=2) matrix x. \n\n Returns the LU factorization(inplace x) and Pivots. low triangular matrix L and \n upper triangular matrix U are combined to a single LU matrix.\n\n Pivoting is done if pivot is set to True.\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n # return ones\n\n Args:\n\n X (Tensor): the tensor to factor of N-dimensions(N>=2).\n\n pivot (bool, optional): controls whether pivoting is done. Default: True.\n\n get_infos (bool, optional): if set to True, returns an info IntTensor. Default: False.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n factorization (Tensor): LU matrix, the factorization of input X.\n\n pivots (IntTensor): the pivots of size(∗(N-2), min(m,n)). `pivots` stores all the \n intermediate transpositions of rows. The final permutation `perm` could be \n reconstructed by this, details refer to upper example.\n\n infos (IntTensor, optional): if `get_infos` is `True`, this is a tensor of size (∗(N-2)) \n where non-zero values indicate whether factorization for the matrix or each minibatch \n has succeeded or failed.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n \n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n if paddle.in_dynamic_mode():\n LU, Piv, Info = _C_ops.lu(x, 'pivots', pivot)\n if get_infos:\n return LU, Piv, Info\n else:\n return LU, Piv\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu')\n helper = LayerHelper('lu', **locals())\n lu = helper.create_variable_for_type_inference(dtype=x.dtype)\n p = helper.create_variable_for_type_inference(dtype='int')\n info = helper.create_variable_for_type_inference(dtype='int')\n attrs = dict()\n attrs['pivots'] = pivot\n helper.append_op(\n type='lu',\n inputs={'X': x},\n outputs={'Out': lu,\n 'Pivots': p,\n 'Infos': info},\n attrs=attrs)\n if get_infos:\n return lu, p, info\n else:\n return lu, p\n\n\ndef lu_unpack(x, y, unpack_ludata=True, unpack_pivots=True, name=None):\n r\"\"\"\n Unpack L U and P to single matrix tensor . \n unpack L and U matrix from LU, unpack permutation matrix P from Pivtos .\n\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n\n\n Args:\n x (Tensor): The LU tensor get from paddle.linalg.lu, which is combined by L and U.\n\n y (Tensor): Pivots get from paddle.linalg.lu.\n\n unpack_ludata (bool,optional): whether to unpack L and U from x. Default: True.\n\n unpack_pivots (bool, optional): whether to unpack permutation matrix P from Pivtos. Default: True.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n P (Tensor): Permutation matrix P of lu factorization.\n\n L (Tensor): The lower triangular matrix tensor of lu factorization.\n\n U (Tensor): The upper triangular matrix tensor of lu factorization.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n\n if paddle.in_dynamic_mode():\n P, L, U = _C_ops.lu_unpack(x, y, 'unpack_ludata', unpack_ludata,\n 'unpack_pivots', unpack_pivots)\n return P, L, U\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu_unpack')\n helper = LayerHelper('lu_unpack', **locals())\n p = helper.create_variable_for_type_inference(dtype=x.dtype)\n l = helper.create_variable_for_type_inference(dtype=x.dtype)\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n attrs = dict()\n attrs['unpack_ludata'] = unpack_ludata\n attrs['unpack_pivots'] = unpack_pivots\n helper.append_op(\n type='lu_unpack',\n inputs={'X': x,\n 'Pivots': y},\n outputs={'Pmat': p,\n 'L': l,\n 'U': u},\n attrs=attrs)\n return p, l, u\n\n\ndef eig(x, name=None):\n \"\"\"\n This API performs the eigenvalue decomposition of a square matrix or a batch of square matrices.\n\n .. note::\n If the matrix is a Hermitian or a real symmetric matrix, please use :ref:`paddle.linalg.eigh` instead, which is much faster.\n If only eigenvalues is needed, please use :ref:`paddle.linalg.eigvals` instead.\n If the matrix is of any shape, please use :ref:`paddle.linalg.svd`.\n This API is only supported on CPU device.\n The output datatype is always complex for both real and complex input.\n\n Args:\n x (Tensor): A tensor with shape math:`[*, N, N]`, The data type of the x should be one of ``float32``,\n ``float64``, ``compplex64`` or ``complex128``.\n name (str, optional): The default value is `None`. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Eigenvalues(Tensors): A tensor with shape math:`[*, N]` refers to the eigen values.\n Eigenvectors(Tensors): A tensor with shape math:`[*, N, N]` refers to the eigen vectors.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n paddle.device.set_device(\"cpu\")\n\n x_data = np.array([[1.6707249, 7.2249975, 6.5045543],\n [9.956216, 8.749598, 6.066444 ],\n [4.4251957, 1.7983172, 0.370647 ]]).astype(\"float32\")\n x = paddle.to_tensor(x_data)\n w, v = paddle.linalg.eig(x)\n print(w)\n # Tensor(shape=[3, 3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [[(-0.5061363550800655+0j) , (-0.7971760990842826+0j) ,\n # (0.18518077798279986+0j)],\n # [(-0.8308237755993192+0j) , (0.3463813401919749+0j) ,\n # (-0.6837005269141947+0j) ],\n # [(-0.23142567697893396+0j), (0.4944999840400175+0j) ,\n # (0.7058765252952796+0j) ]])\n\n print(v)\n # Tensor(shape=[3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [ (16.50471283351188+0j) , (-5.5034820550763515+0j) ,\n # (-0.21026087843552282+0j)])\n \"\"\"\n if paddle.in_dynamic_mode():\n w, v = _C_ops.eig(x)\n return w, v\n\n check_variable_and_dtype(\n x, 'X', ['float32', 'float64', 'complex64', 'complex128'], 'eig')\n helper = LayerHelper('eig', **locals())\n\n w = helper.create_variable_for_type_inference(x.dtype)\n v = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {'X': x}\n outputs = {'Eigenvalues': w, 'Eigenvectors': v}\n helper.append_op(type='eig', inputs=inputs, outputs=outputs)\n\n return w, v\n\n\ndef eigvals(x, name=None):\n \"\"\"\n Compute the eigenvalues of one or more general matrices.\n\n Warning:\n The gradient kernel of this operator does not yet developed.\n If you need back propagation through this operator, please replace it with paddle.linalg.eig.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices whose eigenvalues will be computed.\n Its shape should be `[*, M, M]`, where `*` is zero or more batch dimensions.\n Its data type should be float32, float64, complex64, or complex128.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n Tensor: A tensor containing the unsorted eigenvalues which has the same batch dimensions with `x`.\n The eigenvalues are complex-valued even when `x` is real.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n paddle.seed(1234)\n\n x = paddle.rand(shape=[3, 3], dtype='float64')\n # [[0.02773777, 0.93004224, 0.06911496],\n # [0.24831591, 0.45733623, 0.07717843],\n # [0.48016702, 0.14235102, 0.42620817]])\n\n print(paddle.linalg.eigvals(x))\n # [(-0.27078833542132674+0j), (0.29962280156230725+0j), (0.8824477020120244+0j)] #complex128\n \"\"\"\n\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64',\n 'complex128'], 'eigvals')\n\n x_shape = list(x.shape)\n if len(x_shape) < 2:\n raise ValueError(\n \"The dimension of Input(x) should be at least 2, but received x's dimention = {}, x's shape = {}\".\n format(len(x_shape), x_shape))\n\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The last two dimensions of Input(x) should be equal, but received x's shape = {}\".\n format(x_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.eigvals(x)\n\n helper = LayerHelper('eigvals', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='eigvals', inputs={'X': x}, outputs={'Out': out})\n return out\n\n\ndef multi_dot(x, name=None):\n \"\"\"\n Multi_dot is an operator that calculates multiple matrix multiplications.\n\n Supports inputs of float16(only GPU support), float32 and float64 dtypes. This function does not\n support batched inputs.\n\n The input tensor in [x] must be 2-D except for the first and last can be 1-D.\n If the first tensor is a 1-D vector of shape(n, ) it is treated as row vector\n of shape(1, n), similarly if the last tensor is a 1D vector of shape(n, ), it\n is treated as a column vector of shape(n, 1).\n\n If the first and last tensor are 2-D matrix, then the output is also 2-D matrix,\n otherwise the output is a 1-D vector.\n\n Multi_dot will select the lowest cost multiplication order for calculation. The\n cost of multiplying two matrices with shapes (a, b) and (b, c) is a * b * c.\n Given matrices A, B, C with shapes (20, 5), (5, 100), (100, 10) respectively,\n we can calculate the cost of different multiplication orders as follows:\n - Cost((AB)C) = 20x5x100 + 20x100x10 = 30000\n - Cost(A(BC)) = 5x100x10 + 20x5x10 = 6000\n\n In this case, multiplying B and C first, then multiply A, which is 5 times faster\n than sequential calculation.\n\n Args:\n x ([Tensor]): The input tensors which is a list Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # A * B\n A_data = np.random.random([3, 4]).astype(np.float32)\n B_data = np.random.random([4, 5]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n out = paddle.linalg.multi_dot([A, B])\n print(out.numpy().shape)\n # [3, 5]\n\n # A * B * C\n A_data = np.random.random([10, 5]).astype(np.float32)\n B_data = np.random.random([5, 8]).astype(np.float32)\n C_data = np.random.random([8, 7]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n C = paddle.to_tensor(C_data)\n out = paddle.linalg.multi_dot([A, B, C])\n print(out.numpy().shape)\n # [10, 7]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.multi_dot(x)\n\n check_type(x, 'x', (list, tuple), 'multi_dot')\n for id, item in enumerate(x):\n check_variable_and_dtype(item, 'x[' + str(id) + ']',\n ['float16', 'float32', 'float64'], 'multi_dot')\n if item.dtype != x[0].dtype:\n raise TypeError(\n \"All the Tensors in the input must have the same data type.\")\n\n helper = LayerHelper('multi_dot', **locals())\n dtype = helper.input_dtype(input_param_name='x')\n out = helper.create_variable_for_type_inference(dtype)\n helper.append_op(type='multi_dot', inputs={\"X\": x}, outputs={\"Out\": out})\n return out\n\n\ndef eigh(x, UPLO='L', name=None):\n \"\"\"\n Compute the eigenvalues and eigenvectors of a\n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[*, N, N]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): (string, default 'L'), 'L' represents the lower triangular matrix,\n \"'U' represents the upper triangular matrix.\".\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n\n out_value(Tensor): A Tensor with shape [*, N] and data type of float32 and float64. The eigenvalues of eigh op.\n out_vector(Tensor): A Tensor with shape [*, N, N] and data type of float32,float64,complex64 and complex128. The eigenvectors of eigh op.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value, out_vector = paddle.linalg.eigh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n print(out_vector)\n #[(-0.9238795325112867+0j), (-0.3826834323650898+0j)],\n #[ 0.3826834323650898j , -0.9238795325112867j ]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.eigh(x, 'UPLO', UPLO)\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigh', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'eigh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO})\n return out_value, out_vector\n\n\ndef pinv(x, rcond=1e-15, hermitian=False, name=None):\n r\"\"\"\n Calculate pseudo inverse via SVD(singular value decomposition)\n of one matrix or batches of regular matrix.\n\n .. math::\n\n if hermitian == False:\n x = u * s * vt (SVD)\n out = v * 1/s * ut\n else:\n x = u * s * ut (eigh)\n out = u * 1/s * u.conj().transpose(-2,-1)\n\n If x is hermitian or symmetric matrix, svd will be replaced with eigh.\n\n Args:\n x(Tensor): The input tensor. Its shape should be (*, m, n)\n where * is zero or more batch dimensions. m and n can be\n arbitraty positive number. The data type of x should be\n float32 or float64 or complex64 or complex128. When data\n type is complex64 or cpmplex128, hermitian should be set\n True.\n\n rcond(Tensor, optional): the tolerance value to determine\n when is a singular value zero. Defalut:1e-15.\n\n hermitian(bool, optional): indicates whether x is Hermitian\n if complex or symmetric if real. Default: False.\n\n name(str|None): A name for this layer(optional). If set None,\n the layer will be named automatically.\n\n Returns:\n Tensor: The tensor with same data type with x. it represents\n pseudo inverse of x. Its shape should be (*, n, m).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.arange(15).reshape((3, 5)).astype('float64')\n input = paddle.to_tensor(x)\n out = paddle.linalg.pinv(input)\n print(input)\n print(out)\n\n # input:\n # [[0. , 1. , 2. , 3. , 4. ],\n # [5. , 6. , 7. , 8. , 9. ],\n # [10., 11., 12., 13., 14.]]\n\n # out:\n # [[-0.22666667, -0.06666667, 0.09333333],\n # [-0.12333333, -0.03333333, 0.05666667],\n # [-0.02000000, 0.00000000, 0.02000000],\n # [ 0.08333333, 0.03333333, -0.01666667],\n # [ 0.18666667, 0.06666667, -0.05333333]]\n\n # one can verify : x * out * x = x ;\n # or out * x * out = x ;\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if not hermitian:\n # combine svd and matmul op\n u, s, vt = _C_ops.svd(x, 'full_matrices', False)\n max_singular_val = _C_ops.reduce_max(s, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=x.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=x.dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v, _ = _C_ops.transpose2(vt, 'axis', perm)\n\n out_1 = v * st\n out_2 = _C_ops.matmul_v2(out_1, u, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n # combine eigh and matmul op\n s, u = _C_ops.eigh(x, 'UPLO', 'L')\n s_abs = paddle.abs(s)\n max_singular_val = _C_ops.reduce_max(s_abs, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=s.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=s.dtype)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n out_1 = u * st\n u_conj = _C_ops.conj(u)\n out_2 = _C_ops.matmul_v2(out_1, u_conj, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n if not hermitian:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'pinv')\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(dtype)\n vt = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vt,\n 'S': s},\n attrs={'full_matrices': False}, )\n\n max_singular_val = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=dtype)\n st_shape = helper.create_variable_for_type_inference(dtype=dtype)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v = helper.create_variable_for_type_inference(dtype)\n v_shape = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='transpose2',\n inputs={'X': [vt]},\n outputs={'Out': [v],\n 'XShape': [v_shape]},\n attrs={'axis': perm})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': v,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n else:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64',\n 'complex128'], 'pinv')\n\n if dtype == paddle.complex128:\n s_type = 'float64'\n elif dtype == paddle.complex64:\n s_type = 'float32'\n else:\n s_type = dtype\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': s,\n 'Eigenvectors': u},\n attrs={'UPLO': 'L'})\n s_abs = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='abs', inputs={'X': s}, outputs={'Out': s_abs})\n max_singular_val = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s_abs},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=s_type)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=s_type)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s_type)\n cond_not_int = layers.cast(layers.logical_not(condition), s_type)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=s_type)\n st_shape = helper.create_variable_for_type_inference(dtype=s_type)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': u,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n u_conj = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='conj', inputs={'X': u}, outputs={'Out': [u_conj]})\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u_conj},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n\n\ndef solve(x, y, name=None):\n r\"\"\"\n Computes the solution of a square system of linear equations with a unique solution for input 'X' and 'Y'.\n Let :math: `X` be a sqaure matrix or a batch of square matrices, :math:`Y` be\n a vector/matrix or a batch of vectors/matrices, the equation should be:\n\n .. math::\n Out = X^-1 * Y\n Specifically,\n - This system of linear equations has one solution if and only if input 'X' is invertible.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): A vector/matrix or a batch of vectors/matrices. Its shape should be `[*, M, K]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of a square system of linear equations with a unique solution for input 'x' and 'y'.\n Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # 2*X0 + X1 = 9\n # X0 + 2*X1 = 8\n\n import paddle\n import numpy as np\n\n np_x = np.array([[3, 1],[1, 2]])\n np_y = np.array([9, 8])\n x = paddle.to_tensor(np_x, dtype=\"float64\")\n y = paddle.to_tensor(np_y, dtype=\"float64\")\n out = paddle.linalg.solve(x, y)\n\n print(out)\n # [2., 3.])\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.solve(x, y)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type=\"solve\", inputs={\"X\": x,\n \"Y\": y}, outputs={\"Out\": out})\n return out\n\n\ndef triangular_solve(x,\n y,\n upper=True,\n transpose=False,\n unitriangular=False,\n name=None):\n r\"\"\"\n Computes the solution of a system of equations with a triangular coefficient matrix `x` and\n multiple right-hand sides `y` .\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input triangular coefficient matrix. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): Whether to solve the upper-triangular system of equations (default) or the lower-triangular \n system of equations. Default: True.\n transpose (bool, optional): whether `x` should be transposed before calculation. Default: False.\n unitriangular (bool, optional): whether `x` is unit triangular. If True, the diagonal elements of `x` are assumed \n to be 1 and not referenced from `x` . Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # x1 + x2 + x3 = 0\n # 2*x2 + x3 = -9\n # -x3 = 5\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n y = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.triangular_solve(x, y, upper=True)\n\n print(out)\n # [7, -2, -5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.triangular_solve(x, y, 'upper', upper, 'transpose',\n transpose, 'unitriangular',\n unitriangular)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"triangular_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'triangular_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'triangular_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='triangular_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={\n 'upper': upper,\n 'transpose': transpose,\n 'unitriangular': unitriangular\n })\n return out\n\n\ndef cholesky_solve(x, y, upper=False, name=None):\n r\"\"\"\n Solves a linear system of equations A @ X = B, given A's Cholesky factor matrix u and matrix B.\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input matrix which is upper or lower triangular Cholesky factor of square matrix A. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): whether to consider the Cholesky factor as a lower or upper triangular matrix. Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type is the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n u = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n b = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.cholesky_solve(b, u, upper=True)\n\n print(out)\n # [-2.5, -7, 9.5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky_solve(x, y, 'upper', upper)\n\n helper = LayerHelper(\"cholesky_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'cholesky_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'cholesky_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='cholesky_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef eigvalsh(x, UPLO='L', name=None):\n \"\"\"\n Computes the eigenvalues of a \n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[_, M, M]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): Lower triangular part of a (‘L’, default) or the upper triangular part (‘U’).\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor eigenvalues in ascending order.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value = paddle.eigvalsh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n \"\"\"\n if paddle.in_dynamic_mode():\n is_test = x.stop_gradient\n values, _ = _C_ops.eigvalsh(x, 'UPLO', UPLO, 'is_test', is_test)\n return values\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigvalsh', **locals())\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64', 'complex128'],\n 'eigvalsh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n is_test = x.stop_gradient\n helper.append_op(\n type='eigvalsh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO,\n 'is_test': is_test})\n return out_value\n\n\ndef lstsq(x, y, rcond=None, driver=None, name=None):\n \"\"\"\n Computes a solution to\n the least squares problem of a system of linear equations.\n\n Args:\n x (Tensor): A tensor with shape ``(*, M, N)`` , the data type of the input Tensor ``x``\n should be one of float32, float64.\n y (Tensor): A tensor with shape ``(*, M, K)`` , the data type of the input Tensor ``y`` \n should be one of float32, float64.\n rcond(float, optional): The default value is None. A float pointing number used to determine \n the effective rank of ``x``. If ``rcond`` is None, it will be set to max(M, N) times the \n machine precision of x_dtype.\n driver(str, optional): The default value is None. The name of LAPACK method to be used. For \n CPU inputs the valid values are ‘gels’, ‘gelsy’, ‘gelsd, ‘gelss’. For CUDA input, the only \n valid driver is ‘gels’. If ``driver`` is None, ‘gelsy’ is used for CPU inputs and ‘gels’ \n for CUDA inputs.\n name(str, optional): The default value is None. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple: A tuple of 4 Tensors which is (``solution``, ``residuals``, ``rank``, ``singular_values``). \n ``solution`` is a tensor with shape ``(*, N, K)``, meaning the least squares solution. ``residuals`` \n is a tensor with shape ``(*, K)``, meaning the squared residuals of the solutions, which is computed \n when M > N and every matrix in ``x`` is full-rank, otherwise return an empty tensor. ``rank`` is a tensor \n with shape ``(*)``, meaning the ranks of the matrices in ``x``, which is computed when ``driver`` in \n (‘gelsy’, ‘gelsd’, ‘gelss’), otherwise return an empty tensor. ``singular_values`` is a tensor with \n shape ``(*, min(M, N))``, meaning singular values of the matrices in ``x``, which is computed when \n ``driver`` in (‘gelsd’, ‘gelss’), otherwise return an empty tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n x = paddle.to_tensor([[1, 3], [3, 2], [5, 6.]])\n y = paddle.to_tensor([[3, 4, 6], [5, 3, 4], [1, 2, 1.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gelsd\")\n print(results[0])\n # [[ 0.78350395, -0.22165027, -0.62371236],\n # [-0.11340097, 0.78866047, 1.14948535]]\n print(results[1])\n # [19.81443405, 10.43814468, 30.56185532])\n print(results[2])\n # 2\n print(results[3])\n # [9.03455734, 1.54167950]\n\n x = paddle.to_tensor([[10, 2, 3], [3, 10, 5], [5, 6, 12.]])\n y = paddle.to_tensor([[4, 2, 9], [2, 0, 3], [2, 5, 3.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gels\")\n print(results[0])\n # [[ 0.39386186, 0.10230173, 0.93606132],\n # [ 0.10741687, -0.29028133, 0.11892585],\n # [-0.05115091, 0.51918161, -0.19948854]]\n print(results[1])\n # []\n \"\"\"\n device = paddle.get_device()\n if device == \"cpu\":\n if driver not in (None, \"gels\", \"gelss\", \"gelsd\", \"gelsy\"):\n raise ValueError(\n \"Only support valid driver is 'gels', 'gelss', 'gelsd', 'gelsy' or None for CPU inputs. But got {}\".\n format(driver))\n driver = \"gelsy\" if driver is None else driver\n elif \"gpu\" in device:\n if driver not in (None, \"gels\"):\n raise ValueError(\n \"Only support valid driver is 'gels' or None for CUDA inputs. But got {}\".\n format(driver))\n driver = \"gels\" if driver is None else driver\n else:\n raise RuntimeError(\"Only support lstsq api for CPU or CUDA device.\")\n\n if x.dtype == y.dtype and x.dtype in (paddle.float32, paddle.float64):\n pass\n else:\n raise ValueError(\n \"Only support x and y have the same dtype such as 'float32' and 'float64'.\"\n )\n\n if rcond is None:\n if x.dtype == paddle.float32:\n rcond = 1e-7 * max(x.shape[-2], x.shape[-1])\n elif x.dtype == paddle.float64:\n rcond = 1e-15 * max(x.shape[-2], x.shape[-1])\n\n if paddle.in_dynamic_mode():\n solution, rank, singular_values = _C_ops.lstsq(x, y, \"rcond\", rcond,\n \"driver\", driver)\n if x.shape[-2] > x.shape[-1]:\n matmul_out = _varbase_creator(dtype=x.dtype)\n _C_ops.matmul(x, solution, matmul_out, 'trans_x', False, 'trans_y',\n False)\n minus_out = _C_ops.elementwise_sub(matmul_out, y)\n pow_out = _C_ops.pow(minus_out, 'factor', 2)\n residuals = _C_ops.reduce_sum(pow_out, 'dim', [-2], 'keepdim',\n False, 'reduce_all', False)\n else:\n residuals = paddle.empty(shape=[0], dtype=x.dtype)\n\n if driver == \"gels\":\n rank = paddle.empty(shape=[0], dtype=paddle.int32)\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n elif driver == \"gelsy\":\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n\n return solution, residuals, rank, singular_values\n\n helper = LayerHelper('lstsq', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n check_variable_and_dtype(\n y, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n\n solution = helper.create_variable_for_type_inference(dtype=x.dtype)\n residuals = helper.create_variable_for_type_inference(dtype=x.dtype)\n rank = helper.create_variable_for_type_inference(dtype=paddle.int32)\n singular_values = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='lstsq',\n inputs={'X': x,\n 'Y': y},\n outputs={\n 'Solution': solution,\n 'Rank': rank,\n 'SingularValues': singular_values\n },\n attrs={'rcond': rcond,\n 'driver': driver})\n\n matmul_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n minus_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n pow_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': solution},\n outputs={'Out': matmul_out},\n attrs={\n 'trans_x': False,\n 'trans_y': False,\n })\n\n helper.append_op(\n type='elementwise_sub',\n inputs={'X': matmul_out,\n 'Y': y},\n outputs={'Out': minus_out})\n\n helper.append_op(\n type='pow',\n inputs={'X': minus_out},\n outputs={'Out': pow_out},\n attrs={'factor': 2})\n\n helper.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': residuals},\n attrs={'dim': [-2],\n 'keep_dim': False,\n 'reduce_all': False})\n\n if driver == \"gels\":\n rank = paddle.static.data(name='rank', shape=[0])\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n elif driver == \"gelsy\":\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n\n return solution, residuals, rank, singular_values"},"implementation":{"kind":"string","value":"def histogram(input, bins=100, min=0, max=0, name=None):\n \"\"\"\n Computes the histogram of a tensor. The elements are sorted into equal width bins between min and max.\n If min and max are both zero, the minimum and maximum values of the data are used.\n\n Args:\n input (Tensor): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor\n should be float32, float64, int32, int64.\n bins (int): number of histogram bins\n min (int): lower end of the range (inclusive)\n max (int): upper end of the range (inclusive)\n\n Returns:\n Tensor: data type is int64, shape is (nbins,).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n inputs = paddle.to_tensor([1, 2, 1])\n result = paddle.histogram(inputs, bins=4, min=0, max=3)\n print(result) # [0, 2, 1, 0]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.histogram(input, \"bins\", bins, \"min\", min, \"max\", max)\n\n helper = LayerHelper('histogram', **locals())\n check_variable_and_dtype(\n input, 'X', ['int32', 'int64', 'float32', 'float64'], 'histogram')\n out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)\n helper.append_op(\n type='histogram',\n inputs={'X': input},\n outputs={'Out': out},\n attrs={'bins': bins,\n 'min': min,\n 'max': max})\n return out"},"start_line":{"kind":"number","value":1376,"string":"1,376"},"end_line":{"kind":"number","value":1414,"string":"1,414"},"file_content":{"kind":"string","value":"# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numpy as np\nfrom ..fluid.layer_helper import LayerHelper\nfrom ..framework import _varbase_creator, _dygraph_tracer\nfrom ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype\nfrom ..static import Variable\nfrom ..fluid.framework import _in_legacy_dygraph, in_dygraph_mode\nfrom ..fluid.layers import transpose, cast # noqa: F401\nfrom ..fluid import layers\nimport paddle\nfrom paddle.common_ops_import import core\nfrom paddle.common_ops_import import VarDesc\nfrom paddle import _C_ops\n\n__all__ = []\n\n\ndef matmul(x, y, transpose_x=False, transpose_y=False, name=None):\n \"\"\"\n Applies matrix multiplication to two tensors. `matmul` follows\n the complete broadcast rules,\n and its behavior is consistent with `np.matmul`.\n\n Currently, the input tensors' number of dimensions can be any, `matmul` can be used to\n achieve the `dot`, `matmul` and `batchmatmul`.\n\n The actual behavior depends on the shapes of :math:`x`, :math:`y` and the\n flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:\n\n - If a transpose flag is specified, the last two dimensions of the tensor\n are transposed. If the tensor is ndim-1 of shape, the transpose is invalid. If the tensor\n is ndim-1 of shape :math:`[D]`, then for :math:`x` it is treated as :math:`[1, D]`, whereas\n for :math:`y` it is the opposite: It is treated as :math:`[D, 1]`.\n\n The multiplication behavior depends on the dimensions of `x` and `y`. Specifically:\n\n - If both tensors are 1-dimensional, the dot product result is obtained.\n\n - If both tensors are 2-dimensional, the matrix-matrix product is obtained.\n\n - If the `x` is 1-dimensional and the `y` is 2-dimensional,\n a `1` is prepended to its dimension in order to conduct the matrix multiply.\n After the matrix multiply, the prepended dimension is removed.\n\n - If the `x` is 2-dimensional and `y` is 1-dimensional,\n the matrix-vector product is obtained.\n\n - If both arguments are at least 1-dimensional and at least one argument\n is N-dimensional (where N > 2), then a batched matrix multiply is obtained.\n If the first argument is 1-dimensional, a 1 is prepended to its dimension\n in order to conduct the batched matrix multiply and removed after.\n If the second argument is 1-dimensional, a 1 is appended to its\n dimension for the purpose of the batched matrix multiple and removed after.\n The non-matrix (exclude the last two dimensions) dimensions are\n broadcasted according the broadcast rule.\n For example, if input is a (j, 1, n, m) tensor and the other is a (k, m, p) tensor,\n out will be a (j, k, n, p) tensor.\n\n Args:\n x (Tensor): The input tensor which is a Tensor.\n y (Tensor): The input tensor which is a Tensor.\n transpose_x (bool): Whether to transpose :math:`x` before multiplication.\n transpose_y (bool): Whether to transpose :math:`y` before multiplication.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # vector * vector\n x_data = np.random.random([10]).astype(np.float32)\n y_data = np.random.random([10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [1]\n\n # matrix * vector\n x_data = np.random.random([10, 5]).astype(np.float32)\n y_data = np.random.random([5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10]\n\n # batched matrix * broadcasted vector\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([2]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5]\n\n # batched matrix * batched matrix\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([10, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5, 5]\n\n # batched matrix * broadcasted matrix\n x_data = np.random.random([10, 1, 5, 2]).astype(np.float32)\n y_data = np.random.random([1, 3, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 3, 5, 5]\n\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_matmul(x, y, transpose_x, transpose_y)\n\n if _in_legacy_dygraph():\n op_type = 'matmul_v2'\n op = getattr(_C_ops, op_type)\n return op(x, y, 'trans_x', transpose_x, 'trans_y', transpose_y)\n\n attrs = {\n 'trans_x': transpose_x,\n 'trans_y': transpose_y,\n }\n\n def __check_input(x, y):\n var_names = {'x': x, 'y': y}\n for name, val in var_names.items():\n check_variable_and_dtype(\n val, name,\n ['float16', 'float32', 'float64', 'complex64', 'complex128'],\n 'matmul')\n\n __check_input(x, y)\n\n helper = LayerHelper('matmul_v2', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef norm(x, p='fro', axis=None, keepdim=False, name=None):\n \"\"\"\n\n Returns the matrix norm (Frobenius) or vector norm (the 1-norm, the Euclidean\n or 2-norm, and in general the p-norm for p > 0) of a given tensor.\n\n .. note::\n This norm API is different from `numpy.linalg.norm`.\n This api supports high-order input tensors (rank >= 3), and certain axis need to be pointed out to calculate the norm.\n But `numpy.linalg.norm` only supports 1-D vector or 2-D matrix as input tensor.\n For p-order matrix norm, this api actually treats matrix as a flattened vector to calculate the vector norm, NOT REAL MATRIX NORM.\n\n Args:\n x (Tensor): The input tensor could be N-D tensor, and the input data\n type could be float32 or float64.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `0`, `1`, `2`,\n `inf`, `-inf` and any positive real number yielding the corresponding p-norm. Not supported: ord < 0 and nuclear norm.\n Default value is `fro`.\n axis (int|list|tuple, optional): The axis on which to apply norm operation. If axis is int\n or list(int)/tuple(int) with only one element, the vector norm is computed over the axis.\n If `axis < 0`, the dimension to norm operation is rank(input) + axis.\n If axis is a list(int)/tuple(int) with two elements, the matrix norm is computed over the axis.\n Defalut value is `None`.\n keepdim (bool, optional): Whether to reserve the reduced dimension in the\n output Tensor. The result tensor will have fewer dimension\n than the :attr:`input` unless :attr:`keepdim` is true, default\n value is False.\n name (str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: results of norm operation on the specified axis of input tensor,\n it's data type is the same as input's Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n shape=[2, 3, 4]\n np_input = np.arange(24).astype('float32') - 12\n np_input = np_input.reshape(shape)\n x = paddle.to_tensor(np_input)\n #[[[-12. -11. -10. -9.] [ -8. -7. -6. -5.] [ -4. -3. -2. -1.]]\n # [[ 0. 1. 2. 3.] [ 4. 5. 6. 7.] [ 8. 9. 10. 11.]]]\n\n # compute frobenius norm along last two dimensions.\n out_fro = paddle.linalg.norm(x, p='fro', axis=[0,1])\n # out_fro.numpy() [17.435596 16.911535 16.7332 16.911535]\n\n # compute 2-order vector norm along last dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=-1)\n #out_pnorm.numpy(): [[21.118711 13.190906 5.477226]\n # [ 3.7416575 11.224972 19.131126]]\n\n # compute 2-order norm along [0,1] dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=[0,1])\n #out_pnorm.numpy(): [17.435596 16.911535 16.7332 16.911535]\n\n # compute inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=np.inf)\n #out_pnorm.numpy() = [12.]\n out_pnorm = paddle.linalg.norm(x, p=np.inf, axis=0)\n #out_pnorm.numpy(): [[12. 11. 10. 9.] [8. 7. 6. 7.] [8. 9. 10. 11.]]\n\n # compute -inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=-np.inf)\n #out_pnorm.numpy(): [0.]\n out_pnorm = paddle.linalg.norm(x, p=-np.inf, axis=0)\n #out_pnorm.numpy(): [[0. 1. 2. 3.] [4. 5. 6. 5.] [4. 3. 2. 1.]]\n \"\"\"\n\n def frobenius_norm(input, dim=None, keepdim=False, name=None):\n \"\"\"\n The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n dim (list, optional): None for last two dimensions.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if dim is not None and not (isinstance(dim, list) and len(dim) == 2):\n raise ValueError(\n \"The dim of frobenius norm op should be None or two elements list!\"\n )\n if paddle.in_dynamic_mode():\n if dim is None:\n return _C_ops.frobenius_norm(input, 'keep_dim', keepdim,\n 'reduce_all', True)\n return _C_ops.frobenius_norm(input, 'dim', dim, 'keep_dim', keepdim,\n 'reduce_all', False)\n attrs = {'dim': dim, 'keep_dim': keepdim, 'reduce_all': False}\n if dim is None:\n attrs['reduce_all'] = True\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'frobenius_norm')\n\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='frobenius_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def vector_norm(input,\n porder=None,\n axis=None,\n keepdim=False,\n asvector=False,\n name=None):\n \"\"\"\n Calculate the p-order vector norm for certain dimension of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n porder (float, optional): None for porder=2.0.\n axis (int, optional): None for last dimension.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if paddle.in_dynamic_mode():\n if axis is None: axis = -1\n return _C_ops.p_norm(input, 'porder', porder, 'axis', axis,\n 'keepdim', keepdim, 'asvector', asvector)\n if porder is not None:\n check_type(porder, 'porder', (float, int), 'p_norm')\n if axis is not None:\n check_type(axis, 'axis', (int), 'p_norm')\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'p_norm')\n\n attrs = {\n 'axis': axis if axis is not None else -1,\n 'porder': float(porder) if porder is not None else 2.0,\n 'keepdim': keepdim,\n 'asvector': asvector,\n 'epsilon': 1e-12,\n }\n helper = LayerHelper('p_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='p_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def inf_norm(input,\n porder=None,\n axis=axis,\n keepdim=False,\n asvector=False,\n name=None):\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n helper.append_op(type='abs', inputs={'X': input}, outputs={'Out': out})\n reduce_out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n reduce_all = True if axis == None or axis == [] or asvector == True else False\n axis = axis if axis != None and axis != [] else [0]\n\n reduce_type = 'reduce_max' if porder == np.float(\n 'inf') else 'reduce_min'\n helper.append_op(\n type=reduce_type,\n inputs={'X': out},\n outputs={'Out': reduce_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n\n return reduce_out\n\n def p_matrix_norm(input, porder=1., axis=axis, keepdim=False, name=None):\n \"\"\"\n NOTE:\n This function actually treats the matrix as flattened vector to calculate vector norm instead of matrix norm.\n \"\"\"\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n\n block.append_op(\n type='pow',\n inputs={'X': abs_out},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': True if axis is None else False\n })\n porder\n block.append_op(\n type='pow',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n if axis is None and p is not None:\n if isinstance(p, str):\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n porder=p,\n axis=axis,\n keepdim=keepdim,\n asvector=True,\n name=name)\n else:\n raise ValueError(\"only valid p type is string or float, found {}\".\n format(type(p)))\n\n if isinstance(axis, tuple):\n axis = list(axis)\n if isinstance(axis, list) and len(axis) == 1:\n axis = axis[0]\n\n #calculate vector norm, where axis is int or list with only one integer\n if isinstance(axis, int):\n if isinstance(p, str):\n if p == \"fro\":\n return vector_norm(\n x,\n porder=2,\n axis=axis,\n keepdim=keepdim,\n asvector=False,\n name=name)\n\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n axis=axis,\n porder=p,\n keepdim=keepdim,\n asvector=False,\n name=name)\n else:\n raise ValueError(\n \"unspport p for p-order vector norm. except float, found {}\".\n format(p))\n #calculate matrix norm, where axis is list with two integers\n elif isinstance(axis, list) and len(axis) == 2:\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n elif p == np.inf or p == -np.inf:\n return inf_norm(x, porder=p, axis=axis, keepdim=keepdim, name=name)\n elif p == 0:\n raise ValueError(\n \"just suport axis type int or list (length of list <=1) if p = 0, found {}\".\n format(axis))\n else:\n return p_matrix_norm(\n x, porder=p, axis=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"except axis type int or list (length of list <=2), found {}\".\n format(axis))\n\n\ndef dist(x, y, p=2, name=None):\n r\"\"\"\n\n This OP returns the p-norm of (x - y). It is not a norm in a strict sense, only as a measure\n of distance. The shapes of x and y must be broadcastable. The definition is as follows, for\n details, please refer to the `numpy's broadcasting <https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html>`_:\n\n - Each input has at least one dimension.\n - Match the two input dimensions from back to front, the dimension sizes must either be equal, one of them is 1, or one of them does not exist.\n\n Where, z = x - y, the shapes of x and y are broadcastable, then the shape of z can be\n obtained as follows:\n\n 1. If the number of dimensions of x and y are not equal, prepend 1 to the dimensions of the\n tensor with fewer dimensions.\n\n For example, The shape of x is [8, 1, 6, 1], the shape of y is [7, 1, 5], prepend 1 to the\n dimension of y.\n\n x (4-D Tensor): 8 x 1 x 6 x 1\n\n y (4-D Tensor): 1 x 7 x 1 x 5\n\n 2. Determine the size of each dimension of the output z: choose the maximum value from the\n two input dimensions.\n\n z (4-D Tensor): 8 x 7 x 6 x 5\n\n If the number of dimensions of the two inputs are the same, the size of the output can be\n directly determined in step 2. When p takes different values, the norm formula is as follows:\n\n When p = 0, defining $0^0=0$, the zero-norm of z is simply the number of non-zero elements of z.\n\n .. math::\n\n ||z||_{0}=\\lim_{p \\\\rightarrow 0}\\sum_{i=1}^{m}|z_i|^{p}\n\n When p = inf, the inf-norm of z is the maximum element of z.\n\n .. math::\n\n ||z||_\\infty=\\max_i |z_i|\n\n When p = -inf, the negative-inf-norm of z is the minimum element of z.\n\n .. math::\n\n ||z||_{-\\infty}=\\min_i |z_i|\n\n Otherwise, the p-norm of z follows the formula,\n\n .. math::\n\n ||z||_{p}=(\\sum_{i=1}^{m}|z_i|^p)^{\\\\frac{1}{p}}\n\n Args:\n x (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n y (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n p (float, optional): The norm to be computed, its data type is float32 or float64. Default: 2.\n\n Returns:\n Tensor: Tensor that is the p-norm of (x - y).\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor(np.array([[3, 3],[3, 3]]), \"float32\")\n y = paddle.to_tensor(np.array([[3, 3],[3, 1]]), \"float32\")\n out = paddle.dist(x, y, 0)\n print(out) # out = [1.]\n\n out = paddle.dist(x, y, 2)\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"inf\"))\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"-inf\"))\n print(out) # out = [0.]\n \"\"\"\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'dist')\n check_variable_and_dtype(y, 'dtype', ['float32', 'float64'], 'dist')\n check_type(p, 'p', (float, int), 'dist')\n helper = LayerHelper(\"dist\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n outputs = {'Out': [out]}\n attrs = {\"p\": float(p)}\n helper.append_op(\n type='dist', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef cond(x, p=None, name=None):\n \"\"\"\n\n Computes the condition number of a matrix or batches of matrices with respect to a matrix norm ``p``.\n\n Args:\n x (Tensor): The input tensor could be tensor of shape ``(*, m, n)`` where ``*`` is zero or more batch dimensions\n for ``p`` in ``(2, -2)``, or of shape ``(*, n, n)`` where every matrix is invertible for any supported ``p``.\n And the input data type could be ``float32`` or ``float64``.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `nuc`, `1`, `-1`, `2`, `-2`,\n `inf`, `-inf`. Default value is `None`, meaning that the order of the norm is `2`.\n name (str, optional): The default value is `None`. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: computing results of condition number, its data type is the same as input Tensor ``x``.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1., 0, -1], [0, 1, 0], [1, 0, 1]])\n\n # compute conditional number when p is None\n out = paddle.linalg.cond(x)\n # out.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is 'fro'\n out_fro = paddle.linalg.cond(x, p='fro')\n # out_fro.numpy() [3.1622777]\n\n # compute conditional number when order of the norm is 'nuc'\n out_nuc = paddle.linalg.cond(x, p='nuc')\n # out_nuc.numpy() [9.2426405]\n\n # compute conditional number when order of the norm is 1\n out_1 = paddle.linalg.cond(x, p=1)\n # out_1.numpy() [2.]\n\n # compute conditional number when order of the norm is -1\n out_minus_1 = paddle.linalg.cond(x, p=-1)\n # out_minus_1.numpy() [1.]\n\n # compute conditional number when order of the norm is 2\n out_2 = paddle.linalg.cond(x, p=2)\n # out_2.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is -1\n out_minus_2 = paddle.linalg.cond(x, p=-2)\n # out_minus_2.numpy() [0.70710677]\n\n # compute conditional number when order of the norm is inf\n out_inf = paddle.linalg.cond(x, p=np.inf)\n # out_inf.numpy() [2.]\n\n # compute conditional number when order of the norm is -inf\n out_minus_inf = paddle.linalg.cond(x, p=-np.inf)\n # out_minus_inf.numpy() [1.]\n\n a = paddle.to_tensor(np.random.randn(2, 4, 4).astype('float32'))\n # a.numpy()\n # [[[ 0.14063153 -0.996288 0.7996131 -0.02571543]\n # [-0.16303636 1.5534962 -0.49919784 -0.04402903]\n # [-1.1341571 -0.6022629 0.5445269 0.29154757]\n # [-0.16816919 -0.30972657 1.7521842 -0.5402487 ]]\n # [[-0.58081484 0.12402827 0.7229862 -0.55046535]\n # [-0.15178485 -1.1604939 0.75810957 0.30971205]\n # [-0.9669573 1.0940945 -0.27363303 -0.35416734]\n # [-1.216529 2.0018666 -0.7773689 -0.17556527]]]\n a_cond_fro = paddle.linalg.cond(a, p='fro')\n # a_cond_fro.numpy() [31.572273 28.120834]\n\n b = paddle.to_tensor(np.random.randn(2, 3, 4).astype('float64'))\n # b.numpy()\n # [[[ 1.61707487 0.46829144 0.38130416 0.82546736]\n # [-1.72710298 0.08866375 -0.62518804 0.16128892]\n # [-0.02822879 -1.67764516 0.11141444 0.3220113 ]]\n # [[ 0.22524372 0.62474921 -0.85503233 -1.03960523]\n # [-0.76620689 0.56673047 0.85064753 -0.45158196]\n # [ 1.47595418 2.23646462 1.5701758 0.10497519]]]\n b_cond_2 = paddle.linalg.cond(b, p=2)\n # b_cond_2.numpy() [3.30064451 2.51976252]\n\n \"\"\"\n\n def mat_norm(input, porder=1., axis=None):\n \"\"\"\n NOTE:\n Calculate the matrix norm of a square matrix or batches of square matrices,\n when porder is in (1, -1, inf, -inf)\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n axis = axis if axis != None and axis != [] else [0]\n keepdim = False\n\n if paddle.in_dynamic_mode():\n abs_out = _C_ops.abs(input)\n sum_out = _C_ops.reduce_sum(abs_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == 1 or porder == np.inf:\n return _C_ops.reduce_max(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == -1 or porder == -np.inf:\n return _C_ops.reduce_min(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n\n block = LayerHelper('norm', **locals())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n block.append_op(\n type='reduce_sum',\n inputs={'X': abs_out},\n outputs={'Out': sum_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 1 or porder == np.inf:\n block.append_op(\n type='reduce_max',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n if porder == -1 or porder == -np.inf:\n block.append_op(\n type='reduce_min',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n\n def fro_norm(input, porder=2, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the frobenius norm of a square matrix or batches of square matrices.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n if paddle.in_dynamic_mode():\n pow_out = _C_ops.pow(input, 'factor', porder)\n sum_out_1 = _C_ops.reduce_sum(pow_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n sum_out_2 = _C_ops.reduce_sum(sum_out_1, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n return _C_ops.pow(sum_out_2, 'factor', float(1. / porder))\n\n block = LayerHelper('norm', **locals())\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_1 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_2 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='pow',\n inputs={'X': input},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out_1},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_sum',\n inputs={'X': sum_out_1},\n outputs={'Out': sum_out_2},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='pow',\n inputs={'X': sum_out_2},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n def svd_norm(input, porder, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the matrix norm, which is related to singular values, of a matrix\n or batches of matrices, including nuclear norm, 2-norm and (-2)-norm.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n u, s, vh = svd(input, full_matrices=False)\n\n if paddle.in_dynamic_mode():\n if porder == \"nuc\":\n return _C_ops.reduce_sum(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n max_out = _C_ops.reduce_max(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n min_out = _C_ops.reduce_min(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n if porder == 2:\n return _C_ops.elementwise_div(max_out, min_out, 'aixs', axis,\n 'use_mkldnn', False)\n if porder == -2:\n return _C_ops.elementwise_div(min_out, max_out, 'aixs', axis,\n 'use_mkldnn', False)\n\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n if porder == \"nuc\":\n block.append_op(\n type='reduce_sum',\n inputs={'X': s},\n outputs={'Out': out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n max_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n min_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_min',\n inputs={'X': s},\n outputs={'Out': min_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': max_out,\n 'Y': min_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n if porder == -2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': min_out,\n 'Y': max_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n\n def empty_tensor(input, shape):\n if paddle.in_dynamic_mode():\n return input.reshape(shape)\n raise ValueError(\"only support x is nonempty tensor in static mode\")\n\n x_shape = list(x.shape)\n if not len(x_shape) >= 2:\n raise ValueError(\"input should be a matrix or batches of matrices, \" +\n \"but the dimention of received input is {}\".format(\n len(x_shape)))\n if p == None:\n p = 2\n x_size = 0 if (0 in x_shape) else 1\n if p in (\"fro\", \"nuc\", 1, -1, np.inf, -np.inf):\n if x_shape[len(x_shape) - 1] == x_shape[len(x_shape) - 2]:\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n x_inv = x.inverse()\n if p == \"fro\":\n return fro_norm(x) * fro_norm(x_inv)\n if p == \"nuc\":\n return svd_norm(x, p) * svd_norm(x_inv, p)\n if p in (1, -1):\n return mat_norm(\n x, porder=p, axis=[-2]) * mat_norm(\n x_inv, porder=p, axis=[-2])\n if p in (np.inf, -np.inf):\n return mat_norm(\n x, porder=p, axis=[-1]) * mat_norm(\n x_inv, porder=p, axis=[-1])\n else:\n raise ValueError(\"only support p is {} when input is a \".format(p) +\n \"square matrix or batches of square matrices\")\n elif p in (2, -2):\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n return svd_norm(x, porder=p)\n else:\n raise ValueError(\n \"unsupported {} for p, only supporting ('fro', 'nuc', \".format(\n p) + \"1, -1, 2, -2, inf, -inf) or none\")\n\n\ndef dot(x, y, name=None):\n \"\"\"\n This operator calculates inner product for vectors.\n\n .. note::\n Support 1-d and 2-d Tensor. When it is 2d, the first dimension of this matrix\n is the batch dimension, which means that the vectors of multiple batches are dotted.\n\n Parameters:\n x(Tensor): 1-D or 2-D ``Tensor``. Its dtype should be ``float32``, ``float64``, ``int32``, ``int64``\n y(Tensor): 1-D or 2-D ``Tensor``. Its dtype soulde be ``float32``, ``float64``, ``int32``, ``int64``\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: the calculated result Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x_data = np.random.uniform(0.1, 1, [10]).astype(np.float32)\n y_data = np.random.uniform(1, 3, [10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.dot(x, y)\n print(z)\n\n \"\"\"\n op_type = 'dot'\n # skip var type check in dygraph mode to improve efficiency\n if paddle.in_dynamic_mode():\n op = getattr(_C_ops, op_type)\n return op(x, y)\n\n assert x is not None, 'x cannot be None in {}'.format(op_type)\n assert y is not None, 'y cannot be None in {}'.format(op_type)\n\n check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n check_variable_and_dtype(y, 'y', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n\n helper = LayerHelper(op_type, **locals())\n if name is None:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n else:\n out = helper.create_variable(\n name=name, dtype=x.dtype, persistable=False)\n helper.append_op(\n type=\"dot\", inputs={'X': x,\n 'Y': y}, attrs={}, outputs={\"Out\": out})\n return out\n\n\ndef cov(x, rowvar=True, ddof=True, fweights=None, aweights=None, name=None):\n \"\"\"\n Estimate the covariance matrix of the input variables, given data and weights.\n\n A covariance matrix is a square matrix, indicate the covariance of each pair variables in the input matrix.\n For example, for an N-dimensional samples X=[x1,x2,…xN]T, then the covariance matrix \n element Cij is the covariance of xi and xj. The element Cii is the variance of xi itself.\n\n Parameters:\n x(Tensor): A N-D(N<=2) Tensor containing multiple variables and observations. By default, each row of x represents a variable. Also see rowvar below.\n rowvar(Bool, optional): If rowvar is True (default), then each row represents a variable, with observations in the columns. Default: True\n ddof(Bool, optional): If ddof=True will return the unbiased estimate, and ddof=False will return the simple average. Default: True\n fweights(Tensor, optional): 1-D Tensor of integer frequency weights; The number of times each observation vector should be repeated. Default: None\n aweights(Tensor, optional): 1-D Tensor of observation vector weights. How important of the observation vector, larger data means this element is more important. Default: None\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: The covariance matrix Tensor of the variables.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n\n xt = paddle.rand((3,4))\n paddle.linalg.cov(xt)\n\n '''\n Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n [[0.07918842, 0.06127326, 0.01493049],\n [0.06127326, 0.06166256, 0.00302668],\n [0.01493049, 0.00302668, 0.01632146]])\n '''\n \"\"\"\n op_type = 'cov'\n if len(x.shape) > 2 or len(x.shape) < 1:\n raise ValueError(\n \"Input(x) only support N-D (1<=N<=2) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cov')\n nx = x\n if len(x.shape) == 1:\n nx = x.reshape((1, -1))\n if not rowvar and nx.shape[0] != 1:\n nx = nx.t()\n w = None\n observation_num = nx.shape[1]\n if fweights is not None:\n w = fweights.astype(nx.dtype)\n if len(w.shape) > 1:\n raise ValueError(\n \"Input(fweights) only support N-D (N<=1) tensor in cov, but received \"\n \"shape of Input(input) is %s.\" % len(fweights.shape))\n if fweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(fweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(fweights) is {}.\".format(observation_num,\n fweights.shape[0]))\n if fweights.min() < 0:\n raise ValueError(\n \"The value of Input(fweights) cannot be negtive, but received \"\n \"min of Input(fweights) is {}.\".format(fweights.min()))\n if not paddle.all(fweights == paddle.round(fweights.astype('float64'))):\n raise ValueError(\"Input(fweights) must be integer \")\n\n if aweights is not None:\n aw = aweights.astype(nx.dtype)\n if len(aw.shape) > 1:\n raise ValueError(\n \"Input(aweights) only support N-D (N<=1) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(aweights.shape))\n check_variable_and_dtype(aweights, 'dtype', ['float32', 'float64'],\n 'cov')\n if aweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(aweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(aweights) is {}.\".format(observation_num,\n aweights.shape[0]))\n if aweights.min() < 0:\n raise ValueError(\n \"The value of Input(aweights) cannot be negtive, but received \"\n \"min of Input(aweights) is {}.\".format(aweights.min()))\n if w is not None:\n w = w * aw\n else:\n w = aw\n\n w_sum = paddle.to_tensor(observation_num, dtype=nx.dtype)\n if fweights is not None or aweights is not None:\n w_sum = w.sum()\n if w_sum.item() == 0:\n raise ValueError(\"The sum of weights is zero, can't be normalized.\")\n\n if w is not None:\n nx_w = nx * w\n avg = (nx_w).sum(axis=1) / w_sum\n else:\n avg = nx.sum(axis=1) / w_sum\n nx_w = nx\n\n if w is not None and aweights is not None and ddof == True:\n norm_factor = w_sum - (w * aweights).sum() / w_sum\n else:\n norm_factor = w_sum - ddof\n if norm_factor <= 0:\n norm_factor = paddle.to_tensor(0, dtype=nx.dtype)\n nx = nx - avg.unsqueeze(1)\n xxt = paddle.mm(nx, nx_w.t().conj())\n cov = paddle.divide(xxt, norm_factor).squeeze()\n return cov\n\n\ndef t(input, name=None):\n \"\"\"\n Transpose <=2-D tensor.\n 0-D and 1-D tensors are returned as it is and 2-D tensor is equal to\n the paddle.transpose function which perm dimensions set 0 and 1.\n\n Args:\n input (Tensor): The input Tensor. It is a N-D (N<=2) Tensor of data types float16, float32, float64, int32.\n name(str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`\n Returns:\n Tensor: A transposed n-D Tensor, with data type being float16, float32, float64, int32, int64.\n\n For Example:\n\n .. code-block:: text\n\n # Example 1 (0-D tensor)\n x = tensor([0.79])\n paddle.t(x) = tensor([0.79])\n\n # Example 2 (1-D tensor)\n x = tensor([0.79, 0.84, 0.32])\n paddle.t(x) = tensor([0.79, 0.84, 0.32])\n\n # Example 3 (2-D tensor)\n x = tensor([0.79, 0.84, 0.32],\n [0.64, 0.14, 0.57])\n paddle.t(x) = tensor([0.79, 0.64],\n [0.84, 0.14],\n [0.32, 0.57])\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n x = paddle.ones(shape=[2, 3], dtype='int32')\n x_transposed = paddle.t(x)\n print(x_transposed.shape)\n # [3, 2]\n \"\"\"\n if len(input.shape) > 2:\n raise ValueError(\n \"Input(input) only support N-D (N<=2) tensor, but received \"\n \"length of Input(input) is %s. Perhaps you can use paddle.\"\n \"tensor.transpose() instead.\" % len(input.shape))\n if paddle.in_dynamic_mode():\n if len(input.shape) == 1:\n return input\n # 2-D tensor\n perm = [1, 0]\n out, _ = _C_ops.transpose2(input, 'axis', perm)\n return out\n\n check_variable_and_dtype(\n input, 'input', ['float16', 'float32', 'float64', 'int32',\n 'int64'], 'transpose')\n\n helper = LayerHelper('t', **locals())\n out = helper.create_variable_for_type_inference(input.dtype)\n input_shape = helper.create_variable_for_type_inference(input.dtype)\n if len(input.shape) == 1:\n out = input\n else:\n helper.append_op(\n type='transpose2',\n inputs={'X': [input]},\n outputs={'Out': [out],\n 'XShape': [input_shape]},\n attrs={'axis': [1, 0]})\n return out\n\n\ndef cross(x, y, axis=None, name=None):\n \"\"\"\n Computes the cross product between two tensors along an axis.\n\n Inputs must have the same shape, and the length of their axes should be equal to 3.\n If `axis` is not given, it defaults to the first axis found with the length 3.\n\n Args:\n x (Tensor): The first input tensor.\n y (Tensor): The second input tensor.\n axis (int, optional): The axis along which to compute the cross product. It defaults to the first axis found with the length 3.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor. A Tensor with same data type as `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0],\n [3.0, 3.0, 3.0]])\n y = paddle.to_tensor([[1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0]])\n\n z1 = paddle.cross(x, y)\n # [[-1. -1. -1.]\n # [ 2. 2. 2.]\n # [-1. -1. -1.]]\n\n z2 = paddle.cross(x, y, axis=1)\n # [[0. 0. 0.]\n # [0. 0. 0.]\n # [0. 0. 0.]]\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_cross(x, y, axis)\n else:\n if _in_legacy_dygraph():\n if axis is not None:\n return _C_ops.cross(x, y, 'dim', axis)\n else:\n return _C_ops.cross(x, y)\n else:\n helper = LayerHelper(\"cross\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n attrs = dict()\n attrs['dim'] = axis\n\n helper.append_op(\n type='cross',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef cholesky(x, upper=False, name=None):\n r\"\"\"\n Computes the Cholesky decomposition of one symmetric positive-definite\n matrix or batches of symmetric positive-definite matrice.\n\n If `upper` is `True`, the decomposition has the form :math:`A = U^{T}U` ,\n and the returned matrix :math:`U` is upper-triangular. Otherwise, the\n decomposition has the form :math:`A = LL^{T}` , and the returned matrix\n :math:`L` is lower-triangular.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[*, M, M]`,\n where * is zero or more batch dimensions, and matrices on the\n inner-most 2 dimensions all should be symmetric positive-definite.\n Its data type should be float32 or float64.\n upper (bool): The flag indicating whether to return upper or lower\n triangular matrices. Default: False.\n\n Returns:\n Tensor: A Tensor with same shape and data type as `x`. It represents \\\n triangular matrices generated by Cholesky decomposition.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n a = np.random.rand(3, 3)\n a_t = np.transpose(a, [1, 0])\n x_data = np.matmul(a, a_t) + 1e-03\n x = paddle.to_tensor(x_data)\n out = paddle.linalg.cholesky(x, upper=False)\n print(out)\n # [[1.190523 0. 0. ]\n # [0.9906703 0.27676893 0. ]\n # [1.25450498 0.05600871 0.06400121]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky(x, \"upper\", upper)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cholesky')\n check_type(upper, 'upper', bool, 'cholesky')\n helper = LayerHelper('cholesky', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='cholesky',\n inputs={'X': [x]},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef matrix_rank(x, tol=None, hermitian=False, name=None):\n r\"\"\"\n Computes the rank of a matrix.\n\n The rank of a matrix is the number of singular values that are greater than the specified `tol` threshold when hermitian=False,\n or the number of eigenvalues in absolute value that are greater than the specified `tol` threshold when hermitian=True.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., m, n]`, where `...` is zero or more batch dimensions. If `x` is a batch\n of matrices then the output has the same batch dimensions. The data type of `x` should be float32 or float64.\n tol (float,Tensor,optional): the tolerance value. Default: None. If `tol` is not specified, and `sigma` is the largest\n singular value (or eigenvalues in absolute value), and `eps` is the epsilon value for the dtype of `x`, then `tol` is computed\n with formula `tol=sigma * max(m,n) * eps`. Note that if `x` is a batch of matrices, `tol` is computed this way for every batch.\n hermitian (bool,optional): indicates whether `x` is Hermitian. Default: False. When hermitian=True, `x` is assumed to be Hermitian,\n enabling a more efficient method for finding eigenvalues, but `x` is not checked inside the function. Instead, We just use\n the lower triangular of the matrix to compute.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: Rank of tensor x.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n a = paddle.eye(10)\n b = paddle.linalg.matrix_rank(a)\n print(b)\n # b = [10]\n\n c = paddle.ones(shape=[3, 4, 5, 5])\n d = paddle.linalg.matrix_rank(c, tol=0.01, hermitian=True)\n print(d)\n # d = [[1, 1, 1, 1],\n # [1, 1, 1, 1],\n # [1, 1, 1, 1]]\n\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if tol is None:\n tol_tensor = None\n tol_attr = 0.0\n use_default_tol = True\n elif isinstance(tol, Variable):\n if tol.dtype != x.dtype:\n tol_tensor = cast(tol, x.dtype)\n else:\n tol_tensor = tol\n tol_attr = 0.0\n use_default_tol = False\n else:\n tol_tensor = None\n tol_attr = float(tol)\n use_default_tol = False\n return _C_ops.matrix_rank(x, tol_tensor, \"tol\", tol_attr, 'hermitian',\n hermitian, 'use_default_tol', use_default_tol)\n\n inputs = {}\n attrs = {}\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'matrix_rank')\n inputs['X'] = x\n if tol is None:\n attrs['use_default_tol'] = True\n elif isinstance(tol, Variable):\n check_variable_and_dtype(tol, 'tol', ['float32'], 'matrix_rank')\n attrs['use_default_tol'] = False\n if tol.dtype != x.dtype:\n inputs['TolTensor'] = cast(tol, x.dtype)\n else:\n inputs['TolTensor'] = tol\n else:\n check_type(tol, 'tol', float, 'matrix_rank')\n attrs['use_default_tol'] = False\n attrs['tol'] = tol\n check_type(hermitian, 'hermitian', bool, 'matrix_rank')\n attrs['hermitian'] = hermitian\n\n helper = LayerHelper('matrix_rank', **locals())\n out = helper.create_variable_for_type_inference(dtype='int32')\n helper.append_op(\n type='matrix_rank', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef bmm(x, y, name=None):\n \"\"\"\n Applies batched matrix multiplication to two tensors.\n\n Both of the two input tensors must be three-dementional and share the same batch size.\n\n if x is a (b, m, k) tensor, y is a (b, k, n) tensor, the output will be a (b, m, n) tensor.\n\n Args:\n x (Tensor): The input Tensor.\n y (Tensor): The input Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The product Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n # In imperative mode:\n # size x: (2, 2, 3) and y: (2, 3, 2)\n x = paddle.to_tensor([[[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0]],\n [[3.0, 3.0, 3.0],\n [4.0, 4.0, 4.0]]])\n y = paddle.to_tensor([[[1.0, 1.0],[2.0, 2.0],[3.0, 3.0]],\n [[4.0, 4.0],[5.0, 5.0],[6.0, 6.0]]])\n out = paddle.bmm(x, y)\n #output size: (2, 2, 2)\n #output value:\n #[[[6.0, 6.0],[12.0, 12.0]],[[45.0, 45.0],[60.0, 60.0]]]\n out_np = out.numpy()\n\n \"\"\"\n x_shape = x.shape\n y_shape = y.shape\n if not len(x_shape) == len(y_shape) == 3:\n raise ValueError(\n \"x and y should be 3-dimensional. But received x's dimention: {}, y's dimention: {}\".\n format(x_shape, y_shape))\n if x_shape[2] != y_shape[1]:\n raise ValueError(\n \"x's width must be equal with y's height. But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n if x_shape[0] != y_shape[0]:\n raise ValueError(\n \"x's batch (shape[0]) must be equal with y's batch (shape[0]). But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.bmm(x, y)\n\n helper = LayerHelper('bmm', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='bmm', inputs={'X': x, 'Y': y}, outputs={'Out': out})\n return out\n\n\ndef histogram(input, bins=100, min=0, max=0, name=None):\n \"\"\"\n Computes the histogram of a tensor. The elements are sorted into equal width bins between min and max.\n If min and max are both zero, the minimum and maximum values of the data are used.\n\n Args:\n input (Tensor): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor\n should be float32, float64, int32, int64.\n bins (int): number of histogram bins\n min (int): lower end of the range (inclusive)\n max (int): upper end of the range (inclusive)\n\n Returns:\n Tensor: data type is int64, shape is (nbins,).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n inputs = paddle.to_tensor([1, 2, 1])\n result = paddle.histogram(inputs, bins=4, min=0, max=3)\n print(result) # [0, 2, 1, 0]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.histogram(input, \"bins\", bins, \"min\", min, \"max\", max)\n\n helper = LayerHelper('histogram', **locals())\n check_variable_and_dtype(\n input, 'X', ['int32', 'int64', 'float32', 'float64'], 'histogram')\n out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)\n helper.append_op(\n type='histogram',\n inputs={'X': input},\n outputs={'Out': out},\n attrs={'bins': bins,\n 'min': min,\n 'max': max})\n return out\n\n\ndef bincount(x, weights=None, minlength=0, name=None):\n \"\"\"\n Computes frequency of each value in the input tensor. \n\n Args:\n x (Tensor): A Tensor with non-negative integer. Should be 1-D tensor.\n weights (Tensor, optional): Weight for each value in the input tensor. Should have the same shape as input. Default is None.\n minlength (int, optional): Minimum number of bins. Should be non-negative integer. Default is 0.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor of frequency.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([1, 2, 1, 4, 5])\n result1 = paddle.bincount(x)\n print(result1) # [0, 2, 1, 0, 1, 1]\n\n w = paddle.to_tensor([2.1, 0.4, 0.1, 0.5, 0.5])\n result2 = paddle.bincount(x, weights=w)\n print(result2) # [0., 2.19999981, 0.40000001, 0., 0.50000000, 0.50000000]\n \"\"\"\n if x.dtype not in [paddle.int32, paddle.int64]:\n raise TypeError(\"Elements in Input(x) should all be integers\")\n\n if paddle.in_dynamic_mode():\n return _C_ops.bincount(x, weights, \"minlength\", minlength)\n\n helper = LayerHelper('bincount', **locals())\n\n check_variable_and_dtype(x, 'X', ['int32', 'int64'], 'bincount')\n\n if weights is not None:\n check_variable_and_dtype(weights, 'Weights',\n ['int32', 'int64', 'float32', 'float64'],\n 'bincount')\n out = helper.create_variable_for_type_inference(dtype=weights.dtype)\n else:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='bincount',\n inputs={'X': x,\n 'Weights': weights},\n outputs={'Out': out},\n attrs={'minlength': minlength})\n return out\n\n\ndef mv(x, vec, name=None):\n \"\"\"\n Performs a matrix-vector product of the matrix x and the vector vec.\n\n Args:\n x (Tensor): A tensor with shape :math:`[M, N]` , The data type of the input Tensor x\n should be one of float32, float64.\n vec (Tensor): A tensor with shape :math:`[N]` , The data type of the input Tensor x\n should be one of float32, float64.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor which is producted by x and vec.\n\n Examples:\n .. code-block:: python\n\n # x: [M, N], vec: [N]\n # paddle.mv(x, vec) # out: [M]\n\n import numpy as np\n import paddle\n\n x_data = np.array([[2, 1, 3], [3, 0, 1]]).astype(\"float64\")\n x = paddle.to_tensor(x_data)\n vec_data = np.array([3, 5, 1])\n vec = paddle.to_tensor(vec_data).astype(\"float64\")\n out = paddle.mv(x, vec)\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_mv(x, vec)\n else:\n if _in_legacy_dygraph():\n out = _C_ops.mv(x, vec)\n return out\n else:\n\n def __check_input(x, vec):\n var_names = {'x': x, 'vec': vec}\n for name, val in var_names.items():\n check_variable_and_dtype(val, name, ['float32', 'float64'],\n 'mv')\n x_shape = list(x.shape)\n vec_shape = list(vec.shape)\n if len(x_shape) != 2:\n raise ValueError(\n \"x should be 2-dimensional. But received x's dimention: {}\".\n format(x_shape))\n if len(vec_shape) != 1:\n raise ValueError(\n \"vec should be 1-dimensional. But received vec's dimention: {}\".\n format(vec_shape))\n\n __check_input(x, vec)\n\n helper = LayerHelper('mv', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='mv', inputs={'X': x,\n 'Vec': vec}, outputs={'Out': out})\n return out\n\n\ndef det(x, name=None):\n \"\"\"\n Calculates determinant value of a square matrix or batches of square matrices.\n Args:\n x (Tensor): input (Tensor): the input matrix of size `(n, n)` or the batch of matrices of size\n `(*, n, n)` where `*` is one or more batch dimensions.\n Returns:\n y (Tensor):the determinant value of a square matrix or batches of square matrices.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.det(x)\n\n print(A)\n\n # [ 0.02547996, 2.52317095, -6.15900707])\n\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.determinant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'det')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('determinant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='determinant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef slogdet(x, name=None):\n \"\"\"\n Calculates the sign and natural logarithm of the absolute value of a square matrix's or batches square matrices' determinant.\n The determinant can be computed with ``sign * exp(logabsdet)\n\n Supports input of float, double\n\n Note that for matrices that have zero determinant, this returns ``(0, -inf)``\n Args:\n x (Tensor): the batch of matrices of size :math:`(*, n, n)`\n where math:`*` is one or more batch dimensions.\n\n Returns:\n y (Tensor): A tensor containing the sign of the determinant and the natural logarithm\n of the absolute value of determinant, respectively.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.slogdet(x)\n\n print(A)\n\n # [[ 1. , 1. , -1. ],\n # [-0.98610914, -0.43010661, -0.10872950]])\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.slogdeterminant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'slogdet')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('slogdeterminant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='slogdeterminant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef svd(x, full_matrices=False, name=None):\n r\"\"\"\n Computes the singular value decomposition of one matrix or a batch of regular matrices.\n\n Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies:\n\n .. math::\n X = U * diag(S) * VT\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., N, M]`,\n where `...` is zero or more batch dimensions. N and M can be arbitraty\n positive number. Note that if x is sigular matrices, the grad is numerical\n instable. The data type of x should be float32 or float64.\n full_matrices (bool): A flag to control the behavor of svd.\n If full_matrices = True, svd op will compute full U and V matrics,\n which means shape of U is `[..., N, N]`, shape of V is `[..., M, M]`. K = min(M, N).\n If full_matrices = False, svd op will use a economic method to store U and V.\n which means shape of U is `[..., N, K]`, shape of V is `[..., M, K]`. K = min(M, N).\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple of 3 tensors: (U, S, VH). VH is the conjugate transpose of V. S is the singlar value vectors of matrics with shape `[..., K]`\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 2.0], [1.0, 3.0], [4.0, 6.0]]).astype('float64')\n x = x.reshape([3, 2])\n u, s, vh = paddle.linalg.svd(x)\n print (u)\n #U = [[ 0.27364809, -0.21695147 ],\n # [ 0.37892198, -0.87112408 ],\n # [ 0.8840446 , 0.44053933 ]]\n\n print (s)\n #S = [8.14753743, 0.78589688]\n print (vh)\n #VT= [[ 0.51411221, 0.85772294],\n # [ 0.85772294, -0.51411221]]\n\n # one can verify : U * S * VT == X\n # U * UH == I\n # V * VH == I\n \"\"\"\n\n if paddle.in_dynamic_mode():\n return _C_ops.svd(x, 'full_matrices', full_matrices)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'svd')\n check_type(full_matrices, 'full_matrices', bool, 'svd')\n helper = LayerHelper('svd', **locals())\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n vh = helper.create_variable_for_type_inference(dtype=x.dtype)\n s = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['full_matrices'] = full_matrices\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vh,\n 'S': s},\n attrs=attrs, )\n return u, s, vh\n\n\ndef matrix_power(x, n, name=None):\n r\"\"\"\n Computes the n-th power of a square matrix or a batch of square matrices.\n\n Let :math:`X` be a sqaure matrix or a batch of square matrices, :math:`n` be\n an exponent, the equation should be:\n\n .. math::\n Out = X ^ {n}\n\n Specifically,\n\n - If `n > 0`, it returns the matrix or a batch of matrices raised to the power\n of `n`.\n\n - If `n = 0`, it returns the identity matrix or a batch of identity matrices.\n\n - If `n < 0`, it returns the inverse of each matrix (if invertible) raised to\n the power of `abs(n)`.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices to be raised\n to power `n`. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n n (int): The exponent. It can be any positive, negative integer or zero.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The n-th power of the matrix (or the batch of matrices) `x`. Its\n data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1, 2, 3],\n [1, 4, 9],\n [1, 8, 27]], dtype='float64')\n print(paddle.linalg.matrix_power(x, 2))\n # [[6. , 34. , 102.],\n # [14. , 90. , 282.],\n # [36. , 250., 804.]]\n\n print(paddle.linalg.matrix_power(x, 0))\n # [[1., 0., 0.],\n # [0., 1., 0.],\n # [0., 0., 1.]]\n\n print(paddle.linalg.matrix_power(x, -2))\n # [[ 12.91666667, -12.75000000, 2.83333333 ],\n # [-7.66666667 , 8. , -1.83333333 ],\n # [ 1.80555556 , -1.91666667 , 0.44444444 ]]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.matrix_power(x, \"n\", n)\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'matrix_power')\n check_type(n, 'n', int, 'matrix_power')\n helper = LayerHelper('matrix_power', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matrix_power',\n inputs={'X': x},\n outputs={'Out': out},\n attrs={'n': n})\n return out\n\n\ndef qr(x, mode=\"reduced\", name=None):\n r\"\"\"\n Computes the QR decomposition of one matrix or batches of matrice (backward is unsupported now).\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., M, N]`,\n where ... is zero or more batch dimensions. M and N can be arbitrary\n positive number. The data type of x should be float32 or float64. \n mode (str, optional): A flag to control the behavior of qr, the default is \"reduced\". \n Suppose x's shape is `[..., M, N]` and denoting `K = min(M, N)`:\n If mode = \"reduced\", qr op will return reduced Q and R matrices, \n which means Q's shape is `[..., M, K]` and R's shape is `[..., K, N]`.\n If mode = \"complete\", qr op will return complete Q and R matrices, \n which means Q's shape is `[..., M, M]` and R's shape is `[..., M, N]`.\n If mode = \"r\", qr op will only return reduced R matrix, which means\n R's shape is `[..., K, N]`.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n If mode = \"reduced\" or mode = \"complete\", qr will return a two tensor-tuple, which represents Q and R. \n If mode = \"r\", qr will return a tensor which represents R.\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n q, r = paddle.linalg.qr(x)\n print (q)\n print (r)\n\n # Q = [[-0.16903085, 0.89708523],\n # [-0.50709255, 0.27602622],\n # [-0.84515425, -0.34503278]])\n\n # R = [[-5.91607978, -7.43735744],\n # [ 0. , 0.82807867]])\n \n # one can verify : X = Q * R ; \n \"\"\"\n if paddle.in_dynamic_mode():\n q, r = _C_ops.qr(x, 'mode', mode)\n if mode == \"r\":\n return r\n else:\n return q, r\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'qr')\n check_type(mode, 'mode', str, 'qr')\n helper = LayerHelper('qr', **locals())\n q = helper.create_variable_for_type_inference(dtype=x.dtype)\n r = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['mode'] = mode\n helper.append_op(\n type='qr', inputs={'X': [x]}, outputs={'Q': q,\n 'R': r}, attrs=attrs)\n if mode == \"r\":\n return r\n else:\n return q, r\n\n\ndef lu(x, pivot=True, get_infos=False, name=None):\n r\"\"\"\n Computes the LU factorization of an N-D(N>=2) matrix x. \n\n Returns the LU factorization(inplace x) and Pivots. low triangular matrix L and \n upper triangular matrix U are combined to a single LU matrix.\n\n Pivoting is done if pivot is set to True.\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n # return ones\n\n Args:\n\n X (Tensor): the tensor to factor of N-dimensions(N>=2).\n\n pivot (bool, optional): controls whether pivoting is done. Default: True.\n\n get_infos (bool, optional): if set to True, returns an info IntTensor. Default: False.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n factorization (Tensor): LU matrix, the factorization of input X.\n\n pivots (IntTensor): the pivots of size(∗(N-2), min(m,n)). `pivots` stores all the \n intermediate transpositions of rows. The final permutation `perm` could be \n reconstructed by this, details refer to upper example.\n\n infos (IntTensor, optional): if `get_infos` is `True`, this is a tensor of size (∗(N-2)) \n where non-zero values indicate whether factorization for the matrix or each minibatch \n has succeeded or failed.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n \n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n if paddle.in_dynamic_mode():\n LU, Piv, Info = _C_ops.lu(x, 'pivots', pivot)\n if get_infos:\n return LU, Piv, Info\n else:\n return LU, Piv\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu')\n helper = LayerHelper('lu', **locals())\n lu = helper.create_variable_for_type_inference(dtype=x.dtype)\n p = helper.create_variable_for_type_inference(dtype='int')\n info = helper.create_variable_for_type_inference(dtype='int')\n attrs = dict()\n attrs['pivots'] = pivot\n helper.append_op(\n type='lu',\n inputs={'X': x},\n outputs={'Out': lu,\n 'Pivots': p,\n 'Infos': info},\n attrs=attrs)\n if get_infos:\n return lu, p, info\n else:\n return lu, p\n\n\ndef lu_unpack(x, y, unpack_ludata=True, unpack_pivots=True, name=None):\n r\"\"\"\n Unpack L U and P to single matrix tensor . \n unpack L and U matrix from LU, unpack permutation matrix P from Pivtos .\n\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n\n\n Args:\n x (Tensor): The LU tensor get from paddle.linalg.lu, which is combined by L and U.\n\n y (Tensor): Pivots get from paddle.linalg.lu.\n\n unpack_ludata (bool,optional): whether to unpack L and U from x. Default: True.\n\n unpack_pivots (bool, optional): whether to unpack permutation matrix P from Pivtos. Default: True.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n P (Tensor): Permutation matrix P of lu factorization.\n\n L (Tensor): The lower triangular matrix tensor of lu factorization.\n\n U (Tensor): The upper triangular matrix tensor of lu factorization.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n\n if paddle.in_dynamic_mode():\n P, L, U = _C_ops.lu_unpack(x, y, 'unpack_ludata', unpack_ludata,\n 'unpack_pivots', unpack_pivots)\n return P, L, U\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu_unpack')\n helper = LayerHelper('lu_unpack', **locals())\n p = helper.create_variable_for_type_inference(dtype=x.dtype)\n l = helper.create_variable_for_type_inference(dtype=x.dtype)\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n attrs = dict()\n attrs['unpack_ludata'] = unpack_ludata\n attrs['unpack_pivots'] = unpack_pivots\n helper.append_op(\n type='lu_unpack',\n inputs={'X': x,\n 'Pivots': y},\n outputs={'Pmat': p,\n 'L': l,\n 'U': u},\n attrs=attrs)\n return p, l, u\n\n\ndef eig(x, name=None):\n \"\"\"\n This API performs the eigenvalue decomposition of a square matrix or a batch of square matrices.\n\n .. note::\n If the matrix is a Hermitian or a real symmetric matrix, please use :ref:`paddle.linalg.eigh` instead, which is much faster.\n If only eigenvalues is needed, please use :ref:`paddle.linalg.eigvals` instead.\n If the matrix is of any shape, please use :ref:`paddle.linalg.svd`.\n This API is only supported on CPU device.\n The output datatype is always complex for both real and complex input.\n\n Args:\n x (Tensor): A tensor with shape math:`[*, N, N]`, The data type of the x should be one of ``float32``,\n ``float64``, ``compplex64`` or ``complex128``.\n name (str, optional): The default value is `None`. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Eigenvalues(Tensors): A tensor with shape math:`[*, N]` refers to the eigen values.\n Eigenvectors(Tensors): A tensor with shape math:`[*, N, N]` refers to the eigen vectors.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n paddle.device.set_device(\"cpu\")\n\n x_data = np.array([[1.6707249, 7.2249975, 6.5045543],\n [9.956216, 8.749598, 6.066444 ],\n [4.4251957, 1.7983172, 0.370647 ]]).astype(\"float32\")\n x = paddle.to_tensor(x_data)\n w, v = paddle.linalg.eig(x)\n print(w)\n # Tensor(shape=[3, 3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [[(-0.5061363550800655+0j) , (-0.7971760990842826+0j) ,\n # (0.18518077798279986+0j)],\n # [(-0.8308237755993192+0j) , (0.3463813401919749+0j) ,\n # (-0.6837005269141947+0j) ],\n # [(-0.23142567697893396+0j), (0.4944999840400175+0j) ,\n # (0.7058765252952796+0j) ]])\n\n print(v)\n # Tensor(shape=[3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [ (16.50471283351188+0j) , (-5.5034820550763515+0j) ,\n # (-0.21026087843552282+0j)])\n \"\"\"\n if paddle.in_dynamic_mode():\n w, v = _C_ops.eig(x)\n return w, v\n\n check_variable_and_dtype(\n x, 'X', ['float32', 'float64', 'complex64', 'complex128'], 'eig')\n helper = LayerHelper('eig', **locals())\n\n w = helper.create_variable_for_type_inference(x.dtype)\n v = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {'X': x}\n outputs = {'Eigenvalues': w, 'Eigenvectors': v}\n helper.append_op(type='eig', inputs=inputs, outputs=outputs)\n\n return w, v\n\n\ndef eigvals(x, name=None):\n \"\"\"\n Compute the eigenvalues of one or more general matrices.\n\n Warning:\n The gradient kernel of this operator does not yet developed.\n If you need back propagation through this operator, please replace it with paddle.linalg.eig.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices whose eigenvalues will be computed.\n Its shape should be `[*, M, M]`, where `*` is zero or more batch dimensions.\n Its data type should be float32, float64, complex64, or complex128.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n Tensor: A tensor containing the unsorted eigenvalues which has the same batch dimensions with `x`.\n The eigenvalues are complex-valued even when `x` is real.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n paddle.seed(1234)\n\n x = paddle.rand(shape=[3, 3], dtype='float64')\n # [[0.02773777, 0.93004224, 0.06911496],\n # [0.24831591, 0.45733623, 0.07717843],\n # [0.48016702, 0.14235102, 0.42620817]])\n\n print(paddle.linalg.eigvals(x))\n # [(-0.27078833542132674+0j), (0.29962280156230725+0j), (0.8824477020120244+0j)] #complex128\n \"\"\"\n\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64',\n 'complex128'], 'eigvals')\n\n x_shape = list(x.shape)\n if len(x_shape) < 2:\n raise ValueError(\n \"The dimension of Input(x) should be at least 2, but received x's dimention = {}, x's shape = {}\".\n format(len(x_shape), x_shape))\n\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The last two dimensions of Input(x) should be equal, but received x's shape = {}\".\n format(x_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.eigvals(x)\n\n helper = LayerHelper('eigvals', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='eigvals', inputs={'X': x}, outputs={'Out': out})\n return out\n\n\ndef multi_dot(x, name=None):\n \"\"\"\n Multi_dot is an operator that calculates multiple matrix multiplications.\n\n Supports inputs of float16(only GPU support), float32 and float64 dtypes. This function does not\n support batched inputs.\n\n The input tensor in [x] must be 2-D except for the first and last can be 1-D.\n If the first tensor is a 1-D vector of shape(n, ) it is treated as row vector\n of shape(1, n), similarly if the last tensor is a 1D vector of shape(n, ), it\n is treated as a column vector of shape(n, 1).\n\n If the first and last tensor are 2-D matrix, then the output is also 2-D matrix,\n otherwise the output is a 1-D vector.\n\n Multi_dot will select the lowest cost multiplication order for calculation. The\n cost of multiplying two matrices with shapes (a, b) and (b, c) is a * b * c.\n Given matrices A, B, C with shapes (20, 5), (5, 100), (100, 10) respectively,\n we can calculate the cost of different multiplication orders as follows:\n - Cost((AB)C) = 20x5x100 + 20x100x10 = 30000\n - Cost(A(BC)) = 5x100x10 + 20x5x10 = 6000\n\n In this case, multiplying B and C first, then multiply A, which is 5 times faster\n than sequential calculation.\n\n Args:\n x ([Tensor]): The input tensors which is a list Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # A * B\n A_data = np.random.random([3, 4]).astype(np.float32)\n B_data = np.random.random([4, 5]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n out = paddle.linalg.multi_dot([A, B])\n print(out.numpy().shape)\n # [3, 5]\n\n # A * B * C\n A_data = np.random.random([10, 5]).astype(np.float32)\n B_data = np.random.random([5, 8]).astype(np.float32)\n C_data = np.random.random([8, 7]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n C = paddle.to_tensor(C_data)\n out = paddle.linalg.multi_dot([A, B, C])\n print(out.numpy().shape)\n # [10, 7]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.multi_dot(x)\n\n check_type(x, 'x', (list, tuple), 'multi_dot')\n for id, item in enumerate(x):\n check_variable_and_dtype(item, 'x[' + str(id) + ']',\n ['float16', 'float32', 'float64'], 'multi_dot')\n if item.dtype != x[0].dtype:\n raise TypeError(\n \"All the Tensors in the input must have the same data type.\")\n\n helper = LayerHelper('multi_dot', **locals())\n dtype = helper.input_dtype(input_param_name='x')\n out = helper.create_variable_for_type_inference(dtype)\n helper.append_op(type='multi_dot', inputs={\"X\": x}, outputs={\"Out\": out})\n return out\n\n\ndef eigh(x, UPLO='L', name=None):\n \"\"\"\n Compute the eigenvalues and eigenvectors of a\n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[*, N, N]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): (string, default 'L'), 'L' represents the lower triangular matrix,\n \"'U' represents the upper triangular matrix.\".\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n\n out_value(Tensor): A Tensor with shape [*, N] and data type of float32 and float64. The eigenvalues of eigh op.\n out_vector(Tensor): A Tensor with shape [*, N, N] and data type of float32,float64,complex64 and complex128. The eigenvectors of eigh op.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value, out_vector = paddle.linalg.eigh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n print(out_vector)\n #[(-0.9238795325112867+0j), (-0.3826834323650898+0j)],\n #[ 0.3826834323650898j , -0.9238795325112867j ]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.eigh(x, 'UPLO', UPLO)\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigh', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'eigh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO})\n return out_value, out_vector\n\n\ndef pinv(x, rcond=1e-15, hermitian=False, name=None):\n r\"\"\"\n Calculate pseudo inverse via SVD(singular value decomposition)\n of one matrix or batches of regular matrix.\n\n .. math::\n\n if hermitian == False:\n x = u * s * vt (SVD)\n out = v * 1/s * ut\n else:\n x = u * s * ut (eigh)\n out = u * 1/s * u.conj().transpose(-2,-1)\n\n If x is hermitian or symmetric matrix, svd will be replaced with eigh.\n\n Args:\n x(Tensor): The input tensor. Its shape should be (*, m, n)\n where * is zero or more batch dimensions. m and n can be\n arbitraty positive number. The data type of x should be\n float32 or float64 or complex64 or complex128. When data\n type is complex64 or cpmplex128, hermitian should be set\n True.\n\n rcond(Tensor, optional): the tolerance value to determine\n when is a singular value zero. Defalut:1e-15.\n\n hermitian(bool, optional): indicates whether x is Hermitian\n if complex or symmetric if real. Default: False.\n\n name(str|None): A name for this layer(optional). If set None,\n the layer will be named automatically.\n\n Returns:\n Tensor: The tensor with same data type with x. it represents\n pseudo inverse of x. Its shape should be (*, n, m).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.arange(15).reshape((3, 5)).astype('float64')\n input = paddle.to_tensor(x)\n out = paddle.linalg.pinv(input)\n print(input)\n print(out)\n\n # input:\n # [[0. , 1. , 2. , 3. , 4. ],\n # [5. , 6. , 7. , 8. , 9. ],\n # [10., 11., 12., 13., 14.]]\n\n # out:\n # [[-0.22666667, -0.06666667, 0.09333333],\n # [-0.12333333, -0.03333333, 0.05666667],\n # [-0.02000000, 0.00000000, 0.02000000],\n # [ 0.08333333, 0.03333333, -0.01666667],\n # [ 0.18666667, 0.06666667, -0.05333333]]\n\n # one can verify : x * out * x = x ;\n # or out * x * out = x ;\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if not hermitian:\n # combine svd and matmul op\n u, s, vt = _C_ops.svd(x, 'full_matrices', False)\n max_singular_val = _C_ops.reduce_max(s, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=x.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=x.dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v, _ = _C_ops.transpose2(vt, 'axis', perm)\n\n out_1 = v * st\n out_2 = _C_ops.matmul_v2(out_1, u, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n # combine eigh and matmul op\n s, u = _C_ops.eigh(x, 'UPLO', 'L')\n s_abs = paddle.abs(s)\n max_singular_val = _C_ops.reduce_max(s_abs, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=s.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=s.dtype)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n out_1 = u * st\n u_conj = _C_ops.conj(u)\n out_2 = _C_ops.matmul_v2(out_1, u_conj, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n if not hermitian:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'pinv')\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(dtype)\n vt = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vt,\n 'S': s},\n attrs={'full_matrices': False}, )\n\n max_singular_val = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=dtype)\n st_shape = helper.create_variable_for_type_inference(dtype=dtype)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v = helper.create_variable_for_type_inference(dtype)\n v_shape = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='transpose2',\n inputs={'X': [vt]},\n outputs={'Out': [v],\n 'XShape': [v_shape]},\n attrs={'axis': perm})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': v,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n else:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64',\n 'complex128'], 'pinv')\n\n if dtype == paddle.complex128:\n s_type = 'float64'\n elif dtype == paddle.complex64:\n s_type = 'float32'\n else:\n s_type = dtype\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': s,\n 'Eigenvectors': u},\n attrs={'UPLO': 'L'})\n s_abs = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='abs', inputs={'X': s}, outputs={'Out': s_abs})\n max_singular_val = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s_abs},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=s_type)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=s_type)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s_type)\n cond_not_int = layers.cast(layers.logical_not(condition), s_type)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=s_type)\n st_shape = helper.create_variable_for_type_inference(dtype=s_type)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': u,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n u_conj = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='conj', inputs={'X': u}, outputs={'Out': [u_conj]})\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u_conj},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n\n\ndef solve(x, y, name=None):\n r\"\"\"\n Computes the solution of a square system of linear equations with a unique solution for input 'X' and 'Y'.\n Let :math: `X` be a sqaure matrix or a batch of square matrices, :math:`Y` be\n a vector/matrix or a batch of vectors/matrices, the equation should be:\n\n .. math::\n Out = X^-1 * Y\n Specifically,\n - This system of linear equations has one solution if and only if input 'X' is invertible.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): A vector/matrix or a batch of vectors/matrices. Its shape should be `[*, M, K]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of a square system of linear equations with a unique solution for input 'x' and 'y'.\n Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # 2*X0 + X1 = 9\n # X0 + 2*X1 = 8\n\n import paddle\n import numpy as np\n\n np_x = np.array([[3, 1],[1, 2]])\n np_y = np.array([9, 8])\n x = paddle.to_tensor(np_x, dtype=\"float64\")\n y = paddle.to_tensor(np_y, dtype=\"float64\")\n out = paddle.linalg.solve(x, y)\n\n print(out)\n # [2., 3.])\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.solve(x, y)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type=\"solve\", inputs={\"X\": x,\n \"Y\": y}, outputs={\"Out\": out})\n return out\n\n\ndef triangular_solve(x,\n y,\n upper=True,\n transpose=False,\n unitriangular=False,\n name=None):\n r\"\"\"\n Computes the solution of a system of equations with a triangular coefficient matrix `x` and\n multiple right-hand sides `y` .\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input triangular coefficient matrix. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): Whether to solve the upper-triangular system of equations (default) or the lower-triangular \n system of equations. Default: True.\n transpose (bool, optional): whether `x` should be transposed before calculation. Default: False.\n unitriangular (bool, optional): whether `x` is unit triangular. If True, the diagonal elements of `x` are assumed \n to be 1 and not referenced from `x` . Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # x1 + x2 + x3 = 0\n # 2*x2 + x3 = -9\n # -x3 = 5\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n y = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.triangular_solve(x, y, upper=True)\n\n print(out)\n # [7, -2, -5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.triangular_solve(x, y, 'upper', upper, 'transpose',\n transpose, 'unitriangular',\n unitriangular)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"triangular_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'triangular_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'triangular_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='triangular_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={\n 'upper': upper,\n 'transpose': transpose,\n 'unitriangular': unitriangular\n })\n return out\n\n\ndef cholesky_solve(x, y, upper=False, name=None):\n r\"\"\"\n Solves a linear system of equations A @ X = B, given A's Cholesky factor matrix u and matrix B.\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input matrix which is upper or lower triangular Cholesky factor of square matrix A. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): whether to consider the Cholesky factor as a lower or upper triangular matrix. Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type is the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n u = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n b = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.cholesky_solve(b, u, upper=True)\n\n print(out)\n # [-2.5, -7, 9.5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky_solve(x, y, 'upper', upper)\n\n helper = LayerHelper(\"cholesky_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'cholesky_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'cholesky_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='cholesky_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef eigvalsh(x, UPLO='L', name=None):\n \"\"\"\n Computes the eigenvalues of a \n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[_, M, M]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): Lower triangular part of a (‘L’, default) or the upper triangular part (‘U’).\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor eigenvalues in ascending order.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value = paddle.eigvalsh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n \"\"\"\n if paddle.in_dynamic_mode():\n is_test = x.stop_gradient\n values, _ = _C_ops.eigvalsh(x, 'UPLO', UPLO, 'is_test', is_test)\n return values\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigvalsh', **locals())\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64', 'complex128'],\n 'eigvalsh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n is_test = x.stop_gradient\n helper.append_op(\n type='eigvalsh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO,\n 'is_test': is_test})\n return out_value\n\n\ndef lstsq(x, y, rcond=None, driver=None, name=None):\n \"\"\"\n Computes a solution to\n the least squares problem of a system of linear equations.\n\n Args:\n x (Tensor): A tensor with shape ``(*, M, N)`` , the data type of the input Tensor ``x``\n should be one of float32, float64.\n y (Tensor): A tensor with shape ``(*, M, K)`` , the data type of the input Tensor ``y`` \n should be one of float32, float64.\n rcond(float, optional): The default value is None. A float pointing number used to determine \n the effective rank of ``x``. If ``rcond`` is None, it will be set to max(M, N) times the \n machine precision of x_dtype.\n driver(str, optional): The default value is None. The name of LAPACK method to be used. For \n CPU inputs the valid values are ‘gels’, ‘gelsy’, ‘gelsd, ‘gelss’. For CUDA input, the only \n valid driver is ‘gels’. If ``driver`` is None, ‘gelsy’ is used for CPU inputs and ‘gels’ \n for CUDA inputs.\n name(str, optional): The default value is None. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple: A tuple of 4 Tensors which is (``solution``, ``residuals``, ``rank``, ``singular_values``). \n ``solution`` is a tensor with shape ``(*, N, K)``, meaning the least squares solution. ``residuals`` \n is a tensor with shape ``(*, K)``, meaning the squared residuals of the solutions, which is computed \n when M > N and every matrix in ``x`` is full-rank, otherwise return an empty tensor. ``rank`` is a tensor \n with shape ``(*)``, meaning the ranks of the matrices in ``x``, which is computed when ``driver`` in \n (‘gelsy’, ‘gelsd’, ‘gelss’), otherwise return an empty tensor. ``singular_values`` is a tensor with \n shape ``(*, min(M, N))``, meaning singular values of the matrices in ``x``, which is computed when \n ``driver`` in (‘gelsd’, ‘gelss’), otherwise return an empty tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n x = paddle.to_tensor([[1, 3], [3, 2], [5, 6.]])\n y = paddle.to_tensor([[3, 4, 6], [5, 3, 4], [1, 2, 1.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gelsd\")\n print(results[0])\n # [[ 0.78350395, -0.22165027, -0.62371236],\n # [-0.11340097, 0.78866047, 1.14948535]]\n print(results[1])\n # [19.81443405, 10.43814468, 30.56185532])\n print(results[2])\n # 2\n print(results[3])\n # [9.03455734, 1.54167950]\n\n x = paddle.to_tensor([[10, 2, 3], [3, 10, 5], [5, 6, 12.]])\n y = paddle.to_tensor([[4, 2, 9], [2, 0, 3], [2, 5, 3.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gels\")\n print(results[0])\n # [[ 0.39386186, 0.10230173, 0.93606132],\n # [ 0.10741687, -0.29028133, 0.11892585],\n # [-0.05115091, 0.51918161, -0.19948854]]\n print(results[1])\n # []\n \"\"\"\n device = paddle.get_device()\n if device == \"cpu\":\n if driver not in (None, \"gels\", \"gelss\", \"gelsd\", \"gelsy\"):\n raise ValueError(\n \"Only support valid driver is 'gels', 'gelss', 'gelsd', 'gelsy' or None for CPU inputs. But got {}\".\n format(driver))\n driver = \"gelsy\" if driver is None else driver\n elif \"gpu\" in device:\n if driver not in (None, \"gels\"):\n raise ValueError(\n \"Only support valid driver is 'gels' or None for CUDA inputs. But got {}\".\n format(driver))\n driver = \"gels\" if driver is None else driver\n else:\n raise RuntimeError(\"Only support lstsq api for CPU or CUDA device.\")\n\n if x.dtype == y.dtype and x.dtype in (paddle.float32, paddle.float64):\n pass\n else:\n raise ValueError(\n \"Only support x and y have the same dtype such as 'float32' and 'float64'.\"\n )\n\n if rcond is None:\n if x.dtype == paddle.float32:\n rcond = 1e-7 * max(x.shape[-2], x.shape[-1])\n elif x.dtype == paddle.float64:\n rcond = 1e-15 * max(x.shape[-2], x.shape[-1])\n\n if paddle.in_dynamic_mode():\n solution, rank, singular_values = _C_ops.lstsq(x, y, \"rcond\", rcond,\n \"driver\", driver)\n if x.shape[-2] > x.shape[-1]:\n matmul_out = _varbase_creator(dtype=x.dtype)\n _C_ops.matmul(x, solution, matmul_out, 'trans_x', False, 'trans_y',\n False)\n minus_out = _C_ops.elementwise_sub(matmul_out, y)\n pow_out = _C_ops.pow(minus_out, 'factor', 2)\n residuals = _C_ops.reduce_sum(pow_out, 'dim', [-2], 'keepdim',\n False, 'reduce_all', False)\n else:\n residuals = paddle.empty(shape=[0], dtype=x.dtype)\n\n if driver == \"gels\":\n rank = paddle.empty(shape=[0], dtype=paddle.int32)\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n elif driver == \"gelsy\":\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n\n return solution, residuals, rank, singular_values\n\n helper = LayerHelper('lstsq', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n check_variable_and_dtype(\n y, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n\n solution = helper.create_variable_for_type_inference(dtype=x.dtype)\n residuals = helper.create_variable_for_type_inference(dtype=x.dtype)\n rank = helper.create_variable_for_type_inference(dtype=paddle.int32)\n singular_values = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='lstsq',\n inputs={'X': x,\n 'Y': y},\n outputs={\n 'Solution': solution,\n 'Rank': rank,\n 'SingularValues': singular_values\n },\n attrs={'rcond': rcond,\n 'driver': driver})\n\n matmul_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n minus_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n pow_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': solution},\n outputs={'Out': matmul_out},\n attrs={\n 'trans_x': False,\n 'trans_y': False,\n })\n\n helper.append_op(\n type='elementwise_sub',\n inputs={'X': matmul_out,\n 'Y': y},\n outputs={'Out': minus_out})\n\n helper.append_op(\n type='pow',\n inputs={'X': minus_out},\n outputs={'Out': pow_out},\n attrs={'factor': 2})\n\n helper.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': residuals},\n attrs={'dim': [-2],\n 'keep_dim': False,\n 'reduce_all': False})\n\n if driver == \"gels\":\n rank = paddle.static.data(name='rank', shape=[0])\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n elif driver == \"gelsy\":\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n\n return solution, residuals, rank, singular_values\n"}}},{"rowIdx":35,"cells":{"function_name":{"kind":"string","value":"bincount"},"docstring":{"kind":"string","value":"Computes frequency of each value in the input tensor. \n\nArgs:\n x (Tensor): A Tensor with non-negative integer. Should be 1-D tensor.\n weights (Tensor, optional): Weight for each value in the input tensor. Should have the same shape as input. Default is None.\n minlength (int, optional): Minimum number of bins. Should be non-negative integer. Default is 0.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\nReturns:\n Tensor: The tensor of frequency.\n\nExamples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([1, 2, 1, 4, 5])\n result1 = paddle.bincount(x)\n print(result1) # [0, 2, 1, 0, 1, 1]\n\n w = paddle.to_tensor([2.1, 0.4, 0.1, 0.5, 0.5])\n result2 = paddle.bincount(x, weights=w)\n print(result2) # [0., 2.19999981, 0.40000001, 0., 0.50000000, 0.50000000]"},"masked_code":{"kind":"string","value":"# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numpy as np\nfrom ..fluid.layer_helper import LayerHelper\nfrom ..framework import _varbase_creator, _dygraph_tracer\nfrom ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype\nfrom ..static import Variable\nfrom ..fluid.framework import _in_legacy_dygraph, in_dygraph_mode\nfrom ..fluid.layers import transpose, cast # noqa: F401\nfrom ..fluid import layers\nimport paddle\nfrom paddle.common_ops_import import core\nfrom paddle.common_ops_import import VarDesc\nfrom paddle import _C_ops\n\n__all__ = []\n\n\ndef matmul(x, y, transpose_x=False, transpose_y=False, name=None):\n \"\"\"\n Applies matrix multiplication to two tensors. `matmul` follows\n the complete broadcast rules,\n and its behavior is consistent with `np.matmul`.\n\n Currently, the input tensors' number of dimensions can be any, `matmul` can be used to\n achieve the `dot`, `matmul` and `batchmatmul`.\n\n The actual behavior depends on the shapes of :math:`x`, :math:`y` and the\n flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:\n\n - If a transpose flag is specified, the last two dimensions of the tensor\n are transposed. If the tensor is ndim-1 of shape, the transpose is invalid. If the tensor\n is ndim-1 of shape :math:`[D]`, then for :math:`x` it is treated as :math:`[1, D]`, whereas\n for :math:`y` it is the opposite: It is treated as :math:`[D, 1]`.\n\n The multiplication behavior depends on the dimensions of `x` and `y`. Specifically:\n\n - If both tensors are 1-dimensional, the dot product result is obtained.\n\n - If both tensors are 2-dimensional, the matrix-matrix product is obtained.\n\n - If the `x` is 1-dimensional and the `y` is 2-dimensional,\n a `1` is prepended to its dimension in order to conduct the matrix multiply.\n After the matrix multiply, the prepended dimension is removed.\n\n - If the `x` is 2-dimensional and `y` is 1-dimensional,\n the matrix-vector product is obtained.\n\n - If both arguments are at least 1-dimensional and at least one argument\n is N-dimensional (where N > 2), then a batched matrix multiply is obtained.\n If the first argument is 1-dimensional, a 1 is prepended to its dimension\n in order to conduct the batched matrix multiply and removed after.\n If the second argument is 1-dimensional, a 1 is appended to its\n dimension for the purpose of the batched matrix multiple and removed after.\n The non-matrix (exclude the last two dimensions) dimensions are\n broadcasted according the broadcast rule.\n For example, if input is a (j, 1, n, m) tensor and the other is a (k, m, p) tensor,\n out will be a (j, k, n, p) tensor.\n\n Args:\n x (Tensor): The input tensor which is a Tensor.\n y (Tensor): The input tensor which is a Tensor.\n transpose_x (bool): Whether to transpose :math:`x` before multiplication.\n transpose_y (bool): Whether to transpose :math:`y` before multiplication.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # vector * vector\n x_data = np.random.random([10]).astype(np.float32)\n y_data = np.random.random([10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [1]\n\n # matrix * vector\n x_data = np.random.random([10, 5]).astype(np.float32)\n y_data = np.random.random([5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10]\n\n # batched matrix * broadcasted vector\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([2]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5]\n\n # batched matrix * batched matrix\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([10, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5, 5]\n\n # batched matrix * broadcasted matrix\n x_data = np.random.random([10, 1, 5, 2]).astype(np.float32)\n y_data = np.random.random([1, 3, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 3, 5, 5]\n\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_matmul(x, y, transpose_x, transpose_y)\n\n if _in_legacy_dygraph():\n op_type = 'matmul_v2'\n op = getattr(_C_ops, op_type)\n return op(x, y, 'trans_x', transpose_x, 'trans_y', transpose_y)\n\n attrs = {\n 'trans_x': transpose_x,\n 'trans_y': transpose_y,\n }\n\n def __check_input(x, y):\n var_names = {'x': x, 'y': y}\n for name, val in var_names.items():\n check_variable_and_dtype(\n val, name,\n ['float16', 'float32', 'float64', 'complex64', 'complex128'],\n 'matmul')\n\n __check_input(x, y)\n\n helper = LayerHelper('matmul_v2', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef norm(x, p='fro', axis=None, keepdim=False, name=None):\n \"\"\"\n\n Returns the matrix norm (Frobenius) or vector norm (the 1-norm, the Euclidean\n or 2-norm, and in general the p-norm for p > 0) of a given tensor.\n\n .. note::\n This norm API is different from `numpy.linalg.norm`.\n This api supports high-order input tensors (rank >= 3), and certain axis need to be pointed out to calculate the norm.\n But `numpy.linalg.norm` only supports 1-D vector or 2-D matrix as input tensor.\n For p-order matrix norm, this api actually treats matrix as a flattened vector to calculate the vector norm, NOT REAL MATRIX NORM.\n\n Args:\n x (Tensor): The input tensor could be N-D tensor, and the input data\n type could be float32 or float64.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `0`, `1`, `2`,\n `inf`, `-inf` and any positive real number yielding the corresponding p-norm. Not supported: ord < 0 and nuclear norm.\n Default value is `fro`.\n axis (int|list|tuple, optional): The axis on which to apply norm operation. If axis is int\n or list(int)/tuple(int) with only one element, the vector norm is computed over the axis.\n If `axis < 0`, the dimension to norm operation is rank(input) + axis.\n If axis is a list(int)/tuple(int) with two elements, the matrix norm is computed over the axis.\n Defalut value is `None`.\n keepdim (bool, optional): Whether to reserve the reduced dimension in the\n output Tensor. The result tensor will have fewer dimension\n than the :attr:`input` unless :attr:`keepdim` is true, default\n value is False.\n name (str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: results of norm operation on the specified axis of input tensor,\n it's data type is the same as input's Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n shape=[2, 3, 4]\n np_input = np.arange(24).astype('float32') - 12\n np_input = np_input.reshape(shape)\n x = paddle.to_tensor(np_input)\n #[[[-12. -11. -10. -9.] [ -8. -7. -6. -5.] [ -4. -3. -2. -1.]]\n # [[ 0. 1. 2. 3.] [ 4. 5. 6. 7.] [ 8. 9. 10. 11.]]]\n\n # compute frobenius norm along last two dimensions.\n out_fro = paddle.linalg.norm(x, p='fro', axis=[0,1])\n # out_fro.numpy() [17.435596 16.911535 16.7332 16.911535]\n\n # compute 2-order vector norm along last dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=-1)\n #out_pnorm.numpy(): [[21.118711 13.190906 5.477226]\n # [ 3.7416575 11.224972 19.131126]]\n\n # compute 2-order norm along [0,1] dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=[0,1])\n #out_pnorm.numpy(): [17.435596 16.911535 16.7332 16.911535]\n\n # compute inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=np.inf)\n #out_pnorm.numpy() = [12.]\n out_pnorm = paddle.linalg.norm(x, p=np.inf, axis=0)\n #out_pnorm.numpy(): [[12. 11. 10. 9.] [8. 7. 6. 7.] [8. 9. 10. 11.]]\n\n # compute -inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=-np.inf)\n #out_pnorm.numpy(): [0.]\n out_pnorm = paddle.linalg.norm(x, p=-np.inf, axis=0)\n #out_pnorm.numpy(): [[0. 1. 2. 3.] [4. 5. 6. 5.] [4. 3. 2. 1.]]\n \"\"\"\n\n def frobenius_norm(input, dim=None, keepdim=False, name=None):\n \"\"\"\n The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n dim (list, optional): None for last two dimensions.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if dim is not None and not (isinstance(dim, list) and len(dim) == 2):\n raise ValueError(\n \"The dim of frobenius norm op should be None or two elements list!\"\n )\n if paddle.in_dynamic_mode():\n if dim is None:\n return _C_ops.frobenius_norm(input, 'keep_dim', keepdim,\n 'reduce_all', True)\n return _C_ops.frobenius_norm(input, 'dim', dim, 'keep_dim', keepdim,\n 'reduce_all', False)\n attrs = {'dim': dim, 'keep_dim': keepdim, 'reduce_all': False}\n if dim is None:\n attrs['reduce_all'] = True\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'frobenius_norm')\n\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='frobenius_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def vector_norm(input,\n porder=None,\n axis=None,\n keepdim=False,\n asvector=False,\n name=None):\n \"\"\"\n Calculate the p-order vector norm for certain dimension of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n porder (float, optional): None for porder=2.0.\n axis (int, optional): None for last dimension.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if paddle.in_dynamic_mode():\n if axis is None: axis = -1\n return _C_ops.p_norm(input, 'porder', porder, 'axis', axis,\n 'keepdim', keepdim, 'asvector', asvector)\n if porder is not None:\n check_type(porder, 'porder', (float, int), 'p_norm')\n if axis is not None:\n check_type(axis, 'axis', (int), 'p_norm')\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'p_norm')\n\n attrs = {\n 'axis': axis if axis is not None else -1,\n 'porder': float(porder) if porder is not None else 2.0,\n 'keepdim': keepdim,\n 'asvector': asvector,\n 'epsilon': 1e-12,\n }\n helper = LayerHelper('p_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='p_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def inf_norm(input,\n porder=None,\n axis=axis,\n keepdim=False,\n asvector=False,\n name=None):\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n helper.append_op(type='abs', inputs={'X': input}, outputs={'Out': out})\n reduce_out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n reduce_all = True if axis == None or axis == [] or asvector == True else False\n axis = axis if axis != None and axis != [] else [0]\n\n reduce_type = 'reduce_max' if porder == np.float(\n 'inf') else 'reduce_min'\n helper.append_op(\n type=reduce_type,\n inputs={'X': out},\n outputs={'Out': reduce_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n\n return reduce_out\n\n def p_matrix_norm(input, porder=1., axis=axis, keepdim=False, name=None):\n \"\"\"\n NOTE:\n This function actually treats the matrix as flattened vector to calculate vector norm instead of matrix norm.\n \"\"\"\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n\n block.append_op(\n type='pow',\n inputs={'X': abs_out},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': True if axis is None else False\n })\n porder\n block.append_op(\n type='pow',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n if axis is None and p is not None:\n if isinstance(p, str):\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n porder=p,\n axis=axis,\n keepdim=keepdim,\n asvector=True,\n name=name)\n else:\n raise ValueError(\"only valid p type is string or float, found {}\".\n format(type(p)))\n\n if isinstance(axis, tuple):\n axis = list(axis)\n if isinstance(axis, list) and len(axis) == 1:\n axis = axis[0]\n\n #calculate vector norm, where axis is int or list with only one integer\n if isinstance(axis, int):\n if isinstance(p, str):\n if p == \"fro\":\n return vector_norm(\n x,\n porder=2,\n axis=axis,\n keepdim=keepdim,\n asvector=False,\n name=name)\n\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n axis=axis,\n porder=p,\n keepdim=keepdim,\n asvector=False,\n name=name)\n else:\n raise ValueError(\n \"unspport p for p-order vector norm. except float, found {}\".\n format(p))\n #calculate matrix norm, where axis is list with two integers\n elif isinstance(axis, list) and len(axis) == 2:\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n elif p == np.inf or p == -np.inf:\n return inf_norm(x, porder=p, axis=axis, keepdim=keepdim, name=name)\n elif p == 0:\n raise ValueError(\n \"just suport axis type int or list (length of list <=1) if p = 0, found {}\".\n format(axis))\n else:\n return p_matrix_norm(\n x, porder=p, axis=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"except axis type int or list (length of list <=2), found {}\".\n format(axis))\n\n\ndef dist(x, y, p=2, name=None):\n r\"\"\"\n\n This OP returns the p-norm of (x - y). It is not a norm in a strict sense, only as a measure\n of distance. The shapes of x and y must be broadcastable. The definition is as follows, for\n details, please refer to the `numpy's broadcasting <https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html>`_:\n\n - Each input has at least one dimension.\n - Match the two input dimensions from back to front, the dimension sizes must either be equal, one of them is 1, or one of them does not exist.\n\n Where, z = x - y, the shapes of x and y are broadcastable, then the shape of z can be\n obtained as follows:\n\n 1. If the number of dimensions of x and y are not equal, prepend 1 to the dimensions of the\n tensor with fewer dimensions.\n\n For example, The shape of x is [8, 1, 6, 1], the shape of y is [7, 1, 5], prepend 1 to the\n dimension of y.\n\n x (4-D Tensor): 8 x 1 x 6 x 1\n\n y (4-D Tensor): 1 x 7 x 1 x 5\n\n 2. Determine the size of each dimension of the output z: choose the maximum value from the\n two input dimensions.\n\n z (4-D Tensor): 8 x 7 x 6 x 5\n\n If the number of dimensions of the two inputs are the same, the size of the output can be\n directly determined in step 2. When p takes different values, the norm formula is as follows:\n\n When p = 0, defining $0^0=0$, the zero-norm of z is simply the number of non-zero elements of z.\n\n .. math::\n\n ||z||_{0}=\\lim_{p \\\\rightarrow 0}\\sum_{i=1}^{m}|z_i|^{p}\n\n When p = inf, the inf-norm of z is the maximum element of z.\n\n .. math::\n\n ||z||_\\infty=\\max_i |z_i|\n\n When p = -inf, the negative-inf-norm of z is the minimum element of z.\n\n .. math::\n\n ||z||_{-\\infty}=\\min_i |z_i|\n\n Otherwise, the p-norm of z follows the formula,\n\n .. math::\n\n ||z||_{p}=(\\sum_{i=1}^{m}|z_i|^p)^{\\\\frac{1}{p}}\n\n Args:\n x (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n y (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n p (float, optional): The norm to be computed, its data type is float32 or float64. Default: 2.\n\n Returns:\n Tensor: Tensor that is the p-norm of (x - y).\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor(np.array([[3, 3],[3, 3]]), \"float32\")\n y = paddle.to_tensor(np.array([[3, 3],[3, 1]]), \"float32\")\n out = paddle.dist(x, y, 0)\n print(out) # out = [1.]\n\n out = paddle.dist(x, y, 2)\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"inf\"))\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"-inf\"))\n print(out) # out = [0.]\n \"\"\"\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'dist')\n check_variable_and_dtype(y, 'dtype', ['float32', 'float64'], 'dist')\n check_type(p, 'p', (float, int), 'dist')\n helper = LayerHelper(\"dist\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n outputs = {'Out': [out]}\n attrs = {\"p\": float(p)}\n helper.append_op(\n type='dist', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef cond(x, p=None, name=None):\n \"\"\"\n\n Computes the condition number of a matrix or batches of matrices with respect to a matrix norm ``p``.\n\n Args:\n x (Tensor): The input tensor could be tensor of shape ``(*, m, n)`` where ``*`` is zero or more batch dimensions\n for ``p`` in ``(2, -2)``, or of shape ``(*, n, n)`` where every matrix is invertible for any supported ``p``.\n And the input data type could be ``float32`` or ``float64``.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `nuc`, `1`, `-1`, `2`, `-2`,\n `inf`, `-inf`. Default value is `None`, meaning that the order of the norm is `2`.\n name (str, optional): The default value is `None`. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: computing results of condition number, its data type is the same as input Tensor ``x``.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1., 0, -1], [0, 1, 0], [1, 0, 1]])\n\n # compute conditional number when p is None\n out = paddle.linalg.cond(x)\n # out.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is 'fro'\n out_fro = paddle.linalg.cond(x, p='fro')\n # out_fro.numpy() [3.1622777]\n\n # compute conditional number when order of the norm is 'nuc'\n out_nuc = paddle.linalg.cond(x, p='nuc')\n # out_nuc.numpy() [9.2426405]\n\n # compute conditional number when order of the norm is 1\n out_1 = paddle.linalg.cond(x, p=1)\n # out_1.numpy() [2.]\n\n # compute conditional number when order of the norm is -1\n out_minus_1 = paddle.linalg.cond(x, p=-1)\n # out_minus_1.numpy() [1.]\n\n # compute conditional number when order of the norm is 2\n out_2 = paddle.linalg.cond(x, p=2)\n # out_2.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is -1\n out_minus_2 = paddle.linalg.cond(x, p=-2)\n # out_minus_2.numpy() [0.70710677]\n\n # compute conditional number when order of the norm is inf\n out_inf = paddle.linalg.cond(x, p=np.inf)\n # out_inf.numpy() [2.]\n\n # compute conditional number when order of the norm is -inf\n out_minus_inf = paddle.linalg.cond(x, p=-np.inf)\n # out_minus_inf.numpy() [1.]\n\n a = paddle.to_tensor(np.random.randn(2, 4, 4).astype('float32'))\n # a.numpy()\n # [[[ 0.14063153 -0.996288 0.7996131 -0.02571543]\n # [-0.16303636 1.5534962 -0.49919784 -0.04402903]\n # [-1.1341571 -0.6022629 0.5445269 0.29154757]\n # [-0.16816919 -0.30972657 1.7521842 -0.5402487 ]]\n # [[-0.58081484 0.12402827 0.7229862 -0.55046535]\n # [-0.15178485 -1.1604939 0.75810957 0.30971205]\n # [-0.9669573 1.0940945 -0.27363303 -0.35416734]\n # [-1.216529 2.0018666 -0.7773689 -0.17556527]]]\n a_cond_fro = paddle.linalg.cond(a, p='fro')\n # a_cond_fro.numpy() [31.572273 28.120834]\n\n b = paddle.to_tensor(np.random.randn(2, 3, 4).astype('float64'))\n # b.numpy()\n # [[[ 1.61707487 0.46829144 0.38130416 0.82546736]\n # [-1.72710298 0.08866375 -0.62518804 0.16128892]\n # [-0.02822879 -1.67764516 0.11141444 0.3220113 ]]\n # [[ 0.22524372 0.62474921 -0.85503233 -1.03960523]\n # [-0.76620689 0.56673047 0.85064753 -0.45158196]\n # [ 1.47595418 2.23646462 1.5701758 0.10497519]]]\n b_cond_2 = paddle.linalg.cond(b, p=2)\n # b_cond_2.numpy() [3.30064451 2.51976252]\n\n \"\"\"\n\n def mat_norm(input, porder=1., axis=None):\n \"\"\"\n NOTE:\n Calculate the matrix norm of a square matrix or batches of square matrices,\n when porder is in (1, -1, inf, -inf)\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n axis = axis if axis != None and axis != [] else [0]\n keepdim = False\n\n if paddle.in_dynamic_mode():\n abs_out = _C_ops.abs(input)\n sum_out = _C_ops.reduce_sum(abs_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == 1 or porder == np.inf:\n return _C_ops.reduce_max(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == -1 or porder == -np.inf:\n return _C_ops.reduce_min(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n\n block = LayerHelper('norm', **locals())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n block.append_op(\n type='reduce_sum',\n inputs={'X': abs_out},\n outputs={'Out': sum_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 1 or porder == np.inf:\n block.append_op(\n type='reduce_max',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n if porder == -1 or porder == -np.inf:\n block.append_op(\n type='reduce_min',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n\n def fro_norm(input, porder=2, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the frobenius norm of a square matrix or batches of square matrices.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n if paddle.in_dynamic_mode():\n pow_out = _C_ops.pow(input, 'factor', porder)\n sum_out_1 = _C_ops.reduce_sum(pow_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n sum_out_2 = _C_ops.reduce_sum(sum_out_1, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n return _C_ops.pow(sum_out_2, 'factor', float(1. / porder))\n\n block = LayerHelper('norm', **locals())\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_1 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_2 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='pow',\n inputs={'X': input},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out_1},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_sum',\n inputs={'X': sum_out_1},\n outputs={'Out': sum_out_2},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='pow',\n inputs={'X': sum_out_2},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n def svd_norm(input, porder, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the matrix norm, which is related to singular values, of a matrix\n or batches of matrices, including nuclear norm, 2-norm and (-2)-norm.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n u, s, vh = svd(input, full_matrices=False)\n\n if paddle.in_dynamic_mode():\n if porder == \"nuc\":\n return _C_ops.reduce_sum(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n max_out = _C_ops.reduce_max(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n min_out = _C_ops.reduce_min(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n if porder == 2:\n return _C_ops.elementwise_div(max_out, min_out, 'aixs', axis,\n 'use_mkldnn', False)\n if porder == -2:\n return _C_ops.elementwise_div(min_out, max_out, 'aixs', axis,\n 'use_mkldnn', False)\n\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n if porder == \"nuc\":\n block.append_op(\n type='reduce_sum',\n inputs={'X': s},\n outputs={'Out': out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n max_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n min_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_min',\n inputs={'X': s},\n outputs={'Out': min_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': max_out,\n 'Y': min_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n if porder == -2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': min_out,\n 'Y': max_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n\n def empty_tensor(input, shape):\n if paddle.in_dynamic_mode():\n return input.reshape(shape)\n raise ValueError(\"only support x is nonempty tensor in static mode\")\n\n x_shape = list(x.shape)\n if not len(x_shape) >= 2:\n raise ValueError(\"input should be a matrix or batches of matrices, \" +\n \"but the dimention of received input is {}\".format(\n len(x_shape)))\n if p == None:\n p = 2\n x_size = 0 if (0 in x_shape) else 1\n if p in (\"fro\", \"nuc\", 1, -1, np.inf, -np.inf):\n if x_shape[len(x_shape) - 1] == x_shape[len(x_shape) - 2]:\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n x_inv = x.inverse()\n if p == \"fro\":\n return fro_norm(x) * fro_norm(x_inv)\n if p == \"nuc\":\n return svd_norm(x, p) * svd_norm(x_inv, p)\n if p in (1, -1):\n return mat_norm(\n x, porder=p, axis=[-2]) * mat_norm(\n x_inv, porder=p, axis=[-2])\n if p in (np.inf, -np.inf):\n return mat_norm(\n x, porder=p, axis=[-1]) * mat_norm(\n x_inv, porder=p, axis=[-1])\n else:\n raise ValueError(\"only support p is {} when input is a \".format(p) +\n \"square matrix or batches of square matrices\")\n elif p in (2, -2):\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n return svd_norm(x, porder=p)\n else:\n raise ValueError(\n \"unsupported {} for p, only supporting ('fro', 'nuc', \".format(\n p) + \"1, -1, 2, -2, inf, -inf) or none\")\n\n\ndef dot(x, y, name=None):\n \"\"\"\n This operator calculates inner product for vectors.\n\n .. note::\n Support 1-d and 2-d Tensor. When it is 2d, the first dimension of this matrix\n is the batch dimension, which means that the vectors of multiple batches are dotted.\n\n Parameters:\n x(Tensor): 1-D or 2-D ``Tensor``. Its dtype should be ``float32``, ``float64``, ``int32``, ``int64``\n y(Tensor): 1-D or 2-D ``Tensor``. Its dtype soulde be ``float32``, ``float64``, ``int32``, ``int64``\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: the calculated result Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x_data = np.random.uniform(0.1, 1, [10]).astype(np.float32)\n y_data = np.random.uniform(1, 3, [10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.dot(x, y)\n print(z)\n\n \"\"\"\n op_type = 'dot'\n # skip var type check in dygraph mode to improve efficiency\n if paddle.in_dynamic_mode():\n op = getattr(_C_ops, op_type)\n return op(x, y)\n\n assert x is not None, 'x cannot be None in {}'.format(op_type)\n assert y is not None, 'y cannot be None in {}'.format(op_type)\n\n check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n check_variable_and_dtype(y, 'y', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n\n helper = LayerHelper(op_type, **locals())\n if name is None:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n else:\n out = helper.create_variable(\n name=name, dtype=x.dtype, persistable=False)\n helper.append_op(\n type=\"dot\", inputs={'X': x,\n 'Y': y}, attrs={}, outputs={\"Out\": out})\n return out\n\n\ndef cov(x, rowvar=True, ddof=True, fweights=None, aweights=None, name=None):\n \"\"\"\n Estimate the covariance matrix of the input variables, given data and weights.\n\n A covariance matrix is a square matrix, indicate the covariance of each pair variables in the input matrix.\n For example, for an N-dimensional samples X=[x1,x2,…xN]T, then the covariance matrix \n element Cij is the covariance of xi and xj. The element Cii is the variance of xi itself.\n\n Parameters:\n x(Tensor): A N-D(N<=2) Tensor containing multiple variables and observations. By default, each row of x represents a variable. Also see rowvar below.\n rowvar(Bool, optional): If rowvar is True (default), then each row represents a variable, with observations in the columns. Default: True\n ddof(Bool, optional): If ddof=True will return the unbiased estimate, and ddof=False will return the simple average. Default: True\n fweights(Tensor, optional): 1-D Tensor of integer frequency weights; The number of times each observation vector should be repeated. Default: None\n aweights(Tensor, optional): 1-D Tensor of observation vector weights. How important of the observation vector, larger data means this element is more important. Default: None\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: The covariance matrix Tensor of the variables.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n\n xt = paddle.rand((3,4))\n paddle.linalg.cov(xt)\n\n '''\n Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n [[0.07918842, 0.06127326, 0.01493049],\n [0.06127326, 0.06166256, 0.00302668],\n [0.01493049, 0.00302668, 0.01632146]])\n '''\n \"\"\"\n op_type = 'cov'\n if len(x.shape) > 2 or len(x.shape) < 1:\n raise ValueError(\n \"Input(x) only support N-D (1<=N<=2) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cov')\n nx = x\n if len(x.shape) == 1:\n nx = x.reshape((1, -1))\n if not rowvar and nx.shape[0] != 1:\n nx = nx.t()\n w = None\n observation_num = nx.shape[1]\n if fweights is not None:\n w = fweights.astype(nx.dtype)\n if len(w.shape) > 1:\n raise ValueError(\n \"Input(fweights) only support N-D (N<=1) tensor in cov, but received \"\n \"shape of Input(input) is %s.\" % len(fweights.shape))\n if fweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(fweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(fweights) is {}.\".format(observation_num,\n fweights.shape[0]))\n if fweights.min() < 0:\n raise ValueError(\n \"The value of Input(fweights) cannot be negtive, but received \"\n \"min of Input(fweights) is {}.\".format(fweights.min()))\n if not paddle.all(fweights == paddle.round(fweights.astype('float64'))):\n raise ValueError(\"Input(fweights) must be integer \")\n\n if aweights is not None:\n aw = aweights.astype(nx.dtype)\n if len(aw.shape) > 1:\n raise ValueError(\n \"Input(aweights) only support N-D (N<=1) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(aweights.shape))\n check_variable_and_dtype(aweights, 'dtype', ['float32', 'float64'],\n 'cov')\n if aweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(aweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(aweights) is {}.\".format(observation_num,\n aweights.shape[0]))\n if aweights.min() < 0:\n raise ValueError(\n \"The value of Input(aweights) cannot be negtive, but received \"\n \"min of Input(aweights) is {}.\".format(aweights.min()))\n if w is not None:\n w = w * aw\n else:\n w = aw\n\n w_sum = paddle.to_tensor(observation_num, dtype=nx.dtype)\n if fweights is not None or aweights is not None:\n w_sum = w.sum()\n if w_sum.item() == 0:\n raise ValueError(\"The sum of weights is zero, can't be normalized.\")\n\n if w is not None:\n nx_w = nx * w\n avg = (nx_w).sum(axis=1) / w_sum\n else:\n avg = nx.sum(axis=1) / w_sum\n nx_w = nx\n\n if w is not None and aweights is not None and ddof == True:\n norm_factor = w_sum - (w * aweights).sum() / w_sum\n else:\n norm_factor = w_sum - ddof\n if norm_factor <= 0:\n norm_factor = paddle.to_tensor(0, dtype=nx.dtype)\n nx = nx - avg.unsqueeze(1)\n xxt = paddle.mm(nx, nx_w.t().conj())\n cov = paddle.divide(xxt, norm_factor).squeeze()\n return cov\n\n\ndef t(input, name=None):\n \"\"\"\n Transpose <=2-D tensor.\n 0-D and 1-D tensors are returned as it is and 2-D tensor is equal to\n the paddle.transpose function which perm dimensions set 0 and 1.\n\n Args:\n input (Tensor): The input Tensor. It is a N-D (N<=2) Tensor of data types float16, float32, float64, int32.\n name(str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`\n Returns:\n Tensor: A transposed n-D Tensor, with data type being float16, float32, float64, int32, int64.\n\n For Example:\n\n .. code-block:: text\n\n # Example 1 (0-D tensor)\n x = tensor([0.79])\n paddle.t(x) = tensor([0.79])\n\n # Example 2 (1-D tensor)\n x = tensor([0.79, 0.84, 0.32])\n paddle.t(x) = tensor([0.79, 0.84, 0.32])\n\n # Example 3 (2-D tensor)\n x = tensor([0.79, 0.84, 0.32],\n [0.64, 0.14, 0.57])\n paddle.t(x) = tensor([0.79, 0.64],\n [0.84, 0.14],\n [0.32, 0.57])\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n x = paddle.ones(shape=[2, 3], dtype='int32')\n x_transposed = paddle.t(x)\n print(x_transposed.shape)\n # [3, 2]\n \"\"\"\n if len(input.shape) > 2:\n raise ValueError(\n \"Input(input) only support N-D (N<=2) tensor, but received \"\n \"length of Input(input) is %s. Perhaps you can use paddle.\"\n \"tensor.transpose() instead.\" % len(input.shape))\n if paddle.in_dynamic_mode():\n if len(input.shape) == 1:\n return input\n # 2-D tensor\n perm = [1, 0]\n out, _ = _C_ops.transpose2(input, 'axis', perm)\n return out\n\n check_variable_and_dtype(\n input, 'input', ['float16', 'float32', 'float64', 'int32',\n 'int64'], 'transpose')\n\n helper = LayerHelper('t', **locals())\n out = helper.create_variable_for_type_inference(input.dtype)\n input_shape = helper.create_variable_for_type_inference(input.dtype)\n if len(input.shape) == 1:\n out = input\n else:\n helper.append_op(\n type='transpose2',\n inputs={'X': [input]},\n outputs={'Out': [out],\n 'XShape': [input_shape]},\n attrs={'axis': [1, 0]})\n return out\n\n\ndef cross(x, y, axis=None, name=None):\n \"\"\"\n Computes the cross product between two tensors along an axis.\n\n Inputs must have the same shape, and the length of their axes should be equal to 3.\n If `axis` is not given, it defaults to the first axis found with the length 3.\n\n Args:\n x (Tensor): The first input tensor.\n y (Tensor): The second input tensor.\n axis (int, optional): The axis along which to compute the cross product. It defaults to the first axis found with the length 3.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor. A Tensor with same data type as `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0],\n [3.0, 3.0, 3.0]])\n y = paddle.to_tensor([[1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0]])\n\n z1 = paddle.cross(x, y)\n # [[-1. -1. -1.]\n # [ 2. 2. 2.]\n # [-1. -1. -1.]]\n\n z2 = paddle.cross(x, y, axis=1)\n # [[0. 0. 0.]\n # [0. 0. 0.]\n # [0. 0. 0.]]\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_cross(x, y, axis)\n else:\n if _in_legacy_dygraph():\n if axis is not None:\n return _C_ops.cross(x, y, 'dim', axis)\n else:\n return _C_ops.cross(x, y)\n else:\n helper = LayerHelper(\"cross\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n attrs = dict()\n attrs['dim'] = axis\n\n helper.append_op(\n type='cross',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef cholesky(x, upper=False, name=None):\n r\"\"\"\n Computes the Cholesky decomposition of one symmetric positive-definite\n matrix or batches of symmetric positive-definite matrice.\n\n If `upper` is `True`, the decomposition has the form :math:`A = U^{T}U` ,\n and the returned matrix :math:`U` is upper-triangular. Otherwise, the\n decomposition has the form :math:`A = LL^{T}` , and the returned matrix\n :math:`L` is lower-triangular.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[*, M, M]`,\n where * is zero or more batch dimensions, and matrices on the\n inner-most 2 dimensions all should be symmetric positive-definite.\n Its data type should be float32 or float64.\n upper (bool): The flag indicating whether to return upper or lower\n triangular matrices. Default: False.\n\n Returns:\n Tensor: A Tensor with same shape and data type as `x`. It represents \\\n triangular matrices generated by Cholesky decomposition.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n a = np.random.rand(3, 3)\n a_t = np.transpose(a, [1, 0])\n x_data = np.matmul(a, a_t) + 1e-03\n x = paddle.to_tensor(x_data)\n out = paddle.linalg.cholesky(x, upper=False)\n print(out)\n # [[1.190523 0. 0. ]\n # [0.9906703 0.27676893 0. ]\n # [1.25450498 0.05600871 0.06400121]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky(x, \"upper\", upper)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cholesky')\n check_type(upper, 'upper', bool, 'cholesky')\n helper = LayerHelper('cholesky', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='cholesky',\n inputs={'X': [x]},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef matrix_rank(x, tol=None, hermitian=False, name=None):\n r\"\"\"\n Computes the rank of a matrix.\n\n The rank of a matrix is the number of singular values that are greater than the specified `tol` threshold when hermitian=False,\n or the number of eigenvalues in absolute value that are greater than the specified `tol` threshold when hermitian=True.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., m, n]`, where `...` is zero or more batch dimensions. If `x` is a batch\n of matrices then the output has the same batch dimensions. The data type of `x` should be float32 or float64.\n tol (float,Tensor,optional): the tolerance value. Default: None. If `tol` is not specified, and `sigma` is the largest\n singular value (or eigenvalues in absolute value), and `eps` is the epsilon value for the dtype of `x`, then `tol` is computed\n with formula `tol=sigma * max(m,n) * eps`. Note that if `x` is a batch of matrices, `tol` is computed this way for every batch.\n hermitian (bool,optional): indicates whether `x` is Hermitian. Default: False. When hermitian=True, `x` is assumed to be Hermitian,\n enabling a more efficient method for finding eigenvalues, but `x` is not checked inside the function. Instead, We just use\n the lower triangular of the matrix to compute.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: Rank of tensor x.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n a = paddle.eye(10)\n b = paddle.linalg.matrix_rank(a)\n print(b)\n # b = [10]\n\n c = paddle.ones(shape=[3, 4, 5, 5])\n d = paddle.linalg.matrix_rank(c, tol=0.01, hermitian=True)\n print(d)\n # d = [[1, 1, 1, 1],\n # [1, 1, 1, 1],\n # [1, 1, 1, 1]]\n\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if tol is None:\n tol_tensor = None\n tol_attr = 0.0\n use_default_tol = True\n elif isinstance(tol, Variable):\n if tol.dtype != x.dtype:\n tol_tensor = cast(tol, x.dtype)\n else:\n tol_tensor = tol\n tol_attr = 0.0\n use_default_tol = False\n else:\n tol_tensor = None\n tol_attr = float(tol)\n use_default_tol = False\n return _C_ops.matrix_rank(x, tol_tensor, \"tol\", tol_attr, 'hermitian',\n hermitian, 'use_default_tol', use_default_tol)\n\n inputs = {}\n attrs = {}\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'matrix_rank')\n inputs['X'] = x\n if tol is None:\n attrs['use_default_tol'] = True\n elif isinstance(tol, Variable):\n check_variable_and_dtype(tol, 'tol', ['float32'], 'matrix_rank')\n attrs['use_default_tol'] = False\n if tol.dtype != x.dtype:\n inputs['TolTensor'] = cast(tol, x.dtype)\n else:\n inputs['TolTensor'] = tol\n else:\n check_type(tol, 'tol', float, 'matrix_rank')\n attrs['use_default_tol'] = False\n attrs['tol'] = tol\n check_type(hermitian, 'hermitian', bool, 'matrix_rank')\n attrs['hermitian'] = hermitian\n\n helper = LayerHelper('matrix_rank', **locals())\n out = helper.create_variable_for_type_inference(dtype='int32')\n helper.append_op(\n type='matrix_rank', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef bmm(x, y, name=None):\n \"\"\"\n Applies batched matrix multiplication to two tensors.\n\n Both of the two input tensors must be three-dementional and share the same batch size.\n\n if x is a (b, m, k) tensor, y is a (b, k, n) tensor, the output will be a (b, m, n) tensor.\n\n Args:\n x (Tensor): The input Tensor.\n y (Tensor): The input Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The product Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n # In imperative mode:\n # size x: (2, 2, 3) and y: (2, 3, 2)\n x = paddle.to_tensor([[[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0]],\n [[3.0, 3.0, 3.0],\n [4.0, 4.0, 4.0]]])\n y = paddle.to_tensor([[[1.0, 1.0],[2.0, 2.0],[3.0, 3.0]],\n [[4.0, 4.0],[5.0, 5.0],[6.0, 6.0]]])\n out = paddle.bmm(x, y)\n #output size: (2, 2, 2)\n #output value:\n #[[[6.0, 6.0],[12.0, 12.0]],[[45.0, 45.0],[60.0, 60.0]]]\n out_np = out.numpy()\n\n \"\"\"\n x_shape = x.shape\n y_shape = y.shape\n if not len(x_shape) == len(y_shape) == 3:\n raise ValueError(\n \"x and y should be 3-dimensional. But received x's dimention: {}, y's dimention: {}\".\n format(x_shape, y_shape))\n if x_shape[2] != y_shape[1]:\n raise ValueError(\n \"x's width must be equal with y's height. But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n if x_shape[0] != y_shape[0]:\n raise ValueError(\n \"x's batch (shape[0]) must be equal with y's batch (shape[0]). But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.bmm(x, y)\n\n helper = LayerHelper('bmm', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='bmm', inputs={'X': x, 'Y': y}, outputs={'Out': out})\n return out\n\n\ndef histogram(input, bins=100, min=0, max=0, name=None):\n \"\"\"\n Computes the histogram of a tensor. The elements are sorted into equal width bins between min and max.\n If min and max are both zero, the minimum and maximum values of the data are used.\n\n Args:\n input (Tensor): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor\n should be float32, float64, int32, int64.\n bins (int): number of histogram bins\n min (int): lower end of the range (inclusive)\n max (int): upper end of the range (inclusive)\n\n Returns:\n Tensor: data type is int64, shape is (nbins,).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n inputs = paddle.to_tensor([1, 2, 1])\n result = paddle.histogram(inputs, bins=4, min=0, max=3)\n print(result) # [0, 2, 1, 0]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.histogram(input, \"bins\", bins, \"min\", min, \"max\", max)\n\n helper = LayerHelper('histogram', **locals())\n check_variable_and_dtype(\n input, 'X', ['int32', 'int64', 'float32', 'float64'], 'histogram')\n out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)\n helper.append_op(\n type='histogram',\n inputs={'X': input},\n outputs={'Out': out},\n attrs={'bins': bins,\n 'min': min,\n 'max': max})\n return out\n\n\n# MASKED: bincount function (lines 1417-1467)\n\n\ndef mv(x, vec, name=None):\n \"\"\"\n Performs a matrix-vector product of the matrix x and the vector vec.\n\n Args:\n x (Tensor): A tensor with shape :math:`[M, N]` , The data type of the input Tensor x\n should be one of float32, float64.\n vec (Tensor): A tensor with shape :math:`[N]` , The data type of the input Tensor x\n should be one of float32, float64.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor which is producted by x and vec.\n\n Examples:\n .. code-block:: python\n\n # x: [M, N], vec: [N]\n # paddle.mv(x, vec) # out: [M]\n\n import numpy as np\n import paddle\n\n x_data = np.array([[2, 1, 3], [3, 0, 1]]).astype(\"float64\")\n x = paddle.to_tensor(x_data)\n vec_data = np.array([3, 5, 1])\n vec = paddle.to_tensor(vec_data).astype(\"float64\")\n out = paddle.mv(x, vec)\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_mv(x, vec)\n else:\n if _in_legacy_dygraph():\n out = _C_ops.mv(x, vec)\n return out\n else:\n\n def __check_input(x, vec):\n var_names = {'x': x, 'vec': vec}\n for name, val in var_names.items():\n check_variable_and_dtype(val, name, ['float32', 'float64'],\n 'mv')\n x_shape = list(x.shape)\n vec_shape = list(vec.shape)\n if len(x_shape) != 2:\n raise ValueError(\n \"x should be 2-dimensional. But received x's dimention: {}\".\n format(x_shape))\n if len(vec_shape) != 1:\n raise ValueError(\n \"vec should be 1-dimensional. But received vec's dimention: {}\".\n format(vec_shape))\n\n __check_input(x, vec)\n\n helper = LayerHelper('mv', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='mv', inputs={'X': x,\n 'Vec': vec}, outputs={'Out': out})\n return out\n\n\ndef det(x, name=None):\n \"\"\"\n Calculates determinant value of a square matrix or batches of square matrices.\n Args:\n x (Tensor): input (Tensor): the input matrix of size `(n, n)` or the batch of matrices of size\n `(*, n, n)` where `*` is one or more batch dimensions.\n Returns:\n y (Tensor):the determinant value of a square matrix or batches of square matrices.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.det(x)\n\n print(A)\n\n # [ 0.02547996, 2.52317095, -6.15900707])\n\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.determinant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'det')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('determinant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='determinant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef slogdet(x, name=None):\n \"\"\"\n Calculates the sign and natural logarithm of the absolute value of a square matrix's or batches square matrices' determinant.\n The determinant can be computed with ``sign * exp(logabsdet)\n\n Supports input of float, double\n\n Note that for matrices that have zero determinant, this returns ``(0, -inf)``\n Args:\n x (Tensor): the batch of matrices of size :math:`(*, n, n)`\n where math:`*` is one or more batch dimensions.\n\n Returns:\n y (Tensor): A tensor containing the sign of the determinant and the natural logarithm\n of the absolute value of determinant, respectively.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.slogdet(x)\n\n print(A)\n\n # [[ 1. , 1. , -1. ],\n # [-0.98610914, -0.43010661, -0.10872950]])\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.slogdeterminant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'slogdet')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('slogdeterminant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='slogdeterminant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef svd(x, full_matrices=False, name=None):\n r\"\"\"\n Computes the singular value decomposition of one matrix or a batch of regular matrices.\n\n Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies:\n\n .. math::\n X = U * diag(S) * VT\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., N, M]`,\n where `...` is zero or more batch dimensions. N and M can be arbitraty\n positive number. Note that if x is sigular matrices, the grad is numerical\n instable. The data type of x should be float32 or float64.\n full_matrices (bool): A flag to control the behavor of svd.\n If full_matrices = True, svd op will compute full U and V matrics,\n which means shape of U is `[..., N, N]`, shape of V is `[..., M, M]`. K = min(M, N).\n If full_matrices = False, svd op will use a economic method to store U and V.\n which means shape of U is `[..., N, K]`, shape of V is `[..., M, K]`. K = min(M, N).\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple of 3 tensors: (U, S, VH). VH is the conjugate transpose of V. S is the singlar value vectors of matrics with shape `[..., K]`\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 2.0], [1.0, 3.0], [4.0, 6.0]]).astype('float64')\n x = x.reshape([3, 2])\n u, s, vh = paddle.linalg.svd(x)\n print (u)\n #U = [[ 0.27364809, -0.21695147 ],\n # [ 0.37892198, -0.87112408 ],\n # [ 0.8840446 , 0.44053933 ]]\n\n print (s)\n #S = [8.14753743, 0.78589688]\n print (vh)\n #VT= [[ 0.51411221, 0.85772294],\n # [ 0.85772294, -0.51411221]]\n\n # one can verify : U * S * VT == X\n # U * UH == I\n # V * VH == I\n \"\"\"\n\n if paddle.in_dynamic_mode():\n return _C_ops.svd(x, 'full_matrices', full_matrices)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'svd')\n check_type(full_matrices, 'full_matrices', bool, 'svd')\n helper = LayerHelper('svd', **locals())\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n vh = helper.create_variable_for_type_inference(dtype=x.dtype)\n s = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['full_matrices'] = full_matrices\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vh,\n 'S': s},\n attrs=attrs, )\n return u, s, vh\n\n\ndef matrix_power(x, n, name=None):\n r\"\"\"\n Computes the n-th power of a square matrix or a batch of square matrices.\n\n Let :math:`X` be a sqaure matrix or a batch of square matrices, :math:`n` be\n an exponent, the equation should be:\n\n .. math::\n Out = X ^ {n}\n\n Specifically,\n\n - If `n > 0`, it returns the matrix or a batch of matrices raised to the power\n of `n`.\n\n - If `n = 0`, it returns the identity matrix or a batch of identity matrices.\n\n - If `n < 0`, it returns the inverse of each matrix (if invertible) raised to\n the power of `abs(n)`.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices to be raised\n to power `n`. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n n (int): The exponent. It can be any positive, negative integer or zero.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The n-th power of the matrix (or the batch of matrices) `x`. Its\n data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1, 2, 3],\n [1, 4, 9],\n [1, 8, 27]], dtype='float64')\n print(paddle.linalg.matrix_power(x, 2))\n # [[6. , 34. , 102.],\n # [14. , 90. , 282.],\n # [36. , 250., 804.]]\n\n print(paddle.linalg.matrix_power(x, 0))\n # [[1., 0., 0.],\n # [0., 1., 0.],\n # [0., 0., 1.]]\n\n print(paddle.linalg.matrix_power(x, -2))\n # [[ 12.91666667, -12.75000000, 2.83333333 ],\n # [-7.66666667 , 8. , -1.83333333 ],\n # [ 1.80555556 , -1.91666667 , 0.44444444 ]]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.matrix_power(x, \"n\", n)\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'matrix_power')\n check_type(n, 'n', int, 'matrix_power')\n helper = LayerHelper('matrix_power', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matrix_power',\n inputs={'X': x},\n outputs={'Out': out},\n attrs={'n': n})\n return out\n\n\ndef qr(x, mode=\"reduced\", name=None):\n r\"\"\"\n Computes the QR decomposition of one matrix or batches of matrice (backward is unsupported now).\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., M, N]`,\n where ... is zero or more batch dimensions. M and N can be arbitrary\n positive number. The data type of x should be float32 or float64. \n mode (str, optional): A flag to control the behavior of qr, the default is \"reduced\". \n Suppose x's shape is `[..., M, N]` and denoting `K = min(M, N)`:\n If mode = \"reduced\", qr op will return reduced Q and R matrices, \n which means Q's shape is `[..., M, K]` and R's shape is `[..., K, N]`.\n If mode = \"complete\", qr op will return complete Q and R matrices, \n which means Q's shape is `[..., M, M]` and R's shape is `[..., M, N]`.\n If mode = \"r\", qr op will only return reduced R matrix, which means\n R's shape is `[..., K, N]`.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n If mode = \"reduced\" or mode = \"complete\", qr will return a two tensor-tuple, which represents Q and R. \n If mode = \"r\", qr will return a tensor which represents R.\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n q, r = paddle.linalg.qr(x)\n print (q)\n print (r)\n\n # Q = [[-0.16903085, 0.89708523],\n # [-0.50709255, 0.27602622],\n # [-0.84515425, -0.34503278]])\n\n # R = [[-5.91607978, -7.43735744],\n # [ 0. , 0.82807867]])\n \n # one can verify : X = Q * R ; \n \"\"\"\n if paddle.in_dynamic_mode():\n q, r = _C_ops.qr(x, 'mode', mode)\n if mode == \"r\":\n return r\n else:\n return q, r\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'qr')\n check_type(mode, 'mode', str, 'qr')\n helper = LayerHelper('qr', **locals())\n q = helper.create_variable_for_type_inference(dtype=x.dtype)\n r = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['mode'] = mode\n helper.append_op(\n type='qr', inputs={'X': [x]}, outputs={'Q': q,\n 'R': r}, attrs=attrs)\n if mode == \"r\":\n return r\n else:\n return q, r\n\n\ndef lu(x, pivot=True, get_infos=False, name=None):\n r\"\"\"\n Computes the LU factorization of an N-D(N>=2) matrix x. \n\n Returns the LU factorization(inplace x) and Pivots. low triangular matrix L and \n upper triangular matrix U are combined to a single LU matrix.\n\n Pivoting is done if pivot is set to True.\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n # return ones\n\n Args:\n\n X (Tensor): the tensor to factor of N-dimensions(N>=2).\n\n pivot (bool, optional): controls whether pivoting is done. Default: True.\n\n get_infos (bool, optional): if set to True, returns an info IntTensor. Default: False.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n factorization (Tensor): LU matrix, the factorization of input X.\n\n pivots (IntTensor): the pivots of size(∗(N-2), min(m,n)). `pivots` stores all the \n intermediate transpositions of rows. The final permutation `perm` could be \n reconstructed by this, details refer to upper example.\n\n infos (IntTensor, optional): if `get_infos` is `True`, this is a tensor of size (∗(N-2)) \n where non-zero values indicate whether factorization for the matrix or each minibatch \n has succeeded or failed.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n \n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n if paddle.in_dynamic_mode():\n LU, Piv, Info = _C_ops.lu(x, 'pivots', pivot)\n if get_infos:\n return LU, Piv, Info\n else:\n return LU, Piv\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu')\n helper = LayerHelper('lu', **locals())\n lu = helper.create_variable_for_type_inference(dtype=x.dtype)\n p = helper.create_variable_for_type_inference(dtype='int')\n info = helper.create_variable_for_type_inference(dtype='int')\n attrs = dict()\n attrs['pivots'] = pivot\n helper.append_op(\n type='lu',\n inputs={'X': x},\n outputs={'Out': lu,\n 'Pivots': p,\n 'Infos': info},\n attrs=attrs)\n if get_infos:\n return lu, p, info\n else:\n return lu, p\n\n\ndef lu_unpack(x, y, unpack_ludata=True, unpack_pivots=True, name=None):\n r\"\"\"\n Unpack L U and P to single matrix tensor . \n unpack L and U matrix from LU, unpack permutation matrix P from Pivtos .\n\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n\n\n Args:\n x (Tensor): The LU tensor get from paddle.linalg.lu, which is combined by L and U.\n\n y (Tensor): Pivots get from paddle.linalg.lu.\n\n unpack_ludata (bool,optional): whether to unpack L and U from x. Default: True.\n\n unpack_pivots (bool, optional): whether to unpack permutation matrix P from Pivtos. Default: True.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n P (Tensor): Permutation matrix P of lu factorization.\n\n L (Tensor): The lower triangular matrix tensor of lu factorization.\n\n U (Tensor): The upper triangular matrix tensor of lu factorization.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n\n if paddle.in_dynamic_mode():\n P, L, U = _C_ops.lu_unpack(x, y, 'unpack_ludata', unpack_ludata,\n 'unpack_pivots', unpack_pivots)\n return P, L, U\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu_unpack')\n helper = LayerHelper('lu_unpack', **locals())\n p = helper.create_variable_for_type_inference(dtype=x.dtype)\n l = helper.create_variable_for_type_inference(dtype=x.dtype)\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n attrs = dict()\n attrs['unpack_ludata'] = unpack_ludata\n attrs['unpack_pivots'] = unpack_pivots\n helper.append_op(\n type='lu_unpack',\n inputs={'X': x,\n 'Pivots': y},\n outputs={'Pmat': p,\n 'L': l,\n 'U': u},\n attrs=attrs)\n return p, l, u\n\n\ndef eig(x, name=None):\n \"\"\"\n This API performs the eigenvalue decomposition of a square matrix or a batch of square matrices.\n\n .. note::\n If the matrix is a Hermitian or a real symmetric matrix, please use :ref:`paddle.linalg.eigh` instead, which is much faster.\n If only eigenvalues is needed, please use :ref:`paddle.linalg.eigvals` instead.\n If the matrix is of any shape, please use :ref:`paddle.linalg.svd`.\n This API is only supported on CPU device.\n The output datatype is always complex for both real and complex input.\n\n Args:\n x (Tensor): A tensor with shape math:`[*, N, N]`, The data type of the x should be one of ``float32``,\n ``float64``, ``compplex64`` or ``complex128``.\n name (str, optional): The default value is `None`. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Eigenvalues(Tensors): A tensor with shape math:`[*, N]` refers to the eigen values.\n Eigenvectors(Tensors): A tensor with shape math:`[*, N, N]` refers to the eigen vectors.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n paddle.device.set_device(\"cpu\")\n\n x_data = np.array([[1.6707249, 7.2249975, 6.5045543],\n [9.956216, 8.749598, 6.066444 ],\n [4.4251957, 1.7983172, 0.370647 ]]).astype(\"float32\")\n x = paddle.to_tensor(x_data)\n w, v = paddle.linalg.eig(x)\n print(w)\n # Tensor(shape=[3, 3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [[(-0.5061363550800655+0j) , (-0.7971760990842826+0j) ,\n # (0.18518077798279986+0j)],\n # [(-0.8308237755993192+0j) , (0.3463813401919749+0j) ,\n # (-0.6837005269141947+0j) ],\n # [(-0.23142567697893396+0j), (0.4944999840400175+0j) ,\n # (0.7058765252952796+0j) ]])\n\n print(v)\n # Tensor(shape=[3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [ (16.50471283351188+0j) , (-5.5034820550763515+0j) ,\n # (-0.21026087843552282+0j)])\n \"\"\"\n if paddle.in_dynamic_mode():\n w, v = _C_ops.eig(x)\n return w, v\n\n check_variable_and_dtype(\n x, 'X', ['float32', 'float64', 'complex64', 'complex128'], 'eig')\n helper = LayerHelper('eig', **locals())\n\n w = helper.create_variable_for_type_inference(x.dtype)\n v = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {'X': x}\n outputs = {'Eigenvalues': w, 'Eigenvectors': v}\n helper.append_op(type='eig', inputs=inputs, outputs=outputs)\n\n return w, v\n\n\ndef eigvals(x, name=None):\n \"\"\"\n Compute the eigenvalues of one or more general matrices.\n\n Warning:\n The gradient kernel of this operator does not yet developed.\n If you need back propagation through this operator, please replace it with paddle.linalg.eig.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices whose eigenvalues will be computed.\n Its shape should be `[*, M, M]`, where `*` is zero or more batch dimensions.\n Its data type should be float32, float64, complex64, or complex128.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n Tensor: A tensor containing the unsorted eigenvalues which has the same batch dimensions with `x`.\n The eigenvalues are complex-valued even when `x` is real.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n paddle.seed(1234)\n\n x = paddle.rand(shape=[3, 3], dtype='float64')\n # [[0.02773777, 0.93004224, 0.06911496],\n # [0.24831591, 0.45733623, 0.07717843],\n # [0.48016702, 0.14235102, 0.42620817]])\n\n print(paddle.linalg.eigvals(x))\n # [(-0.27078833542132674+0j), (0.29962280156230725+0j), (0.8824477020120244+0j)] #complex128\n \"\"\"\n\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64',\n 'complex128'], 'eigvals')\n\n x_shape = list(x.shape)\n if len(x_shape) < 2:\n raise ValueError(\n \"The dimension of Input(x) should be at least 2, but received x's dimention = {}, x's shape = {}\".\n format(len(x_shape), x_shape))\n\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The last two dimensions of Input(x) should be equal, but received x's shape = {}\".\n format(x_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.eigvals(x)\n\n helper = LayerHelper('eigvals', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='eigvals', inputs={'X': x}, outputs={'Out': out})\n return out\n\n\ndef multi_dot(x, name=None):\n \"\"\"\n Multi_dot is an operator that calculates multiple matrix multiplications.\n\n Supports inputs of float16(only GPU support), float32 and float64 dtypes. This function does not\n support batched inputs.\n\n The input tensor in [x] must be 2-D except for the first and last can be 1-D.\n If the first tensor is a 1-D vector of shape(n, ) it is treated as row vector\n of shape(1, n), similarly if the last tensor is a 1D vector of shape(n, ), it\n is treated as a column vector of shape(n, 1).\n\n If the first and last tensor are 2-D matrix, then the output is also 2-D matrix,\n otherwise the output is a 1-D vector.\n\n Multi_dot will select the lowest cost multiplication order for calculation. The\n cost of multiplying two matrices with shapes (a, b) and (b, c) is a * b * c.\n Given matrices A, B, C with shapes (20, 5), (5, 100), (100, 10) respectively,\n we can calculate the cost of different multiplication orders as follows:\n - Cost((AB)C) = 20x5x100 + 20x100x10 = 30000\n - Cost(A(BC)) = 5x100x10 + 20x5x10 = 6000\n\n In this case, multiplying B and C first, then multiply A, which is 5 times faster\n than sequential calculation.\n\n Args:\n x ([Tensor]): The input tensors which is a list Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # A * B\n A_data = np.random.random([3, 4]).astype(np.float32)\n B_data = np.random.random([4, 5]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n out = paddle.linalg.multi_dot([A, B])\n print(out.numpy().shape)\n # [3, 5]\n\n # A * B * C\n A_data = np.random.random([10, 5]).astype(np.float32)\n B_data = np.random.random([5, 8]).astype(np.float32)\n C_data = np.random.random([8, 7]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n C = paddle.to_tensor(C_data)\n out = paddle.linalg.multi_dot([A, B, C])\n print(out.numpy().shape)\n # [10, 7]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.multi_dot(x)\n\n check_type(x, 'x', (list, tuple), 'multi_dot')\n for id, item in enumerate(x):\n check_variable_and_dtype(item, 'x[' + str(id) + ']',\n ['float16', 'float32', 'float64'], 'multi_dot')\n if item.dtype != x[0].dtype:\n raise TypeError(\n \"All the Tensors in the input must have the same data type.\")\n\n helper = LayerHelper('multi_dot', **locals())\n dtype = helper.input_dtype(input_param_name='x')\n out = helper.create_variable_for_type_inference(dtype)\n helper.append_op(type='multi_dot', inputs={\"X\": x}, outputs={\"Out\": out})\n return out\n\n\ndef eigh(x, UPLO='L', name=None):\n \"\"\"\n Compute the eigenvalues and eigenvectors of a\n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[*, N, N]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): (string, default 'L'), 'L' represents the lower triangular matrix,\n \"'U' represents the upper triangular matrix.\".\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n\n out_value(Tensor): A Tensor with shape [*, N] and data type of float32 and float64. The eigenvalues of eigh op.\n out_vector(Tensor): A Tensor with shape [*, N, N] and data type of float32,float64,complex64 and complex128. The eigenvectors of eigh op.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value, out_vector = paddle.linalg.eigh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n print(out_vector)\n #[(-0.9238795325112867+0j), (-0.3826834323650898+0j)],\n #[ 0.3826834323650898j , -0.9238795325112867j ]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.eigh(x, 'UPLO', UPLO)\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigh', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'eigh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO})\n return out_value, out_vector\n\n\ndef pinv(x, rcond=1e-15, hermitian=False, name=None):\n r\"\"\"\n Calculate pseudo inverse via SVD(singular value decomposition)\n of one matrix or batches of regular matrix.\n\n .. math::\n\n if hermitian == False:\n x = u * s * vt (SVD)\n out = v * 1/s * ut\n else:\n x = u * s * ut (eigh)\n out = u * 1/s * u.conj().transpose(-2,-1)\n\n If x is hermitian or symmetric matrix, svd will be replaced with eigh.\n\n Args:\n x(Tensor): The input tensor. Its shape should be (*, m, n)\n where * is zero or more batch dimensions. m and n can be\n arbitraty positive number. The data type of x should be\n float32 or float64 or complex64 or complex128. When data\n type is complex64 or cpmplex128, hermitian should be set\n True.\n\n rcond(Tensor, optional): the tolerance value to determine\n when is a singular value zero. Defalut:1e-15.\n\n hermitian(bool, optional): indicates whether x is Hermitian\n if complex or symmetric if real. Default: False.\n\n name(str|None): A name for this layer(optional). If set None,\n the layer will be named automatically.\n\n Returns:\n Tensor: The tensor with same data type with x. it represents\n pseudo inverse of x. Its shape should be (*, n, m).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.arange(15).reshape((3, 5)).astype('float64')\n input = paddle.to_tensor(x)\n out = paddle.linalg.pinv(input)\n print(input)\n print(out)\n\n # input:\n # [[0. , 1. , 2. , 3. , 4. ],\n # [5. , 6. , 7. , 8. , 9. ],\n # [10., 11., 12., 13., 14.]]\n\n # out:\n # [[-0.22666667, -0.06666667, 0.09333333],\n # [-0.12333333, -0.03333333, 0.05666667],\n # [-0.02000000, 0.00000000, 0.02000000],\n # [ 0.08333333, 0.03333333, -0.01666667],\n # [ 0.18666667, 0.06666667, -0.05333333]]\n\n # one can verify : x * out * x = x ;\n # or out * x * out = x ;\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if not hermitian:\n # combine svd and matmul op\n u, s, vt = _C_ops.svd(x, 'full_matrices', False)\n max_singular_val = _C_ops.reduce_max(s, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=x.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=x.dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v, _ = _C_ops.transpose2(vt, 'axis', perm)\n\n out_1 = v * st\n out_2 = _C_ops.matmul_v2(out_1, u, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n # combine eigh and matmul op\n s, u = _C_ops.eigh(x, 'UPLO', 'L')\n s_abs = paddle.abs(s)\n max_singular_val = _C_ops.reduce_max(s_abs, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=s.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=s.dtype)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n out_1 = u * st\n u_conj = _C_ops.conj(u)\n out_2 = _C_ops.matmul_v2(out_1, u_conj, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n if not hermitian:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'pinv')\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(dtype)\n vt = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vt,\n 'S': s},\n attrs={'full_matrices': False}, )\n\n max_singular_val = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=dtype)\n st_shape = helper.create_variable_for_type_inference(dtype=dtype)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v = helper.create_variable_for_type_inference(dtype)\n v_shape = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='transpose2',\n inputs={'X': [vt]},\n outputs={'Out': [v],\n 'XShape': [v_shape]},\n attrs={'axis': perm})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': v,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n else:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64',\n 'complex128'], 'pinv')\n\n if dtype == paddle.complex128:\n s_type = 'float64'\n elif dtype == paddle.complex64:\n s_type = 'float32'\n else:\n s_type = dtype\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': s,\n 'Eigenvectors': u},\n attrs={'UPLO': 'L'})\n s_abs = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='abs', inputs={'X': s}, outputs={'Out': s_abs})\n max_singular_val = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s_abs},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=s_type)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=s_type)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s_type)\n cond_not_int = layers.cast(layers.logical_not(condition), s_type)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=s_type)\n st_shape = helper.create_variable_for_type_inference(dtype=s_type)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': u,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n u_conj = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='conj', inputs={'X': u}, outputs={'Out': [u_conj]})\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u_conj},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n\n\ndef solve(x, y, name=None):\n r\"\"\"\n Computes the solution of a square system of linear equations with a unique solution for input 'X' and 'Y'.\n Let :math: `X` be a sqaure matrix or a batch of square matrices, :math:`Y` be\n a vector/matrix or a batch of vectors/matrices, the equation should be:\n\n .. math::\n Out = X^-1 * Y\n Specifically,\n - This system of linear equations has one solution if and only if input 'X' is invertible.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): A vector/matrix or a batch of vectors/matrices. Its shape should be `[*, M, K]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of a square system of linear equations with a unique solution for input 'x' and 'y'.\n Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # 2*X0 + X1 = 9\n # X0 + 2*X1 = 8\n\n import paddle\n import numpy as np\n\n np_x = np.array([[3, 1],[1, 2]])\n np_y = np.array([9, 8])\n x = paddle.to_tensor(np_x, dtype=\"float64\")\n y = paddle.to_tensor(np_y, dtype=\"float64\")\n out = paddle.linalg.solve(x, y)\n\n print(out)\n # [2., 3.])\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.solve(x, y)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type=\"solve\", inputs={\"X\": x,\n \"Y\": y}, outputs={\"Out\": out})\n return out\n\n\ndef triangular_solve(x,\n y,\n upper=True,\n transpose=False,\n unitriangular=False,\n name=None):\n r\"\"\"\n Computes the solution of a system of equations with a triangular coefficient matrix `x` and\n multiple right-hand sides `y` .\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input triangular coefficient matrix. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): Whether to solve the upper-triangular system of equations (default) or the lower-triangular \n system of equations. Default: True.\n transpose (bool, optional): whether `x` should be transposed before calculation. Default: False.\n unitriangular (bool, optional): whether `x` is unit triangular. If True, the diagonal elements of `x` are assumed \n to be 1 and not referenced from `x` . Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # x1 + x2 + x3 = 0\n # 2*x2 + x3 = -9\n # -x3 = 5\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n y = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.triangular_solve(x, y, upper=True)\n\n print(out)\n # [7, -2, -5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.triangular_solve(x, y, 'upper', upper, 'transpose',\n transpose, 'unitriangular',\n unitriangular)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"triangular_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'triangular_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'triangular_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='triangular_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={\n 'upper': upper,\n 'transpose': transpose,\n 'unitriangular': unitriangular\n })\n return out\n\n\ndef cholesky_solve(x, y, upper=False, name=None):\n r\"\"\"\n Solves a linear system of equations A @ X = B, given A's Cholesky factor matrix u and matrix B.\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input matrix which is upper or lower triangular Cholesky factor of square matrix A. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): whether to consider the Cholesky factor as a lower or upper triangular matrix. Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type is the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n u = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n b = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.cholesky_solve(b, u, upper=True)\n\n print(out)\n # [-2.5, -7, 9.5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky_solve(x, y, 'upper', upper)\n\n helper = LayerHelper(\"cholesky_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'cholesky_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'cholesky_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='cholesky_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef eigvalsh(x, UPLO='L', name=None):\n \"\"\"\n Computes the eigenvalues of a \n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[_, M, M]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): Lower triangular part of a (‘L’, default) or the upper triangular part (‘U’).\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor eigenvalues in ascending order.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value = paddle.eigvalsh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n \"\"\"\n if paddle.in_dynamic_mode():\n is_test = x.stop_gradient\n values, _ = _C_ops.eigvalsh(x, 'UPLO', UPLO, 'is_test', is_test)\n return values\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigvalsh', **locals())\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64', 'complex128'],\n 'eigvalsh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n is_test = x.stop_gradient\n helper.append_op(\n type='eigvalsh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO,\n 'is_test': is_test})\n return out_value\n\n\ndef lstsq(x, y, rcond=None, driver=None, name=None):\n \"\"\"\n Computes a solution to\n the least squares problem of a system of linear equations.\n\n Args:\n x (Tensor): A tensor with shape ``(*, M, N)`` , the data type of the input Tensor ``x``\n should be one of float32, float64.\n y (Tensor): A tensor with shape ``(*, M, K)`` , the data type of the input Tensor ``y`` \n should be one of float32, float64.\n rcond(float, optional): The default value is None. A float pointing number used to determine \n the effective rank of ``x``. If ``rcond`` is None, it will be set to max(M, N) times the \n machine precision of x_dtype.\n driver(str, optional): The default value is None. The name of LAPACK method to be used. For \n CPU inputs the valid values are ‘gels’, ‘gelsy’, ‘gelsd, ‘gelss’. For CUDA input, the only \n valid driver is ‘gels’. If ``driver`` is None, ‘gelsy’ is used for CPU inputs and ‘gels’ \n for CUDA inputs.\n name(str, optional): The default value is None. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple: A tuple of 4 Tensors which is (``solution``, ``residuals``, ``rank``, ``singular_values``). \n ``solution`` is a tensor with shape ``(*, N, K)``, meaning the least squares solution. ``residuals`` \n is a tensor with shape ``(*, K)``, meaning the squared residuals of the solutions, which is computed \n when M > N and every matrix in ``x`` is full-rank, otherwise return an empty tensor. ``rank`` is a tensor \n with shape ``(*)``, meaning the ranks of the matrices in ``x``, which is computed when ``driver`` in \n (‘gelsy’, ‘gelsd’, ‘gelss’), otherwise return an empty tensor. ``singular_values`` is a tensor with \n shape ``(*, min(M, N))``, meaning singular values of the matrices in ``x``, which is computed when \n ``driver`` in (‘gelsd’, ‘gelss’), otherwise return an empty tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n x = paddle.to_tensor([[1, 3], [3, 2], [5, 6.]])\n y = paddle.to_tensor([[3, 4, 6], [5, 3, 4], [1, 2, 1.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gelsd\")\n print(results[0])\n # [[ 0.78350395, -0.22165027, -0.62371236],\n # [-0.11340097, 0.78866047, 1.14948535]]\n print(results[1])\n # [19.81443405, 10.43814468, 30.56185532])\n print(results[2])\n # 2\n print(results[3])\n # [9.03455734, 1.54167950]\n\n x = paddle.to_tensor([[10, 2, 3], [3, 10, 5], [5, 6, 12.]])\n y = paddle.to_tensor([[4, 2, 9], [2, 0, 3], [2, 5, 3.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gels\")\n print(results[0])\n # [[ 0.39386186, 0.10230173, 0.93606132],\n # [ 0.10741687, -0.29028133, 0.11892585],\n # [-0.05115091, 0.51918161, -0.19948854]]\n print(results[1])\n # []\n \"\"\"\n device = paddle.get_device()\n if device == \"cpu\":\n if driver not in (None, \"gels\", \"gelss\", \"gelsd\", \"gelsy\"):\n raise ValueError(\n \"Only support valid driver is 'gels', 'gelss', 'gelsd', 'gelsy' or None for CPU inputs. But got {}\".\n format(driver))\n driver = \"gelsy\" if driver is None else driver\n elif \"gpu\" in device:\n if driver not in (None, \"gels\"):\n raise ValueError(\n \"Only support valid driver is 'gels' or None for CUDA inputs. But got {}\".\n format(driver))\n driver = \"gels\" if driver is None else driver\n else:\n raise RuntimeError(\"Only support lstsq api for CPU or CUDA device.\")\n\n if x.dtype == y.dtype and x.dtype in (paddle.float32, paddle.float64):\n pass\n else:\n raise ValueError(\n \"Only support x and y have the same dtype such as 'float32' and 'float64'.\"\n )\n\n if rcond is None:\n if x.dtype == paddle.float32:\n rcond = 1e-7 * max(x.shape[-2], x.shape[-1])\n elif x.dtype == paddle.float64:\n rcond = 1e-15 * max(x.shape[-2], x.shape[-1])\n\n if paddle.in_dynamic_mode():\n solution, rank, singular_values = _C_ops.lstsq(x, y, \"rcond\", rcond,\n \"driver\", driver)\n if x.shape[-2] > x.shape[-1]:\n matmul_out = _varbase_creator(dtype=x.dtype)\n _C_ops.matmul(x, solution, matmul_out, 'trans_x', False, 'trans_y',\n False)\n minus_out = _C_ops.elementwise_sub(matmul_out, y)\n pow_out = _C_ops.pow(minus_out, 'factor', 2)\n residuals = _C_ops.reduce_sum(pow_out, 'dim', [-2], 'keepdim',\n False, 'reduce_all', False)\n else:\n residuals = paddle.empty(shape=[0], dtype=x.dtype)\n\n if driver == \"gels\":\n rank = paddle.empty(shape=[0], dtype=paddle.int32)\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n elif driver == \"gelsy\":\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n\n return solution, residuals, rank, singular_values\n\n helper = LayerHelper('lstsq', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n check_variable_and_dtype(\n y, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n\n solution = helper.create_variable_for_type_inference(dtype=x.dtype)\n residuals = helper.create_variable_for_type_inference(dtype=x.dtype)\n rank = helper.create_variable_for_type_inference(dtype=paddle.int32)\n singular_values = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='lstsq',\n inputs={'X': x,\n 'Y': y},\n outputs={\n 'Solution': solution,\n 'Rank': rank,\n 'SingularValues': singular_values\n },\n attrs={'rcond': rcond,\n 'driver': driver})\n\n matmul_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n minus_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n pow_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': solution},\n outputs={'Out': matmul_out},\n attrs={\n 'trans_x': False,\n 'trans_y': False,\n })\n\n helper.append_op(\n type='elementwise_sub',\n inputs={'X': matmul_out,\n 'Y': y},\n outputs={'Out': minus_out})\n\n helper.append_op(\n type='pow',\n inputs={'X': minus_out},\n outputs={'Out': pow_out},\n attrs={'factor': 2})\n\n helper.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': residuals},\n attrs={'dim': [-2],\n 'keep_dim': False,\n 'reduce_all': False})\n\n if driver == \"gels\":\n rank = paddle.static.data(name='rank', shape=[0])\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n elif driver == \"gelsy\":\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n\n return solution, residuals, rank, singular_values"},"implementation":{"kind":"string","value":"def bincount(x, weights=None, minlength=0, name=None):\n \"\"\"\n Computes frequency of each value in the input tensor. \n\n Args:\n x (Tensor): A Tensor with non-negative integer. Should be 1-D tensor.\n weights (Tensor, optional): Weight for each value in the input tensor. Should have the same shape as input. Default is None.\n minlength (int, optional): Minimum number of bins. Should be non-negative integer. Default is 0.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor of frequency.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([1, 2, 1, 4, 5])\n result1 = paddle.bincount(x)\n print(result1) # [0, 2, 1, 0, 1, 1]\n\n w = paddle.to_tensor([2.1, 0.4, 0.1, 0.5, 0.5])\n result2 = paddle.bincount(x, weights=w)\n print(result2) # [0., 2.19999981, 0.40000001, 0., 0.50000000, 0.50000000]\n \"\"\"\n if x.dtype not in [paddle.int32, paddle.int64]:\n raise TypeError(\"Elements in Input(x) should all be integers\")\n\n if paddle.in_dynamic_mode():\n return _C_ops.bincount(x, weights, \"minlength\", minlength)\n\n helper = LayerHelper('bincount', **locals())\n\n check_variable_and_dtype(x, 'X', ['int32', 'int64'], 'bincount')\n\n if weights is not None:\n check_variable_and_dtype(weights, 'Weights',\n ['int32', 'int64', 'float32', 'float64'],\n 'bincount')\n out = helper.create_variable_for_type_inference(dtype=weights.dtype)\n else:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='bincount',\n inputs={'X': x,\n 'Weights': weights},\n outputs={'Out': out},\n attrs={'minlength': minlength})\n return out"},"start_line":{"kind":"number","value":1417,"string":"1,417"},"end_line":{"kind":"number","value":1467,"string":"1,467"},"file_content":{"kind":"string","value":"# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numpy as np\nfrom ..fluid.layer_helper import LayerHelper\nfrom ..framework import _varbase_creator, _dygraph_tracer\nfrom ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype\nfrom ..static import Variable\nfrom ..fluid.framework import _in_legacy_dygraph, in_dygraph_mode\nfrom ..fluid.layers import transpose, cast # noqa: F401\nfrom ..fluid import layers\nimport paddle\nfrom paddle.common_ops_import import core\nfrom paddle.common_ops_import import VarDesc\nfrom paddle import _C_ops\n\n__all__ = []\n\n\ndef matmul(x, y, transpose_x=False, transpose_y=False, name=None):\n \"\"\"\n Applies matrix multiplication to two tensors. `matmul` follows\n the complete broadcast rules,\n and its behavior is consistent with `np.matmul`.\n\n Currently, the input tensors' number of dimensions can be any, `matmul` can be used to\n achieve the `dot`, `matmul` and `batchmatmul`.\n\n The actual behavior depends on the shapes of :math:`x`, :math:`y` and the\n flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:\n\n - If a transpose flag is specified, the last two dimensions of the tensor\n are transposed. If the tensor is ndim-1 of shape, the transpose is invalid. If the tensor\n is ndim-1 of shape :math:`[D]`, then for :math:`x` it is treated as :math:`[1, D]`, whereas\n for :math:`y` it is the opposite: It is treated as :math:`[D, 1]`.\n\n The multiplication behavior depends on the dimensions of `x` and `y`. Specifically:\n\n - If both tensors are 1-dimensional, the dot product result is obtained.\n\n - If both tensors are 2-dimensional, the matrix-matrix product is obtained.\n\n - If the `x` is 1-dimensional and the `y` is 2-dimensional,\n a `1` is prepended to its dimension in order to conduct the matrix multiply.\n After the matrix multiply, the prepended dimension is removed.\n\n - If the `x` is 2-dimensional and `y` is 1-dimensional,\n the matrix-vector product is obtained.\n\n - If both arguments are at least 1-dimensional and at least one argument\n is N-dimensional (where N > 2), then a batched matrix multiply is obtained.\n If the first argument is 1-dimensional, a 1 is prepended to its dimension\n in order to conduct the batched matrix multiply and removed after.\n If the second argument is 1-dimensional, a 1 is appended to its\n dimension for the purpose of the batched matrix multiple and removed after.\n The non-matrix (exclude the last two dimensions) dimensions are\n broadcasted according the broadcast rule.\n For example, if input is a (j, 1, n, m) tensor and the other is a (k, m, p) tensor,\n out will be a (j, k, n, p) tensor.\n\n Args:\n x (Tensor): The input tensor which is a Tensor.\n y (Tensor): The input tensor which is a Tensor.\n transpose_x (bool): Whether to transpose :math:`x` before multiplication.\n transpose_y (bool): Whether to transpose :math:`y` before multiplication.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # vector * vector\n x_data = np.random.random([10]).astype(np.float32)\n y_data = np.random.random([10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [1]\n\n # matrix * vector\n x_data = np.random.random([10, 5]).astype(np.float32)\n y_data = np.random.random([5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10]\n\n # batched matrix * broadcasted vector\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([2]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5]\n\n # batched matrix * batched matrix\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([10, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5, 5]\n\n # batched matrix * broadcasted matrix\n x_data = np.random.random([10, 1, 5, 2]).astype(np.float32)\n y_data = np.random.random([1, 3, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 3, 5, 5]\n\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_matmul(x, y, transpose_x, transpose_y)\n\n if _in_legacy_dygraph():\n op_type = 'matmul_v2'\n op = getattr(_C_ops, op_type)\n return op(x, y, 'trans_x', transpose_x, 'trans_y', transpose_y)\n\n attrs = {\n 'trans_x': transpose_x,\n 'trans_y': transpose_y,\n }\n\n def __check_input(x, y):\n var_names = {'x': x, 'y': y}\n for name, val in var_names.items():\n check_variable_and_dtype(\n val, name,\n ['float16', 'float32', 'float64', 'complex64', 'complex128'],\n 'matmul')\n\n __check_input(x, y)\n\n helper = LayerHelper('matmul_v2', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef norm(x, p='fro', axis=None, keepdim=False, name=None):\n \"\"\"\n\n Returns the matrix norm (Frobenius) or vector norm (the 1-norm, the Euclidean\n or 2-norm, and in general the p-norm for p > 0) of a given tensor.\n\n .. note::\n This norm API is different from `numpy.linalg.norm`.\n This api supports high-order input tensors (rank >= 3), and certain axis need to be pointed out to calculate the norm.\n But `numpy.linalg.norm` only supports 1-D vector or 2-D matrix as input tensor.\n For p-order matrix norm, this api actually treats matrix as a flattened vector to calculate the vector norm, NOT REAL MATRIX NORM.\n\n Args:\n x (Tensor): The input tensor could be N-D tensor, and the input data\n type could be float32 or float64.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `0`, `1`, `2`,\n `inf`, `-inf` and any positive real number yielding the corresponding p-norm. Not supported: ord < 0 and nuclear norm.\n Default value is `fro`.\n axis (int|list|tuple, optional): The axis on which to apply norm operation. If axis is int\n or list(int)/tuple(int) with only one element, the vector norm is computed over the axis.\n If `axis < 0`, the dimension to norm operation is rank(input) + axis.\n If axis is a list(int)/tuple(int) with two elements, the matrix norm is computed over the axis.\n Defalut value is `None`.\n keepdim (bool, optional): Whether to reserve the reduced dimension in the\n output Tensor. The result tensor will have fewer dimension\n than the :attr:`input` unless :attr:`keepdim` is true, default\n value is False.\n name (str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: results of norm operation on the specified axis of input tensor,\n it's data type is the same as input's Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n shape=[2, 3, 4]\n np_input = np.arange(24).astype('float32') - 12\n np_input = np_input.reshape(shape)\n x = paddle.to_tensor(np_input)\n #[[[-12. -11. -10. -9.] [ -8. -7. -6. -5.] [ -4. -3. -2. -1.]]\n # [[ 0. 1. 2. 3.] [ 4. 5. 6. 7.] [ 8. 9. 10. 11.]]]\n\n # compute frobenius norm along last two dimensions.\n out_fro = paddle.linalg.norm(x, p='fro', axis=[0,1])\n # out_fro.numpy() [17.435596 16.911535 16.7332 16.911535]\n\n # compute 2-order vector norm along last dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=-1)\n #out_pnorm.numpy(): [[21.118711 13.190906 5.477226]\n # [ 3.7416575 11.224972 19.131126]]\n\n # compute 2-order norm along [0,1] dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=[0,1])\n #out_pnorm.numpy(): [17.435596 16.911535 16.7332 16.911535]\n\n # compute inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=np.inf)\n #out_pnorm.numpy() = [12.]\n out_pnorm = paddle.linalg.norm(x, p=np.inf, axis=0)\n #out_pnorm.numpy(): [[12. 11. 10. 9.] [8. 7. 6. 7.] [8. 9. 10. 11.]]\n\n # compute -inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=-np.inf)\n #out_pnorm.numpy(): [0.]\n out_pnorm = paddle.linalg.norm(x, p=-np.inf, axis=0)\n #out_pnorm.numpy(): [[0. 1. 2. 3.] [4. 5. 6. 5.] [4. 3. 2. 1.]]\n \"\"\"\n\n def frobenius_norm(input, dim=None, keepdim=False, name=None):\n \"\"\"\n The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n dim (list, optional): None for last two dimensions.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if dim is not None and not (isinstance(dim, list) and len(dim) == 2):\n raise ValueError(\n \"The dim of frobenius norm op should be None or two elements list!\"\n )\n if paddle.in_dynamic_mode():\n if dim is None:\n return _C_ops.frobenius_norm(input, 'keep_dim', keepdim,\n 'reduce_all', True)\n return _C_ops.frobenius_norm(input, 'dim', dim, 'keep_dim', keepdim,\n 'reduce_all', False)\n attrs = {'dim': dim, 'keep_dim': keepdim, 'reduce_all': False}\n if dim is None:\n attrs['reduce_all'] = True\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'frobenius_norm')\n\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='frobenius_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def vector_norm(input,\n porder=None,\n axis=None,\n keepdim=False,\n asvector=False,\n name=None):\n \"\"\"\n Calculate the p-order vector norm for certain dimension of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n porder (float, optional): None for porder=2.0.\n axis (int, optional): None for last dimension.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if paddle.in_dynamic_mode():\n if axis is None: axis = -1\n return _C_ops.p_norm(input, 'porder', porder, 'axis', axis,\n 'keepdim', keepdim, 'asvector', asvector)\n if porder is not None:\n check_type(porder, 'porder', (float, int), 'p_norm')\n if axis is not None:\n check_type(axis, 'axis', (int), 'p_norm')\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'p_norm')\n\n attrs = {\n 'axis': axis if axis is not None else -1,\n 'porder': float(porder) if porder is not None else 2.0,\n 'keepdim': keepdim,\n 'asvector': asvector,\n 'epsilon': 1e-12,\n }\n helper = LayerHelper('p_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='p_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def inf_norm(input,\n porder=None,\n axis=axis,\n keepdim=False,\n asvector=False,\n name=None):\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n helper.append_op(type='abs', inputs={'X': input}, outputs={'Out': out})\n reduce_out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n reduce_all = True if axis == None or axis == [] or asvector == True else False\n axis = axis if axis != None and axis != [] else [0]\n\n reduce_type = 'reduce_max' if porder == np.float(\n 'inf') else 'reduce_min'\n helper.append_op(\n type=reduce_type,\n inputs={'X': out},\n outputs={'Out': reduce_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n\n return reduce_out\n\n def p_matrix_norm(input, porder=1., axis=axis, keepdim=False, name=None):\n \"\"\"\n NOTE:\n This function actually treats the matrix as flattened vector to calculate vector norm instead of matrix norm.\n \"\"\"\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n\n block.append_op(\n type='pow',\n inputs={'X': abs_out},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': True if axis is None else False\n })\n porder\n block.append_op(\n type='pow',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n if axis is None and p is not None:\n if isinstance(p, str):\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n porder=p,\n axis=axis,\n keepdim=keepdim,\n asvector=True,\n name=name)\n else:\n raise ValueError(\"only valid p type is string or float, found {}\".\n format(type(p)))\n\n if isinstance(axis, tuple):\n axis = list(axis)\n if isinstance(axis, list) and len(axis) == 1:\n axis = axis[0]\n\n #calculate vector norm, where axis is int or list with only one integer\n if isinstance(axis, int):\n if isinstance(p, str):\n if p == \"fro\":\n return vector_norm(\n x,\n porder=2,\n axis=axis,\n keepdim=keepdim,\n asvector=False,\n name=name)\n\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n axis=axis,\n porder=p,\n keepdim=keepdim,\n asvector=False,\n name=name)\n else:\n raise ValueError(\n \"unspport p for p-order vector norm. except float, found {}\".\n format(p))\n #calculate matrix norm, where axis is list with two integers\n elif isinstance(axis, list) and len(axis) == 2:\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n elif p == np.inf or p == -np.inf:\n return inf_norm(x, porder=p, axis=axis, keepdim=keepdim, name=name)\n elif p == 0:\n raise ValueError(\n \"just suport axis type int or list (length of list <=1) if p = 0, found {}\".\n format(axis))\n else:\n return p_matrix_norm(\n x, porder=p, axis=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"except axis type int or list (length of list <=2), found {}\".\n format(axis))\n\n\ndef dist(x, y, p=2, name=None):\n r\"\"\"\n\n This OP returns the p-norm of (x - y). It is not a norm in a strict sense, only as a measure\n of distance. The shapes of x and y must be broadcastable. The definition is as follows, for\n details, please refer to the `numpy's broadcasting <https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html>`_:\n\n - Each input has at least one dimension.\n - Match the two input dimensions from back to front, the dimension sizes must either be equal, one of them is 1, or one of them does not exist.\n\n Where, z = x - y, the shapes of x and y are broadcastable, then the shape of z can be\n obtained as follows:\n\n 1. If the number of dimensions of x and y are not equal, prepend 1 to the dimensions of the\n tensor with fewer dimensions.\n\n For example, The shape of x is [8, 1, 6, 1], the shape of y is [7, 1, 5], prepend 1 to the\n dimension of y.\n\n x (4-D Tensor): 8 x 1 x 6 x 1\n\n y (4-D Tensor): 1 x 7 x 1 x 5\n\n 2. Determine the size of each dimension of the output z: choose the maximum value from the\n two input dimensions.\n\n z (4-D Tensor): 8 x 7 x 6 x 5\n\n If the number of dimensions of the two inputs are the same, the size of the output can be\n directly determined in step 2. When p takes different values, the norm formula is as follows:\n\n When p = 0, defining $0^0=0$, the zero-norm of z is simply the number of non-zero elements of z.\n\n .. math::\n\n ||z||_{0}=\\lim_{p \\\\rightarrow 0}\\sum_{i=1}^{m}|z_i|^{p}\n\n When p = inf, the inf-norm of z is the maximum element of z.\n\n .. math::\n\n ||z||_\\infty=\\max_i |z_i|\n\n When p = -inf, the negative-inf-norm of z is the minimum element of z.\n\n .. math::\n\n ||z||_{-\\infty}=\\min_i |z_i|\n\n Otherwise, the p-norm of z follows the formula,\n\n .. math::\n\n ||z||_{p}=(\\sum_{i=1}^{m}|z_i|^p)^{\\\\frac{1}{p}}\n\n Args:\n x (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n y (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n p (float, optional): The norm to be computed, its data type is float32 or float64. Default: 2.\n\n Returns:\n Tensor: Tensor that is the p-norm of (x - y).\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor(np.array([[3, 3],[3, 3]]), \"float32\")\n y = paddle.to_tensor(np.array([[3, 3],[3, 1]]), \"float32\")\n out = paddle.dist(x, y, 0)\n print(out) # out = [1.]\n\n out = paddle.dist(x, y, 2)\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"inf\"))\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"-inf\"))\n print(out) # out = [0.]\n \"\"\"\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'dist')\n check_variable_and_dtype(y, 'dtype', ['float32', 'float64'], 'dist')\n check_type(p, 'p', (float, int), 'dist')\n helper = LayerHelper(\"dist\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n outputs = {'Out': [out]}\n attrs = {\"p\": float(p)}\n helper.append_op(\n type='dist', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef cond(x, p=None, name=None):\n \"\"\"\n\n Computes the condition number of a matrix or batches of matrices with respect to a matrix norm ``p``.\n\n Args:\n x (Tensor): The input tensor could be tensor of shape ``(*, m, n)`` where ``*`` is zero or more batch dimensions\n for ``p`` in ``(2, -2)``, or of shape ``(*, n, n)`` where every matrix is invertible for any supported ``p``.\n And the input data type could be ``float32`` or ``float64``.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `nuc`, `1`, `-1`, `2`, `-2`,\n `inf`, `-inf`. Default value is `None`, meaning that the order of the norm is `2`.\n name (str, optional): The default value is `None`. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: computing results of condition number, its data type is the same as input Tensor ``x``.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1., 0, -1], [0, 1, 0], [1, 0, 1]])\n\n # compute conditional number when p is None\n out = paddle.linalg.cond(x)\n # out.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is 'fro'\n out_fro = paddle.linalg.cond(x, p='fro')\n # out_fro.numpy() [3.1622777]\n\n # compute conditional number when order of the norm is 'nuc'\n out_nuc = paddle.linalg.cond(x, p='nuc')\n # out_nuc.numpy() [9.2426405]\n\n # compute conditional number when order of the norm is 1\n out_1 = paddle.linalg.cond(x, p=1)\n # out_1.numpy() [2.]\n\n # compute conditional number when order of the norm is -1\n out_minus_1 = paddle.linalg.cond(x, p=-1)\n # out_minus_1.numpy() [1.]\n\n # compute conditional number when order of the norm is 2\n out_2 = paddle.linalg.cond(x, p=2)\n # out_2.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is -1\n out_minus_2 = paddle.linalg.cond(x, p=-2)\n # out_minus_2.numpy() [0.70710677]\n\n # compute conditional number when order of the norm is inf\n out_inf = paddle.linalg.cond(x, p=np.inf)\n # out_inf.numpy() [2.]\n\n # compute conditional number when order of the norm is -inf\n out_minus_inf = paddle.linalg.cond(x, p=-np.inf)\n # out_minus_inf.numpy() [1.]\n\n a = paddle.to_tensor(np.random.randn(2, 4, 4).astype('float32'))\n # a.numpy()\n # [[[ 0.14063153 -0.996288 0.7996131 -0.02571543]\n # [-0.16303636 1.5534962 -0.49919784 -0.04402903]\n # [-1.1341571 -0.6022629 0.5445269 0.29154757]\n # [-0.16816919 -0.30972657 1.7521842 -0.5402487 ]]\n # [[-0.58081484 0.12402827 0.7229862 -0.55046535]\n # [-0.15178485 -1.1604939 0.75810957 0.30971205]\n # [-0.9669573 1.0940945 -0.27363303 -0.35416734]\n # [-1.216529 2.0018666 -0.7773689 -0.17556527]]]\n a_cond_fro = paddle.linalg.cond(a, p='fro')\n # a_cond_fro.numpy() [31.572273 28.120834]\n\n b = paddle.to_tensor(np.random.randn(2, 3, 4).astype('float64'))\n # b.numpy()\n # [[[ 1.61707487 0.46829144 0.38130416 0.82546736]\n # [-1.72710298 0.08866375 -0.62518804 0.16128892]\n # [-0.02822879 -1.67764516 0.11141444 0.3220113 ]]\n # [[ 0.22524372 0.62474921 -0.85503233 -1.03960523]\n # [-0.76620689 0.56673047 0.85064753 -0.45158196]\n # [ 1.47595418 2.23646462 1.5701758 0.10497519]]]\n b_cond_2 = paddle.linalg.cond(b, p=2)\n # b_cond_2.numpy() [3.30064451 2.51976252]\n\n \"\"\"\n\n def mat_norm(input, porder=1., axis=None):\n \"\"\"\n NOTE:\n Calculate the matrix norm of a square matrix or batches of square matrices,\n when porder is in (1, -1, inf, -inf)\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n axis = axis if axis != None and axis != [] else [0]\n keepdim = False\n\n if paddle.in_dynamic_mode():\n abs_out = _C_ops.abs(input)\n sum_out = _C_ops.reduce_sum(abs_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == 1 or porder == np.inf:\n return _C_ops.reduce_max(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == -1 or porder == -np.inf:\n return _C_ops.reduce_min(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n\n block = LayerHelper('norm', **locals())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n block.append_op(\n type='reduce_sum',\n inputs={'X': abs_out},\n outputs={'Out': sum_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 1 or porder == np.inf:\n block.append_op(\n type='reduce_max',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n if porder == -1 or porder == -np.inf:\n block.append_op(\n type='reduce_min',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n\n def fro_norm(input, porder=2, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the frobenius norm of a square matrix or batches of square matrices.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n if paddle.in_dynamic_mode():\n pow_out = _C_ops.pow(input, 'factor', porder)\n sum_out_1 = _C_ops.reduce_sum(pow_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n sum_out_2 = _C_ops.reduce_sum(sum_out_1, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n return _C_ops.pow(sum_out_2, 'factor', float(1. / porder))\n\n block = LayerHelper('norm', **locals())\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_1 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_2 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='pow',\n inputs={'X': input},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out_1},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_sum',\n inputs={'X': sum_out_1},\n outputs={'Out': sum_out_2},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='pow',\n inputs={'X': sum_out_2},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n def svd_norm(input, porder, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the matrix norm, which is related to singular values, of a matrix\n or batches of matrices, including nuclear norm, 2-norm and (-2)-norm.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n u, s, vh = svd(input, full_matrices=False)\n\n if paddle.in_dynamic_mode():\n if porder == \"nuc\":\n return _C_ops.reduce_sum(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n max_out = _C_ops.reduce_max(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n min_out = _C_ops.reduce_min(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n if porder == 2:\n return _C_ops.elementwise_div(max_out, min_out, 'aixs', axis,\n 'use_mkldnn', False)\n if porder == -2:\n return _C_ops.elementwise_div(min_out, max_out, 'aixs', axis,\n 'use_mkldnn', False)\n\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n if porder == \"nuc\":\n block.append_op(\n type='reduce_sum',\n inputs={'X': s},\n outputs={'Out': out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n max_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n min_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_min',\n inputs={'X': s},\n outputs={'Out': min_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': max_out,\n 'Y': min_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n if porder == -2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': min_out,\n 'Y': max_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n\n def empty_tensor(input, shape):\n if paddle.in_dynamic_mode():\n return input.reshape(shape)\n raise ValueError(\"only support x is nonempty tensor in static mode\")\n\n x_shape = list(x.shape)\n if not len(x_shape) >= 2:\n raise ValueError(\"input should be a matrix or batches of matrices, \" +\n \"but the dimention of received input is {}\".format(\n len(x_shape)))\n if p == None:\n p = 2\n x_size = 0 if (0 in x_shape) else 1\n if p in (\"fro\", \"nuc\", 1, -1, np.inf, -np.inf):\n if x_shape[len(x_shape) - 1] == x_shape[len(x_shape) - 2]:\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n x_inv = x.inverse()\n if p == \"fro\":\n return fro_norm(x) * fro_norm(x_inv)\n if p == \"nuc\":\n return svd_norm(x, p) * svd_norm(x_inv, p)\n if p in (1, -1):\n return mat_norm(\n x, porder=p, axis=[-2]) * mat_norm(\n x_inv, porder=p, axis=[-2])\n if p in (np.inf, -np.inf):\n return mat_norm(\n x, porder=p, axis=[-1]) * mat_norm(\n x_inv, porder=p, axis=[-1])\n else:\n raise ValueError(\"only support p is {} when input is a \".format(p) +\n \"square matrix or batches of square matrices\")\n elif p in (2, -2):\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n return svd_norm(x, porder=p)\n else:\n raise ValueError(\n \"unsupported {} for p, only supporting ('fro', 'nuc', \".format(\n p) + \"1, -1, 2, -2, inf, -inf) or none\")\n\n\ndef dot(x, y, name=None):\n \"\"\"\n This operator calculates inner product for vectors.\n\n .. note::\n Support 1-d and 2-d Tensor. When it is 2d, the first dimension of this matrix\n is the batch dimension, which means that the vectors of multiple batches are dotted.\n\n Parameters:\n x(Tensor): 1-D or 2-D ``Tensor``. Its dtype should be ``float32``, ``float64``, ``int32``, ``int64``\n y(Tensor): 1-D or 2-D ``Tensor``. Its dtype soulde be ``float32``, ``float64``, ``int32``, ``int64``\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: the calculated result Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x_data = np.random.uniform(0.1, 1, [10]).astype(np.float32)\n y_data = np.random.uniform(1, 3, [10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.dot(x, y)\n print(z)\n\n \"\"\"\n op_type = 'dot'\n # skip var type check in dygraph mode to improve efficiency\n if paddle.in_dynamic_mode():\n op = getattr(_C_ops, op_type)\n return op(x, y)\n\n assert x is not None, 'x cannot be None in {}'.format(op_type)\n assert y is not None, 'y cannot be None in {}'.format(op_type)\n\n check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n check_variable_and_dtype(y, 'y', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n\n helper = LayerHelper(op_type, **locals())\n if name is None:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n else:\n out = helper.create_variable(\n name=name, dtype=x.dtype, persistable=False)\n helper.append_op(\n type=\"dot\", inputs={'X': x,\n 'Y': y}, attrs={}, outputs={\"Out\": out})\n return out\n\n\ndef cov(x, rowvar=True, ddof=True, fweights=None, aweights=None, name=None):\n \"\"\"\n Estimate the covariance matrix of the input variables, given data and weights.\n\n A covariance matrix is a square matrix, indicate the covariance of each pair variables in the input matrix.\n For example, for an N-dimensional samples X=[x1,x2,…xN]T, then the covariance matrix \n element Cij is the covariance of xi and xj. The element Cii is the variance of xi itself.\n\n Parameters:\n x(Tensor): A N-D(N<=2) Tensor containing multiple variables and observations. By default, each row of x represents a variable. Also see rowvar below.\n rowvar(Bool, optional): If rowvar is True (default), then each row represents a variable, with observations in the columns. Default: True\n ddof(Bool, optional): If ddof=True will return the unbiased estimate, and ddof=False will return the simple average. Default: True\n fweights(Tensor, optional): 1-D Tensor of integer frequency weights; The number of times each observation vector should be repeated. Default: None\n aweights(Tensor, optional): 1-D Tensor of observation vector weights. How important of the observation vector, larger data means this element is more important. Default: None\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: The covariance matrix Tensor of the variables.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n\n xt = paddle.rand((3,4))\n paddle.linalg.cov(xt)\n\n '''\n Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n [[0.07918842, 0.06127326, 0.01493049],\n [0.06127326, 0.06166256, 0.00302668],\n [0.01493049, 0.00302668, 0.01632146]])\n '''\n \"\"\"\n op_type = 'cov'\n if len(x.shape) > 2 or len(x.shape) < 1:\n raise ValueError(\n \"Input(x) only support N-D (1<=N<=2) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cov')\n nx = x\n if len(x.shape) == 1:\n nx = x.reshape((1, -1))\n if not rowvar and nx.shape[0] != 1:\n nx = nx.t()\n w = None\n observation_num = nx.shape[1]\n if fweights is not None:\n w = fweights.astype(nx.dtype)\n if len(w.shape) > 1:\n raise ValueError(\n \"Input(fweights) only support N-D (N<=1) tensor in cov, but received \"\n \"shape of Input(input) is %s.\" % len(fweights.shape))\n if fweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(fweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(fweights) is {}.\".format(observation_num,\n fweights.shape[0]))\n if fweights.min() < 0:\n raise ValueError(\n \"The value of Input(fweights) cannot be negtive, but received \"\n \"min of Input(fweights) is {}.\".format(fweights.min()))\n if not paddle.all(fweights == paddle.round(fweights.astype('float64'))):\n raise ValueError(\"Input(fweights) must be integer \")\n\n if aweights is not None:\n aw = aweights.astype(nx.dtype)\n if len(aw.shape) > 1:\n raise ValueError(\n \"Input(aweights) only support N-D (N<=1) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(aweights.shape))\n check_variable_and_dtype(aweights, 'dtype', ['float32', 'float64'],\n 'cov')\n if aweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(aweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(aweights) is {}.\".format(observation_num,\n aweights.shape[0]))\n if aweights.min() < 0:\n raise ValueError(\n \"The value of Input(aweights) cannot be negtive, but received \"\n \"min of Input(aweights) is {}.\".format(aweights.min()))\n if w is not None:\n w = w * aw\n else:\n w = aw\n\n w_sum = paddle.to_tensor(observation_num, dtype=nx.dtype)\n if fweights is not None or aweights is not None:\n w_sum = w.sum()\n if w_sum.item() == 0:\n raise ValueError(\"The sum of weights is zero, can't be normalized.\")\n\n if w is not None:\n nx_w = nx * w\n avg = (nx_w).sum(axis=1) / w_sum\n else:\n avg = nx.sum(axis=1) / w_sum\n nx_w = nx\n\n if w is not None and aweights is not None and ddof == True:\n norm_factor = w_sum - (w * aweights).sum() / w_sum\n else:\n norm_factor = w_sum - ddof\n if norm_factor <= 0:\n norm_factor = paddle.to_tensor(0, dtype=nx.dtype)\n nx = nx - avg.unsqueeze(1)\n xxt = paddle.mm(nx, nx_w.t().conj())\n cov = paddle.divide(xxt, norm_factor).squeeze()\n return cov\n\n\ndef t(input, name=None):\n \"\"\"\n Transpose <=2-D tensor.\n 0-D and 1-D tensors are returned as it is and 2-D tensor is equal to\n the paddle.transpose function which perm dimensions set 0 and 1.\n\n Args:\n input (Tensor): The input Tensor. It is a N-D (N<=2) Tensor of data types float16, float32, float64, int32.\n name(str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`\n Returns:\n Tensor: A transposed n-D Tensor, with data type being float16, float32, float64, int32, int64.\n\n For Example:\n\n .. code-block:: text\n\n # Example 1 (0-D tensor)\n x = tensor([0.79])\n paddle.t(x) = tensor([0.79])\n\n # Example 2 (1-D tensor)\n x = tensor([0.79, 0.84, 0.32])\n paddle.t(x) = tensor([0.79, 0.84, 0.32])\n\n # Example 3 (2-D tensor)\n x = tensor([0.79, 0.84, 0.32],\n [0.64, 0.14, 0.57])\n paddle.t(x) = tensor([0.79, 0.64],\n [0.84, 0.14],\n [0.32, 0.57])\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n x = paddle.ones(shape=[2, 3], dtype='int32')\n x_transposed = paddle.t(x)\n print(x_transposed.shape)\n # [3, 2]\n \"\"\"\n if len(input.shape) > 2:\n raise ValueError(\n \"Input(input) only support N-D (N<=2) tensor, but received \"\n \"length of Input(input) is %s. Perhaps you can use paddle.\"\n \"tensor.transpose() instead.\" % len(input.shape))\n if paddle.in_dynamic_mode():\n if len(input.shape) == 1:\n return input\n # 2-D tensor\n perm = [1, 0]\n out, _ = _C_ops.transpose2(input, 'axis', perm)\n return out\n\n check_variable_and_dtype(\n input, 'input', ['float16', 'float32', 'float64', 'int32',\n 'int64'], 'transpose')\n\n helper = LayerHelper('t', **locals())\n out = helper.create_variable_for_type_inference(input.dtype)\n input_shape = helper.create_variable_for_type_inference(input.dtype)\n if len(input.shape) == 1:\n out = input\n else:\n helper.append_op(\n type='transpose2',\n inputs={'X': [input]},\n outputs={'Out': [out],\n 'XShape': [input_shape]},\n attrs={'axis': [1, 0]})\n return out\n\n\ndef cross(x, y, axis=None, name=None):\n \"\"\"\n Computes the cross product between two tensors along an axis.\n\n Inputs must have the same shape, and the length of their axes should be equal to 3.\n If `axis` is not given, it defaults to the first axis found with the length 3.\n\n Args:\n x (Tensor): The first input tensor.\n y (Tensor): The second input tensor.\n axis (int, optional): The axis along which to compute the cross product. It defaults to the first axis found with the length 3.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor. A Tensor with same data type as `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0],\n [3.0, 3.0, 3.0]])\n y = paddle.to_tensor([[1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0]])\n\n z1 = paddle.cross(x, y)\n # [[-1. -1. -1.]\n # [ 2. 2. 2.]\n # [-1. -1. -1.]]\n\n z2 = paddle.cross(x, y, axis=1)\n # [[0. 0. 0.]\n # [0. 0. 0.]\n # [0. 0. 0.]]\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_cross(x, y, axis)\n else:\n if _in_legacy_dygraph():\n if axis is not None:\n return _C_ops.cross(x, y, 'dim', axis)\n else:\n return _C_ops.cross(x, y)\n else:\n helper = LayerHelper(\"cross\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n attrs = dict()\n attrs['dim'] = axis\n\n helper.append_op(\n type='cross',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef cholesky(x, upper=False, name=None):\n r\"\"\"\n Computes the Cholesky decomposition of one symmetric positive-definite\n matrix or batches of symmetric positive-definite matrice.\n\n If `upper` is `True`, the decomposition has the form :math:`A = U^{T}U` ,\n and the returned matrix :math:`U` is upper-triangular. Otherwise, the\n decomposition has the form :math:`A = LL^{T}` , and the returned matrix\n :math:`L` is lower-triangular.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[*, M, M]`,\n where * is zero or more batch dimensions, and matrices on the\n inner-most 2 dimensions all should be symmetric positive-definite.\n Its data type should be float32 or float64.\n upper (bool): The flag indicating whether to return upper or lower\n triangular matrices. Default: False.\n\n Returns:\n Tensor: A Tensor with same shape and data type as `x`. It represents \\\n triangular matrices generated by Cholesky decomposition.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n a = np.random.rand(3, 3)\n a_t = np.transpose(a, [1, 0])\n x_data = np.matmul(a, a_t) + 1e-03\n x = paddle.to_tensor(x_data)\n out = paddle.linalg.cholesky(x, upper=False)\n print(out)\n # [[1.190523 0. 0. ]\n # [0.9906703 0.27676893 0. ]\n # [1.25450498 0.05600871 0.06400121]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky(x, \"upper\", upper)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cholesky')\n check_type(upper, 'upper', bool, 'cholesky')\n helper = LayerHelper('cholesky', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='cholesky',\n inputs={'X': [x]},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef matrix_rank(x, tol=None, hermitian=False, name=None):\n r\"\"\"\n Computes the rank of a matrix.\n\n The rank of a matrix is the number of singular values that are greater than the specified `tol` threshold when hermitian=False,\n or the number of eigenvalues in absolute value that are greater than the specified `tol` threshold when hermitian=True.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., m, n]`, where `...` is zero or more batch dimensions. If `x` is a batch\n of matrices then the output has the same batch dimensions. The data type of `x` should be float32 or float64.\n tol (float,Tensor,optional): the tolerance value. Default: None. If `tol` is not specified, and `sigma` is the largest\n singular value (or eigenvalues in absolute value), and `eps` is the epsilon value for the dtype of `x`, then `tol` is computed\n with formula `tol=sigma * max(m,n) * eps`. Note that if `x` is a batch of matrices, `tol` is computed this way for every batch.\n hermitian (bool,optional): indicates whether `x` is Hermitian. Default: False. When hermitian=True, `x` is assumed to be Hermitian,\n enabling a more efficient method for finding eigenvalues, but `x` is not checked inside the function. Instead, We just use\n the lower triangular of the matrix to compute.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: Rank of tensor x.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n a = paddle.eye(10)\n b = paddle.linalg.matrix_rank(a)\n print(b)\n # b = [10]\n\n c = paddle.ones(shape=[3, 4, 5, 5])\n d = paddle.linalg.matrix_rank(c, tol=0.01, hermitian=True)\n print(d)\n # d = [[1, 1, 1, 1],\n # [1, 1, 1, 1],\n # [1, 1, 1, 1]]\n\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if tol is None:\n tol_tensor = None\n tol_attr = 0.0\n use_default_tol = True\n elif isinstance(tol, Variable):\n if tol.dtype != x.dtype:\n tol_tensor = cast(tol, x.dtype)\n else:\n tol_tensor = tol\n tol_attr = 0.0\n use_default_tol = False\n else:\n tol_tensor = None\n tol_attr = float(tol)\n use_default_tol = False\n return _C_ops.matrix_rank(x, tol_tensor, \"tol\", tol_attr, 'hermitian',\n hermitian, 'use_default_tol', use_default_tol)\n\n inputs = {}\n attrs = {}\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'matrix_rank')\n inputs['X'] = x\n if tol is None:\n attrs['use_default_tol'] = True\n elif isinstance(tol, Variable):\n check_variable_and_dtype(tol, 'tol', ['float32'], 'matrix_rank')\n attrs['use_default_tol'] = False\n if tol.dtype != x.dtype:\n inputs['TolTensor'] = cast(tol, x.dtype)\n else:\n inputs['TolTensor'] = tol\n else:\n check_type(tol, 'tol', float, 'matrix_rank')\n attrs['use_default_tol'] = False\n attrs['tol'] = tol\n check_type(hermitian, 'hermitian', bool, 'matrix_rank')\n attrs['hermitian'] = hermitian\n\n helper = LayerHelper('matrix_rank', **locals())\n out = helper.create_variable_for_type_inference(dtype='int32')\n helper.append_op(\n type='matrix_rank', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef bmm(x, y, name=None):\n \"\"\"\n Applies batched matrix multiplication to two tensors.\n\n Both of the two input tensors must be three-dementional and share the same batch size.\n\n if x is a (b, m, k) tensor, y is a (b, k, n) tensor, the output will be a (b, m, n) tensor.\n\n Args:\n x (Tensor): The input Tensor.\n y (Tensor): The input Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The product Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n # In imperative mode:\n # size x: (2, 2, 3) and y: (2, 3, 2)\n x = paddle.to_tensor([[[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0]],\n [[3.0, 3.0, 3.0],\n [4.0, 4.0, 4.0]]])\n y = paddle.to_tensor([[[1.0, 1.0],[2.0, 2.0],[3.0, 3.0]],\n [[4.0, 4.0],[5.0, 5.0],[6.0, 6.0]]])\n out = paddle.bmm(x, y)\n #output size: (2, 2, 2)\n #output value:\n #[[[6.0, 6.0],[12.0, 12.0]],[[45.0, 45.0],[60.0, 60.0]]]\n out_np = out.numpy()\n\n \"\"\"\n x_shape = x.shape\n y_shape = y.shape\n if not len(x_shape) == len(y_shape) == 3:\n raise ValueError(\n \"x and y should be 3-dimensional. But received x's dimention: {}, y's dimention: {}\".\n format(x_shape, y_shape))\n if x_shape[2] != y_shape[1]:\n raise ValueError(\n \"x's width must be equal with y's height. But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n if x_shape[0] != y_shape[0]:\n raise ValueError(\n \"x's batch (shape[0]) must be equal with y's batch (shape[0]). But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.bmm(x, y)\n\n helper = LayerHelper('bmm', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='bmm', inputs={'X': x, 'Y': y}, outputs={'Out': out})\n return out\n\n\ndef histogram(input, bins=100, min=0, max=0, name=None):\n \"\"\"\n Computes the histogram of a tensor. The elements are sorted into equal width bins between min and max.\n If min and max are both zero, the minimum and maximum values of the data are used.\n\n Args:\n input (Tensor): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor\n should be float32, float64, int32, int64.\n bins (int): number of histogram bins\n min (int): lower end of the range (inclusive)\n max (int): upper end of the range (inclusive)\n\n Returns:\n Tensor: data type is int64, shape is (nbins,).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n inputs = paddle.to_tensor([1, 2, 1])\n result = paddle.histogram(inputs, bins=4, min=0, max=3)\n print(result) # [0, 2, 1, 0]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.histogram(input, \"bins\", bins, \"min\", min, \"max\", max)\n\n helper = LayerHelper('histogram', **locals())\n check_variable_and_dtype(\n input, 'X', ['int32', 'int64', 'float32', 'float64'], 'histogram')\n out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)\n helper.append_op(\n type='histogram',\n inputs={'X': input},\n outputs={'Out': out},\n attrs={'bins': bins,\n 'min': min,\n 'max': max})\n return out\n\n\ndef bincount(x, weights=None, minlength=0, name=None):\n \"\"\"\n Computes frequency of each value in the input tensor. \n\n Args:\n x (Tensor): A Tensor with non-negative integer. Should be 1-D tensor.\n weights (Tensor, optional): Weight for each value in the input tensor. Should have the same shape as input. Default is None.\n minlength (int, optional): Minimum number of bins. Should be non-negative integer. Default is 0.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor of frequency.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([1, 2, 1, 4, 5])\n result1 = paddle.bincount(x)\n print(result1) # [0, 2, 1, 0, 1, 1]\n\n w = paddle.to_tensor([2.1, 0.4, 0.1, 0.5, 0.5])\n result2 = paddle.bincount(x, weights=w)\n print(result2) # [0., 2.19999981, 0.40000001, 0., 0.50000000, 0.50000000]\n \"\"\"\n if x.dtype not in [paddle.int32, paddle.int64]:\n raise TypeError(\"Elements in Input(x) should all be integers\")\n\n if paddle.in_dynamic_mode():\n return _C_ops.bincount(x, weights, \"minlength\", minlength)\n\n helper = LayerHelper('bincount', **locals())\n\n check_variable_and_dtype(x, 'X', ['int32', 'int64'], 'bincount')\n\n if weights is not None:\n check_variable_and_dtype(weights, 'Weights',\n ['int32', 'int64', 'float32', 'float64'],\n 'bincount')\n out = helper.create_variable_for_type_inference(dtype=weights.dtype)\n else:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='bincount',\n inputs={'X': x,\n 'Weights': weights},\n outputs={'Out': out},\n attrs={'minlength': minlength})\n return out\n\n\ndef mv(x, vec, name=None):\n \"\"\"\n Performs a matrix-vector product of the matrix x and the vector vec.\n\n Args:\n x (Tensor): A tensor with shape :math:`[M, N]` , The data type of the input Tensor x\n should be one of float32, float64.\n vec (Tensor): A tensor with shape :math:`[N]` , The data type of the input Tensor x\n should be one of float32, float64.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor which is producted by x and vec.\n\n Examples:\n .. code-block:: python\n\n # x: [M, N], vec: [N]\n # paddle.mv(x, vec) # out: [M]\n\n import numpy as np\n import paddle\n\n x_data = np.array([[2, 1, 3], [3, 0, 1]]).astype(\"float64\")\n x = paddle.to_tensor(x_data)\n vec_data = np.array([3, 5, 1])\n vec = paddle.to_tensor(vec_data).astype(\"float64\")\n out = paddle.mv(x, vec)\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_mv(x, vec)\n else:\n if _in_legacy_dygraph():\n out = _C_ops.mv(x, vec)\n return out\n else:\n\n def __check_input(x, vec):\n var_names = {'x': x, 'vec': vec}\n for name, val in var_names.items():\n check_variable_and_dtype(val, name, ['float32', 'float64'],\n 'mv')\n x_shape = list(x.shape)\n vec_shape = list(vec.shape)\n if len(x_shape) != 2:\n raise ValueError(\n \"x should be 2-dimensional. But received x's dimention: {}\".\n format(x_shape))\n if len(vec_shape) != 1:\n raise ValueError(\n \"vec should be 1-dimensional. But received vec's dimention: {}\".\n format(vec_shape))\n\n __check_input(x, vec)\n\n helper = LayerHelper('mv', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='mv', inputs={'X': x,\n 'Vec': vec}, outputs={'Out': out})\n return out\n\n\ndef det(x, name=None):\n \"\"\"\n Calculates determinant value of a square matrix or batches of square matrices.\n Args:\n x (Tensor): input (Tensor): the input matrix of size `(n, n)` or the batch of matrices of size\n `(*, n, n)` where `*` is one or more batch dimensions.\n Returns:\n y (Tensor):the determinant value of a square matrix or batches of square matrices.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.det(x)\n\n print(A)\n\n # [ 0.02547996, 2.52317095, -6.15900707])\n\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.determinant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'det')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('determinant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='determinant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef slogdet(x, name=None):\n \"\"\"\n Calculates the sign and natural logarithm of the absolute value of a square matrix's or batches square matrices' determinant.\n The determinant can be computed with ``sign * exp(logabsdet)\n\n Supports input of float, double\n\n Note that for matrices that have zero determinant, this returns ``(0, -inf)``\n Args:\n x (Tensor): the batch of matrices of size :math:`(*, n, n)`\n where math:`*` is one or more batch dimensions.\n\n Returns:\n y (Tensor): A tensor containing the sign of the determinant and the natural logarithm\n of the absolute value of determinant, respectively.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.slogdet(x)\n\n print(A)\n\n # [[ 1. , 1. , -1. ],\n # [-0.98610914, -0.43010661, -0.10872950]])\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.slogdeterminant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'slogdet')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('slogdeterminant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='slogdeterminant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef svd(x, full_matrices=False, name=None):\n r\"\"\"\n Computes the singular value decomposition of one matrix or a batch of regular matrices.\n\n Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies:\n\n .. math::\n X = U * diag(S) * VT\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., N, M]`,\n where `...` is zero or more batch dimensions. N and M can be arbitraty\n positive number. Note that if x is sigular matrices, the grad is numerical\n instable. The data type of x should be float32 or float64.\n full_matrices (bool): A flag to control the behavor of svd.\n If full_matrices = True, svd op will compute full U and V matrics,\n which means shape of U is `[..., N, N]`, shape of V is `[..., M, M]`. K = min(M, N).\n If full_matrices = False, svd op will use a economic method to store U and V.\n which means shape of U is `[..., N, K]`, shape of V is `[..., M, K]`. K = min(M, N).\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple of 3 tensors: (U, S, VH). VH is the conjugate transpose of V. S is the singlar value vectors of matrics with shape `[..., K]`\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 2.0], [1.0, 3.0], [4.0, 6.0]]).astype('float64')\n x = x.reshape([3, 2])\n u, s, vh = paddle.linalg.svd(x)\n print (u)\n #U = [[ 0.27364809, -0.21695147 ],\n # [ 0.37892198, -0.87112408 ],\n # [ 0.8840446 , 0.44053933 ]]\n\n print (s)\n #S = [8.14753743, 0.78589688]\n print (vh)\n #VT= [[ 0.51411221, 0.85772294],\n # [ 0.85772294, -0.51411221]]\n\n # one can verify : U * S * VT == X\n # U * UH == I\n # V * VH == I\n \"\"\"\n\n if paddle.in_dynamic_mode():\n return _C_ops.svd(x, 'full_matrices', full_matrices)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'svd')\n check_type(full_matrices, 'full_matrices', bool, 'svd')\n helper = LayerHelper('svd', **locals())\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n vh = helper.create_variable_for_type_inference(dtype=x.dtype)\n s = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['full_matrices'] = full_matrices\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vh,\n 'S': s},\n attrs=attrs, )\n return u, s, vh\n\n\ndef matrix_power(x, n, name=None):\n r\"\"\"\n Computes the n-th power of a square matrix or a batch of square matrices.\n\n Let :math:`X` be a sqaure matrix or a batch of square matrices, :math:`n` be\n an exponent, the equation should be:\n\n .. math::\n Out = X ^ {n}\n\n Specifically,\n\n - If `n > 0`, it returns the matrix or a batch of matrices raised to the power\n of `n`.\n\n - If `n = 0`, it returns the identity matrix or a batch of identity matrices.\n\n - If `n < 0`, it returns the inverse of each matrix (if invertible) raised to\n the power of `abs(n)`.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices to be raised\n to power `n`. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n n (int): The exponent. It can be any positive, negative integer or zero.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The n-th power of the matrix (or the batch of matrices) `x`. Its\n data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1, 2, 3],\n [1, 4, 9],\n [1, 8, 27]], dtype='float64')\n print(paddle.linalg.matrix_power(x, 2))\n # [[6. , 34. , 102.],\n # [14. , 90. , 282.],\n # [36. , 250., 804.]]\n\n print(paddle.linalg.matrix_power(x, 0))\n # [[1., 0., 0.],\n # [0., 1., 0.],\n # [0., 0., 1.]]\n\n print(paddle.linalg.matrix_power(x, -2))\n # [[ 12.91666667, -12.75000000, 2.83333333 ],\n # [-7.66666667 , 8. , -1.83333333 ],\n # [ 1.80555556 , -1.91666667 , 0.44444444 ]]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.matrix_power(x, \"n\", n)\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'matrix_power')\n check_type(n, 'n', int, 'matrix_power')\n helper = LayerHelper('matrix_power', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matrix_power',\n inputs={'X': x},\n outputs={'Out': out},\n attrs={'n': n})\n return out\n\n\ndef qr(x, mode=\"reduced\", name=None):\n r\"\"\"\n Computes the QR decomposition of one matrix or batches of matrice (backward is unsupported now).\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., M, N]`,\n where ... is zero or more batch dimensions. M and N can be arbitrary\n positive number. The data type of x should be float32 or float64. \n mode (str, optional): A flag to control the behavior of qr, the default is \"reduced\". \n Suppose x's shape is `[..., M, N]` and denoting `K = min(M, N)`:\n If mode = \"reduced\", qr op will return reduced Q and R matrices, \n which means Q's shape is `[..., M, K]` and R's shape is `[..., K, N]`.\n If mode = \"complete\", qr op will return complete Q and R matrices, \n which means Q's shape is `[..., M, M]` and R's shape is `[..., M, N]`.\n If mode = \"r\", qr op will only return reduced R matrix, which means\n R's shape is `[..., K, N]`.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n If mode = \"reduced\" or mode = \"complete\", qr will return a two tensor-tuple, which represents Q and R. \n If mode = \"r\", qr will return a tensor which represents R.\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n q, r = paddle.linalg.qr(x)\n print (q)\n print (r)\n\n # Q = [[-0.16903085, 0.89708523],\n # [-0.50709255, 0.27602622],\n # [-0.84515425, -0.34503278]])\n\n # R = [[-5.91607978, -7.43735744],\n # [ 0. , 0.82807867]])\n \n # one can verify : X = Q * R ; \n \"\"\"\n if paddle.in_dynamic_mode():\n q, r = _C_ops.qr(x, 'mode', mode)\n if mode == \"r\":\n return r\n else:\n return q, r\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'qr')\n check_type(mode, 'mode', str, 'qr')\n helper = LayerHelper('qr', **locals())\n q = helper.create_variable_for_type_inference(dtype=x.dtype)\n r = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['mode'] = mode\n helper.append_op(\n type='qr', inputs={'X': [x]}, outputs={'Q': q,\n 'R': r}, attrs=attrs)\n if mode == \"r\":\n return r\n else:\n return q, r\n\n\ndef lu(x, pivot=True, get_infos=False, name=None):\n r\"\"\"\n Computes the LU factorization of an N-D(N>=2) matrix x. \n\n Returns the LU factorization(inplace x) and Pivots. low triangular matrix L and \n upper triangular matrix U are combined to a single LU matrix.\n\n Pivoting is done if pivot is set to True.\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n # return ones\n\n Args:\n\n X (Tensor): the tensor to factor of N-dimensions(N>=2).\n\n pivot (bool, optional): controls whether pivoting is done. Default: True.\n\n get_infos (bool, optional): if set to True, returns an info IntTensor. Default: False.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n factorization (Tensor): LU matrix, the factorization of input X.\n\n pivots (IntTensor): the pivots of size(∗(N-2), min(m,n)). `pivots` stores all the \n intermediate transpositions of rows. The final permutation `perm` could be \n reconstructed by this, details refer to upper example.\n\n infos (IntTensor, optional): if `get_infos` is `True`, this is a tensor of size (∗(N-2)) \n where non-zero values indicate whether factorization for the matrix or each minibatch \n has succeeded or failed.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n \n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n if paddle.in_dynamic_mode():\n LU, Piv, Info = _C_ops.lu(x, 'pivots', pivot)\n if get_infos:\n return LU, Piv, Info\n else:\n return LU, Piv\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu')\n helper = LayerHelper('lu', **locals())\n lu = helper.create_variable_for_type_inference(dtype=x.dtype)\n p = helper.create_variable_for_type_inference(dtype='int')\n info = helper.create_variable_for_type_inference(dtype='int')\n attrs = dict()\n attrs['pivots'] = pivot\n helper.append_op(\n type='lu',\n inputs={'X': x},\n outputs={'Out': lu,\n 'Pivots': p,\n 'Infos': info},\n attrs=attrs)\n if get_infos:\n return lu, p, info\n else:\n return lu, p\n\n\ndef lu_unpack(x, y, unpack_ludata=True, unpack_pivots=True, name=None):\n r\"\"\"\n Unpack L U and P to single matrix tensor . \n unpack L and U matrix from LU, unpack permutation matrix P from Pivtos .\n\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n\n\n Args:\n x (Tensor): The LU tensor get from paddle.linalg.lu, which is combined by L and U.\n\n y (Tensor): Pivots get from paddle.linalg.lu.\n\n unpack_ludata (bool,optional): whether to unpack L and U from x. Default: True.\n\n unpack_pivots (bool, optional): whether to unpack permutation matrix P from Pivtos. Default: True.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n P (Tensor): Permutation matrix P of lu factorization.\n\n L (Tensor): The lower triangular matrix tensor of lu factorization.\n\n U (Tensor): The upper triangular matrix tensor of lu factorization.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n\n if paddle.in_dynamic_mode():\n P, L, U = _C_ops.lu_unpack(x, y, 'unpack_ludata', unpack_ludata,\n 'unpack_pivots', unpack_pivots)\n return P, L, U\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu_unpack')\n helper = LayerHelper('lu_unpack', **locals())\n p = helper.create_variable_for_type_inference(dtype=x.dtype)\n l = helper.create_variable_for_type_inference(dtype=x.dtype)\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n attrs = dict()\n attrs['unpack_ludata'] = unpack_ludata\n attrs['unpack_pivots'] = unpack_pivots\n helper.append_op(\n type='lu_unpack',\n inputs={'X': x,\n 'Pivots': y},\n outputs={'Pmat': p,\n 'L': l,\n 'U': u},\n attrs=attrs)\n return p, l, u\n\n\ndef eig(x, name=None):\n \"\"\"\n This API performs the eigenvalue decomposition of a square matrix or a batch of square matrices.\n\n .. note::\n If the matrix is a Hermitian or a real symmetric matrix, please use :ref:`paddle.linalg.eigh` instead, which is much faster.\n If only eigenvalues is needed, please use :ref:`paddle.linalg.eigvals` instead.\n If the matrix is of any shape, please use :ref:`paddle.linalg.svd`.\n This API is only supported on CPU device.\n The output datatype is always complex for both real and complex input.\n\n Args:\n x (Tensor): A tensor with shape math:`[*, N, N]`, The data type of the x should be one of ``float32``,\n ``float64``, ``compplex64`` or ``complex128``.\n name (str, optional): The default value is `None`. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Eigenvalues(Tensors): A tensor with shape math:`[*, N]` refers to the eigen values.\n Eigenvectors(Tensors): A tensor with shape math:`[*, N, N]` refers to the eigen vectors.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n paddle.device.set_device(\"cpu\")\n\n x_data = np.array([[1.6707249, 7.2249975, 6.5045543],\n [9.956216, 8.749598, 6.066444 ],\n [4.4251957, 1.7983172, 0.370647 ]]).astype(\"float32\")\n x = paddle.to_tensor(x_data)\n w, v = paddle.linalg.eig(x)\n print(w)\n # Tensor(shape=[3, 3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [[(-0.5061363550800655+0j) , (-0.7971760990842826+0j) ,\n # (0.18518077798279986+0j)],\n # [(-0.8308237755993192+0j) , (0.3463813401919749+0j) ,\n # (-0.6837005269141947+0j) ],\n # [(-0.23142567697893396+0j), (0.4944999840400175+0j) ,\n # (0.7058765252952796+0j) ]])\n\n print(v)\n # Tensor(shape=[3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [ (16.50471283351188+0j) , (-5.5034820550763515+0j) ,\n # (-0.21026087843552282+0j)])\n \"\"\"\n if paddle.in_dynamic_mode():\n w, v = _C_ops.eig(x)\n return w, v\n\n check_variable_and_dtype(\n x, 'X', ['float32', 'float64', 'complex64', 'complex128'], 'eig')\n helper = LayerHelper('eig', **locals())\n\n w = helper.create_variable_for_type_inference(x.dtype)\n v = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {'X': x}\n outputs = {'Eigenvalues': w, 'Eigenvectors': v}\n helper.append_op(type='eig', inputs=inputs, outputs=outputs)\n\n return w, v\n\n\ndef eigvals(x, name=None):\n \"\"\"\n Compute the eigenvalues of one or more general matrices.\n\n Warning:\n The gradient kernel of this operator does not yet developed.\n If you need back propagation through this operator, please replace it with paddle.linalg.eig.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices whose eigenvalues will be computed.\n Its shape should be `[*, M, M]`, where `*` is zero or more batch dimensions.\n Its data type should be float32, float64, complex64, or complex128.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n Tensor: A tensor containing the unsorted eigenvalues which has the same batch dimensions with `x`.\n The eigenvalues are complex-valued even when `x` is real.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n paddle.seed(1234)\n\n x = paddle.rand(shape=[3, 3], dtype='float64')\n # [[0.02773777, 0.93004224, 0.06911496],\n # [0.24831591, 0.45733623, 0.07717843],\n # [0.48016702, 0.14235102, 0.42620817]])\n\n print(paddle.linalg.eigvals(x))\n # [(-0.27078833542132674+0j), (0.29962280156230725+0j), (0.8824477020120244+0j)] #complex128\n \"\"\"\n\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64',\n 'complex128'], 'eigvals')\n\n x_shape = list(x.shape)\n if len(x_shape) < 2:\n raise ValueError(\n \"The dimension of Input(x) should be at least 2, but received x's dimention = {}, x's shape = {}\".\n format(len(x_shape), x_shape))\n\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The last two dimensions of Input(x) should be equal, but received x's shape = {}\".\n format(x_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.eigvals(x)\n\n helper = LayerHelper('eigvals', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='eigvals', inputs={'X': x}, outputs={'Out': out})\n return out\n\n\ndef multi_dot(x, name=None):\n \"\"\"\n Multi_dot is an operator that calculates multiple matrix multiplications.\n\n Supports inputs of float16(only GPU support), float32 and float64 dtypes. This function does not\n support batched inputs.\n\n The input tensor in [x] must be 2-D except for the first and last can be 1-D.\n If the first tensor is a 1-D vector of shape(n, ) it is treated as row vector\n of shape(1, n), similarly if the last tensor is a 1D vector of shape(n, ), it\n is treated as a column vector of shape(n, 1).\n\n If the first and last tensor are 2-D matrix, then the output is also 2-D matrix,\n otherwise the output is a 1-D vector.\n\n Multi_dot will select the lowest cost multiplication order for calculation. The\n cost of multiplying two matrices with shapes (a, b) and (b, c) is a * b * c.\n Given matrices A, B, C with shapes (20, 5), (5, 100), (100, 10) respectively,\n we can calculate the cost of different multiplication orders as follows:\n - Cost((AB)C) = 20x5x100 + 20x100x10 = 30000\n - Cost(A(BC)) = 5x100x10 + 20x5x10 = 6000\n\n In this case, multiplying B and C first, then multiply A, which is 5 times faster\n than sequential calculation.\n\n Args:\n x ([Tensor]): The input tensors which is a list Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # A * B\n A_data = np.random.random([3, 4]).astype(np.float32)\n B_data = np.random.random([4, 5]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n out = paddle.linalg.multi_dot([A, B])\n print(out.numpy().shape)\n # [3, 5]\n\n # A * B * C\n A_data = np.random.random([10, 5]).astype(np.float32)\n B_data = np.random.random([5, 8]).astype(np.float32)\n C_data = np.random.random([8, 7]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n C = paddle.to_tensor(C_data)\n out = paddle.linalg.multi_dot([A, B, C])\n print(out.numpy().shape)\n # [10, 7]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.multi_dot(x)\n\n check_type(x, 'x', (list, tuple), 'multi_dot')\n for id, item in enumerate(x):\n check_variable_and_dtype(item, 'x[' + str(id) + ']',\n ['float16', 'float32', 'float64'], 'multi_dot')\n if item.dtype != x[0].dtype:\n raise TypeError(\n \"All the Tensors in the input must have the same data type.\")\n\n helper = LayerHelper('multi_dot', **locals())\n dtype = helper.input_dtype(input_param_name='x')\n out = helper.create_variable_for_type_inference(dtype)\n helper.append_op(type='multi_dot', inputs={\"X\": x}, outputs={\"Out\": out})\n return out\n\n\ndef eigh(x, UPLO='L', name=None):\n \"\"\"\n Compute the eigenvalues and eigenvectors of a\n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[*, N, N]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): (string, default 'L'), 'L' represents the lower triangular matrix,\n \"'U' represents the upper triangular matrix.\".\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n\n out_value(Tensor): A Tensor with shape [*, N] and data type of float32 and float64. The eigenvalues of eigh op.\n out_vector(Tensor): A Tensor with shape [*, N, N] and data type of float32,float64,complex64 and complex128. The eigenvectors of eigh op.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value, out_vector = paddle.linalg.eigh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n print(out_vector)\n #[(-0.9238795325112867+0j), (-0.3826834323650898+0j)],\n #[ 0.3826834323650898j , -0.9238795325112867j ]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.eigh(x, 'UPLO', UPLO)\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigh', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'eigh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO})\n return out_value, out_vector\n\n\ndef pinv(x, rcond=1e-15, hermitian=False, name=None):\n r\"\"\"\n Calculate pseudo inverse via SVD(singular value decomposition)\n of one matrix or batches of regular matrix.\n\n .. math::\n\n if hermitian == False:\n x = u * s * vt (SVD)\n out = v * 1/s * ut\n else:\n x = u * s * ut (eigh)\n out = u * 1/s * u.conj().transpose(-2,-1)\n\n If x is hermitian or symmetric matrix, svd will be replaced with eigh.\n\n Args:\n x(Tensor): The input tensor. Its shape should be (*, m, n)\n where * is zero or more batch dimensions. m and n can be\n arbitraty positive number. The data type of x should be\n float32 or float64 or complex64 or complex128. When data\n type is complex64 or cpmplex128, hermitian should be set\n True.\n\n rcond(Tensor, optional): the tolerance value to determine\n when is a singular value zero. Defalut:1e-15.\n\n hermitian(bool, optional): indicates whether x is Hermitian\n if complex or symmetric if real. Default: False.\n\n name(str|None): A name for this layer(optional). If set None,\n the layer will be named automatically.\n\n Returns:\n Tensor: The tensor with same data type with x. it represents\n pseudo inverse of x. Its shape should be (*, n, m).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.arange(15).reshape((3, 5)).astype('float64')\n input = paddle.to_tensor(x)\n out = paddle.linalg.pinv(input)\n print(input)\n print(out)\n\n # input:\n # [[0. , 1. , 2. , 3. , 4. ],\n # [5. , 6. , 7. , 8. , 9. ],\n # [10., 11., 12., 13., 14.]]\n\n # out:\n # [[-0.22666667, -0.06666667, 0.09333333],\n # [-0.12333333, -0.03333333, 0.05666667],\n # [-0.02000000, 0.00000000, 0.02000000],\n # [ 0.08333333, 0.03333333, -0.01666667],\n # [ 0.18666667, 0.06666667, -0.05333333]]\n\n # one can verify : x * out * x = x ;\n # or out * x * out = x ;\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if not hermitian:\n # combine svd and matmul op\n u, s, vt = _C_ops.svd(x, 'full_matrices', False)\n max_singular_val = _C_ops.reduce_max(s, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=x.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=x.dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v, _ = _C_ops.transpose2(vt, 'axis', perm)\n\n out_1 = v * st\n out_2 = _C_ops.matmul_v2(out_1, u, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n # combine eigh and matmul op\n s, u = _C_ops.eigh(x, 'UPLO', 'L')\n s_abs = paddle.abs(s)\n max_singular_val = _C_ops.reduce_max(s_abs, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=s.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=s.dtype)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n out_1 = u * st\n u_conj = _C_ops.conj(u)\n out_2 = _C_ops.matmul_v2(out_1, u_conj, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n if not hermitian:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'pinv')\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(dtype)\n vt = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vt,\n 'S': s},\n attrs={'full_matrices': False}, )\n\n max_singular_val = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=dtype)\n st_shape = helper.create_variable_for_type_inference(dtype=dtype)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v = helper.create_variable_for_type_inference(dtype)\n v_shape = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='transpose2',\n inputs={'X': [vt]},\n outputs={'Out': [v],\n 'XShape': [v_shape]},\n attrs={'axis': perm})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': v,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n else:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64',\n 'complex128'], 'pinv')\n\n if dtype == paddle.complex128:\n s_type = 'float64'\n elif dtype == paddle.complex64:\n s_type = 'float32'\n else:\n s_type = dtype\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': s,\n 'Eigenvectors': u},\n attrs={'UPLO': 'L'})\n s_abs = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='abs', inputs={'X': s}, outputs={'Out': s_abs})\n max_singular_val = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s_abs},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=s_type)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=s_type)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s_type)\n cond_not_int = layers.cast(layers.logical_not(condition), s_type)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=s_type)\n st_shape = helper.create_variable_for_type_inference(dtype=s_type)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': u,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n u_conj = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='conj', inputs={'X': u}, outputs={'Out': [u_conj]})\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u_conj},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n\n\ndef solve(x, y, name=None):\n r\"\"\"\n Computes the solution of a square system of linear equations with a unique solution for input 'X' and 'Y'.\n Let :math: `X` be a sqaure matrix or a batch of square matrices, :math:`Y` be\n a vector/matrix or a batch of vectors/matrices, the equation should be:\n\n .. math::\n Out = X^-1 * Y\n Specifically,\n - This system of linear equations has one solution if and only if input 'X' is invertible.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): A vector/matrix or a batch of vectors/matrices. Its shape should be `[*, M, K]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of a square system of linear equations with a unique solution for input 'x' and 'y'.\n Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # 2*X0 + X1 = 9\n # X0 + 2*X1 = 8\n\n import paddle\n import numpy as np\n\n np_x = np.array([[3, 1],[1, 2]])\n np_y = np.array([9, 8])\n x = paddle.to_tensor(np_x, dtype=\"float64\")\n y = paddle.to_tensor(np_y, dtype=\"float64\")\n out = paddle.linalg.solve(x, y)\n\n print(out)\n # [2., 3.])\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.solve(x, y)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type=\"solve\", inputs={\"X\": x,\n \"Y\": y}, outputs={\"Out\": out})\n return out\n\n\ndef triangular_solve(x,\n y,\n upper=True,\n transpose=False,\n unitriangular=False,\n name=None):\n r\"\"\"\n Computes the solution of a system of equations with a triangular coefficient matrix `x` and\n multiple right-hand sides `y` .\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input triangular coefficient matrix. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): Whether to solve the upper-triangular system of equations (default) or the lower-triangular \n system of equations. Default: True.\n transpose (bool, optional): whether `x` should be transposed before calculation. Default: False.\n unitriangular (bool, optional): whether `x` is unit triangular. If True, the diagonal elements of `x` are assumed \n to be 1 and not referenced from `x` . Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # x1 + x2 + x3 = 0\n # 2*x2 + x3 = -9\n # -x3 = 5\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n y = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.triangular_solve(x, y, upper=True)\n\n print(out)\n # [7, -2, -5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.triangular_solve(x, y, 'upper', upper, 'transpose',\n transpose, 'unitriangular',\n unitriangular)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"triangular_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'triangular_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'triangular_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='triangular_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={\n 'upper': upper,\n 'transpose': transpose,\n 'unitriangular': unitriangular\n })\n return out\n\n\ndef cholesky_solve(x, y, upper=False, name=None):\n r\"\"\"\n Solves a linear system of equations A @ X = B, given A's Cholesky factor matrix u and matrix B.\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input matrix which is upper or lower triangular Cholesky factor of square matrix A. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): whether to consider the Cholesky factor as a lower or upper triangular matrix. Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type is the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n u = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n b = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.cholesky_solve(b, u, upper=True)\n\n print(out)\n # [-2.5, -7, 9.5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky_solve(x, y, 'upper', upper)\n\n helper = LayerHelper(\"cholesky_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'cholesky_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'cholesky_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='cholesky_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef eigvalsh(x, UPLO='L', name=None):\n \"\"\"\n Computes the eigenvalues of a \n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[_, M, M]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): Lower triangular part of a (‘L’, default) or the upper triangular part (‘U’).\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor eigenvalues in ascending order.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value = paddle.eigvalsh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n \"\"\"\n if paddle.in_dynamic_mode():\n is_test = x.stop_gradient\n values, _ = _C_ops.eigvalsh(x, 'UPLO', UPLO, 'is_test', is_test)\n return values\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigvalsh', **locals())\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64', 'complex128'],\n 'eigvalsh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n is_test = x.stop_gradient\n helper.append_op(\n type='eigvalsh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO,\n 'is_test': is_test})\n return out_value\n\n\ndef lstsq(x, y, rcond=None, driver=None, name=None):\n \"\"\"\n Computes a solution to\n the least squares problem of a system of linear equations.\n\n Args:\n x (Tensor): A tensor with shape ``(*, M, N)`` , the data type of the input Tensor ``x``\n should be one of float32, float64.\n y (Tensor): A tensor with shape ``(*, M, K)`` , the data type of the input Tensor ``y`` \n should be one of float32, float64.\n rcond(float, optional): The default value is None. A float pointing number used to determine \n the effective rank of ``x``. If ``rcond`` is None, it will be set to max(M, N) times the \n machine precision of x_dtype.\n driver(str, optional): The default value is None. The name of LAPACK method to be used. For \n CPU inputs the valid values are ‘gels’, ‘gelsy’, ‘gelsd, ‘gelss’. For CUDA input, the only \n valid driver is ‘gels’. If ``driver`` is None, ‘gelsy’ is used for CPU inputs and ‘gels’ \n for CUDA inputs.\n name(str, optional): The default value is None. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple: A tuple of 4 Tensors which is (``solution``, ``residuals``, ``rank``, ``singular_values``). \n ``solution`` is a tensor with shape ``(*, N, K)``, meaning the least squares solution. ``residuals`` \n is a tensor with shape ``(*, K)``, meaning the squared residuals of the solutions, which is computed \n when M > N and every matrix in ``x`` is full-rank, otherwise return an empty tensor. ``rank`` is a tensor \n with shape ``(*)``, meaning the ranks of the matrices in ``x``, which is computed when ``driver`` in \n (‘gelsy’, ‘gelsd’, ‘gelss’), otherwise return an empty tensor. ``singular_values`` is a tensor with \n shape ``(*, min(M, N))``, meaning singular values of the matrices in ``x``, which is computed when \n ``driver`` in (‘gelsd’, ‘gelss’), otherwise return an empty tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n x = paddle.to_tensor([[1, 3], [3, 2], [5, 6.]])\n y = paddle.to_tensor([[3, 4, 6], [5, 3, 4], [1, 2, 1.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gelsd\")\n print(results[0])\n # [[ 0.78350395, -0.22165027, -0.62371236],\n # [-0.11340097, 0.78866047, 1.14948535]]\n print(results[1])\n # [19.81443405, 10.43814468, 30.56185532])\n print(results[2])\n # 2\n print(results[3])\n # [9.03455734, 1.54167950]\n\n x = paddle.to_tensor([[10, 2, 3], [3, 10, 5], [5, 6, 12.]])\n y = paddle.to_tensor([[4, 2, 9], [2, 0, 3], [2, 5, 3.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gels\")\n print(results[0])\n # [[ 0.39386186, 0.10230173, 0.93606132],\n # [ 0.10741687, -0.29028133, 0.11892585],\n # [-0.05115091, 0.51918161, -0.19948854]]\n print(results[1])\n # []\n \"\"\"\n device = paddle.get_device()\n if device == \"cpu\":\n if driver not in (None, \"gels\", \"gelss\", \"gelsd\", \"gelsy\"):\n raise ValueError(\n \"Only support valid driver is 'gels', 'gelss', 'gelsd', 'gelsy' or None for CPU inputs. But got {}\".\n format(driver))\n driver = \"gelsy\" if driver is None else driver\n elif \"gpu\" in device:\n if driver not in (None, \"gels\"):\n raise ValueError(\n \"Only support valid driver is 'gels' or None for CUDA inputs. But got {}\".\n format(driver))\n driver = \"gels\" if driver is None else driver\n else:\n raise RuntimeError(\"Only support lstsq api for CPU or CUDA device.\")\n\n if x.dtype == y.dtype and x.dtype in (paddle.float32, paddle.float64):\n pass\n else:\n raise ValueError(\n \"Only support x and y have the same dtype such as 'float32' and 'float64'.\"\n )\n\n if rcond is None:\n if x.dtype == paddle.float32:\n rcond = 1e-7 * max(x.shape[-2], x.shape[-1])\n elif x.dtype == paddle.float64:\n rcond = 1e-15 * max(x.shape[-2], x.shape[-1])\n\n if paddle.in_dynamic_mode():\n solution, rank, singular_values = _C_ops.lstsq(x, y, \"rcond\", rcond,\n \"driver\", driver)\n if x.shape[-2] > x.shape[-1]:\n matmul_out = _varbase_creator(dtype=x.dtype)\n _C_ops.matmul(x, solution, matmul_out, 'trans_x', False, 'trans_y',\n False)\n minus_out = _C_ops.elementwise_sub(matmul_out, y)\n pow_out = _C_ops.pow(minus_out, 'factor', 2)\n residuals = _C_ops.reduce_sum(pow_out, 'dim', [-2], 'keepdim',\n False, 'reduce_all', False)\n else:\n residuals = paddle.empty(shape=[0], dtype=x.dtype)\n\n if driver == \"gels\":\n rank = paddle.empty(shape=[0], dtype=paddle.int32)\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n elif driver == \"gelsy\":\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n\n return solution, residuals, rank, singular_values\n\n helper = LayerHelper('lstsq', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n check_variable_and_dtype(\n y, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n\n solution = helper.create_variable_for_type_inference(dtype=x.dtype)\n residuals = helper.create_variable_for_type_inference(dtype=x.dtype)\n rank = helper.create_variable_for_type_inference(dtype=paddle.int32)\n singular_values = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='lstsq',\n inputs={'X': x,\n 'Y': y},\n outputs={\n 'Solution': solution,\n 'Rank': rank,\n 'SingularValues': singular_values\n },\n attrs={'rcond': rcond,\n 'driver': driver})\n\n matmul_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n minus_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n pow_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': solution},\n outputs={'Out': matmul_out},\n attrs={\n 'trans_x': False,\n 'trans_y': False,\n })\n\n helper.append_op(\n type='elementwise_sub',\n inputs={'X': matmul_out,\n 'Y': y},\n outputs={'Out': minus_out})\n\n helper.append_op(\n type='pow',\n inputs={'X': minus_out},\n outputs={'Out': pow_out},\n attrs={'factor': 2})\n\n helper.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': residuals},\n attrs={'dim': [-2],\n 'keep_dim': False,\n 'reduce_all': False})\n\n if driver == \"gels\":\n rank = paddle.static.data(name='rank', shape=[0])\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n elif driver == \"gelsy\":\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n\n return solution, residuals, rank, singular_values\n"}}},{"rowIdx":36,"cells":{"function_name":{"kind":"string","value":"eig"},"docstring":{"kind":"string","value":"This API performs the eigenvalue decomposition of a square matrix or a batch of square matrices.\n\n.. note::\n If the matrix is a Hermitian or a real symmetric matrix, please use :ref:`paddle.linalg.eigh` instead, which is much faster.\n If only eigenvalues is needed, please use :ref:`paddle.linalg.eigvals` instead.\n If the matrix is of any shape, please use :ref:`paddle.linalg.svd`.\n This API is only supported on CPU device.\n The output datatype is always complex for both real and complex input.\n\nArgs:\n x (Tensor): A tensor with shape math:`[*, N, N]`, The data type of the x should be one of ``float32``,\n ``float64``, ``compplex64`` or ``complex128``.\n name (str, optional): The default value is `None`. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\nReturns:\n Eigenvalues(Tensors): A tensor with shape math:`[*, N]` refers to the eigen values.\n Eigenvectors(Tensors): A tensor with shape math:`[*, N, N]` refers to the eigen vectors.\n\nExamples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n paddle.device.set_device(\"cpu\")\n\n x_data = np.array([[1.6707249, 7.2249975, 6.5045543],\n [9.956216, 8.749598, 6.066444 ],\n [4.4251957, 1.7983172, 0.370647 ]]).astype(\"float32\")\n x = paddle.to_tensor(x_data)\n w, v = paddle.linalg.eig(x)\n print(w)\n # Tensor(shape=[3, 3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [[(-0.5061363550800655+0j) , (-0.7971760990842826+0j) ,\n # (0.18518077798279986+0j)],\n # [(-0.8308237755993192+0j) , (0.3463813401919749+0j) ,\n # (-0.6837005269141947+0j) ],\n # [(-0.23142567697893396+0j), (0.4944999840400175+0j) ,\n # (0.7058765252952796+0j) ]])\n\n print(v)\n # Tensor(shape=[3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [ (16.50471283351188+0j) , (-5.5034820550763515+0j) ,\n # (-0.21026087843552282+0j)])"},"masked_code":{"kind":"string","value":"# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numpy as np\nfrom ..fluid.layer_helper import LayerHelper\nfrom ..framework import _varbase_creator, _dygraph_tracer\nfrom ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype\nfrom ..static import Variable\nfrom ..fluid.framework import _in_legacy_dygraph, in_dygraph_mode\nfrom ..fluid.layers import transpose, cast # noqa: F401\nfrom ..fluid import layers\nimport paddle\nfrom paddle.common_ops_import import core\nfrom paddle.common_ops_import import VarDesc\nfrom paddle import _C_ops\n\n__all__ = []\n\n\ndef matmul(x, y, transpose_x=False, transpose_y=False, name=None):\n \"\"\"\n Applies matrix multiplication to two tensors. `matmul` follows\n the complete broadcast rules,\n and its behavior is consistent with `np.matmul`.\n\n Currently, the input tensors' number of dimensions can be any, `matmul` can be used to\n achieve the `dot`, `matmul` and `batchmatmul`.\n\n The actual behavior depends on the shapes of :math:`x`, :math:`y` and the\n flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:\n\n - If a transpose flag is specified, the last two dimensions of the tensor\n are transposed. If the tensor is ndim-1 of shape, the transpose is invalid. If the tensor\n is ndim-1 of shape :math:`[D]`, then for :math:`x` it is treated as :math:`[1, D]`, whereas\n for :math:`y` it is the opposite: It is treated as :math:`[D, 1]`.\n\n The multiplication behavior depends on the dimensions of `x` and `y`. Specifically:\n\n - If both tensors are 1-dimensional, the dot product result is obtained.\n\n - If both tensors are 2-dimensional, the matrix-matrix product is obtained.\n\n - If the `x` is 1-dimensional and the `y` is 2-dimensional,\n a `1` is prepended to its dimension in order to conduct the matrix multiply.\n After the matrix multiply, the prepended dimension is removed.\n\n - If the `x` is 2-dimensional and `y` is 1-dimensional,\n the matrix-vector product is obtained.\n\n - If both arguments are at least 1-dimensional and at least one argument\n is N-dimensional (where N > 2), then a batched matrix multiply is obtained.\n If the first argument is 1-dimensional, a 1 is prepended to its dimension\n in order to conduct the batched matrix multiply and removed after.\n If the second argument is 1-dimensional, a 1 is appended to its\n dimension for the purpose of the batched matrix multiple and removed after.\n The non-matrix (exclude the last two dimensions) dimensions are\n broadcasted according the broadcast rule.\n For example, if input is a (j, 1, n, m) tensor and the other is a (k, m, p) tensor,\n out will be a (j, k, n, p) tensor.\n\n Args:\n x (Tensor): The input tensor which is a Tensor.\n y (Tensor): The input tensor which is a Tensor.\n transpose_x (bool): Whether to transpose :math:`x` before multiplication.\n transpose_y (bool): Whether to transpose :math:`y` before multiplication.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # vector * vector\n x_data = np.random.random([10]).astype(np.float32)\n y_data = np.random.random([10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [1]\n\n # matrix * vector\n x_data = np.random.random([10, 5]).astype(np.float32)\n y_data = np.random.random([5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10]\n\n # batched matrix * broadcasted vector\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([2]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5]\n\n # batched matrix * batched matrix\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([10, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5, 5]\n\n # batched matrix * broadcasted matrix\n x_data = np.random.random([10, 1, 5, 2]).astype(np.float32)\n y_data = np.random.random([1, 3, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 3, 5, 5]\n\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_matmul(x, y, transpose_x, transpose_y)\n\n if _in_legacy_dygraph():\n op_type = 'matmul_v2'\n op = getattr(_C_ops, op_type)\n return op(x, y, 'trans_x', transpose_x, 'trans_y', transpose_y)\n\n attrs = {\n 'trans_x': transpose_x,\n 'trans_y': transpose_y,\n }\n\n def __check_input(x, y):\n var_names = {'x': x, 'y': y}\n for name, val in var_names.items():\n check_variable_and_dtype(\n val, name,\n ['float16', 'float32', 'float64', 'complex64', 'complex128'],\n 'matmul')\n\n __check_input(x, y)\n\n helper = LayerHelper('matmul_v2', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef norm(x, p='fro', axis=None, keepdim=False, name=None):\n \"\"\"\n\n Returns the matrix norm (Frobenius) or vector norm (the 1-norm, the Euclidean\n or 2-norm, and in general the p-norm for p > 0) of a given tensor.\n\n .. note::\n This norm API is different from `numpy.linalg.norm`.\n This api supports high-order input tensors (rank >= 3), and certain axis need to be pointed out to calculate the norm.\n But `numpy.linalg.norm` only supports 1-D vector or 2-D matrix as input tensor.\n For p-order matrix norm, this api actually treats matrix as a flattened vector to calculate the vector norm, NOT REAL MATRIX NORM.\n\n Args:\n x (Tensor): The input tensor could be N-D tensor, and the input data\n type could be float32 or float64.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `0`, `1`, `2`,\n `inf`, `-inf` and any positive real number yielding the corresponding p-norm. Not supported: ord < 0 and nuclear norm.\n Default value is `fro`.\n axis (int|list|tuple, optional): The axis on which to apply norm operation. If axis is int\n or list(int)/tuple(int) with only one element, the vector norm is computed over the axis.\n If `axis < 0`, the dimension to norm operation is rank(input) + axis.\n If axis is a list(int)/tuple(int) with two elements, the matrix norm is computed over the axis.\n Defalut value is `None`.\n keepdim (bool, optional): Whether to reserve the reduced dimension in the\n output Tensor. The result tensor will have fewer dimension\n than the :attr:`input` unless :attr:`keepdim` is true, default\n value is False.\n name (str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: results of norm operation on the specified axis of input tensor,\n it's data type is the same as input's Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n shape=[2, 3, 4]\n np_input = np.arange(24).astype('float32') - 12\n np_input = np_input.reshape(shape)\n x = paddle.to_tensor(np_input)\n #[[[-12. -11. -10. -9.] [ -8. -7. -6. -5.] [ -4. -3. -2. -1.]]\n # [[ 0. 1. 2. 3.] [ 4. 5. 6. 7.] [ 8. 9. 10. 11.]]]\n\n # compute frobenius norm along last two dimensions.\n out_fro = paddle.linalg.norm(x, p='fro', axis=[0,1])\n # out_fro.numpy() [17.435596 16.911535 16.7332 16.911535]\n\n # compute 2-order vector norm along last dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=-1)\n #out_pnorm.numpy(): [[21.118711 13.190906 5.477226]\n # [ 3.7416575 11.224972 19.131126]]\n\n # compute 2-order norm along [0,1] dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=[0,1])\n #out_pnorm.numpy(): [17.435596 16.911535 16.7332 16.911535]\n\n # compute inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=np.inf)\n #out_pnorm.numpy() = [12.]\n out_pnorm = paddle.linalg.norm(x, p=np.inf, axis=0)\n #out_pnorm.numpy(): [[12. 11. 10. 9.] [8. 7. 6. 7.] [8. 9. 10. 11.]]\n\n # compute -inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=-np.inf)\n #out_pnorm.numpy(): [0.]\n out_pnorm = paddle.linalg.norm(x, p=-np.inf, axis=0)\n #out_pnorm.numpy(): [[0. 1. 2. 3.] [4. 5. 6. 5.] [4. 3. 2. 1.]]\n \"\"\"\n\n def frobenius_norm(input, dim=None, keepdim=False, name=None):\n \"\"\"\n The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n dim (list, optional): None for last two dimensions.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if dim is not None and not (isinstance(dim, list) and len(dim) == 2):\n raise ValueError(\n \"The dim of frobenius norm op should be None or two elements list!\"\n )\n if paddle.in_dynamic_mode():\n if dim is None:\n return _C_ops.frobenius_norm(input, 'keep_dim', keepdim,\n 'reduce_all', True)\n return _C_ops.frobenius_norm(input, 'dim', dim, 'keep_dim', keepdim,\n 'reduce_all', False)\n attrs = {'dim': dim, 'keep_dim': keepdim, 'reduce_all': False}\n if dim is None:\n attrs['reduce_all'] = True\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'frobenius_norm')\n\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='frobenius_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def vector_norm(input,\n porder=None,\n axis=None,\n keepdim=False,\n asvector=False,\n name=None):\n \"\"\"\n Calculate the p-order vector norm for certain dimension of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n porder (float, optional): None for porder=2.0.\n axis (int, optional): None for last dimension.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if paddle.in_dynamic_mode():\n if axis is None: axis = -1\n return _C_ops.p_norm(input, 'porder', porder, 'axis', axis,\n 'keepdim', keepdim, 'asvector', asvector)\n if porder is not None:\n check_type(porder, 'porder', (float, int), 'p_norm')\n if axis is not None:\n check_type(axis, 'axis', (int), 'p_norm')\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'p_norm')\n\n attrs = {\n 'axis': axis if axis is not None else -1,\n 'porder': float(porder) if porder is not None else 2.0,\n 'keepdim': keepdim,\n 'asvector': asvector,\n 'epsilon': 1e-12,\n }\n helper = LayerHelper('p_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='p_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def inf_norm(input,\n porder=None,\n axis=axis,\n keepdim=False,\n asvector=False,\n name=None):\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n helper.append_op(type='abs', inputs={'X': input}, outputs={'Out': out})\n reduce_out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n reduce_all = True if axis == None or axis == [] or asvector == True else False\n axis = axis if axis != None and axis != [] else [0]\n\n reduce_type = 'reduce_max' if porder == np.float(\n 'inf') else 'reduce_min'\n helper.append_op(\n type=reduce_type,\n inputs={'X': out},\n outputs={'Out': reduce_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n\n return reduce_out\n\n def p_matrix_norm(input, porder=1., axis=axis, keepdim=False, name=None):\n \"\"\"\n NOTE:\n This function actually treats the matrix as flattened vector to calculate vector norm instead of matrix norm.\n \"\"\"\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n\n block.append_op(\n type='pow',\n inputs={'X': abs_out},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': True if axis is None else False\n })\n porder\n block.append_op(\n type='pow',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n if axis is None and p is not None:\n if isinstance(p, str):\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n porder=p,\n axis=axis,\n keepdim=keepdim,\n asvector=True,\n name=name)\n else:\n raise ValueError(\"only valid p type is string or float, found {}\".\n format(type(p)))\n\n if isinstance(axis, tuple):\n axis = list(axis)\n if isinstance(axis, list) and len(axis) == 1:\n axis = axis[0]\n\n #calculate vector norm, where axis is int or list with only one integer\n if isinstance(axis, int):\n if isinstance(p, str):\n if p == \"fro\":\n return vector_norm(\n x,\n porder=2,\n axis=axis,\n keepdim=keepdim,\n asvector=False,\n name=name)\n\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n axis=axis,\n porder=p,\n keepdim=keepdim,\n asvector=False,\n name=name)\n else:\n raise ValueError(\n \"unspport p for p-order vector norm. except float, found {}\".\n format(p))\n #calculate matrix norm, where axis is list with two integers\n elif isinstance(axis, list) and len(axis) == 2:\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n elif p == np.inf or p == -np.inf:\n return inf_norm(x, porder=p, axis=axis, keepdim=keepdim, name=name)\n elif p == 0:\n raise ValueError(\n \"just suport axis type int or list (length of list <=1) if p = 0, found {}\".\n format(axis))\n else:\n return p_matrix_norm(\n x, porder=p, axis=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"except axis type int or list (length of list <=2), found {}\".\n format(axis))\n\n\ndef dist(x, y, p=2, name=None):\n r\"\"\"\n\n This OP returns the p-norm of (x - y). It is not a norm in a strict sense, only as a measure\n of distance. The shapes of x and y must be broadcastable. The definition is as follows, for\n details, please refer to the `numpy's broadcasting <https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html>`_:\n\n - Each input has at least one dimension.\n - Match the two input dimensions from back to front, the dimension sizes must either be equal, one of them is 1, or one of them does not exist.\n\n Where, z = x - y, the shapes of x and y are broadcastable, then the shape of z can be\n obtained as follows:\n\n 1. If the number of dimensions of x and y are not equal, prepend 1 to the dimensions of the\n tensor with fewer dimensions.\n\n For example, The shape of x is [8, 1, 6, 1], the shape of y is [7, 1, 5], prepend 1 to the\n dimension of y.\n\n x (4-D Tensor): 8 x 1 x 6 x 1\n\n y (4-D Tensor): 1 x 7 x 1 x 5\n\n 2. Determine the size of each dimension of the output z: choose the maximum value from the\n two input dimensions.\n\n z (4-D Tensor): 8 x 7 x 6 x 5\n\n If the number of dimensions of the two inputs are the same, the size of the output can be\n directly determined in step 2. When p takes different values, the norm formula is as follows:\n\n When p = 0, defining $0^0=0$, the zero-norm of z is simply the number of non-zero elements of z.\n\n .. math::\n\n ||z||_{0}=\\lim_{p \\\\rightarrow 0}\\sum_{i=1}^{m}|z_i|^{p}\n\n When p = inf, the inf-norm of z is the maximum element of z.\n\n .. math::\n\n ||z||_\\infty=\\max_i |z_i|\n\n When p = -inf, the negative-inf-norm of z is the minimum element of z.\n\n .. math::\n\n ||z||_{-\\infty}=\\min_i |z_i|\n\n Otherwise, the p-norm of z follows the formula,\n\n .. math::\n\n ||z||_{p}=(\\sum_{i=1}^{m}|z_i|^p)^{\\\\frac{1}{p}}\n\n Args:\n x (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n y (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n p (float, optional): The norm to be computed, its data type is float32 or float64. Default: 2.\n\n Returns:\n Tensor: Tensor that is the p-norm of (x - y).\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor(np.array([[3, 3],[3, 3]]), \"float32\")\n y = paddle.to_tensor(np.array([[3, 3],[3, 1]]), \"float32\")\n out = paddle.dist(x, y, 0)\n print(out) # out = [1.]\n\n out = paddle.dist(x, y, 2)\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"inf\"))\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"-inf\"))\n print(out) # out = [0.]\n \"\"\"\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'dist')\n check_variable_and_dtype(y, 'dtype', ['float32', 'float64'], 'dist')\n check_type(p, 'p', (float, int), 'dist')\n helper = LayerHelper(\"dist\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n outputs = {'Out': [out]}\n attrs = {\"p\": float(p)}\n helper.append_op(\n type='dist', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef cond(x, p=None, name=None):\n \"\"\"\n\n Computes the condition number of a matrix or batches of matrices with respect to a matrix norm ``p``.\n\n Args:\n x (Tensor): The input tensor could be tensor of shape ``(*, m, n)`` where ``*`` is zero or more batch dimensions\n for ``p`` in ``(2, -2)``, or of shape ``(*, n, n)`` where every matrix is invertible for any supported ``p``.\n And the input data type could be ``float32`` or ``float64``.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `nuc`, `1`, `-1`, `2`, `-2`,\n `inf`, `-inf`. Default value is `None`, meaning that the order of the norm is `2`.\n name (str, optional): The default value is `None`. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: computing results of condition number, its data type is the same as input Tensor ``x``.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1., 0, -1], [0, 1, 0], [1, 0, 1]])\n\n # compute conditional number when p is None\n out = paddle.linalg.cond(x)\n # out.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is 'fro'\n out_fro = paddle.linalg.cond(x, p='fro')\n # out_fro.numpy() [3.1622777]\n\n # compute conditional number when order of the norm is 'nuc'\n out_nuc = paddle.linalg.cond(x, p='nuc')\n # out_nuc.numpy() [9.2426405]\n\n # compute conditional number when order of the norm is 1\n out_1 = paddle.linalg.cond(x, p=1)\n # out_1.numpy() [2.]\n\n # compute conditional number when order of the norm is -1\n out_minus_1 = paddle.linalg.cond(x, p=-1)\n # out_minus_1.numpy() [1.]\n\n # compute conditional number when order of the norm is 2\n out_2 = paddle.linalg.cond(x, p=2)\n # out_2.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is -1\n out_minus_2 = paddle.linalg.cond(x, p=-2)\n # out_minus_2.numpy() [0.70710677]\n\n # compute conditional number when order of the norm is inf\n out_inf = paddle.linalg.cond(x, p=np.inf)\n # out_inf.numpy() [2.]\n\n # compute conditional number when order of the norm is -inf\n out_minus_inf = paddle.linalg.cond(x, p=-np.inf)\n # out_minus_inf.numpy() [1.]\n\n a = paddle.to_tensor(np.random.randn(2, 4, 4).astype('float32'))\n # a.numpy()\n # [[[ 0.14063153 -0.996288 0.7996131 -0.02571543]\n # [-0.16303636 1.5534962 -0.49919784 -0.04402903]\n # [-1.1341571 -0.6022629 0.5445269 0.29154757]\n # [-0.16816919 -0.30972657 1.7521842 -0.5402487 ]]\n # [[-0.58081484 0.12402827 0.7229862 -0.55046535]\n # [-0.15178485 -1.1604939 0.75810957 0.30971205]\n # [-0.9669573 1.0940945 -0.27363303 -0.35416734]\n # [-1.216529 2.0018666 -0.7773689 -0.17556527]]]\n a_cond_fro = paddle.linalg.cond(a, p='fro')\n # a_cond_fro.numpy() [31.572273 28.120834]\n\n b = paddle.to_tensor(np.random.randn(2, 3, 4).astype('float64'))\n # b.numpy()\n # [[[ 1.61707487 0.46829144 0.38130416 0.82546736]\n # [-1.72710298 0.08866375 -0.62518804 0.16128892]\n # [-0.02822879 -1.67764516 0.11141444 0.3220113 ]]\n # [[ 0.22524372 0.62474921 -0.85503233 -1.03960523]\n # [-0.76620689 0.56673047 0.85064753 -0.45158196]\n # [ 1.47595418 2.23646462 1.5701758 0.10497519]]]\n b_cond_2 = paddle.linalg.cond(b, p=2)\n # b_cond_2.numpy() [3.30064451 2.51976252]\n\n \"\"\"\n\n def mat_norm(input, porder=1., axis=None):\n \"\"\"\n NOTE:\n Calculate the matrix norm of a square matrix or batches of square matrices,\n when porder is in (1, -1, inf, -inf)\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n axis = axis if axis != None and axis != [] else [0]\n keepdim = False\n\n if paddle.in_dynamic_mode():\n abs_out = _C_ops.abs(input)\n sum_out = _C_ops.reduce_sum(abs_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == 1 or porder == np.inf:\n return _C_ops.reduce_max(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == -1 or porder == -np.inf:\n return _C_ops.reduce_min(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n\n block = LayerHelper('norm', **locals())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n block.append_op(\n type='reduce_sum',\n inputs={'X': abs_out},\n outputs={'Out': sum_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 1 or porder == np.inf:\n block.append_op(\n type='reduce_max',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n if porder == -1 or porder == -np.inf:\n block.append_op(\n type='reduce_min',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n\n def fro_norm(input, porder=2, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the frobenius norm of a square matrix or batches of square matrices.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n if paddle.in_dynamic_mode():\n pow_out = _C_ops.pow(input, 'factor', porder)\n sum_out_1 = _C_ops.reduce_sum(pow_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n sum_out_2 = _C_ops.reduce_sum(sum_out_1, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n return _C_ops.pow(sum_out_2, 'factor', float(1. / porder))\n\n block = LayerHelper('norm', **locals())\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_1 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_2 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='pow',\n inputs={'X': input},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out_1},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_sum',\n inputs={'X': sum_out_1},\n outputs={'Out': sum_out_2},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='pow',\n inputs={'X': sum_out_2},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n def svd_norm(input, porder, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the matrix norm, which is related to singular values, of a matrix\n or batches of matrices, including nuclear norm, 2-norm and (-2)-norm.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n u, s, vh = svd(input, full_matrices=False)\n\n if paddle.in_dynamic_mode():\n if porder == \"nuc\":\n return _C_ops.reduce_sum(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n max_out = _C_ops.reduce_max(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n min_out = _C_ops.reduce_min(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n if porder == 2:\n return _C_ops.elementwise_div(max_out, min_out, 'aixs', axis,\n 'use_mkldnn', False)\n if porder == -2:\n return _C_ops.elementwise_div(min_out, max_out, 'aixs', axis,\n 'use_mkldnn', False)\n\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n if porder == \"nuc\":\n block.append_op(\n type='reduce_sum',\n inputs={'X': s},\n outputs={'Out': out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n max_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n min_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_min',\n inputs={'X': s},\n outputs={'Out': min_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': max_out,\n 'Y': min_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n if porder == -2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': min_out,\n 'Y': max_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n\n def empty_tensor(input, shape):\n if paddle.in_dynamic_mode():\n return input.reshape(shape)\n raise ValueError(\"only support x is nonempty tensor in static mode\")\n\n x_shape = list(x.shape)\n if not len(x_shape) >= 2:\n raise ValueError(\"input should be a matrix or batches of matrices, \" +\n \"but the dimention of received input is {}\".format(\n len(x_shape)))\n if p == None:\n p = 2\n x_size = 0 if (0 in x_shape) else 1\n if p in (\"fro\", \"nuc\", 1, -1, np.inf, -np.inf):\n if x_shape[len(x_shape) - 1] == x_shape[len(x_shape) - 2]:\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n x_inv = x.inverse()\n if p == \"fro\":\n return fro_norm(x) * fro_norm(x_inv)\n if p == \"nuc\":\n return svd_norm(x, p) * svd_norm(x_inv, p)\n if p in (1, -1):\n return mat_norm(\n x, porder=p, axis=[-2]) * mat_norm(\n x_inv, porder=p, axis=[-2])\n if p in (np.inf, -np.inf):\n return mat_norm(\n x, porder=p, axis=[-1]) * mat_norm(\n x_inv, porder=p, axis=[-1])\n else:\n raise ValueError(\"only support p is {} when input is a \".format(p) +\n \"square matrix or batches of square matrices\")\n elif p in (2, -2):\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n return svd_norm(x, porder=p)\n else:\n raise ValueError(\n \"unsupported {} for p, only supporting ('fro', 'nuc', \".format(\n p) + \"1, -1, 2, -2, inf, -inf) or none\")\n\n\ndef dot(x, y, name=None):\n \"\"\"\n This operator calculates inner product for vectors.\n\n .. note::\n Support 1-d and 2-d Tensor. When it is 2d, the first dimension of this matrix\n is the batch dimension, which means that the vectors of multiple batches are dotted.\n\n Parameters:\n x(Tensor): 1-D or 2-D ``Tensor``. Its dtype should be ``float32``, ``float64``, ``int32``, ``int64``\n y(Tensor): 1-D or 2-D ``Tensor``. Its dtype soulde be ``float32``, ``float64``, ``int32``, ``int64``\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: the calculated result Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x_data = np.random.uniform(0.1, 1, [10]).astype(np.float32)\n y_data = np.random.uniform(1, 3, [10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.dot(x, y)\n print(z)\n\n \"\"\"\n op_type = 'dot'\n # skip var type check in dygraph mode to improve efficiency\n if paddle.in_dynamic_mode():\n op = getattr(_C_ops, op_type)\n return op(x, y)\n\n assert x is not None, 'x cannot be None in {}'.format(op_type)\n assert y is not None, 'y cannot be None in {}'.format(op_type)\n\n check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n check_variable_and_dtype(y, 'y', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n\n helper = LayerHelper(op_type, **locals())\n if name is None:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n else:\n out = helper.create_variable(\n name=name, dtype=x.dtype, persistable=False)\n helper.append_op(\n type=\"dot\", inputs={'X': x,\n 'Y': y}, attrs={}, outputs={\"Out\": out})\n return out\n\n\ndef cov(x, rowvar=True, ddof=True, fweights=None, aweights=None, name=None):\n \"\"\"\n Estimate the covariance matrix of the input variables, given data and weights.\n\n A covariance matrix is a square matrix, indicate the covariance of each pair variables in the input matrix.\n For example, for an N-dimensional samples X=[x1,x2,…xN]T, then the covariance matrix \n element Cij is the covariance of xi and xj. The element Cii is the variance of xi itself.\n\n Parameters:\n x(Tensor): A N-D(N<=2) Tensor containing multiple variables and observations. By default, each row of x represents a variable. Also see rowvar below.\n rowvar(Bool, optional): If rowvar is True (default), then each row represents a variable, with observations in the columns. Default: True\n ddof(Bool, optional): If ddof=True will return the unbiased estimate, and ddof=False will return the simple average. Default: True\n fweights(Tensor, optional): 1-D Tensor of integer frequency weights; The number of times each observation vector should be repeated. Default: None\n aweights(Tensor, optional): 1-D Tensor of observation vector weights. How important of the observation vector, larger data means this element is more important. Default: None\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: The covariance matrix Tensor of the variables.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n\n xt = paddle.rand((3,4))\n paddle.linalg.cov(xt)\n\n '''\n Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n [[0.07918842, 0.06127326, 0.01493049],\n [0.06127326, 0.06166256, 0.00302668],\n [0.01493049, 0.00302668, 0.01632146]])\n '''\n \"\"\"\n op_type = 'cov'\n if len(x.shape) > 2 or len(x.shape) < 1:\n raise ValueError(\n \"Input(x) only support N-D (1<=N<=2) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cov')\n nx = x\n if len(x.shape) == 1:\n nx = x.reshape((1, -1))\n if not rowvar and nx.shape[0] != 1:\n nx = nx.t()\n w = None\n observation_num = nx.shape[1]\n if fweights is not None:\n w = fweights.astype(nx.dtype)\n if len(w.shape) > 1:\n raise ValueError(\n \"Input(fweights) only support N-D (N<=1) tensor in cov, but received \"\n \"shape of Input(input) is %s.\" % len(fweights.shape))\n if fweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(fweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(fweights) is {}.\".format(observation_num,\n fweights.shape[0]))\n if fweights.min() < 0:\n raise ValueError(\n \"The value of Input(fweights) cannot be negtive, but received \"\n \"min of Input(fweights) is {}.\".format(fweights.min()))\n if not paddle.all(fweights == paddle.round(fweights.astype('float64'))):\n raise ValueError(\"Input(fweights) must be integer \")\n\n if aweights is not None:\n aw = aweights.astype(nx.dtype)\n if len(aw.shape) > 1:\n raise ValueError(\n \"Input(aweights) only support N-D (N<=1) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(aweights.shape))\n check_variable_and_dtype(aweights, 'dtype', ['float32', 'float64'],\n 'cov')\n if aweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(aweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(aweights) is {}.\".format(observation_num,\n aweights.shape[0]))\n if aweights.min() < 0:\n raise ValueError(\n \"The value of Input(aweights) cannot be negtive, but received \"\n \"min of Input(aweights) is {}.\".format(aweights.min()))\n if w is not None:\n w = w * aw\n else:\n w = aw\n\n w_sum = paddle.to_tensor(observation_num, dtype=nx.dtype)\n if fweights is not None or aweights is not None:\n w_sum = w.sum()\n if w_sum.item() == 0:\n raise ValueError(\"The sum of weights is zero, can't be normalized.\")\n\n if w is not None:\n nx_w = nx * w\n avg = (nx_w).sum(axis=1) / w_sum\n else:\n avg = nx.sum(axis=1) / w_sum\n nx_w = nx\n\n if w is not None and aweights is not None and ddof == True:\n norm_factor = w_sum - (w * aweights).sum() / w_sum\n else:\n norm_factor = w_sum - ddof\n if norm_factor <= 0:\n norm_factor = paddle.to_tensor(0, dtype=nx.dtype)\n nx = nx - avg.unsqueeze(1)\n xxt = paddle.mm(nx, nx_w.t().conj())\n cov = paddle.divide(xxt, norm_factor).squeeze()\n return cov\n\n\ndef t(input, name=None):\n \"\"\"\n Transpose <=2-D tensor.\n 0-D and 1-D tensors are returned as it is and 2-D tensor is equal to\n the paddle.transpose function which perm dimensions set 0 and 1.\n\n Args:\n input (Tensor): The input Tensor. It is a N-D (N<=2) Tensor of data types float16, float32, float64, int32.\n name(str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`\n Returns:\n Tensor: A transposed n-D Tensor, with data type being float16, float32, float64, int32, int64.\n\n For Example:\n\n .. code-block:: text\n\n # Example 1 (0-D tensor)\n x = tensor([0.79])\n paddle.t(x) = tensor([0.79])\n\n # Example 2 (1-D tensor)\n x = tensor([0.79, 0.84, 0.32])\n paddle.t(x) = tensor([0.79, 0.84, 0.32])\n\n # Example 3 (2-D tensor)\n x = tensor([0.79, 0.84, 0.32],\n [0.64, 0.14, 0.57])\n paddle.t(x) = tensor([0.79, 0.64],\n [0.84, 0.14],\n [0.32, 0.57])\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n x = paddle.ones(shape=[2, 3], dtype='int32')\n x_transposed = paddle.t(x)\n print(x_transposed.shape)\n # [3, 2]\n \"\"\"\n if len(input.shape) > 2:\n raise ValueError(\n \"Input(input) only support N-D (N<=2) tensor, but received \"\n \"length of Input(input) is %s. Perhaps you can use paddle.\"\n \"tensor.transpose() instead.\" % len(input.shape))\n if paddle.in_dynamic_mode():\n if len(input.shape) == 1:\n return input\n # 2-D tensor\n perm = [1, 0]\n out, _ = _C_ops.transpose2(input, 'axis', perm)\n return out\n\n check_variable_and_dtype(\n input, 'input', ['float16', 'float32', 'float64', 'int32',\n 'int64'], 'transpose')\n\n helper = LayerHelper('t', **locals())\n out = helper.create_variable_for_type_inference(input.dtype)\n input_shape = helper.create_variable_for_type_inference(input.dtype)\n if len(input.shape) == 1:\n out = input\n else:\n helper.append_op(\n type='transpose2',\n inputs={'X': [input]},\n outputs={'Out': [out],\n 'XShape': [input_shape]},\n attrs={'axis': [1, 0]})\n return out\n\n\ndef cross(x, y, axis=None, name=None):\n \"\"\"\n Computes the cross product between two tensors along an axis.\n\n Inputs must have the same shape, and the length of their axes should be equal to 3.\n If `axis` is not given, it defaults to the first axis found with the length 3.\n\n Args:\n x (Tensor): The first input tensor.\n y (Tensor): The second input tensor.\n axis (int, optional): The axis along which to compute the cross product. It defaults to the first axis found with the length 3.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor. A Tensor with same data type as `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0],\n [3.0, 3.0, 3.0]])\n y = paddle.to_tensor([[1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0]])\n\n z1 = paddle.cross(x, y)\n # [[-1. -1. -1.]\n # [ 2. 2. 2.]\n # [-1. -1. -1.]]\n\n z2 = paddle.cross(x, y, axis=1)\n # [[0. 0. 0.]\n # [0. 0. 0.]\n # [0. 0. 0.]]\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_cross(x, y, axis)\n else:\n if _in_legacy_dygraph():\n if axis is not None:\n return _C_ops.cross(x, y, 'dim', axis)\n else:\n return _C_ops.cross(x, y)\n else:\n helper = LayerHelper(\"cross\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n attrs = dict()\n attrs['dim'] = axis\n\n helper.append_op(\n type='cross',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef cholesky(x, upper=False, name=None):\n r\"\"\"\n Computes the Cholesky decomposition of one symmetric positive-definite\n matrix or batches of symmetric positive-definite matrice.\n\n If `upper` is `True`, the decomposition has the form :math:`A = U^{T}U` ,\n and the returned matrix :math:`U` is upper-triangular. Otherwise, the\n decomposition has the form :math:`A = LL^{T}` , and the returned matrix\n :math:`L` is lower-triangular.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[*, M, M]`,\n where * is zero or more batch dimensions, and matrices on the\n inner-most 2 dimensions all should be symmetric positive-definite.\n Its data type should be float32 or float64.\n upper (bool): The flag indicating whether to return upper or lower\n triangular matrices. Default: False.\n\n Returns:\n Tensor: A Tensor with same shape and data type as `x`. It represents \\\n triangular matrices generated by Cholesky decomposition.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n a = np.random.rand(3, 3)\n a_t = np.transpose(a, [1, 0])\n x_data = np.matmul(a, a_t) + 1e-03\n x = paddle.to_tensor(x_data)\n out = paddle.linalg.cholesky(x, upper=False)\n print(out)\n # [[1.190523 0. 0. ]\n # [0.9906703 0.27676893 0. ]\n # [1.25450498 0.05600871 0.06400121]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky(x, \"upper\", upper)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cholesky')\n check_type(upper, 'upper', bool, 'cholesky')\n helper = LayerHelper('cholesky', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='cholesky',\n inputs={'X': [x]},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef matrix_rank(x, tol=None, hermitian=False, name=None):\n r\"\"\"\n Computes the rank of a matrix.\n\n The rank of a matrix is the number of singular values that are greater than the specified `tol` threshold when hermitian=False,\n or the number of eigenvalues in absolute value that are greater than the specified `tol` threshold when hermitian=True.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., m, n]`, where `...` is zero or more batch dimensions. If `x` is a batch\n of matrices then the output has the same batch dimensions. The data type of `x` should be float32 or float64.\n tol (float,Tensor,optional): the tolerance value. Default: None. If `tol` is not specified, and `sigma` is the largest\n singular value (or eigenvalues in absolute value), and `eps` is the epsilon value for the dtype of `x`, then `tol` is computed\n with formula `tol=sigma * max(m,n) * eps`. Note that if `x` is a batch of matrices, `tol` is computed this way for every batch.\n hermitian (bool,optional): indicates whether `x` is Hermitian. Default: False. When hermitian=True, `x` is assumed to be Hermitian,\n enabling a more efficient method for finding eigenvalues, but `x` is not checked inside the function. Instead, We just use\n the lower triangular of the matrix to compute.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: Rank of tensor x.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n a = paddle.eye(10)\n b = paddle.linalg.matrix_rank(a)\n print(b)\n # b = [10]\n\n c = paddle.ones(shape=[3, 4, 5, 5])\n d = paddle.linalg.matrix_rank(c, tol=0.01, hermitian=True)\n print(d)\n # d = [[1, 1, 1, 1],\n # [1, 1, 1, 1],\n # [1, 1, 1, 1]]\n\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if tol is None:\n tol_tensor = None\n tol_attr = 0.0\n use_default_tol = True\n elif isinstance(tol, Variable):\n if tol.dtype != x.dtype:\n tol_tensor = cast(tol, x.dtype)\n else:\n tol_tensor = tol\n tol_attr = 0.0\n use_default_tol = False\n else:\n tol_tensor = None\n tol_attr = float(tol)\n use_default_tol = False\n return _C_ops.matrix_rank(x, tol_tensor, \"tol\", tol_attr, 'hermitian',\n hermitian, 'use_default_tol', use_default_tol)\n\n inputs = {}\n attrs = {}\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'matrix_rank')\n inputs['X'] = x\n if tol is None:\n attrs['use_default_tol'] = True\n elif isinstance(tol, Variable):\n check_variable_and_dtype(tol, 'tol', ['float32'], 'matrix_rank')\n attrs['use_default_tol'] = False\n if tol.dtype != x.dtype:\n inputs['TolTensor'] = cast(tol, x.dtype)\n else:\n inputs['TolTensor'] = tol\n else:\n check_type(tol, 'tol', float, 'matrix_rank')\n attrs['use_default_tol'] = False\n attrs['tol'] = tol\n check_type(hermitian, 'hermitian', bool, 'matrix_rank')\n attrs['hermitian'] = hermitian\n\n helper = LayerHelper('matrix_rank', **locals())\n out = helper.create_variable_for_type_inference(dtype='int32')\n helper.append_op(\n type='matrix_rank', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef bmm(x, y, name=None):\n \"\"\"\n Applies batched matrix multiplication to two tensors.\n\n Both of the two input tensors must be three-dementional and share the same batch size.\n\n if x is a (b, m, k) tensor, y is a (b, k, n) tensor, the output will be a (b, m, n) tensor.\n\n Args:\n x (Tensor): The input Tensor.\n y (Tensor): The input Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The product Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n # In imperative mode:\n # size x: (2, 2, 3) and y: (2, 3, 2)\n x = paddle.to_tensor([[[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0]],\n [[3.0, 3.0, 3.0],\n [4.0, 4.0, 4.0]]])\n y = paddle.to_tensor([[[1.0, 1.0],[2.0, 2.0],[3.0, 3.0]],\n [[4.0, 4.0],[5.0, 5.0],[6.0, 6.0]]])\n out = paddle.bmm(x, y)\n #output size: (2, 2, 2)\n #output value:\n #[[[6.0, 6.0],[12.0, 12.0]],[[45.0, 45.0],[60.0, 60.0]]]\n out_np = out.numpy()\n\n \"\"\"\n x_shape = x.shape\n y_shape = y.shape\n if not len(x_shape) == len(y_shape) == 3:\n raise ValueError(\n \"x and y should be 3-dimensional. But received x's dimention: {}, y's dimention: {}\".\n format(x_shape, y_shape))\n if x_shape[2] != y_shape[1]:\n raise ValueError(\n \"x's width must be equal with y's height. But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n if x_shape[0] != y_shape[0]:\n raise ValueError(\n \"x's batch (shape[0]) must be equal with y's batch (shape[0]). But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.bmm(x, y)\n\n helper = LayerHelper('bmm', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='bmm', inputs={'X': x, 'Y': y}, outputs={'Out': out})\n return out\n\n\ndef histogram(input, bins=100, min=0, max=0, name=None):\n \"\"\"\n Computes the histogram of a tensor. The elements are sorted into equal width bins between min and max.\n If min and max are both zero, the minimum and maximum values of the data are used.\n\n Args:\n input (Tensor): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor\n should be float32, float64, int32, int64.\n bins (int): number of histogram bins\n min (int): lower end of the range (inclusive)\n max (int): upper end of the range (inclusive)\n\n Returns:\n Tensor: data type is int64, shape is (nbins,).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n inputs = paddle.to_tensor([1, 2, 1])\n result = paddle.histogram(inputs, bins=4, min=0, max=3)\n print(result) # [0, 2, 1, 0]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.histogram(input, \"bins\", bins, \"min\", min, \"max\", max)\n\n helper = LayerHelper('histogram', **locals())\n check_variable_and_dtype(\n input, 'X', ['int32', 'int64', 'float32', 'float64'], 'histogram')\n out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)\n helper.append_op(\n type='histogram',\n inputs={'X': input},\n outputs={'Out': out},\n attrs={'bins': bins,\n 'min': min,\n 'max': max})\n return out\n\n\ndef bincount(x, weights=None, minlength=0, name=None):\n \"\"\"\n Computes frequency of each value in the input tensor. \n\n Args:\n x (Tensor): A Tensor with non-negative integer. Should be 1-D tensor.\n weights (Tensor, optional): Weight for each value in the input tensor. Should have the same shape as input. Default is None.\n minlength (int, optional): Minimum number of bins. Should be non-negative integer. Default is 0.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor of frequency.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([1, 2, 1, 4, 5])\n result1 = paddle.bincount(x)\n print(result1) # [0, 2, 1, 0, 1, 1]\n\n w = paddle.to_tensor([2.1, 0.4, 0.1, 0.5, 0.5])\n result2 = paddle.bincount(x, weights=w)\n print(result2) # [0., 2.19999981, 0.40000001, 0., 0.50000000, 0.50000000]\n \"\"\"\n if x.dtype not in [paddle.int32, paddle.int64]:\n raise TypeError(\"Elements in Input(x) should all be integers\")\n\n if paddle.in_dynamic_mode():\n return _C_ops.bincount(x, weights, \"minlength\", minlength)\n\n helper = LayerHelper('bincount', **locals())\n\n check_variable_and_dtype(x, 'X', ['int32', 'int64'], 'bincount')\n\n if weights is not None:\n check_variable_and_dtype(weights, 'Weights',\n ['int32', 'int64', 'float32', 'float64'],\n 'bincount')\n out = helper.create_variable_for_type_inference(dtype=weights.dtype)\n else:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='bincount',\n inputs={'X': x,\n 'Weights': weights},\n outputs={'Out': out},\n attrs={'minlength': minlength})\n return out\n\n\ndef mv(x, vec, name=None):\n \"\"\"\n Performs a matrix-vector product of the matrix x and the vector vec.\n\n Args:\n x (Tensor): A tensor with shape :math:`[M, N]` , The data type of the input Tensor x\n should be one of float32, float64.\n vec (Tensor): A tensor with shape :math:`[N]` , The data type of the input Tensor x\n should be one of float32, float64.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor which is producted by x and vec.\n\n Examples:\n .. code-block:: python\n\n # x: [M, N], vec: [N]\n # paddle.mv(x, vec) # out: [M]\n\n import numpy as np\n import paddle\n\n x_data = np.array([[2, 1, 3], [3, 0, 1]]).astype(\"float64\")\n x = paddle.to_tensor(x_data)\n vec_data = np.array([3, 5, 1])\n vec = paddle.to_tensor(vec_data).astype(\"float64\")\n out = paddle.mv(x, vec)\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_mv(x, vec)\n else:\n if _in_legacy_dygraph():\n out = _C_ops.mv(x, vec)\n return out\n else:\n\n def __check_input(x, vec):\n var_names = {'x': x, 'vec': vec}\n for name, val in var_names.items():\n check_variable_and_dtype(val, name, ['float32', 'float64'],\n 'mv')\n x_shape = list(x.shape)\n vec_shape = list(vec.shape)\n if len(x_shape) != 2:\n raise ValueError(\n \"x should be 2-dimensional. But received x's dimention: {}\".\n format(x_shape))\n if len(vec_shape) != 1:\n raise ValueError(\n \"vec should be 1-dimensional. But received vec's dimention: {}\".\n format(vec_shape))\n\n __check_input(x, vec)\n\n helper = LayerHelper('mv', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='mv', inputs={'X': x,\n 'Vec': vec}, outputs={'Out': out})\n return out\n\n\ndef det(x, name=None):\n \"\"\"\n Calculates determinant value of a square matrix or batches of square matrices.\n Args:\n x (Tensor): input (Tensor): the input matrix of size `(n, n)` or the batch of matrices of size\n `(*, n, n)` where `*` is one or more batch dimensions.\n Returns:\n y (Tensor):the determinant value of a square matrix or batches of square matrices.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.det(x)\n\n print(A)\n\n # [ 0.02547996, 2.52317095, -6.15900707])\n\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.determinant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'det')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('determinant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='determinant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef slogdet(x, name=None):\n \"\"\"\n Calculates the sign and natural logarithm of the absolute value of a square matrix's or batches square matrices' determinant.\n The determinant can be computed with ``sign * exp(logabsdet)\n\n Supports input of float, double\n\n Note that for matrices that have zero determinant, this returns ``(0, -inf)``\n Args:\n x (Tensor): the batch of matrices of size :math:`(*, n, n)`\n where math:`*` is one or more batch dimensions.\n\n Returns:\n y (Tensor): A tensor containing the sign of the determinant and the natural logarithm\n of the absolute value of determinant, respectively.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.slogdet(x)\n\n print(A)\n\n # [[ 1. , 1. , -1. ],\n # [-0.98610914, -0.43010661, -0.10872950]])\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.slogdeterminant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'slogdet')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('slogdeterminant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='slogdeterminant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef svd(x, full_matrices=False, name=None):\n r\"\"\"\n Computes the singular value decomposition of one matrix or a batch of regular matrices.\n\n Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies:\n\n .. math::\n X = U * diag(S) * VT\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., N, M]`,\n where `...` is zero or more batch dimensions. N and M can be arbitraty\n positive number. Note that if x is sigular matrices, the grad is numerical\n instable. The data type of x should be float32 or float64.\n full_matrices (bool): A flag to control the behavor of svd.\n If full_matrices = True, svd op will compute full U and V matrics,\n which means shape of U is `[..., N, N]`, shape of V is `[..., M, M]`. K = min(M, N).\n If full_matrices = False, svd op will use a economic method to store U and V.\n which means shape of U is `[..., N, K]`, shape of V is `[..., M, K]`. K = min(M, N).\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple of 3 tensors: (U, S, VH). VH is the conjugate transpose of V. S is the singlar value vectors of matrics with shape `[..., K]`\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 2.0], [1.0, 3.0], [4.0, 6.0]]).astype('float64')\n x = x.reshape([3, 2])\n u, s, vh = paddle.linalg.svd(x)\n print (u)\n #U = [[ 0.27364809, -0.21695147 ],\n # [ 0.37892198, -0.87112408 ],\n # [ 0.8840446 , 0.44053933 ]]\n\n print (s)\n #S = [8.14753743, 0.78589688]\n print (vh)\n #VT= [[ 0.51411221, 0.85772294],\n # [ 0.85772294, -0.51411221]]\n\n # one can verify : U * S * VT == X\n # U * UH == I\n # V * VH == I\n \"\"\"\n\n if paddle.in_dynamic_mode():\n return _C_ops.svd(x, 'full_matrices', full_matrices)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'svd')\n check_type(full_matrices, 'full_matrices', bool, 'svd')\n helper = LayerHelper('svd', **locals())\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n vh = helper.create_variable_for_type_inference(dtype=x.dtype)\n s = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['full_matrices'] = full_matrices\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vh,\n 'S': s},\n attrs=attrs, )\n return u, s, vh\n\n\ndef matrix_power(x, n, name=None):\n r\"\"\"\n Computes the n-th power of a square matrix or a batch of square matrices.\n\n Let :math:`X` be a sqaure matrix or a batch of square matrices, :math:`n` be\n an exponent, the equation should be:\n\n .. math::\n Out = X ^ {n}\n\n Specifically,\n\n - If `n > 0`, it returns the matrix or a batch of matrices raised to the power\n of `n`.\n\n - If `n = 0`, it returns the identity matrix or a batch of identity matrices.\n\n - If `n < 0`, it returns the inverse of each matrix (if invertible) raised to\n the power of `abs(n)`.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices to be raised\n to power `n`. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n n (int): The exponent. It can be any positive, negative integer or zero.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The n-th power of the matrix (or the batch of matrices) `x`. Its\n data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1, 2, 3],\n [1, 4, 9],\n [1, 8, 27]], dtype='float64')\n print(paddle.linalg.matrix_power(x, 2))\n # [[6. , 34. , 102.],\n # [14. , 90. , 282.],\n # [36. , 250., 804.]]\n\n print(paddle.linalg.matrix_power(x, 0))\n # [[1., 0., 0.],\n # [0., 1., 0.],\n # [0., 0., 1.]]\n\n print(paddle.linalg.matrix_power(x, -2))\n # [[ 12.91666667, -12.75000000, 2.83333333 ],\n # [-7.66666667 , 8. , -1.83333333 ],\n # [ 1.80555556 , -1.91666667 , 0.44444444 ]]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.matrix_power(x, \"n\", n)\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'matrix_power')\n check_type(n, 'n', int, 'matrix_power')\n helper = LayerHelper('matrix_power', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matrix_power',\n inputs={'X': x},\n outputs={'Out': out},\n attrs={'n': n})\n return out\n\n\ndef qr(x, mode=\"reduced\", name=None):\n r\"\"\"\n Computes the QR decomposition of one matrix or batches of matrice (backward is unsupported now).\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., M, N]`,\n where ... is zero or more batch dimensions. M and N can be arbitrary\n positive number. The data type of x should be float32 or float64. \n mode (str, optional): A flag to control the behavior of qr, the default is \"reduced\". \n Suppose x's shape is `[..., M, N]` and denoting `K = min(M, N)`:\n If mode = \"reduced\", qr op will return reduced Q and R matrices, \n which means Q's shape is `[..., M, K]` and R's shape is `[..., K, N]`.\n If mode = \"complete\", qr op will return complete Q and R matrices, \n which means Q's shape is `[..., M, M]` and R's shape is `[..., M, N]`.\n If mode = \"r\", qr op will only return reduced R matrix, which means\n R's shape is `[..., K, N]`.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n If mode = \"reduced\" or mode = \"complete\", qr will return a two tensor-tuple, which represents Q and R. \n If mode = \"r\", qr will return a tensor which represents R.\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n q, r = paddle.linalg.qr(x)\n print (q)\n print (r)\n\n # Q = [[-0.16903085, 0.89708523],\n # [-0.50709255, 0.27602622],\n # [-0.84515425, -0.34503278]])\n\n # R = [[-5.91607978, -7.43735744],\n # [ 0. , 0.82807867]])\n \n # one can verify : X = Q * R ; \n \"\"\"\n if paddle.in_dynamic_mode():\n q, r = _C_ops.qr(x, 'mode', mode)\n if mode == \"r\":\n return r\n else:\n return q, r\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'qr')\n check_type(mode, 'mode', str, 'qr')\n helper = LayerHelper('qr', **locals())\n q = helper.create_variable_for_type_inference(dtype=x.dtype)\n r = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['mode'] = mode\n helper.append_op(\n type='qr', inputs={'X': [x]}, outputs={'Q': q,\n 'R': r}, attrs=attrs)\n if mode == \"r\":\n return r\n else:\n return q, r\n\n\ndef lu(x, pivot=True, get_infos=False, name=None):\n r\"\"\"\n Computes the LU factorization of an N-D(N>=2) matrix x. \n\n Returns the LU factorization(inplace x) and Pivots. low triangular matrix L and \n upper triangular matrix U are combined to a single LU matrix.\n\n Pivoting is done if pivot is set to True.\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n # return ones\n\n Args:\n\n X (Tensor): the tensor to factor of N-dimensions(N>=2).\n\n pivot (bool, optional): controls whether pivoting is done. Default: True.\n\n get_infos (bool, optional): if set to True, returns an info IntTensor. Default: False.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n factorization (Tensor): LU matrix, the factorization of input X.\n\n pivots (IntTensor): the pivots of size(∗(N-2), min(m,n)). `pivots` stores all the \n intermediate transpositions of rows. The final permutation `perm` could be \n reconstructed by this, details refer to upper example.\n\n infos (IntTensor, optional): if `get_infos` is `True`, this is a tensor of size (∗(N-2)) \n where non-zero values indicate whether factorization for the matrix or each minibatch \n has succeeded or failed.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n \n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n if paddle.in_dynamic_mode():\n LU, Piv, Info = _C_ops.lu(x, 'pivots', pivot)\n if get_infos:\n return LU, Piv, Info\n else:\n return LU, Piv\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu')\n helper = LayerHelper('lu', **locals())\n lu = helper.create_variable_for_type_inference(dtype=x.dtype)\n p = helper.create_variable_for_type_inference(dtype='int')\n info = helper.create_variable_for_type_inference(dtype='int')\n attrs = dict()\n attrs['pivots'] = pivot\n helper.append_op(\n type='lu',\n inputs={'X': x},\n outputs={'Out': lu,\n 'Pivots': p,\n 'Infos': info},\n attrs=attrs)\n if get_infos:\n return lu, p, info\n else:\n return lu, p\n\n\ndef lu_unpack(x, y, unpack_ludata=True, unpack_pivots=True, name=None):\n r\"\"\"\n Unpack L U and P to single matrix tensor . \n unpack L and U matrix from LU, unpack permutation matrix P from Pivtos .\n\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n\n\n Args:\n x (Tensor): The LU tensor get from paddle.linalg.lu, which is combined by L and U.\n\n y (Tensor): Pivots get from paddle.linalg.lu.\n\n unpack_ludata (bool,optional): whether to unpack L and U from x. Default: True.\n\n unpack_pivots (bool, optional): whether to unpack permutation matrix P from Pivtos. Default: True.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n P (Tensor): Permutation matrix P of lu factorization.\n\n L (Tensor): The lower triangular matrix tensor of lu factorization.\n\n U (Tensor): The upper triangular matrix tensor of lu factorization.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n\n if paddle.in_dynamic_mode():\n P, L, U = _C_ops.lu_unpack(x, y, 'unpack_ludata', unpack_ludata,\n 'unpack_pivots', unpack_pivots)\n return P, L, U\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu_unpack')\n helper = LayerHelper('lu_unpack', **locals())\n p = helper.create_variable_for_type_inference(dtype=x.dtype)\n l = helper.create_variable_for_type_inference(dtype=x.dtype)\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n attrs = dict()\n attrs['unpack_ludata'] = unpack_ludata\n attrs['unpack_pivots'] = unpack_pivots\n helper.append_op(\n type='lu_unpack',\n inputs={'X': x,\n 'Pivots': y},\n outputs={'Pmat': p,\n 'L': l,\n 'U': u},\n attrs=attrs)\n return p, l, u\n\n\n# MASKED: eig function (lines 2039-2102)\n\n\ndef eigvals(x, name=None):\n \"\"\"\n Compute the eigenvalues of one or more general matrices.\n\n Warning:\n The gradient kernel of this operator does not yet developed.\n If you need back propagation through this operator, please replace it with paddle.linalg.eig.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices whose eigenvalues will be computed.\n Its shape should be `[*, M, M]`, where `*` is zero or more batch dimensions.\n Its data type should be float32, float64, complex64, or complex128.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n Tensor: A tensor containing the unsorted eigenvalues which has the same batch dimensions with `x`.\n The eigenvalues are complex-valued even when `x` is real.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n paddle.seed(1234)\n\n x = paddle.rand(shape=[3, 3], dtype='float64')\n # [[0.02773777, 0.93004224, 0.06911496],\n # [0.24831591, 0.45733623, 0.07717843],\n # [0.48016702, 0.14235102, 0.42620817]])\n\n print(paddle.linalg.eigvals(x))\n # [(-0.27078833542132674+0j), (0.29962280156230725+0j), (0.8824477020120244+0j)] #complex128\n \"\"\"\n\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64',\n 'complex128'], 'eigvals')\n\n x_shape = list(x.shape)\n if len(x_shape) < 2:\n raise ValueError(\n \"The dimension of Input(x) should be at least 2, but received x's dimention = {}, x's shape = {}\".\n format(len(x_shape), x_shape))\n\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The last two dimensions of Input(x) should be equal, but received x's shape = {}\".\n format(x_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.eigvals(x)\n\n helper = LayerHelper('eigvals', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='eigvals', inputs={'X': x}, outputs={'Out': out})\n return out\n\n\ndef multi_dot(x, name=None):\n \"\"\"\n Multi_dot is an operator that calculates multiple matrix multiplications.\n\n Supports inputs of float16(only GPU support), float32 and float64 dtypes. This function does not\n support batched inputs.\n\n The input tensor in [x] must be 2-D except for the first and last can be 1-D.\n If the first tensor is a 1-D vector of shape(n, ) it is treated as row vector\n of shape(1, n), similarly if the last tensor is a 1D vector of shape(n, ), it\n is treated as a column vector of shape(n, 1).\n\n If the first and last tensor are 2-D matrix, then the output is also 2-D matrix,\n otherwise the output is a 1-D vector.\n\n Multi_dot will select the lowest cost multiplication order for calculation. The\n cost of multiplying two matrices with shapes (a, b) and (b, c) is a * b * c.\n Given matrices A, B, C with shapes (20, 5), (5, 100), (100, 10) respectively,\n we can calculate the cost of different multiplication orders as follows:\n - Cost((AB)C) = 20x5x100 + 20x100x10 = 30000\n - Cost(A(BC)) = 5x100x10 + 20x5x10 = 6000\n\n In this case, multiplying B and C first, then multiply A, which is 5 times faster\n than sequential calculation.\n\n Args:\n x ([Tensor]): The input tensors which is a list Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # A * B\n A_data = np.random.random([3, 4]).astype(np.float32)\n B_data = np.random.random([4, 5]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n out = paddle.linalg.multi_dot([A, B])\n print(out.numpy().shape)\n # [3, 5]\n\n # A * B * C\n A_data = np.random.random([10, 5]).astype(np.float32)\n B_data = np.random.random([5, 8]).astype(np.float32)\n C_data = np.random.random([8, 7]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n C = paddle.to_tensor(C_data)\n out = paddle.linalg.multi_dot([A, B, C])\n print(out.numpy().shape)\n # [10, 7]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.multi_dot(x)\n\n check_type(x, 'x', (list, tuple), 'multi_dot')\n for id, item in enumerate(x):\n check_variable_and_dtype(item, 'x[' + str(id) + ']',\n ['float16', 'float32', 'float64'], 'multi_dot')\n if item.dtype != x[0].dtype:\n raise TypeError(\n \"All the Tensors in the input must have the same data type.\")\n\n helper = LayerHelper('multi_dot', **locals())\n dtype = helper.input_dtype(input_param_name='x')\n out = helper.create_variable_for_type_inference(dtype)\n helper.append_op(type='multi_dot', inputs={\"X\": x}, outputs={\"Out\": out})\n return out\n\n\ndef eigh(x, UPLO='L', name=None):\n \"\"\"\n Compute the eigenvalues and eigenvectors of a\n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[*, N, N]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): (string, default 'L'), 'L' represents the lower triangular matrix,\n \"'U' represents the upper triangular matrix.\".\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n\n out_value(Tensor): A Tensor with shape [*, N] and data type of float32 and float64. The eigenvalues of eigh op.\n out_vector(Tensor): A Tensor with shape [*, N, N] and data type of float32,float64,complex64 and complex128. The eigenvectors of eigh op.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value, out_vector = paddle.linalg.eigh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n print(out_vector)\n #[(-0.9238795325112867+0j), (-0.3826834323650898+0j)],\n #[ 0.3826834323650898j , -0.9238795325112867j ]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.eigh(x, 'UPLO', UPLO)\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigh', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'eigh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO})\n return out_value, out_vector\n\n\ndef pinv(x, rcond=1e-15, hermitian=False, name=None):\n r\"\"\"\n Calculate pseudo inverse via SVD(singular value decomposition)\n of one matrix or batches of regular matrix.\n\n .. math::\n\n if hermitian == False:\n x = u * s * vt (SVD)\n out = v * 1/s * ut\n else:\n x = u * s * ut (eigh)\n out = u * 1/s * u.conj().transpose(-2,-1)\n\n If x is hermitian or symmetric matrix, svd will be replaced with eigh.\n\n Args:\n x(Tensor): The input tensor. Its shape should be (*, m, n)\n where * is zero or more batch dimensions. m and n can be\n arbitraty positive number. The data type of x should be\n float32 or float64 or complex64 or complex128. When data\n type is complex64 or cpmplex128, hermitian should be set\n True.\n\n rcond(Tensor, optional): the tolerance value to determine\n when is a singular value zero. Defalut:1e-15.\n\n hermitian(bool, optional): indicates whether x is Hermitian\n if complex or symmetric if real. Default: False.\n\n name(str|None): A name for this layer(optional). If set None,\n the layer will be named automatically.\n\n Returns:\n Tensor: The tensor with same data type with x. it represents\n pseudo inverse of x. Its shape should be (*, n, m).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.arange(15).reshape((3, 5)).astype('float64')\n input = paddle.to_tensor(x)\n out = paddle.linalg.pinv(input)\n print(input)\n print(out)\n\n # input:\n # [[0. , 1. , 2. , 3. , 4. ],\n # [5. , 6. , 7. , 8. , 9. ],\n # [10., 11., 12., 13., 14.]]\n\n # out:\n # [[-0.22666667, -0.06666667, 0.09333333],\n # [-0.12333333, -0.03333333, 0.05666667],\n # [-0.02000000, 0.00000000, 0.02000000],\n # [ 0.08333333, 0.03333333, -0.01666667],\n # [ 0.18666667, 0.06666667, -0.05333333]]\n\n # one can verify : x * out * x = x ;\n # or out * x * out = x ;\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if not hermitian:\n # combine svd and matmul op\n u, s, vt = _C_ops.svd(x, 'full_matrices', False)\n max_singular_val = _C_ops.reduce_max(s, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=x.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=x.dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v, _ = _C_ops.transpose2(vt, 'axis', perm)\n\n out_1 = v * st\n out_2 = _C_ops.matmul_v2(out_1, u, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n # combine eigh and matmul op\n s, u = _C_ops.eigh(x, 'UPLO', 'L')\n s_abs = paddle.abs(s)\n max_singular_val = _C_ops.reduce_max(s_abs, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=s.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=s.dtype)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n out_1 = u * st\n u_conj = _C_ops.conj(u)\n out_2 = _C_ops.matmul_v2(out_1, u_conj, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n if not hermitian:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'pinv')\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(dtype)\n vt = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vt,\n 'S': s},\n attrs={'full_matrices': False}, )\n\n max_singular_val = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=dtype)\n st_shape = helper.create_variable_for_type_inference(dtype=dtype)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v = helper.create_variable_for_type_inference(dtype)\n v_shape = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='transpose2',\n inputs={'X': [vt]},\n outputs={'Out': [v],\n 'XShape': [v_shape]},\n attrs={'axis': perm})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': v,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n else:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64',\n 'complex128'], 'pinv')\n\n if dtype == paddle.complex128:\n s_type = 'float64'\n elif dtype == paddle.complex64:\n s_type = 'float32'\n else:\n s_type = dtype\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': s,\n 'Eigenvectors': u},\n attrs={'UPLO': 'L'})\n s_abs = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='abs', inputs={'X': s}, outputs={'Out': s_abs})\n max_singular_val = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s_abs},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=s_type)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=s_type)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s_type)\n cond_not_int = layers.cast(layers.logical_not(condition), s_type)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=s_type)\n st_shape = helper.create_variable_for_type_inference(dtype=s_type)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': u,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n u_conj = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='conj', inputs={'X': u}, outputs={'Out': [u_conj]})\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u_conj},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n\n\ndef solve(x, y, name=None):\n r\"\"\"\n Computes the solution of a square system of linear equations with a unique solution for input 'X' and 'Y'.\n Let :math: `X` be a sqaure matrix or a batch of square matrices, :math:`Y` be\n a vector/matrix or a batch of vectors/matrices, the equation should be:\n\n .. math::\n Out = X^-1 * Y\n Specifically,\n - This system of linear equations has one solution if and only if input 'X' is invertible.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): A vector/matrix or a batch of vectors/matrices. Its shape should be `[*, M, K]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of a square system of linear equations with a unique solution for input 'x' and 'y'.\n Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # 2*X0 + X1 = 9\n # X0 + 2*X1 = 8\n\n import paddle\n import numpy as np\n\n np_x = np.array([[3, 1],[1, 2]])\n np_y = np.array([9, 8])\n x = paddle.to_tensor(np_x, dtype=\"float64\")\n y = paddle.to_tensor(np_y, dtype=\"float64\")\n out = paddle.linalg.solve(x, y)\n\n print(out)\n # [2., 3.])\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.solve(x, y)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type=\"solve\", inputs={\"X\": x,\n \"Y\": y}, outputs={\"Out\": out})\n return out\n\n\ndef triangular_solve(x,\n y,\n upper=True,\n transpose=False,\n unitriangular=False,\n name=None):\n r\"\"\"\n Computes the solution of a system of equations with a triangular coefficient matrix `x` and\n multiple right-hand sides `y` .\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input triangular coefficient matrix. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): Whether to solve the upper-triangular system of equations (default) or the lower-triangular \n system of equations. Default: True.\n transpose (bool, optional): whether `x` should be transposed before calculation. Default: False.\n unitriangular (bool, optional): whether `x` is unit triangular. If True, the diagonal elements of `x` are assumed \n to be 1 and not referenced from `x` . Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # x1 + x2 + x3 = 0\n # 2*x2 + x3 = -9\n # -x3 = 5\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n y = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.triangular_solve(x, y, upper=True)\n\n print(out)\n # [7, -2, -5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.triangular_solve(x, y, 'upper', upper, 'transpose',\n transpose, 'unitriangular',\n unitriangular)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"triangular_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'triangular_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'triangular_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='triangular_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={\n 'upper': upper,\n 'transpose': transpose,\n 'unitriangular': unitriangular\n })\n return out\n\n\ndef cholesky_solve(x, y, upper=False, name=None):\n r\"\"\"\n Solves a linear system of equations A @ X = B, given A's Cholesky factor matrix u and matrix B.\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input matrix which is upper or lower triangular Cholesky factor of square matrix A. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): whether to consider the Cholesky factor as a lower or upper triangular matrix. Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type is the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n u = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n b = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.cholesky_solve(b, u, upper=True)\n\n print(out)\n # [-2.5, -7, 9.5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky_solve(x, y, 'upper', upper)\n\n helper = LayerHelper(\"cholesky_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'cholesky_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'cholesky_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='cholesky_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef eigvalsh(x, UPLO='L', name=None):\n \"\"\"\n Computes the eigenvalues of a \n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[_, M, M]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): Lower triangular part of a (‘L’, default) or the upper triangular part (‘U’).\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor eigenvalues in ascending order.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value = paddle.eigvalsh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n \"\"\"\n if paddle.in_dynamic_mode():\n is_test = x.stop_gradient\n values, _ = _C_ops.eigvalsh(x, 'UPLO', UPLO, 'is_test', is_test)\n return values\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigvalsh', **locals())\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64', 'complex128'],\n 'eigvalsh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n is_test = x.stop_gradient\n helper.append_op(\n type='eigvalsh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO,\n 'is_test': is_test})\n return out_value\n\n\ndef lstsq(x, y, rcond=None, driver=None, name=None):\n \"\"\"\n Computes a solution to\n the least squares problem of a system of linear equations.\n\n Args:\n x (Tensor): A tensor with shape ``(*, M, N)`` , the data type of the input Tensor ``x``\n should be one of float32, float64.\n y (Tensor): A tensor with shape ``(*, M, K)`` , the data type of the input Tensor ``y`` \n should be one of float32, float64.\n rcond(float, optional): The default value is None. A float pointing number used to determine \n the effective rank of ``x``. If ``rcond`` is None, it will be set to max(M, N) times the \n machine precision of x_dtype.\n driver(str, optional): The default value is None. The name of LAPACK method to be used. For \n CPU inputs the valid values are ‘gels’, ‘gelsy’, ‘gelsd, ‘gelss’. For CUDA input, the only \n valid driver is ‘gels’. If ``driver`` is None, ‘gelsy’ is used for CPU inputs and ‘gels’ \n for CUDA inputs.\n name(str, optional): The default value is None. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple: A tuple of 4 Tensors which is (``solution``, ``residuals``, ``rank``, ``singular_values``). \n ``solution`` is a tensor with shape ``(*, N, K)``, meaning the least squares solution. ``residuals`` \n is a tensor with shape ``(*, K)``, meaning the squared residuals of the solutions, which is computed \n when M > N and every matrix in ``x`` is full-rank, otherwise return an empty tensor. ``rank`` is a tensor \n with shape ``(*)``, meaning the ranks of the matrices in ``x``, which is computed when ``driver`` in \n (‘gelsy’, ‘gelsd’, ‘gelss’), otherwise return an empty tensor. ``singular_values`` is a tensor with \n shape ``(*, min(M, N))``, meaning singular values of the matrices in ``x``, which is computed when \n ``driver`` in (‘gelsd’, ‘gelss’), otherwise return an empty tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n x = paddle.to_tensor([[1, 3], [3, 2], [5, 6.]])\n y = paddle.to_tensor([[3, 4, 6], [5, 3, 4], [1, 2, 1.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gelsd\")\n print(results[0])\n # [[ 0.78350395, -0.22165027, -0.62371236],\n # [-0.11340097, 0.78866047, 1.14948535]]\n print(results[1])\n # [19.81443405, 10.43814468, 30.56185532])\n print(results[2])\n # 2\n print(results[3])\n # [9.03455734, 1.54167950]\n\n x = paddle.to_tensor([[10, 2, 3], [3, 10, 5], [5, 6, 12.]])\n y = paddle.to_tensor([[4, 2, 9], [2, 0, 3], [2, 5, 3.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gels\")\n print(results[0])\n # [[ 0.39386186, 0.10230173, 0.93606132],\n # [ 0.10741687, -0.29028133, 0.11892585],\n # [-0.05115091, 0.51918161, -0.19948854]]\n print(results[1])\n # []\n \"\"\"\n device = paddle.get_device()\n if device == \"cpu\":\n if driver not in (None, \"gels\", \"gelss\", \"gelsd\", \"gelsy\"):\n raise ValueError(\n \"Only support valid driver is 'gels', 'gelss', 'gelsd', 'gelsy' or None for CPU inputs. But got {}\".\n format(driver))\n driver = \"gelsy\" if driver is None else driver\n elif \"gpu\" in device:\n if driver not in (None, \"gels\"):\n raise ValueError(\n \"Only support valid driver is 'gels' or None for CUDA inputs. But got {}\".\n format(driver))\n driver = \"gels\" if driver is None else driver\n else:\n raise RuntimeError(\"Only support lstsq api for CPU or CUDA device.\")\n\n if x.dtype == y.dtype and x.dtype in (paddle.float32, paddle.float64):\n pass\n else:\n raise ValueError(\n \"Only support x and y have the same dtype such as 'float32' and 'float64'.\"\n )\n\n if rcond is None:\n if x.dtype == paddle.float32:\n rcond = 1e-7 * max(x.shape[-2], x.shape[-1])\n elif x.dtype == paddle.float64:\n rcond = 1e-15 * max(x.shape[-2], x.shape[-1])\n\n if paddle.in_dynamic_mode():\n solution, rank, singular_values = _C_ops.lstsq(x, y, \"rcond\", rcond,\n \"driver\", driver)\n if x.shape[-2] > x.shape[-1]:\n matmul_out = _varbase_creator(dtype=x.dtype)\n _C_ops.matmul(x, solution, matmul_out, 'trans_x', False, 'trans_y',\n False)\n minus_out = _C_ops.elementwise_sub(matmul_out, y)\n pow_out = _C_ops.pow(minus_out, 'factor', 2)\n residuals = _C_ops.reduce_sum(pow_out, 'dim', [-2], 'keepdim',\n False, 'reduce_all', False)\n else:\n residuals = paddle.empty(shape=[0], dtype=x.dtype)\n\n if driver == \"gels\":\n rank = paddle.empty(shape=[0], dtype=paddle.int32)\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n elif driver == \"gelsy\":\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n\n return solution, residuals, rank, singular_values\n\n helper = LayerHelper('lstsq', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n check_variable_and_dtype(\n y, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n\n solution = helper.create_variable_for_type_inference(dtype=x.dtype)\n residuals = helper.create_variable_for_type_inference(dtype=x.dtype)\n rank = helper.create_variable_for_type_inference(dtype=paddle.int32)\n singular_values = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='lstsq',\n inputs={'X': x,\n 'Y': y},\n outputs={\n 'Solution': solution,\n 'Rank': rank,\n 'SingularValues': singular_values\n },\n attrs={'rcond': rcond,\n 'driver': driver})\n\n matmul_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n minus_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n pow_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': solution},\n outputs={'Out': matmul_out},\n attrs={\n 'trans_x': False,\n 'trans_y': False,\n })\n\n helper.append_op(\n type='elementwise_sub',\n inputs={'X': matmul_out,\n 'Y': y},\n outputs={'Out': minus_out})\n\n helper.append_op(\n type='pow',\n inputs={'X': minus_out},\n outputs={'Out': pow_out},\n attrs={'factor': 2})\n\n helper.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': residuals},\n attrs={'dim': [-2],\n 'keep_dim': False,\n 'reduce_all': False})\n\n if driver == \"gels\":\n rank = paddle.static.data(name='rank', shape=[0])\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n elif driver == \"gelsy\":\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n\n return solution, residuals, rank, singular_values"},"implementation":{"kind":"string","value":"def eig(x, name=None):\n \"\"\"\n This API performs the eigenvalue decomposition of a square matrix or a batch of square matrices.\n\n .. note::\n If the matrix is a Hermitian or a real symmetric matrix, please use :ref:`paddle.linalg.eigh` instead, which is much faster.\n If only eigenvalues is needed, please use :ref:`paddle.linalg.eigvals` instead.\n If the matrix is of any shape, please use :ref:`paddle.linalg.svd`.\n This API is only supported on CPU device.\n The output datatype is always complex for both real and complex input.\n\n Args:\n x (Tensor): A tensor with shape math:`[*, N, N]`, The data type of the x should be one of ``float32``,\n ``float64``, ``compplex64`` or ``complex128``.\n name (str, optional): The default value is `None`. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Eigenvalues(Tensors): A tensor with shape math:`[*, N]` refers to the eigen values.\n Eigenvectors(Tensors): A tensor with shape math:`[*, N, N]` refers to the eigen vectors.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n paddle.device.set_device(\"cpu\")\n\n x_data = np.array([[1.6707249, 7.2249975, 6.5045543],\n [9.956216, 8.749598, 6.066444 ],\n [4.4251957, 1.7983172, 0.370647 ]]).astype(\"float32\")\n x = paddle.to_tensor(x_data)\n w, v = paddle.linalg.eig(x)\n print(w)\n # Tensor(shape=[3, 3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [[(-0.5061363550800655+0j) , (-0.7971760990842826+0j) ,\n # (0.18518077798279986+0j)],\n # [(-0.8308237755993192+0j) , (0.3463813401919749+0j) ,\n # (-0.6837005269141947+0j) ],\n # [(-0.23142567697893396+0j), (0.4944999840400175+0j) ,\n # (0.7058765252952796+0j) ]])\n\n print(v)\n # Tensor(shape=[3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [ (16.50471283351188+0j) , (-5.5034820550763515+0j) ,\n # (-0.21026087843552282+0j)])\n \"\"\"\n if paddle.in_dynamic_mode():\n w, v = _C_ops.eig(x)\n return w, v\n\n check_variable_and_dtype(\n x, 'X', ['float32', 'float64', 'complex64', 'complex128'], 'eig')\n helper = LayerHelper('eig', **locals())\n\n w = helper.create_variable_for_type_inference(x.dtype)\n v = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {'X': x}\n outputs = {'Eigenvalues': w, 'Eigenvectors': v}\n helper.append_op(type='eig', inputs=inputs, outputs=outputs)\n\n return w, v"},"start_line":{"kind":"number","value":2039,"string":"2,039"},"end_line":{"kind":"number","value":2102,"string":"2,102"},"file_content":{"kind":"string","value":"# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numpy as np\nfrom ..fluid.layer_helper import LayerHelper\nfrom ..framework import _varbase_creator, _dygraph_tracer\nfrom ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype\nfrom ..static import Variable\nfrom ..fluid.framework import _in_legacy_dygraph, in_dygraph_mode\nfrom ..fluid.layers import transpose, cast # noqa: F401\nfrom ..fluid import layers\nimport paddle\nfrom paddle.common_ops_import import core\nfrom paddle.common_ops_import import VarDesc\nfrom paddle import _C_ops\n\n__all__ = []\n\n\ndef matmul(x, y, transpose_x=False, transpose_y=False, name=None):\n \"\"\"\n Applies matrix multiplication to two tensors. `matmul` follows\n the complete broadcast rules,\n and its behavior is consistent with `np.matmul`.\n\n Currently, the input tensors' number of dimensions can be any, `matmul` can be used to\n achieve the `dot`, `matmul` and `batchmatmul`.\n\n The actual behavior depends on the shapes of :math:`x`, :math:`y` and the\n flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:\n\n - If a transpose flag is specified, the last two dimensions of the tensor\n are transposed. If the tensor is ndim-1 of shape, the transpose is invalid. If the tensor\n is ndim-1 of shape :math:`[D]`, then for :math:`x` it is treated as :math:`[1, D]`, whereas\n for :math:`y` it is the opposite: It is treated as :math:`[D, 1]`.\n\n The multiplication behavior depends on the dimensions of `x` and `y`. Specifically:\n\n - If both tensors are 1-dimensional, the dot product result is obtained.\n\n - If both tensors are 2-dimensional, the matrix-matrix product is obtained.\n\n - If the `x` is 1-dimensional and the `y` is 2-dimensional,\n a `1` is prepended to its dimension in order to conduct the matrix multiply.\n After the matrix multiply, the prepended dimension is removed.\n\n - If the `x` is 2-dimensional and `y` is 1-dimensional,\n the matrix-vector product is obtained.\n\n - If both arguments are at least 1-dimensional and at least one argument\n is N-dimensional (where N > 2), then a batched matrix multiply is obtained.\n If the first argument is 1-dimensional, a 1 is prepended to its dimension\n in order to conduct the batched matrix multiply and removed after.\n If the second argument is 1-dimensional, a 1 is appended to its\n dimension for the purpose of the batched matrix multiple and removed after.\n The non-matrix (exclude the last two dimensions) dimensions are\n broadcasted according the broadcast rule.\n For example, if input is a (j, 1, n, m) tensor and the other is a (k, m, p) tensor,\n out will be a (j, k, n, p) tensor.\n\n Args:\n x (Tensor): The input tensor which is a Tensor.\n y (Tensor): The input tensor which is a Tensor.\n transpose_x (bool): Whether to transpose :math:`x` before multiplication.\n transpose_y (bool): Whether to transpose :math:`y` before multiplication.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # vector * vector\n x_data = np.random.random([10]).astype(np.float32)\n y_data = np.random.random([10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [1]\n\n # matrix * vector\n x_data = np.random.random([10, 5]).astype(np.float32)\n y_data = np.random.random([5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10]\n\n # batched matrix * broadcasted vector\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([2]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5]\n\n # batched matrix * batched matrix\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([10, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5, 5]\n\n # batched matrix * broadcasted matrix\n x_data = np.random.random([10, 1, 5, 2]).astype(np.float32)\n y_data = np.random.random([1, 3, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 3, 5, 5]\n\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_matmul(x, y, transpose_x, transpose_y)\n\n if _in_legacy_dygraph():\n op_type = 'matmul_v2'\n op = getattr(_C_ops, op_type)\n return op(x, y, 'trans_x', transpose_x, 'trans_y', transpose_y)\n\n attrs = {\n 'trans_x': transpose_x,\n 'trans_y': transpose_y,\n }\n\n def __check_input(x, y):\n var_names = {'x': x, 'y': y}\n for name, val in var_names.items():\n check_variable_and_dtype(\n val, name,\n ['float16', 'float32', 'float64', 'complex64', 'complex128'],\n 'matmul')\n\n __check_input(x, y)\n\n helper = LayerHelper('matmul_v2', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef norm(x, p='fro', axis=None, keepdim=False, name=None):\n \"\"\"\n\n Returns the matrix norm (Frobenius) or vector norm (the 1-norm, the Euclidean\n or 2-norm, and in general the p-norm for p > 0) of a given tensor.\n\n .. note::\n This norm API is different from `numpy.linalg.norm`.\n This api supports high-order input tensors (rank >= 3), and certain axis need to be pointed out to calculate the norm.\n But `numpy.linalg.norm` only supports 1-D vector or 2-D matrix as input tensor.\n For p-order matrix norm, this api actually treats matrix as a flattened vector to calculate the vector norm, NOT REAL MATRIX NORM.\n\n Args:\n x (Tensor): The input tensor could be N-D tensor, and the input data\n type could be float32 or float64.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `0`, `1`, `2`,\n `inf`, `-inf` and any positive real number yielding the corresponding p-norm. Not supported: ord < 0 and nuclear norm.\n Default value is `fro`.\n axis (int|list|tuple, optional): The axis on which to apply norm operation. If axis is int\n or list(int)/tuple(int) with only one element, the vector norm is computed over the axis.\n If `axis < 0`, the dimension to norm operation is rank(input) + axis.\n If axis is a list(int)/tuple(int) with two elements, the matrix norm is computed over the axis.\n Defalut value is `None`.\n keepdim (bool, optional): Whether to reserve the reduced dimension in the\n output Tensor. The result tensor will have fewer dimension\n than the :attr:`input` unless :attr:`keepdim` is true, default\n value is False.\n name (str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: results of norm operation on the specified axis of input tensor,\n it's data type is the same as input's Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n shape=[2, 3, 4]\n np_input = np.arange(24).astype('float32') - 12\n np_input = np_input.reshape(shape)\n x = paddle.to_tensor(np_input)\n #[[[-12. -11. -10. -9.] [ -8. -7. -6. -5.] [ -4. -3. -2. -1.]]\n # [[ 0. 1. 2. 3.] [ 4. 5. 6. 7.] [ 8. 9. 10. 11.]]]\n\n # compute frobenius norm along last two dimensions.\n out_fro = paddle.linalg.norm(x, p='fro', axis=[0,1])\n # out_fro.numpy() [17.435596 16.911535 16.7332 16.911535]\n\n # compute 2-order vector norm along last dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=-1)\n #out_pnorm.numpy(): [[21.118711 13.190906 5.477226]\n # [ 3.7416575 11.224972 19.131126]]\n\n # compute 2-order norm along [0,1] dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=[0,1])\n #out_pnorm.numpy(): [17.435596 16.911535 16.7332 16.911535]\n\n # compute inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=np.inf)\n #out_pnorm.numpy() = [12.]\n out_pnorm = paddle.linalg.norm(x, p=np.inf, axis=0)\n #out_pnorm.numpy(): [[12. 11. 10. 9.] [8. 7. 6. 7.] [8. 9. 10. 11.]]\n\n # compute -inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=-np.inf)\n #out_pnorm.numpy(): [0.]\n out_pnorm = paddle.linalg.norm(x, p=-np.inf, axis=0)\n #out_pnorm.numpy(): [[0. 1. 2. 3.] [4. 5. 6. 5.] [4. 3. 2. 1.]]\n \"\"\"\n\n def frobenius_norm(input, dim=None, keepdim=False, name=None):\n \"\"\"\n The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n dim (list, optional): None for last two dimensions.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if dim is not None and not (isinstance(dim, list) and len(dim) == 2):\n raise ValueError(\n \"The dim of frobenius norm op should be None or two elements list!\"\n )\n if paddle.in_dynamic_mode():\n if dim is None:\n return _C_ops.frobenius_norm(input, 'keep_dim', keepdim,\n 'reduce_all', True)\n return _C_ops.frobenius_norm(input, 'dim', dim, 'keep_dim', keepdim,\n 'reduce_all', False)\n attrs = {'dim': dim, 'keep_dim': keepdim, 'reduce_all': False}\n if dim is None:\n attrs['reduce_all'] = True\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'frobenius_norm')\n\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='frobenius_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def vector_norm(input,\n porder=None,\n axis=None,\n keepdim=False,\n asvector=False,\n name=None):\n \"\"\"\n Calculate the p-order vector norm for certain dimension of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n porder (float, optional): None for porder=2.0.\n axis (int, optional): None for last dimension.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if paddle.in_dynamic_mode():\n if axis is None: axis = -1\n return _C_ops.p_norm(input, 'porder', porder, 'axis', axis,\n 'keepdim', keepdim, 'asvector', asvector)\n if porder is not None:\n check_type(porder, 'porder', (float, int), 'p_norm')\n if axis is not None:\n check_type(axis, 'axis', (int), 'p_norm')\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'p_norm')\n\n attrs = {\n 'axis': axis if axis is not None else -1,\n 'porder': float(porder) if porder is not None else 2.0,\n 'keepdim': keepdim,\n 'asvector': asvector,\n 'epsilon': 1e-12,\n }\n helper = LayerHelper('p_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='p_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def inf_norm(input,\n porder=None,\n axis=axis,\n keepdim=False,\n asvector=False,\n name=None):\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n helper.append_op(type='abs', inputs={'X': input}, outputs={'Out': out})\n reduce_out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n reduce_all = True if axis == None or axis == [] or asvector == True else False\n axis = axis if axis != None and axis != [] else [0]\n\n reduce_type = 'reduce_max' if porder == np.float(\n 'inf') else 'reduce_min'\n helper.append_op(\n type=reduce_type,\n inputs={'X': out},\n outputs={'Out': reduce_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n\n return reduce_out\n\n def p_matrix_norm(input, porder=1., axis=axis, keepdim=False, name=None):\n \"\"\"\n NOTE:\n This function actually treats the matrix as flattened vector to calculate vector norm instead of matrix norm.\n \"\"\"\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n\n block.append_op(\n type='pow',\n inputs={'X': abs_out},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': True if axis is None else False\n })\n porder\n block.append_op(\n type='pow',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n if axis is None and p is not None:\n if isinstance(p, str):\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n porder=p,\n axis=axis,\n keepdim=keepdim,\n asvector=True,\n name=name)\n else:\n raise ValueError(\"only valid p type is string or float, found {}\".\n format(type(p)))\n\n if isinstance(axis, tuple):\n axis = list(axis)\n if isinstance(axis, list) and len(axis) == 1:\n axis = axis[0]\n\n #calculate vector norm, where axis is int or list with only one integer\n if isinstance(axis, int):\n if isinstance(p, str):\n if p == \"fro\":\n return vector_norm(\n x,\n porder=2,\n axis=axis,\n keepdim=keepdim,\n asvector=False,\n name=name)\n\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n axis=axis,\n porder=p,\n keepdim=keepdim,\n asvector=False,\n name=name)\n else:\n raise ValueError(\n \"unspport p for p-order vector norm. except float, found {}\".\n format(p))\n #calculate matrix norm, where axis is list with two integers\n elif isinstance(axis, list) and len(axis) == 2:\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n elif p == np.inf or p == -np.inf:\n return inf_norm(x, porder=p, axis=axis, keepdim=keepdim, name=name)\n elif p == 0:\n raise ValueError(\n \"just suport axis type int or list (length of list <=1) if p = 0, found {}\".\n format(axis))\n else:\n return p_matrix_norm(\n x, porder=p, axis=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"except axis type int or list (length of list <=2), found {}\".\n format(axis))\n\n\ndef dist(x, y, p=2, name=None):\n r\"\"\"\n\n This OP returns the p-norm of (x - y). It is not a norm in a strict sense, only as a measure\n of distance. The shapes of x and y must be broadcastable. The definition is as follows, for\n details, please refer to the `numpy's broadcasting <https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html>`_:\n\n - Each input has at least one dimension.\n - Match the two input dimensions from back to front, the dimension sizes must either be equal, one of them is 1, or one of them does not exist.\n\n Where, z = x - y, the shapes of x and y are broadcastable, then the shape of z can be\n obtained as follows:\n\n 1. If the number of dimensions of x and y are not equal, prepend 1 to the dimensions of the\n tensor with fewer dimensions.\n\n For example, The shape of x is [8, 1, 6, 1], the shape of y is [7, 1, 5], prepend 1 to the\n dimension of y.\n\n x (4-D Tensor): 8 x 1 x 6 x 1\n\n y (4-D Tensor): 1 x 7 x 1 x 5\n\n 2. Determine the size of each dimension of the output z: choose the maximum value from the\n two input dimensions.\n\n z (4-D Tensor): 8 x 7 x 6 x 5\n\n If the number of dimensions of the two inputs are the same, the size of the output can be\n directly determined in step 2. When p takes different values, the norm formula is as follows:\n\n When p = 0, defining $0^0=0$, the zero-norm of z is simply the number of non-zero elements of z.\n\n .. math::\n\n ||z||_{0}=\\lim_{p \\\\rightarrow 0}\\sum_{i=1}^{m}|z_i|^{p}\n\n When p = inf, the inf-norm of z is the maximum element of z.\n\n .. math::\n\n ||z||_\\infty=\\max_i |z_i|\n\n When p = -inf, the negative-inf-norm of z is the minimum element of z.\n\n .. math::\n\n ||z||_{-\\infty}=\\min_i |z_i|\n\n Otherwise, the p-norm of z follows the formula,\n\n .. math::\n\n ||z||_{p}=(\\sum_{i=1}^{m}|z_i|^p)^{\\\\frac{1}{p}}\n\n Args:\n x (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n y (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n p (float, optional): The norm to be computed, its data type is float32 or float64. Default: 2.\n\n Returns:\n Tensor: Tensor that is the p-norm of (x - y).\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor(np.array([[3, 3],[3, 3]]), \"float32\")\n y = paddle.to_tensor(np.array([[3, 3],[3, 1]]), \"float32\")\n out = paddle.dist(x, y, 0)\n print(out) # out = [1.]\n\n out = paddle.dist(x, y, 2)\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"inf\"))\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"-inf\"))\n print(out) # out = [0.]\n \"\"\"\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'dist')\n check_variable_and_dtype(y, 'dtype', ['float32', 'float64'], 'dist')\n check_type(p, 'p', (float, int), 'dist')\n helper = LayerHelper(\"dist\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n outputs = {'Out': [out]}\n attrs = {\"p\": float(p)}\n helper.append_op(\n type='dist', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef cond(x, p=None, name=None):\n \"\"\"\n\n Computes the condition number of a matrix or batches of matrices with respect to a matrix norm ``p``.\n\n Args:\n x (Tensor): The input tensor could be tensor of shape ``(*, m, n)`` where ``*`` is zero or more batch dimensions\n for ``p`` in ``(2, -2)``, or of shape ``(*, n, n)`` where every matrix is invertible for any supported ``p``.\n And the input data type could be ``float32`` or ``float64``.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `nuc`, `1`, `-1`, `2`, `-2`,\n `inf`, `-inf`. Default value is `None`, meaning that the order of the norm is `2`.\n name (str, optional): The default value is `None`. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: computing results of condition number, its data type is the same as input Tensor ``x``.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1., 0, -1], [0, 1, 0], [1, 0, 1]])\n\n # compute conditional number when p is None\n out = paddle.linalg.cond(x)\n # out.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is 'fro'\n out_fro = paddle.linalg.cond(x, p='fro')\n # out_fro.numpy() [3.1622777]\n\n # compute conditional number when order of the norm is 'nuc'\n out_nuc = paddle.linalg.cond(x, p='nuc')\n # out_nuc.numpy() [9.2426405]\n\n # compute conditional number when order of the norm is 1\n out_1 = paddle.linalg.cond(x, p=1)\n # out_1.numpy() [2.]\n\n # compute conditional number when order of the norm is -1\n out_minus_1 = paddle.linalg.cond(x, p=-1)\n # out_minus_1.numpy() [1.]\n\n # compute conditional number when order of the norm is 2\n out_2 = paddle.linalg.cond(x, p=2)\n # out_2.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is -1\n out_minus_2 = paddle.linalg.cond(x, p=-2)\n # out_minus_2.numpy() [0.70710677]\n\n # compute conditional number when order of the norm is inf\n out_inf = paddle.linalg.cond(x, p=np.inf)\n # out_inf.numpy() [2.]\n\n # compute conditional number when order of the norm is -inf\n out_minus_inf = paddle.linalg.cond(x, p=-np.inf)\n # out_minus_inf.numpy() [1.]\n\n a = paddle.to_tensor(np.random.randn(2, 4, 4).astype('float32'))\n # a.numpy()\n # [[[ 0.14063153 -0.996288 0.7996131 -0.02571543]\n # [-0.16303636 1.5534962 -0.49919784 -0.04402903]\n # [-1.1341571 -0.6022629 0.5445269 0.29154757]\n # [-0.16816919 -0.30972657 1.7521842 -0.5402487 ]]\n # [[-0.58081484 0.12402827 0.7229862 -0.55046535]\n # [-0.15178485 -1.1604939 0.75810957 0.30971205]\n # [-0.9669573 1.0940945 -0.27363303 -0.35416734]\n # [-1.216529 2.0018666 -0.7773689 -0.17556527]]]\n a_cond_fro = paddle.linalg.cond(a, p='fro')\n # a_cond_fro.numpy() [31.572273 28.120834]\n\n b = paddle.to_tensor(np.random.randn(2, 3, 4).astype('float64'))\n # b.numpy()\n # [[[ 1.61707487 0.46829144 0.38130416 0.82546736]\n # [-1.72710298 0.08866375 -0.62518804 0.16128892]\n # [-0.02822879 -1.67764516 0.11141444 0.3220113 ]]\n # [[ 0.22524372 0.62474921 -0.85503233 -1.03960523]\n # [-0.76620689 0.56673047 0.85064753 -0.45158196]\n # [ 1.47595418 2.23646462 1.5701758 0.10497519]]]\n b_cond_2 = paddle.linalg.cond(b, p=2)\n # b_cond_2.numpy() [3.30064451 2.51976252]\n\n \"\"\"\n\n def mat_norm(input, porder=1., axis=None):\n \"\"\"\n NOTE:\n Calculate the matrix norm of a square matrix or batches of square matrices,\n when porder is in (1, -1, inf, -inf)\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n axis = axis if axis != None and axis != [] else [0]\n keepdim = False\n\n if paddle.in_dynamic_mode():\n abs_out = _C_ops.abs(input)\n sum_out = _C_ops.reduce_sum(abs_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == 1 or porder == np.inf:\n return _C_ops.reduce_max(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == -1 or porder == -np.inf:\n return _C_ops.reduce_min(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n\n block = LayerHelper('norm', **locals())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n block.append_op(\n type='reduce_sum',\n inputs={'X': abs_out},\n outputs={'Out': sum_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 1 or porder == np.inf:\n block.append_op(\n type='reduce_max',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n if porder == -1 or porder == -np.inf:\n block.append_op(\n type='reduce_min',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n\n def fro_norm(input, porder=2, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the frobenius norm of a square matrix or batches of square matrices.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n if paddle.in_dynamic_mode():\n pow_out = _C_ops.pow(input, 'factor', porder)\n sum_out_1 = _C_ops.reduce_sum(pow_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n sum_out_2 = _C_ops.reduce_sum(sum_out_1, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n return _C_ops.pow(sum_out_2, 'factor', float(1. / porder))\n\n block = LayerHelper('norm', **locals())\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_1 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_2 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='pow',\n inputs={'X': input},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out_1},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_sum',\n inputs={'X': sum_out_1},\n outputs={'Out': sum_out_2},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='pow',\n inputs={'X': sum_out_2},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n def svd_norm(input, porder, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the matrix norm, which is related to singular values, of a matrix\n or batches of matrices, including nuclear norm, 2-norm and (-2)-norm.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n u, s, vh = svd(input, full_matrices=False)\n\n if paddle.in_dynamic_mode():\n if porder == \"nuc\":\n return _C_ops.reduce_sum(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n max_out = _C_ops.reduce_max(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n min_out = _C_ops.reduce_min(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n if porder == 2:\n return _C_ops.elementwise_div(max_out, min_out, 'aixs', axis,\n 'use_mkldnn', False)\n if porder == -2:\n return _C_ops.elementwise_div(min_out, max_out, 'aixs', axis,\n 'use_mkldnn', False)\n\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n if porder == \"nuc\":\n block.append_op(\n type='reduce_sum',\n inputs={'X': s},\n outputs={'Out': out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n max_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n min_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_min',\n inputs={'X': s},\n outputs={'Out': min_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': max_out,\n 'Y': min_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n if porder == -2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': min_out,\n 'Y': max_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n\n def empty_tensor(input, shape):\n if paddle.in_dynamic_mode():\n return input.reshape(shape)\n raise ValueError(\"only support x is nonempty tensor in static mode\")\n\n x_shape = list(x.shape)\n if not len(x_shape) >= 2:\n raise ValueError(\"input should be a matrix or batches of matrices, \" +\n \"but the dimention of received input is {}\".format(\n len(x_shape)))\n if p == None:\n p = 2\n x_size = 0 if (0 in x_shape) else 1\n if p in (\"fro\", \"nuc\", 1, -1, np.inf, -np.inf):\n if x_shape[len(x_shape) - 1] == x_shape[len(x_shape) - 2]:\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n x_inv = x.inverse()\n if p == \"fro\":\n return fro_norm(x) * fro_norm(x_inv)\n if p == \"nuc\":\n return svd_norm(x, p) * svd_norm(x_inv, p)\n if p in (1, -1):\n return mat_norm(\n x, porder=p, axis=[-2]) * mat_norm(\n x_inv, porder=p, axis=[-2])\n if p in (np.inf, -np.inf):\n return mat_norm(\n x, porder=p, axis=[-1]) * mat_norm(\n x_inv, porder=p, axis=[-1])\n else:\n raise ValueError(\"only support p is {} when input is a \".format(p) +\n \"square matrix or batches of square matrices\")\n elif p in (2, -2):\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n return svd_norm(x, porder=p)\n else:\n raise ValueError(\n \"unsupported {} for p, only supporting ('fro', 'nuc', \".format(\n p) + \"1, -1, 2, -2, inf, -inf) or none\")\n\n\ndef dot(x, y, name=None):\n \"\"\"\n This operator calculates inner product for vectors.\n\n .. note::\n Support 1-d and 2-d Tensor. When it is 2d, the first dimension of this matrix\n is the batch dimension, which means that the vectors of multiple batches are dotted.\n\n Parameters:\n x(Tensor): 1-D or 2-D ``Tensor``. Its dtype should be ``float32``, ``float64``, ``int32``, ``int64``\n y(Tensor): 1-D or 2-D ``Tensor``. Its dtype soulde be ``float32``, ``float64``, ``int32``, ``int64``\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: the calculated result Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x_data = np.random.uniform(0.1, 1, [10]).astype(np.float32)\n y_data = np.random.uniform(1, 3, [10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.dot(x, y)\n print(z)\n\n \"\"\"\n op_type = 'dot'\n # skip var type check in dygraph mode to improve efficiency\n if paddle.in_dynamic_mode():\n op = getattr(_C_ops, op_type)\n return op(x, y)\n\n assert x is not None, 'x cannot be None in {}'.format(op_type)\n assert y is not None, 'y cannot be None in {}'.format(op_type)\n\n check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n check_variable_and_dtype(y, 'y', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n\n helper = LayerHelper(op_type, **locals())\n if name is None:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n else:\n out = helper.create_variable(\n name=name, dtype=x.dtype, persistable=False)\n helper.append_op(\n type=\"dot\", inputs={'X': x,\n 'Y': y}, attrs={}, outputs={\"Out\": out})\n return out\n\n\ndef cov(x, rowvar=True, ddof=True, fweights=None, aweights=None, name=None):\n \"\"\"\n Estimate the covariance matrix of the input variables, given data and weights.\n\n A covariance matrix is a square matrix, indicate the covariance of each pair variables in the input matrix.\n For example, for an N-dimensional samples X=[x1,x2,…xN]T, then the covariance matrix \n element Cij is the covariance of xi and xj. The element Cii is the variance of xi itself.\n\n Parameters:\n x(Tensor): A N-D(N<=2) Tensor containing multiple variables and observations. By default, each row of x represents a variable. Also see rowvar below.\n rowvar(Bool, optional): If rowvar is True (default), then each row represents a variable, with observations in the columns. Default: True\n ddof(Bool, optional): If ddof=True will return the unbiased estimate, and ddof=False will return the simple average. Default: True\n fweights(Tensor, optional): 1-D Tensor of integer frequency weights; The number of times each observation vector should be repeated. Default: None\n aweights(Tensor, optional): 1-D Tensor of observation vector weights. How important of the observation vector, larger data means this element is more important. Default: None\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: The covariance matrix Tensor of the variables.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n\n xt = paddle.rand((3,4))\n paddle.linalg.cov(xt)\n\n '''\n Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n [[0.07918842, 0.06127326, 0.01493049],\n [0.06127326, 0.06166256, 0.00302668],\n [0.01493049, 0.00302668, 0.01632146]])\n '''\n \"\"\"\n op_type = 'cov'\n if len(x.shape) > 2 or len(x.shape) < 1:\n raise ValueError(\n \"Input(x) only support N-D (1<=N<=2) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cov')\n nx = x\n if len(x.shape) == 1:\n nx = x.reshape((1, -1))\n if not rowvar and nx.shape[0] != 1:\n nx = nx.t()\n w = None\n observation_num = nx.shape[1]\n if fweights is not None:\n w = fweights.astype(nx.dtype)\n if len(w.shape) > 1:\n raise ValueError(\n \"Input(fweights) only support N-D (N<=1) tensor in cov, but received \"\n \"shape of Input(input) is %s.\" % len(fweights.shape))\n if fweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(fweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(fweights) is {}.\".format(observation_num,\n fweights.shape[0]))\n if fweights.min() < 0:\n raise ValueError(\n \"The value of Input(fweights) cannot be negtive, but received \"\n \"min of Input(fweights) is {}.\".format(fweights.min()))\n if not paddle.all(fweights == paddle.round(fweights.astype('float64'))):\n raise ValueError(\"Input(fweights) must be integer \")\n\n if aweights is not None:\n aw = aweights.astype(nx.dtype)\n if len(aw.shape) > 1:\n raise ValueError(\n \"Input(aweights) only support N-D (N<=1) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(aweights.shape))\n check_variable_and_dtype(aweights, 'dtype', ['float32', 'float64'],\n 'cov')\n if aweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(aweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(aweights) is {}.\".format(observation_num,\n aweights.shape[0]))\n if aweights.min() < 0:\n raise ValueError(\n \"The value of Input(aweights) cannot be negtive, but received \"\n \"min of Input(aweights) is {}.\".format(aweights.min()))\n if w is not None:\n w = w * aw\n else:\n w = aw\n\n w_sum = paddle.to_tensor(observation_num, dtype=nx.dtype)\n if fweights is not None or aweights is not None:\n w_sum = w.sum()\n if w_sum.item() == 0:\n raise ValueError(\"The sum of weights is zero, can't be normalized.\")\n\n if w is not None:\n nx_w = nx * w\n avg = (nx_w).sum(axis=1) / w_sum\n else:\n avg = nx.sum(axis=1) / w_sum\n nx_w = nx\n\n if w is not None and aweights is not None and ddof == True:\n norm_factor = w_sum - (w * aweights).sum() / w_sum\n else:\n norm_factor = w_sum - ddof\n if norm_factor <= 0:\n norm_factor = paddle.to_tensor(0, dtype=nx.dtype)\n nx = nx - avg.unsqueeze(1)\n xxt = paddle.mm(nx, nx_w.t().conj())\n cov = paddle.divide(xxt, norm_factor).squeeze()\n return cov\n\n\ndef t(input, name=None):\n \"\"\"\n Transpose <=2-D tensor.\n 0-D and 1-D tensors are returned as it is and 2-D tensor is equal to\n the paddle.transpose function which perm dimensions set 0 and 1.\n\n Args:\n input (Tensor): The input Tensor. It is a N-D (N<=2) Tensor of data types float16, float32, float64, int32.\n name(str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`\n Returns:\n Tensor: A transposed n-D Tensor, with data type being float16, float32, float64, int32, int64.\n\n For Example:\n\n .. code-block:: text\n\n # Example 1 (0-D tensor)\n x = tensor([0.79])\n paddle.t(x) = tensor([0.79])\n\n # Example 2 (1-D tensor)\n x = tensor([0.79, 0.84, 0.32])\n paddle.t(x) = tensor([0.79, 0.84, 0.32])\n\n # Example 3 (2-D tensor)\n x = tensor([0.79, 0.84, 0.32],\n [0.64, 0.14, 0.57])\n paddle.t(x) = tensor([0.79, 0.64],\n [0.84, 0.14],\n [0.32, 0.57])\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n x = paddle.ones(shape=[2, 3], dtype='int32')\n x_transposed = paddle.t(x)\n print(x_transposed.shape)\n # [3, 2]\n \"\"\"\n if len(input.shape) > 2:\n raise ValueError(\n \"Input(input) only support N-D (N<=2) tensor, but received \"\n \"length of Input(input) is %s. Perhaps you can use paddle.\"\n \"tensor.transpose() instead.\" % len(input.shape))\n if paddle.in_dynamic_mode():\n if len(input.shape) == 1:\n return input\n # 2-D tensor\n perm = [1, 0]\n out, _ = _C_ops.transpose2(input, 'axis', perm)\n return out\n\n check_variable_and_dtype(\n input, 'input', ['float16', 'float32', 'float64', 'int32',\n 'int64'], 'transpose')\n\n helper = LayerHelper('t', **locals())\n out = helper.create_variable_for_type_inference(input.dtype)\n input_shape = helper.create_variable_for_type_inference(input.dtype)\n if len(input.shape) == 1:\n out = input\n else:\n helper.append_op(\n type='transpose2',\n inputs={'X': [input]},\n outputs={'Out': [out],\n 'XShape': [input_shape]},\n attrs={'axis': [1, 0]})\n return out\n\n\ndef cross(x, y, axis=None, name=None):\n \"\"\"\n Computes the cross product between two tensors along an axis.\n\n Inputs must have the same shape, and the length of their axes should be equal to 3.\n If `axis` is not given, it defaults to the first axis found with the length 3.\n\n Args:\n x (Tensor): The first input tensor.\n y (Tensor): The second input tensor.\n axis (int, optional): The axis along which to compute the cross product. It defaults to the first axis found with the length 3.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor. A Tensor with same data type as `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0],\n [3.0, 3.0, 3.0]])\n y = paddle.to_tensor([[1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0]])\n\n z1 = paddle.cross(x, y)\n # [[-1. -1. -1.]\n # [ 2. 2. 2.]\n # [-1. -1. -1.]]\n\n z2 = paddle.cross(x, y, axis=1)\n # [[0. 0. 0.]\n # [0. 0. 0.]\n # [0. 0. 0.]]\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_cross(x, y, axis)\n else:\n if _in_legacy_dygraph():\n if axis is not None:\n return _C_ops.cross(x, y, 'dim', axis)\n else:\n return _C_ops.cross(x, y)\n else:\n helper = LayerHelper(\"cross\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n attrs = dict()\n attrs['dim'] = axis\n\n helper.append_op(\n type='cross',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef cholesky(x, upper=False, name=None):\n r\"\"\"\n Computes the Cholesky decomposition of one symmetric positive-definite\n matrix or batches of symmetric positive-definite matrice.\n\n If `upper` is `True`, the decomposition has the form :math:`A = U^{T}U` ,\n and the returned matrix :math:`U` is upper-triangular. Otherwise, the\n decomposition has the form :math:`A = LL^{T}` , and the returned matrix\n :math:`L` is lower-triangular.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[*, M, M]`,\n where * is zero or more batch dimensions, and matrices on the\n inner-most 2 dimensions all should be symmetric positive-definite.\n Its data type should be float32 or float64.\n upper (bool): The flag indicating whether to return upper or lower\n triangular matrices. Default: False.\n\n Returns:\n Tensor: A Tensor with same shape and data type as `x`. It represents \\\n triangular matrices generated by Cholesky decomposition.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n a = np.random.rand(3, 3)\n a_t = np.transpose(a, [1, 0])\n x_data = np.matmul(a, a_t) + 1e-03\n x = paddle.to_tensor(x_data)\n out = paddle.linalg.cholesky(x, upper=False)\n print(out)\n # [[1.190523 0. 0. ]\n # [0.9906703 0.27676893 0. ]\n # [1.25450498 0.05600871 0.06400121]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky(x, \"upper\", upper)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cholesky')\n check_type(upper, 'upper', bool, 'cholesky')\n helper = LayerHelper('cholesky', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='cholesky',\n inputs={'X': [x]},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef matrix_rank(x, tol=None, hermitian=False, name=None):\n r\"\"\"\n Computes the rank of a matrix.\n\n The rank of a matrix is the number of singular values that are greater than the specified `tol` threshold when hermitian=False,\n or the number of eigenvalues in absolute value that are greater than the specified `tol` threshold when hermitian=True.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., m, n]`, where `...` is zero or more batch dimensions. If `x` is a batch\n of matrices then the output has the same batch dimensions. The data type of `x` should be float32 or float64.\n tol (float,Tensor,optional): the tolerance value. Default: None. If `tol` is not specified, and `sigma` is the largest\n singular value (or eigenvalues in absolute value), and `eps` is the epsilon value for the dtype of `x`, then `tol` is computed\n with formula `tol=sigma * max(m,n) * eps`. Note that if `x` is a batch of matrices, `tol` is computed this way for every batch.\n hermitian (bool,optional): indicates whether `x` is Hermitian. Default: False. When hermitian=True, `x` is assumed to be Hermitian,\n enabling a more efficient method for finding eigenvalues, but `x` is not checked inside the function. Instead, We just use\n the lower triangular of the matrix to compute.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: Rank of tensor x.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n a = paddle.eye(10)\n b = paddle.linalg.matrix_rank(a)\n print(b)\n # b = [10]\n\n c = paddle.ones(shape=[3, 4, 5, 5])\n d = paddle.linalg.matrix_rank(c, tol=0.01, hermitian=True)\n print(d)\n # d = [[1, 1, 1, 1],\n # [1, 1, 1, 1],\n # [1, 1, 1, 1]]\n\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if tol is None:\n tol_tensor = None\n tol_attr = 0.0\n use_default_tol = True\n elif isinstance(tol, Variable):\n if tol.dtype != x.dtype:\n tol_tensor = cast(tol, x.dtype)\n else:\n tol_tensor = tol\n tol_attr = 0.0\n use_default_tol = False\n else:\n tol_tensor = None\n tol_attr = float(tol)\n use_default_tol = False\n return _C_ops.matrix_rank(x, tol_tensor, \"tol\", tol_attr, 'hermitian',\n hermitian, 'use_default_tol', use_default_tol)\n\n inputs = {}\n attrs = {}\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'matrix_rank')\n inputs['X'] = x\n if tol is None:\n attrs['use_default_tol'] = True\n elif isinstance(tol, Variable):\n check_variable_and_dtype(tol, 'tol', ['float32'], 'matrix_rank')\n attrs['use_default_tol'] = False\n if tol.dtype != x.dtype:\n inputs['TolTensor'] = cast(tol, x.dtype)\n else:\n inputs['TolTensor'] = tol\n else:\n check_type(tol, 'tol', float, 'matrix_rank')\n attrs['use_default_tol'] = False\n attrs['tol'] = tol\n check_type(hermitian, 'hermitian', bool, 'matrix_rank')\n attrs['hermitian'] = hermitian\n\n helper = LayerHelper('matrix_rank', **locals())\n out = helper.create_variable_for_type_inference(dtype='int32')\n helper.append_op(\n type='matrix_rank', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef bmm(x, y, name=None):\n \"\"\"\n Applies batched matrix multiplication to two tensors.\n\n Both of the two input tensors must be three-dementional and share the same batch size.\n\n if x is a (b, m, k) tensor, y is a (b, k, n) tensor, the output will be a (b, m, n) tensor.\n\n Args:\n x (Tensor): The input Tensor.\n y (Tensor): The input Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The product Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n # In imperative mode:\n # size x: (2, 2, 3) and y: (2, 3, 2)\n x = paddle.to_tensor([[[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0]],\n [[3.0, 3.0, 3.0],\n [4.0, 4.0, 4.0]]])\n y = paddle.to_tensor([[[1.0, 1.0],[2.0, 2.0],[3.0, 3.0]],\n [[4.0, 4.0],[5.0, 5.0],[6.0, 6.0]]])\n out = paddle.bmm(x, y)\n #output size: (2, 2, 2)\n #output value:\n #[[[6.0, 6.0],[12.0, 12.0]],[[45.0, 45.0],[60.0, 60.0]]]\n out_np = out.numpy()\n\n \"\"\"\n x_shape = x.shape\n y_shape = y.shape\n if not len(x_shape) == len(y_shape) == 3:\n raise ValueError(\n \"x and y should be 3-dimensional. But received x's dimention: {}, y's dimention: {}\".\n format(x_shape, y_shape))\n if x_shape[2] != y_shape[1]:\n raise ValueError(\n \"x's width must be equal with y's height. But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n if x_shape[0] != y_shape[0]:\n raise ValueError(\n \"x's batch (shape[0]) must be equal with y's batch (shape[0]). But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.bmm(x, y)\n\n helper = LayerHelper('bmm', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='bmm', inputs={'X': x, 'Y': y}, outputs={'Out': out})\n return out\n\n\ndef histogram(input, bins=100, min=0, max=0, name=None):\n \"\"\"\n Computes the histogram of a tensor. The elements are sorted into equal width bins between min and max.\n If min and max are both zero, the minimum and maximum values of the data are used.\n\n Args:\n input (Tensor): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor\n should be float32, float64, int32, int64.\n bins (int): number of histogram bins\n min (int): lower end of the range (inclusive)\n max (int): upper end of the range (inclusive)\n\n Returns:\n Tensor: data type is int64, shape is (nbins,).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n inputs = paddle.to_tensor([1, 2, 1])\n result = paddle.histogram(inputs, bins=4, min=0, max=3)\n print(result) # [0, 2, 1, 0]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.histogram(input, \"bins\", bins, \"min\", min, \"max\", max)\n\n helper = LayerHelper('histogram', **locals())\n check_variable_and_dtype(\n input, 'X', ['int32', 'int64', 'float32', 'float64'], 'histogram')\n out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)\n helper.append_op(\n type='histogram',\n inputs={'X': input},\n outputs={'Out': out},\n attrs={'bins': bins,\n 'min': min,\n 'max': max})\n return out\n\n\ndef bincount(x, weights=None, minlength=0, name=None):\n \"\"\"\n Computes frequency of each value in the input tensor. \n\n Args:\n x (Tensor): A Tensor with non-negative integer. Should be 1-D tensor.\n weights (Tensor, optional): Weight for each value in the input tensor. Should have the same shape as input. Default is None.\n minlength (int, optional): Minimum number of bins. Should be non-negative integer. Default is 0.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor of frequency.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([1, 2, 1, 4, 5])\n result1 = paddle.bincount(x)\n print(result1) # [0, 2, 1, 0, 1, 1]\n\n w = paddle.to_tensor([2.1, 0.4, 0.1, 0.5, 0.5])\n result2 = paddle.bincount(x, weights=w)\n print(result2) # [0., 2.19999981, 0.40000001, 0., 0.50000000, 0.50000000]\n \"\"\"\n if x.dtype not in [paddle.int32, paddle.int64]:\n raise TypeError(\"Elements in Input(x) should all be integers\")\n\n if paddle.in_dynamic_mode():\n return _C_ops.bincount(x, weights, \"minlength\", minlength)\n\n helper = LayerHelper('bincount', **locals())\n\n check_variable_and_dtype(x, 'X', ['int32', 'int64'], 'bincount')\n\n if weights is not None:\n check_variable_and_dtype(weights, 'Weights',\n ['int32', 'int64', 'float32', 'float64'],\n 'bincount')\n out = helper.create_variable_for_type_inference(dtype=weights.dtype)\n else:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='bincount',\n inputs={'X': x,\n 'Weights': weights},\n outputs={'Out': out},\n attrs={'minlength': minlength})\n return out\n\n\ndef mv(x, vec, name=None):\n \"\"\"\n Performs a matrix-vector product of the matrix x and the vector vec.\n\n Args:\n x (Tensor): A tensor with shape :math:`[M, N]` , The data type of the input Tensor x\n should be one of float32, float64.\n vec (Tensor): A tensor with shape :math:`[N]` , The data type of the input Tensor x\n should be one of float32, float64.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor which is producted by x and vec.\n\n Examples:\n .. code-block:: python\n\n # x: [M, N], vec: [N]\n # paddle.mv(x, vec) # out: [M]\n\n import numpy as np\n import paddle\n\n x_data = np.array([[2, 1, 3], [3, 0, 1]]).astype(\"float64\")\n x = paddle.to_tensor(x_data)\n vec_data = np.array([3, 5, 1])\n vec = paddle.to_tensor(vec_data).astype(\"float64\")\n out = paddle.mv(x, vec)\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_mv(x, vec)\n else:\n if _in_legacy_dygraph():\n out = _C_ops.mv(x, vec)\n return out\n else:\n\n def __check_input(x, vec):\n var_names = {'x': x, 'vec': vec}\n for name, val in var_names.items():\n check_variable_and_dtype(val, name, ['float32', 'float64'],\n 'mv')\n x_shape = list(x.shape)\n vec_shape = list(vec.shape)\n if len(x_shape) != 2:\n raise ValueError(\n \"x should be 2-dimensional. But received x's dimention: {}\".\n format(x_shape))\n if len(vec_shape) != 1:\n raise ValueError(\n \"vec should be 1-dimensional. But received vec's dimention: {}\".\n format(vec_shape))\n\n __check_input(x, vec)\n\n helper = LayerHelper('mv', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='mv', inputs={'X': x,\n 'Vec': vec}, outputs={'Out': out})\n return out\n\n\ndef det(x, name=None):\n \"\"\"\n Calculates determinant value of a square matrix or batches of square matrices.\n Args:\n x (Tensor): input (Tensor): the input matrix of size `(n, n)` or the batch of matrices of size\n `(*, n, n)` where `*` is one or more batch dimensions.\n Returns:\n y (Tensor):the determinant value of a square matrix or batches of square matrices.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.det(x)\n\n print(A)\n\n # [ 0.02547996, 2.52317095, -6.15900707])\n\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.determinant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'det')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('determinant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='determinant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef slogdet(x, name=None):\n \"\"\"\n Calculates the sign and natural logarithm of the absolute value of a square matrix's or batches square matrices' determinant.\n The determinant can be computed with ``sign * exp(logabsdet)\n\n Supports input of float, double\n\n Note that for matrices that have zero determinant, this returns ``(0, -inf)``\n Args:\n x (Tensor): the batch of matrices of size :math:`(*, n, n)`\n where math:`*` is one or more batch dimensions.\n\n Returns:\n y (Tensor): A tensor containing the sign of the determinant and the natural logarithm\n of the absolute value of determinant, respectively.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.slogdet(x)\n\n print(A)\n\n # [[ 1. , 1. , -1. ],\n # [-0.98610914, -0.43010661, -0.10872950]])\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.slogdeterminant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'slogdet')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('slogdeterminant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='slogdeterminant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef svd(x, full_matrices=False, name=None):\n r\"\"\"\n Computes the singular value decomposition of one matrix or a batch of regular matrices.\n\n Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies:\n\n .. math::\n X = U * diag(S) * VT\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., N, M]`,\n where `...` is zero or more batch dimensions. N and M can be arbitraty\n positive number. Note that if x is sigular matrices, the grad is numerical\n instable. The data type of x should be float32 or float64.\n full_matrices (bool): A flag to control the behavor of svd.\n If full_matrices = True, svd op will compute full U and V matrics,\n which means shape of U is `[..., N, N]`, shape of V is `[..., M, M]`. K = min(M, N).\n If full_matrices = False, svd op will use a economic method to store U and V.\n which means shape of U is `[..., N, K]`, shape of V is `[..., M, K]`. K = min(M, N).\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple of 3 tensors: (U, S, VH). VH is the conjugate transpose of V. S is the singlar value vectors of matrics with shape `[..., K]`\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 2.0], [1.0, 3.0], [4.0, 6.0]]).astype('float64')\n x = x.reshape([3, 2])\n u, s, vh = paddle.linalg.svd(x)\n print (u)\n #U = [[ 0.27364809, -0.21695147 ],\n # [ 0.37892198, -0.87112408 ],\n # [ 0.8840446 , 0.44053933 ]]\n\n print (s)\n #S = [8.14753743, 0.78589688]\n print (vh)\n #VT= [[ 0.51411221, 0.85772294],\n # [ 0.85772294, -0.51411221]]\n\n # one can verify : U * S * VT == X\n # U * UH == I\n # V * VH == I\n \"\"\"\n\n if paddle.in_dynamic_mode():\n return _C_ops.svd(x, 'full_matrices', full_matrices)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'svd')\n check_type(full_matrices, 'full_matrices', bool, 'svd')\n helper = LayerHelper('svd', **locals())\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n vh = helper.create_variable_for_type_inference(dtype=x.dtype)\n s = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['full_matrices'] = full_matrices\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vh,\n 'S': s},\n attrs=attrs, )\n return u, s, vh\n\n\ndef matrix_power(x, n, name=None):\n r\"\"\"\n Computes the n-th power of a square matrix or a batch of square matrices.\n\n Let :math:`X` be a sqaure matrix or a batch of square matrices, :math:`n` be\n an exponent, the equation should be:\n\n .. math::\n Out = X ^ {n}\n\n Specifically,\n\n - If `n > 0`, it returns the matrix or a batch of matrices raised to the power\n of `n`.\n\n - If `n = 0`, it returns the identity matrix or a batch of identity matrices.\n\n - If `n < 0`, it returns the inverse of each matrix (if invertible) raised to\n the power of `abs(n)`.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices to be raised\n to power `n`. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n n (int): The exponent. It can be any positive, negative integer or zero.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The n-th power of the matrix (or the batch of matrices) `x`. Its\n data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1, 2, 3],\n [1, 4, 9],\n [1, 8, 27]], dtype='float64')\n print(paddle.linalg.matrix_power(x, 2))\n # [[6. , 34. , 102.],\n # [14. , 90. , 282.],\n # [36. , 250., 804.]]\n\n print(paddle.linalg.matrix_power(x, 0))\n # [[1., 0., 0.],\n # [0., 1., 0.],\n # [0., 0., 1.]]\n\n print(paddle.linalg.matrix_power(x, -2))\n # [[ 12.91666667, -12.75000000, 2.83333333 ],\n # [-7.66666667 , 8. , -1.83333333 ],\n # [ 1.80555556 , -1.91666667 , 0.44444444 ]]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.matrix_power(x, \"n\", n)\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'matrix_power')\n check_type(n, 'n', int, 'matrix_power')\n helper = LayerHelper('matrix_power', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matrix_power',\n inputs={'X': x},\n outputs={'Out': out},\n attrs={'n': n})\n return out\n\n\ndef qr(x, mode=\"reduced\", name=None):\n r\"\"\"\n Computes the QR decomposition of one matrix or batches of matrice (backward is unsupported now).\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., M, N]`,\n where ... is zero or more batch dimensions. M and N can be arbitrary\n positive number. The data type of x should be float32 or float64. \n mode (str, optional): A flag to control the behavior of qr, the default is \"reduced\". \n Suppose x's shape is `[..., M, N]` and denoting `K = min(M, N)`:\n If mode = \"reduced\", qr op will return reduced Q and R matrices, \n which means Q's shape is `[..., M, K]` and R's shape is `[..., K, N]`.\n If mode = \"complete\", qr op will return complete Q and R matrices, \n which means Q's shape is `[..., M, M]` and R's shape is `[..., M, N]`.\n If mode = \"r\", qr op will only return reduced R matrix, which means\n R's shape is `[..., K, N]`.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n If mode = \"reduced\" or mode = \"complete\", qr will return a two tensor-tuple, which represents Q and R. \n If mode = \"r\", qr will return a tensor which represents R.\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n q, r = paddle.linalg.qr(x)\n print (q)\n print (r)\n\n # Q = [[-0.16903085, 0.89708523],\n # [-0.50709255, 0.27602622],\n # [-0.84515425, -0.34503278]])\n\n # R = [[-5.91607978, -7.43735744],\n # [ 0. , 0.82807867]])\n \n # one can verify : X = Q * R ; \n \"\"\"\n if paddle.in_dynamic_mode():\n q, r = _C_ops.qr(x, 'mode', mode)\n if mode == \"r\":\n return r\n else:\n return q, r\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'qr')\n check_type(mode, 'mode', str, 'qr')\n helper = LayerHelper('qr', **locals())\n q = helper.create_variable_for_type_inference(dtype=x.dtype)\n r = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['mode'] = mode\n helper.append_op(\n type='qr', inputs={'X': [x]}, outputs={'Q': q,\n 'R': r}, attrs=attrs)\n if mode == \"r\":\n return r\n else:\n return q, r\n\n\ndef lu(x, pivot=True, get_infos=False, name=None):\n r\"\"\"\n Computes the LU factorization of an N-D(N>=2) matrix x. \n\n Returns the LU factorization(inplace x) and Pivots. low triangular matrix L and \n upper triangular matrix U are combined to a single LU matrix.\n\n Pivoting is done if pivot is set to True.\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n # return ones\n\n Args:\n\n X (Tensor): the tensor to factor of N-dimensions(N>=2).\n\n pivot (bool, optional): controls whether pivoting is done. Default: True.\n\n get_infos (bool, optional): if set to True, returns an info IntTensor. Default: False.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n factorization (Tensor): LU matrix, the factorization of input X.\n\n pivots (IntTensor): the pivots of size(∗(N-2), min(m,n)). `pivots` stores all the \n intermediate transpositions of rows. The final permutation `perm` could be \n reconstructed by this, details refer to upper example.\n\n infos (IntTensor, optional): if `get_infos` is `True`, this is a tensor of size (∗(N-2)) \n where non-zero values indicate whether factorization for the matrix or each minibatch \n has succeeded or failed.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n \n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n if paddle.in_dynamic_mode():\n LU, Piv, Info = _C_ops.lu(x, 'pivots', pivot)\n if get_infos:\n return LU, Piv, Info\n else:\n return LU, Piv\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu')\n helper = LayerHelper('lu', **locals())\n lu = helper.create_variable_for_type_inference(dtype=x.dtype)\n p = helper.create_variable_for_type_inference(dtype='int')\n info = helper.create_variable_for_type_inference(dtype='int')\n attrs = dict()\n attrs['pivots'] = pivot\n helper.append_op(\n type='lu',\n inputs={'X': x},\n outputs={'Out': lu,\n 'Pivots': p,\n 'Infos': info},\n attrs=attrs)\n if get_infos:\n return lu, p, info\n else:\n return lu, p\n\n\ndef lu_unpack(x, y, unpack_ludata=True, unpack_pivots=True, name=None):\n r\"\"\"\n Unpack L U and P to single matrix tensor . \n unpack L and U matrix from LU, unpack permutation matrix P from Pivtos .\n\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n\n\n Args:\n x (Tensor): The LU tensor get from paddle.linalg.lu, which is combined by L and U.\n\n y (Tensor): Pivots get from paddle.linalg.lu.\n\n unpack_ludata (bool,optional): whether to unpack L and U from x. Default: True.\n\n unpack_pivots (bool, optional): whether to unpack permutation matrix P from Pivtos. Default: True.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n P (Tensor): Permutation matrix P of lu factorization.\n\n L (Tensor): The lower triangular matrix tensor of lu factorization.\n\n U (Tensor): The upper triangular matrix tensor of lu factorization.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n\n if paddle.in_dynamic_mode():\n P, L, U = _C_ops.lu_unpack(x, y, 'unpack_ludata', unpack_ludata,\n 'unpack_pivots', unpack_pivots)\n return P, L, U\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu_unpack')\n helper = LayerHelper('lu_unpack', **locals())\n p = helper.create_variable_for_type_inference(dtype=x.dtype)\n l = helper.create_variable_for_type_inference(dtype=x.dtype)\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n attrs = dict()\n attrs['unpack_ludata'] = unpack_ludata\n attrs['unpack_pivots'] = unpack_pivots\n helper.append_op(\n type='lu_unpack',\n inputs={'X': x,\n 'Pivots': y},\n outputs={'Pmat': p,\n 'L': l,\n 'U': u},\n attrs=attrs)\n return p, l, u\n\n\ndef eig(x, name=None):\n \"\"\"\n This API performs the eigenvalue decomposition of a square matrix or a batch of square matrices.\n\n .. note::\n If the matrix is a Hermitian or a real symmetric matrix, please use :ref:`paddle.linalg.eigh` instead, which is much faster.\n If only eigenvalues is needed, please use :ref:`paddle.linalg.eigvals` instead.\n If the matrix is of any shape, please use :ref:`paddle.linalg.svd`.\n This API is only supported on CPU device.\n The output datatype is always complex for both real and complex input.\n\n Args:\n x (Tensor): A tensor with shape math:`[*, N, N]`, The data type of the x should be one of ``float32``,\n ``float64``, ``compplex64`` or ``complex128``.\n name (str, optional): The default value is `None`. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Eigenvalues(Tensors): A tensor with shape math:`[*, N]` refers to the eigen values.\n Eigenvectors(Tensors): A tensor with shape math:`[*, N, N]` refers to the eigen vectors.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n paddle.device.set_device(\"cpu\")\n\n x_data = np.array([[1.6707249, 7.2249975, 6.5045543],\n [9.956216, 8.749598, 6.066444 ],\n [4.4251957, 1.7983172, 0.370647 ]]).astype(\"float32\")\n x = paddle.to_tensor(x_data)\n w, v = paddle.linalg.eig(x)\n print(w)\n # Tensor(shape=[3, 3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [[(-0.5061363550800655+0j) , (-0.7971760990842826+0j) ,\n # (0.18518077798279986+0j)],\n # [(-0.8308237755993192+0j) , (0.3463813401919749+0j) ,\n # (-0.6837005269141947+0j) ],\n # [(-0.23142567697893396+0j), (0.4944999840400175+0j) ,\n # (0.7058765252952796+0j) ]])\n\n print(v)\n # Tensor(shape=[3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [ (16.50471283351188+0j) , (-5.5034820550763515+0j) ,\n # (-0.21026087843552282+0j)])\n \"\"\"\n if paddle.in_dynamic_mode():\n w, v = _C_ops.eig(x)\n return w, v\n\n check_variable_and_dtype(\n x, 'X', ['float32', 'float64', 'complex64', 'complex128'], 'eig')\n helper = LayerHelper('eig', **locals())\n\n w = helper.create_variable_for_type_inference(x.dtype)\n v = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {'X': x}\n outputs = {'Eigenvalues': w, 'Eigenvectors': v}\n helper.append_op(type='eig', inputs=inputs, outputs=outputs)\n\n return w, v\n\n\ndef eigvals(x, name=None):\n \"\"\"\n Compute the eigenvalues of one or more general matrices.\n\n Warning:\n The gradient kernel of this operator does not yet developed.\n If you need back propagation through this operator, please replace it with paddle.linalg.eig.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices whose eigenvalues will be computed.\n Its shape should be `[*, M, M]`, where `*` is zero or more batch dimensions.\n Its data type should be float32, float64, complex64, or complex128.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n Tensor: A tensor containing the unsorted eigenvalues which has the same batch dimensions with `x`.\n The eigenvalues are complex-valued even when `x` is real.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n paddle.seed(1234)\n\n x = paddle.rand(shape=[3, 3], dtype='float64')\n # [[0.02773777, 0.93004224, 0.06911496],\n # [0.24831591, 0.45733623, 0.07717843],\n # [0.48016702, 0.14235102, 0.42620817]])\n\n print(paddle.linalg.eigvals(x))\n # [(-0.27078833542132674+0j), (0.29962280156230725+0j), (0.8824477020120244+0j)] #complex128\n \"\"\"\n\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64',\n 'complex128'], 'eigvals')\n\n x_shape = list(x.shape)\n if len(x_shape) < 2:\n raise ValueError(\n \"The dimension of Input(x) should be at least 2, but received x's dimention = {}, x's shape = {}\".\n format(len(x_shape), x_shape))\n\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The last two dimensions of Input(x) should be equal, but received x's shape = {}\".\n format(x_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.eigvals(x)\n\n helper = LayerHelper('eigvals', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='eigvals', inputs={'X': x}, outputs={'Out': out})\n return out\n\n\ndef multi_dot(x, name=None):\n \"\"\"\n Multi_dot is an operator that calculates multiple matrix multiplications.\n\n Supports inputs of float16(only GPU support), float32 and float64 dtypes. This function does not\n support batched inputs.\n\n The input tensor in [x] must be 2-D except for the first and last can be 1-D.\n If the first tensor is a 1-D vector of shape(n, ) it is treated as row vector\n of shape(1, n), similarly if the last tensor is a 1D vector of shape(n, ), it\n is treated as a column vector of shape(n, 1).\n\n If the first and last tensor are 2-D matrix, then the output is also 2-D matrix,\n otherwise the output is a 1-D vector.\n\n Multi_dot will select the lowest cost multiplication order for calculation. The\n cost of multiplying two matrices with shapes (a, b) and (b, c) is a * b * c.\n Given matrices A, B, C with shapes (20, 5), (5, 100), (100, 10) respectively,\n we can calculate the cost of different multiplication orders as follows:\n - Cost((AB)C) = 20x5x100 + 20x100x10 = 30000\n - Cost(A(BC)) = 5x100x10 + 20x5x10 = 6000\n\n In this case, multiplying B and C first, then multiply A, which is 5 times faster\n than sequential calculation.\n\n Args:\n x ([Tensor]): The input tensors which is a list Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # A * B\n A_data = np.random.random([3, 4]).astype(np.float32)\n B_data = np.random.random([4, 5]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n out = paddle.linalg.multi_dot([A, B])\n print(out.numpy().shape)\n # [3, 5]\n\n # A * B * C\n A_data = np.random.random([10, 5]).astype(np.float32)\n B_data = np.random.random([5, 8]).astype(np.float32)\n C_data = np.random.random([8, 7]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n C = paddle.to_tensor(C_data)\n out = paddle.linalg.multi_dot([A, B, C])\n print(out.numpy().shape)\n # [10, 7]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.multi_dot(x)\n\n check_type(x, 'x', (list, tuple), 'multi_dot')\n for id, item in enumerate(x):\n check_variable_and_dtype(item, 'x[' + str(id) + ']',\n ['float16', 'float32', 'float64'], 'multi_dot')\n if item.dtype != x[0].dtype:\n raise TypeError(\n \"All the Tensors in the input must have the same data type.\")\n\n helper = LayerHelper('multi_dot', **locals())\n dtype = helper.input_dtype(input_param_name='x')\n out = helper.create_variable_for_type_inference(dtype)\n helper.append_op(type='multi_dot', inputs={\"X\": x}, outputs={\"Out\": out})\n return out\n\n\ndef eigh(x, UPLO='L', name=None):\n \"\"\"\n Compute the eigenvalues and eigenvectors of a\n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[*, N, N]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): (string, default 'L'), 'L' represents the lower triangular matrix,\n \"'U' represents the upper triangular matrix.\".\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n\n out_value(Tensor): A Tensor with shape [*, N] and data type of float32 and float64. The eigenvalues of eigh op.\n out_vector(Tensor): A Tensor with shape [*, N, N] and data type of float32,float64,complex64 and complex128. The eigenvectors of eigh op.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value, out_vector = paddle.linalg.eigh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n print(out_vector)\n #[(-0.9238795325112867+0j), (-0.3826834323650898+0j)],\n #[ 0.3826834323650898j , -0.9238795325112867j ]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.eigh(x, 'UPLO', UPLO)\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigh', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'eigh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO})\n return out_value, out_vector\n\n\ndef pinv(x, rcond=1e-15, hermitian=False, name=None):\n r\"\"\"\n Calculate pseudo inverse via SVD(singular value decomposition)\n of one matrix or batches of regular matrix.\n\n .. math::\n\n if hermitian == False:\n x = u * s * vt (SVD)\n out = v * 1/s * ut\n else:\n x = u * s * ut (eigh)\n out = u * 1/s * u.conj().transpose(-2,-1)\n\n If x is hermitian or symmetric matrix, svd will be replaced with eigh.\n\n Args:\n x(Tensor): The input tensor. Its shape should be (*, m, n)\n where * is zero or more batch dimensions. m and n can be\n arbitraty positive number. The data type of x should be\n float32 or float64 or complex64 or complex128. When data\n type is complex64 or cpmplex128, hermitian should be set\n True.\n\n rcond(Tensor, optional): the tolerance value to determine\n when is a singular value zero. Defalut:1e-15.\n\n hermitian(bool, optional): indicates whether x is Hermitian\n if complex or symmetric if real. Default: False.\n\n name(str|None): A name for this layer(optional). If set None,\n the layer will be named automatically.\n\n Returns:\n Tensor: The tensor with same data type with x. it represents\n pseudo inverse of x. Its shape should be (*, n, m).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.arange(15).reshape((3, 5)).astype('float64')\n input = paddle.to_tensor(x)\n out = paddle.linalg.pinv(input)\n print(input)\n print(out)\n\n # input:\n # [[0. , 1. , 2. , 3. , 4. ],\n # [5. , 6. , 7. , 8. , 9. ],\n # [10., 11., 12., 13., 14.]]\n\n # out:\n # [[-0.22666667, -0.06666667, 0.09333333],\n # [-0.12333333, -0.03333333, 0.05666667],\n # [-0.02000000, 0.00000000, 0.02000000],\n # [ 0.08333333, 0.03333333, -0.01666667],\n # [ 0.18666667, 0.06666667, -0.05333333]]\n\n # one can verify : x * out * x = x ;\n # or out * x * out = x ;\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if not hermitian:\n # combine svd and matmul op\n u, s, vt = _C_ops.svd(x, 'full_matrices', False)\n max_singular_val = _C_ops.reduce_max(s, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=x.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=x.dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v, _ = _C_ops.transpose2(vt, 'axis', perm)\n\n out_1 = v * st\n out_2 = _C_ops.matmul_v2(out_1, u, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n # combine eigh and matmul op\n s, u = _C_ops.eigh(x, 'UPLO', 'L')\n s_abs = paddle.abs(s)\n max_singular_val = _C_ops.reduce_max(s_abs, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=s.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=s.dtype)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n out_1 = u * st\n u_conj = _C_ops.conj(u)\n out_2 = _C_ops.matmul_v2(out_1, u_conj, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n if not hermitian:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'pinv')\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(dtype)\n vt = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vt,\n 'S': s},\n attrs={'full_matrices': False}, )\n\n max_singular_val = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=dtype)\n st_shape = helper.create_variable_for_type_inference(dtype=dtype)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v = helper.create_variable_for_type_inference(dtype)\n v_shape = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='transpose2',\n inputs={'X': [vt]},\n outputs={'Out': [v],\n 'XShape': [v_shape]},\n attrs={'axis': perm})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': v,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n else:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64',\n 'complex128'], 'pinv')\n\n if dtype == paddle.complex128:\n s_type = 'float64'\n elif dtype == paddle.complex64:\n s_type = 'float32'\n else:\n s_type = dtype\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': s,\n 'Eigenvectors': u},\n attrs={'UPLO': 'L'})\n s_abs = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='abs', inputs={'X': s}, outputs={'Out': s_abs})\n max_singular_val = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s_abs},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=s_type)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=s_type)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s_type)\n cond_not_int = layers.cast(layers.logical_not(condition), s_type)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=s_type)\n st_shape = helper.create_variable_for_type_inference(dtype=s_type)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': u,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n u_conj = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='conj', inputs={'X': u}, outputs={'Out': [u_conj]})\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u_conj},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n\n\ndef solve(x, y, name=None):\n r\"\"\"\n Computes the solution of a square system of linear equations with a unique solution for input 'X' and 'Y'.\n Let :math: `X` be a sqaure matrix or a batch of square matrices, :math:`Y` be\n a vector/matrix or a batch of vectors/matrices, the equation should be:\n\n .. math::\n Out = X^-1 * Y\n Specifically,\n - This system of linear equations has one solution if and only if input 'X' is invertible.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): A vector/matrix or a batch of vectors/matrices. Its shape should be `[*, M, K]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of a square system of linear equations with a unique solution for input 'x' and 'y'.\n Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # 2*X0 + X1 = 9\n # X0 + 2*X1 = 8\n\n import paddle\n import numpy as np\n\n np_x = np.array([[3, 1],[1, 2]])\n np_y = np.array([9, 8])\n x = paddle.to_tensor(np_x, dtype=\"float64\")\n y = paddle.to_tensor(np_y, dtype=\"float64\")\n out = paddle.linalg.solve(x, y)\n\n print(out)\n # [2., 3.])\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.solve(x, y)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type=\"solve\", inputs={\"X\": x,\n \"Y\": y}, outputs={\"Out\": out})\n return out\n\n\ndef triangular_solve(x,\n y,\n upper=True,\n transpose=False,\n unitriangular=False,\n name=None):\n r\"\"\"\n Computes the solution of a system of equations with a triangular coefficient matrix `x` and\n multiple right-hand sides `y` .\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input triangular coefficient matrix. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): Whether to solve the upper-triangular system of equations (default) or the lower-triangular \n system of equations. Default: True.\n transpose (bool, optional): whether `x` should be transposed before calculation. Default: False.\n unitriangular (bool, optional): whether `x` is unit triangular. If True, the diagonal elements of `x` are assumed \n to be 1 and not referenced from `x` . Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # x1 + x2 + x3 = 0\n # 2*x2 + x3 = -9\n # -x3 = 5\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n y = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.triangular_solve(x, y, upper=True)\n\n print(out)\n # [7, -2, -5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.triangular_solve(x, y, 'upper', upper, 'transpose',\n transpose, 'unitriangular',\n unitriangular)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"triangular_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'triangular_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'triangular_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='triangular_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={\n 'upper': upper,\n 'transpose': transpose,\n 'unitriangular': unitriangular\n })\n return out\n\n\ndef cholesky_solve(x, y, upper=False, name=None):\n r\"\"\"\n Solves a linear system of equations A @ X = B, given A's Cholesky factor matrix u and matrix B.\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input matrix which is upper or lower triangular Cholesky factor of square matrix A. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): whether to consider the Cholesky factor as a lower or upper triangular matrix. Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type is the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n u = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n b = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.cholesky_solve(b, u, upper=True)\n\n print(out)\n # [-2.5, -7, 9.5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky_solve(x, y, 'upper', upper)\n\n helper = LayerHelper(\"cholesky_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'cholesky_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'cholesky_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='cholesky_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef eigvalsh(x, UPLO='L', name=None):\n \"\"\"\n Computes the eigenvalues of a \n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[_, M, M]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): Lower triangular part of a (‘L’, default) or the upper triangular part (‘U’).\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor eigenvalues in ascending order.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value = paddle.eigvalsh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n \"\"\"\n if paddle.in_dynamic_mode():\n is_test = x.stop_gradient\n values, _ = _C_ops.eigvalsh(x, 'UPLO', UPLO, 'is_test', is_test)\n return values\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigvalsh', **locals())\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64', 'complex128'],\n 'eigvalsh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n is_test = x.stop_gradient\n helper.append_op(\n type='eigvalsh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO,\n 'is_test': is_test})\n return out_value\n\n\ndef lstsq(x, y, rcond=None, driver=None, name=None):\n \"\"\"\n Computes a solution to\n the least squares problem of a system of linear equations.\n\n Args:\n x (Tensor): A tensor with shape ``(*, M, N)`` , the data type of the input Tensor ``x``\n should be one of float32, float64.\n y (Tensor): A tensor with shape ``(*, M, K)`` , the data type of the input Tensor ``y`` \n should be one of float32, float64.\n rcond(float, optional): The default value is None. A float pointing number used to determine \n the effective rank of ``x``. If ``rcond`` is None, it will be set to max(M, N) times the \n machine precision of x_dtype.\n driver(str, optional): The default value is None. The name of LAPACK method to be used. For \n CPU inputs the valid values are ‘gels’, ‘gelsy’, ‘gelsd, ‘gelss’. For CUDA input, the only \n valid driver is ‘gels’. If ``driver`` is None, ‘gelsy’ is used for CPU inputs and ‘gels’ \n for CUDA inputs.\n name(str, optional): The default value is None. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple: A tuple of 4 Tensors which is (``solution``, ``residuals``, ``rank``, ``singular_values``). \n ``solution`` is a tensor with shape ``(*, N, K)``, meaning the least squares solution. ``residuals`` \n is a tensor with shape ``(*, K)``, meaning the squared residuals of the solutions, which is computed \n when M > N and every matrix in ``x`` is full-rank, otherwise return an empty tensor. ``rank`` is a tensor \n with shape ``(*)``, meaning the ranks of the matrices in ``x``, which is computed when ``driver`` in \n (‘gelsy’, ‘gelsd’, ‘gelss’), otherwise return an empty tensor. ``singular_values`` is a tensor with \n shape ``(*, min(M, N))``, meaning singular values of the matrices in ``x``, which is computed when \n ``driver`` in (‘gelsd’, ‘gelss’), otherwise return an empty tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n x = paddle.to_tensor([[1, 3], [3, 2], [5, 6.]])\n y = paddle.to_tensor([[3, 4, 6], [5, 3, 4], [1, 2, 1.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gelsd\")\n print(results[0])\n # [[ 0.78350395, -0.22165027, -0.62371236],\n # [-0.11340097, 0.78866047, 1.14948535]]\n print(results[1])\n # [19.81443405, 10.43814468, 30.56185532])\n print(results[2])\n # 2\n print(results[3])\n # [9.03455734, 1.54167950]\n\n x = paddle.to_tensor([[10, 2, 3], [3, 10, 5], [5, 6, 12.]])\n y = paddle.to_tensor([[4, 2, 9], [2, 0, 3], [2, 5, 3.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gels\")\n print(results[0])\n # [[ 0.39386186, 0.10230173, 0.93606132],\n # [ 0.10741687, -0.29028133, 0.11892585],\n # [-0.05115091, 0.51918161, -0.19948854]]\n print(results[1])\n # []\n \"\"\"\n device = paddle.get_device()\n if device == \"cpu\":\n if driver not in (None, \"gels\", \"gelss\", \"gelsd\", \"gelsy\"):\n raise ValueError(\n \"Only support valid driver is 'gels', 'gelss', 'gelsd', 'gelsy' or None for CPU inputs. But got {}\".\n format(driver))\n driver = \"gelsy\" if driver is None else driver\n elif \"gpu\" in device:\n if driver not in (None, \"gels\"):\n raise ValueError(\n \"Only support valid driver is 'gels' or None for CUDA inputs. But got {}\".\n format(driver))\n driver = \"gels\" if driver is None else driver\n else:\n raise RuntimeError(\"Only support lstsq api for CPU or CUDA device.\")\n\n if x.dtype == y.dtype and x.dtype in (paddle.float32, paddle.float64):\n pass\n else:\n raise ValueError(\n \"Only support x and y have the same dtype such as 'float32' and 'float64'.\"\n )\n\n if rcond is None:\n if x.dtype == paddle.float32:\n rcond = 1e-7 * max(x.shape[-2], x.shape[-1])\n elif x.dtype == paddle.float64:\n rcond = 1e-15 * max(x.shape[-2], x.shape[-1])\n\n if paddle.in_dynamic_mode():\n solution, rank, singular_values = _C_ops.lstsq(x, y, \"rcond\", rcond,\n \"driver\", driver)\n if x.shape[-2] > x.shape[-1]:\n matmul_out = _varbase_creator(dtype=x.dtype)\n _C_ops.matmul(x, solution, matmul_out, 'trans_x', False, 'trans_y',\n False)\n minus_out = _C_ops.elementwise_sub(matmul_out, y)\n pow_out = _C_ops.pow(minus_out, 'factor', 2)\n residuals = _C_ops.reduce_sum(pow_out, 'dim', [-2], 'keepdim',\n False, 'reduce_all', False)\n else:\n residuals = paddle.empty(shape=[0], dtype=x.dtype)\n\n if driver == \"gels\":\n rank = paddle.empty(shape=[0], dtype=paddle.int32)\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n elif driver == \"gelsy\":\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n\n return solution, residuals, rank, singular_values\n\n helper = LayerHelper('lstsq', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n check_variable_and_dtype(\n y, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n\n solution = helper.create_variable_for_type_inference(dtype=x.dtype)\n residuals = helper.create_variable_for_type_inference(dtype=x.dtype)\n rank = helper.create_variable_for_type_inference(dtype=paddle.int32)\n singular_values = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='lstsq',\n inputs={'X': x,\n 'Y': y},\n outputs={\n 'Solution': solution,\n 'Rank': rank,\n 'SingularValues': singular_values\n },\n attrs={'rcond': rcond,\n 'driver': driver})\n\n matmul_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n minus_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n pow_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': solution},\n outputs={'Out': matmul_out},\n attrs={\n 'trans_x': False,\n 'trans_y': False,\n })\n\n helper.append_op(\n type='elementwise_sub',\n inputs={'X': matmul_out,\n 'Y': y},\n outputs={'Out': minus_out})\n\n helper.append_op(\n type='pow',\n inputs={'X': minus_out},\n outputs={'Out': pow_out},\n attrs={'factor': 2})\n\n helper.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': residuals},\n attrs={'dim': [-2],\n 'keep_dim': False,\n 'reduce_all': False})\n\n if driver == \"gels\":\n rank = paddle.static.data(name='rank', shape=[0])\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n elif driver == \"gelsy\":\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n\n return solution, residuals, rank, singular_values\n"}}},{"rowIdx":37,"cells":{"function_name":{"kind":"string","value":"eigh"},"docstring":{"kind":"string","value":"Compute the eigenvalues and eigenvectors of a\ncomplex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\nArgs:\n x (Tensor): A tensor with shape :math:`[*, N, N]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): (string, default 'L'), 'L' represents the lower triangular matrix,\n \"'U' represents the upper triangular matrix.\".\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\nReturns:\n\n out_value(Tensor): A Tensor with shape [*, N] and data type of float32 and float64. The eigenvalues of eigh op.\n out_vector(Tensor): A Tensor with shape [*, N, N] and data type of float32,float64,complex64 and complex128. The eigenvectors of eigh op.\n\nExamples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value, out_vector = paddle.linalg.eigh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n print(out_vector)\n #[(-0.9238795325112867+0j), (-0.3826834323650898+0j)],\n #[ 0.3826834323650898j , -0.9238795325112867j ]]"},"masked_code":{"kind":"string","value":"# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numpy as np\nfrom ..fluid.layer_helper import LayerHelper\nfrom ..framework import _varbase_creator, _dygraph_tracer\nfrom ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype\nfrom ..static import Variable\nfrom ..fluid.framework import _in_legacy_dygraph, in_dygraph_mode\nfrom ..fluid.layers import transpose, cast # noqa: F401\nfrom ..fluid import layers\nimport paddle\nfrom paddle.common_ops_import import core\nfrom paddle.common_ops_import import VarDesc\nfrom paddle import _C_ops\n\n__all__ = []\n\n\ndef matmul(x, y, transpose_x=False, transpose_y=False, name=None):\n \"\"\"\n Applies matrix multiplication to two tensors. `matmul` follows\n the complete broadcast rules,\n and its behavior is consistent with `np.matmul`.\n\n Currently, the input tensors' number of dimensions can be any, `matmul` can be used to\n achieve the `dot`, `matmul` and `batchmatmul`.\n\n The actual behavior depends on the shapes of :math:`x`, :math:`y` and the\n flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:\n\n - If a transpose flag is specified, the last two dimensions of the tensor\n are transposed. If the tensor is ndim-1 of shape, the transpose is invalid. If the tensor\n is ndim-1 of shape :math:`[D]`, then for :math:`x` it is treated as :math:`[1, D]`, whereas\n for :math:`y` it is the opposite: It is treated as :math:`[D, 1]`.\n\n The multiplication behavior depends on the dimensions of `x` and `y`. Specifically:\n\n - If both tensors are 1-dimensional, the dot product result is obtained.\n\n - If both tensors are 2-dimensional, the matrix-matrix product is obtained.\n\n - If the `x` is 1-dimensional and the `y` is 2-dimensional,\n a `1` is prepended to its dimension in order to conduct the matrix multiply.\n After the matrix multiply, the prepended dimension is removed.\n\n - If the `x` is 2-dimensional and `y` is 1-dimensional,\n the matrix-vector product is obtained.\n\n - If both arguments are at least 1-dimensional and at least one argument\n is N-dimensional (where N > 2), then a batched matrix multiply is obtained.\n If the first argument is 1-dimensional, a 1 is prepended to its dimension\n in order to conduct the batched matrix multiply and removed after.\n If the second argument is 1-dimensional, a 1 is appended to its\n dimension for the purpose of the batched matrix multiple and removed after.\n The non-matrix (exclude the last two dimensions) dimensions are\n broadcasted according the broadcast rule.\n For example, if input is a (j, 1, n, m) tensor and the other is a (k, m, p) tensor,\n out will be a (j, k, n, p) tensor.\n\n Args:\n x (Tensor): The input tensor which is a Tensor.\n y (Tensor): The input tensor which is a Tensor.\n transpose_x (bool): Whether to transpose :math:`x` before multiplication.\n transpose_y (bool): Whether to transpose :math:`y` before multiplication.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # vector * vector\n x_data = np.random.random([10]).astype(np.float32)\n y_data = np.random.random([10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [1]\n\n # matrix * vector\n x_data = np.random.random([10, 5]).astype(np.float32)\n y_data = np.random.random([5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10]\n\n # batched matrix * broadcasted vector\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([2]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5]\n\n # batched matrix * batched matrix\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([10, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5, 5]\n\n # batched matrix * broadcasted matrix\n x_data = np.random.random([10, 1, 5, 2]).astype(np.float32)\n y_data = np.random.random([1, 3, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 3, 5, 5]\n\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_matmul(x, y, transpose_x, transpose_y)\n\n if _in_legacy_dygraph():\n op_type = 'matmul_v2'\n op = getattr(_C_ops, op_type)\n return op(x, y, 'trans_x', transpose_x, 'trans_y', transpose_y)\n\n attrs = {\n 'trans_x': transpose_x,\n 'trans_y': transpose_y,\n }\n\n def __check_input(x, y):\n var_names = {'x': x, 'y': y}\n for name, val in var_names.items():\n check_variable_and_dtype(\n val, name,\n ['float16', 'float32', 'float64', 'complex64', 'complex128'],\n 'matmul')\n\n __check_input(x, y)\n\n helper = LayerHelper('matmul_v2', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef norm(x, p='fro', axis=None, keepdim=False, name=None):\n \"\"\"\n\n Returns the matrix norm (Frobenius) or vector norm (the 1-norm, the Euclidean\n or 2-norm, and in general the p-norm for p > 0) of a given tensor.\n\n .. note::\n This norm API is different from `numpy.linalg.norm`.\n This api supports high-order input tensors (rank >= 3), and certain axis need to be pointed out to calculate the norm.\n But `numpy.linalg.norm` only supports 1-D vector or 2-D matrix as input tensor.\n For p-order matrix norm, this api actually treats matrix as a flattened vector to calculate the vector norm, NOT REAL MATRIX NORM.\n\n Args:\n x (Tensor): The input tensor could be N-D tensor, and the input data\n type could be float32 or float64.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `0`, `1`, `2`,\n `inf`, `-inf` and any positive real number yielding the corresponding p-norm. Not supported: ord < 0 and nuclear norm.\n Default value is `fro`.\n axis (int|list|tuple, optional): The axis on which to apply norm operation. If axis is int\n or list(int)/tuple(int) with only one element, the vector norm is computed over the axis.\n If `axis < 0`, the dimension to norm operation is rank(input) + axis.\n If axis is a list(int)/tuple(int) with two elements, the matrix norm is computed over the axis.\n Defalut value is `None`.\n keepdim (bool, optional): Whether to reserve the reduced dimension in the\n output Tensor. The result tensor will have fewer dimension\n than the :attr:`input` unless :attr:`keepdim` is true, default\n value is False.\n name (str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: results of norm operation on the specified axis of input tensor,\n it's data type is the same as input's Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n shape=[2, 3, 4]\n np_input = np.arange(24).astype('float32') - 12\n np_input = np_input.reshape(shape)\n x = paddle.to_tensor(np_input)\n #[[[-12. -11. -10. -9.] [ -8. -7. -6. -5.] [ -4. -3. -2. -1.]]\n # [[ 0. 1. 2. 3.] [ 4. 5. 6. 7.] [ 8. 9. 10. 11.]]]\n\n # compute frobenius norm along last two dimensions.\n out_fro = paddle.linalg.norm(x, p='fro', axis=[0,1])\n # out_fro.numpy() [17.435596 16.911535 16.7332 16.911535]\n\n # compute 2-order vector norm along last dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=-1)\n #out_pnorm.numpy(): [[21.118711 13.190906 5.477226]\n # [ 3.7416575 11.224972 19.131126]]\n\n # compute 2-order norm along [0,1] dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=[0,1])\n #out_pnorm.numpy(): [17.435596 16.911535 16.7332 16.911535]\n\n # compute inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=np.inf)\n #out_pnorm.numpy() = [12.]\n out_pnorm = paddle.linalg.norm(x, p=np.inf, axis=0)\n #out_pnorm.numpy(): [[12. 11. 10. 9.] [8. 7. 6. 7.] [8. 9. 10. 11.]]\n\n # compute -inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=-np.inf)\n #out_pnorm.numpy(): [0.]\n out_pnorm = paddle.linalg.norm(x, p=-np.inf, axis=0)\n #out_pnorm.numpy(): [[0. 1. 2. 3.] [4. 5. 6. 5.] [4. 3. 2. 1.]]\n \"\"\"\n\n def frobenius_norm(input, dim=None, keepdim=False, name=None):\n \"\"\"\n The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n dim (list, optional): None for last two dimensions.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if dim is not None and not (isinstance(dim, list) and len(dim) == 2):\n raise ValueError(\n \"The dim of frobenius norm op should be None or two elements list!\"\n )\n if paddle.in_dynamic_mode():\n if dim is None:\n return _C_ops.frobenius_norm(input, 'keep_dim', keepdim,\n 'reduce_all', True)\n return _C_ops.frobenius_norm(input, 'dim', dim, 'keep_dim', keepdim,\n 'reduce_all', False)\n attrs = {'dim': dim, 'keep_dim': keepdim, 'reduce_all': False}\n if dim is None:\n attrs['reduce_all'] = True\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'frobenius_norm')\n\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='frobenius_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def vector_norm(input,\n porder=None,\n axis=None,\n keepdim=False,\n asvector=False,\n name=None):\n \"\"\"\n Calculate the p-order vector norm for certain dimension of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n porder (float, optional): None for porder=2.0.\n axis (int, optional): None for last dimension.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if paddle.in_dynamic_mode():\n if axis is None: axis = -1\n return _C_ops.p_norm(input, 'porder', porder, 'axis', axis,\n 'keepdim', keepdim, 'asvector', asvector)\n if porder is not None:\n check_type(porder, 'porder', (float, int), 'p_norm')\n if axis is not None:\n check_type(axis, 'axis', (int), 'p_norm')\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'p_norm')\n\n attrs = {\n 'axis': axis if axis is not None else -1,\n 'porder': float(porder) if porder is not None else 2.0,\n 'keepdim': keepdim,\n 'asvector': asvector,\n 'epsilon': 1e-12,\n }\n helper = LayerHelper('p_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='p_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def inf_norm(input,\n porder=None,\n axis=axis,\n keepdim=False,\n asvector=False,\n name=None):\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n helper.append_op(type='abs', inputs={'X': input}, outputs={'Out': out})\n reduce_out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n reduce_all = True if axis == None or axis == [] or asvector == True else False\n axis = axis if axis != None and axis != [] else [0]\n\n reduce_type = 'reduce_max' if porder == np.float(\n 'inf') else 'reduce_min'\n helper.append_op(\n type=reduce_type,\n inputs={'X': out},\n outputs={'Out': reduce_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n\n return reduce_out\n\n def p_matrix_norm(input, porder=1., axis=axis, keepdim=False, name=None):\n \"\"\"\n NOTE:\n This function actually treats the matrix as flattened vector to calculate vector norm instead of matrix norm.\n \"\"\"\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n\n block.append_op(\n type='pow',\n inputs={'X': abs_out},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': True if axis is None else False\n })\n porder\n block.append_op(\n type='pow',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n if axis is None and p is not None:\n if isinstance(p, str):\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n porder=p,\n axis=axis,\n keepdim=keepdim,\n asvector=True,\n name=name)\n else:\n raise ValueError(\"only valid p type is string or float, found {}\".\n format(type(p)))\n\n if isinstance(axis, tuple):\n axis = list(axis)\n if isinstance(axis, list) and len(axis) == 1:\n axis = axis[0]\n\n #calculate vector norm, where axis is int or list with only one integer\n if isinstance(axis, int):\n if isinstance(p, str):\n if p == \"fro\":\n return vector_norm(\n x,\n porder=2,\n axis=axis,\n keepdim=keepdim,\n asvector=False,\n name=name)\n\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n axis=axis,\n porder=p,\n keepdim=keepdim,\n asvector=False,\n name=name)\n else:\n raise ValueError(\n \"unspport p for p-order vector norm. except float, found {}\".\n format(p))\n #calculate matrix norm, where axis is list with two integers\n elif isinstance(axis, list) and len(axis) == 2:\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n elif p == np.inf or p == -np.inf:\n return inf_norm(x, porder=p, axis=axis, keepdim=keepdim, name=name)\n elif p == 0:\n raise ValueError(\n \"just suport axis type int or list (length of list <=1) if p = 0, found {}\".\n format(axis))\n else:\n return p_matrix_norm(\n x, porder=p, axis=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"except axis type int or list (length of list <=2), found {}\".\n format(axis))\n\n\ndef dist(x, y, p=2, name=None):\n r\"\"\"\n\n This OP returns the p-norm of (x - y). It is not a norm in a strict sense, only as a measure\n of distance. The shapes of x and y must be broadcastable. The definition is as follows, for\n details, please refer to the `numpy's broadcasting <https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html>`_:\n\n - Each input has at least one dimension.\n - Match the two input dimensions from back to front, the dimension sizes must either be equal, one of them is 1, or one of them does not exist.\n\n Where, z = x - y, the shapes of x and y are broadcastable, then the shape of z can be\n obtained as follows:\n\n 1. If the number of dimensions of x and y are not equal, prepend 1 to the dimensions of the\n tensor with fewer dimensions.\n\n For example, The shape of x is [8, 1, 6, 1], the shape of y is [7, 1, 5], prepend 1 to the\n dimension of y.\n\n x (4-D Tensor): 8 x 1 x 6 x 1\n\n y (4-D Tensor): 1 x 7 x 1 x 5\n\n 2. Determine the size of each dimension of the output z: choose the maximum value from the\n two input dimensions.\n\n z (4-D Tensor): 8 x 7 x 6 x 5\n\n If the number of dimensions of the two inputs are the same, the size of the output can be\n directly determined in step 2. When p takes different values, the norm formula is as follows:\n\n When p = 0, defining $0^0=0$, the zero-norm of z is simply the number of non-zero elements of z.\n\n .. math::\n\n ||z||_{0}=\\lim_{p \\\\rightarrow 0}\\sum_{i=1}^{m}|z_i|^{p}\n\n When p = inf, the inf-norm of z is the maximum element of z.\n\n .. math::\n\n ||z||_\\infty=\\max_i |z_i|\n\n When p = -inf, the negative-inf-norm of z is the minimum element of z.\n\n .. math::\n\n ||z||_{-\\infty}=\\min_i |z_i|\n\n Otherwise, the p-norm of z follows the formula,\n\n .. math::\n\n ||z||_{p}=(\\sum_{i=1}^{m}|z_i|^p)^{\\\\frac{1}{p}}\n\n Args:\n x (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n y (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n p (float, optional): The norm to be computed, its data type is float32 or float64. Default: 2.\n\n Returns:\n Tensor: Tensor that is the p-norm of (x - y).\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor(np.array([[3, 3],[3, 3]]), \"float32\")\n y = paddle.to_tensor(np.array([[3, 3],[3, 1]]), \"float32\")\n out = paddle.dist(x, y, 0)\n print(out) # out = [1.]\n\n out = paddle.dist(x, y, 2)\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"inf\"))\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"-inf\"))\n print(out) # out = [0.]\n \"\"\"\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'dist')\n check_variable_and_dtype(y, 'dtype', ['float32', 'float64'], 'dist')\n check_type(p, 'p', (float, int), 'dist')\n helper = LayerHelper(\"dist\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n outputs = {'Out': [out]}\n attrs = {\"p\": float(p)}\n helper.append_op(\n type='dist', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef cond(x, p=None, name=None):\n \"\"\"\n\n Computes the condition number of a matrix or batches of matrices with respect to a matrix norm ``p``.\n\n Args:\n x (Tensor): The input tensor could be tensor of shape ``(*, m, n)`` where ``*`` is zero or more batch dimensions\n for ``p`` in ``(2, -2)``, or of shape ``(*, n, n)`` where every matrix is invertible for any supported ``p``.\n And the input data type could be ``float32`` or ``float64``.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `nuc`, `1`, `-1`, `2`, `-2`,\n `inf`, `-inf`. Default value is `None`, meaning that the order of the norm is `2`.\n name (str, optional): The default value is `None`. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: computing results of condition number, its data type is the same as input Tensor ``x``.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1., 0, -1], [0, 1, 0], [1, 0, 1]])\n\n # compute conditional number when p is None\n out = paddle.linalg.cond(x)\n # out.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is 'fro'\n out_fro = paddle.linalg.cond(x, p='fro')\n # out_fro.numpy() [3.1622777]\n\n # compute conditional number when order of the norm is 'nuc'\n out_nuc = paddle.linalg.cond(x, p='nuc')\n # out_nuc.numpy() [9.2426405]\n\n # compute conditional number when order of the norm is 1\n out_1 = paddle.linalg.cond(x, p=1)\n # out_1.numpy() [2.]\n\n # compute conditional number when order of the norm is -1\n out_minus_1 = paddle.linalg.cond(x, p=-1)\n # out_minus_1.numpy() [1.]\n\n # compute conditional number when order of the norm is 2\n out_2 = paddle.linalg.cond(x, p=2)\n # out_2.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is -1\n out_minus_2 = paddle.linalg.cond(x, p=-2)\n # out_minus_2.numpy() [0.70710677]\n\n # compute conditional number when order of the norm is inf\n out_inf = paddle.linalg.cond(x, p=np.inf)\n # out_inf.numpy() [2.]\n\n # compute conditional number when order of the norm is -inf\n out_minus_inf = paddle.linalg.cond(x, p=-np.inf)\n # out_minus_inf.numpy() [1.]\n\n a = paddle.to_tensor(np.random.randn(2, 4, 4).astype('float32'))\n # a.numpy()\n # [[[ 0.14063153 -0.996288 0.7996131 -0.02571543]\n # [-0.16303636 1.5534962 -0.49919784 -0.04402903]\n # [-1.1341571 -0.6022629 0.5445269 0.29154757]\n # [-0.16816919 -0.30972657 1.7521842 -0.5402487 ]]\n # [[-0.58081484 0.12402827 0.7229862 -0.55046535]\n # [-0.15178485 -1.1604939 0.75810957 0.30971205]\n # [-0.9669573 1.0940945 -0.27363303 -0.35416734]\n # [-1.216529 2.0018666 -0.7773689 -0.17556527]]]\n a_cond_fro = paddle.linalg.cond(a, p='fro')\n # a_cond_fro.numpy() [31.572273 28.120834]\n\n b = paddle.to_tensor(np.random.randn(2, 3, 4).astype('float64'))\n # b.numpy()\n # [[[ 1.61707487 0.46829144 0.38130416 0.82546736]\n # [-1.72710298 0.08866375 -0.62518804 0.16128892]\n # [-0.02822879 -1.67764516 0.11141444 0.3220113 ]]\n # [[ 0.22524372 0.62474921 -0.85503233 -1.03960523]\n # [-0.76620689 0.56673047 0.85064753 -0.45158196]\n # [ 1.47595418 2.23646462 1.5701758 0.10497519]]]\n b_cond_2 = paddle.linalg.cond(b, p=2)\n # b_cond_2.numpy() [3.30064451 2.51976252]\n\n \"\"\"\n\n def mat_norm(input, porder=1., axis=None):\n \"\"\"\n NOTE:\n Calculate the matrix norm of a square matrix or batches of square matrices,\n when porder is in (1, -1, inf, -inf)\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n axis = axis if axis != None and axis != [] else [0]\n keepdim = False\n\n if paddle.in_dynamic_mode():\n abs_out = _C_ops.abs(input)\n sum_out = _C_ops.reduce_sum(abs_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == 1 or porder == np.inf:\n return _C_ops.reduce_max(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == -1 or porder == -np.inf:\n return _C_ops.reduce_min(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n\n block = LayerHelper('norm', **locals())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n block.append_op(\n type='reduce_sum',\n inputs={'X': abs_out},\n outputs={'Out': sum_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 1 or porder == np.inf:\n block.append_op(\n type='reduce_max',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n if porder == -1 or porder == -np.inf:\n block.append_op(\n type='reduce_min',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n\n def fro_norm(input, porder=2, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the frobenius norm of a square matrix or batches of square matrices.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n if paddle.in_dynamic_mode():\n pow_out = _C_ops.pow(input, 'factor', porder)\n sum_out_1 = _C_ops.reduce_sum(pow_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n sum_out_2 = _C_ops.reduce_sum(sum_out_1, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n return _C_ops.pow(sum_out_2, 'factor', float(1. / porder))\n\n block = LayerHelper('norm', **locals())\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_1 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_2 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='pow',\n inputs={'X': input},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out_1},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_sum',\n inputs={'X': sum_out_1},\n outputs={'Out': sum_out_2},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='pow',\n inputs={'X': sum_out_2},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n def svd_norm(input, porder, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the matrix norm, which is related to singular values, of a matrix\n or batches of matrices, including nuclear norm, 2-norm and (-2)-norm.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n u, s, vh = svd(input, full_matrices=False)\n\n if paddle.in_dynamic_mode():\n if porder == \"nuc\":\n return _C_ops.reduce_sum(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n max_out = _C_ops.reduce_max(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n min_out = _C_ops.reduce_min(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n if porder == 2:\n return _C_ops.elementwise_div(max_out, min_out, 'aixs', axis,\n 'use_mkldnn', False)\n if porder == -2:\n return _C_ops.elementwise_div(min_out, max_out, 'aixs', axis,\n 'use_mkldnn', False)\n\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n if porder == \"nuc\":\n block.append_op(\n type='reduce_sum',\n inputs={'X': s},\n outputs={'Out': out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n max_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n min_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_min',\n inputs={'X': s},\n outputs={'Out': min_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': max_out,\n 'Y': min_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n if porder == -2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': min_out,\n 'Y': max_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n\n def empty_tensor(input, shape):\n if paddle.in_dynamic_mode():\n return input.reshape(shape)\n raise ValueError(\"only support x is nonempty tensor in static mode\")\n\n x_shape = list(x.shape)\n if not len(x_shape) >= 2:\n raise ValueError(\"input should be a matrix or batches of matrices, \" +\n \"but the dimention of received input is {}\".format(\n len(x_shape)))\n if p == None:\n p = 2\n x_size = 0 if (0 in x_shape) else 1\n if p in (\"fro\", \"nuc\", 1, -1, np.inf, -np.inf):\n if x_shape[len(x_shape) - 1] == x_shape[len(x_shape) - 2]:\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n x_inv = x.inverse()\n if p == \"fro\":\n return fro_norm(x) * fro_norm(x_inv)\n if p == \"nuc\":\n return svd_norm(x, p) * svd_norm(x_inv, p)\n if p in (1, -1):\n return mat_norm(\n x, porder=p, axis=[-2]) * mat_norm(\n x_inv, porder=p, axis=[-2])\n if p in (np.inf, -np.inf):\n return mat_norm(\n x, porder=p, axis=[-1]) * mat_norm(\n x_inv, porder=p, axis=[-1])\n else:\n raise ValueError(\"only support p is {} when input is a \".format(p) +\n \"square matrix or batches of square matrices\")\n elif p in (2, -2):\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n return svd_norm(x, porder=p)\n else:\n raise ValueError(\n \"unsupported {} for p, only supporting ('fro', 'nuc', \".format(\n p) + \"1, -1, 2, -2, inf, -inf) or none\")\n\n\ndef dot(x, y, name=None):\n \"\"\"\n This operator calculates inner product for vectors.\n\n .. note::\n Support 1-d and 2-d Tensor. When it is 2d, the first dimension of this matrix\n is the batch dimension, which means that the vectors of multiple batches are dotted.\n\n Parameters:\n x(Tensor): 1-D or 2-D ``Tensor``. Its dtype should be ``float32``, ``float64``, ``int32``, ``int64``\n y(Tensor): 1-D or 2-D ``Tensor``. Its dtype soulde be ``float32``, ``float64``, ``int32``, ``int64``\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: the calculated result Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x_data = np.random.uniform(0.1, 1, [10]).astype(np.float32)\n y_data = np.random.uniform(1, 3, [10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.dot(x, y)\n print(z)\n\n \"\"\"\n op_type = 'dot'\n # skip var type check in dygraph mode to improve efficiency\n if paddle.in_dynamic_mode():\n op = getattr(_C_ops, op_type)\n return op(x, y)\n\n assert x is not None, 'x cannot be None in {}'.format(op_type)\n assert y is not None, 'y cannot be None in {}'.format(op_type)\n\n check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n check_variable_and_dtype(y, 'y', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n\n helper = LayerHelper(op_type, **locals())\n if name is None:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n else:\n out = helper.create_variable(\n name=name, dtype=x.dtype, persistable=False)\n helper.append_op(\n type=\"dot\", inputs={'X': x,\n 'Y': y}, attrs={}, outputs={\"Out\": out})\n return out\n\n\ndef cov(x, rowvar=True, ddof=True, fweights=None, aweights=None, name=None):\n \"\"\"\n Estimate the covariance matrix of the input variables, given data and weights.\n\n A covariance matrix is a square matrix, indicate the covariance of each pair variables in the input matrix.\n For example, for an N-dimensional samples X=[x1,x2,…xN]T, then the covariance matrix \n element Cij is the covariance of xi and xj. The element Cii is the variance of xi itself.\n\n Parameters:\n x(Tensor): A N-D(N<=2) Tensor containing multiple variables and observations. By default, each row of x represents a variable. Also see rowvar below.\n rowvar(Bool, optional): If rowvar is True (default), then each row represents a variable, with observations in the columns. Default: True\n ddof(Bool, optional): If ddof=True will return the unbiased estimate, and ddof=False will return the simple average. Default: True\n fweights(Tensor, optional): 1-D Tensor of integer frequency weights; The number of times each observation vector should be repeated. Default: None\n aweights(Tensor, optional): 1-D Tensor of observation vector weights. How important of the observation vector, larger data means this element is more important. Default: None\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: The covariance matrix Tensor of the variables.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n\n xt = paddle.rand((3,4))\n paddle.linalg.cov(xt)\n\n '''\n Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n [[0.07918842, 0.06127326, 0.01493049],\n [0.06127326, 0.06166256, 0.00302668],\n [0.01493049, 0.00302668, 0.01632146]])\n '''\n \"\"\"\n op_type = 'cov'\n if len(x.shape) > 2 or len(x.shape) < 1:\n raise ValueError(\n \"Input(x) only support N-D (1<=N<=2) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cov')\n nx = x\n if len(x.shape) == 1:\n nx = x.reshape((1, -1))\n if not rowvar and nx.shape[0] != 1:\n nx = nx.t()\n w = None\n observation_num = nx.shape[1]\n if fweights is not None:\n w = fweights.astype(nx.dtype)\n if len(w.shape) > 1:\n raise ValueError(\n \"Input(fweights) only support N-D (N<=1) tensor in cov, but received \"\n \"shape of Input(input) is %s.\" % len(fweights.shape))\n if fweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(fweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(fweights) is {}.\".format(observation_num,\n fweights.shape[0]))\n if fweights.min() < 0:\n raise ValueError(\n \"The value of Input(fweights) cannot be negtive, but received \"\n \"min of Input(fweights) is {}.\".format(fweights.min()))\n if not paddle.all(fweights == paddle.round(fweights.astype('float64'))):\n raise ValueError(\"Input(fweights) must be integer \")\n\n if aweights is not None:\n aw = aweights.astype(nx.dtype)\n if len(aw.shape) > 1:\n raise ValueError(\n \"Input(aweights) only support N-D (N<=1) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(aweights.shape))\n check_variable_and_dtype(aweights, 'dtype', ['float32', 'float64'],\n 'cov')\n if aweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(aweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(aweights) is {}.\".format(observation_num,\n aweights.shape[0]))\n if aweights.min() < 0:\n raise ValueError(\n \"The value of Input(aweights) cannot be negtive, but received \"\n \"min of Input(aweights) is {}.\".format(aweights.min()))\n if w is not None:\n w = w * aw\n else:\n w = aw\n\n w_sum = paddle.to_tensor(observation_num, dtype=nx.dtype)\n if fweights is not None or aweights is not None:\n w_sum = w.sum()\n if w_sum.item() == 0:\n raise ValueError(\"The sum of weights is zero, can't be normalized.\")\n\n if w is not None:\n nx_w = nx * w\n avg = (nx_w).sum(axis=1) / w_sum\n else:\n avg = nx.sum(axis=1) / w_sum\n nx_w = nx\n\n if w is not None and aweights is not None and ddof == True:\n norm_factor = w_sum - (w * aweights).sum() / w_sum\n else:\n norm_factor = w_sum - ddof\n if norm_factor <= 0:\n norm_factor = paddle.to_tensor(0, dtype=nx.dtype)\n nx = nx - avg.unsqueeze(1)\n xxt = paddle.mm(nx, nx_w.t().conj())\n cov = paddle.divide(xxt, norm_factor).squeeze()\n return cov\n\n\ndef t(input, name=None):\n \"\"\"\n Transpose <=2-D tensor.\n 0-D and 1-D tensors are returned as it is and 2-D tensor is equal to\n the paddle.transpose function which perm dimensions set 0 and 1.\n\n Args:\n input (Tensor): The input Tensor. It is a N-D (N<=2) Tensor of data types float16, float32, float64, int32.\n name(str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`\n Returns:\n Tensor: A transposed n-D Tensor, with data type being float16, float32, float64, int32, int64.\n\n For Example:\n\n .. code-block:: text\n\n # Example 1 (0-D tensor)\n x = tensor([0.79])\n paddle.t(x) = tensor([0.79])\n\n # Example 2 (1-D tensor)\n x = tensor([0.79, 0.84, 0.32])\n paddle.t(x) = tensor([0.79, 0.84, 0.32])\n\n # Example 3 (2-D tensor)\n x = tensor([0.79, 0.84, 0.32],\n [0.64, 0.14, 0.57])\n paddle.t(x) = tensor([0.79, 0.64],\n [0.84, 0.14],\n [0.32, 0.57])\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n x = paddle.ones(shape=[2, 3], dtype='int32')\n x_transposed = paddle.t(x)\n print(x_transposed.shape)\n # [3, 2]\n \"\"\"\n if len(input.shape) > 2:\n raise ValueError(\n \"Input(input) only support N-D (N<=2) tensor, but received \"\n \"length of Input(input) is %s. Perhaps you can use paddle.\"\n \"tensor.transpose() instead.\" % len(input.shape))\n if paddle.in_dynamic_mode():\n if len(input.shape) == 1:\n return input\n # 2-D tensor\n perm = [1, 0]\n out, _ = _C_ops.transpose2(input, 'axis', perm)\n return out\n\n check_variable_and_dtype(\n input, 'input', ['float16', 'float32', 'float64', 'int32',\n 'int64'], 'transpose')\n\n helper = LayerHelper('t', **locals())\n out = helper.create_variable_for_type_inference(input.dtype)\n input_shape = helper.create_variable_for_type_inference(input.dtype)\n if len(input.shape) == 1:\n out = input\n else:\n helper.append_op(\n type='transpose2',\n inputs={'X': [input]},\n outputs={'Out': [out],\n 'XShape': [input_shape]},\n attrs={'axis': [1, 0]})\n return out\n\n\ndef cross(x, y, axis=None, name=None):\n \"\"\"\n Computes the cross product between two tensors along an axis.\n\n Inputs must have the same shape, and the length of their axes should be equal to 3.\n If `axis` is not given, it defaults to the first axis found with the length 3.\n\n Args:\n x (Tensor): The first input tensor.\n y (Tensor): The second input tensor.\n axis (int, optional): The axis along which to compute the cross product. It defaults to the first axis found with the length 3.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor. A Tensor with same data type as `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0],\n [3.0, 3.0, 3.0]])\n y = paddle.to_tensor([[1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0]])\n\n z1 = paddle.cross(x, y)\n # [[-1. -1. -1.]\n # [ 2. 2. 2.]\n # [-1. -1. -1.]]\n\n z2 = paddle.cross(x, y, axis=1)\n # [[0. 0. 0.]\n # [0. 0. 0.]\n # [0. 0. 0.]]\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_cross(x, y, axis)\n else:\n if _in_legacy_dygraph():\n if axis is not None:\n return _C_ops.cross(x, y, 'dim', axis)\n else:\n return _C_ops.cross(x, y)\n else:\n helper = LayerHelper(\"cross\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n attrs = dict()\n attrs['dim'] = axis\n\n helper.append_op(\n type='cross',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef cholesky(x, upper=False, name=None):\n r\"\"\"\n Computes the Cholesky decomposition of one symmetric positive-definite\n matrix or batches of symmetric positive-definite matrice.\n\n If `upper` is `True`, the decomposition has the form :math:`A = U^{T}U` ,\n and the returned matrix :math:`U` is upper-triangular. Otherwise, the\n decomposition has the form :math:`A = LL^{T}` , and the returned matrix\n :math:`L` is lower-triangular.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[*, M, M]`,\n where * is zero or more batch dimensions, and matrices on the\n inner-most 2 dimensions all should be symmetric positive-definite.\n Its data type should be float32 or float64.\n upper (bool): The flag indicating whether to return upper or lower\n triangular matrices. Default: False.\n\n Returns:\n Tensor: A Tensor with same shape and data type as `x`. It represents \\\n triangular matrices generated by Cholesky decomposition.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n a = np.random.rand(3, 3)\n a_t = np.transpose(a, [1, 0])\n x_data = np.matmul(a, a_t) + 1e-03\n x = paddle.to_tensor(x_data)\n out = paddle.linalg.cholesky(x, upper=False)\n print(out)\n # [[1.190523 0. 0. ]\n # [0.9906703 0.27676893 0. ]\n # [1.25450498 0.05600871 0.06400121]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky(x, \"upper\", upper)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cholesky')\n check_type(upper, 'upper', bool, 'cholesky')\n helper = LayerHelper('cholesky', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='cholesky',\n inputs={'X': [x]},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef matrix_rank(x, tol=None, hermitian=False, name=None):\n r\"\"\"\n Computes the rank of a matrix.\n\n The rank of a matrix is the number of singular values that are greater than the specified `tol` threshold when hermitian=False,\n or the number of eigenvalues in absolute value that are greater than the specified `tol` threshold when hermitian=True.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., m, n]`, where `...` is zero or more batch dimensions. If `x` is a batch\n of matrices then the output has the same batch dimensions. The data type of `x` should be float32 or float64.\n tol (float,Tensor,optional): the tolerance value. Default: None. If `tol` is not specified, and `sigma` is the largest\n singular value (or eigenvalues in absolute value), and `eps` is the epsilon value for the dtype of `x`, then `tol` is computed\n with formula `tol=sigma * max(m,n) * eps`. Note that if `x` is a batch of matrices, `tol` is computed this way for every batch.\n hermitian (bool,optional): indicates whether `x` is Hermitian. Default: False. When hermitian=True, `x` is assumed to be Hermitian,\n enabling a more efficient method for finding eigenvalues, but `x` is not checked inside the function. Instead, We just use\n the lower triangular of the matrix to compute.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: Rank of tensor x.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n a = paddle.eye(10)\n b = paddle.linalg.matrix_rank(a)\n print(b)\n # b = [10]\n\n c = paddle.ones(shape=[3, 4, 5, 5])\n d = paddle.linalg.matrix_rank(c, tol=0.01, hermitian=True)\n print(d)\n # d = [[1, 1, 1, 1],\n # [1, 1, 1, 1],\n # [1, 1, 1, 1]]\n\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if tol is None:\n tol_tensor = None\n tol_attr = 0.0\n use_default_tol = True\n elif isinstance(tol, Variable):\n if tol.dtype != x.dtype:\n tol_tensor = cast(tol, x.dtype)\n else:\n tol_tensor = tol\n tol_attr = 0.0\n use_default_tol = False\n else:\n tol_tensor = None\n tol_attr = float(tol)\n use_default_tol = False\n return _C_ops.matrix_rank(x, tol_tensor, \"tol\", tol_attr, 'hermitian',\n hermitian, 'use_default_tol', use_default_tol)\n\n inputs = {}\n attrs = {}\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'matrix_rank')\n inputs['X'] = x\n if tol is None:\n attrs['use_default_tol'] = True\n elif isinstance(tol, Variable):\n check_variable_and_dtype(tol, 'tol', ['float32'], 'matrix_rank')\n attrs['use_default_tol'] = False\n if tol.dtype != x.dtype:\n inputs['TolTensor'] = cast(tol, x.dtype)\n else:\n inputs['TolTensor'] = tol\n else:\n check_type(tol, 'tol', float, 'matrix_rank')\n attrs['use_default_tol'] = False\n attrs['tol'] = tol\n check_type(hermitian, 'hermitian', bool, 'matrix_rank')\n attrs['hermitian'] = hermitian\n\n helper = LayerHelper('matrix_rank', **locals())\n out = helper.create_variable_for_type_inference(dtype='int32')\n helper.append_op(\n type='matrix_rank', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef bmm(x, y, name=None):\n \"\"\"\n Applies batched matrix multiplication to two tensors.\n\n Both of the two input tensors must be three-dementional and share the same batch size.\n\n if x is a (b, m, k) tensor, y is a (b, k, n) tensor, the output will be a (b, m, n) tensor.\n\n Args:\n x (Tensor): The input Tensor.\n y (Tensor): The input Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The product Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n # In imperative mode:\n # size x: (2, 2, 3) and y: (2, 3, 2)\n x = paddle.to_tensor([[[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0]],\n [[3.0, 3.0, 3.0],\n [4.0, 4.0, 4.0]]])\n y = paddle.to_tensor([[[1.0, 1.0],[2.0, 2.0],[3.0, 3.0]],\n [[4.0, 4.0],[5.0, 5.0],[6.0, 6.0]]])\n out = paddle.bmm(x, y)\n #output size: (2, 2, 2)\n #output value:\n #[[[6.0, 6.0],[12.0, 12.0]],[[45.0, 45.0],[60.0, 60.0]]]\n out_np = out.numpy()\n\n \"\"\"\n x_shape = x.shape\n y_shape = y.shape\n if not len(x_shape) == len(y_shape) == 3:\n raise ValueError(\n \"x and y should be 3-dimensional. But received x's dimention: {}, y's dimention: {}\".\n format(x_shape, y_shape))\n if x_shape[2] != y_shape[1]:\n raise ValueError(\n \"x's width must be equal with y's height. But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n if x_shape[0] != y_shape[0]:\n raise ValueError(\n \"x's batch (shape[0]) must be equal with y's batch (shape[0]). But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.bmm(x, y)\n\n helper = LayerHelper('bmm', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='bmm', inputs={'X': x, 'Y': y}, outputs={'Out': out})\n return out\n\n\ndef histogram(input, bins=100, min=0, max=0, name=None):\n \"\"\"\n Computes the histogram of a tensor. The elements are sorted into equal width bins between min and max.\n If min and max are both zero, the minimum and maximum values of the data are used.\n\n Args:\n input (Tensor): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor\n should be float32, float64, int32, int64.\n bins (int): number of histogram bins\n min (int): lower end of the range (inclusive)\n max (int): upper end of the range (inclusive)\n\n Returns:\n Tensor: data type is int64, shape is (nbins,).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n inputs = paddle.to_tensor([1, 2, 1])\n result = paddle.histogram(inputs, bins=4, min=0, max=3)\n print(result) # [0, 2, 1, 0]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.histogram(input, \"bins\", bins, \"min\", min, \"max\", max)\n\n helper = LayerHelper('histogram', **locals())\n check_variable_and_dtype(\n input, 'X', ['int32', 'int64', 'float32', 'float64'], 'histogram')\n out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)\n helper.append_op(\n type='histogram',\n inputs={'X': input},\n outputs={'Out': out},\n attrs={'bins': bins,\n 'min': min,\n 'max': max})\n return out\n\n\ndef bincount(x, weights=None, minlength=0, name=None):\n \"\"\"\n Computes frequency of each value in the input tensor. \n\n Args:\n x (Tensor): A Tensor with non-negative integer. Should be 1-D tensor.\n weights (Tensor, optional): Weight for each value in the input tensor. Should have the same shape as input. Default is None.\n minlength (int, optional): Minimum number of bins. Should be non-negative integer. Default is 0.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor of frequency.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([1, 2, 1, 4, 5])\n result1 = paddle.bincount(x)\n print(result1) # [0, 2, 1, 0, 1, 1]\n\n w = paddle.to_tensor([2.1, 0.4, 0.1, 0.5, 0.5])\n result2 = paddle.bincount(x, weights=w)\n print(result2) # [0., 2.19999981, 0.40000001, 0., 0.50000000, 0.50000000]\n \"\"\"\n if x.dtype not in [paddle.int32, paddle.int64]:\n raise TypeError(\"Elements in Input(x) should all be integers\")\n\n if paddle.in_dynamic_mode():\n return _C_ops.bincount(x, weights, \"minlength\", minlength)\n\n helper = LayerHelper('bincount', **locals())\n\n check_variable_and_dtype(x, 'X', ['int32', 'int64'], 'bincount')\n\n if weights is not None:\n check_variable_and_dtype(weights, 'Weights',\n ['int32', 'int64', 'float32', 'float64'],\n 'bincount')\n out = helper.create_variable_for_type_inference(dtype=weights.dtype)\n else:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='bincount',\n inputs={'X': x,\n 'Weights': weights},\n outputs={'Out': out},\n attrs={'minlength': minlength})\n return out\n\n\ndef mv(x, vec, name=None):\n \"\"\"\n Performs a matrix-vector product of the matrix x and the vector vec.\n\n Args:\n x (Tensor): A tensor with shape :math:`[M, N]` , The data type of the input Tensor x\n should be one of float32, float64.\n vec (Tensor): A tensor with shape :math:`[N]` , The data type of the input Tensor x\n should be one of float32, float64.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor which is producted by x and vec.\n\n Examples:\n .. code-block:: python\n\n # x: [M, N], vec: [N]\n # paddle.mv(x, vec) # out: [M]\n\n import numpy as np\n import paddle\n\n x_data = np.array([[2, 1, 3], [3, 0, 1]]).astype(\"float64\")\n x = paddle.to_tensor(x_data)\n vec_data = np.array([3, 5, 1])\n vec = paddle.to_tensor(vec_data).astype(\"float64\")\n out = paddle.mv(x, vec)\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_mv(x, vec)\n else:\n if _in_legacy_dygraph():\n out = _C_ops.mv(x, vec)\n return out\n else:\n\n def __check_input(x, vec):\n var_names = {'x': x, 'vec': vec}\n for name, val in var_names.items():\n check_variable_and_dtype(val, name, ['float32', 'float64'],\n 'mv')\n x_shape = list(x.shape)\n vec_shape = list(vec.shape)\n if len(x_shape) != 2:\n raise ValueError(\n \"x should be 2-dimensional. But received x's dimention: {}\".\n format(x_shape))\n if len(vec_shape) != 1:\n raise ValueError(\n \"vec should be 1-dimensional. But received vec's dimention: {}\".\n format(vec_shape))\n\n __check_input(x, vec)\n\n helper = LayerHelper('mv', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='mv', inputs={'X': x,\n 'Vec': vec}, outputs={'Out': out})\n return out\n\n\ndef det(x, name=None):\n \"\"\"\n Calculates determinant value of a square matrix or batches of square matrices.\n Args:\n x (Tensor): input (Tensor): the input matrix of size `(n, n)` or the batch of matrices of size\n `(*, n, n)` where `*` is one or more batch dimensions.\n Returns:\n y (Tensor):the determinant value of a square matrix or batches of square matrices.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.det(x)\n\n print(A)\n\n # [ 0.02547996, 2.52317095, -6.15900707])\n\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.determinant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'det')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('determinant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='determinant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef slogdet(x, name=None):\n \"\"\"\n Calculates the sign and natural logarithm of the absolute value of a square matrix's or batches square matrices' determinant.\n The determinant can be computed with ``sign * exp(logabsdet)\n\n Supports input of float, double\n\n Note that for matrices that have zero determinant, this returns ``(0, -inf)``\n Args:\n x (Tensor): the batch of matrices of size :math:`(*, n, n)`\n where math:`*` is one or more batch dimensions.\n\n Returns:\n y (Tensor): A tensor containing the sign of the determinant and the natural logarithm\n of the absolute value of determinant, respectively.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.slogdet(x)\n\n print(A)\n\n # [[ 1. , 1. , -1. ],\n # [-0.98610914, -0.43010661, -0.10872950]])\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.slogdeterminant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'slogdet')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('slogdeterminant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='slogdeterminant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef svd(x, full_matrices=False, name=None):\n r\"\"\"\n Computes the singular value decomposition of one matrix or a batch of regular matrices.\n\n Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies:\n\n .. math::\n X = U * diag(S) * VT\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., N, M]`,\n where `...` is zero or more batch dimensions. N and M can be arbitraty\n positive number. Note that if x is sigular matrices, the grad is numerical\n instable. The data type of x should be float32 or float64.\n full_matrices (bool): A flag to control the behavor of svd.\n If full_matrices = True, svd op will compute full U and V matrics,\n which means shape of U is `[..., N, N]`, shape of V is `[..., M, M]`. K = min(M, N).\n If full_matrices = False, svd op will use a economic method to store U and V.\n which means shape of U is `[..., N, K]`, shape of V is `[..., M, K]`. K = min(M, N).\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple of 3 tensors: (U, S, VH). VH is the conjugate transpose of V. S is the singlar value vectors of matrics with shape `[..., K]`\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 2.0], [1.0, 3.0], [4.0, 6.0]]).astype('float64')\n x = x.reshape([3, 2])\n u, s, vh = paddle.linalg.svd(x)\n print (u)\n #U = [[ 0.27364809, -0.21695147 ],\n # [ 0.37892198, -0.87112408 ],\n # [ 0.8840446 , 0.44053933 ]]\n\n print (s)\n #S = [8.14753743, 0.78589688]\n print (vh)\n #VT= [[ 0.51411221, 0.85772294],\n # [ 0.85772294, -0.51411221]]\n\n # one can verify : U * S * VT == X\n # U * UH == I\n # V * VH == I\n \"\"\"\n\n if paddle.in_dynamic_mode():\n return _C_ops.svd(x, 'full_matrices', full_matrices)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'svd')\n check_type(full_matrices, 'full_matrices', bool, 'svd')\n helper = LayerHelper('svd', **locals())\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n vh = helper.create_variable_for_type_inference(dtype=x.dtype)\n s = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['full_matrices'] = full_matrices\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vh,\n 'S': s},\n attrs=attrs, )\n return u, s, vh\n\n\ndef matrix_power(x, n, name=None):\n r\"\"\"\n Computes the n-th power of a square matrix or a batch of square matrices.\n\n Let :math:`X` be a sqaure matrix or a batch of square matrices, :math:`n` be\n an exponent, the equation should be:\n\n .. math::\n Out = X ^ {n}\n\n Specifically,\n\n - If `n > 0`, it returns the matrix or a batch of matrices raised to the power\n of `n`.\n\n - If `n = 0`, it returns the identity matrix or a batch of identity matrices.\n\n - If `n < 0`, it returns the inverse of each matrix (if invertible) raised to\n the power of `abs(n)`.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices to be raised\n to power `n`. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n n (int): The exponent. It can be any positive, negative integer or zero.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The n-th power of the matrix (or the batch of matrices) `x`. Its\n data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1, 2, 3],\n [1, 4, 9],\n [1, 8, 27]], dtype='float64')\n print(paddle.linalg.matrix_power(x, 2))\n # [[6. , 34. , 102.],\n # [14. , 90. , 282.],\n # [36. , 250., 804.]]\n\n print(paddle.linalg.matrix_power(x, 0))\n # [[1., 0., 0.],\n # [0., 1., 0.],\n # [0., 0., 1.]]\n\n print(paddle.linalg.matrix_power(x, -2))\n # [[ 12.91666667, -12.75000000, 2.83333333 ],\n # [-7.66666667 , 8. , -1.83333333 ],\n # [ 1.80555556 , -1.91666667 , 0.44444444 ]]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.matrix_power(x, \"n\", n)\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'matrix_power')\n check_type(n, 'n', int, 'matrix_power')\n helper = LayerHelper('matrix_power', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matrix_power',\n inputs={'X': x},\n outputs={'Out': out},\n attrs={'n': n})\n return out\n\n\ndef qr(x, mode=\"reduced\", name=None):\n r\"\"\"\n Computes the QR decomposition of one matrix or batches of matrice (backward is unsupported now).\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., M, N]`,\n where ... is zero or more batch dimensions. M and N can be arbitrary\n positive number. The data type of x should be float32 or float64. \n mode (str, optional): A flag to control the behavior of qr, the default is \"reduced\". \n Suppose x's shape is `[..., M, N]` and denoting `K = min(M, N)`:\n If mode = \"reduced\", qr op will return reduced Q and R matrices, \n which means Q's shape is `[..., M, K]` and R's shape is `[..., K, N]`.\n If mode = \"complete\", qr op will return complete Q and R matrices, \n which means Q's shape is `[..., M, M]` and R's shape is `[..., M, N]`.\n If mode = \"r\", qr op will only return reduced R matrix, which means\n R's shape is `[..., K, N]`.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n If mode = \"reduced\" or mode = \"complete\", qr will return a two tensor-tuple, which represents Q and R. \n If mode = \"r\", qr will return a tensor which represents R.\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n q, r = paddle.linalg.qr(x)\n print (q)\n print (r)\n\n # Q = [[-0.16903085, 0.89708523],\n # [-0.50709255, 0.27602622],\n # [-0.84515425, -0.34503278]])\n\n # R = [[-5.91607978, -7.43735744],\n # [ 0. , 0.82807867]])\n \n # one can verify : X = Q * R ; \n \"\"\"\n if paddle.in_dynamic_mode():\n q, r = _C_ops.qr(x, 'mode', mode)\n if mode == \"r\":\n return r\n else:\n return q, r\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'qr')\n check_type(mode, 'mode', str, 'qr')\n helper = LayerHelper('qr', **locals())\n q = helper.create_variable_for_type_inference(dtype=x.dtype)\n r = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['mode'] = mode\n helper.append_op(\n type='qr', inputs={'X': [x]}, outputs={'Q': q,\n 'R': r}, attrs=attrs)\n if mode == \"r\":\n return r\n else:\n return q, r\n\n\ndef lu(x, pivot=True, get_infos=False, name=None):\n r\"\"\"\n Computes the LU factorization of an N-D(N>=2) matrix x. \n\n Returns the LU factorization(inplace x) and Pivots. low triangular matrix L and \n upper triangular matrix U are combined to a single LU matrix.\n\n Pivoting is done if pivot is set to True.\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n # return ones\n\n Args:\n\n X (Tensor): the tensor to factor of N-dimensions(N>=2).\n\n pivot (bool, optional): controls whether pivoting is done. Default: True.\n\n get_infos (bool, optional): if set to True, returns an info IntTensor. Default: False.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n factorization (Tensor): LU matrix, the factorization of input X.\n\n pivots (IntTensor): the pivots of size(∗(N-2), min(m,n)). `pivots` stores all the \n intermediate transpositions of rows. The final permutation `perm` could be \n reconstructed by this, details refer to upper example.\n\n infos (IntTensor, optional): if `get_infos` is `True`, this is a tensor of size (∗(N-2)) \n where non-zero values indicate whether factorization for the matrix or each minibatch \n has succeeded or failed.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n \n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n if paddle.in_dynamic_mode():\n LU, Piv, Info = _C_ops.lu(x, 'pivots', pivot)\n if get_infos:\n return LU, Piv, Info\n else:\n return LU, Piv\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu')\n helper = LayerHelper('lu', **locals())\n lu = helper.create_variable_for_type_inference(dtype=x.dtype)\n p = helper.create_variable_for_type_inference(dtype='int')\n info = helper.create_variable_for_type_inference(dtype='int')\n attrs = dict()\n attrs['pivots'] = pivot\n helper.append_op(\n type='lu',\n inputs={'X': x},\n outputs={'Out': lu,\n 'Pivots': p,\n 'Infos': info},\n attrs=attrs)\n if get_infos:\n return lu, p, info\n else:\n return lu, p\n\n\ndef lu_unpack(x, y, unpack_ludata=True, unpack_pivots=True, name=None):\n r\"\"\"\n Unpack L U and P to single matrix tensor . \n unpack L and U matrix from LU, unpack permutation matrix P from Pivtos .\n\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n\n\n Args:\n x (Tensor): The LU tensor get from paddle.linalg.lu, which is combined by L and U.\n\n y (Tensor): Pivots get from paddle.linalg.lu.\n\n unpack_ludata (bool,optional): whether to unpack L and U from x. Default: True.\n\n unpack_pivots (bool, optional): whether to unpack permutation matrix P from Pivtos. Default: True.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n P (Tensor): Permutation matrix P of lu factorization.\n\n L (Tensor): The lower triangular matrix tensor of lu factorization.\n\n U (Tensor): The upper triangular matrix tensor of lu factorization.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n\n if paddle.in_dynamic_mode():\n P, L, U = _C_ops.lu_unpack(x, y, 'unpack_ludata', unpack_ludata,\n 'unpack_pivots', unpack_pivots)\n return P, L, U\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu_unpack')\n helper = LayerHelper('lu_unpack', **locals())\n p = helper.create_variable_for_type_inference(dtype=x.dtype)\n l = helper.create_variable_for_type_inference(dtype=x.dtype)\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n attrs = dict()\n attrs['unpack_ludata'] = unpack_ludata\n attrs['unpack_pivots'] = unpack_pivots\n helper.append_op(\n type='lu_unpack',\n inputs={'X': x,\n 'Pivots': y},\n outputs={'Pmat': p,\n 'L': l,\n 'U': u},\n attrs=attrs)\n return p, l, u\n\n\ndef eig(x, name=None):\n \"\"\"\n This API performs the eigenvalue decomposition of a square matrix or a batch of square matrices.\n\n .. note::\n If the matrix is a Hermitian or a real symmetric matrix, please use :ref:`paddle.linalg.eigh` instead, which is much faster.\n If only eigenvalues is needed, please use :ref:`paddle.linalg.eigvals` instead.\n If the matrix is of any shape, please use :ref:`paddle.linalg.svd`.\n This API is only supported on CPU device.\n The output datatype is always complex for both real and complex input.\n\n Args:\n x (Tensor): A tensor with shape math:`[*, N, N]`, The data type of the x should be one of ``float32``,\n ``float64``, ``compplex64`` or ``complex128``.\n name (str, optional): The default value is `None`. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Eigenvalues(Tensors): A tensor with shape math:`[*, N]` refers to the eigen values.\n Eigenvectors(Tensors): A tensor with shape math:`[*, N, N]` refers to the eigen vectors.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n paddle.device.set_device(\"cpu\")\n\n x_data = np.array([[1.6707249, 7.2249975, 6.5045543],\n [9.956216, 8.749598, 6.066444 ],\n [4.4251957, 1.7983172, 0.370647 ]]).astype(\"float32\")\n x = paddle.to_tensor(x_data)\n w, v = paddle.linalg.eig(x)\n print(w)\n # Tensor(shape=[3, 3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [[(-0.5061363550800655+0j) , (-0.7971760990842826+0j) ,\n # (0.18518077798279986+0j)],\n # [(-0.8308237755993192+0j) , (0.3463813401919749+0j) ,\n # (-0.6837005269141947+0j) ],\n # [(-0.23142567697893396+0j), (0.4944999840400175+0j) ,\n # (0.7058765252952796+0j) ]])\n\n print(v)\n # Tensor(shape=[3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [ (16.50471283351188+0j) , (-5.5034820550763515+0j) ,\n # (-0.21026087843552282+0j)])\n \"\"\"\n if paddle.in_dynamic_mode():\n w, v = _C_ops.eig(x)\n return w, v\n\n check_variable_and_dtype(\n x, 'X', ['float32', 'float64', 'complex64', 'complex128'], 'eig')\n helper = LayerHelper('eig', **locals())\n\n w = helper.create_variable_for_type_inference(x.dtype)\n v = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {'X': x}\n outputs = {'Eigenvalues': w, 'Eigenvectors': v}\n helper.append_op(type='eig', inputs=inputs, outputs=outputs)\n\n return w, v\n\n\ndef eigvals(x, name=None):\n \"\"\"\n Compute the eigenvalues of one or more general matrices.\n\n Warning:\n The gradient kernel of this operator does not yet developed.\n If you need back propagation through this operator, please replace it with paddle.linalg.eig.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices whose eigenvalues will be computed.\n Its shape should be `[*, M, M]`, where `*` is zero or more batch dimensions.\n Its data type should be float32, float64, complex64, or complex128.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n Tensor: A tensor containing the unsorted eigenvalues which has the same batch dimensions with `x`.\n The eigenvalues are complex-valued even when `x` is real.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n paddle.seed(1234)\n\n x = paddle.rand(shape=[3, 3], dtype='float64')\n # [[0.02773777, 0.93004224, 0.06911496],\n # [0.24831591, 0.45733623, 0.07717843],\n # [0.48016702, 0.14235102, 0.42620817]])\n\n print(paddle.linalg.eigvals(x))\n # [(-0.27078833542132674+0j), (0.29962280156230725+0j), (0.8824477020120244+0j)] #complex128\n \"\"\"\n\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64',\n 'complex128'], 'eigvals')\n\n x_shape = list(x.shape)\n if len(x_shape) < 2:\n raise ValueError(\n \"The dimension of Input(x) should be at least 2, but received x's dimention = {}, x's shape = {}\".\n format(len(x_shape), x_shape))\n\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The last two dimensions of Input(x) should be equal, but received x's shape = {}\".\n format(x_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.eigvals(x)\n\n helper = LayerHelper('eigvals', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='eigvals', inputs={'X': x}, outputs={'Out': out})\n return out\n\n\ndef multi_dot(x, name=None):\n \"\"\"\n Multi_dot is an operator that calculates multiple matrix multiplications.\n\n Supports inputs of float16(only GPU support), float32 and float64 dtypes. This function does not\n support batched inputs.\n\n The input tensor in [x] must be 2-D except for the first and last can be 1-D.\n If the first tensor is a 1-D vector of shape(n, ) it is treated as row vector\n of shape(1, n), similarly if the last tensor is a 1D vector of shape(n, ), it\n is treated as a column vector of shape(n, 1).\n\n If the first and last tensor are 2-D matrix, then the output is also 2-D matrix,\n otherwise the output is a 1-D vector.\n\n Multi_dot will select the lowest cost multiplication order for calculation. The\n cost of multiplying two matrices with shapes (a, b) and (b, c) is a * b * c.\n Given matrices A, B, C with shapes (20, 5), (5, 100), (100, 10) respectively,\n we can calculate the cost of different multiplication orders as follows:\n - Cost((AB)C) = 20x5x100 + 20x100x10 = 30000\n - Cost(A(BC)) = 5x100x10 + 20x5x10 = 6000\n\n In this case, multiplying B and C first, then multiply A, which is 5 times faster\n than sequential calculation.\n\n Args:\n x ([Tensor]): The input tensors which is a list Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # A * B\n A_data = np.random.random([3, 4]).astype(np.float32)\n B_data = np.random.random([4, 5]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n out = paddle.linalg.multi_dot([A, B])\n print(out.numpy().shape)\n # [3, 5]\n\n # A * B * C\n A_data = np.random.random([10, 5]).astype(np.float32)\n B_data = np.random.random([5, 8]).astype(np.float32)\n C_data = np.random.random([8, 7]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n C = paddle.to_tensor(C_data)\n out = paddle.linalg.multi_dot([A, B, C])\n print(out.numpy().shape)\n # [10, 7]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.multi_dot(x)\n\n check_type(x, 'x', (list, tuple), 'multi_dot')\n for id, item in enumerate(x):\n check_variable_and_dtype(item, 'x[' + str(id) + ']',\n ['float16', 'float32', 'float64'], 'multi_dot')\n if item.dtype != x[0].dtype:\n raise TypeError(\n \"All the Tensors in the input must have the same data type.\")\n\n helper = LayerHelper('multi_dot', **locals())\n dtype = helper.input_dtype(input_param_name='x')\n out = helper.create_variable_for_type_inference(dtype)\n helper.append_op(type='multi_dot', inputs={\"X\": x}, outputs={\"Out\": out})\n return out\n\n\n# MASKED: eigh function (lines 2245-2311)\n\n\ndef pinv(x, rcond=1e-15, hermitian=False, name=None):\n r\"\"\"\n Calculate pseudo inverse via SVD(singular value decomposition)\n of one matrix or batches of regular matrix.\n\n .. math::\n\n if hermitian == False:\n x = u * s * vt (SVD)\n out = v * 1/s * ut\n else:\n x = u * s * ut (eigh)\n out = u * 1/s * u.conj().transpose(-2,-1)\n\n If x is hermitian or symmetric matrix, svd will be replaced with eigh.\n\n Args:\n x(Tensor): The input tensor. Its shape should be (*, m, n)\n where * is zero or more batch dimensions. m and n can be\n arbitraty positive number. The data type of x should be\n float32 or float64 or complex64 or complex128. When data\n type is complex64 or cpmplex128, hermitian should be set\n True.\n\n rcond(Tensor, optional): the tolerance value to determine\n when is a singular value zero. Defalut:1e-15.\n\n hermitian(bool, optional): indicates whether x is Hermitian\n if complex or symmetric if real. Default: False.\n\n name(str|None): A name for this layer(optional). If set None,\n the layer will be named automatically.\n\n Returns:\n Tensor: The tensor with same data type with x. it represents\n pseudo inverse of x. Its shape should be (*, n, m).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.arange(15).reshape((3, 5)).astype('float64')\n input = paddle.to_tensor(x)\n out = paddle.linalg.pinv(input)\n print(input)\n print(out)\n\n # input:\n # [[0. , 1. , 2. , 3. , 4. ],\n # [5. , 6. , 7. , 8. , 9. ],\n # [10., 11., 12., 13., 14.]]\n\n # out:\n # [[-0.22666667, -0.06666667, 0.09333333],\n # [-0.12333333, -0.03333333, 0.05666667],\n # [-0.02000000, 0.00000000, 0.02000000],\n # [ 0.08333333, 0.03333333, -0.01666667],\n # [ 0.18666667, 0.06666667, -0.05333333]]\n\n # one can verify : x * out * x = x ;\n # or out * x * out = x ;\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if not hermitian:\n # combine svd and matmul op\n u, s, vt = _C_ops.svd(x, 'full_matrices', False)\n max_singular_val = _C_ops.reduce_max(s, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=x.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=x.dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v, _ = _C_ops.transpose2(vt, 'axis', perm)\n\n out_1 = v * st\n out_2 = _C_ops.matmul_v2(out_1, u, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n # combine eigh and matmul op\n s, u = _C_ops.eigh(x, 'UPLO', 'L')\n s_abs = paddle.abs(s)\n max_singular_val = _C_ops.reduce_max(s_abs, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=s.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=s.dtype)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n out_1 = u * st\n u_conj = _C_ops.conj(u)\n out_2 = _C_ops.matmul_v2(out_1, u_conj, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n if not hermitian:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'pinv')\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(dtype)\n vt = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vt,\n 'S': s},\n attrs={'full_matrices': False}, )\n\n max_singular_val = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=dtype)\n st_shape = helper.create_variable_for_type_inference(dtype=dtype)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v = helper.create_variable_for_type_inference(dtype)\n v_shape = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='transpose2',\n inputs={'X': [vt]},\n outputs={'Out': [v],\n 'XShape': [v_shape]},\n attrs={'axis': perm})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': v,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n else:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64',\n 'complex128'], 'pinv')\n\n if dtype == paddle.complex128:\n s_type = 'float64'\n elif dtype == paddle.complex64:\n s_type = 'float32'\n else:\n s_type = dtype\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': s,\n 'Eigenvectors': u},\n attrs={'UPLO': 'L'})\n s_abs = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='abs', inputs={'X': s}, outputs={'Out': s_abs})\n max_singular_val = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s_abs},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=s_type)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=s_type)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s_type)\n cond_not_int = layers.cast(layers.logical_not(condition), s_type)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=s_type)\n st_shape = helper.create_variable_for_type_inference(dtype=s_type)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': u,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n u_conj = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='conj', inputs={'X': u}, outputs={'Out': [u_conj]})\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u_conj},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n\n\ndef solve(x, y, name=None):\n r\"\"\"\n Computes the solution of a square system of linear equations with a unique solution for input 'X' and 'Y'.\n Let :math: `X` be a sqaure matrix or a batch of square matrices, :math:`Y` be\n a vector/matrix or a batch of vectors/matrices, the equation should be:\n\n .. math::\n Out = X^-1 * Y\n Specifically,\n - This system of linear equations has one solution if and only if input 'X' is invertible.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): A vector/matrix or a batch of vectors/matrices. Its shape should be `[*, M, K]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of a square system of linear equations with a unique solution for input 'x' and 'y'.\n Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # 2*X0 + X1 = 9\n # X0 + 2*X1 = 8\n\n import paddle\n import numpy as np\n\n np_x = np.array([[3, 1],[1, 2]])\n np_y = np.array([9, 8])\n x = paddle.to_tensor(np_x, dtype=\"float64\")\n y = paddle.to_tensor(np_y, dtype=\"float64\")\n out = paddle.linalg.solve(x, y)\n\n print(out)\n # [2., 3.])\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.solve(x, y)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type=\"solve\", inputs={\"X\": x,\n \"Y\": y}, outputs={\"Out\": out})\n return out\n\n\ndef triangular_solve(x,\n y,\n upper=True,\n transpose=False,\n unitriangular=False,\n name=None):\n r\"\"\"\n Computes the solution of a system of equations with a triangular coefficient matrix `x` and\n multiple right-hand sides `y` .\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input triangular coefficient matrix. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): Whether to solve the upper-triangular system of equations (default) or the lower-triangular \n system of equations. Default: True.\n transpose (bool, optional): whether `x` should be transposed before calculation. Default: False.\n unitriangular (bool, optional): whether `x` is unit triangular. If True, the diagonal elements of `x` are assumed \n to be 1 and not referenced from `x` . Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # x1 + x2 + x3 = 0\n # 2*x2 + x3 = -9\n # -x3 = 5\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n y = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.triangular_solve(x, y, upper=True)\n\n print(out)\n # [7, -2, -5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.triangular_solve(x, y, 'upper', upper, 'transpose',\n transpose, 'unitriangular',\n unitriangular)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"triangular_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'triangular_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'triangular_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='triangular_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={\n 'upper': upper,\n 'transpose': transpose,\n 'unitriangular': unitriangular\n })\n return out\n\n\ndef cholesky_solve(x, y, upper=False, name=None):\n r\"\"\"\n Solves a linear system of equations A @ X = B, given A's Cholesky factor matrix u and matrix B.\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input matrix which is upper or lower triangular Cholesky factor of square matrix A. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): whether to consider the Cholesky factor as a lower or upper triangular matrix. Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type is the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n u = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n b = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.cholesky_solve(b, u, upper=True)\n\n print(out)\n # [-2.5, -7, 9.5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky_solve(x, y, 'upper', upper)\n\n helper = LayerHelper(\"cholesky_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'cholesky_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'cholesky_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='cholesky_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef eigvalsh(x, UPLO='L', name=None):\n \"\"\"\n Computes the eigenvalues of a \n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[_, M, M]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): Lower triangular part of a (‘L’, default) or the upper triangular part (‘U’).\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor eigenvalues in ascending order.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value = paddle.eigvalsh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n \"\"\"\n if paddle.in_dynamic_mode():\n is_test = x.stop_gradient\n values, _ = _C_ops.eigvalsh(x, 'UPLO', UPLO, 'is_test', is_test)\n return values\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigvalsh', **locals())\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64', 'complex128'],\n 'eigvalsh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n is_test = x.stop_gradient\n helper.append_op(\n type='eigvalsh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO,\n 'is_test': is_test})\n return out_value\n\n\ndef lstsq(x, y, rcond=None, driver=None, name=None):\n \"\"\"\n Computes a solution to\n the least squares problem of a system of linear equations.\n\n Args:\n x (Tensor): A tensor with shape ``(*, M, N)`` , the data type of the input Tensor ``x``\n should be one of float32, float64.\n y (Tensor): A tensor with shape ``(*, M, K)`` , the data type of the input Tensor ``y`` \n should be one of float32, float64.\n rcond(float, optional): The default value is None. A float pointing number used to determine \n the effective rank of ``x``. If ``rcond`` is None, it will be set to max(M, N) times the \n machine precision of x_dtype.\n driver(str, optional): The default value is None. The name of LAPACK method to be used. For \n CPU inputs the valid values are ‘gels’, ‘gelsy’, ‘gelsd, ‘gelss’. For CUDA input, the only \n valid driver is ‘gels’. If ``driver`` is None, ‘gelsy’ is used for CPU inputs and ‘gels’ \n for CUDA inputs.\n name(str, optional): The default value is None. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple: A tuple of 4 Tensors which is (``solution``, ``residuals``, ``rank``, ``singular_values``). \n ``solution`` is a tensor with shape ``(*, N, K)``, meaning the least squares solution. ``residuals`` \n is a tensor with shape ``(*, K)``, meaning the squared residuals of the solutions, which is computed \n when M > N and every matrix in ``x`` is full-rank, otherwise return an empty tensor. ``rank`` is a tensor \n with shape ``(*)``, meaning the ranks of the matrices in ``x``, which is computed when ``driver`` in \n (‘gelsy’, ‘gelsd’, ‘gelss’), otherwise return an empty tensor. ``singular_values`` is a tensor with \n shape ``(*, min(M, N))``, meaning singular values of the matrices in ``x``, which is computed when \n ``driver`` in (‘gelsd’, ‘gelss’), otherwise return an empty tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n x = paddle.to_tensor([[1, 3], [3, 2], [5, 6.]])\n y = paddle.to_tensor([[3, 4, 6], [5, 3, 4], [1, 2, 1.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gelsd\")\n print(results[0])\n # [[ 0.78350395, -0.22165027, -0.62371236],\n # [-0.11340097, 0.78866047, 1.14948535]]\n print(results[1])\n # [19.81443405, 10.43814468, 30.56185532])\n print(results[2])\n # 2\n print(results[3])\n # [9.03455734, 1.54167950]\n\n x = paddle.to_tensor([[10, 2, 3], [3, 10, 5], [5, 6, 12.]])\n y = paddle.to_tensor([[4, 2, 9], [2, 0, 3], [2, 5, 3.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gels\")\n print(results[0])\n # [[ 0.39386186, 0.10230173, 0.93606132],\n # [ 0.10741687, -0.29028133, 0.11892585],\n # [-0.05115091, 0.51918161, -0.19948854]]\n print(results[1])\n # []\n \"\"\"\n device = paddle.get_device()\n if device == \"cpu\":\n if driver not in (None, \"gels\", \"gelss\", \"gelsd\", \"gelsy\"):\n raise ValueError(\n \"Only support valid driver is 'gels', 'gelss', 'gelsd', 'gelsy' or None for CPU inputs. But got {}\".\n format(driver))\n driver = \"gelsy\" if driver is None else driver\n elif \"gpu\" in device:\n if driver not in (None, \"gels\"):\n raise ValueError(\n \"Only support valid driver is 'gels' or None for CUDA inputs. But got {}\".\n format(driver))\n driver = \"gels\" if driver is None else driver\n else:\n raise RuntimeError(\"Only support lstsq api for CPU or CUDA device.\")\n\n if x.dtype == y.dtype and x.dtype in (paddle.float32, paddle.float64):\n pass\n else:\n raise ValueError(\n \"Only support x and y have the same dtype such as 'float32' and 'float64'.\"\n )\n\n if rcond is None:\n if x.dtype == paddle.float32:\n rcond = 1e-7 * max(x.shape[-2], x.shape[-1])\n elif x.dtype == paddle.float64:\n rcond = 1e-15 * max(x.shape[-2], x.shape[-1])\n\n if paddle.in_dynamic_mode():\n solution, rank, singular_values = _C_ops.lstsq(x, y, \"rcond\", rcond,\n \"driver\", driver)\n if x.shape[-2] > x.shape[-1]:\n matmul_out = _varbase_creator(dtype=x.dtype)\n _C_ops.matmul(x, solution, matmul_out, 'trans_x', False, 'trans_y',\n False)\n minus_out = _C_ops.elementwise_sub(matmul_out, y)\n pow_out = _C_ops.pow(minus_out, 'factor', 2)\n residuals = _C_ops.reduce_sum(pow_out, 'dim', [-2], 'keepdim',\n False, 'reduce_all', False)\n else:\n residuals = paddle.empty(shape=[0], dtype=x.dtype)\n\n if driver == \"gels\":\n rank = paddle.empty(shape=[0], dtype=paddle.int32)\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n elif driver == \"gelsy\":\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n\n return solution, residuals, rank, singular_values\n\n helper = LayerHelper('lstsq', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n check_variable_and_dtype(\n y, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n\n solution = helper.create_variable_for_type_inference(dtype=x.dtype)\n residuals = helper.create_variable_for_type_inference(dtype=x.dtype)\n rank = helper.create_variable_for_type_inference(dtype=paddle.int32)\n singular_values = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='lstsq',\n inputs={'X': x,\n 'Y': y},\n outputs={\n 'Solution': solution,\n 'Rank': rank,\n 'SingularValues': singular_values\n },\n attrs={'rcond': rcond,\n 'driver': driver})\n\n matmul_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n minus_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n pow_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': solution},\n outputs={'Out': matmul_out},\n attrs={\n 'trans_x': False,\n 'trans_y': False,\n })\n\n helper.append_op(\n type='elementwise_sub',\n inputs={'X': matmul_out,\n 'Y': y},\n outputs={'Out': minus_out})\n\n helper.append_op(\n type='pow',\n inputs={'X': minus_out},\n outputs={'Out': pow_out},\n attrs={'factor': 2})\n\n helper.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': residuals},\n attrs={'dim': [-2],\n 'keep_dim': False,\n 'reduce_all': False})\n\n if driver == \"gels\":\n rank = paddle.static.data(name='rank', shape=[0])\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n elif driver == \"gelsy\":\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n\n return solution, residuals, rank, singular_values"},"implementation":{"kind":"string","value":"def eigh(x, UPLO='L', name=None):\n \"\"\"\n Compute the eigenvalues and eigenvectors of a\n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[*, N, N]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): (string, default 'L'), 'L' represents the lower triangular matrix,\n \"'U' represents the upper triangular matrix.\".\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n\n out_value(Tensor): A Tensor with shape [*, N] and data type of float32 and float64. The eigenvalues of eigh op.\n out_vector(Tensor): A Tensor with shape [*, N, N] and data type of float32,float64,complex64 and complex128. The eigenvectors of eigh op.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value, out_vector = paddle.linalg.eigh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n print(out_vector)\n #[(-0.9238795325112867+0j), (-0.3826834323650898+0j)],\n #[ 0.3826834323650898j , -0.9238795325112867j ]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.eigh(x, 'UPLO', UPLO)\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigh', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'eigh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO})\n return out_value, out_vector"},"start_line":{"kind":"number","value":2245,"string":"2,245"},"end_line":{"kind":"number","value":2311,"string":"2,311"},"file_content":{"kind":"string","value":"# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numpy as np\nfrom ..fluid.layer_helper import LayerHelper\nfrom ..framework import _varbase_creator, _dygraph_tracer\nfrom ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype\nfrom ..static import Variable\nfrom ..fluid.framework import _in_legacy_dygraph, in_dygraph_mode\nfrom ..fluid.layers import transpose, cast # noqa: F401\nfrom ..fluid import layers\nimport paddle\nfrom paddle.common_ops_import import core\nfrom paddle.common_ops_import import VarDesc\nfrom paddle import _C_ops\n\n__all__ = []\n\n\ndef matmul(x, y, transpose_x=False, transpose_y=False, name=None):\n \"\"\"\n Applies matrix multiplication to two tensors. `matmul` follows\n the complete broadcast rules,\n and its behavior is consistent with `np.matmul`.\n\n Currently, the input tensors' number of dimensions can be any, `matmul` can be used to\n achieve the `dot`, `matmul` and `batchmatmul`.\n\n The actual behavior depends on the shapes of :math:`x`, :math:`y` and the\n flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:\n\n - If a transpose flag is specified, the last two dimensions of the tensor\n are transposed. If the tensor is ndim-1 of shape, the transpose is invalid. If the tensor\n is ndim-1 of shape :math:`[D]`, then for :math:`x` it is treated as :math:`[1, D]`, whereas\n for :math:`y` it is the opposite: It is treated as :math:`[D, 1]`.\n\n The multiplication behavior depends on the dimensions of `x` and `y`. Specifically:\n\n - If both tensors are 1-dimensional, the dot product result is obtained.\n\n - If both tensors are 2-dimensional, the matrix-matrix product is obtained.\n\n - If the `x` is 1-dimensional and the `y` is 2-dimensional,\n a `1` is prepended to its dimension in order to conduct the matrix multiply.\n After the matrix multiply, the prepended dimension is removed.\n\n - If the `x` is 2-dimensional and `y` is 1-dimensional,\n the matrix-vector product is obtained.\n\n - If both arguments are at least 1-dimensional and at least one argument\n is N-dimensional (where N > 2), then a batched matrix multiply is obtained.\n If the first argument is 1-dimensional, a 1 is prepended to its dimension\n in order to conduct the batched matrix multiply and removed after.\n If the second argument is 1-dimensional, a 1 is appended to its\n dimension for the purpose of the batched matrix multiple and removed after.\n The non-matrix (exclude the last two dimensions) dimensions are\n broadcasted according the broadcast rule.\n For example, if input is a (j, 1, n, m) tensor and the other is a (k, m, p) tensor,\n out will be a (j, k, n, p) tensor.\n\n Args:\n x (Tensor): The input tensor which is a Tensor.\n y (Tensor): The input tensor which is a Tensor.\n transpose_x (bool): Whether to transpose :math:`x` before multiplication.\n transpose_y (bool): Whether to transpose :math:`y` before multiplication.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # vector * vector\n x_data = np.random.random([10]).astype(np.float32)\n y_data = np.random.random([10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [1]\n\n # matrix * vector\n x_data = np.random.random([10, 5]).astype(np.float32)\n y_data = np.random.random([5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10]\n\n # batched matrix * broadcasted vector\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([2]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5]\n\n # batched matrix * batched matrix\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([10, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5, 5]\n\n # batched matrix * broadcasted matrix\n x_data = np.random.random([10, 1, 5, 2]).astype(np.float32)\n y_data = np.random.random([1, 3, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 3, 5, 5]\n\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_matmul(x, y, transpose_x, transpose_y)\n\n if _in_legacy_dygraph():\n op_type = 'matmul_v2'\n op = getattr(_C_ops, op_type)\n return op(x, y, 'trans_x', transpose_x, 'trans_y', transpose_y)\n\n attrs = {\n 'trans_x': transpose_x,\n 'trans_y': transpose_y,\n }\n\n def __check_input(x, y):\n var_names = {'x': x, 'y': y}\n for name, val in var_names.items():\n check_variable_and_dtype(\n val, name,\n ['float16', 'float32', 'float64', 'complex64', 'complex128'],\n 'matmul')\n\n __check_input(x, y)\n\n helper = LayerHelper('matmul_v2', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef norm(x, p='fro', axis=None, keepdim=False, name=None):\n \"\"\"\n\n Returns the matrix norm (Frobenius) or vector norm (the 1-norm, the Euclidean\n or 2-norm, and in general the p-norm for p > 0) of a given tensor.\n\n .. note::\n This norm API is different from `numpy.linalg.norm`.\n This api supports high-order input tensors (rank >= 3), and certain axis need to be pointed out to calculate the norm.\n But `numpy.linalg.norm` only supports 1-D vector or 2-D matrix as input tensor.\n For p-order matrix norm, this api actually treats matrix as a flattened vector to calculate the vector norm, NOT REAL MATRIX NORM.\n\n Args:\n x (Tensor): The input tensor could be N-D tensor, and the input data\n type could be float32 or float64.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `0`, `1`, `2`,\n `inf`, `-inf` and any positive real number yielding the corresponding p-norm. Not supported: ord < 0 and nuclear norm.\n Default value is `fro`.\n axis (int|list|tuple, optional): The axis on which to apply norm operation. If axis is int\n or list(int)/tuple(int) with only one element, the vector norm is computed over the axis.\n If `axis < 0`, the dimension to norm operation is rank(input) + axis.\n If axis is a list(int)/tuple(int) with two elements, the matrix norm is computed over the axis.\n Defalut value is `None`.\n keepdim (bool, optional): Whether to reserve the reduced dimension in the\n output Tensor. The result tensor will have fewer dimension\n than the :attr:`input` unless :attr:`keepdim` is true, default\n value is False.\n name (str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: results of norm operation on the specified axis of input tensor,\n it's data type is the same as input's Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n shape=[2, 3, 4]\n np_input = np.arange(24).astype('float32') - 12\n np_input = np_input.reshape(shape)\n x = paddle.to_tensor(np_input)\n #[[[-12. -11. -10. -9.] [ -8. -7. -6. -5.] [ -4. -3. -2. -1.]]\n # [[ 0. 1. 2. 3.] [ 4. 5. 6. 7.] [ 8. 9. 10. 11.]]]\n\n # compute frobenius norm along last two dimensions.\n out_fro = paddle.linalg.norm(x, p='fro', axis=[0,1])\n # out_fro.numpy() [17.435596 16.911535 16.7332 16.911535]\n\n # compute 2-order vector norm along last dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=-1)\n #out_pnorm.numpy(): [[21.118711 13.190906 5.477226]\n # [ 3.7416575 11.224972 19.131126]]\n\n # compute 2-order norm along [0,1] dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=[0,1])\n #out_pnorm.numpy(): [17.435596 16.911535 16.7332 16.911535]\n\n # compute inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=np.inf)\n #out_pnorm.numpy() = [12.]\n out_pnorm = paddle.linalg.norm(x, p=np.inf, axis=0)\n #out_pnorm.numpy(): [[12. 11. 10. 9.] [8. 7. 6. 7.] [8. 9. 10. 11.]]\n\n # compute -inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=-np.inf)\n #out_pnorm.numpy(): [0.]\n out_pnorm = paddle.linalg.norm(x, p=-np.inf, axis=0)\n #out_pnorm.numpy(): [[0. 1. 2. 3.] [4. 5. 6. 5.] [4. 3. 2. 1.]]\n \"\"\"\n\n def frobenius_norm(input, dim=None, keepdim=False, name=None):\n \"\"\"\n The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n dim (list, optional): None for last two dimensions.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if dim is not None and not (isinstance(dim, list) and len(dim) == 2):\n raise ValueError(\n \"The dim of frobenius norm op should be None or two elements list!\"\n )\n if paddle.in_dynamic_mode():\n if dim is None:\n return _C_ops.frobenius_norm(input, 'keep_dim', keepdim,\n 'reduce_all', True)\n return _C_ops.frobenius_norm(input, 'dim', dim, 'keep_dim', keepdim,\n 'reduce_all', False)\n attrs = {'dim': dim, 'keep_dim': keepdim, 'reduce_all': False}\n if dim is None:\n attrs['reduce_all'] = True\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'frobenius_norm')\n\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='frobenius_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def vector_norm(input,\n porder=None,\n axis=None,\n keepdim=False,\n asvector=False,\n name=None):\n \"\"\"\n Calculate the p-order vector norm for certain dimension of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n porder (float, optional): None for porder=2.0.\n axis (int, optional): None for last dimension.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if paddle.in_dynamic_mode():\n if axis is None: axis = -1\n return _C_ops.p_norm(input, 'porder', porder, 'axis', axis,\n 'keepdim', keepdim, 'asvector', asvector)\n if porder is not None:\n check_type(porder, 'porder', (float, int), 'p_norm')\n if axis is not None:\n check_type(axis, 'axis', (int), 'p_norm')\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'p_norm')\n\n attrs = {\n 'axis': axis if axis is not None else -1,\n 'porder': float(porder) if porder is not None else 2.0,\n 'keepdim': keepdim,\n 'asvector': asvector,\n 'epsilon': 1e-12,\n }\n helper = LayerHelper('p_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='p_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def inf_norm(input,\n porder=None,\n axis=axis,\n keepdim=False,\n asvector=False,\n name=None):\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n helper.append_op(type='abs', inputs={'X': input}, outputs={'Out': out})\n reduce_out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n reduce_all = True if axis == None or axis == [] or asvector == True else False\n axis = axis if axis != None and axis != [] else [0]\n\n reduce_type = 'reduce_max' if porder == np.float(\n 'inf') else 'reduce_min'\n helper.append_op(\n type=reduce_type,\n inputs={'X': out},\n outputs={'Out': reduce_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n\n return reduce_out\n\n def p_matrix_norm(input, porder=1., axis=axis, keepdim=False, name=None):\n \"\"\"\n NOTE:\n This function actually treats the matrix as flattened vector to calculate vector norm instead of matrix norm.\n \"\"\"\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n\n block.append_op(\n type='pow',\n inputs={'X': abs_out},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': True if axis is None else False\n })\n porder\n block.append_op(\n type='pow',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n if axis is None and p is not None:\n if isinstance(p, str):\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n porder=p,\n axis=axis,\n keepdim=keepdim,\n asvector=True,\n name=name)\n else:\n raise ValueError(\"only valid p type is string or float, found {}\".\n format(type(p)))\n\n if isinstance(axis, tuple):\n axis = list(axis)\n if isinstance(axis, list) and len(axis) == 1:\n axis = axis[0]\n\n #calculate vector norm, where axis is int or list with only one integer\n if isinstance(axis, int):\n if isinstance(p, str):\n if p == \"fro\":\n return vector_norm(\n x,\n porder=2,\n axis=axis,\n keepdim=keepdim,\n asvector=False,\n name=name)\n\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n axis=axis,\n porder=p,\n keepdim=keepdim,\n asvector=False,\n name=name)\n else:\n raise ValueError(\n \"unspport p for p-order vector norm. except float, found {}\".\n format(p))\n #calculate matrix norm, where axis is list with two integers\n elif isinstance(axis, list) and len(axis) == 2:\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n elif p == np.inf or p == -np.inf:\n return inf_norm(x, porder=p, axis=axis, keepdim=keepdim, name=name)\n elif p == 0:\n raise ValueError(\n \"just suport axis type int or list (length of list <=1) if p = 0, found {}\".\n format(axis))\n else:\n return p_matrix_norm(\n x, porder=p, axis=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"except axis type int or list (length of list <=2), found {}\".\n format(axis))\n\n\ndef dist(x, y, p=2, name=None):\n r\"\"\"\n\n This OP returns the p-norm of (x - y). It is not a norm in a strict sense, only as a measure\n of distance. The shapes of x and y must be broadcastable. The definition is as follows, for\n details, please refer to the `numpy's broadcasting <https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html>`_:\n\n - Each input has at least one dimension.\n - Match the two input dimensions from back to front, the dimension sizes must either be equal, one of them is 1, or one of them does not exist.\n\n Where, z = x - y, the shapes of x and y are broadcastable, then the shape of z can be\n obtained as follows:\n\n 1. If the number of dimensions of x and y are not equal, prepend 1 to the dimensions of the\n tensor with fewer dimensions.\n\n For example, The shape of x is [8, 1, 6, 1], the shape of y is [7, 1, 5], prepend 1 to the\n dimension of y.\n\n x (4-D Tensor): 8 x 1 x 6 x 1\n\n y (4-D Tensor): 1 x 7 x 1 x 5\n\n 2. Determine the size of each dimension of the output z: choose the maximum value from the\n two input dimensions.\n\n z (4-D Tensor): 8 x 7 x 6 x 5\n\n If the number of dimensions of the two inputs are the same, the size of the output can be\n directly determined in step 2. When p takes different values, the norm formula is as follows:\n\n When p = 0, defining $0^0=0$, the zero-norm of z is simply the number of non-zero elements of z.\n\n .. math::\n\n ||z||_{0}=\\lim_{p \\\\rightarrow 0}\\sum_{i=1}^{m}|z_i|^{p}\n\n When p = inf, the inf-norm of z is the maximum element of z.\n\n .. math::\n\n ||z||_\\infty=\\max_i |z_i|\n\n When p = -inf, the negative-inf-norm of z is the minimum element of z.\n\n .. math::\n\n ||z||_{-\\infty}=\\min_i |z_i|\n\n Otherwise, the p-norm of z follows the formula,\n\n .. math::\n\n ||z||_{p}=(\\sum_{i=1}^{m}|z_i|^p)^{\\\\frac{1}{p}}\n\n Args:\n x (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n y (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n p (float, optional): The norm to be computed, its data type is float32 or float64. Default: 2.\n\n Returns:\n Tensor: Tensor that is the p-norm of (x - y).\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor(np.array([[3, 3],[3, 3]]), \"float32\")\n y = paddle.to_tensor(np.array([[3, 3],[3, 1]]), \"float32\")\n out = paddle.dist(x, y, 0)\n print(out) # out = [1.]\n\n out = paddle.dist(x, y, 2)\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"inf\"))\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"-inf\"))\n print(out) # out = [0.]\n \"\"\"\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'dist')\n check_variable_and_dtype(y, 'dtype', ['float32', 'float64'], 'dist')\n check_type(p, 'p', (float, int), 'dist')\n helper = LayerHelper(\"dist\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n outputs = {'Out': [out]}\n attrs = {\"p\": float(p)}\n helper.append_op(\n type='dist', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef cond(x, p=None, name=None):\n \"\"\"\n\n Computes the condition number of a matrix or batches of matrices with respect to a matrix norm ``p``.\n\n Args:\n x (Tensor): The input tensor could be tensor of shape ``(*, m, n)`` where ``*`` is zero or more batch dimensions\n for ``p`` in ``(2, -2)``, or of shape ``(*, n, n)`` where every matrix is invertible for any supported ``p``.\n And the input data type could be ``float32`` or ``float64``.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `nuc`, `1`, `-1`, `2`, `-2`,\n `inf`, `-inf`. Default value is `None`, meaning that the order of the norm is `2`.\n name (str, optional): The default value is `None`. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: computing results of condition number, its data type is the same as input Tensor ``x``.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1., 0, -1], [0, 1, 0], [1, 0, 1]])\n\n # compute conditional number when p is None\n out = paddle.linalg.cond(x)\n # out.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is 'fro'\n out_fro = paddle.linalg.cond(x, p='fro')\n # out_fro.numpy() [3.1622777]\n\n # compute conditional number when order of the norm is 'nuc'\n out_nuc = paddle.linalg.cond(x, p='nuc')\n # out_nuc.numpy() [9.2426405]\n\n # compute conditional number when order of the norm is 1\n out_1 = paddle.linalg.cond(x, p=1)\n # out_1.numpy() [2.]\n\n # compute conditional number when order of the norm is -1\n out_minus_1 = paddle.linalg.cond(x, p=-1)\n # out_minus_1.numpy() [1.]\n\n # compute conditional number when order of the norm is 2\n out_2 = paddle.linalg.cond(x, p=2)\n # out_2.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is -1\n out_minus_2 = paddle.linalg.cond(x, p=-2)\n # out_minus_2.numpy() [0.70710677]\n\n # compute conditional number when order of the norm is inf\n out_inf = paddle.linalg.cond(x, p=np.inf)\n # out_inf.numpy() [2.]\n\n # compute conditional number when order of the norm is -inf\n out_minus_inf = paddle.linalg.cond(x, p=-np.inf)\n # out_minus_inf.numpy() [1.]\n\n a = paddle.to_tensor(np.random.randn(2, 4, 4).astype('float32'))\n # a.numpy()\n # [[[ 0.14063153 -0.996288 0.7996131 -0.02571543]\n # [-0.16303636 1.5534962 -0.49919784 -0.04402903]\n # [-1.1341571 -0.6022629 0.5445269 0.29154757]\n # [-0.16816919 -0.30972657 1.7521842 -0.5402487 ]]\n # [[-0.58081484 0.12402827 0.7229862 -0.55046535]\n # [-0.15178485 -1.1604939 0.75810957 0.30971205]\n # [-0.9669573 1.0940945 -0.27363303 -0.35416734]\n # [-1.216529 2.0018666 -0.7773689 -0.17556527]]]\n a_cond_fro = paddle.linalg.cond(a, p='fro')\n # a_cond_fro.numpy() [31.572273 28.120834]\n\n b = paddle.to_tensor(np.random.randn(2, 3, 4).astype('float64'))\n # b.numpy()\n # [[[ 1.61707487 0.46829144 0.38130416 0.82546736]\n # [-1.72710298 0.08866375 -0.62518804 0.16128892]\n # [-0.02822879 -1.67764516 0.11141444 0.3220113 ]]\n # [[ 0.22524372 0.62474921 -0.85503233 -1.03960523]\n # [-0.76620689 0.56673047 0.85064753 -0.45158196]\n # [ 1.47595418 2.23646462 1.5701758 0.10497519]]]\n b_cond_2 = paddle.linalg.cond(b, p=2)\n # b_cond_2.numpy() [3.30064451 2.51976252]\n\n \"\"\"\n\n def mat_norm(input, porder=1., axis=None):\n \"\"\"\n NOTE:\n Calculate the matrix norm of a square matrix or batches of square matrices,\n when porder is in (1, -1, inf, -inf)\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n axis = axis if axis != None and axis != [] else [0]\n keepdim = False\n\n if paddle.in_dynamic_mode():\n abs_out = _C_ops.abs(input)\n sum_out = _C_ops.reduce_sum(abs_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == 1 or porder == np.inf:\n return _C_ops.reduce_max(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == -1 or porder == -np.inf:\n return _C_ops.reduce_min(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n\n block = LayerHelper('norm', **locals())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n block.append_op(\n type='reduce_sum',\n inputs={'X': abs_out},\n outputs={'Out': sum_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 1 or porder == np.inf:\n block.append_op(\n type='reduce_max',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n if porder == -1 or porder == -np.inf:\n block.append_op(\n type='reduce_min',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n\n def fro_norm(input, porder=2, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the frobenius norm of a square matrix or batches of square matrices.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n if paddle.in_dynamic_mode():\n pow_out = _C_ops.pow(input, 'factor', porder)\n sum_out_1 = _C_ops.reduce_sum(pow_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n sum_out_2 = _C_ops.reduce_sum(sum_out_1, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n return _C_ops.pow(sum_out_2, 'factor', float(1. / porder))\n\n block = LayerHelper('norm', **locals())\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_1 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_2 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='pow',\n inputs={'X': input},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out_1},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_sum',\n inputs={'X': sum_out_1},\n outputs={'Out': sum_out_2},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='pow',\n inputs={'X': sum_out_2},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n def svd_norm(input, porder, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the matrix norm, which is related to singular values, of a matrix\n or batches of matrices, including nuclear norm, 2-norm and (-2)-norm.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n u, s, vh = svd(input, full_matrices=False)\n\n if paddle.in_dynamic_mode():\n if porder == \"nuc\":\n return _C_ops.reduce_sum(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n max_out = _C_ops.reduce_max(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n min_out = _C_ops.reduce_min(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n if porder == 2:\n return _C_ops.elementwise_div(max_out, min_out, 'aixs', axis,\n 'use_mkldnn', False)\n if porder == -2:\n return _C_ops.elementwise_div(min_out, max_out, 'aixs', axis,\n 'use_mkldnn', False)\n\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n if porder == \"nuc\":\n block.append_op(\n type='reduce_sum',\n inputs={'X': s},\n outputs={'Out': out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n max_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n min_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_min',\n inputs={'X': s},\n outputs={'Out': min_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': max_out,\n 'Y': min_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n if porder == -2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': min_out,\n 'Y': max_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n\n def empty_tensor(input, shape):\n if paddle.in_dynamic_mode():\n return input.reshape(shape)\n raise ValueError(\"only support x is nonempty tensor in static mode\")\n\n x_shape = list(x.shape)\n if not len(x_shape) >= 2:\n raise ValueError(\"input should be a matrix or batches of matrices, \" +\n \"but the dimention of received input is {}\".format(\n len(x_shape)))\n if p == None:\n p = 2\n x_size = 0 if (0 in x_shape) else 1\n if p in (\"fro\", \"nuc\", 1, -1, np.inf, -np.inf):\n if x_shape[len(x_shape) - 1] == x_shape[len(x_shape) - 2]:\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n x_inv = x.inverse()\n if p == \"fro\":\n return fro_norm(x) * fro_norm(x_inv)\n if p == \"nuc\":\n return svd_norm(x, p) * svd_norm(x_inv, p)\n if p in (1, -1):\n return mat_norm(\n x, porder=p, axis=[-2]) * mat_norm(\n x_inv, porder=p, axis=[-2])\n if p in (np.inf, -np.inf):\n return mat_norm(\n x, porder=p, axis=[-1]) * mat_norm(\n x_inv, porder=p, axis=[-1])\n else:\n raise ValueError(\"only support p is {} when input is a \".format(p) +\n \"square matrix or batches of square matrices\")\n elif p in (2, -2):\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n return svd_norm(x, porder=p)\n else:\n raise ValueError(\n \"unsupported {} for p, only supporting ('fro', 'nuc', \".format(\n p) + \"1, -1, 2, -2, inf, -inf) or none\")\n\n\ndef dot(x, y, name=None):\n \"\"\"\n This operator calculates inner product for vectors.\n\n .. note::\n Support 1-d and 2-d Tensor. When it is 2d, the first dimension of this matrix\n is the batch dimension, which means that the vectors of multiple batches are dotted.\n\n Parameters:\n x(Tensor): 1-D or 2-D ``Tensor``. Its dtype should be ``float32``, ``float64``, ``int32``, ``int64``\n y(Tensor): 1-D or 2-D ``Tensor``. Its dtype soulde be ``float32``, ``float64``, ``int32``, ``int64``\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: the calculated result Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x_data = np.random.uniform(0.1, 1, [10]).astype(np.float32)\n y_data = np.random.uniform(1, 3, [10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.dot(x, y)\n print(z)\n\n \"\"\"\n op_type = 'dot'\n # skip var type check in dygraph mode to improve efficiency\n if paddle.in_dynamic_mode():\n op = getattr(_C_ops, op_type)\n return op(x, y)\n\n assert x is not None, 'x cannot be None in {}'.format(op_type)\n assert y is not None, 'y cannot be None in {}'.format(op_type)\n\n check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n check_variable_and_dtype(y, 'y', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n\n helper = LayerHelper(op_type, **locals())\n if name is None:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n else:\n out = helper.create_variable(\n name=name, dtype=x.dtype, persistable=False)\n helper.append_op(\n type=\"dot\", inputs={'X': x,\n 'Y': y}, attrs={}, outputs={\"Out\": out})\n return out\n\n\ndef cov(x, rowvar=True, ddof=True, fweights=None, aweights=None, name=None):\n \"\"\"\n Estimate the covariance matrix of the input variables, given data and weights.\n\n A covariance matrix is a square matrix, indicate the covariance of each pair variables in the input matrix.\n For example, for an N-dimensional samples X=[x1,x2,…xN]T, then the covariance matrix \n element Cij is the covariance of xi and xj. The element Cii is the variance of xi itself.\n\n Parameters:\n x(Tensor): A N-D(N<=2) Tensor containing multiple variables and observations. By default, each row of x represents a variable. Also see rowvar below.\n rowvar(Bool, optional): If rowvar is True (default), then each row represents a variable, with observations in the columns. Default: True\n ddof(Bool, optional): If ddof=True will return the unbiased estimate, and ddof=False will return the simple average. Default: True\n fweights(Tensor, optional): 1-D Tensor of integer frequency weights; The number of times each observation vector should be repeated. Default: None\n aweights(Tensor, optional): 1-D Tensor of observation vector weights. How important of the observation vector, larger data means this element is more important. Default: None\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: The covariance matrix Tensor of the variables.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n\n xt = paddle.rand((3,4))\n paddle.linalg.cov(xt)\n\n '''\n Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n [[0.07918842, 0.06127326, 0.01493049],\n [0.06127326, 0.06166256, 0.00302668],\n [0.01493049, 0.00302668, 0.01632146]])\n '''\n \"\"\"\n op_type = 'cov'\n if len(x.shape) > 2 or len(x.shape) < 1:\n raise ValueError(\n \"Input(x) only support N-D (1<=N<=2) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cov')\n nx = x\n if len(x.shape) == 1:\n nx = x.reshape((1, -1))\n if not rowvar and nx.shape[0] != 1:\n nx = nx.t()\n w = None\n observation_num = nx.shape[1]\n if fweights is not None:\n w = fweights.astype(nx.dtype)\n if len(w.shape) > 1:\n raise ValueError(\n \"Input(fweights) only support N-D (N<=1) tensor in cov, but received \"\n \"shape of Input(input) is %s.\" % len(fweights.shape))\n if fweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(fweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(fweights) is {}.\".format(observation_num,\n fweights.shape[0]))\n if fweights.min() < 0:\n raise ValueError(\n \"The value of Input(fweights) cannot be negtive, but received \"\n \"min of Input(fweights) is {}.\".format(fweights.min()))\n if not paddle.all(fweights == paddle.round(fweights.astype('float64'))):\n raise ValueError(\"Input(fweights) must be integer \")\n\n if aweights is not None:\n aw = aweights.astype(nx.dtype)\n if len(aw.shape) > 1:\n raise ValueError(\n \"Input(aweights) only support N-D (N<=1) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(aweights.shape))\n check_variable_and_dtype(aweights, 'dtype', ['float32', 'float64'],\n 'cov')\n if aweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(aweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(aweights) is {}.\".format(observation_num,\n aweights.shape[0]))\n if aweights.min() < 0:\n raise ValueError(\n \"The value of Input(aweights) cannot be negtive, but received \"\n \"min of Input(aweights) is {}.\".format(aweights.min()))\n if w is not None:\n w = w * aw\n else:\n w = aw\n\n w_sum = paddle.to_tensor(observation_num, dtype=nx.dtype)\n if fweights is not None or aweights is not None:\n w_sum = w.sum()\n if w_sum.item() == 0:\n raise ValueError(\"The sum of weights is zero, can't be normalized.\")\n\n if w is not None:\n nx_w = nx * w\n avg = (nx_w).sum(axis=1) / w_sum\n else:\n avg = nx.sum(axis=1) / w_sum\n nx_w = nx\n\n if w is not None and aweights is not None and ddof == True:\n norm_factor = w_sum - (w * aweights).sum() / w_sum\n else:\n norm_factor = w_sum - ddof\n if norm_factor <= 0:\n norm_factor = paddle.to_tensor(0, dtype=nx.dtype)\n nx = nx - avg.unsqueeze(1)\n xxt = paddle.mm(nx, nx_w.t().conj())\n cov = paddle.divide(xxt, norm_factor).squeeze()\n return cov\n\n\ndef t(input, name=None):\n \"\"\"\n Transpose <=2-D tensor.\n 0-D and 1-D tensors are returned as it is and 2-D tensor is equal to\n the paddle.transpose function which perm dimensions set 0 and 1.\n\n Args:\n input (Tensor): The input Tensor. It is a N-D (N<=2) Tensor of data types float16, float32, float64, int32.\n name(str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`\n Returns:\n Tensor: A transposed n-D Tensor, with data type being float16, float32, float64, int32, int64.\n\n For Example:\n\n .. code-block:: text\n\n # Example 1 (0-D tensor)\n x = tensor([0.79])\n paddle.t(x) = tensor([0.79])\n\n # Example 2 (1-D tensor)\n x = tensor([0.79, 0.84, 0.32])\n paddle.t(x) = tensor([0.79, 0.84, 0.32])\n\n # Example 3 (2-D tensor)\n x = tensor([0.79, 0.84, 0.32],\n [0.64, 0.14, 0.57])\n paddle.t(x) = tensor([0.79, 0.64],\n [0.84, 0.14],\n [0.32, 0.57])\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n x = paddle.ones(shape=[2, 3], dtype='int32')\n x_transposed = paddle.t(x)\n print(x_transposed.shape)\n # [3, 2]\n \"\"\"\n if len(input.shape) > 2:\n raise ValueError(\n \"Input(input) only support N-D (N<=2) tensor, but received \"\n \"length of Input(input) is %s. Perhaps you can use paddle.\"\n \"tensor.transpose() instead.\" % len(input.shape))\n if paddle.in_dynamic_mode():\n if len(input.shape) == 1:\n return input\n # 2-D tensor\n perm = [1, 0]\n out, _ = _C_ops.transpose2(input, 'axis', perm)\n return out\n\n check_variable_and_dtype(\n input, 'input', ['float16', 'float32', 'float64', 'int32',\n 'int64'], 'transpose')\n\n helper = LayerHelper('t', **locals())\n out = helper.create_variable_for_type_inference(input.dtype)\n input_shape = helper.create_variable_for_type_inference(input.dtype)\n if len(input.shape) == 1:\n out = input\n else:\n helper.append_op(\n type='transpose2',\n inputs={'X': [input]},\n outputs={'Out': [out],\n 'XShape': [input_shape]},\n attrs={'axis': [1, 0]})\n return out\n\n\ndef cross(x, y, axis=None, name=None):\n \"\"\"\n Computes the cross product between two tensors along an axis.\n\n Inputs must have the same shape, and the length of their axes should be equal to 3.\n If `axis` is not given, it defaults to the first axis found with the length 3.\n\n Args:\n x (Tensor): The first input tensor.\n y (Tensor): The second input tensor.\n axis (int, optional): The axis along which to compute the cross product. It defaults to the first axis found with the length 3.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor. A Tensor with same data type as `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0],\n [3.0, 3.0, 3.0]])\n y = paddle.to_tensor([[1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0]])\n\n z1 = paddle.cross(x, y)\n # [[-1. -1. -1.]\n # [ 2. 2. 2.]\n # [-1. -1. -1.]]\n\n z2 = paddle.cross(x, y, axis=1)\n # [[0. 0. 0.]\n # [0. 0. 0.]\n # [0. 0. 0.]]\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_cross(x, y, axis)\n else:\n if _in_legacy_dygraph():\n if axis is not None:\n return _C_ops.cross(x, y, 'dim', axis)\n else:\n return _C_ops.cross(x, y)\n else:\n helper = LayerHelper(\"cross\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n attrs = dict()\n attrs['dim'] = axis\n\n helper.append_op(\n type='cross',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef cholesky(x, upper=False, name=None):\n r\"\"\"\n Computes the Cholesky decomposition of one symmetric positive-definite\n matrix or batches of symmetric positive-definite matrice.\n\n If `upper` is `True`, the decomposition has the form :math:`A = U^{T}U` ,\n and the returned matrix :math:`U` is upper-triangular. Otherwise, the\n decomposition has the form :math:`A = LL^{T}` , and the returned matrix\n :math:`L` is lower-triangular.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[*, M, M]`,\n where * is zero or more batch dimensions, and matrices on the\n inner-most 2 dimensions all should be symmetric positive-definite.\n Its data type should be float32 or float64.\n upper (bool): The flag indicating whether to return upper or lower\n triangular matrices. Default: False.\n\n Returns:\n Tensor: A Tensor with same shape and data type as `x`. It represents \\\n triangular matrices generated by Cholesky decomposition.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n a = np.random.rand(3, 3)\n a_t = np.transpose(a, [1, 0])\n x_data = np.matmul(a, a_t) + 1e-03\n x = paddle.to_tensor(x_data)\n out = paddle.linalg.cholesky(x, upper=False)\n print(out)\n # [[1.190523 0. 0. ]\n # [0.9906703 0.27676893 0. ]\n # [1.25450498 0.05600871 0.06400121]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky(x, \"upper\", upper)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cholesky')\n check_type(upper, 'upper', bool, 'cholesky')\n helper = LayerHelper('cholesky', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='cholesky',\n inputs={'X': [x]},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef matrix_rank(x, tol=None, hermitian=False, name=None):\n r\"\"\"\n Computes the rank of a matrix.\n\n The rank of a matrix is the number of singular values that are greater than the specified `tol` threshold when hermitian=False,\n or the number of eigenvalues in absolute value that are greater than the specified `tol` threshold when hermitian=True.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., m, n]`, where `...` is zero or more batch dimensions. If `x` is a batch\n of matrices then the output has the same batch dimensions. The data type of `x` should be float32 or float64.\n tol (float,Tensor,optional): the tolerance value. Default: None. If `tol` is not specified, and `sigma` is the largest\n singular value (or eigenvalues in absolute value), and `eps` is the epsilon value for the dtype of `x`, then `tol` is computed\n with formula `tol=sigma * max(m,n) * eps`. Note that if `x` is a batch of matrices, `tol` is computed this way for every batch.\n hermitian (bool,optional): indicates whether `x` is Hermitian. Default: False. When hermitian=True, `x` is assumed to be Hermitian,\n enabling a more efficient method for finding eigenvalues, but `x` is not checked inside the function. Instead, We just use\n the lower triangular of the matrix to compute.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: Rank of tensor x.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n a = paddle.eye(10)\n b = paddle.linalg.matrix_rank(a)\n print(b)\n # b = [10]\n\n c = paddle.ones(shape=[3, 4, 5, 5])\n d = paddle.linalg.matrix_rank(c, tol=0.01, hermitian=True)\n print(d)\n # d = [[1, 1, 1, 1],\n # [1, 1, 1, 1],\n # [1, 1, 1, 1]]\n\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if tol is None:\n tol_tensor = None\n tol_attr = 0.0\n use_default_tol = True\n elif isinstance(tol, Variable):\n if tol.dtype != x.dtype:\n tol_tensor = cast(tol, x.dtype)\n else:\n tol_tensor = tol\n tol_attr = 0.0\n use_default_tol = False\n else:\n tol_tensor = None\n tol_attr = float(tol)\n use_default_tol = False\n return _C_ops.matrix_rank(x, tol_tensor, \"tol\", tol_attr, 'hermitian',\n hermitian, 'use_default_tol', use_default_tol)\n\n inputs = {}\n attrs = {}\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'matrix_rank')\n inputs['X'] = x\n if tol is None:\n attrs['use_default_tol'] = True\n elif isinstance(tol, Variable):\n check_variable_and_dtype(tol, 'tol', ['float32'], 'matrix_rank')\n attrs['use_default_tol'] = False\n if tol.dtype != x.dtype:\n inputs['TolTensor'] = cast(tol, x.dtype)\n else:\n inputs['TolTensor'] = tol\n else:\n check_type(tol, 'tol', float, 'matrix_rank')\n attrs['use_default_tol'] = False\n attrs['tol'] = tol\n check_type(hermitian, 'hermitian', bool, 'matrix_rank')\n attrs['hermitian'] = hermitian\n\n helper = LayerHelper('matrix_rank', **locals())\n out = helper.create_variable_for_type_inference(dtype='int32')\n helper.append_op(\n type='matrix_rank', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef bmm(x, y, name=None):\n \"\"\"\n Applies batched matrix multiplication to two tensors.\n\n Both of the two input tensors must be three-dementional and share the same batch size.\n\n if x is a (b, m, k) tensor, y is a (b, k, n) tensor, the output will be a (b, m, n) tensor.\n\n Args:\n x (Tensor): The input Tensor.\n y (Tensor): The input Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The product Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n # In imperative mode:\n # size x: (2, 2, 3) and y: (2, 3, 2)\n x = paddle.to_tensor([[[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0]],\n [[3.0, 3.0, 3.0],\n [4.0, 4.0, 4.0]]])\n y = paddle.to_tensor([[[1.0, 1.0],[2.0, 2.0],[3.0, 3.0]],\n [[4.0, 4.0],[5.0, 5.0],[6.0, 6.0]]])\n out = paddle.bmm(x, y)\n #output size: (2, 2, 2)\n #output value:\n #[[[6.0, 6.0],[12.0, 12.0]],[[45.0, 45.0],[60.0, 60.0]]]\n out_np = out.numpy()\n\n \"\"\"\n x_shape = x.shape\n y_shape = y.shape\n if not len(x_shape) == len(y_shape) == 3:\n raise ValueError(\n \"x and y should be 3-dimensional. But received x's dimention: {}, y's dimention: {}\".\n format(x_shape, y_shape))\n if x_shape[2] != y_shape[1]:\n raise ValueError(\n \"x's width must be equal with y's height. But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n if x_shape[0] != y_shape[0]:\n raise ValueError(\n \"x's batch (shape[0]) must be equal with y's batch (shape[0]). But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.bmm(x, y)\n\n helper = LayerHelper('bmm', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='bmm', inputs={'X': x, 'Y': y}, outputs={'Out': out})\n return out\n\n\ndef histogram(input, bins=100, min=0, max=0, name=None):\n \"\"\"\n Computes the histogram of a tensor. The elements are sorted into equal width bins between min and max.\n If min and max are both zero, the minimum and maximum values of the data are used.\n\n Args:\n input (Tensor): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor\n should be float32, float64, int32, int64.\n bins (int): number of histogram bins\n min (int): lower end of the range (inclusive)\n max (int): upper end of the range (inclusive)\n\n Returns:\n Tensor: data type is int64, shape is (nbins,).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n inputs = paddle.to_tensor([1, 2, 1])\n result = paddle.histogram(inputs, bins=4, min=0, max=3)\n print(result) # [0, 2, 1, 0]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.histogram(input, \"bins\", bins, \"min\", min, \"max\", max)\n\n helper = LayerHelper('histogram', **locals())\n check_variable_and_dtype(\n input, 'X', ['int32', 'int64', 'float32', 'float64'], 'histogram')\n out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)\n helper.append_op(\n type='histogram',\n inputs={'X': input},\n outputs={'Out': out},\n attrs={'bins': bins,\n 'min': min,\n 'max': max})\n return out\n\n\ndef bincount(x, weights=None, minlength=0, name=None):\n \"\"\"\n Computes frequency of each value in the input tensor. \n\n Args:\n x (Tensor): A Tensor with non-negative integer. Should be 1-D tensor.\n weights (Tensor, optional): Weight for each value in the input tensor. Should have the same shape as input. Default is None.\n minlength (int, optional): Minimum number of bins. Should be non-negative integer. Default is 0.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor of frequency.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([1, 2, 1, 4, 5])\n result1 = paddle.bincount(x)\n print(result1) # [0, 2, 1, 0, 1, 1]\n\n w = paddle.to_tensor([2.1, 0.4, 0.1, 0.5, 0.5])\n result2 = paddle.bincount(x, weights=w)\n print(result2) # [0., 2.19999981, 0.40000001, 0., 0.50000000, 0.50000000]\n \"\"\"\n if x.dtype not in [paddle.int32, paddle.int64]:\n raise TypeError(\"Elements in Input(x) should all be integers\")\n\n if paddle.in_dynamic_mode():\n return _C_ops.bincount(x, weights, \"minlength\", minlength)\n\n helper = LayerHelper('bincount', **locals())\n\n check_variable_and_dtype(x, 'X', ['int32', 'int64'], 'bincount')\n\n if weights is not None:\n check_variable_and_dtype(weights, 'Weights',\n ['int32', 'int64', 'float32', 'float64'],\n 'bincount')\n out = helper.create_variable_for_type_inference(dtype=weights.dtype)\n else:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='bincount',\n inputs={'X': x,\n 'Weights': weights},\n outputs={'Out': out},\n attrs={'minlength': minlength})\n return out\n\n\ndef mv(x, vec, name=None):\n \"\"\"\n Performs a matrix-vector product of the matrix x and the vector vec.\n\n Args:\n x (Tensor): A tensor with shape :math:`[M, N]` , The data type of the input Tensor x\n should be one of float32, float64.\n vec (Tensor): A tensor with shape :math:`[N]` , The data type of the input Tensor x\n should be one of float32, float64.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor which is producted by x and vec.\n\n Examples:\n .. code-block:: python\n\n # x: [M, N], vec: [N]\n # paddle.mv(x, vec) # out: [M]\n\n import numpy as np\n import paddle\n\n x_data = np.array([[2, 1, 3], [3, 0, 1]]).astype(\"float64\")\n x = paddle.to_tensor(x_data)\n vec_data = np.array([3, 5, 1])\n vec = paddle.to_tensor(vec_data).astype(\"float64\")\n out = paddle.mv(x, vec)\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_mv(x, vec)\n else:\n if _in_legacy_dygraph():\n out = _C_ops.mv(x, vec)\n return out\n else:\n\n def __check_input(x, vec):\n var_names = {'x': x, 'vec': vec}\n for name, val in var_names.items():\n check_variable_and_dtype(val, name, ['float32', 'float64'],\n 'mv')\n x_shape = list(x.shape)\n vec_shape = list(vec.shape)\n if len(x_shape) != 2:\n raise ValueError(\n \"x should be 2-dimensional. But received x's dimention: {}\".\n format(x_shape))\n if len(vec_shape) != 1:\n raise ValueError(\n \"vec should be 1-dimensional. But received vec's dimention: {}\".\n format(vec_shape))\n\n __check_input(x, vec)\n\n helper = LayerHelper('mv', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='mv', inputs={'X': x,\n 'Vec': vec}, outputs={'Out': out})\n return out\n\n\ndef det(x, name=None):\n \"\"\"\n Calculates determinant value of a square matrix or batches of square matrices.\n Args:\n x (Tensor): input (Tensor): the input matrix of size `(n, n)` or the batch of matrices of size\n `(*, n, n)` where `*` is one or more batch dimensions.\n Returns:\n y (Tensor):the determinant value of a square matrix or batches of square matrices.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.det(x)\n\n print(A)\n\n # [ 0.02547996, 2.52317095, -6.15900707])\n\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.determinant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'det')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('determinant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='determinant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef slogdet(x, name=None):\n \"\"\"\n Calculates the sign and natural logarithm of the absolute value of a square matrix's or batches square matrices' determinant.\n The determinant can be computed with ``sign * exp(logabsdet)\n\n Supports input of float, double\n\n Note that for matrices that have zero determinant, this returns ``(0, -inf)``\n Args:\n x (Tensor): the batch of matrices of size :math:`(*, n, n)`\n where math:`*` is one or more batch dimensions.\n\n Returns:\n y (Tensor): A tensor containing the sign of the determinant and the natural logarithm\n of the absolute value of determinant, respectively.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.slogdet(x)\n\n print(A)\n\n # [[ 1. , 1. , -1. ],\n # [-0.98610914, -0.43010661, -0.10872950]])\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.slogdeterminant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'slogdet')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('slogdeterminant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='slogdeterminant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef svd(x, full_matrices=False, name=None):\n r\"\"\"\n Computes the singular value decomposition of one matrix or a batch of regular matrices.\n\n Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies:\n\n .. math::\n X = U * diag(S) * VT\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., N, M]`,\n where `...` is zero or more batch dimensions. N and M can be arbitraty\n positive number. Note that if x is sigular matrices, the grad is numerical\n instable. The data type of x should be float32 or float64.\n full_matrices (bool): A flag to control the behavor of svd.\n If full_matrices = True, svd op will compute full U and V matrics,\n which means shape of U is `[..., N, N]`, shape of V is `[..., M, M]`. K = min(M, N).\n If full_matrices = False, svd op will use a economic method to store U and V.\n which means shape of U is `[..., N, K]`, shape of V is `[..., M, K]`. K = min(M, N).\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple of 3 tensors: (U, S, VH). VH is the conjugate transpose of V. S is the singlar value vectors of matrics with shape `[..., K]`\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 2.0], [1.0, 3.0], [4.0, 6.0]]).astype('float64')\n x = x.reshape([3, 2])\n u, s, vh = paddle.linalg.svd(x)\n print (u)\n #U = [[ 0.27364809, -0.21695147 ],\n # [ 0.37892198, -0.87112408 ],\n # [ 0.8840446 , 0.44053933 ]]\n\n print (s)\n #S = [8.14753743, 0.78589688]\n print (vh)\n #VT= [[ 0.51411221, 0.85772294],\n # [ 0.85772294, -0.51411221]]\n\n # one can verify : U * S * VT == X\n # U * UH == I\n # V * VH == I\n \"\"\"\n\n if paddle.in_dynamic_mode():\n return _C_ops.svd(x, 'full_matrices', full_matrices)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'svd')\n check_type(full_matrices, 'full_matrices', bool, 'svd')\n helper = LayerHelper('svd', **locals())\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n vh = helper.create_variable_for_type_inference(dtype=x.dtype)\n s = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['full_matrices'] = full_matrices\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vh,\n 'S': s},\n attrs=attrs, )\n return u, s, vh\n\n\ndef matrix_power(x, n, name=None):\n r\"\"\"\n Computes the n-th power of a square matrix or a batch of square matrices.\n\n Let :math:`X` be a sqaure matrix or a batch of square matrices, :math:`n` be\n an exponent, the equation should be:\n\n .. math::\n Out = X ^ {n}\n\n Specifically,\n\n - If `n > 0`, it returns the matrix or a batch of matrices raised to the power\n of `n`.\n\n - If `n = 0`, it returns the identity matrix or a batch of identity matrices.\n\n - If `n < 0`, it returns the inverse of each matrix (if invertible) raised to\n the power of `abs(n)`.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices to be raised\n to power `n`. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n n (int): The exponent. It can be any positive, negative integer or zero.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The n-th power of the matrix (or the batch of matrices) `x`. Its\n data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1, 2, 3],\n [1, 4, 9],\n [1, 8, 27]], dtype='float64')\n print(paddle.linalg.matrix_power(x, 2))\n # [[6. , 34. , 102.],\n # [14. , 90. , 282.],\n # [36. , 250., 804.]]\n\n print(paddle.linalg.matrix_power(x, 0))\n # [[1., 0., 0.],\n # [0., 1., 0.],\n # [0., 0., 1.]]\n\n print(paddle.linalg.matrix_power(x, -2))\n # [[ 12.91666667, -12.75000000, 2.83333333 ],\n # [-7.66666667 , 8. , -1.83333333 ],\n # [ 1.80555556 , -1.91666667 , 0.44444444 ]]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.matrix_power(x, \"n\", n)\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'matrix_power')\n check_type(n, 'n', int, 'matrix_power')\n helper = LayerHelper('matrix_power', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matrix_power',\n inputs={'X': x},\n outputs={'Out': out},\n attrs={'n': n})\n return out\n\n\ndef qr(x, mode=\"reduced\", name=None):\n r\"\"\"\n Computes the QR decomposition of one matrix or batches of matrice (backward is unsupported now).\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., M, N]`,\n where ... is zero or more batch dimensions. M and N can be arbitrary\n positive number. The data type of x should be float32 or float64. \n mode (str, optional): A flag to control the behavior of qr, the default is \"reduced\". \n Suppose x's shape is `[..., M, N]` and denoting `K = min(M, N)`:\n If mode = \"reduced\", qr op will return reduced Q and R matrices, \n which means Q's shape is `[..., M, K]` and R's shape is `[..., K, N]`.\n If mode = \"complete\", qr op will return complete Q and R matrices, \n which means Q's shape is `[..., M, M]` and R's shape is `[..., M, N]`.\n If mode = \"r\", qr op will only return reduced R matrix, which means\n R's shape is `[..., K, N]`.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n If mode = \"reduced\" or mode = \"complete\", qr will return a two tensor-tuple, which represents Q and R. \n If mode = \"r\", qr will return a tensor which represents R.\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n q, r = paddle.linalg.qr(x)\n print (q)\n print (r)\n\n # Q = [[-0.16903085, 0.89708523],\n # [-0.50709255, 0.27602622],\n # [-0.84515425, -0.34503278]])\n\n # R = [[-5.91607978, -7.43735744],\n # [ 0. , 0.82807867]])\n \n # one can verify : X = Q * R ; \n \"\"\"\n if paddle.in_dynamic_mode():\n q, r = _C_ops.qr(x, 'mode', mode)\n if mode == \"r\":\n return r\n else:\n return q, r\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'qr')\n check_type(mode, 'mode', str, 'qr')\n helper = LayerHelper('qr', **locals())\n q = helper.create_variable_for_type_inference(dtype=x.dtype)\n r = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['mode'] = mode\n helper.append_op(\n type='qr', inputs={'X': [x]}, outputs={'Q': q,\n 'R': r}, attrs=attrs)\n if mode == \"r\":\n return r\n else:\n return q, r\n\n\ndef lu(x, pivot=True, get_infos=False, name=None):\n r\"\"\"\n Computes the LU factorization of an N-D(N>=2) matrix x. \n\n Returns the LU factorization(inplace x) and Pivots. low triangular matrix L and \n upper triangular matrix U are combined to a single LU matrix.\n\n Pivoting is done if pivot is set to True.\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n # return ones\n\n Args:\n\n X (Tensor): the tensor to factor of N-dimensions(N>=2).\n\n pivot (bool, optional): controls whether pivoting is done. Default: True.\n\n get_infos (bool, optional): if set to True, returns an info IntTensor. Default: False.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n factorization (Tensor): LU matrix, the factorization of input X.\n\n pivots (IntTensor): the pivots of size(∗(N-2), min(m,n)). `pivots` stores all the \n intermediate transpositions of rows. The final permutation `perm` could be \n reconstructed by this, details refer to upper example.\n\n infos (IntTensor, optional): if `get_infos` is `True`, this is a tensor of size (∗(N-2)) \n where non-zero values indicate whether factorization for the matrix or each minibatch \n has succeeded or failed.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n \n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n if paddle.in_dynamic_mode():\n LU, Piv, Info = _C_ops.lu(x, 'pivots', pivot)\n if get_infos:\n return LU, Piv, Info\n else:\n return LU, Piv\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu')\n helper = LayerHelper('lu', **locals())\n lu = helper.create_variable_for_type_inference(dtype=x.dtype)\n p = helper.create_variable_for_type_inference(dtype='int')\n info = helper.create_variable_for_type_inference(dtype='int')\n attrs = dict()\n attrs['pivots'] = pivot\n helper.append_op(\n type='lu',\n inputs={'X': x},\n outputs={'Out': lu,\n 'Pivots': p,\n 'Infos': info},\n attrs=attrs)\n if get_infos:\n return lu, p, info\n else:\n return lu, p\n\n\ndef lu_unpack(x, y, unpack_ludata=True, unpack_pivots=True, name=None):\n r\"\"\"\n Unpack L U and P to single matrix tensor . \n unpack L and U matrix from LU, unpack permutation matrix P from Pivtos .\n\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n\n\n Args:\n x (Tensor): The LU tensor get from paddle.linalg.lu, which is combined by L and U.\n\n y (Tensor): Pivots get from paddle.linalg.lu.\n\n unpack_ludata (bool,optional): whether to unpack L and U from x. Default: True.\n\n unpack_pivots (bool, optional): whether to unpack permutation matrix P from Pivtos. Default: True.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n P (Tensor): Permutation matrix P of lu factorization.\n\n L (Tensor): The lower triangular matrix tensor of lu factorization.\n\n U (Tensor): The upper triangular matrix tensor of lu factorization.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n\n if paddle.in_dynamic_mode():\n P, L, U = _C_ops.lu_unpack(x, y, 'unpack_ludata', unpack_ludata,\n 'unpack_pivots', unpack_pivots)\n return P, L, U\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu_unpack')\n helper = LayerHelper('lu_unpack', **locals())\n p = helper.create_variable_for_type_inference(dtype=x.dtype)\n l = helper.create_variable_for_type_inference(dtype=x.dtype)\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n attrs = dict()\n attrs['unpack_ludata'] = unpack_ludata\n attrs['unpack_pivots'] = unpack_pivots\n helper.append_op(\n type='lu_unpack',\n inputs={'X': x,\n 'Pivots': y},\n outputs={'Pmat': p,\n 'L': l,\n 'U': u},\n attrs=attrs)\n return p, l, u\n\n\ndef eig(x, name=None):\n \"\"\"\n This API performs the eigenvalue decomposition of a square matrix or a batch of square matrices.\n\n .. note::\n If the matrix is a Hermitian or a real symmetric matrix, please use :ref:`paddle.linalg.eigh` instead, which is much faster.\n If only eigenvalues is needed, please use :ref:`paddle.linalg.eigvals` instead.\n If the matrix is of any shape, please use :ref:`paddle.linalg.svd`.\n This API is only supported on CPU device.\n The output datatype is always complex for both real and complex input.\n\n Args:\n x (Tensor): A tensor with shape math:`[*, N, N]`, The data type of the x should be one of ``float32``,\n ``float64``, ``compplex64`` or ``complex128``.\n name (str, optional): The default value is `None`. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Eigenvalues(Tensors): A tensor with shape math:`[*, N]` refers to the eigen values.\n Eigenvectors(Tensors): A tensor with shape math:`[*, N, N]` refers to the eigen vectors.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n paddle.device.set_device(\"cpu\")\n\n x_data = np.array([[1.6707249, 7.2249975, 6.5045543],\n [9.956216, 8.749598, 6.066444 ],\n [4.4251957, 1.7983172, 0.370647 ]]).astype(\"float32\")\n x = paddle.to_tensor(x_data)\n w, v = paddle.linalg.eig(x)\n print(w)\n # Tensor(shape=[3, 3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [[(-0.5061363550800655+0j) , (-0.7971760990842826+0j) ,\n # (0.18518077798279986+0j)],\n # [(-0.8308237755993192+0j) , (0.3463813401919749+0j) ,\n # (-0.6837005269141947+0j) ],\n # [(-0.23142567697893396+0j), (0.4944999840400175+0j) ,\n # (0.7058765252952796+0j) ]])\n\n print(v)\n # Tensor(shape=[3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [ (16.50471283351188+0j) , (-5.5034820550763515+0j) ,\n # (-0.21026087843552282+0j)])\n \"\"\"\n if paddle.in_dynamic_mode():\n w, v = _C_ops.eig(x)\n return w, v\n\n check_variable_and_dtype(\n x, 'X', ['float32', 'float64', 'complex64', 'complex128'], 'eig')\n helper = LayerHelper('eig', **locals())\n\n w = helper.create_variable_for_type_inference(x.dtype)\n v = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {'X': x}\n outputs = {'Eigenvalues': w, 'Eigenvectors': v}\n helper.append_op(type='eig', inputs=inputs, outputs=outputs)\n\n return w, v\n\n\ndef eigvals(x, name=None):\n \"\"\"\n Compute the eigenvalues of one or more general matrices.\n\n Warning:\n The gradient kernel of this operator does not yet developed.\n If you need back propagation through this operator, please replace it with paddle.linalg.eig.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices whose eigenvalues will be computed.\n Its shape should be `[*, M, M]`, where `*` is zero or more batch dimensions.\n Its data type should be float32, float64, complex64, or complex128.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n Tensor: A tensor containing the unsorted eigenvalues which has the same batch dimensions with `x`.\n The eigenvalues are complex-valued even when `x` is real.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n paddle.seed(1234)\n\n x = paddle.rand(shape=[3, 3], dtype='float64')\n # [[0.02773777, 0.93004224, 0.06911496],\n # [0.24831591, 0.45733623, 0.07717843],\n # [0.48016702, 0.14235102, 0.42620817]])\n\n print(paddle.linalg.eigvals(x))\n # [(-0.27078833542132674+0j), (0.29962280156230725+0j), (0.8824477020120244+0j)] #complex128\n \"\"\"\n\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64',\n 'complex128'], 'eigvals')\n\n x_shape = list(x.shape)\n if len(x_shape) < 2:\n raise ValueError(\n \"The dimension of Input(x) should be at least 2, but received x's dimention = {}, x's shape = {}\".\n format(len(x_shape), x_shape))\n\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The last two dimensions of Input(x) should be equal, but received x's shape = {}\".\n format(x_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.eigvals(x)\n\n helper = LayerHelper('eigvals', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='eigvals', inputs={'X': x}, outputs={'Out': out})\n return out\n\n\ndef multi_dot(x, name=None):\n \"\"\"\n Multi_dot is an operator that calculates multiple matrix multiplications.\n\n Supports inputs of float16(only GPU support), float32 and float64 dtypes. This function does not\n support batched inputs.\n\n The input tensor in [x] must be 2-D except for the first and last can be 1-D.\n If the first tensor is a 1-D vector of shape(n, ) it is treated as row vector\n of shape(1, n), similarly if the last tensor is a 1D vector of shape(n, ), it\n is treated as a column vector of shape(n, 1).\n\n If the first and last tensor are 2-D matrix, then the output is also 2-D matrix,\n otherwise the output is a 1-D vector.\n\n Multi_dot will select the lowest cost multiplication order for calculation. The\n cost of multiplying two matrices with shapes (a, b) and (b, c) is a * b * c.\n Given matrices A, B, C with shapes (20, 5), (5, 100), (100, 10) respectively,\n we can calculate the cost of different multiplication orders as follows:\n - Cost((AB)C) = 20x5x100 + 20x100x10 = 30000\n - Cost(A(BC)) = 5x100x10 + 20x5x10 = 6000\n\n In this case, multiplying B and C first, then multiply A, which is 5 times faster\n than sequential calculation.\n\n Args:\n x ([Tensor]): The input tensors which is a list Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # A * B\n A_data = np.random.random([3, 4]).astype(np.float32)\n B_data = np.random.random([4, 5]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n out = paddle.linalg.multi_dot([A, B])\n print(out.numpy().shape)\n # [3, 5]\n\n # A * B * C\n A_data = np.random.random([10, 5]).astype(np.float32)\n B_data = np.random.random([5, 8]).astype(np.float32)\n C_data = np.random.random([8, 7]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n C = paddle.to_tensor(C_data)\n out = paddle.linalg.multi_dot([A, B, C])\n print(out.numpy().shape)\n # [10, 7]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.multi_dot(x)\n\n check_type(x, 'x', (list, tuple), 'multi_dot')\n for id, item in enumerate(x):\n check_variable_and_dtype(item, 'x[' + str(id) + ']',\n ['float16', 'float32', 'float64'], 'multi_dot')\n if item.dtype != x[0].dtype:\n raise TypeError(\n \"All the Tensors in the input must have the same data type.\")\n\n helper = LayerHelper('multi_dot', **locals())\n dtype = helper.input_dtype(input_param_name='x')\n out = helper.create_variable_for_type_inference(dtype)\n helper.append_op(type='multi_dot', inputs={\"X\": x}, outputs={\"Out\": out})\n return out\n\n\ndef eigh(x, UPLO='L', name=None):\n \"\"\"\n Compute the eigenvalues and eigenvectors of a\n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[*, N, N]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): (string, default 'L'), 'L' represents the lower triangular matrix,\n \"'U' represents the upper triangular matrix.\".\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n\n out_value(Tensor): A Tensor with shape [*, N] and data type of float32 and float64. The eigenvalues of eigh op.\n out_vector(Tensor): A Tensor with shape [*, N, N] and data type of float32,float64,complex64 and complex128. The eigenvectors of eigh op.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value, out_vector = paddle.linalg.eigh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n print(out_vector)\n #[(-0.9238795325112867+0j), (-0.3826834323650898+0j)],\n #[ 0.3826834323650898j , -0.9238795325112867j ]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.eigh(x, 'UPLO', UPLO)\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigh', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'eigh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO})\n return out_value, out_vector\n\n\ndef pinv(x, rcond=1e-15, hermitian=False, name=None):\n r\"\"\"\n Calculate pseudo inverse via SVD(singular value decomposition)\n of one matrix or batches of regular matrix.\n\n .. math::\n\n if hermitian == False:\n x = u * s * vt (SVD)\n out = v * 1/s * ut\n else:\n x = u * s * ut (eigh)\n out = u * 1/s * u.conj().transpose(-2,-1)\n\n If x is hermitian or symmetric matrix, svd will be replaced with eigh.\n\n Args:\n x(Tensor): The input tensor. Its shape should be (*, m, n)\n where * is zero or more batch dimensions. m and n can be\n arbitraty positive number. The data type of x should be\n float32 or float64 or complex64 or complex128. When data\n type is complex64 or cpmplex128, hermitian should be set\n True.\n\n rcond(Tensor, optional): the tolerance value to determine\n when is a singular value zero. Defalut:1e-15.\n\n hermitian(bool, optional): indicates whether x is Hermitian\n if complex or symmetric if real. Default: False.\n\n name(str|None): A name for this layer(optional). If set None,\n the layer will be named automatically.\n\n Returns:\n Tensor: The tensor with same data type with x. it represents\n pseudo inverse of x. Its shape should be (*, n, m).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.arange(15).reshape((3, 5)).astype('float64')\n input = paddle.to_tensor(x)\n out = paddle.linalg.pinv(input)\n print(input)\n print(out)\n\n # input:\n # [[0. , 1. , 2. , 3. , 4. ],\n # [5. , 6. , 7. , 8. , 9. ],\n # [10., 11., 12., 13., 14.]]\n\n # out:\n # [[-0.22666667, -0.06666667, 0.09333333],\n # [-0.12333333, -0.03333333, 0.05666667],\n # [-0.02000000, 0.00000000, 0.02000000],\n # [ 0.08333333, 0.03333333, -0.01666667],\n # [ 0.18666667, 0.06666667, -0.05333333]]\n\n # one can verify : x * out * x = x ;\n # or out * x * out = x ;\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if not hermitian:\n # combine svd and matmul op\n u, s, vt = _C_ops.svd(x, 'full_matrices', False)\n max_singular_val = _C_ops.reduce_max(s, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=x.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=x.dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v, _ = _C_ops.transpose2(vt, 'axis', perm)\n\n out_1 = v * st\n out_2 = _C_ops.matmul_v2(out_1, u, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n # combine eigh and matmul op\n s, u = _C_ops.eigh(x, 'UPLO', 'L')\n s_abs = paddle.abs(s)\n max_singular_val = _C_ops.reduce_max(s_abs, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=s.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=s.dtype)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n out_1 = u * st\n u_conj = _C_ops.conj(u)\n out_2 = _C_ops.matmul_v2(out_1, u_conj, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n if not hermitian:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'pinv')\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(dtype)\n vt = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vt,\n 'S': s},\n attrs={'full_matrices': False}, )\n\n max_singular_val = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=dtype)\n st_shape = helper.create_variable_for_type_inference(dtype=dtype)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v = helper.create_variable_for_type_inference(dtype)\n v_shape = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='transpose2',\n inputs={'X': [vt]},\n outputs={'Out': [v],\n 'XShape': [v_shape]},\n attrs={'axis': perm})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': v,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n else:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64',\n 'complex128'], 'pinv')\n\n if dtype == paddle.complex128:\n s_type = 'float64'\n elif dtype == paddle.complex64:\n s_type = 'float32'\n else:\n s_type = dtype\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': s,\n 'Eigenvectors': u},\n attrs={'UPLO': 'L'})\n s_abs = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='abs', inputs={'X': s}, outputs={'Out': s_abs})\n max_singular_val = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s_abs},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=s_type)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=s_type)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s_type)\n cond_not_int = layers.cast(layers.logical_not(condition), s_type)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=s_type)\n st_shape = helper.create_variable_for_type_inference(dtype=s_type)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': u,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n u_conj = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='conj', inputs={'X': u}, outputs={'Out': [u_conj]})\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u_conj},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n\n\ndef solve(x, y, name=None):\n r\"\"\"\n Computes the solution of a square system of linear equations with a unique solution for input 'X' and 'Y'.\n Let :math: `X` be a sqaure matrix or a batch of square matrices, :math:`Y` be\n a vector/matrix or a batch of vectors/matrices, the equation should be:\n\n .. math::\n Out = X^-1 * Y\n Specifically,\n - This system of linear equations has one solution if and only if input 'X' is invertible.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): A vector/matrix or a batch of vectors/matrices. Its shape should be `[*, M, K]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of a square system of linear equations with a unique solution for input 'x' and 'y'.\n Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # 2*X0 + X1 = 9\n # X0 + 2*X1 = 8\n\n import paddle\n import numpy as np\n\n np_x = np.array([[3, 1],[1, 2]])\n np_y = np.array([9, 8])\n x = paddle.to_tensor(np_x, dtype=\"float64\")\n y = paddle.to_tensor(np_y, dtype=\"float64\")\n out = paddle.linalg.solve(x, y)\n\n print(out)\n # [2., 3.])\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.solve(x, y)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type=\"solve\", inputs={\"X\": x,\n \"Y\": y}, outputs={\"Out\": out})\n return out\n\n\ndef triangular_solve(x,\n y,\n upper=True,\n transpose=False,\n unitriangular=False,\n name=None):\n r\"\"\"\n Computes the solution of a system of equations with a triangular coefficient matrix `x` and\n multiple right-hand sides `y` .\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input triangular coefficient matrix. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): Whether to solve the upper-triangular system of equations (default) or the lower-triangular \n system of equations. Default: True.\n transpose (bool, optional): whether `x` should be transposed before calculation. Default: False.\n unitriangular (bool, optional): whether `x` is unit triangular. If True, the diagonal elements of `x` are assumed \n to be 1 and not referenced from `x` . Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # x1 + x2 + x3 = 0\n # 2*x2 + x3 = -9\n # -x3 = 5\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n y = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.triangular_solve(x, y, upper=True)\n\n print(out)\n # [7, -2, -5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.triangular_solve(x, y, 'upper', upper, 'transpose',\n transpose, 'unitriangular',\n unitriangular)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"triangular_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'triangular_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'triangular_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='triangular_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={\n 'upper': upper,\n 'transpose': transpose,\n 'unitriangular': unitriangular\n })\n return out\n\n\ndef cholesky_solve(x, y, upper=False, name=None):\n r\"\"\"\n Solves a linear system of equations A @ X = B, given A's Cholesky factor matrix u and matrix B.\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input matrix which is upper or lower triangular Cholesky factor of square matrix A. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): whether to consider the Cholesky factor as a lower or upper triangular matrix. Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type is the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n u = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n b = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.cholesky_solve(b, u, upper=True)\n\n print(out)\n # [-2.5, -7, 9.5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky_solve(x, y, 'upper', upper)\n\n helper = LayerHelper(\"cholesky_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'cholesky_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'cholesky_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='cholesky_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef eigvalsh(x, UPLO='L', name=None):\n \"\"\"\n Computes the eigenvalues of a \n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[_, M, M]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): Lower triangular part of a (‘L’, default) or the upper triangular part (‘U’).\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor eigenvalues in ascending order.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value = paddle.eigvalsh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n \"\"\"\n if paddle.in_dynamic_mode():\n is_test = x.stop_gradient\n values, _ = _C_ops.eigvalsh(x, 'UPLO', UPLO, 'is_test', is_test)\n return values\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigvalsh', **locals())\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64', 'complex128'],\n 'eigvalsh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n is_test = x.stop_gradient\n helper.append_op(\n type='eigvalsh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO,\n 'is_test': is_test})\n return out_value\n\n\ndef lstsq(x, y, rcond=None, driver=None, name=None):\n \"\"\"\n Computes a solution to\n the least squares problem of a system of linear equations.\n\n Args:\n x (Tensor): A tensor with shape ``(*, M, N)`` , the data type of the input Tensor ``x``\n should be one of float32, float64.\n y (Tensor): A tensor with shape ``(*, M, K)`` , the data type of the input Tensor ``y`` \n should be one of float32, float64.\n rcond(float, optional): The default value is None. A float pointing number used to determine \n the effective rank of ``x``. If ``rcond`` is None, it will be set to max(M, N) times the \n machine precision of x_dtype.\n driver(str, optional): The default value is None. The name of LAPACK method to be used. For \n CPU inputs the valid values are ‘gels’, ‘gelsy’, ‘gelsd, ‘gelss’. For CUDA input, the only \n valid driver is ‘gels’. If ``driver`` is None, ‘gelsy’ is used for CPU inputs and ‘gels’ \n for CUDA inputs.\n name(str, optional): The default value is None. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple: A tuple of 4 Tensors which is (``solution``, ``residuals``, ``rank``, ``singular_values``). \n ``solution`` is a tensor with shape ``(*, N, K)``, meaning the least squares solution. ``residuals`` \n is a tensor with shape ``(*, K)``, meaning the squared residuals of the solutions, which is computed \n when M > N and every matrix in ``x`` is full-rank, otherwise return an empty tensor. ``rank`` is a tensor \n with shape ``(*)``, meaning the ranks of the matrices in ``x``, which is computed when ``driver`` in \n (‘gelsy’, ‘gelsd’, ‘gelss’), otherwise return an empty tensor. ``singular_values`` is a tensor with \n shape ``(*, min(M, N))``, meaning singular values of the matrices in ``x``, which is computed when \n ``driver`` in (‘gelsd’, ‘gelss’), otherwise return an empty tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n x = paddle.to_tensor([[1, 3], [3, 2], [5, 6.]])\n y = paddle.to_tensor([[3, 4, 6], [5, 3, 4], [1, 2, 1.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gelsd\")\n print(results[0])\n # [[ 0.78350395, -0.22165027, -0.62371236],\n # [-0.11340097, 0.78866047, 1.14948535]]\n print(results[1])\n # [19.81443405, 10.43814468, 30.56185532])\n print(results[2])\n # 2\n print(results[3])\n # [9.03455734, 1.54167950]\n\n x = paddle.to_tensor([[10, 2, 3], [3, 10, 5], [5, 6, 12.]])\n y = paddle.to_tensor([[4, 2, 9], [2, 0, 3], [2, 5, 3.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gels\")\n print(results[0])\n # [[ 0.39386186, 0.10230173, 0.93606132],\n # [ 0.10741687, -0.29028133, 0.11892585],\n # [-0.05115091, 0.51918161, -0.19948854]]\n print(results[1])\n # []\n \"\"\"\n device = paddle.get_device()\n if device == \"cpu\":\n if driver not in (None, \"gels\", \"gelss\", \"gelsd\", \"gelsy\"):\n raise ValueError(\n \"Only support valid driver is 'gels', 'gelss', 'gelsd', 'gelsy' or None for CPU inputs. But got {}\".\n format(driver))\n driver = \"gelsy\" if driver is None else driver\n elif \"gpu\" in device:\n if driver not in (None, \"gels\"):\n raise ValueError(\n \"Only support valid driver is 'gels' or None for CUDA inputs. But got {}\".\n format(driver))\n driver = \"gels\" if driver is None else driver\n else:\n raise RuntimeError(\"Only support lstsq api for CPU or CUDA device.\")\n\n if x.dtype == y.dtype and x.dtype in (paddle.float32, paddle.float64):\n pass\n else:\n raise ValueError(\n \"Only support x and y have the same dtype such as 'float32' and 'float64'.\"\n )\n\n if rcond is None:\n if x.dtype == paddle.float32:\n rcond = 1e-7 * max(x.shape[-2], x.shape[-1])\n elif x.dtype == paddle.float64:\n rcond = 1e-15 * max(x.shape[-2], x.shape[-1])\n\n if paddle.in_dynamic_mode():\n solution, rank, singular_values = _C_ops.lstsq(x, y, \"rcond\", rcond,\n \"driver\", driver)\n if x.shape[-2] > x.shape[-1]:\n matmul_out = _varbase_creator(dtype=x.dtype)\n _C_ops.matmul(x, solution, matmul_out, 'trans_x', False, 'trans_y',\n False)\n minus_out = _C_ops.elementwise_sub(matmul_out, y)\n pow_out = _C_ops.pow(minus_out, 'factor', 2)\n residuals = _C_ops.reduce_sum(pow_out, 'dim', [-2], 'keepdim',\n False, 'reduce_all', False)\n else:\n residuals = paddle.empty(shape=[0], dtype=x.dtype)\n\n if driver == \"gels\":\n rank = paddle.empty(shape=[0], dtype=paddle.int32)\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n elif driver == \"gelsy\":\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n\n return solution, residuals, rank, singular_values\n\n helper = LayerHelper('lstsq', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n check_variable_and_dtype(\n y, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n\n solution = helper.create_variable_for_type_inference(dtype=x.dtype)\n residuals = helper.create_variable_for_type_inference(dtype=x.dtype)\n rank = helper.create_variable_for_type_inference(dtype=paddle.int32)\n singular_values = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='lstsq',\n inputs={'X': x,\n 'Y': y},\n outputs={\n 'Solution': solution,\n 'Rank': rank,\n 'SingularValues': singular_values\n },\n attrs={'rcond': rcond,\n 'driver': driver})\n\n matmul_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n minus_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n pow_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': solution},\n outputs={'Out': matmul_out},\n attrs={\n 'trans_x': False,\n 'trans_y': False,\n })\n\n helper.append_op(\n type='elementwise_sub',\n inputs={'X': matmul_out,\n 'Y': y},\n outputs={'Out': minus_out})\n\n helper.append_op(\n type='pow',\n inputs={'X': minus_out},\n outputs={'Out': pow_out},\n attrs={'factor': 2})\n\n helper.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': residuals},\n attrs={'dim': [-2],\n 'keep_dim': False,\n 'reduce_all': False})\n\n if driver == \"gels\":\n rank = paddle.static.data(name='rank', shape=[0])\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n elif driver == \"gelsy\":\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n\n return solution, residuals, rank, singular_values\n"}}},{"rowIdx":38,"cells":{"function_name":{"kind":"string","value":"eigvalsh"},"docstring":{"kind":"string","value":"Computes the eigenvalues of a \ncomplex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\nArgs:\n x (Tensor): A tensor with shape :math:`[_, M, M]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): Lower triangular part of a (‘L’, default) or the upper triangular part (‘U’).\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\nReturns:\n Tensor: The tensor eigenvalues in ascending order.\n\nExamples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value = paddle.eigvalsh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]"},"masked_code":{"kind":"string","value":"# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numpy as np\nfrom ..fluid.layer_helper import LayerHelper\nfrom ..framework import _varbase_creator, _dygraph_tracer\nfrom ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype\nfrom ..static import Variable\nfrom ..fluid.framework import _in_legacy_dygraph, in_dygraph_mode\nfrom ..fluid.layers import transpose, cast # noqa: F401\nfrom ..fluid import layers\nimport paddle\nfrom paddle.common_ops_import import core\nfrom paddle.common_ops_import import VarDesc\nfrom paddle import _C_ops\n\n__all__ = []\n\n\ndef matmul(x, y, transpose_x=False, transpose_y=False, name=None):\n \"\"\"\n Applies matrix multiplication to two tensors. `matmul` follows\n the complete broadcast rules,\n and its behavior is consistent with `np.matmul`.\n\n Currently, the input tensors' number of dimensions can be any, `matmul` can be used to\n achieve the `dot`, `matmul` and `batchmatmul`.\n\n The actual behavior depends on the shapes of :math:`x`, :math:`y` and the\n flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:\n\n - If a transpose flag is specified, the last two dimensions of the tensor\n are transposed. If the tensor is ndim-1 of shape, the transpose is invalid. If the tensor\n is ndim-1 of shape :math:`[D]`, then for :math:`x` it is treated as :math:`[1, D]`, whereas\n for :math:`y` it is the opposite: It is treated as :math:`[D, 1]`.\n\n The multiplication behavior depends on the dimensions of `x` and `y`. Specifically:\n\n - If both tensors are 1-dimensional, the dot product result is obtained.\n\n - If both tensors are 2-dimensional, the matrix-matrix product is obtained.\n\n - If the `x` is 1-dimensional and the `y` is 2-dimensional,\n a `1` is prepended to its dimension in order to conduct the matrix multiply.\n After the matrix multiply, the prepended dimension is removed.\n\n - If the `x` is 2-dimensional and `y` is 1-dimensional,\n the matrix-vector product is obtained.\n\n - If both arguments are at least 1-dimensional and at least one argument\n is N-dimensional (where N > 2), then a batched matrix multiply is obtained.\n If the first argument is 1-dimensional, a 1 is prepended to its dimension\n in order to conduct the batched matrix multiply and removed after.\n If the second argument is 1-dimensional, a 1 is appended to its\n dimension for the purpose of the batched matrix multiple and removed after.\n The non-matrix (exclude the last two dimensions) dimensions are\n broadcasted according the broadcast rule.\n For example, if input is a (j, 1, n, m) tensor and the other is a (k, m, p) tensor,\n out will be a (j, k, n, p) tensor.\n\n Args:\n x (Tensor): The input tensor which is a Tensor.\n y (Tensor): The input tensor which is a Tensor.\n transpose_x (bool): Whether to transpose :math:`x` before multiplication.\n transpose_y (bool): Whether to transpose :math:`y` before multiplication.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # vector * vector\n x_data = np.random.random([10]).astype(np.float32)\n y_data = np.random.random([10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [1]\n\n # matrix * vector\n x_data = np.random.random([10, 5]).astype(np.float32)\n y_data = np.random.random([5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10]\n\n # batched matrix * broadcasted vector\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([2]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5]\n\n # batched matrix * batched matrix\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([10, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5, 5]\n\n # batched matrix * broadcasted matrix\n x_data = np.random.random([10, 1, 5, 2]).astype(np.float32)\n y_data = np.random.random([1, 3, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 3, 5, 5]\n\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_matmul(x, y, transpose_x, transpose_y)\n\n if _in_legacy_dygraph():\n op_type = 'matmul_v2'\n op = getattr(_C_ops, op_type)\n return op(x, y, 'trans_x', transpose_x, 'trans_y', transpose_y)\n\n attrs = {\n 'trans_x': transpose_x,\n 'trans_y': transpose_y,\n }\n\n def __check_input(x, y):\n var_names = {'x': x, 'y': y}\n for name, val in var_names.items():\n check_variable_and_dtype(\n val, name,\n ['float16', 'float32', 'float64', 'complex64', 'complex128'],\n 'matmul')\n\n __check_input(x, y)\n\n helper = LayerHelper('matmul_v2', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef norm(x, p='fro', axis=None, keepdim=False, name=None):\n \"\"\"\n\n Returns the matrix norm (Frobenius) or vector norm (the 1-norm, the Euclidean\n or 2-norm, and in general the p-norm for p > 0) of a given tensor.\n\n .. note::\n This norm API is different from `numpy.linalg.norm`.\n This api supports high-order input tensors (rank >= 3), and certain axis need to be pointed out to calculate the norm.\n But `numpy.linalg.norm` only supports 1-D vector or 2-D matrix as input tensor.\n For p-order matrix norm, this api actually treats matrix as a flattened vector to calculate the vector norm, NOT REAL MATRIX NORM.\n\n Args:\n x (Tensor): The input tensor could be N-D tensor, and the input data\n type could be float32 or float64.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `0`, `1`, `2`,\n `inf`, `-inf` and any positive real number yielding the corresponding p-norm. Not supported: ord < 0 and nuclear norm.\n Default value is `fro`.\n axis (int|list|tuple, optional): The axis on which to apply norm operation. If axis is int\n or list(int)/tuple(int) with only one element, the vector norm is computed over the axis.\n If `axis < 0`, the dimension to norm operation is rank(input) + axis.\n If axis is a list(int)/tuple(int) with two elements, the matrix norm is computed over the axis.\n Defalut value is `None`.\n keepdim (bool, optional): Whether to reserve the reduced dimension in the\n output Tensor. The result tensor will have fewer dimension\n than the :attr:`input` unless :attr:`keepdim` is true, default\n value is False.\n name (str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: results of norm operation on the specified axis of input tensor,\n it's data type is the same as input's Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n shape=[2, 3, 4]\n np_input = np.arange(24).astype('float32') - 12\n np_input = np_input.reshape(shape)\n x = paddle.to_tensor(np_input)\n #[[[-12. -11. -10. -9.] [ -8. -7. -6. -5.] [ -4. -3. -2. -1.]]\n # [[ 0. 1. 2. 3.] [ 4. 5. 6. 7.] [ 8. 9. 10. 11.]]]\n\n # compute frobenius norm along last two dimensions.\n out_fro = paddle.linalg.norm(x, p='fro', axis=[0,1])\n # out_fro.numpy() [17.435596 16.911535 16.7332 16.911535]\n\n # compute 2-order vector norm along last dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=-1)\n #out_pnorm.numpy(): [[21.118711 13.190906 5.477226]\n # [ 3.7416575 11.224972 19.131126]]\n\n # compute 2-order norm along [0,1] dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=[0,1])\n #out_pnorm.numpy(): [17.435596 16.911535 16.7332 16.911535]\n\n # compute inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=np.inf)\n #out_pnorm.numpy() = [12.]\n out_pnorm = paddle.linalg.norm(x, p=np.inf, axis=0)\n #out_pnorm.numpy(): [[12. 11. 10. 9.] [8. 7. 6. 7.] [8. 9. 10. 11.]]\n\n # compute -inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=-np.inf)\n #out_pnorm.numpy(): [0.]\n out_pnorm = paddle.linalg.norm(x, p=-np.inf, axis=0)\n #out_pnorm.numpy(): [[0. 1. 2. 3.] [4. 5. 6. 5.] [4. 3. 2. 1.]]\n \"\"\"\n\n def frobenius_norm(input, dim=None, keepdim=False, name=None):\n \"\"\"\n The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n dim (list, optional): None for last two dimensions.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if dim is not None and not (isinstance(dim, list) and len(dim) == 2):\n raise ValueError(\n \"The dim of frobenius norm op should be None or two elements list!\"\n )\n if paddle.in_dynamic_mode():\n if dim is None:\n return _C_ops.frobenius_norm(input, 'keep_dim', keepdim,\n 'reduce_all', True)\n return _C_ops.frobenius_norm(input, 'dim', dim, 'keep_dim', keepdim,\n 'reduce_all', False)\n attrs = {'dim': dim, 'keep_dim': keepdim, 'reduce_all': False}\n if dim is None:\n attrs['reduce_all'] = True\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'frobenius_norm')\n\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='frobenius_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def vector_norm(input,\n porder=None,\n axis=None,\n keepdim=False,\n asvector=False,\n name=None):\n \"\"\"\n Calculate the p-order vector norm for certain dimension of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n porder (float, optional): None for porder=2.0.\n axis (int, optional): None for last dimension.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if paddle.in_dynamic_mode():\n if axis is None: axis = -1\n return _C_ops.p_norm(input, 'porder', porder, 'axis', axis,\n 'keepdim', keepdim, 'asvector', asvector)\n if porder is not None:\n check_type(porder, 'porder', (float, int), 'p_norm')\n if axis is not None:\n check_type(axis, 'axis', (int), 'p_norm')\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'p_norm')\n\n attrs = {\n 'axis': axis if axis is not None else -1,\n 'porder': float(porder) if porder is not None else 2.0,\n 'keepdim': keepdim,\n 'asvector': asvector,\n 'epsilon': 1e-12,\n }\n helper = LayerHelper('p_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='p_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def inf_norm(input,\n porder=None,\n axis=axis,\n keepdim=False,\n asvector=False,\n name=None):\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n helper.append_op(type='abs', inputs={'X': input}, outputs={'Out': out})\n reduce_out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n reduce_all = True if axis == None or axis == [] or asvector == True else False\n axis = axis if axis != None and axis != [] else [0]\n\n reduce_type = 'reduce_max' if porder == np.float(\n 'inf') else 'reduce_min'\n helper.append_op(\n type=reduce_type,\n inputs={'X': out},\n outputs={'Out': reduce_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n\n return reduce_out\n\n def p_matrix_norm(input, porder=1., axis=axis, keepdim=False, name=None):\n \"\"\"\n NOTE:\n This function actually treats the matrix as flattened vector to calculate vector norm instead of matrix norm.\n \"\"\"\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n\n block.append_op(\n type='pow',\n inputs={'X': abs_out},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': True if axis is None else False\n })\n porder\n block.append_op(\n type='pow',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n if axis is None and p is not None:\n if isinstance(p, str):\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n porder=p,\n axis=axis,\n keepdim=keepdim,\n asvector=True,\n name=name)\n else:\n raise ValueError(\"only valid p type is string or float, found {}\".\n format(type(p)))\n\n if isinstance(axis, tuple):\n axis = list(axis)\n if isinstance(axis, list) and len(axis) == 1:\n axis = axis[0]\n\n #calculate vector norm, where axis is int or list with only one integer\n if isinstance(axis, int):\n if isinstance(p, str):\n if p == \"fro\":\n return vector_norm(\n x,\n porder=2,\n axis=axis,\n keepdim=keepdim,\n asvector=False,\n name=name)\n\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n axis=axis,\n porder=p,\n keepdim=keepdim,\n asvector=False,\n name=name)\n else:\n raise ValueError(\n \"unspport p for p-order vector norm. except float, found {}\".\n format(p))\n #calculate matrix norm, where axis is list with two integers\n elif isinstance(axis, list) and len(axis) == 2:\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n elif p == np.inf or p == -np.inf:\n return inf_norm(x, porder=p, axis=axis, keepdim=keepdim, name=name)\n elif p == 0:\n raise ValueError(\n \"just suport axis type int or list (length of list <=1) if p = 0, found {}\".\n format(axis))\n else:\n return p_matrix_norm(\n x, porder=p, axis=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"except axis type int or list (length of list <=2), found {}\".\n format(axis))\n\n\ndef dist(x, y, p=2, name=None):\n r\"\"\"\n\n This OP returns the p-norm of (x - y). It is not a norm in a strict sense, only as a measure\n of distance. The shapes of x and y must be broadcastable. The definition is as follows, for\n details, please refer to the `numpy's broadcasting <https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html>`_:\n\n - Each input has at least one dimension.\n - Match the two input dimensions from back to front, the dimension sizes must either be equal, one of them is 1, or one of them does not exist.\n\n Where, z = x - y, the shapes of x and y are broadcastable, then the shape of z can be\n obtained as follows:\n\n 1. If the number of dimensions of x and y are not equal, prepend 1 to the dimensions of the\n tensor with fewer dimensions.\n\n For example, The shape of x is [8, 1, 6, 1], the shape of y is [7, 1, 5], prepend 1 to the\n dimension of y.\n\n x (4-D Tensor): 8 x 1 x 6 x 1\n\n y (4-D Tensor): 1 x 7 x 1 x 5\n\n 2. Determine the size of each dimension of the output z: choose the maximum value from the\n two input dimensions.\n\n z (4-D Tensor): 8 x 7 x 6 x 5\n\n If the number of dimensions of the two inputs are the same, the size of the output can be\n directly determined in step 2. When p takes different values, the norm formula is as follows:\n\n When p = 0, defining $0^0=0$, the zero-norm of z is simply the number of non-zero elements of z.\n\n .. math::\n\n ||z||_{0}=\\lim_{p \\\\rightarrow 0}\\sum_{i=1}^{m}|z_i|^{p}\n\n When p = inf, the inf-norm of z is the maximum element of z.\n\n .. math::\n\n ||z||_\\infty=\\max_i |z_i|\n\n When p = -inf, the negative-inf-norm of z is the minimum element of z.\n\n .. math::\n\n ||z||_{-\\infty}=\\min_i |z_i|\n\n Otherwise, the p-norm of z follows the formula,\n\n .. math::\n\n ||z||_{p}=(\\sum_{i=1}^{m}|z_i|^p)^{\\\\frac{1}{p}}\n\n Args:\n x (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n y (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n p (float, optional): The norm to be computed, its data type is float32 or float64. Default: 2.\n\n Returns:\n Tensor: Tensor that is the p-norm of (x - y).\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor(np.array([[3, 3],[3, 3]]), \"float32\")\n y = paddle.to_tensor(np.array([[3, 3],[3, 1]]), \"float32\")\n out = paddle.dist(x, y, 0)\n print(out) # out = [1.]\n\n out = paddle.dist(x, y, 2)\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"inf\"))\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"-inf\"))\n print(out) # out = [0.]\n \"\"\"\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'dist')\n check_variable_and_dtype(y, 'dtype', ['float32', 'float64'], 'dist')\n check_type(p, 'p', (float, int), 'dist')\n helper = LayerHelper(\"dist\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n outputs = {'Out': [out]}\n attrs = {\"p\": float(p)}\n helper.append_op(\n type='dist', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef cond(x, p=None, name=None):\n \"\"\"\n\n Computes the condition number of a matrix or batches of matrices with respect to a matrix norm ``p``.\n\n Args:\n x (Tensor): The input tensor could be tensor of shape ``(*, m, n)`` where ``*`` is zero or more batch dimensions\n for ``p`` in ``(2, -2)``, or of shape ``(*, n, n)`` where every matrix is invertible for any supported ``p``.\n And the input data type could be ``float32`` or ``float64``.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `nuc`, `1`, `-1`, `2`, `-2`,\n `inf`, `-inf`. Default value is `None`, meaning that the order of the norm is `2`.\n name (str, optional): The default value is `None`. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: computing results of condition number, its data type is the same as input Tensor ``x``.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1., 0, -1], [0, 1, 0], [1, 0, 1]])\n\n # compute conditional number when p is None\n out = paddle.linalg.cond(x)\n # out.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is 'fro'\n out_fro = paddle.linalg.cond(x, p='fro')\n # out_fro.numpy() [3.1622777]\n\n # compute conditional number when order of the norm is 'nuc'\n out_nuc = paddle.linalg.cond(x, p='nuc')\n # out_nuc.numpy() [9.2426405]\n\n # compute conditional number when order of the norm is 1\n out_1 = paddle.linalg.cond(x, p=1)\n # out_1.numpy() [2.]\n\n # compute conditional number when order of the norm is -1\n out_minus_1 = paddle.linalg.cond(x, p=-1)\n # out_minus_1.numpy() [1.]\n\n # compute conditional number when order of the norm is 2\n out_2 = paddle.linalg.cond(x, p=2)\n # out_2.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is -1\n out_minus_2 = paddle.linalg.cond(x, p=-2)\n # out_minus_2.numpy() [0.70710677]\n\n # compute conditional number when order of the norm is inf\n out_inf = paddle.linalg.cond(x, p=np.inf)\n # out_inf.numpy() [2.]\n\n # compute conditional number when order of the norm is -inf\n out_minus_inf = paddle.linalg.cond(x, p=-np.inf)\n # out_minus_inf.numpy() [1.]\n\n a = paddle.to_tensor(np.random.randn(2, 4, 4).astype('float32'))\n # a.numpy()\n # [[[ 0.14063153 -0.996288 0.7996131 -0.02571543]\n # [-0.16303636 1.5534962 -0.49919784 -0.04402903]\n # [-1.1341571 -0.6022629 0.5445269 0.29154757]\n # [-0.16816919 -0.30972657 1.7521842 -0.5402487 ]]\n # [[-0.58081484 0.12402827 0.7229862 -0.55046535]\n # [-0.15178485 -1.1604939 0.75810957 0.30971205]\n # [-0.9669573 1.0940945 -0.27363303 -0.35416734]\n # [-1.216529 2.0018666 -0.7773689 -0.17556527]]]\n a_cond_fro = paddle.linalg.cond(a, p='fro')\n # a_cond_fro.numpy() [31.572273 28.120834]\n\n b = paddle.to_tensor(np.random.randn(2, 3, 4).astype('float64'))\n # b.numpy()\n # [[[ 1.61707487 0.46829144 0.38130416 0.82546736]\n # [-1.72710298 0.08866375 -0.62518804 0.16128892]\n # [-0.02822879 -1.67764516 0.11141444 0.3220113 ]]\n # [[ 0.22524372 0.62474921 -0.85503233 -1.03960523]\n # [-0.76620689 0.56673047 0.85064753 -0.45158196]\n # [ 1.47595418 2.23646462 1.5701758 0.10497519]]]\n b_cond_2 = paddle.linalg.cond(b, p=2)\n # b_cond_2.numpy() [3.30064451 2.51976252]\n\n \"\"\"\n\n def mat_norm(input, porder=1., axis=None):\n \"\"\"\n NOTE:\n Calculate the matrix norm of a square matrix or batches of square matrices,\n when porder is in (1, -1, inf, -inf)\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n axis = axis if axis != None and axis != [] else [0]\n keepdim = False\n\n if paddle.in_dynamic_mode():\n abs_out = _C_ops.abs(input)\n sum_out = _C_ops.reduce_sum(abs_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == 1 or porder == np.inf:\n return _C_ops.reduce_max(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == -1 or porder == -np.inf:\n return _C_ops.reduce_min(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n\n block = LayerHelper('norm', **locals())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n block.append_op(\n type='reduce_sum',\n inputs={'X': abs_out},\n outputs={'Out': sum_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 1 or porder == np.inf:\n block.append_op(\n type='reduce_max',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n if porder == -1 or porder == -np.inf:\n block.append_op(\n type='reduce_min',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n\n def fro_norm(input, porder=2, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the frobenius norm of a square matrix or batches of square matrices.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n if paddle.in_dynamic_mode():\n pow_out = _C_ops.pow(input, 'factor', porder)\n sum_out_1 = _C_ops.reduce_sum(pow_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n sum_out_2 = _C_ops.reduce_sum(sum_out_1, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n return _C_ops.pow(sum_out_2, 'factor', float(1. / porder))\n\n block = LayerHelper('norm', **locals())\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_1 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_2 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='pow',\n inputs={'X': input},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out_1},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_sum',\n inputs={'X': sum_out_1},\n outputs={'Out': sum_out_2},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='pow',\n inputs={'X': sum_out_2},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n def svd_norm(input, porder, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the matrix norm, which is related to singular values, of a matrix\n or batches of matrices, including nuclear norm, 2-norm and (-2)-norm.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n u, s, vh = svd(input, full_matrices=False)\n\n if paddle.in_dynamic_mode():\n if porder == \"nuc\":\n return _C_ops.reduce_sum(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n max_out = _C_ops.reduce_max(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n min_out = _C_ops.reduce_min(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n if porder == 2:\n return _C_ops.elementwise_div(max_out, min_out, 'aixs', axis,\n 'use_mkldnn', False)\n if porder == -2:\n return _C_ops.elementwise_div(min_out, max_out, 'aixs', axis,\n 'use_mkldnn', False)\n\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n if porder == \"nuc\":\n block.append_op(\n type='reduce_sum',\n inputs={'X': s},\n outputs={'Out': out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n max_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n min_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_min',\n inputs={'X': s},\n outputs={'Out': min_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': max_out,\n 'Y': min_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n if porder == -2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': min_out,\n 'Y': max_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n\n def empty_tensor(input, shape):\n if paddle.in_dynamic_mode():\n return input.reshape(shape)\n raise ValueError(\"only support x is nonempty tensor in static mode\")\n\n x_shape = list(x.shape)\n if not len(x_shape) >= 2:\n raise ValueError(\"input should be a matrix or batches of matrices, \" +\n \"but the dimention of received input is {}\".format(\n len(x_shape)))\n if p == None:\n p = 2\n x_size = 0 if (0 in x_shape) else 1\n if p in (\"fro\", \"nuc\", 1, -1, np.inf, -np.inf):\n if x_shape[len(x_shape) - 1] == x_shape[len(x_shape) - 2]:\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n x_inv = x.inverse()\n if p == \"fro\":\n return fro_norm(x) * fro_norm(x_inv)\n if p == \"nuc\":\n return svd_norm(x, p) * svd_norm(x_inv, p)\n if p in (1, -1):\n return mat_norm(\n x, porder=p, axis=[-2]) * mat_norm(\n x_inv, porder=p, axis=[-2])\n if p in (np.inf, -np.inf):\n return mat_norm(\n x, porder=p, axis=[-1]) * mat_norm(\n x_inv, porder=p, axis=[-1])\n else:\n raise ValueError(\"only support p is {} when input is a \".format(p) +\n \"square matrix or batches of square matrices\")\n elif p in (2, -2):\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n return svd_norm(x, porder=p)\n else:\n raise ValueError(\n \"unsupported {} for p, only supporting ('fro', 'nuc', \".format(\n p) + \"1, -1, 2, -2, inf, -inf) or none\")\n\n\ndef dot(x, y, name=None):\n \"\"\"\n This operator calculates inner product for vectors.\n\n .. note::\n Support 1-d and 2-d Tensor. When it is 2d, the first dimension of this matrix\n is the batch dimension, which means that the vectors of multiple batches are dotted.\n\n Parameters:\n x(Tensor): 1-D or 2-D ``Tensor``. Its dtype should be ``float32``, ``float64``, ``int32``, ``int64``\n y(Tensor): 1-D or 2-D ``Tensor``. Its dtype soulde be ``float32``, ``float64``, ``int32``, ``int64``\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: the calculated result Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x_data = np.random.uniform(0.1, 1, [10]).astype(np.float32)\n y_data = np.random.uniform(1, 3, [10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.dot(x, y)\n print(z)\n\n \"\"\"\n op_type = 'dot'\n # skip var type check in dygraph mode to improve efficiency\n if paddle.in_dynamic_mode():\n op = getattr(_C_ops, op_type)\n return op(x, y)\n\n assert x is not None, 'x cannot be None in {}'.format(op_type)\n assert y is not None, 'y cannot be None in {}'.format(op_type)\n\n check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n check_variable_and_dtype(y, 'y', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n\n helper = LayerHelper(op_type, **locals())\n if name is None:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n else:\n out = helper.create_variable(\n name=name, dtype=x.dtype, persistable=False)\n helper.append_op(\n type=\"dot\", inputs={'X': x,\n 'Y': y}, attrs={}, outputs={\"Out\": out})\n return out\n\n\ndef cov(x, rowvar=True, ddof=True, fweights=None, aweights=None, name=None):\n \"\"\"\n Estimate the covariance matrix of the input variables, given data and weights.\n\n A covariance matrix is a square matrix, indicate the covariance of each pair variables in the input matrix.\n For example, for an N-dimensional samples X=[x1,x2,…xN]T, then the covariance matrix \n element Cij is the covariance of xi and xj. The element Cii is the variance of xi itself.\n\n Parameters:\n x(Tensor): A N-D(N<=2) Tensor containing multiple variables and observations. By default, each row of x represents a variable. Also see rowvar below.\n rowvar(Bool, optional): If rowvar is True (default), then each row represents a variable, with observations in the columns. Default: True\n ddof(Bool, optional): If ddof=True will return the unbiased estimate, and ddof=False will return the simple average. Default: True\n fweights(Tensor, optional): 1-D Tensor of integer frequency weights; The number of times each observation vector should be repeated. Default: None\n aweights(Tensor, optional): 1-D Tensor of observation vector weights. How important of the observation vector, larger data means this element is more important. Default: None\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: The covariance matrix Tensor of the variables.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n\n xt = paddle.rand((3,4))\n paddle.linalg.cov(xt)\n\n '''\n Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n [[0.07918842, 0.06127326, 0.01493049],\n [0.06127326, 0.06166256, 0.00302668],\n [0.01493049, 0.00302668, 0.01632146]])\n '''\n \"\"\"\n op_type = 'cov'\n if len(x.shape) > 2 or len(x.shape) < 1:\n raise ValueError(\n \"Input(x) only support N-D (1<=N<=2) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cov')\n nx = x\n if len(x.shape) == 1:\n nx = x.reshape((1, -1))\n if not rowvar and nx.shape[0] != 1:\n nx = nx.t()\n w = None\n observation_num = nx.shape[1]\n if fweights is not None:\n w = fweights.astype(nx.dtype)\n if len(w.shape) > 1:\n raise ValueError(\n \"Input(fweights) only support N-D (N<=1) tensor in cov, but received \"\n \"shape of Input(input) is %s.\" % len(fweights.shape))\n if fweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(fweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(fweights) is {}.\".format(observation_num,\n fweights.shape[0]))\n if fweights.min() < 0:\n raise ValueError(\n \"The value of Input(fweights) cannot be negtive, but received \"\n \"min of Input(fweights) is {}.\".format(fweights.min()))\n if not paddle.all(fweights == paddle.round(fweights.astype('float64'))):\n raise ValueError(\"Input(fweights) must be integer \")\n\n if aweights is not None:\n aw = aweights.astype(nx.dtype)\n if len(aw.shape) > 1:\n raise ValueError(\n \"Input(aweights) only support N-D (N<=1) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(aweights.shape))\n check_variable_and_dtype(aweights, 'dtype', ['float32', 'float64'],\n 'cov')\n if aweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(aweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(aweights) is {}.\".format(observation_num,\n aweights.shape[0]))\n if aweights.min() < 0:\n raise ValueError(\n \"The value of Input(aweights) cannot be negtive, but received \"\n \"min of Input(aweights) is {}.\".format(aweights.min()))\n if w is not None:\n w = w * aw\n else:\n w = aw\n\n w_sum = paddle.to_tensor(observation_num, dtype=nx.dtype)\n if fweights is not None or aweights is not None:\n w_sum = w.sum()\n if w_sum.item() == 0:\n raise ValueError(\"The sum of weights is zero, can't be normalized.\")\n\n if w is not None:\n nx_w = nx * w\n avg = (nx_w).sum(axis=1) / w_sum\n else:\n avg = nx.sum(axis=1) / w_sum\n nx_w = nx\n\n if w is not None and aweights is not None and ddof == True:\n norm_factor = w_sum - (w * aweights).sum() / w_sum\n else:\n norm_factor = w_sum - ddof\n if norm_factor <= 0:\n norm_factor = paddle.to_tensor(0, dtype=nx.dtype)\n nx = nx - avg.unsqueeze(1)\n xxt = paddle.mm(nx, nx_w.t().conj())\n cov = paddle.divide(xxt, norm_factor).squeeze()\n return cov\n\n\ndef t(input, name=None):\n \"\"\"\n Transpose <=2-D tensor.\n 0-D and 1-D tensors are returned as it is and 2-D tensor is equal to\n the paddle.transpose function which perm dimensions set 0 and 1.\n\n Args:\n input (Tensor): The input Tensor. It is a N-D (N<=2) Tensor of data types float16, float32, float64, int32.\n name(str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`\n Returns:\n Tensor: A transposed n-D Tensor, with data type being float16, float32, float64, int32, int64.\n\n For Example:\n\n .. code-block:: text\n\n # Example 1 (0-D tensor)\n x = tensor([0.79])\n paddle.t(x) = tensor([0.79])\n\n # Example 2 (1-D tensor)\n x = tensor([0.79, 0.84, 0.32])\n paddle.t(x) = tensor([0.79, 0.84, 0.32])\n\n # Example 3 (2-D tensor)\n x = tensor([0.79, 0.84, 0.32],\n [0.64, 0.14, 0.57])\n paddle.t(x) = tensor([0.79, 0.64],\n [0.84, 0.14],\n [0.32, 0.57])\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n x = paddle.ones(shape=[2, 3], dtype='int32')\n x_transposed = paddle.t(x)\n print(x_transposed.shape)\n # [3, 2]\n \"\"\"\n if len(input.shape) > 2:\n raise ValueError(\n \"Input(input) only support N-D (N<=2) tensor, but received \"\n \"length of Input(input) is %s. Perhaps you can use paddle.\"\n \"tensor.transpose() instead.\" % len(input.shape))\n if paddle.in_dynamic_mode():\n if len(input.shape) == 1:\n return input\n # 2-D tensor\n perm = [1, 0]\n out, _ = _C_ops.transpose2(input, 'axis', perm)\n return out\n\n check_variable_and_dtype(\n input, 'input', ['float16', 'float32', 'float64', 'int32',\n 'int64'], 'transpose')\n\n helper = LayerHelper('t', **locals())\n out = helper.create_variable_for_type_inference(input.dtype)\n input_shape = helper.create_variable_for_type_inference(input.dtype)\n if len(input.shape) == 1:\n out = input\n else:\n helper.append_op(\n type='transpose2',\n inputs={'X': [input]},\n outputs={'Out': [out],\n 'XShape': [input_shape]},\n attrs={'axis': [1, 0]})\n return out\n\n\ndef cross(x, y, axis=None, name=None):\n \"\"\"\n Computes the cross product between two tensors along an axis.\n\n Inputs must have the same shape, and the length of their axes should be equal to 3.\n If `axis` is not given, it defaults to the first axis found with the length 3.\n\n Args:\n x (Tensor): The first input tensor.\n y (Tensor): The second input tensor.\n axis (int, optional): The axis along which to compute the cross product. It defaults to the first axis found with the length 3.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor. A Tensor with same data type as `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0],\n [3.0, 3.0, 3.0]])\n y = paddle.to_tensor([[1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0]])\n\n z1 = paddle.cross(x, y)\n # [[-1. -1. -1.]\n # [ 2. 2. 2.]\n # [-1. -1. -1.]]\n\n z2 = paddle.cross(x, y, axis=1)\n # [[0. 0. 0.]\n # [0. 0. 0.]\n # [0. 0. 0.]]\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_cross(x, y, axis)\n else:\n if _in_legacy_dygraph():\n if axis is not None:\n return _C_ops.cross(x, y, 'dim', axis)\n else:\n return _C_ops.cross(x, y)\n else:\n helper = LayerHelper(\"cross\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n attrs = dict()\n attrs['dim'] = axis\n\n helper.append_op(\n type='cross',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef cholesky(x, upper=False, name=None):\n r\"\"\"\n Computes the Cholesky decomposition of one symmetric positive-definite\n matrix or batches of symmetric positive-definite matrice.\n\n If `upper` is `True`, the decomposition has the form :math:`A = U^{T}U` ,\n and the returned matrix :math:`U` is upper-triangular. Otherwise, the\n decomposition has the form :math:`A = LL^{T}` , and the returned matrix\n :math:`L` is lower-triangular.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[*, M, M]`,\n where * is zero or more batch dimensions, and matrices on the\n inner-most 2 dimensions all should be symmetric positive-definite.\n Its data type should be float32 or float64.\n upper (bool): The flag indicating whether to return upper or lower\n triangular matrices. Default: False.\n\n Returns:\n Tensor: A Tensor with same shape and data type as `x`. It represents \\\n triangular matrices generated by Cholesky decomposition.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n a = np.random.rand(3, 3)\n a_t = np.transpose(a, [1, 0])\n x_data = np.matmul(a, a_t) + 1e-03\n x = paddle.to_tensor(x_data)\n out = paddle.linalg.cholesky(x, upper=False)\n print(out)\n # [[1.190523 0. 0. ]\n # [0.9906703 0.27676893 0. ]\n # [1.25450498 0.05600871 0.06400121]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky(x, \"upper\", upper)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cholesky')\n check_type(upper, 'upper', bool, 'cholesky')\n helper = LayerHelper('cholesky', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='cholesky',\n inputs={'X': [x]},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef matrix_rank(x, tol=None, hermitian=False, name=None):\n r\"\"\"\n Computes the rank of a matrix.\n\n The rank of a matrix is the number of singular values that are greater than the specified `tol` threshold when hermitian=False,\n or the number of eigenvalues in absolute value that are greater than the specified `tol` threshold when hermitian=True.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., m, n]`, where `...` is zero or more batch dimensions. If `x` is a batch\n of matrices then the output has the same batch dimensions. The data type of `x` should be float32 or float64.\n tol (float,Tensor,optional): the tolerance value. Default: None. If `tol` is not specified, and `sigma` is the largest\n singular value (or eigenvalues in absolute value), and `eps` is the epsilon value for the dtype of `x`, then `tol` is computed\n with formula `tol=sigma * max(m,n) * eps`. Note that if `x` is a batch of matrices, `tol` is computed this way for every batch.\n hermitian (bool,optional): indicates whether `x` is Hermitian. Default: False. When hermitian=True, `x` is assumed to be Hermitian,\n enabling a more efficient method for finding eigenvalues, but `x` is not checked inside the function. Instead, We just use\n the lower triangular of the matrix to compute.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: Rank of tensor x.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n a = paddle.eye(10)\n b = paddle.linalg.matrix_rank(a)\n print(b)\n # b = [10]\n\n c = paddle.ones(shape=[3, 4, 5, 5])\n d = paddle.linalg.matrix_rank(c, tol=0.01, hermitian=True)\n print(d)\n # d = [[1, 1, 1, 1],\n # [1, 1, 1, 1],\n # [1, 1, 1, 1]]\n\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if tol is None:\n tol_tensor = None\n tol_attr = 0.0\n use_default_tol = True\n elif isinstance(tol, Variable):\n if tol.dtype != x.dtype:\n tol_tensor = cast(tol, x.dtype)\n else:\n tol_tensor = tol\n tol_attr = 0.0\n use_default_tol = False\n else:\n tol_tensor = None\n tol_attr = float(tol)\n use_default_tol = False\n return _C_ops.matrix_rank(x, tol_tensor, \"tol\", tol_attr, 'hermitian',\n hermitian, 'use_default_tol', use_default_tol)\n\n inputs = {}\n attrs = {}\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'matrix_rank')\n inputs['X'] = x\n if tol is None:\n attrs['use_default_tol'] = True\n elif isinstance(tol, Variable):\n check_variable_and_dtype(tol, 'tol', ['float32'], 'matrix_rank')\n attrs['use_default_tol'] = False\n if tol.dtype != x.dtype:\n inputs['TolTensor'] = cast(tol, x.dtype)\n else:\n inputs['TolTensor'] = tol\n else:\n check_type(tol, 'tol', float, 'matrix_rank')\n attrs['use_default_tol'] = False\n attrs['tol'] = tol\n check_type(hermitian, 'hermitian', bool, 'matrix_rank')\n attrs['hermitian'] = hermitian\n\n helper = LayerHelper('matrix_rank', **locals())\n out = helper.create_variable_for_type_inference(dtype='int32')\n helper.append_op(\n type='matrix_rank', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef bmm(x, y, name=None):\n \"\"\"\n Applies batched matrix multiplication to two tensors.\n\n Both of the two input tensors must be three-dementional and share the same batch size.\n\n if x is a (b, m, k) tensor, y is a (b, k, n) tensor, the output will be a (b, m, n) tensor.\n\n Args:\n x (Tensor): The input Tensor.\n y (Tensor): The input Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The product Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n # In imperative mode:\n # size x: (2, 2, 3) and y: (2, 3, 2)\n x = paddle.to_tensor([[[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0]],\n [[3.0, 3.0, 3.0],\n [4.0, 4.0, 4.0]]])\n y = paddle.to_tensor([[[1.0, 1.0],[2.0, 2.0],[3.0, 3.0]],\n [[4.0, 4.0],[5.0, 5.0],[6.0, 6.0]]])\n out = paddle.bmm(x, y)\n #output size: (2, 2, 2)\n #output value:\n #[[[6.0, 6.0],[12.0, 12.0]],[[45.0, 45.0],[60.0, 60.0]]]\n out_np = out.numpy()\n\n \"\"\"\n x_shape = x.shape\n y_shape = y.shape\n if not len(x_shape) == len(y_shape) == 3:\n raise ValueError(\n \"x and y should be 3-dimensional. But received x's dimention: {}, y's dimention: {}\".\n format(x_shape, y_shape))\n if x_shape[2] != y_shape[1]:\n raise ValueError(\n \"x's width must be equal with y's height. But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n if x_shape[0] != y_shape[0]:\n raise ValueError(\n \"x's batch (shape[0]) must be equal with y's batch (shape[0]). But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.bmm(x, y)\n\n helper = LayerHelper('bmm', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='bmm', inputs={'X': x, 'Y': y}, outputs={'Out': out})\n return out\n\n\ndef histogram(input, bins=100, min=0, max=0, name=None):\n \"\"\"\n Computes the histogram of a tensor. The elements are sorted into equal width bins between min and max.\n If min and max are both zero, the minimum and maximum values of the data are used.\n\n Args:\n input (Tensor): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor\n should be float32, float64, int32, int64.\n bins (int): number of histogram bins\n min (int): lower end of the range (inclusive)\n max (int): upper end of the range (inclusive)\n\n Returns:\n Tensor: data type is int64, shape is (nbins,).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n inputs = paddle.to_tensor([1, 2, 1])\n result = paddle.histogram(inputs, bins=4, min=0, max=3)\n print(result) # [0, 2, 1, 0]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.histogram(input, \"bins\", bins, \"min\", min, \"max\", max)\n\n helper = LayerHelper('histogram', **locals())\n check_variable_and_dtype(\n input, 'X', ['int32', 'int64', 'float32', 'float64'], 'histogram')\n out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)\n helper.append_op(\n type='histogram',\n inputs={'X': input},\n outputs={'Out': out},\n attrs={'bins': bins,\n 'min': min,\n 'max': max})\n return out\n\n\ndef bincount(x, weights=None, minlength=0, name=None):\n \"\"\"\n Computes frequency of each value in the input tensor. \n\n Args:\n x (Tensor): A Tensor with non-negative integer. Should be 1-D tensor.\n weights (Tensor, optional): Weight for each value in the input tensor. Should have the same shape as input. Default is None.\n minlength (int, optional): Minimum number of bins. Should be non-negative integer. Default is 0.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor of frequency.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([1, 2, 1, 4, 5])\n result1 = paddle.bincount(x)\n print(result1) # [0, 2, 1, 0, 1, 1]\n\n w = paddle.to_tensor([2.1, 0.4, 0.1, 0.5, 0.5])\n result2 = paddle.bincount(x, weights=w)\n print(result2) # [0., 2.19999981, 0.40000001, 0., 0.50000000, 0.50000000]\n \"\"\"\n if x.dtype not in [paddle.int32, paddle.int64]:\n raise TypeError(\"Elements in Input(x) should all be integers\")\n\n if paddle.in_dynamic_mode():\n return _C_ops.bincount(x, weights, \"minlength\", minlength)\n\n helper = LayerHelper('bincount', **locals())\n\n check_variable_and_dtype(x, 'X', ['int32', 'int64'], 'bincount')\n\n if weights is not None:\n check_variable_and_dtype(weights, 'Weights',\n ['int32', 'int64', 'float32', 'float64'],\n 'bincount')\n out = helper.create_variable_for_type_inference(dtype=weights.dtype)\n else:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='bincount',\n inputs={'X': x,\n 'Weights': weights},\n outputs={'Out': out},\n attrs={'minlength': minlength})\n return out\n\n\ndef mv(x, vec, name=None):\n \"\"\"\n Performs a matrix-vector product of the matrix x and the vector vec.\n\n Args:\n x (Tensor): A tensor with shape :math:`[M, N]` , The data type of the input Tensor x\n should be one of float32, float64.\n vec (Tensor): A tensor with shape :math:`[N]` , The data type of the input Tensor x\n should be one of float32, float64.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor which is producted by x and vec.\n\n Examples:\n .. code-block:: python\n\n # x: [M, N], vec: [N]\n # paddle.mv(x, vec) # out: [M]\n\n import numpy as np\n import paddle\n\n x_data = np.array([[2, 1, 3], [3, 0, 1]]).astype(\"float64\")\n x = paddle.to_tensor(x_data)\n vec_data = np.array([3, 5, 1])\n vec = paddle.to_tensor(vec_data).astype(\"float64\")\n out = paddle.mv(x, vec)\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_mv(x, vec)\n else:\n if _in_legacy_dygraph():\n out = _C_ops.mv(x, vec)\n return out\n else:\n\n def __check_input(x, vec):\n var_names = {'x': x, 'vec': vec}\n for name, val in var_names.items():\n check_variable_and_dtype(val, name, ['float32', 'float64'],\n 'mv')\n x_shape = list(x.shape)\n vec_shape = list(vec.shape)\n if len(x_shape) != 2:\n raise ValueError(\n \"x should be 2-dimensional. But received x's dimention: {}\".\n format(x_shape))\n if len(vec_shape) != 1:\n raise ValueError(\n \"vec should be 1-dimensional. But received vec's dimention: {}\".\n format(vec_shape))\n\n __check_input(x, vec)\n\n helper = LayerHelper('mv', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='mv', inputs={'X': x,\n 'Vec': vec}, outputs={'Out': out})\n return out\n\n\ndef det(x, name=None):\n \"\"\"\n Calculates determinant value of a square matrix or batches of square matrices.\n Args:\n x (Tensor): input (Tensor): the input matrix of size `(n, n)` or the batch of matrices of size\n `(*, n, n)` where `*` is one or more batch dimensions.\n Returns:\n y (Tensor):the determinant value of a square matrix or batches of square matrices.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.det(x)\n\n print(A)\n\n # [ 0.02547996, 2.52317095, -6.15900707])\n\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.determinant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'det')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('determinant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='determinant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef slogdet(x, name=None):\n \"\"\"\n Calculates the sign and natural logarithm of the absolute value of a square matrix's or batches square matrices' determinant.\n The determinant can be computed with ``sign * exp(logabsdet)\n\n Supports input of float, double\n\n Note that for matrices that have zero determinant, this returns ``(0, -inf)``\n Args:\n x (Tensor): the batch of matrices of size :math:`(*, n, n)`\n where math:`*` is one or more batch dimensions.\n\n Returns:\n y (Tensor): A tensor containing the sign of the determinant and the natural logarithm\n of the absolute value of determinant, respectively.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.slogdet(x)\n\n print(A)\n\n # [[ 1. , 1. , -1. ],\n # [-0.98610914, -0.43010661, -0.10872950]])\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.slogdeterminant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'slogdet')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('slogdeterminant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='slogdeterminant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef svd(x, full_matrices=False, name=None):\n r\"\"\"\n Computes the singular value decomposition of one matrix or a batch of regular matrices.\n\n Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies:\n\n .. math::\n X = U * diag(S) * VT\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., N, M]`,\n where `...` is zero or more batch dimensions. N and M can be arbitraty\n positive number. Note that if x is sigular matrices, the grad is numerical\n instable. The data type of x should be float32 or float64.\n full_matrices (bool): A flag to control the behavor of svd.\n If full_matrices = True, svd op will compute full U and V matrics,\n which means shape of U is `[..., N, N]`, shape of V is `[..., M, M]`. K = min(M, N).\n If full_matrices = False, svd op will use a economic method to store U and V.\n which means shape of U is `[..., N, K]`, shape of V is `[..., M, K]`. K = min(M, N).\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple of 3 tensors: (U, S, VH). VH is the conjugate transpose of V. S is the singlar value vectors of matrics with shape `[..., K]`\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 2.0], [1.0, 3.0], [4.0, 6.0]]).astype('float64')\n x = x.reshape([3, 2])\n u, s, vh = paddle.linalg.svd(x)\n print (u)\n #U = [[ 0.27364809, -0.21695147 ],\n # [ 0.37892198, -0.87112408 ],\n # [ 0.8840446 , 0.44053933 ]]\n\n print (s)\n #S = [8.14753743, 0.78589688]\n print (vh)\n #VT= [[ 0.51411221, 0.85772294],\n # [ 0.85772294, -0.51411221]]\n\n # one can verify : U * S * VT == X\n # U * UH == I\n # V * VH == I\n \"\"\"\n\n if paddle.in_dynamic_mode():\n return _C_ops.svd(x, 'full_matrices', full_matrices)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'svd')\n check_type(full_matrices, 'full_matrices', bool, 'svd')\n helper = LayerHelper('svd', **locals())\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n vh = helper.create_variable_for_type_inference(dtype=x.dtype)\n s = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['full_matrices'] = full_matrices\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vh,\n 'S': s},\n attrs=attrs, )\n return u, s, vh\n\n\ndef matrix_power(x, n, name=None):\n r\"\"\"\n Computes the n-th power of a square matrix or a batch of square matrices.\n\n Let :math:`X` be a sqaure matrix or a batch of square matrices, :math:`n` be\n an exponent, the equation should be:\n\n .. math::\n Out = X ^ {n}\n\n Specifically,\n\n - If `n > 0`, it returns the matrix or a batch of matrices raised to the power\n of `n`.\n\n - If `n = 0`, it returns the identity matrix or a batch of identity matrices.\n\n - If `n < 0`, it returns the inverse of each matrix (if invertible) raised to\n the power of `abs(n)`.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices to be raised\n to power `n`. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n n (int): The exponent. It can be any positive, negative integer or zero.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The n-th power of the matrix (or the batch of matrices) `x`. Its\n data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1, 2, 3],\n [1, 4, 9],\n [1, 8, 27]], dtype='float64')\n print(paddle.linalg.matrix_power(x, 2))\n # [[6. , 34. , 102.],\n # [14. , 90. , 282.],\n # [36. , 250., 804.]]\n\n print(paddle.linalg.matrix_power(x, 0))\n # [[1., 0., 0.],\n # [0., 1., 0.],\n # [0., 0., 1.]]\n\n print(paddle.linalg.matrix_power(x, -2))\n # [[ 12.91666667, -12.75000000, 2.83333333 ],\n # [-7.66666667 , 8. , -1.83333333 ],\n # [ 1.80555556 , -1.91666667 , 0.44444444 ]]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.matrix_power(x, \"n\", n)\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'matrix_power')\n check_type(n, 'n', int, 'matrix_power')\n helper = LayerHelper('matrix_power', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matrix_power',\n inputs={'X': x},\n outputs={'Out': out},\n attrs={'n': n})\n return out\n\n\ndef qr(x, mode=\"reduced\", name=None):\n r\"\"\"\n Computes the QR decomposition of one matrix or batches of matrice (backward is unsupported now).\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., M, N]`,\n where ... is zero or more batch dimensions. M and N can be arbitrary\n positive number. The data type of x should be float32 or float64. \n mode (str, optional): A flag to control the behavior of qr, the default is \"reduced\". \n Suppose x's shape is `[..., M, N]` and denoting `K = min(M, N)`:\n If mode = \"reduced\", qr op will return reduced Q and R matrices, \n which means Q's shape is `[..., M, K]` and R's shape is `[..., K, N]`.\n If mode = \"complete\", qr op will return complete Q and R matrices, \n which means Q's shape is `[..., M, M]` and R's shape is `[..., M, N]`.\n If mode = \"r\", qr op will only return reduced R matrix, which means\n R's shape is `[..., K, N]`.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n If mode = \"reduced\" or mode = \"complete\", qr will return a two tensor-tuple, which represents Q and R. \n If mode = \"r\", qr will return a tensor which represents R.\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n q, r = paddle.linalg.qr(x)\n print (q)\n print (r)\n\n # Q = [[-0.16903085, 0.89708523],\n # [-0.50709255, 0.27602622],\n # [-0.84515425, -0.34503278]])\n\n # R = [[-5.91607978, -7.43735744],\n # [ 0. , 0.82807867]])\n \n # one can verify : X = Q * R ; \n \"\"\"\n if paddle.in_dynamic_mode():\n q, r = _C_ops.qr(x, 'mode', mode)\n if mode == \"r\":\n return r\n else:\n return q, r\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'qr')\n check_type(mode, 'mode', str, 'qr')\n helper = LayerHelper('qr', **locals())\n q = helper.create_variable_for_type_inference(dtype=x.dtype)\n r = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['mode'] = mode\n helper.append_op(\n type='qr', inputs={'X': [x]}, outputs={'Q': q,\n 'R': r}, attrs=attrs)\n if mode == \"r\":\n return r\n else:\n return q, r\n\n\ndef lu(x, pivot=True, get_infos=False, name=None):\n r\"\"\"\n Computes the LU factorization of an N-D(N>=2) matrix x. \n\n Returns the LU factorization(inplace x) and Pivots. low triangular matrix L and \n upper triangular matrix U are combined to a single LU matrix.\n\n Pivoting is done if pivot is set to True.\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n # return ones\n\n Args:\n\n X (Tensor): the tensor to factor of N-dimensions(N>=2).\n\n pivot (bool, optional): controls whether pivoting is done. Default: True.\n\n get_infos (bool, optional): if set to True, returns an info IntTensor. Default: False.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n factorization (Tensor): LU matrix, the factorization of input X.\n\n pivots (IntTensor): the pivots of size(∗(N-2), min(m,n)). `pivots` stores all the \n intermediate transpositions of rows. The final permutation `perm` could be \n reconstructed by this, details refer to upper example.\n\n infos (IntTensor, optional): if `get_infos` is `True`, this is a tensor of size (∗(N-2)) \n where non-zero values indicate whether factorization for the matrix or each minibatch \n has succeeded or failed.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n \n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n if paddle.in_dynamic_mode():\n LU, Piv, Info = _C_ops.lu(x, 'pivots', pivot)\n if get_infos:\n return LU, Piv, Info\n else:\n return LU, Piv\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu')\n helper = LayerHelper('lu', **locals())\n lu = helper.create_variable_for_type_inference(dtype=x.dtype)\n p = helper.create_variable_for_type_inference(dtype='int')\n info = helper.create_variable_for_type_inference(dtype='int')\n attrs = dict()\n attrs['pivots'] = pivot\n helper.append_op(\n type='lu',\n inputs={'X': x},\n outputs={'Out': lu,\n 'Pivots': p,\n 'Infos': info},\n attrs=attrs)\n if get_infos:\n return lu, p, info\n else:\n return lu, p\n\n\ndef lu_unpack(x, y, unpack_ludata=True, unpack_pivots=True, name=None):\n r\"\"\"\n Unpack L U and P to single matrix tensor . \n unpack L and U matrix from LU, unpack permutation matrix P from Pivtos .\n\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n\n\n Args:\n x (Tensor): The LU tensor get from paddle.linalg.lu, which is combined by L and U.\n\n y (Tensor): Pivots get from paddle.linalg.lu.\n\n unpack_ludata (bool,optional): whether to unpack L and U from x. Default: True.\n\n unpack_pivots (bool, optional): whether to unpack permutation matrix P from Pivtos. Default: True.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n P (Tensor): Permutation matrix P of lu factorization.\n\n L (Tensor): The lower triangular matrix tensor of lu factorization.\n\n U (Tensor): The upper triangular matrix tensor of lu factorization.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n\n if paddle.in_dynamic_mode():\n P, L, U = _C_ops.lu_unpack(x, y, 'unpack_ludata', unpack_ludata,\n 'unpack_pivots', unpack_pivots)\n return P, L, U\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu_unpack')\n helper = LayerHelper('lu_unpack', **locals())\n p = helper.create_variable_for_type_inference(dtype=x.dtype)\n l = helper.create_variable_for_type_inference(dtype=x.dtype)\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n attrs = dict()\n attrs['unpack_ludata'] = unpack_ludata\n attrs['unpack_pivots'] = unpack_pivots\n helper.append_op(\n type='lu_unpack',\n inputs={'X': x,\n 'Pivots': y},\n outputs={'Pmat': p,\n 'L': l,\n 'U': u},\n attrs=attrs)\n return p, l, u\n\n\ndef eig(x, name=None):\n \"\"\"\n This API performs the eigenvalue decomposition of a square matrix or a batch of square matrices.\n\n .. note::\n If the matrix is a Hermitian or a real symmetric matrix, please use :ref:`paddle.linalg.eigh` instead, which is much faster.\n If only eigenvalues is needed, please use :ref:`paddle.linalg.eigvals` instead.\n If the matrix is of any shape, please use :ref:`paddle.linalg.svd`.\n This API is only supported on CPU device.\n The output datatype is always complex for both real and complex input.\n\n Args:\n x (Tensor): A tensor with shape math:`[*, N, N]`, The data type of the x should be one of ``float32``,\n ``float64``, ``compplex64`` or ``complex128``.\n name (str, optional): The default value is `None`. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Eigenvalues(Tensors): A tensor with shape math:`[*, N]` refers to the eigen values.\n Eigenvectors(Tensors): A tensor with shape math:`[*, N, N]` refers to the eigen vectors.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n paddle.device.set_device(\"cpu\")\n\n x_data = np.array([[1.6707249, 7.2249975, 6.5045543],\n [9.956216, 8.749598, 6.066444 ],\n [4.4251957, 1.7983172, 0.370647 ]]).astype(\"float32\")\n x = paddle.to_tensor(x_data)\n w, v = paddle.linalg.eig(x)\n print(w)\n # Tensor(shape=[3, 3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [[(-0.5061363550800655+0j) , (-0.7971760990842826+0j) ,\n # (0.18518077798279986+0j)],\n # [(-0.8308237755993192+0j) , (0.3463813401919749+0j) ,\n # (-0.6837005269141947+0j) ],\n # [(-0.23142567697893396+0j), (0.4944999840400175+0j) ,\n # (0.7058765252952796+0j) ]])\n\n print(v)\n # Tensor(shape=[3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [ (16.50471283351188+0j) , (-5.5034820550763515+0j) ,\n # (-0.21026087843552282+0j)])\n \"\"\"\n if paddle.in_dynamic_mode():\n w, v = _C_ops.eig(x)\n return w, v\n\n check_variable_and_dtype(\n x, 'X', ['float32', 'float64', 'complex64', 'complex128'], 'eig')\n helper = LayerHelper('eig', **locals())\n\n w = helper.create_variable_for_type_inference(x.dtype)\n v = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {'X': x}\n outputs = {'Eigenvalues': w, 'Eigenvectors': v}\n helper.append_op(type='eig', inputs=inputs, outputs=outputs)\n\n return w, v\n\n\ndef eigvals(x, name=None):\n \"\"\"\n Compute the eigenvalues of one or more general matrices.\n\n Warning:\n The gradient kernel of this operator does not yet developed.\n If you need back propagation through this operator, please replace it with paddle.linalg.eig.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices whose eigenvalues will be computed.\n Its shape should be `[*, M, M]`, where `*` is zero or more batch dimensions.\n Its data type should be float32, float64, complex64, or complex128.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n Tensor: A tensor containing the unsorted eigenvalues which has the same batch dimensions with `x`.\n The eigenvalues are complex-valued even when `x` is real.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n paddle.seed(1234)\n\n x = paddle.rand(shape=[3, 3], dtype='float64')\n # [[0.02773777, 0.93004224, 0.06911496],\n # [0.24831591, 0.45733623, 0.07717843],\n # [0.48016702, 0.14235102, 0.42620817]])\n\n print(paddle.linalg.eigvals(x))\n # [(-0.27078833542132674+0j), (0.29962280156230725+0j), (0.8824477020120244+0j)] #complex128\n \"\"\"\n\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64',\n 'complex128'], 'eigvals')\n\n x_shape = list(x.shape)\n if len(x_shape) < 2:\n raise ValueError(\n \"The dimension of Input(x) should be at least 2, but received x's dimention = {}, x's shape = {}\".\n format(len(x_shape), x_shape))\n\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The last two dimensions of Input(x) should be equal, but received x's shape = {}\".\n format(x_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.eigvals(x)\n\n helper = LayerHelper('eigvals', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='eigvals', inputs={'X': x}, outputs={'Out': out})\n return out\n\n\ndef multi_dot(x, name=None):\n \"\"\"\n Multi_dot is an operator that calculates multiple matrix multiplications.\n\n Supports inputs of float16(only GPU support), float32 and float64 dtypes. This function does not\n support batched inputs.\n\n The input tensor in [x] must be 2-D except for the first and last can be 1-D.\n If the first tensor is a 1-D vector of shape(n, ) it is treated as row vector\n of shape(1, n), similarly if the last tensor is a 1D vector of shape(n, ), it\n is treated as a column vector of shape(n, 1).\n\n If the first and last tensor are 2-D matrix, then the output is also 2-D matrix,\n otherwise the output is a 1-D vector.\n\n Multi_dot will select the lowest cost multiplication order for calculation. The\n cost of multiplying two matrices with shapes (a, b) and (b, c) is a * b * c.\n Given matrices A, B, C with shapes (20, 5), (5, 100), (100, 10) respectively,\n we can calculate the cost of different multiplication orders as follows:\n - Cost((AB)C) = 20x5x100 + 20x100x10 = 30000\n - Cost(A(BC)) = 5x100x10 + 20x5x10 = 6000\n\n In this case, multiplying B and C first, then multiply A, which is 5 times faster\n than sequential calculation.\n\n Args:\n x ([Tensor]): The input tensors which is a list Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # A * B\n A_data = np.random.random([3, 4]).astype(np.float32)\n B_data = np.random.random([4, 5]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n out = paddle.linalg.multi_dot([A, B])\n print(out.numpy().shape)\n # [3, 5]\n\n # A * B * C\n A_data = np.random.random([10, 5]).astype(np.float32)\n B_data = np.random.random([5, 8]).astype(np.float32)\n C_data = np.random.random([8, 7]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n C = paddle.to_tensor(C_data)\n out = paddle.linalg.multi_dot([A, B, C])\n print(out.numpy().shape)\n # [10, 7]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.multi_dot(x)\n\n check_type(x, 'x', (list, tuple), 'multi_dot')\n for id, item in enumerate(x):\n check_variable_and_dtype(item, 'x[' + str(id) + ']',\n ['float16', 'float32', 'float64'], 'multi_dot')\n if item.dtype != x[0].dtype:\n raise TypeError(\n \"All the Tensors in the input must have the same data type.\")\n\n helper = LayerHelper('multi_dot', **locals())\n dtype = helper.input_dtype(input_param_name='x')\n out = helper.create_variable_for_type_inference(dtype)\n helper.append_op(type='multi_dot', inputs={\"X\": x}, outputs={\"Out\": out})\n return out\n\n\ndef eigh(x, UPLO='L', name=None):\n \"\"\"\n Compute the eigenvalues and eigenvectors of a\n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[*, N, N]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): (string, default 'L'), 'L' represents the lower triangular matrix,\n \"'U' represents the upper triangular matrix.\".\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n\n out_value(Tensor): A Tensor with shape [*, N] and data type of float32 and float64. The eigenvalues of eigh op.\n out_vector(Tensor): A Tensor with shape [*, N, N] and data type of float32,float64,complex64 and complex128. The eigenvectors of eigh op.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value, out_vector = paddle.linalg.eigh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n print(out_vector)\n #[(-0.9238795325112867+0j), (-0.3826834323650898+0j)],\n #[ 0.3826834323650898j , -0.9238795325112867j ]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.eigh(x, 'UPLO', UPLO)\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigh', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'eigh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO})\n return out_value, out_vector\n\n\ndef pinv(x, rcond=1e-15, hermitian=False, name=None):\n r\"\"\"\n Calculate pseudo inverse via SVD(singular value decomposition)\n of one matrix or batches of regular matrix.\n\n .. math::\n\n if hermitian == False:\n x = u * s * vt (SVD)\n out = v * 1/s * ut\n else:\n x = u * s * ut (eigh)\n out = u * 1/s * u.conj().transpose(-2,-1)\n\n If x is hermitian or symmetric matrix, svd will be replaced with eigh.\n\n Args:\n x(Tensor): The input tensor. Its shape should be (*, m, n)\n where * is zero or more batch dimensions. m and n can be\n arbitraty positive number. The data type of x should be\n float32 or float64 or complex64 or complex128. When data\n type is complex64 or cpmplex128, hermitian should be set\n True.\n\n rcond(Tensor, optional): the tolerance value to determine\n when is a singular value zero. Defalut:1e-15.\n\n hermitian(bool, optional): indicates whether x is Hermitian\n if complex or symmetric if real. Default: False.\n\n name(str|None): A name for this layer(optional). If set None,\n the layer will be named automatically.\n\n Returns:\n Tensor: The tensor with same data type with x. it represents\n pseudo inverse of x. Its shape should be (*, n, m).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.arange(15).reshape((3, 5)).astype('float64')\n input = paddle.to_tensor(x)\n out = paddle.linalg.pinv(input)\n print(input)\n print(out)\n\n # input:\n # [[0. , 1. , 2. , 3. , 4. ],\n # [5. , 6. , 7. , 8. , 9. ],\n # [10., 11., 12., 13., 14.]]\n\n # out:\n # [[-0.22666667, -0.06666667, 0.09333333],\n # [-0.12333333, -0.03333333, 0.05666667],\n # [-0.02000000, 0.00000000, 0.02000000],\n # [ 0.08333333, 0.03333333, -0.01666667],\n # [ 0.18666667, 0.06666667, -0.05333333]]\n\n # one can verify : x * out * x = x ;\n # or out * x * out = x ;\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if not hermitian:\n # combine svd and matmul op\n u, s, vt = _C_ops.svd(x, 'full_matrices', False)\n max_singular_val = _C_ops.reduce_max(s, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=x.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=x.dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v, _ = _C_ops.transpose2(vt, 'axis', perm)\n\n out_1 = v * st\n out_2 = _C_ops.matmul_v2(out_1, u, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n # combine eigh and matmul op\n s, u = _C_ops.eigh(x, 'UPLO', 'L')\n s_abs = paddle.abs(s)\n max_singular_val = _C_ops.reduce_max(s_abs, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=s.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=s.dtype)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n out_1 = u * st\n u_conj = _C_ops.conj(u)\n out_2 = _C_ops.matmul_v2(out_1, u_conj, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n if not hermitian:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'pinv')\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(dtype)\n vt = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vt,\n 'S': s},\n attrs={'full_matrices': False}, )\n\n max_singular_val = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=dtype)\n st_shape = helper.create_variable_for_type_inference(dtype=dtype)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v = helper.create_variable_for_type_inference(dtype)\n v_shape = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='transpose2',\n inputs={'X': [vt]},\n outputs={'Out': [v],\n 'XShape': [v_shape]},\n attrs={'axis': perm})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': v,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n else:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64',\n 'complex128'], 'pinv')\n\n if dtype == paddle.complex128:\n s_type = 'float64'\n elif dtype == paddle.complex64:\n s_type = 'float32'\n else:\n s_type = dtype\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': s,\n 'Eigenvectors': u},\n attrs={'UPLO': 'L'})\n s_abs = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='abs', inputs={'X': s}, outputs={'Out': s_abs})\n max_singular_val = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s_abs},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=s_type)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=s_type)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s_type)\n cond_not_int = layers.cast(layers.logical_not(condition), s_type)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=s_type)\n st_shape = helper.create_variable_for_type_inference(dtype=s_type)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': u,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n u_conj = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='conj', inputs={'X': u}, outputs={'Out': [u_conj]})\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u_conj},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n\n\ndef solve(x, y, name=None):\n r\"\"\"\n Computes the solution of a square system of linear equations with a unique solution for input 'X' and 'Y'.\n Let :math: `X` be a sqaure matrix or a batch of square matrices, :math:`Y` be\n a vector/matrix or a batch of vectors/matrices, the equation should be:\n\n .. math::\n Out = X^-1 * Y\n Specifically,\n - This system of linear equations has one solution if and only if input 'X' is invertible.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): A vector/matrix or a batch of vectors/matrices. Its shape should be `[*, M, K]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of a square system of linear equations with a unique solution for input 'x' and 'y'.\n Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # 2*X0 + X1 = 9\n # X0 + 2*X1 = 8\n\n import paddle\n import numpy as np\n\n np_x = np.array([[3, 1],[1, 2]])\n np_y = np.array([9, 8])\n x = paddle.to_tensor(np_x, dtype=\"float64\")\n y = paddle.to_tensor(np_y, dtype=\"float64\")\n out = paddle.linalg.solve(x, y)\n\n print(out)\n # [2., 3.])\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.solve(x, y)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type=\"solve\", inputs={\"X\": x,\n \"Y\": y}, outputs={\"Out\": out})\n return out\n\n\ndef triangular_solve(x,\n y,\n upper=True,\n transpose=False,\n unitriangular=False,\n name=None):\n r\"\"\"\n Computes the solution of a system of equations with a triangular coefficient matrix `x` and\n multiple right-hand sides `y` .\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input triangular coefficient matrix. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): Whether to solve the upper-triangular system of equations (default) or the lower-triangular \n system of equations. Default: True.\n transpose (bool, optional): whether `x` should be transposed before calculation. Default: False.\n unitriangular (bool, optional): whether `x` is unit triangular. If True, the diagonal elements of `x` are assumed \n to be 1 and not referenced from `x` . Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # x1 + x2 + x3 = 0\n # 2*x2 + x3 = -9\n # -x3 = 5\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n y = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.triangular_solve(x, y, upper=True)\n\n print(out)\n # [7, -2, -5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.triangular_solve(x, y, 'upper', upper, 'transpose',\n transpose, 'unitriangular',\n unitriangular)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"triangular_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'triangular_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'triangular_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='triangular_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={\n 'upper': upper,\n 'transpose': transpose,\n 'unitriangular': unitriangular\n })\n return out\n\n\ndef cholesky_solve(x, y, upper=False, name=None):\n r\"\"\"\n Solves a linear system of equations A @ X = B, given A's Cholesky factor matrix u and matrix B.\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input matrix which is upper or lower triangular Cholesky factor of square matrix A. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): whether to consider the Cholesky factor as a lower or upper triangular matrix. Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type is the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n u = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n b = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.cholesky_solve(b, u, upper=True)\n\n print(out)\n # [-2.5, -7, 9.5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky_solve(x, y, 'upper', upper)\n\n helper = LayerHelper(\"cholesky_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'cholesky_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'cholesky_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='cholesky_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\n# MASKED: eigvalsh function (lines 2766-2830)\n\n\ndef lstsq(x, y, rcond=None, driver=None, name=None):\n \"\"\"\n Computes a solution to\n the least squares problem of a system of linear equations.\n\n Args:\n x (Tensor): A tensor with shape ``(*, M, N)`` , the data type of the input Tensor ``x``\n should be one of float32, float64.\n y (Tensor): A tensor with shape ``(*, M, K)`` , the data type of the input Tensor ``y`` \n should be one of float32, float64.\n rcond(float, optional): The default value is None. A float pointing number used to determine \n the effective rank of ``x``. If ``rcond`` is None, it will be set to max(M, N) times the \n machine precision of x_dtype.\n driver(str, optional): The default value is None. The name of LAPACK method to be used. For \n CPU inputs the valid values are ‘gels’, ‘gelsy’, ‘gelsd, ‘gelss’. For CUDA input, the only \n valid driver is ‘gels’. If ``driver`` is None, ‘gelsy’ is used for CPU inputs and ‘gels’ \n for CUDA inputs.\n name(str, optional): The default value is None. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple: A tuple of 4 Tensors which is (``solution``, ``residuals``, ``rank``, ``singular_values``). \n ``solution`` is a tensor with shape ``(*, N, K)``, meaning the least squares solution. ``residuals`` \n is a tensor with shape ``(*, K)``, meaning the squared residuals of the solutions, which is computed \n when M > N and every matrix in ``x`` is full-rank, otherwise return an empty tensor. ``rank`` is a tensor \n with shape ``(*)``, meaning the ranks of the matrices in ``x``, which is computed when ``driver`` in \n (‘gelsy’, ‘gelsd’, ‘gelss’), otherwise return an empty tensor. ``singular_values`` is a tensor with \n shape ``(*, min(M, N))``, meaning singular values of the matrices in ``x``, which is computed when \n ``driver`` in (‘gelsd’, ‘gelss’), otherwise return an empty tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n x = paddle.to_tensor([[1, 3], [3, 2], [5, 6.]])\n y = paddle.to_tensor([[3, 4, 6], [5, 3, 4], [1, 2, 1.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gelsd\")\n print(results[0])\n # [[ 0.78350395, -0.22165027, -0.62371236],\n # [-0.11340097, 0.78866047, 1.14948535]]\n print(results[1])\n # [19.81443405, 10.43814468, 30.56185532])\n print(results[2])\n # 2\n print(results[3])\n # [9.03455734, 1.54167950]\n\n x = paddle.to_tensor([[10, 2, 3], [3, 10, 5], [5, 6, 12.]])\n y = paddle.to_tensor([[4, 2, 9], [2, 0, 3], [2, 5, 3.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gels\")\n print(results[0])\n # [[ 0.39386186, 0.10230173, 0.93606132],\n # [ 0.10741687, -0.29028133, 0.11892585],\n # [-0.05115091, 0.51918161, -0.19948854]]\n print(results[1])\n # []\n \"\"\"\n device = paddle.get_device()\n if device == \"cpu\":\n if driver not in (None, \"gels\", \"gelss\", \"gelsd\", \"gelsy\"):\n raise ValueError(\n \"Only support valid driver is 'gels', 'gelss', 'gelsd', 'gelsy' or None for CPU inputs. But got {}\".\n format(driver))\n driver = \"gelsy\" if driver is None else driver\n elif \"gpu\" in device:\n if driver not in (None, \"gels\"):\n raise ValueError(\n \"Only support valid driver is 'gels' or None for CUDA inputs. But got {}\".\n format(driver))\n driver = \"gels\" if driver is None else driver\n else:\n raise RuntimeError(\"Only support lstsq api for CPU or CUDA device.\")\n\n if x.dtype == y.dtype and x.dtype in (paddle.float32, paddle.float64):\n pass\n else:\n raise ValueError(\n \"Only support x and y have the same dtype such as 'float32' and 'float64'.\"\n )\n\n if rcond is None:\n if x.dtype == paddle.float32:\n rcond = 1e-7 * max(x.shape[-2], x.shape[-1])\n elif x.dtype == paddle.float64:\n rcond = 1e-15 * max(x.shape[-2], x.shape[-1])\n\n if paddle.in_dynamic_mode():\n solution, rank, singular_values = _C_ops.lstsq(x, y, \"rcond\", rcond,\n \"driver\", driver)\n if x.shape[-2] > x.shape[-1]:\n matmul_out = _varbase_creator(dtype=x.dtype)\n _C_ops.matmul(x, solution, matmul_out, 'trans_x', False, 'trans_y',\n False)\n minus_out = _C_ops.elementwise_sub(matmul_out, y)\n pow_out = _C_ops.pow(minus_out, 'factor', 2)\n residuals = _C_ops.reduce_sum(pow_out, 'dim', [-2], 'keepdim',\n False, 'reduce_all', False)\n else:\n residuals = paddle.empty(shape=[0], dtype=x.dtype)\n\n if driver == \"gels\":\n rank = paddle.empty(shape=[0], dtype=paddle.int32)\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n elif driver == \"gelsy\":\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n\n return solution, residuals, rank, singular_values\n\n helper = LayerHelper('lstsq', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n check_variable_and_dtype(\n y, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n\n solution = helper.create_variable_for_type_inference(dtype=x.dtype)\n residuals = helper.create_variable_for_type_inference(dtype=x.dtype)\n rank = helper.create_variable_for_type_inference(dtype=paddle.int32)\n singular_values = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='lstsq',\n inputs={'X': x,\n 'Y': y},\n outputs={\n 'Solution': solution,\n 'Rank': rank,\n 'SingularValues': singular_values\n },\n attrs={'rcond': rcond,\n 'driver': driver})\n\n matmul_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n minus_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n pow_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': solution},\n outputs={'Out': matmul_out},\n attrs={\n 'trans_x': False,\n 'trans_y': False,\n })\n\n helper.append_op(\n type='elementwise_sub',\n inputs={'X': matmul_out,\n 'Y': y},\n outputs={'Out': minus_out})\n\n helper.append_op(\n type='pow',\n inputs={'X': minus_out},\n outputs={'Out': pow_out},\n attrs={'factor': 2})\n\n helper.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': residuals},\n attrs={'dim': [-2],\n 'keep_dim': False,\n 'reduce_all': False})\n\n if driver == \"gels\":\n rank = paddle.static.data(name='rank', shape=[0])\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n elif driver == \"gelsy\":\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n\n return solution, residuals, rank, singular_values"},"implementation":{"kind":"string","value":"def eigvalsh(x, UPLO='L', name=None):\n \"\"\"\n Computes the eigenvalues of a \n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[_, M, M]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): Lower triangular part of a (‘L’, default) or the upper triangular part (‘U’).\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor eigenvalues in ascending order.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value = paddle.eigvalsh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n \"\"\"\n if paddle.in_dynamic_mode():\n is_test = x.stop_gradient\n values, _ = _C_ops.eigvalsh(x, 'UPLO', UPLO, 'is_test', is_test)\n return values\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigvalsh', **locals())\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64', 'complex128'],\n 'eigvalsh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n is_test = x.stop_gradient\n helper.append_op(\n type='eigvalsh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO,\n 'is_test': is_test})\n return out_value"},"start_line":{"kind":"number","value":2766,"string":"2,766"},"end_line":{"kind":"number","value":2830,"string":"2,830"},"file_content":{"kind":"string","value":"# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numpy as np\nfrom ..fluid.layer_helper import LayerHelper\nfrom ..framework import _varbase_creator, _dygraph_tracer\nfrom ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype\nfrom ..static import Variable\nfrom ..fluid.framework import _in_legacy_dygraph, in_dygraph_mode\nfrom ..fluid.layers import transpose, cast # noqa: F401\nfrom ..fluid import layers\nimport paddle\nfrom paddle.common_ops_import import core\nfrom paddle.common_ops_import import VarDesc\nfrom paddle import _C_ops\n\n__all__ = []\n\n\ndef matmul(x, y, transpose_x=False, transpose_y=False, name=None):\n \"\"\"\n Applies matrix multiplication to two tensors. `matmul` follows\n the complete broadcast rules,\n and its behavior is consistent with `np.matmul`.\n\n Currently, the input tensors' number of dimensions can be any, `matmul` can be used to\n achieve the `dot`, `matmul` and `batchmatmul`.\n\n The actual behavior depends on the shapes of :math:`x`, :math:`y` and the\n flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:\n\n - If a transpose flag is specified, the last two dimensions of the tensor\n are transposed. If the tensor is ndim-1 of shape, the transpose is invalid. If the tensor\n is ndim-1 of shape :math:`[D]`, then for :math:`x` it is treated as :math:`[1, D]`, whereas\n for :math:`y` it is the opposite: It is treated as :math:`[D, 1]`.\n\n The multiplication behavior depends on the dimensions of `x` and `y`. Specifically:\n\n - If both tensors are 1-dimensional, the dot product result is obtained.\n\n - If both tensors are 2-dimensional, the matrix-matrix product is obtained.\n\n - If the `x` is 1-dimensional and the `y` is 2-dimensional,\n a `1` is prepended to its dimension in order to conduct the matrix multiply.\n After the matrix multiply, the prepended dimension is removed.\n\n - If the `x` is 2-dimensional and `y` is 1-dimensional,\n the matrix-vector product is obtained.\n\n - If both arguments are at least 1-dimensional and at least one argument\n is N-dimensional (where N > 2), then a batched matrix multiply is obtained.\n If the first argument is 1-dimensional, a 1 is prepended to its dimension\n in order to conduct the batched matrix multiply and removed after.\n If the second argument is 1-dimensional, a 1 is appended to its\n dimension for the purpose of the batched matrix multiple and removed after.\n The non-matrix (exclude the last two dimensions) dimensions are\n broadcasted according the broadcast rule.\n For example, if input is a (j, 1, n, m) tensor and the other is a (k, m, p) tensor,\n out will be a (j, k, n, p) tensor.\n\n Args:\n x (Tensor): The input tensor which is a Tensor.\n y (Tensor): The input tensor which is a Tensor.\n transpose_x (bool): Whether to transpose :math:`x` before multiplication.\n transpose_y (bool): Whether to transpose :math:`y` before multiplication.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # vector * vector\n x_data = np.random.random([10]).astype(np.float32)\n y_data = np.random.random([10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [1]\n\n # matrix * vector\n x_data = np.random.random([10, 5]).astype(np.float32)\n y_data = np.random.random([5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10]\n\n # batched matrix * broadcasted vector\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([2]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5]\n\n # batched matrix * batched matrix\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([10, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5, 5]\n\n # batched matrix * broadcasted matrix\n x_data = np.random.random([10, 1, 5, 2]).astype(np.float32)\n y_data = np.random.random([1, 3, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 3, 5, 5]\n\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_matmul(x, y, transpose_x, transpose_y)\n\n if _in_legacy_dygraph():\n op_type = 'matmul_v2'\n op = getattr(_C_ops, op_type)\n return op(x, y, 'trans_x', transpose_x, 'trans_y', transpose_y)\n\n attrs = {\n 'trans_x': transpose_x,\n 'trans_y': transpose_y,\n }\n\n def __check_input(x, y):\n var_names = {'x': x, 'y': y}\n for name, val in var_names.items():\n check_variable_and_dtype(\n val, name,\n ['float16', 'float32', 'float64', 'complex64', 'complex128'],\n 'matmul')\n\n __check_input(x, y)\n\n helper = LayerHelper('matmul_v2', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef norm(x, p='fro', axis=None, keepdim=False, name=None):\n \"\"\"\n\n Returns the matrix norm (Frobenius) or vector norm (the 1-norm, the Euclidean\n or 2-norm, and in general the p-norm for p > 0) of a given tensor.\n\n .. note::\n This norm API is different from `numpy.linalg.norm`.\n This api supports high-order input tensors (rank >= 3), and certain axis need to be pointed out to calculate the norm.\n But `numpy.linalg.norm` only supports 1-D vector or 2-D matrix as input tensor.\n For p-order matrix norm, this api actually treats matrix as a flattened vector to calculate the vector norm, NOT REAL MATRIX NORM.\n\n Args:\n x (Tensor): The input tensor could be N-D tensor, and the input data\n type could be float32 or float64.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `0`, `1`, `2`,\n `inf`, `-inf` and any positive real number yielding the corresponding p-norm. Not supported: ord < 0 and nuclear norm.\n Default value is `fro`.\n axis (int|list|tuple, optional): The axis on which to apply norm operation. If axis is int\n or list(int)/tuple(int) with only one element, the vector norm is computed over the axis.\n If `axis < 0`, the dimension to norm operation is rank(input) + axis.\n If axis is a list(int)/tuple(int) with two elements, the matrix norm is computed over the axis.\n Defalut value is `None`.\n keepdim (bool, optional): Whether to reserve the reduced dimension in the\n output Tensor. The result tensor will have fewer dimension\n than the :attr:`input` unless :attr:`keepdim` is true, default\n value is False.\n name (str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: results of norm operation on the specified axis of input tensor,\n it's data type is the same as input's Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n shape=[2, 3, 4]\n np_input = np.arange(24).astype('float32') - 12\n np_input = np_input.reshape(shape)\n x = paddle.to_tensor(np_input)\n #[[[-12. -11. -10. -9.] [ -8. -7. -6. -5.] [ -4. -3. -2. -1.]]\n # [[ 0. 1. 2. 3.] [ 4. 5. 6. 7.] [ 8. 9. 10. 11.]]]\n\n # compute frobenius norm along last two dimensions.\n out_fro = paddle.linalg.norm(x, p='fro', axis=[0,1])\n # out_fro.numpy() [17.435596 16.911535 16.7332 16.911535]\n\n # compute 2-order vector norm along last dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=-1)\n #out_pnorm.numpy(): [[21.118711 13.190906 5.477226]\n # [ 3.7416575 11.224972 19.131126]]\n\n # compute 2-order norm along [0,1] dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=[0,1])\n #out_pnorm.numpy(): [17.435596 16.911535 16.7332 16.911535]\n\n # compute inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=np.inf)\n #out_pnorm.numpy() = [12.]\n out_pnorm = paddle.linalg.norm(x, p=np.inf, axis=0)\n #out_pnorm.numpy(): [[12. 11. 10. 9.] [8. 7. 6. 7.] [8. 9. 10. 11.]]\n\n # compute -inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=-np.inf)\n #out_pnorm.numpy(): [0.]\n out_pnorm = paddle.linalg.norm(x, p=-np.inf, axis=0)\n #out_pnorm.numpy(): [[0. 1. 2. 3.] [4. 5. 6. 5.] [4. 3. 2. 1.]]\n \"\"\"\n\n def frobenius_norm(input, dim=None, keepdim=False, name=None):\n \"\"\"\n The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n dim (list, optional): None for last two dimensions.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if dim is not None and not (isinstance(dim, list) and len(dim) == 2):\n raise ValueError(\n \"The dim of frobenius norm op should be None or two elements list!\"\n )\n if paddle.in_dynamic_mode():\n if dim is None:\n return _C_ops.frobenius_norm(input, 'keep_dim', keepdim,\n 'reduce_all', True)\n return _C_ops.frobenius_norm(input, 'dim', dim, 'keep_dim', keepdim,\n 'reduce_all', False)\n attrs = {'dim': dim, 'keep_dim': keepdim, 'reduce_all': False}\n if dim is None:\n attrs['reduce_all'] = True\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'frobenius_norm')\n\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='frobenius_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def vector_norm(input,\n porder=None,\n axis=None,\n keepdim=False,\n asvector=False,\n name=None):\n \"\"\"\n Calculate the p-order vector norm for certain dimension of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n porder (float, optional): None for porder=2.0.\n axis (int, optional): None for last dimension.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if paddle.in_dynamic_mode():\n if axis is None: axis = -1\n return _C_ops.p_norm(input, 'porder', porder, 'axis', axis,\n 'keepdim', keepdim, 'asvector', asvector)\n if porder is not None:\n check_type(porder, 'porder', (float, int), 'p_norm')\n if axis is not None:\n check_type(axis, 'axis', (int), 'p_norm')\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'p_norm')\n\n attrs = {\n 'axis': axis if axis is not None else -1,\n 'porder': float(porder) if porder is not None else 2.0,\n 'keepdim': keepdim,\n 'asvector': asvector,\n 'epsilon': 1e-12,\n }\n helper = LayerHelper('p_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='p_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def inf_norm(input,\n porder=None,\n axis=axis,\n keepdim=False,\n asvector=False,\n name=None):\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n helper.append_op(type='abs', inputs={'X': input}, outputs={'Out': out})\n reduce_out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n reduce_all = True if axis == None or axis == [] or asvector == True else False\n axis = axis if axis != None and axis != [] else [0]\n\n reduce_type = 'reduce_max' if porder == np.float(\n 'inf') else 'reduce_min'\n helper.append_op(\n type=reduce_type,\n inputs={'X': out},\n outputs={'Out': reduce_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n\n return reduce_out\n\n def p_matrix_norm(input, porder=1., axis=axis, keepdim=False, name=None):\n \"\"\"\n NOTE:\n This function actually treats the matrix as flattened vector to calculate vector norm instead of matrix norm.\n \"\"\"\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n\n block.append_op(\n type='pow',\n inputs={'X': abs_out},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': True if axis is None else False\n })\n porder\n block.append_op(\n type='pow',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n if axis is None and p is not None:\n if isinstance(p, str):\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n porder=p,\n axis=axis,\n keepdim=keepdim,\n asvector=True,\n name=name)\n else:\n raise ValueError(\"only valid p type is string or float, found {}\".\n format(type(p)))\n\n if isinstance(axis, tuple):\n axis = list(axis)\n if isinstance(axis, list) and len(axis) == 1:\n axis = axis[0]\n\n #calculate vector norm, where axis is int or list with only one integer\n if isinstance(axis, int):\n if isinstance(p, str):\n if p == \"fro\":\n return vector_norm(\n x,\n porder=2,\n axis=axis,\n keepdim=keepdim,\n asvector=False,\n name=name)\n\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n axis=axis,\n porder=p,\n keepdim=keepdim,\n asvector=False,\n name=name)\n else:\n raise ValueError(\n \"unspport p for p-order vector norm. except float, found {}\".\n format(p))\n #calculate matrix norm, where axis is list with two integers\n elif isinstance(axis, list) and len(axis) == 2:\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n elif p == np.inf or p == -np.inf:\n return inf_norm(x, porder=p, axis=axis, keepdim=keepdim, name=name)\n elif p == 0:\n raise ValueError(\n \"just suport axis type int or list (length of list <=1) if p = 0, found {}\".\n format(axis))\n else:\n return p_matrix_norm(\n x, porder=p, axis=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"except axis type int or list (length of list <=2), found {}\".\n format(axis))\n\n\ndef dist(x, y, p=2, name=None):\n r\"\"\"\n\n This OP returns the p-norm of (x - y). It is not a norm in a strict sense, only as a measure\n of distance. The shapes of x and y must be broadcastable. The definition is as follows, for\n details, please refer to the `numpy's broadcasting <https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html>`_:\n\n - Each input has at least one dimension.\n - Match the two input dimensions from back to front, the dimension sizes must either be equal, one of them is 1, or one of them does not exist.\n\n Where, z = x - y, the shapes of x and y are broadcastable, then the shape of z can be\n obtained as follows:\n\n 1. If the number of dimensions of x and y are not equal, prepend 1 to the dimensions of the\n tensor with fewer dimensions.\n\n For example, The shape of x is [8, 1, 6, 1], the shape of y is [7, 1, 5], prepend 1 to the\n dimension of y.\n\n x (4-D Tensor): 8 x 1 x 6 x 1\n\n y (4-D Tensor): 1 x 7 x 1 x 5\n\n 2. Determine the size of each dimension of the output z: choose the maximum value from the\n two input dimensions.\n\n z (4-D Tensor): 8 x 7 x 6 x 5\n\n If the number of dimensions of the two inputs are the same, the size of the output can be\n directly determined in step 2. When p takes different values, the norm formula is as follows:\n\n When p = 0, defining $0^0=0$, the zero-norm of z is simply the number of non-zero elements of z.\n\n .. math::\n\n ||z||_{0}=\\lim_{p \\\\rightarrow 0}\\sum_{i=1}^{m}|z_i|^{p}\n\n When p = inf, the inf-norm of z is the maximum element of z.\n\n .. math::\n\n ||z||_\\infty=\\max_i |z_i|\n\n When p = -inf, the negative-inf-norm of z is the minimum element of z.\n\n .. math::\n\n ||z||_{-\\infty}=\\min_i |z_i|\n\n Otherwise, the p-norm of z follows the formula,\n\n .. math::\n\n ||z||_{p}=(\\sum_{i=1}^{m}|z_i|^p)^{\\\\frac{1}{p}}\n\n Args:\n x (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n y (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n p (float, optional): The norm to be computed, its data type is float32 or float64. Default: 2.\n\n Returns:\n Tensor: Tensor that is the p-norm of (x - y).\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor(np.array([[3, 3],[3, 3]]), \"float32\")\n y = paddle.to_tensor(np.array([[3, 3],[3, 1]]), \"float32\")\n out = paddle.dist(x, y, 0)\n print(out) # out = [1.]\n\n out = paddle.dist(x, y, 2)\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"inf\"))\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"-inf\"))\n print(out) # out = [0.]\n \"\"\"\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'dist')\n check_variable_and_dtype(y, 'dtype', ['float32', 'float64'], 'dist')\n check_type(p, 'p', (float, int), 'dist')\n helper = LayerHelper(\"dist\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n outputs = {'Out': [out]}\n attrs = {\"p\": float(p)}\n helper.append_op(\n type='dist', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef cond(x, p=None, name=None):\n \"\"\"\n\n Computes the condition number of a matrix or batches of matrices with respect to a matrix norm ``p``.\n\n Args:\n x (Tensor): The input tensor could be tensor of shape ``(*, m, n)`` where ``*`` is zero or more batch dimensions\n for ``p`` in ``(2, -2)``, or of shape ``(*, n, n)`` where every matrix is invertible for any supported ``p``.\n And the input data type could be ``float32`` or ``float64``.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `nuc`, `1`, `-1`, `2`, `-2`,\n `inf`, `-inf`. Default value is `None`, meaning that the order of the norm is `2`.\n name (str, optional): The default value is `None`. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: computing results of condition number, its data type is the same as input Tensor ``x``.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1., 0, -1], [0, 1, 0], [1, 0, 1]])\n\n # compute conditional number when p is None\n out = paddle.linalg.cond(x)\n # out.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is 'fro'\n out_fro = paddle.linalg.cond(x, p='fro')\n # out_fro.numpy() [3.1622777]\n\n # compute conditional number when order of the norm is 'nuc'\n out_nuc = paddle.linalg.cond(x, p='nuc')\n # out_nuc.numpy() [9.2426405]\n\n # compute conditional number when order of the norm is 1\n out_1 = paddle.linalg.cond(x, p=1)\n # out_1.numpy() [2.]\n\n # compute conditional number when order of the norm is -1\n out_minus_1 = paddle.linalg.cond(x, p=-1)\n # out_minus_1.numpy() [1.]\n\n # compute conditional number when order of the norm is 2\n out_2 = paddle.linalg.cond(x, p=2)\n # out_2.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is -1\n out_minus_2 = paddle.linalg.cond(x, p=-2)\n # out_minus_2.numpy() [0.70710677]\n\n # compute conditional number when order of the norm is inf\n out_inf = paddle.linalg.cond(x, p=np.inf)\n # out_inf.numpy() [2.]\n\n # compute conditional number when order of the norm is -inf\n out_minus_inf = paddle.linalg.cond(x, p=-np.inf)\n # out_minus_inf.numpy() [1.]\n\n a = paddle.to_tensor(np.random.randn(2, 4, 4).astype('float32'))\n # a.numpy()\n # [[[ 0.14063153 -0.996288 0.7996131 -0.02571543]\n # [-0.16303636 1.5534962 -0.49919784 -0.04402903]\n # [-1.1341571 -0.6022629 0.5445269 0.29154757]\n # [-0.16816919 -0.30972657 1.7521842 -0.5402487 ]]\n # [[-0.58081484 0.12402827 0.7229862 -0.55046535]\n # [-0.15178485 -1.1604939 0.75810957 0.30971205]\n # [-0.9669573 1.0940945 -0.27363303 -0.35416734]\n # [-1.216529 2.0018666 -0.7773689 -0.17556527]]]\n a_cond_fro = paddle.linalg.cond(a, p='fro')\n # a_cond_fro.numpy() [31.572273 28.120834]\n\n b = paddle.to_tensor(np.random.randn(2, 3, 4).astype('float64'))\n # b.numpy()\n # [[[ 1.61707487 0.46829144 0.38130416 0.82546736]\n # [-1.72710298 0.08866375 -0.62518804 0.16128892]\n # [-0.02822879 -1.67764516 0.11141444 0.3220113 ]]\n # [[ 0.22524372 0.62474921 -0.85503233 -1.03960523]\n # [-0.76620689 0.56673047 0.85064753 -0.45158196]\n # [ 1.47595418 2.23646462 1.5701758 0.10497519]]]\n b_cond_2 = paddle.linalg.cond(b, p=2)\n # b_cond_2.numpy() [3.30064451 2.51976252]\n\n \"\"\"\n\n def mat_norm(input, porder=1., axis=None):\n \"\"\"\n NOTE:\n Calculate the matrix norm of a square matrix or batches of square matrices,\n when porder is in (1, -1, inf, -inf)\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n axis = axis if axis != None and axis != [] else [0]\n keepdim = False\n\n if paddle.in_dynamic_mode():\n abs_out = _C_ops.abs(input)\n sum_out = _C_ops.reduce_sum(abs_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == 1 or porder == np.inf:\n return _C_ops.reduce_max(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == -1 or porder == -np.inf:\n return _C_ops.reduce_min(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n\n block = LayerHelper('norm', **locals())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n block.append_op(\n type='reduce_sum',\n inputs={'X': abs_out},\n outputs={'Out': sum_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 1 or porder == np.inf:\n block.append_op(\n type='reduce_max',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n if porder == -1 or porder == -np.inf:\n block.append_op(\n type='reduce_min',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n\n def fro_norm(input, porder=2, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the frobenius norm of a square matrix or batches of square matrices.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n if paddle.in_dynamic_mode():\n pow_out = _C_ops.pow(input, 'factor', porder)\n sum_out_1 = _C_ops.reduce_sum(pow_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n sum_out_2 = _C_ops.reduce_sum(sum_out_1, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n return _C_ops.pow(sum_out_2, 'factor', float(1. / porder))\n\n block = LayerHelper('norm', **locals())\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_1 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_2 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='pow',\n inputs={'X': input},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out_1},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_sum',\n inputs={'X': sum_out_1},\n outputs={'Out': sum_out_2},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='pow',\n inputs={'X': sum_out_2},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n def svd_norm(input, porder, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the matrix norm, which is related to singular values, of a matrix\n or batches of matrices, including nuclear norm, 2-norm and (-2)-norm.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n u, s, vh = svd(input, full_matrices=False)\n\n if paddle.in_dynamic_mode():\n if porder == \"nuc\":\n return _C_ops.reduce_sum(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n max_out = _C_ops.reduce_max(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n min_out = _C_ops.reduce_min(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n if porder == 2:\n return _C_ops.elementwise_div(max_out, min_out, 'aixs', axis,\n 'use_mkldnn', False)\n if porder == -2:\n return _C_ops.elementwise_div(min_out, max_out, 'aixs', axis,\n 'use_mkldnn', False)\n\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n if porder == \"nuc\":\n block.append_op(\n type='reduce_sum',\n inputs={'X': s},\n outputs={'Out': out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n max_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n min_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_min',\n inputs={'X': s},\n outputs={'Out': min_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': max_out,\n 'Y': min_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n if porder == -2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': min_out,\n 'Y': max_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n\n def empty_tensor(input, shape):\n if paddle.in_dynamic_mode():\n return input.reshape(shape)\n raise ValueError(\"only support x is nonempty tensor in static mode\")\n\n x_shape = list(x.shape)\n if not len(x_shape) >= 2:\n raise ValueError(\"input should be a matrix or batches of matrices, \" +\n \"but the dimention of received input is {}\".format(\n len(x_shape)))\n if p == None:\n p = 2\n x_size = 0 if (0 in x_shape) else 1\n if p in (\"fro\", \"nuc\", 1, -1, np.inf, -np.inf):\n if x_shape[len(x_shape) - 1] == x_shape[len(x_shape) - 2]:\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n x_inv = x.inverse()\n if p == \"fro\":\n return fro_norm(x) * fro_norm(x_inv)\n if p == \"nuc\":\n return svd_norm(x, p) * svd_norm(x_inv, p)\n if p in (1, -1):\n return mat_norm(\n x, porder=p, axis=[-2]) * mat_norm(\n x_inv, porder=p, axis=[-2])\n if p in (np.inf, -np.inf):\n return mat_norm(\n x, porder=p, axis=[-1]) * mat_norm(\n x_inv, porder=p, axis=[-1])\n else:\n raise ValueError(\"only support p is {} when input is a \".format(p) +\n \"square matrix or batches of square matrices\")\n elif p in (2, -2):\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n return svd_norm(x, porder=p)\n else:\n raise ValueError(\n \"unsupported {} for p, only supporting ('fro', 'nuc', \".format(\n p) + \"1, -1, 2, -2, inf, -inf) or none\")\n\n\ndef dot(x, y, name=None):\n \"\"\"\n This operator calculates inner product for vectors.\n\n .. note::\n Support 1-d and 2-d Tensor. When it is 2d, the first dimension of this matrix\n is the batch dimension, which means that the vectors of multiple batches are dotted.\n\n Parameters:\n x(Tensor): 1-D or 2-D ``Tensor``. Its dtype should be ``float32``, ``float64``, ``int32``, ``int64``\n y(Tensor): 1-D or 2-D ``Tensor``. Its dtype soulde be ``float32``, ``float64``, ``int32``, ``int64``\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: the calculated result Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x_data = np.random.uniform(0.1, 1, [10]).astype(np.float32)\n y_data = np.random.uniform(1, 3, [10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.dot(x, y)\n print(z)\n\n \"\"\"\n op_type = 'dot'\n # skip var type check in dygraph mode to improve efficiency\n if paddle.in_dynamic_mode():\n op = getattr(_C_ops, op_type)\n return op(x, y)\n\n assert x is not None, 'x cannot be None in {}'.format(op_type)\n assert y is not None, 'y cannot be None in {}'.format(op_type)\n\n check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n check_variable_and_dtype(y, 'y', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n\n helper = LayerHelper(op_type, **locals())\n if name is None:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n else:\n out = helper.create_variable(\n name=name, dtype=x.dtype, persistable=False)\n helper.append_op(\n type=\"dot\", inputs={'X': x,\n 'Y': y}, attrs={}, outputs={\"Out\": out})\n return out\n\n\ndef cov(x, rowvar=True, ddof=True, fweights=None, aweights=None, name=None):\n \"\"\"\n Estimate the covariance matrix of the input variables, given data and weights.\n\n A covariance matrix is a square matrix, indicate the covariance of each pair variables in the input matrix.\n For example, for an N-dimensional samples X=[x1,x2,…xN]T, then the covariance matrix \n element Cij is the covariance of xi and xj. The element Cii is the variance of xi itself.\n\n Parameters:\n x(Tensor): A N-D(N<=2) Tensor containing multiple variables and observations. By default, each row of x represents a variable. Also see rowvar below.\n rowvar(Bool, optional): If rowvar is True (default), then each row represents a variable, with observations in the columns. Default: True\n ddof(Bool, optional): If ddof=True will return the unbiased estimate, and ddof=False will return the simple average. Default: True\n fweights(Tensor, optional): 1-D Tensor of integer frequency weights; The number of times each observation vector should be repeated. Default: None\n aweights(Tensor, optional): 1-D Tensor of observation vector weights. How important of the observation vector, larger data means this element is more important. Default: None\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: The covariance matrix Tensor of the variables.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n\n xt = paddle.rand((3,4))\n paddle.linalg.cov(xt)\n\n '''\n Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n [[0.07918842, 0.06127326, 0.01493049],\n [0.06127326, 0.06166256, 0.00302668],\n [0.01493049, 0.00302668, 0.01632146]])\n '''\n \"\"\"\n op_type = 'cov'\n if len(x.shape) > 2 or len(x.shape) < 1:\n raise ValueError(\n \"Input(x) only support N-D (1<=N<=2) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cov')\n nx = x\n if len(x.shape) == 1:\n nx = x.reshape((1, -1))\n if not rowvar and nx.shape[0] != 1:\n nx = nx.t()\n w = None\n observation_num = nx.shape[1]\n if fweights is not None:\n w = fweights.astype(nx.dtype)\n if len(w.shape) > 1:\n raise ValueError(\n \"Input(fweights) only support N-D (N<=1) tensor in cov, but received \"\n \"shape of Input(input) is %s.\" % len(fweights.shape))\n if fweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(fweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(fweights) is {}.\".format(observation_num,\n fweights.shape[0]))\n if fweights.min() < 0:\n raise ValueError(\n \"The value of Input(fweights) cannot be negtive, but received \"\n \"min of Input(fweights) is {}.\".format(fweights.min()))\n if not paddle.all(fweights == paddle.round(fweights.astype('float64'))):\n raise ValueError(\"Input(fweights) must be integer \")\n\n if aweights is not None:\n aw = aweights.astype(nx.dtype)\n if len(aw.shape) > 1:\n raise ValueError(\n \"Input(aweights) only support N-D (N<=1) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(aweights.shape))\n check_variable_and_dtype(aweights, 'dtype', ['float32', 'float64'],\n 'cov')\n if aweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(aweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(aweights) is {}.\".format(observation_num,\n aweights.shape[0]))\n if aweights.min() < 0:\n raise ValueError(\n \"The value of Input(aweights) cannot be negtive, but received \"\n \"min of Input(aweights) is {}.\".format(aweights.min()))\n if w is not None:\n w = w * aw\n else:\n w = aw\n\n w_sum = paddle.to_tensor(observation_num, dtype=nx.dtype)\n if fweights is not None or aweights is not None:\n w_sum = w.sum()\n if w_sum.item() == 0:\n raise ValueError(\"The sum of weights is zero, can't be normalized.\")\n\n if w is not None:\n nx_w = nx * w\n avg = (nx_w).sum(axis=1) / w_sum\n else:\n avg = nx.sum(axis=1) / w_sum\n nx_w = nx\n\n if w is not None and aweights is not None and ddof == True:\n norm_factor = w_sum - (w * aweights).sum() / w_sum\n else:\n norm_factor = w_sum - ddof\n if norm_factor <= 0:\n norm_factor = paddle.to_tensor(0, dtype=nx.dtype)\n nx = nx - avg.unsqueeze(1)\n xxt = paddle.mm(nx, nx_w.t().conj())\n cov = paddle.divide(xxt, norm_factor).squeeze()\n return cov\n\n\ndef t(input, name=None):\n \"\"\"\n Transpose <=2-D tensor.\n 0-D and 1-D tensors are returned as it is and 2-D tensor is equal to\n the paddle.transpose function which perm dimensions set 0 and 1.\n\n Args:\n input (Tensor): The input Tensor. It is a N-D (N<=2) Tensor of data types float16, float32, float64, int32.\n name(str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`\n Returns:\n Tensor: A transposed n-D Tensor, with data type being float16, float32, float64, int32, int64.\n\n For Example:\n\n .. code-block:: text\n\n # Example 1 (0-D tensor)\n x = tensor([0.79])\n paddle.t(x) = tensor([0.79])\n\n # Example 2 (1-D tensor)\n x = tensor([0.79, 0.84, 0.32])\n paddle.t(x) = tensor([0.79, 0.84, 0.32])\n\n # Example 3 (2-D tensor)\n x = tensor([0.79, 0.84, 0.32],\n [0.64, 0.14, 0.57])\n paddle.t(x) = tensor([0.79, 0.64],\n [0.84, 0.14],\n [0.32, 0.57])\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n x = paddle.ones(shape=[2, 3], dtype='int32')\n x_transposed = paddle.t(x)\n print(x_transposed.shape)\n # [3, 2]\n \"\"\"\n if len(input.shape) > 2:\n raise ValueError(\n \"Input(input) only support N-D (N<=2) tensor, but received \"\n \"length of Input(input) is %s. Perhaps you can use paddle.\"\n \"tensor.transpose() instead.\" % len(input.shape))\n if paddle.in_dynamic_mode():\n if len(input.shape) == 1:\n return input\n # 2-D tensor\n perm = [1, 0]\n out, _ = _C_ops.transpose2(input, 'axis', perm)\n return out\n\n check_variable_and_dtype(\n input, 'input', ['float16', 'float32', 'float64', 'int32',\n 'int64'], 'transpose')\n\n helper = LayerHelper('t', **locals())\n out = helper.create_variable_for_type_inference(input.dtype)\n input_shape = helper.create_variable_for_type_inference(input.dtype)\n if len(input.shape) == 1:\n out = input\n else:\n helper.append_op(\n type='transpose2',\n inputs={'X': [input]},\n outputs={'Out': [out],\n 'XShape': [input_shape]},\n attrs={'axis': [1, 0]})\n return out\n\n\ndef cross(x, y, axis=None, name=None):\n \"\"\"\n Computes the cross product between two tensors along an axis.\n\n Inputs must have the same shape, and the length of their axes should be equal to 3.\n If `axis` is not given, it defaults to the first axis found with the length 3.\n\n Args:\n x (Tensor): The first input tensor.\n y (Tensor): The second input tensor.\n axis (int, optional): The axis along which to compute the cross product. It defaults to the first axis found with the length 3.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor. A Tensor with same data type as `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0],\n [3.0, 3.0, 3.0]])\n y = paddle.to_tensor([[1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0]])\n\n z1 = paddle.cross(x, y)\n # [[-1. -1. -1.]\n # [ 2. 2. 2.]\n # [-1. -1. -1.]]\n\n z2 = paddle.cross(x, y, axis=1)\n # [[0. 0. 0.]\n # [0. 0. 0.]\n # [0. 0. 0.]]\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_cross(x, y, axis)\n else:\n if _in_legacy_dygraph():\n if axis is not None:\n return _C_ops.cross(x, y, 'dim', axis)\n else:\n return _C_ops.cross(x, y)\n else:\n helper = LayerHelper(\"cross\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n attrs = dict()\n attrs['dim'] = axis\n\n helper.append_op(\n type='cross',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef cholesky(x, upper=False, name=None):\n r\"\"\"\n Computes the Cholesky decomposition of one symmetric positive-definite\n matrix or batches of symmetric positive-definite matrice.\n\n If `upper` is `True`, the decomposition has the form :math:`A = U^{T}U` ,\n and the returned matrix :math:`U` is upper-triangular. Otherwise, the\n decomposition has the form :math:`A = LL^{T}` , and the returned matrix\n :math:`L` is lower-triangular.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[*, M, M]`,\n where * is zero or more batch dimensions, and matrices on the\n inner-most 2 dimensions all should be symmetric positive-definite.\n Its data type should be float32 or float64.\n upper (bool): The flag indicating whether to return upper or lower\n triangular matrices. Default: False.\n\n Returns:\n Tensor: A Tensor with same shape and data type as `x`. It represents \\\n triangular matrices generated by Cholesky decomposition.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n a = np.random.rand(3, 3)\n a_t = np.transpose(a, [1, 0])\n x_data = np.matmul(a, a_t) + 1e-03\n x = paddle.to_tensor(x_data)\n out = paddle.linalg.cholesky(x, upper=False)\n print(out)\n # [[1.190523 0. 0. ]\n # [0.9906703 0.27676893 0. ]\n # [1.25450498 0.05600871 0.06400121]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky(x, \"upper\", upper)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cholesky')\n check_type(upper, 'upper', bool, 'cholesky')\n helper = LayerHelper('cholesky', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='cholesky',\n inputs={'X': [x]},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef matrix_rank(x, tol=None, hermitian=False, name=None):\n r\"\"\"\n Computes the rank of a matrix.\n\n The rank of a matrix is the number of singular values that are greater than the specified `tol` threshold when hermitian=False,\n or the number of eigenvalues in absolute value that are greater than the specified `tol` threshold when hermitian=True.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., m, n]`, where `...` is zero or more batch dimensions. If `x` is a batch\n of matrices then the output has the same batch dimensions. The data type of `x` should be float32 or float64.\n tol (float,Tensor,optional): the tolerance value. Default: None. If `tol` is not specified, and `sigma` is the largest\n singular value (or eigenvalues in absolute value), and `eps` is the epsilon value for the dtype of `x`, then `tol` is computed\n with formula `tol=sigma * max(m,n) * eps`. Note that if `x` is a batch of matrices, `tol` is computed this way for every batch.\n hermitian (bool,optional): indicates whether `x` is Hermitian. Default: False. When hermitian=True, `x` is assumed to be Hermitian,\n enabling a more efficient method for finding eigenvalues, but `x` is not checked inside the function. Instead, We just use\n the lower triangular of the matrix to compute.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: Rank of tensor x.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n a = paddle.eye(10)\n b = paddle.linalg.matrix_rank(a)\n print(b)\n # b = [10]\n\n c = paddle.ones(shape=[3, 4, 5, 5])\n d = paddle.linalg.matrix_rank(c, tol=0.01, hermitian=True)\n print(d)\n # d = [[1, 1, 1, 1],\n # [1, 1, 1, 1],\n # [1, 1, 1, 1]]\n\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if tol is None:\n tol_tensor = None\n tol_attr = 0.0\n use_default_tol = True\n elif isinstance(tol, Variable):\n if tol.dtype != x.dtype:\n tol_tensor = cast(tol, x.dtype)\n else:\n tol_tensor = tol\n tol_attr = 0.0\n use_default_tol = False\n else:\n tol_tensor = None\n tol_attr = float(tol)\n use_default_tol = False\n return _C_ops.matrix_rank(x, tol_tensor, \"tol\", tol_attr, 'hermitian',\n hermitian, 'use_default_tol', use_default_tol)\n\n inputs = {}\n attrs = {}\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'matrix_rank')\n inputs['X'] = x\n if tol is None:\n attrs['use_default_tol'] = True\n elif isinstance(tol, Variable):\n check_variable_and_dtype(tol, 'tol', ['float32'], 'matrix_rank')\n attrs['use_default_tol'] = False\n if tol.dtype != x.dtype:\n inputs['TolTensor'] = cast(tol, x.dtype)\n else:\n inputs['TolTensor'] = tol\n else:\n check_type(tol, 'tol', float, 'matrix_rank')\n attrs['use_default_tol'] = False\n attrs['tol'] = tol\n check_type(hermitian, 'hermitian', bool, 'matrix_rank')\n attrs['hermitian'] = hermitian\n\n helper = LayerHelper('matrix_rank', **locals())\n out = helper.create_variable_for_type_inference(dtype='int32')\n helper.append_op(\n type='matrix_rank', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef bmm(x, y, name=None):\n \"\"\"\n Applies batched matrix multiplication to two tensors.\n\n Both of the two input tensors must be three-dementional and share the same batch size.\n\n if x is a (b, m, k) tensor, y is a (b, k, n) tensor, the output will be a (b, m, n) tensor.\n\n Args:\n x (Tensor): The input Tensor.\n y (Tensor): The input Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The product Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n # In imperative mode:\n # size x: (2, 2, 3) and y: (2, 3, 2)\n x = paddle.to_tensor([[[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0]],\n [[3.0, 3.0, 3.0],\n [4.0, 4.0, 4.0]]])\n y = paddle.to_tensor([[[1.0, 1.0],[2.0, 2.0],[3.0, 3.0]],\n [[4.0, 4.0],[5.0, 5.0],[6.0, 6.0]]])\n out = paddle.bmm(x, y)\n #output size: (2, 2, 2)\n #output value:\n #[[[6.0, 6.0],[12.0, 12.0]],[[45.0, 45.0],[60.0, 60.0]]]\n out_np = out.numpy()\n\n \"\"\"\n x_shape = x.shape\n y_shape = y.shape\n if not len(x_shape) == len(y_shape) == 3:\n raise ValueError(\n \"x and y should be 3-dimensional. But received x's dimention: {}, y's dimention: {}\".\n format(x_shape, y_shape))\n if x_shape[2] != y_shape[1]:\n raise ValueError(\n \"x's width must be equal with y's height. But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n if x_shape[0] != y_shape[0]:\n raise ValueError(\n \"x's batch (shape[0]) must be equal with y's batch (shape[0]). But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.bmm(x, y)\n\n helper = LayerHelper('bmm', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='bmm', inputs={'X': x, 'Y': y}, outputs={'Out': out})\n return out\n\n\ndef histogram(input, bins=100, min=0, max=0, name=None):\n \"\"\"\n Computes the histogram of a tensor. The elements are sorted into equal width bins between min and max.\n If min and max are both zero, the minimum and maximum values of the data are used.\n\n Args:\n input (Tensor): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor\n should be float32, float64, int32, int64.\n bins (int): number of histogram bins\n min (int): lower end of the range (inclusive)\n max (int): upper end of the range (inclusive)\n\n Returns:\n Tensor: data type is int64, shape is (nbins,).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n inputs = paddle.to_tensor([1, 2, 1])\n result = paddle.histogram(inputs, bins=4, min=0, max=3)\n print(result) # [0, 2, 1, 0]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.histogram(input, \"bins\", bins, \"min\", min, \"max\", max)\n\n helper = LayerHelper('histogram', **locals())\n check_variable_and_dtype(\n input, 'X', ['int32', 'int64', 'float32', 'float64'], 'histogram')\n out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)\n helper.append_op(\n type='histogram',\n inputs={'X': input},\n outputs={'Out': out},\n attrs={'bins': bins,\n 'min': min,\n 'max': max})\n return out\n\n\ndef bincount(x, weights=None, minlength=0, name=None):\n \"\"\"\n Computes frequency of each value in the input tensor. \n\n Args:\n x (Tensor): A Tensor with non-negative integer. Should be 1-D tensor.\n weights (Tensor, optional): Weight for each value in the input tensor. Should have the same shape as input. Default is None.\n minlength (int, optional): Minimum number of bins. Should be non-negative integer. Default is 0.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor of frequency.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([1, 2, 1, 4, 5])\n result1 = paddle.bincount(x)\n print(result1) # [0, 2, 1, 0, 1, 1]\n\n w = paddle.to_tensor([2.1, 0.4, 0.1, 0.5, 0.5])\n result2 = paddle.bincount(x, weights=w)\n print(result2) # [0., 2.19999981, 0.40000001, 0., 0.50000000, 0.50000000]\n \"\"\"\n if x.dtype not in [paddle.int32, paddle.int64]:\n raise TypeError(\"Elements in Input(x) should all be integers\")\n\n if paddle.in_dynamic_mode():\n return _C_ops.bincount(x, weights, \"minlength\", minlength)\n\n helper = LayerHelper('bincount', **locals())\n\n check_variable_and_dtype(x, 'X', ['int32', 'int64'], 'bincount')\n\n if weights is not None:\n check_variable_and_dtype(weights, 'Weights',\n ['int32', 'int64', 'float32', 'float64'],\n 'bincount')\n out = helper.create_variable_for_type_inference(dtype=weights.dtype)\n else:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='bincount',\n inputs={'X': x,\n 'Weights': weights},\n outputs={'Out': out},\n attrs={'minlength': minlength})\n return out\n\n\ndef mv(x, vec, name=None):\n \"\"\"\n Performs a matrix-vector product of the matrix x and the vector vec.\n\n Args:\n x (Tensor): A tensor with shape :math:`[M, N]` , The data type of the input Tensor x\n should be one of float32, float64.\n vec (Tensor): A tensor with shape :math:`[N]` , The data type of the input Tensor x\n should be one of float32, float64.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor which is producted by x and vec.\n\n Examples:\n .. code-block:: python\n\n # x: [M, N], vec: [N]\n # paddle.mv(x, vec) # out: [M]\n\n import numpy as np\n import paddle\n\n x_data = np.array([[2, 1, 3], [3, 0, 1]]).astype(\"float64\")\n x = paddle.to_tensor(x_data)\n vec_data = np.array([3, 5, 1])\n vec = paddle.to_tensor(vec_data).astype(\"float64\")\n out = paddle.mv(x, vec)\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_mv(x, vec)\n else:\n if _in_legacy_dygraph():\n out = _C_ops.mv(x, vec)\n return out\n else:\n\n def __check_input(x, vec):\n var_names = {'x': x, 'vec': vec}\n for name, val in var_names.items():\n check_variable_and_dtype(val, name, ['float32', 'float64'],\n 'mv')\n x_shape = list(x.shape)\n vec_shape = list(vec.shape)\n if len(x_shape) != 2:\n raise ValueError(\n \"x should be 2-dimensional. But received x's dimention: {}\".\n format(x_shape))\n if len(vec_shape) != 1:\n raise ValueError(\n \"vec should be 1-dimensional. But received vec's dimention: {}\".\n format(vec_shape))\n\n __check_input(x, vec)\n\n helper = LayerHelper('mv', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='mv', inputs={'X': x,\n 'Vec': vec}, outputs={'Out': out})\n return out\n\n\ndef det(x, name=None):\n \"\"\"\n Calculates determinant value of a square matrix or batches of square matrices.\n Args:\n x (Tensor): input (Tensor): the input matrix of size `(n, n)` or the batch of matrices of size\n `(*, n, n)` where `*` is one or more batch dimensions.\n Returns:\n y (Tensor):the determinant value of a square matrix or batches of square matrices.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.det(x)\n\n print(A)\n\n # [ 0.02547996, 2.52317095, -6.15900707])\n\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.determinant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'det')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('determinant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='determinant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef slogdet(x, name=None):\n \"\"\"\n Calculates the sign and natural logarithm of the absolute value of a square matrix's or batches square matrices' determinant.\n The determinant can be computed with ``sign * exp(logabsdet)\n\n Supports input of float, double\n\n Note that for matrices that have zero determinant, this returns ``(0, -inf)``\n Args:\n x (Tensor): the batch of matrices of size :math:`(*, n, n)`\n where math:`*` is one or more batch dimensions.\n\n Returns:\n y (Tensor): A tensor containing the sign of the determinant and the natural logarithm\n of the absolute value of determinant, respectively.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.slogdet(x)\n\n print(A)\n\n # [[ 1. , 1. , -1. ],\n # [-0.98610914, -0.43010661, -0.10872950]])\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.slogdeterminant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'slogdet')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('slogdeterminant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='slogdeterminant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef svd(x, full_matrices=False, name=None):\n r\"\"\"\n Computes the singular value decomposition of one matrix or a batch of regular matrices.\n\n Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies:\n\n .. math::\n X = U * diag(S) * VT\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., N, M]`,\n where `...` is zero or more batch dimensions. N and M can be arbitraty\n positive number. Note that if x is sigular matrices, the grad is numerical\n instable. The data type of x should be float32 or float64.\n full_matrices (bool): A flag to control the behavor of svd.\n If full_matrices = True, svd op will compute full U and V matrics,\n which means shape of U is `[..., N, N]`, shape of V is `[..., M, M]`. K = min(M, N).\n If full_matrices = False, svd op will use a economic method to store U and V.\n which means shape of U is `[..., N, K]`, shape of V is `[..., M, K]`. K = min(M, N).\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple of 3 tensors: (U, S, VH). VH is the conjugate transpose of V. S is the singlar value vectors of matrics with shape `[..., K]`\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 2.0], [1.0, 3.0], [4.0, 6.0]]).astype('float64')\n x = x.reshape([3, 2])\n u, s, vh = paddle.linalg.svd(x)\n print (u)\n #U = [[ 0.27364809, -0.21695147 ],\n # [ 0.37892198, -0.87112408 ],\n # [ 0.8840446 , 0.44053933 ]]\n\n print (s)\n #S = [8.14753743, 0.78589688]\n print (vh)\n #VT= [[ 0.51411221, 0.85772294],\n # [ 0.85772294, -0.51411221]]\n\n # one can verify : U * S * VT == X\n # U * UH == I\n # V * VH == I\n \"\"\"\n\n if paddle.in_dynamic_mode():\n return _C_ops.svd(x, 'full_matrices', full_matrices)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'svd')\n check_type(full_matrices, 'full_matrices', bool, 'svd')\n helper = LayerHelper('svd', **locals())\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n vh = helper.create_variable_for_type_inference(dtype=x.dtype)\n s = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['full_matrices'] = full_matrices\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vh,\n 'S': s},\n attrs=attrs, )\n return u, s, vh\n\n\ndef matrix_power(x, n, name=None):\n r\"\"\"\n Computes the n-th power of a square matrix or a batch of square matrices.\n\n Let :math:`X` be a sqaure matrix or a batch of square matrices, :math:`n` be\n an exponent, the equation should be:\n\n .. math::\n Out = X ^ {n}\n\n Specifically,\n\n - If `n > 0`, it returns the matrix or a batch of matrices raised to the power\n of `n`.\n\n - If `n = 0`, it returns the identity matrix or a batch of identity matrices.\n\n - If `n < 0`, it returns the inverse of each matrix (if invertible) raised to\n the power of `abs(n)`.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices to be raised\n to power `n`. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n n (int): The exponent. It can be any positive, negative integer or zero.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The n-th power of the matrix (or the batch of matrices) `x`. Its\n data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1, 2, 3],\n [1, 4, 9],\n [1, 8, 27]], dtype='float64')\n print(paddle.linalg.matrix_power(x, 2))\n # [[6. , 34. , 102.],\n # [14. , 90. , 282.],\n # [36. , 250., 804.]]\n\n print(paddle.linalg.matrix_power(x, 0))\n # [[1., 0., 0.],\n # [0., 1., 0.],\n # [0., 0., 1.]]\n\n print(paddle.linalg.matrix_power(x, -2))\n # [[ 12.91666667, -12.75000000, 2.83333333 ],\n # [-7.66666667 , 8. , -1.83333333 ],\n # [ 1.80555556 , -1.91666667 , 0.44444444 ]]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.matrix_power(x, \"n\", n)\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'matrix_power')\n check_type(n, 'n', int, 'matrix_power')\n helper = LayerHelper('matrix_power', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matrix_power',\n inputs={'X': x},\n outputs={'Out': out},\n attrs={'n': n})\n return out\n\n\ndef qr(x, mode=\"reduced\", name=None):\n r\"\"\"\n Computes the QR decomposition of one matrix or batches of matrice (backward is unsupported now).\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., M, N]`,\n where ... is zero or more batch dimensions. M and N can be arbitrary\n positive number. The data type of x should be float32 or float64. \n mode (str, optional): A flag to control the behavior of qr, the default is \"reduced\". \n Suppose x's shape is `[..., M, N]` and denoting `K = min(M, N)`:\n If mode = \"reduced\", qr op will return reduced Q and R matrices, \n which means Q's shape is `[..., M, K]` and R's shape is `[..., K, N]`.\n If mode = \"complete\", qr op will return complete Q and R matrices, \n which means Q's shape is `[..., M, M]` and R's shape is `[..., M, N]`.\n If mode = \"r\", qr op will only return reduced R matrix, which means\n R's shape is `[..., K, N]`.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n If mode = \"reduced\" or mode = \"complete\", qr will return a two tensor-tuple, which represents Q and R. \n If mode = \"r\", qr will return a tensor which represents R.\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n q, r = paddle.linalg.qr(x)\n print (q)\n print (r)\n\n # Q = [[-0.16903085, 0.89708523],\n # [-0.50709255, 0.27602622],\n # [-0.84515425, -0.34503278]])\n\n # R = [[-5.91607978, -7.43735744],\n # [ 0. , 0.82807867]])\n \n # one can verify : X = Q * R ; \n \"\"\"\n if paddle.in_dynamic_mode():\n q, r = _C_ops.qr(x, 'mode', mode)\n if mode == \"r\":\n return r\n else:\n return q, r\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'qr')\n check_type(mode, 'mode', str, 'qr')\n helper = LayerHelper('qr', **locals())\n q = helper.create_variable_for_type_inference(dtype=x.dtype)\n r = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['mode'] = mode\n helper.append_op(\n type='qr', inputs={'X': [x]}, outputs={'Q': q,\n 'R': r}, attrs=attrs)\n if mode == \"r\":\n return r\n else:\n return q, r\n\n\ndef lu(x, pivot=True, get_infos=False, name=None):\n r\"\"\"\n Computes the LU factorization of an N-D(N>=2) matrix x. \n\n Returns the LU factorization(inplace x) and Pivots. low triangular matrix L and \n upper triangular matrix U are combined to a single LU matrix.\n\n Pivoting is done if pivot is set to True.\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n # return ones\n\n Args:\n\n X (Tensor): the tensor to factor of N-dimensions(N>=2).\n\n pivot (bool, optional): controls whether pivoting is done. Default: True.\n\n get_infos (bool, optional): if set to True, returns an info IntTensor. Default: False.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n factorization (Tensor): LU matrix, the factorization of input X.\n\n pivots (IntTensor): the pivots of size(∗(N-2), min(m,n)). `pivots` stores all the \n intermediate transpositions of rows. The final permutation `perm` could be \n reconstructed by this, details refer to upper example.\n\n infos (IntTensor, optional): if `get_infos` is `True`, this is a tensor of size (∗(N-2)) \n where non-zero values indicate whether factorization for the matrix or each minibatch \n has succeeded or failed.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n \n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n if paddle.in_dynamic_mode():\n LU, Piv, Info = _C_ops.lu(x, 'pivots', pivot)\n if get_infos:\n return LU, Piv, Info\n else:\n return LU, Piv\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu')\n helper = LayerHelper('lu', **locals())\n lu = helper.create_variable_for_type_inference(dtype=x.dtype)\n p = helper.create_variable_for_type_inference(dtype='int')\n info = helper.create_variable_for_type_inference(dtype='int')\n attrs = dict()\n attrs['pivots'] = pivot\n helper.append_op(\n type='lu',\n inputs={'X': x},\n outputs={'Out': lu,\n 'Pivots': p,\n 'Infos': info},\n attrs=attrs)\n if get_infos:\n return lu, p, info\n else:\n return lu, p\n\n\ndef lu_unpack(x, y, unpack_ludata=True, unpack_pivots=True, name=None):\n r\"\"\"\n Unpack L U and P to single matrix tensor . \n unpack L and U matrix from LU, unpack permutation matrix P from Pivtos .\n\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n\n\n Args:\n x (Tensor): The LU tensor get from paddle.linalg.lu, which is combined by L and U.\n\n y (Tensor): Pivots get from paddle.linalg.lu.\n\n unpack_ludata (bool,optional): whether to unpack L and U from x. Default: True.\n\n unpack_pivots (bool, optional): whether to unpack permutation matrix P from Pivtos. Default: True.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n P (Tensor): Permutation matrix P of lu factorization.\n\n L (Tensor): The lower triangular matrix tensor of lu factorization.\n\n U (Tensor): The upper triangular matrix tensor of lu factorization.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n\n if paddle.in_dynamic_mode():\n P, L, U = _C_ops.lu_unpack(x, y, 'unpack_ludata', unpack_ludata,\n 'unpack_pivots', unpack_pivots)\n return P, L, U\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu_unpack')\n helper = LayerHelper('lu_unpack', **locals())\n p = helper.create_variable_for_type_inference(dtype=x.dtype)\n l = helper.create_variable_for_type_inference(dtype=x.dtype)\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n attrs = dict()\n attrs['unpack_ludata'] = unpack_ludata\n attrs['unpack_pivots'] = unpack_pivots\n helper.append_op(\n type='lu_unpack',\n inputs={'X': x,\n 'Pivots': y},\n outputs={'Pmat': p,\n 'L': l,\n 'U': u},\n attrs=attrs)\n return p, l, u\n\n\ndef eig(x, name=None):\n \"\"\"\n This API performs the eigenvalue decomposition of a square matrix or a batch of square matrices.\n\n .. note::\n If the matrix is a Hermitian or a real symmetric matrix, please use :ref:`paddle.linalg.eigh` instead, which is much faster.\n If only eigenvalues is needed, please use :ref:`paddle.linalg.eigvals` instead.\n If the matrix is of any shape, please use :ref:`paddle.linalg.svd`.\n This API is only supported on CPU device.\n The output datatype is always complex for both real and complex input.\n\n Args:\n x (Tensor): A tensor with shape math:`[*, N, N]`, The data type of the x should be one of ``float32``,\n ``float64``, ``compplex64`` or ``complex128``.\n name (str, optional): The default value is `None`. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Eigenvalues(Tensors): A tensor with shape math:`[*, N]` refers to the eigen values.\n Eigenvectors(Tensors): A tensor with shape math:`[*, N, N]` refers to the eigen vectors.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n paddle.device.set_device(\"cpu\")\n\n x_data = np.array([[1.6707249, 7.2249975, 6.5045543],\n [9.956216, 8.749598, 6.066444 ],\n [4.4251957, 1.7983172, 0.370647 ]]).astype(\"float32\")\n x = paddle.to_tensor(x_data)\n w, v = paddle.linalg.eig(x)\n print(w)\n # Tensor(shape=[3, 3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [[(-0.5061363550800655+0j) , (-0.7971760990842826+0j) ,\n # (0.18518077798279986+0j)],\n # [(-0.8308237755993192+0j) , (0.3463813401919749+0j) ,\n # (-0.6837005269141947+0j) ],\n # [(-0.23142567697893396+0j), (0.4944999840400175+0j) ,\n # (0.7058765252952796+0j) ]])\n\n print(v)\n # Tensor(shape=[3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [ (16.50471283351188+0j) , (-5.5034820550763515+0j) ,\n # (-0.21026087843552282+0j)])\n \"\"\"\n if paddle.in_dynamic_mode():\n w, v = _C_ops.eig(x)\n return w, v\n\n check_variable_and_dtype(\n x, 'X', ['float32', 'float64', 'complex64', 'complex128'], 'eig')\n helper = LayerHelper('eig', **locals())\n\n w = helper.create_variable_for_type_inference(x.dtype)\n v = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {'X': x}\n outputs = {'Eigenvalues': w, 'Eigenvectors': v}\n helper.append_op(type='eig', inputs=inputs, outputs=outputs)\n\n return w, v\n\n\ndef eigvals(x, name=None):\n \"\"\"\n Compute the eigenvalues of one or more general matrices.\n\n Warning:\n The gradient kernel of this operator does not yet developed.\n If you need back propagation through this operator, please replace it with paddle.linalg.eig.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices whose eigenvalues will be computed.\n Its shape should be `[*, M, M]`, where `*` is zero or more batch dimensions.\n Its data type should be float32, float64, complex64, or complex128.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n Tensor: A tensor containing the unsorted eigenvalues which has the same batch dimensions with `x`.\n The eigenvalues are complex-valued even when `x` is real.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n paddle.seed(1234)\n\n x = paddle.rand(shape=[3, 3], dtype='float64')\n # [[0.02773777, 0.93004224, 0.06911496],\n # [0.24831591, 0.45733623, 0.07717843],\n # [0.48016702, 0.14235102, 0.42620817]])\n\n print(paddle.linalg.eigvals(x))\n # [(-0.27078833542132674+0j), (0.29962280156230725+0j), (0.8824477020120244+0j)] #complex128\n \"\"\"\n\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64',\n 'complex128'], 'eigvals')\n\n x_shape = list(x.shape)\n if len(x_shape) < 2:\n raise ValueError(\n \"The dimension of Input(x) should be at least 2, but received x's dimention = {}, x's shape = {}\".\n format(len(x_shape), x_shape))\n\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The last two dimensions of Input(x) should be equal, but received x's shape = {}\".\n format(x_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.eigvals(x)\n\n helper = LayerHelper('eigvals', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='eigvals', inputs={'X': x}, outputs={'Out': out})\n return out\n\n\ndef multi_dot(x, name=None):\n \"\"\"\n Multi_dot is an operator that calculates multiple matrix multiplications.\n\n Supports inputs of float16(only GPU support), float32 and float64 dtypes. This function does not\n support batched inputs.\n\n The input tensor in [x] must be 2-D except for the first and last can be 1-D.\n If the first tensor is a 1-D vector of shape(n, ) it is treated as row vector\n of shape(1, n), similarly if the last tensor is a 1D vector of shape(n, ), it\n is treated as a column vector of shape(n, 1).\n\n If the first and last tensor are 2-D matrix, then the output is also 2-D matrix,\n otherwise the output is a 1-D vector.\n\n Multi_dot will select the lowest cost multiplication order for calculation. The\n cost of multiplying two matrices with shapes (a, b) and (b, c) is a * b * c.\n Given matrices A, B, C with shapes (20, 5), (5, 100), (100, 10) respectively,\n we can calculate the cost of different multiplication orders as follows:\n - Cost((AB)C) = 20x5x100 + 20x100x10 = 30000\n - Cost(A(BC)) = 5x100x10 + 20x5x10 = 6000\n\n In this case, multiplying B and C first, then multiply A, which is 5 times faster\n than sequential calculation.\n\n Args:\n x ([Tensor]): The input tensors which is a list Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # A * B\n A_data = np.random.random([3, 4]).astype(np.float32)\n B_data = np.random.random([4, 5]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n out = paddle.linalg.multi_dot([A, B])\n print(out.numpy().shape)\n # [3, 5]\n\n # A * B * C\n A_data = np.random.random([10, 5]).astype(np.float32)\n B_data = np.random.random([5, 8]).astype(np.float32)\n C_data = np.random.random([8, 7]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n C = paddle.to_tensor(C_data)\n out = paddle.linalg.multi_dot([A, B, C])\n print(out.numpy().shape)\n # [10, 7]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.multi_dot(x)\n\n check_type(x, 'x', (list, tuple), 'multi_dot')\n for id, item in enumerate(x):\n check_variable_and_dtype(item, 'x[' + str(id) + ']',\n ['float16', 'float32', 'float64'], 'multi_dot')\n if item.dtype != x[0].dtype:\n raise TypeError(\n \"All the Tensors in the input must have the same data type.\")\n\n helper = LayerHelper('multi_dot', **locals())\n dtype = helper.input_dtype(input_param_name='x')\n out = helper.create_variable_for_type_inference(dtype)\n helper.append_op(type='multi_dot', inputs={\"X\": x}, outputs={\"Out\": out})\n return out\n\n\ndef eigh(x, UPLO='L', name=None):\n \"\"\"\n Compute the eigenvalues and eigenvectors of a\n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[*, N, N]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): (string, default 'L'), 'L' represents the lower triangular matrix,\n \"'U' represents the upper triangular matrix.\".\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n\n out_value(Tensor): A Tensor with shape [*, N] and data type of float32 and float64. The eigenvalues of eigh op.\n out_vector(Tensor): A Tensor with shape [*, N, N] and data type of float32,float64,complex64 and complex128. The eigenvectors of eigh op.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value, out_vector = paddle.linalg.eigh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n print(out_vector)\n #[(-0.9238795325112867+0j), (-0.3826834323650898+0j)],\n #[ 0.3826834323650898j , -0.9238795325112867j ]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.eigh(x, 'UPLO', UPLO)\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigh', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'eigh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO})\n return out_value, out_vector\n\n\ndef pinv(x, rcond=1e-15, hermitian=False, name=None):\n r\"\"\"\n Calculate pseudo inverse via SVD(singular value decomposition)\n of one matrix or batches of regular matrix.\n\n .. math::\n\n if hermitian == False:\n x = u * s * vt (SVD)\n out = v * 1/s * ut\n else:\n x = u * s * ut (eigh)\n out = u * 1/s * u.conj().transpose(-2,-1)\n\n If x is hermitian or symmetric matrix, svd will be replaced with eigh.\n\n Args:\n x(Tensor): The input tensor. Its shape should be (*, m, n)\n where * is zero or more batch dimensions. m and n can be\n arbitraty positive number. The data type of x should be\n float32 or float64 or complex64 or complex128. When data\n type is complex64 or cpmplex128, hermitian should be set\n True.\n\n rcond(Tensor, optional): the tolerance value to determine\n when is a singular value zero. Defalut:1e-15.\n\n hermitian(bool, optional): indicates whether x is Hermitian\n if complex or symmetric if real. Default: False.\n\n name(str|None): A name for this layer(optional). If set None,\n the layer will be named automatically.\n\n Returns:\n Tensor: The tensor with same data type with x. it represents\n pseudo inverse of x. Its shape should be (*, n, m).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.arange(15).reshape((3, 5)).astype('float64')\n input = paddle.to_tensor(x)\n out = paddle.linalg.pinv(input)\n print(input)\n print(out)\n\n # input:\n # [[0. , 1. , 2. , 3. , 4. ],\n # [5. , 6. , 7. , 8. , 9. ],\n # [10., 11., 12., 13., 14.]]\n\n # out:\n # [[-0.22666667, -0.06666667, 0.09333333],\n # [-0.12333333, -0.03333333, 0.05666667],\n # [-0.02000000, 0.00000000, 0.02000000],\n # [ 0.08333333, 0.03333333, -0.01666667],\n # [ 0.18666667, 0.06666667, -0.05333333]]\n\n # one can verify : x * out * x = x ;\n # or out * x * out = x ;\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if not hermitian:\n # combine svd and matmul op\n u, s, vt = _C_ops.svd(x, 'full_matrices', False)\n max_singular_val = _C_ops.reduce_max(s, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=x.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=x.dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v, _ = _C_ops.transpose2(vt, 'axis', perm)\n\n out_1 = v * st\n out_2 = _C_ops.matmul_v2(out_1, u, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n # combine eigh and matmul op\n s, u = _C_ops.eigh(x, 'UPLO', 'L')\n s_abs = paddle.abs(s)\n max_singular_val = _C_ops.reduce_max(s_abs, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=s.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=s.dtype)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n out_1 = u * st\n u_conj = _C_ops.conj(u)\n out_2 = _C_ops.matmul_v2(out_1, u_conj, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n if not hermitian:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'pinv')\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(dtype)\n vt = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vt,\n 'S': s},\n attrs={'full_matrices': False}, )\n\n max_singular_val = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=dtype)\n st_shape = helper.create_variable_for_type_inference(dtype=dtype)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v = helper.create_variable_for_type_inference(dtype)\n v_shape = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='transpose2',\n inputs={'X': [vt]},\n outputs={'Out': [v],\n 'XShape': [v_shape]},\n attrs={'axis': perm})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': v,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n else:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64',\n 'complex128'], 'pinv')\n\n if dtype == paddle.complex128:\n s_type = 'float64'\n elif dtype == paddle.complex64:\n s_type = 'float32'\n else:\n s_type = dtype\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': s,\n 'Eigenvectors': u},\n attrs={'UPLO': 'L'})\n s_abs = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='abs', inputs={'X': s}, outputs={'Out': s_abs})\n max_singular_val = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s_abs},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=s_type)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=s_type)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s_type)\n cond_not_int = layers.cast(layers.logical_not(condition), s_type)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=s_type)\n st_shape = helper.create_variable_for_type_inference(dtype=s_type)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': u,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n u_conj = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='conj', inputs={'X': u}, outputs={'Out': [u_conj]})\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u_conj},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n\n\ndef solve(x, y, name=None):\n r\"\"\"\n Computes the solution of a square system of linear equations with a unique solution for input 'X' and 'Y'.\n Let :math: `X` be a sqaure matrix or a batch of square matrices, :math:`Y` be\n a vector/matrix or a batch of vectors/matrices, the equation should be:\n\n .. math::\n Out = X^-1 * Y\n Specifically,\n - This system of linear equations has one solution if and only if input 'X' is invertible.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): A vector/matrix or a batch of vectors/matrices. Its shape should be `[*, M, K]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of a square system of linear equations with a unique solution for input 'x' and 'y'.\n Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # 2*X0 + X1 = 9\n # X0 + 2*X1 = 8\n\n import paddle\n import numpy as np\n\n np_x = np.array([[3, 1],[1, 2]])\n np_y = np.array([9, 8])\n x = paddle.to_tensor(np_x, dtype=\"float64\")\n y = paddle.to_tensor(np_y, dtype=\"float64\")\n out = paddle.linalg.solve(x, y)\n\n print(out)\n # [2., 3.])\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.solve(x, y)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type=\"solve\", inputs={\"X\": x,\n \"Y\": y}, outputs={\"Out\": out})\n return out\n\n\ndef triangular_solve(x,\n y,\n upper=True,\n transpose=False,\n unitriangular=False,\n name=None):\n r\"\"\"\n Computes the solution of a system of equations with a triangular coefficient matrix `x` and\n multiple right-hand sides `y` .\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input triangular coefficient matrix. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): Whether to solve the upper-triangular system of equations (default) or the lower-triangular \n system of equations. Default: True.\n transpose (bool, optional): whether `x` should be transposed before calculation. Default: False.\n unitriangular (bool, optional): whether `x` is unit triangular. If True, the diagonal elements of `x` are assumed \n to be 1 and not referenced from `x` . Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # x1 + x2 + x3 = 0\n # 2*x2 + x3 = -9\n # -x3 = 5\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n y = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.triangular_solve(x, y, upper=True)\n\n print(out)\n # [7, -2, -5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.triangular_solve(x, y, 'upper', upper, 'transpose',\n transpose, 'unitriangular',\n unitriangular)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"triangular_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'triangular_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'triangular_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='triangular_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={\n 'upper': upper,\n 'transpose': transpose,\n 'unitriangular': unitriangular\n })\n return out\n\n\ndef cholesky_solve(x, y, upper=False, name=None):\n r\"\"\"\n Solves a linear system of equations A @ X = B, given A's Cholesky factor matrix u and matrix B.\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input matrix which is upper or lower triangular Cholesky factor of square matrix A. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): whether to consider the Cholesky factor as a lower or upper triangular matrix. Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type is the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n u = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n b = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.cholesky_solve(b, u, upper=True)\n\n print(out)\n # [-2.5, -7, 9.5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky_solve(x, y, 'upper', upper)\n\n helper = LayerHelper(\"cholesky_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'cholesky_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'cholesky_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='cholesky_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef eigvalsh(x, UPLO='L', name=None):\n \"\"\"\n Computes the eigenvalues of a \n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[_, M, M]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): Lower triangular part of a (‘L’, default) or the upper triangular part (‘U’).\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor eigenvalues in ascending order.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value = paddle.eigvalsh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n \"\"\"\n if paddle.in_dynamic_mode():\n is_test = x.stop_gradient\n values, _ = _C_ops.eigvalsh(x, 'UPLO', UPLO, 'is_test', is_test)\n return values\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigvalsh', **locals())\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64', 'complex128'],\n 'eigvalsh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n is_test = x.stop_gradient\n helper.append_op(\n type='eigvalsh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO,\n 'is_test': is_test})\n return out_value\n\n\ndef lstsq(x, y, rcond=None, driver=None, name=None):\n \"\"\"\n Computes a solution to\n the least squares problem of a system of linear equations.\n\n Args:\n x (Tensor): A tensor with shape ``(*, M, N)`` , the data type of the input Tensor ``x``\n should be one of float32, float64.\n y (Tensor): A tensor with shape ``(*, M, K)`` , the data type of the input Tensor ``y`` \n should be one of float32, float64.\n rcond(float, optional): The default value is None. A float pointing number used to determine \n the effective rank of ``x``. If ``rcond`` is None, it will be set to max(M, N) times the \n machine precision of x_dtype.\n driver(str, optional): The default value is None. The name of LAPACK method to be used. For \n CPU inputs the valid values are ‘gels’, ‘gelsy’, ‘gelsd, ‘gelss’. For CUDA input, the only \n valid driver is ‘gels’. If ``driver`` is None, ‘gelsy’ is used for CPU inputs and ‘gels’ \n for CUDA inputs.\n name(str, optional): The default value is None. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple: A tuple of 4 Tensors which is (``solution``, ``residuals``, ``rank``, ``singular_values``). \n ``solution`` is a tensor with shape ``(*, N, K)``, meaning the least squares solution. ``residuals`` \n is a tensor with shape ``(*, K)``, meaning the squared residuals of the solutions, which is computed \n when M > N and every matrix in ``x`` is full-rank, otherwise return an empty tensor. ``rank`` is a tensor \n with shape ``(*)``, meaning the ranks of the matrices in ``x``, which is computed when ``driver`` in \n (‘gelsy’, ‘gelsd’, ‘gelss’), otherwise return an empty tensor. ``singular_values`` is a tensor with \n shape ``(*, min(M, N))``, meaning singular values of the matrices in ``x``, which is computed when \n ``driver`` in (‘gelsd’, ‘gelss’), otherwise return an empty tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n x = paddle.to_tensor([[1, 3], [3, 2], [5, 6.]])\n y = paddle.to_tensor([[3, 4, 6], [5, 3, 4], [1, 2, 1.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gelsd\")\n print(results[0])\n # [[ 0.78350395, -0.22165027, -0.62371236],\n # [-0.11340097, 0.78866047, 1.14948535]]\n print(results[1])\n # [19.81443405, 10.43814468, 30.56185532])\n print(results[2])\n # 2\n print(results[3])\n # [9.03455734, 1.54167950]\n\n x = paddle.to_tensor([[10, 2, 3], [3, 10, 5], [5, 6, 12.]])\n y = paddle.to_tensor([[4, 2, 9], [2, 0, 3], [2, 5, 3.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gels\")\n print(results[0])\n # [[ 0.39386186, 0.10230173, 0.93606132],\n # [ 0.10741687, -0.29028133, 0.11892585],\n # [-0.05115091, 0.51918161, -0.19948854]]\n print(results[1])\n # []\n \"\"\"\n device = paddle.get_device()\n if device == \"cpu\":\n if driver not in (None, \"gels\", \"gelss\", \"gelsd\", \"gelsy\"):\n raise ValueError(\n \"Only support valid driver is 'gels', 'gelss', 'gelsd', 'gelsy' or None for CPU inputs. But got {}\".\n format(driver))\n driver = \"gelsy\" if driver is None else driver\n elif \"gpu\" in device:\n if driver not in (None, \"gels\"):\n raise ValueError(\n \"Only support valid driver is 'gels' or None for CUDA inputs. But got {}\".\n format(driver))\n driver = \"gels\" if driver is None else driver\n else:\n raise RuntimeError(\"Only support lstsq api for CPU or CUDA device.\")\n\n if x.dtype == y.dtype and x.dtype in (paddle.float32, paddle.float64):\n pass\n else:\n raise ValueError(\n \"Only support x and y have the same dtype such as 'float32' and 'float64'.\"\n )\n\n if rcond is None:\n if x.dtype == paddle.float32:\n rcond = 1e-7 * max(x.shape[-2], x.shape[-1])\n elif x.dtype == paddle.float64:\n rcond = 1e-15 * max(x.shape[-2], x.shape[-1])\n\n if paddle.in_dynamic_mode():\n solution, rank, singular_values = _C_ops.lstsq(x, y, \"rcond\", rcond,\n \"driver\", driver)\n if x.shape[-2] > x.shape[-1]:\n matmul_out = _varbase_creator(dtype=x.dtype)\n _C_ops.matmul(x, solution, matmul_out, 'trans_x', False, 'trans_y',\n False)\n minus_out = _C_ops.elementwise_sub(matmul_out, y)\n pow_out = _C_ops.pow(minus_out, 'factor', 2)\n residuals = _C_ops.reduce_sum(pow_out, 'dim', [-2], 'keepdim',\n False, 'reduce_all', False)\n else:\n residuals = paddle.empty(shape=[0], dtype=x.dtype)\n\n if driver == \"gels\":\n rank = paddle.empty(shape=[0], dtype=paddle.int32)\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n elif driver == \"gelsy\":\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n\n return solution, residuals, rank, singular_values\n\n helper = LayerHelper('lstsq', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n check_variable_and_dtype(\n y, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n\n solution = helper.create_variable_for_type_inference(dtype=x.dtype)\n residuals = helper.create_variable_for_type_inference(dtype=x.dtype)\n rank = helper.create_variable_for_type_inference(dtype=paddle.int32)\n singular_values = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='lstsq',\n inputs={'X': x,\n 'Y': y},\n outputs={\n 'Solution': solution,\n 'Rank': rank,\n 'SingularValues': singular_values\n },\n attrs={'rcond': rcond,\n 'driver': driver})\n\n matmul_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n minus_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n pow_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': solution},\n outputs={'Out': matmul_out},\n attrs={\n 'trans_x': False,\n 'trans_y': False,\n })\n\n helper.append_op(\n type='elementwise_sub',\n inputs={'X': matmul_out,\n 'Y': y},\n outputs={'Out': minus_out})\n\n helper.append_op(\n type='pow',\n inputs={'X': minus_out},\n outputs={'Out': pow_out},\n attrs={'factor': 2})\n\n helper.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': residuals},\n attrs={'dim': [-2],\n 'keep_dim': False,\n 'reduce_all': False})\n\n if driver == \"gels\":\n rank = paddle.static.data(name='rank', shape=[0])\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n elif driver == \"gelsy\":\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n\n return solution, residuals, rank, singular_values\n"}}},{"rowIdx":39,"cells":{"function_name":{"kind":"string","value":"lstsq"},"docstring":{"kind":"string","value":"Computes a solution to\nthe least squares problem of a system of linear equations.\n\nArgs:\n x (Tensor): A tensor with shape ``(*, M, N)`` , the data type of the input Tensor ``x``\n should be one of float32, float64.\n y (Tensor): A tensor with shape ``(*, M, K)`` , the data type of the input Tensor ``y`` \n should be one of float32, float64.\n rcond(float, optional): The default value is None. A float pointing number used to determine \n the effective rank of ``x``. If ``rcond`` is None, it will be set to max(M, N) times the \n machine precision of x_dtype.\n driver(str, optional): The default value is None. The name of LAPACK method to be used. For \n CPU inputs the valid values are ‘gels’, ‘gelsy’, ‘gelsd, ‘gelss’. For CUDA input, the only \n valid driver is ‘gels’. If ``driver`` is None, ‘gelsy’ is used for CPU inputs and ‘gels’ \n for CUDA inputs.\n name(str, optional): The default value is None. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\nReturns:\n Tuple: A tuple of 4 Tensors which is (``solution``, ``residuals``, ``rank``, ``singular_values``). \n ``solution`` is a tensor with shape ``(*, N, K)``, meaning the least squares solution. ``residuals`` \n is a tensor with shape ``(*, K)``, meaning the squared residuals of the solutions, which is computed \n when M > N and every matrix in ``x`` is full-rank, otherwise return an empty tensor. ``rank`` is a tensor \n with shape ``(*)``, meaning the ranks of the matrices in ``x``, which is computed when ``driver`` in \n (‘gelsy’, ‘gelsd’, ‘gelss’), otherwise return an empty tensor. ``singular_values`` is a tensor with \n shape ``(*, min(M, N))``, meaning singular values of the matrices in ``x``, which is computed when \n ``driver`` in (‘gelsd’, ‘gelss’), otherwise return an empty tensor.\n\nExamples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n x = paddle.to_tensor([[1, 3], [3, 2], [5, 6.]])\n y = paddle.to_tensor([[3, 4, 6], [5, 3, 4], [1, 2, 1.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gelsd\")\n print(results[0])\n # [[ 0.78350395, -0.22165027, -0.62371236],\n # [-0.11340097, 0.78866047, 1.14948535]]\n print(results[1])\n # [19.81443405, 10.43814468, 30.56185532])\n print(results[2])\n # 2\n print(results[3])\n # [9.03455734, 1.54167950]\n\n x = paddle.to_tensor([[10, 2, 3], [3, 10, 5], [5, 6, 12.]])\n y = paddle.to_tensor([[4, 2, 9], [2, 0, 3], [2, 5, 3.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gels\")\n print(results[0])\n # [[ 0.39386186, 0.10230173, 0.93606132],\n # [ 0.10741687, -0.29028133, 0.11892585],\n # [-0.05115091, 0.51918161, -0.19948854]]\n print(results[1])\n # []"},"masked_code":{"kind":"string","value":"# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numpy as np\nfrom ..fluid.layer_helper import LayerHelper\nfrom ..framework import _varbase_creator, _dygraph_tracer\nfrom ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype\nfrom ..static import Variable\nfrom ..fluid.framework import _in_legacy_dygraph, in_dygraph_mode\nfrom ..fluid.layers import transpose, cast # noqa: F401\nfrom ..fluid import layers\nimport paddle\nfrom paddle.common_ops_import import core\nfrom paddle.common_ops_import import VarDesc\nfrom paddle import _C_ops\n\n__all__ = []\n\n\ndef matmul(x, y, transpose_x=False, transpose_y=False, name=None):\n \"\"\"\n Applies matrix multiplication to two tensors. `matmul` follows\n the complete broadcast rules,\n and its behavior is consistent with `np.matmul`.\n\n Currently, the input tensors' number of dimensions can be any, `matmul` can be used to\n achieve the `dot`, `matmul` and `batchmatmul`.\n\n The actual behavior depends on the shapes of :math:`x`, :math:`y` and the\n flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:\n\n - If a transpose flag is specified, the last two dimensions of the tensor\n are transposed. If the tensor is ndim-1 of shape, the transpose is invalid. If the tensor\n is ndim-1 of shape :math:`[D]`, then for :math:`x` it is treated as :math:`[1, D]`, whereas\n for :math:`y` it is the opposite: It is treated as :math:`[D, 1]`.\n\n The multiplication behavior depends on the dimensions of `x` and `y`. Specifically:\n\n - If both tensors are 1-dimensional, the dot product result is obtained.\n\n - If both tensors are 2-dimensional, the matrix-matrix product is obtained.\n\n - If the `x` is 1-dimensional and the `y` is 2-dimensional,\n a `1` is prepended to its dimension in order to conduct the matrix multiply.\n After the matrix multiply, the prepended dimension is removed.\n\n - If the `x` is 2-dimensional and `y` is 1-dimensional,\n the matrix-vector product is obtained.\n\n - If both arguments are at least 1-dimensional and at least one argument\n is N-dimensional (where N > 2), then a batched matrix multiply is obtained.\n If the first argument is 1-dimensional, a 1 is prepended to its dimension\n in order to conduct the batched matrix multiply and removed after.\n If the second argument is 1-dimensional, a 1 is appended to its\n dimension for the purpose of the batched matrix multiple and removed after.\n The non-matrix (exclude the last two dimensions) dimensions are\n broadcasted according the broadcast rule.\n For example, if input is a (j, 1, n, m) tensor and the other is a (k, m, p) tensor,\n out will be a (j, k, n, p) tensor.\n\n Args:\n x (Tensor): The input tensor which is a Tensor.\n y (Tensor): The input tensor which is a Tensor.\n transpose_x (bool): Whether to transpose :math:`x` before multiplication.\n transpose_y (bool): Whether to transpose :math:`y` before multiplication.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # vector * vector\n x_data = np.random.random([10]).astype(np.float32)\n y_data = np.random.random([10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [1]\n\n # matrix * vector\n x_data = np.random.random([10, 5]).astype(np.float32)\n y_data = np.random.random([5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10]\n\n # batched matrix * broadcasted vector\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([2]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5]\n\n # batched matrix * batched matrix\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([10, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5, 5]\n\n # batched matrix * broadcasted matrix\n x_data = np.random.random([10, 1, 5, 2]).astype(np.float32)\n y_data = np.random.random([1, 3, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 3, 5, 5]\n\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_matmul(x, y, transpose_x, transpose_y)\n\n if _in_legacy_dygraph():\n op_type = 'matmul_v2'\n op = getattr(_C_ops, op_type)\n return op(x, y, 'trans_x', transpose_x, 'trans_y', transpose_y)\n\n attrs = {\n 'trans_x': transpose_x,\n 'trans_y': transpose_y,\n }\n\n def __check_input(x, y):\n var_names = {'x': x, 'y': y}\n for name, val in var_names.items():\n check_variable_and_dtype(\n val, name,\n ['float16', 'float32', 'float64', 'complex64', 'complex128'],\n 'matmul')\n\n __check_input(x, y)\n\n helper = LayerHelper('matmul_v2', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef norm(x, p='fro', axis=None, keepdim=False, name=None):\n \"\"\"\n\n Returns the matrix norm (Frobenius) or vector norm (the 1-norm, the Euclidean\n or 2-norm, and in general the p-norm for p > 0) of a given tensor.\n\n .. note::\n This norm API is different from `numpy.linalg.norm`.\n This api supports high-order input tensors (rank >= 3), and certain axis need to be pointed out to calculate the norm.\n But `numpy.linalg.norm` only supports 1-D vector or 2-D matrix as input tensor.\n For p-order matrix norm, this api actually treats matrix as a flattened vector to calculate the vector norm, NOT REAL MATRIX NORM.\n\n Args:\n x (Tensor): The input tensor could be N-D tensor, and the input data\n type could be float32 or float64.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `0`, `1`, `2`,\n `inf`, `-inf` and any positive real number yielding the corresponding p-norm. Not supported: ord < 0 and nuclear norm.\n Default value is `fro`.\n axis (int|list|tuple, optional): The axis on which to apply norm operation. If axis is int\n or list(int)/tuple(int) with only one element, the vector norm is computed over the axis.\n If `axis < 0`, the dimension to norm operation is rank(input) + axis.\n If axis is a list(int)/tuple(int) with two elements, the matrix norm is computed over the axis.\n Defalut value is `None`.\n keepdim (bool, optional): Whether to reserve the reduced dimension in the\n output Tensor. The result tensor will have fewer dimension\n than the :attr:`input` unless :attr:`keepdim` is true, default\n value is False.\n name (str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: results of norm operation on the specified axis of input tensor,\n it's data type is the same as input's Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n shape=[2, 3, 4]\n np_input = np.arange(24).astype('float32') - 12\n np_input = np_input.reshape(shape)\n x = paddle.to_tensor(np_input)\n #[[[-12. -11. -10. -9.] [ -8. -7. -6. -5.] [ -4. -3. -2. -1.]]\n # [[ 0. 1. 2. 3.] [ 4. 5. 6. 7.] [ 8. 9. 10. 11.]]]\n\n # compute frobenius norm along last two dimensions.\n out_fro = paddle.linalg.norm(x, p='fro', axis=[0,1])\n # out_fro.numpy() [17.435596 16.911535 16.7332 16.911535]\n\n # compute 2-order vector norm along last dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=-1)\n #out_pnorm.numpy(): [[21.118711 13.190906 5.477226]\n # [ 3.7416575 11.224972 19.131126]]\n\n # compute 2-order norm along [0,1] dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=[0,1])\n #out_pnorm.numpy(): [17.435596 16.911535 16.7332 16.911535]\n\n # compute inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=np.inf)\n #out_pnorm.numpy() = [12.]\n out_pnorm = paddle.linalg.norm(x, p=np.inf, axis=0)\n #out_pnorm.numpy(): [[12. 11. 10. 9.] [8. 7. 6. 7.] [8. 9. 10. 11.]]\n\n # compute -inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=-np.inf)\n #out_pnorm.numpy(): [0.]\n out_pnorm = paddle.linalg.norm(x, p=-np.inf, axis=0)\n #out_pnorm.numpy(): [[0. 1. 2. 3.] [4. 5. 6. 5.] [4. 3. 2. 1.]]\n \"\"\"\n\n def frobenius_norm(input, dim=None, keepdim=False, name=None):\n \"\"\"\n The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n dim (list, optional): None for last two dimensions.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if dim is not None and not (isinstance(dim, list) and len(dim) == 2):\n raise ValueError(\n \"The dim of frobenius norm op should be None or two elements list!\"\n )\n if paddle.in_dynamic_mode():\n if dim is None:\n return _C_ops.frobenius_norm(input, 'keep_dim', keepdim,\n 'reduce_all', True)\n return _C_ops.frobenius_norm(input, 'dim', dim, 'keep_dim', keepdim,\n 'reduce_all', False)\n attrs = {'dim': dim, 'keep_dim': keepdim, 'reduce_all': False}\n if dim is None:\n attrs['reduce_all'] = True\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'frobenius_norm')\n\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='frobenius_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def vector_norm(input,\n porder=None,\n axis=None,\n keepdim=False,\n asvector=False,\n name=None):\n \"\"\"\n Calculate the p-order vector norm for certain dimension of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n porder (float, optional): None for porder=2.0.\n axis (int, optional): None for last dimension.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if paddle.in_dynamic_mode():\n if axis is None: axis = -1\n return _C_ops.p_norm(input, 'porder', porder, 'axis', axis,\n 'keepdim', keepdim, 'asvector', asvector)\n if porder is not None:\n check_type(porder, 'porder', (float, int), 'p_norm')\n if axis is not None:\n check_type(axis, 'axis', (int), 'p_norm')\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'p_norm')\n\n attrs = {\n 'axis': axis if axis is not None else -1,\n 'porder': float(porder) if porder is not None else 2.0,\n 'keepdim': keepdim,\n 'asvector': asvector,\n 'epsilon': 1e-12,\n }\n helper = LayerHelper('p_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='p_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def inf_norm(input,\n porder=None,\n axis=axis,\n keepdim=False,\n asvector=False,\n name=None):\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n helper.append_op(type='abs', inputs={'X': input}, outputs={'Out': out})\n reduce_out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n reduce_all = True if axis == None or axis == [] or asvector == True else False\n axis = axis if axis != None and axis != [] else [0]\n\n reduce_type = 'reduce_max' if porder == np.float(\n 'inf') else 'reduce_min'\n helper.append_op(\n type=reduce_type,\n inputs={'X': out},\n outputs={'Out': reduce_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n\n return reduce_out\n\n def p_matrix_norm(input, porder=1., axis=axis, keepdim=False, name=None):\n \"\"\"\n NOTE:\n This function actually treats the matrix as flattened vector to calculate vector norm instead of matrix norm.\n \"\"\"\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n\n block.append_op(\n type='pow',\n inputs={'X': abs_out},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': True if axis is None else False\n })\n porder\n block.append_op(\n type='pow',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n if axis is None and p is not None:\n if isinstance(p, str):\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n porder=p,\n axis=axis,\n keepdim=keepdim,\n asvector=True,\n name=name)\n else:\n raise ValueError(\"only valid p type is string or float, found {}\".\n format(type(p)))\n\n if isinstance(axis, tuple):\n axis = list(axis)\n if isinstance(axis, list) and len(axis) == 1:\n axis = axis[0]\n\n #calculate vector norm, where axis is int or list with only one integer\n if isinstance(axis, int):\n if isinstance(p, str):\n if p == \"fro\":\n return vector_norm(\n x,\n porder=2,\n axis=axis,\n keepdim=keepdim,\n asvector=False,\n name=name)\n\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n axis=axis,\n porder=p,\n keepdim=keepdim,\n asvector=False,\n name=name)\n else:\n raise ValueError(\n \"unspport p for p-order vector norm. except float, found {}\".\n format(p))\n #calculate matrix norm, where axis is list with two integers\n elif isinstance(axis, list) and len(axis) == 2:\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n elif p == np.inf or p == -np.inf:\n return inf_norm(x, porder=p, axis=axis, keepdim=keepdim, name=name)\n elif p == 0:\n raise ValueError(\n \"just suport axis type int or list (length of list <=1) if p = 0, found {}\".\n format(axis))\n else:\n return p_matrix_norm(\n x, porder=p, axis=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"except axis type int or list (length of list <=2), found {}\".\n format(axis))\n\n\ndef dist(x, y, p=2, name=None):\n r\"\"\"\n\n This OP returns the p-norm of (x - y). It is not a norm in a strict sense, only as a measure\n of distance. The shapes of x and y must be broadcastable. The definition is as follows, for\n details, please refer to the `numpy's broadcasting <https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html>`_:\n\n - Each input has at least one dimension.\n - Match the two input dimensions from back to front, the dimension sizes must either be equal, one of them is 1, or one of them does not exist.\n\n Where, z = x - y, the shapes of x and y are broadcastable, then the shape of z can be\n obtained as follows:\n\n 1. If the number of dimensions of x and y are not equal, prepend 1 to the dimensions of the\n tensor with fewer dimensions.\n\n For example, The shape of x is [8, 1, 6, 1], the shape of y is [7, 1, 5], prepend 1 to the\n dimension of y.\n\n x (4-D Tensor): 8 x 1 x 6 x 1\n\n y (4-D Tensor): 1 x 7 x 1 x 5\n\n 2. Determine the size of each dimension of the output z: choose the maximum value from the\n two input dimensions.\n\n z (4-D Tensor): 8 x 7 x 6 x 5\n\n If the number of dimensions of the two inputs are the same, the size of the output can be\n directly determined in step 2. When p takes different values, the norm formula is as follows:\n\n When p = 0, defining $0^0=0$, the zero-norm of z is simply the number of non-zero elements of z.\n\n .. math::\n\n ||z||_{0}=\\lim_{p \\\\rightarrow 0}\\sum_{i=1}^{m}|z_i|^{p}\n\n When p = inf, the inf-norm of z is the maximum element of z.\n\n .. math::\n\n ||z||_\\infty=\\max_i |z_i|\n\n When p = -inf, the negative-inf-norm of z is the minimum element of z.\n\n .. math::\n\n ||z||_{-\\infty}=\\min_i |z_i|\n\n Otherwise, the p-norm of z follows the formula,\n\n .. math::\n\n ||z||_{p}=(\\sum_{i=1}^{m}|z_i|^p)^{\\\\frac{1}{p}}\n\n Args:\n x (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n y (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n p (float, optional): The norm to be computed, its data type is float32 or float64. Default: 2.\n\n Returns:\n Tensor: Tensor that is the p-norm of (x - y).\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor(np.array([[3, 3],[3, 3]]), \"float32\")\n y = paddle.to_tensor(np.array([[3, 3],[3, 1]]), \"float32\")\n out = paddle.dist(x, y, 0)\n print(out) # out = [1.]\n\n out = paddle.dist(x, y, 2)\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"inf\"))\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"-inf\"))\n print(out) # out = [0.]\n \"\"\"\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'dist')\n check_variable_and_dtype(y, 'dtype', ['float32', 'float64'], 'dist')\n check_type(p, 'p', (float, int), 'dist')\n helper = LayerHelper(\"dist\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n outputs = {'Out': [out]}\n attrs = {\"p\": float(p)}\n helper.append_op(\n type='dist', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef cond(x, p=None, name=None):\n \"\"\"\n\n Computes the condition number of a matrix or batches of matrices with respect to a matrix norm ``p``.\n\n Args:\n x (Tensor): The input tensor could be tensor of shape ``(*, m, n)`` where ``*`` is zero or more batch dimensions\n for ``p`` in ``(2, -2)``, or of shape ``(*, n, n)`` where every matrix is invertible for any supported ``p``.\n And the input data type could be ``float32`` or ``float64``.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `nuc`, `1`, `-1`, `2`, `-2`,\n `inf`, `-inf`. Default value is `None`, meaning that the order of the norm is `2`.\n name (str, optional): The default value is `None`. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: computing results of condition number, its data type is the same as input Tensor ``x``.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1., 0, -1], [0, 1, 0], [1, 0, 1]])\n\n # compute conditional number when p is None\n out = paddle.linalg.cond(x)\n # out.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is 'fro'\n out_fro = paddle.linalg.cond(x, p='fro')\n # out_fro.numpy() [3.1622777]\n\n # compute conditional number when order of the norm is 'nuc'\n out_nuc = paddle.linalg.cond(x, p='nuc')\n # out_nuc.numpy() [9.2426405]\n\n # compute conditional number when order of the norm is 1\n out_1 = paddle.linalg.cond(x, p=1)\n # out_1.numpy() [2.]\n\n # compute conditional number when order of the norm is -1\n out_minus_1 = paddle.linalg.cond(x, p=-1)\n # out_minus_1.numpy() [1.]\n\n # compute conditional number when order of the norm is 2\n out_2 = paddle.linalg.cond(x, p=2)\n # out_2.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is -1\n out_minus_2 = paddle.linalg.cond(x, p=-2)\n # out_minus_2.numpy() [0.70710677]\n\n # compute conditional number when order of the norm is inf\n out_inf = paddle.linalg.cond(x, p=np.inf)\n # out_inf.numpy() [2.]\n\n # compute conditional number when order of the norm is -inf\n out_minus_inf = paddle.linalg.cond(x, p=-np.inf)\n # out_minus_inf.numpy() [1.]\n\n a = paddle.to_tensor(np.random.randn(2, 4, 4).astype('float32'))\n # a.numpy()\n # [[[ 0.14063153 -0.996288 0.7996131 -0.02571543]\n # [-0.16303636 1.5534962 -0.49919784 -0.04402903]\n # [-1.1341571 -0.6022629 0.5445269 0.29154757]\n # [-0.16816919 -0.30972657 1.7521842 -0.5402487 ]]\n # [[-0.58081484 0.12402827 0.7229862 -0.55046535]\n # [-0.15178485 -1.1604939 0.75810957 0.30971205]\n # [-0.9669573 1.0940945 -0.27363303 -0.35416734]\n # [-1.216529 2.0018666 -0.7773689 -0.17556527]]]\n a_cond_fro = paddle.linalg.cond(a, p='fro')\n # a_cond_fro.numpy() [31.572273 28.120834]\n\n b = paddle.to_tensor(np.random.randn(2, 3, 4).astype('float64'))\n # b.numpy()\n # [[[ 1.61707487 0.46829144 0.38130416 0.82546736]\n # [-1.72710298 0.08866375 -0.62518804 0.16128892]\n # [-0.02822879 -1.67764516 0.11141444 0.3220113 ]]\n # [[ 0.22524372 0.62474921 -0.85503233 -1.03960523]\n # [-0.76620689 0.56673047 0.85064753 -0.45158196]\n # [ 1.47595418 2.23646462 1.5701758 0.10497519]]]\n b_cond_2 = paddle.linalg.cond(b, p=2)\n # b_cond_2.numpy() [3.30064451 2.51976252]\n\n \"\"\"\n\n def mat_norm(input, porder=1., axis=None):\n \"\"\"\n NOTE:\n Calculate the matrix norm of a square matrix or batches of square matrices,\n when porder is in (1, -1, inf, -inf)\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n axis = axis if axis != None and axis != [] else [0]\n keepdim = False\n\n if paddle.in_dynamic_mode():\n abs_out = _C_ops.abs(input)\n sum_out = _C_ops.reduce_sum(abs_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == 1 or porder == np.inf:\n return _C_ops.reduce_max(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == -1 or porder == -np.inf:\n return _C_ops.reduce_min(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n\n block = LayerHelper('norm', **locals())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n block.append_op(\n type='reduce_sum',\n inputs={'X': abs_out},\n outputs={'Out': sum_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 1 or porder == np.inf:\n block.append_op(\n type='reduce_max',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n if porder == -1 or porder == -np.inf:\n block.append_op(\n type='reduce_min',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n\n def fro_norm(input, porder=2, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the frobenius norm of a square matrix or batches of square matrices.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n if paddle.in_dynamic_mode():\n pow_out = _C_ops.pow(input, 'factor', porder)\n sum_out_1 = _C_ops.reduce_sum(pow_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n sum_out_2 = _C_ops.reduce_sum(sum_out_1, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n return _C_ops.pow(sum_out_2, 'factor', float(1. / porder))\n\n block = LayerHelper('norm', **locals())\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_1 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_2 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='pow',\n inputs={'X': input},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out_1},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_sum',\n inputs={'X': sum_out_1},\n outputs={'Out': sum_out_2},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='pow',\n inputs={'X': sum_out_2},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n def svd_norm(input, porder, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the matrix norm, which is related to singular values, of a matrix\n or batches of matrices, including nuclear norm, 2-norm and (-2)-norm.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n u, s, vh = svd(input, full_matrices=False)\n\n if paddle.in_dynamic_mode():\n if porder == \"nuc\":\n return _C_ops.reduce_sum(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n max_out = _C_ops.reduce_max(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n min_out = _C_ops.reduce_min(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n if porder == 2:\n return _C_ops.elementwise_div(max_out, min_out, 'aixs', axis,\n 'use_mkldnn', False)\n if porder == -2:\n return _C_ops.elementwise_div(min_out, max_out, 'aixs', axis,\n 'use_mkldnn', False)\n\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n if porder == \"nuc\":\n block.append_op(\n type='reduce_sum',\n inputs={'X': s},\n outputs={'Out': out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n max_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n min_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_min',\n inputs={'X': s},\n outputs={'Out': min_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': max_out,\n 'Y': min_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n if porder == -2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': min_out,\n 'Y': max_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n\n def empty_tensor(input, shape):\n if paddle.in_dynamic_mode():\n return input.reshape(shape)\n raise ValueError(\"only support x is nonempty tensor in static mode\")\n\n x_shape = list(x.shape)\n if not len(x_shape) >= 2:\n raise ValueError(\"input should be a matrix or batches of matrices, \" +\n \"but the dimention of received input is {}\".format(\n len(x_shape)))\n if p == None:\n p = 2\n x_size = 0 if (0 in x_shape) else 1\n if p in (\"fro\", \"nuc\", 1, -1, np.inf, -np.inf):\n if x_shape[len(x_shape) - 1] == x_shape[len(x_shape) - 2]:\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n x_inv = x.inverse()\n if p == \"fro\":\n return fro_norm(x) * fro_norm(x_inv)\n if p == \"nuc\":\n return svd_norm(x, p) * svd_norm(x_inv, p)\n if p in (1, -1):\n return mat_norm(\n x, porder=p, axis=[-2]) * mat_norm(\n x_inv, porder=p, axis=[-2])\n if p in (np.inf, -np.inf):\n return mat_norm(\n x, porder=p, axis=[-1]) * mat_norm(\n x_inv, porder=p, axis=[-1])\n else:\n raise ValueError(\"only support p is {} when input is a \".format(p) +\n \"square matrix or batches of square matrices\")\n elif p in (2, -2):\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n return svd_norm(x, porder=p)\n else:\n raise ValueError(\n \"unsupported {} for p, only supporting ('fro', 'nuc', \".format(\n p) + \"1, -1, 2, -2, inf, -inf) or none\")\n\n\ndef dot(x, y, name=None):\n \"\"\"\n This operator calculates inner product for vectors.\n\n .. note::\n Support 1-d and 2-d Tensor. When it is 2d, the first dimension of this matrix\n is the batch dimension, which means that the vectors of multiple batches are dotted.\n\n Parameters:\n x(Tensor): 1-D or 2-D ``Tensor``. Its dtype should be ``float32``, ``float64``, ``int32``, ``int64``\n y(Tensor): 1-D or 2-D ``Tensor``. Its dtype soulde be ``float32``, ``float64``, ``int32``, ``int64``\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: the calculated result Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x_data = np.random.uniform(0.1, 1, [10]).astype(np.float32)\n y_data = np.random.uniform(1, 3, [10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.dot(x, y)\n print(z)\n\n \"\"\"\n op_type = 'dot'\n # skip var type check in dygraph mode to improve efficiency\n if paddle.in_dynamic_mode():\n op = getattr(_C_ops, op_type)\n return op(x, y)\n\n assert x is not None, 'x cannot be None in {}'.format(op_type)\n assert y is not None, 'y cannot be None in {}'.format(op_type)\n\n check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n check_variable_and_dtype(y, 'y', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n\n helper = LayerHelper(op_type, **locals())\n if name is None:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n else:\n out = helper.create_variable(\n name=name, dtype=x.dtype, persistable=False)\n helper.append_op(\n type=\"dot\", inputs={'X': x,\n 'Y': y}, attrs={}, outputs={\"Out\": out})\n return out\n\n\ndef cov(x, rowvar=True, ddof=True, fweights=None, aweights=None, name=None):\n \"\"\"\n Estimate the covariance matrix of the input variables, given data and weights.\n\n A covariance matrix is a square matrix, indicate the covariance of each pair variables in the input matrix.\n For example, for an N-dimensional samples X=[x1,x2,…xN]T, then the covariance matrix \n element Cij is the covariance of xi and xj. The element Cii is the variance of xi itself.\n\n Parameters:\n x(Tensor): A N-D(N<=2) Tensor containing multiple variables and observations. By default, each row of x represents a variable. Also see rowvar below.\n rowvar(Bool, optional): If rowvar is True (default), then each row represents a variable, with observations in the columns. Default: True\n ddof(Bool, optional): If ddof=True will return the unbiased estimate, and ddof=False will return the simple average. Default: True\n fweights(Tensor, optional): 1-D Tensor of integer frequency weights; The number of times each observation vector should be repeated. Default: None\n aweights(Tensor, optional): 1-D Tensor of observation vector weights. How important of the observation vector, larger data means this element is more important. Default: None\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: The covariance matrix Tensor of the variables.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n\n xt = paddle.rand((3,4))\n paddle.linalg.cov(xt)\n\n '''\n Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n [[0.07918842, 0.06127326, 0.01493049],\n [0.06127326, 0.06166256, 0.00302668],\n [0.01493049, 0.00302668, 0.01632146]])\n '''\n \"\"\"\n op_type = 'cov'\n if len(x.shape) > 2 or len(x.shape) < 1:\n raise ValueError(\n \"Input(x) only support N-D (1<=N<=2) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cov')\n nx = x\n if len(x.shape) == 1:\n nx = x.reshape((1, -1))\n if not rowvar and nx.shape[0] != 1:\n nx = nx.t()\n w = None\n observation_num = nx.shape[1]\n if fweights is not None:\n w = fweights.astype(nx.dtype)\n if len(w.shape) > 1:\n raise ValueError(\n \"Input(fweights) only support N-D (N<=1) tensor in cov, but received \"\n \"shape of Input(input) is %s.\" % len(fweights.shape))\n if fweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(fweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(fweights) is {}.\".format(observation_num,\n fweights.shape[0]))\n if fweights.min() < 0:\n raise ValueError(\n \"The value of Input(fweights) cannot be negtive, but received \"\n \"min of Input(fweights) is {}.\".format(fweights.min()))\n if not paddle.all(fweights == paddle.round(fweights.astype('float64'))):\n raise ValueError(\"Input(fweights) must be integer \")\n\n if aweights is not None:\n aw = aweights.astype(nx.dtype)\n if len(aw.shape) > 1:\n raise ValueError(\n \"Input(aweights) only support N-D (N<=1) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(aweights.shape))\n check_variable_and_dtype(aweights, 'dtype', ['float32', 'float64'],\n 'cov')\n if aweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(aweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(aweights) is {}.\".format(observation_num,\n aweights.shape[0]))\n if aweights.min() < 0:\n raise ValueError(\n \"The value of Input(aweights) cannot be negtive, but received \"\n \"min of Input(aweights) is {}.\".format(aweights.min()))\n if w is not None:\n w = w * aw\n else:\n w = aw\n\n w_sum = paddle.to_tensor(observation_num, dtype=nx.dtype)\n if fweights is not None or aweights is not None:\n w_sum = w.sum()\n if w_sum.item() == 0:\n raise ValueError(\"The sum of weights is zero, can't be normalized.\")\n\n if w is not None:\n nx_w = nx * w\n avg = (nx_w).sum(axis=1) / w_sum\n else:\n avg = nx.sum(axis=1) / w_sum\n nx_w = nx\n\n if w is not None and aweights is not None and ddof == True:\n norm_factor = w_sum - (w * aweights).sum() / w_sum\n else:\n norm_factor = w_sum - ddof\n if norm_factor <= 0:\n norm_factor = paddle.to_tensor(0, dtype=nx.dtype)\n nx = nx - avg.unsqueeze(1)\n xxt = paddle.mm(nx, nx_w.t().conj())\n cov = paddle.divide(xxt, norm_factor).squeeze()\n return cov\n\n\ndef t(input, name=None):\n \"\"\"\n Transpose <=2-D tensor.\n 0-D and 1-D tensors are returned as it is and 2-D tensor is equal to\n the paddle.transpose function which perm dimensions set 0 and 1.\n\n Args:\n input (Tensor): The input Tensor. It is a N-D (N<=2) Tensor of data types float16, float32, float64, int32.\n name(str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`\n Returns:\n Tensor: A transposed n-D Tensor, with data type being float16, float32, float64, int32, int64.\n\n For Example:\n\n .. code-block:: text\n\n # Example 1 (0-D tensor)\n x = tensor([0.79])\n paddle.t(x) = tensor([0.79])\n\n # Example 2 (1-D tensor)\n x = tensor([0.79, 0.84, 0.32])\n paddle.t(x) = tensor([0.79, 0.84, 0.32])\n\n # Example 3 (2-D tensor)\n x = tensor([0.79, 0.84, 0.32],\n [0.64, 0.14, 0.57])\n paddle.t(x) = tensor([0.79, 0.64],\n [0.84, 0.14],\n [0.32, 0.57])\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n x = paddle.ones(shape=[2, 3], dtype='int32')\n x_transposed = paddle.t(x)\n print(x_transposed.shape)\n # [3, 2]\n \"\"\"\n if len(input.shape) > 2:\n raise ValueError(\n \"Input(input) only support N-D (N<=2) tensor, but received \"\n \"length of Input(input) is %s. Perhaps you can use paddle.\"\n \"tensor.transpose() instead.\" % len(input.shape))\n if paddle.in_dynamic_mode():\n if len(input.shape) == 1:\n return input\n # 2-D tensor\n perm = [1, 0]\n out, _ = _C_ops.transpose2(input, 'axis', perm)\n return out\n\n check_variable_and_dtype(\n input, 'input', ['float16', 'float32', 'float64', 'int32',\n 'int64'], 'transpose')\n\n helper = LayerHelper('t', **locals())\n out = helper.create_variable_for_type_inference(input.dtype)\n input_shape = helper.create_variable_for_type_inference(input.dtype)\n if len(input.shape) == 1:\n out = input\n else:\n helper.append_op(\n type='transpose2',\n inputs={'X': [input]},\n outputs={'Out': [out],\n 'XShape': [input_shape]},\n attrs={'axis': [1, 0]})\n return out\n\n\ndef cross(x, y, axis=None, name=None):\n \"\"\"\n Computes the cross product between two tensors along an axis.\n\n Inputs must have the same shape, and the length of their axes should be equal to 3.\n If `axis` is not given, it defaults to the first axis found with the length 3.\n\n Args:\n x (Tensor): The first input tensor.\n y (Tensor): The second input tensor.\n axis (int, optional): The axis along which to compute the cross product. It defaults to the first axis found with the length 3.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor. A Tensor with same data type as `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0],\n [3.0, 3.0, 3.0]])\n y = paddle.to_tensor([[1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0]])\n\n z1 = paddle.cross(x, y)\n # [[-1. -1. -1.]\n # [ 2. 2. 2.]\n # [-1. -1. -1.]]\n\n z2 = paddle.cross(x, y, axis=1)\n # [[0. 0. 0.]\n # [0. 0. 0.]\n # [0. 0. 0.]]\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_cross(x, y, axis)\n else:\n if _in_legacy_dygraph():\n if axis is not None:\n return _C_ops.cross(x, y, 'dim', axis)\n else:\n return _C_ops.cross(x, y)\n else:\n helper = LayerHelper(\"cross\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n attrs = dict()\n attrs['dim'] = axis\n\n helper.append_op(\n type='cross',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef cholesky(x, upper=False, name=None):\n r\"\"\"\n Computes the Cholesky decomposition of one symmetric positive-definite\n matrix or batches of symmetric positive-definite matrice.\n\n If `upper` is `True`, the decomposition has the form :math:`A = U^{T}U` ,\n and the returned matrix :math:`U` is upper-triangular. Otherwise, the\n decomposition has the form :math:`A = LL^{T}` , and the returned matrix\n :math:`L` is lower-triangular.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[*, M, M]`,\n where * is zero or more batch dimensions, and matrices on the\n inner-most 2 dimensions all should be symmetric positive-definite.\n Its data type should be float32 or float64.\n upper (bool): The flag indicating whether to return upper or lower\n triangular matrices. Default: False.\n\n Returns:\n Tensor: A Tensor with same shape and data type as `x`. It represents \\\n triangular matrices generated by Cholesky decomposition.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n a = np.random.rand(3, 3)\n a_t = np.transpose(a, [1, 0])\n x_data = np.matmul(a, a_t) + 1e-03\n x = paddle.to_tensor(x_data)\n out = paddle.linalg.cholesky(x, upper=False)\n print(out)\n # [[1.190523 0. 0. ]\n # [0.9906703 0.27676893 0. ]\n # [1.25450498 0.05600871 0.06400121]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky(x, \"upper\", upper)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cholesky')\n check_type(upper, 'upper', bool, 'cholesky')\n helper = LayerHelper('cholesky', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='cholesky',\n inputs={'X': [x]},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef matrix_rank(x, tol=None, hermitian=False, name=None):\n r\"\"\"\n Computes the rank of a matrix.\n\n The rank of a matrix is the number of singular values that are greater than the specified `tol` threshold when hermitian=False,\n or the number of eigenvalues in absolute value that are greater than the specified `tol` threshold when hermitian=True.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., m, n]`, where `...` is zero or more batch dimensions. If `x` is a batch\n of matrices then the output has the same batch dimensions. The data type of `x` should be float32 or float64.\n tol (float,Tensor,optional): the tolerance value. Default: None. If `tol` is not specified, and `sigma` is the largest\n singular value (or eigenvalues in absolute value), and `eps` is the epsilon value for the dtype of `x`, then `tol` is computed\n with formula `tol=sigma * max(m,n) * eps`. Note that if `x` is a batch of matrices, `tol` is computed this way for every batch.\n hermitian (bool,optional): indicates whether `x` is Hermitian. Default: False. When hermitian=True, `x` is assumed to be Hermitian,\n enabling a more efficient method for finding eigenvalues, but `x` is not checked inside the function. Instead, We just use\n the lower triangular of the matrix to compute.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: Rank of tensor x.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n a = paddle.eye(10)\n b = paddle.linalg.matrix_rank(a)\n print(b)\n # b = [10]\n\n c = paddle.ones(shape=[3, 4, 5, 5])\n d = paddle.linalg.matrix_rank(c, tol=0.01, hermitian=True)\n print(d)\n # d = [[1, 1, 1, 1],\n # [1, 1, 1, 1],\n # [1, 1, 1, 1]]\n\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if tol is None:\n tol_tensor = None\n tol_attr = 0.0\n use_default_tol = True\n elif isinstance(tol, Variable):\n if tol.dtype != x.dtype:\n tol_tensor = cast(tol, x.dtype)\n else:\n tol_tensor = tol\n tol_attr = 0.0\n use_default_tol = False\n else:\n tol_tensor = None\n tol_attr = float(tol)\n use_default_tol = False\n return _C_ops.matrix_rank(x, tol_tensor, \"tol\", tol_attr, 'hermitian',\n hermitian, 'use_default_tol', use_default_tol)\n\n inputs = {}\n attrs = {}\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'matrix_rank')\n inputs['X'] = x\n if tol is None:\n attrs['use_default_tol'] = True\n elif isinstance(tol, Variable):\n check_variable_and_dtype(tol, 'tol', ['float32'], 'matrix_rank')\n attrs['use_default_tol'] = False\n if tol.dtype != x.dtype:\n inputs['TolTensor'] = cast(tol, x.dtype)\n else:\n inputs['TolTensor'] = tol\n else:\n check_type(tol, 'tol', float, 'matrix_rank')\n attrs['use_default_tol'] = False\n attrs['tol'] = tol\n check_type(hermitian, 'hermitian', bool, 'matrix_rank')\n attrs['hermitian'] = hermitian\n\n helper = LayerHelper('matrix_rank', **locals())\n out = helper.create_variable_for_type_inference(dtype='int32')\n helper.append_op(\n type='matrix_rank', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef bmm(x, y, name=None):\n \"\"\"\n Applies batched matrix multiplication to two tensors.\n\n Both of the two input tensors must be three-dementional and share the same batch size.\n\n if x is a (b, m, k) tensor, y is a (b, k, n) tensor, the output will be a (b, m, n) tensor.\n\n Args:\n x (Tensor): The input Tensor.\n y (Tensor): The input Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The product Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n # In imperative mode:\n # size x: (2, 2, 3) and y: (2, 3, 2)\n x = paddle.to_tensor([[[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0]],\n [[3.0, 3.0, 3.0],\n [4.0, 4.0, 4.0]]])\n y = paddle.to_tensor([[[1.0, 1.0],[2.0, 2.0],[3.0, 3.0]],\n [[4.0, 4.0],[5.0, 5.0],[6.0, 6.0]]])\n out = paddle.bmm(x, y)\n #output size: (2, 2, 2)\n #output value:\n #[[[6.0, 6.0],[12.0, 12.0]],[[45.0, 45.0],[60.0, 60.0]]]\n out_np = out.numpy()\n\n \"\"\"\n x_shape = x.shape\n y_shape = y.shape\n if not len(x_shape) == len(y_shape) == 3:\n raise ValueError(\n \"x and y should be 3-dimensional. But received x's dimention: {}, y's dimention: {}\".\n format(x_shape, y_shape))\n if x_shape[2] != y_shape[1]:\n raise ValueError(\n \"x's width must be equal with y's height. But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n if x_shape[0] != y_shape[0]:\n raise ValueError(\n \"x's batch (shape[0]) must be equal with y's batch (shape[0]). But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.bmm(x, y)\n\n helper = LayerHelper('bmm', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='bmm', inputs={'X': x, 'Y': y}, outputs={'Out': out})\n return out\n\n\ndef histogram(input, bins=100, min=0, max=0, name=None):\n \"\"\"\n Computes the histogram of a tensor. The elements are sorted into equal width bins between min and max.\n If min and max are both zero, the minimum and maximum values of the data are used.\n\n Args:\n input (Tensor): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor\n should be float32, float64, int32, int64.\n bins (int): number of histogram bins\n min (int): lower end of the range (inclusive)\n max (int): upper end of the range (inclusive)\n\n Returns:\n Tensor: data type is int64, shape is (nbins,).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n inputs = paddle.to_tensor([1, 2, 1])\n result = paddle.histogram(inputs, bins=4, min=0, max=3)\n print(result) # [0, 2, 1, 0]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.histogram(input, \"bins\", bins, \"min\", min, \"max\", max)\n\n helper = LayerHelper('histogram', **locals())\n check_variable_and_dtype(\n input, 'X', ['int32', 'int64', 'float32', 'float64'], 'histogram')\n out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)\n helper.append_op(\n type='histogram',\n inputs={'X': input},\n outputs={'Out': out},\n attrs={'bins': bins,\n 'min': min,\n 'max': max})\n return out\n\n\ndef bincount(x, weights=None, minlength=0, name=None):\n \"\"\"\n Computes frequency of each value in the input tensor. \n\n Args:\n x (Tensor): A Tensor with non-negative integer. Should be 1-D tensor.\n weights (Tensor, optional): Weight for each value in the input tensor. Should have the same shape as input. Default is None.\n minlength (int, optional): Minimum number of bins. Should be non-negative integer. Default is 0.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor of frequency.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([1, 2, 1, 4, 5])\n result1 = paddle.bincount(x)\n print(result1) # [0, 2, 1, 0, 1, 1]\n\n w = paddle.to_tensor([2.1, 0.4, 0.1, 0.5, 0.5])\n result2 = paddle.bincount(x, weights=w)\n print(result2) # [0., 2.19999981, 0.40000001, 0., 0.50000000, 0.50000000]\n \"\"\"\n if x.dtype not in [paddle.int32, paddle.int64]:\n raise TypeError(\"Elements in Input(x) should all be integers\")\n\n if paddle.in_dynamic_mode():\n return _C_ops.bincount(x, weights, \"minlength\", minlength)\n\n helper = LayerHelper('bincount', **locals())\n\n check_variable_and_dtype(x, 'X', ['int32', 'int64'], 'bincount')\n\n if weights is not None:\n check_variable_and_dtype(weights, 'Weights',\n ['int32', 'int64', 'float32', 'float64'],\n 'bincount')\n out = helper.create_variable_for_type_inference(dtype=weights.dtype)\n else:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='bincount',\n inputs={'X': x,\n 'Weights': weights},\n outputs={'Out': out},\n attrs={'minlength': minlength})\n return out\n\n\ndef mv(x, vec, name=None):\n \"\"\"\n Performs a matrix-vector product of the matrix x and the vector vec.\n\n Args:\n x (Tensor): A tensor with shape :math:`[M, N]` , The data type of the input Tensor x\n should be one of float32, float64.\n vec (Tensor): A tensor with shape :math:`[N]` , The data type of the input Tensor x\n should be one of float32, float64.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor which is producted by x and vec.\n\n Examples:\n .. code-block:: python\n\n # x: [M, N], vec: [N]\n # paddle.mv(x, vec) # out: [M]\n\n import numpy as np\n import paddle\n\n x_data = np.array([[2, 1, 3], [3, 0, 1]]).astype(\"float64\")\n x = paddle.to_tensor(x_data)\n vec_data = np.array([3, 5, 1])\n vec = paddle.to_tensor(vec_data).astype(\"float64\")\n out = paddle.mv(x, vec)\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_mv(x, vec)\n else:\n if _in_legacy_dygraph():\n out = _C_ops.mv(x, vec)\n return out\n else:\n\n def __check_input(x, vec):\n var_names = {'x': x, 'vec': vec}\n for name, val in var_names.items():\n check_variable_and_dtype(val, name, ['float32', 'float64'],\n 'mv')\n x_shape = list(x.shape)\n vec_shape = list(vec.shape)\n if len(x_shape) != 2:\n raise ValueError(\n \"x should be 2-dimensional. But received x's dimention: {}\".\n format(x_shape))\n if len(vec_shape) != 1:\n raise ValueError(\n \"vec should be 1-dimensional. But received vec's dimention: {}\".\n format(vec_shape))\n\n __check_input(x, vec)\n\n helper = LayerHelper('mv', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='mv', inputs={'X': x,\n 'Vec': vec}, outputs={'Out': out})\n return out\n\n\ndef det(x, name=None):\n \"\"\"\n Calculates determinant value of a square matrix or batches of square matrices.\n Args:\n x (Tensor): input (Tensor): the input matrix of size `(n, n)` or the batch of matrices of size\n `(*, n, n)` where `*` is one or more batch dimensions.\n Returns:\n y (Tensor):the determinant value of a square matrix or batches of square matrices.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.det(x)\n\n print(A)\n\n # [ 0.02547996, 2.52317095, -6.15900707])\n\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.determinant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'det')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('determinant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='determinant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef slogdet(x, name=None):\n \"\"\"\n Calculates the sign and natural logarithm of the absolute value of a square matrix's or batches square matrices' determinant.\n The determinant can be computed with ``sign * exp(logabsdet)\n\n Supports input of float, double\n\n Note that for matrices that have zero determinant, this returns ``(0, -inf)``\n Args:\n x (Tensor): the batch of matrices of size :math:`(*, n, n)`\n where math:`*` is one or more batch dimensions.\n\n Returns:\n y (Tensor): A tensor containing the sign of the determinant and the natural logarithm\n of the absolute value of determinant, respectively.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.slogdet(x)\n\n print(A)\n\n # [[ 1. , 1. , -1. ],\n # [-0.98610914, -0.43010661, -0.10872950]])\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.slogdeterminant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'slogdet')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('slogdeterminant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='slogdeterminant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef svd(x, full_matrices=False, name=None):\n r\"\"\"\n Computes the singular value decomposition of one matrix or a batch of regular matrices.\n\n Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies:\n\n .. math::\n X = U * diag(S) * VT\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., N, M]`,\n where `...` is zero or more batch dimensions. N and M can be arbitraty\n positive number. Note that if x is sigular matrices, the grad is numerical\n instable. The data type of x should be float32 or float64.\n full_matrices (bool): A flag to control the behavor of svd.\n If full_matrices = True, svd op will compute full U and V matrics,\n which means shape of U is `[..., N, N]`, shape of V is `[..., M, M]`. K = min(M, N).\n If full_matrices = False, svd op will use a economic method to store U and V.\n which means shape of U is `[..., N, K]`, shape of V is `[..., M, K]`. K = min(M, N).\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple of 3 tensors: (U, S, VH). VH is the conjugate transpose of V. S is the singlar value vectors of matrics with shape `[..., K]`\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 2.0], [1.0, 3.0], [4.0, 6.0]]).astype('float64')\n x = x.reshape([3, 2])\n u, s, vh = paddle.linalg.svd(x)\n print (u)\n #U = [[ 0.27364809, -0.21695147 ],\n # [ 0.37892198, -0.87112408 ],\n # [ 0.8840446 , 0.44053933 ]]\n\n print (s)\n #S = [8.14753743, 0.78589688]\n print (vh)\n #VT= [[ 0.51411221, 0.85772294],\n # [ 0.85772294, -0.51411221]]\n\n # one can verify : U * S * VT == X\n # U * UH == I\n # V * VH == I\n \"\"\"\n\n if paddle.in_dynamic_mode():\n return _C_ops.svd(x, 'full_matrices', full_matrices)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'svd')\n check_type(full_matrices, 'full_matrices', bool, 'svd')\n helper = LayerHelper('svd', **locals())\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n vh = helper.create_variable_for_type_inference(dtype=x.dtype)\n s = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['full_matrices'] = full_matrices\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vh,\n 'S': s},\n attrs=attrs, )\n return u, s, vh\n\n\ndef matrix_power(x, n, name=None):\n r\"\"\"\n Computes the n-th power of a square matrix or a batch of square matrices.\n\n Let :math:`X` be a sqaure matrix or a batch of square matrices, :math:`n` be\n an exponent, the equation should be:\n\n .. math::\n Out = X ^ {n}\n\n Specifically,\n\n - If `n > 0`, it returns the matrix or a batch of matrices raised to the power\n of `n`.\n\n - If `n = 0`, it returns the identity matrix or a batch of identity matrices.\n\n - If `n < 0`, it returns the inverse of each matrix (if invertible) raised to\n the power of `abs(n)`.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices to be raised\n to power `n`. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n n (int): The exponent. It can be any positive, negative integer or zero.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The n-th power of the matrix (or the batch of matrices) `x`. Its\n data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1, 2, 3],\n [1, 4, 9],\n [1, 8, 27]], dtype='float64')\n print(paddle.linalg.matrix_power(x, 2))\n # [[6. , 34. , 102.],\n # [14. , 90. , 282.],\n # [36. , 250., 804.]]\n\n print(paddle.linalg.matrix_power(x, 0))\n # [[1., 0., 0.],\n # [0., 1., 0.],\n # [0., 0., 1.]]\n\n print(paddle.linalg.matrix_power(x, -2))\n # [[ 12.91666667, -12.75000000, 2.83333333 ],\n # [-7.66666667 , 8. , -1.83333333 ],\n # [ 1.80555556 , -1.91666667 , 0.44444444 ]]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.matrix_power(x, \"n\", n)\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'matrix_power')\n check_type(n, 'n', int, 'matrix_power')\n helper = LayerHelper('matrix_power', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matrix_power',\n inputs={'X': x},\n outputs={'Out': out},\n attrs={'n': n})\n return out\n\n\ndef qr(x, mode=\"reduced\", name=None):\n r\"\"\"\n Computes the QR decomposition of one matrix or batches of matrice (backward is unsupported now).\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., M, N]`,\n where ... is zero or more batch dimensions. M and N can be arbitrary\n positive number. The data type of x should be float32 or float64. \n mode (str, optional): A flag to control the behavior of qr, the default is \"reduced\". \n Suppose x's shape is `[..., M, N]` and denoting `K = min(M, N)`:\n If mode = \"reduced\", qr op will return reduced Q and R matrices, \n which means Q's shape is `[..., M, K]` and R's shape is `[..., K, N]`.\n If mode = \"complete\", qr op will return complete Q and R matrices, \n which means Q's shape is `[..., M, M]` and R's shape is `[..., M, N]`.\n If mode = \"r\", qr op will only return reduced R matrix, which means\n R's shape is `[..., K, N]`.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n If mode = \"reduced\" or mode = \"complete\", qr will return a two tensor-tuple, which represents Q and R. \n If mode = \"r\", qr will return a tensor which represents R.\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n q, r = paddle.linalg.qr(x)\n print (q)\n print (r)\n\n # Q = [[-0.16903085, 0.89708523],\n # [-0.50709255, 0.27602622],\n # [-0.84515425, -0.34503278]])\n\n # R = [[-5.91607978, -7.43735744],\n # [ 0. , 0.82807867]])\n \n # one can verify : X = Q * R ; \n \"\"\"\n if paddle.in_dynamic_mode():\n q, r = _C_ops.qr(x, 'mode', mode)\n if mode == \"r\":\n return r\n else:\n return q, r\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'qr')\n check_type(mode, 'mode', str, 'qr')\n helper = LayerHelper('qr', **locals())\n q = helper.create_variable_for_type_inference(dtype=x.dtype)\n r = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['mode'] = mode\n helper.append_op(\n type='qr', inputs={'X': [x]}, outputs={'Q': q,\n 'R': r}, attrs=attrs)\n if mode == \"r\":\n return r\n else:\n return q, r\n\n\ndef lu(x, pivot=True, get_infos=False, name=None):\n r\"\"\"\n Computes the LU factorization of an N-D(N>=2) matrix x. \n\n Returns the LU factorization(inplace x) and Pivots. low triangular matrix L and \n upper triangular matrix U are combined to a single LU matrix.\n\n Pivoting is done if pivot is set to True.\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n # return ones\n\n Args:\n\n X (Tensor): the tensor to factor of N-dimensions(N>=2).\n\n pivot (bool, optional): controls whether pivoting is done. Default: True.\n\n get_infos (bool, optional): if set to True, returns an info IntTensor. Default: False.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n factorization (Tensor): LU matrix, the factorization of input X.\n\n pivots (IntTensor): the pivots of size(∗(N-2), min(m,n)). `pivots` stores all the \n intermediate transpositions of rows. The final permutation `perm` could be \n reconstructed by this, details refer to upper example.\n\n infos (IntTensor, optional): if `get_infos` is `True`, this is a tensor of size (∗(N-2)) \n where non-zero values indicate whether factorization for the matrix or each minibatch \n has succeeded or failed.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n \n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n if paddle.in_dynamic_mode():\n LU, Piv, Info = _C_ops.lu(x, 'pivots', pivot)\n if get_infos:\n return LU, Piv, Info\n else:\n return LU, Piv\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu')\n helper = LayerHelper('lu', **locals())\n lu = helper.create_variable_for_type_inference(dtype=x.dtype)\n p = helper.create_variable_for_type_inference(dtype='int')\n info = helper.create_variable_for_type_inference(dtype='int')\n attrs = dict()\n attrs['pivots'] = pivot\n helper.append_op(\n type='lu',\n inputs={'X': x},\n outputs={'Out': lu,\n 'Pivots': p,\n 'Infos': info},\n attrs=attrs)\n if get_infos:\n return lu, p, info\n else:\n return lu, p\n\n\ndef lu_unpack(x, y, unpack_ludata=True, unpack_pivots=True, name=None):\n r\"\"\"\n Unpack L U and P to single matrix tensor . \n unpack L and U matrix from LU, unpack permutation matrix P from Pivtos .\n\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n\n\n Args:\n x (Tensor): The LU tensor get from paddle.linalg.lu, which is combined by L and U.\n\n y (Tensor): Pivots get from paddle.linalg.lu.\n\n unpack_ludata (bool,optional): whether to unpack L and U from x. Default: True.\n\n unpack_pivots (bool, optional): whether to unpack permutation matrix P from Pivtos. Default: True.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n P (Tensor): Permutation matrix P of lu factorization.\n\n L (Tensor): The lower triangular matrix tensor of lu factorization.\n\n U (Tensor): The upper triangular matrix tensor of lu factorization.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n\n if paddle.in_dynamic_mode():\n P, L, U = _C_ops.lu_unpack(x, y, 'unpack_ludata', unpack_ludata,\n 'unpack_pivots', unpack_pivots)\n return P, L, U\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu_unpack')\n helper = LayerHelper('lu_unpack', **locals())\n p = helper.create_variable_for_type_inference(dtype=x.dtype)\n l = helper.create_variable_for_type_inference(dtype=x.dtype)\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n attrs = dict()\n attrs['unpack_ludata'] = unpack_ludata\n attrs['unpack_pivots'] = unpack_pivots\n helper.append_op(\n type='lu_unpack',\n inputs={'X': x,\n 'Pivots': y},\n outputs={'Pmat': p,\n 'L': l,\n 'U': u},\n attrs=attrs)\n return p, l, u\n\n\ndef eig(x, name=None):\n \"\"\"\n This API performs the eigenvalue decomposition of a square matrix or a batch of square matrices.\n\n .. note::\n If the matrix is a Hermitian or a real symmetric matrix, please use :ref:`paddle.linalg.eigh` instead, which is much faster.\n If only eigenvalues is needed, please use :ref:`paddle.linalg.eigvals` instead.\n If the matrix is of any shape, please use :ref:`paddle.linalg.svd`.\n This API is only supported on CPU device.\n The output datatype is always complex for both real and complex input.\n\n Args:\n x (Tensor): A tensor with shape math:`[*, N, N]`, The data type of the x should be one of ``float32``,\n ``float64``, ``compplex64`` or ``complex128``.\n name (str, optional): The default value is `None`. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Eigenvalues(Tensors): A tensor with shape math:`[*, N]` refers to the eigen values.\n Eigenvectors(Tensors): A tensor with shape math:`[*, N, N]` refers to the eigen vectors.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n paddle.device.set_device(\"cpu\")\n\n x_data = np.array([[1.6707249, 7.2249975, 6.5045543],\n [9.956216, 8.749598, 6.066444 ],\n [4.4251957, 1.7983172, 0.370647 ]]).astype(\"float32\")\n x = paddle.to_tensor(x_data)\n w, v = paddle.linalg.eig(x)\n print(w)\n # Tensor(shape=[3, 3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [[(-0.5061363550800655+0j) , (-0.7971760990842826+0j) ,\n # (0.18518077798279986+0j)],\n # [(-0.8308237755993192+0j) , (0.3463813401919749+0j) ,\n # (-0.6837005269141947+0j) ],\n # [(-0.23142567697893396+0j), (0.4944999840400175+0j) ,\n # (0.7058765252952796+0j) ]])\n\n print(v)\n # Tensor(shape=[3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [ (16.50471283351188+0j) , (-5.5034820550763515+0j) ,\n # (-0.21026087843552282+0j)])\n \"\"\"\n if paddle.in_dynamic_mode():\n w, v = _C_ops.eig(x)\n return w, v\n\n check_variable_and_dtype(\n x, 'X', ['float32', 'float64', 'complex64', 'complex128'], 'eig')\n helper = LayerHelper('eig', **locals())\n\n w = helper.create_variable_for_type_inference(x.dtype)\n v = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {'X': x}\n outputs = {'Eigenvalues': w, 'Eigenvectors': v}\n helper.append_op(type='eig', inputs=inputs, outputs=outputs)\n\n return w, v\n\n\ndef eigvals(x, name=None):\n \"\"\"\n Compute the eigenvalues of one or more general matrices.\n\n Warning:\n The gradient kernel of this operator does not yet developed.\n If you need back propagation through this operator, please replace it with paddle.linalg.eig.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices whose eigenvalues will be computed.\n Its shape should be `[*, M, M]`, where `*` is zero or more batch dimensions.\n Its data type should be float32, float64, complex64, or complex128.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n Tensor: A tensor containing the unsorted eigenvalues which has the same batch dimensions with `x`.\n The eigenvalues are complex-valued even when `x` is real.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n paddle.seed(1234)\n\n x = paddle.rand(shape=[3, 3], dtype='float64')\n # [[0.02773777, 0.93004224, 0.06911496],\n # [0.24831591, 0.45733623, 0.07717843],\n # [0.48016702, 0.14235102, 0.42620817]])\n\n print(paddle.linalg.eigvals(x))\n # [(-0.27078833542132674+0j), (0.29962280156230725+0j), (0.8824477020120244+0j)] #complex128\n \"\"\"\n\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64',\n 'complex128'], 'eigvals')\n\n x_shape = list(x.shape)\n if len(x_shape) < 2:\n raise ValueError(\n \"The dimension of Input(x) should be at least 2, but received x's dimention = {}, x's shape = {}\".\n format(len(x_shape), x_shape))\n\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The last two dimensions of Input(x) should be equal, but received x's shape = {}\".\n format(x_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.eigvals(x)\n\n helper = LayerHelper('eigvals', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='eigvals', inputs={'X': x}, outputs={'Out': out})\n return out\n\n\ndef multi_dot(x, name=None):\n \"\"\"\n Multi_dot is an operator that calculates multiple matrix multiplications.\n\n Supports inputs of float16(only GPU support), float32 and float64 dtypes. This function does not\n support batched inputs.\n\n The input tensor in [x] must be 2-D except for the first and last can be 1-D.\n If the first tensor is a 1-D vector of shape(n, ) it is treated as row vector\n of shape(1, n), similarly if the last tensor is a 1D vector of shape(n, ), it\n is treated as a column vector of shape(n, 1).\n\n If the first and last tensor are 2-D matrix, then the output is also 2-D matrix,\n otherwise the output is a 1-D vector.\n\n Multi_dot will select the lowest cost multiplication order for calculation. The\n cost of multiplying two matrices with shapes (a, b) and (b, c) is a * b * c.\n Given matrices A, B, C with shapes (20, 5), (5, 100), (100, 10) respectively,\n we can calculate the cost of different multiplication orders as follows:\n - Cost((AB)C) = 20x5x100 + 20x100x10 = 30000\n - Cost(A(BC)) = 5x100x10 + 20x5x10 = 6000\n\n In this case, multiplying B and C first, then multiply A, which is 5 times faster\n than sequential calculation.\n\n Args:\n x ([Tensor]): The input tensors which is a list Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # A * B\n A_data = np.random.random([3, 4]).astype(np.float32)\n B_data = np.random.random([4, 5]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n out = paddle.linalg.multi_dot([A, B])\n print(out.numpy().shape)\n # [3, 5]\n\n # A * B * C\n A_data = np.random.random([10, 5]).astype(np.float32)\n B_data = np.random.random([5, 8]).astype(np.float32)\n C_data = np.random.random([8, 7]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n C = paddle.to_tensor(C_data)\n out = paddle.linalg.multi_dot([A, B, C])\n print(out.numpy().shape)\n # [10, 7]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.multi_dot(x)\n\n check_type(x, 'x', (list, tuple), 'multi_dot')\n for id, item in enumerate(x):\n check_variable_and_dtype(item, 'x[' + str(id) + ']',\n ['float16', 'float32', 'float64'], 'multi_dot')\n if item.dtype != x[0].dtype:\n raise TypeError(\n \"All the Tensors in the input must have the same data type.\")\n\n helper = LayerHelper('multi_dot', **locals())\n dtype = helper.input_dtype(input_param_name='x')\n out = helper.create_variable_for_type_inference(dtype)\n helper.append_op(type='multi_dot', inputs={\"X\": x}, outputs={\"Out\": out})\n return out\n\n\ndef eigh(x, UPLO='L', name=None):\n \"\"\"\n Compute the eigenvalues and eigenvectors of a\n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[*, N, N]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): (string, default 'L'), 'L' represents the lower triangular matrix,\n \"'U' represents the upper triangular matrix.\".\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n\n out_value(Tensor): A Tensor with shape [*, N] and data type of float32 and float64. The eigenvalues of eigh op.\n out_vector(Tensor): A Tensor with shape [*, N, N] and data type of float32,float64,complex64 and complex128. The eigenvectors of eigh op.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value, out_vector = paddle.linalg.eigh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n print(out_vector)\n #[(-0.9238795325112867+0j), (-0.3826834323650898+0j)],\n #[ 0.3826834323650898j , -0.9238795325112867j ]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.eigh(x, 'UPLO', UPLO)\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigh', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'eigh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO})\n return out_value, out_vector\n\n\ndef pinv(x, rcond=1e-15, hermitian=False, name=None):\n r\"\"\"\n Calculate pseudo inverse via SVD(singular value decomposition)\n of one matrix or batches of regular matrix.\n\n .. math::\n\n if hermitian == False:\n x = u * s * vt (SVD)\n out = v * 1/s * ut\n else:\n x = u * s * ut (eigh)\n out = u * 1/s * u.conj().transpose(-2,-1)\n\n If x is hermitian or symmetric matrix, svd will be replaced with eigh.\n\n Args:\n x(Tensor): The input tensor. Its shape should be (*, m, n)\n where * is zero or more batch dimensions. m and n can be\n arbitraty positive number. The data type of x should be\n float32 or float64 or complex64 or complex128. When data\n type is complex64 or cpmplex128, hermitian should be set\n True.\n\n rcond(Tensor, optional): the tolerance value to determine\n when is a singular value zero. Defalut:1e-15.\n\n hermitian(bool, optional): indicates whether x is Hermitian\n if complex or symmetric if real. Default: False.\n\n name(str|None): A name for this layer(optional). If set None,\n the layer will be named automatically.\n\n Returns:\n Tensor: The tensor with same data type with x. it represents\n pseudo inverse of x. Its shape should be (*, n, m).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.arange(15).reshape((3, 5)).astype('float64')\n input = paddle.to_tensor(x)\n out = paddle.linalg.pinv(input)\n print(input)\n print(out)\n\n # input:\n # [[0. , 1. , 2. , 3. , 4. ],\n # [5. , 6. , 7. , 8. , 9. ],\n # [10., 11., 12., 13., 14.]]\n\n # out:\n # [[-0.22666667, -0.06666667, 0.09333333],\n # [-0.12333333, -0.03333333, 0.05666667],\n # [-0.02000000, 0.00000000, 0.02000000],\n # [ 0.08333333, 0.03333333, -0.01666667],\n # [ 0.18666667, 0.06666667, -0.05333333]]\n\n # one can verify : x * out * x = x ;\n # or out * x * out = x ;\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if not hermitian:\n # combine svd and matmul op\n u, s, vt = _C_ops.svd(x, 'full_matrices', False)\n max_singular_val = _C_ops.reduce_max(s, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=x.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=x.dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v, _ = _C_ops.transpose2(vt, 'axis', perm)\n\n out_1 = v * st\n out_2 = _C_ops.matmul_v2(out_1, u, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n # combine eigh and matmul op\n s, u = _C_ops.eigh(x, 'UPLO', 'L')\n s_abs = paddle.abs(s)\n max_singular_val = _C_ops.reduce_max(s_abs, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=s.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=s.dtype)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n out_1 = u * st\n u_conj = _C_ops.conj(u)\n out_2 = _C_ops.matmul_v2(out_1, u_conj, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n if not hermitian:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'pinv')\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(dtype)\n vt = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vt,\n 'S': s},\n attrs={'full_matrices': False}, )\n\n max_singular_val = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=dtype)\n st_shape = helper.create_variable_for_type_inference(dtype=dtype)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v = helper.create_variable_for_type_inference(dtype)\n v_shape = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='transpose2',\n inputs={'X': [vt]},\n outputs={'Out': [v],\n 'XShape': [v_shape]},\n attrs={'axis': perm})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': v,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n else:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64',\n 'complex128'], 'pinv')\n\n if dtype == paddle.complex128:\n s_type = 'float64'\n elif dtype == paddle.complex64:\n s_type = 'float32'\n else:\n s_type = dtype\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': s,\n 'Eigenvectors': u},\n attrs={'UPLO': 'L'})\n s_abs = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='abs', inputs={'X': s}, outputs={'Out': s_abs})\n max_singular_val = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s_abs},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=s_type)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=s_type)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s_type)\n cond_not_int = layers.cast(layers.logical_not(condition), s_type)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=s_type)\n st_shape = helper.create_variable_for_type_inference(dtype=s_type)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': u,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n u_conj = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='conj', inputs={'X': u}, outputs={'Out': [u_conj]})\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u_conj},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n\n\ndef solve(x, y, name=None):\n r\"\"\"\n Computes the solution of a square system of linear equations with a unique solution for input 'X' and 'Y'.\n Let :math: `X` be a sqaure matrix or a batch of square matrices, :math:`Y` be\n a vector/matrix or a batch of vectors/matrices, the equation should be:\n\n .. math::\n Out = X^-1 * Y\n Specifically,\n - This system of linear equations has one solution if and only if input 'X' is invertible.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): A vector/matrix or a batch of vectors/matrices. Its shape should be `[*, M, K]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of a square system of linear equations with a unique solution for input 'x' and 'y'.\n Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # 2*X0 + X1 = 9\n # X0 + 2*X1 = 8\n\n import paddle\n import numpy as np\n\n np_x = np.array([[3, 1],[1, 2]])\n np_y = np.array([9, 8])\n x = paddle.to_tensor(np_x, dtype=\"float64\")\n y = paddle.to_tensor(np_y, dtype=\"float64\")\n out = paddle.linalg.solve(x, y)\n\n print(out)\n # [2., 3.])\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.solve(x, y)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type=\"solve\", inputs={\"X\": x,\n \"Y\": y}, outputs={\"Out\": out})\n return out\n\n\ndef triangular_solve(x,\n y,\n upper=True,\n transpose=False,\n unitriangular=False,\n name=None):\n r\"\"\"\n Computes the solution of a system of equations with a triangular coefficient matrix `x` and\n multiple right-hand sides `y` .\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input triangular coefficient matrix. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): Whether to solve the upper-triangular system of equations (default) or the lower-triangular \n system of equations. Default: True.\n transpose (bool, optional): whether `x` should be transposed before calculation. Default: False.\n unitriangular (bool, optional): whether `x` is unit triangular. If True, the diagonal elements of `x` are assumed \n to be 1 and not referenced from `x` . Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # x1 + x2 + x3 = 0\n # 2*x2 + x3 = -9\n # -x3 = 5\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n y = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.triangular_solve(x, y, upper=True)\n\n print(out)\n # [7, -2, -5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.triangular_solve(x, y, 'upper', upper, 'transpose',\n transpose, 'unitriangular',\n unitriangular)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"triangular_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'triangular_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'triangular_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='triangular_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={\n 'upper': upper,\n 'transpose': transpose,\n 'unitriangular': unitriangular\n })\n return out\n\n\ndef cholesky_solve(x, y, upper=False, name=None):\n r\"\"\"\n Solves a linear system of equations A @ X = B, given A's Cholesky factor matrix u and matrix B.\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input matrix which is upper or lower triangular Cholesky factor of square matrix A. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): whether to consider the Cholesky factor as a lower or upper triangular matrix. Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type is the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n u = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n b = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.cholesky_solve(b, u, upper=True)\n\n print(out)\n # [-2.5, -7, 9.5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky_solve(x, y, 'upper', upper)\n\n helper = LayerHelper(\"cholesky_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'cholesky_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'cholesky_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='cholesky_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef eigvalsh(x, UPLO='L', name=None):\n \"\"\"\n Computes the eigenvalues of a \n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[_, M, M]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): Lower triangular part of a (‘L’, default) or the upper triangular part (‘U’).\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor eigenvalues in ascending order.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value = paddle.eigvalsh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n \"\"\"\n if paddle.in_dynamic_mode():\n is_test = x.stop_gradient\n values, _ = _C_ops.eigvalsh(x, 'UPLO', UPLO, 'is_test', is_test)\n return values\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigvalsh', **locals())\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64', 'complex128'],\n 'eigvalsh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n is_test = x.stop_gradient\n helper.append_op(\n type='eigvalsh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO,\n 'is_test': is_test})\n return out_value\n\n\n# MASKED: lstsq function (lines 2833-3005)"},"implementation":{"kind":"string","value":"def lstsq(x, y, rcond=None, driver=None, name=None):\n \"\"\"\n Computes a solution to\n the least squares problem of a system of linear equations.\n\n Args:\n x (Tensor): A tensor with shape ``(*, M, N)`` , the data type of the input Tensor ``x``\n should be one of float32, float64.\n y (Tensor): A tensor with shape ``(*, M, K)`` , the data type of the input Tensor ``y`` \n should be one of float32, float64.\n rcond(float, optional): The default value is None. A float pointing number used to determine \n the effective rank of ``x``. If ``rcond`` is None, it will be set to max(M, N) times the \n machine precision of x_dtype.\n driver(str, optional): The default value is None. The name of LAPACK method to be used. For \n CPU inputs the valid values are ‘gels’, ‘gelsy’, ‘gelsd, ‘gelss’. For CUDA input, the only \n valid driver is ‘gels’. If ``driver`` is None, ‘gelsy’ is used for CPU inputs and ‘gels’ \n for CUDA inputs.\n name(str, optional): The default value is None. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple: A tuple of 4 Tensors which is (``solution``, ``residuals``, ``rank``, ``singular_values``). \n ``solution`` is a tensor with shape ``(*, N, K)``, meaning the least squares solution. ``residuals`` \n is a tensor with shape ``(*, K)``, meaning the squared residuals of the solutions, which is computed \n when M > N and every matrix in ``x`` is full-rank, otherwise return an empty tensor. ``rank`` is a tensor \n with shape ``(*)``, meaning the ranks of the matrices in ``x``, which is computed when ``driver`` in \n (‘gelsy’, ‘gelsd’, ‘gelss’), otherwise return an empty tensor. ``singular_values`` is a tensor with \n shape ``(*, min(M, N))``, meaning singular values of the matrices in ``x``, which is computed when \n ``driver`` in (‘gelsd’, ‘gelss’), otherwise return an empty tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n x = paddle.to_tensor([[1, 3], [3, 2], [5, 6.]])\n y = paddle.to_tensor([[3, 4, 6], [5, 3, 4], [1, 2, 1.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gelsd\")\n print(results[0])\n # [[ 0.78350395, -0.22165027, -0.62371236],\n # [-0.11340097, 0.78866047, 1.14948535]]\n print(results[1])\n # [19.81443405, 10.43814468, 30.56185532])\n print(results[2])\n # 2\n print(results[3])\n # [9.03455734, 1.54167950]\n\n x = paddle.to_tensor([[10, 2, 3], [3, 10, 5], [5, 6, 12.]])\n y = paddle.to_tensor([[4, 2, 9], [2, 0, 3], [2, 5, 3.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gels\")\n print(results[0])\n # [[ 0.39386186, 0.10230173, 0.93606132],\n # [ 0.10741687, -0.29028133, 0.11892585],\n # [-0.05115091, 0.51918161, -0.19948854]]\n print(results[1])\n # []\n \"\"\"\n device = paddle.get_device()\n if device == \"cpu\":\n if driver not in (None, \"gels\", \"gelss\", \"gelsd\", \"gelsy\"):\n raise ValueError(\n \"Only support valid driver is 'gels', 'gelss', 'gelsd', 'gelsy' or None for CPU inputs. But got {}\".\n format(driver))\n driver = \"gelsy\" if driver is None else driver\n elif \"gpu\" in device:\n if driver not in (None, \"gels\"):\n raise ValueError(\n \"Only support valid driver is 'gels' or None for CUDA inputs. But got {}\".\n format(driver))\n driver = \"gels\" if driver is None else driver\n else:\n raise RuntimeError(\"Only support lstsq api for CPU or CUDA device.\")\n\n if x.dtype == y.dtype and x.dtype in (paddle.float32, paddle.float64):\n pass\n else:\n raise ValueError(\n \"Only support x and y have the same dtype such as 'float32' and 'float64'.\"\n )\n\n if rcond is None:\n if x.dtype == paddle.float32:\n rcond = 1e-7 * max(x.shape[-2], x.shape[-1])\n elif x.dtype == paddle.float64:\n rcond = 1e-15 * max(x.shape[-2], x.shape[-1])\n\n if paddle.in_dynamic_mode():\n solution, rank, singular_values = _C_ops.lstsq(x, y, \"rcond\", rcond,\n \"driver\", driver)\n if x.shape[-2] > x.shape[-1]:\n matmul_out = _varbase_creator(dtype=x.dtype)\n _C_ops.matmul(x, solution, matmul_out, 'trans_x', False, 'trans_y',\n False)\n minus_out = _C_ops.elementwise_sub(matmul_out, y)\n pow_out = _C_ops.pow(minus_out, 'factor', 2)\n residuals = _C_ops.reduce_sum(pow_out, 'dim', [-2], 'keepdim',\n False, 'reduce_all', False)\n else:\n residuals = paddle.empty(shape=[0], dtype=x.dtype)\n\n if driver == \"gels\":\n rank = paddle.empty(shape=[0], dtype=paddle.int32)\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n elif driver == \"gelsy\":\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n\n return solution, residuals, rank, singular_values\n\n helper = LayerHelper('lstsq', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n check_variable_and_dtype(\n y, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n\n solution = helper.create_variable_for_type_inference(dtype=x.dtype)\n residuals = helper.create_variable_for_type_inference(dtype=x.dtype)\n rank = helper.create_variable_for_type_inference(dtype=paddle.int32)\n singular_values = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='lstsq',\n inputs={'X': x,\n 'Y': y},\n outputs={\n 'Solution': solution,\n 'Rank': rank,\n 'SingularValues': singular_values\n },\n attrs={'rcond': rcond,\n 'driver': driver})\n\n matmul_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n minus_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n pow_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': solution},\n outputs={'Out': matmul_out},\n attrs={\n 'trans_x': False,\n 'trans_y': False,\n })\n\n helper.append_op(\n type='elementwise_sub',\n inputs={'X': matmul_out,\n 'Y': y},\n outputs={'Out': minus_out})\n\n helper.append_op(\n type='pow',\n inputs={'X': minus_out},\n outputs={'Out': pow_out},\n attrs={'factor': 2})\n\n helper.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': residuals},\n attrs={'dim': [-2],\n 'keep_dim': False,\n 'reduce_all': False})\n\n if driver == \"gels\":\n rank = paddle.static.data(name='rank', shape=[0])\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n elif driver == \"gelsy\":\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n\n return solution, residuals, rank, singular_values"},"start_line":{"kind":"number","value":2833,"string":"2,833"},"end_line":{"kind":"number","value":3005,"string":"3,005"},"file_content":{"kind":"string","value":"# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numpy as np\nfrom ..fluid.layer_helper import LayerHelper\nfrom ..framework import _varbase_creator, _dygraph_tracer\nfrom ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype\nfrom ..static import Variable\nfrom ..fluid.framework import _in_legacy_dygraph, in_dygraph_mode\nfrom ..fluid.layers import transpose, cast # noqa: F401\nfrom ..fluid import layers\nimport paddle\nfrom paddle.common_ops_import import core\nfrom paddle.common_ops_import import VarDesc\nfrom paddle import _C_ops\n\n__all__ = []\n\n\ndef matmul(x, y, transpose_x=False, transpose_y=False, name=None):\n \"\"\"\n Applies matrix multiplication to two tensors. `matmul` follows\n the complete broadcast rules,\n and its behavior is consistent with `np.matmul`.\n\n Currently, the input tensors' number of dimensions can be any, `matmul` can be used to\n achieve the `dot`, `matmul` and `batchmatmul`.\n\n The actual behavior depends on the shapes of :math:`x`, :math:`y` and the\n flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:\n\n - If a transpose flag is specified, the last two dimensions of the tensor\n are transposed. If the tensor is ndim-1 of shape, the transpose is invalid. If the tensor\n is ndim-1 of shape :math:`[D]`, then for :math:`x` it is treated as :math:`[1, D]`, whereas\n for :math:`y` it is the opposite: It is treated as :math:`[D, 1]`.\n\n The multiplication behavior depends on the dimensions of `x` and `y`. Specifically:\n\n - If both tensors are 1-dimensional, the dot product result is obtained.\n\n - If both tensors are 2-dimensional, the matrix-matrix product is obtained.\n\n - If the `x` is 1-dimensional and the `y` is 2-dimensional,\n a `1` is prepended to its dimension in order to conduct the matrix multiply.\n After the matrix multiply, the prepended dimension is removed.\n\n - If the `x` is 2-dimensional and `y` is 1-dimensional,\n the matrix-vector product is obtained.\n\n - If both arguments are at least 1-dimensional and at least one argument\n is N-dimensional (where N > 2), then a batched matrix multiply is obtained.\n If the first argument is 1-dimensional, a 1 is prepended to its dimension\n in order to conduct the batched matrix multiply and removed after.\n If the second argument is 1-dimensional, a 1 is appended to its\n dimension for the purpose of the batched matrix multiple and removed after.\n The non-matrix (exclude the last two dimensions) dimensions are\n broadcasted according the broadcast rule.\n For example, if input is a (j, 1, n, m) tensor and the other is a (k, m, p) tensor,\n out will be a (j, k, n, p) tensor.\n\n Args:\n x (Tensor): The input tensor which is a Tensor.\n y (Tensor): The input tensor which is a Tensor.\n transpose_x (bool): Whether to transpose :math:`x` before multiplication.\n transpose_y (bool): Whether to transpose :math:`y` before multiplication.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # vector * vector\n x_data = np.random.random([10]).astype(np.float32)\n y_data = np.random.random([10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [1]\n\n # matrix * vector\n x_data = np.random.random([10, 5]).astype(np.float32)\n y_data = np.random.random([5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10]\n\n # batched matrix * broadcasted vector\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([2]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5]\n\n # batched matrix * batched matrix\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([10, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5, 5]\n\n # batched matrix * broadcasted matrix\n x_data = np.random.random([10, 1, 5, 2]).astype(np.float32)\n y_data = np.random.random([1, 3, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 3, 5, 5]\n\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_matmul(x, y, transpose_x, transpose_y)\n\n if _in_legacy_dygraph():\n op_type = 'matmul_v2'\n op = getattr(_C_ops, op_type)\n return op(x, y, 'trans_x', transpose_x, 'trans_y', transpose_y)\n\n attrs = {\n 'trans_x': transpose_x,\n 'trans_y': transpose_y,\n }\n\n def __check_input(x, y):\n var_names = {'x': x, 'y': y}\n for name, val in var_names.items():\n check_variable_and_dtype(\n val, name,\n ['float16', 'float32', 'float64', 'complex64', 'complex128'],\n 'matmul')\n\n __check_input(x, y)\n\n helper = LayerHelper('matmul_v2', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef norm(x, p='fro', axis=None, keepdim=False, name=None):\n \"\"\"\n\n Returns the matrix norm (Frobenius) or vector norm (the 1-norm, the Euclidean\n or 2-norm, and in general the p-norm for p > 0) of a given tensor.\n\n .. note::\n This norm API is different from `numpy.linalg.norm`.\n This api supports high-order input tensors (rank >= 3), and certain axis need to be pointed out to calculate the norm.\n But `numpy.linalg.norm` only supports 1-D vector or 2-D matrix as input tensor.\n For p-order matrix norm, this api actually treats matrix as a flattened vector to calculate the vector norm, NOT REAL MATRIX NORM.\n\n Args:\n x (Tensor): The input tensor could be N-D tensor, and the input data\n type could be float32 or float64.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `0`, `1`, `2`,\n `inf`, `-inf` and any positive real number yielding the corresponding p-norm. Not supported: ord < 0 and nuclear norm.\n Default value is `fro`.\n axis (int|list|tuple, optional): The axis on which to apply norm operation. If axis is int\n or list(int)/tuple(int) with only one element, the vector norm is computed over the axis.\n If `axis < 0`, the dimension to norm operation is rank(input) + axis.\n If axis is a list(int)/tuple(int) with two elements, the matrix norm is computed over the axis.\n Defalut value is `None`.\n keepdim (bool, optional): Whether to reserve the reduced dimension in the\n output Tensor. The result tensor will have fewer dimension\n than the :attr:`input` unless :attr:`keepdim` is true, default\n value is False.\n name (str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: results of norm operation on the specified axis of input tensor,\n it's data type is the same as input's Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n shape=[2, 3, 4]\n np_input = np.arange(24).astype('float32') - 12\n np_input = np_input.reshape(shape)\n x = paddle.to_tensor(np_input)\n #[[[-12. -11. -10. -9.] [ -8. -7. -6. -5.] [ -4. -3. -2. -1.]]\n # [[ 0. 1. 2. 3.] [ 4. 5. 6. 7.] [ 8. 9. 10. 11.]]]\n\n # compute frobenius norm along last two dimensions.\n out_fro = paddle.linalg.norm(x, p='fro', axis=[0,1])\n # out_fro.numpy() [17.435596 16.911535 16.7332 16.911535]\n\n # compute 2-order vector norm along last dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=-1)\n #out_pnorm.numpy(): [[21.118711 13.190906 5.477226]\n # [ 3.7416575 11.224972 19.131126]]\n\n # compute 2-order norm along [0,1] dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=[0,1])\n #out_pnorm.numpy(): [17.435596 16.911535 16.7332 16.911535]\n\n # compute inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=np.inf)\n #out_pnorm.numpy() = [12.]\n out_pnorm = paddle.linalg.norm(x, p=np.inf, axis=0)\n #out_pnorm.numpy(): [[12. 11. 10. 9.] [8. 7. 6. 7.] [8. 9. 10. 11.]]\n\n # compute -inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=-np.inf)\n #out_pnorm.numpy(): [0.]\n out_pnorm = paddle.linalg.norm(x, p=-np.inf, axis=0)\n #out_pnorm.numpy(): [[0. 1. 2. 3.] [4. 5. 6. 5.] [4. 3. 2. 1.]]\n \"\"\"\n\n def frobenius_norm(input, dim=None, keepdim=False, name=None):\n \"\"\"\n The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n dim (list, optional): None for last two dimensions.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if dim is not None and not (isinstance(dim, list) and len(dim) == 2):\n raise ValueError(\n \"The dim of frobenius norm op should be None or two elements list!\"\n )\n if paddle.in_dynamic_mode():\n if dim is None:\n return _C_ops.frobenius_norm(input, 'keep_dim', keepdim,\n 'reduce_all', True)\n return _C_ops.frobenius_norm(input, 'dim', dim, 'keep_dim', keepdim,\n 'reduce_all', False)\n attrs = {'dim': dim, 'keep_dim': keepdim, 'reduce_all': False}\n if dim is None:\n attrs['reduce_all'] = True\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'frobenius_norm')\n\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='frobenius_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def vector_norm(input,\n porder=None,\n axis=None,\n keepdim=False,\n asvector=False,\n name=None):\n \"\"\"\n Calculate the p-order vector norm for certain dimension of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n porder (float, optional): None for porder=2.0.\n axis (int, optional): None for last dimension.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if paddle.in_dynamic_mode():\n if axis is None: axis = -1\n return _C_ops.p_norm(input, 'porder', porder, 'axis', axis,\n 'keepdim', keepdim, 'asvector', asvector)\n if porder is not None:\n check_type(porder, 'porder', (float, int), 'p_norm')\n if axis is not None:\n check_type(axis, 'axis', (int), 'p_norm')\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'p_norm')\n\n attrs = {\n 'axis': axis if axis is not None else -1,\n 'porder': float(porder) if porder is not None else 2.0,\n 'keepdim': keepdim,\n 'asvector': asvector,\n 'epsilon': 1e-12,\n }\n helper = LayerHelper('p_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='p_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def inf_norm(input,\n porder=None,\n axis=axis,\n keepdim=False,\n asvector=False,\n name=None):\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n helper.append_op(type='abs', inputs={'X': input}, outputs={'Out': out})\n reduce_out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n reduce_all = True if axis == None or axis == [] or asvector == True else False\n axis = axis if axis != None and axis != [] else [0]\n\n reduce_type = 'reduce_max' if porder == np.float(\n 'inf') else 'reduce_min'\n helper.append_op(\n type=reduce_type,\n inputs={'X': out},\n outputs={'Out': reduce_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n\n return reduce_out\n\n def p_matrix_norm(input, porder=1., axis=axis, keepdim=False, name=None):\n \"\"\"\n NOTE:\n This function actually treats the matrix as flattened vector to calculate vector norm instead of matrix norm.\n \"\"\"\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n\n block.append_op(\n type='pow',\n inputs={'X': abs_out},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': True if axis is None else False\n })\n porder\n block.append_op(\n type='pow',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n if axis is None and p is not None:\n if isinstance(p, str):\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n porder=p,\n axis=axis,\n keepdim=keepdim,\n asvector=True,\n name=name)\n else:\n raise ValueError(\"only valid p type is string or float, found {}\".\n format(type(p)))\n\n if isinstance(axis, tuple):\n axis = list(axis)\n if isinstance(axis, list) and len(axis) == 1:\n axis = axis[0]\n\n #calculate vector norm, where axis is int or list with only one integer\n if isinstance(axis, int):\n if isinstance(p, str):\n if p == \"fro\":\n return vector_norm(\n x,\n porder=2,\n axis=axis,\n keepdim=keepdim,\n asvector=False,\n name=name)\n\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n axis=axis,\n porder=p,\n keepdim=keepdim,\n asvector=False,\n name=name)\n else:\n raise ValueError(\n \"unspport p for p-order vector norm. except float, found {}\".\n format(p))\n #calculate matrix norm, where axis is list with two integers\n elif isinstance(axis, list) and len(axis) == 2:\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n elif p == np.inf or p == -np.inf:\n return inf_norm(x, porder=p, axis=axis, keepdim=keepdim, name=name)\n elif p == 0:\n raise ValueError(\n \"just suport axis type int or list (length of list <=1) if p = 0, found {}\".\n format(axis))\n else:\n return p_matrix_norm(\n x, porder=p, axis=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"except axis type int or list (length of list <=2), found {}\".\n format(axis))\n\n\ndef dist(x, y, p=2, name=None):\n r\"\"\"\n\n This OP returns the p-norm of (x - y). It is not a norm in a strict sense, only as a measure\n of distance. The shapes of x and y must be broadcastable. The definition is as follows, for\n details, please refer to the `numpy's broadcasting <https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html>`_:\n\n - Each input has at least one dimension.\n - Match the two input dimensions from back to front, the dimension sizes must either be equal, one of them is 1, or one of them does not exist.\n\n Where, z = x - y, the shapes of x and y are broadcastable, then the shape of z can be\n obtained as follows:\n\n 1. If the number of dimensions of x and y are not equal, prepend 1 to the dimensions of the\n tensor with fewer dimensions.\n\n For example, The shape of x is [8, 1, 6, 1], the shape of y is [7, 1, 5], prepend 1 to the\n dimension of y.\n\n x (4-D Tensor): 8 x 1 x 6 x 1\n\n y (4-D Tensor): 1 x 7 x 1 x 5\n\n 2. Determine the size of each dimension of the output z: choose the maximum value from the\n two input dimensions.\n\n z (4-D Tensor): 8 x 7 x 6 x 5\n\n If the number of dimensions of the two inputs are the same, the size of the output can be\n directly determined in step 2. When p takes different values, the norm formula is as follows:\n\n When p = 0, defining $0^0=0$, the zero-norm of z is simply the number of non-zero elements of z.\n\n .. math::\n\n ||z||_{0}=\\lim_{p \\\\rightarrow 0}\\sum_{i=1}^{m}|z_i|^{p}\n\n When p = inf, the inf-norm of z is the maximum element of z.\n\n .. math::\n\n ||z||_\\infty=\\max_i |z_i|\n\n When p = -inf, the negative-inf-norm of z is the minimum element of z.\n\n .. math::\n\n ||z||_{-\\infty}=\\min_i |z_i|\n\n Otherwise, the p-norm of z follows the formula,\n\n .. math::\n\n ||z||_{p}=(\\sum_{i=1}^{m}|z_i|^p)^{\\\\frac{1}{p}}\n\n Args:\n x (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n y (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n p (float, optional): The norm to be computed, its data type is float32 or float64. Default: 2.\n\n Returns:\n Tensor: Tensor that is the p-norm of (x - y).\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor(np.array([[3, 3],[3, 3]]), \"float32\")\n y = paddle.to_tensor(np.array([[3, 3],[3, 1]]), \"float32\")\n out = paddle.dist(x, y, 0)\n print(out) # out = [1.]\n\n out = paddle.dist(x, y, 2)\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"inf\"))\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"-inf\"))\n print(out) # out = [0.]\n \"\"\"\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'dist')\n check_variable_and_dtype(y, 'dtype', ['float32', 'float64'], 'dist')\n check_type(p, 'p', (float, int), 'dist')\n helper = LayerHelper(\"dist\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n outputs = {'Out': [out]}\n attrs = {\"p\": float(p)}\n helper.append_op(\n type='dist', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef cond(x, p=None, name=None):\n \"\"\"\n\n Computes the condition number of a matrix or batches of matrices with respect to a matrix norm ``p``.\n\n Args:\n x (Tensor): The input tensor could be tensor of shape ``(*, m, n)`` where ``*`` is zero or more batch dimensions\n for ``p`` in ``(2, -2)``, or of shape ``(*, n, n)`` where every matrix is invertible for any supported ``p``.\n And the input data type could be ``float32`` or ``float64``.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `nuc`, `1`, `-1`, `2`, `-2`,\n `inf`, `-inf`. Default value is `None`, meaning that the order of the norm is `2`.\n name (str, optional): The default value is `None`. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: computing results of condition number, its data type is the same as input Tensor ``x``.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1., 0, -1], [0, 1, 0], [1, 0, 1]])\n\n # compute conditional number when p is None\n out = paddle.linalg.cond(x)\n # out.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is 'fro'\n out_fro = paddle.linalg.cond(x, p='fro')\n # out_fro.numpy() [3.1622777]\n\n # compute conditional number when order of the norm is 'nuc'\n out_nuc = paddle.linalg.cond(x, p='nuc')\n # out_nuc.numpy() [9.2426405]\n\n # compute conditional number when order of the norm is 1\n out_1 = paddle.linalg.cond(x, p=1)\n # out_1.numpy() [2.]\n\n # compute conditional number when order of the norm is -1\n out_minus_1 = paddle.linalg.cond(x, p=-1)\n # out_minus_1.numpy() [1.]\n\n # compute conditional number when order of the norm is 2\n out_2 = paddle.linalg.cond(x, p=2)\n # out_2.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is -1\n out_minus_2 = paddle.linalg.cond(x, p=-2)\n # out_minus_2.numpy() [0.70710677]\n\n # compute conditional number when order of the norm is inf\n out_inf = paddle.linalg.cond(x, p=np.inf)\n # out_inf.numpy() [2.]\n\n # compute conditional number when order of the norm is -inf\n out_minus_inf = paddle.linalg.cond(x, p=-np.inf)\n # out_minus_inf.numpy() [1.]\n\n a = paddle.to_tensor(np.random.randn(2, 4, 4).astype('float32'))\n # a.numpy()\n # [[[ 0.14063153 -0.996288 0.7996131 -0.02571543]\n # [-0.16303636 1.5534962 -0.49919784 -0.04402903]\n # [-1.1341571 -0.6022629 0.5445269 0.29154757]\n # [-0.16816919 -0.30972657 1.7521842 -0.5402487 ]]\n # [[-0.58081484 0.12402827 0.7229862 -0.55046535]\n # [-0.15178485 -1.1604939 0.75810957 0.30971205]\n # [-0.9669573 1.0940945 -0.27363303 -0.35416734]\n # [-1.216529 2.0018666 -0.7773689 -0.17556527]]]\n a_cond_fro = paddle.linalg.cond(a, p='fro')\n # a_cond_fro.numpy() [31.572273 28.120834]\n\n b = paddle.to_tensor(np.random.randn(2, 3, 4).astype('float64'))\n # b.numpy()\n # [[[ 1.61707487 0.46829144 0.38130416 0.82546736]\n # [-1.72710298 0.08866375 -0.62518804 0.16128892]\n # [-0.02822879 -1.67764516 0.11141444 0.3220113 ]]\n # [[ 0.22524372 0.62474921 -0.85503233 -1.03960523]\n # [-0.76620689 0.56673047 0.85064753 -0.45158196]\n # [ 1.47595418 2.23646462 1.5701758 0.10497519]]]\n b_cond_2 = paddle.linalg.cond(b, p=2)\n # b_cond_2.numpy() [3.30064451 2.51976252]\n\n \"\"\"\n\n def mat_norm(input, porder=1., axis=None):\n \"\"\"\n NOTE:\n Calculate the matrix norm of a square matrix or batches of square matrices,\n when porder is in (1, -1, inf, -inf)\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n axis = axis if axis != None and axis != [] else [0]\n keepdim = False\n\n if paddle.in_dynamic_mode():\n abs_out = _C_ops.abs(input)\n sum_out = _C_ops.reduce_sum(abs_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == 1 or porder == np.inf:\n return _C_ops.reduce_max(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == -1 or porder == -np.inf:\n return _C_ops.reduce_min(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n\n block = LayerHelper('norm', **locals())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n block.append_op(\n type='reduce_sum',\n inputs={'X': abs_out},\n outputs={'Out': sum_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 1 or porder == np.inf:\n block.append_op(\n type='reduce_max',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n if porder == -1 or porder == -np.inf:\n block.append_op(\n type='reduce_min',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n\n def fro_norm(input, porder=2, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the frobenius norm of a square matrix or batches of square matrices.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n if paddle.in_dynamic_mode():\n pow_out = _C_ops.pow(input, 'factor', porder)\n sum_out_1 = _C_ops.reduce_sum(pow_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n sum_out_2 = _C_ops.reduce_sum(sum_out_1, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n return _C_ops.pow(sum_out_2, 'factor', float(1. / porder))\n\n block = LayerHelper('norm', **locals())\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_1 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_2 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='pow',\n inputs={'X': input},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out_1},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_sum',\n inputs={'X': sum_out_1},\n outputs={'Out': sum_out_2},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='pow',\n inputs={'X': sum_out_2},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n def svd_norm(input, porder, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the matrix norm, which is related to singular values, of a matrix\n or batches of matrices, including nuclear norm, 2-norm and (-2)-norm.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n u, s, vh = svd(input, full_matrices=False)\n\n if paddle.in_dynamic_mode():\n if porder == \"nuc\":\n return _C_ops.reduce_sum(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n max_out = _C_ops.reduce_max(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n min_out = _C_ops.reduce_min(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n if porder == 2:\n return _C_ops.elementwise_div(max_out, min_out, 'aixs', axis,\n 'use_mkldnn', False)\n if porder == -2:\n return _C_ops.elementwise_div(min_out, max_out, 'aixs', axis,\n 'use_mkldnn', False)\n\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n if porder == \"nuc\":\n block.append_op(\n type='reduce_sum',\n inputs={'X': s},\n outputs={'Out': out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n max_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n min_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_min',\n inputs={'X': s},\n outputs={'Out': min_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': max_out,\n 'Y': min_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n if porder == -2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': min_out,\n 'Y': max_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n\n def empty_tensor(input, shape):\n if paddle.in_dynamic_mode():\n return input.reshape(shape)\n raise ValueError(\"only support x is nonempty tensor in static mode\")\n\n x_shape = list(x.shape)\n if not len(x_shape) >= 2:\n raise ValueError(\"input should be a matrix or batches of matrices, \" +\n \"but the dimention of received input is {}\".format(\n len(x_shape)))\n if p == None:\n p = 2\n x_size = 0 if (0 in x_shape) else 1\n if p in (\"fro\", \"nuc\", 1, -1, np.inf, -np.inf):\n if x_shape[len(x_shape) - 1] == x_shape[len(x_shape) - 2]:\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n x_inv = x.inverse()\n if p == \"fro\":\n return fro_norm(x) * fro_norm(x_inv)\n if p == \"nuc\":\n return svd_norm(x, p) * svd_norm(x_inv, p)\n if p in (1, -1):\n return mat_norm(\n x, porder=p, axis=[-2]) * mat_norm(\n x_inv, porder=p, axis=[-2])\n if p in (np.inf, -np.inf):\n return mat_norm(\n x, porder=p, axis=[-1]) * mat_norm(\n x_inv, porder=p, axis=[-1])\n else:\n raise ValueError(\"only support p is {} when input is a \".format(p) +\n \"square matrix or batches of square matrices\")\n elif p in (2, -2):\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n return svd_norm(x, porder=p)\n else:\n raise ValueError(\n \"unsupported {} for p, only supporting ('fro', 'nuc', \".format(\n p) + \"1, -1, 2, -2, inf, -inf) or none\")\n\n\ndef dot(x, y, name=None):\n \"\"\"\n This operator calculates inner product for vectors.\n\n .. note::\n Support 1-d and 2-d Tensor. When it is 2d, the first dimension of this matrix\n is the batch dimension, which means that the vectors of multiple batches are dotted.\n\n Parameters:\n x(Tensor): 1-D or 2-D ``Tensor``. Its dtype should be ``float32``, ``float64``, ``int32``, ``int64``\n y(Tensor): 1-D or 2-D ``Tensor``. Its dtype soulde be ``float32``, ``float64``, ``int32``, ``int64``\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: the calculated result Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x_data = np.random.uniform(0.1, 1, [10]).astype(np.float32)\n y_data = np.random.uniform(1, 3, [10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.dot(x, y)\n print(z)\n\n \"\"\"\n op_type = 'dot'\n # skip var type check in dygraph mode to improve efficiency\n if paddle.in_dynamic_mode():\n op = getattr(_C_ops, op_type)\n return op(x, y)\n\n assert x is not None, 'x cannot be None in {}'.format(op_type)\n assert y is not None, 'y cannot be None in {}'.format(op_type)\n\n check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n check_variable_and_dtype(y, 'y', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n\n helper = LayerHelper(op_type, **locals())\n if name is None:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n else:\n out = helper.create_variable(\n name=name, dtype=x.dtype, persistable=False)\n helper.append_op(\n type=\"dot\", inputs={'X': x,\n 'Y': y}, attrs={}, outputs={\"Out\": out})\n return out\n\n\ndef cov(x, rowvar=True, ddof=True, fweights=None, aweights=None, name=None):\n \"\"\"\n Estimate the covariance matrix of the input variables, given data and weights.\n\n A covariance matrix is a square matrix, indicate the covariance of each pair variables in the input matrix.\n For example, for an N-dimensional samples X=[x1,x2,…xN]T, then the covariance matrix \n element Cij is the covariance of xi and xj. The element Cii is the variance of xi itself.\n\n Parameters:\n x(Tensor): A N-D(N<=2) Tensor containing multiple variables and observations. By default, each row of x represents a variable. Also see rowvar below.\n rowvar(Bool, optional): If rowvar is True (default), then each row represents a variable, with observations in the columns. Default: True\n ddof(Bool, optional): If ddof=True will return the unbiased estimate, and ddof=False will return the simple average. Default: True\n fweights(Tensor, optional): 1-D Tensor of integer frequency weights; The number of times each observation vector should be repeated. Default: None\n aweights(Tensor, optional): 1-D Tensor of observation vector weights. How important of the observation vector, larger data means this element is more important. Default: None\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: The covariance matrix Tensor of the variables.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n\n xt = paddle.rand((3,4))\n paddle.linalg.cov(xt)\n\n '''\n Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n [[0.07918842, 0.06127326, 0.01493049],\n [0.06127326, 0.06166256, 0.00302668],\n [0.01493049, 0.00302668, 0.01632146]])\n '''\n \"\"\"\n op_type = 'cov'\n if len(x.shape) > 2 or len(x.shape) < 1:\n raise ValueError(\n \"Input(x) only support N-D (1<=N<=2) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cov')\n nx = x\n if len(x.shape) == 1:\n nx = x.reshape((1, -1))\n if not rowvar and nx.shape[0] != 1:\n nx = nx.t()\n w = None\n observation_num = nx.shape[1]\n if fweights is not None:\n w = fweights.astype(nx.dtype)\n if len(w.shape) > 1:\n raise ValueError(\n \"Input(fweights) only support N-D (N<=1) tensor in cov, but received \"\n \"shape of Input(input) is %s.\" % len(fweights.shape))\n if fweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(fweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(fweights) is {}.\".format(observation_num,\n fweights.shape[0]))\n if fweights.min() < 0:\n raise ValueError(\n \"The value of Input(fweights) cannot be negtive, but received \"\n \"min of Input(fweights) is {}.\".format(fweights.min()))\n if not paddle.all(fweights == paddle.round(fweights.astype('float64'))):\n raise ValueError(\"Input(fweights) must be integer \")\n\n if aweights is not None:\n aw = aweights.astype(nx.dtype)\n if len(aw.shape) > 1:\n raise ValueError(\n \"Input(aweights) only support N-D (N<=1) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(aweights.shape))\n check_variable_and_dtype(aweights, 'dtype', ['float32', 'float64'],\n 'cov')\n if aweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(aweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(aweights) is {}.\".format(observation_num,\n aweights.shape[0]))\n if aweights.min() < 0:\n raise ValueError(\n \"The value of Input(aweights) cannot be negtive, but received \"\n \"min of Input(aweights) is {}.\".format(aweights.min()))\n if w is not None:\n w = w * aw\n else:\n w = aw\n\n w_sum = paddle.to_tensor(observation_num, dtype=nx.dtype)\n if fweights is not None or aweights is not None:\n w_sum = w.sum()\n if w_sum.item() == 0:\n raise ValueError(\"The sum of weights is zero, can't be normalized.\")\n\n if w is not None:\n nx_w = nx * w\n avg = (nx_w).sum(axis=1) / w_sum\n else:\n avg = nx.sum(axis=1) / w_sum\n nx_w = nx\n\n if w is not None and aweights is not None and ddof == True:\n norm_factor = w_sum - (w * aweights).sum() / w_sum\n else:\n norm_factor = w_sum - ddof\n if norm_factor <= 0:\n norm_factor = paddle.to_tensor(0, dtype=nx.dtype)\n nx = nx - avg.unsqueeze(1)\n xxt = paddle.mm(nx, nx_w.t().conj())\n cov = paddle.divide(xxt, norm_factor).squeeze()\n return cov\n\n\ndef t(input, name=None):\n \"\"\"\n Transpose <=2-D tensor.\n 0-D and 1-D tensors are returned as it is and 2-D tensor is equal to\n the paddle.transpose function which perm dimensions set 0 and 1.\n\n Args:\n input (Tensor): The input Tensor. It is a N-D (N<=2) Tensor of data types float16, float32, float64, int32.\n name(str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`\n Returns:\n Tensor: A transposed n-D Tensor, with data type being float16, float32, float64, int32, int64.\n\n For Example:\n\n .. code-block:: text\n\n # Example 1 (0-D tensor)\n x = tensor([0.79])\n paddle.t(x) = tensor([0.79])\n\n # Example 2 (1-D tensor)\n x = tensor([0.79, 0.84, 0.32])\n paddle.t(x) = tensor([0.79, 0.84, 0.32])\n\n # Example 3 (2-D tensor)\n x = tensor([0.79, 0.84, 0.32],\n [0.64, 0.14, 0.57])\n paddle.t(x) = tensor([0.79, 0.64],\n [0.84, 0.14],\n [0.32, 0.57])\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n x = paddle.ones(shape=[2, 3], dtype='int32')\n x_transposed = paddle.t(x)\n print(x_transposed.shape)\n # [3, 2]\n \"\"\"\n if len(input.shape) > 2:\n raise ValueError(\n \"Input(input) only support N-D (N<=2) tensor, but received \"\n \"length of Input(input) is %s. Perhaps you can use paddle.\"\n \"tensor.transpose() instead.\" % len(input.shape))\n if paddle.in_dynamic_mode():\n if len(input.shape) == 1:\n return input\n # 2-D tensor\n perm = [1, 0]\n out, _ = _C_ops.transpose2(input, 'axis', perm)\n return out\n\n check_variable_and_dtype(\n input, 'input', ['float16', 'float32', 'float64', 'int32',\n 'int64'], 'transpose')\n\n helper = LayerHelper('t', **locals())\n out = helper.create_variable_for_type_inference(input.dtype)\n input_shape = helper.create_variable_for_type_inference(input.dtype)\n if len(input.shape) == 1:\n out = input\n else:\n helper.append_op(\n type='transpose2',\n inputs={'X': [input]},\n outputs={'Out': [out],\n 'XShape': [input_shape]},\n attrs={'axis': [1, 0]})\n return out\n\n\ndef cross(x, y, axis=None, name=None):\n \"\"\"\n Computes the cross product between two tensors along an axis.\n\n Inputs must have the same shape, and the length of their axes should be equal to 3.\n If `axis` is not given, it defaults to the first axis found with the length 3.\n\n Args:\n x (Tensor): The first input tensor.\n y (Tensor): The second input tensor.\n axis (int, optional): The axis along which to compute the cross product. It defaults to the first axis found with the length 3.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor. A Tensor with same data type as `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0],\n [3.0, 3.0, 3.0]])\n y = paddle.to_tensor([[1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0]])\n\n z1 = paddle.cross(x, y)\n # [[-1. -1. -1.]\n # [ 2. 2. 2.]\n # [-1. -1. -1.]]\n\n z2 = paddle.cross(x, y, axis=1)\n # [[0. 0. 0.]\n # [0. 0. 0.]\n # [0. 0. 0.]]\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_cross(x, y, axis)\n else:\n if _in_legacy_dygraph():\n if axis is not None:\n return _C_ops.cross(x, y, 'dim', axis)\n else:\n return _C_ops.cross(x, y)\n else:\n helper = LayerHelper(\"cross\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n attrs = dict()\n attrs['dim'] = axis\n\n helper.append_op(\n type='cross',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef cholesky(x, upper=False, name=None):\n r\"\"\"\n Computes the Cholesky decomposition of one symmetric positive-definite\n matrix or batches of symmetric positive-definite matrice.\n\n If `upper` is `True`, the decomposition has the form :math:`A = U^{T}U` ,\n and the returned matrix :math:`U` is upper-triangular. Otherwise, the\n decomposition has the form :math:`A = LL^{T}` , and the returned matrix\n :math:`L` is lower-triangular.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[*, M, M]`,\n where * is zero or more batch dimensions, and matrices on the\n inner-most 2 dimensions all should be symmetric positive-definite.\n Its data type should be float32 or float64.\n upper (bool): The flag indicating whether to return upper or lower\n triangular matrices. Default: False.\n\n Returns:\n Tensor: A Tensor with same shape and data type as `x`. It represents \\\n triangular matrices generated by Cholesky decomposition.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n a = np.random.rand(3, 3)\n a_t = np.transpose(a, [1, 0])\n x_data = np.matmul(a, a_t) + 1e-03\n x = paddle.to_tensor(x_data)\n out = paddle.linalg.cholesky(x, upper=False)\n print(out)\n # [[1.190523 0. 0. ]\n # [0.9906703 0.27676893 0. ]\n # [1.25450498 0.05600871 0.06400121]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky(x, \"upper\", upper)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cholesky')\n check_type(upper, 'upper', bool, 'cholesky')\n helper = LayerHelper('cholesky', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='cholesky',\n inputs={'X': [x]},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef matrix_rank(x, tol=None, hermitian=False, name=None):\n r\"\"\"\n Computes the rank of a matrix.\n\n The rank of a matrix is the number of singular values that are greater than the specified `tol` threshold when hermitian=False,\n or the number of eigenvalues in absolute value that are greater than the specified `tol` threshold when hermitian=True.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., m, n]`, where `...` is zero or more batch dimensions. If `x` is a batch\n of matrices then the output has the same batch dimensions. The data type of `x` should be float32 or float64.\n tol (float,Tensor,optional): the tolerance value. Default: None. If `tol` is not specified, and `sigma` is the largest\n singular value (or eigenvalues in absolute value), and `eps` is the epsilon value for the dtype of `x`, then `tol` is computed\n with formula `tol=sigma * max(m,n) * eps`. Note that if `x` is a batch of matrices, `tol` is computed this way for every batch.\n hermitian (bool,optional): indicates whether `x` is Hermitian. Default: False. When hermitian=True, `x` is assumed to be Hermitian,\n enabling a more efficient method for finding eigenvalues, but `x` is not checked inside the function. Instead, We just use\n the lower triangular of the matrix to compute.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: Rank of tensor x.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n a = paddle.eye(10)\n b = paddle.linalg.matrix_rank(a)\n print(b)\n # b = [10]\n\n c = paddle.ones(shape=[3, 4, 5, 5])\n d = paddle.linalg.matrix_rank(c, tol=0.01, hermitian=True)\n print(d)\n # d = [[1, 1, 1, 1],\n # [1, 1, 1, 1],\n # [1, 1, 1, 1]]\n\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if tol is None:\n tol_tensor = None\n tol_attr = 0.0\n use_default_tol = True\n elif isinstance(tol, Variable):\n if tol.dtype != x.dtype:\n tol_tensor = cast(tol, x.dtype)\n else:\n tol_tensor = tol\n tol_attr = 0.0\n use_default_tol = False\n else:\n tol_tensor = None\n tol_attr = float(tol)\n use_default_tol = False\n return _C_ops.matrix_rank(x, tol_tensor, \"tol\", tol_attr, 'hermitian',\n hermitian, 'use_default_tol', use_default_tol)\n\n inputs = {}\n attrs = {}\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'matrix_rank')\n inputs['X'] = x\n if tol is None:\n attrs['use_default_tol'] = True\n elif isinstance(tol, Variable):\n check_variable_and_dtype(tol, 'tol', ['float32'], 'matrix_rank')\n attrs['use_default_tol'] = False\n if tol.dtype != x.dtype:\n inputs['TolTensor'] = cast(tol, x.dtype)\n else:\n inputs['TolTensor'] = tol\n else:\n check_type(tol, 'tol', float, 'matrix_rank')\n attrs['use_default_tol'] = False\n attrs['tol'] = tol\n check_type(hermitian, 'hermitian', bool, 'matrix_rank')\n attrs['hermitian'] = hermitian\n\n helper = LayerHelper('matrix_rank', **locals())\n out = helper.create_variable_for_type_inference(dtype='int32')\n helper.append_op(\n type='matrix_rank', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef bmm(x, y, name=None):\n \"\"\"\n Applies batched matrix multiplication to two tensors.\n\n Both of the two input tensors must be three-dementional and share the same batch size.\n\n if x is a (b, m, k) tensor, y is a (b, k, n) tensor, the output will be a (b, m, n) tensor.\n\n Args:\n x (Tensor): The input Tensor.\n y (Tensor): The input Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The product Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n # In imperative mode:\n # size x: (2, 2, 3) and y: (2, 3, 2)\n x = paddle.to_tensor([[[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0]],\n [[3.0, 3.0, 3.0],\n [4.0, 4.0, 4.0]]])\n y = paddle.to_tensor([[[1.0, 1.0],[2.0, 2.0],[3.0, 3.0]],\n [[4.0, 4.0],[5.0, 5.0],[6.0, 6.0]]])\n out = paddle.bmm(x, y)\n #output size: (2, 2, 2)\n #output value:\n #[[[6.0, 6.0],[12.0, 12.0]],[[45.0, 45.0],[60.0, 60.0]]]\n out_np = out.numpy()\n\n \"\"\"\n x_shape = x.shape\n y_shape = y.shape\n if not len(x_shape) == len(y_shape) == 3:\n raise ValueError(\n \"x and y should be 3-dimensional. But received x's dimention: {}, y's dimention: {}\".\n format(x_shape, y_shape))\n if x_shape[2] != y_shape[1]:\n raise ValueError(\n \"x's width must be equal with y's height. But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n if x_shape[0] != y_shape[0]:\n raise ValueError(\n \"x's batch (shape[0]) must be equal with y's batch (shape[0]). But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.bmm(x, y)\n\n helper = LayerHelper('bmm', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='bmm', inputs={'X': x, 'Y': y}, outputs={'Out': out})\n return out\n\n\ndef histogram(input, bins=100, min=0, max=0, name=None):\n \"\"\"\n Computes the histogram of a tensor. The elements are sorted into equal width bins between min and max.\n If min and max are both zero, the minimum and maximum values of the data are used.\n\n Args:\n input (Tensor): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor\n should be float32, float64, int32, int64.\n bins (int): number of histogram bins\n min (int): lower end of the range (inclusive)\n max (int): upper end of the range (inclusive)\n\n Returns:\n Tensor: data type is int64, shape is (nbins,).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n inputs = paddle.to_tensor([1, 2, 1])\n result = paddle.histogram(inputs, bins=4, min=0, max=3)\n print(result) # [0, 2, 1, 0]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.histogram(input, \"bins\", bins, \"min\", min, \"max\", max)\n\n helper = LayerHelper('histogram', **locals())\n check_variable_and_dtype(\n input, 'X', ['int32', 'int64', 'float32', 'float64'], 'histogram')\n out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)\n helper.append_op(\n type='histogram',\n inputs={'X': input},\n outputs={'Out': out},\n attrs={'bins': bins,\n 'min': min,\n 'max': max})\n return out\n\n\ndef bincount(x, weights=None, minlength=0, name=None):\n \"\"\"\n Computes frequency of each value in the input tensor. \n\n Args:\n x (Tensor): A Tensor with non-negative integer. Should be 1-D tensor.\n weights (Tensor, optional): Weight for each value in the input tensor. Should have the same shape as input. Default is None.\n minlength (int, optional): Minimum number of bins. Should be non-negative integer. Default is 0.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor of frequency.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([1, 2, 1, 4, 5])\n result1 = paddle.bincount(x)\n print(result1) # [0, 2, 1, 0, 1, 1]\n\n w = paddle.to_tensor([2.1, 0.4, 0.1, 0.5, 0.5])\n result2 = paddle.bincount(x, weights=w)\n print(result2) # [0., 2.19999981, 0.40000001, 0., 0.50000000, 0.50000000]\n \"\"\"\n if x.dtype not in [paddle.int32, paddle.int64]:\n raise TypeError(\"Elements in Input(x) should all be integers\")\n\n if paddle.in_dynamic_mode():\n return _C_ops.bincount(x, weights, \"minlength\", minlength)\n\n helper = LayerHelper('bincount', **locals())\n\n check_variable_and_dtype(x, 'X', ['int32', 'int64'], 'bincount')\n\n if weights is not None:\n check_variable_and_dtype(weights, 'Weights',\n ['int32', 'int64', 'float32', 'float64'],\n 'bincount')\n out = helper.create_variable_for_type_inference(dtype=weights.dtype)\n else:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='bincount',\n inputs={'X': x,\n 'Weights': weights},\n outputs={'Out': out},\n attrs={'minlength': minlength})\n return out\n\n\ndef mv(x, vec, name=None):\n \"\"\"\n Performs a matrix-vector product of the matrix x and the vector vec.\n\n Args:\n x (Tensor): A tensor with shape :math:`[M, N]` , The data type of the input Tensor x\n should be one of float32, float64.\n vec (Tensor): A tensor with shape :math:`[N]` , The data type of the input Tensor x\n should be one of float32, float64.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor which is producted by x and vec.\n\n Examples:\n .. code-block:: python\n\n # x: [M, N], vec: [N]\n # paddle.mv(x, vec) # out: [M]\n\n import numpy as np\n import paddle\n\n x_data = np.array([[2, 1, 3], [3, 0, 1]]).astype(\"float64\")\n x = paddle.to_tensor(x_data)\n vec_data = np.array([3, 5, 1])\n vec = paddle.to_tensor(vec_data).astype(\"float64\")\n out = paddle.mv(x, vec)\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_mv(x, vec)\n else:\n if _in_legacy_dygraph():\n out = _C_ops.mv(x, vec)\n return out\n else:\n\n def __check_input(x, vec):\n var_names = {'x': x, 'vec': vec}\n for name, val in var_names.items():\n check_variable_and_dtype(val, name, ['float32', 'float64'],\n 'mv')\n x_shape = list(x.shape)\n vec_shape = list(vec.shape)\n if len(x_shape) != 2:\n raise ValueError(\n \"x should be 2-dimensional. But received x's dimention: {}\".\n format(x_shape))\n if len(vec_shape) != 1:\n raise ValueError(\n \"vec should be 1-dimensional. But received vec's dimention: {}\".\n format(vec_shape))\n\n __check_input(x, vec)\n\n helper = LayerHelper('mv', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='mv', inputs={'X': x,\n 'Vec': vec}, outputs={'Out': out})\n return out\n\n\ndef det(x, name=None):\n \"\"\"\n Calculates determinant value of a square matrix or batches of square matrices.\n Args:\n x (Tensor): input (Tensor): the input matrix of size `(n, n)` or the batch of matrices of size\n `(*, n, n)` where `*` is one or more batch dimensions.\n Returns:\n y (Tensor):the determinant value of a square matrix or batches of square matrices.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.det(x)\n\n print(A)\n\n # [ 0.02547996, 2.52317095, -6.15900707])\n\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.determinant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'det')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('determinant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='determinant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef slogdet(x, name=None):\n \"\"\"\n Calculates the sign and natural logarithm of the absolute value of a square matrix's or batches square matrices' determinant.\n The determinant can be computed with ``sign * exp(logabsdet)\n\n Supports input of float, double\n\n Note that for matrices that have zero determinant, this returns ``(0, -inf)``\n Args:\n x (Tensor): the batch of matrices of size :math:`(*, n, n)`\n where math:`*` is one or more batch dimensions.\n\n Returns:\n y (Tensor): A tensor containing the sign of the determinant and the natural logarithm\n of the absolute value of determinant, respectively.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.slogdet(x)\n\n print(A)\n\n # [[ 1. , 1. , -1. ],\n # [-0.98610914, -0.43010661, -0.10872950]])\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.slogdeterminant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'slogdet')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('slogdeterminant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='slogdeterminant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef svd(x, full_matrices=False, name=None):\n r\"\"\"\n Computes the singular value decomposition of one matrix or a batch of regular matrices.\n\n Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies:\n\n .. math::\n X = U * diag(S) * VT\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., N, M]`,\n where `...` is zero or more batch dimensions. N and M can be arbitraty\n positive number. Note that if x is sigular matrices, the grad is numerical\n instable. The data type of x should be float32 or float64.\n full_matrices (bool): A flag to control the behavor of svd.\n If full_matrices = True, svd op will compute full U and V matrics,\n which means shape of U is `[..., N, N]`, shape of V is `[..., M, M]`. K = min(M, N).\n If full_matrices = False, svd op will use a economic method to store U and V.\n which means shape of U is `[..., N, K]`, shape of V is `[..., M, K]`. K = min(M, N).\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple of 3 tensors: (U, S, VH). VH is the conjugate transpose of V. S is the singlar value vectors of matrics with shape `[..., K]`\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 2.0], [1.0, 3.0], [4.0, 6.0]]).astype('float64')\n x = x.reshape([3, 2])\n u, s, vh = paddle.linalg.svd(x)\n print (u)\n #U = [[ 0.27364809, -0.21695147 ],\n # [ 0.37892198, -0.87112408 ],\n # [ 0.8840446 , 0.44053933 ]]\n\n print (s)\n #S = [8.14753743, 0.78589688]\n print (vh)\n #VT= [[ 0.51411221, 0.85772294],\n # [ 0.85772294, -0.51411221]]\n\n # one can verify : U * S * VT == X\n # U * UH == I\n # V * VH == I\n \"\"\"\n\n if paddle.in_dynamic_mode():\n return _C_ops.svd(x, 'full_matrices', full_matrices)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'svd')\n check_type(full_matrices, 'full_matrices', bool, 'svd')\n helper = LayerHelper('svd', **locals())\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n vh = helper.create_variable_for_type_inference(dtype=x.dtype)\n s = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['full_matrices'] = full_matrices\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vh,\n 'S': s},\n attrs=attrs, )\n return u, s, vh\n\n\ndef matrix_power(x, n, name=None):\n r\"\"\"\n Computes the n-th power of a square matrix or a batch of square matrices.\n\n Let :math:`X` be a sqaure matrix or a batch of square matrices, :math:`n` be\n an exponent, the equation should be:\n\n .. math::\n Out = X ^ {n}\n\n Specifically,\n\n - If `n > 0`, it returns the matrix or a batch of matrices raised to the power\n of `n`.\n\n - If `n = 0`, it returns the identity matrix or a batch of identity matrices.\n\n - If `n < 0`, it returns the inverse of each matrix (if invertible) raised to\n the power of `abs(n)`.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices to be raised\n to power `n`. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n n (int): The exponent. It can be any positive, negative integer or zero.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The n-th power of the matrix (or the batch of matrices) `x`. Its\n data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1, 2, 3],\n [1, 4, 9],\n [1, 8, 27]], dtype='float64')\n print(paddle.linalg.matrix_power(x, 2))\n # [[6. , 34. , 102.],\n # [14. , 90. , 282.],\n # [36. , 250., 804.]]\n\n print(paddle.linalg.matrix_power(x, 0))\n # [[1., 0., 0.],\n # [0., 1., 0.],\n # [0., 0., 1.]]\n\n print(paddle.linalg.matrix_power(x, -2))\n # [[ 12.91666667, -12.75000000, 2.83333333 ],\n # [-7.66666667 , 8. , -1.83333333 ],\n # [ 1.80555556 , -1.91666667 , 0.44444444 ]]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.matrix_power(x, \"n\", n)\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'matrix_power')\n check_type(n, 'n', int, 'matrix_power')\n helper = LayerHelper('matrix_power', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matrix_power',\n inputs={'X': x},\n outputs={'Out': out},\n attrs={'n': n})\n return out\n\n\ndef qr(x, mode=\"reduced\", name=None):\n r\"\"\"\n Computes the QR decomposition of one matrix or batches of matrice (backward is unsupported now).\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., M, N]`,\n where ... is zero or more batch dimensions. M and N can be arbitrary\n positive number. The data type of x should be float32 or float64. \n mode (str, optional): A flag to control the behavior of qr, the default is \"reduced\". \n Suppose x's shape is `[..., M, N]` and denoting `K = min(M, N)`:\n If mode = \"reduced\", qr op will return reduced Q and R matrices, \n which means Q's shape is `[..., M, K]` and R's shape is `[..., K, N]`.\n If mode = \"complete\", qr op will return complete Q and R matrices, \n which means Q's shape is `[..., M, M]` and R's shape is `[..., M, N]`.\n If mode = \"r\", qr op will only return reduced R matrix, which means\n R's shape is `[..., K, N]`.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n If mode = \"reduced\" or mode = \"complete\", qr will return a two tensor-tuple, which represents Q and R. \n If mode = \"r\", qr will return a tensor which represents R.\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n q, r = paddle.linalg.qr(x)\n print (q)\n print (r)\n\n # Q = [[-0.16903085, 0.89708523],\n # [-0.50709255, 0.27602622],\n # [-0.84515425, -0.34503278]])\n\n # R = [[-5.91607978, -7.43735744],\n # [ 0. , 0.82807867]])\n \n # one can verify : X = Q * R ; \n \"\"\"\n if paddle.in_dynamic_mode():\n q, r = _C_ops.qr(x, 'mode', mode)\n if mode == \"r\":\n return r\n else:\n return q, r\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'qr')\n check_type(mode, 'mode', str, 'qr')\n helper = LayerHelper('qr', **locals())\n q = helper.create_variable_for_type_inference(dtype=x.dtype)\n r = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['mode'] = mode\n helper.append_op(\n type='qr', inputs={'X': [x]}, outputs={'Q': q,\n 'R': r}, attrs=attrs)\n if mode == \"r\":\n return r\n else:\n return q, r\n\n\ndef lu(x, pivot=True, get_infos=False, name=None):\n r\"\"\"\n Computes the LU factorization of an N-D(N>=2) matrix x. \n\n Returns the LU factorization(inplace x) and Pivots. low triangular matrix L and \n upper triangular matrix U are combined to a single LU matrix.\n\n Pivoting is done if pivot is set to True.\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n # return ones\n\n Args:\n\n X (Tensor): the tensor to factor of N-dimensions(N>=2).\n\n pivot (bool, optional): controls whether pivoting is done. Default: True.\n\n get_infos (bool, optional): if set to True, returns an info IntTensor. Default: False.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n factorization (Tensor): LU matrix, the factorization of input X.\n\n pivots (IntTensor): the pivots of size(∗(N-2), min(m,n)). `pivots` stores all the \n intermediate transpositions of rows. The final permutation `perm` could be \n reconstructed by this, details refer to upper example.\n\n infos (IntTensor, optional): if `get_infos` is `True`, this is a tensor of size (∗(N-2)) \n where non-zero values indicate whether factorization for the matrix or each minibatch \n has succeeded or failed.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n \n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n if paddle.in_dynamic_mode():\n LU, Piv, Info = _C_ops.lu(x, 'pivots', pivot)\n if get_infos:\n return LU, Piv, Info\n else:\n return LU, Piv\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu')\n helper = LayerHelper('lu', **locals())\n lu = helper.create_variable_for_type_inference(dtype=x.dtype)\n p = helper.create_variable_for_type_inference(dtype='int')\n info = helper.create_variable_for_type_inference(dtype='int')\n attrs = dict()\n attrs['pivots'] = pivot\n helper.append_op(\n type='lu',\n inputs={'X': x},\n outputs={'Out': lu,\n 'Pivots': p,\n 'Infos': info},\n attrs=attrs)\n if get_infos:\n return lu, p, info\n else:\n return lu, p\n\n\ndef lu_unpack(x, y, unpack_ludata=True, unpack_pivots=True, name=None):\n r\"\"\"\n Unpack L U and P to single matrix tensor . \n unpack L and U matrix from LU, unpack permutation matrix P from Pivtos .\n\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n\n\n Args:\n x (Tensor): The LU tensor get from paddle.linalg.lu, which is combined by L and U.\n\n y (Tensor): Pivots get from paddle.linalg.lu.\n\n unpack_ludata (bool,optional): whether to unpack L and U from x. Default: True.\n\n unpack_pivots (bool, optional): whether to unpack permutation matrix P from Pivtos. Default: True.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n P (Tensor): Permutation matrix P of lu factorization.\n\n L (Tensor): The lower triangular matrix tensor of lu factorization.\n\n U (Tensor): The upper triangular matrix tensor of lu factorization.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n\n if paddle.in_dynamic_mode():\n P, L, U = _C_ops.lu_unpack(x, y, 'unpack_ludata', unpack_ludata,\n 'unpack_pivots', unpack_pivots)\n return P, L, U\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu_unpack')\n helper = LayerHelper('lu_unpack', **locals())\n p = helper.create_variable_for_type_inference(dtype=x.dtype)\n l = helper.create_variable_for_type_inference(dtype=x.dtype)\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n attrs = dict()\n attrs['unpack_ludata'] = unpack_ludata\n attrs['unpack_pivots'] = unpack_pivots\n helper.append_op(\n type='lu_unpack',\n inputs={'X': x,\n 'Pivots': y},\n outputs={'Pmat': p,\n 'L': l,\n 'U': u},\n attrs=attrs)\n return p, l, u\n\n\ndef eig(x, name=None):\n \"\"\"\n This API performs the eigenvalue decomposition of a square matrix or a batch of square matrices.\n\n .. note::\n If the matrix is a Hermitian or a real symmetric matrix, please use :ref:`paddle.linalg.eigh` instead, which is much faster.\n If only eigenvalues is needed, please use :ref:`paddle.linalg.eigvals` instead.\n If the matrix is of any shape, please use :ref:`paddle.linalg.svd`.\n This API is only supported on CPU device.\n The output datatype is always complex for both real and complex input.\n\n Args:\n x (Tensor): A tensor with shape math:`[*, N, N]`, The data type of the x should be one of ``float32``,\n ``float64``, ``compplex64`` or ``complex128``.\n name (str, optional): The default value is `None`. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Eigenvalues(Tensors): A tensor with shape math:`[*, N]` refers to the eigen values.\n Eigenvectors(Tensors): A tensor with shape math:`[*, N, N]` refers to the eigen vectors.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n paddle.device.set_device(\"cpu\")\n\n x_data = np.array([[1.6707249, 7.2249975, 6.5045543],\n [9.956216, 8.749598, 6.066444 ],\n [4.4251957, 1.7983172, 0.370647 ]]).astype(\"float32\")\n x = paddle.to_tensor(x_data)\n w, v = paddle.linalg.eig(x)\n print(w)\n # Tensor(shape=[3, 3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [[(-0.5061363550800655+0j) , (-0.7971760990842826+0j) ,\n # (0.18518077798279986+0j)],\n # [(-0.8308237755993192+0j) , (0.3463813401919749+0j) ,\n # (-0.6837005269141947+0j) ],\n # [(-0.23142567697893396+0j), (0.4944999840400175+0j) ,\n # (0.7058765252952796+0j) ]])\n\n print(v)\n # Tensor(shape=[3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [ (16.50471283351188+0j) , (-5.5034820550763515+0j) ,\n # (-0.21026087843552282+0j)])\n \"\"\"\n if paddle.in_dynamic_mode():\n w, v = _C_ops.eig(x)\n return w, v\n\n check_variable_and_dtype(\n x, 'X', ['float32', 'float64', 'complex64', 'complex128'], 'eig')\n helper = LayerHelper('eig', **locals())\n\n w = helper.create_variable_for_type_inference(x.dtype)\n v = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {'X': x}\n outputs = {'Eigenvalues': w, 'Eigenvectors': v}\n helper.append_op(type='eig', inputs=inputs, outputs=outputs)\n\n return w, v\n\n\ndef eigvals(x, name=None):\n \"\"\"\n Compute the eigenvalues of one or more general matrices.\n\n Warning:\n The gradient kernel of this operator does not yet developed.\n If you need back propagation through this operator, please replace it with paddle.linalg.eig.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices whose eigenvalues will be computed.\n Its shape should be `[*, M, M]`, where `*` is zero or more batch dimensions.\n Its data type should be float32, float64, complex64, or complex128.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n Tensor: A tensor containing the unsorted eigenvalues which has the same batch dimensions with `x`.\n The eigenvalues are complex-valued even when `x` is real.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n paddle.seed(1234)\n\n x = paddle.rand(shape=[3, 3], dtype='float64')\n # [[0.02773777, 0.93004224, 0.06911496],\n # [0.24831591, 0.45733623, 0.07717843],\n # [0.48016702, 0.14235102, 0.42620817]])\n\n print(paddle.linalg.eigvals(x))\n # [(-0.27078833542132674+0j), (0.29962280156230725+0j), (0.8824477020120244+0j)] #complex128\n \"\"\"\n\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64',\n 'complex128'], 'eigvals')\n\n x_shape = list(x.shape)\n if len(x_shape) < 2:\n raise ValueError(\n \"The dimension of Input(x) should be at least 2, but received x's dimention = {}, x's shape = {}\".\n format(len(x_shape), x_shape))\n\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The last two dimensions of Input(x) should be equal, but received x's shape = {}\".\n format(x_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.eigvals(x)\n\n helper = LayerHelper('eigvals', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='eigvals', inputs={'X': x}, outputs={'Out': out})\n return out\n\n\ndef multi_dot(x, name=None):\n \"\"\"\n Multi_dot is an operator that calculates multiple matrix multiplications.\n\n Supports inputs of float16(only GPU support), float32 and float64 dtypes. This function does not\n support batched inputs.\n\n The input tensor in [x] must be 2-D except for the first and last can be 1-D.\n If the first tensor is a 1-D vector of shape(n, ) it is treated as row vector\n of shape(1, n), similarly if the last tensor is a 1D vector of shape(n, ), it\n is treated as a column vector of shape(n, 1).\n\n If the first and last tensor are 2-D matrix, then the output is also 2-D matrix,\n otherwise the output is a 1-D vector.\n\n Multi_dot will select the lowest cost multiplication order for calculation. The\n cost of multiplying two matrices with shapes (a, b) and (b, c) is a * b * c.\n Given matrices A, B, C with shapes (20, 5), (5, 100), (100, 10) respectively,\n we can calculate the cost of different multiplication orders as follows:\n - Cost((AB)C) = 20x5x100 + 20x100x10 = 30000\n - Cost(A(BC)) = 5x100x10 + 20x5x10 = 6000\n\n In this case, multiplying B and C first, then multiply A, which is 5 times faster\n than sequential calculation.\n\n Args:\n x ([Tensor]): The input tensors which is a list Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # A * B\n A_data = np.random.random([3, 4]).astype(np.float32)\n B_data = np.random.random([4, 5]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n out = paddle.linalg.multi_dot([A, B])\n print(out.numpy().shape)\n # [3, 5]\n\n # A * B * C\n A_data = np.random.random([10, 5]).astype(np.float32)\n B_data = np.random.random([5, 8]).astype(np.float32)\n C_data = np.random.random([8, 7]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n C = paddle.to_tensor(C_data)\n out = paddle.linalg.multi_dot([A, B, C])\n print(out.numpy().shape)\n # [10, 7]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.multi_dot(x)\n\n check_type(x, 'x', (list, tuple), 'multi_dot')\n for id, item in enumerate(x):\n check_variable_and_dtype(item, 'x[' + str(id) + ']',\n ['float16', 'float32', 'float64'], 'multi_dot')\n if item.dtype != x[0].dtype:\n raise TypeError(\n \"All the Tensors in the input must have the same data type.\")\n\n helper = LayerHelper('multi_dot', **locals())\n dtype = helper.input_dtype(input_param_name='x')\n out = helper.create_variable_for_type_inference(dtype)\n helper.append_op(type='multi_dot', inputs={\"X\": x}, outputs={\"Out\": out})\n return out\n\n\ndef eigh(x, UPLO='L', name=None):\n \"\"\"\n Compute the eigenvalues and eigenvectors of a\n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[*, N, N]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): (string, default 'L'), 'L' represents the lower triangular matrix,\n \"'U' represents the upper triangular matrix.\".\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n\n out_value(Tensor): A Tensor with shape [*, N] and data type of float32 and float64. The eigenvalues of eigh op.\n out_vector(Tensor): A Tensor with shape [*, N, N] and data type of float32,float64,complex64 and complex128. The eigenvectors of eigh op.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value, out_vector = paddle.linalg.eigh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n print(out_vector)\n #[(-0.9238795325112867+0j), (-0.3826834323650898+0j)],\n #[ 0.3826834323650898j , -0.9238795325112867j ]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.eigh(x, 'UPLO', UPLO)\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigh', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'eigh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO})\n return out_value, out_vector\n\n\ndef pinv(x, rcond=1e-15, hermitian=False, name=None):\n r\"\"\"\n Calculate pseudo inverse via SVD(singular value decomposition)\n of one matrix or batches of regular matrix.\n\n .. math::\n\n if hermitian == False:\n x = u * s * vt (SVD)\n out = v * 1/s * ut\n else:\n x = u * s * ut (eigh)\n out = u * 1/s * u.conj().transpose(-2,-1)\n\n If x is hermitian or symmetric matrix, svd will be replaced with eigh.\n\n Args:\n x(Tensor): The input tensor. Its shape should be (*, m, n)\n where * is zero or more batch dimensions. m and n can be\n arbitraty positive number. The data type of x should be\n float32 or float64 or complex64 or complex128. When data\n type is complex64 or cpmplex128, hermitian should be set\n True.\n\n rcond(Tensor, optional): the tolerance value to determine\n when is a singular value zero. Defalut:1e-15.\n\n hermitian(bool, optional): indicates whether x is Hermitian\n if complex or symmetric if real. Default: False.\n\n name(str|None): A name for this layer(optional). If set None,\n the layer will be named automatically.\n\n Returns:\n Tensor: The tensor with same data type with x. it represents\n pseudo inverse of x. Its shape should be (*, n, m).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.arange(15).reshape((3, 5)).astype('float64')\n input = paddle.to_tensor(x)\n out = paddle.linalg.pinv(input)\n print(input)\n print(out)\n\n # input:\n # [[0. , 1. , 2. , 3. , 4. ],\n # [5. , 6. , 7. , 8. , 9. ],\n # [10., 11., 12., 13., 14.]]\n\n # out:\n # [[-0.22666667, -0.06666667, 0.09333333],\n # [-0.12333333, -0.03333333, 0.05666667],\n # [-0.02000000, 0.00000000, 0.02000000],\n # [ 0.08333333, 0.03333333, -0.01666667],\n # [ 0.18666667, 0.06666667, -0.05333333]]\n\n # one can verify : x * out * x = x ;\n # or out * x * out = x ;\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if not hermitian:\n # combine svd and matmul op\n u, s, vt = _C_ops.svd(x, 'full_matrices', False)\n max_singular_val = _C_ops.reduce_max(s, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=x.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=x.dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v, _ = _C_ops.transpose2(vt, 'axis', perm)\n\n out_1 = v * st\n out_2 = _C_ops.matmul_v2(out_1, u, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n # combine eigh and matmul op\n s, u = _C_ops.eigh(x, 'UPLO', 'L')\n s_abs = paddle.abs(s)\n max_singular_val = _C_ops.reduce_max(s_abs, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=s.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=s.dtype)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n out_1 = u * st\n u_conj = _C_ops.conj(u)\n out_2 = _C_ops.matmul_v2(out_1, u_conj, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n if not hermitian:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'pinv')\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(dtype)\n vt = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vt,\n 'S': s},\n attrs={'full_matrices': False}, )\n\n max_singular_val = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=dtype)\n st_shape = helper.create_variable_for_type_inference(dtype=dtype)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v = helper.create_variable_for_type_inference(dtype)\n v_shape = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='transpose2',\n inputs={'X': [vt]},\n outputs={'Out': [v],\n 'XShape': [v_shape]},\n attrs={'axis': perm})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': v,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n else:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64',\n 'complex128'], 'pinv')\n\n if dtype == paddle.complex128:\n s_type = 'float64'\n elif dtype == paddle.complex64:\n s_type = 'float32'\n else:\n s_type = dtype\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': s,\n 'Eigenvectors': u},\n attrs={'UPLO': 'L'})\n s_abs = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='abs', inputs={'X': s}, outputs={'Out': s_abs})\n max_singular_val = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s_abs},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=s_type)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=s_type)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s_type)\n cond_not_int = layers.cast(layers.logical_not(condition), s_type)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=s_type)\n st_shape = helper.create_variable_for_type_inference(dtype=s_type)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': u,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n u_conj = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='conj', inputs={'X': u}, outputs={'Out': [u_conj]})\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u_conj},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n\n\ndef solve(x, y, name=None):\n r\"\"\"\n Computes the solution of a square system of linear equations with a unique solution for input 'X' and 'Y'.\n Let :math: `X` be a sqaure matrix or a batch of square matrices, :math:`Y` be\n a vector/matrix or a batch of vectors/matrices, the equation should be:\n\n .. math::\n Out = X^-1 * Y\n Specifically,\n - This system of linear equations has one solution if and only if input 'X' is invertible.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): A vector/matrix or a batch of vectors/matrices. Its shape should be `[*, M, K]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of a square system of linear equations with a unique solution for input 'x' and 'y'.\n Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # 2*X0 + X1 = 9\n # X0 + 2*X1 = 8\n\n import paddle\n import numpy as np\n\n np_x = np.array([[3, 1],[1, 2]])\n np_y = np.array([9, 8])\n x = paddle.to_tensor(np_x, dtype=\"float64\")\n y = paddle.to_tensor(np_y, dtype=\"float64\")\n out = paddle.linalg.solve(x, y)\n\n print(out)\n # [2., 3.])\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.solve(x, y)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type=\"solve\", inputs={\"X\": x,\n \"Y\": y}, outputs={\"Out\": out})\n return out\n\n\ndef triangular_solve(x,\n y,\n upper=True,\n transpose=False,\n unitriangular=False,\n name=None):\n r\"\"\"\n Computes the solution of a system of equations with a triangular coefficient matrix `x` and\n multiple right-hand sides `y` .\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input triangular coefficient matrix. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): Whether to solve the upper-triangular system of equations (default) or the lower-triangular \n system of equations. Default: True.\n transpose (bool, optional): whether `x` should be transposed before calculation. Default: False.\n unitriangular (bool, optional): whether `x` is unit triangular. If True, the diagonal elements of `x` are assumed \n to be 1 and not referenced from `x` . Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # x1 + x2 + x3 = 0\n # 2*x2 + x3 = -9\n # -x3 = 5\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n y = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.triangular_solve(x, y, upper=True)\n\n print(out)\n # [7, -2, -5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.triangular_solve(x, y, 'upper', upper, 'transpose',\n transpose, 'unitriangular',\n unitriangular)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"triangular_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'triangular_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'triangular_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='triangular_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={\n 'upper': upper,\n 'transpose': transpose,\n 'unitriangular': unitriangular\n })\n return out\n\n\ndef cholesky_solve(x, y, upper=False, name=None):\n r\"\"\"\n Solves a linear system of equations A @ X = B, given A's Cholesky factor matrix u and matrix B.\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input matrix which is upper or lower triangular Cholesky factor of square matrix A. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): whether to consider the Cholesky factor as a lower or upper triangular matrix. Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type is the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n u = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n b = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.cholesky_solve(b, u, upper=True)\n\n print(out)\n # [-2.5, -7, 9.5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky_solve(x, y, 'upper', upper)\n\n helper = LayerHelper(\"cholesky_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'cholesky_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'cholesky_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='cholesky_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef eigvalsh(x, UPLO='L', name=None):\n \"\"\"\n Computes the eigenvalues of a \n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[_, M, M]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): Lower triangular part of a (‘L’, default) or the upper triangular part (‘U’).\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor eigenvalues in ascending order.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value = paddle.eigvalsh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n \"\"\"\n if paddle.in_dynamic_mode():\n is_test = x.stop_gradient\n values, _ = _C_ops.eigvalsh(x, 'UPLO', UPLO, 'is_test', is_test)\n return values\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigvalsh', **locals())\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64', 'complex128'],\n 'eigvalsh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n is_test = x.stop_gradient\n helper.append_op(\n type='eigvalsh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO,\n 'is_test': is_test})\n return out_value\n\n\ndef lstsq(x, y, rcond=None, driver=None, name=None):\n \"\"\"\n Computes a solution to\n the least squares problem of a system of linear equations.\n\n Args:\n x (Tensor): A tensor with shape ``(*, M, N)`` , the data type of the input Tensor ``x``\n should be one of float32, float64.\n y (Tensor): A tensor with shape ``(*, M, K)`` , the data type of the input Tensor ``y`` \n should be one of float32, float64.\n rcond(float, optional): The default value is None. A float pointing number used to determine \n the effective rank of ``x``. If ``rcond`` is None, it will be set to max(M, N) times the \n machine precision of x_dtype.\n driver(str, optional): The default value is None. The name of LAPACK method to be used. For \n CPU inputs the valid values are ‘gels’, ‘gelsy’, ‘gelsd, ‘gelss’. For CUDA input, the only \n valid driver is ‘gels’. If ``driver`` is None, ‘gelsy’ is used for CPU inputs and ‘gels’ \n for CUDA inputs.\n name(str, optional): The default value is None. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple: A tuple of 4 Tensors which is (``solution``, ``residuals``, ``rank``, ``singular_values``). \n ``solution`` is a tensor with shape ``(*, N, K)``, meaning the least squares solution. ``residuals`` \n is a tensor with shape ``(*, K)``, meaning the squared residuals of the solutions, which is computed \n when M > N and every matrix in ``x`` is full-rank, otherwise return an empty tensor. ``rank`` is a tensor \n with shape ``(*)``, meaning the ranks of the matrices in ``x``, which is computed when ``driver`` in \n (‘gelsy’, ‘gelsd’, ‘gelss’), otherwise return an empty tensor. ``singular_values`` is a tensor with \n shape ``(*, min(M, N))``, meaning singular values of the matrices in ``x``, which is computed when \n ``driver`` in (‘gelsd’, ‘gelss’), otherwise return an empty tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n x = paddle.to_tensor([[1, 3], [3, 2], [5, 6.]])\n y = paddle.to_tensor([[3, 4, 6], [5, 3, 4], [1, 2, 1.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gelsd\")\n print(results[0])\n # [[ 0.78350395, -0.22165027, -0.62371236],\n # [-0.11340097, 0.78866047, 1.14948535]]\n print(results[1])\n # [19.81443405, 10.43814468, 30.56185532])\n print(results[2])\n # 2\n print(results[3])\n # [9.03455734, 1.54167950]\n\n x = paddle.to_tensor([[10, 2, 3], [3, 10, 5], [5, 6, 12.]])\n y = paddle.to_tensor([[4, 2, 9], [2, 0, 3], [2, 5, 3.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gels\")\n print(results[0])\n # [[ 0.39386186, 0.10230173, 0.93606132],\n # [ 0.10741687, -0.29028133, 0.11892585],\n # [-0.05115091, 0.51918161, -0.19948854]]\n print(results[1])\n # []\n \"\"\"\n device = paddle.get_device()\n if device == \"cpu\":\n if driver not in (None, \"gels\", \"gelss\", \"gelsd\", \"gelsy\"):\n raise ValueError(\n \"Only support valid driver is 'gels', 'gelss', 'gelsd', 'gelsy' or None for CPU inputs. But got {}\".\n format(driver))\n driver = \"gelsy\" if driver is None else driver\n elif \"gpu\" in device:\n if driver not in (None, \"gels\"):\n raise ValueError(\n \"Only support valid driver is 'gels' or None for CUDA inputs. But got {}\".\n format(driver))\n driver = \"gels\" if driver is None else driver\n else:\n raise RuntimeError(\"Only support lstsq api for CPU or CUDA device.\")\n\n if x.dtype == y.dtype and x.dtype in (paddle.float32, paddle.float64):\n pass\n else:\n raise ValueError(\n \"Only support x and y have the same dtype such as 'float32' and 'float64'.\"\n )\n\n if rcond is None:\n if x.dtype == paddle.float32:\n rcond = 1e-7 * max(x.shape[-2], x.shape[-1])\n elif x.dtype == paddle.float64:\n rcond = 1e-15 * max(x.shape[-2], x.shape[-1])\n\n if paddle.in_dynamic_mode():\n solution, rank, singular_values = _C_ops.lstsq(x, y, \"rcond\", rcond,\n \"driver\", driver)\n if x.shape[-2] > x.shape[-1]:\n matmul_out = _varbase_creator(dtype=x.dtype)\n _C_ops.matmul(x, solution, matmul_out, 'trans_x', False, 'trans_y',\n False)\n minus_out = _C_ops.elementwise_sub(matmul_out, y)\n pow_out = _C_ops.pow(minus_out, 'factor', 2)\n residuals = _C_ops.reduce_sum(pow_out, 'dim', [-2], 'keepdim',\n False, 'reduce_all', False)\n else:\n residuals = paddle.empty(shape=[0], dtype=x.dtype)\n\n if driver == \"gels\":\n rank = paddle.empty(shape=[0], dtype=paddle.int32)\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n elif driver == \"gelsy\":\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n\n return solution, residuals, rank, singular_values\n\n helper = LayerHelper('lstsq', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n check_variable_and_dtype(\n y, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n\n solution = helper.create_variable_for_type_inference(dtype=x.dtype)\n residuals = helper.create_variable_for_type_inference(dtype=x.dtype)\n rank = helper.create_variable_for_type_inference(dtype=paddle.int32)\n singular_values = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='lstsq',\n inputs={'X': x,\n 'Y': y},\n outputs={\n 'Solution': solution,\n 'Rank': rank,\n 'SingularValues': singular_values\n },\n attrs={'rcond': rcond,\n 'driver': driver})\n\n matmul_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n minus_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n pow_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': solution},\n outputs={'Out': matmul_out},\n attrs={\n 'trans_x': False,\n 'trans_y': False,\n })\n\n helper.append_op(\n type='elementwise_sub',\n inputs={'X': matmul_out,\n 'Y': y},\n outputs={'Out': minus_out})\n\n helper.append_op(\n type='pow',\n inputs={'X': minus_out},\n outputs={'Out': pow_out},\n attrs={'factor': 2})\n\n helper.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': residuals},\n attrs={'dim': [-2],\n 'keep_dim': False,\n 'reduce_all': False})\n\n if driver == \"gels\":\n rank = paddle.static.data(name='rank', shape=[0])\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n elif driver == \"gelsy\":\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n\n return solution, residuals, rank, singular_values\n"}}},{"rowIdx":40,"cells":{"function_name":{"kind":"string","value":"frobenius_norm"},"docstring":{"kind":"string","value":"The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`.\nArgs:\n input (Variable): Tensor, data type float32, float64.\n dim (list, optional): None for last two dimensions.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False."},"masked_code":{"kind":"string","value":"# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numpy as np\nfrom ..fluid.layer_helper import LayerHelper\nfrom ..framework import _varbase_creator, _dygraph_tracer\nfrom ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype\nfrom ..static import Variable\nfrom ..fluid.framework import _in_legacy_dygraph, in_dygraph_mode\nfrom ..fluid.layers import transpose, cast # noqa: F401\nfrom ..fluid import layers\nimport paddle\nfrom paddle.common_ops_import import core\nfrom paddle.common_ops_import import VarDesc\nfrom paddle import _C_ops\n\n__all__ = []\n\n\ndef matmul(x, y, transpose_x=False, transpose_y=False, name=None):\n \"\"\"\n Applies matrix multiplication to two tensors. `matmul` follows\n the complete broadcast rules,\n and its behavior is consistent with `np.matmul`.\n\n Currently, the input tensors' number of dimensions can be any, `matmul` can be used to\n achieve the `dot`, `matmul` and `batchmatmul`.\n\n The actual behavior depends on the shapes of :math:`x`, :math:`y` and the\n flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:\n\n - If a transpose flag is specified, the last two dimensions of the tensor\n are transposed. If the tensor is ndim-1 of shape, the transpose is invalid. If the tensor\n is ndim-1 of shape :math:`[D]`, then for :math:`x` it is treated as :math:`[1, D]`, whereas\n for :math:`y` it is the opposite: It is treated as :math:`[D, 1]`.\n\n The multiplication behavior depends on the dimensions of `x` and `y`. Specifically:\n\n - If both tensors are 1-dimensional, the dot product result is obtained.\n\n - If both tensors are 2-dimensional, the matrix-matrix product is obtained.\n\n - If the `x` is 1-dimensional and the `y` is 2-dimensional,\n a `1` is prepended to its dimension in order to conduct the matrix multiply.\n After the matrix multiply, the prepended dimension is removed.\n\n - If the `x` is 2-dimensional and `y` is 1-dimensional,\n the matrix-vector product is obtained.\n\n - If both arguments are at least 1-dimensional and at least one argument\n is N-dimensional (where N > 2), then a batched matrix multiply is obtained.\n If the first argument is 1-dimensional, a 1 is prepended to its dimension\n in order to conduct the batched matrix multiply and removed after.\n If the second argument is 1-dimensional, a 1 is appended to its\n dimension for the purpose of the batched matrix multiple and removed after.\n The non-matrix (exclude the last two dimensions) dimensions are\n broadcasted according the broadcast rule.\n For example, if input is a (j, 1, n, m) tensor and the other is a (k, m, p) tensor,\n out will be a (j, k, n, p) tensor.\n\n Args:\n x (Tensor): The input tensor which is a Tensor.\n y (Tensor): The input tensor which is a Tensor.\n transpose_x (bool): Whether to transpose :math:`x` before multiplication.\n transpose_y (bool): Whether to transpose :math:`y` before multiplication.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # vector * vector\n x_data = np.random.random([10]).astype(np.float32)\n y_data = np.random.random([10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [1]\n\n # matrix * vector\n x_data = np.random.random([10, 5]).astype(np.float32)\n y_data = np.random.random([5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10]\n\n # batched matrix * broadcasted vector\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([2]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5]\n\n # batched matrix * batched matrix\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([10, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5, 5]\n\n # batched matrix * broadcasted matrix\n x_data = np.random.random([10, 1, 5, 2]).astype(np.float32)\n y_data = np.random.random([1, 3, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 3, 5, 5]\n\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_matmul(x, y, transpose_x, transpose_y)\n\n if _in_legacy_dygraph():\n op_type = 'matmul_v2'\n op = getattr(_C_ops, op_type)\n return op(x, y, 'trans_x', transpose_x, 'trans_y', transpose_y)\n\n attrs = {\n 'trans_x': transpose_x,\n 'trans_y': transpose_y,\n }\n\n def __check_input(x, y):\n var_names = {'x': x, 'y': y}\n for name, val in var_names.items():\n check_variable_and_dtype(\n val, name,\n ['float16', 'float32', 'float64', 'complex64', 'complex128'],\n 'matmul')\n\n __check_input(x, y)\n\n helper = LayerHelper('matmul_v2', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef norm(x, p='fro', axis=None, keepdim=False, name=None):\n \"\"\"\n\n Returns the matrix norm (Frobenius) or vector norm (the 1-norm, the Euclidean\n or 2-norm, and in general the p-norm for p > 0) of a given tensor.\n\n .. note::\n This norm API is different from `numpy.linalg.norm`.\n This api supports high-order input tensors (rank >= 3), and certain axis need to be pointed out to calculate the norm.\n But `numpy.linalg.norm` only supports 1-D vector or 2-D matrix as input tensor.\n For p-order matrix norm, this api actually treats matrix as a flattened vector to calculate the vector norm, NOT REAL MATRIX NORM.\n\n Args:\n x (Tensor): The input tensor could be N-D tensor, and the input data\n type could be float32 or float64.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `0`, `1`, `2`,\n `inf`, `-inf` and any positive real number yielding the corresponding p-norm. Not supported: ord < 0 and nuclear norm.\n Default value is `fro`.\n axis (int|list|tuple, optional): The axis on which to apply norm operation. If axis is int\n or list(int)/tuple(int) with only one element, the vector norm is computed over the axis.\n If `axis < 0`, the dimension to norm operation is rank(input) + axis.\n If axis is a list(int)/tuple(int) with two elements, the matrix norm is computed over the axis.\n Defalut value is `None`.\n keepdim (bool, optional): Whether to reserve the reduced dimension in the\n output Tensor. The result tensor will have fewer dimension\n than the :attr:`input` unless :attr:`keepdim` is true, default\n value is False.\n name (str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: results of norm operation on the specified axis of input tensor,\n it's data type is the same as input's Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n shape=[2, 3, 4]\n np_input = np.arange(24).astype('float32') - 12\n np_input = np_input.reshape(shape)\n x = paddle.to_tensor(np_input)\n #[[[-12. -11. -10. -9.] [ -8. -7. -6. -5.] [ -4. -3. -2. -1.]]\n # [[ 0. 1. 2. 3.] [ 4. 5. 6. 7.] [ 8. 9. 10. 11.]]]\n\n # compute frobenius norm along last two dimensions.\n out_fro = paddle.linalg.norm(x, p='fro', axis=[0,1])\n # out_fro.numpy() [17.435596 16.911535 16.7332 16.911535]\n\n # compute 2-order vector norm along last dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=-1)\n #out_pnorm.numpy(): [[21.118711 13.190906 5.477226]\n # [ 3.7416575 11.224972 19.131126]]\n\n # compute 2-order norm along [0,1] dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=[0,1])\n #out_pnorm.numpy(): [17.435596 16.911535 16.7332 16.911535]\n\n # compute inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=np.inf)\n #out_pnorm.numpy() = [12.]\n out_pnorm = paddle.linalg.norm(x, p=np.inf, axis=0)\n #out_pnorm.numpy(): [[12. 11. 10. 9.] [8. 7. 6. 7.] [8. 9. 10. 11.]]\n\n # compute -inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=-np.inf)\n #out_pnorm.numpy(): [0.]\n out_pnorm = paddle.linalg.norm(x, p=-np.inf, axis=0)\n #out_pnorm.numpy(): [[0. 1. 2. 3.] [4. 5. 6. 5.] [4. 3. 2. 1.]]\n \"\"\"\n\n# MASKED: frobenius_norm function (lines 242-275)\n\n def vector_norm(input,\n porder=None,\n axis=None,\n keepdim=False,\n asvector=False,\n name=None):\n \"\"\"\n Calculate the p-order vector norm for certain dimension of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n porder (float, optional): None for porder=2.0.\n axis (int, optional): None for last dimension.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if paddle.in_dynamic_mode():\n if axis is None: axis = -1\n return _C_ops.p_norm(input, 'porder', porder, 'axis', axis,\n 'keepdim', keepdim, 'asvector', asvector)\n if porder is not None:\n check_type(porder, 'porder', (float, int), 'p_norm')\n if axis is not None:\n check_type(axis, 'axis', (int), 'p_norm')\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'p_norm')\n\n attrs = {\n 'axis': axis if axis is not None else -1,\n 'porder': float(porder) if porder is not None else 2.0,\n 'keepdim': keepdim,\n 'asvector': asvector,\n 'epsilon': 1e-12,\n }\n helper = LayerHelper('p_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='p_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def inf_norm(input,\n porder=None,\n axis=axis,\n keepdim=False,\n asvector=False,\n name=None):\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n helper.append_op(type='abs', inputs={'X': input}, outputs={'Out': out})\n reduce_out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n reduce_all = True if axis == None or axis == [] or asvector == True else False\n axis = axis if axis != None and axis != [] else [0]\n\n reduce_type = 'reduce_max' if porder == np.float(\n 'inf') else 'reduce_min'\n helper.append_op(\n type=reduce_type,\n inputs={'X': out},\n outputs={'Out': reduce_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n\n return reduce_out\n\n def p_matrix_norm(input, porder=1., axis=axis, keepdim=False, name=None):\n \"\"\"\n NOTE:\n This function actually treats the matrix as flattened vector to calculate vector norm instead of matrix norm.\n \"\"\"\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n\n block.append_op(\n type='pow',\n inputs={'X': abs_out},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': True if axis is None else False\n })\n porder\n block.append_op(\n type='pow',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n if axis is None and p is not None:\n if isinstance(p, str):\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n porder=p,\n axis=axis,\n keepdim=keepdim,\n asvector=True,\n name=name)\n else:\n raise ValueError(\"only valid p type is string or float, found {}\".\n format(type(p)))\n\n if isinstance(axis, tuple):\n axis = list(axis)\n if isinstance(axis, list) and len(axis) == 1:\n axis = axis[0]\n\n #calculate vector norm, where axis is int or list with only one integer\n if isinstance(axis, int):\n if isinstance(p, str):\n if p == \"fro\":\n return vector_norm(\n x,\n porder=2,\n axis=axis,\n keepdim=keepdim,\n asvector=False,\n name=name)\n\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n axis=axis,\n porder=p,\n keepdim=keepdim,\n asvector=False,\n name=name)\n else:\n raise ValueError(\n \"unspport p for p-order vector norm. except float, found {}\".\n format(p))\n #calculate matrix norm, where axis is list with two integers\n elif isinstance(axis, list) and len(axis) == 2:\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n elif p == np.inf or p == -np.inf:\n return inf_norm(x, porder=p, axis=axis, keepdim=keepdim, name=name)\n elif p == 0:\n raise ValueError(\n \"just suport axis type int or list (length of list <=1) if p = 0, found {}\".\n format(axis))\n else:\n return p_matrix_norm(\n x, porder=p, axis=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"except axis type int or list (length of list <=2), found {}\".\n format(axis))\n\n\ndef dist(x, y, p=2, name=None):\n r\"\"\"\n\n This OP returns the p-norm of (x - y). It is not a norm in a strict sense, only as a measure\n of distance. The shapes of x and y must be broadcastable. The definition is as follows, for\n details, please refer to the `numpy's broadcasting <https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html>`_:\n\n - Each input has at least one dimension.\n - Match the two input dimensions from back to front, the dimension sizes must either be equal, one of them is 1, or one of them does not exist.\n\n Where, z = x - y, the shapes of x and y are broadcastable, then the shape of z can be\n obtained as follows:\n\n 1. If the number of dimensions of x and y are not equal, prepend 1 to the dimensions of the\n tensor with fewer dimensions.\n\n For example, The shape of x is [8, 1, 6, 1], the shape of y is [7, 1, 5], prepend 1 to the\n dimension of y.\n\n x (4-D Tensor): 8 x 1 x 6 x 1\n\n y (4-D Tensor): 1 x 7 x 1 x 5\n\n 2. Determine the size of each dimension of the output z: choose the maximum value from the\n two input dimensions.\n\n z (4-D Tensor): 8 x 7 x 6 x 5\n\n If the number of dimensions of the two inputs are the same, the size of the output can be\n directly determined in step 2. When p takes different values, the norm formula is as follows:\n\n When p = 0, defining $0^0=0$, the zero-norm of z is simply the number of non-zero elements of z.\n\n .. math::\n\n ||z||_{0}=\\lim_{p \\\\rightarrow 0}\\sum_{i=1}^{m}|z_i|^{p}\n\n When p = inf, the inf-norm of z is the maximum element of z.\n\n .. math::\n\n ||z||_\\infty=\\max_i |z_i|\n\n When p = -inf, the negative-inf-norm of z is the minimum element of z.\n\n .. math::\n\n ||z||_{-\\infty}=\\min_i |z_i|\n\n Otherwise, the p-norm of z follows the formula,\n\n .. math::\n\n ||z||_{p}=(\\sum_{i=1}^{m}|z_i|^p)^{\\\\frac{1}{p}}\n\n Args:\n x (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n y (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n p (float, optional): The norm to be computed, its data type is float32 or float64. Default: 2.\n\n Returns:\n Tensor: Tensor that is the p-norm of (x - y).\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor(np.array([[3, 3],[3, 3]]), \"float32\")\n y = paddle.to_tensor(np.array([[3, 3],[3, 1]]), \"float32\")\n out = paddle.dist(x, y, 0)\n print(out) # out = [1.]\n\n out = paddle.dist(x, y, 2)\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"inf\"))\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"-inf\"))\n print(out) # out = [0.]\n \"\"\"\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'dist')\n check_variable_and_dtype(y, 'dtype', ['float32', 'float64'], 'dist')\n check_type(p, 'p', (float, int), 'dist')\n helper = LayerHelper(\"dist\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n outputs = {'Out': [out]}\n attrs = {\"p\": float(p)}\n helper.append_op(\n type='dist', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef cond(x, p=None, name=None):\n \"\"\"\n\n Computes the condition number of a matrix or batches of matrices with respect to a matrix norm ``p``.\n\n Args:\n x (Tensor): The input tensor could be tensor of shape ``(*, m, n)`` where ``*`` is zero or more batch dimensions\n for ``p`` in ``(2, -2)``, or of shape ``(*, n, n)`` where every matrix is invertible for any supported ``p``.\n And the input data type could be ``float32`` or ``float64``.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `nuc`, `1`, `-1`, `2`, `-2`,\n `inf`, `-inf`. Default value is `None`, meaning that the order of the norm is `2`.\n name (str, optional): The default value is `None`. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: computing results of condition number, its data type is the same as input Tensor ``x``.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1., 0, -1], [0, 1, 0], [1, 0, 1]])\n\n # compute conditional number when p is None\n out = paddle.linalg.cond(x)\n # out.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is 'fro'\n out_fro = paddle.linalg.cond(x, p='fro')\n # out_fro.numpy() [3.1622777]\n\n # compute conditional number when order of the norm is 'nuc'\n out_nuc = paddle.linalg.cond(x, p='nuc')\n # out_nuc.numpy() [9.2426405]\n\n # compute conditional number when order of the norm is 1\n out_1 = paddle.linalg.cond(x, p=1)\n # out_1.numpy() [2.]\n\n # compute conditional number when order of the norm is -1\n out_minus_1 = paddle.linalg.cond(x, p=-1)\n # out_minus_1.numpy() [1.]\n\n # compute conditional number when order of the norm is 2\n out_2 = paddle.linalg.cond(x, p=2)\n # out_2.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is -1\n out_minus_2 = paddle.linalg.cond(x, p=-2)\n # out_minus_2.numpy() [0.70710677]\n\n # compute conditional number when order of the norm is inf\n out_inf = paddle.linalg.cond(x, p=np.inf)\n # out_inf.numpy() [2.]\n\n # compute conditional number when order of the norm is -inf\n out_minus_inf = paddle.linalg.cond(x, p=-np.inf)\n # out_minus_inf.numpy() [1.]\n\n a = paddle.to_tensor(np.random.randn(2, 4, 4).astype('float32'))\n # a.numpy()\n # [[[ 0.14063153 -0.996288 0.7996131 -0.02571543]\n # [-0.16303636 1.5534962 -0.49919784 -0.04402903]\n # [-1.1341571 -0.6022629 0.5445269 0.29154757]\n # [-0.16816919 -0.30972657 1.7521842 -0.5402487 ]]\n # [[-0.58081484 0.12402827 0.7229862 -0.55046535]\n # [-0.15178485 -1.1604939 0.75810957 0.30971205]\n # [-0.9669573 1.0940945 -0.27363303 -0.35416734]\n # [-1.216529 2.0018666 -0.7773689 -0.17556527]]]\n a_cond_fro = paddle.linalg.cond(a, p='fro')\n # a_cond_fro.numpy() [31.572273 28.120834]\n\n b = paddle.to_tensor(np.random.randn(2, 3, 4).astype('float64'))\n # b.numpy()\n # [[[ 1.61707487 0.46829144 0.38130416 0.82546736]\n # [-1.72710298 0.08866375 -0.62518804 0.16128892]\n # [-0.02822879 -1.67764516 0.11141444 0.3220113 ]]\n # [[ 0.22524372 0.62474921 -0.85503233 -1.03960523]\n # [-0.76620689 0.56673047 0.85064753 -0.45158196]\n # [ 1.47595418 2.23646462 1.5701758 0.10497519]]]\n b_cond_2 = paddle.linalg.cond(b, p=2)\n # b_cond_2.numpy() [3.30064451 2.51976252]\n\n \"\"\"\n\n def mat_norm(input, porder=1., axis=None):\n \"\"\"\n NOTE:\n Calculate the matrix norm of a square matrix or batches of square matrices,\n when porder is in (1, -1, inf, -inf)\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n axis = axis if axis != None and axis != [] else [0]\n keepdim = False\n\n if paddle.in_dynamic_mode():\n abs_out = _C_ops.abs(input)\n sum_out = _C_ops.reduce_sum(abs_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == 1 or porder == np.inf:\n return _C_ops.reduce_max(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == -1 or porder == -np.inf:\n return _C_ops.reduce_min(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n\n block = LayerHelper('norm', **locals())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n block.append_op(\n type='reduce_sum',\n inputs={'X': abs_out},\n outputs={'Out': sum_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 1 or porder == np.inf:\n block.append_op(\n type='reduce_max',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n if porder == -1 or porder == -np.inf:\n block.append_op(\n type='reduce_min',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n\n def fro_norm(input, porder=2, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the frobenius norm of a square matrix or batches of square matrices.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n if paddle.in_dynamic_mode():\n pow_out = _C_ops.pow(input, 'factor', porder)\n sum_out_1 = _C_ops.reduce_sum(pow_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n sum_out_2 = _C_ops.reduce_sum(sum_out_1, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n return _C_ops.pow(sum_out_2, 'factor', float(1. / porder))\n\n block = LayerHelper('norm', **locals())\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_1 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_2 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='pow',\n inputs={'X': input},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out_1},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_sum',\n inputs={'X': sum_out_1},\n outputs={'Out': sum_out_2},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='pow',\n inputs={'X': sum_out_2},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n def svd_norm(input, porder, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the matrix norm, which is related to singular values, of a matrix\n or batches of matrices, including nuclear norm, 2-norm and (-2)-norm.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n u, s, vh = svd(input, full_matrices=False)\n\n if paddle.in_dynamic_mode():\n if porder == \"nuc\":\n return _C_ops.reduce_sum(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n max_out = _C_ops.reduce_max(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n min_out = _C_ops.reduce_min(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n if porder == 2:\n return _C_ops.elementwise_div(max_out, min_out, 'aixs', axis,\n 'use_mkldnn', False)\n if porder == -2:\n return _C_ops.elementwise_div(min_out, max_out, 'aixs', axis,\n 'use_mkldnn', False)\n\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n if porder == \"nuc\":\n block.append_op(\n type='reduce_sum',\n inputs={'X': s},\n outputs={'Out': out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n max_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n min_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_min',\n inputs={'X': s},\n outputs={'Out': min_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': max_out,\n 'Y': min_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n if porder == -2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': min_out,\n 'Y': max_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n\n def empty_tensor(input, shape):\n if paddle.in_dynamic_mode():\n return input.reshape(shape)\n raise ValueError(\"only support x is nonempty tensor in static mode\")\n\n x_shape = list(x.shape)\n if not len(x_shape) >= 2:\n raise ValueError(\"input should be a matrix or batches of matrices, \" +\n \"but the dimention of received input is {}\".format(\n len(x_shape)))\n if p == None:\n p = 2\n x_size = 0 if (0 in x_shape) else 1\n if p in (\"fro\", \"nuc\", 1, -1, np.inf, -np.inf):\n if x_shape[len(x_shape) - 1] == x_shape[len(x_shape) - 2]:\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n x_inv = x.inverse()\n if p == \"fro\":\n return fro_norm(x) * fro_norm(x_inv)\n if p == \"nuc\":\n return svd_norm(x, p) * svd_norm(x_inv, p)\n if p in (1, -1):\n return mat_norm(\n x, porder=p, axis=[-2]) * mat_norm(\n x_inv, porder=p, axis=[-2])\n if p in (np.inf, -np.inf):\n return mat_norm(\n x, porder=p, axis=[-1]) * mat_norm(\n x_inv, porder=p, axis=[-1])\n else:\n raise ValueError(\"only support p is {} when input is a \".format(p) +\n \"square matrix or batches of square matrices\")\n elif p in (2, -2):\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n return svd_norm(x, porder=p)\n else:\n raise ValueError(\n \"unsupported {} for p, only supporting ('fro', 'nuc', \".format(\n p) + \"1, -1, 2, -2, inf, -inf) or none\")\n\n\ndef dot(x, y, name=None):\n \"\"\"\n This operator calculates inner product for vectors.\n\n .. note::\n Support 1-d and 2-d Tensor. When it is 2d, the first dimension of this matrix\n is the batch dimension, which means that the vectors of multiple batches are dotted.\n\n Parameters:\n x(Tensor): 1-D or 2-D ``Tensor``. Its dtype should be ``float32``, ``float64``, ``int32``, ``int64``\n y(Tensor): 1-D or 2-D ``Tensor``. Its dtype soulde be ``float32``, ``float64``, ``int32``, ``int64``\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: the calculated result Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x_data = np.random.uniform(0.1, 1, [10]).astype(np.float32)\n y_data = np.random.uniform(1, 3, [10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.dot(x, y)\n print(z)\n\n \"\"\"\n op_type = 'dot'\n # skip var type check in dygraph mode to improve efficiency\n if paddle.in_dynamic_mode():\n op = getattr(_C_ops, op_type)\n return op(x, y)\n\n assert x is not None, 'x cannot be None in {}'.format(op_type)\n assert y is not None, 'y cannot be None in {}'.format(op_type)\n\n check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n check_variable_and_dtype(y, 'y', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n\n helper = LayerHelper(op_type, **locals())\n if name is None:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n else:\n out = helper.create_variable(\n name=name, dtype=x.dtype, persistable=False)\n helper.append_op(\n type=\"dot\", inputs={'X': x,\n 'Y': y}, attrs={}, outputs={\"Out\": out})\n return out\n\n\ndef cov(x, rowvar=True, ddof=True, fweights=None, aweights=None, name=None):\n \"\"\"\n Estimate the covariance matrix of the input variables, given data and weights.\n\n A covariance matrix is a square matrix, indicate the covariance of each pair variables in the input matrix.\n For example, for an N-dimensional samples X=[x1,x2,…xN]T, then the covariance matrix \n element Cij is the covariance of xi and xj. The element Cii is the variance of xi itself.\n\n Parameters:\n x(Tensor): A N-D(N<=2) Tensor containing multiple variables and observations. By default, each row of x represents a variable. Also see rowvar below.\n rowvar(Bool, optional): If rowvar is True (default), then each row represents a variable, with observations in the columns. Default: True\n ddof(Bool, optional): If ddof=True will return the unbiased estimate, and ddof=False will return the simple average. Default: True\n fweights(Tensor, optional): 1-D Tensor of integer frequency weights; The number of times each observation vector should be repeated. Default: None\n aweights(Tensor, optional): 1-D Tensor of observation vector weights. How important of the observation vector, larger data means this element is more important. Default: None\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: The covariance matrix Tensor of the variables.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n\n xt = paddle.rand((3,4))\n paddle.linalg.cov(xt)\n\n '''\n Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n [[0.07918842, 0.06127326, 0.01493049],\n [0.06127326, 0.06166256, 0.00302668],\n [0.01493049, 0.00302668, 0.01632146]])\n '''\n \"\"\"\n op_type = 'cov'\n if len(x.shape) > 2 or len(x.shape) < 1:\n raise ValueError(\n \"Input(x) only support N-D (1<=N<=2) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cov')\n nx = x\n if len(x.shape) == 1:\n nx = x.reshape((1, -1))\n if not rowvar and nx.shape[0] != 1:\n nx = nx.t()\n w = None\n observation_num = nx.shape[1]\n if fweights is not None:\n w = fweights.astype(nx.dtype)\n if len(w.shape) > 1:\n raise ValueError(\n \"Input(fweights) only support N-D (N<=1) tensor in cov, but received \"\n \"shape of Input(input) is %s.\" % len(fweights.shape))\n if fweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(fweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(fweights) is {}.\".format(observation_num,\n fweights.shape[0]))\n if fweights.min() < 0:\n raise ValueError(\n \"The value of Input(fweights) cannot be negtive, but received \"\n \"min of Input(fweights) is {}.\".format(fweights.min()))\n if not paddle.all(fweights == paddle.round(fweights.astype('float64'))):\n raise ValueError(\"Input(fweights) must be integer \")\n\n if aweights is not None:\n aw = aweights.astype(nx.dtype)\n if len(aw.shape) > 1:\n raise ValueError(\n \"Input(aweights) only support N-D (N<=1) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(aweights.shape))\n check_variable_and_dtype(aweights, 'dtype', ['float32', 'float64'],\n 'cov')\n if aweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(aweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(aweights) is {}.\".format(observation_num,\n aweights.shape[0]))\n if aweights.min() < 0:\n raise ValueError(\n \"The value of Input(aweights) cannot be negtive, but received \"\n \"min of Input(aweights) is {}.\".format(aweights.min()))\n if w is not None:\n w = w * aw\n else:\n w = aw\n\n w_sum = paddle.to_tensor(observation_num, dtype=nx.dtype)\n if fweights is not None or aweights is not None:\n w_sum = w.sum()\n if w_sum.item() == 0:\n raise ValueError(\"The sum of weights is zero, can't be normalized.\")\n\n if w is not None:\n nx_w = nx * w\n avg = (nx_w).sum(axis=1) / w_sum\n else:\n avg = nx.sum(axis=1) / w_sum\n nx_w = nx\n\n if w is not None and aweights is not None and ddof == True:\n norm_factor = w_sum - (w * aweights).sum() / w_sum\n else:\n norm_factor = w_sum - ddof\n if norm_factor <= 0:\n norm_factor = paddle.to_tensor(0, dtype=nx.dtype)\n nx = nx - avg.unsqueeze(1)\n xxt = paddle.mm(nx, nx_w.t().conj())\n cov = paddle.divide(xxt, norm_factor).squeeze()\n return cov\n\n\ndef t(input, name=None):\n \"\"\"\n Transpose <=2-D tensor.\n 0-D and 1-D tensors are returned as it is and 2-D tensor is equal to\n the paddle.transpose function which perm dimensions set 0 and 1.\n\n Args:\n input (Tensor): The input Tensor. It is a N-D (N<=2) Tensor of data types float16, float32, float64, int32.\n name(str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`\n Returns:\n Tensor: A transposed n-D Tensor, with data type being float16, float32, float64, int32, int64.\n\n For Example:\n\n .. code-block:: text\n\n # Example 1 (0-D tensor)\n x = tensor([0.79])\n paddle.t(x) = tensor([0.79])\n\n # Example 2 (1-D tensor)\n x = tensor([0.79, 0.84, 0.32])\n paddle.t(x) = tensor([0.79, 0.84, 0.32])\n\n # Example 3 (2-D tensor)\n x = tensor([0.79, 0.84, 0.32],\n [0.64, 0.14, 0.57])\n paddle.t(x) = tensor([0.79, 0.64],\n [0.84, 0.14],\n [0.32, 0.57])\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n x = paddle.ones(shape=[2, 3], dtype='int32')\n x_transposed = paddle.t(x)\n print(x_transposed.shape)\n # [3, 2]\n \"\"\"\n if len(input.shape) > 2:\n raise ValueError(\n \"Input(input) only support N-D (N<=2) tensor, but received \"\n \"length of Input(input) is %s. Perhaps you can use paddle.\"\n \"tensor.transpose() instead.\" % len(input.shape))\n if paddle.in_dynamic_mode():\n if len(input.shape) == 1:\n return input\n # 2-D tensor\n perm = [1, 0]\n out, _ = _C_ops.transpose2(input, 'axis', perm)\n return out\n\n check_variable_and_dtype(\n input, 'input', ['float16', 'float32', 'float64', 'int32',\n 'int64'], 'transpose')\n\n helper = LayerHelper('t', **locals())\n out = helper.create_variable_for_type_inference(input.dtype)\n input_shape = helper.create_variable_for_type_inference(input.dtype)\n if len(input.shape) == 1:\n out = input\n else:\n helper.append_op(\n type='transpose2',\n inputs={'X': [input]},\n outputs={'Out': [out],\n 'XShape': [input_shape]},\n attrs={'axis': [1, 0]})\n return out\n\n\ndef cross(x, y, axis=None, name=None):\n \"\"\"\n Computes the cross product between two tensors along an axis.\n\n Inputs must have the same shape, and the length of their axes should be equal to 3.\n If `axis` is not given, it defaults to the first axis found with the length 3.\n\n Args:\n x (Tensor): The first input tensor.\n y (Tensor): The second input tensor.\n axis (int, optional): The axis along which to compute the cross product. It defaults to the first axis found with the length 3.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor. A Tensor with same data type as `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0],\n [3.0, 3.0, 3.0]])\n y = paddle.to_tensor([[1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0]])\n\n z1 = paddle.cross(x, y)\n # [[-1. -1. -1.]\n # [ 2. 2. 2.]\n # [-1. -1. -1.]]\n\n z2 = paddle.cross(x, y, axis=1)\n # [[0. 0. 0.]\n # [0. 0. 0.]\n # [0. 0. 0.]]\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_cross(x, y, axis)\n else:\n if _in_legacy_dygraph():\n if axis is not None:\n return _C_ops.cross(x, y, 'dim', axis)\n else:\n return _C_ops.cross(x, y)\n else:\n helper = LayerHelper(\"cross\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n attrs = dict()\n attrs['dim'] = axis\n\n helper.append_op(\n type='cross',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef cholesky(x, upper=False, name=None):\n r\"\"\"\n Computes the Cholesky decomposition of one symmetric positive-definite\n matrix or batches of symmetric positive-definite matrice.\n\n If `upper` is `True`, the decomposition has the form :math:`A = U^{T}U` ,\n and the returned matrix :math:`U` is upper-triangular. Otherwise, the\n decomposition has the form :math:`A = LL^{T}` , and the returned matrix\n :math:`L` is lower-triangular.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[*, M, M]`,\n where * is zero or more batch dimensions, and matrices on the\n inner-most 2 dimensions all should be symmetric positive-definite.\n Its data type should be float32 or float64.\n upper (bool): The flag indicating whether to return upper or lower\n triangular matrices. Default: False.\n\n Returns:\n Tensor: A Tensor with same shape and data type as `x`. It represents \\\n triangular matrices generated by Cholesky decomposition.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n a = np.random.rand(3, 3)\n a_t = np.transpose(a, [1, 0])\n x_data = np.matmul(a, a_t) + 1e-03\n x = paddle.to_tensor(x_data)\n out = paddle.linalg.cholesky(x, upper=False)\n print(out)\n # [[1.190523 0. 0. ]\n # [0.9906703 0.27676893 0. ]\n # [1.25450498 0.05600871 0.06400121]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky(x, \"upper\", upper)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cholesky')\n check_type(upper, 'upper', bool, 'cholesky')\n helper = LayerHelper('cholesky', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='cholesky',\n inputs={'X': [x]},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef matrix_rank(x, tol=None, hermitian=False, name=None):\n r\"\"\"\n Computes the rank of a matrix.\n\n The rank of a matrix is the number of singular values that are greater than the specified `tol` threshold when hermitian=False,\n or the number of eigenvalues in absolute value that are greater than the specified `tol` threshold when hermitian=True.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., m, n]`, where `...` is zero or more batch dimensions. If `x` is a batch\n of matrices then the output has the same batch dimensions. The data type of `x` should be float32 or float64.\n tol (float,Tensor,optional): the tolerance value. Default: None. If `tol` is not specified, and `sigma` is the largest\n singular value (or eigenvalues in absolute value), and `eps` is the epsilon value for the dtype of `x`, then `tol` is computed\n with formula `tol=sigma * max(m,n) * eps`. Note that if `x` is a batch of matrices, `tol` is computed this way for every batch.\n hermitian (bool,optional): indicates whether `x` is Hermitian. Default: False. When hermitian=True, `x` is assumed to be Hermitian,\n enabling a more efficient method for finding eigenvalues, but `x` is not checked inside the function. Instead, We just use\n the lower triangular of the matrix to compute.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: Rank of tensor x.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n a = paddle.eye(10)\n b = paddle.linalg.matrix_rank(a)\n print(b)\n # b = [10]\n\n c = paddle.ones(shape=[3, 4, 5, 5])\n d = paddle.linalg.matrix_rank(c, tol=0.01, hermitian=True)\n print(d)\n # d = [[1, 1, 1, 1],\n # [1, 1, 1, 1],\n # [1, 1, 1, 1]]\n\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if tol is None:\n tol_tensor = None\n tol_attr = 0.0\n use_default_tol = True\n elif isinstance(tol, Variable):\n if tol.dtype != x.dtype:\n tol_tensor = cast(tol, x.dtype)\n else:\n tol_tensor = tol\n tol_attr = 0.0\n use_default_tol = False\n else:\n tol_tensor = None\n tol_attr = float(tol)\n use_default_tol = False\n return _C_ops.matrix_rank(x, tol_tensor, \"tol\", tol_attr, 'hermitian',\n hermitian, 'use_default_tol', use_default_tol)\n\n inputs = {}\n attrs = {}\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'matrix_rank')\n inputs['X'] = x\n if tol is None:\n attrs['use_default_tol'] = True\n elif isinstance(tol, Variable):\n check_variable_and_dtype(tol, 'tol', ['float32'], 'matrix_rank')\n attrs['use_default_tol'] = False\n if tol.dtype != x.dtype:\n inputs['TolTensor'] = cast(tol, x.dtype)\n else:\n inputs['TolTensor'] = tol\n else:\n check_type(tol, 'tol', float, 'matrix_rank')\n attrs['use_default_tol'] = False\n attrs['tol'] = tol\n check_type(hermitian, 'hermitian', bool, 'matrix_rank')\n attrs['hermitian'] = hermitian\n\n helper = LayerHelper('matrix_rank', **locals())\n out = helper.create_variable_for_type_inference(dtype='int32')\n helper.append_op(\n type='matrix_rank', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef bmm(x, y, name=None):\n \"\"\"\n Applies batched matrix multiplication to two tensors.\n\n Both of the two input tensors must be three-dementional and share the same batch size.\n\n if x is a (b, m, k) tensor, y is a (b, k, n) tensor, the output will be a (b, m, n) tensor.\n\n Args:\n x (Tensor): The input Tensor.\n y (Tensor): The input Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The product Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n # In imperative mode:\n # size x: (2, 2, 3) and y: (2, 3, 2)\n x = paddle.to_tensor([[[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0]],\n [[3.0, 3.0, 3.0],\n [4.0, 4.0, 4.0]]])\n y = paddle.to_tensor([[[1.0, 1.0],[2.0, 2.0],[3.0, 3.0]],\n [[4.0, 4.0],[5.0, 5.0],[6.0, 6.0]]])\n out = paddle.bmm(x, y)\n #output size: (2, 2, 2)\n #output value:\n #[[[6.0, 6.0],[12.0, 12.0]],[[45.0, 45.0],[60.0, 60.0]]]\n out_np = out.numpy()\n\n \"\"\"\n x_shape = x.shape\n y_shape = y.shape\n if not len(x_shape) == len(y_shape) == 3:\n raise ValueError(\n \"x and y should be 3-dimensional. But received x's dimention: {}, y's dimention: {}\".\n format(x_shape, y_shape))\n if x_shape[2] != y_shape[1]:\n raise ValueError(\n \"x's width must be equal with y's height. But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n if x_shape[0] != y_shape[0]:\n raise ValueError(\n \"x's batch (shape[0]) must be equal with y's batch (shape[0]). But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.bmm(x, y)\n\n helper = LayerHelper('bmm', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='bmm', inputs={'X': x, 'Y': y}, outputs={'Out': out})\n return out\n\n\ndef histogram(input, bins=100, min=0, max=0, name=None):\n \"\"\"\n Computes the histogram of a tensor. The elements are sorted into equal width bins between min and max.\n If min and max are both zero, the minimum and maximum values of the data are used.\n\n Args:\n input (Tensor): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor\n should be float32, float64, int32, int64.\n bins (int): number of histogram bins\n min (int): lower end of the range (inclusive)\n max (int): upper end of the range (inclusive)\n\n Returns:\n Tensor: data type is int64, shape is (nbins,).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n inputs = paddle.to_tensor([1, 2, 1])\n result = paddle.histogram(inputs, bins=4, min=0, max=3)\n print(result) # [0, 2, 1, 0]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.histogram(input, \"bins\", bins, \"min\", min, \"max\", max)\n\n helper = LayerHelper('histogram', **locals())\n check_variable_and_dtype(\n input, 'X', ['int32', 'int64', 'float32', 'float64'], 'histogram')\n out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)\n helper.append_op(\n type='histogram',\n inputs={'X': input},\n outputs={'Out': out},\n attrs={'bins': bins,\n 'min': min,\n 'max': max})\n return out\n\n\ndef bincount(x, weights=None, minlength=0, name=None):\n \"\"\"\n Computes frequency of each value in the input tensor. \n\n Args:\n x (Tensor): A Tensor with non-negative integer. Should be 1-D tensor.\n weights (Tensor, optional): Weight for each value in the input tensor. Should have the same shape as input. Default is None.\n minlength (int, optional): Minimum number of bins. Should be non-negative integer. Default is 0.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor of frequency.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([1, 2, 1, 4, 5])\n result1 = paddle.bincount(x)\n print(result1) # [0, 2, 1, 0, 1, 1]\n\n w = paddle.to_tensor([2.1, 0.4, 0.1, 0.5, 0.5])\n result2 = paddle.bincount(x, weights=w)\n print(result2) # [0., 2.19999981, 0.40000001, 0., 0.50000000, 0.50000000]\n \"\"\"\n if x.dtype not in [paddle.int32, paddle.int64]:\n raise TypeError(\"Elements in Input(x) should all be integers\")\n\n if paddle.in_dynamic_mode():\n return _C_ops.bincount(x, weights, \"minlength\", minlength)\n\n helper = LayerHelper('bincount', **locals())\n\n check_variable_and_dtype(x, 'X', ['int32', 'int64'], 'bincount')\n\n if weights is not None:\n check_variable_and_dtype(weights, 'Weights',\n ['int32', 'int64', 'float32', 'float64'],\n 'bincount')\n out = helper.create_variable_for_type_inference(dtype=weights.dtype)\n else:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='bincount',\n inputs={'X': x,\n 'Weights': weights},\n outputs={'Out': out},\n attrs={'minlength': minlength})\n return out\n\n\ndef mv(x, vec, name=None):\n \"\"\"\n Performs a matrix-vector product of the matrix x and the vector vec.\n\n Args:\n x (Tensor): A tensor with shape :math:`[M, N]` , The data type of the input Tensor x\n should be one of float32, float64.\n vec (Tensor): A tensor with shape :math:`[N]` , The data type of the input Tensor x\n should be one of float32, float64.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor which is producted by x and vec.\n\n Examples:\n .. code-block:: python\n\n # x: [M, N], vec: [N]\n # paddle.mv(x, vec) # out: [M]\n\n import numpy as np\n import paddle\n\n x_data = np.array([[2, 1, 3], [3, 0, 1]]).astype(\"float64\")\n x = paddle.to_tensor(x_data)\n vec_data = np.array([3, 5, 1])\n vec = paddle.to_tensor(vec_data).astype(\"float64\")\n out = paddle.mv(x, vec)\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_mv(x, vec)\n else:\n if _in_legacy_dygraph():\n out = _C_ops.mv(x, vec)\n return out\n else:\n\n def __check_input(x, vec):\n var_names = {'x': x, 'vec': vec}\n for name, val in var_names.items():\n check_variable_and_dtype(val, name, ['float32', 'float64'],\n 'mv')\n x_shape = list(x.shape)\n vec_shape = list(vec.shape)\n if len(x_shape) != 2:\n raise ValueError(\n \"x should be 2-dimensional. But received x's dimention: {}\".\n format(x_shape))\n if len(vec_shape) != 1:\n raise ValueError(\n \"vec should be 1-dimensional. But received vec's dimention: {}\".\n format(vec_shape))\n\n __check_input(x, vec)\n\n helper = LayerHelper('mv', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='mv', inputs={'X': x,\n 'Vec': vec}, outputs={'Out': out})\n return out\n\n\ndef det(x, name=None):\n \"\"\"\n Calculates determinant value of a square matrix or batches of square matrices.\n Args:\n x (Tensor): input (Tensor): the input matrix of size `(n, n)` or the batch of matrices of size\n `(*, n, n)` where `*` is one or more batch dimensions.\n Returns:\n y (Tensor):the determinant value of a square matrix or batches of square matrices.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.det(x)\n\n print(A)\n\n # [ 0.02547996, 2.52317095, -6.15900707])\n\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.determinant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'det')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('determinant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='determinant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef slogdet(x, name=None):\n \"\"\"\n Calculates the sign and natural logarithm of the absolute value of a square matrix's or batches square matrices' determinant.\n The determinant can be computed with ``sign * exp(logabsdet)\n\n Supports input of float, double\n\n Note that for matrices that have zero determinant, this returns ``(0, -inf)``\n Args:\n x (Tensor): the batch of matrices of size :math:`(*, n, n)`\n where math:`*` is one or more batch dimensions.\n\n Returns:\n y (Tensor): A tensor containing the sign of the determinant and the natural logarithm\n of the absolute value of determinant, respectively.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.slogdet(x)\n\n print(A)\n\n # [[ 1. , 1. , -1. ],\n # [-0.98610914, -0.43010661, -0.10872950]])\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.slogdeterminant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'slogdet')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('slogdeterminant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='slogdeterminant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef svd(x, full_matrices=False, name=None):\n r\"\"\"\n Computes the singular value decomposition of one matrix or a batch of regular matrices.\n\n Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies:\n\n .. math::\n X = U * diag(S) * VT\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., N, M]`,\n where `...` is zero or more batch dimensions. N and M can be arbitraty\n positive number. Note that if x is sigular matrices, the grad is numerical\n instable. The data type of x should be float32 or float64.\n full_matrices (bool): A flag to control the behavor of svd.\n If full_matrices = True, svd op will compute full U and V matrics,\n which means shape of U is `[..., N, N]`, shape of V is `[..., M, M]`. K = min(M, N).\n If full_matrices = False, svd op will use a economic method to store U and V.\n which means shape of U is `[..., N, K]`, shape of V is `[..., M, K]`. K = min(M, N).\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple of 3 tensors: (U, S, VH). VH is the conjugate transpose of V. S is the singlar value vectors of matrics with shape `[..., K]`\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 2.0], [1.0, 3.0], [4.0, 6.0]]).astype('float64')\n x = x.reshape([3, 2])\n u, s, vh = paddle.linalg.svd(x)\n print (u)\n #U = [[ 0.27364809, -0.21695147 ],\n # [ 0.37892198, -0.87112408 ],\n # [ 0.8840446 , 0.44053933 ]]\n\n print (s)\n #S = [8.14753743, 0.78589688]\n print (vh)\n #VT= [[ 0.51411221, 0.85772294],\n # [ 0.85772294, -0.51411221]]\n\n # one can verify : U * S * VT == X\n # U * UH == I\n # V * VH == I\n \"\"\"\n\n if paddle.in_dynamic_mode():\n return _C_ops.svd(x, 'full_matrices', full_matrices)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'svd')\n check_type(full_matrices, 'full_matrices', bool, 'svd')\n helper = LayerHelper('svd', **locals())\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n vh = helper.create_variable_for_type_inference(dtype=x.dtype)\n s = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['full_matrices'] = full_matrices\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vh,\n 'S': s},\n attrs=attrs, )\n return u, s, vh\n\n\ndef matrix_power(x, n, name=None):\n r\"\"\"\n Computes the n-th power of a square matrix or a batch of square matrices.\n\n Let :math:`X` be a sqaure matrix or a batch of square matrices, :math:`n` be\n an exponent, the equation should be:\n\n .. math::\n Out = X ^ {n}\n\n Specifically,\n\n - If `n > 0`, it returns the matrix or a batch of matrices raised to the power\n of `n`.\n\n - If `n = 0`, it returns the identity matrix or a batch of identity matrices.\n\n - If `n < 0`, it returns the inverse of each matrix (if invertible) raised to\n the power of `abs(n)`.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices to be raised\n to power `n`. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n n (int): The exponent. It can be any positive, negative integer or zero.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The n-th power of the matrix (or the batch of matrices) `x`. Its\n data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1, 2, 3],\n [1, 4, 9],\n [1, 8, 27]], dtype='float64')\n print(paddle.linalg.matrix_power(x, 2))\n # [[6. , 34. , 102.],\n # [14. , 90. , 282.],\n # [36. , 250., 804.]]\n\n print(paddle.linalg.matrix_power(x, 0))\n # [[1., 0., 0.],\n # [0., 1., 0.],\n # [0., 0., 1.]]\n\n print(paddle.linalg.matrix_power(x, -2))\n # [[ 12.91666667, -12.75000000, 2.83333333 ],\n # [-7.66666667 , 8. , -1.83333333 ],\n # [ 1.80555556 , -1.91666667 , 0.44444444 ]]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.matrix_power(x, \"n\", n)\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'matrix_power')\n check_type(n, 'n', int, 'matrix_power')\n helper = LayerHelper('matrix_power', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matrix_power',\n inputs={'X': x},\n outputs={'Out': out},\n attrs={'n': n})\n return out\n\n\ndef qr(x, mode=\"reduced\", name=None):\n r\"\"\"\n Computes the QR decomposition of one matrix or batches of matrice (backward is unsupported now).\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., M, N]`,\n where ... is zero or more batch dimensions. M and N can be arbitrary\n positive number. The data type of x should be float32 or float64. \n mode (str, optional): A flag to control the behavior of qr, the default is \"reduced\". \n Suppose x's shape is `[..., M, N]` and denoting `K = min(M, N)`:\n If mode = \"reduced\", qr op will return reduced Q and R matrices, \n which means Q's shape is `[..., M, K]` and R's shape is `[..., K, N]`.\n If mode = \"complete\", qr op will return complete Q and R matrices, \n which means Q's shape is `[..., M, M]` and R's shape is `[..., M, N]`.\n If mode = \"r\", qr op will only return reduced R matrix, which means\n R's shape is `[..., K, N]`.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n If mode = \"reduced\" or mode = \"complete\", qr will return a two tensor-tuple, which represents Q and R. \n If mode = \"r\", qr will return a tensor which represents R.\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n q, r = paddle.linalg.qr(x)\n print (q)\n print (r)\n\n # Q = [[-0.16903085, 0.89708523],\n # [-0.50709255, 0.27602622],\n # [-0.84515425, -0.34503278]])\n\n # R = [[-5.91607978, -7.43735744],\n # [ 0. , 0.82807867]])\n \n # one can verify : X = Q * R ; \n \"\"\"\n if paddle.in_dynamic_mode():\n q, r = _C_ops.qr(x, 'mode', mode)\n if mode == \"r\":\n return r\n else:\n return q, r\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'qr')\n check_type(mode, 'mode', str, 'qr')\n helper = LayerHelper('qr', **locals())\n q = helper.create_variable_for_type_inference(dtype=x.dtype)\n r = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['mode'] = mode\n helper.append_op(\n type='qr', inputs={'X': [x]}, outputs={'Q': q,\n 'R': r}, attrs=attrs)\n if mode == \"r\":\n return r\n else:\n return q, r\n\n\ndef lu(x, pivot=True, get_infos=False, name=None):\n r\"\"\"\n Computes the LU factorization of an N-D(N>=2) matrix x. \n\n Returns the LU factorization(inplace x) and Pivots. low triangular matrix L and \n upper triangular matrix U are combined to a single LU matrix.\n\n Pivoting is done if pivot is set to True.\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n # return ones\n\n Args:\n\n X (Tensor): the tensor to factor of N-dimensions(N>=2).\n\n pivot (bool, optional): controls whether pivoting is done. Default: True.\n\n get_infos (bool, optional): if set to True, returns an info IntTensor. Default: False.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n factorization (Tensor): LU matrix, the factorization of input X.\n\n pivots (IntTensor): the pivots of size(∗(N-2), min(m,n)). `pivots` stores all the \n intermediate transpositions of rows. The final permutation `perm` could be \n reconstructed by this, details refer to upper example.\n\n infos (IntTensor, optional): if `get_infos` is `True`, this is a tensor of size (∗(N-2)) \n where non-zero values indicate whether factorization for the matrix or each minibatch \n has succeeded or failed.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n \n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n if paddle.in_dynamic_mode():\n LU, Piv, Info = _C_ops.lu(x, 'pivots', pivot)\n if get_infos:\n return LU, Piv, Info\n else:\n return LU, Piv\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu')\n helper = LayerHelper('lu', **locals())\n lu = helper.create_variable_for_type_inference(dtype=x.dtype)\n p = helper.create_variable_for_type_inference(dtype='int')\n info = helper.create_variable_for_type_inference(dtype='int')\n attrs = dict()\n attrs['pivots'] = pivot\n helper.append_op(\n type='lu',\n inputs={'X': x},\n outputs={'Out': lu,\n 'Pivots': p,\n 'Infos': info},\n attrs=attrs)\n if get_infos:\n return lu, p, info\n else:\n return lu, p\n\n\ndef lu_unpack(x, y, unpack_ludata=True, unpack_pivots=True, name=None):\n r\"\"\"\n Unpack L U and P to single matrix tensor . \n unpack L and U matrix from LU, unpack permutation matrix P from Pivtos .\n\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n\n\n Args:\n x (Tensor): The LU tensor get from paddle.linalg.lu, which is combined by L and U.\n\n y (Tensor): Pivots get from paddle.linalg.lu.\n\n unpack_ludata (bool,optional): whether to unpack L and U from x. Default: True.\n\n unpack_pivots (bool, optional): whether to unpack permutation matrix P from Pivtos. Default: True.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n P (Tensor): Permutation matrix P of lu factorization.\n\n L (Tensor): The lower triangular matrix tensor of lu factorization.\n\n U (Tensor): The upper triangular matrix tensor of lu factorization.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n\n if paddle.in_dynamic_mode():\n P, L, U = _C_ops.lu_unpack(x, y, 'unpack_ludata', unpack_ludata,\n 'unpack_pivots', unpack_pivots)\n return P, L, U\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu_unpack')\n helper = LayerHelper('lu_unpack', **locals())\n p = helper.create_variable_for_type_inference(dtype=x.dtype)\n l = helper.create_variable_for_type_inference(dtype=x.dtype)\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n attrs = dict()\n attrs['unpack_ludata'] = unpack_ludata\n attrs['unpack_pivots'] = unpack_pivots\n helper.append_op(\n type='lu_unpack',\n inputs={'X': x,\n 'Pivots': y},\n outputs={'Pmat': p,\n 'L': l,\n 'U': u},\n attrs=attrs)\n return p, l, u\n\n\ndef eig(x, name=None):\n \"\"\"\n This API performs the eigenvalue decomposition of a square matrix or a batch of square matrices.\n\n .. note::\n If the matrix is a Hermitian or a real symmetric matrix, please use :ref:`paddle.linalg.eigh` instead, which is much faster.\n If only eigenvalues is needed, please use :ref:`paddle.linalg.eigvals` instead.\n If the matrix is of any shape, please use :ref:`paddle.linalg.svd`.\n This API is only supported on CPU device.\n The output datatype is always complex for both real and complex input.\n\n Args:\n x (Tensor): A tensor with shape math:`[*, N, N]`, The data type of the x should be one of ``float32``,\n ``float64``, ``compplex64`` or ``complex128``.\n name (str, optional): The default value is `None`. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Eigenvalues(Tensors): A tensor with shape math:`[*, N]` refers to the eigen values.\n Eigenvectors(Tensors): A tensor with shape math:`[*, N, N]` refers to the eigen vectors.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n paddle.device.set_device(\"cpu\")\n\n x_data = np.array([[1.6707249, 7.2249975, 6.5045543],\n [9.956216, 8.749598, 6.066444 ],\n [4.4251957, 1.7983172, 0.370647 ]]).astype(\"float32\")\n x = paddle.to_tensor(x_data)\n w, v = paddle.linalg.eig(x)\n print(w)\n # Tensor(shape=[3, 3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [[(-0.5061363550800655+0j) , (-0.7971760990842826+0j) ,\n # (0.18518077798279986+0j)],\n # [(-0.8308237755993192+0j) , (0.3463813401919749+0j) ,\n # (-0.6837005269141947+0j) ],\n # [(-0.23142567697893396+0j), (0.4944999840400175+0j) ,\n # (0.7058765252952796+0j) ]])\n\n print(v)\n # Tensor(shape=[3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [ (16.50471283351188+0j) , (-5.5034820550763515+0j) ,\n # (-0.21026087843552282+0j)])\n \"\"\"\n if paddle.in_dynamic_mode():\n w, v = _C_ops.eig(x)\n return w, v\n\n check_variable_and_dtype(\n x, 'X', ['float32', 'float64', 'complex64', 'complex128'], 'eig')\n helper = LayerHelper('eig', **locals())\n\n w = helper.create_variable_for_type_inference(x.dtype)\n v = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {'X': x}\n outputs = {'Eigenvalues': w, 'Eigenvectors': v}\n helper.append_op(type='eig', inputs=inputs, outputs=outputs)\n\n return w, v\n\n\ndef eigvals(x, name=None):\n \"\"\"\n Compute the eigenvalues of one or more general matrices.\n\n Warning:\n The gradient kernel of this operator does not yet developed.\n If you need back propagation through this operator, please replace it with paddle.linalg.eig.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices whose eigenvalues will be computed.\n Its shape should be `[*, M, M]`, where `*` is zero or more batch dimensions.\n Its data type should be float32, float64, complex64, or complex128.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n Tensor: A tensor containing the unsorted eigenvalues which has the same batch dimensions with `x`.\n The eigenvalues are complex-valued even when `x` is real.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n paddle.seed(1234)\n\n x = paddle.rand(shape=[3, 3], dtype='float64')\n # [[0.02773777, 0.93004224, 0.06911496],\n # [0.24831591, 0.45733623, 0.07717843],\n # [0.48016702, 0.14235102, 0.42620817]])\n\n print(paddle.linalg.eigvals(x))\n # [(-0.27078833542132674+0j), (0.29962280156230725+0j), (0.8824477020120244+0j)] #complex128\n \"\"\"\n\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64',\n 'complex128'], 'eigvals')\n\n x_shape = list(x.shape)\n if len(x_shape) < 2:\n raise ValueError(\n \"The dimension of Input(x) should be at least 2, but received x's dimention = {}, x's shape = {}\".\n format(len(x_shape), x_shape))\n\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The last two dimensions of Input(x) should be equal, but received x's shape = {}\".\n format(x_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.eigvals(x)\n\n helper = LayerHelper('eigvals', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='eigvals', inputs={'X': x}, outputs={'Out': out})\n return out\n\n\ndef multi_dot(x, name=None):\n \"\"\"\n Multi_dot is an operator that calculates multiple matrix multiplications.\n\n Supports inputs of float16(only GPU support), float32 and float64 dtypes. This function does not\n support batched inputs.\n\n The input tensor in [x] must be 2-D except for the first and last can be 1-D.\n If the first tensor is a 1-D vector of shape(n, ) it is treated as row vector\n of shape(1, n), similarly if the last tensor is a 1D vector of shape(n, ), it\n is treated as a column vector of shape(n, 1).\n\n If the first and last tensor are 2-D matrix, then the output is also 2-D matrix,\n otherwise the output is a 1-D vector.\n\n Multi_dot will select the lowest cost multiplication order for calculation. The\n cost of multiplying two matrices with shapes (a, b) and (b, c) is a * b * c.\n Given matrices A, B, C with shapes (20, 5), (5, 100), (100, 10) respectively,\n we can calculate the cost of different multiplication orders as follows:\n - Cost((AB)C) = 20x5x100 + 20x100x10 = 30000\n - Cost(A(BC)) = 5x100x10 + 20x5x10 = 6000\n\n In this case, multiplying B and C first, then multiply A, which is 5 times faster\n than sequential calculation.\n\n Args:\n x ([Tensor]): The input tensors which is a list Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # A * B\n A_data = np.random.random([3, 4]).astype(np.float32)\n B_data = np.random.random([4, 5]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n out = paddle.linalg.multi_dot([A, B])\n print(out.numpy().shape)\n # [3, 5]\n\n # A * B * C\n A_data = np.random.random([10, 5]).astype(np.float32)\n B_data = np.random.random([5, 8]).astype(np.float32)\n C_data = np.random.random([8, 7]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n C = paddle.to_tensor(C_data)\n out = paddle.linalg.multi_dot([A, B, C])\n print(out.numpy().shape)\n # [10, 7]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.multi_dot(x)\n\n check_type(x, 'x', (list, tuple), 'multi_dot')\n for id, item in enumerate(x):\n check_variable_and_dtype(item, 'x[' + str(id) + ']',\n ['float16', 'float32', 'float64'], 'multi_dot')\n if item.dtype != x[0].dtype:\n raise TypeError(\n \"All the Tensors in the input must have the same data type.\")\n\n helper = LayerHelper('multi_dot', **locals())\n dtype = helper.input_dtype(input_param_name='x')\n out = helper.create_variable_for_type_inference(dtype)\n helper.append_op(type='multi_dot', inputs={\"X\": x}, outputs={\"Out\": out})\n return out\n\n\ndef eigh(x, UPLO='L', name=None):\n \"\"\"\n Compute the eigenvalues and eigenvectors of a\n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[*, N, N]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): (string, default 'L'), 'L' represents the lower triangular matrix,\n \"'U' represents the upper triangular matrix.\".\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n\n out_value(Tensor): A Tensor with shape [*, N] and data type of float32 and float64. The eigenvalues of eigh op.\n out_vector(Tensor): A Tensor with shape [*, N, N] and data type of float32,float64,complex64 and complex128. The eigenvectors of eigh op.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value, out_vector = paddle.linalg.eigh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n print(out_vector)\n #[(-0.9238795325112867+0j), (-0.3826834323650898+0j)],\n #[ 0.3826834323650898j , -0.9238795325112867j ]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.eigh(x, 'UPLO', UPLO)\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigh', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'eigh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO})\n return out_value, out_vector\n\n\ndef pinv(x, rcond=1e-15, hermitian=False, name=None):\n r\"\"\"\n Calculate pseudo inverse via SVD(singular value decomposition)\n of one matrix or batches of regular matrix.\n\n .. math::\n\n if hermitian == False:\n x = u * s * vt (SVD)\n out = v * 1/s * ut\n else:\n x = u * s * ut (eigh)\n out = u * 1/s * u.conj().transpose(-2,-1)\n\n If x is hermitian or symmetric matrix, svd will be replaced with eigh.\n\n Args:\n x(Tensor): The input tensor. Its shape should be (*, m, n)\n where * is zero or more batch dimensions. m and n can be\n arbitraty positive number. The data type of x should be\n float32 or float64 or complex64 or complex128. When data\n type is complex64 or cpmplex128, hermitian should be set\n True.\n\n rcond(Tensor, optional): the tolerance value to determine\n when is a singular value zero. Defalut:1e-15.\n\n hermitian(bool, optional): indicates whether x is Hermitian\n if complex or symmetric if real. Default: False.\n\n name(str|None): A name for this layer(optional). If set None,\n the layer will be named automatically.\n\n Returns:\n Tensor: The tensor with same data type with x. it represents\n pseudo inverse of x. Its shape should be (*, n, m).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.arange(15).reshape((3, 5)).astype('float64')\n input = paddle.to_tensor(x)\n out = paddle.linalg.pinv(input)\n print(input)\n print(out)\n\n # input:\n # [[0. , 1. , 2. , 3. , 4. ],\n # [5. , 6. , 7. , 8. , 9. ],\n # [10., 11., 12., 13., 14.]]\n\n # out:\n # [[-0.22666667, -0.06666667, 0.09333333],\n # [-0.12333333, -0.03333333, 0.05666667],\n # [-0.02000000, 0.00000000, 0.02000000],\n # [ 0.08333333, 0.03333333, -0.01666667],\n # [ 0.18666667, 0.06666667, -0.05333333]]\n\n # one can verify : x * out * x = x ;\n # or out * x * out = x ;\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if not hermitian:\n # combine svd and matmul op\n u, s, vt = _C_ops.svd(x, 'full_matrices', False)\n max_singular_val = _C_ops.reduce_max(s, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=x.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=x.dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v, _ = _C_ops.transpose2(vt, 'axis', perm)\n\n out_1 = v * st\n out_2 = _C_ops.matmul_v2(out_1, u, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n # combine eigh and matmul op\n s, u = _C_ops.eigh(x, 'UPLO', 'L')\n s_abs = paddle.abs(s)\n max_singular_val = _C_ops.reduce_max(s_abs, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=s.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=s.dtype)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n out_1 = u * st\n u_conj = _C_ops.conj(u)\n out_2 = _C_ops.matmul_v2(out_1, u_conj, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n if not hermitian:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'pinv')\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(dtype)\n vt = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vt,\n 'S': s},\n attrs={'full_matrices': False}, )\n\n max_singular_val = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=dtype)\n st_shape = helper.create_variable_for_type_inference(dtype=dtype)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v = helper.create_variable_for_type_inference(dtype)\n v_shape = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='transpose2',\n inputs={'X': [vt]},\n outputs={'Out': [v],\n 'XShape': [v_shape]},\n attrs={'axis': perm})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': v,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n else:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64',\n 'complex128'], 'pinv')\n\n if dtype == paddle.complex128:\n s_type = 'float64'\n elif dtype == paddle.complex64:\n s_type = 'float32'\n else:\n s_type = dtype\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': s,\n 'Eigenvectors': u},\n attrs={'UPLO': 'L'})\n s_abs = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='abs', inputs={'X': s}, outputs={'Out': s_abs})\n max_singular_val = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s_abs},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=s_type)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=s_type)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s_type)\n cond_not_int = layers.cast(layers.logical_not(condition), s_type)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=s_type)\n st_shape = helper.create_variable_for_type_inference(dtype=s_type)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': u,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n u_conj = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='conj', inputs={'X': u}, outputs={'Out': [u_conj]})\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u_conj},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n\n\ndef solve(x, y, name=None):\n r\"\"\"\n Computes the solution of a square system of linear equations with a unique solution for input 'X' and 'Y'.\n Let :math: `X` be a sqaure matrix or a batch of square matrices, :math:`Y` be\n a vector/matrix or a batch of vectors/matrices, the equation should be:\n\n .. math::\n Out = X^-1 * Y\n Specifically,\n - This system of linear equations has one solution if and only if input 'X' is invertible.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): A vector/matrix or a batch of vectors/matrices. Its shape should be `[*, M, K]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of a square system of linear equations with a unique solution for input 'x' and 'y'.\n Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # 2*X0 + X1 = 9\n # X0 + 2*X1 = 8\n\n import paddle\n import numpy as np\n\n np_x = np.array([[3, 1],[1, 2]])\n np_y = np.array([9, 8])\n x = paddle.to_tensor(np_x, dtype=\"float64\")\n y = paddle.to_tensor(np_y, dtype=\"float64\")\n out = paddle.linalg.solve(x, y)\n\n print(out)\n # [2., 3.])\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.solve(x, y)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type=\"solve\", inputs={\"X\": x,\n \"Y\": y}, outputs={\"Out\": out})\n return out\n\n\ndef triangular_solve(x,\n y,\n upper=True,\n transpose=False,\n unitriangular=False,\n name=None):\n r\"\"\"\n Computes the solution of a system of equations with a triangular coefficient matrix `x` and\n multiple right-hand sides `y` .\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input triangular coefficient matrix. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): Whether to solve the upper-triangular system of equations (default) or the lower-triangular \n system of equations. Default: True.\n transpose (bool, optional): whether `x` should be transposed before calculation. Default: False.\n unitriangular (bool, optional): whether `x` is unit triangular. If True, the diagonal elements of `x` are assumed \n to be 1 and not referenced from `x` . Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # x1 + x2 + x3 = 0\n # 2*x2 + x3 = -9\n # -x3 = 5\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n y = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.triangular_solve(x, y, upper=True)\n\n print(out)\n # [7, -2, -5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.triangular_solve(x, y, 'upper', upper, 'transpose',\n transpose, 'unitriangular',\n unitriangular)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"triangular_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'triangular_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'triangular_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='triangular_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={\n 'upper': upper,\n 'transpose': transpose,\n 'unitriangular': unitriangular\n })\n return out\n\n\ndef cholesky_solve(x, y, upper=False, name=None):\n r\"\"\"\n Solves a linear system of equations A @ X = B, given A's Cholesky factor matrix u and matrix B.\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input matrix which is upper or lower triangular Cholesky factor of square matrix A. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): whether to consider the Cholesky factor as a lower or upper triangular matrix. Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type is the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n u = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n b = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.cholesky_solve(b, u, upper=True)\n\n print(out)\n # [-2.5, -7, 9.5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky_solve(x, y, 'upper', upper)\n\n helper = LayerHelper(\"cholesky_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'cholesky_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'cholesky_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='cholesky_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef eigvalsh(x, UPLO='L', name=None):\n \"\"\"\n Computes the eigenvalues of a \n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[_, M, M]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): Lower triangular part of a (‘L’, default) or the upper triangular part (‘U’).\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor eigenvalues in ascending order.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value = paddle.eigvalsh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n \"\"\"\n if paddle.in_dynamic_mode():\n is_test = x.stop_gradient\n values, _ = _C_ops.eigvalsh(x, 'UPLO', UPLO, 'is_test', is_test)\n return values\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigvalsh', **locals())\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64', 'complex128'],\n 'eigvalsh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n is_test = x.stop_gradient\n helper.append_op(\n type='eigvalsh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO,\n 'is_test': is_test})\n return out_value\n\n\ndef lstsq(x, y, rcond=None, driver=None, name=None):\n \"\"\"\n Computes a solution to\n the least squares problem of a system of linear equations.\n\n Args:\n x (Tensor): A tensor with shape ``(*, M, N)`` , the data type of the input Tensor ``x``\n should be one of float32, float64.\n y (Tensor): A tensor with shape ``(*, M, K)`` , the data type of the input Tensor ``y`` \n should be one of float32, float64.\n rcond(float, optional): The default value is None. A float pointing number used to determine \n the effective rank of ``x``. If ``rcond`` is None, it will be set to max(M, N) times the \n machine precision of x_dtype.\n driver(str, optional): The default value is None. The name of LAPACK method to be used. For \n CPU inputs the valid values are ‘gels’, ‘gelsy’, ‘gelsd, ‘gelss’. For CUDA input, the only \n valid driver is ‘gels’. If ``driver`` is None, ‘gelsy’ is used for CPU inputs and ‘gels’ \n for CUDA inputs.\n name(str, optional): The default value is None. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple: A tuple of 4 Tensors which is (``solution``, ``residuals``, ``rank``, ``singular_values``). \n ``solution`` is a tensor with shape ``(*, N, K)``, meaning the least squares solution. ``residuals`` \n is a tensor with shape ``(*, K)``, meaning the squared residuals of the solutions, which is computed \n when M > N and every matrix in ``x`` is full-rank, otherwise return an empty tensor. ``rank`` is a tensor \n with shape ``(*)``, meaning the ranks of the matrices in ``x``, which is computed when ``driver`` in \n (‘gelsy’, ‘gelsd’, ‘gelss’), otherwise return an empty tensor. ``singular_values`` is a tensor with \n shape ``(*, min(M, N))``, meaning singular values of the matrices in ``x``, which is computed when \n ``driver`` in (‘gelsd’, ‘gelss’), otherwise return an empty tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n x = paddle.to_tensor([[1, 3], [3, 2], [5, 6.]])\n y = paddle.to_tensor([[3, 4, 6], [5, 3, 4], [1, 2, 1.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gelsd\")\n print(results[0])\n # [[ 0.78350395, -0.22165027, -0.62371236],\n # [-0.11340097, 0.78866047, 1.14948535]]\n print(results[1])\n # [19.81443405, 10.43814468, 30.56185532])\n print(results[2])\n # 2\n print(results[3])\n # [9.03455734, 1.54167950]\n\n x = paddle.to_tensor([[10, 2, 3], [3, 10, 5], [5, 6, 12.]])\n y = paddle.to_tensor([[4, 2, 9], [2, 0, 3], [2, 5, 3.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gels\")\n print(results[0])\n # [[ 0.39386186, 0.10230173, 0.93606132],\n # [ 0.10741687, -0.29028133, 0.11892585],\n # [-0.05115091, 0.51918161, -0.19948854]]\n print(results[1])\n # []\n \"\"\"\n device = paddle.get_device()\n if device == \"cpu\":\n if driver not in (None, \"gels\", \"gelss\", \"gelsd\", \"gelsy\"):\n raise ValueError(\n \"Only support valid driver is 'gels', 'gelss', 'gelsd', 'gelsy' or None for CPU inputs. But got {}\".\n format(driver))\n driver = \"gelsy\" if driver is None else driver\n elif \"gpu\" in device:\n if driver not in (None, \"gels\"):\n raise ValueError(\n \"Only support valid driver is 'gels' or None for CUDA inputs. But got {}\".\n format(driver))\n driver = \"gels\" if driver is None else driver\n else:\n raise RuntimeError(\"Only support lstsq api for CPU or CUDA device.\")\n\n if x.dtype == y.dtype and x.dtype in (paddle.float32, paddle.float64):\n pass\n else:\n raise ValueError(\n \"Only support x and y have the same dtype such as 'float32' and 'float64'.\"\n )\n\n if rcond is None:\n if x.dtype == paddle.float32:\n rcond = 1e-7 * max(x.shape[-2], x.shape[-1])\n elif x.dtype == paddle.float64:\n rcond = 1e-15 * max(x.shape[-2], x.shape[-1])\n\n if paddle.in_dynamic_mode():\n solution, rank, singular_values = _C_ops.lstsq(x, y, \"rcond\", rcond,\n \"driver\", driver)\n if x.shape[-2] > x.shape[-1]:\n matmul_out = _varbase_creator(dtype=x.dtype)\n _C_ops.matmul(x, solution, matmul_out, 'trans_x', False, 'trans_y',\n False)\n minus_out = _C_ops.elementwise_sub(matmul_out, y)\n pow_out = _C_ops.pow(minus_out, 'factor', 2)\n residuals = _C_ops.reduce_sum(pow_out, 'dim', [-2], 'keepdim',\n False, 'reduce_all', False)\n else:\n residuals = paddle.empty(shape=[0], dtype=x.dtype)\n\n if driver == \"gels\":\n rank = paddle.empty(shape=[0], dtype=paddle.int32)\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n elif driver == \"gelsy\":\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n\n return solution, residuals, rank, singular_values\n\n helper = LayerHelper('lstsq', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n check_variable_and_dtype(\n y, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n\n solution = helper.create_variable_for_type_inference(dtype=x.dtype)\n residuals = helper.create_variable_for_type_inference(dtype=x.dtype)\n rank = helper.create_variable_for_type_inference(dtype=paddle.int32)\n singular_values = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='lstsq',\n inputs={'X': x,\n 'Y': y},\n outputs={\n 'Solution': solution,\n 'Rank': rank,\n 'SingularValues': singular_values\n },\n attrs={'rcond': rcond,\n 'driver': driver})\n\n matmul_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n minus_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n pow_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': solution},\n outputs={'Out': matmul_out},\n attrs={\n 'trans_x': False,\n 'trans_y': False,\n })\n\n helper.append_op(\n type='elementwise_sub',\n inputs={'X': matmul_out,\n 'Y': y},\n outputs={'Out': minus_out})\n\n helper.append_op(\n type='pow',\n inputs={'X': minus_out},\n outputs={'Out': pow_out},\n attrs={'factor': 2})\n\n helper.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': residuals},\n attrs={'dim': [-2],\n 'keep_dim': False,\n 'reduce_all': False})\n\n if driver == \"gels\":\n rank = paddle.static.data(name='rank', shape=[0])\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n elif driver == \"gelsy\":\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n\n return solution, residuals, rank, singular_values"},"implementation":{"kind":"string","value":" def frobenius_norm(input, dim=None, keepdim=False, name=None):\n \"\"\"\n The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n dim (list, optional): None for last two dimensions.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if dim is not None and not (isinstance(dim, list) and len(dim) == 2):\n raise ValueError(\n \"The dim of frobenius norm op should be None or two elements list!\"\n )\n if paddle.in_dynamic_mode():\n if dim is None:\n return _C_ops.frobenius_norm(input, 'keep_dim', keepdim,\n 'reduce_all', True)\n return _C_ops.frobenius_norm(input, 'dim', dim, 'keep_dim', keepdim,\n 'reduce_all', False)\n attrs = {'dim': dim, 'keep_dim': keepdim, 'reduce_all': False}\n if dim is None:\n attrs['reduce_all'] = True\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'frobenius_norm')\n\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='frobenius_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out"},"start_line":{"kind":"number","value":242,"string":"242"},"end_line":{"kind":"number","value":275,"string":"275"},"file_content":{"kind":"string","value":"# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numpy as np\nfrom ..fluid.layer_helper import LayerHelper\nfrom ..framework import _varbase_creator, _dygraph_tracer\nfrom ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype\nfrom ..static import Variable\nfrom ..fluid.framework import _in_legacy_dygraph, in_dygraph_mode\nfrom ..fluid.layers import transpose, cast # noqa: F401\nfrom ..fluid import layers\nimport paddle\nfrom paddle.common_ops_import import core\nfrom paddle.common_ops_import import VarDesc\nfrom paddle import _C_ops\n\n__all__ = []\n\n\ndef matmul(x, y, transpose_x=False, transpose_y=False, name=None):\n \"\"\"\n Applies matrix multiplication to two tensors. `matmul` follows\n the complete broadcast rules,\n and its behavior is consistent with `np.matmul`.\n\n Currently, the input tensors' number of dimensions can be any, `matmul` can be used to\n achieve the `dot`, `matmul` and `batchmatmul`.\n\n The actual behavior depends on the shapes of :math:`x`, :math:`y` and the\n flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:\n\n - If a transpose flag is specified, the last two dimensions of the tensor\n are transposed. If the tensor is ndim-1 of shape, the transpose is invalid. If the tensor\n is ndim-1 of shape :math:`[D]`, then for :math:`x` it is treated as :math:`[1, D]`, whereas\n for :math:`y` it is the opposite: It is treated as :math:`[D, 1]`.\n\n The multiplication behavior depends on the dimensions of `x` and `y`. Specifically:\n\n - If both tensors are 1-dimensional, the dot product result is obtained.\n\n - If both tensors are 2-dimensional, the matrix-matrix product is obtained.\n\n - If the `x` is 1-dimensional and the `y` is 2-dimensional,\n a `1` is prepended to its dimension in order to conduct the matrix multiply.\n After the matrix multiply, the prepended dimension is removed.\n\n - If the `x` is 2-dimensional and `y` is 1-dimensional,\n the matrix-vector product is obtained.\n\n - If both arguments are at least 1-dimensional and at least one argument\n is N-dimensional (where N > 2), then a batched matrix multiply is obtained.\n If the first argument is 1-dimensional, a 1 is prepended to its dimension\n in order to conduct the batched matrix multiply and removed after.\n If the second argument is 1-dimensional, a 1 is appended to its\n dimension for the purpose of the batched matrix multiple and removed after.\n The non-matrix (exclude the last two dimensions) dimensions are\n broadcasted according the broadcast rule.\n For example, if input is a (j, 1, n, m) tensor and the other is a (k, m, p) tensor,\n out will be a (j, k, n, p) tensor.\n\n Args:\n x (Tensor): The input tensor which is a Tensor.\n y (Tensor): The input tensor which is a Tensor.\n transpose_x (bool): Whether to transpose :math:`x` before multiplication.\n transpose_y (bool): Whether to transpose :math:`y` before multiplication.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # vector * vector\n x_data = np.random.random([10]).astype(np.float32)\n y_data = np.random.random([10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [1]\n\n # matrix * vector\n x_data = np.random.random([10, 5]).astype(np.float32)\n y_data = np.random.random([5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10]\n\n # batched matrix * broadcasted vector\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([2]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5]\n\n # batched matrix * batched matrix\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([10, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5, 5]\n\n # batched matrix * broadcasted matrix\n x_data = np.random.random([10, 1, 5, 2]).astype(np.float32)\n y_data = np.random.random([1, 3, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 3, 5, 5]\n\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_matmul(x, y, transpose_x, transpose_y)\n\n if _in_legacy_dygraph():\n op_type = 'matmul_v2'\n op = getattr(_C_ops, op_type)\n return op(x, y, 'trans_x', transpose_x, 'trans_y', transpose_y)\n\n attrs = {\n 'trans_x': transpose_x,\n 'trans_y': transpose_y,\n }\n\n def __check_input(x, y):\n var_names = {'x': x, 'y': y}\n for name, val in var_names.items():\n check_variable_and_dtype(\n val, name,\n ['float16', 'float32', 'float64', 'complex64', 'complex128'],\n 'matmul')\n\n __check_input(x, y)\n\n helper = LayerHelper('matmul_v2', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef norm(x, p='fro', axis=None, keepdim=False, name=None):\n \"\"\"\n\n Returns the matrix norm (Frobenius) or vector norm (the 1-norm, the Euclidean\n or 2-norm, and in general the p-norm for p > 0) of a given tensor.\n\n .. note::\n This norm API is different from `numpy.linalg.norm`.\n This api supports high-order input tensors (rank >= 3), and certain axis need to be pointed out to calculate the norm.\n But `numpy.linalg.norm` only supports 1-D vector or 2-D matrix as input tensor.\n For p-order matrix norm, this api actually treats matrix as a flattened vector to calculate the vector norm, NOT REAL MATRIX NORM.\n\n Args:\n x (Tensor): The input tensor could be N-D tensor, and the input data\n type could be float32 or float64.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `0`, `1`, `2`,\n `inf`, `-inf` and any positive real number yielding the corresponding p-norm. Not supported: ord < 0 and nuclear norm.\n Default value is `fro`.\n axis (int|list|tuple, optional): The axis on which to apply norm operation. If axis is int\n or list(int)/tuple(int) with only one element, the vector norm is computed over the axis.\n If `axis < 0`, the dimension to norm operation is rank(input) + axis.\n If axis is a list(int)/tuple(int) with two elements, the matrix norm is computed over the axis.\n Defalut value is `None`.\n keepdim (bool, optional): Whether to reserve the reduced dimension in the\n output Tensor. The result tensor will have fewer dimension\n than the :attr:`input` unless :attr:`keepdim` is true, default\n value is False.\n name (str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: results of norm operation on the specified axis of input tensor,\n it's data type is the same as input's Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n shape=[2, 3, 4]\n np_input = np.arange(24).astype('float32') - 12\n np_input = np_input.reshape(shape)\n x = paddle.to_tensor(np_input)\n #[[[-12. -11. -10. -9.] [ -8. -7. -6. -5.] [ -4. -3. -2. -1.]]\n # [[ 0. 1. 2. 3.] [ 4. 5. 6. 7.] [ 8. 9. 10. 11.]]]\n\n # compute frobenius norm along last two dimensions.\n out_fro = paddle.linalg.norm(x, p='fro', axis=[0,1])\n # out_fro.numpy() [17.435596 16.911535 16.7332 16.911535]\n\n # compute 2-order vector norm along last dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=-1)\n #out_pnorm.numpy(): [[21.118711 13.190906 5.477226]\n # [ 3.7416575 11.224972 19.131126]]\n\n # compute 2-order norm along [0,1] dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=[0,1])\n #out_pnorm.numpy(): [17.435596 16.911535 16.7332 16.911535]\n\n # compute inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=np.inf)\n #out_pnorm.numpy() = [12.]\n out_pnorm = paddle.linalg.norm(x, p=np.inf, axis=0)\n #out_pnorm.numpy(): [[12. 11. 10. 9.] [8. 7. 6. 7.] [8. 9. 10. 11.]]\n\n # compute -inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=-np.inf)\n #out_pnorm.numpy(): [0.]\n out_pnorm = paddle.linalg.norm(x, p=-np.inf, axis=0)\n #out_pnorm.numpy(): [[0. 1. 2. 3.] [4. 5. 6. 5.] [4. 3. 2. 1.]]\n \"\"\"\n\n def frobenius_norm(input, dim=None, keepdim=False, name=None):\n \"\"\"\n The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n dim (list, optional): None for last two dimensions.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if dim is not None and not (isinstance(dim, list) and len(dim) == 2):\n raise ValueError(\n \"The dim of frobenius norm op should be None or two elements list!\"\n )\n if paddle.in_dynamic_mode():\n if dim is None:\n return _C_ops.frobenius_norm(input, 'keep_dim', keepdim,\n 'reduce_all', True)\n return _C_ops.frobenius_norm(input, 'dim', dim, 'keep_dim', keepdim,\n 'reduce_all', False)\n attrs = {'dim': dim, 'keep_dim': keepdim, 'reduce_all': False}\n if dim is None:\n attrs['reduce_all'] = True\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'frobenius_norm')\n\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='frobenius_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def vector_norm(input,\n porder=None,\n axis=None,\n keepdim=False,\n asvector=False,\n name=None):\n \"\"\"\n Calculate the p-order vector norm for certain dimension of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n porder (float, optional): None for porder=2.0.\n axis (int, optional): None for last dimension.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if paddle.in_dynamic_mode():\n if axis is None: axis = -1\n return _C_ops.p_norm(input, 'porder', porder, 'axis', axis,\n 'keepdim', keepdim, 'asvector', asvector)\n if porder is not None:\n check_type(porder, 'porder', (float, int), 'p_norm')\n if axis is not None:\n check_type(axis, 'axis', (int), 'p_norm')\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'p_norm')\n\n attrs = {\n 'axis': axis if axis is not None else -1,\n 'porder': float(porder) if porder is not None else 2.0,\n 'keepdim': keepdim,\n 'asvector': asvector,\n 'epsilon': 1e-12,\n }\n helper = LayerHelper('p_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='p_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def inf_norm(input,\n porder=None,\n axis=axis,\n keepdim=False,\n asvector=False,\n name=None):\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n helper.append_op(type='abs', inputs={'X': input}, outputs={'Out': out})\n reduce_out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n reduce_all = True if axis == None or axis == [] or asvector == True else False\n axis = axis if axis != None and axis != [] else [0]\n\n reduce_type = 'reduce_max' if porder == np.float(\n 'inf') else 'reduce_min'\n helper.append_op(\n type=reduce_type,\n inputs={'X': out},\n outputs={'Out': reduce_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n\n return reduce_out\n\n def p_matrix_norm(input, porder=1., axis=axis, keepdim=False, name=None):\n \"\"\"\n NOTE:\n This function actually treats the matrix as flattened vector to calculate vector norm instead of matrix norm.\n \"\"\"\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n\n block.append_op(\n type='pow',\n inputs={'X': abs_out},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': True if axis is None else False\n })\n porder\n block.append_op(\n type='pow',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n if axis is None and p is not None:\n if isinstance(p, str):\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n porder=p,\n axis=axis,\n keepdim=keepdim,\n asvector=True,\n name=name)\n else:\n raise ValueError(\"only valid p type is string or float, found {}\".\n format(type(p)))\n\n if isinstance(axis, tuple):\n axis = list(axis)\n if isinstance(axis, list) and len(axis) == 1:\n axis = axis[0]\n\n #calculate vector norm, where axis is int or list with only one integer\n if isinstance(axis, int):\n if isinstance(p, str):\n if p == \"fro\":\n return vector_norm(\n x,\n porder=2,\n axis=axis,\n keepdim=keepdim,\n asvector=False,\n name=name)\n\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n axis=axis,\n porder=p,\n keepdim=keepdim,\n asvector=False,\n name=name)\n else:\n raise ValueError(\n \"unspport p for p-order vector norm. except float, found {}\".\n format(p))\n #calculate matrix norm, where axis is list with two integers\n elif isinstance(axis, list) and len(axis) == 2:\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n elif p == np.inf or p == -np.inf:\n return inf_norm(x, porder=p, axis=axis, keepdim=keepdim, name=name)\n elif p == 0:\n raise ValueError(\n \"just suport axis type int or list (length of list <=1) if p = 0, found {}\".\n format(axis))\n else:\n return p_matrix_norm(\n x, porder=p, axis=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"except axis type int or list (length of list <=2), found {}\".\n format(axis))\n\n\ndef dist(x, y, p=2, name=None):\n r\"\"\"\n\n This OP returns the p-norm of (x - y). It is not a norm in a strict sense, only as a measure\n of distance. The shapes of x and y must be broadcastable. The definition is as follows, for\n details, please refer to the `numpy's broadcasting <https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html>`_:\n\n - Each input has at least one dimension.\n - Match the two input dimensions from back to front, the dimension sizes must either be equal, one of them is 1, or one of them does not exist.\n\n Where, z = x - y, the shapes of x and y are broadcastable, then the shape of z can be\n obtained as follows:\n\n 1. If the number of dimensions of x and y are not equal, prepend 1 to the dimensions of the\n tensor with fewer dimensions.\n\n For example, The shape of x is [8, 1, 6, 1], the shape of y is [7, 1, 5], prepend 1 to the\n dimension of y.\n\n x (4-D Tensor): 8 x 1 x 6 x 1\n\n y (4-D Tensor): 1 x 7 x 1 x 5\n\n 2. Determine the size of each dimension of the output z: choose the maximum value from the\n two input dimensions.\n\n z (4-D Tensor): 8 x 7 x 6 x 5\n\n If the number of dimensions of the two inputs are the same, the size of the output can be\n directly determined in step 2. When p takes different values, the norm formula is as follows:\n\n When p = 0, defining $0^0=0$, the zero-norm of z is simply the number of non-zero elements of z.\n\n .. math::\n\n ||z||_{0}=\\lim_{p \\\\rightarrow 0}\\sum_{i=1}^{m}|z_i|^{p}\n\n When p = inf, the inf-norm of z is the maximum element of z.\n\n .. math::\n\n ||z||_\\infty=\\max_i |z_i|\n\n When p = -inf, the negative-inf-norm of z is the minimum element of z.\n\n .. math::\n\n ||z||_{-\\infty}=\\min_i |z_i|\n\n Otherwise, the p-norm of z follows the formula,\n\n .. math::\n\n ||z||_{p}=(\\sum_{i=1}^{m}|z_i|^p)^{\\\\frac{1}{p}}\n\n Args:\n x (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n y (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n p (float, optional): The norm to be computed, its data type is float32 or float64. Default: 2.\n\n Returns:\n Tensor: Tensor that is the p-norm of (x - y).\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor(np.array([[3, 3],[3, 3]]), \"float32\")\n y = paddle.to_tensor(np.array([[3, 3],[3, 1]]), \"float32\")\n out = paddle.dist(x, y, 0)\n print(out) # out = [1.]\n\n out = paddle.dist(x, y, 2)\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"inf\"))\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"-inf\"))\n print(out) # out = [0.]\n \"\"\"\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'dist')\n check_variable_and_dtype(y, 'dtype', ['float32', 'float64'], 'dist')\n check_type(p, 'p', (float, int), 'dist')\n helper = LayerHelper(\"dist\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n outputs = {'Out': [out]}\n attrs = {\"p\": float(p)}\n helper.append_op(\n type='dist', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef cond(x, p=None, name=None):\n \"\"\"\n\n Computes the condition number of a matrix or batches of matrices with respect to a matrix norm ``p``.\n\n Args:\n x (Tensor): The input tensor could be tensor of shape ``(*, m, n)`` where ``*`` is zero or more batch dimensions\n for ``p`` in ``(2, -2)``, or of shape ``(*, n, n)`` where every matrix is invertible for any supported ``p``.\n And the input data type could be ``float32`` or ``float64``.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `nuc`, `1`, `-1`, `2`, `-2`,\n `inf`, `-inf`. Default value is `None`, meaning that the order of the norm is `2`.\n name (str, optional): The default value is `None`. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: computing results of condition number, its data type is the same as input Tensor ``x``.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1., 0, -1], [0, 1, 0], [1, 0, 1]])\n\n # compute conditional number when p is None\n out = paddle.linalg.cond(x)\n # out.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is 'fro'\n out_fro = paddle.linalg.cond(x, p='fro')\n # out_fro.numpy() [3.1622777]\n\n # compute conditional number when order of the norm is 'nuc'\n out_nuc = paddle.linalg.cond(x, p='nuc')\n # out_nuc.numpy() [9.2426405]\n\n # compute conditional number when order of the norm is 1\n out_1 = paddle.linalg.cond(x, p=1)\n # out_1.numpy() [2.]\n\n # compute conditional number when order of the norm is -1\n out_minus_1 = paddle.linalg.cond(x, p=-1)\n # out_minus_1.numpy() [1.]\n\n # compute conditional number when order of the norm is 2\n out_2 = paddle.linalg.cond(x, p=2)\n # out_2.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is -1\n out_minus_2 = paddle.linalg.cond(x, p=-2)\n # out_minus_2.numpy() [0.70710677]\n\n # compute conditional number when order of the norm is inf\n out_inf = paddle.linalg.cond(x, p=np.inf)\n # out_inf.numpy() [2.]\n\n # compute conditional number when order of the norm is -inf\n out_minus_inf = paddle.linalg.cond(x, p=-np.inf)\n # out_minus_inf.numpy() [1.]\n\n a = paddle.to_tensor(np.random.randn(2, 4, 4).astype('float32'))\n # a.numpy()\n # [[[ 0.14063153 -0.996288 0.7996131 -0.02571543]\n # [-0.16303636 1.5534962 -0.49919784 -0.04402903]\n # [-1.1341571 -0.6022629 0.5445269 0.29154757]\n # [-0.16816919 -0.30972657 1.7521842 -0.5402487 ]]\n # [[-0.58081484 0.12402827 0.7229862 -0.55046535]\n # [-0.15178485 -1.1604939 0.75810957 0.30971205]\n # [-0.9669573 1.0940945 -0.27363303 -0.35416734]\n # [-1.216529 2.0018666 -0.7773689 -0.17556527]]]\n a_cond_fro = paddle.linalg.cond(a, p='fro')\n # a_cond_fro.numpy() [31.572273 28.120834]\n\n b = paddle.to_tensor(np.random.randn(2, 3, 4).astype('float64'))\n # b.numpy()\n # [[[ 1.61707487 0.46829144 0.38130416 0.82546736]\n # [-1.72710298 0.08866375 -0.62518804 0.16128892]\n # [-0.02822879 -1.67764516 0.11141444 0.3220113 ]]\n # [[ 0.22524372 0.62474921 -0.85503233 -1.03960523]\n # [-0.76620689 0.56673047 0.85064753 -0.45158196]\n # [ 1.47595418 2.23646462 1.5701758 0.10497519]]]\n b_cond_2 = paddle.linalg.cond(b, p=2)\n # b_cond_2.numpy() [3.30064451 2.51976252]\n\n \"\"\"\n\n def mat_norm(input, porder=1., axis=None):\n \"\"\"\n NOTE:\n Calculate the matrix norm of a square matrix or batches of square matrices,\n when porder is in (1, -1, inf, -inf)\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n axis = axis if axis != None and axis != [] else [0]\n keepdim = False\n\n if paddle.in_dynamic_mode():\n abs_out = _C_ops.abs(input)\n sum_out = _C_ops.reduce_sum(abs_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == 1 or porder == np.inf:\n return _C_ops.reduce_max(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == -1 or porder == -np.inf:\n return _C_ops.reduce_min(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n\n block = LayerHelper('norm', **locals())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n block.append_op(\n type='reduce_sum',\n inputs={'X': abs_out},\n outputs={'Out': sum_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 1 or porder == np.inf:\n block.append_op(\n type='reduce_max',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n if porder == -1 or porder == -np.inf:\n block.append_op(\n type='reduce_min',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n\n def fro_norm(input, porder=2, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the frobenius norm of a square matrix or batches of square matrices.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n if paddle.in_dynamic_mode():\n pow_out = _C_ops.pow(input, 'factor', porder)\n sum_out_1 = _C_ops.reduce_sum(pow_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n sum_out_2 = _C_ops.reduce_sum(sum_out_1, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n return _C_ops.pow(sum_out_2, 'factor', float(1. / porder))\n\n block = LayerHelper('norm', **locals())\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_1 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_2 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='pow',\n inputs={'X': input},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out_1},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_sum',\n inputs={'X': sum_out_1},\n outputs={'Out': sum_out_2},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='pow',\n inputs={'X': sum_out_2},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n def svd_norm(input, porder, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the matrix norm, which is related to singular values, of a matrix\n or batches of matrices, including nuclear norm, 2-norm and (-2)-norm.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n u, s, vh = svd(input, full_matrices=False)\n\n if paddle.in_dynamic_mode():\n if porder == \"nuc\":\n return _C_ops.reduce_sum(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n max_out = _C_ops.reduce_max(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n min_out = _C_ops.reduce_min(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n if porder == 2:\n return _C_ops.elementwise_div(max_out, min_out, 'aixs', axis,\n 'use_mkldnn', False)\n if porder == -2:\n return _C_ops.elementwise_div(min_out, max_out, 'aixs', axis,\n 'use_mkldnn', False)\n\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n if porder == \"nuc\":\n block.append_op(\n type='reduce_sum',\n inputs={'X': s},\n outputs={'Out': out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n max_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n min_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_min',\n inputs={'X': s},\n outputs={'Out': min_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': max_out,\n 'Y': min_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n if porder == -2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': min_out,\n 'Y': max_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n\n def empty_tensor(input, shape):\n if paddle.in_dynamic_mode():\n return input.reshape(shape)\n raise ValueError(\"only support x is nonempty tensor in static mode\")\n\n x_shape = list(x.shape)\n if not len(x_shape) >= 2:\n raise ValueError(\"input should be a matrix or batches of matrices, \" +\n \"but the dimention of received input is {}\".format(\n len(x_shape)))\n if p == None:\n p = 2\n x_size = 0 if (0 in x_shape) else 1\n if p in (\"fro\", \"nuc\", 1, -1, np.inf, -np.inf):\n if x_shape[len(x_shape) - 1] == x_shape[len(x_shape) - 2]:\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n x_inv = x.inverse()\n if p == \"fro\":\n return fro_norm(x) * fro_norm(x_inv)\n if p == \"nuc\":\n return svd_norm(x, p) * svd_norm(x_inv, p)\n if p in (1, -1):\n return mat_norm(\n x, porder=p, axis=[-2]) * mat_norm(\n x_inv, porder=p, axis=[-2])\n if p in (np.inf, -np.inf):\n return mat_norm(\n x, porder=p, axis=[-1]) * mat_norm(\n x_inv, porder=p, axis=[-1])\n else:\n raise ValueError(\"only support p is {} when input is a \".format(p) +\n \"square matrix or batches of square matrices\")\n elif p in (2, -2):\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n return svd_norm(x, porder=p)\n else:\n raise ValueError(\n \"unsupported {} for p, only supporting ('fro', 'nuc', \".format(\n p) + \"1, -1, 2, -2, inf, -inf) or none\")\n\n\ndef dot(x, y, name=None):\n \"\"\"\n This operator calculates inner product for vectors.\n\n .. note::\n Support 1-d and 2-d Tensor. When it is 2d, the first dimension of this matrix\n is the batch dimension, which means that the vectors of multiple batches are dotted.\n\n Parameters:\n x(Tensor): 1-D or 2-D ``Tensor``. Its dtype should be ``float32``, ``float64``, ``int32``, ``int64``\n y(Tensor): 1-D or 2-D ``Tensor``. Its dtype soulde be ``float32``, ``float64``, ``int32``, ``int64``\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: the calculated result Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x_data = np.random.uniform(0.1, 1, [10]).astype(np.float32)\n y_data = np.random.uniform(1, 3, [10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.dot(x, y)\n print(z)\n\n \"\"\"\n op_type = 'dot'\n # skip var type check in dygraph mode to improve efficiency\n if paddle.in_dynamic_mode():\n op = getattr(_C_ops, op_type)\n return op(x, y)\n\n assert x is not None, 'x cannot be None in {}'.format(op_type)\n assert y is not None, 'y cannot be None in {}'.format(op_type)\n\n check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n check_variable_and_dtype(y, 'y', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n\n helper = LayerHelper(op_type, **locals())\n if name is None:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n else:\n out = helper.create_variable(\n name=name, dtype=x.dtype, persistable=False)\n helper.append_op(\n type=\"dot\", inputs={'X': x,\n 'Y': y}, attrs={}, outputs={\"Out\": out})\n return out\n\n\ndef cov(x, rowvar=True, ddof=True, fweights=None, aweights=None, name=None):\n \"\"\"\n Estimate the covariance matrix of the input variables, given data and weights.\n\n A covariance matrix is a square matrix, indicate the covariance of each pair variables in the input matrix.\n For example, for an N-dimensional samples X=[x1,x2,…xN]T, then the covariance matrix \n element Cij is the covariance of xi and xj. The element Cii is the variance of xi itself.\n\n Parameters:\n x(Tensor): A N-D(N<=2) Tensor containing multiple variables and observations. By default, each row of x represents a variable. Also see rowvar below.\n rowvar(Bool, optional): If rowvar is True (default), then each row represents a variable, with observations in the columns. Default: True\n ddof(Bool, optional): If ddof=True will return the unbiased estimate, and ddof=False will return the simple average. Default: True\n fweights(Tensor, optional): 1-D Tensor of integer frequency weights; The number of times each observation vector should be repeated. Default: None\n aweights(Tensor, optional): 1-D Tensor of observation vector weights. How important of the observation vector, larger data means this element is more important. Default: None\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: The covariance matrix Tensor of the variables.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n\n xt = paddle.rand((3,4))\n paddle.linalg.cov(xt)\n\n '''\n Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n [[0.07918842, 0.06127326, 0.01493049],\n [0.06127326, 0.06166256, 0.00302668],\n [0.01493049, 0.00302668, 0.01632146]])\n '''\n \"\"\"\n op_type = 'cov'\n if len(x.shape) > 2 or len(x.shape) < 1:\n raise ValueError(\n \"Input(x) only support N-D (1<=N<=2) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cov')\n nx = x\n if len(x.shape) == 1:\n nx = x.reshape((1, -1))\n if not rowvar and nx.shape[0] != 1:\n nx = nx.t()\n w = None\n observation_num = nx.shape[1]\n if fweights is not None:\n w = fweights.astype(nx.dtype)\n if len(w.shape) > 1:\n raise ValueError(\n \"Input(fweights) only support N-D (N<=1) tensor in cov, but received \"\n \"shape of Input(input) is %s.\" % len(fweights.shape))\n if fweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(fweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(fweights) is {}.\".format(observation_num,\n fweights.shape[0]))\n if fweights.min() < 0:\n raise ValueError(\n \"The value of Input(fweights) cannot be negtive, but received \"\n \"min of Input(fweights) is {}.\".format(fweights.min()))\n if not paddle.all(fweights == paddle.round(fweights.astype('float64'))):\n raise ValueError(\"Input(fweights) must be integer \")\n\n if aweights is not None:\n aw = aweights.astype(nx.dtype)\n if len(aw.shape) > 1:\n raise ValueError(\n \"Input(aweights) only support N-D (N<=1) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(aweights.shape))\n check_variable_and_dtype(aweights, 'dtype', ['float32', 'float64'],\n 'cov')\n if aweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(aweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(aweights) is {}.\".format(observation_num,\n aweights.shape[0]))\n if aweights.min() < 0:\n raise ValueError(\n \"The value of Input(aweights) cannot be negtive, but received \"\n \"min of Input(aweights) is {}.\".format(aweights.min()))\n if w is not None:\n w = w * aw\n else:\n w = aw\n\n w_sum = paddle.to_tensor(observation_num, dtype=nx.dtype)\n if fweights is not None or aweights is not None:\n w_sum = w.sum()\n if w_sum.item() == 0:\n raise ValueError(\"The sum of weights is zero, can't be normalized.\")\n\n if w is not None:\n nx_w = nx * w\n avg = (nx_w).sum(axis=1) / w_sum\n else:\n avg = nx.sum(axis=1) / w_sum\n nx_w = nx\n\n if w is not None and aweights is not None and ddof == True:\n norm_factor = w_sum - (w * aweights).sum() / w_sum\n else:\n norm_factor = w_sum - ddof\n if norm_factor <= 0:\n norm_factor = paddle.to_tensor(0, dtype=nx.dtype)\n nx = nx - avg.unsqueeze(1)\n xxt = paddle.mm(nx, nx_w.t().conj())\n cov = paddle.divide(xxt, norm_factor).squeeze()\n return cov\n\n\ndef t(input, name=None):\n \"\"\"\n Transpose <=2-D tensor.\n 0-D and 1-D tensors are returned as it is and 2-D tensor is equal to\n the paddle.transpose function which perm dimensions set 0 and 1.\n\n Args:\n input (Tensor): The input Tensor. It is a N-D (N<=2) Tensor of data types float16, float32, float64, int32.\n name(str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`\n Returns:\n Tensor: A transposed n-D Tensor, with data type being float16, float32, float64, int32, int64.\n\n For Example:\n\n .. code-block:: text\n\n # Example 1 (0-D tensor)\n x = tensor([0.79])\n paddle.t(x) = tensor([0.79])\n\n # Example 2 (1-D tensor)\n x = tensor([0.79, 0.84, 0.32])\n paddle.t(x) = tensor([0.79, 0.84, 0.32])\n\n # Example 3 (2-D tensor)\n x = tensor([0.79, 0.84, 0.32],\n [0.64, 0.14, 0.57])\n paddle.t(x) = tensor([0.79, 0.64],\n [0.84, 0.14],\n [0.32, 0.57])\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n x = paddle.ones(shape=[2, 3], dtype='int32')\n x_transposed = paddle.t(x)\n print(x_transposed.shape)\n # [3, 2]\n \"\"\"\n if len(input.shape) > 2:\n raise ValueError(\n \"Input(input) only support N-D (N<=2) tensor, but received \"\n \"length of Input(input) is %s. Perhaps you can use paddle.\"\n \"tensor.transpose() instead.\" % len(input.shape))\n if paddle.in_dynamic_mode():\n if len(input.shape) == 1:\n return input\n # 2-D tensor\n perm = [1, 0]\n out, _ = _C_ops.transpose2(input, 'axis', perm)\n return out\n\n check_variable_and_dtype(\n input, 'input', ['float16', 'float32', 'float64', 'int32',\n 'int64'], 'transpose')\n\n helper = LayerHelper('t', **locals())\n out = helper.create_variable_for_type_inference(input.dtype)\n input_shape = helper.create_variable_for_type_inference(input.dtype)\n if len(input.shape) == 1:\n out = input\n else:\n helper.append_op(\n type='transpose2',\n inputs={'X': [input]},\n outputs={'Out': [out],\n 'XShape': [input_shape]},\n attrs={'axis': [1, 0]})\n return out\n\n\ndef cross(x, y, axis=None, name=None):\n \"\"\"\n Computes the cross product between two tensors along an axis.\n\n Inputs must have the same shape, and the length of their axes should be equal to 3.\n If `axis` is not given, it defaults to the first axis found with the length 3.\n\n Args:\n x (Tensor): The first input tensor.\n y (Tensor): The second input tensor.\n axis (int, optional): The axis along which to compute the cross product. It defaults to the first axis found with the length 3.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor. A Tensor with same data type as `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0],\n [3.0, 3.0, 3.0]])\n y = paddle.to_tensor([[1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0]])\n\n z1 = paddle.cross(x, y)\n # [[-1. -1. -1.]\n # [ 2. 2. 2.]\n # [-1. -1. -1.]]\n\n z2 = paddle.cross(x, y, axis=1)\n # [[0. 0. 0.]\n # [0. 0. 0.]\n # [0. 0. 0.]]\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_cross(x, y, axis)\n else:\n if _in_legacy_dygraph():\n if axis is not None:\n return _C_ops.cross(x, y, 'dim', axis)\n else:\n return _C_ops.cross(x, y)\n else:\n helper = LayerHelper(\"cross\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n attrs = dict()\n attrs['dim'] = axis\n\n helper.append_op(\n type='cross',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef cholesky(x, upper=False, name=None):\n r\"\"\"\n Computes the Cholesky decomposition of one symmetric positive-definite\n matrix or batches of symmetric positive-definite matrice.\n\n If `upper` is `True`, the decomposition has the form :math:`A = U^{T}U` ,\n and the returned matrix :math:`U` is upper-triangular. Otherwise, the\n decomposition has the form :math:`A = LL^{T}` , and the returned matrix\n :math:`L` is lower-triangular.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[*, M, M]`,\n where * is zero or more batch dimensions, and matrices on the\n inner-most 2 dimensions all should be symmetric positive-definite.\n Its data type should be float32 or float64.\n upper (bool): The flag indicating whether to return upper or lower\n triangular matrices. Default: False.\n\n Returns:\n Tensor: A Tensor with same shape and data type as `x`. It represents \\\n triangular matrices generated by Cholesky decomposition.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n a = np.random.rand(3, 3)\n a_t = np.transpose(a, [1, 0])\n x_data = np.matmul(a, a_t) + 1e-03\n x = paddle.to_tensor(x_data)\n out = paddle.linalg.cholesky(x, upper=False)\n print(out)\n # [[1.190523 0. 0. ]\n # [0.9906703 0.27676893 0. ]\n # [1.25450498 0.05600871 0.06400121]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky(x, \"upper\", upper)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cholesky')\n check_type(upper, 'upper', bool, 'cholesky')\n helper = LayerHelper('cholesky', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='cholesky',\n inputs={'X': [x]},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef matrix_rank(x, tol=None, hermitian=False, name=None):\n r\"\"\"\n Computes the rank of a matrix.\n\n The rank of a matrix is the number of singular values that are greater than the specified `tol` threshold when hermitian=False,\n or the number of eigenvalues in absolute value that are greater than the specified `tol` threshold when hermitian=True.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., m, n]`, where `...` is zero or more batch dimensions. If `x` is a batch\n of matrices then the output has the same batch dimensions. The data type of `x` should be float32 or float64.\n tol (float,Tensor,optional): the tolerance value. Default: None. If `tol` is not specified, and `sigma` is the largest\n singular value (or eigenvalues in absolute value), and `eps` is the epsilon value for the dtype of `x`, then `tol` is computed\n with formula `tol=sigma * max(m,n) * eps`. Note that if `x` is a batch of matrices, `tol` is computed this way for every batch.\n hermitian (bool,optional): indicates whether `x` is Hermitian. Default: False. When hermitian=True, `x` is assumed to be Hermitian,\n enabling a more efficient method for finding eigenvalues, but `x` is not checked inside the function. Instead, We just use\n the lower triangular of the matrix to compute.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: Rank of tensor x.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n a = paddle.eye(10)\n b = paddle.linalg.matrix_rank(a)\n print(b)\n # b = [10]\n\n c = paddle.ones(shape=[3, 4, 5, 5])\n d = paddle.linalg.matrix_rank(c, tol=0.01, hermitian=True)\n print(d)\n # d = [[1, 1, 1, 1],\n # [1, 1, 1, 1],\n # [1, 1, 1, 1]]\n\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if tol is None:\n tol_tensor = None\n tol_attr = 0.0\n use_default_tol = True\n elif isinstance(tol, Variable):\n if tol.dtype != x.dtype:\n tol_tensor = cast(tol, x.dtype)\n else:\n tol_tensor = tol\n tol_attr = 0.0\n use_default_tol = False\n else:\n tol_tensor = None\n tol_attr = float(tol)\n use_default_tol = False\n return _C_ops.matrix_rank(x, tol_tensor, \"tol\", tol_attr, 'hermitian',\n hermitian, 'use_default_tol', use_default_tol)\n\n inputs = {}\n attrs = {}\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'matrix_rank')\n inputs['X'] = x\n if tol is None:\n attrs['use_default_tol'] = True\n elif isinstance(tol, Variable):\n check_variable_and_dtype(tol, 'tol', ['float32'], 'matrix_rank')\n attrs['use_default_tol'] = False\n if tol.dtype != x.dtype:\n inputs['TolTensor'] = cast(tol, x.dtype)\n else:\n inputs['TolTensor'] = tol\n else:\n check_type(tol, 'tol', float, 'matrix_rank')\n attrs['use_default_tol'] = False\n attrs['tol'] = tol\n check_type(hermitian, 'hermitian', bool, 'matrix_rank')\n attrs['hermitian'] = hermitian\n\n helper = LayerHelper('matrix_rank', **locals())\n out = helper.create_variable_for_type_inference(dtype='int32')\n helper.append_op(\n type='matrix_rank', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef bmm(x, y, name=None):\n \"\"\"\n Applies batched matrix multiplication to two tensors.\n\n Both of the two input tensors must be three-dementional and share the same batch size.\n\n if x is a (b, m, k) tensor, y is a (b, k, n) tensor, the output will be a (b, m, n) tensor.\n\n Args:\n x (Tensor): The input Tensor.\n y (Tensor): The input Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The product Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n # In imperative mode:\n # size x: (2, 2, 3) and y: (2, 3, 2)\n x = paddle.to_tensor([[[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0]],\n [[3.0, 3.0, 3.0],\n [4.0, 4.0, 4.0]]])\n y = paddle.to_tensor([[[1.0, 1.0],[2.0, 2.0],[3.0, 3.0]],\n [[4.0, 4.0],[5.0, 5.0],[6.0, 6.0]]])\n out = paddle.bmm(x, y)\n #output size: (2, 2, 2)\n #output value:\n #[[[6.0, 6.0],[12.0, 12.0]],[[45.0, 45.0],[60.0, 60.0]]]\n out_np = out.numpy()\n\n \"\"\"\n x_shape = x.shape\n y_shape = y.shape\n if not len(x_shape) == len(y_shape) == 3:\n raise ValueError(\n \"x and y should be 3-dimensional. But received x's dimention: {}, y's dimention: {}\".\n format(x_shape, y_shape))\n if x_shape[2] != y_shape[1]:\n raise ValueError(\n \"x's width must be equal with y's height. But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n if x_shape[0] != y_shape[0]:\n raise ValueError(\n \"x's batch (shape[0]) must be equal with y's batch (shape[0]). But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.bmm(x, y)\n\n helper = LayerHelper('bmm', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='bmm', inputs={'X': x, 'Y': y}, outputs={'Out': out})\n return out\n\n\ndef histogram(input, bins=100, min=0, max=0, name=None):\n \"\"\"\n Computes the histogram of a tensor. The elements are sorted into equal width bins between min and max.\n If min and max are both zero, the minimum and maximum values of the data are used.\n\n Args:\n input (Tensor): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor\n should be float32, float64, int32, int64.\n bins (int): number of histogram bins\n min (int): lower end of the range (inclusive)\n max (int): upper end of the range (inclusive)\n\n Returns:\n Tensor: data type is int64, shape is (nbins,).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n inputs = paddle.to_tensor([1, 2, 1])\n result = paddle.histogram(inputs, bins=4, min=0, max=3)\n print(result) # [0, 2, 1, 0]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.histogram(input, \"bins\", bins, \"min\", min, \"max\", max)\n\n helper = LayerHelper('histogram', **locals())\n check_variable_and_dtype(\n input, 'X', ['int32', 'int64', 'float32', 'float64'], 'histogram')\n out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)\n helper.append_op(\n type='histogram',\n inputs={'X': input},\n outputs={'Out': out},\n attrs={'bins': bins,\n 'min': min,\n 'max': max})\n return out\n\n\ndef bincount(x, weights=None, minlength=0, name=None):\n \"\"\"\n Computes frequency of each value in the input tensor. \n\n Args:\n x (Tensor): A Tensor with non-negative integer. Should be 1-D tensor.\n weights (Tensor, optional): Weight for each value in the input tensor. Should have the same shape as input. Default is None.\n minlength (int, optional): Minimum number of bins. Should be non-negative integer. Default is 0.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor of frequency.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([1, 2, 1, 4, 5])\n result1 = paddle.bincount(x)\n print(result1) # [0, 2, 1, 0, 1, 1]\n\n w = paddle.to_tensor([2.1, 0.4, 0.1, 0.5, 0.5])\n result2 = paddle.bincount(x, weights=w)\n print(result2) # [0., 2.19999981, 0.40000001, 0., 0.50000000, 0.50000000]\n \"\"\"\n if x.dtype not in [paddle.int32, paddle.int64]:\n raise TypeError(\"Elements in Input(x) should all be integers\")\n\n if paddle.in_dynamic_mode():\n return _C_ops.bincount(x, weights, \"minlength\", minlength)\n\n helper = LayerHelper('bincount', **locals())\n\n check_variable_and_dtype(x, 'X', ['int32', 'int64'], 'bincount')\n\n if weights is not None:\n check_variable_and_dtype(weights, 'Weights',\n ['int32', 'int64', 'float32', 'float64'],\n 'bincount')\n out = helper.create_variable_for_type_inference(dtype=weights.dtype)\n else:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='bincount',\n inputs={'X': x,\n 'Weights': weights},\n outputs={'Out': out},\n attrs={'minlength': minlength})\n return out\n\n\ndef mv(x, vec, name=None):\n \"\"\"\n Performs a matrix-vector product of the matrix x and the vector vec.\n\n Args:\n x (Tensor): A tensor with shape :math:`[M, N]` , The data type of the input Tensor x\n should be one of float32, float64.\n vec (Tensor): A tensor with shape :math:`[N]` , The data type of the input Tensor x\n should be one of float32, float64.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor which is producted by x and vec.\n\n Examples:\n .. code-block:: python\n\n # x: [M, N], vec: [N]\n # paddle.mv(x, vec) # out: [M]\n\n import numpy as np\n import paddle\n\n x_data = np.array([[2, 1, 3], [3, 0, 1]]).astype(\"float64\")\n x = paddle.to_tensor(x_data)\n vec_data = np.array([3, 5, 1])\n vec = paddle.to_tensor(vec_data).astype(\"float64\")\n out = paddle.mv(x, vec)\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_mv(x, vec)\n else:\n if _in_legacy_dygraph():\n out = _C_ops.mv(x, vec)\n return out\n else:\n\n def __check_input(x, vec):\n var_names = {'x': x, 'vec': vec}\n for name, val in var_names.items():\n check_variable_and_dtype(val, name, ['float32', 'float64'],\n 'mv')\n x_shape = list(x.shape)\n vec_shape = list(vec.shape)\n if len(x_shape) != 2:\n raise ValueError(\n \"x should be 2-dimensional. But received x's dimention: {}\".\n format(x_shape))\n if len(vec_shape) != 1:\n raise ValueError(\n \"vec should be 1-dimensional. But received vec's dimention: {}\".\n format(vec_shape))\n\n __check_input(x, vec)\n\n helper = LayerHelper('mv', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='mv', inputs={'X': x,\n 'Vec': vec}, outputs={'Out': out})\n return out\n\n\ndef det(x, name=None):\n \"\"\"\n Calculates determinant value of a square matrix or batches of square matrices.\n Args:\n x (Tensor): input (Tensor): the input matrix of size `(n, n)` or the batch of matrices of size\n `(*, n, n)` where `*` is one or more batch dimensions.\n Returns:\n y (Tensor):the determinant value of a square matrix or batches of square matrices.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.det(x)\n\n print(A)\n\n # [ 0.02547996, 2.52317095, -6.15900707])\n\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.determinant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'det')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('determinant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='determinant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef slogdet(x, name=None):\n \"\"\"\n Calculates the sign and natural logarithm of the absolute value of a square matrix's or batches square matrices' determinant.\n The determinant can be computed with ``sign * exp(logabsdet)\n\n Supports input of float, double\n\n Note that for matrices that have zero determinant, this returns ``(0, -inf)``\n Args:\n x (Tensor): the batch of matrices of size :math:`(*, n, n)`\n where math:`*` is one or more batch dimensions.\n\n Returns:\n y (Tensor): A tensor containing the sign of the determinant and the natural logarithm\n of the absolute value of determinant, respectively.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.slogdet(x)\n\n print(A)\n\n # [[ 1. , 1. , -1. ],\n # [-0.98610914, -0.43010661, -0.10872950]])\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.slogdeterminant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'slogdet')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('slogdeterminant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='slogdeterminant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef svd(x, full_matrices=False, name=None):\n r\"\"\"\n Computes the singular value decomposition of one matrix or a batch of regular matrices.\n\n Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies:\n\n .. math::\n X = U * diag(S) * VT\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., N, M]`,\n where `...` is zero or more batch dimensions. N and M can be arbitraty\n positive number. Note that if x is sigular matrices, the grad is numerical\n instable. The data type of x should be float32 or float64.\n full_matrices (bool): A flag to control the behavor of svd.\n If full_matrices = True, svd op will compute full U and V matrics,\n which means shape of U is `[..., N, N]`, shape of V is `[..., M, M]`. K = min(M, N).\n If full_matrices = False, svd op will use a economic method to store U and V.\n which means shape of U is `[..., N, K]`, shape of V is `[..., M, K]`. K = min(M, N).\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple of 3 tensors: (U, S, VH). VH is the conjugate transpose of V. S is the singlar value vectors of matrics with shape `[..., K]`\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 2.0], [1.0, 3.0], [4.0, 6.0]]).astype('float64')\n x = x.reshape([3, 2])\n u, s, vh = paddle.linalg.svd(x)\n print (u)\n #U = [[ 0.27364809, -0.21695147 ],\n # [ 0.37892198, -0.87112408 ],\n # [ 0.8840446 , 0.44053933 ]]\n\n print (s)\n #S = [8.14753743, 0.78589688]\n print (vh)\n #VT= [[ 0.51411221, 0.85772294],\n # [ 0.85772294, -0.51411221]]\n\n # one can verify : U * S * VT == X\n # U * UH == I\n # V * VH == I\n \"\"\"\n\n if paddle.in_dynamic_mode():\n return _C_ops.svd(x, 'full_matrices', full_matrices)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'svd')\n check_type(full_matrices, 'full_matrices', bool, 'svd')\n helper = LayerHelper('svd', **locals())\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n vh = helper.create_variable_for_type_inference(dtype=x.dtype)\n s = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['full_matrices'] = full_matrices\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vh,\n 'S': s},\n attrs=attrs, )\n return u, s, vh\n\n\ndef matrix_power(x, n, name=None):\n r\"\"\"\n Computes the n-th power of a square matrix or a batch of square matrices.\n\n Let :math:`X` be a sqaure matrix or a batch of square matrices, :math:`n` be\n an exponent, the equation should be:\n\n .. math::\n Out = X ^ {n}\n\n Specifically,\n\n - If `n > 0`, it returns the matrix or a batch of matrices raised to the power\n of `n`.\n\n - If `n = 0`, it returns the identity matrix or a batch of identity matrices.\n\n - If `n < 0`, it returns the inverse of each matrix (if invertible) raised to\n the power of `abs(n)`.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices to be raised\n to power `n`. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n n (int): The exponent. It can be any positive, negative integer or zero.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The n-th power of the matrix (or the batch of matrices) `x`. Its\n data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1, 2, 3],\n [1, 4, 9],\n [1, 8, 27]], dtype='float64')\n print(paddle.linalg.matrix_power(x, 2))\n # [[6. , 34. , 102.],\n # [14. , 90. , 282.],\n # [36. , 250., 804.]]\n\n print(paddle.linalg.matrix_power(x, 0))\n # [[1., 0., 0.],\n # [0., 1., 0.],\n # [0., 0., 1.]]\n\n print(paddle.linalg.matrix_power(x, -2))\n # [[ 12.91666667, -12.75000000, 2.83333333 ],\n # [-7.66666667 , 8. , -1.83333333 ],\n # [ 1.80555556 , -1.91666667 , 0.44444444 ]]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.matrix_power(x, \"n\", n)\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'matrix_power')\n check_type(n, 'n', int, 'matrix_power')\n helper = LayerHelper('matrix_power', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matrix_power',\n inputs={'X': x},\n outputs={'Out': out},\n attrs={'n': n})\n return out\n\n\ndef qr(x, mode=\"reduced\", name=None):\n r\"\"\"\n Computes the QR decomposition of one matrix or batches of matrice (backward is unsupported now).\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., M, N]`,\n where ... is zero or more batch dimensions. M and N can be arbitrary\n positive number. The data type of x should be float32 or float64. \n mode (str, optional): A flag to control the behavior of qr, the default is \"reduced\". \n Suppose x's shape is `[..., M, N]` and denoting `K = min(M, N)`:\n If mode = \"reduced\", qr op will return reduced Q and R matrices, \n which means Q's shape is `[..., M, K]` and R's shape is `[..., K, N]`.\n If mode = \"complete\", qr op will return complete Q and R matrices, \n which means Q's shape is `[..., M, M]` and R's shape is `[..., M, N]`.\n If mode = \"r\", qr op will only return reduced R matrix, which means\n R's shape is `[..., K, N]`.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n If mode = \"reduced\" or mode = \"complete\", qr will return a two tensor-tuple, which represents Q and R. \n If mode = \"r\", qr will return a tensor which represents R.\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n q, r = paddle.linalg.qr(x)\n print (q)\n print (r)\n\n # Q = [[-0.16903085, 0.89708523],\n # [-0.50709255, 0.27602622],\n # [-0.84515425, -0.34503278]])\n\n # R = [[-5.91607978, -7.43735744],\n # [ 0. , 0.82807867]])\n \n # one can verify : X = Q * R ; \n \"\"\"\n if paddle.in_dynamic_mode():\n q, r = _C_ops.qr(x, 'mode', mode)\n if mode == \"r\":\n return r\n else:\n return q, r\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'qr')\n check_type(mode, 'mode', str, 'qr')\n helper = LayerHelper('qr', **locals())\n q = helper.create_variable_for_type_inference(dtype=x.dtype)\n r = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['mode'] = mode\n helper.append_op(\n type='qr', inputs={'X': [x]}, outputs={'Q': q,\n 'R': r}, attrs=attrs)\n if mode == \"r\":\n return r\n else:\n return q, r\n\n\ndef lu(x, pivot=True, get_infos=False, name=None):\n r\"\"\"\n Computes the LU factorization of an N-D(N>=2) matrix x. \n\n Returns the LU factorization(inplace x) and Pivots. low triangular matrix L and \n upper triangular matrix U are combined to a single LU matrix.\n\n Pivoting is done if pivot is set to True.\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n # return ones\n\n Args:\n\n X (Tensor): the tensor to factor of N-dimensions(N>=2).\n\n pivot (bool, optional): controls whether pivoting is done. Default: True.\n\n get_infos (bool, optional): if set to True, returns an info IntTensor. Default: False.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n factorization (Tensor): LU matrix, the factorization of input X.\n\n pivots (IntTensor): the pivots of size(∗(N-2), min(m,n)). `pivots` stores all the \n intermediate transpositions of rows. The final permutation `perm` could be \n reconstructed by this, details refer to upper example.\n\n infos (IntTensor, optional): if `get_infos` is `True`, this is a tensor of size (∗(N-2)) \n where non-zero values indicate whether factorization for the matrix or each minibatch \n has succeeded or failed.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n \n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n if paddle.in_dynamic_mode():\n LU, Piv, Info = _C_ops.lu(x, 'pivots', pivot)\n if get_infos:\n return LU, Piv, Info\n else:\n return LU, Piv\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu')\n helper = LayerHelper('lu', **locals())\n lu = helper.create_variable_for_type_inference(dtype=x.dtype)\n p = helper.create_variable_for_type_inference(dtype='int')\n info = helper.create_variable_for_type_inference(dtype='int')\n attrs = dict()\n attrs['pivots'] = pivot\n helper.append_op(\n type='lu',\n inputs={'X': x},\n outputs={'Out': lu,\n 'Pivots': p,\n 'Infos': info},\n attrs=attrs)\n if get_infos:\n return lu, p, info\n else:\n return lu, p\n\n\ndef lu_unpack(x, y, unpack_ludata=True, unpack_pivots=True, name=None):\n r\"\"\"\n Unpack L U and P to single matrix tensor . \n unpack L and U matrix from LU, unpack permutation matrix P from Pivtos .\n\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n\n\n Args:\n x (Tensor): The LU tensor get from paddle.linalg.lu, which is combined by L and U.\n\n y (Tensor): Pivots get from paddle.linalg.lu.\n\n unpack_ludata (bool,optional): whether to unpack L and U from x. Default: True.\n\n unpack_pivots (bool, optional): whether to unpack permutation matrix P from Pivtos. Default: True.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n P (Tensor): Permutation matrix P of lu factorization.\n\n L (Tensor): The lower triangular matrix tensor of lu factorization.\n\n U (Tensor): The upper triangular matrix tensor of lu factorization.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n\n if paddle.in_dynamic_mode():\n P, L, U = _C_ops.lu_unpack(x, y, 'unpack_ludata', unpack_ludata,\n 'unpack_pivots', unpack_pivots)\n return P, L, U\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu_unpack')\n helper = LayerHelper('lu_unpack', **locals())\n p = helper.create_variable_for_type_inference(dtype=x.dtype)\n l = helper.create_variable_for_type_inference(dtype=x.dtype)\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n attrs = dict()\n attrs['unpack_ludata'] = unpack_ludata\n attrs['unpack_pivots'] = unpack_pivots\n helper.append_op(\n type='lu_unpack',\n inputs={'X': x,\n 'Pivots': y},\n outputs={'Pmat': p,\n 'L': l,\n 'U': u},\n attrs=attrs)\n return p, l, u\n\n\ndef eig(x, name=None):\n \"\"\"\n This API performs the eigenvalue decomposition of a square matrix or a batch of square matrices.\n\n .. note::\n If the matrix is a Hermitian or a real symmetric matrix, please use :ref:`paddle.linalg.eigh` instead, which is much faster.\n If only eigenvalues is needed, please use :ref:`paddle.linalg.eigvals` instead.\n If the matrix is of any shape, please use :ref:`paddle.linalg.svd`.\n This API is only supported on CPU device.\n The output datatype is always complex for both real and complex input.\n\n Args:\n x (Tensor): A tensor with shape math:`[*, N, N]`, The data type of the x should be one of ``float32``,\n ``float64``, ``compplex64`` or ``complex128``.\n name (str, optional): The default value is `None`. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Eigenvalues(Tensors): A tensor with shape math:`[*, N]` refers to the eigen values.\n Eigenvectors(Tensors): A tensor with shape math:`[*, N, N]` refers to the eigen vectors.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n paddle.device.set_device(\"cpu\")\n\n x_data = np.array([[1.6707249, 7.2249975, 6.5045543],\n [9.956216, 8.749598, 6.066444 ],\n [4.4251957, 1.7983172, 0.370647 ]]).astype(\"float32\")\n x = paddle.to_tensor(x_data)\n w, v = paddle.linalg.eig(x)\n print(w)\n # Tensor(shape=[3, 3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [[(-0.5061363550800655+0j) , (-0.7971760990842826+0j) ,\n # (0.18518077798279986+0j)],\n # [(-0.8308237755993192+0j) , (0.3463813401919749+0j) ,\n # (-0.6837005269141947+0j) ],\n # [(-0.23142567697893396+0j), (0.4944999840400175+0j) ,\n # (0.7058765252952796+0j) ]])\n\n print(v)\n # Tensor(shape=[3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [ (16.50471283351188+0j) , (-5.5034820550763515+0j) ,\n # (-0.21026087843552282+0j)])\n \"\"\"\n if paddle.in_dynamic_mode():\n w, v = _C_ops.eig(x)\n return w, v\n\n check_variable_and_dtype(\n x, 'X', ['float32', 'float64', 'complex64', 'complex128'], 'eig')\n helper = LayerHelper('eig', **locals())\n\n w = helper.create_variable_for_type_inference(x.dtype)\n v = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {'X': x}\n outputs = {'Eigenvalues': w, 'Eigenvectors': v}\n helper.append_op(type='eig', inputs=inputs, outputs=outputs)\n\n return w, v\n\n\ndef eigvals(x, name=None):\n \"\"\"\n Compute the eigenvalues of one or more general matrices.\n\n Warning:\n The gradient kernel of this operator does not yet developed.\n If you need back propagation through this operator, please replace it with paddle.linalg.eig.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices whose eigenvalues will be computed.\n Its shape should be `[*, M, M]`, where `*` is zero or more batch dimensions.\n Its data type should be float32, float64, complex64, or complex128.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n Tensor: A tensor containing the unsorted eigenvalues which has the same batch dimensions with `x`.\n The eigenvalues are complex-valued even when `x` is real.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n paddle.seed(1234)\n\n x = paddle.rand(shape=[3, 3], dtype='float64')\n # [[0.02773777, 0.93004224, 0.06911496],\n # [0.24831591, 0.45733623, 0.07717843],\n # [0.48016702, 0.14235102, 0.42620817]])\n\n print(paddle.linalg.eigvals(x))\n # [(-0.27078833542132674+0j), (0.29962280156230725+0j), (0.8824477020120244+0j)] #complex128\n \"\"\"\n\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64',\n 'complex128'], 'eigvals')\n\n x_shape = list(x.shape)\n if len(x_shape) < 2:\n raise ValueError(\n \"The dimension of Input(x) should be at least 2, but received x's dimention = {}, x's shape = {}\".\n format(len(x_shape), x_shape))\n\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The last two dimensions of Input(x) should be equal, but received x's shape = {}\".\n format(x_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.eigvals(x)\n\n helper = LayerHelper('eigvals', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='eigvals', inputs={'X': x}, outputs={'Out': out})\n return out\n\n\ndef multi_dot(x, name=None):\n \"\"\"\n Multi_dot is an operator that calculates multiple matrix multiplications.\n\n Supports inputs of float16(only GPU support), float32 and float64 dtypes. This function does not\n support batched inputs.\n\n The input tensor in [x] must be 2-D except for the first and last can be 1-D.\n If the first tensor is a 1-D vector of shape(n, ) it is treated as row vector\n of shape(1, n), similarly if the last tensor is a 1D vector of shape(n, ), it\n is treated as a column vector of shape(n, 1).\n\n If the first and last tensor are 2-D matrix, then the output is also 2-D matrix,\n otherwise the output is a 1-D vector.\n\n Multi_dot will select the lowest cost multiplication order for calculation. The\n cost of multiplying two matrices with shapes (a, b) and (b, c) is a * b * c.\n Given matrices A, B, C with shapes (20, 5), (5, 100), (100, 10) respectively,\n we can calculate the cost of different multiplication orders as follows:\n - Cost((AB)C) = 20x5x100 + 20x100x10 = 30000\n - Cost(A(BC)) = 5x100x10 + 20x5x10 = 6000\n\n In this case, multiplying B and C first, then multiply A, which is 5 times faster\n than sequential calculation.\n\n Args:\n x ([Tensor]): The input tensors which is a list Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # A * B\n A_data = np.random.random([3, 4]).astype(np.float32)\n B_data = np.random.random([4, 5]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n out = paddle.linalg.multi_dot([A, B])\n print(out.numpy().shape)\n # [3, 5]\n\n # A * B * C\n A_data = np.random.random([10, 5]).astype(np.float32)\n B_data = np.random.random([5, 8]).astype(np.float32)\n C_data = np.random.random([8, 7]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n C = paddle.to_tensor(C_data)\n out = paddle.linalg.multi_dot([A, B, C])\n print(out.numpy().shape)\n # [10, 7]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.multi_dot(x)\n\n check_type(x, 'x', (list, tuple), 'multi_dot')\n for id, item in enumerate(x):\n check_variable_and_dtype(item, 'x[' + str(id) + ']',\n ['float16', 'float32', 'float64'], 'multi_dot')\n if item.dtype != x[0].dtype:\n raise TypeError(\n \"All the Tensors in the input must have the same data type.\")\n\n helper = LayerHelper('multi_dot', **locals())\n dtype = helper.input_dtype(input_param_name='x')\n out = helper.create_variable_for_type_inference(dtype)\n helper.append_op(type='multi_dot', inputs={\"X\": x}, outputs={\"Out\": out})\n return out\n\n\ndef eigh(x, UPLO='L', name=None):\n \"\"\"\n Compute the eigenvalues and eigenvectors of a\n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[*, N, N]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): (string, default 'L'), 'L' represents the lower triangular matrix,\n \"'U' represents the upper triangular matrix.\".\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n\n out_value(Tensor): A Tensor with shape [*, N] and data type of float32 and float64. The eigenvalues of eigh op.\n out_vector(Tensor): A Tensor with shape [*, N, N] and data type of float32,float64,complex64 and complex128. The eigenvectors of eigh op.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value, out_vector = paddle.linalg.eigh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n print(out_vector)\n #[(-0.9238795325112867+0j), (-0.3826834323650898+0j)],\n #[ 0.3826834323650898j , -0.9238795325112867j ]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.eigh(x, 'UPLO', UPLO)\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigh', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'eigh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO})\n return out_value, out_vector\n\n\ndef pinv(x, rcond=1e-15, hermitian=False, name=None):\n r\"\"\"\n Calculate pseudo inverse via SVD(singular value decomposition)\n of one matrix or batches of regular matrix.\n\n .. math::\n\n if hermitian == False:\n x = u * s * vt (SVD)\n out = v * 1/s * ut\n else:\n x = u * s * ut (eigh)\n out = u * 1/s * u.conj().transpose(-2,-1)\n\n If x is hermitian or symmetric matrix, svd will be replaced with eigh.\n\n Args:\n x(Tensor): The input tensor. Its shape should be (*, m, n)\n where * is zero or more batch dimensions. m and n can be\n arbitraty positive number. The data type of x should be\n float32 or float64 or complex64 or complex128. When data\n type is complex64 or cpmplex128, hermitian should be set\n True.\n\n rcond(Tensor, optional): the tolerance value to determine\n when is a singular value zero. Defalut:1e-15.\n\n hermitian(bool, optional): indicates whether x is Hermitian\n if complex or symmetric if real. Default: False.\n\n name(str|None): A name for this layer(optional). If set None,\n the layer will be named automatically.\n\n Returns:\n Tensor: The tensor with same data type with x. it represents\n pseudo inverse of x. Its shape should be (*, n, m).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.arange(15).reshape((3, 5)).astype('float64')\n input = paddle.to_tensor(x)\n out = paddle.linalg.pinv(input)\n print(input)\n print(out)\n\n # input:\n # [[0. , 1. , 2. , 3. , 4. ],\n # [5. , 6. , 7. , 8. , 9. ],\n # [10., 11., 12., 13., 14.]]\n\n # out:\n # [[-0.22666667, -0.06666667, 0.09333333],\n # [-0.12333333, -0.03333333, 0.05666667],\n # [-0.02000000, 0.00000000, 0.02000000],\n # [ 0.08333333, 0.03333333, -0.01666667],\n # [ 0.18666667, 0.06666667, -0.05333333]]\n\n # one can verify : x * out * x = x ;\n # or out * x * out = x ;\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if not hermitian:\n # combine svd and matmul op\n u, s, vt = _C_ops.svd(x, 'full_matrices', False)\n max_singular_val = _C_ops.reduce_max(s, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=x.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=x.dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v, _ = _C_ops.transpose2(vt, 'axis', perm)\n\n out_1 = v * st\n out_2 = _C_ops.matmul_v2(out_1, u, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n # combine eigh and matmul op\n s, u = _C_ops.eigh(x, 'UPLO', 'L')\n s_abs = paddle.abs(s)\n max_singular_val = _C_ops.reduce_max(s_abs, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=s.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=s.dtype)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n out_1 = u * st\n u_conj = _C_ops.conj(u)\n out_2 = _C_ops.matmul_v2(out_1, u_conj, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n if not hermitian:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'pinv')\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(dtype)\n vt = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vt,\n 'S': s},\n attrs={'full_matrices': False}, )\n\n max_singular_val = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=dtype)\n st_shape = helper.create_variable_for_type_inference(dtype=dtype)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v = helper.create_variable_for_type_inference(dtype)\n v_shape = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='transpose2',\n inputs={'X': [vt]},\n outputs={'Out': [v],\n 'XShape': [v_shape]},\n attrs={'axis': perm})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': v,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n else:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64',\n 'complex128'], 'pinv')\n\n if dtype == paddle.complex128:\n s_type = 'float64'\n elif dtype == paddle.complex64:\n s_type = 'float32'\n else:\n s_type = dtype\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': s,\n 'Eigenvectors': u},\n attrs={'UPLO': 'L'})\n s_abs = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='abs', inputs={'X': s}, outputs={'Out': s_abs})\n max_singular_val = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s_abs},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=s_type)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=s_type)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s_type)\n cond_not_int = layers.cast(layers.logical_not(condition), s_type)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=s_type)\n st_shape = helper.create_variable_for_type_inference(dtype=s_type)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': u,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n u_conj = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='conj', inputs={'X': u}, outputs={'Out': [u_conj]})\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u_conj},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n\n\ndef solve(x, y, name=None):\n r\"\"\"\n Computes the solution of a square system of linear equations with a unique solution for input 'X' and 'Y'.\n Let :math: `X` be a sqaure matrix or a batch of square matrices, :math:`Y` be\n a vector/matrix or a batch of vectors/matrices, the equation should be:\n\n .. math::\n Out = X^-1 * Y\n Specifically,\n - This system of linear equations has one solution if and only if input 'X' is invertible.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): A vector/matrix or a batch of vectors/matrices. Its shape should be `[*, M, K]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of a square system of linear equations with a unique solution for input 'x' and 'y'.\n Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # 2*X0 + X1 = 9\n # X0 + 2*X1 = 8\n\n import paddle\n import numpy as np\n\n np_x = np.array([[3, 1],[1, 2]])\n np_y = np.array([9, 8])\n x = paddle.to_tensor(np_x, dtype=\"float64\")\n y = paddle.to_tensor(np_y, dtype=\"float64\")\n out = paddle.linalg.solve(x, y)\n\n print(out)\n # [2., 3.])\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.solve(x, y)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type=\"solve\", inputs={\"X\": x,\n \"Y\": y}, outputs={\"Out\": out})\n return out\n\n\ndef triangular_solve(x,\n y,\n upper=True,\n transpose=False,\n unitriangular=False,\n name=None):\n r\"\"\"\n Computes the solution of a system of equations with a triangular coefficient matrix `x` and\n multiple right-hand sides `y` .\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input triangular coefficient matrix. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): Whether to solve the upper-triangular system of equations (default) or the lower-triangular \n system of equations. Default: True.\n transpose (bool, optional): whether `x` should be transposed before calculation. Default: False.\n unitriangular (bool, optional): whether `x` is unit triangular. If True, the diagonal elements of `x` are assumed \n to be 1 and not referenced from `x` . Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # x1 + x2 + x3 = 0\n # 2*x2 + x3 = -9\n # -x3 = 5\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n y = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.triangular_solve(x, y, upper=True)\n\n print(out)\n # [7, -2, -5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.triangular_solve(x, y, 'upper', upper, 'transpose',\n transpose, 'unitriangular',\n unitriangular)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"triangular_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'triangular_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'triangular_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='triangular_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={\n 'upper': upper,\n 'transpose': transpose,\n 'unitriangular': unitriangular\n })\n return out\n\n\ndef cholesky_solve(x, y, upper=False, name=None):\n r\"\"\"\n Solves a linear system of equations A @ X = B, given A's Cholesky factor matrix u and matrix B.\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input matrix which is upper or lower triangular Cholesky factor of square matrix A. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): whether to consider the Cholesky factor as a lower or upper triangular matrix. Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type is the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n u = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n b = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.cholesky_solve(b, u, upper=True)\n\n print(out)\n # [-2.5, -7, 9.5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky_solve(x, y, 'upper', upper)\n\n helper = LayerHelper(\"cholesky_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'cholesky_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'cholesky_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='cholesky_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef eigvalsh(x, UPLO='L', name=None):\n \"\"\"\n Computes the eigenvalues of a \n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[_, M, M]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): Lower triangular part of a (‘L’, default) or the upper triangular part (‘U’).\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor eigenvalues in ascending order.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value = paddle.eigvalsh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n \"\"\"\n if paddle.in_dynamic_mode():\n is_test = x.stop_gradient\n values, _ = _C_ops.eigvalsh(x, 'UPLO', UPLO, 'is_test', is_test)\n return values\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigvalsh', **locals())\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64', 'complex128'],\n 'eigvalsh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n is_test = x.stop_gradient\n helper.append_op(\n type='eigvalsh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO,\n 'is_test': is_test})\n return out_value\n\n\ndef lstsq(x, y, rcond=None, driver=None, name=None):\n \"\"\"\n Computes a solution to\n the least squares problem of a system of linear equations.\n\n Args:\n x (Tensor): A tensor with shape ``(*, M, N)`` , the data type of the input Tensor ``x``\n should be one of float32, float64.\n y (Tensor): A tensor with shape ``(*, M, K)`` , the data type of the input Tensor ``y`` \n should be one of float32, float64.\n rcond(float, optional): The default value is None. A float pointing number used to determine \n the effective rank of ``x``. If ``rcond`` is None, it will be set to max(M, N) times the \n machine precision of x_dtype.\n driver(str, optional): The default value is None. The name of LAPACK method to be used. For \n CPU inputs the valid values are ‘gels’, ‘gelsy’, ‘gelsd, ‘gelss’. For CUDA input, the only \n valid driver is ‘gels’. If ``driver`` is None, ‘gelsy’ is used for CPU inputs and ‘gels’ \n for CUDA inputs.\n name(str, optional): The default value is None. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple: A tuple of 4 Tensors which is (``solution``, ``residuals``, ``rank``, ``singular_values``). \n ``solution`` is a tensor with shape ``(*, N, K)``, meaning the least squares solution. ``residuals`` \n is a tensor with shape ``(*, K)``, meaning the squared residuals of the solutions, which is computed \n when M > N and every matrix in ``x`` is full-rank, otherwise return an empty tensor. ``rank`` is a tensor \n with shape ``(*)``, meaning the ranks of the matrices in ``x``, which is computed when ``driver`` in \n (‘gelsy’, ‘gelsd’, ‘gelss’), otherwise return an empty tensor. ``singular_values`` is a tensor with \n shape ``(*, min(M, N))``, meaning singular values of the matrices in ``x``, which is computed when \n ``driver`` in (‘gelsd’, ‘gelss’), otherwise return an empty tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n x = paddle.to_tensor([[1, 3], [3, 2], [5, 6.]])\n y = paddle.to_tensor([[3, 4, 6], [5, 3, 4], [1, 2, 1.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gelsd\")\n print(results[0])\n # [[ 0.78350395, -0.22165027, -0.62371236],\n # [-0.11340097, 0.78866047, 1.14948535]]\n print(results[1])\n # [19.81443405, 10.43814468, 30.56185532])\n print(results[2])\n # 2\n print(results[3])\n # [9.03455734, 1.54167950]\n\n x = paddle.to_tensor([[10, 2, 3], [3, 10, 5], [5, 6, 12.]])\n y = paddle.to_tensor([[4, 2, 9], [2, 0, 3], [2, 5, 3.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gels\")\n print(results[0])\n # [[ 0.39386186, 0.10230173, 0.93606132],\n # [ 0.10741687, -0.29028133, 0.11892585],\n # [-0.05115091, 0.51918161, -0.19948854]]\n print(results[1])\n # []\n \"\"\"\n device = paddle.get_device()\n if device == \"cpu\":\n if driver not in (None, \"gels\", \"gelss\", \"gelsd\", \"gelsy\"):\n raise ValueError(\n \"Only support valid driver is 'gels', 'gelss', 'gelsd', 'gelsy' or None for CPU inputs. But got {}\".\n format(driver))\n driver = \"gelsy\" if driver is None else driver\n elif \"gpu\" in device:\n if driver not in (None, \"gels\"):\n raise ValueError(\n \"Only support valid driver is 'gels' or None for CUDA inputs. But got {}\".\n format(driver))\n driver = \"gels\" if driver is None else driver\n else:\n raise RuntimeError(\"Only support lstsq api for CPU or CUDA device.\")\n\n if x.dtype == y.dtype and x.dtype in (paddle.float32, paddle.float64):\n pass\n else:\n raise ValueError(\n \"Only support x and y have the same dtype such as 'float32' and 'float64'.\"\n )\n\n if rcond is None:\n if x.dtype == paddle.float32:\n rcond = 1e-7 * max(x.shape[-2], x.shape[-1])\n elif x.dtype == paddle.float64:\n rcond = 1e-15 * max(x.shape[-2], x.shape[-1])\n\n if paddle.in_dynamic_mode():\n solution, rank, singular_values = _C_ops.lstsq(x, y, \"rcond\", rcond,\n \"driver\", driver)\n if x.shape[-2] > x.shape[-1]:\n matmul_out = _varbase_creator(dtype=x.dtype)\n _C_ops.matmul(x, solution, matmul_out, 'trans_x', False, 'trans_y',\n False)\n minus_out = _C_ops.elementwise_sub(matmul_out, y)\n pow_out = _C_ops.pow(minus_out, 'factor', 2)\n residuals = _C_ops.reduce_sum(pow_out, 'dim', [-2], 'keepdim',\n False, 'reduce_all', False)\n else:\n residuals = paddle.empty(shape=[0], dtype=x.dtype)\n\n if driver == \"gels\":\n rank = paddle.empty(shape=[0], dtype=paddle.int32)\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n elif driver == \"gelsy\":\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n\n return solution, residuals, rank, singular_values\n\n helper = LayerHelper('lstsq', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n check_variable_and_dtype(\n y, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n\n solution = helper.create_variable_for_type_inference(dtype=x.dtype)\n residuals = helper.create_variable_for_type_inference(dtype=x.dtype)\n rank = helper.create_variable_for_type_inference(dtype=paddle.int32)\n singular_values = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='lstsq',\n inputs={'X': x,\n 'Y': y},\n outputs={\n 'Solution': solution,\n 'Rank': rank,\n 'SingularValues': singular_values\n },\n attrs={'rcond': rcond,\n 'driver': driver})\n\n matmul_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n minus_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n pow_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': solution},\n outputs={'Out': matmul_out},\n attrs={\n 'trans_x': False,\n 'trans_y': False,\n })\n\n helper.append_op(\n type='elementwise_sub',\n inputs={'X': matmul_out,\n 'Y': y},\n outputs={'Out': minus_out})\n\n helper.append_op(\n type='pow',\n inputs={'X': minus_out},\n outputs={'Out': pow_out},\n attrs={'factor': 2})\n\n helper.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': residuals},\n attrs={'dim': [-2],\n 'keep_dim': False,\n 'reduce_all': False})\n\n if driver == \"gels\":\n rank = paddle.static.data(name='rank', shape=[0])\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n elif driver == \"gelsy\":\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n\n return solution, residuals, rank, singular_values\n"}}},{"rowIdx":41,"cells":{"function_name":{"kind":"string","value":"vector_norm"},"docstring":{"kind":"string","value":"Calculate the p-order vector norm for certain dimension of Tensor `input`.\nArgs:\n input (Variable): Tensor, data type float32, float64.\n porder (float, optional): None for porder=2.0.\n axis (int, optional): None for last dimension.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False."},"masked_code":{"kind":"string","value":"# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numpy as np\nfrom ..fluid.layer_helper import LayerHelper\nfrom ..framework import _varbase_creator, _dygraph_tracer\nfrom ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype\nfrom ..static import Variable\nfrom ..fluid.framework import _in_legacy_dygraph, in_dygraph_mode\nfrom ..fluid.layers import transpose, cast # noqa: F401\nfrom ..fluid import layers\nimport paddle\nfrom paddle.common_ops_import import core\nfrom paddle.common_ops_import import VarDesc\nfrom paddle import _C_ops\n\n__all__ = []\n\n\ndef matmul(x, y, transpose_x=False, transpose_y=False, name=None):\n \"\"\"\n Applies matrix multiplication to two tensors. `matmul` follows\n the complete broadcast rules,\n and its behavior is consistent with `np.matmul`.\n\n Currently, the input tensors' number of dimensions can be any, `matmul` can be used to\n achieve the `dot`, `matmul` and `batchmatmul`.\n\n The actual behavior depends on the shapes of :math:`x`, :math:`y` and the\n flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:\n\n - If a transpose flag is specified, the last two dimensions of the tensor\n are transposed. If the tensor is ndim-1 of shape, the transpose is invalid. If the tensor\n is ndim-1 of shape :math:`[D]`, then for :math:`x` it is treated as :math:`[1, D]`, whereas\n for :math:`y` it is the opposite: It is treated as :math:`[D, 1]`.\n\n The multiplication behavior depends on the dimensions of `x` and `y`. Specifically:\n\n - If both tensors are 1-dimensional, the dot product result is obtained.\n\n - If both tensors are 2-dimensional, the matrix-matrix product is obtained.\n\n - If the `x` is 1-dimensional and the `y` is 2-dimensional,\n a `1` is prepended to its dimension in order to conduct the matrix multiply.\n After the matrix multiply, the prepended dimension is removed.\n\n - If the `x` is 2-dimensional and `y` is 1-dimensional,\n the matrix-vector product is obtained.\n\n - If both arguments are at least 1-dimensional and at least one argument\n is N-dimensional (where N > 2), then a batched matrix multiply is obtained.\n If the first argument is 1-dimensional, a 1 is prepended to its dimension\n in order to conduct the batched matrix multiply and removed after.\n If the second argument is 1-dimensional, a 1 is appended to its\n dimension for the purpose of the batched matrix multiple and removed after.\n The non-matrix (exclude the last two dimensions) dimensions are\n broadcasted according the broadcast rule.\n For example, if input is a (j, 1, n, m) tensor and the other is a (k, m, p) tensor,\n out will be a (j, k, n, p) tensor.\n\n Args:\n x (Tensor): The input tensor which is a Tensor.\n y (Tensor): The input tensor which is a Tensor.\n transpose_x (bool): Whether to transpose :math:`x` before multiplication.\n transpose_y (bool): Whether to transpose :math:`y` before multiplication.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # vector * vector\n x_data = np.random.random([10]).astype(np.float32)\n y_data = np.random.random([10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [1]\n\n # matrix * vector\n x_data = np.random.random([10, 5]).astype(np.float32)\n y_data = np.random.random([5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10]\n\n # batched matrix * broadcasted vector\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([2]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5]\n\n # batched matrix * batched matrix\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([10, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5, 5]\n\n # batched matrix * broadcasted matrix\n x_data = np.random.random([10, 1, 5, 2]).astype(np.float32)\n y_data = np.random.random([1, 3, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 3, 5, 5]\n\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_matmul(x, y, transpose_x, transpose_y)\n\n if _in_legacy_dygraph():\n op_type = 'matmul_v2'\n op = getattr(_C_ops, op_type)\n return op(x, y, 'trans_x', transpose_x, 'trans_y', transpose_y)\n\n attrs = {\n 'trans_x': transpose_x,\n 'trans_y': transpose_y,\n }\n\n def __check_input(x, y):\n var_names = {'x': x, 'y': y}\n for name, val in var_names.items():\n check_variable_and_dtype(\n val, name,\n ['float16', 'float32', 'float64', 'complex64', 'complex128'],\n 'matmul')\n\n __check_input(x, y)\n\n helper = LayerHelper('matmul_v2', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef norm(x, p='fro', axis=None, keepdim=False, name=None):\n \"\"\"\n\n Returns the matrix norm (Frobenius) or vector norm (the 1-norm, the Euclidean\n or 2-norm, and in general the p-norm for p > 0) of a given tensor.\n\n .. note::\n This norm API is different from `numpy.linalg.norm`.\n This api supports high-order input tensors (rank >= 3), and certain axis need to be pointed out to calculate the norm.\n But `numpy.linalg.norm` only supports 1-D vector or 2-D matrix as input tensor.\n For p-order matrix norm, this api actually treats matrix as a flattened vector to calculate the vector norm, NOT REAL MATRIX NORM.\n\n Args:\n x (Tensor): The input tensor could be N-D tensor, and the input data\n type could be float32 or float64.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `0`, `1`, `2`,\n `inf`, `-inf` and any positive real number yielding the corresponding p-norm. Not supported: ord < 0 and nuclear norm.\n Default value is `fro`.\n axis (int|list|tuple, optional): The axis on which to apply norm operation. If axis is int\n or list(int)/tuple(int) with only one element, the vector norm is computed over the axis.\n If `axis < 0`, the dimension to norm operation is rank(input) + axis.\n If axis is a list(int)/tuple(int) with two elements, the matrix norm is computed over the axis.\n Defalut value is `None`.\n keepdim (bool, optional): Whether to reserve the reduced dimension in the\n output Tensor. The result tensor will have fewer dimension\n than the :attr:`input` unless :attr:`keepdim` is true, default\n value is False.\n name (str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: results of norm operation on the specified axis of input tensor,\n it's data type is the same as input's Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n shape=[2, 3, 4]\n np_input = np.arange(24).astype('float32') - 12\n np_input = np_input.reshape(shape)\n x = paddle.to_tensor(np_input)\n #[[[-12. -11. -10. -9.] [ -8. -7. -6. -5.] [ -4. -3. -2. -1.]]\n # [[ 0. 1. 2. 3.] [ 4. 5. 6. 7.] [ 8. 9. 10. 11.]]]\n\n # compute frobenius norm along last two dimensions.\n out_fro = paddle.linalg.norm(x, p='fro', axis=[0,1])\n # out_fro.numpy() [17.435596 16.911535 16.7332 16.911535]\n\n # compute 2-order vector norm along last dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=-1)\n #out_pnorm.numpy(): [[21.118711 13.190906 5.477226]\n # [ 3.7416575 11.224972 19.131126]]\n\n # compute 2-order norm along [0,1] dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=[0,1])\n #out_pnorm.numpy(): [17.435596 16.911535 16.7332 16.911535]\n\n # compute inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=np.inf)\n #out_pnorm.numpy() = [12.]\n out_pnorm = paddle.linalg.norm(x, p=np.inf, axis=0)\n #out_pnorm.numpy(): [[12. 11. 10. 9.] [8. 7. 6. 7.] [8. 9. 10. 11.]]\n\n # compute -inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=-np.inf)\n #out_pnorm.numpy(): [0.]\n out_pnorm = paddle.linalg.norm(x, p=-np.inf, axis=0)\n #out_pnorm.numpy(): [[0. 1. 2. 3.] [4. 5. 6. 5.] [4. 3. 2. 1.]]\n \"\"\"\n\n def frobenius_norm(input, dim=None, keepdim=False, name=None):\n \"\"\"\n The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n dim (list, optional): None for last two dimensions.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if dim is not None and not (isinstance(dim, list) and len(dim) == 2):\n raise ValueError(\n \"The dim of frobenius norm op should be None or two elements list!\"\n )\n if paddle.in_dynamic_mode():\n if dim is None:\n return _C_ops.frobenius_norm(input, 'keep_dim', keepdim,\n 'reduce_all', True)\n return _C_ops.frobenius_norm(input, 'dim', dim, 'keep_dim', keepdim,\n 'reduce_all', False)\n attrs = {'dim': dim, 'keep_dim': keepdim, 'reduce_all': False}\n if dim is None:\n attrs['reduce_all'] = True\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'frobenius_norm')\n\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='frobenius_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n# MASKED: vector_norm function (lines 277-318)\n\n def inf_norm(input,\n porder=None,\n axis=axis,\n keepdim=False,\n asvector=False,\n name=None):\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n helper.append_op(type='abs', inputs={'X': input}, outputs={'Out': out})\n reduce_out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n reduce_all = True if axis == None or axis == [] or asvector == True else False\n axis = axis if axis != None and axis != [] else [0]\n\n reduce_type = 'reduce_max' if porder == np.float(\n 'inf') else 'reduce_min'\n helper.append_op(\n type=reduce_type,\n inputs={'X': out},\n outputs={'Out': reduce_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n\n return reduce_out\n\n def p_matrix_norm(input, porder=1., axis=axis, keepdim=False, name=None):\n \"\"\"\n NOTE:\n This function actually treats the matrix as flattened vector to calculate vector norm instead of matrix norm.\n \"\"\"\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n\n block.append_op(\n type='pow',\n inputs={'X': abs_out},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': True if axis is None else False\n })\n porder\n block.append_op(\n type='pow',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n if axis is None and p is not None:\n if isinstance(p, str):\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n porder=p,\n axis=axis,\n keepdim=keepdim,\n asvector=True,\n name=name)\n else:\n raise ValueError(\"only valid p type is string or float, found {}\".\n format(type(p)))\n\n if isinstance(axis, tuple):\n axis = list(axis)\n if isinstance(axis, list) and len(axis) == 1:\n axis = axis[0]\n\n #calculate vector norm, where axis is int or list with only one integer\n if isinstance(axis, int):\n if isinstance(p, str):\n if p == \"fro\":\n return vector_norm(\n x,\n porder=2,\n axis=axis,\n keepdim=keepdim,\n asvector=False,\n name=name)\n\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n axis=axis,\n porder=p,\n keepdim=keepdim,\n asvector=False,\n name=name)\n else:\n raise ValueError(\n \"unspport p for p-order vector norm. except float, found {}\".\n format(p))\n #calculate matrix norm, where axis is list with two integers\n elif isinstance(axis, list) and len(axis) == 2:\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n elif p == np.inf or p == -np.inf:\n return inf_norm(x, porder=p, axis=axis, keepdim=keepdim, name=name)\n elif p == 0:\n raise ValueError(\n \"just suport axis type int or list (length of list <=1) if p = 0, found {}\".\n format(axis))\n else:\n return p_matrix_norm(\n x, porder=p, axis=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"except axis type int or list (length of list <=2), found {}\".\n format(axis))\n\n\ndef dist(x, y, p=2, name=None):\n r\"\"\"\n\n This OP returns the p-norm of (x - y). It is not a norm in a strict sense, only as a measure\n of distance. The shapes of x and y must be broadcastable. The definition is as follows, for\n details, please refer to the `numpy's broadcasting <https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html>`_:\n\n - Each input has at least one dimension.\n - Match the two input dimensions from back to front, the dimension sizes must either be equal, one of them is 1, or one of them does not exist.\n\n Where, z = x - y, the shapes of x and y are broadcastable, then the shape of z can be\n obtained as follows:\n\n 1. If the number of dimensions of x and y are not equal, prepend 1 to the dimensions of the\n tensor with fewer dimensions.\n\n For example, The shape of x is [8, 1, 6, 1], the shape of y is [7, 1, 5], prepend 1 to the\n dimension of y.\n\n x (4-D Tensor): 8 x 1 x 6 x 1\n\n y (4-D Tensor): 1 x 7 x 1 x 5\n\n 2. Determine the size of each dimension of the output z: choose the maximum value from the\n two input dimensions.\n\n z (4-D Tensor): 8 x 7 x 6 x 5\n\n If the number of dimensions of the two inputs are the same, the size of the output can be\n directly determined in step 2. When p takes different values, the norm formula is as follows:\n\n When p = 0, defining $0^0=0$, the zero-norm of z is simply the number of non-zero elements of z.\n\n .. math::\n\n ||z||_{0}=\\lim_{p \\\\rightarrow 0}\\sum_{i=1}^{m}|z_i|^{p}\n\n When p = inf, the inf-norm of z is the maximum element of z.\n\n .. math::\n\n ||z||_\\infty=\\max_i |z_i|\n\n When p = -inf, the negative-inf-norm of z is the minimum element of z.\n\n .. math::\n\n ||z||_{-\\infty}=\\min_i |z_i|\n\n Otherwise, the p-norm of z follows the formula,\n\n .. math::\n\n ||z||_{p}=(\\sum_{i=1}^{m}|z_i|^p)^{\\\\frac{1}{p}}\n\n Args:\n x (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n y (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n p (float, optional): The norm to be computed, its data type is float32 or float64. Default: 2.\n\n Returns:\n Tensor: Tensor that is the p-norm of (x - y).\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor(np.array([[3, 3],[3, 3]]), \"float32\")\n y = paddle.to_tensor(np.array([[3, 3],[3, 1]]), \"float32\")\n out = paddle.dist(x, y, 0)\n print(out) # out = [1.]\n\n out = paddle.dist(x, y, 2)\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"inf\"))\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"-inf\"))\n print(out) # out = [0.]\n \"\"\"\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'dist')\n check_variable_and_dtype(y, 'dtype', ['float32', 'float64'], 'dist')\n check_type(p, 'p', (float, int), 'dist')\n helper = LayerHelper(\"dist\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n outputs = {'Out': [out]}\n attrs = {\"p\": float(p)}\n helper.append_op(\n type='dist', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef cond(x, p=None, name=None):\n \"\"\"\n\n Computes the condition number of a matrix or batches of matrices with respect to a matrix norm ``p``.\n\n Args:\n x (Tensor): The input tensor could be tensor of shape ``(*, m, n)`` where ``*`` is zero or more batch dimensions\n for ``p`` in ``(2, -2)``, or of shape ``(*, n, n)`` where every matrix is invertible for any supported ``p``.\n And the input data type could be ``float32`` or ``float64``.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `nuc`, `1`, `-1`, `2`, `-2`,\n `inf`, `-inf`. Default value is `None`, meaning that the order of the norm is `2`.\n name (str, optional): The default value is `None`. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: computing results of condition number, its data type is the same as input Tensor ``x``.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1., 0, -1], [0, 1, 0], [1, 0, 1]])\n\n # compute conditional number when p is None\n out = paddle.linalg.cond(x)\n # out.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is 'fro'\n out_fro = paddle.linalg.cond(x, p='fro')\n # out_fro.numpy() [3.1622777]\n\n # compute conditional number when order of the norm is 'nuc'\n out_nuc = paddle.linalg.cond(x, p='nuc')\n # out_nuc.numpy() [9.2426405]\n\n # compute conditional number when order of the norm is 1\n out_1 = paddle.linalg.cond(x, p=1)\n # out_1.numpy() [2.]\n\n # compute conditional number when order of the norm is -1\n out_minus_1 = paddle.linalg.cond(x, p=-1)\n # out_minus_1.numpy() [1.]\n\n # compute conditional number when order of the norm is 2\n out_2 = paddle.linalg.cond(x, p=2)\n # out_2.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is -1\n out_minus_2 = paddle.linalg.cond(x, p=-2)\n # out_minus_2.numpy() [0.70710677]\n\n # compute conditional number when order of the norm is inf\n out_inf = paddle.linalg.cond(x, p=np.inf)\n # out_inf.numpy() [2.]\n\n # compute conditional number when order of the norm is -inf\n out_minus_inf = paddle.linalg.cond(x, p=-np.inf)\n # out_minus_inf.numpy() [1.]\n\n a = paddle.to_tensor(np.random.randn(2, 4, 4).astype('float32'))\n # a.numpy()\n # [[[ 0.14063153 -0.996288 0.7996131 -0.02571543]\n # [-0.16303636 1.5534962 -0.49919784 -0.04402903]\n # [-1.1341571 -0.6022629 0.5445269 0.29154757]\n # [-0.16816919 -0.30972657 1.7521842 -0.5402487 ]]\n # [[-0.58081484 0.12402827 0.7229862 -0.55046535]\n # [-0.15178485 -1.1604939 0.75810957 0.30971205]\n # [-0.9669573 1.0940945 -0.27363303 -0.35416734]\n # [-1.216529 2.0018666 -0.7773689 -0.17556527]]]\n a_cond_fro = paddle.linalg.cond(a, p='fro')\n # a_cond_fro.numpy() [31.572273 28.120834]\n\n b = paddle.to_tensor(np.random.randn(2, 3, 4).astype('float64'))\n # b.numpy()\n # [[[ 1.61707487 0.46829144 0.38130416 0.82546736]\n # [-1.72710298 0.08866375 -0.62518804 0.16128892]\n # [-0.02822879 -1.67764516 0.11141444 0.3220113 ]]\n # [[ 0.22524372 0.62474921 -0.85503233 -1.03960523]\n # [-0.76620689 0.56673047 0.85064753 -0.45158196]\n # [ 1.47595418 2.23646462 1.5701758 0.10497519]]]\n b_cond_2 = paddle.linalg.cond(b, p=2)\n # b_cond_2.numpy() [3.30064451 2.51976252]\n\n \"\"\"\n\n def mat_norm(input, porder=1., axis=None):\n \"\"\"\n NOTE:\n Calculate the matrix norm of a square matrix or batches of square matrices,\n when porder is in (1, -1, inf, -inf)\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n axis = axis if axis != None and axis != [] else [0]\n keepdim = False\n\n if paddle.in_dynamic_mode():\n abs_out = _C_ops.abs(input)\n sum_out = _C_ops.reduce_sum(abs_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == 1 or porder == np.inf:\n return _C_ops.reduce_max(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == -1 or porder == -np.inf:\n return _C_ops.reduce_min(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n\n block = LayerHelper('norm', **locals())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n block.append_op(\n type='reduce_sum',\n inputs={'X': abs_out},\n outputs={'Out': sum_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 1 or porder == np.inf:\n block.append_op(\n type='reduce_max',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n if porder == -1 or porder == -np.inf:\n block.append_op(\n type='reduce_min',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n\n def fro_norm(input, porder=2, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the frobenius norm of a square matrix or batches of square matrices.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n if paddle.in_dynamic_mode():\n pow_out = _C_ops.pow(input, 'factor', porder)\n sum_out_1 = _C_ops.reduce_sum(pow_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n sum_out_2 = _C_ops.reduce_sum(sum_out_1, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n return _C_ops.pow(sum_out_2, 'factor', float(1. / porder))\n\n block = LayerHelper('norm', **locals())\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_1 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_2 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='pow',\n inputs={'X': input},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out_1},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_sum',\n inputs={'X': sum_out_1},\n outputs={'Out': sum_out_2},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='pow',\n inputs={'X': sum_out_2},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n def svd_norm(input, porder, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the matrix norm, which is related to singular values, of a matrix\n or batches of matrices, including nuclear norm, 2-norm and (-2)-norm.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n u, s, vh = svd(input, full_matrices=False)\n\n if paddle.in_dynamic_mode():\n if porder == \"nuc\":\n return _C_ops.reduce_sum(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n max_out = _C_ops.reduce_max(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n min_out = _C_ops.reduce_min(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n if porder == 2:\n return _C_ops.elementwise_div(max_out, min_out, 'aixs', axis,\n 'use_mkldnn', False)\n if porder == -2:\n return _C_ops.elementwise_div(min_out, max_out, 'aixs', axis,\n 'use_mkldnn', False)\n\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n if porder == \"nuc\":\n block.append_op(\n type='reduce_sum',\n inputs={'X': s},\n outputs={'Out': out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n max_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n min_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_min',\n inputs={'X': s},\n outputs={'Out': min_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': max_out,\n 'Y': min_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n if porder == -2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': min_out,\n 'Y': max_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n\n def empty_tensor(input, shape):\n if paddle.in_dynamic_mode():\n return input.reshape(shape)\n raise ValueError(\"only support x is nonempty tensor in static mode\")\n\n x_shape = list(x.shape)\n if not len(x_shape) >= 2:\n raise ValueError(\"input should be a matrix or batches of matrices, \" +\n \"but the dimention of received input is {}\".format(\n len(x_shape)))\n if p == None:\n p = 2\n x_size = 0 if (0 in x_shape) else 1\n if p in (\"fro\", \"nuc\", 1, -1, np.inf, -np.inf):\n if x_shape[len(x_shape) - 1] == x_shape[len(x_shape) - 2]:\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n x_inv = x.inverse()\n if p == \"fro\":\n return fro_norm(x) * fro_norm(x_inv)\n if p == \"nuc\":\n return svd_norm(x, p) * svd_norm(x_inv, p)\n if p in (1, -1):\n return mat_norm(\n x, porder=p, axis=[-2]) * mat_norm(\n x_inv, porder=p, axis=[-2])\n if p in (np.inf, -np.inf):\n return mat_norm(\n x, porder=p, axis=[-1]) * mat_norm(\n x_inv, porder=p, axis=[-1])\n else:\n raise ValueError(\"only support p is {} when input is a \".format(p) +\n \"square matrix or batches of square matrices\")\n elif p in (2, -2):\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n return svd_norm(x, porder=p)\n else:\n raise ValueError(\n \"unsupported {} for p, only supporting ('fro', 'nuc', \".format(\n p) + \"1, -1, 2, -2, inf, -inf) or none\")\n\n\ndef dot(x, y, name=None):\n \"\"\"\n This operator calculates inner product for vectors.\n\n .. note::\n Support 1-d and 2-d Tensor. When it is 2d, the first dimension of this matrix\n is the batch dimension, which means that the vectors of multiple batches are dotted.\n\n Parameters:\n x(Tensor): 1-D or 2-D ``Tensor``. Its dtype should be ``float32``, ``float64``, ``int32``, ``int64``\n y(Tensor): 1-D or 2-D ``Tensor``. Its dtype soulde be ``float32``, ``float64``, ``int32``, ``int64``\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: the calculated result Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x_data = np.random.uniform(0.1, 1, [10]).astype(np.float32)\n y_data = np.random.uniform(1, 3, [10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.dot(x, y)\n print(z)\n\n \"\"\"\n op_type = 'dot'\n # skip var type check in dygraph mode to improve efficiency\n if paddle.in_dynamic_mode():\n op = getattr(_C_ops, op_type)\n return op(x, y)\n\n assert x is not None, 'x cannot be None in {}'.format(op_type)\n assert y is not None, 'y cannot be None in {}'.format(op_type)\n\n check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n check_variable_and_dtype(y, 'y', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n\n helper = LayerHelper(op_type, **locals())\n if name is None:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n else:\n out = helper.create_variable(\n name=name, dtype=x.dtype, persistable=False)\n helper.append_op(\n type=\"dot\", inputs={'X': x,\n 'Y': y}, attrs={}, outputs={\"Out\": out})\n return out\n\n\ndef cov(x, rowvar=True, ddof=True, fweights=None, aweights=None, name=None):\n \"\"\"\n Estimate the covariance matrix of the input variables, given data and weights.\n\n A covariance matrix is a square matrix, indicate the covariance of each pair variables in the input matrix.\n For example, for an N-dimensional samples X=[x1,x2,…xN]T, then the covariance matrix \n element Cij is the covariance of xi and xj. The element Cii is the variance of xi itself.\n\n Parameters:\n x(Tensor): A N-D(N<=2) Tensor containing multiple variables and observations. By default, each row of x represents a variable. Also see rowvar below.\n rowvar(Bool, optional): If rowvar is True (default), then each row represents a variable, with observations in the columns. Default: True\n ddof(Bool, optional): If ddof=True will return the unbiased estimate, and ddof=False will return the simple average. Default: True\n fweights(Tensor, optional): 1-D Tensor of integer frequency weights; The number of times each observation vector should be repeated. Default: None\n aweights(Tensor, optional): 1-D Tensor of observation vector weights. How important of the observation vector, larger data means this element is more important. Default: None\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: The covariance matrix Tensor of the variables.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n\n xt = paddle.rand((3,4))\n paddle.linalg.cov(xt)\n\n '''\n Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n [[0.07918842, 0.06127326, 0.01493049],\n [0.06127326, 0.06166256, 0.00302668],\n [0.01493049, 0.00302668, 0.01632146]])\n '''\n \"\"\"\n op_type = 'cov'\n if len(x.shape) > 2 or len(x.shape) < 1:\n raise ValueError(\n \"Input(x) only support N-D (1<=N<=2) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cov')\n nx = x\n if len(x.shape) == 1:\n nx = x.reshape((1, -1))\n if not rowvar and nx.shape[0] != 1:\n nx = nx.t()\n w = None\n observation_num = nx.shape[1]\n if fweights is not None:\n w = fweights.astype(nx.dtype)\n if len(w.shape) > 1:\n raise ValueError(\n \"Input(fweights) only support N-D (N<=1) tensor in cov, but received \"\n \"shape of Input(input) is %s.\" % len(fweights.shape))\n if fweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(fweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(fweights) is {}.\".format(observation_num,\n fweights.shape[0]))\n if fweights.min() < 0:\n raise ValueError(\n \"The value of Input(fweights) cannot be negtive, but received \"\n \"min of Input(fweights) is {}.\".format(fweights.min()))\n if not paddle.all(fweights == paddle.round(fweights.astype('float64'))):\n raise ValueError(\"Input(fweights) must be integer \")\n\n if aweights is not None:\n aw = aweights.astype(nx.dtype)\n if len(aw.shape) > 1:\n raise ValueError(\n \"Input(aweights) only support N-D (N<=1) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(aweights.shape))\n check_variable_and_dtype(aweights, 'dtype', ['float32', 'float64'],\n 'cov')\n if aweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(aweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(aweights) is {}.\".format(observation_num,\n aweights.shape[0]))\n if aweights.min() < 0:\n raise ValueError(\n \"The value of Input(aweights) cannot be negtive, but received \"\n \"min of Input(aweights) is {}.\".format(aweights.min()))\n if w is not None:\n w = w * aw\n else:\n w = aw\n\n w_sum = paddle.to_tensor(observation_num, dtype=nx.dtype)\n if fweights is not None or aweights is not None:\n w_sum = w.sum()\n if w_sum.item() == 0:\n raise ValueError(\"The sum of weights is zero, can't be normalized.\")\n\n if w is not None:\n nx_w = nx * w\n avg = (nx_w).sum(axis=1) / w_sum\n else:\n avg = nx.sum(axis=1) / w_sum\n nx_w = nx\n\n if w is not None and aweights is not None and ddof == True:\n norm_factor = w_sum - (w * aweights).sum() / w_sum\n else:\n norm_factor = w_sum - ddof\n if norm_factor <= 0:\n norm_factor = paddle.to_tensor(0, dtype=nx.dtype)\n nx = nx - avg.unsqueeze(1)\n xxt = paddle.mm(nx, nx_w.t().conj())\n cov = paddle.divide(xxt, norm_factor).squeeze()\n return cov\n\n\ndef t(input, name=None):\n \"\"\"\n Transpose <=2-D tensor.\n 0-D and 1-D tensors are returned as it is and 2-D tensor is equal to\n the paddle.transpose function which perm dimensions set 0 and 1.\n\n Args:\n input (Tensor): The input Tensor. It is a N-D (N<=2) Tensor of data types float16, float32, float64, int32.\n name(str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`\n Returns:\n Tensor: A transposed n-D Tensor, with data type being float16, float32, float64, int32, int64.\n\n For Example:\n\n .. code-block:: text\n\n # Example 1 (0-D tensor)\n x = tensor([0.79])\n paddle.t(x) = tensor([0.79])\n\n # Example 2 (1-D tensor)\n x = tensor([0.79, 0.84, 0.32])\n paddle.t(x) = tensor([0.79, 0.84, 0.32])\n\n # Example 3 (2-D tensor)\n x = tensor([0.79, 0.84, 0.32],\n [0.64, 0.14, 0.57])\n paddle.t(x) = tensor([0.79, 0.64],\n [0.84, 0.14],\n [0.32, 0.57])\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n x = paddle.ones(shape=[2, 3], dtype='int32')\n x_transposed = paddle.t(x)\n print(x_transposed.shape)\n # [3, 2]\n \"\"\"\n if len(input.shape) > 2:\n raise ValueError(\n \"Input(input) only support N-D (N<=2) tensor, but received \"\n \"length of Input(input) is %s. Perhaps you can use paddle.\"\n \"tensor.transpose() instead.\" % len(input.shape))\n if paddle.in_dynamic_mode():\n if len(input.shape) == 1:\n return input\n # 2-D tensor\n perm = [1, 0]\n out, _ = _C_ops.transpose2(input, 'axis', perm)\n return out\n\n check_variable_and_dtype(\n input, 'input', ['float16', 'float32', 'float64', 'int32',\n 'int64'], 'transpose')\n\n helper = LayerHelper('t', **locals())\n out = helper.create_variable_for_type_inference(input.dtype)\n input_shape = helper.create_variable_for_type_inference(input.dtype)\n if len(input.shape) == 1:\n out = input\n else:\n helper.append_op(\n type='transpose2',\n inputs={'X': [input]},\n outputs={'Out': [out],\n 'XShape': [input_shape]},\n attrs={'axis': [1, 0]})\n return out\n\n\ndef cross(x, y, axis=None, name=None):\n \"\"\"\n Computes the cross product between two tensors along an axis.\n\n Inputs must have the same shape, and the length of their axes should be equal to 3.\n If `axis` is not given, it defaults to the first axis found with the length 3.\n\n Args:\n x (Tensor): The first input tensor.\n y (Tensor): The second input tensor.\n axis (int, optional): The axis along which to compute the cross product. It defaults to the first axis found with the length 3.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor. A Tensor with same data type as `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0],\n [3.0, 3.0, 3.0]])\n y = paddle.to_tensor([[1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0]])\n\n z1 = paddle.cross(x, y)\n # [[-1. -1. -1.]\n # [ 2. 2. 2.]\n # [-1. -1. -1.]]\n\n z2 = paddle.cross(x, y, axis=1)\n # [[0. 0. 0.]\n # [0. 0. 0.]\n # [0. 0. 0.]]\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_cross(x, y, axis)\n else:\n if _in_legacy_dygraph():\n if axis is not None:\n return _C_ops.cross(x, y, 'dim', axis)\n else:\n return _C_ops.cross(x, y)\n else:\n helper = LayerHelper(\"cross\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n attrs = dict()\n attrs['dim'] = axis\n\n helper.append_op(\n type='cross',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef cholesky(x, upper=False, name=None):\n r\"\"\"\n Computes the Cholesky decomposition of one symmetric positive-definite\n matrix or batches of symmetric positive-definite matrice.\n\n If `upper` is `True`, the decomposition has the form :math:`A = U^{T}U` ,\n and the returned matrix :math:`U` is upper-triangular. Otherwise, the\n decomposition has the form :math:`A = LL^{T}` , and the returned matrix\n :math:`L` is lower-triangular.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[*, M, M]`,\n where * is zero or more batch dimensions, and matrices on the\n inner-most 2 dimensions all should be symmetric positive-definite.\n Its data type should be float32 or float64.\n upper (bool): The flag indicating whether to return upper or lower\n triangular matrices. Default: False.\n\n Returns:\n Tensor: A Tensor with same shape and data type as `x`. It represents \\\n triangular matrices generated by Cholesky decomposition.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n a = np.random.rand(3, 3)\n a_t = np.transpose(a, [1, 0])\n x_data = np.matmul(a, a_t) + 1e-03\n x = paddle.to_tensor(x_data)\n out = paddle.linalg.cholesky(x, upper=False)\n print(out)\n # [[1.190523 0. 0. ]\n # [0.9906703 0.27676893 0. ]\n # [1.25450498 0.05600871 0.06400121]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky(x, \"upper\", upper)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cholesky')\n check_type(upper, 'upper', bool, 'cholesky')\n helper = LayerHelper('cholesky', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='cholesky',\n inputs={'X': [x]},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef matrix_rank(x, tol=None, hermitian=False, name=None):\n r\"\"\"\n Computes the rank of a matrix.\n\n The rank of a matrix is the number of singular values that are greater than the specified `tol` threshold when hermitian=False,\n or the number of eigenvalues in absolute value that are greater than the specified `tol` threshold when hermitian=True.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., m, n]`, where `...` is zero or more batch dimensions. If `x` is a batch\n of matrices then the output has the same batch dimensions. The data type of `x` should be float32 or float64.\n tol (float,Tensor,optional): the tolerance value. Default: None. If `tol` is not specified, and `sigma` is the largest\n singular value (or eigenvalues in absolute value), and `eps` is the epsilon value for the dtype of `x`, then `tol` is computed\n with formula `tol=sigma * max(m,n) * eps`. Note that if `x` is a batch of matrices, `tol` is computed this way for every batch.\n hermitian (bool,optional): indicates whether `x` is Hermitian. Default: False. When hermitian=True, `x` is assumed to be Hermitian,\n enabling a more efficient method for finding eigenvalues, but `x` is not checked inside the function. Instead, We just use\n the lower triangular of the matrix to compute.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: Rank of tensor x.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n a = paddle.eye(10)\n b = paddle.linalg.matrix_rank(a)\n print(b)\n # b = [10]\n\n c = paddle.ones(shape=[3, 4, 5, 5])\n d = paddle.linalg.matrix_rank(c, tol=0.01, hermitian=True)\n print(d)\n # d = [[1, 1, 1, 1],\n # [1, 1, 1, 1],\n # [1, 1, 1, 1]]\n\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if tol is None:\n tol_tensor = None\n tol_attr = 0.0\n use_default_tol = True\n elif isinstance(tol, Variable):\n if tol.dtype != x.dtype:\n tol_tensor = cast(tol, x.dtype)\n else:\n tol_tensor = tol\n tol_attr = 0.0\n use_default_tol = False\n else:\n tol_tensor = None\n tol_attr = float(tol)\n use_default_tol = False\n return _C_ops.matrix_rank(x, tol_tensor, \"tol\", tol_attr, 'hermitian',\n hermitian, 'use_default_tol', use_default_tol)\n\n inputs = {}\n attrs = {}\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'matrix_rank')\n inputs['X'] = x\n if tol is None:\n attrs['use_default_tol'] = True\n elif isinstance(tol, Variable):\n check_variable_and_dtype(tol, 'tol', ['float32'], 'matrix_rank')\n attrs['use_default_tol'] = False\n if tol.dtype != x.dtype:\n inputs['TolTensor'] = cast(tol, x.dtype)\n else:\n inputs['TolTensor'] = tol\n else:\n check_type(tol, 'tol', float, 'matrix_rank')\n attrs['use_default_tol'] = False\n attrs['tol'] = tol\n check_type(hermitian, 'hermitian', bool, 'matrix_rank')\n attrs['hermitian'] = hermitian\n\n helper = LayerHelper('matrix_rank', **locals())\n out = helper.create_variable_for_type_inference(dtype='int32')\n helper.append_op(\n type='matrix_rank', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef bmm(x, y, name=None):\n \"\"\"\n Applies batched matrix multiplication to two tensors.\n\n Both of the two input tensors must be three-dementional and share the same batch size.\n\n if x is a (b, m, k) tensor, y is a (b, k, n) tensor, the output will be a (b, m, n) tensor.\n\n Args:\n x (Tensor): The input Tensor.\n y (Tensor): The input Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The product Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n # In imperative mode:\n # size x: (2, 2, 3) and y: (2, 3, 2)\n x = paddle.to_tensor([[[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0]],\n [[3.0, 3.0, 3.0],\n [4.0, 4.0, 4.0]]])\n y = paddle.to_tensor([[[1.0, 1.0],[2.0, 2.0],[3.0, 3.0]],\n [[4.0, 4.0],[5.0, 5.0],[6.0, 6.0]]])\n out = paddle.bmm(x, y)\n #output size: (2, 2, 2)\n #output value:\n #[[[6.0, 6.0],[12.0, 12.0]],[[45.0, 45.0],[60.0, 60.0]]]\n out_np = out.numpy()\n\n \"\"\"\n x_shape = x.shape\n y_shape = y.shape\n if not len(x_shape) == len(y_shape) == 3:\n raise ValueError(\n \"x and y should be 3-dimensional. But received x's dimention: {}, y's dimention: {}\".\n format(x_shape, y_shape))\n if x_shape[2] != y_shape[1]:\n raise ValueError(\n \"x's width must be equal with y's height. But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n if x_shape[0] != y_shape[0]:\n raise ValueError(\n \"x's batch (shape[0]) must be equal with y's batch (shape[0]). But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.bmm(x, y)\n\n helper = LayerHelper('bmm', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='bmm', inputs={'X': x, 'Y': y}, outputs={'Out': out})\n return out\n\n\ndef histogram(input, bins=100, min=0, max=0, name=None):\n \"\"\"\n Computes the histogram of a tensor. The elements are sorted into equal width bins between min and max.\n If min and max are both zero, the minimum and maximum values of the data are used.\n\n Args:\n input (Tensor): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor\n should be float32, float64, int32, int64.\n bins (int): number of histogram bins\n min (int): lower end of the range (inclusive)\n max (int): upper end of the range (inclusive)\n\n Returns:\n Tensor: data type is int64, shape is (nbins,).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n inputs = paddle.to_tensor([1, 2, 1])\n result = paddle.histogram(inputs, bins=4, min=0, max=3)\n print(result) # [0, 2, 1, 0]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.histogram(input, \"bins\", bins, \"min\", min, \"max\", max)\n\n helper = LayerHelper('histogram', **locals())\n check_variable_and_dtype(\n input, 'X', ['int32', 'int64', 'float32', 'float64'], 'histogram')\n out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)\n helper.append_op(\n type='histogram',\n inputs={'X': input},\n outputs={'Out': out},\n attrs={'bins': bins,\n 'min': min,\n 'max': max})\n return out\n\n\ndef bincount(x, weights=None, minlength=0, name=None):\n \"\"\"\n Computes frequency of each value in the input tensor. \n\n Args:\n x (Tensor): A Tensor with non-negative integer. Should be 1-D tensor.\n weights (Tensor, optional): Weight for each value in the input tensor. Should have the same shape as input. Default is None.\n minlength (int, optional): Minimum number of bins. Should be non-negative integer. Default is 0.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor of frequency.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([1, 2, 1, 4, 5])\n result1 = paddle.bincount(x)\n print(result1) # [0, 2, 1, 0, 1, 1]\n\n w = paddle.to_tensor([2.1, 0.4, 0.1, 0.5, 0.5])\n result2 = paddle.bincount(x, weights=w)\n print(result2) # [0., 2.19999981, 0.40000001, 0., 0.50000000, 0.50000000]\n \"\"\"\n if x.dtype not in [paddle.int32, paddle.int64]:\n raise TypeError(\"Elements in Input(x) should all be integers\")\n\n if paddle.in_dynamic_mode():\n return _C_ops.bincount(x, weights, \"minlength\", minlength)\n\n helper = LayerHelper('bincount', **locals())\n\n check_variable_and_dtype(x, 'X', ['int32', 'int64'], 'bincount')\n\n if weights is not None:\n check_variable_and_dtype(weights, 'Weights',\n ['int32', 'int64', 'float32', 'float64'],\n 'bincount')\n out = helper.create_variable_for_type_inference(dtype=weights.dtype)\n else:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='bincount',\n inputs={'X': x,\n 'Weights': weights},\n outputs={'Out': out},\n attrs={'minlength': minlength})\n return out\n\n\ndef mv(x, vec, name=None):\n \"\"\"\n Performs a matrix-vector product of the matrix x and the vector vec.\n\n Args:\n x (Tensor): A tensor with shape :math:`[M, N]` , The data type of the input Tensor x\n should be one of float32, float64.\n vec (Tensor): A tensor with shape :math:`[N]` , The data type of the input Tensor x\n should be one of float32, float64.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor which is producted by x and vec.\n\n Examples:\n .. code-block:: python\n\n # x: [M, N], vec: [N]\n # paddle.mv(x, vec) # out: [M]\n\n import numpy as np\n import paddle\n\n x_data = np.array([[2, 1, 3], [3, 0, 1]]).astype(\"float64\")\n x = paddle.to_tensor(x_data)\n vec_data = np.array([3, 5, 1])\n vec = paddle.to_tensor(vec_data).astype(\"float64\")\n out = paddle.mv(x, vec)\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_mv(x, vec)\n else:\n if _in_legacy_dygraph():\n out = _C_ops.mv(x, vec)\n return out\n else:\n\n def __check_input(x, vec):\n var_names = {'x': x, 'vec': vec}\n for name, val in var_names.items():\n check_variable_and_dtype(val, name, ['float32', 'float64'],\n 'mv')\n x_shape = list(x.shape)\n vec_shape = list(vec.shape)\n if len(x_shape) != 2:\n raise ValueError(\n \"x should be 2-dimensional. But received x's dimention: {}\".\n format(x_shape))\n if len(vec_shape) != 1:\n raise ValueError(\n \"vec should be 1-dimensional. But received vec's dimention: {}\".\n format(vec_shape))\n\n __check_input(x, vec)\n\n helper = LayerHelper('mv', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='mv', inputs={'X': x,\n 'Vec': vec}, outputs={'Out': out})\n return out\n\n\ndef det(x, name=None):\n \"\"\"\n Calculates determinant value of a square matrix or batches of square matrices.\n Args:\n x (Tensor): input (Tensor): the input matrix of size `(n, n)` or the batch of matrices of size\n `(*, n, n)` where `*` is one or more batch dimensions.\n Returns:\n y (Tensor):the determinant value of a square matrix or batches of square matrices.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.det(x)\n\n print(A)\n\n # [ 0.02547996, 2.52317095, -6.15900707])\n\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.determinant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'det')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('determinant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='determinant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef slogdet(x, name=None):\n \"\"\"\n Calculates the sign and natural logarithm of the absolute value of a square matrix's or batches square matrices' determinant.\n The determinant can be computed with ``sign * exp(logabsdet)\n\n Supports input of float, double\n\n Note that for matrices that have zero determinant, this returns ``(0, -inf)``\n Args:\n x (Tensor): the batch of matrices of size :math:`(*, n, n)`\n where math:`*` is one or more batch dimensions.\n\n Returns:\n y (Tensor): A tensor containing the sign of the determinant and the natural logarithm\n of the absolute value of determinant, respectively.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.slogdet(x)\n\n print(A)\n\n # [[ 1. , 1. , -1. ],\n # [-0.98610914, -0.43010661, -0.10872950]])\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.slogdeterminant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'slogdet')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('slogdeterminant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='slogdeterminant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef svd(x, full_matrices=False, name=None):\n r\"\"\"\n Computes the singular value decomposition of one matrix or a batch of regular matrices.\n\n Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies:\n\n .. math::\n X = U * diag(S) * VT\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., N, M]`,\n where `...` is zero or more batch dimensions. N and M can be arbitraty\n positive number. Note that if x is sigular matrices, the grad is numerical\n instable. The data type of x should be float32 or float64.\n full_matrices (bool): A flag to control the behavor of svd.\n If full_matrices = True, svd op will compute full U and V matrics,\n which means shape of U is `[..., N, N]`, shape of V is `[..., M, M]`. K = min(M, N).\n If full_matrices = False, svd op will use a economic method to store U and V.\n which means shape of U is `[..., N, K]`, shape of V is `[..., M, K]`. K = min(M, N).\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple of 3 tensors: (U, S, VH). VH is the conjugate transpose of V. S is the singlar value vectors of matrics with shape `[..., K]`\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 2.0], [1.0, 3.0], [4.0, 6.0]]).astype('float64')\n x = x.reshape([3, 2])\n u, s, vh = paddle.linalg.svd(x)\n print (u)\n #U = [[ 0.27364809, -0.21695147 ],\n # [ 0.37892198, -0.87112408 ],\n # [ 0.8840446 , 0.44053933 ]]\n\n print (s)\n #S = [8.14753743, 0.78589688]\n print (vh)\n #VT= [[ 0.51411221, 0.85772294],\n # [ 0.85772294, -0.51411221]]\n\n # one can verify : U * S * VT == X\n # U * UH == I\n # V * VH == I\n \"\"\"\n\n if paddle.in_dynamic_mode():\n return _C_ops.svd(x, 'full_matrices', full_matrices)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'svd')\n check_type(full_matrices, 'full_matrices', bool, 'svd')\n helper = LayerHelper('svd', **locals())\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n vh = helper.create_variable_for_type_inference(dtype=x.dtype)\n s = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['full_matrices'] = full_matrices\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vh,\n 'S': s},\n attrs=attrs, )\n return u, s, vh\n\n\ndef matrix_power(x, n, name=None):\n r\"\"\"\n Computes the n-th power of a square matrix or a batch of square matrices.\n\n Let :math:`X` be a sqaure matrix or a batch of square matrices, :math:`n` be\n an exponent, the equation should be:\n\n .. math::\n Out = X ^ {n}\n\n Specifically,\n\n - If `n > 0`, it returns the matrix or a batch of matrices raised to the power\n of `n`.\n\n - If `n = 0`, it returns the identity matrix or a batch of identity matrices.\n\n - If `n < 0`, it returns the inverse of each matrix (if invertible) raised to\n the power of `abs(n)`.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices to be raised\n to power `n`. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n n (int): The exponent. It can be any positive, negative integer or zero.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The n-th power of the matrix (or the batch of matrices) `x`. Its\n data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1, 2, 3],\n [1, 4, 9],\n [1, 8, 27]], dtype='float64')\n print(paddle.linalg.matrix_power(x, 2))\n # [[6. , 34. , 102.],\n # [14. , 90. , 282.],\n # [36. , 250., 804.]]\n\n print(paddle.linalg.matrix_power(x, 0))\n # [[1., 0., 0.],\n # [0., 1., 0.],\n # [0., 0., 1.]]\n\n print(paddle.linalg.matrix_power(x, -2))\n # [[ 12.91666667, -12.75000000, 2.83333333 ],\n # [-7.66666667 , 8. , -1.83333333 ],\n # [ 1.80555556 , -1.91666667 , 0.44444444 ]]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.matrix_power(x, \"n\", n)\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'matrix_power')\n check_type(n, 'n', int, 'matrix_power')\n helper = LayerHelper('matrix_power', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matrix_power',\n inputs={'X': x},\n outputs={'Out': out},\n attrs={'n': n})\n return out\n\n\ndef qr(x, mode=\"reduced\", name=None):\n r\"\"\"\n Computes the QR decomposition of one matrix or batches of matrice (backward is unsupported now).\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., M, N]`,\n where ... is zero or more batch dimensions. M and N can be arbitrary\n positive number. The data type of x should be float32 or float64. \n mode (str, optional): A flag to control the behavior of qr, the default is \"reduced\". \n Suppose x's shape is `[..., M, N]` and denoting `K = min(M, N)`:\n If mode = \"reduced\", qr op will return reduced Q and R matrices, \n which means Q's shape is `[..., M, K]` and R's shape is `[..., K, N]`.\n If mode = \"complete\", qr op will return complete Q and R matrices, \n which means Q's shape is `[..., M, M]` and R's shape is `[..., M, N]`.\n If mode = \"r\", qr op will only return reduced R matrix, which means\n R's shape is `[..., K, N]`.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n If mode = \"reduced\" or mode = \"complete\", qr will return a two tensor-tuple, which represents Q and R. \n If mode = \"r\", qr will return a tensor which represents R.\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n q, r = paddle.linalg.qr(x)\n print (q)\n print (r)\n\n # Q = [[-0.16903085, 0.89708523],\n # [-0.50709255, 0.27602622],\n # [-0.84515425, -0.34503278]])\n\n # R = [[-5.91607978, -7.43735744],\n # [ 0. , 0.82807867]])\n \n # one can verify : X = Q * R ; \n \"\"\"\n if paddle.in_dynamic_mode():\n q, r = _C_ops.qr(x, 'mode', mode)\n if mode == \"r\":\n return r\n else:\n return q, r\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'qr')\n check_type(mode, 'mode', str, 'qr')\n helper = LayerHelper('qr', **locals())\n q = helper.create_variable_for_type_inference(dtype=x.dtype)\n r = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['mode'] = mode\n helper.append_op(\n type='qr', inputs={'X': [x]}, outputs={'Q': q,\n 'R': r}, attrs=attrs)\n if mode == \"r\":\n return r\n else:\n return q, r\n\n\ndef lu(x, pivot=True, get_infos=False, name=None):\n r\"\"\"\n Computes the LU factorization of an N-D(N>=2) matrix x. \n\n Returns the LU factorization(inplace x) and Pivots. low triangular matrix L and \n upper triangular matrix U are combined to a single LU matrix.\n\n Pivoting is done if pivot is set to True.\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n # return ones\n\n Args:\n\n X (Tensor): the tensor to factor of N-dimensions(N>=2).\n\n pivot (bool, optional): controls whether pivoting is done. Default: True.\n\n get_infos (bool, optional): if set to True, returns an info IntTensor. Default: False.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n factorization (Tensor): LU matrix, the factorization of input X.\n\n pivots (IntTensor): the pivots of size(∗(N-2), min(m,n)). `pivots` stores all the \n intermediate transpositions of rows. The final permutation `perm` could be \n reconstructed by this, details refer to upper example.\n\n infos (IntTensor, optional): if `get_infos` is `True`, this is a tensor of size (∗(N-2)) \n where non-zero values indicate whether factorization for the matrix or each minibatch \n has succeeded or failed.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n \n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n if paddle.in_dynamic_mode():\n LU, Piv, Info = _C_ops.lu(x, 'pivots', pivot)\n if get_infos:\n return LU, Piv, Info\n else:\n return LU, Piv\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu')\n helper = LayerHelper('lu', **locals())\n lu = helper.create_variable_for_type_inference(dtype=x.dtype)\n p = helper.create_variable_for_type_inference(dtype='int')\n info = helper.create_variable_for_type_inference(dtype='int')\n attrs = dict()\n attrs['pivots'] = pivot\n helper.append_op(\n type='lu',\n inputs={'X': x},\n outputs={'Out': lu,\n 'Pivots': p,\n 'Infos': info},\n attrs=attrs)\n if get_infos:\n return lu, p, info\n else:\n return lu, p\n\n\ndef lu_unpack(x, y, unpack_ludata=True, unpack_pivots=True, name=None):\n r\"\"\"\n Unpack L U and P to single matrix tensor . \n unpack L and U matrix from LU, unpack permutation matrix P from Pivtos .\n\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n\n\n Args:\n x (Tensor): The LU tensor get from paddle.linalg.lu, which is combined by L and U.\n\n y (Tensor): Pivots get from paddle.linalg.lu.\n\n unpack_ludata (bool,optional): whether to unpack L and U from x. Default: True.\n\n unpack_pivots (bool, optional): whether to unpack permutation matrix P from Pivtos. Default: True.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n P (Tensor): Permutation matrix P of lu factorization.\n\n L (Tensor): The lower triangular matrix tensor of lu factorization.\n\n U (Tensor): The upper triangular matrix tensor of lu factorization.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n\n if paddle.in_dynamic_mode():\n P, L, U = _C_ops.lu_unpack(x, y, 'unpack_ludata', unpack_ludata,\n 'unpack_pivots', unpack_pivots)\n return P, L, U\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu_unpack')\n helper = LayerHelper('lu_unpack', **locals())\n p = helper.create_variable_for_type_inference(dtype=x.dtype)\n l = helper.create_variable_for_type_inference(dtype=x.dtype)\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n attrs = dict()\n attrs['unpack_ludata'] = unpack_ludata\n attrs['unpack_pivots'] = unpack_pivots\n helper.append_op(\n type='lu_unpack',\n inputs={'X': x,\n 'Pivots': y},\n outputs={'Pmat': p,\n 'L': l,\n 'U': u},\n attrs=attrs)\n return p, l, u\n\n\ndef eig(x, name=None):\n \"\"\"\n This API performs the eigenvalue decomposition of a square matrix or a batch of square matrices.\n\n .. note::\n If the matrix is a Hermitian or a real symmetric matrix, please use :ref:`paddle.linalg.eigh` instead, which is much faster.\n If only eigenvalues is needed, please use :ref:`paddle.linalg.eigvals` instead.\n If the matrix is of any shape, please use :ref:`paddle.linalg.svd`.\n This API is only supported on CPU device.\n The output datatype is always complex for both real and complex input.\n\n Args:\n x (Tensor): A tensor with shape math:`[*, N, N]`, The data type of the x should be one of ``float32``,\n ``float64``, ``compplex64`` or ``complex128``.\n name (str, optional): The default value is `None`. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Eigenvalues(Tensors): A tensor with shape math:`[*, N]` refers to the eigen values.\n Eigenvectors(Tensors): A tensor with shape math:`[*, N, N]` refers to the eigen vectors.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n paddle.device.set_device(\"cpu\")\n\n x_data = np.array([[1.6707249, 7.2249975, 6.5045543],\n [9.956216, 8.749598, 6.066444 ],\n [4.4251957, 1.7983172, 0.370647 ]]).astype(\"float32\")\n x = paddle.to_tensor(x_data)\n w, v = paddle.linalg.eig(x)\n print(w)\n # Tensor(shape=[3, 3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [[(-0.5061363550800655+0j) , (-0.7971760990842826+0j) ,\n # (0.18518077798279986+0j)],\n # [(-0.8308237755993192+0j) , (0.3463813401919749+0j) ,\n # (-0.6837005269141947+0j) ],\n # [(-0.23142567697893396+0j), (0.4944999840400175+0j) ,\n # (0.7058765252952796+0j) ]])\n\n print(v)\n # Tensor(shape=[3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [ (16.50471283351188+0j) , (-5.5034820550763515+0j) ,\n # (-0.21026087843552282+0j)])\n \"\"\"\n if paddle.in_dynamic_mode():\n w, v = _C_ops.eig(x)\n return w, v\n\n check_variable_and_dtype(\n x, 'X', ['float32', 'float64', 'complex64', 'complex128'], 'eig')\n helper = LayerHelper('eig', **locals())\n\n w = helper.create_variable_for_type_inference(x.dtype)\n v = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {'X': x}\n outputs = {'Eigenvalues': w, 'Eigenvectors': v}\n helper.append_op(type='eig', inputs=inputs, outputs=outputs)\n\n return w, v\n\n\ndef eigvals(x, name=None):\n \"\"\"\n Compute the eigenvalues of one or more general matrices.\n\n Warning:\n The gradient kernel of this operator does not yet developed.\n If you need back propagation through this operator, please replace it with paddle.linalg.eig.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices whose eigenvalues will be computed.\n Its shape should be `[*, M, M]`, where `*` is zero or more batch dimensions.\n Its data type should be float32, float64, complex64, or complex128.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n Tensor: A tensor containing the unsorted eigenvalues which has the same batch dimensions with `x`.\n The eigenvalues are complex-valued even when `x` is real.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n paddle.seed(1234)\n\n x = paddle.rand(shape=[3, 3], dtype='float64')\n # [[0.02773777, 0.93004224, 0.06911496],\n # [0.24831591, 0.45733623, 0.07717843],\n # [0.48016702, 0.14235102, 0.42620817]])\n\n print(paddle.linalg.eigvals(x))\n # [(-0.27078833542132674+0j), (0.29962280156230725+0j), (0.8824477020120244+0j)] #complex128\n \"\"\"\n\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64',\n 'complex128'], 'eigvals')\n\n x_shape = list(x.shape)\n if len(x_shape) < 2:\n raise ValueError(\n \"The dimension of Input(x) should be at least 2, but received x's dimention = {}, x's shape = {}\".\n format(len(x_shape), x_shape))\n\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The last two dimensions of Input(x) should be equal, but received x's shape = {}\".\n format(x_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.eigvals(x)\n\n helper = LayerHelper('eigvals', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='eigvals', inputs={'X': x}, outputs={'Out': out})\n return out\n\n\ndef multi_dot(x, name=None):\n \"\"\"\n Multi_dot is an operator that calculates multiple matrix multiplications.\n\n Supports inputs of float16(only GPU support), float32 and float64 dtypes. This function does not\n support batched inputs.\n\n The input tensor in [x] must be 2-D except for the first and last can be 1-D.\n If the first tensor is a 1-D vector of shape(n, ) it is treated as row vector\n of shape(1, n), similarly if the last tensor is a 1D vector of shape(n, ), it\n is treated as a column vector of shape(n, 1).\n\n If the first and last tensor are 2-D matrix, then the output is also 2-D matrix,\n otherwise the output is a 1-D vector.\n\n Multi_dot will select the lowest cost multiplication order for calculation. The\n cost of multiplying two matrices with shapes (a, b) and (b, c) is a * b * c.\n Given matrices A, B, C with shapes (20, 5), (5, 100), (100, 10) respectively,\n we can calculate the cost of different multiplication orders as follows:\n - Cost((AB)C) = 20x5x100 + 20x100x10 = 30000\n - Cost(A(BC)) = 5x100x10 + 20x5x10 = 6000\n\n In this case, multiplying B and C first, then multiply A, which is 5 times faster\n than sequential calculation.\n\n Args:\n x ([Tensor]): The input tensors which is a list Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # A * B\n A_data = np.random.random([3, 4]).astype(np.float32)\n B_data = np.random.random([4, 5]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n out = paddle.linalg.multi_dot([A, B])\n print(out.numpy().shape)\n # [3, 5]\n\n # A * B * C\n A_data = np.random.random([10, 5]).astype(np.float32)\n B_data = np.random.random([5, 8]).astype(np.float32)\n C_data = np.random.random([8, 7]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n C = paddle.to_tensor(C_data)\n out = paddle.linalg.multi_dot([A, B, C])\n print(out.numpy().shape)\n # [10, 7]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.multi_dot(x)\n\n check_type(x, 'x', (list, tuple), 'multi_dot')\n for id, item in enumerate(x):\n check_variable_and_dtype(item, 'x[' + str(id) + ']',\n ['float16', 'float32', 'float64'], 'multi_dot')\n if item.dtype != x[0].dtype:\n raise TypeError(\n \"All the Tensors in the input must have the same data type.\")\n\n helper = LayerHelper('multi_dot', **locals())\n dtype = helper.input_dtype(input_param_name='x')\n out = helper.create_variable_for_type_inference(dtype)\n helper.append_op(type='multi_dot', inputs={\"X\": x}, outputs={\"Out\": out})\n return out\n\n\ndef eigh(x, UPLO='L', name=None):\n \"\"\"\n Compute the eigenvalues and eigenvectors of a\n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[*, N, N]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): (string, default 'L'), 'L' represents the lower triangular matrix,\n \"'U' represents the upper triangular matrix.\".\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n\n out_value(Tensor): A Tensor with shape [*, N] and data type of float32 and float64. The eigenvalues of eigh op.\n out_vector(Tensor): A Tensor with shape [*, N, N] and data type of float32,float64,complex64 and complex128. The eigenvectors of eigh op.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value, out_vector = paddle.linalg.eigh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n print(out_vector)\n #[(-0.9238795325112867+0j), (-0.3826834323650898+0j)],\n #[ 0.3826834323650898j , -0.9238795325112867j ]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.eigh(x, 'UPLO', UPLO)\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigh', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'eigh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO})\n return out_value, out_vector\n\n\ndef pinv(x, rcond=1e-15, hermitian=False, name=None):\n r\"\"\"\n Calculate pseudo inverse via SVD(singular value decomposition)\n of one matrix or batches of regular matrix.\n\n .. math::\n\n if hermitian == False:\n x = u * s * vt (SVD)\n out = v * 1/s * ut\n else:\n x = u * s * ut (eigh)\n out = u * 1/s * u.conj().transpose(-2,-1)\n\n If x is hermitian or symmetric matrix, svd will be replaced with eigh.\n\n Args:\n x(Tensor): The input tensor. Its shape should be (*, m, n)\n where * is zero or more batch dimensions. m and n can be\n arbitraty positive number. The data type of x should be\n float32 or float64 or complex64 or complex128. When data\n type is complex64 or cpmplex128, hermitian should be set\n True.\n\n rcond(Tensor, optional): the tolerance value to determine\n when is a singular value zero. Defalut:1e-15.\n\n hermitian(bool, optional): indicates whether x is Hermitian\n if complex or symmetric if real. Default: False.\n\n name(str|None): A name for this layer(optional). If set None,\n the layer will be named automatically.\n\n Returns:\n Tensor: The tensor with same data type with x. it represents\n pseudo inverse of x. Its shape should be (*, n, m).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.arange(15).reshape((3, 5)).astype('float64')\n input = paddle.to_tensor(x)\n out = paddle.linalg.pinv(input)\n print(input)\n print(out)\n\n # input:\n # [[0. , 1. , 2. , 3. , 4. ],\n # [5. , 6. , 7. , 8. , 9. ],\n # [10., 11., 12., 13., 14.]]\n\n # out:\n # [[-0.22666667, -0.06666667, 0.09333333],\n # [-0.12333333, -0.03333333, 0.05666667],\n # [-0.02000000, 0.00000000, 0.02000000],\n # [ 0.08333333, 0.03333333, -0.01666667],\n # [ 0.18666667, 0.06666667, -0.05333333]]\n\n # one can verify : x * out * x = x ;\n # or out * x * out = x ;\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if not hermitian:\n # combine svd and matmul op\n u, s, vt = _C_ops.svd(x, 'full_matrices', False)\n max_singular_val = _C_ops.reduce_max(s, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=x.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=x.dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v, _ = _C_ops.transpose2(vt, 'axis', perm)\n\n out_1 = v * st\n out_2 = _C_ops.matmul_v2(out_1, u, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n # combine eigh and matmul op\n s, u = _C_ops.eigh(x, 'UPLO', 'L')\n s_abs = paddle.abs(s)\n max_singular_val = _C_ops.reduce_max(s_abs, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=s.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=s.dtype)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n out_1 = u * st\n u_conj = _C_ops.conj(u)\n out_2 = _C_ops.matmul_v2(out_1, u_conj, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n if not hermitian:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'pinv')\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(dtype)\n vt = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vt,\n 'S': s},\n attrs={'full_matrices': False}, )\n\n max_singular_val = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=dtype)\n st_shape = helper.create_variable_for_type_inference(dtype=dtype)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v = helper.create_variable_for_type_inference(dtype)\n v_shape = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='transpose2',\n inputs={'X': [vt]},\n outputs={'Out': [v],\n 'XShape': [v_shape]},\n attrs={'axis': perm})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': v,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n else:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64',\n 'complex128'], 'pinv')\n\n if dtype == paddle.complex128:\n s_type = 'float64'\n elif dtype == paddle.complex64:\n s_type = 'float32'\n else:\n s_type = dtype\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': s,\n 'Eigenvectors': u},\n attrs={'UPLO': 'L'})\n s_abs = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='abs', inputs={'X': s}, outputs={'Out': s_abs})\n max_singular_val = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s_abs},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=s_type)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=s_type)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s_type)\n cond_not_int = layers.cast(layers.logical_not(condition), s_type)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=s_type)\n st_shape = helper.create_variable_for_type_inference(dtype=s_type)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': u,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n u_conj = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='conj', inputs={'X': u}, outputs={'Out': [u_conj]})\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u_conj},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n\n\ndef solve(x, y, name=None):\n r\"\"\"\n Computes the solution of a square system of linear equations with a unique solution for input 'X' and 'Y'.\n Let :math: `X` be a sqaure matrix or a batch of square matrices, :math:`Y` be\n a vector/matrix or a batch of vectors/matrices, the equation should be:\n\n .. math::\n Out = X^-1 * Y\n Specifically,\n - This system of linear equations has one solution if and only if input 'X' is invertible.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): A vector/matrix or a batch of vectors/matrices. Its shape should be `[*, M, K]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of a square system of linear equations with a unique solution for input 'x' and 'y'.\n Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # 2*X0 + X1 = 9\n # X0 + 2*X1 = 8\n\n import paddle\n import numpy as np\n\n np_x = np.array([[3, 1],[1, 2]])\n np_y = np.array([9, 8])\n x = paddle.to_tensor(np_x, dtype=\"float64\")\n y = paddle.to_tensor(np_y, dtype=\"float64\")\n out = paddle.linalg.solve(x, y)\n\n print(out)\n # [2., 3.])\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.solve(x, y)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type=\"solve\", inputs={\"X\": x,\n \"Y\": y}, outputs={\"Out\": out})\n return out\n\n\ndef triangular_solve(x,\n y,\n upper=True,\n transpose=False,\n unitriangular=False,\n name=None):\n r\"\"\"\n Computes the solution of a system of equations with a triangular coefficient matrix `x` and\n multiple right-hand sides `y` .\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input triangular coefficient matrix. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): Whether to solve the upper-triangular system of equations (default) or the lower-triangular \n system of equations. Default: True.\n transpose (bool, optional): whether `x` should be transposed before calculation. Default: False.\n unitriangular (bool, optional): whether `x` is unit triangular. If True, the diagonal elements of `x` are assumed \n to be 1 and not referenced from `x` . Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # x1 + x2 + x3 = 0\n # 2*x2 + x3 = -9\n # -x3 = 5\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n y = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.triangular_solve(x, y, upper=True)\n\n print(out)\n # [7, -2, -5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.triangular_solve(x, y, 'upper', upper, 'transpose',\n transpose, 'unitriangular',\n unitriangular)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"triangular_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'triangular_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'triangular_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='triangular_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={\n 'upper': upper,\n 'transpose': transpose,\n 'unitriangular': unitriangular\n })\n return out\n\n\ndef cholesky_solve(x, y, upper=False, name=None):\n r\"\"\"\n Solves a linear system of equations A @ X = B, given A's Cholesky factor matrix u and matrix B.\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input matrix which is upper or lower triangular Cholesky factor of square matrix A. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): whether to consider the Cholesky factor as a lower or upper triangular matrix. Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type is the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n u = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n b = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.cholesky_solve(b, u, upper=True)\n\n print(out)\n # [-2.5, -7, 9.5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky_solve(x, y, 'upper', upper)\n\n helper = LayerHelper(\"cholesky_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'cholesky_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'cholesky_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='cholesky_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef eigvalsh(x, UPLO='L', name=None):\n \"\"\"\n Computes the eigenvalues of a \n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[_, M, M]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): Lower triangular part of a (‘L’, default) or the upper triangular part (‘U’).\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor eigenvalues in ascending order.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value = paddle.eigvalsh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n \"\"\"\n if paddle.in_dynamic_mode():\n is_test = x.stop_gradient\n values, _ = _C_ops.eigvalsh(x, 'UPLO', UPLO, 'is_test', is_test)\n return values\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigvalsh', **locals())\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64', 'complex128'],\n 'eigvalsh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n is_test = x.stop_gradient\n helper.append_op(\n type='eigvalsh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO,\n 'is_test': is_test})\n return out_value\n\n\ndef lstsq(x, y, rcond=None, driver=None, name=None):\n \"\"\"\n Computes a solution to\n the least squares problem of a system of linear equations.\n\n Args:\n x (Tensor): A tensor with shape ``(*, M, N)`` , the data type of the input Tensor ``x``\n should be one of float32, float64.\n y (Tensor): A tensor with shape ``(*, M, K)`` , the data type of the input Tensor ``y`` \n should be one of float32, float64.\n rcond(float, optional): The default value is None. A float pointing number used to determine \n the effective rank of ``x``. If ``rcond`` is None, it will be set to max(M, N) times the \n machine precision of x_dtype.\n driver(str, optional): The default value is None. The name of LAPACK method to be used. For \n CPU inputs the valid values are ‘gels’, ‘gelsy’, ‘gelsd, ‘gelss’. For CUDA input, the only \n valid driver is ‘gels’. If ``driver`` is None, ‘gelsy’ is used for CPU inputs and ‘gels’ \n for CUDA inputs.\n name(str, optional): The default value is None. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple: A tuple of 4 Tensors which is (``solution``, ``residuals``, ``rank``, ``singular_values``). \n ``solution`` is a tensor with shape ``(*, N, K)``, meaning the least squares solution. ``residuals`` \n is a tensor with shape ``(*, K)``, meaning the squared residuals of the solutions, which is computed \n when M > N and every matrix in ``x`` is full-rank, otherwise return an empty tensor. ``rank`` is a tensor \n with shape ``(*)``, meaning the ranks of the matrices in ``x``, which is computed when ``driver`` in \n (‘gelsy’, ‘gelsd’, ‘gelss’), otherwise return an empty tensor. ``singular_values`` is a tensor with \n shape ``(*, min(M, N))``, meaning singular values of the matrices in ``x``, which is computed when \n ``driver`` in (‘gelsd’, ‘gelss’), otherwise return an empty tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n x = paddle.to_tensor([[1, 3], [3, 2], [5, 6.]])\n y = paddle.to_tensor([[3, 4, 6], [5, 3, 4], [1, 2, 1.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gelsd\")\n print(results[0])\n # [[ 0.78350395, -0.22165027, -0.62371236],\n # [-0.11340097, 0.78866047, 1.14948535]]\n print(results[1])\n # [19.81443405, 10.43814468, 30.56185532])\n print(results[2])\n # 2\n print(results[3])\n # [9.03455734, 1.54167950]\n\n x = paddle.to_tensor([[10, 2, 3], [3, 10, 5], [5, 6, 12.]])\n y = paddle.to_tensor([[4, 2, 9], [2, 0, 3], [2, 5, 3.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gels\")\n print(results[0])\n # [[ 0.39386186, 0.10230173, 0.93606132],\n # [ 0.10741687, -0.29028133, 0.11892585],\n # [-0.05115091, 0.51918161, -0.19948854]]\n print(results[1])\n # []\n \"\"\"\n device = paddle.get_device()\n if device == \"cpu\":\n if driver not in (None, \"gels\", \"gelss\", \"gelsd\", \"gelsy\"):\n raise ValueError(\n \"Only support valid driver is 'gels', 'gelss', 'gelsd', 'gelsy' or None for CPU inputs. But got {}\".\n format(driver))\n driver = \"gelsy\" if driver is None else driver\n elif \"gpu\" in device:\n if driver not in (None, \"gels\"):\n raise ValueError(\n \"Only support valid driver is 'gels' or None for CUDA inputs. But got {}\".\n format(driver))\n driver = \"gels\" if driver is None else driver\n else:\n raise RuntimeError(\"Only support lstsq api for CPU or CUDA device.\")\n\n if x.dtype == y.dtype and x.dtype in (paddle.float32, paddle.float64):\n pass\n else:\n raise ValueError(\n \"Only support x and y have the same dtype such as 'float32' and 'float64'.\"\n )\n\n if rcond is None:\n if x.dtype == paddle.float32:\n rcond = 1e-7 * max(x.shape[-2], x.shape[-1])\n elif x.dtype == paddle.float64:\n rcond = 1e-15 * max(x.shape[-2], x.shape[-1])\n\n if paddle.in_dynamic_mode():\n solution, rank, singular_values = _C_ops.lstsq(x, y, \"rcond\", rcond,\n \"driver\", driver)\n if x.shape[-2] > x.shape[-1]:\n matmul_out = _varbase_creator(dtype=x.dtype)\n _C_ops.matmul(x, solution, matmul_out, 'trans_x', False, 'trans_y',\n False)\n minus_out = _C_ops.elementwise_sub(matmul_out, y)\n pow_out = _C_ops.pow(minus_out, 'factor', 2)\n residuals = _C_ops.reduce_sum(pow_out, 'dim', [-2], 'keepdim',\n False, 'reduce_all', False)\n else:\n residuals = paddle.empty(shape=[0], dtype=x.dtype)\n\n if driver == \"gels\":\n rank = paddle.empty(shape=[0], dtype=paddle.int32)\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n elif driver == \"gelsy\":\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n\n return solution, residuals, rank, singular_values\n\n helper = LayerHelper('lstsq', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n check_variable_and_dtype(\n y, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n\n solution = helper.create_variable_for_type_inference(dtype=x.dtype)\n residuals = helper.create_variable_for_type_inference(dtype=x.dtype)\n rank = helper.create_variable_for_type_inference(dtype=paddle.int32)\n singular_values = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='lstsq',\n inputs={'X': x,\n 'Y': y},\n outputs={\n 'Solution': solution,\n 'Rank': rank,\n 'SingularValues': singular_values\n },\n attrs={'rcond': rcond,\n 'driver': driver})\n\n matmul_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n minus_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n pow_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': solution},\n outputs={'Out': matmul_out},\n attrs={\n 'trans_x': False,\n 'trans_y': False,\n })\n\n helper.append_op(\n type='elementwise_sub',\n inputs={'X': matmul_out,\n 'Y': y},\n outputs={'Out': minus_out})\n\n helper.append_op(\n type='pow',\n inputs={'X': minus_out},\n outputs={'Out': pow_out},\n attrs={'factor': 2})\n\n helper.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': residuals},\n attrs={'dim': [-2],\n 'keep_dim': False,\n 'reduce_all': False})\n\n if driver == \"gels\":\n rank = paddle.static.data(name='rank', shape=[0])\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n elif driver == \"gelsy\":\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n\n return solution, residuals, rank, singular_values"},"implementation":{"kind":"string","value":" def vector_norm(input,\n porder=None,\n axis=None,\n keepdim=False,\n asvector=False,\n name=None):\n \"\"\"\n Calculate the p-order vector norm for certain dimension of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n porder (float, optional): None for porder=2.0.\n axis (int, optional): None for last dimension.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if paddle.in_dynamic_mode():\n if axis is None: axis = -1\n return _C_ops.p_norm(input, 'porder', porder, 'axis', axis,\n 'keepdim', keepdim, 'asvector', asvector)\n if porder is not None:\n check_type(porder, 'porder', (float, int), 'p_norm')\n if axis is not None:\n check_type(axis, 'axis', (int), 'p_norm')\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'p_norm')\n\n attrs = {\n 'axis': axis if axis is not None else -1,\n 'porder': float(porder) if porder is not None else 2.0,\n 'keepdim': keepdim,\n 'asvector': asvector,\n 'epsilon': 1e-12,\n }\n helper = LayerHelper('p_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='p_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out"},"start_line":{"kind":"number","value":277,"string":"277"},"end_line":{"kind":"number","value":318,"string":"318"},"file_content":{"kind":"string","value":"# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numpy as np\nfrom ..fluid.layer_helper import LayerHelper\nfrom ..framework import _varbase_creator, _dygraph_tracer\nfrom ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype\nfrom ..static import Variable\nfrom ..fluid.framework import _in_legacy_dygraph, in_dygraph_mode\nfrom ..fluid.layers import transpose, cast # noqa: F401\nfrom ..fluid import layers\nimport paddle\nfrom paddle.common_ops_import import core\nfrom paddle.common_ops_import import VarDesc\nfrom paddle import _C_ops\n\n__all__ = []\n\n\ndef matmul(x, y, transpose_x=False, transpose_y=False, name=None):\n \"\"\"\n Applies matrix multiplication to two tensors. `matmul` follows\n the complete broadcast rules,\n and its behavior is consistent with `np.matmul`.\n\n Currently, the input tensors' number of dimensions can be any, `matmul` can be used to\n achieve the `dot`, `matmul` and `batchmatmul`.\n\n The actual behavior depends on the shapes of :math:`x`, :math:`y` and the\n flag values of :attr:`transpose_x`, :attr:`transpose_y`. Specifically:\n\n - If a transpose flag is specified, the last two dimensions of the tensor\n are transposed. If the tensor is ndim-1 of shape, the transpose is invalid. If the tensor\n is ndim-1 of shape :math:`[D]`, then for :math:`x` it is treated as :math:`[1, D]`, whereas\n for :math:`y` it is the opposite: It is treated as :math:`[D, 1]`.\n\n The multiplication behavior depends on the dimensions of `x` and `y`. Specifically:\n\n - If both tensors are 1-dimensional, the dot product result is obtained.\n\n - If both tensors are 2-dimensional, the matrix-matrix product is obtained.\n\n - If the `x` is 1-dimensional and the `y` is 2-dimensional,\n a `1` is prepended to its dimension in order to conduct the matrix multiply.\n After the matrix multiply, the prepended dimension is removed.\n\n - If the `x` is 2-dimensional and `y` is 1-dimensional,\n the matrix-vector product is obtained.\n\n - If both arguments are at least 1-dimensional and at least one argument\n is N-dimensional (where N > 2), then a batched matrix multiply is obtained.\n If the first argument is 1-dimensional, a 1 is prepended to its dimension\n in order to conduct the batched matrix multiply and removed after.\n If the second argument is 1-dimensional, a 1 is appended to its\n dimension for the purpose of the batched matrix multiple and removed after.\n The non-matrix (exclude the last two dimensions) dimensions are\n broadcasted according the broadcast rule.\n For example, if input is a (j, 1, n, m) tensor and the other is a (k, m, p) tensor,\n out will be a (j, k, n, p) tensor.\n\n Args:\n x (Tensor): The input tensor which is a Tensor.\n y (Tensor): The input tensor which is a Tensor.\n transpose_x (bool): Whether to transpose :math:`x` before multiplication.\n transpose_y (bool): Whether to transpose :math:`y` before multiplication.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # vector * vector\n x_data = np.random.random([10]).astype(np.float32)\n y_data = np.random.random([10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [1]\n\n # matrix * vector\n x_data = np.random.random([10, 5]).astype(np.float32)\n y_data = np.random.random([5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10]\n\n # batched matrix * broadcasted vector\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([2]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5]\n\n # batched matrix * batched matrix\n x_data = np.random.random([10, 5, 2]).astype(np.float32)\n y_data = np.random.random([10, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 5, 5]\n\n # batched matrix * broadcasted matrix\n x_data = np.random.random([10, 1, 5, 2]).astype(np.float32)\n y_data = np.random.random([1, 3, 2, 5]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.matmul(x, y)\n print(z.numpy().shape)\n # [10, 3, 5, 5]\n\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_matmul(x, y, transpose_x, transpose_y)\n\n if _in_legacy_dygraph():\n op_type = 'matmul_v2'\n op = getattr(_C_ops, op_type)\n return op(x, y, 'trans_x', transpose_x, 'trans_y', transpose_y)\n\n attrs = {\n 'trans_x': transpose_x,\n 'trans_y': transpose_y,\n }\n\n def __check_input(x, y):\n var_names = {'x': x, 'y': y}\n for name, val in var_names.items():\n check_variable_and_dtype(\n val, name,\n ['float16', 'float32', 'float64', 'complex64', 'complex128'],\n 'matmul')\n\n __check_input(x, y)\n\n helper = LayerHelper('matmul_v2', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef norm(x, p='fro', axis=None, keepdim=False, name=None):\n \"\"\"\n\n Returns the matrix norm (Frobenius) or vector norm (the 1-norm, the Euclidean\n or 2-norm, and in general the p-norm for p > 0) of a given tensor.\n\n .. note::\n This norm API is different from `numpy.linalg.norm`.\n This api supports high-order input tensors (rank >= 3), and certain axis need to be pointed out to calculate the norm.\n But `numpy.linalg.norm` only supports 1-D vector or 2-D matrix as input tensor.\n For p-order matrix norm, this api actually treats matrix as a flattened vector to calculate the vector norm, NOT REAL MATRIX NORM.\n\n Args:\n x (Tensor): The input tensor could be N-D tensor, and the input data\n type could be float32 or float64.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `0`, `1`, `2`,\n `inf`, `-inf` and any positive real number yielding the corresponding p-norm. Not supported: ord < 0 and nuclear norm.\n Default value is `fro`.\n axis (int|list|tuple, optional): The axis on which to apply norm operation. If axis is int\n or list(int)/tuple(int) with only one element, the vector norm is computed over the axis.\n If `axis < 0`, the dimension to norm operation is rank(input) + axis.\n If axis is a list(int)/tuple(int) with two elements, the matrix norm is computed over the axis.\n Defalut value is `None`.\n keepdim (bool, optional): Whether to reserve the reduced dimension in the\n output Tensor. The result tensor will have fewer dimension\n than the :attr:`input` unless :attr:`keepdim` is true, default\n value is False.\n name (str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: results of norm operation on the specified axis of input tensor,\n it's data type is the same as input's Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n shape=[2, 3, 4]\n np_input = np.arange(24).astype('float32') - 12\n np_input = np_input.reshape(shape)\n x = paddle.to_tensor(np_input)\n #[[[-12. -11. -10. -9.] [ -8. -7. -6. -5.] [ -4. -3. -2. -1.]]\n # [[ 0. 1. 2. 3.] [ 4. 5. 6. 7.] [ 8. 9. 10. 11.]]]\n\n # compute frobenius norm along last two dimensions.\n out_fro = paddle.linalg.norm(x, p='fro', axis=[0,1])\n # out_fro.numpy() [17.435596 16.911535 16.7332 16.911535]\n\n # compute 2-order vector norm along last dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=-1)\n #out_pnorm.numpy(): [[21.118711 13.190906 5.477226]\n # [ 3.7416575 11.224972 19.131126]]\n\n # compute 2-order norm along [0,1] dimension.\n out_pnorm = paddle.linalg.norm(x, p=2, axis=[0,1])\n #out_pnorm.numpy(): [17.435596 16.911535 16.7332 16.911535]\n\n # compute inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=np.inf)\n #out_pnorm.numpy() = [12.]\n out_pnorm = paddle.linalg.norm(x, p=np.inf, axis=0)\n #out_pnorm.numpy(): [[12. 11. 10. 9.] [8. 7. 6. 7.] [8. 9. 10. 11.]]\n\n # compute -inf-order norm\n out_pnorm = paddle.linalg.norm(x, p=-np.inf)\n #out_pnorm.numpy(): [0.]\n out_pnorm = paddle.linalg.norm(x, p=-np.inf, axis=0)\n #out_pnorm.numpy(): [[0. 1. 2. 3.] [4. 5. 6. 5.] [4. 3. 2. 1.]]\n \"\"\"\n\n def frobenius_norm(input, dim=None, keepdim=False, name=None):\n \"\"\"\n The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n dim (list, optional): None for last two dimensions.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if dim is not None and not (isinstance(dim, list) and len(dim) == 2):\n raise ValueError(\n \"The dim of frobenius norm op should be None or two elements list!\"\n )\n if paddle.in_dynamic_mode():\n if dim is None:\n return _C_ops.frobenius_norm(input, 'keep_dim', keepdim,\n 'reduce_all', True)\n return _C_ops.frobenius_norm(input, 'dim', dim, 'keep_dim', keepdim,\n 'reduce_all', False)\n attrs = {'dim': dim, 'keep_dim': keepdim, 'reduce_all': False}\n if dim is None:\n attrs['reduce_all'] = True\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'frobenius_norm')\n\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='frobenius_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def vector_norm(input,\n porder=None,\n axis=None,\n keepdim=False,\n asvector=False,\n name=None):\n \"\"\"\n Calculate the p-order vector norm for certain dimension of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n porder (float, optional): None for porder=2.0.\n axis (int, optional): None for last dimension.\n keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False.\n \"\"\"\n if paddle.in_dynamic_mode():\n if axis is None: axis = -1\n return _C_ops.p_norm(input, 'porder', porder, 'axis', axis,\n 'keepdim', keepdim, 'asvector', asvector)\n if porder is not None:\n check_type(porder, 'porder', (float, int), 'p_norm')\n if axis is not None:\n check_type(axis, 'axis', (int), 'p_norm')\n check_variable_and_dtype(input, 'input', ['float32', 'float64'],\n 'p_norm')\n\n attrs = {\n 'axis': axis if axis is not None else -1,\n 'porder': float(porder) if porder is not None else 2.0,\n 'keepdim': keepdim,\n 'asvector': asvector,\n 'epsilon': 1e-12,\n }\n helper = LayerHelper('p_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n helper.append_op(\n type='p_norm',\n inputs={'X': input},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n def inf_norm(input,\n porder=None,\n axis=axis,\n keepdim=False,\n asvector=False,\n name=None):\n helper = LayerHelper('frobenius_norm', **locals())\n out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n helper.append_op(type='abs', inputs={'X': input}, outputs={'Out': out})\n reduce_out = helper.create_variable_for_type_inference(\n dtype=helper.input_dtype())\n\n reduce_all = True if axis == None or axis == [] or asvector == True else False\n axis = axis if axis != None and axis != [] else [0]\n\n reduce_type = 'reduce_max' if porder == np.float(\n 'inf') else 'reduce_min'\n helper.append_op(\n type=reduce_type,\n inputs={'X': out},\n outputs={'Out': reduce_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n\n return reduce_out\n\n def p_matrix_norm(input, porder=1., axis=axis, keepdim=False, name=None):\n \"\"\"\n NOTE:\n This function actually treats the matrix as flattened vector to calculate vector norm instead of matrix norm.\n \"\"\"\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n\n block.append_op(\n type='pow',\n inputs={'X': abs_out},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': True if axis is None else False\n })\n porder\n block.append_op(\n type='pow',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n if axis is None and p is not None:\n if isinstance(p, str):\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n porder=p,\n axis=axis,\n keepdim=keepdim,\n asvector=True,\n name=name)\n else:\n raise ValueError(\"only valid p type is string or float, found {}\".\n format(type(p)))\n\n if isinstance(axis, tuple):\n axis = list(axis)\n if isinstance(axis, list) and len(axis) == 1:\n axis = axis[0]\n\n #calculate vector norm, where axis is int or list with only one integer\n if isinstance(axis, int):\n if isinstance(p, str):\n if p == \"fro\":\n return vector_norm(\n x,\n porder=2,\n axis=axis,\n keepdim=keepdim,\n asvector=False,\n name=name)\n\n else:\n raise ValueError(\n \"only valid string values are 'fro', found {}\".format(p))\n elif isinstance(p, (int, float)):\n return vector_norm(\n x,\n axis=axis,\n porder=p,\n keepdim=keepdim,\n asvector=False,\n name=name)\n else:\n raise ValueError(\n \"unspport p for p-order vector norm. except float, found {}\".\n format(p))\n #calculate matrix norm, where axis is list with two integers\n elif isinstance(axis, list) and len(axis) == 2:\n if p == \"fro\":\n return frobenius_norm(x, dim=axis, keepdim=keepdim, name=name)\n elif p == np.inf or p == -np.inf:\n return inf_norm(x, porder=p, axis=axis, keepdim=keepdim, name=name)\n elif p == 0:\n raise ValueError(\n \"just suport axis type int or list (length of list <=1) if p = 0, found {}\".\n format(axis))\n else:\n return p_matrix_norm(\n x, porder=p, axis=axis, keepdim=keepdim, name=name)\n else:\n raise ValueError(\n \"except axis type int or list (length of list <=2), found {}\".\n format(axis))\n\n\ndef dist(x, y, p=2, name=None):\n r\"\"\"\n\n This OP returns the p-norm of (x - y). It is not a norm in a strict sense, only as a measure\n of distance. The shapes of x and y must be broadcastable. The definition is as follows, for\n details, please refer to the `numpy's broadcasting <https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html>`_:\n\n - Each input has at least one dimension.\n - Match the two input dimensions from back to front, the dimension sizes must either be equal, one of them is 1, or one of them does not exist.\n\n Where, z = x - y, the shapes of x and y are broadcastable, then the shape of z can be\n obtained as follows:\n\n 1. If the number of dimensions of x and y are not equal, prepend 1 to the dimensions of the\n tensor with fewer dimensions.\n\n For example, The shape of x is [8, 1, 6, 1], the shape of y is [7, 1, 5], prepend 1 to the\n dimension of y.\n\n x (4-D Tensor): 8 x 1 x 6 x 1\n\n y (4-D Tensor): 1 x 7 x 1 x 5\n\n 2. Determine the size of each dimension of the output z: choose the maximum value from the\n two input dimensions.\n\n z (4-D Tensor): 8 x 7 x 6 x 5\n\n If the number of dimensions of the two inputs are the same, the size of the output can be\n directly determined in step 2. When p takes different values, the norm formula is as follows:\n\n When p = 0, defining $0^0=0$, the zero-norm of z is simply the number of non-zero elements of z.\n\n .. math::\n\n ||z||_{0}=\\lim_{p \\\\rightarrow 0}\\sum_{i=1}^{m}|z_i|^{p}\n\n When p = inf, the inf-norm of z is the maximum element of z.\n\n .. math::\n\n ||z||_\\infty=\\max_i |z_i|\n\n When p = -inf, the negative-inf-norm of z is the minimum element of z.\n\n .. math::\n\n ||z||_{-\\infty}=\\min_i |z_i|\n\n Otherwise, the p-norm of z follows the formula,\n\n .. math::\n\n ||z||_{p}=(\\sum_{i=1}^{m}|z_i|^p)^{\\\\frac{1}{p}}\n\n Args:\n x (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n y (Tensor): 1-D to 6-D Tensor, its data type is float32 or float64.\n p (float, optional): The norm to be computed, its data type is float32 or float64. Default: 2.\n\n Returns:\n Tensor: Tensor that is the p-norm of (x - y).\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor(np.array([[3, 3],[3, 3]]), \"float32\")\n y = paddle.to_tensor(np.array([[3, 3],[3, 1]]), \"float32\")\n out = paddle.dist(x, y, 0)\n print(out) # out = [1.]\n\n out = paddle.dist(x, y, 2)\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"inf\"))\n print(out) # out = [2.]\n\n out = paddle.dist(x, y, float(\"-inf\"))\n print(out) # out = [0.]\n \"\"\"\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'dist')\n check_variable_and_dtype(y, 'dtype', ['float32', 'float64'], 'dist')\n check_type(p, 'p', (float, int), 'dist')\n helper = LayerHelper(\"dist\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n outputs = {'Out': [out]}\n attrs = {\"p\": float(p)}\n helper.append_op(\n type='dist', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef cond(x, p=None, name=None):\n \"\"\"\n\n Computes the condition number of a matrix or batches of matrices with respect to a matrix norm ``p``.\n\n Args:\n x (Tensor): The input tensor could be tensor of shape ``(*, m, n)`` where ``*`` is zero or more batch dimensions\n for ``p`` in ``(2, -2)``, or of shape ``(*, n, n)`` where every matrix is invertible for any supported ``p``.\n And the input data type could be ``float32`` or ``float64``.\n p (float|string, optional): Order of the norm. Supported values are `fro`, `nuc`, `1`, `-1`, `2`, `-2`,\n `inf`, `-inf`. Default value is `None`, meaning that the order of the norm is `2`.\n name (str, optional): The default value is `None`. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: computing results of condition number, its data type is the same as input Tensor ``x``.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1., 0, -1], [0, 1, 0], [1, 0, 1]])\n\n # compute conditional number when p is None\n out = paddle.linalg.cond(x)\n # out.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is 'fro'\n out_fro = paddle.linalg.cond(x, p='fro')\n # out_fro.numpy() [3.1622777]\n\n # compute conditional number when order of the norm is 'nuc'\n out_nuc = paddle.linalg.cond(x, p='nuc')\n # out_nuc.numpy() [9.2426405]\n\n # compute conditional number when order of the norm is 1\n out_1 = paddle.linalg.cond(x, p=1)\n # out_1.numpy() [2.]\n\n # compute conditional number when order of the norm is -1\n out_minus_1 = paddle.linalg.cond(x, p=-1)\n # out_minus_1.numpy() [1.]\n\n # compute conditional number when order of the norm is 2\n out_2 = paddle.linalg.cond(x, p=2)\n # out_2.numpy() [1.4142135]\n\n # compute conditional number when order of the norm is -1\n out_minus_2 = paddle.linalg.cond(x, p=-2)\n # out_minus_2.numpy() [0.70710677]\n\n # compute conditional number when order of the norm is inf\n out_inf = paddle.linalg.cond(x, p=np.inf)\n # out_inf.numpy() [2.]\n\n # compute conditional number when order of the norm is -inf\n out_minus_inf = paddle.linalg.cond(x, p=-np.inf)\n # out_minus_inf.numpy() [1.]\n\n a = paddle.to_tensor(np.random.randn(2, 4, 4).astype('float32'))\n # a.numpy()\n # [[[ 0.14063153 -0.996288 0.7996131 -0.02571543]\n # [-0.16303636 1.5534962 -0.49919784 -0.04402903]\n # [-1.1341571 -0.6022629 0.5445269 0.29154757]\n # [-0.16816919 -0.30972657 1.7521842 -0.5402487 ]]\n # [[-0.58081484 0.12402827 0.7229862 -0.55046535]\n # [-0.15178485 -1.1604939 0.75810957 0.30971205]\n # [-0.9669573 1.0940945 -0.27363303 -0.35416734]\n # [-1.216529 2.0018666 -0.7773689 -0.17556527]]]\n a_cond_fro = paddle.linalg.cond(a, p='fro')\n # a_cond_fro.numpy() [31.572273 28.120834]\n\n b = paddle.to_tensor(np.random.randn(2, 3, 4).astype('float64'))\n # b.numpy()\n # [[[ 1.61707487 0.46829144 0.38130416 0.82546736]\n # [-1.72710298 0.08866375 -0.62518804 0.16128892]\n # [-0.02822879 -1.67764516 0.11141444 0.3220113 ]]\n # [[ 0.22524372 0.62474921 -0.85503233 -1.03960523]\n # [-0.76620689 0.56673047 0.85064753 -0.45158196]\n # [ 1.47595418 2.23646462 1.5701758 0.10497519]]]\n b_cond_2 = paddle.linalg.cond(b, p=2)\n # b_cond_2.numpy() [3.30064451 2.51976252]\n\n \"\"\"\n\n def mat_norm(input, porder=1., axis=None):\n \"\"\"\n NOTE:\n Calculate the matrix norm of a square matrix or batches of square matrices,\n when porder is in (1, -1, inf, -inf)\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n axis = axis if axis != None and axis != [] else [0]\n keepdim = False\n\n if paddle.in_dynamic_mode():\n abs_out = _C_ops.abs(input)\n sum_out = _C_ops.reduce_sum(abs_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == 1 or porder == np.inf:\n return _C_ops.reduce_max(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n if porder == -1 or porder == -np.inf:\n return _C_ops.reduce_min(sum_out, 'dim', [-1], 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n\n block = LayerHelper('norm', **locals())\n abs_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='abs', inputs={'X': input}, outputs={'Out': abs_out})\n block.append_op(\n type='reduce_sum',\n inputs={'X': abs_out},\n outputs={'Out': sum_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 1 or porder == np.inf:\n block.append_op(\n type='reduce_max',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n if porder == -1 or porder == -np.inf:\n block.append_op(\n type='reduce_min',\n inputs={'X': sum_out},\n outputs={'Out': out},\n attrs={\n 'dim': [-1],\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n\n def fro_norm(input, porder=2, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the frobenius norm of a square matrix or batches of square matrices.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n if paddle.in_dynamic_mode():\n pow_out = _C_ops.pow(input, 'factor', porder)\n sum_out_1 = _C_ops.reduce_sum(pow_out, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n sum_out_2 = _C_ops.reduce_sum(sum_out_1, 'dim', axis, 'keepdim',\n keepdim, 'reduce_all', reduce_all)\n return _C_ops.pow(sum_out_2, 'factor', float(1. / porder))\n\n block = LayerHelper('norm', **locals())\n pow_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_1 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n sum_out_2 = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='pow',\n inputs={'X': input},\n outputs={'Out': pow_out},\n attrs={'factor': porder})\n block.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': sum_out_1},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_sum',\n inputs={'X': sum_out_1},\n outputs={'Out': sum_out_2},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='pow',\n inputs={'X': sum_out_2},\n outputs={'Out': out},\n attrs={'factor': float(1. / porder)})\n return out\n\n def svd_norm(input, porder, axis=[-1]):\n \"\"\"\n NOTE:\n Calculate the matrix norm, which is related to singular values, of a matrix\n or batches of matrices, including nuclear norm, 2-norm and (-2)-norm.\n \"\"\"\n reduce_all = True if axis is None or axis == [] else False\n keepdim = False\n\n u, s, vh = svd(input, full_matrices=False)\n\n if paddle.in_dynamic_mode():\n if porder == \"nuc\":\n return _C_ops.reduce_sum(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n max_out = _C_ops.reduce_max(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n min_out = _C_ops.reduce_min(s, 'dim', axis, 'keepdim', keepdim,\n 'reduce_all', reduce_all)\n if porder == 2:\n return _C_ops.elementwise_div(max_out, min_out, 'aixs', axis,\n 'use_mkldnn', False)\n if porder == -2:\n return _C_ops.elementwise_div(min_out, max_out, 'aixs', axis,\n 'use_mkldnn', False)\n\n block = LayerHelper('norm', **locals())\n out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n if porder == \"nuc\":\n block.append_op(\n type='reduce_sum',\n inputs={'X': s},\n outputs={'Out': out},\n attrs={\n 'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all\n })\n return out\n max_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n min_out = block.create_variable_for_type_inference(\n dtype=block.input_dtype())\n block.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n block.append_op(\n type='reduce_min',\n inputs={'X': s},\n outputs={'Out': min_out},\n attrs={'dim': axis,\n 'keep_dim': keepdim,\n 'reduce_all': reduce_all})\n if porder == 2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': max_out,\n 'Y': min_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n if porder == -2:\n block.append_op(\n type='elementwise_div',\n inputs={'X': min_out,\n 'Y': max_out},\n outputs={'Out': out},\n attrs={'aixs': axis,\n 'use_mkldnn': False})\n return out\n\n def empty_tensor(input, shape):\n if paddle.in_dynamic_mode():\n return input.reshape(shape)\n raise ValueError(\"only support x is nonempty tensor in static mode\")\n\n x_shape = list(x.shape)\n if not len(x_shape) >= 2:\n raise ValueError(\"input should be a matrix or batches of matrices, \" +\n \"but the dimention of received input is {}\".format(\n len(x_shape)))\n if p == None:\n p = 2\n x_size = 0 if (0 in x_shape) else 1\n if p in (\"fro\", \"nuc\", 1, -1, np.inf, -np.inf):\n if x_shape[len(x_shape) - 1] == x_shape[len(x_shape) - 2]:\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n x_inv = x.inverse()\n if p == \"fro\":\n return fro_norm(x) * fro_norm(x_inv)\n if p == \"nuc\":\n return svd_norm(x, p) * svd_norm(x_inv, p)\n if p in (1, -1):\n return mat_norm(\n x, porder=p, axis=[-2]) * mat_norm(\n x_inv, porder=p, axis=[-2])\n if p in (np.inf, -np.inf):\n return mat_norm(\n x, porder=p, axis=[-1]) * mat_norm(\n x_inv, porder=p, axis=[-1])\n else:\n raise ValueError(\"only support p is {} when input is a \".format(p) +\n \"square matrix or batches of square matrices\")\n elif p in (2, -2):\n if x_size == 0:\n return empty_tensor(x, x_shape[:-2])\n return svd_norm(x, porder=p)\n else:\n raise ValueError(\n \"unsupported {} for p, only supporting ('fro', 'nuc', \".format(\n p) + \"1, -1, 2, -2, inf, -inf) or none\")\n\n\ndef dot(x, y, name=None):\n \"\"\"\n This operator calculates inner product for vectors.\n\n .. note::\n Support 1-d and 2-d Tensor. When it is 2d, the first dimension of this matrix\n is the batch dimension, which means that the vectors of multiple batches are dotted.\n\n Parameters:\n x(Tensor): 1-D or 2-D ``Tensor``. Its dtype should be ``float32``, ``float64``, ``int32``, ``int64``\n y(Tensor): 1-D or 2-D ``Tensor``. Its dtype soulde be ``float32``, ``float64``, ``int32``, ``int64``\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: the calculated result Tensor.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n x_data = np.random.uniform(0.1, 1, [10]).astype(np.float32)\n y_data = np.random.uniform(1, 3, [10]).astype(np.float32)\n x = paddle.to_tensor(x_data)\n y = paddle.to_tensor(y_data)\n z = paddle.dot(x, y)\n print(z)\n\n \"\"\"\n op_type = 'dot'\n # skip var type check in dygraph mode to improve efficiency\n if paddle.in_dynamic_mode():\n op = getattr(_C_ops, op_type)\n return op(x, y)\n\n assert x is not None, 'x cannot be None in {}'.format(op_type)\n assert y is not None, 'y cannot be None in {}'.format(op_type)\n\n check_variable_and_dtype(x, 'x', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n check_variable_and_dtype(y, 'y', ['float32', 'float64', 'int32', 'int64'],\n op_type)\n\n helper = LayerHelper(op_type, **locals())\n if name is None:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n else:\n out = helper.create_variable(\n name=name, dtype=x.dtype, persistable=False)\n helper.append_op(\n type=\"dot\", inputs={'X': x,\n 'Y': y}, attrs={}, outputs={\"Out\": out})\n return out\n\n\ndef cov(x, rowvar=True, ddof=True, fweights=None, aweights=None, name=None):\n \"\"\"\n Estimate the covariance matrix of the input variables, given data and weights.\n\n A covariance matrix is a square matrix, indicate the covariance of each pair variables in the input matrix.\n For example, for an N-dimensional samples X=[x1,x2,…xN]T, then the covariance matrix \n element Cij is the covariance of xi and xj. The element Cii is the variance of xi itself.\n\n Parameters:\n x(Tensor): A N-D(N<=2) Tensor containing multiple variables and observations. By default, each row of x represents a variable. Also see rowvar below.\n rowvar(Bool, optional): If rowvar is True (default), then each row represents a variable, with observations in the columns. Default: True\n ddof(Bool, optional): If ddof=True will return the unbiased estimate, and ddof=False will return the simple average. Default: True\n fweights(Tensor, optional): 1-D Tensor of integer frequency weights; The number of times each observation vector should be repeated. Default: None\n aweights(Tensor, optional): 1-D Tensor of observation vector weights. How important of the observation vector, larger data means this element is more important. Default: None\n name(str, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name`\n\n Returns:\n Tensor: The covariance matrix Tensor of the variables.\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n\n xt = paddle.rand((3,4))\n paddle.linalg.cov(xt)\n\n '''\n Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n [[0.07918842, 0.06127326, 0.01493049],\n [0.06127326, 0.06166256, 0.00302668],\n [0.01493049, 0.00302668, 0.01632146]])\n '''\n \"\"\"\n op_type = 'cov'\n if len(x.shape) > 2 or len(x.shape) < 1:\n raise ValueError(\n \"Input(x) only support N-D (1<=N<=2) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cov')\n nx = x\n if len(x.shape) == 1:\n nx = x.reshape((1, -1))\n if not rowvar and nx.shape[0] != 1:\n nx = nx.t()\n w = None\n observation_num = nx.shape[1]\n if fweights is not None:\n w = fweights.astype(nx.dtype)\n if len(w.shape) > 1:\n raise ValueError(\n \"Input(fweights) only support N-D (N<=1) tensor in cov, but received \"\n \"shape of Input(input) is %s.\" % len(fweights.shape))\n if fweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(fweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(fweights) is {}.\".format(observation_num,\n fweights.shape[0]))\n if fweights.min() < 0:\n raise ValueError(\n \"The value of Input(fweights) cannot be negtive, but received \"\n \"min of Input(fweights) is {}.\".format(fweights.min()))\n if not paddle.all(fweights == paddle.round(fweights.astype('float64'))):\n raise ValueError(\"Input(fweights) must be integer \")\n\n if aweights is not None:\n aw = aweights.astype(nx.dtype)\n if len(aw.shape) > 1:\n raise ValueError(\n \"Input(aweights) only support N-D (N<=1) tensor in cov, but received \"\n \"length of Input(input) is %s.\" % len(aweights.shape))\n check_variable_and_dtype(aweights, 'dtype', ['float32', 'float64'],\n 'cov')\n if aweights.shape[0] != observation_num:\n raise ValueError(\n \"The number of Input(aweights) should equal to x's dim[1]: {}, but received \"\n \"size of Input(aweights) is {}.\".format(observation_num,\n aweights.shape[0]))\n if aweights.min() < 0:\n raise ValueError(\n \"The value of Input(aweights) cannot be negtive, but received \"\n \"min of Input(aweights) is {}.\".format(aweights.min()))\n if w is not None:\n w = w * aw\n else:\n w = aw\n\n w_sum = paddle.to_tensor(observation_num, dtype=nx.dtype)\n if fweights is not None or aweights is not None:\n w_sum = w.sum()\n if w_sum.item() == 0:\n raise ValueError(\"The sum of weights is zero, can't be normalized.\")\n\n if w is not None:\n nx_w = nx * w\n avg = (nx_w).sum(axis=1) / w_sum\n else:\n avg = nx.sum(axis=1) / w_sum\n nx_w = nx\n\n if w is not None and aweights is not None and ddof == True:\n norm_factor = w_sum - (w * aweights).sum() / w_sum\n else:\n norm_factor = w_sum - ddof\n if norm_factor <= 0:\n norm_factor = paddle.to_tensor(0, dtype=nx.dtype)\n nx = nx - avg.unsqueeze(1)\n xxt = paddle.mm(nx, nx_w.t().conj())\n cov = paddle.divide(xxt, norm_factor).squeeze()\n return cov\n\n\ndef t(input, name=None):\n \"\"\"\n Transpose <=2-D tensor.\n 0-D and 1-D tensors are returned as it is and 2-D tensor is equal to\n the paddle.transpose function which perm dimensions set 0 and 1.\n\n Args:\n input (Tensor): The input Tensor. It is a N-D (N<=2) Tensor of data types float16, float32, float64, int32.\n name(str, optional): The default value is None. Normally there is no need for\n user to set this property. For more information, please refer to :ref:`api_guide_Name`\n Returns:\n Tensor: A transposed n-D Tensor, with data type being float16, float32, float64, int32, int64.\n\n For Example:\n\n .. code-block:: text\n\n # Example 1 (0-D tensor)\n x = tensor([0.79])\n paddle.t(x) = tensor([0.79])\n\n # Example 2 (1-D tensor)\n x = tensor([0.79, 0.84, 0.32])\n paddle.t(x) = tensor([0.79, 0.84, 0.32])\n\n # Example 3 (2-D tensor)\n x = tensor([0.79, 0.84, 0.32],\n [0.64, 0.14, 0.57])\n paddle.t(x) = tensor([0.79, 0.64],\n [0.84, 0.14],\n [0.32, 0.57])\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n x = paddle.ones(shape=[2, 3], dtype='int32')\n x_transposed = paddle.t(x)\n print(x_transposed.shape)\n # [3, 2]\n \"\"\"\n if len(input.shape) > 2:\n raise ValueError(\n \"Input(input) only support N-D (N<=2) tensor, but received \"\n \"length of Input(input) is %s. Perhaps you can use paddle.\"\n \"tensor.transpose() instead.\" % len(input.shape))\n if paddle.in_dynamic_mode():\n if len(input.shape) == 1:\n return input\n # 2-D tensor\n perm = [1, 0]\n out, _ = _C_ops.transpose2(input, 'axis', perm)\n return out\n\n check_variable_and_dtype(\n input, 'input', ['float16', 'float32', 'float64', 'int32',\n 'int64'], 'transpose')\n\n helper = LayerHelper('t', **locals())\n out = helper.create_variable_for_type_inference(input.dtype)\n input_shape = helper.create_variable_for_type_inference(input.dtype)\n if len(input.shape) == 1:\n out = input\n else:\n helper.append_op(\n type='transpose2',\n inputs={'X': [input]},\n outputs={'Out': [out],\n 'XShape': [input_shape]},\n attrs={'axis': [1, 0]})\n return out\n\n\ndef cross(x, y, axis=None, name=None):\n \"\"\"\n Computes the cross product between two tensors along an axis.\n\n Inputs must have the same shape, and the length of their axes should be equal to 3.\n If `axis` is not given, it defaults to the first axis found with the length 3.\n\n Args:\n x (Tensor): The first input tensor.\n y (Tensor): The second input tensor.\n axis (int, optional): The axis along which to compute the cross product. It defaults to the first axis found with the length 3.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor. A Tensor with same data type as `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0],\n [3.0, 3.0, 3.0]])\n y = paddle.to_tensor([[1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0],\n [1.0, 1.0, 1.0]])\n\n z1 = paddle.cross(x, y)\n # [[-1. -1. -1.]\n # [ 2. 2. 2.]\n # [-1. -1. -1.]]\n\n z2 = paddle.cross(x, y, axis=1)\n # [[0. 0. 0.]\n # [0. 0. 0.]\n # [0. 0. 0.]]\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_cross(x, y, axis)\n else:\n if _in_legacy_dygraph():\n if axis is not None:\n return _C_ops.cross(x, y, 'dim', axis)\n else:\n return _C_ops.cross(x, y)\n else:\n helper = LayerHelper(\"cross\", **locals())\n out = helper.create_variable_for_type_inference(x.dtype)\n attrs = dict()\n attrs['dim'] = axis\n\n helper.append_op(\n type='cross',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs=attrs)\n return out\n\n\ndef cholesky(x, upper=False, name=None):\n r\"\"\"\n Computes the Cholesky decomposition of one symmetric positive-definite\n matrix or batches of symmetric positive-definite matrice.\n\n If `upper` is `True`, the decomposition has the form :math:`A = U^{T}U` ,\n and the returned matrix :math:`U` is upper-triangular. Otherwise, the\n decomposition has the form :math:`A = LL^{T}` , and the returned matrix\n :math:`L` is lower-triangular.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[*, M, M]`,\n where * is zero or more batch dimensions, and matrices on the\n inner-most 2 dimensions all should be symmetric positive-definite.\n Its data type should be float32 or float64.\n upper (bool): The flag indicating whether to return upper or lower\n triangular matrices. Default: False.\n\n Returns:\n Tensor: A Tensor with same shape and data type as `x`. It represents \\\n triangular matrices generated by Cholesky decomposition.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n a = np.random.rand(3, 3)\n a_t = np.transpose(a, [1, 0])\n x_data = np.matmul(a, a_t) + 1e-03\n x = paddle.to_tensor(x_data)\n out = paddle.linalg.cholesky(x, upper=False)\n print(out)\n # [[1.190523 0. 0. ]\n # [0.9906703 0.27676893 0. ]\n # [1.25450498 0.05600871 0.06400121]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky(x, \"upper\", upper)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'cholesky')\n check_type(upper, 'upper', bool, 'cholesky')\n helper = LayerHelper('cholesky', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='cholesky',\n inputs={'X': [x]},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef matrix_rank(x, tol=None, hermitian=False, name=None):\n r\"\"\"\n Computes the rank of a matrix.\n\n The rank of a matrix is the number of singular values that are greater than the specified `tol` threshold when hermitian=False,\n or the number of eigenvalues in absolute value that are greater than the specified `tol` threshold when hermitian=True.\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., m, n]`, where `...` is zero or more batch dimensions. If `x` is a batch\n of matrices then the output has the same batch dimensions. The data type of `x` should be float32 or float64.\n tol (float,Tensor,optional): the tolerance value. Default: None. If `tol` is not specified, and `sigma` is the largest\n singular value (or eigenvalues in absolute value), and `eps` is the epsilon value for the dtype of `x`, then `tol` is computed\n with formula `tol=sigma * max(m,n) * eps`. Note that if `x` is a batch of matrices, `tol` is computed this way for every batch.\n hermitian (bool,optional): indicates whether `x` is Hermitian. Default: False. When hermitian=True, `x` is assumed to be Hermitian,\n enabling a more efficient method for finding eigenvalues, but `x` is not checked inside the function. Instead, We just use\n the lower triangular of the matrix to compute.\n name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: Rank of tensor x.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n a = paddle.eye(10)\n b = paddle.linalg.matrix_rank(a)\n print(b)\n # b = [10]\n\n c = paddle.ones(shape=[3, 4, 5, 5])\n d = paddle.linalg.matrix_rank(c, tol=0.01, hermitian=True)\n print(d)\n # d = [[1, 1, 1, 1],\n # [1, 1, 1, 1],\n # [1, 1, 1, 1]]\n\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if tol is None:\n tol_tensor = None\n tol_attr = 0.0\n use_default_tol = True\n elif isinstance(tol, Variable):\n if tol.dtype != x.dtype:\n tol_tensor = cast(tol, x.dtype)\n else:\n tol_tensor = tol\n tol_attr = 0.0\n use_default_tol = False\n else:\n tol_tensor = None\n tol_attr = float(tol)\n use_default_tol = False\n return _C_ops.matrix_rank(x, tol_tensor, \"tol\", tol_attr, 'hermitian',\n hermitian, 'use_default_tol', use_default_tol)\n\n inputs = {}\n attrs = {}\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'matrix_rank')\n inputs['X'] = x\n if tol is None:\n attrs['use_default_tol'] = True\n elif isinstance(tol, Variable):\n check_variable_and_dtype(tol, 'tol', ['float32'], 'matrix_rank')\n attrs['use_default_tol'] = False\n if tol.dtype != x.dtype:\n inputs['TolTensor'] = cast(tol, x.dtype)\n else:\n inputs['TolTensor'] = tol\n else:\n check_type(tol, 'tol', float, 'matrix_rank')\n attrs['use_default_tol'] = False\n attrs['tol'] = tol\n check_type(hermitian, 'hermitian', bool, 'matrix_rank')\n attrs['hermitian'] = hermitian\n\n helper = LayerHelper('matrix_rank', **locals())\n out = helper.create_variable_for_type_inference(dtype='int32')\n helper.append_op(\n type='matrix_rank', inputs=inputs, outputs={'Out': out}, attrs=attrs)\n return out\n\n\ndef bmm(x, y, name=None):\n \"\"\"\n Applies batched matrix multiplication to two tensors.\n\n Both of the two input tensors must be three-dementional and share the same batch size.\n\n if x is a (b, m, k) tensor, y is a (b, k, n) tensor, the output will be a (b, m, n) tensor.\n\n Args:\n x (Tensor): The input Tensor.\n y (Tensor): The input Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The product Tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n # In imperative mode:\n # size x: (2, 2, 3) and y: (2, 3, 2)\n x = paddle.to_tensor([[[1.0, 1.0, 1.0],\n [2.0, 2.0, 2.0]],\n [[3.0, 3.0, 3.0],\n [4.0, 4.0, 4.0]]])\n y = paddle.to_tensor([[[1.0, 1.0],[2.0, 2.0],[3.0, 3.0]],\n [[4.0, 4.0],[5.0, 5.0],[6.0, 6.0]]])\n out = paddle.bmm(x, y)\n #output size: (2, 2, 2)\n #output value:\n #[[[6.0, 6.0],[12.0, 12.0]],[[45.0, 45.0],[60.0, 60.0]]]\n out_np = out.numpy()\n\n \"\"\"\n x_shape = x.shape\n y_shape = y.shape\n if not len(x_shape) == len(y_shape) == 3:\n raise ValueError(\n \"x and y should be 3-dimensional. But received x's dimention: {}, y's dimention: {}\".\n format(x_shape, y_shape))\n if x_shape[2] != y_shape[1]:\n raise ValueError(\n \"x's width must be equal with y's height. But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n if x_shape[0] != y_shape[0]:\n raise ValueError(\n \"x's batch (shape[0]) must be equal with y's batch (shape[0]). But received x's shape: {}, y's shape: {}\".\n format(x_shape, y_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.bmm(x, y)\n\n helper = LayerHelper('bmm', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='bmm', inputs={'X': x, 'Y': y}, outputs={'Out': out})\n return out\n\n\ndef histogram(input, bins=100, min=0, max=0, name=None):\n \"\"\"\n Computes the histogram of a tensor. The elements are sorted into equal width bins between min and max.\n If min and max are both zero, the minimum and maximum values of the data are used.\n\n Args:\n input (Tensor): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor\n should be float32, float64, int32, int64.\n bins (int): number of histogram bins\n min (int): lower end of the range (inclusive)\n max (int): upper end of the range (inclusive)\n\n Returns:\n Tensor: data type is int64, shape is (nbins,).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n inputs = paddle.to_tensor([1, 2, 1])\n result = paddle.histogram(inputs, bins=4, min=0, max=3)\n print(result) # [0, 2, 1, 0]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.histogram(input, \"bins\", bins, \"min\", min, \"max\", max)\n\n helper = LayerHelper('histogram', **locals())\n check_variable_and_dtype(\n input, 'X', ['int32', 'int64', 'float32', 'float64'], 'histogram')\n out = helper.create_variable_for_type_inference(VarDesc.VarType.INT64)\n helper.append_op(\n type='histogram',\n inputs={'X': input},\n outputs={'Out': out},\n attrs={'bins': bins,\n 'min': min,\n 'max': max})\n return out\n\n\ndef bincount(x, weights=None, minlength=0, name=None):\n \"\"\"\n Computes frequency of each value in the input tensor. \n\n Args:\n x (Tensor): A Tensor with non-negative integer. Should be 1-D tensor.\n weights (Tensor, optional): Weight for each value in the input tensor. Should have the same shape as input. Default is None.\n minlength (int, optional): Minimum number of bins. Should be non-negative integer. Default is 0.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor of frequency.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([1, 2, 1, 4, 5])\n result1 = paddle.bincount(x)\n print(result1) # [0, 2, 1, 0, 1, 1]\n\n w = paddle.to_tensor([2.1, 0.4, 0.1, 0.5, 0.5])\n result2 = paddle.bincount(x, weights=w)\n print(result2) # [0., 2.19999981, 0.40000001, 0., 0.50000000, 0.50000000]\n \"\"\"\n if x.dtype not in [paddle.int32, paddle.int64]:\n raise TypeError(\"Elements in Input(x) should all be integers\")\n\n if paddle.in_dynamic_mode():\n return _C_ops.bincount(x, weights, \"minlength\", minlength)\n\n helper = LayerHelper('bincount', **locals())\n\n check_variable_and_dtype(x, 'X', ['int32', 'int64'], 'bincount')\n\n if weights is not None:\n check_variable_and_dtype(weights, 'Weights',\n ['int32', 'int64', 'float32', 'float64'],\n 'bincount')\n out = helper.create_variable_for_type_inference(dtype=weights.dtype)\n else:\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='bincount',\n inputs={'X': x,\n 'Weights': weights},\n outputs={'Out': out},\n attrs={'minlength': minlength})\n return out\n\n\ndef mv(x, vec, name=None):\n \"\"\"\n Performs a matrix-vector product of the matrix x and the vector vec.\n\n Args:\n x (Tensor): A tensor with shape :math:`[M, N]` , The data type of the input Tensor x\n should be one of float32, float64.\n vec (Tensor): A tensor with shape :math:`[N]` , The data type of the input Tensor x\n should be one of float32, float64.\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor which is producted by x and vec.\n\n Examples:\n .. code-block:: python\n\n # x: [M, N], vec: [N]\n # paddle.mv(x, vec) # out: [M]\n\n import numpy as np\n import paddle\n\n x_data = np.array([[2, 1, 3], [3, 0, 1]]).astype(\"float64\")\n x = paddle.to_tensor(x_data)\n vec_data = np.array([3, 5, 1])\n vec = paddle.to_tensor(vec_data).astype(\"float64\")\n out = paddle.mv(x, vec)\n \"\"\"\n if in_dygraph_mode():\n return _C_ops.final_state_mv(x, vec)\n else:\n if _in_legacy_dygraph():\n out = _C_ops.mv(x, vec)\n return out\n else:\n\n def __check_input(x, vec):\n var_names = {'x': x, 'vec': vec}\n for name, val in var_names.items():\n check_variable_and_dtype(val, name, ['float32', 'float64'],\n 'mv')\n x_shape = list(x.shape)\n vec_shape = list(vec.shape)\n if len(x_shape) != 2:\n raise ValueError(\n \"x should be 2-dimensional. But received x's dimention: {}\".\n format(x_shape))\n if len(vec_shape) != 1:\n raise ValueError(\n \"vec should be 1-dimensional. But received vec's dimention: {}\".\n format(vec_shape))\n\n __check_input(x, vec)\n\n helper = LayerHelper('mv', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='mv', inputs={'X': x,\n 'Vec': vec}, outputs={'Out': out})\n return out\n\n\ndef det(x, name=None):\n \"\"\"\n Calculates determinant value of a square matrix or batches of square matrices.\n Args:\n x (Tensor): input (Tensor): the input matrix of size `(n, n)` or the batch of matrices of size\n `(*, n, n)` where `*` is one or more batch dimensions.\n Returns:\n y (Tensor):the determinant value of a square matrix or batches of square matrices.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.det(x)\n\n print(A)\n\n # [ 0.02547996, 2.52317095, -6.15900707])\n\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.determinant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'det')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('determinant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='determinant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef slogdet(x, name=None):\n \"\"\"\n Calculates the sign and natural logarithm of the absolute value of a square matrix's or batches square matrices' determinant.\n The determinant can be computed with ``sign * exp(logabsdet)\n\n Supports input of float, double\n\n Note that for matrices that have zero determinant, this returns ``(0, -inf)``\n Args:\n x (Tensor): the batch of matrices of size :math:`(*, n, n)`\n where math:`*` is one or more batch dimensions.\n\n Returns:\n y (Tensor): A tensor containing the sign of the determinant and the natural logarithm\n of the absolute value of determinant, respectively.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.randn([3,3,3])\n\n A = paddle.linalg.slogdet(x)\n\n print(A)\n\n # [[ 1. , 1. , -1. ],\n # [-0.98610914, -0.43010661, -0.10872950]])\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.slogdeterminant(x)\n\n check_dtype(x.dtype, 'Input', ['float32', 'float64'], 'slogdet')\n\n input_shape = list(x.shape)\n assert len(input_shape) >= 2, \\\n \"The x must be at least 2-dimensional, \" \\\n \"but received Input x's dimensional: %s.\\n\" % \\\n len(input_shape)\n\n assert (input_shape[-1] == input_shape[-2]), \\\n \"Expect squared input,\" \\\n \"but received %s by %s matrix.\\n\" \\\n %(input_shape[-2], input_shape[-1]) \\\n\n helper = LayerHelper('slogdeterminant', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='slogdeterminant', inputs={'Input': [x]}, outputs={'Out': [out]})\n return out\n\n\ndef svd(x, full_matrices=False, name=None):\n r\"\"\"\n Computes the singular value decomposition of one matrix or a batch of regular matrices.\n\n Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies:\n\n .. math::\n X = U * diag(S) * VT\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., N, M]`,\n where `...` is zero or more batch dimensions. N and M can be arbitraty\n positive number. Note that if x is sigular matrices, the grad is numerical\n instable. The data type of x should be float32 or float64.\n full_matrices (bool): A flag to control the behavor of svd.\n If full_matrices = True, svd op will compute full U and V matrics,\n which means shape of U is `[..., N, N]`, shape of V is `[..., M, M]`. K = min(M, N).\n If full_matrices = False, svd op will use a economic method to store U and V.\n which means shape of U is `[..., N, K]`, shape of V is `[..., M, K]`. K = min(M, N).\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple of 3 tensors: (U, S, VH). VH is the conjugate transpose of V. S is the singlar value vectors of matrics with shape `[..., K]`\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1.0, 2.0], [1.0, 3.0], [4.0, 6.0]]).astype('float64')\n x = x.reshape([3, 2])\n u, s, vh = paddle.linalg.svd(x)\n print (u)\n #U = [[ 0.27364809, -0.21695147 ],\n # [ 0.37892198, -0.87112408 ],\n # [ 0.8840446 , 0.44053933 ]]\n\n print (s)\n #S = [8.14753743, 0.78589688]\n print (vh)\n #VT= [[ 0.51411221, 0.85772294],\n # [ 0.85772294, -0.51411221]]\n\n # one can verify : U * S * VT == X\n # U * UH == I\n # V * VH == I\n \"\"\"\n\n if paddle.in_dynamic_mode():\n return _C_ops.svd(x, 'full_matrices', full_matrices)\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'svd')\n check_type(full_matrices, 'full_matrices', bool, 'svd')\n helper = LayerHelper('svd', **locals())\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n vh = helper.create_variable_for_type_inference(dtype=x.dtype)\n s = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['full_matrices'] = full_matrices\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vh,\n 'S': s},\n attrs=attrs, )\n return u, s, vh\n\n\ndef matrix_power(x, n, name=None):\n r\"\"\"\n Computes the n-th power of a square matrix or a batch of square matrices.\n\n Let :math:`X` be a sqaure matrix or a batch of square matrices, :math:`n` be\n an exponent, the equation should be:\n\n .. math::\n Out = X ^ {n}\n\n Specifically,\n\n - If `n > 0`, it returns the matrix or a batch of matrices raised to the power\n of `n`.\n\n - If `n = 0`, it returns the identity matrix or a batch of identity matrices.\n\n - If `n < 0`, it returns the inverse of each matrix (if invertible) raised to\n the power of `abs(n)`.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices to be raised\n to power `n`. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n n (int): The exponent. It can be any positive, negative integer or zero.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The n-th power of the matrix (or the batch of matrices) `x`. Its\n data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.to_tensor([[1, 2, 3],\n [1, 4, 9],\n [1, 8, 27]], dtype='float64')\n print(paddle.linalg.matrix_power(x, 2))\n # [[6. , 34. , 102.],\n # [14. , 90. , 282.],\n # [36. , 250., 804.]]\n\n print(paddle.linalg.matrix_power(x, 0))\n # [[1., 0., 0.],\n # [0., 1., 0.],\n # [0., 0., 1.]]\n\n print(paddle.linalg.matrix_power(x, -2))\n # [[ 12.91666667, -12.75000000, 2.83333333 ],\n # [-7.66666667 , 8. , -1.83333333 ],\n # [ 1.80555556 , -1.91666667 , 0.44444444 ]]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.matrix_power(x, \"n\", n)\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'matrix_power')\n check_type(n, 'n', int, 'matrix_power')\n helper = LayerHelper('matrix_power', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matrix_power',\n inputs={'X': x},\n outputs={'Out': out},\n attrs={'n': n})\n return out\n\n\ndef qr(x, mode=\"reduced\", name=None):\n r\"\"\"\n Computes the QR decomposition of one matrix or batches of matrice (backward is unsupported now).\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., M, N]`,\n where ... is zero or more batch dimensions. M and N can be arbitrary\n positive number. The data type of x should be float32 or float64. \n mode (str, optional): A flag to control the behavior of qr, the default is \"reduced\". \n Suppose x's shape is `[..., M, N]` and denoting `K = min(M, N)`:\n If mode = \"reduced\", qr op will return reduced Q and R matrices, \n which means Q's shape is `[..., M, K]` and R's shape is `[..., K, N]`.\n If mode = \"complete\", qr op will return complete Q and R matrices, \n which means Q's shape is `[..., M, M]` and R's shape is `[..., M, N]`.\n If mode = \"r\", qr op will only return reduced R matrix, which means\n R's shape is `[..., K, N]`.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n If mode = \"reduced\" or mode = \"complete\", qr will return a two tensor-tuple, which represents Q and R. \n If mode = \"r\", qr will return a tensor which represents R.\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n q, r = paddle.linalg.qr(x)\n print (q)\n print (r)\n\n # Q = [[-0.16903085, 0.89708523],\n # [-0.50709255, 0.27602622],\n # [-0.84515425, -0.34503278]])\n\n # R = [[-5.91607978, -7.43735744],\n # [ 0. , 0.82807867]])\n \n # one can verify : X = Q * R ; \n \"\"\"\n if paddle.in_dynamic_mode():\n q, r = _C_ops.qr(x, 'mode', mode)\n if mode == \"r\":\n return r\n else:\n return q, r\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'qr')\n check_type(mode, 'mode', str, 'qr')\n helper = LayerHelper('qr', **locals())\n q = helper.create_variable_for_type_inference(dtype=x.dtype)\n r = helper.create_variable_for_type_inference(dtype=x.dtype)\n attrs = dict()\n attrs['mode'] = mode\n helper.append_op(\n type='qr', inputs={'X': [x]}, outputs={'Q': q,\n 'R': r}, attrs=attrs)\n if mode == \"r\":\n return r\n else:\n return q, r\n\n\ndef lu(x, pivot=True, get_infos=False, name=None):\n r\"\"\"\n Computes the LU factorization of an N-D(N>=2) matrix x. \n\n Returns the LU factorization(inplace x) and Pivots. low triangular matrix L and \n upper triangular matrix U are combined to a single LU matrix.\n\n Pivoting is done if pivot is set to True.\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n # return ones\n\n Args:\n\n X (Tensor): the tensor to factor of N-dimensions(N>=2).\n\n pivot (bool, optional): controls whether pivoting is done. Default: True.\n\n get_infos (bool, optional): if set to True, returns an info IntTensor. Default: False.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n factorization (Tensor): LU matrix, the factorization of input X.\n\n pivots (IntTensor): the pivots of size(∗(N-2), min(m,n)). `pivots` stores all the \n intermediate transpositions of rows. The final permutation `perm` could be \n reconstructed by this, details refer to upper example.\n\n infos (IntTensor, optional): if `get_infos` is `True`, this is a tensor of size (∗(N-2)) \n where non-zero values indicate whether factorization for the matrix or each minibatch \n has succeeded or failed.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n \n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n if paddle.in_dynamic_mode():\n LU, Piv, Info = _C_ops.lu(x, 'pivots', pivot)\n if get_infos:\n return LU, Piv, Info\n else:\n return LU, Piv\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu')\n helper = LayerHelper('lu', **locals())\n lu = helper.create_variable_for_type_inference(dtype=x.dtype)\n p = helper.create_variable_for_type_inference(dtype='int')\n info = helper.create_variable_for_type_inference(dtype='int')\n attrs = dict()\n attrs['pivots'] = pivot\n helper.append_op(\n type='lu',\n inputs={'X': x},\n outputs={'Out': lu,\n 'Pivots': p,\n 'Infos': info},\n attrs=attrs)\n if get_infos:\n return lu, p, info\n else:\n return lu, p\n\n\ndef lu_unpack(x, y, unpack_ludata=True, unpack_pivots=True, name=None):\n r\"\"\"\n Unpack L U and P to single matrix tensor . \n unpack L and U matrix from LU, unpack permutation matrix P from Pivtos .\n\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n # swap(ones[i], ones[pivots[i]])\n\n\n Args:\n x (Tensor): The LU tensor get from paddle.linalg.lu, which is combined by L and U.\n\n y (Tensor): Pivots get from paddle.linalg.lu.\n\n unpack_ludata (bool,optional): whether to unpack L and U from x. Default: True.\n\n unpack_pivots (bool, optional): whether to unpack permutation matrix P from Pivtos. Default: True.\n\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n P (Tensor): Permutation matrix P of lu factorization.\n\n L (Tensor): The lower triangular matrix tensor of lu factorization.\n\n U (Tensor): The upper triangular matrix tensor of lu factorization.\n\n \n Examples: \n .. code-block:: python\n\n import paddle \n\n x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]).astype('float64')\n lu,p,info = paddle.linalg.lu(x, get_infos=True)\n\n # >>> lu:\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0.20000000, 0.80000000],\n # [0.60000000, 0.50000000]])\n # >>> p\n # Tensor(shape=[2], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # [3, 3])\n # >>> info\n # Tensor(shape=[], dtype=int32, place=CUDAPlace(0), stop_gradient=True,\n # 0)\n \n P,L,U = paddle.linalg.lu_unpack(lu,p)\n\n # >>> P\n # (Tensor(shape=[3, 3], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[0., 1., 0.],\n # [0., 0., 1.],\n # [1., 0., 0.]]), \n # >>> L\n # Tensor(shape=[3, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[1. , 0. ],\n # [0.20000000, 1. ],\n # [0.60000000, 0.50000000]]), \n # >>> U\n # Tensor(shape=[2, 2], dtype=float64, place=CUDAPlace(0), stop_gradient=True,\n # [[5. , 6. ],\n # [0. , 0.80000000]]))\n\n # one can verify : X = P @ L @ U ; \n \"\"\"\n\n if paddle.in_dynamic_mode():\n P, L, U = _C_ops.lu_unpack(x, y, 'unpack_ludata', unpack_ludata,\n 'unpack_pivots', unpack_pivots)\n return P, L, U\n\n check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'lu_unpack')\n helper = LayerHelper('lu_unpack', **locals())\n p = helper.create_variable_for_type_inference(dtype=x.dtype)\n l = helper.create_variable_for_type_inference(dtype=x.dtype)\n u = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n attrs = dict()\n attrs['unpack_ludata'] = unpack_ludata\n attrs['unpack_pivots'] = unpack_pivots\n helper.append_op(\n type='lu_unpack',\n inputs={'X': x,\n 'Pivots': y},\n outputs={'Pmat': p,\n 'L': l,\n 'U': u},\n attrs=attrs)\n return p, l, u\n\n\ndef eig(x, name=None):\n \"\"\"\n This API performs the eigenvalue decomposition of a square matrix or a batch of square matrices.\n\n .. note::\n If the matrix is a Hermitian or a real symmetric matrix, please use :ref:`paddle.linalg.eigh` instead, which is much faster.\n If only eigenvalues is needed, please use :ref:`paddle.linalg.eigvals` instead.\n If the matrix is of any shape, please use :ref:`paddle.linalg.svd`.\n This API is only supported on CPU device.\n The output datatype is always complex for both real and complex input.\n\n Args:\n x (Tensor): A tensor with shape math:`[*, N, N]`, The data type of the x should be one of ``float32``,\n ``float64``, ``compplex64`` or ``complex128``.\n name (str, optional): The default value is `None`. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Eigenvalues(Tensors): A tensor with shape math:`[*, N]` refers to the eigen values.\n Eigenvectors(Tensors): A tensor with shape math:`[*, N, N]` refers to the eigen vectors.\n\n Examples:\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n paddle.device.set_device(\"cpu\")\n\n x_data = np.array([[1.6707249, 7.2249975, 6.5045543],\n [9.956216, 8.749598, 6.066444 ],\n [4.4251957, 1.7983172, 0.370647 ]]).astype(\"float32\")\n x = paddle.to_tensor(x_data)\n w, v = paddle.linalg.eig(x)\n print(w)\n # Tensor(shape=[3, 3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [[(-0.5061363550800655+0j) , (-0.7971760990842826+0j) ,\n # (0.18518077798279986+0j)],\n # [(-0.8308237755993192+0j) , (0.3463813401919749+0j) ,\n # (-0.6837005269141947+0j) ],\n # [(-0.23142567697893396+0j), (0.4944999840400175+0j) ,\n # (0.7058765252952796+0j) ]])\n\n print(v)\n # Tensor(shape=[3], dtype=complex128, place=CPUPlace, stop_gradient=False,\n # [ (16.50471283351188+0j) , (-5.5034820550763515+0j) ,\n # (-0.21026087843552282+0j)])\n \"\"\"\n if paddle.in_dynamic_mode():\n w, v = _C_ops.eig(x)\n return w, v\n\n check_variable_and_dtype(\n x, 'X', ['float32', 'float64', 'complex64', 'complex128'], 'eig')\n helper = LayerHelper('eig', **locals())\n\n w = helper.create_variable_for_type_inference(x.dtype)\n v = helper.create_variable_for_type_inference(x.dtype)\n\n inputs = {'X': x}\n outputs = {'Eigenvalues': w, 'Eigenvectors': v}\n helper.append_op(type='eig', inputs=inputs, outputs=outputs)\n\n return w, v\n\n\ndef eigvals(x, name=None):\n \"\"\"\n Compute the eigenvalues of one or more general matrices.\n\n Warning:\n The gradient kernel of this operator does not yet developed.\n If you need back propagation through this operator, please replace it with paddle.linalg.eig.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices whose eigenvalues will be computed.\n Its shape should be `[*, M, M]`, where `*` is zero or more batch dimensions.\n Its data type should be float32, float64, complex64, or complex128.\n name (str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n \n Returns:\n Tensor: A tensor containing the unsorted eigenvalues which has the same batch dimensions with `x`.\n The eigenvalues are complex-valued even when `x` is real.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n paddle.seed(1234)\n\n x = paddle.rand(shape=[3, 3], dtype='float64')\n # [[0.02773777, 0.93004224, 0.06911496],\n # [0.24831591, 0.45733623, 0.07717843],\n # [0.48016702, 0.14235102, 0.42620817]])\n\n print(paddle.linalg.eigvals(x))\n # [(-0.27078833542132674+0j), (0.29962280156230725+0j), (0.8824477020120244+0j)] #complex128\n \"\"\"\n\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64',\n 'complex128'], 'eigvals')\n\n x_shape = list(x.shape)\n if len(x_shape) < 2:\n raise ValueError(\n \"The dimension of Input(x) should be at least 2, but received x's dimention = {}, x's shape = {}\".\n format(len(x_shape), x_shape))\n\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The last two dimensions of Input(x) should be equal, but received x's shape = {}\".\n format(x_shape))\n\n if paddle.in_dynamic_mode():\n return _C_ops.eigvals(x)\n\n helper = LayerHelper('eigvals', **locals())\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(type='eigvals', inputs={'X': x}, outputs={'Out': out})\n return out\n\n\ndef multi_dot(x, name=None):\n \"\"\"\n Multi_dot is an operator that calculates multiple matrix multiplications.\n\n Supports inputs of float16(only GPU support), float32 and float64 dtypes. This function does not\n support batched inputs.\n\n The input tensor in [x] must be 2-D except for the first and last can be 1-D.\n If the first tensor is a 1-D vector of shape(n, ) it is treated as row vector\n of shape(1, n), similarly if the last tensor is a 1D vector of shape(n, ), it\n is treated as a column vector of shape(n, 1).\n\n If the first and last tensor are 2-D matrix, then the output is also 2-D matrix,\n otherwise the output is a 1-D vector.\n\n Multi_dot will select the lowest cost multiplication order for calculation. The\n cost of multiplying two matrices with shapes (a, b) and (b, c) is a * b * c.\n Given matrices A, B, C with shapes (20, 5), (5, 100), (100, 10) respectively,\n we can calculate the cost of different multiplication orders as follows:\n - Cost((AB)C) = 20x5x100 + 20x100x10 = 30000\n - Cost(A(BC)) = 5x100x10 + 20x5x10 = 6000\n\n In this case, multiplying B and C first, then multiply A, which is 5 times faster\n than sequential calculation.\n\n Args:\n x ([Tensor]): The input tensors which is a list Tensor.\n name(str|None): A name for this layer(optional). If set None, the layer\n will be named automatically.\n\n Returns:\n Tensor: The output Tensor.\n\n\n Examples:\n\n .. code-block:: python\n\n import paddle\n import numpy as np\n\n # A * B\n A_data = np.random.random([3, 4]).astype(np.float32)\n B_data = np.random.random([4, 5]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n out = paddle.linalg.multi_dot([A, B])\n print(out.numpy().shape)\n # [3, 5]\n\n # A * B * C\n A_data = np.random.random([10, 5]).astype(np.float32)\n B_data = np.random.random([5, 8]).astype(np.float32)\n C_data = np.random.random([8, 7]).astype(np.float32)\n A = paddle.to_tensor(A_data)\n B = paddle.to_tensor(B_data)\n C = paddle.to_tensor(C_data)\n out = paddle.linalg.multi_dot([A, B, C])\n print(out.numpy().shape)\n # [10, 7]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.multi_dot(x)\n\n check_type(x, 'x', (list, tuple), 'multi_dot')\n for id, item in enumerate(x):\n check_variable_and_dtype(item, 'x[' + str(id) + ']',\n ['float16', 'float32', 'float64'], 'multi_dot')\n if item.dtype != x[0].dtype:\n raise TypeError(\n \"All the Tensors in the input must have the same data type.\")\n\n helper = LayerHelper('multi_dot', **locals())\n dtype = helper.input_dtype(input_param_name='x')\n out = helper.create_variable_for_type_inference(dtype)\n helper.append_op(type='multi_dot', inputs={\"X\": x}, outputs={\"Out\": out})\n return out\n\n\ndef eigh(x, UPLO='L', name=None):\n \"\"\"\n Compute the eigenvalues and eigenvectors of a\n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[*, N, N]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): (string, default 'L'), 'L' represents the lower triangular matrix,\n \"'U' represents the upper triangular matrix.\".\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n\n out_value(Tensor): A Tensor with shape [*, N] and data type of float32 and float64. The eigenvalues of eigh op.\n out_vector(Tensor): A Tensor with shape [*, N, N] and data type of float32,float64,complex64 and complex128. The eigenvectors of eigh op.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value, out_vector = paddle.linalg.eigh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n print(out_vector)\n #[(-0.9238795325112867+0j), (-0.3826834323650898+0j)],\n #[ 0.3826834323650898j , -0.9238795325112867j ]]\n\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.eigh(x, 'UPLO', UPLO)\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigh', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'eigh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO})\n return out_value, out_vector\n\n\ndef pinv(x, rcond=1e-15, hermitian=False, name=None):\n r\"\"\"\n Calculate pseudo inverse via SVD(singular value decomposition)\n of one matrix or batches of regular matrix.\n\n .. math::\n\n if hermitian == False:\n x = u * s * vt (SVD)\n out = v * 1/s * ut\n else:\n x = u * s * ut (eigh)\n out = u * 1/s * u.conj().transpose(-2,-1)\n\n If x is hermitian or symmetric matrix, svd will be replaced with eigh.\n\n Args:\n x(Tensor): The input tensor. Its shape should be (*, m, n)\n where * is zero or more batch dimensions. m and n can be\n arbitraty positive number. The data type of x should be\n float32 or float64 or complex64 or complex128. When data\n type is complex64 or cpmplex128, hermitian should be set\n True.\n\n rcond(Tensor, optional): the tolerance value to determine\n when is a singular value zero. Defalut:1e-15.\n\n hermitian(bool, optional): indicates whether x is Hermitian\n if complex or symmetric if real. Default: False.\n\n name(str|None): A name for this layer(optional). If set None,\n the layer will be named automatically.\n\n Returns:\n Tensor: The tensor with same data type with x. it represents\n pseudo inverse of x. Its shape should be (*, n, m).\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n x = paddle.arange(15).reshape((3, 5)).astype('float64')\n input = paddle.to_tensor(x)\n out = paddle.linalg.pinv(input)\n print(input)\n print(out)\n\n # input:\n # [[0. , 1. , 2. , 3. , 4. ],\n # [5. , 6. , 7. , 8. , 9. ],\n # [10., 11., 12., 13., 14.]]\n\n # out:\n # [[-0.22666667, -0.06666667, 0.09333333],\n # [-0.12333333, -0.03333333, 0.05666667],\n # [-0.02000000, 0.00000000, 0.02000000],\n # [ 0.08333333, 0.03333333, -0.01666667],\n # [ 0.18666667, 0.06666667, -0.05333333]]\n\n # one can verify : x * out * x = x ;\n # or out * x * out = x ;\n \"\"\"\n\n if paddle.in_dynamic_mode():\n if not hermitian:\n # combine svd and matmul op\n u, s, vt = _C_ops.svd(x, 'full_matrices', False)\n max_singular_val = _C_ops.reduce_max(s, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=x.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=x.dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v, _ = _C_ops.transpose2(vt, 'axis', perm)\n\n out_1 = v * st\n out_2 = _C_ops.matmul_v2(out_1, u, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n # combine eigh and matmul op\n s, u = _C_ops.eigh(x, 'UPLO', 'L')\n s_abs = paddle.abs(s)\n max_singular_val = _C_ops.reduce_max(s_abs, 'dim', [-1], 'keep_dim', True, \\\n 'reduce_all', False)\n rcond = paddle.to_tensor(rcond, dtype=s.dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = paddle.to_tensor(y, dtype=s.dtype)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s.dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), s.dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n st, _ = _C_ops.unsqueeze2(singular, 'axes', [-2])\n\n out_1 = u * st\n u_conj = _C_ops.conj(u)\n out_2 = _C_ops.matmul_v2(out_1, u_conj, 'trans_x', False, 'trans_y',\n True)\n return out_2\n else:\n if not hermitian:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'pinv')\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(dtype)\n vt = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='svd',\n inputs={'X': [x]},\n outputs={'U': u,\n 'VH': vt,\n 'S': s},\n attrs={'full_matrices': False}, )\n\n max_singular_val = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=dtype)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=dtype)\n\n condition = s > cutoff\n cond_int = layers.cast(condition, dtype)\n cond_not_int = layers.cast(layers.logical_not(condition), dtype)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=dtype)\n st_shape = helper.create_variable_for_type_inference(dtype=dtype)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n dims = list(range(len(vt.shape)))\n perm = dims[:-2] + [dims[-1]] + [dims[-2]]\n v = helper.create_variable_for_type_inference(dtype)\n v_shape = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='transpose2',\n inputs={'X': [vt]},\n outputs={'Out': [v],\n 'XShape': [v_shape]},\n attrs={'axis': perm})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': v,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n else:\n helper = LayerHelper('pinv', **locals())\n dtype = x.dtype\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64',\n 'complex128'], 'pinv')\n\n if dtype == paddle.complex128:\n s_type = 'float64'\n elif dtype == paddle.complex64:\n s_type = 'float32'\n else:\n s_type = dtype\n\n u = helper.create_variable_for_type_inference(dtype)\n s = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='eigh',\n inputs={'X': x},\n outputs={'Eigenvalues': s,\n 'Eigenvectors': u},\n attrs={'UPLO': 'L'})\n s_abs = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='abs', inputs={'X': s}, outputs={'Out': s_abs})\n max_singular_val = helper.create_variable_for_type_inference(s_type)\n helper.append_op(\n type='reduce_max',\n inputs={'X': s_abs},\n outputs={'Out': max_singular_val},\n attrs={'dim': [-1],\n 'keep_dim': True,\n 'reduce_all': False})\n\n rcond = layers.fill_constant(shape=[1], value=rcond, dtype=s_type)\n cutoff = rcond * max_singular_val\n y = float('inf')\n y = layers.fill_constant(shape=[1], value=y, dtype=s_type)\n\n condition = s_abs > cutoff\n cond_int = layers.cast(condition, s_type)\n cond_not_int = layers.cast(layers.logical_not(condition), s_type)\n out1 = layers.elementwise_mul(1 / s, cond_int)\n out2 = layers.elementwise_mul(1 / y, cond_not_int)\n singular = layers.elementwise_add(out1, out2)\n\n st = helper.create_variable_for_type_inference(dtype=s_type)\n st_shape = helper.create_variable_for_type_inference(dtype=s_type)\n helper.append_op(\n type='unsqueeze2',\n inputs={'X': singular},\n attrs={'axes': [-2]},\n outputs={'Out': st,\n 'XShape': st_shape})\n\n out_1 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='elementwise_mul',\n inputs={'X': u,\n 'Y': st},\n outputs={'Out': out_1},\n attrs={'axis': -1,\n 'use_mkldnn': False})\n out_1 = helper.append_activation(out_1)\n\n u_conj = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='conj', inputs={'X': u}, outputs={'Out': [u_conj]})\n\n out_2 = helper.create_variable_for_type_inference(dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': out_1,\n 'Y': u_conj},\n outputs={'Out': out_2},\n attrs={'trans_x': False,\n 'trans_y': True}, )\n return out_2\n\n\ndef solve(x, y, name=None):\n r\"\"\"\n Computes the solution of a square system of linear equations with a unique solution for input 'X' and 'Y'.\n Let :math: `X` be a sqaure matrix or a batch of square matrices, :math:`Y` be\n a vector/matrix or a batch of vectors/matrices, the equation should be:\n\n .. math::\n Out = X^-1 * Y\n Specifically,\n - This system of linear equations has one solution if and only if input 'X' is invertible.\n\n Args:\n x (Tensor): A square matrix or a batch of square matrices. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): A vector/matrix or a batch of vectors/matrices. Its shape should be `[*, M, K]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of a square system of linear equations with a unique solution for input 'x' and 'y'.\n Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # 2*X0 + X1 = 9\n # X0 + 2*X1 = 8\n\n import paddle\n import numpy as np\n\n np_x = np.array([[3, 1],[1, 2]])\n np_y = np.array([9, 8])\n x = paddle.to_tensor(np_x, dtype=\"float64\")\n y = paddle.to_tensor(np_y, dtype=\"float64\")\n out = paddle.linalg.solve(x, y)\n\n print(out)\n # [2., 3.])\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.solve(x, y)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type=\"solve\", inputs={\"X\": x,\n \"Y\": y}, outputs={\"Out\": out})\n return out\n\n\ndef triangular_solve(x,\n y,\n upper=True,\n transpose=False,\n unitriangular=False,\n name=None):\n r\"\"\"\n Computes the solution of a system of equations with a triangular coefficient matrix `x` and\n multiple right-hand sides `y` .\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input triangular coefficient matrix. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): Whether to solve the upper-triangular system of equations (default) or the lower-triangular \n system of equations. Default: True.\n transpose (bool, optional): whether `x` should be transposed before calculation. Default: False.\n unitriangular (bool, optional): whether `x` is unit triangular. If True, the diagonal elements of `x` are assumed \n to be 1 and not referenced from `x` . Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type should be the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n # a square system of linear equations:\n # x1 + x2 + x3 = 0\n # 2*x2 + x3 = -9\n # -x3 = 5\n\n import paddle\n import numpy as np\n\n x = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n y = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.triangular_solve(x, y, upper=True)\n\n print(out)\n # [7, -2, -5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.triangular_solve(x, y, 'upper', upper, 'transpose',\n transpose, 'unitriangular',\n unitriangular)\n\n inputs = {\"X\": [x], \"Y\": [y]}\n helper = LayerHelper(\"triangular_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'triangular_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'triangular_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='triangular_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={\n 'upper': upper,\n 'transpose': transpose,\n 'unitriangular': unitriangular\n })\n return out\n\n\ndef cholesky_solve(x, y, upper=False, name=None):\n r\"\"\"\n Solves a linear system of equations A @ X = B, given A's Cholesky factor matrix u and matrix B.\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor): The input matrix which is upper or lower triangular Cholesky factor of square matrix A. Its shape should be `[*, M, M]`, where `*` is zero or\n more batch dimensions. Its data type should be float32 or float64.\n y (Tensor): Multiple right-hand sides of system of equations. Its shape should be `[*, M, K]`, where `*` is \n zero or more batch dimensions. Its data type should be float32 or float64.\n upper (bool, optional): whether to consider the Cholesky factor as a lower or upper triangular matrix. Default: False.\n name(str, optional): Name for the operation (optional, default is None).\n For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The solution of the system of equations. Its data type is the same as that of `x`.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n u = paddle.to_tensor([[1, 1, 1], \n [0, 2, 1],\n [0, 0,-1]], dtype=\"float64\")\n b = paddle.to_tensor([[0], [-9], [5]], dtype=\"float64\")\n out = paddle.linalg.cholesky_solve(b, u, upper=True)\n\n print(out)\n # [-2.5, -7, 9.5]\n \"\"\"\n if paddle.in_dynamic_mode():\n return _C_ops.cholesky_solve(x, y, 'upper', upper)\n\n helper = LayerHelper(\"cholesky_solve\", **locals())\n check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'cholesky_solve')\n check_variable_and_dtype(y, 'y', ['float32', 'float64'], 'cholesky_solve')\n out = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='cholesky_solve',\n inputs={'X': x,\n 'Y': y},\n outputs={'Out': out},\n attrs={'upper': upper})\n return out\n\n\ndef eigvalsh(x, UPLO='L', name=None):\n \"\"\"\n Computes the eigenvalues of a \n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[_, M, M]` , The data type of the input Tensor x\n should be one of float32, float64, complex64, complex128.\n UPLO(str, optional): Lower triangular part of a (‘L’, default) or the upper triangular part (‘U’).\n name(str, optional): The default value is None. Normally there is no need for user to set this\n property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tensor: The tensor eigenvalues in ascending order.\n\n Examples:\n .. code-block:: python\n\n import numpy as np\n import paddle\n\n x_data = np.array([[1, -2j], [2j, 5]])\n x = paddle.to_tensor(x_data)\n out_value = paddle.eigvalsh(x, UPLO='L')\n print(out_value)\n #[0.17157288, 5.82842712]\n \"\"\"\n if paddle.in_dynamic_mode():\n is_test = x.stop_gradient\n values, _ = _C_ops.eigvalsh(x, 'UPLO', UPLO, 'is_test', is_test)\n return values\n\n def __check_input(x, UPLO):\n x_shape = list(x.shape)\n if len(x.shape) < 2:\n raise ValueError(\n \"Input(input) only support >=2 tensor, but received \"\n \"length of Input(input) is %s.\" % len(x.shape))\n if x_shape[-1] != x_shape[-2]:\n raise ValueError(\n \"The input matrix must be batches of square matrices. But received x's dimention: {}\".\n format(x_shape))\n if UPLO != 'L' and UPLO != 'U':\n raise ValueError(\n \"UPLO must be L or U. But received UPLO is: {}\".format(UPLO))\n\n __check_input(x, UPLO)\n\n helper = LayerHelper('eigvalsh', **locals())\n check_variable_and_dtype(x, 'dtype',\n ['float32', 'float64', 'complex64', 'complex128'],\n 'eigvalsh')\n\n out_value = helper.create_variable_for_type_inference(dtype=x.dtype)\n out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n is_test = x.stop_gradient\n helper.append_op(\n type='eigvalsh',\n inputs={'X': x},\n outputs={'Eigenvalues': out_value,\n 'Eigenvectors': out_vector},\n attrs={'UPLO': UPLO,\n 'is_test': is_test})\n return out_value\n\n\ndef lstsq(x, y, rcond=None, driver=None, name=None):\n \"\"\"\n Computes a solution to\n the least squares problem of a system of linear equations.\n\n Args:\n x (Tensor): A tensor with shape ``(*, M, N)`` , the data type of the input Tensor ``x``\n should be one of float32, float64.\n y (Tensor): A tensor with shape ``(*, M, K)`` , the data type of the input Tensor ``y`` \n should be one of float32, float64.\n rcond(float, optional): The default value is None. A float pointing number used to determine \n the effective rank of ``x``. If ``rcond`` is None, it will be set to max(M, N) times the \n machine precision of x_dtype.\n driver(str, optional): The default value is None. The name of LAPACK method to be used. For \n CPU inputs the valid values are ‘gels’, ‘gelsy’, ‘gelsd, ‘gelss’. For CUDA input, the only \n valid driver is ‘gels’. If ``driver`` is None, ‘gelsy’ is used for CPU inputs and ‘gels’ \n for CUDA inputs.\n name(str, optional): The default value is None. Normally there is no need for user to set \n this property. For more information, please refer to :ref:`api_guide_Name`.\n\n Returns:\n Tuple: A tuple of 4 Tensors which is (``solution``, ``residuals``, ``rank``, ``singular_values``). \n ``solution`` is a tensor with shape ``(*, N, K)``, meaning the least squares solution. ``residuals`` \n is a tensor with shape ``(*, K)``, meaning the squared residuals of the solutions, which is computed \n when M > N and every matrix in ``x`` is full-rank, otherwise return an empty tensor. ``rank`` is a tensor \n with shape ``(*)``, meaning the ranks of the matrices in ``x``, which is computed when ``driver`` in \n (‘gelsy’, ‘gelsd’, ‘gelss’), otherwise return an empty tensor. ``singular_values`` is a tensor with \n shape ``(*, min(M, N))``, meaning singular values of the matrices in ``x``, which is computed when \n ``driver`` in (‘gelsd’, ‘gelss’), otherwise return an empty tensor.\n\n Examples:\n .. code-block:: python\n\n import paddle\n\n paddle.set_device(\"cpu\")\n x = paddle.to_tensor([[1, 3], [3, 2], [5, 6.]])\n y = paddle.to_tensor([[3, 4, 6], [5, 3, 4], [1, 2, 1.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gelsd\")\n print(results[0])\n # [[ 0.78350395, -0.22165027, -0.62371236],\n # [-0.11340097, 0.78866047, 1.14948535]]\n print(results[1])\n # [19.81443405, 10.43814468, 30.56185532])\n print(results[2])\n # 2\n print(results[3])\n # [9.03455734, 1.54167950]\n\n x = paddle.to_tensor([[10, 2, 3], [3, 10, 5], [5, 6, 12.]])\n y = paddle.to_tensor([[4, 2, 9], [2, 0, 3], [2, 5, 3.]])\n results = paddle.linalg.lstsq(x, y, driver=\"gels\")\n print(results[0])\n # [[ 0.39386186, 0.10230173, 0.93606132],\n # [ 0.10741687, -0.29028133, 0.11892585],\n # [-0.05115091, 0.51918161, -0.19948854]]\n print(results[1])\n # []\n \"\"\"\n device = paddle.get_device()\n if device == \"cpu\":\n if driver not in (None, \"gels\", \"gelss\", \"gelsd\", \"gelsy\"):\n raise ValueError(\n \"Only support valid driver is 'gels', 'gelss', 'gelsd', 'gelsy' or None for CPU inputs. But got {}\".\n format(driver))\n driver = \"gelsy\" if driver is None else driver\n elif \"gpu\" in device:\n if driver not in (None, \"gels\"):\n raise ValueError(\n \"Only support valid driver is 'gels' or None for CUDA inputs. But got {}\".\n format(driver))\n driver = \"gels\" if driver is None else driver\n else:\n raise RuntimeError(\"Only support lstsq api for CPU or CUDA device.\")\n\n if x.dtype == y.dtype and x.dtype in (paddle.float32, paddle.float64):\n pass\n else:\n raise ValueError(\n \"Only support x and y have the same dtype such as 'float32' and 'float64'.\"\n )\n\n if rcond is None:\n if x.dtype == paddle.float32:\n rcond = 1e-7 * max(x.shape[-2], x.shape[-1])\n elif x.dtype == paddle.float64:\n rcond = 1e-15 * max(x.shape[-2], x.shape[-1])\n\n if paddle.in_dynamic_mode():\n solution, rank, singular_values = _C_ops.lstsq(x, y, \"rcond\", rcond,\n \"driver\", driver)\n if x.shape[-2] > x.shape[-1]:\n matmul_out = _varbase_creator(dtype=x.dtype)\n _C_ops.matmul(x, solution, matmul_out, 'trans_x', False, 'trans_y',\n False)\n minus_out = _C_ops.elementwise_sub(matmul_out, y)\n pow_out = _C_ops.pow(minus_out, 'factor', 2)\n residuals = _C_ops.reduce_sum(pow_out, 'dim', [-2], 'keepdim',\n False, 'reduce_all', False)\n else:\n residuals = paddle.empty(shape=[0], dtype=x.dtype)\n\n if driver == \"gels\":\n rank = paddle.empty(shape=[0], dtype=paddle.int32)\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n elif driver == \"gelsy\":\n singular_values = paddle.empty(shape=[0], dtype=x.dtype)\n\n return solution, residuals, rank, singular_values\n\n helper = LayerHelper('lstsq', **locals())\n check_variable_and_dtype(\n x, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n check_variable_and_dtype(\n y, 'dtype', ['float32', 'float64', 'complex64', 'complex128'], 'lstsq')\n\n solution = helper.create_variable_for_type_inference(dtype=x.dtype)\n residuals = helper.create_variable_for_type_inference(dtype=x.dtype)\n rank = helper.create_variable_for_type_inference(dtype=paddle.int32)\n singular_values = helper.create_variable_for_type_inference(dtype=x.dtype)\n\n helper.append_op(\n type='lstsq',\n inputs={'X': x,\n 'Y': y},\n outputs={\n 'Solution': solution,\n 'Rank': rank,\n 'SingularValues': singular_values\n },\n attrs={'rcond': rcond,\n 'driver': driver})\n\n matmul_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n minus_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n pow_out = helper.create_variable_for_type_inference(dtype=x.dtype)\n helper.append_op(\n type='matmul_v2',\n inputs={'X': x,\n 'Y': solution},\n outputs={'Out': matmul_out},\n attrs={\n 'trans_x': False,\n 'trans_y': False,\n })\n\n helper.append_op(\n type='elementwise_sub',\n inputs={'X': matmul_out,\n 'Y': y},\n outputs={'Out': minus_out})\n\n helper.append_op(\n type='pow',\n inputs={'X': minus_out},\n outputs={'Out': pow_out},\n attrs={'factor': 2})\n\n helper.append_op(\n type='reduce_sum',\n inputs={'X': pow_out},\n outputs={'Out': residuals},\n attrs={'dim': [-2],\n 'keep_dim': False,\n 'reduce_all': False})\n\n if driver == \"gels\":\n rank = paddle.static.data(name='rank', shape=[0])\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n elif driver == \"gelsy\":\n singular_values = paddle.static.data(name='singular_values', shape=[0])\n\n return solution, residuals, rank, singular_values\n"}}},{"rowIdx":42,"cells":{"function_name":{"kind":"string","value":"on_off_command"},"docstring":{"kind":"string","value":"Send an on or off command to an appliance\n\nSends the specified command to the homevision through netio interface to control the specified appliance.\n\nArgs:\n details: {\"appliance\": string, \"state\": string} "},"masked_code":{"kind":"string","value":"\nimport socket\n\nclass UserException(Exception):\n pass\n\ndef user_exception(s): raise UserException(s)\n\nclass Macro:\n \"\"\"Represents a macro to be run\"\"\"\n def __init__(self, code):\n \"\"\"code: int - index of macro to run\"\"\"\n self.code = code\n\nclass Command:\n \"\"\"Represents a macro to be run\"\"\"\n def __init__(self, command):\n \"\"\"command: string - command to send\"\"\"\n self.command = command\n \nclass HomeVisionController:\n\n def __init__(\n self,\n ip_address,\n port,\n auth,\n on_off_appliance_codes={},\n actions={},\n process_actions={},\n var_queries={},\n flag_queries={},\n flag_return_values = {True: [\"True\", \"On\", \"Yes\", \"Occupied\", \"Set\", \"1\"], False: [\"False\", \"Off\", \"No\", \"Vacant\", \"Clear\", \"0\"]},\n on_off_commands = None\n ):\n \"\"\"\n Args:\n ip_address: string\n port: int\n auth: string\n - key for authenticating with netio\n on_off_appliance_codes: dict[string] => int - codes to be fed to 'on_off_commands' for each appliance\n actions: dict[string] => Macro/Command/(_, _, ...) - named actions to be completed\n process_actions: dict[string] => {\"START\": X, \"STOP\": X} where X is Macro/Command/(_, _, ...) - named processes to be started and stopped\n var_queries: dict[string] => int - mapping of names to variable indexes\n flag_queries: dict[string] => int - mapping of names to flag indexes\n flag_return_values: {True: [string], False: [string]} - synonyms for true and false that are returned by netio 'read flag command'. (ignore if you haven't set them up)\n on_off_commands: {\"ON\": (int) => Macro/Command/(_, _, ...), \"OFF\": (int) => Macro/Command} - how to handle on and off commands\n \"\"\"\n self.ip_address = ip_address\n self.port = port\n self.auth = auth\n self.on_off_appliance_codes = on_off_appliance_codes\n self.actions = actions\n self.process_actions = process_actions\n self.var_queries = var_queries\n self.flag_queries = flag_queries\n self.flag_return_values = flag_return_values\n self.on_off_commands = on_off_commands\n \n# MASKED: on_off_command function (lines 61-84)\n\n def action_command(self, details):\n \"\"\"Send an action command\n \n Sends the specified command to the homevision through netio interface.\n \n Args:\n details: {\"command\": string} \n \"\"\"\n if \"command\" not in details:\n raise Exception(\"Command not specified\")\n\n if details[\"command\"] not in self.actions.keys():\n raise Exception(\"Command not supported. Must be one of: \" + \",\".join(self.actions.keys()))\n\n self._handle_action(self.actions[details[\"command\"]])\n\n def start_stop_command(self, details):\n \"\"\"Starts or stops a process\n \n Sends the specified command to the homevision through netio interface to control the specified process.\n \n Args:\n details: {\"action\": string, \"process\": string} \n \"\"\"\n if \"action\" not in details:\n raise Exception(\"action not specified\")\n elif \"process\" not in details:\n raise Exception(\"process not specified\")\n\n if details[\"process\"] not in self.process_actions.keys():\n raise Exception(\"process not supported. Must be one of: \" + \",\".join(self.process_actions.keys()))\n \n if details['action'] == \"START\":\n self._handle_action(self.process_actions[details[\"process\"]][\"START\"])\n elif details[\"action\"] == \"STOP\":\n self._handle_action(self.process_actions[details[\"process\"]][\"STOP\"])\n else:\n raise Exception(\"action not supported. Must be either \\\"START\\\" or \\\"STOP\\\".\")\n \n def _handle_action(self, action):\n def handle_single(a):\n if type(a) == Macro:\n self._run_macro(a.code)\n elif type(a) == Command:\n self._send_command(a.command)\n elif type(a) == Exception:\n raise a\n else:\n raise Exception(\"Internal Error: invalid action type. Should be Macro, Command or Exception\")\n \n if type(action) == tuple:\n for a in action:\n handle_single(a)\n else:\n handle_single(action)\n\n def var_query(self, details):\n \"\"\"Returns the answer to a query on variable\n \n Returns the answer to a query on the specified variable using netio\n \n Args:\n details: {\"query\": string} \n \"\"\"\n if \"query\" not in details:\n raise Exception(\"query not specified\")\n \n if details[\"query\"] not in self.var_queries.keys():\n raise Exception(\"query not supported. Must be one of: \" + \",\".join(self.var_queries.keys()))\n \n code = self.var_queries[details[\"query\"]]\n if type(code) == int:\n val = self._get_var(code)\n elif type(code) == tuple:\n val = [self._get_var(c) for c in code]\n else:\n raise Exception(\"Internal Exception: variable code is not valid\")\n \n return val\n \n def flag_query(self, details):\n \"\"\"Returns the answer to a query on flag\n \n Returns the answer to a query on the specified variable using netio\n \n Args:\n details: {\"query\": string} \n \"\"\"\n if \"query\" not in details:\n raise Exception(\"query not specified\")\n \n if details[\"query\"] not in self.flag_queries.keys():\n raise Exception(\"query not supported. Must be one of: \" + \",\".join(self.flag_queries.keys()))\n \n val = self._get_flag(self.flag_queries[details[\"query\"]])\n \n return \"yes\" if val else \"no\"\n \n def _switch_on(self, code):\n if self.on_off_commands == None:\n raise Exception(\"No On/Off command set\")\n self._handle_action(self.on_off_commands[\"ON\"](code))\n \n def _switch_off(self, code):\n if self.on_off_commands == None:\n raise Exception(\"No On/Off command set\")\n self._handle_action(self.on_off_commands[\"OFF\"](code))\n\n def _run_macro(self, code):\n self._send_command(b'action macro run ' + bytes(str(code), encoding=\"ascii\") + b'; __wait 100')\n\n def _send_command(self, command):\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((self.ip_address, self.port))\n s.send(bytes(\"auth \" + self.auth + \"\\n\", encoding=\"ascii\"))\n s.send(command)\n s.close()\n\n def _get_var(self, id):\n return int(self._run_read_command(b\"get var state \" + bytes(str(id), encoding=\"ascii\")))\n\n def _get_flag(self, id):\n ret = self._run_read_command(b\"get flag state \" + bytes(str(id), encoding=\"ascii\"))\n if ret in self.flag_return_values[False]:\n return False\n elif ret in self.flag_return_values[True]:\n return True\n else:\n raise Exception(\"Flag value not supported: \" + ret)\n\n def _run_read_command(self, command):\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((self.ip_address, self.port))\n s.send(bytes(\"auth \" + self.auth + \"\\n\", encoding=\"ascii\"))\n s.recv(10)\n s.send(command)\n s.send(b'\\n')\n response = s.recv(10).decode(encoding=\"ascii\").rstrip()\n s.close()\n return response\n "},"implementation":{"kind":"string","value":" def on_off_command(self, details):\n \"\"\"Send an on or off command to an appliance\n \n Sends the specified command to the homevision through netio interface to control the specified appliance.\n \n Args:\n details: {\"appliance\": string, \"state\": string} \n \"\"\"\n if \"appliance\" not in details:\n raise Exception(\"appliance not specified\")\n elif \"state\" not in details:\n raise Exception(\"state not specified\")\n\n if details[\"appliance\"] not in self.on_off_appliance_codes.keys():\n raise Exception(\"appliance not supported. Must be one of: \" + \",\".join(self.on_off_appliance_codes.keys()))\n\n appliance_code = self.on_off_appliance_codes[details[\"appliance\"]]\n \n if details['state'] == \"ON\":\n self._switch_on(appliance_code)\n elif details[\"state\"] == \"OFF\":\n self._switch_off(appliance_code)\n else:\n raise Exception(\"state not supported. Must be either \\\"ON\\\" or \\\"OFF\\\".\")"},"start_line":{"kind":"number","value":61,"string":"61"},"end_line":{"kind":"number","value":84,"string":"84"},"file_content":{"kind":"string","value":"\nimport socket\n\nclass UserException(Exception):\n pass\n\ndef user_exception(s): raise UserException(s)\n\nclass Macro:\n \"\"\"Represents a macro to be run\"\"\"\n def __init__(self, code):\n \"\"\"code: int - index of macro to run\"\"\"\n self.code = code\n\nclass Command:\n \"\"\"Represents a macro to be run\"\"\"\n def __init__(self, command):\n \"\"\"command: string - command to send\"\"\"\n self.command = command\n \nclass HomeVisionController:\n\n def __init__(\n self,\n ip_address,\n port,\n auth,\n on_off_appliance_codes={},\n actions={},\n process_actions={},\n var_queries={},\n flag_queries={},\n flag_return_values = {True: [\"True\", \"On\", \"Yes\", \"Occupied\", \"Set\", \"1\"], False: [\"False\", \"Off\", \"No\", \"Vacant\", \"Clear\", \"0\"]},\n on_off_commands = None\n ):\n \"\"\"\n Args:\n ip_address: string\n port: int\n auth: string\n - key for authenticating with netio\n on_off_appliance_codes: dict[string] => int - codes to be fed to 'on_off_commands' for each appliance\n actions: dict[string] => Macro/Command/(_, _, ...) - named actions to be completed\n process_actions: dict[string] => {\"START\": X, \"STOP\": X} where X is Macro/Command/(_, _, ...) - named processes to be started and stopped\n var_queries: dict[string] => int - mapping of names to variable indexes\n flag_queries: dict[string] => int - mapping of names to flag indexes\n flag_return_values: {True: [string], False: [string]} - synonyms for true and false that are returned by netio 'read flag command'. (ignore if you haven't set them up)\n on_off_commands: {\"ON\": (int) => Macro/Command/(_, _, ...), \"OFF\": (int) => Macro/Command} - how to handle on and off commands\n \"\"\"\n self.ip_address = ip_address\n self.port = port\n self.auth = auth\n self.on_off_appliance_codes = on_off_appliance_codes\n self.actions = actions\n self.process_actions = process_actions\n self.var_queries = var_queries\n self.flag_queries = flag_queries\n self.flag_return_values = flag_return_values\n self.on_off_commands = on_off_commands\n \n def on_off_command(self, details):\n \"\"\"Send an on or off command to an appliance\n \n Sends the specified command to the homevision through netio interface to control the specified appliance.\n \n Args:\n details: {\"appliance\": string, \"state\": string} \n \"\"\"\n if \"appliance\" not in details:\n raise Exception(\"appliance not specified\")\n elif \"state\" not in details:\n raise Exception(\"state not specified\")\n\n if details[\"appliance\"] not in self.on_off_appliance_codes.keys():\n raise Exception(\"appliance not supported. Must be one of: \" + \",\".join(self.on_off_appliance_codes.keys()))\n\n appliance_code = self.on_off_appliance_codes[details[\"appliance\"]]\n \n if details['state'] == \"ON\":\n self._switch_on(appliance_code)\n elif details[\"state\"] == \"OFF\":\n self._switch_off(appliance_code)\n else:\n raise Exception(\"state not supported. Must be either \\\"ON\\\" or \\\"OFF\\\".\")\n\n def action_command(self, details):\n \"\"\"Send an action command\n \n Sends the specified command to the homevision through netio interface.\n \n Args:\n details: {\"command\": string} \n \"\"\"\n if \"command\" not in details:\n raise Exception(\"Command not specified\")\n\n if details[\"command\"] not in self.actions.keys():\n raise Exception(\"Command not supported. Must be one of: \" + \",\".join(self.actions.keys()))\n\n self._handle_action(self.actions[details[\"command\"]])\n\n def start_stop_command(self, details):\n \"\"\"Starts or stops a process\n \n Sends the specified command to the homevision through netio interface to control the specified process.\n \n Args:\n details: {\"action\": string, \"process\": string} \n \"\"\"\n if \"action\" not in details:\n raise Exception(\"action not specified\")\n elif \"process\" not in details:\n raise Exception(\"process not specified\")\n\n if details[\"process\"] not in self.process_actions.keys():\n raise Exception(\"process not supported. Must be one of: \" + \",\".join(self.process_actions.keys()))\n \n if details['action'] == \"START\":\n self._handle_action(self.process_actions[details[\"process\"]][\"START\"])\n elif details[\"action\"] == \"STOP\":\n self._handle_action(self.process_actions[details[\"process\"]][\"STOP\"])\n else:\n raise Exception(\"action not supported. Must be either \\\"START\\\" or \\\"STOP\\\".\")\n \n def _handle_action(self, action):\n def handle_single(a):\n if type(a) == Macro:\n self._run_macro(a.code)\n elif type(a) == Command:\n self._send_command(a.command)\n elif type(a) == Exception:\n raise a\n else:\n raise Exception(\"Internal Error: invalid action type. Should be Macro, Command or Exception\")\n \n if type(action) == tuple:\n for a in action:\n handle_single(a)\n else:\n handle_single(action)\n\n def var_query(self, details):\n \"\"\"Returns the answer to a query on variable\n \n Returns the answer to a query on the specified variable using netio\n \n Args:\n details: {\"query\": string} \n \"\"\"\n if \"query\" not in details:\n raise Exception(\"query not specified\")\n \n if details[\"query\"] not in self.var_queries.keys():\n raise Exception(\"query not supported. Must be one of: \" + \",\".join(self.var_queries.keys()))\n \n code = self.var_queries[details[\"query\"]]\n if type(code) == int:\n val = self._get_var(code)\n elif type(code) == tuple:\n val = [self._get_var(c) for c in code]\n else:\n raise Exception(\"Internal Exception: variable code is not valid\")\n \n return val\n \n def flag_query(self, details):\n \"\"\"Returns the answer to a query on flag\n \n Returns the answer to a query on the specified variable using netio\n \n Args:\n details: {\"query\": string} \n \"\"\"\n if \"query\" not in details:\n raise Exception(\"query not specified\")\n \n if details[\"query\"] not in self.flag_queries.keys():\n raise Exception(\"query not supported. Must be one of: \" + \",\".join(self.flag_queries.keys()))\n \n val = self._get_flag(self.flag_queries[details[\"query\"]])\n \n return \"yes\" if val else \"no\"\n \n def _switch_on(self, code):\n if self.on_off_commands == None:\n raise Exception(\"No On/Off command set\")\n self._handle_action(self.on_off_commands[\"ON\"](code))\n \n def _switch_off(self, code):\n if self.on_off_commands == None:\n raise Exception(\"No On/Off command set\")\n self._handle_action(self.on_off_commands[\"OFF\"](code))\n\n def _run_macro(self, code):\n self._send_command(b'action macro run ' + bytes(str(code), encoding=\"ascii\") + b'; __wait 100')\n\n def _send_command(self, command):\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((self.ip_address, self.port))\n s.send(bytes(\"auth \" + self.auth + \"\\n\", encoding=\"ascii\"))\n s.send(command)\n s.close()\n\n def _get_var(self, id):\n return int(self._run_read_command(b\"get var state \" + bytes(str(id), encoding=\"ascii\")))\n\n def _get_flag(self, id):\n ret = self._run_read_command(b\"get flag state \" + bytes(str(id), encoding=\"ascii\"))\n if ret in self.flag_return_values[False]:\n return False\n elif ret in self.flag_return_values[True]:\n return True\n else:\n raise Exception(\"Flag value not supported: \" + ret)\n\n def _run_read_command(self, command):\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((self.ip_address, self.port))\n s.send(bytes(\"auth \" + self.auth + \"\\n\", encoding=\"ascii\"))\n s.recv(10)\n s.send(command)\n s.send(b'\\n')\n response = s.recv(10).decode(encoding=\"ascii\").rstrip()\n s.close()\n return response\n "}}},{"rowIdx":43,"cells":{"function_name":{"kind":"string","value":"action_command"},"docstring":{"kind":"string","value":"Send an action command\n\nSends the specified command to the homevision through netio interface.\n\nArgs:\n details: {\"command\": string} "},"masked_code":{"kind":"string","value":"\nimport socket\n\nclass UserException(Exception):\n pass\n\ndef user_exception(s): raise UserException(s)\n\nclass Macro:\n \"\"\"Represents a macro to be run\"\"\"\n def __init__(self, code):\n \"\"\"code: int - index of macro to run\"\"\"\n self.code = code\n\nclass Command:\n \"\"\"Represents a macro to be run\"\"\"\n def __init__(self, command):\n \"\"\"command: string - command to send\"\"\"\n self.command = command\n \nclass HomeVisionController:\n\n def __init__(\n self,\n ip_address,\n port,\n auth,\n on_off_appliance_codes={},\n actions={},\n process_actions={},\n var_queries={},\n flag_queries={},\n flag_return_values = {True: [\"True\", \"On\", \"Yes\", \"Occupied\", \"Set\", \"1\"], False: [\"False\", \"Off\", \"No\", \"Vacant\", \"Clear\", \"0\"]},\n on_off_commands = None\n ):\n \"\"\"\n Args:\n ip_address: string\n port: int\n auth: string\n - key for authenticating with netio\n on_off_appliance_codes: dict[string] => int - codes to be fed to 'on_off_commands' for each appliance\n actions: dict[string] => Macro/Command/(_, _, ...) - named actions to be completed\n process_actions: dict[string] => {\"START\": X, \"STOP\": X} where X is Macro/Command/(_, _, ...) - named processes to be started and stopped\n var_queries: dict[string] => int - mapping of names to variable indexes\n flag_queries: dict[string] => int - mapping of names to flag indexes\n flag_return_values: {True: [string], False: [string]} - synonyms for true and false that are returned by netio 'read flag command'. (ignore if you haven't set them up)\n on_off_commands: {\"ON\": (int) => Macro/Command/(_, _, ...), \"OFF\": (int) => Macro/Command} - how to handle on and off commands\n \"\"\"\n self.ip_address = ip_address\n self.port = port\n self.auth = auth\n self.on_off_appliance_codes = on_off_appliance_codes\n self.actions = actions\n self.process_actions = process_actions\n self.var_queries = var_queries\n self.flag_queries = flag_queries\n self.flag_return_values = flag_return_values\n self.on_off_commands = on_off_commands\n \n def on_off_command(self, details):\n \"\"\"Send an on or off command to an appliance\n \n Sends the specified command to the homevision through netio interface to control the specified appliance.\n \n Args:\n details: {\"appliance\": string, \"state\": string} \n \"\"\"\n if \"appliance\" not in details:\n raise Exception(\"appliance not specified\")\n elif \"state\" not in details:\n raise Exception(\"state not specified\")\n\n if details[\"appliance\"] not in self.on_off_appliance_codes.keys():\n raise Exception(\"appliance not supported. Must be one of: \" + \",\".join(self.on_off_appliance_codes.keys()))\n\n appliance_code = self.on_off_appliance_codes[details[\"appliance\"]]\n \n if details['state'] == \"ON\":\n self._switch_on(appliance_code)\n elif details[\"state\"] == \"OFF\":\n self._switch_off(appliance_code)\n else:\n raise Exception(\"state not supported. Must be either \\\"ON\\\" or \\\"OFF\\\".\")\n\n# MASKED: action_command function (lines 86-100)\n\n def start_stop_command(self, details):\n \"\"\"Starts or stops a process\n \n Sends the specified command to the homevision through netio interface to control the specified process.\n \n Args:\n details: {\"action\": string, \"process\": string} \n \"\"\"\n if \"action\" not in details:\n raise Exception(\"action not specified\")\n elif \"process\" not in details:\n raise Exception(\"process not specified\")\n\n if details[\"process\"] not in self.process_actions.keys():\n raise Exception(\"process not supported. Must be one of: \" + \",\".join(self.process_actions.keys()))\n \n if details['action'] == \"START\":\n self._handle_action(self.process_actions[details[\"process\"]][\"START\"])\n elif details[\"action\"] == \"STOP\":\n self._handle_action(self.process_actions[details[\"process\"]][\"STOP\"])\n else:\n raise Exception(\"action not supported. Must be either \\\"START\\\" or \\\"STOP\\\".\")\n \n def _handle_action(self, action):\n def handle_single(a):\n if type(a) == Macro:\n self._run_macro(a.code)\n elif type(a) == Command:\n self._send_command(a.command)\n elif type(a) == Exception:\n raise a\n else:\n raise Exception(\"Internal Error: invalid action type. Should be Macro, Command or Exception\")\n \n if type(action) == tuple:\n for a in action:\n handle_single(a)\n else:\n handle_single(action)\n\n def var_query(self, details):\n \"\"\"Returns the answer to a query on variable\n \n Returns the answer to a query on the specified variable using netio\n \n Args:\n details: {\"query\": string} \n \"\"\"\n if \"query\" not in details:\n raise Exception(\"query not specified\")\n \n if details[\"query\"] not in self.var_queries.keys():\n raise Exception(\"query not supported. Must be one of: \" + \",\".join(self.var_queries.keys()))\n \n code = self.var_queries[details[\"query\"]]\n if type(code) == int:\n val = self._get_var(code)\n elif type(code) == tuple:\n val = [self._get_var(c) for c in code]\n else:\n raise Exception(\"Internal Exception: variable code is not valid\")\n \n return val\n \n def flag_query(self, details):\n \"\"\"Returns the answer to a query on flag\n \n Returns the answer to a query on the specified variable using netio\n \n Args:\n details: {\"query\": string} \n \"\"\"\n if \"query\" not in details:\n raise Exception(\"query not specified\")\n \n if details[\"query\"] not in self.flag_queries.keys():\n raise Exception(\"query not supported. Must be one of: \" + \",\".join(self.flag_queries.keys()))\n \n val = self._get_flag(self.flag_queries[details[\"query\"]])\n \n return \"yes\" if val else \"no\"\n \n def _switch_on(self, code):\n if self.on_off_commands == None:\n raise Exception(\"No On/Off command set\")\n self._handle_action(self.on_off_commands[\"ON\"](code))\n \n def _switch_off(self, code):\n if self.on_off_commands == None:\n raise Exception(\"No On/Off command set\")\n self._handle_action(self.on_off_commands[\"OFF\"](code))\n\n def _run_macro(self, code):\n self._send_command(b'action macro run ' + bytes(str(code), encoding=\"ascii\") + b'; __wait 100')\n\n def _send_command(self, command):\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((self.ip_address, self.port))\n s.send(bytes(\"auth \" + self.auth + \"\\n\", encoding=\"ascii\"))\n s.send(command)\n s.close()\n\n def _get_var(self, id):\n return int(self._run_read_command(b\"get var state \" + bytes(str(id), encoding=\"ascii\")))\n\n def _get_flag(self, id):\n ret = self._run_read_command(b\"get flag state \" + bytes(str(id), encoding=\"ascii\"))\n if ret in self.flag_return_values[False]:\n return False\n elif ret in self.flag_return_values[True]:\n return True\n else:\n raise Exception(\"Flag value not supported: \" + ret)\n\n def _run_read_command(self, command):\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((self.ip_address, self.port))\n s.send(bytes(\"auth \" + self.auth + \"\\n\", encoding=\"ascii\"))\n s.recv(10)\n s.send(command)\n s.send(b'\\n')\n response = s.recv(10).decode(encoding=\"ascii\").rstrip()\n s.close()\n return response\n "},"implementation":{"kind":"string","value":" def action_command(self, details):\n \"\"\"Send an action command\n \n Sends the specified command to the homevision through netio interface.\n \n Args:\n details: {\"command\": string} \n \"\"\"\n if \"command\" not in details:\n raise Exception(\"Command not specified\")\n\n if details[\"command\"] not in self.actions.keys():\n raise Exception(\"Command not supported. Must be one of: \" + \",\".join(self.actions.keys()))\n\n self._handle_action(self.actions[details[\"command\"]])"},"start_line":{"kind":"number","value":86,"string":"86"},"end_line":{"kind":"number","value":100,"string":"100"},"file_content":{"kind":"string","value":"\nimport socket\n\nclass UserException(Exception):\n pass\n\ndef user_exception(s): raise UserException(s)\n\nclass Macro:\n \"\"\"Represents a macro to be run\"\"\"\n def __init__(self, code):\n \"\"\"code: int - index of macro to run\"\"\"\n self.code = code\n\nclass Command:\n \"\"\"Represents a macro to be run\"\"\"\n def __init__(self, command):\n \"\"\"command: string - command to send\"\"\"\n self.command = command\n \nclass HomeVisionController:\n\n def __init__(\n self,\n ip_address,\n port,\n auth,\n on_off_appliance_codes={},\n actions={},\n process_actions={},\n var_queries={},\n flag_queries={},\n flag_return_values = {True: [\"True\", \"On\", \"Yes\", \"Occupied\", \"Set\", \"1\"], False: [\"False\", \"Off\", \"No\", \"Vacant\", \"Clear\", \"0\"]},\n on_off_commands = None\n ):\n \"\"\"\n Args:\n ip_address: string\n port: int\n auth: string\n - key for authenticating with netio\n on_off_appliance_codes: dict[string] => int - codes to be fed to 'on_off_commands' for each appliance\n actions: dict[string] => Macro/Command/(_, _, ...) - named actions to be completed\n process_actions: dict[string] => {\"START\": X, \"STOP\": X} where X is Macro/Command/(_, _, ...) - named processes to be started and stopped\n var_queries: dict[string] => int - mapping of names to variable indexes\n flag_queries: dict[string] => int - mapping of names to flag indexes\n flag_return_values: {True: [string], False: [string]} - synonyms for true and false that are returned by netio 'read flag command'. (ignore if you haven't set them up)\n on_off_commands: {\"ON\": (int) => Macro/Command/(_, _, ...), \"OFF\": (int) => Macro/Command} - how to handle on and off commands\n \"\"\"\n self.ip_address = ip_address\n self.port = port\n self.auth = auth\n self.on_off_appliance_codes = on_off_appliance_codes\n self.actions = actions\n self.process_actions = process_actions\n self.var_queries = var_queries\n self.flag_queries = flag_queries\n self.flag_return_values = flag_return_values\n self.on_off_commands = on_off_commands\n \n def on_off_command(self, details):\n \"\"\"Send an on or off command to an appliance\n \n Sends the specified command to the homevision through netio interface to control the specified appliance.\n \n Args:\n details: {\"appliance\": string, \"state\": string} \n \"\"\"\n if \"appliance\" not in details:\n raise Exception(\"appliance not specified\")\n elif \"state\" not in details:\n raise Exception(\"state not specified\")\n\n if details[\"appliance\"] not in self.on_off_appliance_codes.keys():\n raise Exception(\"appliance not supported. Must be one of: \" + \",\".join(self.on_off_appliance_codes.keys()))\n\n appliance_code = self.on_off_appliance_codes[details[\"appliance\"]]\n \n if details['state'] == \"ON\":\n self._switch_on(appliance_code)\n elif details[\"state\"] == \"OFF\":\n self._switch_off(appliance_code)\n else:\n raise Exception(\"state not supported. Must be either \\\"ON\\\" or \\\"OFF\\\".\")\n\n def action_command(self, details):\n \"\"\"Send an action command\n \n Sends the specified command to the homevision through netio interface.\n \n Args:\n details: {\"command\": string} \n \"\"\"\n if \"command\" not in details:\n raise Exception(\"Command not specified\")\n\n if details[\"command\"] not in self.actions.keys():\n raise Exception(\"Command not supported. Must be one of: \" + \",\".join(self.actions.keys()))\n\n self._handle_action(self.actions[details[\"command\"]])\n\n def start_stop_command(self, details):\n \"\"\"Starts or stops a process\n \n Sends the specified command to the homevision through netio interface to control the specified process.\n \n Args:\n details: {\"action\": string, \"process\": string} \n \"\"\"\n if \"action\" not in details:\n raise Exception(\"action not specified\")\n elif \"process\" not in details:\n raise Exception(\"process not specified\")\n\n if details[\"process\"] not in self.process_actions.keys():\n raise Exception(\"process not supported. Must be one of: \" + \",\".join(self.process_actions.keys()))\n \n if details['action'] == \"START\":\n self._handle_action(self.process_actions[details[\"process\"]][\"START\"])\n elif details[\"action\"] == \"STOP\":\n self._handle_action(self.process_actions[details[\"process\"]][\"STOP\"])\n else:\n raise Exception(\"action not supported. Must be either \\\"START\\\" or \\\"STOP\\\".\")\n \n def _handle_action(self, action):\n def handle_single(a):\n if type(a) == Macro:\n self._run_macro(a.code)\n elif type(a) == Command:\n self._send_command(a.command)\n elif type(a) == Exception:\n raise a\n else:\n raise Exception(\"Internal Error: invalid action type. Should be Macro, Command or Exception\")\n \n if type(action) == tuple:\n for a in action:\n handle_single(a)\n else:\n handle_single(action)\n\n def var_query(self, details):\n \"\"\"Returns the answer to a query on variable\n \n Returns the answer to a query on the specified variable using netio\n \n Args:\n details: {\"query\": string} \n \"\"\"\n if \"query\" not in details:\n raise Exception(\"query not specified\")\n \n if details[\"query\"] not in self.var_queries.keys():\n raise Exception(\"query not supported. Must be one of: \" + \",\".join(self.var_queries.keys()))\n \n code = self.var_queries[details[\"query\"]]\n if type(code) == int:\n val = self._get_var(code)\n elif type(code) == tuple:\n val = [self._get_var(c) for c in code]\n else:\n raise Exception(\"Internal Exception: variable code is not valid\")\n \n return val\n \n def flag_query(self, details):\n \"\"\"Returns the answer to a query on flag\n \n Returns the answer to a query on the specified variable using netio\n \n Args:\n details: {\"query\": string} \n \"\"\"\n if \"query\" not in details:\n raise Exception(\"query not specified\")\n \n if details[\"query\"] not in self.flag_queries.keys():\n raise Exception(\"query not supported. Must be one of: \" + \",\".join(self.flag_queries.keys()))\n \n val = self._get_flag(self.flag_queries[details[\"query\"]])\n \n return \"yes\" if val else \"no\"\n \n def _switch_on(self, code):\n if self.on_off_commands == None:\n raise Exception(\"No On/Off command set\")\n self._handle_action(self.on_off_commands[\"ON\"](code))\n \n def _switch_off(self, code):\n if self.on_off_commands == None:\n raise Exception(\"No On/Off command set\")\n self._handle_action(self.on_off_commands[\"OFF\"](code))\n\n def _run_macro(self, code):\n self._send_command(b'action macro run ' + bytes(str(code), encoding=\"ascii\") + b'; __wait 100')\n\n def _send_command(self, command):\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((self.ip_address, self.port))\n s.send(bytes(\"auth \" + self.auth + \"\\n\", encoding=\"ascii\"))\n s.send(command)\n s.close()\n\n def _get_var(self, id):\n return int(self._run_read_command(b\"get var state \" + bytes(str(id), encoding=\"ascii\")))\n\n def _get_flag(self, id):\n ret = self._run_read_command(b\"get flag state \" + bytes(str(id), encoding=\"ascii\"))\n if ret in self.flag_return_values[False]:\n return False\n elif ret in self.flag_return_values[True]:\n return True\n else:\n raise Exception(\"Flag value not supported: \" + ret)\n\n def _run_read_command(self, command):\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((self.ip_address, self.port))\n s.send(bytes(\"auth \" + self.auth + \"\\n\", encoding=\"ascii\"))\n s.recv(10)\n s.send(command)\n s.send(b'\\n')\n response = s.recv(10).decode(encoding=\"ascii\").rstrip()\n s.close()\n return response\n "}}},{"rowIdx":44,"cells":{"function_name":{"kind":"string","value":"start_stop_command"},"docstring":{"kind":"string","value":"Starts or stops a process\n\nSends the specified command to the homevision through netio interface to control the specified process.\n\nArgs:\n details: {\"action\": string, \"process\": string} "},"masked_code":{"kind":"string","value":"\nimport socket\n\nclass UserException(Exception):\n pass\n\ndef user_exception(s): raise UserException(s)\n\nclass Macro:\n \"\"\"Represents a macro to be run\"\"\"\n def __init__(self, code):\n \"\"\"code: int - index of macro to run\"\"\"\n self.code = code\n\nclass Command:\n \"\"\"Represents a macro to be run\"\"\"\n def __init__(self, command):\n \"\"\"command: string - command to send\"\"\"\n self.command = command\n \nclass HomeVisionController:\n\n def __init__(\n self,\n ip_address,\n port,\n auth,\n on_off_appliance_codes={},\n actions={},\n process_actions={},\n var_queries={},\n flag_queries={},\n flag_return_values = {True: [\"True\", \"On\", \"Yes\", \"Occupied\", \"Set\", \"1\"], False: [\"False\", \"Off\", \"No\", \"Vacant\", \"Clear\", \"0\"]},\n on_off_commands = None\n ):\n \"\"\"\n Args:\n ip_address: string\n port: int\n auth: string\n - key for authenticating with netio\n on_off_appliance_codes: dict[string] => int - codes to be fed to 'on_off_commands' for each appliance\n actions: dict[string] => Macro/Command/(_, _, ...) - named actions to be completed\n process_actions: dict[string] => {\"START\": X, \"STOP\": X} where X is Macro/Command/(_, _, ...) - named processes to be started and stopped\n var_queries: dict[string] => int - mapping of names to variable indexes\n flag_queries: dict[string] => int - mapping of names to flag indexes\n flag_return_values: {True: [string], False: [string]} - synonyms for true and false that are returned by netio 'read flag command'. (ignore if you haven't set them up)\n on_off_commands: {\"ON\": (int) => Macro/Command/(_, _, ...), \"OFF\": (int) => Macro/Command} - how to handle on and off commands\n \"\"\"\n self.ip_address = ip_address\n self.port = port\n self.auth = auth\n self.on_off_appliance_codes = on_off_appliance_codes\n self.actions = actions\n self.process_actions = process_actions\n self.var_queries = var_queries\n self.flag_queries = flag_queries\n self.flag_return_values = flag_return_values\n self.on_off_commands = on_off_commands\n \n def on_off_command(self, details):\n \"\"\"Send an on or off command to an appliance\n \n Sends the specified command to the homevision through netio interface to control the specified appliance.\n \n Args:\n details: {\"appliance\": string, \"state\": string} \n \"\"\"\n if \"appliance\" not in details:\n raise Exception(\"appliance not specified\")\n elif \"state\" not in details:\n raise Exception(\"state not specified\")\n\n if details[\"appliance\"] not in self.on_off_appliance_codes.keys():\n raise Exception(\"appliance not supported. Must be one of: \" + \",\".join(self.on_off_appliance_codes.keys()))\n\n appliance_code = self.on_off_appliance_codes[details[\"appliance\"]]\n \n if details['state'] == \"ON\":\n self._switch_on(appliance_code)\n elif details[\"state\"] == \"OFF\":\n self._switch_off(appliance_code)\n else:\n raise Exception(\"state not supported. Must be either \\\"ON\\\" or \\\"OFF\\\".\")\n\n def action_command(self, details):\n \"\"\"Send an action command\n \n Sends the specified command to the homevision through netio interface.\n \n Args:\n details: {\"command\": string} \n \"\"\"\n if \"command\" not in details:\n raise Exception(\"Command not specified\")\n\n if details[\"command\"] not in self.actions.keys():\n raise Exception(\"Command not supported. Must be one of: \" + \",\".join(self.actions.keys()))\n\n self._handle_action(self.actions[details[\"command\"]])\n\n# MASKED: start_stop_command function (lines 102-123)\n \n def _handle_action(self, action):\n def handle_single(a):\n if type(a) == Macro:\n self._run_macro(a.code)\n elif type(a) == Command:\n self._send_command(a.command)\n elif type(a) == Exception:\n raise a\n else:\n raise Exception(\"Internal Error: invalid action type. Should be Macro, Command or Exception\")\n \n if type(action) == tuple:\n for a in action:\n handle_single(a)\n else:\n handle_single(action)\n\n def var_query(self, details):\n \"\"\"Returns the answer to a query on variable\n \n Returns the answer to a query on the specified variable using netio\n \n Args:\n details: {\"query\": string} \n \"\"\"\n if \"query\" not in details:\n raise Exception(\"query not specified\")\n \n if details[\"query\"] not in self.var_queries.keys():\n raise Exception(\"query not supported. Must be one of: \" + \",\".join(self.var_queries.keys()))\n \n code = self.var_queries[details[\"query\"]]\n if type(code) == int:\n val = self._get_var(code)\n elif type(code) == tuple:\n val = [self._get_var(c) for c in code]\n else:\n raise Exception(\"Internal Exception: variable code is not valid\")\n \n return val\n \n def flag_query(self, details):\n \"\"\"Returns the answer to a query on flag\n \n Returns the answer to a query on the specified variable using netio\n \n Args:\n details: {\"query\": string} \n \"\"\"\n if \"query\" not in details:\n raise Exception(\"query not specified\")\n \n if details[\"query\"] not in self.flag_queries.keys():\n raise Exception(\"query not supported. Must be one of: \" + \",\".join(self.flag_queries.keys()))\n \n val = self._get_flag(self.flag_queries[details[\"query\"]])\n \n return \"yes\" if val else \"no\"\n \n def _switch_on(self, code):\n if self.on_off_commands == None:\n raise Exception(\"No On/Off command set\")\n self._handle_action(self.on_off_commands[\"ON\"](code))\n \n def _switch_off(self, code):\n if self.on_off_commands == None:\n raise Exception(\"No On/Off command set\")\n self._handle_action(self.on_off_commands[\"OFF\"](code))\n\n def _run_macro(self, code):\n self._send_command(b'action macro run ' + bytes(str(code), encoding=\"ascii\") + b'; __wait 100')\n\n def _send_command(self, command):\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((self.ip_address, self.port))\n s.send(bytes(\"auth \" + self.auth + \"\\n\", encoding=\"ascii\"))\n s.send(command)\n s.close()\n\n def _get_var(self, id):\n return int(self._run_read_command(b\"get var state \" + bytes(str(id), encoding=\"ascii\")))\n\n def _get_flag(self, id):\n ret = self._run_read_command(b\"get flag state \" + bytes(str(id), encoding=\"ascii\"))\n if ret in self.flag_return_values[False]:\n return False\n elif ret in self.flag_return_values[True]:\n return True\n else:\n raise Exception(\"Flag value not supported: \" + ret)\n\n def _run_read_command(self, command):\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((self.ip_address, self.port))\n s.send(bytes(\"auth \" + self.auth + \"\\n\", encoding=\"ascii\"))\n s.recv(10)\n s.send(command)\n s.send(b'\\n')\n response = s.recv(10).decode(encoding=\"ascii\").rstrip()\n s.close()\n return response\n "},"implementation":{"kind":"string","value":" def start_stop_command(self, details):\n \"\"\"Starts or stops a process\n \n Sends the specified command to the homevision through netio interface to control the specified process.\n \n Args:\n details: {\"action\": string, \"process\": string} \n \"\"\"\n if \"action\" not in details:\n raise Exception(\"action not specified\")\n elif \"process\" not in details:\n raise Exception(\"process not specified\")\n\n if details[\"process\"] not in self.process_actions.keys():\n raise Exception(\"process not supported. Must be one of: \" + \",\".join(self.process_actions.keys()))\n \n if details['action'] == \"START\":\n self._handle_action(self.process_actions[details[\"process\"]][\"START\"])\n elif details[\"action\"] == \"STOP\":\n self._handle_action(self.process_actions[details[\"process\"]][\"STOP\"])\n else:\n raise Exception(\"action not supported. Must be either \\\"START\\\" or \\\"STOP\\\".\")"},"start_line":{"kind":"number","value":102,"string":"102"},"end_line":{"kind":"number","value":123,"string":"123"},"file_content":{"kind":"string","value":"\nimport socket\n\nclass UserException(Exception):\n pass\n\ndef user_exception(s): raise UserException(s)\n\nclass Macro:\n \"\"\"Represents a macro to be run\"\"\"\n def __init__(self, code):\n \"\"\"code: int - index of macro to run\"\"\"\n self.code = code\n\nclass Command:\n \"\"\"Represents a macro to be run\"\"\"\n def __init__(self, command):\n \"\"\"command: string - command to send\"\"\"\n self.command = command\n \nclass HomeVisionController:\n\n def __init__(\n self,\n ip_address,\n port,\n auth,\n on_off_appliance_codes={},\n actions={},\n process_actions={},\n var_queries={},\n flag_queries={},\n flag_return_values = {True: [\"True\", \"On\", \"Yes\", \"Occupied\", \"Set\", \"1\"], False: [\"False\", \"Off\", \"No\", \"Vacant\", \"Clear\", \"0\"]},\n on_off_commands = None\n ):\n \"\"\"\n Args:\n ip_address: string\n port: int\n auth: string\n - key for authenticating with netio\n on_off_appliance_codes: dict[string] => int - codes to be fed to 'on_off_commands' for each appliance\n actions: dict[string] => Macro/Command/(_, _, ...) - named actions to be completed\n process_actions: dict[string] => {\"START\": X, \"STOP\": X} where X is Macro/Command/(_, _, ...) - named processes to be started and stopped\n var_queries: dict[string] => int - mapping of names to variable indexes\n flag_queries: dict[string] => int - mapping of names to flag indexes\n flag_return_values: {True: [string], False: [string]} - synonyms for true and false that are returned by netio 'read flag command'. (ignore if you haven't set them up)\n on_off_commands: {\"ON\": (int) => Macro/Command/(_, _, ...), \"OFF\": (int) => Macro/Command} - how to handle on and off commands\n \"\"\"\n self.ip_address = ip_address\n self.port = port\n self.auth = auth\n self.on_off_appliance_codes = on_off_appliance_codes\n self.actions = actions\n self.process_actions = process_actions\n self.var_queries = var_queries\n self.flag_queries = flag_queries\n self.flag_return_values = flag_return_values\n self.on_off_commands = on_off_commands\n \n def on_off_command(self, details):\n \"\"\"Send an on or off command to an appliance\n \n Sends the specified command to the homevision through netio interface to control the specified appliance.\n \n Args:\n details: {\"appliance\": string, \"state\": string} \n \"\"\"\n if \"appliance\" not in details:\n raise Exception(\"appliance not specified\")\n elif \"state\" not in details:\n raise Exception(\"state not specified\")\n\n if details[\"appliance\"] not in self.on_off_appliance_codes.keys():\n raise Exception(\"appliance not supported. Must be one of: \" + \",\".join(self.on_off_appliance_codes.keys()))\n\n appliance_code = self.on_off_appliance_codes[details[\"appliance\"]]\n \n if details['state'] == \"ON\":\n self._switch_on(appliance_code)\n elif details[\"state\"] == \"OFF\":\n self._switch_off(appliance_code)\n else:\n raise Exception(\"state not supported. Must be either \\\"ON\\\" or \\\"OFF\\\".\")\n\n def action_command(self, details):\n \"\"\"Send an action command\n \n Sends the specified command to the homevision through netio interface.\n \n Args:\n details: {\"command\": string} \n \"\"\"\n if \"command\" not in details:\n raise Exception(\"Command not specified\")\n\n if details[\"command\"] not in self.actions.keys():\n raise Exception(\"Command not supported. Must be one of: \" + \",\".join(self.actions.keys()))\n\n self._handle_action(self.actions[details[\"command\"]])\n\n def start_stop_command(self, details):\n \"\"\"Starts or stops a process\n \n Sends the specified command to the homevision through netio interface to control the specified process.\n \n Args:\n details: {\"action\": string, \"process\": string} \n \"\"\"\n if \"action\" not in details:\n raise Exception(\"action not specified\")\n elif \"process\" not in details:\n raise Exception(\"process not specified\")\n\n if details[\"process\"] not in self.process_actions.keys():\n raise Exception(\"process not supported. Must be one of: \" + \",\".join(self.process_actions.keys()))\n \n if details['action'] == \"START\":\n self._handle_action(self.process_actions[details[\"process\"]][\"START\"])\n elif details[\"action\"] == \"STOP\":\n self._handle_action(self.process_actions[details[\"process\"]][\"STOP\"])\n else:\n raise Exception(\"action not supported. Must be either \\\"START\\\" or \\\"STOP\\\".\")\n \n def _handle_action(self, action):\n def handle_single(a):\n if type(a) == Macro:\n self._run_macro(a.code)\n elif type(a) == Command:\n self._send_command(a.command)\n elif type(a) == Exception:\n raise a\n else:\n raise Exception(\"Internal Error: invalid action type. Should be Macro, Command or Exception\")\n \n if type(action) == tuple:\n for a in action:\n handle_single(a)\n else:\n handle_single(action)\n\n def var_query(self, details):\n \"\"\"Returns the answer to a query on variable\n \n Returns the answer to a query on the specified variable using netio\n \n Args:\n details: {\"query\": string} \n \"\"\"\n if \"query\" not in details:\n raise Exception(\"query not specified\")\n \n if details[\"query\"] not in self.var_queries.keys():\n raise Exception(\"query not supported. Must be one of: \" + \",\".join(self.var_queries.keys()))\n \n code = self.var_queries[details[\"query\"]]\n if type(code) == int:\n val = self._get_var(code)\n elif type(code) == tuple:\n val = [self._get_var(c) for c in code]\n else:\n raise Exception(\"Internal Exception: variable code is not valid\")\n \n return val\n \n def flag_query(self, details):\n \"\"\"Returns the answer to a query on flag\n \n Returns the answer to a query on the specified variable using netio\n \n Args:\n details: {\"query\": string} \n \"\"\"\n if \"query\" not in details:\n raise Exception(\"query not specified\")\n \n if details[\"query\"] not in self.flag_queries.keys():\n raise Exception(\"query not supported. Must be one of: \" + \",\".join(self.flag_queries.keys()))\n \n val = self._get_flag(self.flag_queries[details[\"query\"]])\n \n return \"yes\" if val else \"no\"\n \n def _switch_on(self, code):\n if self.on_off_commands == None:\n raise Exception(\"No On/Off command set\")\n self._handle_action(self.on_off_commands[\"ON\"](code))\n \n def _switch_off(self, code):\n if self.on_off_commands == None:\n raise Exception(\"No On/Off command set\")\n self._handle_action(self.on_off_commands[\"OFF\"](code))\n\n def _run_macro(self, code):\n self._send_command(b'action macro run ' + bytes(str(code), encoding=\"ascii\") + b'; __wait 100')\n\n def _send_command(self, command):\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((self.ip_address, self.port))\n s.send(bytes(\"auth \" + self.auth + \"\\n\", encoding=\"ascii\"))\n s.send(command)\n s.close()\n\n def _get_var(self, id):\n return int(self._run_read_command(b\"get var state \" + bytes(str(id), encoding=\"ascii\")))\n\n def _get_flag(self, id):\n ret = self._run_read_command(b\"get flag state \" + bytes(str(id), encoding=\"ascii\"))\n if ret in self.flag_return_values[False]:\n return False\n elif ret in self.flag_return_values[True]:\n return True\n else:\n raise Exception(\"Flag value not supported: \" + ret)\n\n def _run_read_command(self, command):\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((self.ip_address, self.port))\n s.send(bytes(\"auth \" + self.auth + \"\\n\", encoding=\"ascii\"))\n s.recv(10)\n s.send(command)\n s.send(b'\\n')\n response = s.recv(10).decode(encoding=\"ascii\").rstrip()\n s.close()\n return response\n "}}},{"rowIdx":45,"cells":{"function_name":{"kind":"string","value":"var_query"},"docstring":{"kind":"string","value":"Returns the answer to a query on variable\n\nReturns the answer to a query on the specified variable using netio\n\nArgs:\n details: {\"query\": string} "},"masked_code":{"kind":"string","value":"\nimport socket\n\nclass UserException(Exception):\n pass\n\ndef user_exception(s): raise UserException(s)\n\nclass Macro:\n \"\"\"Represents a macro to be run\"\"\"\n def __init__(self, code):\n \"\"\"code: int - index of macro to run\"\"\"\n self.code = code\n\nclass Command:\n \"\"\"Represents a macro to be run\"\"\"\n def __init__(self, command):\n \"\"\"command: string - command to send\"\"\"\n self.command = command\n \nclass HomeVisionController:\n\n def __init__(\n self,\n ip_address,\n port,\n auth,\n on_off_appliance_codes={},\n actions={},\n process_actions={},\n var_queries={},\n flag_queries={},\n flag_return_values = {True: [\"True\", \"On\", \"Yes\", \"Occupied\", \"Set\", \"1\"], False: [\"False\", \"Off\", \"No\", \"Vacant\", \"Clear\", \"0\"]},\n on_off_commands = None\n ):\n \"\"\"\n Args:\n ip_address: string\n port: int\n auth: string\n - key for authenticating with netio\n on_off_appliance_codes: dict[string] => int - codes to be fed to 'on_off_commands' for each appliance\n actions: dict[string] => Macro/Command/(_, _, ...) - named actions to be completed\n process_actions: dict[string] => {\"START\": X, \"STOP\": X} where X is Macro/Command/(_, _, ...) - named processes to be started and stopped\n var_queries: dict[string] => int - mapping of names to variable indexes\n flag_queries: dict[string] => int - mapping of names to flag indexes\n flag_return_values: {True: [string], False: [string]} - synonyms for true and false that are returned by netio 'read flag command'. (ignore if you haven't set them up)\n on_off_commands: {\"ON\": (int) => Macro/Command/(_, _, ...), \"OFF\": (int) => Macro/Command} - how to handle on and off commands\n \"\"\"\n self.ip_address = ip_address\n self.port = port\n self.auth = auth\n self.on_off_appliance_codes = on_off_appliance_codes\n self.actions = actions\n self.process_actions = process_actions\n self.var_queries = var_queries\n self.flag_queries = flag_queries\n self.flag_return_values = flag_return_values\n self.on_off_commands = on_off_commands\n \n def on_off_command(self, details):\n \"\"\"Send an on or off command to an appliance\n \n Sends the specified command to the homevision through netio interface to control the specified appliance.\n \n Args:\n details: {\"appliance\": string, \"state\": string} \n \"\"\"\n if \"appliance\" not in details:\n raise Exception(\"appliance not specified\")\n elif \"state\" not in details:\n raise Exception(\"state not specified\")\n\n if details[\"appliance\"] not in self.on_off_appliance_codes.keys():\n raise Exception(\"appliance not supported. Must be one of: \" + \",\".join(self.on_off_appliance_codes.keys()))\n\n appliance_code = self.on_off_appliance_codes[details[\"appliance\"]]\n \n if details['state'] == \"ON\":\n self._switch_on(appliance_code)\n elif details[\"state\"] == \"OFF\":\n self._switch_off(appliance_code)\n else:\n raise Exception(\"state not supported. Must be either \\\"ON\\\" or \\\"OFF\\\".\")\n\n def action_command(self, details):\n \"\"\"Send an action command\n \n Sends the specified command to the homevision through netio interface.\n \n Args:\n details: {\"command\": string} \n \"\"\"\n if \"command\" not in details:\n raise Exception(\"Command not specified\")\n\n if details[\"command\"] not in self.actions.keys():\n raise Exception(\"Command not supported. Must be one of: \" + \",\".join(self.actions.keys()))\n\n self._handle_action(self.actions[details[\"command\"]])\n\n def start_stop_command(self, details):\n \"\"\"Starts or stops a process\n \n Sends the specified command to the homevision through netio interface to control the specified process.\n \n Args:\n details: {\"action\": string, \"process\": string} \n \"\"\"\n if \"action\" not in details:\n raise Exception(\"action not specified\")\n elif \"process\" not in details:\n raise Exception(\"process not specified\")\n\n if details[\"process\"] not in self.process_actions.keys():\n raise Exception(\"process not supported. Must be one of: \" + \",\".join(self.process_actions.keys()))\n \n if details['action'] == \"START\":\n self._handle_action(self.process_actions[details[\"process\"]][\"START\"])\n elif details[\"action\"] == \"STOP\":\n self._handle_action(self.process_actions[details[\"process\"]][\"STOP\"])\n else:\n raise Exception(\"action not supported. Must be either \\\"START\\\" or \\\"STOP\\\".\")\n \n def _handle_action(self, action):\n def handle_single(a):\n if type(a) == Macro:\n self._run_macro(a.code)\n elif type(a) == Command:\n self._send_command(a.command)\n elif type(a) == Exception:\n raise a\n else:\n raise Exception(\"Internal Error: invalid action type. Should be Macro, Command or Exception\")\n \n if type(action) == tuple:\n for a in action:\n handle_single(a)\n else:\n handle_single(action)\n\n# MASKED: var_query function (lines 142-164)\n \n def flag_query(self, details):\n \"\"\"Returns the answer to a query on flag\n \n Returns the answer to a query on the specified variable using netio\n \n Args:\n details: {\"query\": string} \n \"\"\"\n if \"query\" not in details:\n raise Exception(\"query not specified\")\n \n if details[\"query\"] not in self.flag_queries.keys():\n raise Exception(\"query not supported. Must be one of: \" + \",\".join(self.flag_queries.keys()))\n \n val = self._get_flag(self.flag_queries[details[\"query\"]])\n \n return \"yes\" if val else \"no\"\n \n def _switch_on(self, code):\n if self.on_off_commands == None:\n raise Exception(\"No On/Off command set\")\n self._handle_action(self.on_off_commands[\"ON\"](code))\n \n def _switch_off(self, code):\n if self.on_off_commands == None:\n raise Exception(\"No On/Off command set\")\n self._handle_action(self.on_off_commands[\"OFF\"](code))\n\n def _run_macro(self, code):\n self._send_command(b'action macro run ' + bytes(str(code), encoding=\"ascii\") + b'; __wait 100')\n\n def _send_command(self, command):\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((self.ip_address, self.port))\n s.send(bytes(\"auth \" + self.auth + \"\\n\", encoding=\"ascii\"))\n s.send(command)\n s.close()\n\n def _get_var(self, id):\n return int(self._run_read_command(b\"get var state \" + bytes(str(id), encoding=\"ascii\")))\n\n def _get_flag(self, id):\n ret = self._run_read_command(b\"get flag state \" + bytes(str(id), encoding=\"ascii\"))\n if ret in self.flag_return_values[False]:\n return False\n elif ret in self.flag_return_values[True]:\n return True\n else:\n raise Exception(\"Flag value not supported: \" + ret)\n\n def _run_read_command(self, command):\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((self.ip_address, self.port))\n s.send(bytes(\"auth \" + self.auth + \"\\n\", encoding=\"ascii\"))\n s.recv(10)\n s.send(command)\n s.send(b'\\n')\n response = s.recv(10).decode(encoding=\"ascii\").rstrip()\n s.close()\n return response\n "},"implementation":{"kind":"string","value":" def var_query(self, details):\n \"\"\"Returns the answer to a query on variable\n \n Returns the answer to a query on the specified variable using netio\n \n Args:\n details: {\"query\": string} \n \"\"\"\n if \"query\" not in details:\n raise Exception(\"query not specified\")\n \n if details[\"query\"] not in self.var_queries.keys():\n raise Exception(\"query not supported. Must be one of: \" + \",\".join(self.var_queries.keys()))\n \n code = self.var_queries[details[\"query\"]]\n if type(code) == int:\n val = self._get_var(code)\n elif type(code) == tuple:\n val = [self._get_var(c) for c in code]\n else:\n raise Exception(\"Internal Exception: variable code is not valid\")\n \n return val"},"start_line":{"kind":"number","value":142,"string":"142"},"end_line":{"kind":"number","value":164,"string":"164"},"file_content":{"kind":"string","value":"\nimport socket\n\nclass UserException(Exception):\n pass\n\ndef user_exception(s): raise UserException(s)\n\nclass Macro:\n \"\"\"Represents a macro to be run\"\"\"\n def __init__(self, code):\n \"\"\"code: int - index of macro to run\"\"\"\n self.code = code\n\nclass Command:\n \"\"\"Represents a macro to be run\"\"\"\n def __init__(self, command):\n \"\"\"command: string - command to send\"\"\"\n self.command = command\n \nclass HomeVisionController:\n\n def __init__(\n self,\n ip_address,\n port,\n auth,\n on_off_appliance_codes={},\n actions={},\n process_actions={},\n var_queries={},\n flag_queries={},\n flag_return_values = {True: [\"True\", \"On\", \"Yes\", \"Occupied\", \"Set\", \"1\"], False: [\"False\", \"Off\", \"No\", \"Vacant\", \"Clear\", \"0\"]},\n on_off_commands = None\n ):\n \"\"\"\n Args:\n ip_address: string\n port: int\n auth: string\n - key for authenticating with netio\n on_off_appliance_codes: dict[string] => int - codes to be fed to 'on_off_commands' for each appliance\n actions: dict[string] => Macro/Command/(_, _, ...) - named actions to be completed\n process_actions: dict[string] => {\"START\": X, \"STOP\": X} where X is Macro/Command/(_, _, ...) - named processes to be started and stopped\n var_queries: dict[string] => int - mapping of names to variable indexes\n flag_queries: dict[string] => int - mapping of names to flag indexes\n flag_return_values: {True: [string], False: [string]} - synonyms for true and false that are returned by netio 'read flag command'. (ignore if you haven't set them up)\n on_off_commands: {\"ON\": (int) => Macro/Command/(_, _, ...), \"OFF\": (int) => Macro/Command} - how to handle on and off commands\n \"\"\"\n self.ip_address = ip_address\n self.port = port\n self.auth = auth\n self.on_off_appliance_codes = on_off_appliance_codes\n self.actions = actions\n self.process_actions = process_actions\n self.var_queries = var_queries\n self.flag_queries = flag_queries\n self.flag_return_values = flag_return_values\n self.on_off_commands = on_off_commands\n \n def on_off_command(self, details):\n \"\"\"Send an on or off command to an appliance\n \n Sends the specified command to the homevision through netio interface to control the specified appliance.\n \n Args:\n details: {\"appliance\": string, \"state\": string} \n \"\"\"\n if \"appliance\" not in details:\n raise Exception(\"appliance not specified\")\n elif \"state\" not in details:\n raise Exception(\"state not specified\")\n\n if details[\"appliance\"] not in self.on_off_appliance_codes.keys():\n raise Exception(\"appliance not supported. Must be one of: \" + \",\".join(self.on_off_appliance_codes.keys()))\n\n appliance_code = self.on_off_appliance_codes[details[\"appliance\"]]\n \n if details['state'] == \"ON\":\n self._switch_on(appliance_code)\n elif details[\"state\"] == \"OFF\":\n self._switch_off(appliance_code)\n else:\n raise Exception(\"state not supported. Must be either \\\"ON\\\" or \\\"OFF\\\".\")\n\n def action_command(self, details):\n \"\"\"Send an action command\n \n Sends the specified command to the homevision through netio interface.\n \n Args:\n details: {\"command\": string} \n \"\"\"\n if \"command\" not in details:\n raise Exception(\"Command not specified\")\n\n if details[\"command\"] not in self.actions.keys():\n raise Exception(\"Command not supported. Must be one of: \" + \",\".join(self.actions.keys()))\n\n self._handle_action(self.actions[details[\"command\"]])\n\n def start_stop_command(self, details):\n \"\"\"Starts or stops a process\n \n Sends the specified command to the homevision through netio interface to control the specified process.\n \n Args:\n details: {\"action\": string, \"process\": string} \n \"\"\"\n if \"action\" not in details:\n raise Exception(\"action not specified\")\n elif \"process\" not in details:\n raise Exception(\"process not specified\")\n\n if details[\"process\"] not in self.process_actions.keys():\n raise Exception(\"process not supported. Must be one of: \" + \",\".join(self.process_actions.keys()))\n \n if details['action'] == \"START\":\n self._handle_action(self.process_actions[details[\"process\"]][\"START\"])\n elif details[\"action\"] == \"STOP\":\n self._handle_action(self.process_actions[details[\"process\"]][\"STOP\"])\n else:\n raise Exception(\"action not supported. Must be either \\\"START\\\" or \\\"STOP\\\".\")\n \n def _handle_action(self, action):\n def handle_single(a):\n if type(a) == Macro:\n self._run_macro(a.code)\n elif type(a) == Command:\n self._send_command(a.command)\n elif type(a) == Exception:\n raise a\n else:\n raise Exception(\"Internal Error: invalid action type. Should be Macro, Command or Exception\")\n \n if type(action) == tuple:\n for a in action:\n handle_single(a)\n else:\n handle_single(action)\n\n def var_query(self, details):\n \"\"\"Returns the answer to a query on variable\n \n Returns the answer to a query on the specified variable using netio\n \n Args:\n details: {\"query\": string} \n \"\"\"\n if \"query\" not in details:\n raise Exception(\"query not specified\")\n \n if details[\"query\"] not in self.var_queries.keys():\n raise Exception(\"query not supported. Must be one of: \" + \",\".join(self.var_queries.keys()))\n \n code = self.var_queries[details[\"query\"]]\n if type(code) == int:\n val = self._get_var(code)\n elif type(code) == tuple:\n val = [self._get_var(c) for c in code]\n else:\n raise Exception(\"Internal Exception: variable code is not valid\")\n \n return val\n \n def flag_query(self, details):\n \"\"\"Returns the answer to a query on flag\n \n Returns the answer to a query on the specified variable using netio\n \n Args:\n details: {\"query\": string} \n \"\"\"\n if \"query\" not in details:\n raise Exception(\"query not specified\")\n \n if details[\"query\"] not in self.flag_queries.keys():\n raise Exception(\"query not supported. Must be one of: \" + \",\".join(self.flag_queries.keys()))\n \n val = self._get_flag(self.flag_queries[details[\"query\"]])\n \n return \"yes\" if val else \"no\"\n \n def _switch_on(self, code):\n if self.on_off_commands == None:\n raise Exception(\"No On/Off command set\")\n self._handle_action(self.on_off_commands[\"ON\"](code))\n \n def _switch_off(self, code):\n if self.on_off_commands == None:\n raise Exception(\"No On/Off command set\")\n self._handle_action(self.on_off_commands[\"OFF\"](code))\n\n def _run_macro(self, code):\n self._send_command(b'action macro run ' + bytes(str(code), encoding=\"ascii\") + b'; __wait 100')\n\n def _send_command(self, command):\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((self.ip_address, self.port))\n s.send(bytes(\"auth \" + self.auth + \"\\n\", encoding=\"ascii\"))\n s.send(command)\n s.close()\n\n def _get_var(self, id):\n return int(self._run_read_command(b\"get var state \" + bytes(str(id), encoding=\"ascii\")))\n\n def _get_flag(self, id):\n ret = self._run_read_command(b\"get flag state \" + bytes(str(id), encoding=\"ascii\"))\n if ret in self.flag_return_values[False]:\n return False\n elif ret in self.flag_return_values[True]:\n return True\n else:\n raise Exception(\"Flag value not supported: \" + ret)\n\n def _run_read_command(self, command):\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((self.ip_address, self.port))\n s.send(bytes(\"auth \" + self.auth + \"\\n\", encoding=\"ascii\"))\n s.recv(10)\n s.send(command)\n s.send(b'\\n')\n response = s.recv(10).decode(encoding=\"ascii\").rstrip()\n s.close()\n return response\n "}}},{"rowIdx":46,"cells":{"function_name":{"kind":"string","value":"flag_query"},"docstring":{"kind":"string","value":"Returns the answer to a query on flag\n\nReturns the answer to a query on the specified variable using netio\n\nArgs:\n details: {\"query\": string} "},"masked_code":{"kind":"string","value":"\nimport socket\n\nclass UserException(Exception):\n pass\n\ndef user_exception(s): raise UserException(s)\n\nclass Macro:\n \"\"\"Represents a macro to be run\"\"\"\n def __init__(self, code):\n \"\"\"code: int - index of macro to run\"\"\"\n self.code = code\n\nclass Command:\n \"\"\"Represents a macro to be run\"\"\"\n def __init__(self, command):\n \"\"\"command: string - command to send\"\"\"\n self.command = command\n \nclass HomeVisionController:\n\n def __init__(\n self,\n ip_address,\n port,\n auth,\n on_off_appliance_codes={},\n actions={},\n process_actions={},\n var_queries={},\n flag_queries={},\n flag_return_values = {True: [\"True\", \"On\", \"Yes\", \"Occupied\", \"Set\", \"1\"], False: [\"False\", \"Off\", \"No\", \"Vacant\", \"Clear\", \"0\"]},\n on_off_commands = None\n ):\n \"\"\"\n Args:\n ip_address: string\n port: int\n auth: string\n - key for authenticating with netio\n on_off_appliance_codes: dict[string] => int - codes to be fed to 'on_off_commands' for each appliance\n actions: dict[string] => Macro/Command/(_, _, ...) - named actions to be completed\n process_actions: dict[string] => {\"START\": X, \"STOP\": X} where X is Macro/Command/(_, _, ...) - named processes to be started and stopped\n var_queries: dict[string] => int - mapping of names to variable indexes\n flag_queries: dict[string] => int - mapping of names to flag indexes\n flag_return_values: {True: [string], False: [string]} - synonyms for true and false that are returned by netio 'read flag command'. (ignore if you haven't set them up)\n on_off_commands: {\"ON\": (int) => Macro/Command/(_, _, ...), \"OFF\": (int) => Macro/Command} - how to handle on and off commands\n \"\"\"\n self.ip_address = ip_address\n self.port = port\n self.auth = auth\n self.on_off_appliance_codes = on_off_appliance_codes\n self.actions = actions\n self.process_actions = process_actions\n self.var_queries = var_queries\n self.flag_queries = flag_queries\n self.flag_return_values = flag_return_values\n self.on_off_commands = on_off_commands\n \n def on_off_command(self, details):\n \"\"\"Send an on or off command to an appliance\n \n Sends the specified command to the homevision through netio interface to control the specified appliance.\n \n Args:\n details: {\"appliance\": string, \"state\": string} \n \"\"\"\n if \"appliance\" not in details:\n raise Exception(\"appliance not specified\")\n elif \"state\" not in details:\n raise Exception(\"state not specified\")\n\n if details[\"appliance\"] not in self.on_off_appliance_codes.keys():\n raise Exception(\"appliance not supported. Must be one of: \" + \",\".join(self.on_off_appliance_codes.keys()))\n\n appliance_code = self.on_off_appliance_codes[details[\"appliance\"]]\n \n if details['state'] == \"ON\":\n self._switch_on(appliance_code)\n elif details[\"state\"] == \"OFF\":\n self._switch_off(appliance_code)\n else:\n raise Exception(\"state not supported. Must be either \\\"ON\\\" or \\\"OFF\\\".\")\n\n def action_command(self, details):\n \"\"\"Send an action command\n \n Sends the specified command to the homevision through netio interface.\n \n Args:\n details: {\"command\": string} \n \"\"\"\n if \"command\" not in details:\n raise Exception(\"Command not specified\")\n\n if details[\"command\"] not in self.actions.keys():\n raise Exception(\"Command not supported. Must be one of: \" + \",\".join(self.actions.keys()))\n\n self._handle_action(self.actions[details[\"command\"]])\n\n def start_stop_command(self, details):\n \"\"\"Starts or stops a process\n \n Sends the specified command to the homevision through netio interface to control the specified process.\n \n Args:\n details: {\"action\": string, \"process\": string} \n \"\"\"\n if \"action\" not in details:\n raise Exception(\"action not specified\")\n elif \"process\" not in details:\n raise Exception(\"process not specified\")\n\n if details[\"process\"] not in self.process_actions.keys():\n raise Exception(\"process not supported. Must be one of: \" + \",\".join(self.process_actions.keys()))\n \n if details['action'] == \"START\":\n self._handle_action(self.process_actions[details[\"process\"]][\"START\"])\n elif details[\"action\"] == \"STOP\":\n self._handle_action(self.process_actions[details[\"process\"]][\"STOP\"])\n else:\n raise Exception(\"action not supported. Must be either \\\"START\\\" or \\\"STOP\\\".\")\n \n def _handle_action(self, action):\n def handle_single(a):\n if type(a) == Macro:\n self._run_macro(a.code)\n elif type(a) == Command:\n self._send_command(a.command)\n elif type(a) == Exception:\n raise a\n else:\n raise Exception(\"Internal Error: invalid action type. Should be Macro, Command or Exception\")\n \n if type(action) == tuple:\n for a in action:\n handle_single(a)\n else:\n handle_single(action)\n\n def var_query(self, details):\n \"\"\"Returns the answer to a query on variable\n \n Returns the answer to a query on the specified variable using netio\n \n Args:\n details: {\"query\": string} \n \"\"\"\n if \"query\" not in details:\n raise Exception(\"query not specified\")\n \n if details[\"query\"] not in self.var_queries.keys():\n raise Exception(\"query not supported. Must be one of: \" + \",\".join(self.var_queries.keys()))\n \n code = self.var_queries[details[\"query\"]]\n if type(code) == int:\n val = self._get_var(code)\n elif type(code) == tuple:\n val = [self._get_var(c) for c in code]\n else:\n raise Exception(\"Internal Exception: variable code is not valid\")\n \n return val\n \n# MASKED: flag_query function (lines 166-182)\n \n def _switch_on(self, code):\n if self.on_off_commands == None:\n raise Exception(\"No On/Off command set\")\n self._handle_action(self.on_off_commands[\"ON\"](code))\n \n def _switch_off(self, code):\n if self.on_off_commands == None:\n raise Exception(\"No On/Off command set\")\n self._handle_action(self.on_off_commands[\"OFF\"](code))\n\n def _run_macro(self, code):\n self._send_command(b'action macro run ' + bytes(str(code), encoding=\"ascii\") + b'; __wait 100')\n\n def _send_command(self, command):\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((self.ip_address, self.port))\n s.send(bytes(\"auth \" + self.auth + \"\\n\", encoding=\"ascii\"))\n s.send(command)\n s.close()\n\n def _get_var(self, id):\n return int(self._run_read_command(b\"get var state \" + bytes(str(id), encoding=\"ascii\")))\n\n def _get_flag(self, id):\n ret = self._run_read_command(b\"get flag state \" + bytes(str(id), encoding=\"ascii\"))\n if ret in self.flag_return_values[False]:\n return False\n elif ret in self.flag_return_values[True]:\n return True\n else:\n raise Exception(\"Flag value not supported: \" + ret)\n\n def _run_read_command(self, command):\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((self.ip_address, self.port))\n s.send(bytes(\"auth \" + self.auth + \"\\n\", encoding=\"ascii\"))\n s.recv(10)\n s.send(command)\n s.send(b'\\n')\n response = s.recv(10).decode(encoding=\"ascii\").rstrip()\n s.close()\n return response\n "},"implementation":{"kind":"string","value":" def flag_query(self, details):\n \"\"\"Returns the answer to a query on flag\n \n Returns the answer to a query on the specified variable using netio\n \n Args:\n details: {\"query\": string} \n \"\"\"\n if \"query\" not in details:\n raise Exception(\"query not specified\")\n \n if details[\"query\"] not in self.flag_queries.keys():\n raise Exception(\"query not supported. Must be one of: \" + \",\".join(self.flag_queries.keys()))\n \n val = self._get_flag(self.flag_queries[details[\"query\"]])\n \n return \"yes\" if val else \"no\""},"start_line":{"kind":"number","value":166,"string":"166"},"end_line":{"kind":"number","value":182,"string":"182"},"file_content":{"kind":"string","value":"\nimport socket\n\nclass UserException(Exception):\n pass\n\ndef user_exception(s): raise UserException(s)\n\nclass Macro:\n \"\"\"Represents a macro to be run\"\"\"\n def __init__(self, code):\n \"\"\"code: int - index of macro to run\"\"\"\n self.code = code\n\nclass Command:\n \"\"\"Represents a macro to be run\"\"\"\n def __init__(self, command):\n \"\"\"command: string - command to send\"\"\"\n self.command = command\n \nclass HomeVisionController:\n\n def __init__(\n self,\n ip_address,\n port,\n auth,\n on_off_appliance_codes={},\n actions={},\n process_actions={},\n var_queries={},\n flag_queries={},\n flag_return_values = {True: [\"True\", \"On\", \"Yes\", \"Occupied\", \"Set\", \"1\"], False: [\"False\", \"Off\", \"No\", \"Vacant\", \"Clear\", \"0\"]},\n on_off_commands = None\n ):\n \"\"\"\n Args:\n ip_address: string\n port: int\n auth: string\n - key for authenticating with netio\n on_off_appliance_codes: dict[string] => int - codes to be fed to 'on_off_commands' for each appliance\n actions: dict[string] => Macro/Command/(_, _, ...) - named actions to be completed\n process_actions: dict[string] => {\"START\": X, \"STOP\": X} where X is Macro/Command/(_, _, ...) - named processes to be started and stopped\n var_queries: dict[string] => int - mapping of names to variable indexes\n flag_queries: dict[string] => int - mapping of names to flag indexes\n flag_return_values: {True: [string], False: [string]} - synonyms for true and false that are returned by netio 'read flag command'. (ignore if you haven't set them up)\n on_off_commands: {\"ON\": (int) => Macro/Command/(_, _, ...), \"OFF\": (int) => Macro/Command} - how to handle on and off commands\n \"\"\"\n self.ip_address = ip_address\n self.port = port\n self.auth = auth\n self.on_off_appliance_codes = on_off_appliance_codes\n self.actions = actions\n self.process_actions = process_actions\n self.var_queries = var_queries\n self.flag_queries = flag_queries\n self.flag_return_values = flag_return_values\n self.on_off_commands = on_off_commands\n \n def on_off_command(self, details):\n \"\"\"Send an on or off command to an appliance\n \n Sends the specified command to the homevision through netio interface to control the specified appliance.\n \n Args:\n details: {\"appliance\": string, \"state\": string} \n \"\"\"\n if \"appliance\" not in details:\n raise Exception(\"appliance not specified\")\n elif \"state\" not in details:\n raise Exception(\"state not specified\")\n\n if details[\"appliance\"] not in self.on_off_appliance_codes.keys():\n raise Exception(\"appliance not supported. Must be one of: \" + \",\".join(self.on_off_appliance_codes.keys()))\n\n appliance_code = self.on_off_appliance_codes[details[\"appliance\"]]\n \n if details['state'] == \"ON\":\n self._switch_on(appliance_code)\n elif details[\"state\"] == \"OFF\":\n self._switch_off(appliance_code)\n else:\n raise Exception(\"state not supported. Must be either \\\"ON\\\" or \\\"OFF\\\".\")\n\n def action_command(self, details):\n \"\"\"Send an action command\n \n Sends the specified command to the homevision through netio interface.\n \n Args:\n details: {\"command\": string} \n \"\"\"\n if \"command\" not in details:\n raise Exception(\"Command not specified\")\n\n if details[\"command\"] not in self.actions.keys():\n raise Exception(\"Command not supported. Must be one of: \" + \",\".join(self.actions.keys()))\n\n self._handle_action(self.actions[details[\"command\"]])\n\n def start_stop_command(self, details):\n \"\"\"Starts or stops a process\n \n Sends the specified command to the homevision through netio interface to control the specified process.\n \n Args:\n details: {\"action\": string, \"process\": string} \n \"\"\"\n if \"action\" not in details:\n raise Exception(\"action not specified\")\n elif \"process\" not in details:\n raise Exception(\"process not specified\")\n\n if details[\"process\"] not in self.process_actions.keys():\n raise Exception(\"process not supported. Must be one of: \" + \",\".join(self.process_actions.keys()))\n \n if details['action'] == \"START\":\n self._handle_action(self.process_actions[details[\"process\"]][\"START\"])\n elif details[\"action\"] == \"STOP\":\n self._handle_action(self.process_actions[details[\"process\"]][\"STOP\"])\n else:\n raise Exception(\"action not supported. Must be either \\\"START\\\" or \\\"STOP\\\".\")\n \n def _handle_action(self, action):\n def handle_single(a):\n if type(a) == Macro:\n self._run_macro(a.code)\n elif type(a) == Command:\n self._send_command(a.command)\n elif type(a) == Exception:\n raise a\n else:\n raise Exception(\"Internal Error: invalid action type. Should be Macro, Command or Exception\")\n \n if type(action) == tuple:\n for a in action:\n handle_single(a)\n else:\n handle_single(action)\n\n def var_query(self, details):\n \"\"\"Returns the answer to a query on variable\n \n Returns the answer to a query on the specified variable using netio\n \n Args:\n details: {\"query\": string} \n \"\"\"\n if \"query\" not in details:\n raise Exception(\"query not specified\")\n \n if details[\"query\"] not in self.var_queries.keys():\n raise Exception(\"query not supported. Must be one of: \" + \",\".join(self.var_queries.keys()))\n \n code = self.var_queries[details[\"query\"]]\n if type(code) == int:\n val = self._get_var(code)\n elif type(code) == tuple:\n val = [self._get_var(c) for c in code]\n else:\n raise Exception(\"Internal Exception: variable code is not valid\")\n \n return val\n \n def flag_query(self, details):\n \"\"\"Returns the answer to a query on flag\n \n Returns the answer to a query on the specified variable using netio\n \n Args:\n details: {\"query\": string} \n \"\"\"\n if \"query\" not in details:\n raise Exception(\"query not specified\")\n \n if details[\"query\"] not in self.flag_queries.keys():\n raise Exception(\"query not supported. Must be one of: \" + \",\".join(self.flag_queries.keys()))\n \n val = self._get_flag(self.flag_queries[details[\"query\"]])\n \n return \"yes\" if val else \"no\"\n \n def _switch_on(self, code):\n if self.on_off_commands == None:\n raise Exception(\"No On/Off command set\")\n self._handle_action(self.on_off_commands[\"ON\"](code))\n \n def _switch_off(self, code):\n if self.on_off_commands == None:\n raise Exception(\"No On/Off command set\")\n self._handle_action(self.on_off_commands[\"OFF\"](code))\n\n def _run_macro(self, code):\n self._send_command(b'action macro run ' + bytes(str(code), encoding=\"ascii\") + b'; __wait 100')\n\n def _send_command(self, command):\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((self.ip_address, self.port))\n s.send(bytes(\"auth \" + self.auth + \"\\n\", encoding=\"ascii\"))\n s.send(command)\n s.close()\n\n def _get_var(self, id):\n return int(self._run_read_command(b\"get var state \" + bytes(str(id), encoding=\"ascii\")))\n\n def _get_flag(self, id):\n ret = self._run_read_command(b\"get flag state \" + bytes(str(id), encoding=\"ascii\"))\n if ret in self.flag_return_values[False]:\n return False\n elif ret in self.flag_return_values[True]:\n return True\n else:\n raise Exception(\"Flag value not supported: \" + ret)\n\n def _run_read_command(self, command):\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.connect((self.ip_address, self.port))\n s.send(bytes(\"auth \" + self.auth + \"\\n\", encoding=\"ascii\"))\n s.recv(10)\n s.send(command)\n s.send(b'\\n')\n response = s.recv(10).decode(encoding=\"ascii\").rstrip()\n s.close()\n return response\n "}}},{"rowIdx":47,"cells":{"function_name":{"kind":"string","value":"_find_files"},"docstring":{"kind":"string","value":"Find files in directory with optional extensions.\n\nArgs:\n directory (string)\n keywords (list): e.g. [\"SelfpacedRota\", \"ButtonPress] (optional)\n extensions (list): e.g. [\".json\" or \"tsv\"] (optional)\n verbose (bool): verbosity level (optional, default=True)"},"masked_code":{"kind":"string","value":"\"\"\"Define abstract base classes to construct FileFinder classes.\"\"\"\n\nimport os\nimport shutil\nfrom abc import ABC, abstractmethod\nfrom dataclasses import dataclass, field\nfrom pathlib import Path\nfrom typing import Optional, Sequence, Union\n\nimport mne_bids\n\n\n@dataclass\nclass FileFinder(ABC):\n \"\"\"Basic representation of class for finding and filtering files.\"\"\"\n\n hemispheres: Union[dict, None] = field(default_factory=dict)\n directory: Union[Path, str] = field(init=False)\n files: list = field(init=False, default_factory=list)\n\n def __str__(self):\n if not self.files:\n return \"No corresponding files found.\"\n headers = [\"Index\", \"Filename\"]\n col_width = max(len(os.path.basename(file)) for file in self.files)\n format_row = f\"{{:>{len(headers[0]) + 2}}}{{:>{col_width + 2}}}\"\n terminal_size = \"\\u2500\" * shutil.get_terminal_size().columns\n return \"\\n\".join(\n (\n \"Corresponding files found:\",\n \"\".join(\n f\"{{:>{len(header) + 2}}}\".format(header)\n for header in headers\n ),\n terminal_size,\n *(\n format_row.format(idx, os.path.basename(file))\n for idx, file in enumerate(self.files)\n ),\n )\n )\n\n def __len__(self) -> int:\n if not self.files:\n return 0\n return len(self.files)\n\n @abstractmethod\n def find_files(\n self,\n directory: Union[str, Path],\n extensions: Optional[Union[Sequence, str]] = None,\n keywords: Optional[Union[list, str]] = None,\n hemisphere: Optional[str] = None,\n stimulation: Optional[str] = None,\n medication: Optional[str] = None,\n exclude: Optional[Union[str, list]] = None,\n verbose: bool = False,\n ) -> None:\n \"\"\"Find files in directory with optional\n keywords and extensions.\"\"\"\n\n @abstractmethod\n def filter_files(\n self,\n keywords: Optional[Union[str, list]] = None,\n hemisphere: Optional[str] = None,\n stimulation: Optional[str] = None,\n medication: Optional[str] = None,\n exclude: Optional[Union[str, list]] = None,\n verbose: bool = False,\n ) -> None:\n \"\"\"Filter list of filepaths for given parameters.\"\"\"\n\n @staticmethod\n def _keyword_search(\n files: list[str], keywords: Optional[Union[str, list]]\n ) -> list:\n if not keywords:\n return files\n if not isinstance(keywords, list):\n keywords = [keywords]\n filtered_files = [\n file for file in files if any(key in file for key in keywords)\n ]\n return filtered_files\n\n# MASKED: _find_files function (lines 88-108)\n\n def _filter_files(\n self,\n keywords: Optional[Union[str, list[str]]] = None,\n hemisphere: Optional[str] = None,\n stimulation: Optional[str] = None,\n medication: Optional[str] = None,\n exclude: Optional[Union[str, list[str]]] = None,\n ) -> None:\n \"\"\"Filter filepaths for given parameters.\"\"\"\n filtered_files = self.files\n if exclude:\n if not isinstance(exclude, list):\n exclude = [exclude]\n filtered_files = [\n file\n for file in filtered_files\n if not any(item in file for item in exclude)\n ]\n if keywords:\n if not isinstance(keywords, list):\n keywords = [keywords]\n filtered_files = self._keyword_search(filtered_files, keywords)\n if stimulation:\n if stimulation.lower() in \"stimon\":\n stim = \"StimOn\"\n elif stimulation.lower() in \"stimoff\":\n stim = \"StimOff\"\n else:\n raise ValueError(\"Keyword for stimulation not valid.\")\n filtered_files = self._keyword_search(filtered_files, [stim])\n if medication:\n if medication.lower() in \"medon\":\n med = \"MedOn\"\n elif medication.lower() in \"medoff\":\n med = \"MedOff\"\n else:\n raise ValueError(\"Keyword for medication not valid.\")\n filtered_files = self._keyword_search(filtered_files, [med])\n if hemisphere:\n matching_files = []\n for file in filtered_files:\n subject = mne_bids.get_entities_from_fname(file)[\"subject\"]\n if (\n subject not in self.hemispheres\n or self.hemispheres[subject] is None\n ):\n raise HemisphereNotSpecifiedError(\n subject, self.hemispheres\n )\n hem = self.hemispheres[subject] + \"_\"\n if hemisphere.lower() in \"ipsilateral\" and hem in file:\n matching_files.append(file)\n if hemisphere.lower() in \"contralateral\" and hem not in file:\n matching_files.append(file)\n filtered_files = matching_files\n self.files = filtered_files\n\n\nclass DirectoryNotFoundError(Exception):\n \"\"\"Exception raised when invalid Reader is passed.\n\n Attributes:\n directory -- input directory which caused the error\n \"\"\"\n\n def __init__(\n self,\n directory: Union[Path, str],\n message=\"Input directory was not found.\",\n ):\n self.directory = directory\n self.message = message\n super().__init__(self.message)\n\n def __str__(self):\n return f\"{self.message} Got: {self.directory}.\"\n\n\nclass HemisphereNotSpecifiedError(Exception):\n \"\"\"Exception raised when electrode hemisphere is not specified in settings.\n\n Attributes:\n subject -- input subject which caused the error\n hemisphere -- specified hemispheres\n message -- explanation of the error\n \"\"\"\n\n def __init__(\n self,\n subject,\n hemispheres,\n message=(\n \"Input ECOG hemisphere is not specified in\"\n \" `filefinder_settings.py` for given subject.\"\n ),\n ) -> None:\n self.subject = subject\n self.hemispheres = hemispheres\n self.message = message\n super().__init__(self.message)\n\n def __str__(self):\n return (\n f\"{self.message} Unspecified subject: {self.subject}.\"\n f\" Specified hemispheres: {self.hemispheres}.\"\n )"},"implementation":{"kind":"string","value":" def _find_files(\n self,\n directory: Union[Path, str],\n extensions: Optional[Union[list, str]] = None,\n ) -> None:\n \"\"\"Find files in directory with optional extensions.\n\n Args:\n directory (string)\n keywords (list): e.g. [\"SelfpacedRota\", \"ButtonPress] (optional)\n extensions (list): e.g. [\".json\" or \"tsv\"] (optional)\n verbose (bool): verbosity level (optional, default=True)\n \"\"\"\n\n files = []\n for root, _, fnames in os.walk(directory):\n fnames = [os.path.join(root, file) for file in fnames]\n fnames = self._keyword_search(fnames, extensions)\n if fnames:\n files.extend(fnames)\n self.files = files"},"start_line":{"kind":"number","value":88,"string":"88"},"end_line":{"kind":"number","value":108,"string":"108"},"file_content":{"kind":"string","value":"\"\"\"Define abstract base classes to construct FileFinder classes.\"\"\"\n\nimport os\nimport shutil\nfrom abc import ABC, abstractmethod\nfrom dataclasses import dataclass, field\nfrom pathlib import Path\nfrom typing import Optional, Sequence, Union\n\nimport mne_bids\n\n\n@dataclass\nclass FileFinder(ABC):\n \"\"\"Basic representation of class for finding and filtering files.\"\"\"\n\n hemispheres: Union[dict, None] = field(default_factory=dict)\n directory: Union[Path, str] = field(init=False)\n files: list = field(init=False, default_factory=list)\n\n def __str__(self):\n if not self.files:\n return \"No corresponding files found.\"\n headers = [\"Index\", \"Filename\"]\n col_width = max(len(os.path.basename(file)) for file in self.files)\n format_row = f\"{{:>{len(headers[0]) + 2}}}{{:>{col_width + 2}}}\"\n terminal_size = \"\\u2500\" * shutil.get_terminal_size().columns\n return \"\\n\".join(\n (\n \"Corresponding files found:\",\n \"\".join(\n f\"{{:>{len(header) + 2}}}\".format(header)\n for header in headers\n ),\n terminal_size,\n *(\n format_row.format(idx, os.path.basename(file))\n for idx, file in enumerate(self.files)\n ),\n )\n )\n\n def __len__(self) -> int:\n if not self.files:\n return 0\n return len(self.files)\n\n @abstractmethod\n def find_files(\n self,\n directory: Union[str, Path],\n extensions: Optional[Union[Sequence, str]] = None,\n keywords: Optional[Union[list, str]] = None,\n hemisphere: Optional[str] = None,\n stimulation: Optional[str] = None,\n medication: Optional[str] = None,\n exclude: Optional[Union[str, list]] = None,\n verbose: bool = False,\n ) -> None:\n \"\"\"Find files in directory with optional\n keywords and extensions.\"\"\"\n\n @abstractmethod\n def filter_files(\n self,\n keywords: Optional[Union[str, list]] = None,\n hemisphere: Optional[str] = None,\n stimulation: Optional[str] = None,\n medication: Optional[str] = None,\n exclude: Optional[Union[str, list]] = None,\n verbose: bool = False,\n ) -> None:\n \"\"\"Filter list of filepaths for given parameters.\"\"\"\n\n @staticmethod\n def _keyword_search(\n files: list[str], keywords: Optional[Union[str, list]]\n ) -> list:\n if not keywords:\n return files\n if not isinstance(keywords, list):\n keywords = [keywords]\n filtered_files = [\n file for file in files if any(key in file for key in keywords)\n ]\n return filtered_files\n\n def _find_files(\n self,\n directory: Union[Path, str],\n extensions: Optional[Union[list, str]] = None,\n ) -> None:\n \"\"\"Find files in directory with optional extensions.\n\n Args:\n directory (string)\n keywords (list): e.g. [\"SelfpacedRota\", \"ButtonPress] (optional)\n extensions (list): e.g. [\".json\" or \"tsv\"] (optional)\n verbose (bool): verbosity level (optional, default=True)\n \"\"\"\n\n files = []\n for root, _, fnames in os.walk(directory):\n fnames = [os.path.join(root, file) for file in fnames]\n fnames = self._keyword_search(fnames, extensions)\n if fnames:\n files.extend(fnames)\n self.files = files\n\n def _filter_files(\n self,\n keywords: Optional[Union[str, list[str]]] = None,\n hemisphere: Optional[str] = None,\n stimulation: Optional[str] = None,\n medication: Optional[str] = None,\n exclude: Optional[Union[str, list[str]]] = None,\n ) -> None:\n \"\"\"Filter filepaths for given parameters.\"\"\"\n filtered_files = self.files\n if exclude:\n if not isinstance(exclude, list):\n exclude = [exclude]\n filtered_files = [\n file\n for file in filtered_files\n if not any(item in file for item in exclude)\n ]\n if keywords:\n if not isinstance(keywords, list):\n keywords = [keywords]\n filtered_files = self._keyword_search(filtered_files, keywords)\n if stimulation:\n if stimulation.lower() in \"stimon\":\n stim = \"StimOn\"\n elif stimulation.lower() in \"stimoff\":\n stim = \"StimOff\"\n else:\n raise ValueError(\"Keyword for stimulation not valid.\")\n filtered_files = self._keyword_search(filtered_files, [stim])\n if medication:\n if medication.lower() in \"medon\":\n med = \"MedOn\"\n elif medication.lower() in \"medoff\":\n med = \"MedOff\"\n else:\n raise ValueError(\"Keyword for medication not valid.\")\n filtered_files = self._keyword_search(filtered_files, [med])\n if hemisphere:\n matching_files = []\n for file in filtered_files:\n subject = mne_bids.get_entities_from_fname(file)[\"subject\"]\n if (\n subject not in self.hemispheres\n or self.hemispheres[subject] is None\n ):\n raise HemisphereNotSpecifiedError(\n subject, self.hemispheres\n )\n hem = self.hemispheres[subject] + \"_\"\n if hemisphere.lower() in \"ipsilateral\" and hem in file:\n matching_files.append(file)\n if hemisphere.lower() in \"contralateral\" and hem not in file:\n matching_files.append(file)\n filtered_files = matching_files\n self.files = filtered_files\n\n\nclass DirectoryNotFoundError(Exception):\n \"\"\"Exception raised when invalid Reader is passed.\n\n Attributes:\n directory -- input directory which caused the error\n \"\"\"\n\n def __init__(\n self,\n directory: Union[Path, str],\n message=\"Input directory was not found.\",\n ):\n self.directory = directory\n self.message = message\n super().__init__(self.message)\n\n def __str__(self):\n return f\"{self.message} Got: {self.directory}.\"\n\n\nclass HemisphereNotSpecifiedError(Exception):\n \"\"\"Exception raised when electrode hemisphere is not specified in settings.\n\n Attributes:\n subject -- input subject which caused the error\n hemisphere -- specified hemispheres\n message -- explanation of the error\n \"\"\"\n\n def __init__(\n self,\n subject,\n hemispheres,\n message=(\n \"Input ECOG hemisphere is not specified in\"\n \" `filefinder_settings.py` for given subject.\"\n ),\n ) -> None:\n self.subject = subject\n self.hemispheres = hemispheres\n self.message = message\n super().__init__(self.message)\n\n def __str__(self):\n return (\n f\"{self.message} Unspecified subject: {self.subject}.\"\n f\" Specified hemispheres: {self.hemispheres}.\"\n )\n"}}},{"rowIdx":48,"cells":{"function_name":{"kind":"string","value":"irfft2"},"docstring":{"kind":"string","value":"Compute the 2-dimensional inverse FFT of a real array.\n\nParameters\n----------\na : array_like\n The input tensor\ns : sequence of ints, optional\n Shape of the inverse FFT.\naxes : sequence of ints, optional\n The axes over which to compute the inverse fft.\n Default is the last two axes.\nnorm : {None, \"ortho\"}, optional\n Normalization mode (see `mt.fft`). Default is None.\n\nReturns\n-------\nout : Tensor\n The result of the inverse real 2-D FFT.\n\nSee Also\n--------\nirfftn : Compute the inverse of the N-dimensional FFT of real input.\n\nNotes\n-----\nThis is really `irfftn` with different defaults.\nFor more details see `irfftn`."},"masked_code":{"kind":"string","value":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Copyright 1999-2020 Alibaba Group Holding Ltd.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numpy as np\n\nfrom ... import opcodes as OperandDef\nfrom ..datasource import tensor as astensor\nfrom .core import TensorRealIFFTNMixin, validate_fftn, TensorRealFFTN\n\n\nclass TensorIRFFT2(TensorRealFFTN, TensorRealIFFTNMixin):\n _op_type_ = OperandDef.IRFFT2\n\n def __init__(self, shape=None, axes=None, norm=None, **kw):\n super().__init__(_shape=shape, _axes=axes, _norm=norm, **kw)\n\n\n# MASKED: irfft2 function (lines 31-67)"},"implementation":{"kind":"string","value":"def irfft2(a, s=None, axes=(-2, -1), norm=None):\n \"\"\"\n Compute the 2-dimensional inverse FFT of a real array.\n\n Parameters\n ----------\n a : array_like\n The input tensor\n s : sequence of ints, optional\n Shape of the inverse FFT.\n axes : sequence of ints, optional\n The axes over which to compute the inverse fft.\n Default is the last two axes.\n norm : {None, \"ortho\"}, optional\n Normalization mode (see `mt.fft`). Default is None.\n\n Returns\n -------\n out : Tensor\n The result of the inverse real 2-D FFT.\n\n See Also\n --------\n irfftn : Compute the inverse of the N-dimensional FFT of real input.\n\n Notes\n -----\n This is really `irfftn` with different defaults.\n For more details see `irfftn`.\n\n \"\"\"\n if len(axes) != 2:\n raise ValueError(\"axes length should be 2\")\n a = astensor(a)\n axes = validate_fftn(a, s=s, axes=axes, norm=norm)\n op = TensorIRFFT2(shape=s, axes=axes, norm=norm, dtype=np.dtype(np.float_))\n return op(a)"},"start_line":{"kind":"number","value":31,"string":"31"},"end_line":{"kind":"number","value":67,"string":"67"},"file_content":{"kind":"string","value":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Copyright 1999-2020 Alibaba Group Holding Ltd.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport numpy as np\n\nfrom ... import opcodes as OperandDef\nfrom ..datasource import tensor as astensor\nfrom .core import TensorRealIFFTNMixin, validate_fftn, TensorRealFFTN\n\n\nclass TensorIRFFT2(TensorRealFFTN, TensorRealIFFTNMixin):\n _op_type_ = OperandDef.IRFFT2\n\n def __init__(self, shape=None, axes=None, norm=None, **kw):\n super().__init__(_shape=shape, _axes=axes, _norm=norm, **kw)\n\n\ndef irfft2(a, s=None, axes=(-2, -1), norm=None):\n \"\"\"\n Compute the 2-dimensional inverse FFT of a real array.\n\n Parameters\n ----------\n a : array_like\n The input tensor\n s : sequence of ints, optional\n Shape of the inverse FFT.\n axes : sequence of ints, optional\n The axes over which to compute the inverse fft.\n Default is the last two axes.\n norm : {None, \"ortho\"}, optional\n Normalization mode (see `mt.fft`). Default is None.\n\n Returns\n -------\n out : Tensor\n The result of the inverse real 2-D FFT.\n\n See Also\n --------\n irfftn : Compute the inverse of the N-dimensional FFT of real input.\n\n Notes\n -----\n This is really `irfftn` with different defaults.\n For more details see `irfftn`.\n\n \"\"\"\n if len(axes) != 2:\n raise ValueError(\"axes length should be 2\")\n a = astensor(a)\n axes = validate_fftn(a, s=s, axes=axes, norm=norm)\n op = TensorIRFFT2(shape=s, axes=axes, norm=norm, dtype=np.dtype(np.float_))\n return op(a)\n"}}},{"rowIdx":49,"cells":{"function_name":{"kind":"string","value":"_send_notification"},"docstring":{"kind":"string","value":"Send a notification to a specific node_id\n\nThis version of the overriden method includes the necessary crypto\nheaders for the notification.\n\n:type notification: autopush.utils.WebPushNotification"},"masked_code":{"kind":"string","value":"\"\"\"WebPush Style Autopush Router\n\nThis router handles notifications that should be dispatched to an Autopush\nnode, or stores each individual message, along with its data, in a Message\ntable for retrieval by the client.\n\n\"\"\"\nimport json\nimport time\nfrom StringIO import StringIO\nfrom typing import Any # noqa\n\nfrom botocore.exceptions import ClientError\nfrom twisted.internet.threads import deferToThread\nfrom twisted.web.client import FileBodyProducer\nfrom twisted.internet.defer import (\n inlineCallbacks,\n returnValue,\n CancelledError,\n)\nfrom twisted.internet.error import (\n ConnectError,\n ConnectionClosed,\n ConnectionRefusedError,\n)\nfrom twisted.logger import Logger\nfrom twisted.web._newclient import ResponseFailed\nfrom twisted.web.http import PotentialDataLoss\n\nfrom autopush.exceptions import ItemNotFound, RouterException\nfrom autopush.metrics import make_tags\nfrom autopush.protocol import IgnoreBody\nfrom autopush.router.interface import RouterResponse\nfrom autopush.types import JSONDict # noqa\n\nTTL_URL = \"https://webpush-wg.github.io/webpush-protocol/#rfc.section.6.2\"\n\n\nclass WebPushRouter(object):\n \"\"\"Implements :class: `autopush.router.interface.IRouter` for internal\n routing to an autopush node\n\n \"\"\"\n log = Logger()\n\n def __init__(self, conf, router_conf, db, agent):\n \"\"\"Create a new Router\"\"\"\n self.conf = conf\n self.router_conf = router_conf\n self.db = db\n self.agent = agent\n\n @property\n def metrics(self):\n return self.db.metrics\n\n def register(self, uaid, router_data, app_id, *args, **kwargs):\n # type: (str, JSONDict, str, *Any, **Any) -> None\n \"\"\"No additional routing data\"\"\"\n\n def amend_endpoint_response(self, response, router_data):\n # type: (JSONDict, JSONDict) -> None\n \"\"\"Stubbed out for this router\"\"\"\n\n @inlineCallbacks\n def route_notification(self, notification, uaid_data):\n \"\"\"Route a notification to an internal node, and store it if the node\n can't deliver immediately or is no longer a valid node\n \"\"\"\n # Determine if they're connected at the moment\n node_id = uaid_data.get(\"node_id\")\n uaid = uaid_data[\"uaid\"]\n router = self.db.router\n\n # Node_id is present, attempt delivery.\n # - Send Notification to node\n # - Success: Done, return 200\n # - Error (Node busy): Jump to Save notification below\n # - Error (Client gone, node gone/dead): Clear node entry for user\n # - Both: Done, return 503\n if node_id:\n result = None\n try:\n result = yield self._send_notification(uaid, node_id,\n notification)\n except (ConnectError, ConnectionClosed, ResponseFailed,\n CancelledError, PotentialDataLoss) as exc:\n self.metrics.increment(\"updates.client.host_gone\")\n yield deferToThread(router.clear_node,\n uaid_data).addErrback(self._eat_db_err)\n if isinstance(exc, ConnectionRefusedError):\n # Occurs if an IP record is now used by some other node\n # in AWS or if the connection timesout.\n self.log.debug(\"Could not route message: {exc}\", exc=exc)\n if result and result.code == 200:\n returnValue(self.delivered_response(notification))\n\n # Save notification, node is not present or busy\n # - Save notification\n # - Success (older version): Done, return 202\n # - Error (db error): Done, return 503\n try:\n yield self._save_notification(uaid_data, notification)\n except ClientError as e:\n log_exception = (e.response[\"Error\"][\"Code\"] !=\n \"ProvisionedThroughputExceededException\")\n raise RouterException(\"Error saving to database\",\n status_code=503,\n response_body=\"Retry Request\",\n log_exception=log_exception,\n errno=201)\n\n # - Lookup client again to get latest node state after save.\n # - Success (node found): Notify node of new notification\n # - Success: Done, return 200\n # - Error (no client): Done, return 202\n # - Error (no node): Clear node entry\n # - Both: Done, return 202\n # - Success (no node): Done, return 202\n # - Error (db error): Done, return 202\n # - Error (no client) : Done, return 404\n try:\n uaid_data = yield deferToThread(router.get_uaid, uaid)\n except ClientError:\n returnValue(self.stored_response(notification))\n except ItemNotFound:\n self.metrics.increment(\"updates.client.deleted\")\n raise RouterException(\"User was deleted\",\n status_code=410,\n response_body=\"Invalid UAID\",\n log_exception=False,\n errno=105)\n\n # Verify there's a node_id in here, if not we're done\n node_id = uaid_data.get(\"node_id\")\n if not node_id:\n returnValue(self.stored_response(notification))\n try:\n result = yield self._send_notification_check(uaid, node_id)\n except (ConnectError, ConnectionClosed, ResponseFailed) as exc:\n self.metrics.increment(\"updates.client.host_gone\")\n if isinstance(exc, ConnectionRefusedError):\n self.log.debug(\"Could not route message: {exc}\", exc=exc)\n yield deferToThread(\n router.clear_node,\n uaid_data).addErrback(self._eat_db_err)\n returnValue(self.stored_response(notification))\n\n if result.code == 200:\n returnValue(self.delivered_response(notification))\n else:\n ret_val = self.stored_response(notification)\n returnValue(ret_val)\n\n def delivered_response(self, notification):\n self.metrics.increment(\"notification.message_data\",\n notification.data_length,\n tags=make_tags(destination='Stored'))\n location = \"%s/m/%s\" % (self.conf.endpoint_url, notification.location)\n return RouterResponse(status_code=201, response_body=\"\",\n headers={\"Location\": location,\n \"TTL\": notification.ttl or 0},\n logged_status=200)\n\n def stored_response(self, notification):\n self.metrics.increment(\"notification.message_data\",\n notification.data_length,\n tags=make_tags(destination='Direct'))\n location = \"%s/m/%s\" % (self.conf.endpoint_url, notification.location)\n return RouterResponse(status_code=201, response_body=\"\",\n headers={\"Location\": location,\n \"TTL\": notification.ttl},\n logged_status=202)\n\n #############################################################\n # Blocking Helper Functions\n #############################################################\n\n# MASKED: _send_notification function (lines 179-197)\n\n def _send_notification_check(self, uaid, node_id):\n \"\"\"Send a command to the node to check for notifications\"\"\"\n url = node_id + \"/notif/\" + uaid\n return self.agent.request(\n \"PUT\",\n url.encode(\"utf8\"),\n ).addCallback(IgnoreBody.ignore)\n\n def _save_notification(self, uaid_data, notification):\n \"\"\"Saves a notification, returns a deferred.\n\n This version of the overridden method saves each individual message\n to the message table along with relevant request headers if\n available.\n\n :type uaid_data: dict\n\n \"\"\"\n month_table = uaid_data[\"current_month\"]\n if notification.ttl is None:\n # Note that this URL is temporary, as well as this warning as\n # we will 400 all missing TTL's eventually\n raise RouterException(\n \"Missing TTL Header\",\n response_body=\"Missing TTL Header, see: %s\" % TTL_URL,\n status_code=400,\n errno=111,\n log_exception=False,\n )\n if notification.ttl == 0:\n location = \"%s/m/%s\" % (self.conf.endpoint_url,\n notification.version)\n raise RouterException(\"Finished Routing\", status_code=201,\n log_exception=False,\n headers={\"TTL\": str(notification.ttl),\n \"Location\": location},\n logged_status=204)\n return deferToThread(\n self.db.message_table(month_table).store_message,\n notification=notification,\n )\n\n #############################################################\n # Error Callbacks\n #############################################################\n def _eat_db_err(self, fail):\n \"\"\"errBack for ignoring provisioned throughput errors\"\"\"\n fail.trap(ClientError)"},"implementation":{"kind":"string","value":" def _send_notification(self, uaid, node_id, notification):\n \"\"\"Send a notification to a specific node_id\n\n This version of the overriden method includes the necessary crypto\n headers for the notification.\n\n :type notification: autopush.utils.WebPushNotification\n\n \"\"\"\n payload = notification.serialize()\n payload[\"timestamp\"] = int(time.time())\n url = node_id + \"/push/\" + uaid\n request = self.agent.request(\n \"PUT\",\n url.encode(\"utf8\"),\n bodyProducer=FileBodyProducer(StringIO(json.dumps(payload))),\n )\n request.addCallback(IgnoreBody.ignore)\n return request"},"start_line":{"kind":"number","value":179,"string":"179"},"end_line":{"kind":"number","value":197,"string":"197"},"file_content":{"kind":"string","value":"\"\"\"WebPush Style Autopush Router\n\nThis router handles notifications that should be dispatched to an Autopush\nnode, or stores each individual message, along with its data, in a Message\ntable for retrieval by the client.\n\n\"\"\"\nimport json\nimport time\nfrom StringIO import StringIO\nfrom typing import Any # noqa\n\nfrom botocore.exceptions import ClientError\nfrom twisted.internet.threads import deferToThread\nfrom twisted.web.client import FileBodyProducer\nfrom twisted.internet.defer import (\n inlineCallbacks,\n returnValue,\n CancelledError,\n)\nfrom twisted.internet.error import (\n ConnectError,\n ConnectionClosed,\n ConnectionRefusedError,\n)\nfrom twisted.logger import Logger\nfrom twisted.web._newclient import ResponseFailed\nfrom twisted.web.http import PotentialDataLoss\n\nfrom autopush.exceptions import ItemNotFound, RouterException\nfrom autopush.metrics import make_tags\nfrom autopush.protocol import IgnoreBody\nfrom autopush.router.interface import RouterResponse\nfrom autopush.types import JSONDict # noqa\n\nTTL_URL = \"https://webpush-wg.github.io/webpush-protocol/#rfc.section.6.2\"\n\n\nclass WebPushRouter(object):\n \"\"\"Implements :class: `autopush.router.interface.IRouter` for internal\n routing to an autopush node\n\n \"\"\"\n log = Logger()\n\n def __init__(self, conf, router_conf, db, agent):\n \"\"\"Create a new Router\"\"\"\n self.conf = conf\n self.router_conf = router_conf\n self.db = db\n self.agent = agent\n\n @property\n def metrics(self):\n return self.db.metrics\n\n def register(self, uaid, router_data, app_id, *args, **kwargs):\n # type: (str, JSONDict, str, *Any, **Any) -> None\n \"\"\"No additional routing data\"\"\"\n\n def amend_endpoint_response(self, response, router_data):\n # type: (JSONDict, JSONDict) -> None\n \"\"\"Stubbed out for this router\"\"\"\n\n @inlineCallbacks\n def route_notification(self, notification, uaid_data):\n \"\"\"Route a notification to an internal node, and store it if the node\n can't deliver immediately or is no longer a valid node\n \"\"\"\n # Determine if they're connected at the moment\n node_id = uaid_data.get(\"node_id\")\n uaid = uaid_data[\"uaid\"]\n router = self.db.router\n\n # Node_id is present, attempt delivery.\n # - Send Notification to node\n # - Success: Done, return 200\n # - Error (Node busy): Jump to Save notification below\n # - Error (Client gone, node gone/dead): Clear node entry for user\n # - Both: Done, return 503\n if node_id:\n result = None\n try:\n result = yield self._send_notification(uaid, node_id,\n notification)\n except (ConnectError, ConnectionClosed, ResponseFailed,\n CancelledError, PotentialDataLoss) as exc:\n self.metrics.increment(\"updates.client.host_gone\")\n yield deferToThread(router.clear_node,\n uaid_data).addErrback(self._eat_db_err)\n if isinstance(exc, ConnectionRefusedError):\n # Occurs if an IP record is now used by some other node\n # in AWS or if the connection timesout.\n self.log.debug(\"Could not route message: {exc}\", exc=exc)\n if result and result.code == 200:\n returnValue(self.delivered_response(notification))\n\n # Save notification, node is not present or busy\n # - Save notification\n # - Success (older version): Done, return 202\n # - Error (db error): Done, return 503\n try:\n yield self._save_notification(uaid_data, notification)\n except ClientError as e:\n log_exception = (e.response[\"Error\"][\"Code\"] !=\n \"ProvisionedThroughputExceededException\")\n raise RouterException(\"Error saving to database\",\n status_code=503,\n response_body=\"Retry Request\",\n log_exception=log_exception,\n errno=201)\n\n # - Lookup client again to get latest node state after save.\n # - Success (node found): Notify node of new notification\n # - Success: Done, return 200\n # - Error (no client): Done, return 202\n # - Error (no node): Clear node entry\n # - Both: Done, return 202\n # - Success (no node): Done, return 202\n # - Error (db error): Done, return 202\n # - Error (no client) : Done, return 404\n try:\n uaid_data = yield deferToThread(router.get_uaid, uaid)\n except ClientError:\n returnValue(self.stored_response(notification))\n except ItemNotFound:\n self.metrics.increment(\"updates.client.deleted\")\n raise RouterException(\"User was deleted\",\n status_code=410,\n response_body=\"Invalid UAID\",\n log_exception=False,\n errno=105)\n\n # Verify there's a node_id in here, if not we're done\n node_id = uaid_data.get(\"node_id\")\n if not node_id:\n returnValue(self.stored_response(notification))\n try:\n result = yield self._send_notification_check(uaid, node_id)\n except (ConnectError, ConnectionClosed, ResponseFailed) as exc:\n self.metrics.increment(\"updates.client.host_gone\")\n if isinstance(exc, ConnectionRefusedError):\n self.log.debug(\"Could not route message: {exc}\", exc=exc)\n yield deferToThread(\n router.clear_node,\n uaid_data).addErrback(self._eat_db_err)\n returnValue(self.stored_response(notification))\n\n if result.code == 200:\n returnValue(self.delivered_response(notification))\n else:\n ret_val = self.stored_response(notification)\n returnValue(ret_val)\n\n def delivered_response(self, notification):\n self.metrics.increment(\"notification.message_data\",\n notification.data_length,\n tags=make_tags(destination='Stored'))\n location = \"%s/m/%s\" % (self.conf.endpoint_url, notification.location)\n return RouterResponse(status_code=201, response_body=\"\",\n headers={\"Location\": location,\n \"TTL\": notification.ttl or 0},\n logged_status=200)\n\n def stored_response(self, notification):\n self.metrics.increment(\"notification.message_data\",\n notification.data_length,\n tags=make_tags(destination='Direct'))\n location = \"%s/m/%s\" % (self.conf.endpoint_url, notification.location)\n return RouterResponse(status_code=201, response_body=\"\",\n headers={\"Location\": location,\n \"TTL\": notification.ttl},\n logged_status=202)\n\n #############################################################\n # Blocking Helper Functions\n #############################################################\n\n def _send_notification(self, uaid, node_id, notification):\n \"\"\"Send a notification to a specific node_id\n\n This version of the overriden method includes the necessary crypto\n headers for the notification.\n\n :type notification: autopush.utils.WebPushNotification\n\n \"\"\"\n payload = notification.serialize()\n payload[\"timestamp\"] = int(time.time())\n url = node_id + \"/push/\" + uaid\n request = self.agent.request(\n \"PUT\",\n url.encode(\"utf8\"),\n bodyProducer=FileBodyProducer(StringIO(json.dumps(payload))),\n )\n request.addCallback(IgnoreBody.ignore)\n return request\n\n def _send_notification_check(self, uaid, node_id):\n \"\"\"Send a command to the node to check for notifications\"\"\"\n url = node_id + \"/notif/\" + uaid\n return self.agent.request(\n \"PUT\",\n url.encode(\"utf8\"),\n ).addCallback(IgnoreBody.ignore)\n\n def _save_notification(self, uaid_data, notification):\n \"\"\"Saves a notification, returns a deferred.\n\n This version of the overridden method saves each individual message\n to the message table along with relevant request headers if\n available.\n\n :type uaid_data: dict\n\n \"\"\"\n month_table = uaid_data[\"current_month\"]\n if notification.ttl is None:\n # Note that this URL is temporary, as well as this warning as\n # we will 400 all missing TTL's eventually\n raise RouterException(\n \"Missing TTL Header\",\n response_body=\"Missing TTL Header, see: %s\" % TTL_URL,\n status_code=400,\n errno=111,\n log_exception=False,\n )\n if notification.ttl == 0:\n location = \"%s/m/%s\" % (self.conf.endpoint_url,\n notification.version)\n raise RouterException(\"Finished Routing\", status_code=201,\n log_exception=False,\n headers={\"TTL\": str(notification.ttl),\n \"Location\": location},\n logged_status=204)\n return deferToThread(\n self.db.message_table(month_table).store_message,\n notification=notification,\n )\n\n #############################################################\n # Error Callbacks\n #############################################################\n def _eat_db_err(self, fail):\n \"\"\"errBack for ignoring provisioned throughput errors\"\"\"\n fail.trap(ClientError)\n"}}},{"rowIdx":50,"cells":{"function_name":{"kind":"string","value":"_save_notification"},"docstring":{"kind":"string","value":"Saves a notification, returns a deferred.\n\nThis version of the overridden method saves each individual message\nto the message table along with relevant request headers if\navailable.\n\n:type uaid_data: dict"},"masked_code":{"kind":"string","value":"\"\"\"WebPush Style Autopush Router\n\nThis router handles notifications that should be dispatched to an Autopush\nnode, or stores each individual message, along with its data, in a Message\ntable for retrieval by the client.\n\n\"\"\"\nimport json\nimport time\nfrom StringIO import StringIO\nfrom typing import Any # noqa\n\nfrom botocore.exceptions import ClientError\nfrom twisted.internet.threads import deferToThread\nfrom twisted.web.client import FileBodyProducer\nfrom twisted.internet.defer import (\n inlineCallbacks,\n returnValue,\n CancelledError,\n)\nfrom twisted.internet.error import (\n ConnectError,\n ConnectionClosed,\n ConnectionRefusedError,\n)\nfrom twisted.logger import Logger\nfrom twisted.web._newclient import ResponseFailed\nfrom twisted.web.http import PotentialDataLoss\n\nfrom autopush.exceptions import ItemNotFound, RouterException\nfrom autopush.metrics import make_tags\nfrom autopush.protocol import IgnoreBody\nfrom autopush.router.interface import RouterResponse\nfrom autopush.types import JSONDict # noqa\n\nTTL_URL = \"https://webpush-wg.github.io/webpush-protocol/#rfc.section.6.2\"\n\n\nclass WebPushRouter(object):\n \"\"\"Implements :class: `autopush.router.interface.IRouter` for internal\n routing to an autopush node\n\n \"\"\"\n log = Logger()\n\n def __init__(self, conf, router_conf, db, agent):\n \"\"\"Create a new Router\"\"\"\n self.conf = conf\n self.router_conf = router_conf\n self.db = db\n self.agent = agent\n\n @property\n def metrics(self):\n return self.db.metrics\n\n def register(self, uaid, router_data, app_id, *args, **kwargs):\n # type: (str, JSONDict, str, *Any, **Any) -> None\n \"\"\"No additional routing data\"\"\"\n\n def amend_endpoint_response(self, response, router_data):\n # type: (JSONDict, JSONDict) -> None\n \"\"\"Stubbed out for this router\"\"\"\n\n @inlineCallbacks\n def route_notification(self, notification, uaid_data):\n \"\"\"Route a notification to an internal node, and store it if the node\n can't deliver immediately or is no longer a valid node\n \"\"\"\n # Determine if they're connected at the moment\n node_id = uaid_data.get(\"node_id\")\n uaid = uaid_data[\"uaid\"]\n router = self.db.router\n\n # Node_id is present, attempt delivery.\n # - Send Notification to node\n # - Success: Done, return 200\n # - Error (Node busy): Jump to Save notification below\n # - Error (Client gone, node gone/dead): Clear node entry for user\n # - Both: Done, return 503\n if node_id:\n result = None\n try:\n result = yield self._send_notification(uaid, node_id,\n notification)\n except (ConnectError, ConnectionClosed, ResponseFailed,\n CancelledError, PotentialDataLoss) as exc:\n self.metrics.increment(\"updates.client.host_gone\")\n yield deferToThread(router.clear_node,\n uaid_data).addErrback(self._eat_db_err)\n if isinstance(exc, ConnectionRefusedError):\n # Occurs if an IP record is now used by some other node\n # in AWS or if the connection timesout.\n self.log.debug(\"Could not route message: {exc}\", exc=exc)\n if result and result.code == 200:\n returnValue(self.delivered_response(notification))\n\n # Save notification, node is not present or busy\n # - Save notification\n # - Success (older version): Done, return 202\n # - Error (db error): Done, return 503\n try:\n yield self._save_notification(uaid_data, notification)\n except ClientError as e:\n log_exception = (e.response[\"Error\"][\"Code\"] !=\n \"ProvisionedThroughputExceededException\")\n raise RouterException(\"Error saving to database\",\n status_code=503,\n response_body=\"Retry Request\",\n log_exception=log_exception,\n errno=201)\n\n # - Lookup client again to get latest node state after save.\n # - Success (node found): Notify node of new notification\n # - Success: Done, return 200\n # - Error (no client): Done, return 202\n # - Error (no node): Clear node entry\n # - Both: Done, return 202\n # - Success (no node): Done, return 202\n # - Error (db error): Done, return 202\n # - Error (no client) : Done, return 404\n try:\n uaid_data = yield deferToThread(router.get_uaid, uaid)\n except ClientError:\n returnValue(self.stored_response(notification))\n except ItemNotFound:\n self.metrics.increment(\"updates.client.deleted\")\n raise RouterException(\"User was deleted\",\n status_code=410,\n response_body=\"Invalid UAID\",\n log_exception=False,\n errno=105)\n\n # Verify there's a node_id in here, if not we're done\n node_id = uaid_data.get(\"node_id\")\n if not node_id:\n returnValue(self.stored_response(notification))\n try:\n result = yield self._send_notification_check(uaid, node_id)\n except (ConnectError, ConnectionClosed, ResponseFailed) as exc:\n self.metrics.increment(\"updates.client.host_gone\")\n if isinstance(exc, ConnectionRefusedError):\n self.log.debug(\"Could not route message: {exc}\", exc=exc)\n yield deferToThread(\n router.clear_node,\n uaid_data).addErrback(self._eat_db_err)\n returnValue(self.stored_response(notification))\n\n if result.code == 200:\n returnValue(self.delivered_response(notification))\n else:\n ret_val = self.stored_response(notification)\n returnValue(ret_val)\n\n def delivered_response(self, notification):\n self.metrics.increment(\"notification.message_data\",\n notification.data_length,\n tags=make_tags(destination='Stored'))\n location = \"%s/m/%s\" % (self.conf.endpoint_url, notification.location)\n return RouterResponse(status_code=201, response_body=\"\",\n headers={\"Location\": location,\n \"TTL\": notification.ttl or 0},\n logged_status=200)\n\n def stored_response(self, notification):\n self.metrics.increment(\"notification.message_data\",\n notification.data_length,\n tags=make_tags(destination='Direct'))\n location = \"%s/m/%s\" % (self.conf.endpoint_url, notification.location)\n return RouterResponse(status_code=201, response_body=\"\",\n headers={\"Location\": location,\n \"TTL\": notification.ttl},\n logged_status=202)\n\n #############################################################\n # Blocking Helper Functions\n #############################################################\n\n def _send_notification(self, uaid, node_id, notification):\n \"\"\"Send a notification to a specific node_id\n\n This version of the overriden method includes the necessary crypto\n headers for the notification.\n\n :type notification: autopush.utils.WebPushNotification\n\n \"\"\"\n payload = notification.serialize()\n payload[\"timestamp\"] = int(time.time())\n url = node_id + \"/push/\" + uaid\n request = self.agent.request(\n \"PUT\",\n url.encode(\"utf8\"),\n bodyProducer=FileBodyProducer(StringIO(json.dumps(payload))),\n )\n request.addCallback(IgnoreBody.ignore)\n return request\n\n def _send_notification_check(self, uaid, node_id):\n \"\"\"Send a command to the node to check for notifications\"\"\"\n url = node_id + \"/notif/\" + uaid\n return self.agent.request(\n \"PUT\",\n url.encode(\"utf8\"),\n ).addCallback(IgnoreBody.ignore)\n\n# MASKED: _save_notification function (lines 207-239)\n\n #############################################################\n # Error Callbacks\n #############################################################\n def _eat_db_err(self, fail):\n \"\"\"errBack for ignoring provisioned throughput errors\"\"\"\n fail.trap(ClientError)"},"implementation":{"kind":"string","value":" def _save_notification(self, uaid_data, notification):\n \"\"\"Saves a notification, returns a deferred.\n\n This version of the overridden method saves each individual message\n to the message table along with relevant request headers if\n available.\n\n :type uaid_data: dict\n\n \"\"\"\n month_table = uaid_data[\"current_month\"]\n if notification.ttl is None:\n # Note that this URL is temporary, as well as this warning as\n # we will 400 all missing TTL's eventually\n raise RouterException(\n \"Missing TTL Header\",\n response_body=\"Missing TTL Header, see: %s\" % TTL_URL,\n status_code=400,\n errno=111,\n log_exception=False,\n )\n if notification.ttl == 0:\n location = \"%s/m/%s\" % (self.conf.endpoint_url,\n notification.version)\n raise RouterException(\"Finished Routing\", status_code=201,\n log_exception=False,\n headers={\"TTL\": str(notification.ttl),\n \"Location\": location},\n logged_status=204)\n return deferToThread(\n self.db.message_table(month_table).store_message,\n notification=notification,\n )"},"start_line":{"kind":"number","value":207,"string":"207"},"end_line":{"kind":"number","value":239,"string":"239"},"file_content":{"kind":"string","value":"\"\"\"WebPush Style Autopush Router\n\nThis router handles notifications that should be dispatched to an Autopush\nnode, or stores each individual message, along with its data, in a Message\ntable for retrieval by the client.\n\n\"\"\"\nimport json\nimport time\nfrom StringIO import StringIO\nfrom typing import Any # noqa\n\nfrom botocore.exceptions import ClientError\nfrom twisted.internet.threads import deferToThread\nfrom twisted.web.client import FileBodyProducer\nfrom twisted.internet.defer import (\n inlineCallbacks,\n returnValue,\n CancelledError,\n)\nfrom twisted.internet.error import (\n ConnectError,\n ConnectionClosed,\n ConnectionRefusedError,\n)\nfrom twisted.logger import Logger\nfrom twisted.web._newclient import ResponseFailed\nfrom twisted.web.http import PotentialDataLoss\n\nfrom autopush.exceptions import ItemNotFound, RouterException\nfrom autopush.metrics import make_tags\nfrom autopush.protocol import IgnoreBody\nfrom autopush.router.interface import RouterResponse\nfrom autopush.types import JSONDict # noqa\n\nTTL_URL = \"https://webpush-wg.github.io/webpush-protocol/#rfc.section.6.2\"\n\n\nclass WebPushRouter(object):\n \"\"\"Implements :class: `autopush.router.interface.IRouter` for internal\n routing to an autopush node\n\n \"\"\"\n log = Logger()\n\n def __init__(self, conf, router_conf, db, agent):\n \"\"\"Create a new Router\"\"\"\n self.conf = conf\n self.router_conf = router_conf\n self.db = db\n self.agent = agent\n\n @property\n def metrics(self):\n return self.db.metrics\n\n def register(self, uaid, router_data, app_id, *args, **kwargs):\n # type: (str, JSONDict, str, *Any, **Any) -> None\n \"\"\"No additional routing data\"\"\"\n\n def amend_endpoint_response(self, response, router_data):\n # type: (JSONDict, JSONDict) -> None\n \"\"\"Stubbed out for this router\"\"\"\n\n @inlineCallbacks\n def route_notification(self, notification, uaid_data):\n \"\"\"Route a notification to an internal node, and store it if the node\n can't deliver immediately or is no longer a valid node\n \"\"\"\n # Determine if they're connected at the moment\n node_id = uaid_data.get(\"node_id\")\n uaid = uaid_data[\"uaid\"]\n router = self.db.router\n\n # Node_id is present, attempt delivery.\n # - Send Notification to node\n # - Success: Done, return 200\n # - Error (Node busy): Jump to Save notification below\n # - Error (Client gone, node gone/dead): Clear node entry for user\n # - Both: Done, return 503\n if node_id:\n result = None\n try:\n result = yield self._send_notification(uaid, node_id,\n notification)\n except (ConnectError, ConnectionClosed, ResponseFailed,\n CancelledError, PotentialDataLoss) as exc:\n self.metrics.increment(\"updates.client.host_gone\")\n yield deferToThread(router.clear_node,\n uaid_data).addErrback(self._eat_db_err)\n if isinstance(exc, ConnectionRefusedError):\n # Occurs if an IP record is now used by some other node\n # in AWS or if the connection timesout.\n self.log.debug(\"Could not route message: {exc}\", exc=exc)\n if result and result.code == 200:\n returnValue(self.delivered_response(notification))\n\n # Save notification, node is not present or busy\n # - Save notification\n # - Success (older version): Done, return 202\n # - Error (db error): Done, return 503\n try:\n yield self._save_notification(uaid_data, notification)\n except ClientError as e:\n log_exception = (e.response[\"Error\"][\"Code\"] !=\n \"ProvisionedThroughputExceededException\")\n raise RouterException(\"Error saving to database\",\n status_code=503,\n response_body=\"Retry Request\",\n log_exception=log_exception,\n errno=201)\n\n # - Lookup client again to get latest node state after save.\n # - Success (node found): Notify node of new notification\n # - Success: Done, return 200\n # - Error (no client): Done, return 202\n # - Error (no node): Clear node entry\n # - Both: Done, return 202\n # - Success (no node): Done, return 202\n # - Error (db error): Done, return 202\n # - Error (no client) : Done, return 404\n try:\n uaid_data = yield deferToThread(router.get_uaid, uaid)\n except ClientError:\n returnValue(self.stored_response(notification))\n except ItemNotFound:\n self.metrics.increment(\"updates.client.deleted\")\n raise RouterException(\"User was deleted\",\n status_code=410,\n response_body=\"Invalid UAID\",\n log_exception=False,\n errno=105)\n\n # Verify there's a node_id in here, if not we're done\n node_id = uaid_data.get(\"node_id\")\n if not node_id:\n returnValue(self.stored_response(notification))\n try:\n result = yield self._send_notification_check(uaid, node_id)\n except (ConnectError, ConnectionClosed, ResponseFailed) as exc:\n self.metrics.increment(\"updates.client.host_gone\")\n if isinstance(exc, ConnectionRefusedError):\n self.log.debug(\"Could not route message: {exc}\", exc=exc)\n yield deferToThread(\n router.clear_node,\n uaid_data).addErrback(self._eat_db_err)\n returnValue(self.stored_response(notification))\n\n if result.code == 200:\n returnValue(self.delivered_response(notification))\n else:\n ret_val = self.stored_response(notification)\n returnValue(ret_val)\n\n def delivered_response(self, notification):\n self.metrics.increment(\"notification.message_data\",\n notification.data_length,\n tags=make_tags(destination='Stored'))\n location = \"%s/m/%s\" % (self.conf.endpoint_url, notification.location)\n return RouterResponse(status_code=201, response_body=\"\",\n headers={\"Location\": location,\n \"TTL\": notification.ttl or 0},\n logged_status=200)\n\n def stored_response(self, notification):\n self.metrics.increment(\"notification.message_data\",\n notification.data_length,\n tags=make_tags(destination='Direct'))\n location = \"%s/m/%s\" % (self.conf.endpoint_url, notification.location)\n return RouterResponse(status_code=201, response_body=\"\",\n headers={\"Location\": location,\n \"TTL\": notification.ttl},\n logged_status=202)\n\n #############################################################\n # Blocking Helper Functions\n #############################################################\n\n def _send_notification(self, uaid, node_id, notification):\n \"\"\"Send a notification to a specific node_id\n\n This version of the overriden method includes the necessary crypto\n headers for the notification.\n\n :type notification: autopush.utils.WebPushNotification\n\n \"\"\"\n payload = notification.serialize()\n payload[\"timestamp\"] = int(time.time())\n url = node_id + \"/push/\" + uaid\n request = self.agent.request(\n \"PUT\",\n url.encode(\"utf8\"),\n bodyProducer=FileBodyProducer(StringIO(json.dumps(payload))),\n )\n request.addCallback(IgnoreBody.ignore)\n return request\n\n def _send_notification_check(self, uaid, node_id):\n \"\"\"Send a command to the node to check for notifications\"\"\"\n url = node_id + \"/notif/\" + uaid\n return self.agent.request(\n \"PUT\",\n url.encode(\"utf8\"),\n ).addCallback(IgnoreBody.ignore)\n\n def _save_notification(self, uaid_data, notification):\n \"\"\"Saves a notification, returns a deferred.\n\n This version of the overridden method saves each individual message\n to the message table along with relevant request headers if\n available.\n\n :type uaid_data: dict\n\n \"\"\"\n month_table = uaid_data[\"current_month\"]\n if notification.ttl is None:\n # Note that this URL is temporary, as well as this warning as\n # we will 400 all missing TTL's eventually\n raise RouterException(\n \"Missing TTL Header\",\n response_body=\"Missing TTL Header, see: %s\" % TTL_URL,\n status_code=400,\n errno=111,\n log_exception=False,\n )\n if notification.ttl == 0:\n location = \"%s/m/%s\" % (self.conf.endpoint_url,\n notification.version)\n raise RouterException(\"Finished Routing\", status_code=201,\n log_exception=False,\n headers={\"TTL\": str(notification.ttl),\n \"Location\": location},\n logged_status=204)\n return deferToThread(\n self.db.message_table(month_table).store_message,\n notification=notification,\n )\n\n #############################################################\n # Error Callbacks\n #############################################################\n def _eat_db_err(self, fail):\n \"\"\"errBack for ignoring provisioned throughput errors\"\"\"\n fail.trap(ClientError)\n"}}},{"rowIdx":51,"cells":{"function_name":{"kind":"string","value":"_recursive_apply"},"docstring":{"kind":"string","value":"This function is \"applied\" to every child in the block. This function in turn\nregisters the forward hook to each module. It helps logging the input output tensors\nof that module."},"masked_code":{"kind":"string","value":"# Third Party\nimport mxnet as mx\nfrom mxnet.ndarray import NDArray\n\n# First Party\nfrom smdebug.core.collection import DEFAULT_MXNET_COLLECTIONS, CollectionKeys\nfrom smdebug.core.hook import CallbackHook\nfrom smdebug.core.json_config import DEFAULT_WORKER_NAME\nfrom smdebug.core.utils import FRAMEWORK, error_handling_agent\nfrom smdebug.mxnet.collection import CollectionManager\nfrom smdebug.mxnet.graph import _net2pb\nfrom smdebug.mxnet.singleton_utils import set_hook\nfrom smdebug.mxnet.utils import get_reduction_of_data, make_numpy_array\nfrom smdebug.profiler.profiler_config_parser import get_profiler_config_parser\n\nDEFAULT_INCLUDE_COLLECTIONS = [CollectionKeys.LOSSES]\n\nCOLLECTIONS_NOT_REQUIRING_RECURSIVE_HOOK = [\n CollectionKeys.WEIGHTS,\n CollectionKeys.BIASES,\n CollectionKeys.GRADIENTS,\n CollectionKeys.LOSSES,\n]\n\n\nprofiler_config_parser = get_profiler_config_parser(FRAMEWORK.PYTORCH)\n\n\nclass Hook(CallbackHook):\n def __init__(\n self,\n out_dir=None,\n export_tensorboard=False,\n tensorboard_dir=None,\n dry_run=False,\n reduction_config=None,\n save_config=None,\n include_regex=None,\n include_collections=None,\n save_all=False,\n include_workers=\"one\",\n ):\n collection_manager = CollectionManager()\n super().__init__(\n collection_manager=collection_manager,\n default_include_collections=DEFAULT_INCLUDE_COLLECTIONS,\n profiler_config_parser=profiler_config_parser,\n data_type_name=mx.ndarray.NDArray.__name__,\n out_dir=out_dir,\n export_tensorboard=export_tensorboard,\n tensorboard_dir=tensorboard_dir,\n dry_run=dry_run,\n reduction_config=reduction_config,\n save_config=save_config,\n include_regex=include_regex,\n include_collections=include_collections,\n save_all=save_all,\n include_workers=include_workers,\n )\n self.last_block = None\n\n self.model = None\n self.exported_model = False\n # Keep the set of blocks to which this hook is registered. The blocks include loss blocks as well.\n self.registered_blocks = set()\n self.worker = self._get_worker_name()\n set_hook(self)\n\n def _get_worker_name(self):\n try:\n import horovod.mxnet as hvd\n\n if hvd.size():\n return f\"worker_{hvd.rank()}\"\n except (ModuleNotFoundError, ValueError, ImportError):\n pass\n\n return DEFAULT_WORKER_NAME\n\n def _get_num_workers(self):\n try:\n import horovod.mxnet as hvd\n\n if hvd.size():\n return hvd.size()\n except (ModuleNotFoundError, ValueError, ImportError):\n pass\n return 1\n\n def _cleanup(self):\n # Write the gradients of the past step if the writer is still available.\n if self.writer is not None and self.last_block is not None:\n self._log_params(self.last_block)\n if self.exported_model is False:\n self._export_model()\n super()._cleanup()\n\n def _log_params(self, block):\n params = block.collect_params().values()\n for param in params:\n self._log_param(param)\n\n def _log_param(self, param):\n try:\n self._save_for_tensor(\n tensor_name=param.name, tensor_value=param.data(param.list_ctx()[0])\n )\n # If Gradient for this param is available\n if param.grad_req != \"null\":\n self._save_for_tensor(\n tensor_name=self.GRADIENT_PREFIX + param.name,\n tensor_value=param.grad(param.list_ctx()[0]),\n )\n except RuntimeError as e:\n self.logger.warning(\n f\"Could not log parameter {param.name} due to the mxnet exception: {e}\"\n )\n\n def _export_model(self):\n if self.model is not None:\n try:\n tb_writer = self._maybe_get_tb_writer()\n if tb_writer:\n tb_writer.write_graph(_net2pb(self.model))\n except (RuntimeError, TypeError) as e:\n self.logger.warning(\n f\"Could not export model graph for tensorboard \"\n f\"due to the mxnet exception: {e}\"\n )\n\n def _get_default_collections(self):\n return DEFAULT_MXNET_COLLECTIONS\n\n # This hook is invoked by trainer prior to running the forward pass.\n @error_handling_agent.catch_smdebug_errors()\n def forward_pre_hook(self, block, inputs):\n if self.writer is not None:\n # Write the params and gradients of the\n # past step if the writer is still available.\n self._log_params(block)\n self._close_writers()\n self._close_tb_writer()\n\n if not self.prepared_collections:\n # at this point we need all collections to be ready\n # this may not be the case at creation of hook\n # as user's code after hook might add collections\n self._prepare_collections()\n self.prepared_collections = True\n\n self._increment_step()\n\n if self._get_collections_to_save_for_step():\n self._initialize_writers()\n\n if self.exported_model is False:\n self._export_model()\n self.exported_model = True\n\n if self.last_saved_step is not None and not self.exported_collections:\n self.export_collections()\n self.exported_collections = True\n\n self.last_block = block\n self._save_custom_tensors_post_step()\n\n # This hook is invoked by trainer after running the forward pass.\n @error_handling_agent.catch_smdebug_errors()\n def forward_hook(self, block, inputs, outputs):\n if not self._get_collections_to_save_for_step():\n return\n\n block_name = block.name\n # This overwhelms the logs; turn back on if you really need it\n # logger.debug(\"Processing the global step {0} for block {1}\".format(self.step, block_name))\n\n # Output input tensor\n self._write_inputs(block_name, inputs)\n\n # Output output tensors\n self._write_outputs(block_name, outputs)\n\n self.last_saved_step = self.step\n\n# MASKED: _recursive_apply function (lines 185-196)\n\n def _is_recursive_needed(self):\n collections_to_save = self.include_collections\n\n # Check if default collection has a regex associated with it.\n # If it does we would need to apply hook recursively.\n if (\n len(self.collection_manager.get(CollectionKeys.DEFAULT).include_regex) != 0\n and CollectionKeys.DEFAULT in collections_to_save\n ):\n return True\n\n # Get the collections that are to be saved but are not part of default collections\n # We will need to apply hook recursively to get tensors specified in those collections.\n extra_coll = [\n value\n for value in collections_to_save\n if value not in COLLECTIONS_NOT_REQUIRING_RECURSIVE_HOOK\n ]\n\n # extra_coll contains the collections that are not part of default collections.\n return len(extra_coll) != 0\n\n def register_hook(self, block):\n # for compatibility with ZCC patches which call this\n self.register_block(block)\n\n @error_handling_agent.catch_smdebug_errors()\n def register_block(self, block):\n \"\"\"\n This function registers the forward hook. If user wants to register the hook\n for every child in the given block, then the function calls \"apply\" API for\n registration of the hook.\n The hook is registered recursively, if user has specified the collections that are more than\n the default collectors viz. gradients, weight and bias\n \"\"\"\n\n if not isinstance(block, mx.gluon.Block):\n self.logger.error(f\"The given block type {block.__class__.__name__} is unsupported.\")\n return\n\n # Check if the hook is already registered for this block.\n if block in self.registered_blocks:\n self.logger.warning(f\"The hook is already registered to block {block.name}\")\n return\n\n # Skip the forward pre hook for the Loss blocks.\n if isinstance(block, mx.gluon.loss.Loss):\n self.logger.info(f\"Registering hook for block {block.name}\")\n block.register_forward_hook(self.forward_hook)\n self.registered_blocks.add(block)\n return\n else:\n self.model = block\n\n is_recursive = self._is_recursive_needed()\n block.register_forward_pre_hook(self.forward_pre_hook)\n if is_recursive is True:\n block.apply(self._recursive_apply)\n else:\n block.register_forward_hook(self.forward_hook)\n self.registered_blocks.add(block)\n\n @staticmethod\n def _get_reduction_of_data(reduction_name, tensor_value, tensor_name, abs):\n return get_reduction_of_data(reduction_name, tensor_value, tensor_name, abs)\n\n @staticmethod\n def _make_numpy_array(tensor_value):\n if isinstance(tensor_value, NDArray):\n return tensor_value.asnumpy()\n return make_numpy_array(tensor_value)"},"implementation":{"kind":"string","value":" def _recursive_apply(self, block):\n \"\"\"\n This function is \"applied\" to every child in the block. This function in turn\n registers the forward hook to each module. It helps logging the input output tensors\n of that module.\n \"\"\"\n # Check if the hook is already registered for this block.\n if block in self.registered_blocks:\n self.logger.warning(f\"The hook is already registered to block {block.name}\")\n return\n block.register_forward_hook(self.forward_hook)\n self.registered_blocks.add(block)"},"start_line":{"kind":"number","value":185,"string":"185"},"end_line":{"kind":"number","value":196,"string":"196"},"file_content":{"kind":"string","value":"# Third Party\nimport mxnet as mx\nfrom mxnet.ndarray import NDArray\n\n# First Party\nfrom smdebug.core.collection import DEFAULT_MXNET_COLLECTIONS, CollectionKeys\nfrom smdebug.core.hook import CallbackHook\nfrom smdebug.core.json_config import DEFAULT_WORKER_NAME\nfrom smdebug.core.utils import FRAMEWORK, error_handling_agent\nfrom smdebug.mxnet.collection import CollectionManager\nfrom smdebug.mxnet.graph import _net2pb\nfrom smdebug.mxnet.singleton_utils import set_hook\nfrom smdebug.mxnet.utils import get_reduction_of_data, make_numpy_array\nfrom smdebug.profiler.profiler_config_parser import get_profiler_config_parser\n\nDEFAULT_INCLUDE_COLLECTIONS = [CollectionKeys.LOSSES]\n\nCOLLECTIONS_NOT_REQUIRING_RECURSIVE_HOOK = [\n CollectionKeys.WEIGHTS,\n CollectionKeys.BIASES,\n CollectionKeys.GRADIENTS,\n CollectionKeys.LOSSES,\n]\n\n\nprofiler_config_parser = get_profiler_config_parser(FRAMEWORK.PYTORCH)\n\n\nclass Hook(CallbackHook):\n def __init__(\n self,\n out_dir=None,\n export_tensorboard=False,\n tensorboard_dir=None,\n dry_run=False,\n reduction_config=None,\n save_config=None,\n include_regex=None,\n include_collections=None,\n save_all=False,\n include_workers=\"one\",\n ):\n collection_manager = CollectionManager()\n super().__init__(\n collection_manager=collection_manager,\n default_include_collections=DEFAULT_INCLUDE_COLLECTIONS,\n profiler_config_parser=profiler_config_parser,\n data_type_name=mx.ndarray.NDArray.__name__,\n out_dir=out_dir,\n export_tensorboard=export_tensorboard,\n tensorboard_dir=tensorboard_dir,\n dry_run=dry_run,\n reduction_config=reduction_config,\n save_config=save_config,\n include_regex=include_regex,\n include_collections=include_collections,\n save_all=save_all,\n include_workers=include_workers,\n )\n self.last_block = None\n\n self.model = None\n self.exported_model = False\n # Keep the set of blocks to which this hook is registered. The blocks include loss blocks as well.\n self.registered_blocks = set()\n self.worker = self._get_worker_name()\n set_hook(self)\n\n def _get_worker_name(self):\n try:\n import horovod.mxnet as hvd\n\n if hvd.size():\n return f\"worker_{hvd.rank()}\"\n except (ModuleNotFoundError, ValueError, ImportError):\n pass\n\n return DEFAULT_WORKER_NAME\n\n def _get_num_workers(self):\n try:\n import horovod.mxnet as hvd\n\n if hvd.size():\n return hvd.size()\n except (ModuleNotFoundError, ValueError, ImportError):\n pass\n return 1\n\n def _cleanup(self):\n # Write the gradients of the past step if the writer is still available.\n if self.writer is not None and self.last_block is not None:\n self._log_params(self.last_block)\n if self.exported_model is False:\n self._export_model()\n super()._cleanup()\n\n def _log_params(self, block):\n params = block.collect_params().values()\n for param in params:\n self._log_param(param)\n\n def _log_param(self, param):\n try:\n self._save_for_tensor(\n tensor_name=param.name, tensor_value=param.data(param.list_ctx()[0])\n )\n # If Gradient for this param is available\n if param.grad_req != \"null\":\n self._save_for_tensor(\n tensor_name=self.GRADIENT_PREFIX + param.name,\n tensor_value=param.grad(param.list_ctx()[0]),\n )\n except RuntimeError as e:\n self.logger.warning(\n f\"Could not log parameter {param.name} due to the mxnet exception: {e}\"\n )\n\n def _export_model(self):\n if self.model is not None:\n try:\n tb_writer = self._maybe_get_tb_writer()\n if tb_writer:\n tb_writer.write_graph(_net2pb(self.model))\n except (RuntimeError, TypeError) as e:\n self.logger.warning(\n f\"Could not export model graph for tensorboard \"\n f\"due to the mxnet exception: {e}\"\n )\n\n def _get_default_collections(self):\n return DEFAULT_MXNET_COLLECTIONS\n\n # This hook is invoked by trainer prior to running the forward pass.\n @error_handling_agent.catch_smdebug_errors()\n def forward_pre_hook(self, block, inputs):\n if self.writer is not None:\n # Write the params and gradients of the\n # past step if the writer is still available.\n self._log_params(block)\n self._close_writers()\n self._close_tb_writer()\n\n if not self.prepared_collections:\n # at this point we need all collections to be ready\n # this may not be the case at creation of hook\n # as user's code after hook might add collections\n self._prepare_collections()\n self.prepared_collections = True\n\n self._increment_step()\n\n if self._get_collections_to_save_for_step():\n self._initialize_writers()\n\n if self.exported_model is False:\n self._export_model()\n self.exported_model = True\n\n if self.last_saved_step is not None and not self.exported_collections:\n self.export_collections()\n self.exported_collections = True\n\n self.last_block = block\n self._save_custom_tensors_post_step()\n\n # This hook is invoked by trainer after running the forward pass.\n @error_handling_agent.catch_smdebug_errors()\n def forward_hook(self, block, inputs, outputs):\n if not self._get_collections_to_save_for_step():\n return\n\n block_name = block.name\n # This overwhelms the logs; turn back on if you really need it\n # logger.debug(\"Processing the global step {0} for block {1}\".format(self.step, block_name))\n\n # Output input tensor\n self._write_inputs(block_name, inputs)\n\n # Output output tensors\n self._write_outputs(block_name, outputs)\n\n self.last_saved_step = self.step\n\n def _recursive_apply(self, block):\n \"\"\"\n This function is \"applied\" to every child in the block. This function in turn\n registers the forward hook to each module. It helps logging the input output tensors\n of that module.\n \"\"\"\n # Check if the hook is already registered for this block.\n if block in self.registered_blocks:\n self.logger.warning(f\"The hook is already registered to block {block.name}\")\n return\n block.register_forward_hook(self.forward_hook)\n self.registered_blocks.add(block)\n\n def _is_recursive_needed(self):\n collections_to_save = self.include_collections\n\n # Check if default collection has a regex associated with it.\n # If it does we would need to apply hook recursively.\n if (\n len(self.collection_manager.get(CollectionKeys.DEFAULT).include_regex) != 0\n and CollectionKeys.DEFAULT in collections_to_save\n ):\n return True\n\n # Get the collections that are to be saved but are not part of default collections\n # We will need to apply hook recursively to get tensors specified in those collections.\n extra_coll = [\n value\n for value in collections_to_save\n if value not in COLLECTIONS_NOT_REQUIRING_RECURSIVE_HOOK\n ]\n\n # extra_coll contains the collections that are not part of default collections.\n return len(extra_coll) != 0\n\n def register_hook(self, block):\n # for compatibility with ZCC patches which call this\n self.register_block(block)\n\n @error_handling_agent.catch_smdebug_errors()\n def register_block(self, block):\n \"\"\"\n This function registers the forward hook. If user wants to register the hook\n for every child in the given block, then the function calls \"apply\" API for\n registration of the hook.\n The hook is registered recursively, if user has specified the collections that are more than\n the default collectors viz. gradients, weight and bias\n \"\"\"\n\n if not isinstance(block, mx.gluon.Block):\n self.logger.error(f\"The given block type {block.__class__.__name__} is unsupported.\")\n return\n\n # Check if the hook is already registered for this block.\n if block in self.registered_blocks:\n self.logger.warning(f\"The hook is already registered to block {block.name}\")\n return\n\n # Skip the forward pre hook for the Loss blocks.\n if isinstance(block, mx.gluon.loss.Loss):\n self.logger.info(f\"Registering hook for block {block.name}\")\n block.register_forward_hook(self.forward_hook)\n self.registered_blocks.add(block)\n return\n else:\n self.model = block\n\n is_recursive = self._is_recursive_needed()\n block.register_forward_pre_hook(self.forward_pre_hook)\n if is_recursive is True:\n block.apply(self._recursive_apply)\n else:\n block.register_forward_hook(self.forward_hook)\n self.registered_blocks.add(block)\n\n @staticmethod\n def _get_reduction_of_data(reduction_name, tensor_value, tensor_name, abs):\n return get_reduction_of_data(reduction_name, tensor_value, tensor_name, abs)\n\n @staticmethod\n def _make_numpy_array(tensor_value):\n if isinstance(tensor_value, NDArray):\n return tensor_value.asnumpy()\n return make_numpy_array(tensor_value)\n"}}},{"rowIdx":52,"cells":{"function_name":{"kind":"string","value":"register_block"},"docstring":{"kind":"string","value":"This function registers the forward hook. If user wants to register the hook\nfor every child in the given block, then the function calls \"apply\" API for\nregistration of the hook.\nThe hook is registered recursively, if user has specified the collections that are more than\nthe default collectors viz. gradients, weight and bias"},"masked_code":{"kind":"string","value":"# Third Party\nimport mxnet as mx\nfrom mxnet.ndarray import NDArray\n\n# First Party\nfrom smdebug.core.collection import DEFAULT_MXNET_COLLECTIONS, CollectionKeys\nfrom smdebug.core.hook import CallbackHook\nfrom smdebug.core.json_config import DEFAULT_WORKER_NAME\nfrom smdebug.core.utils import FRAMEWORK, error_handling_agent\nfrom smdebug.mxnet.collection import CollectionManager\nfrom smdebug.mxnet.graph import _net2pb\nfrom smdebug.mxnet.singleton_utils import set_hook\nfrom smdebug.mxnet.utils import get_reduction_of_data, make_numpy_array\nfrom smdebug.profiler.profiler_config_parser import get_profiler_config_parser\n\nDEFAULT_INCLUDE_COLLECTIONS = [CollectionKeys.LOSSES]\n\nCOLLECTIONS_NOT_REQUIRING_RECURSIVE_HOOK = [\n CollectionKeys.WEIGHTS,\n CollectionKeys.BIASES,\n CollectionKeys.GRADIENTS,\n CollectionKeys.LOSSES,\n]\n\n\nprofiler_config_parser = get_profiler_config_parser(FRAMEWORK.PYTORCH)\n\n\nclass Hook(CallbackHook):\n def __init__(\n self,\n out_dir=None,\n export_tensorboard=False,\n tensorboard_dir=None,\n dry_run=False,\n reduction_config=None,\n save_config=None,\n include_regex=None,\n include_collections=None,\n save_all=False,\n include_workers=\"one\",\n ):\n collection_manager = CollectionManager()\n super().__init__(\n collection_manager=collection_manager,\n default_include_collections=DEFAULT_INCLUDE_COLLECTIONS,\n profiler_config_parser=profiler_config_parser,\n data_type_name=mx.ndarray.NDArray.__name__,\n out_dir=out_dir,\n export_tensorboard=export_tensorboard,\n tensorboard_dir=tensorboard_dir,\n dry_run=dry_run,\n reduction_config=reduction_config,\n save_config=save_config,\n include_regex=include_regex,\n include_collections=include_collections,\n save_all=save_all,\n include_workers=include_workers,\n )\n self.last_block = None\n\n self.model = None\n self.exported_model = False\n # Keep the set of blocks to which this hook is registered. The blocks include loss blocks as well.\n self.registered_blocks = set()\n self.worker = self._get_worker_name()\n set_hook(self)\n\n def _get_worker_name(self):\n try:\n import horovod.mxnet as hvd\n\n if hvd.size():\n return f\"worker_{hvd.rank()}\"\n except (ModuleNotFoundError, ValueError, ImportError):\n pass\n\n return DEFAULT_WORKER_NAME\n\n def _get_num_workers(self):\n try:\n import horovod.mxnet as hvd\n\n if hvd.size():\n return hvd.size()\n except (ModuleNotFoundError, ValueError, ImportError):\n pass\n return 1\n\n def _cleanup(self):\n # Write the gradients of the past step if the writer is still available.\n if self.writer is not None and self.last_block is not None:\n self._log_params(self.last_block)\n if self.exported_model is False:\n self._export_model()\n super()._cleanup()\n\n def _log_params(self, block):\n params = block.collect_params().values()\n for param in params:\n self._log_param(param)\n\n def _log_param(self, param):\n try:\n self._save_for_tensor(\n tensor_name=param.name, tensor_value=param.data(param.list_ctx()[0])\n )\n # If Gradient for this param is available\n if param.grad_req != \"null\":\n self._save_for_tensor(\n tensor_name=self.GRADIENT_PREFIX + param.name,\n tensor_value=param.grad(param.list_ctx()[0]),\n )\n except RuntimeError as e:\n self.logger.warning(\n f\"Could not log parameter {param.name} due to the mxnet exception: {e}\"\n )\n\n def _export_model(self):\n if self.model is not None:\n try:\n tb_writer = self._maybe_get_tb_writer()\n if tb_writer:\n tb_writer.write_graph(_net2pb(self.model))\n except (RuntimeError, TypeError) as e:\n self.logger.warning(\n f\"Could not export model graph for tensorboard \"\n f\"due to the mxnet exception: {e}\"\n )\n\n def _get_default_collections(self):\n return DEFAULT_MXNET_COLLECTIONS\n\n # This hook is invoked by trainer prior to running the forward pass.\n @error_handling_agent.catch_smdebug_errors()\n def forward_pre_hook(self, block, inputs):\n if self.writer is not None:\n # Write the params and gradients of the\n # past step if the writer is still available.\n self._log_params(block)\n self._close_writers()\n self._close_tb_writer()\n\n if not self.prepared_collections:\n # at this point we need all collections to be ready\n # this may not be the case at creation of hook\n # as user's code after hook might add collections\n self._prepare_collections()\n self.prepared_collections = True\n\n self._increment_step()\n\n if self._get_collections_to_save_for_step():\n self._initialize_writers()\n\n if self.exported_model is False:\n self._export_model()\n self.exported_model = True\n\n if self.last_saved_step is not None and not self.exported_collections:\n self.export_collections()\n self.exported_collections = True\n\n self.last_block = block\n self._save_custom_tensors_post_step()\n\n # This hook is invoked by trainer after running the forward pass.\n @error_handling_agent.catch_smdebug_errors()\n def forward_hook(self, block, inputs, outputs):\n if not self._get_collections_to_save_for_step():\n return\n\n block_name = block.name\n # This overwhelms the logs; turn back on if you really need it\n # logger.debug(\"Processing the global step {0} for block {1}\".format(self.step, block_name))\n\n # Output input tensor\n self._write_inputs(block_name, inputs)\n\n # Output output tensors\n self._write_outputs(block_name, outputs)\n\n self.last_saved_step = self.step\n\n def _recursive_apply(self, block):\n \"\"\"\n This function is \"applied\" to every child in the block. This function in turn\n registers the forward hook to each module. It helps logging the input output tensors\n of that module.\n \"\"\"\n # Check if the hook is already registered for this block.\n if block in self.registered_blocks:\n self.logger.warning(f\"The hook is already registered to block {block.name}\")\n return\n block.register_forward_hook(self.forward_hook)\n self.registered_blocks.add(block)\n\n def _is_recursive_needed(self):\n collections_to_save = self.include_collections\n\n # Check if default collection has a regex associated with it.\n # If it does we would need to apply hook recursively.\n if (\n len(self.collection_manager.get(CollectionKeys.DEFAULT).include_regex) != 0\n and CollectionKeys.DEFAULT in collections_to_save\n ):\n return True\n\n # Get the collections that are to be saved but are not part of default collections\n # We will need to apply hook recursively to get tensors specified in those collections.\n extra_coll = [\n value\n for value in collections_to_save\n if value not in COLLECTIONS_NOT_REQUIRING_RECURSIVE_HOOK\n ]\n\n # extra_coll contains the collections that are not part of default collections.\n return len(extra_coll) != 0\n\n def register_hook(self, block):\n # for compatibility with ZCC patches which call this\n self.register_block(block)\n\n# MASKED: register_block function (lines 224-258)\n\n @staticmethod\n def _get_reduction_of_data(reduction_name, tensor_value, tensor_name, abs):\n return get_reduction_of_data(reduction_name, tensor_value, tensor_name, abs)\n\n @staticmethod\n def _make_numpy_array(tensor_value):\n if isinstance(tensor_value, NDArray):\n return tensor_value.asnumpy()\n return make_numpy_array(tensor_value)"},"implementation":{"kind":"string","value":" @error_handling_agent.catch_smdebug_errors()\n def register_block(self, block):\n \"\"\"\n This function registers the forward hook. If user wants to register the hook\n for every child in the given block, then the function calls \"apply\" API for\n registration of the hook.\n The hook is registered recursively, if user has specified the collections that are more than\n the default collectors viz. gradients, weight and bias\n \"\"\"\n\n if not isinstance(block, mx.gluon.Block):\n self.logger.error(f\"The given block type {block.__class__.__name__} is unsupported.\")\n return\n\n # Check if the hook is already registered for this block.\n if block in self.registered_blocks:\n self.logger.warning(f\"The hook is already registered to block {block.name}\")\n return\n\n # Skip the forward pre hook for the Loss blocks.\n if isinstance(block, mx.gluon.loss.Loss):\n self.logger.info(f\"Registering hook for block {block.name}\")\n block.register_forward_hook(self.forward_hook)\n self.registered_blocks.add(block)\n return\n else:\n self.model = block\n\n is_recursive = self._is_recursive_needed()\n block.register_forward_pre_hook(self.forward_pre_hook)\n if is_recursive is True:\n block.apply(self._recursive_apply)\n else:\n block.register_forward_hook(self.forward_hook)\n self.registered_blocks.add(block)"},"start_line":{"kind":"number","value":224,"string":"224"},"end_line":{"kind":"number","value":258,"string":"258"},"file_content":{"kind":"string","value":"# Third Party\nimport mxnet as mx\nfrom mxnet.ndarray import NDArray\n\n# First Party\nfrom smdebug.core.collection import DEFAULT_MXNET_COLLECTIONS, CollectionKeys\nfrom smdebug.core.hook import CallbackHook\nfrom smdebug.core.json_config import DEFAULT_WORKER_NAME\nfrom smdebug.core.utils import FRAMEWORK, error_handling_agent\nfrom smdebug.mxnet.collection import CollectionManager\nfrom smdebug.mxnet.graph import _net2pb\nfrom smdebug.mxnet.singleton_utils import set_hook\nfrom smdebug.mxnet.utils import get_reduction_of_data, make_numpy_array\nfrom smdebug.profiler.profiler_config_parser import get_profiler_config_parser\n\nDEFAULT_INCLUDE_COLLECTIONS = [CollectionKeys.LOSSES]\n\nCOLLECTIONS_NOT_REQUIRING_RECURSIVE_HOOK = [\n CollectionKeys.WEIGHTS,\n CollectionKeys.BIASES,\n CollectionKeys.GRADIENTS,\n CollectionKeys.LOSSES,\n]\n\n\nprofiler_config_parser = get_profiler_config_parser(FRAMEWORK.PYTORCH)\n\n\nclass Hook(CallbackHook):\n def __init__(\n self,\n out_dir=None,\n export_tensorboard=False,\n tensorboard_dir=None,\n dry_run=False,\n reduction_config=None,\n save_config=None,\n include_regex=None,\n include_collections=None,\n save_all=False,\n include_workers=\"one\",\n ):\n collection_manager = CollectionManager()\n super().__init__(\n collection_manager=collection_manager,\n default_include_collections=DEFAULT_INCLUDE_COLLECTIONS,\n profiler_config_parser=profiler_config_parser,\n data_type_name=mx.ndarray.NDArray.__name__,\n out_dir=out_dir,\n export_tensorboard=export_tensorboard,\n tensorboard_dir=tensorboard_dir,\n dry_run=dry_run,\n reduction_config=reduction_config,\n save_config=save_config,\n include_regex=include_regex,\n include_collections=include_collections,\n save_all=save_all,\n include_workers=include_workers,\n )\n self.last_block = None\n\n self.model = None\n self.exported_model = False\n # Keep the set of blocks to which this hook is registered. The blocks include loss blocks as well.\n self.registered_blocks = set()\n self.worker = self._get_worker_name()\n set_hook(self)\n\n def _get_worker_name(self):\n try:\n import horovod.mxnet as hvd\n\n if hvd.size():\n return f\"worker_{hvd.rank()}\"\n except (ModuleNotFoundError, ValueError, ImportError):\n pass\n\n return DEFAULT_WORKER_NAME\n\n def _get_num_workers(self):\n try:\n import horovod.mxnet as hvd\n\n if hvd.size():\n return hvd.size()\n except (ModuleNotFoundError, ValueError, ImportError):\n pass\n return 1\n\n def _cleanup(self):\n # Write the gradients of the past step if the writer is still available.\n if self.writer is not None and self.last_block is not None:\n self._log_params(self.last_block)\n if self.exported_model is False:\n self._export_model()\n super()._cleanup()\n\n def _log_params(self, block):\n params = block.collect_params().values()\n for param in params:\n self._log_param(param)\n\n def _log_param(self, param):\n try:\n self._save_for_tensor(\n tensor_name=param.name, tensor_value=param.data(param.list_ctx()[0])\n )\n # If Gradient for this param is available\n if param.grad_req != \"null\":\n self._save_for_tensor(\n tensor_name=self.GRADIENT_PREFIX + param.name,\n tensor_value=param.grad(param.list_ctx()[0]),\n )\n except RuntimeError as e:\n self.logger.warning(\n f\"Could not log parameter {param.name} due to the mxnet exception: {e}\"\n )\n\n def _export_model(self):\n if self.model is not None:\n try:\n tb_writer = self._maybe_get_tb_writer()\n if tb_writer:\n tb_writer.write_graph(_net2pb(self.model))\n except (RuntimeError, TypeError) as e:\n self.logger.warning(\n f\"Could not export model graph for tensorboard \"\n f\"due to the mxnet exception: {e}\"\n )\n\n def _get_default_collections(self):\n return DEFAULT_MXNET_COLLECTIONS\n\n # This hook is invoked by trainer prior to running the forward pass.\n @error_handling_agent.catch_smdebug_errors()\n def forward_pre_hook(self, block, inputs):\n if self.writer is not None:\n # Write the params and gradients of the\n # past step if the writer is still available.\n self._log_params(block)\n self._close_writers()\n self._close_tb_writer()\n\n if not self.prepared_collections:\n # at this point we need all collections to be ready\n # this may not be the case at creation of hook\n # as user's code after hook might add collections\n self._prepare_collections()\n self.prepared_collections = True\n\n self._increment_step()\n\n if self._get_collections_to_save_for_step():\n self._initialize_writers()\n\n if self.exported_model is False:\n self._export_model()\n self.exported_model = True\n\n if self.last_saved_step is not None and not self.exported_collections:\n self.export_collections()\n self.exported_collections = True\n\n self.last_block = block\n self._save_custom_tensors_post_step()\n\n # This hook is invoked by trainer after running the forward pass.\n @error_handling_agent.catch_smdebug_errors()\n def forward_hook(self, block, inputs, outputs):\n if not self._get_collections_to_save_for_step():\n return\n\n block_name = block.name\n # This overwhelms the logs; turn back on if you really need it\n # logger.debug(\"Processing the global step {0} for block {1}\".format(self.step, block_name))\n\n # Output input tensor\n self._write_inputs(block_name, inputs)\n\n # Output output tensors\n self._write_outputs(block_name, outputs)\n\n self.last_saved_step = self.step\n\n def _recursive_apply(self, block):\n \"\"\"\n This function is \"applied\" to every child in the block. This function in turn\n registers the forward hook to each module. It helps logging the input output tensors\n of that module.\n \"\"\"\n # Check if the hook is already registered for this block.\n if block in self.registered_blocks:\n self.logger.warning(f\"The hook is already registered to block {block.name}\")\n return\n block.register_forward_hook(self.forward_hook)\n self.registered_blocks.add(block)\n\n def _is_recursive_needed(self):\n collections_to_save = self.include_collections\n\n # Check if default collection has a regex associated with it.\n # If it does we would need to apply hook recursively.\n if (\n len(self.collection_manager.get(CollectionKeys.DEFAULT).include_regex) != 0\n and CollectionKeys.DEFAULT in collections_to_save\n ):\n return True\n\n # Get the collections that are to be saved but are not part of default collections\n # We will need to apply hook recursively to get tensors specified in those collections.\n extra_coll = [\n value\n for value in collections_to_save\n if value not in COLLECTIONS_NOT_REQUIRING_RECURSIVE_HOOK\n ]\n\n # extra_coll contains the collections that are not part of default collections.\n return len(extra_coll) != 0\n\n def register_hook(self, block):\n # for compatibility with ZCC patches which call this\n self.register_block(block)\n\n @error_handling_agent.catch_smdebug_errors()\n def register_block(self, block):\n \"\"\"\n This function registers the forward hook. If user wants to register the hook\n for every child in the given block, then the function calls \"apply\" API for\n registration of the hook.\n The hook is registered recursively, if user has specified the collections that are more than\n the default collectors viz. gradients, weight and bias\n \"\"\"\n\n if not isinstance(block, mx.gluon.Block):\n self.logger.error(f\"The given block type {block.__class__.__name__} is unsupported.\")\n return\n\n # Check if the hook is already registered for this block.\n if block in self.registered_blocks:\n self.logger.warning(f\"The hook is already registered to block {block.name}\")\n return\n\n # Skip the forward pre hook for the Loss blocks.\n if isinstance(block, mx.gluon.loss.Loss):\n self.logger.info(f\"Registering hook for block {block.name}\")\n block.register_forward_hook(self.forward_hook)\n self.registered_blocks.add(block)\n return\n else:\n self.model = block\n\n is_recursive = self._is_recursive_needed()\n block.register_forward_pre_hook(self.forward_pre_hook)\n if is_recursive is True:\n block.apply(self._recursive_apply)\n else:\n block.register_forward_hook(self.forward_hook)\n self.registered_blocks.add(block)\n\n @staticmethod\n def _get_reduction_of_data(reduction_name, tensor_value, tensor_name, abs):\n return get_reduction_of_data(reduction_name, tensor_value, tensor_name, abs)\n\n @staticmethod\n def _make_numpy_array(tensor_value):\n if isinstance(tensor_value, NDArray):\n return tensor_value.asnumpy()\n return make_numpy_array(tensor_value)\n"}}},{"rowIdx":53,"cells":{"function_name":{"kind":"string","value":"step"},"docstring":{"kind":"string","value":"Performs a single optimization step.\n\nArguments:\n closure (callable, optional): A closure that reevaluates the model\n and returns the loss."},"masked_code":{"kind":"string","value":"\"\"\"\n\"\"\"\nfrom __future__ import division\nfrom torch.optim.optimizer import Optimizer, required\n\nimport numpy as np\nimport torch\n\nfrom typing import NamedTuple, List\nfrom dataclasses import dataclass\nfrom enum import Enum\nfrom typing import Union, Tuple\n# from scipy.sparse.linalg import svds\nfrom scipy.optimize import minimize_scalar\n\n\nclass LayerType(Enum):\n CONV = 1\n FC = 2\n NON_CONV = 3\n\n\n@dataclass\nclass LayerMetrics:\n rank: float\n KG: float\n condition: float\n\n\n@dataclass\nclass ConvLayerMetrics:\n input_channel: LayerMetrics\n output_channel: LayerMetrics\n\n\nclass LRMetrics(NamedTuple):\n rank_velocity: List[float]\n r_conv: List[float]\n\n\ndef EVBMF(Y, sigma2=None, H=None):\n \"\"\"Implementation of the analytical solution to Empirical Variational\n Bayes Matrix Factorization.\n\n This function can be used to calculate the analytical solution to\n empirical VBMF.\n This is based on the paper and MatLab code by Nakajima et al.:\n \"Global analytic solution of fully-observed variational Bayesian matrix\n factorization.\"\n\n Notes\n -----\n If sigma2 is unspecified, it is estimated by minimizing the free\n energy.\n If H is unspecified, it is set to the smallest of the sides of the\n input Y.\n\n Attributes\n ----------\n Y : numpy-array\n Input matrix that is to be factorized. Y has shape (L,M), where L<=M.\n\n sigma2 : int or None (default=None)\n Variance of the noise on Y.\n\n H : int or None (default = None)\n Maximum rank of the factorized matrices.\n\n Returns\n -------\n U : numpy-array\n Left-singular vectors.\n\n S : numpy-array\n Diagonal matrix of singular values.\n\n V : numpy-array\n Right-singular vectors.\n\n post : dictionary\n Dictionary containing the computed posterior values.\n\n\n References\n ----------\n .. [1] Nakajima, Shinichi, et al. \"Global analytic solution of\n fully-observed variational Bayesian matrix factorization.\" Journal of\n Machine Learning Research 14.Jan (2013): 1-37.\n\n .. [2] Nakajima, Shinichi, et al. \"Perfect dimensionality recovery by\n variational Bayesian PCA.\" Advances in Neural Information Processing\n Systems. 2012.\n \"\"\"\n L, M = Y.shape # has to be L<=M\n\n if H is None:\n H = L\n\n alpha = L / M\n tauubar = 2.5129 * np.sqrt(alpha)\n\n # SVD of the input matrix, max rank of H\n # U, s, V = np.linalg.svd(Y)\n U, s, V = torch.svd(Y)\n U = U[:, :H]\n s = s[:H]\n V = V[:H].T\n\n # Calculate residual\n residual = 0.\n if H < L:\n # residual = np.sum(np.sum(Y**2)-np.sum(s**2))\n residual = torch.sum(np.sum(Y**2) - np.sum(s**2))\n\n # Estimation of the variance when sigma2 is unspecified\n if sigma2 is None:\n xubar = (1 + tauubar) * (1 + alpha / tauubar)\n eH_ub = int(np.min([np.ceil(L / (1 + alpha)) - 1, H])) - 1\n # upper_bound = (np.sum(s**2)+residual)/(L*M)\n # lower_bound = np.max(\n # [s[eH_ub+1]**2/(M*xubar), np.mean(s[eH_ub+1:]**2)/M])\n upper_bound = (torch.sum(s**2) + residual) / (L * M)\n lower_bound = torch.max(torch.stack(\n [s[eH_ub + 1]**2 / (M * xubar), torch.mean(s[eH_ub + 1:]**2) / M], dim=0))\n\n scale = 1. # /lower_bound\n s = s * np.sqrt(scale)\n residual = residual * scale\n lower_bound = lower_bound * scale\n upper_bound = upper_bound * scale\n\n sigma2_opt = minimize_scalar(\n EVBsigma2, args=(L, M, s.cpu().numpy(), residual, xubar),\n bounds=[lower_bound.cpu().numpy(), upper_bound.cpu().numpy()],\n method='Bounded')\n sigma2 = sigma2_opt.x\n\n # Threshold gamma term\n threshold = np.sqrt(M * sigma2 * (1 + tauubar) * (1 + alpha / tauubar))\n # pos = np.sum(s > threshold)\n pos = torch.sum(s > threshold)\n\n # Formula (15) from [2]\n # d = torch.multiply(s[:pos]/2,\n # 1-torch.divide(\n # torch.tensor((L+M)*sigma2, device=s.device),\n # s[:pos]**2) + torch.sqrt((1-torch.divide(\n # torch.tensor(\n # (L+M)*sigma2, device=s.device),\n # s[:pos]**2))**2 -\n # 4*L*M*sigma2**2/s[:pos]**4))\n # d = np.multiply(s[:pos]/2, 1-np.divide((L+M)*sigma2, s[:pos]**2) + np.sqrt(\n # (1-np.divide((L+M)*sigma2, s[:pos]**2))**2 - 4*L*M*sigma2**2/s[:pos]**4))\n d = (s[:pos] / 2) * (1 - (L + M) * sigma2 / s[:pos]**2\n + torch.sqrt((1 -\n (L + M) * sigma2 / s[:pos]**2)**2 - 4 * L * M * sigma2**2 / s[:pos]**4))\n\n # Computation of the posterior\n # post = {}\n # post['ma'] = np.zeros(H)\n # post['mb'] = np.zeros(H)\n # post['sa2'] = np.zeros(H)\n # post['sb2'] = np.zeros(H)\n # post['cacb'] = np.zeros(H)\n\n # tau = np.multiply(d, s[:pos])/(M*sigma2)\n # delta = np.multiply(np.sqrt(np.divide(M*d, L*s[:pos])), 1+alpha/tau)\n\n # post['ma'][:pos] = np.sqrt(np.multiply(d, delta))\n # post['mb'][:pos] = np.sqrt(np.divide(d, delta))\n # post['sa2'][:pos] = np.divide(sigma2*delta, s[:pos])\n # post['sb2'][:pos] = np.divide(sigma2, np.multiply(delta, s[:pos]))\n # post['cacb'][:pos] = np.sqrt(np.multiply(d, s[:pos])/(L*M))\n # post['sigma2'] = sigma2\n # post['F'] = 0.5*(L*M*np.log(2*np.pi*sigma2) +\n # (residual+np.sum(s**2))/sigma2 + np.sum(\n # M*np.log(tau+1) + L*np.log(tau/alpha + 1) - M*tau))\n\n return U[:, :pos], torch.diag(d), V[:, :pos] # , post\n\n\ndef EVBsigma2(sigma2, L, M, s, residual, xubar):\n H = len(s)\n\n alpha = L / M\n x = s**2 / (M * sigma2)\n\n z1 = x[x > xubar]\n z2 = x[x <= xubar]\n tau_z1 = tau(z1, alpha)\n\n term1 = np.sum(z2 - np.log(z2))\n term2 = np.sum(z1 - tau_z1)\n term3 = np.sum(np.log(np.divide(tau_z1 + 1, z1)))\n term4 = alpha * np.sum(np.log(tau_z1 / alpha + 1))\n\n obj = term1 + term2 + term3 + term4 + residual / (M * sigma2) + (L - H) * np.log(sigma2)\n\n return obj\n\n\ndef phi0(x):\n return x - np.log(x)\n\n\ndef phi1(x, alpha):\n return np.log(tau(x, alpha) + 1) + alpha * np.log(tau(x, alpha) / alpha + 1\n ) - tau(x, alpha)\n\n\ndef tau(x, alpha):\n return 0.5 * (x - (1 + alpha) + np.sqrt((x - (1 + alpha))**2 - 4 * alpha))\n\n\nclass Metrics:\n def __init__(self, params, linear: bool = False) -> None:\n '''\n parameters: list of torch.nn.Module.parameters()\n '''\n self.params = params\n self.history = list()\n mask = list()\n for param_idx, param in enumerate(params):\n param_shape = param.shape\n if not linear:\n if len(param_shape) != 4:\n mask.append(param_idx)\n else:\n if len(param_shape) != 4 and len(param_shape) != 2:\n mask.append(param_idx)\n self.mask = set(mask)\n\n def compute_low_rank(self,\n tensor: torch.Tensor,\n normalizer: float) -> torch.Tensor:\n if tensor.requires_grad:\n tensor = tensor.detach()\n try:\n tensor_size = tensor.shape\n if tensor_size[0] > tensor_size[1]:\n tensor = tensor.T\n U_approx, S_approx, V_approx = EVBMF(tensor)\n except RuntimeError:\n return None, None, None\n rank = S_approx.shape[0] / tensor_size[0] # normalizer\n low_rank_eigen = torch.diag(S_approx).data.cpu().numpy()\n if len(low_rank_eigen) != 0:\n condition = low_rank_eigen[0] / low_rank_eigen[-1]\n sum_low_rank_eigen = low_rank_eigen / \\\n max(low_rank_eigen)\n sum_low_rank_eigen = np.sum(sum_low_rank_eigen)\n else:\n condition = 0\n sum_low_rank_eigen = 0\n KG = sum_low_rank_eigen / tensor_size[0] # normalizer\n return rank, KG, condition\n\n def KG(self, epoch: int) -> np.ndarray:\n KG_list = list()\n for i, (index, metric) in enumerate(self.history[epoch]):\n if isinstance(metric, ConvLayerMetrics):\n KG_list.append((metric.input_channel.KG\n + metric.output_channel.KG) / 2)\n elif isinstance(metric, LayerMetrics):\n KG_list.append(metric.KG)\n return np.array(KG_list)\n\n def __call__(self) -> List[Tuple[int, Union[LayerMetrics,\n ConvLayerMetrics]]]:\n '''\n Computes the knowledge gain (S) and mapping condition (condition)\n '''\n metrics: List[Tuple[int, Union[LayerMetrics,\n ConvLayerMetrics]]] = list()\n for layer_index, layer in enumerate(self.params):\n if layer_index in self.mask:\n metrics.append((layer_index, None))\n continue\n # if np.less(np.prod(layer.shape), 10_000):\n # metrics.append((layer_index, None))\n if len(layer.shape) == 4:\n layer_tensor = layer.data\n tensor_size = layer_tensor.shape\n mode_3_unfold = layer_tensor.permute(1, 0, 2, 3)\n mode_3_unfold = torch.reshape(\n mode_3_unfold, [tensor_size[1], tensor_size[0]\n * tensor_size[2] * tensor_size[3]])\n mode_4_unfold = layer_tensor\n mode_4_unfold = torch.reshape(\n mode_4_unfold, [tensor_size[0], tensor_size[1]\n * tensor_size[2] * tensor_size[3]])\n in_rank, in_KG, in_condition = self.compute_low_rank(\n mode_3_unfold, tensor_size[1])\n if in_rank is None and in_KG is None and in_condition is None:\n if len(self.history) > 0:\n in_rank = self.history[-1][\n layer_index][1].input_channel.rank\n in_KG = self.history[-1][\n layer_index][1].input_channel.KG\n in_condition = self.history[-1][\n layer_index][1].input_channel.condition\n else:\n in_rank = in_KG = in_condition = 0.\n out_rank, out_KG, out_condition = self.compute_low_rank(\n mode_4_unfold, tensor_size[0])\n if out_rank is None and out_KG is None and out_condition is None:\n if len(self.history) > 0:\n out_rank = self.history[-1][\n layer_index][1].output_channel.rank\n out_KG = self.history[-1][\n layer_index][1].output_channel.KG\n out_condition = self.history[-1][\n layer_index][1].output_channel.condition\n else:\n out_rank = out_KG = out_condition = 0.\n metrics.append((layer_index, ConvLayerMetrics(\n input_channel=LayerMetrics(\n rank=in_rank,\n KG=in_KG,\n condition=in_condition),\n output_channel=LayerMetrics(\n rank=out_rank,\n KG=out_KG,\n condition=out_condition))))\n elif len(layer.shape) == 2:\n rank, KG, condition = self.compute_low_rank(\n layer, layer.shape[0])\n if rank is None and KG is None and condition is None:\n if len(self.history) > 0:\n rank = self.history[-1][layer_index][1].rank\n KG = self.history[-1][layer_index][1].KG\n condition = self.history[-1][layer_index][1].condition\n else:\n rank = KG = condition = 0.\n metrics.append((layer_index, LayerMetrics(\n rank=rank,\n KG=KG,\n condition=condition)))\n else:\n metrics.append((layer_index, None))\n self.history.append(metrics)\n return metrics\n\n\nclass Adas(Optimizer):\n \"\"\"\n Vectorized SGD from torch.optim.SGD\n \"\"\"\n\n def __init__(self,\n params,\n lr: float = required,\n beta: float = 0.8,\n step_size: int = None,\n linear: bool = True,\n gamma: float = 1,\n momentum: float = 0,\n dampening: float = 0,\n weight_decay: float = 0,\n nesterov: bool = False):\n if lr is not required and lr < 0.0:\n raise ValueError(\"Invalid learning rate: {}\".format(lr))\n if momentum < 0.0:\n raise ValueError(\"Invalid momentum value: {}\".format(momentum))\n if weight_decay < 0.0:\n raise ValueError(\n \"Invalid weight_decay value: {}\".format(weight_decay))\n\n defaults = dict(lr=lr, momentum=momentum, dampening=dampening,\n weight_decay=weight_decay, nesterov=nesterov)\n if nesterov and (momentum <= 0 or dampening != 0):\n raise ValueError(\n \"Nesterov momentum requires a momentum and zero dampening\")\n super(Adas, self).__init__(params[:2], defaults)\n\n # Adas Specific stuff (not SGD)\n if np.less(beta, 0) or np.greater_equal(beta, 1):\n raise ValueError(f'Invalid beta: {beta}')\n if np.less(gamma, 0):\n raise ValueError(f'Invalid gamma: {gamma}')\n if step_size is not None:\n if np.less_equal(step_size, 0):\n raise ValueError(f'Invalid step_size: {step_size}')\n self.step_size = step_size\n self.gamma = gamma\n self.beta = beta\n self.metrics = metrics = Metrics(params=params[2][\"all_params\"], linear=linear)\n self.lr_vector = np.repeat(a=lr, repeats=len(metrics.params))\n self.velocity = np.zeros(\n len(self.metrics.params) - len(self.metrics.mask))\n self.not_ready = list(range(len(self.velocity)))\n self.init_lr = lr\n self.zeta = 1.\n self.KG = 0.\n\n def __setstate__(self, state):\n super(Adas, self).__setstate__(state)\n for group in self.param_groups:\n group.setdefault('nesterov', False)\n\n def epoch_step(self, epoch: int) -> None:\n self.metrics()\n if epoch == 0:\n velocity = self.init_lr * np.ones(len(self.velocity))\n self.KG = self.metrics.KG(epoch)\n else:\n KG = self.metrics.KG(epoch)\n velocity = KG - self.KG\n self.KG = KG\n for idx in self.not_ready:\n if np.isclose(KG[idx], 0.):\n velocity[idx] = self.init_lr - \\\n self.beta * self.velocity[idx]\n else:\n self.not_ready.remove(idx)\n\n if self.step_size is not None:\n if epoch % self.step_size == 0 and epoch > 0:\n self.lr_vector *= self.gamma\n self.zeta *= self.gamma\n\n self.velocity = np.maximum(\n self.beta * self.velocity + self.zeta * velocity, 0.)\n count = 0\n for i in range(len(self.metrics.params)):\n if i in self.metrics.mask:\n self.lr_vector[i] = self.lr_vector[i - (1 if i > 0 else 0)]\n else:\n self.lr_vector[i] = self.velocity[count]\n count += 1\n\n# MASKED: step function (lines 432-473)"},"implementation":{"kind":"string","value":" def step(self, closure: callable = None):\n \"\"\"Performs a single optimization step.\n\n Arguments:\n closure (callable, optional): A closure that reevaluates the model\n and returns the loss.\n \"\"\"\n loss = None\n if closure is not None:\n loss = closure()\n\n iteration_group = 0\n for group in self.param_groups:\n iteration_group += 1\n weight_decay = group['weight_decay']\n momentum = group['momentum']\n dampening = group['dampening']\n nesterov = group['nesterov']\n\n for p_index, p in enumerate(group['params']):\n if p.grad is None:\n continue\n d_p = p.grad.data\n if weight_decay != 0:\n d_p.add_(p.data, alpha=weight_decay)\n if momentum != 0:\n param_state = self.state[p]\n if 'momentum_buffer' not in param_state:\n buf = param_state['momentum_buffer'] = torch.clone(\n d_p).detach()\n else:\n buf = param_state['momentum_buffer']\n buf.mul_(momentum).add_(d_p, alpha=1 - dampening)\n if nesterov:\n d_p = d_p.add(momentum, buf)\n else:\n d_p = buf\n\n # p.data.add_(-group['lr'], d_p)\n p.data.add_(d_p, alpha=-self.lr_vector[p_index])\n\n return loss"},"start_line":{"kind":"number","value":432,"string":"432"},"end_line":{"kind":"number","value":473,"string":"473"},"file_content":{"kind":"string","value":"\"\"\"\n\"\"\"\nfrom __future__ import division\nfrom torch.optim.optimizer import Optimizer, required\n\nimport numpy as np\nimport torch\n\nfrom typing import NamedTuple, List\nfrom dataclasses import dataclass\nfrom enum import Enum\nfrom typing import Union, Tuple\n# from scipy.sparse.linalg import svds\nfrom scipy.optimize import minimize_scalar\n\n\nclass LayerType(Enum):\n CONV = 1\n FC = 2\n NON_CONV = 3\n\n\n@dataclass\nclass LayerMetrics:\n rank: float\n KG: float\n condition: float\n\n\n@dataclass\nclass ConvLayerMetrics:\n input_channel: LayerMetrics\n output_channel: LayerMetrics\n\n\nclass LRMetrics(NamedTuple):\n rank_velocity: List[float]\n r_conv: List[float]\n\n\ndef EVBMF(Y, sigma2=None, H=None):\n \"\"\"Implementation of the analytical solution to Empirical Variational\n Bayes Matrix Factorization.\n\n This function can be used to calculate the analytical solution to\n empirical VBMF.\n This is based on the paper and MatLab code by Nakajima et al.:\n \"Global analytic solution of fully-observed variational Bayesian matrix\n factorization.\"\n\n Notes\n -----\n If sigma2 is unspecified, it is estimated by minimizing the free\n energy.\n If H is unspecified, it is set to the smallest of the sides of the\n input Y.\n\n Attributes\n ----------\n Y : numpy-array\n Input matrix that is to be factorized. Y has shape (L,M), where L<=M.\n\n sigma2 : int or None (default=None)\n Variance of the noise on Y.\n\n H : int or None (default = None)\n Maximum rank of the factorized matrices.\n\n Returns\n -------\n U : numpy-array\n Left-singular vectors.\n\n S : numpy-array\n Diagonal matrix of singular values.\n\n V : numpy-array\n Right-singular vectors.\n\n post : dictionary\n Dictionary containing the computed posterior values.\n\n\n References\n ----------\n .. [1] Nakajima, Shinichi, et al. \"Global analytic solution of\n fully-observed variational Bayesian matrix factorization.\" Journal of\n Machine Learning Research 14.Jan (2013): 1-37.\n\n .. [2] Nakajima, Shinichi, et al. \"Perfect dimensionality recovery by\n variational Bayesian PCA.\" Advances in Neural Information Processing\n Systems. 2012.\n \"\"\"\n L, M = Y.shape # has to be L<=M\n\n if H is None:\n H = L\n\n alpha = L / M\n tauubar = 2.5129 * np.sqrt(alpha)\n\n # SVD of the input matrix, max rank of H\n # U, s, V = np.linalg.svd(Y)\n U, s, V = torch.svd(Y)\n U = U[:, :H]\n s = s[:H]\n V = V[:H].T\n\n # Calculate residual\n residual = 0.\n if H < L:\n # residual = np.sum(np.sum(Y**2)-np.sum(s**2))\n residual = torch.sum(np.sum(Y**2) - np.sum(s**2))\n\n # Estimation of the variance when sigma2 is unspecified\n if sigma2 is None:\n xubar = (1 + tauubar) * (1 + alpha / tauubar)\n eH_ub = int(np.min([np.ceil(L / (1 + alpha)) - 1, H])) - 1\n # upper_bound = (np.sum(s**2)+residual)/(L*M)\n # lower_bound = np.max(\n # [s[eH_ub+1]**2/(M*xubar), np.mean(s[eH_ub+1:]**2)/M])\n upper_bound = (torch.sum(s**2) + residual) / (L * M)\n lower_bound = torch.max(torch.stack(\n [s[eH_ub + 1]**2 / (M * xubar), torch.mean(s[eH_ub + 1:]**2) / M], dim=0))\n\n scale = 1. # /lower_bound\n s = s * np.sqrt(scale)\n residual = residual * scale\n lower_bound = lower_bound * scale\n upper_bound = upper_bound * scale\n\n sigma2_opt = minimize_scalar(\n EVBsigma2, args=(L, M, s.cpu().numpy(), residual, xubar),\n bounds=[lower_bound.cpu().numpy(), upper_bound.cpu().numpy()],\n method='Bounded')\n sigma2 = sigma2_opt.x\n\n # Threshold gamma term\n threshold = np.sqrt(M * sigma2 * (1 + tauubar) * (1 + alpha / tauubar))\n # pos = np.sum(s > threshold)\n pos = torch.sum(s > threshold)\n\n # Formula (15) from [2]\n # d = torch.multiply(s[:pos]/2,\n # 1-torch.divide(\n # torch.tensor((L+M)*sigma2, device=s.device),\n # s[:pos]**2) + torch.sqrt((1-torch.divide(\n # torch.tensor(\n # (L+M)*sigma2, device=s.device),\n # s[:pos]**2))**2 -\n # 4*L*M*sigma2**2/s[:pos]**4))\n # d = np.multiply(s[:pos]/2, 1-np.divide((L+M)*sigma2, s[:pos]**2) + np.sqrt(\n # (1-np.divide((L+M)*sigma2, s[:pos]**2))**2 - 4*L*M*sigma2**2/s[:pos]**4))\n d = (s[:pos] / 2) * (1 - (L + M) * sigma2 / s[:pos]**2\n + torch.sqrt((1 -\n (L + M) * sigma2 / s[:pos]**2)**2 - 4 * L * M * sigma2**2 / s[:pos]**4))\n\n # Computation of the posterior\n # post = {}\n # post['ma'] = np.zeros(H)\n # post['mb'] = np.zeros(H)\n # post['sa2'] = np.zeros(H)\n # post['sb2'] = np.zeros(H)\n # post['cacb'] = np.zeros(H)\n\n # tau = np.multiply(d, s[:pos])/(M*sigma2)\n # delta = np.multiply(np.sqrt(np.divide(M*d, L*s[:pos])), 1+alpha/tau)\n\n # post['ma'][:pos] = np.sqrt(np.multiply(d, delta))\n # post['mb'][:pos] = np.sqrt(np.divide(d, delta))\n # post['sa2'][:pos] = np.divide(sigma2*delta, s[:pos])\n # post['sb2'][:pos] = np.divide(sigma2, np.multiply(delta, s[:pos]))\n # post['cacb'][:pos] = np.sqrt(np.multiply(d, s[:pos])/(L*M))\n # post['sigma2'] = sigma2\n # post['F'] = 0.5*(L*M*np.log(2*np.pi*sigma2) +\n # (residual+np.sum(s**2))/sigma2 + np.sum(\n # M*np.log(tau+1) + L*np.log(tau/alpha + 1) - M*tau))\n\n return U[:, :pos], torch.diag(d), V[:, :pos] # , post\n\n\ndef EVBsigma2(sigma2, L, M, s, residual, xubar):\n H = len(s)\n\n alpha = L / M\n x = s**2 / (M * sigma2)\n\n z1 = x[x > xubar]\n z2 = x[x <= xubar]\n tau_z1 = tau(z1, alpha)\n\n term1 = np.sum(z2 - np.log(z2))\n term2 = np.sum(z1 - tau_z1)\n term3 = np.sum(np.log(np.divide(tau_z1 + 1, z1)))\n term4 = alpha * np.sum(np.log(tau_z1 / alpha + 1))\n\n obj = term1 + term2 + term3 + term4 + residual / (M * sigma2) + (L - H) * np.log(sigma2)\n\n return obj\n\n\ndef phi0(x):\n return x - np.log(x)\n\n\ndef phi1(x, alpha):\n return np.log(tau(x, alpha) + 1) + alpha * np.log(tau(x, alpha) / alpha + 1\n ) - tau(x, alpha)\n\n\ndef tau(x, alpha):\n return 0.5 * (x - (1 + alpha) + np.sqrt((x - (1 + alpha))**2 - 4 * alpha))\n\n\nclass Metrics:\n def __init__(self, params, linear: bool = False) -> None:\n '''\n parameters: list of torch.nn.Module.parameters()\n '''\n self.params = params\n self.history = list()\n mask = list()\n for param_idx, param in enumerate(params):\n param_shape = param.shape\n if not linear:\n if len(param_shape) != 4:\n mask.append(param_idx)\n else:\n if len(param_shape) != 4 and len(param_shape) != 2:\n mask.append(param_idx)\n self.mask = set(mask)\n\n def compute_low_rank(self,\n tensor: torch.Tensor,\n normalizer: float) -> torch.Tensor:\n if tensor.requires_grad:\n tensor = tensor.detach()\n try:\n tensor_size = tensor.shape\n if tensor_size[0] > tensor_size[1]:\n tensor = tensor.T\n U_approx, S_approx, V_approx = EVBMF(tensor)\n except RuntimeError:\n return None, None, None\n rank = S_approx.shape[0] / tensor_size[0] # normalizer\n low_rank_eigen = torch.diag(S_approx).data.cpu().numpy()\n if len(low_rank_eigen) != 0:\n condition = low_rank_eigen[0] / low_rank_eigen[-1]\n sum_low_rank_eigen = low_rank_eigen / \\\n max(low_rank_eigen)\n sum_low_rank_eigen = np.sum(sum_low_rank_eigen)\n else:\n condition = 0\n sum_low_rank_eigen = 0\n KG = sum_low_rank_eigen / tensor_size[0] # normalizer\n return rank, KG, condition\n\n def KG(self, epoch: int) -> np.ndarray:\n KG_list = list()\n for i, (index, metric) in enumerate(self.history[epoch]):\n if isinstance(metric, ConvLayerMetrics):\n KG_list.append((metric.input_channel.KG\n + metric.output_channel.KG) / 2)\n elif isinstance(metric, LayerMetrics):\n KG_list.append(metric.KG)\n return np.array(KG_list)\n\n def __call__(self) -> List[Tuple[int, Union[LayerMetrics,\n ConvLayerMetrics]]]:\n '''\n Computes the knowledge gain (S) and mapping condition (condition)\n '''\n metrics: List[Tuple[int, Union[LayerMetrics,\n ConvLayerMetrics]]] = list()\n for layer_index, layer in enumerate(self.params):\n if layer_index in self.mask:\n metrics.append((layer_index, None))\n continue\n # if np.less(np.prod(layer.shape), 10_000):\n # metrics.append((layer_index, None))\n if len(layer.shape) == 4:\n layer_tensor = layer.data\n tensor_size = layer_tensor.shape\n mode_3_unfold = layer_tensor.permute(1, 0, 2, 3)\n mode_3_unfold = torch.reshape(\n mode_3_unfold, [tensor_size[1], tensor_size[0]\n * tensor_size[2] * tensor_size[3]])\n mode_4_unfold = layer_tensor\n mode_4_unfold = torch.reshape(\n mode_4_unfold, [tensor_size[0], tensor_size[1]\n * tensor_size[2] * tensor_size[3]])\n in_rank, in_KG, in_condition = self.compute_low_rank(\n mode_3_unfold, tensor_size[1])\n if in_rank is None and in_KG is None and in_condition is None:\n if len(self.history) > 0:\n in_rank = self.history[-1][\n layer_index][1].input_channel.rank\n in_KG = self.history[-1][\n layer_index][1].input_channel.KG\n in_condition = self.history[-1][\n layer_index][1].input_channel.condition\n else:\n in_rank = in_KG = in_condition = 0.\n out_rank, out_KG, out_condition = self.compute_low_rank(\n mode_4_unfold, tensor_size[0])\n if out_rank is None and out_KG is None and out_condition is None:\n if len(self.history) > 0:\n out_rank = self.history[-1][\n layer_index][1].output_channel.rank\n out_KG = self.history[-1][\n layer_index][1].output_channel.KG\n out_condition = self.history[-1][\n layer_index][1].output_channel.condition\n else:\n out_rank = out_KG = out_condition = 0.\n metrics.append((layer_index, ConvLayerMetrics(\n input_channel=LayerMetrics(\n rank=in_rank,\n KG=in_KG,\n condition=in_condition),\n output_channel=LayerMetrics(\n rank=out_rank,\n KG=out_KG,\n condition=out_condition))))\n elif len(layer.shape) == 2:\n rank, KG, condition = self.compute_low_rank(\n layer, layer.shape[0])\n if rank is None and KG is None and condition is None:\n if len(self.history) > 0:\n rank = self.history[-1][layer_index][1].rank\n KG = self.history[-1][layer_index][1].KG\n condition = self.history[-1][layer_index][1].condition\n else:\n rank = KG = condition = 0.\n metrics.append((layer_index, LayerMetrics(\n rank=rank,\n KG=KG,\n condition=condition)))\n else:\n metrics.append((layer_index, None))\n self.history.append(metrics)\n return metrics\n\n\nclass Adas(Optimizer):\n \"\"\"\n Vectorized SGD from torch.optim.SGD\n \"\"\"\n\n def __init__(self,\n params,\n lr: float = required,\n beta: float = 0.8,\n step_size: int = None,\n linear: bool = True,\n gamma: float = 1,\n momentum: float = 0,\n dampening: float = 0,\n weight_decay: float = 0,\n nesterov: bool = False):\n if lr is not required and lr < 0.0:\n raise ValueError(\"Invalid learning rate: {}\".format(lr))\n if momentum < 0.0:\n raise ValueError(\"Invalid momentum value: {}\".format(momentum))\n if weight_decay < 0.0:\n raise ValueError(\n \"Invalid weight_decay value: {}\".format(weight_decay))\n\n defaults = dict(lr=lr, momentum=momentum, dampening=dampening,\n weight_decay=weight_decay, nesterov=nesterov)\n if nesterov and (momentum <= 0 or dampening != 0):\n raise ValueError(\n \"Nesterov momentum requires a momentum and zero dampening\")\n super(Adas, self).__init__(params[:2], defaults)\n\n # Adas Specific stuff (not SGD)\n if np.less(beta, 0) or np.greater_equal(beta, 1):\n raise ValueError(f'Invalid beta: {beta}')\n if np.less(gamma, 0):\n raise ValueError(f'Invalid gamma: {gamma}')\n if step_size is not None:\n if np.less_equal(step_size, 0):\n raise ValueError(f'Invalid step_size: {step_size}')\n self.step_size = step_size\n self.gamma = gamma\n self.beta = beta\n self.metrics = metrics = Metrics(params=params[2][\"all_params\"], linear=linear)\n self.lr_vector = np.repeat(a=lr, repeats=len(metrics.params))\n self.velocity = np.zeros(\n len(self.metrics.params) - len(self.metrics.mask))\n self.not_ready = list(range(len(self.velocity)))\n self.init_lr = lr\n self.zeta = 1.\n self.KG = 0.\n\n def __setstate__(self, state):\n super(Adas, self).__setstate__(state)\n for group in self.param_groups:\n group.setdefault('nesterov', False)\n\n def epoch_step(self, epoch: int) -> None:\n self.metrics()\n if epoch == 0:\n velocity = self.init_lr * np.ones(len(self.velocity))\n self.KG = self.metrics.KG(epoch)\n else:\n KG = self.metrics.KG(epoch)\n velocity = KG - self.KG\n self.KG = KG\n for idx in self.not_ready:\n if np.isclose(KG[idx], 0.):\n velocity[idx] = self.init_lr - \\\n self.beta * self.velocity[idx]\n else:\n self.not_ready.remove(idx)\n\n if self.step_size is not None:\n if epoch % self.step_size == 0 and epoch > 0:\n self.lr_vector *= self.gamma\n self.zeta *= self.gamma\n\n self.velocity = np.maximum(\n self.beta * self.velocity + self.zeta * velocity, 0.)\n count = 0\n for i in range(len(self.metrics.params)):\n if i in self.metrics.mask:\n self.lr_vector[i] = self.lr_vector[i - (1 if i > 0 else 0)]\n else:\n self.lr_vector[i] = self.velocity[count]\n count += 1\n\n def step(self, closure: callable = None):\n \"\"\"Performs a single optimization step.\n\n Arguments:\n closure (callable, optional): A closure that reevaluates the model\n and returns the loss.\n \"\"\"\n loss = None\n if closure is not None:\n loss = closure()\n\n iteration_group = 0\n for group in self.param_groups:\n iteration_group += 1\n weight_decay = group['weight_decay']\n momentum = group['momentum']\n dampening = group['dampening']\n nesterov = group['nesterov']\n\n for p_index, p in enumerate(group['params']):\n if p.grad is None:\n continue\n d_p = p.grad.data\n if weight_decay != 0:\n d_p.add_(p.data, alpha=weight_decay)\n if momentum != 0:\n param_state = self.state[p]\n if 'momentum_buffer' not in param_state:\n buf = param_state['momentum_buffer'] = torch.clone(\n d_p).detach()\n else:\n buf = param_state['momentum_buffer']\n buf.mul_(momentum).add_(d_p, alpha=1 - dampening)\n if nesterov:\n d_p = d_p.add(momentum, buf)\n else:\n d_p = buf\n\n # p.data.add_(-group['lr'], d_p)\n p.data.add_(d_p, alpha=-self.lr_vector[p_index])\n\n return loss\n"}}},{"rowIdx":54,"cells":{"function_name":{"kind":"string","value":"get_value"},"docstring":{"kind":"string","value":"Returns the displayed text for the desired column in this row.\n\nThe formula or input which generated the displayed value is not accessible\nthrough the list feed, to see the user's input, use the cells feed.\n\nIf a column is not present in this spreadsheet, or there is no value\nfor a column in this row, this method will return None."},"masked_code":{"kind":"string","value":"#!/usr/bin/env python\n#\n# Copyright (C) 2009 Google Inc.\n#\n# Licensed under the Apache License 2.0;\n\n\n\n# This module is used for version 2 of the Google Data APIs.\n\n\n\"\"\"Provides classes and constants for the XML in the Google Spreadsheets API.\n\nDocumentation for the raw XML which these classes represent can be found here:\nhttp://code.google.com/apis/spreadsheets/docs/3.0/reference.html#Elements\n\"\"\"\n\n# __author__ = 'j.s@google.com (Jeff Scudder)'\n\nimport atom.core\nimport gdata.data\n\nGS_TEMPLATE = '{http://schemas.google.com/spreadsheets/2006}%s'\nGSX_NAMESPACE = 'http://schemas.google.com/spreadsheets/2006/extended'\n\nINSERT_MODE = 'insert'\nOVERWRITE_MODE = 'overwrite'\n\nWORKSHEETS_REL = 'http://schemas.google.com/spreadsheets/2006#worksheetsfeed'\n\nBATCH_POST_ID_TEMPLATE = ('https://spreadsheets.google.com/feeds/cells'\n '/%s/%s/private/full')\nBATCH_ENTRY_ID_TEMPLATE = '%s/R%sC%s'\nBATCH_EDIT_LINK_TEMPLATE = '%s/batch'\n\n\nclass Error(Exception):\n pass\n\n\nclass FieldMissing(Exception):\n pass\n\n\nclass HeaderNotSet(Error):\n \"\"\"The desired column header had no value for the row in the list feed.\"\"\"\n\n\nclass Cell(atom.core.XmlElement):\n \"\"\"The gs:cell element.\n\n A cell in the worksheet. The <gs:cell> element can appear only as a child\n of <atom:entry>.\n \"\"\"\n _qname = GS_TEMPLATE % 'cell'\n col = 'col'\n input_value = 'inputValue'\n numeric_value = 'numericValue'\n row = 'row'\n\n\nclass ColCount(atom.core.XmlElement):\n \"\"\"The gs:colCount element.\n\n Indicates the number of columns in the worksheet, including columns that\n contain only empty cells. The <gs:colCount> element can appear as a child\n of <atom:entry> or <atom:feed>\n \"\"\"\n _qname = GS_TEMPLATE % 'colCount'\n\n\nclass Field(atom.core.XmlElement):\n \"\"\"The gs:field element.\n\n A field single cell within a record. Contained in an <atom:entry>.\n \"\"\"\n _qname = GS_TEMPLATE % 'field'\n index = 'index'\n name = 'name'\n\n\nclass Column(Field):\n \"\"\"The gs:column element.\"\"\"\n _qname = GS_TEMPLATE % 'column'\n\n\nclass Data(atom.core.XmlElement):\n \"\"\"The gs:data element.\n\n A data region of a table. Contained in an <atom:entry> element.\n \"\"\"\n _qname = GS_TEMPLATE % 'data'\n column = [Column]\n insertion_mode = 'insertionMode'\n num_rows = 'numRows'\n start_row = 'startRow'\n\n\nclass Header(atom.core.XmlElement):\n \"\"\"The gs:header element.\n\n Indicates which row is the header row. Contained in an <atom:entry>.\n \"\"\"\n _qname = GS_TEMPLATE % 'header'\n row = 'row'\n\n\nclass RowCount(atom.core.XmlElement):\n \"\"\"The gs:rowCount element.\n\n Indicates the number of total rows in the worksheet, including rows that\n contain only empty cells. The <gs:rowCount> element can appear as a\n child of <atom:entry> or <atom:feed>.\n \"\"\"\n _qname = GS_TEMPLATE % 'rowCount'\n\n\nclass Worksheet(atom.core.XmlElement):\n \"\"\"The gs:worksheet element.\n\n The worksheet where the table lives.Contained in an <atom:entry>.\n \"\"\"\n _qname = GS_TEMPLATE % 'worksheet'\n name = 'name'\n\n\nclass Spreadsheet(gdata.data.GDEntry):\n \"\"\"An Atom entry which represents a Google Spreadsheet.\"\"\"\n\n def find_worksheets_feed(self):\n return self.find_url(WORKSHEETS_REL)\n\n FindWorksheetsFeed = find_worksheets_feed\n\n def get_spreadsheet_key(self):\n \"\"\"Extracts the spreadsheet key unique to this spreadsheet.\"\"\"\n return self.get_id().split('/')[-1]\n\n GetSpreadsheetKey = get_spreadsheet_key\n\n\nclass SpreadsheetsFeed(gdata.data.GDFeed):\n \"\"\"An Atom feed listing a user's Google Spreadsheets.\"\"\"\n entry = [Spreadsheet]\n\n\nclass WorksheetEntry(gdata.data.GDEntry):\n \"\"\"An Atom entry representing a single worksheet in a spreadsheet.\"\"\"\n row_count = RowCount\n col_count = ColCount\n\n def get_worksheet_id(self):\n \"\"\"The worksheet ID identifies this worksheet in its spreadsheet.\"\"\"\n return self.get_id().split('/')[-1]\n\n GetWorksheetId = get_worksheet_id\n\n\nclass WorksheetsFeed(gdata.data.GDFeed):\n \"\"\"A feed containing the worksheets in a single spreadsheet.\"\"\"\n entry = [WorksheetEntry]\n\n\nclass Table(gdata.data.GDEntry):\n \"\"\"An Atom entry that represents a subsection of a worksheet.\n\n A table allows you to treat part or all of a worksheet somewhat like a\n table in a database that is, as a set of structured data items. Tables\n don't exist until you explicitly create them before you can use a table\n feed, you have to explicitly define where the table data comes from.\n \"\"\"\n data = Data\n header = Header\n worksheet = Worksheet\n\n def get_table_id(self):\n if self.id.text:\n return self.id.text.split('/')[-1]\n return None\n\n GetTableId = get_table_id\n\n\nclass TablesFeed(gdata.data.GDFeed):\n \"\"\"An Atom feed containing the tables defined within a worksheet.\"\"\"\n entry = [Table]\n\n\nclass Record(gdata.data.GDEntry):\n \"\"\"An Atom entry representing a single record in a table.\n\n Note that the order of items in each record is the same as the order of\n columns in the table definition, which may not match the order of\n columns in the GUI.\n \"\"\"\n field = [Field]\n\n def value_for_index(self, column_index):\n for field in self.field:\n if field.index == column_index:\n return field.text\n raise FieldMissing('There is no field for %s' % column_index)\n\n ValueForIndex = value_for_index\n\n def value_for_name(self, name):\n for field in self.field:\n if field.name == name:\n return field.text\n raise FieldMissing('There is no field for %s' % name)\n\n ValueForName = value_for_name\n\n def get_record_id(self):\n if self.id.text:\n return self.id.text.split('/')[-1]\n return None\n\n\nclass RecordsFeed(gdata.data.GDFeed):\n \"\"\"An Atom feed containing the individuals records in a table.\"\"\"\n entry = [Record]\n\n\nclass ListRow(atom.core.XmlElement):\n \"\"\"A gsx column value within a row.\n\n The local tag in the _qname is blank and must be set to the column\n name. For example, when adding to a ListEntry, do:\n col_value = ListRow(text='something')\n col_value._qname = col_value._qname % 'mycolumnname'\n \"\"\"\n _qname = '{http://schemas.google.com/spreadsheets/2006/extended}%s'\n\n\nclass ListEntry(gdata.data.GDEntry):\n \"\"\"An Atom entry representing a worksheet row in the list feed.\n\n The values for a particular column can be get and set using\n x.get_value('columnheader') and x.set_value('columnheader', 'value').\n See also the explanation of column names in the ListFeed class.\n \"\"\"\n\n# MASKED: get_value function (lines 244-256)\n\n def set_value(self, column_name, value):\n \"\"\"Changes the value of cell in this row under the desired column name.\n\n Warning: if the cell contained a formula, it will be wiped out by setting\n the value using the list feed since the list feed only works with\n displayed values.\n\n No client side checking is performed on the column_name, you need to\n ensure that the column_name is the local tag name in the gsx tag for the\n column. For example, the column_name will not contain special characters,\n spaces, uppercase letters, etc.\n \"\"\"\n # Try to find the column in this row to change an existing value.\n values = self.get_elements(column_name, GSX_NAMESPACE)\n if len(values) > 0:\n values[0].text = value\n else:\n # There is no value in this row for the desired column, so add a new\n # gsx:column_name element.\n new_value = ListRow(text=value)\n new_value._qname = new_value._qname % (column_name,)\n self._other_elements.append(new_value)\n\n def to_dict(self):\n \"\"\"Converts this row to a mapping of column names to their values.\"\"\"\n result = {}\n values = self.get_elements(namespace=GSX_NAMESPACE)\n for item in values:\n result[item._get_tag()] = item.text\n return result\n\n def from_dict(self, values):\n \"\"\"Sets values for this row from the dictionary.\n\n Old values which are already in the entry will not be removed unless\n they are overwritten with new values from the dict.\n \"\"\"\n for column, value in values.items():\n self.set_value(column, value)\n\n\nclass ListsFeed(gdata.data.GDFeed):\n \"\"\"An Atom feed in which each entry represents a row in a worksheet.\n\n The first row in the worksheet is used as the column names for the values\n in each row. If a header cell is empty, then a unique column ID is used\n for the gsx element name.\n\n Spaces in a column name are removed from the name of the corresponding\n gsx element.\n\n Caution: The columnNames are case-insensitive. For example, if you see\n a <gsx:e-mail> element in a feed, you can't know whether the column\n heading in the original worksheet was \"e-mail\" or \"E-Mail\".\n\n Note: If two or more columns have the same name, then subsequent columns\n of the same name have _n appended to the columnName. For example, if the\n first column name is \"e-mail\", followed by columns named \"E-Mail\" and\n \"E-mail\", then the columnNames will be gsx:e-mail, gsx:e-mail_2, and\n gsx:e-mail_3 respectively.\n \"\"\"\n entry = [ListEntry]\n\n\nclass CellEntry(gdata.data.BatchEntry):\n \"\"\"An Atom entry representing a single cell in a worksheet.\"\"\"\n cell = Cell\n\n\nclass CellsFeed(gdata.data.BatchFeed):\n \"\"\"An Atom feed contains one entry per cell in a worksheet.\n\n The cell feed supports batch operations, you can send multiple cell\n operations in one HTTP request.\n \"\"\"\n entry = [CellEntry]\n\n def add_set_cell(self, row, col, input_value):\n \"\"\"Adds a request to change the contents of a cell to this batch request.\n\n Args:\n row: int, The row number for this cell. Numbering starts at 1.\n col: int, The column number for this cell. Starts at 1.\n input_value: str, The desired formula/content this cell should contain.\n \"\"\"\n self.add_update(CellEntry(\n id=atom.data.Id(text=BATCH_ENTRY_ID_TEMPLATE % (\n self.id.text, row, col)),\n cell=Cell(col=str(col), row=str(row), input_value=input_value)))\n return self\n\n AddSetCell = add_set_cell\n\n\ndef build_batch_cells_update(spreadsheet_key, worksheet_id):\n \"\"\"Creates an empty cells feed for adding batch cell updates to.\n\n Call batch_set_cell on the resulting CellsFeed instance then send the batch\n request TODO: fill in\n\n Args:\n spreadsheet_key: The ID of the spreadsheet\n worksheet_id:\n \"\"\"\n feed_id_text = BATCH_POST_ID_TEMPLATE % (spreadsheet_key, worksheet_id)\n return CellsFeed(\n id=atom.data.Id(text=feed_id_text),\n link=[atom.data.Link(\n rel='edit', href=BATCH_EDIT_LINK_TEMPLATE % (feed_id_text,))])\n\n\nBuildBatchCellsUpdate = build_batch_cells_update"},"implementation":{"kind":"string","value":" def get_value(self, column_name):\n \"\"\"Returns the displayed text for the desired column in this row.\n\n The formula or input which generated the displayed value is not accessible\n through the list feed, to see the user's input, use the cells feed.\n\n If a column is not present in this spreadsheet, or there is no value\n for a column in this row, this method will return None.\n \"\"\"\n values = self.get_elements(column_name, GSX_NAMESPACE)\n if len(values) == 0:\n return None\n return values[0].text"},"start_line":{"kind":"number","value":244,"string":"244"},"end_line":{"kind":"number","value":256,"string":"256"},"file_content":{"kind":"string","value":"#!/usr/bin/env python\n#\n# Copyright (C) 2009 Google Inc.\n#\n# Licensed under the Apache License 2.0;\n\n\n\n# This module is used for version 2 of the Google Data APIs.\n\n\n\"\"\"Provides classes and constants for the XML in the Google Spreadsheets API.\n\nDocumentation for the raw XML which these classes represent can be found here:\nhttp://code.google.com/apis/spreadsheets/docs/3.0/reference.html#Elements\n\"\"\"\n\n# __author__ = 'j.s@google.com (Jeff Scudder)'\n\nimport atom.core\nimport gdata.data\n\nGS_TEMPLATE = '{http://schemas.google.com/spreadsheets/2006}%s'\nGSX_NAMESPACE = 'http://schemas.google.com/spreadsheets/2006/extended'\n\nINSERT_MODE = 'insert'\nOVERWRITE_MODE = 'overwrite'\n\nWORKSHEETS_REL = 'http://schemas.google.com/spreadsheets/2006#worksheetsfeed'\n\nBATCH_POST_ID_TEMPLATE = ('https://spreadsheets.google.com/feeds/cells'\n '/%s/%s/private/full')\nBATCH_ENTRY_ID_TEMPLATE = '%s/R%sC%s'\nBATCH_EDIT_LINK_TEMPLATE = '%s/batch'\n\n\nclass Error(Exception):\n pass\n\n\nclass FieldMissing(Exception):\n pass\n\n\nclass HeaderNotSet(Error):\n \"\"\"The desired column header had no value for the row in the list feed.\"\"\"\n\n\nclass Cell(atom.core.XmlElement):\n \"\"\"The gs:cell element.\n\n A cell in the worksheet. The <gs:cell> element can appear only as a child\n of <atom:entry>.\n \"\"\"\n _qname = GS_TEMPLATE % 'cell'\n col = 'col'\n input_value = 'inputValue'\n numeric_value = 'numericValue'\n row = 'row'\n\n\nclass ColCount(atom.core.XmlElement):\n \"\"\"The gs:colCount element.\n\n Indicates the number of columns in the worksheet, including columns that\n contain only empty cells. The <gs:colCount> element can appear as a child\n of <atom:entry> or <atom:feed>\n \"\"\"\n _qname = GS_TEMPLATE % 'colCount'\n\n\nclass Field(atom.core.XmlElement):\n \"\"\"The gs:field element.\n\n A field single cell within a record. Contained in an <atom:entry>.\n \"\"\"\n _qname = GS_TEMPLATE % 'field'\n index = 'index'\n name = 'name'\n\n\nclass Column(Field):\n \"\"\"The gs:column element.\"\"\"\n _qname = GS_TEMPLATE % 'column'\n\n\nclass Data(atom.core.XmlElement):\n \"\"\"The gs:data element.\n\n A data region of a table. Contained in an <atom:entry> element.\n \"\"\"\n _qname = GS_TEMPLATE % 'data'\n column = [Column]\n insertion_mode = 'insertionMode'\n num_rows = 'numRows'\n start_row = 'startRow'\n\n\nclass Header(atom.core.XmlElement):\n \"\"\"The gs:header element.\n\n Indicates which row is the header row. Contained in an <atom:entry>.\n \"\"\"\n _qname = GS_TEMPLATE % 'header'\n row = 'row'\n\n\nclass RowCount(atom.core.XmlElement):\n \"\"\"The gs:rowCount element.\n\n Indicates the number of total rows in the worksheet, including rows that\n contain only empty cells. The <gs:rowCount> element can appear as a\n child of <atom:entry> or <atom:feed>.\n \"\"\"\n _qname = GS_TEMPLATE % 'rowCount'\n\n\nclass Worksheet(atom.core.XmlElement):\n \"\"\"The gs:worksheet element.\n\n The worksheet where the table lives.Contained in an <atom:entry>.\n \"\"\"\n _qname = GS_TEMPLATE % 'worksheet'\n name = 'name'\n\n\nclass Spreadsheet(gdata.data.GDEntry):\n \"\"\"An Atom entry which represents a Google Spreadsheet.\"\"\"\n\n def find_worksheets_feed(self):\n return self.find_url(WORKSHEETS_REL)\n\n FindWorksheetsFeed = find_worksheets_feed\n\n def get_spreadsheet_key(self):\n \"\"\"Extracts the spreadsheet key unique to this spreadsheet.\"\"\"\n return self.get_id().split('/')[-1]\n\n GetSpreadsheetKey = get_spreadsheet_key\n\n\nclass SpreadsheetsFeed(gdata.data.GDFeed):\n \"\"\"An Atom feed listing a user's Google Spreadsheets.\"\"\"\n entry = [Spreadsheet]\n\n\nclass WorksheetEntry(gdata.data.GDEntry):\n \"\"\"An Atom entry representing a single worksheet in a spreadsheet.\"\"\"\n row_count = RowCount\n col_count = ColCount\n\n def get_worksheet_id(self):\n \"\"\"The worksheet ID identifies this worksheet in its spreadsheet.\"\"\"\n return self.get_id().split('/')[-1]\n\n GetWorksheetId = get_worksheet_id\n\n\nclass WorksheetsFeed(gdata.data.GDFeed):\n \"\"\"A feed containing the worksheets in a single spreadsheet.\"\"\"\n entry = [WorksheetEntry]\n\n\nclass Table(gdata.data.GDEntry):\n \"\"\"An Atom entry that represents a subsection of a worksheet.\n\n A table allows you to treat part or all of a worksheet somewhat like a\n table in a database that is, as a set of structured data items. Tables\n don't exist until you explicitly create them before you can use a table\n feed, you have to explicitly define where the table data comes from.\n \"\"\"\n data = Data\n header = Header\n worksheet = Worksheet\n\n def get_table_id(self):\n if self.id.text:\n return self.id.text.split('/')[-1]\n return None\n\n GetTableId = get_table_id\n\n\nclass TablesFeed(gdata.data.GDFeed):\n \"\"\"An Atom feed containing the tables defined within a worksheet.\"\"\"\n entry = [Table]\n\n\nclass Record(gdata.data.GDEntry):\n \"\"\"An Atom entry representing a single record in a table.\n\n Note that the order of items in each record is the same as the order of\n columns in the table definition, which may not match the order of\n columns in the GUI.\n \"\"\"\n field = [Field]\n\n def value_for_index(self, column_index):\n for field in self.field:\n if field.index == column_index:\n return field.text\n raise FieldMissing('There is no field for %s' % column_index)\n\n ValueForIndex = value_for_index\n\n def value_for_name(self, name):\n for field in self.field:\n if field.name == name:\n return field.text\n raise FieldMissing('There is no field for %s' % name)\n\n ValueForName = value_for_name\n\n def get_record_id(self):\n if self.id.text:\n return self.id.text.split('/')[-1]\n return None\n\n\nclass RecordsFeed(gdata.data.GDFeed):\n \"\"\"An Atom feed containing the individuals records in a table.\"\"\"\n entry = [Record]\n\n\nclass ListRow(atom.core.XmlElement):\n \"\"\"A gsx column value within a row.\n\n The local tag in the _qname is blank and must be set to the column\n name. For example, when adding to a ListEntry, do:\n col_value = ListRow(text='something')\n col_value._qname = col_value._qname % 'mycolumnname'\n \"\"\"\n _qname = '{http://schemas.google.com/spreadsheets/2006/extended}%s'\n\n\nclass ListEntry(gdata.data.GDEntry):\n \"\"\"An Atom entry representing a worksheet row in the list feed.\n\n The values for a particular column can be get and set using\n x.get_value('columnheader') and x.set_value('columnheader', 'value').\n See also the explanation of column names in the ListFeed class.\n \"\"\"\n\n def get_value(self, column_name):\n \"\"\"Returns the displayed text for the desired column in this row.\n\n The formula or input which generated the displayed value is not accessible\n through the list feed, to see the user's input, use the cells feed.\n\n If a column is not present in this spreadsheet, or there is no value\n for a column in this row, this method will return None.\n \"\"\"\n values = self.get_elements(column_name, GSX_NAMESPACE)\n if len(values) == 0:\n return None\n return values[0].text\n\n def set_value(self, column_name, value):\n \"\"\"Changes the value of cell in this row under the desired column name.\n\n Warning: if the cell contained a formula, it will be wiped out by setting\n the value using the list feed since the list feed only works with\n displayed values.\n\n No client side checking is performed on the column_name, you need to\n ensure that the column_name is the local tag name in the gsx tag for the\n column. For example, the column_name will not contain special characters,\n spaces, uppercase letters, etc.\n \"\"\"\n # Try to find the column in this row to change an existing value.\n values = self.get_elements(column_name, GSX_NAMESPACE)\n if len(values) > 0:\n values[0].text = value\n else:\n # There is no value in this row for the desired column, so add a new\n # gsx:column_name element.\n new_value = ListRow(text=value)\n new_value._qname = new_value._qname % (column_name,)\n self._other_elements.append(new_value)\n\n def to_dict(self):\n \"\"\"Converts this row to a mapping of column names to their values.\"\"\"\n result = {}\n values = self.get_elements(namespace=GSX_NAMESPACE)\n for item in values:\n result[item._get_tag()] = item.text\n return result\n\n def from_dict(self, values):\n \"\"\"Sets values for this row from the dictionary.\n\n Old values which are already in the entry will not be removed unless\n they are overwritten with new values from the dict.\n \"\"\"\n for column, value in values.items():\n self.set_value(column, value)\n\n\nclass ListsFeed(gdata.data.GDFeed):\n \"\"\"An Atom feed in which each entry represents a row in a worksheet.\n\n The first row in the worksheet is used as the column names for the values\n in each row. If a header cell is empty, then a unique column ID is used\n for the gsx element name.\n\n Spaces in a column name are removed from the name of the corresponding\n gsx element.\n\n Caution: The columnNames are case-insensitive. For example, if you see\n a <gsx:e-mail> element in a feed, you can't know whether the column\n heading in the original worksheet was \"e-mail\" or \"E-Mail\".\n\n Note: If two or more columns have the same name, then subsequent columns\n of the same name have _n appended to the columnName. For example, if the\n first column name is \"e-mail\", followed by columns named \"E-Mail\" and\n \"E-mail\", then the columnNames will be gsx:e-mail, gsx:e-mail_2, and\n gsx:e-mail_3 respectively.\n \"\"\"\n entry = [ListEntry]\n\n\nclass CellEntry(gdata.data.BatchEntry):\n \"\"\"An Atom entry representing a single cell in a worksheet.\"\"\"\n cell = Cell\n\n\nclass CellsFeed(gdata.data.BatchFeed):\n \"\"\"An Atom feed contains one entry per cell in a worksheet.\n\n The cell feed supports batch operations, you can send multiple cell\n operations in one HTTP request.\n \"\"\"\n entry = [CellEntry]\n\n def add_set_cell(self, row, col, input_value):\n \"\"\"Adds a request to change the contents of a cell to this batch request.\n\n Args:\n row: int, The row number for this cell. Numbering starts at 1.\n col: int, The column number for this cell. Starts at 1.\n input_value: str, The desired formula/content this cell should contain.\n \"\"\"\n self.add_update(CellEntry(\n id=atom.data.Id(text=BATCH_ENTRY_ID_TEMPLATE % (\n self.id.text, row, col)),\n cell=Cell(col=str(col), row=str(row), input_value=input_value)))\n return self\n\n AddSetCell = add_set_cell\n\n\ndef build_batch_cells_update(spreadsheet_key, worksheet_id):\n \"\"\"Creates an empty cells feed for adding batch cell updates to.\n\n Call batch_set_cell on the resulting CellsFeed instance then send the batch\n request TODO: fill in\n\n Args:\n spreadsheet_key: The ID of the spreadsheet\n worksheet_id:\n \"\"\"\n feed_id_text = BATCH_POST_ID_TEMPLATE % (spreadsheet_key, worksheet_id)\n return CellsFeed(\n id=atom.data.Id(text=feed_id_text),\n link=[atom.data.Link(\n rel='edit', href=BATCH_EDIT_LINK_TEMPLATE % (feed_id_text,))])\n\n\nBuildBatchCellsUpdate = build_batch_cells_update\n"}}},{"rowIdx":55,"cells":{"function_name":{"kind":"string","value":"set_value"},"docstring":{"kind":"string","value":"Changes the value of cell in this row under the desired column name.\n\nWarning: if the cell contained a formula, it will be wiped out by setting\nthe value using the list feed since the list feed only works with\ndisplayed values.\n\nNo client side checking is performed on the column_name, you need to\nensure that the column_name is the local tag name in the gsx tag for the\ncolumn. For example, the column_name will not contain special characters,\nspaces, uppercase letters, etc."},"masked_code":{"kind":"string","value":"#!/usr/bin/env python\n#\n# Copyright (C) 2009 Google Inc.\n#\n# Licensed under the Apache License 2.0;\n\n\n\n# This module is used for version 2 of the Google Data APIs.\n\n\n\"\"\"Provides classes and constants for the XML in the Google Spreadsheets API.\n\nDocumentation for the raw XML which these classes represent can be found here:\nhttp://code.google.com/apis/spreadsheets/docs/3.0/reference.html#Elements\n\"\"\"\n\n# __author__ = 'j.s@google.com (Jeff Scudder)'\n\nimport atom.core\nimport gdata.data\n\nGS_TEMPLATE = '{http://schemas.google.com/spreadsheets/2006}%s'\nGSX_NAMESPACE = 'http://schemas.google.com/spreadsheets/2006/extended'\n\nINSERT_MODE = 'insert'\nOVERWRITE_MODE = 'overwrite'\n\nWORKSHEETS_REL = 'http://schemas.google.com/spreadsheets/2006#worksheetsfeed'\n\nBATCH_POST_ID_TEMPLATE = ('https://spreadsheets.google.com/feeds/cells'\n '/%s/%s/private/full')\nBATCH_ENTRY_ID_TEMPLATE = '%s/R%sC%s'\nBATCH_EDIT_LINK_TEMPLATE = '%s/batch'\n\n\nclass Error(Exception):\n pass\n\n\nclass FieldMissing(Exception):\n pass\n\n\nclass HeaderNotSet(Error):\n \"\"\"The desired column header had no value for the row in the list feed.\"\"\"\n\n\nclass Cell(atom.core.XmlElement):\n \"\"\"The gs:cell element.\n\n A cell in the worksheet. The <gs:cell> element can appear only as a child\n of <atom:entry>.\n \"\"\"\n _qname = GS_TEMPLATE % 'cell'\n col = 'col'\n input_value = 'inputValue'\n numeric_value = 'numericValue'\n row = 'row'\n\n\nclass ColCount(atom.core.XmlElement):\n \"\"\"The gs:colCount element.\n\n Indicates the number of columns in the worksheet, including columns that\n contain only empty cells. The <gs:colCount> element can appear as a child\n of <atom:entry> or <atom:feed>\n \"\"\"\n _qname = GS_TEMPLATE % 'colCount'\n\n\nclass Field(atom.core.XmlElement):\n \"\"\"The gs:field element.\n\n A field single cell within a record. Contained in an <atom:entry>.\n \"\"\"\n _qname = GS_TEMPLATE % 'field'\n index = 'index'\n name = 'name'\n\n\nclass Column(Field):\n \"\"\"The gs:column element.\"\"\"\n _qname = GS_TEMPLATE % 'column'\n\n\nclass Data(atom.core.XmlElement):\n \"\"\"The gs:data element.\n\n A data region of a table. Contained in an <atom:entry> element.\n \"\"\"\n _qname = GS_TEMPLATE % 'data'\n column = [Column]\n insertion_mode = 'insertionMode'\n num_rows = 'numRows'\n start_row = 'startRow'\n\n\nclass Header(atom.core.XmlElement):\n \"\"\"The gs:header element.\n\n Indicates which row is the header row. Contained in an <atom:entry>.\n \"\"\"\n _qname = GS_TEMPLATE % 'header'\n row = 'row'\n\n\nclass RowCount(atom.core.XmlElement):\n \"\"\"The gs:rowCount element.\n\n Indicates the number of total rows in the worksheet, including rows that\n contain only empty cells. The <gs:rowCount> element can appear as a\n child of <atom:entry> or <atom:feed>.\n \"\"\"\n _qname = GS_TEMPLATE % 'rowCount'\n\n\nclass Worksheet(atom.core.XmlElement):\n \"\"\"The gs:worksheet element.\n\n The worksheet where the table lives.Contained in an <atom:entry>.\n \"\"\"\n _qname = GS_TEMPLATE % 'worksheet'\n name = 'name'\n\n\nclass Spreadsheet(gdata.data.GDEntry):\n \"\"\"An Atom entry which represents a Google Spreadsheet.\"\"\"\n\n def find_worksheets_feed(self):\n return self.find_url(WORKSHEETS_REL)\n\n FindWorksheetsFeed = find_worksheets_feed\n\n def get_spreadsheet_key(self):\n \"\"\"Extracts the spreadsheet key unique to this spreadsheet.\"\"\"\n return self.get_id().split('/')[-1]\n\n GetSpreadsheetKey = get_spreadsheet_key\n\n\nclass SpreadsheetsFeed(gdata.data.GDFeed):\n \"\"\"An Atom feed listing a user's Google Spreadsheets.\"\"\"\n entry = [Spreadsheet]\n\n\nclass WorksheetEntry(gdata.data.GDEntry):\n \"\"\"An Atom entry representing a single worksheet in a spreadsheet.\"\"\"\n row_count = RowCount\n col_count = ColCount\n\n def get_worksheet_id(self):\n \"\"\"The worksheet ID identifies this worksheet in its spreadsheet.\"\"\"\n return self.get_id().split('/')[-1]\n\n GetWorksheetId = get_worksheet_id\n\n\nclass WorksheetsFeed(gdata.data.GDFeed):\n \"\"\"A feed containing the worksheets in a single spreadsheet.\"\"\"\n entry = [WorksheetEntry]\n\n\nclass Table(gdata.data.GDEntry):\n \"\"\"An Atom entry that represents a subsection of a worksheet.\n\n A table allows you to treat part or all of a worksheet somewhat like a\n table in a database that is, as a set of structured data items. Tables\n don't exist until you explicitly create them before you can use a table\n feed, you have to explicitly define where the table data comes from.\n \"\"\"\n data = Data\n header = Header\n worksheet = Worksheet\n\n def get_table_id(self):\n if self.id.text:\n return self.id.text.split('/')[-1]\n return None\n\n GetTableId = get_table_id\n\n\nclass TablesFeed(gdata.data.GDFeed):\n \"\"\"An Atom feed containing the tables defined within a worksheet.\"\"\"\n entry = [Table]\n\n\nclass Record(gdata.data.GDEntry):\n \"\"\"An Atom entry representing a single record in a table.\n\n Note that the order of items in each record is the same as the order of\n columns in the table definition, which may not match the order of\n columns in the GUI.\n \"\"\"\n field = [Field]\n\n def value_for_index(self, column_index):\n for field in self.field:\n if field.index == column_index:\n return field.text\n raise FieldMissing('There is no field for %s' % column_index)\n\n ValueForIndex = value_for_index\n\n def value_for_name(self, name):\n for field in self.field:\n if field.name == name:\n return field.text\n raise FieldMissing('There is no field for %s' % name)\n\n ValueForName = value_for_name\n\n def get_record_id(self):\n if self.id.text:\n return self.id.text.split('/')[-1]\n return None\n\n\nclass RecordsFeed(gdata.data.GDFeed):\n \"\"\"An Atom feed containing the individuals records in a table.\"\"\"\n entry = [Record]\n\n\nclass ListRow(atom.core.XmlElement):\n \"\"\"A gsx column value within a row.\n\n The local tag in the _qname is blank and must be set to the column\n name. For example, when adding to a ListEntry, do:\n col_value = ListRow(text='something')\n col_value._qname = col_value._qname % 'mycolumnname'\n \"\"\"\n _qname = '{http://schemas.google.com/spreadsheets/2006/extended}%s'\n\n\nclass ListEntry(gdata.data.GDEntry):\n \"\"\"An Atom entry representing a worksheet row in the list feed.\n\n The values for a particular column can be get and set using\n x.get_value('columnheader') and x.set_value('columnheader', 'value').\n See also the explanation of column names in the ListFeed class.\n \"\"\"\n\n def get_value(self, column_name):\n \"\"\"Returns the displayed text for the desired column in this row.\n\n The formula or input which generated the displayed value is not accessible\n through the list feed, to see the user's input, use the cells feed.\n\n If a column is not present in this spreadsheet, or there is no value\n for a column in this row, this method will return None.\n \"\"\"\n values = self.get_elements(column_name, GSX_NAMESPACE)\n if len(values) == 0:\n return None\n return values[0].text\n\n# MASKED: set_value function (lines 258-279)\n\n def to_dict(self):\n \"\"\"Converts this row to a mapping of column names to their values.\"\"\"\n result = {}\n values = self.get_elements(namespace=GSX_NAMESPACE)\n for item in values:\n result[item._get_tag()] = item.text\n return result\n\n def from_dict(self, values):\n \"\"\"Sets values for this row from the dictionary.\n\n Old values which are already in the entry will not be removed unless\n they are overwritten with new values from the dict.\n \"\"\"\n for column, value in values.items():\n self.set_value(column, value)\n\n\nclass ListsFeed(gdata.data.GDFeed):\n \"\"\"An Atom feed in which each entry represents a row in a worksheet.\n\n The first row in the worksheet is used as the column names for the values\n in each row. If a header cell is empty, then a unique column ID is used\n for the gsx element name.\n\n Spaces in a column name are removed from the name of the corresponding\n gsx element.\n\n Caution: The columnNames are case-insensitive. For example, if you see\n a <gsx:e-mail> element in a feed, you can't know whether the column\n heading in the original worksheet was \"e-mail\" or \"E-Mail\".\n\n Note: If two or more columns have the same name, then subsequent columns\n of the same name have _n appended to the columnName. For example, if the\n first column name is \"e-mail\", followed by columns named \"E-Mail\" and\n \"E-mail\", then the columnNames will be gsx:e-mail, gsx:e-mail_2, and\n gsx:e-mail_3 respectively.\n \"\"\"\n entry = [ListEntry]\n\n\nclass CellEntry(gdata.data.BatchEntry):\n \"\"\"An Atom entry representing a single cell in a worksheet.\"\"\"\n cell = Cell\n\n\nclass CellsFeed(gdata.data.BatchFeed):\n \"\"\"An Atom feed contains one entry per cell in a worksheet.\n\n The cell feed supports batch operations, you can send multiple cell\n operations in one HTTP request.\n \"\"\"\n entry = [CellEntry]\n\n def add_set_cell(self, row, col, input_value):\n \"\"\"Adds a request to change the contents of a cell to this batch request.\n\n Args:\n row: int, The row number for this cell. Numbering starts at 1.\n col: int, The column number for this cell. Starts at 1.\n input_value: str, The desired formula/content this cell should contain.\n \"\"\"\n self.add_update(CellEntry(\n id=atom.data.Id(text=BATCH_ENTRY_ID_TEMPLATE % (\n self.id.text, row, col)),\n cell=Cell(col=str(col), row=str(row), input_value=input_value)))\n return self\n\n AddSetCell = add_set_cell\n\n\ndef build_batch_cells_update(spreadsheet_key, worksheet_id):\n \"\"\"Creates an empty cells feed for adding batch cell updates to.\n\n Call batch_set_cell on the resulting CellsFeed instance then send the batch\n request TODO: fill in\n\n Args:\n spreadsheet_key: The ID of the spreadsheet\n worksheet_id:\n \"\"\"\n feed_id_text = BATCH_POST_ID_TEMPLATE % (spreadsheet_key, worksheet_id)\n return CellsFeed(\n id=atom.data.Id(text=feed_id_text),\n link=[atom.data.Link(\n rel='edit', href=BATCH_EDIT_LINK_TEMPLATE % (feed_id_text,))])\n\n\nBuildBatchCellsUpdate = build_batch_cells_update"},"implementation":{"kind":"string","value":" def set_value(self, column_name, value):\n \"\"\"Changes the value of cell in this row under the desired column name.\n\n Warning: if the cell contained a formula, it will be wiped out by setting\n the value using the list feed since the list feed only works with\n displayed values.\n\n No client side checking is performed on the column_name, you need to\n ensure that the column_name is the local tag name in the gsx tag for the\n column. For example, the column_name will not contain special characters,\n spaces, uppercase letters, etc.\n \"\"\"\n # Try to find the column in this row to change an existing value.\n values = self.get_elements(column_name, GSX_NAMESPACE)\n if len(values) > 0:\n values[0].text = value\n else:\n # There is no value in this row for the desired column, so add a new\n # gsx:column_name element.\n new_value = ListRow(text=value)\n new_value._qname = new_value._qname % (column_name,)\n self._other_elements.append(new_value)"},"start_line":{"kind":"number","value":258,"string":"258"},"end_line":{"kind":"number","value":279,"string":"279"},"file_content":{"kind":"string","value":"#!/usr/bin/env python\n#\n# Copyright (C) 2009 Google Inc.\n#\n# Licensed under the Apache License 2.0;\n\n\n\n# This module is used for version 2 of the Google Data APIs.\n\n\n\"\"\"Provides classes and constants for the XML in the Google Spreadsheets API.\n\nDocumentation for the raw XML which these classes represent can be found here:\nhttp://code.google.com/apis/spreadsheets/docs/3.0/reference.html#Elements\n\"\"\"\n\n# __author__ = 'j.s@google.com (Jeff Scudder)'\n\nimport atom.core\nimport gdata.data\n\nGS_TEMPLATE = '{http://schemas.google.com/spreadsheets/2006}%s'\nGSX_NAMESPACE = 'http://schemas.google.com/spreadsheets/2006/extended'\n\nINSERT_MODE = 'insert'\nOVERWRITE_MODE = 'overwrite'\n\nWORKSHEETS_REL = 'http://schemas.google.com/spreadsheets/2006#worksheetsfeed'\n\nBATCH_POST_ID_TEMPLATE = ('https://spreadsheets.google.com/feeds/cells'\n '/%s/%s/private/full')\nBATCH_ENTRY_ID_TEMPLATE = '%s/R%sC%s'\nBATCH_EDIT_LINK_TEMPLATE = '%s/batch'\n\n\nclass Error(Exception):\n pass\n\n\nclass FieldMissing(Exception):\n pass\n\n\nclass HeaderNotSet(Error):\n \"\"\"The desired column header had no value for the row in the list feed.\"\"\"\n\n\nclass Cell(atom.core.XmlElement):\n \"\"\"The gs:cell element.\n\n A cell in the worksheet. The <gs:cell> element can appear only as a child\n of <atom:entry>.\n \"\"\"\n _qname = GS_TEMPLATE % 'cell'\n col = 'col'\n input_value = 'inputValue'\n numeric_value = 'numericValue'\n row = 'row'\n\n\nclass ColCount(atom.core.XmlElement):\n \"\"\"The gs:colCount element.\n\n Indicates the number of columns in the worksheet, including columns that\n contain only empty cells. The <gs:colCount> element can appear as a child\n of <atom:entry> or <atom:feed>\n \"\"\"\n _qname = GS_TEMPLATE % 'colCount'\n\n\nclass Field(atom.core.XmlElement):\n \"\"\"The gs:field element.\n\n A field single cell within a record. Contained in an <atom:entry>.\n \"\"\"\n _qname = GS_TEMPLATE % 'field'\n index = 'index'\n name = 'name'\n\n\nclass Column(Field):\n \"\"\"The gs:column element.\"\"\"\n _qname = GS_TEMPLATE % 'column'\n\n\nclass Data(atom.core.XmlElement):\n \"\"\"The gs:data element.\n\n A data region of a table. Contained in an <atom:entry> element.\n \"\"\"\n _qname = GS_TEMPLATE % 'data'\n column = [Column]\n insertion_mode = 'insertionMode'\n num_rows = 'numRows'\n start_row = 'startRow'\n\n\nclass Header(atom.core.XmlElement):\n \"\"\"The gs:header element.\n\n Indicates which row is the header row. Contained in an <atom:entry>.\n \"\"\"\n _qname = GS_TEMPLATE % 'header'\n row = 'row'\n\n\nclass RowCount(atom.core.XmlElement):\n \"\"\"The gs:rowCount element.\n\n Indicates the number of total rows in the worksheet, including rows that\n contain only empty cells. The <gs:rowCount> element can appear as a\n child of <atom:entry> or <atom:feed>.\n \"\"\"\n _qname = GS_TEMPLATE % 'rowCount'\n\n\nclass Worksheet(atom.core.XmlElement):\n \"\"\"The gs:worksheet element.\n\n The worksheet where the table lives.Contained in an <atom:entry>.\n \"\"\"\n _qname = GS_TEMPLATE % 'worksheet'\n name = 'name'\n\n\nclass Spreadsheet(gdata.data.GDEntry):\n \"\"\"An Atom entry which represents a Google Spreadsheet.\"\"\"\n\n def find_worksheets_feed(self):\n return self.find_url(WORKSHEETS_REL)\n\n FindWorksheetsFeed = find_worksheets_feed\n\n def get_spreadsheet_key(self):\n \"\"\"Extracts the spreadsheet key unique to this spreadsheet.\"\"\"\n return self.get_id().split('/')[-1]\n\n GetSpreadsheetKey = get_spreadsheet_key\n\n\nclass SpreadsheetsFeed(gdata.data.GDFeed):\n \"\"\"An Atom feed listing a user's Google Spreadsheets.\"\"\"\n entry = [Spreadsheet]\n\n\nclass WorksheetEntry(gdata.data.GDEntry):\n \"\"\"An Atom entry representing a single worksheet in a spreadsheet.\"\"\"\n row_count = RowCount\n col_count = ColCount\n\n def get_worksheet_id(self):\n \"\"\"The worksheet ID identifies this worksheet in its spreadsheet.\"\"\"\n return self.get_id().split('/')[-1]\n\n GetWorksheetId = get_worksheet_id\n\n\nclass WorksheetsFeed(gdata.data.GDFeed):\n \"\"\"A feed containing the worksheets in a single spreadsheet.\"\"\"\n entry = [WorksheetEntry]\n\n\nclass Table(gdata.data.GDEntry):\n \"\"\"An Atom entry that represents a subsection of a worksheet.\n\n A table allows you to treat part or all of a worksheet somewhat like a\n table in a database that is, as a set of structured data items. Tables\n don't exist until you explicitly create them before you can use a table\n feed, you have to explicitly define where the table data comes from.\n \"\"\"\n data = Data\n header = Header\n worksheet = Worksheet\n\n def get_table_id(self):\n if self.id.text:\n return self.id.text.split('/')[-1]\n return None\n\n GetTableId = get_table_id\n\n\nclass TablesFeed(gdata.data.GDFeed):\n \"\"\"An Atom feed containing the tables defined within a worksheet.\"\"\"\n entry = [Table]\n\n\nclass Record(gdata.data.GDEntry):\n \"\"\"An Atom entry representing a single record in a table.\n\n Note that the order of items in each record is the same as the order of\n columns in the table definition, which may not match the order of\n columns in the GUI.\n \"\"\"\n field = [Field]\n\n def value_for_index(self, column_index):\n for field in self.field:\n if field.index == column_index:\n return field.text\n raise FieldMissing('There is no field for %s' % column_index)\n\n ValueForIndex = value_for_index\n\n def value_for_name(self, name):\n for field in self.field:\n if field.name == name:\n return field.text\n raise FieldMissing('There is no field for %s' % name)\n\n ValueForName = value_for_name\n\n def get_record_id(self):\n if self.id.text:\n return self.id.text.split('/')[-1]\n return None\n\n\nclass RecordsFeed(gdata.data.GDFeed):\n \"\"\"An Atom feed containing the individuals records in a table.\"\"\"\n entry = [Record]\n\n\nclass ListRow(atom.core.XmlElement):\n \"\"\"A gsx column value within a row.\n\n The local tag in the _qname is blank and must be set to the column\n name. For example, when adding to a ListEntry, do:\n col_value = ListRow(text='something')\n col_value._qname = col_value._qname % 'mycolumnname'\n \"\"\"\n _qname = '{http://schemas.google.com/spreadsheets/2006/extended}%s'\n\n\nclass ListEntry(gdata.data.GDEntry):\n \"\"\"An Atom entry representing a worksheet row in the list feed.\n\n The values for a particular column can be get and set using\n x.get_value('columnheader') and x.set_value('columnheader', 'value').\n See also the explanation of column names in the ListFeed class.\n \"\"\"\n\n def get_value(self, column_name):\n \"\"\"Returns the displayed text for the desired column in this row.\n\n The formula or input which generated the displayed value is not accessible\n through the list feed, to see the user's input, use the cells feed.\n\n If a column is not present in this spreadsheet, or there is no value\n for a column in this row, this method will return None.\n \"\"\"\n values = self.get_elements(column_name, GSX_NAMESPACE)\n if len(values) == 0:\n return None\n return values[0].text\n\n def set_value(self, column_name, value):\n \"\"\"Changes the value of cell in this row under the desired column name.\n\n Warning: if the cell contained a formula, it will be wiped out by setting\n the value using the list feed since the list feed only works with\n displayed values.\n\n No client side checking is performed on the column_name, you need to\n ensure that the column_name is the local tag name in the gsx tag for the\n column. For example, the column_name will not contain special characters,\n spaces, uppercase letters, etc.\n \"\"\"\n # Try to find the column in this row to change an existing value.\n values = self.get_elements(column_name, GSX_NAMESPACE)\n if len(values) > 0:\n values[0].text = value\n else:\n # There is no value in this row for the desired column, so add a new\n # gsx:column_name element.\n new_value = ListRow(text=value)\n new_value._qname = new_value._qname % (column_name,)\n self._other_elements.append(new_value)\n\n def to_dict(self):\n \"\"\"Converts this row to a mapping of column names to their values.\"\"\"\n result = {}\n values = self.get_elements(namespace=GSX_NAMESPACE)\n for item in values:\n result[item._get_tag()] = item.text\n return result\n\n def from_dict(self, values):\n \"\"\"Sets values for this row from the dictionary.\n\n Old values which are already in the entry will not be removed unless\n they are overwritten with new values from the dict.\n \"\"\"\n for column, value in values.items():\n self.set_value(column, value)\n\n\nclass ListsFeed(gdata.data.GDFeed):\n \"\"\"An Atom feed in which each entry represents a row in a worksheet.\n\n The first row in the worksheet is used as the column names for the values\n in each row. If a header cell is empty, then a unique column ID is used\n for the gsx element name.\n\n Spaces in a column name are removed from the name of the corresponding\n gsx element.\n\n Caution: The columnNames are case-insensitive. For example, if you see\n a <gsx:e-mail> element in a feed, you can't know whether the column\n heading in the original worksheet was \"e-mail\" or \"E-Mail\".\n\n Note: If two or more columns have the same name, then subsequent columns\n of the same name have _n appended to the columnName. For example, if the\n first column name is \"e-mail\", followed by columns named \"E-Mail\" and\n \"E-mail\", then the columnNames will be gsx:e-mail, gsx:e-mail_2, and\n gsx:e-mail_3 respectively.\n \"\"\"\n entry = [ListEntry]\n\n\nclass CellEntry(gdata.data.BatchEntry):\n \"\"\"An Atom entry representing a single cell in a worksheet.\"\"\"\n cell = Cell\n\n\nclass CellsFeed(gdata.data.BatchFeed):\n \"\"\"An Atom feed contains one entry per cell in a worksheet.\n\n The cell feed supports batch operations, you can send multiple cell\n operations in one HTTP request.\n \"\"\"\n entry = [CellEntry]\n\n def add_set_cell(self, row, col, input_value):\n \"\"\"Adds a request to change the contents of a cell to this batch request.\n\n Args:\n row: int, The row number for this cell. Numbering starts at 1.\n col: int, The column number for this cell. Starts at 1.\n input_value: str, The desired formula/content this cell should contain.\n \"\"\"\n self.add_update(CellEntry(\n id=atom.data.Id(text=BATCH_ENTRY_ID_TEMPLATE % (\n self.id.text, row, col)),\n cell=Cell(col=str(col), row=str(row), input_value=input_value)))\n return self\n\n AddSetCell = add_set_cell\n\n\ndef build_batch_cells_update(spreadsheet_key, worksheet_id):\n \"\"\"Creates an empty cells feed for adding batch cell updates to.\n\n Call batch_set_cell on the resulting CellsFeed instance then send the batch\n request TODO: fill in\n\n Args:\n spreadsheet_key: The ID of the spreadsheet\n worksheet_id:\n \"\"\"\n feed_id_text = BATCH_POST_ID_TEMPLATE % (spreadsheet_key, worksheet_id)\n return CellsFeed(\n id=atom.data.Id(text=feed_id_text),\n link=[atom.data.Link(\n rel='edit', href=BATCH_EDIT_LINK_TEMPLATE % (feed_id_text,))])\n\n\nBuildBatchCellsUpdate = build_batch_cells_update\n"}}},{"rowIdx":56,"cells":{"function_name":{"kind":"string","value":"__getattribute__"},"docstring":{"kind":"string","value":"Attribute lookup method for custom class attributes.\n\nReFrame test variables are descriptors injected at the class level.\nIf a variable descriptor has already been injected into the class,\ndo not return the descriptor object and return the default value\nassociated with that variable instead.\n\n.. warning::\n .. versionchanged:: 3.7.0\n Prior versions exposed the variable descriptor object if this\n was already present in the class, instead of returning the\n variable's default value."},"masked_code":{"kind":"string","value":"# Copyright 2016-2021 Swiss National Supercomputing Centre (CSCS/ETH Zurich)\n# ReFrame Project Developers. See the top-level LICENSE file for details.\n#\n# SPDX-License-Identifier: BSD-3-Clause\n\n#\n# Meta-class for creating regression tests.\n#\n\nimport functools\nimport types\n\nimport reframe.core.namespaces as namespaces\nimport reframe.core.parameters as parameters\nimport reframe.core.variables as variables\nimport reframe.core.hooks as hooks\nimport reframe.utility as utils\n\nfrom reframe.core.exceptions import ReframeSyntaxError\nfrom reframe.core.deferrable import deferrable, _DeferredPerformanceExpression\n\n\nclass RegressionTestMeta(type):\n\n class MetaNamespace(namespaces.LocalNamespace):\n '''Custom namespace to control the cls attribute assignment.\n\n Regular Python class attributes can be overridden by either\n parameters or variables respecting the order of execution.\n A variable or a parameter may not be declared more than once in the\n same class body. Overriding a variable with a parameter or the other\n way around has an undefined behavior. A variable's value may be\n updated multiple times within the same class body. A parameter's\n value cannot be updated more than once within the same class body.\n '''\n\n def __setitem__(self, key, value):\n if isinstance(value, variables.TestVar):\n # Insert the attribute in the variable namespace\n try:\n self['_rfm_local_var_space'][key] = value\n value.__set_name__(self, key)\n except KeyError:\n raise ReframeSyntaxError(\n f'variable {key!r} is already declared'\n ) from None\n\n # Override the regular class attribute (if present) and return\n self._namespace.pop(key, None)\n return\n\n elif isinstance(value, parameters.TestParam):\n # Insert the attribute in the parameter namespace\n try:\n self['_rfm_local_param_space'][key] = value\n except KeyError:\n raise ReframeSyntaxError(\n f'parameter {key!r} is already declared in this class'\n ) from None\n\n # Override the regular class attribute (if present) and return\n self._namespace.pop(key, None)\n return\n\n elif key in self['_rfm_local_param_space']:\n raise ReframeSyntaxError(\n f'cannot override parameter {key!r}'\n )\n else:\n # Insert the items manually to overide the namespace clash\n # check from the base namespace.\n self._namespace[key] = value\n\n # Register functions decorated with either @sanity_function or\n # @performance_variables or @performance_function decorators.\n if hasattr(value, '_rfm_sanity_fn'):\n try:\n super().__setitem__('_rfm_sanity', value)\n except KeyError:\n raise ReframeSyntaxError(\n 'the @sanity_function decorator can only be used '\n 'once in the class body'\n ) from None\n elif hasattr(value, '_rfm_perf_key'):\n try:\n self['_rfm_perf_fns'][key] = value\n except KeyError:\n raise ReframeSyntaxError(\n f'the performance function {key!r} has already been '\n f'defined in this class'\n ) from None\n\n # Register the final methods\n if hasattr(value, '_rfm_final'):\n self['_rfm_final_methods'].add(key)\n\n # Register the hooks - if a value does not meet the conditions\n # it will be simply ignored\n self['_rfm_hook_registry'].add(value)\n\n def __getitem__(self, key):\n '''Expose and control access to the local namespaces.\n\n Variables may only be retrieved if their value has been previously\n set. Accessing a parameter in the class body is disallowed (the\n actual test parameter is set during the class instantiation).\n '''\n\n try:\n return super().__getitem__(key)\n except KeyError as err:\n try:\n # Handle variable access\n return self['_rfm_local_var_space'][key]\n\n except KeyError:\n # Handle parameter access\n if key in self['_rfm_local_param_space']:\n raise ReframeSyntaxError(\n 'accessing a test parameter from the class '\n 'body is disallowed'\n ) from None\n else:\n # As the last resource, look if key is a variable in\n # any of the base classes. If so, make its value\n # available in the current class' namespace.\n for b in self['_rfm_bases']:\n if key in b._rfm_var_space:\n # Store a deep-copy of the variable's\n # value and return.\n v = b._rfm_var_space[key].default_value\n self._namespace[key] = v\n return self._namespace[key]\n\n # If 'key' is neither a variable nor a parameter,\n # raise the exception from the base __getitem__.\n raise err from None\n\n def reset(self, key):\n '''Reset an item to rerun it through the __setitem__ logic.'''\n self[key] = self[key]\n\n class WrappedFunction:\n '''Descriptor to wrap a free function as a bound-method.\n\n The free function object is wrapped by the constructor. Instances\n of this class should be inserted into the namespace of the target class\n with the desired name for the bound-method. Since this class is a\n descriptor, the `__get__` method will return the right bound-method\n when accessed from a class instance.\n\n :meta private:\n '''\n\n __slots__ = ('fn')\n\n def __init__(self, fn, name=None):\n @functools.wraps(fn)\n def _fn(*args, **kwargs):\n return fn(*args, **kwargs)\n\n self.fn = _fn\n if name:\n self.fn.__name__ = name\n\n def __get__(self, obj, objtype=None):\n if objtype is None:\n objtype = type(obj)\n\n self.fn.__qualname__ = '.'.join(\n [objtype.__qualname__, self.fn.__name__]\n )\n if obj is None:\n return self.fn\n\n return types.MethodType(self.fn, obj)\n\n def __call__(self, *args, **kwargs):\n return self.fn(*args, **kwargs)\n\n def __getattr__(self, name):\n if name in self.__slots__:\n return super().__getattr__(name)\n else:\n return getattr(self.fn, name)\n\n def __setattr__(self, name, value):\n if name in self.__slots__:\n super().__setattr__(name, value)\n else:\n setattr(self.fn, name, value)\n\n @classmethod\n def __prepare__(metacls, name, bases, **kwargs):\n namespace = super().__prepare__(name, bases, **kwargs)\n\n # Keep reference to the bases inside the namespace\n namespace['_rfm_bases'] = [\n b for b in bases if hasattr(b, '_rfm_var_space')\n ]\n\n # Regression test parameter space defined at the class level\n local_param_space = namespaces.LocalNamespace()\n namespace['_rfm_local_param_space'] = local_param_space\n\n # Directive to insert a regression test parameter directly in the\n # class body as: `P0 = parameter([0,1,2,3])`.\n namespace['parameter'] = parameters.TestParam\n\n # Regression test var space defined at the class level\n local_var_space = namespaces.LocalNamespace()\n namespace['_rfm_local_var_space'] = local_var_space\n\n # Directives to add/modify a regression test variable\n namespace['variable'] = variables.TestVar\n namespace['required'] = variables.Undefined\n\n # Utility decorators\n namespace['_rfm_ext_bound'] = set()\n\n def bind(fn, name=None):\n '''Directive to bind a free function to a class.\n\n See online docs for more information.\n\n .. note::\n Functions bound using this directive must be re-inspected after\n the class body execution has completed. This directive attaches\n the external method into the class namespace and returns the\n associated instance of the :class:`WrappedFunction`. However,\n this instance may be further modified by other ReFrame builtins\n such as :func:`run_before`, :func:`run_after`, :func:`final` and\n so on after it was added to the namespace, which would bypass\n the logic implemented in the :func:`__setitem__` method from the\n :class:`MetaNamespace` class. Hence, we track the items set by\n this directive in the ``_rfm_ext_bound`` set, so they can be\n later re-inspected.\n '''\n\n inst = metacls.WrappedFunction(fn, name)\n namespace[inst.__name__] = inst\n\n # Track the imported external functions\n namespace['_rfm_ext_bound'].add(inst.__name__)\n return inst\n\n def final(fn):\n '''Indicate that a function is final and cannot be overridden.'''\n\n fn._rfm_final = True\n return fn\n\n namespace['bind'] = bind\n namespace['final'] = final\n namespace['_rfm_final_methods'] = set()\n\n # Hook-related functionality\n def run_before(stage):\n '''Decorator for attaching a test method to a given stage.\n\n See online docs for more information.\n '''\n return hooks.attach_to('pre_' + stage)\n\n def run_after(stage):\n '''Decorator for attaching a test method to a given stage.\n\n See online docs for more information.\n '''\n return hooks.attach_to('post_' + stage)\n\n namespace['run_before'] = run_before\n namespace['run_after'] = run_after\n namespace['require_deps'] = hooks.require_deps\n namespace['_rfm_hook_registry'] = hooks.HookRegistry()\n\n # Machinery to add a sanity function\n def sanity_function(fn):\n '''Mark a function as the test's sanity function.\n\n Decorated functions must be unary and they will be converted into\n deferred expressions.\n '''\n\n _def_fn = deferrable(fn)\n setattr(_def_fn, '_rfm_sanity_fn', True)\n return _def_fn\n\n namespace['sanity_function'] = sanity_function\n namespace['deferrable'] = deferrable\n\n # Machinery to add performance functions\n def performance_function(units, *, perf_key=None):\n '''Decorate a function to extract a performance variable.\n\n The ``units`` argument indicates the units of the performance\n variable to be extracted.\n The ``perf_key`` optional arg will be used as the name of the\n performance variable. If not provided, the function name will\n be used as the performance variable name.\n '''\n if not isinstance(units, str):\n raise TypeError('performance units must be a string')\n\n if perf_key and not isinstance(perf_key, str):\n raise TypeError(\"'perf_key' must be a string\")\n\n def _deco_wrapper(func):\n if not utils.is_trivially_callable(func, non_def_args=1):\n raise TypeError(\n f'performance function {func.__name__!r} has more '\n f'than one argument without a default value'\n )\n\n @functools.wraps(func)\n def _perf_fn(*args, **kwargs):\n return _DeferredPerformanceExpression(\n func, units, *args, **kwargs\n )\n\n _perf_key = perf_key if perf_key else func.__name__\n setattr(_perf_fn, '_rfm_perf_key', _perf_key)\n return _perf_fn\n\n return _deco_wrapper\n\n namespace['performance_function'] = performance_function\n namespace['_rfm_perf_fns'] = namespaces.LocalNamespace()\n return metacls.MetaNamespace(namespace)\n\n def __new__(metacls, name, bases, namespace, **kwargs):\n '''Remove directives from the class namespace.\n\n It does not make sense to have some directives available after the\n class was created or even at the instance level (e.g. doing\n ``self.parameter([1, 2, 3])`` does not make sense). So here, we\n intercept those directives out of the namespace before the class is\n constructed.\n '''\n\n directives = [\n 'parameter', 'variable', 'bind', 'run_before', 'run_after',\n 'require_deps', 'required', 'deferrable', 'sanity_function',\n 'final', 'performance_function'\n ]\n for b in directives:\n namespace.pop(b, None)\n\n # Reset the external functions imported through the bind directive.\n for item in namespace.pop('_rfm_ext_bound'):\n namespace.reset(item)\n\n return super().__new__(metacls, name, bases, dict(namespace), **kwargs)\n\n def __init__(cls, name, bases, namespace, **kwargs):\n super().__init__(name, bases, namespace, **kwargs)\n\n # Create a set with the attribute names already in use.\n cls._rfm_dir = set()\n for base in (b for b in bases if hasattr(b, '_rfm_dir')):\n cls._rfm_dir.update(base._rfm_dir)\n\n used_attribute_names = set(cls._rfm_dir)\n\n # Build the var space and extend the target namespace\n variables.VarSpace(cls, used_attribute_names)\n used_attribute_names.update(cls._rfm_var_space.vars)\n\n # Build the parameter space\n parameters.ParamSpace(cls, used_attribute_names)\n\n # Update used names set with the local __dict__\n cls._rfm_dir.update(cls.__dict__)\n\n # Update the hook registry with the bases\n for base in cls._rfm_bases:\n cls._rfm_hook_registry.update(\n base._rfm_hook_registry, denied_hooks=namespace\n )\n\n # Search the bases if no local sanity functions exist.\n if '_rfm_sanity' not in namespace:\n for base in cls._rfm_bases:\n if hasattr(base, '_rfm_sanity'):\n cls._rfm_sanity = getattr(base, '_rfm_sanity')\n if cls._rfm_sanity.__name__ in namespace:\n raise ReframeSyntaxError(\n f'{cls.__qualname__!r} overrides the candidate '\n f'sanity function '\n f'{cls._rfm_sanity.__qualname__!r} without '\n f'defining an alternative'\n )\n\n break\n\n # Update the performance function dict with the bases.\n for base in cls._rfm_bases:\n for k, v in base._rfm_perf_fns.items():\n if k not in namespace:\n try:\n cls._rfm_perf_fns[k] = v\n except KeyError:\n '''Performance function overridden by other class'''\n\n # Add the final functions from its parents\n cls._rfm_final_methods.update(\n *(b._rfm_final_methods for b in cls._rfm_bases)\n )\n\n if getattr(cls, '_rfm_override_final', None):\n return\n\n for b in cls._rfm_bases:\n for key in b._rfm_final_methods:\n if key in namespace and callable(namespace[key]):\n msg = (f\"'{cls.__qualname__}.{key}' attempts to \"\n f\"override final method \"\n f\"'{b.__qualname__}.{key}'; \"\n f\"you should use the pipeline hooks instead\")\n raise ReframeSyntaxError(msg)\n\n def __call__(cls, *args, **kwargs):\n '''Inject parameter and variable spaces during object construction.\n\n When a class is instantiated, this method intercepts the arguments\n associated to the parameter and variable spaces. This prevents both\n :func:`__new__` and :func:`__init__` methods from ever seing these\n arguments.\n\n The parameter and variable spaces are injected into the object after\n construction and before initialization.\n '''\n\n # Intercept constructor arguments\n _rfm_use_params = kwargs.pop('_rfm_use_params', False)\n\n obj = cls.__new__(cls, *args, **kwargs)\n\n # Insert the var & param spaces\n cls._rfm_var_space.inject(obj, cls)\n cls._rfm_param_space.inject(obj, cls, _rfm_use_params)\n\n obj.__init__(*args, **kwargs)\n return obj\n\n# MASKED: __getattribute__ function (lines 446-471)\n\n def __getattr__(cls, name):\n '''Backup attribute lookup method into custom namespaces.\n\n Some ReFrame built-in types are stored under their own sub-namespaces.\n This method will perform an attribute lookup on these sub-namespaces\n if a call to the default :func:`__getattribute__` method fails to\n retrieve the requested class attribute.\n '''\n\n try:\n var_space = super().__getattribute__('_rfm_var_space')\n return var_space.vars[name]\n except AttributeError:\n '''Catch early access attempt to the variable space.'''\n except KeyError:\n '''Requested name not in variable space.'''\n\n try:\n param_space = super().__getattribute__('_rfm_param_space')\n return param_space.params[name]\n except AttributeError:\n '''Catch early access attempt to the parameter space.'''\n except KeyError:\n '''Requested name not in parameter space.'''\n\n raise AttributeError(\n f'class {cls.__qualname__!r} has no attribute {name!r}'\n ) from None\n\n def setvar(cls, name, value):\n '''Set the value of a variable.\n\n :param name: The name of the variable.\n :param value: The value of the variable.\n\n :returns: :class:`True` if the variable was set.\n A variable will *not* be set, if it does not exist or when an\n attempt is made to set it with its underlying descriptor.\n This happens during the variable injection time and it should be\n delegated to the class' :func:`__setattr__` method.\n\n :raises ReframeSyntaxError: If an attempt is made to override a\n variable with a descriptor other than its underlying one.\n\n '''\n\n try:\n var_space = super().__getattribute__('_rfm_var_space')\n if name in var_space:\n if not hasattr(value, '__get__'):\n var_space[name].define(value)\n return True\n elif var_space[name].field is not value:\n desc = '.'.join([cls.__qualname__, name])\n raise ReframeSyntaxError(\n f'cannot override variable descriptor {desc!r}'\n )\n else:\n # Variable is being injected\n return False\n except AttributeError:\n '''Catch early access attempt to the variable space.'''\n return False\n\n def __setattr__(cls, name, value):\n '''Handle the special treatment required for variables and parameters.\n\n A variable's default value can be updated when accessed as a regular\n class attribute. This behavior does not apply when the assigned value\n is a descriptor object. In that case, the task of setting the value is\n delegated to the base :func:`__setattr__` (this is to comply with\n standard Python behavior). However, since the variables are already\n descriptors which are injected during class instantiation, we disallow\n any attempt to override this descriptor (since it would be silently\n re-overridden in any case).\n\n Altering the value of a parameter when accessed as a class attribute\n is not allowed. This would break the parameter space internals.\n '''\n\n # Try to treat `name` as variable\n if cls.setvar(name, value):\n return\n\n # Try to treat `name` as a parameter\n try:\n # Catch attempts to override a test parameter\n param_space = super().__getattribute__('_rfm_param_space')\n if name in param_space.params:\n raise ReframeSyntaxError(f'cannot override parameter {name!r}')\n except AttributeError:\n '''Catch early access attempt to the parameter space.'''\n\n # Treat `name` as normal class attribute\n super().__setattr__(name, value)\n\n @property\n def param_space(cls):\n ''' Make the parameter space available as read-only.'''\n return cls._rfm_param_space\n\n def is_abstract(cls):\n '''Check if the class is an abstract test.\n\n This is the case when some parameters are undefined, which results in\n the length of the parameter space being 0.\n\n :return: bool indicating whether the test has undefined parameters.\n\n :meta private:\n '''\n return len(cls.param_space) == 0"},"implementation":{"kind":"string","value":" def __getattribute__(cls, name):\n '''Attribute lookup method for custom class attributes.\n\n ReFrame test variables are descriptors injected at the class level.\n If a variable descriptor has already been injected into the class,\n do not return the descriptor object and return the default value\n associated with that variable instead.\n\n .. warning::\n .. versionchanged:: 3.7.0\n Prior versions exposed the variable descriptor object if this\n was already present in the class, instead of returning the\n variable's default value.\n '''\n\n try:\n var_space = super().__getattribute__('_rfm_var_space')\n except AttributeError:\n var_space = None\n\n # If the variable is already injected, delegate lookup to __getattr__.\n if var_space and name in var_space.injected_vars:\n raise AttributeError('delegate variable lookup to __getattr__')\n\n # Default back to the base method if no special treatment required.\n return super().__getattribute__(name)"},"start_line":{"kind":"number","value":446,"string":"446"},"end_line":{"kind":"number","value":471,"string":"471"},"file_content":{"kind":"string","value":"# Copyright 2016-2021 Swiss National Supercomputing Centre (CSCS/ETH Zurich)\n# ReFrame Project Developers. See the top-level LICENSE file for details.\n#\n# SPDX-License-Identifier: BSD-3-Clause\n\n#\n# Meta-class for creating regression tests.\n#\n\nimport functools\nimport types\n\nimport reframe.core.namespaces as namespaces\nimport reframe.core.parameters as parameters\nimport reframe.core.variables as variables\nimport reframe.core.hooks as hooks\nimport reframe.utility as utils\n\nfrom reframe.core.exceptions import ReframeSyntaxError\nfrom reframe.core.deferrable import deferrable, _DeferredPerformanceExpression\n\n\nclass RegressionTestMeta(type):\n\n class MetaNamespace(namespaces.LocalNamespace):\n '''Custom namespace to control the cls attribute assignment.\n\n Regular Python class attributes can be overridden by either\n parameters or variables respecting the order of execution.\n A variable or a parameter may not be declared more than once in the\n same class body. Overriding a variable with a parameter or the other\n way around has an undefined behavior. A variable's value may be\n updated multiple times within the same class body. A parameter's\n value cannot be updated more than once within the same class body.\n '''\n\n def __setitem__(self, key, value):\n if isinstance(value, variables.TestVar):\n # Insert the attribute in the variable namespace\n try:\n self['_rfm_local_var_space'][key] = value\n value.__set_name__(self, key)\n except KeyError:\n raise ReframeSyntaxError(\n f'variable {key!r} is already declared'\n ) from None\n\n # Override the regular class attribute (if present) and return\n self._namespace.pop(key, None)\n return\n\n elif isinstance(value, parameters.TestParam):\n # Insert the attribute in the parameter namespace\n try:\n self['_rfm_local_param_space'][key] = value\n except KeyError:\n raise ReframeSyntaxError(\n f'parameter {key!r} is already declared in this class'\n ) from None\n\n # Override the regular class attribute (if present) and return\n self._namespace.pop(key, None)\n return\n\n elif key in self['_rfm_local_param_space']:\n raise ReframeSyntaxError(\n f'cannot override parameter {key!r}'\n )\n else:\n # Insert the items manually to overide the namespace clash\n # check from the base namespace.\n self._namespace[key] = value\n\n # Register functions decorated with either @sanity_function or\n # @performance_variables or @performance_function decorators.\n if hasattr(value, '_rfm_sanity_fn'):\n try:\n super().__setitem__('_rfm_sanity', value)\n except KeyError:\n raise ReframeSyntaxError(\n 'the @sanity_function decorator can only be used '\n 'once in the class body'\n ) from None\n elif hasattr(value, '_rfm_perf_key'):\n try:\n self['_rfm_perf_fns'][key] = value\n except KeyError:\n raise ReframeSyntaxError(\n f'the performance function {key!r} has already been '\n f'defined in this class'\n ) from None\n\n # Register the final methods\n if hasattr(value, '_rfm_final'):\n self['_rfm_final_methods'].add(key)\n\n # Register the hooks - if a value does not meet the conditions\n # it will be simply ignored\n self['_rfm_hook_registry'].add(value)\n\n def __getitem__(self, key):\n '''Expose and control access to the local namespaces.\n\n Variables may only be retrieved if their value has been previously\n set. Accessing a parameter in the class body is disallowed (the\n actual test parameter is set during the class instantiation).\n '''\n\n try:\n return super().__getitem__(key)\n except KeyError as err:\n try:\n # Handle variable access\n return self['_rfm_local_var_space'][key]\n\n except KeyError:\n # Handle parameter access\n if key in self['_rfm_local_param_space']:\n raise ReframeSyntaxError(\n 'accessing a test parameter from the class '\n 'body is disallowed'\n ) from None\n else:\n # As the last resource, look if key is a variable in\n # any of the base classes. If so, make its value\n # available in the current class' namespace.\n for b in self['_rfm_bases']:\n if key in b._rfm_var_space:\n # Store a deep-copy of the variable's\n # value and return.\n v = b._rfm_var_space[key].default_value\n self._namespace[key] = v\n return self._namespace[key]\n\n # If 'key' is neither a variable nor a parameter,\n # raise the exception from the base __getitem__.\n raise err from None\n\n def reset(self, key):\n '''Reset an item to rerun it through the __setitem__ logic.'''\n self[key] = self[key]\n\n class WrappedFunction:\n '''Descriptor to wrap a free function as a bound-method.\n\n The free function object is wrapped by the constructor. Instances\n of this class should be inserted into the namespace of the target class\n with the desired name for the bound-method. Since this class is a\n descriptor, the `__get__` method will return the right bound-method\n when accessed from a class instance.\n\n :meta private:\n '''\n\n __slots__ = ('fn')\n\n def __init__(self, fn, name=None):\n @functools.wraps(fn)\n def _fn(*args, **kwargs):\n return fn(*args, **kwargs)\n\n self.fn = _fn\n if name:\n self.fn.__name__ = name\n\n def __get__(self, obj, objtype=None):\n if objtype is None:\n objtype = type(obj)\n\n self.fn.__qualname__ = '.'.join(\n [objtype.__qualname__, self.fn.__name__]\n )\n if obj is None:\n return self.fn\n\n return types.MethodType(self.fn, obj)\n\n def __call__(self, *args, **kwargs):\n return self.fn(*args, **kwargs)\n\n def __getattr__(self, name):\n if name in self.__slots__:\n return super().__getattr__(name)\n else:\n return getattr(self.fn, name)\n\n def __setattr__(self, name, value):\n if name in self.__slots__:\n super().__setattr__(name, value)\n else:\n setattr(self.fn, name, value)\n\n @classmethod\n def __prepare__(metacls, name, bases, **kwargs):\n namespace = super().__prepare__(name, bases, **kwargs)\n\n # Keep reference to the bases inside the namespace\n namespace['_rfm_bases'] = [\n b for b in bases if hasattr(b, '_rfm_var_space')\n ]\n\n # Regression test parameter space defined at the class level\n local_param_space = namespaces.LocalNamespace()\n namespace['_rfm_local_param_space'] = local_param_space\n\n # Directive to insert a regression test parameter directly in the\n # class body as: `P0 = parameter([0,1,2,3])`.\n namespace['parameter'] = parameters.TestParam\n\n # Regression test var space defined at the class level\n local_var_space = namespaces.LocalNamespace()\n namespace['_rfm_local_var_space'] = local_var_space\n\n # Directives to add/modify a regression test variable\n namespace['variable'] = variables.TestVar\n namespace['required'] = variables.Undefined\n\n # Utility decorators\n namespace['_rfm_ext_bound'] = set()\n\n def bind(fn, name=None):\n '''Directive to bind a free function to a class.\n\n See online docs for more information.\n\n .. note::\n Functions bound using this directive must be re-inspected after\n the class body execution has completed. This directive attaches\n the external method into the class namespace and returns the\n associated instance of the :class:`WrappedFunction`. However,\n this instance may be further modified by other ReFrame builtins\n such as :func:`run_before`, :func:`run_after`, :func:`final` and\n so on after it was added to the namespace, which would bypass\n the logic implemented in the :func:`__setitem__` method from the\n :class:`MetaNamespace` class. Hence, we track the items set by\n this directive in the ``_rfm_ext_bound`` set, so they can be\n later re-inspected.\n '''\n\n inst = metacls.WrappedFunction(fn, name)\n namespace[inst.__name__] = inst\n\n # Track the imported external functions\n namespace['_rfm_ext_bound'].add(inst.__name__)\n return inst\n\n def final(fn):\n '''Indicate that a function is final and cannot be overridden.'''\n\n fn._rfm_final = True\n return fn\n\n namespace['bind'] = bind\n namespace['final'] = final\n namespace['_rfm_final_methods'] = set()\n\n # Hook-related functionality\n def run_before(stage):\n '''Decorator for attaching a test method to a given stage.\n\n See online docs for more information.\n '''\n return hooks.attach_to('pre_' + stage)\n\n def run_after(stage):\n '''Decorator for attaching a test method to a given stage.\n\n See online docs for more information.\n '''\n return hooks.attach_to('post_' + stage)\n\n namespace['run_before'] = run_before\n namespace['run_after'] = run_after\n namespace['require_deps'] = hooks.require_deps\n namespace['_rfm_hook_registry'] = hooks.HookRegistry()\n\n # Machinery to add a sanity function\n def sanity_function(fn):\n '''Mark a function as the test's sanity function.\n\n Decorated functions must be unary and they will be converted into\n deferred expressions.\n '''\n\n _def_fn = deferrable(fn)\n setattr(_def_fn, '_rfm_sanity_fn', True)\n return _def_fn\n\n namespace['sanity_function'] = sanity_function\n namespace['deferrable'] = deferrable\n\n # Machinery to add performance functions\n def performance_function(units, *, perf_key=None):\n '''Decorate a function to extract a performance variable.\n\n The ``units`` argument indicates the units of the performance\n variable to be extracted.\n The ``perf_key`` optional arg will be used as the name of the\n performance variable. If not provided, the function name will\n be used as the performance variable name.\n '''\n if not isinstance(units, str):\n raise TypeError('performance units must be a string')\n\n if perf_key and not isinstance(perf_key, str):\n raise TypeError(\"'perf_key' must be a string\")\n\n def _deco_wrapper(func):\n if not utils.is_trivially_callable(func, non_def_args=1):\n raise TypeError(\n f'performance function {func.__name__!r} has more '\n f'than one argument without a default value'\n )\n\n @functools.wraps(func)\n def _perf_fn(*args, **kwargs):\n return _DeferredPerformanceExpression(\n func, units, *args, **kwargs\n )\n\n _perf_key = perf_key if perf_key else func.__name__\n setattr(_perf_fn, '_rfm_perf_key', _perf_key)\n return _perf_fn\n\n return _deco_wrapper\n\n namespace['performance_function'] = performance_function\n namespace['_rfm_perf_fns'] = namespaces.LocalNamespace()\n return metacls.MetaNamespace(namespace)\n\n def __new__(metacls, name, bases, namespace, **kwargs):\n '''Remove directives from the class namespace.\n\n It does not make sense to have some directives available after the\n class was created or even at the instance level (e.g. doing\n ``self.parameter([1, 2, 3])`` does not make sense). So here, we\n intercept those directives out of the namespace before the class is\n constructed.\n '''\n\n directives = [\n 'parameter', 'variable', 'bind', 'run_before', 'run_after',\n 'require_deps', 'required', 'deferrable', 'sanity_function',\n 'final', 'performance_function'\n ]\n for b in directives:\n namespace.pop(b, None)\n\n # Reset the external functions imported through the bind directive.\n for item in namespace.pop('_rfm_ext_bound'):\n namespace.reset(item)\n\n return super().__new__(metacls, name, bases, dict(namespace), **kwargs)\n\n def __init__(cls, name, bases, namespace, **kwargs):\n super().__init__(name, bases, namespace, **kwargs)\n\n # Create a set with the attribute names already in use.\n cls._rfm_dir = set()\n for base in (b for b in bases if hasattr(b, '_rfm_dir')):\n cls._rfm_dir.update(base._rfm_dir)\n\n used_attribute_names = set(cls._rfm_dir)\n\n # Build the var space and extend the target namespace\n variables.VarSpace(cls, used_attribute_names)\n used_attribute_names.update(cls._rfm_var_space.vars)\n\n # Build the parameter space\n parameters.ParamSpace(cls, used_attribute_names)\n\n # Update used names set with the local __dict__\n cls._rfm_dir.update(cls.__dict__)\n\n # Update the hook registry with the bases\n for base in cls._rfm_bases:\n cls._rfm_hook_registry.update(\n base._rfm_hook_registry, denied_hooks=namespace\n )\n\n # Search the bases if no local sanity functions exist.\n if '_rfm_sanity' not in namespace:\n for base in cls._rfm_bases:\n if hasattr(base, '_rfm_sanity'):\n cls._rfm_sanity = getattr(base, '_rfm_sanity')\n if cls._rfm_sanity.__name__ in namespace:\n raise ReframeSyntaxError(\n f'{cls.__qualname__!r} overrides the candidate '\n f'sanity function '\n f'{cls._rfm_sanity.__qualname__!r} without '\n f'defining an alternative'\n )\n\n break\n\n # Update the performance function dict with the bases.\n for base in cls._rfm_bases:\n for k, v in base._rfm_perf_fns.items():\n if k not in namespace:\n try:\n cls._rfm_perf_fns[k] = v\n except KeyError:\n '''Performance function overridden by other class'''\n\n # Add the final functions from its parents\n cls._rfm_final_methods.update(\n *(b._rfm_final_methods for b in cls._rfm_bases)\n )\n\n if getattr(cls, '_rfm_override_final', None):\n return\n\n for b in cls._rfm_bases:\n for key in b._rfm_final_methods:\n if key in namespace and callable(namespace[key]):\n msg = (f\"'{cls.__qualname__}.{key}' attempts to \"\n f\"override final method \"\n f\"'{b.__qualname__}.{key}'; \"\n f\"you should use the pipeline hooks instead\")\n raise ReframeSyntaxError(msg)\n\n def __call__(cls, *args, **kwargs):\n '''Inject parameter and variable spaces during object construction.\n\n When a class is instantiated, this method intercepts the arguments\n associated to the parameter and variable spaces. This prevents both\n :func:`__new__` and :func:`__init__` methods from ever seing these\n arguments.\n\n The parameter and variable spaces are injected into the object after\n construction and before initialization.\n '''\n\n # Intercept constructor arguments\n _rfm_use_params = kwargs.pop('_rfm_use_params', False)\n\n obj = cls.__new__(cls, *args, **kwargs)\n\n # Insert the var & param spaces\n cls._rfm_var_space.inject(obj, cls)\n cls._rfm_param_space.inject(obj, cls, _rfm_use_params)\n\n obj.__init__(*args, **kwargs)\n return obj\n\n def __getattribute__(cls, name):\n '''Attribute lookup method for custom class attributes.\n\n ReFrame test variables are descriptors injected at the class level.\n If a variable descriptor has already been injected into the class,\n do not return the descriptor object and return the default value\n associated with that variable instead.\n\n .. warning::\n .. versionchanged:: 3.7.0\n Prior versions exposed the variable descriptor object if this\n was already present in the class, instead of returning the\n variable's default value.\n '''\n\n try:\n var_space = super().__getattribute__('_rfm_var_space')\n except AttributeError:\n var_space = None\n\n # If the variable is already injected, delegate lookup to __getattr__.\n if var_space and name in var_space.injected_vars:\n raise AttributeError('delegate variable lookup to __getattr__')\n\n # Default back to the base method if no special treatment required.\n return super().__getattribute__(name)\n\n def __getattr__(cls, name):\n '''Backup attribute lookup method into custom namespaces.\n\n Some ReFrame built-in types are stored under their own sub-namespaces.\n This method will perform an attribute lookup on these sub-namespaces\n if a call to the default :func:`__getattribute__` method fails to\n retrieve the requested class attribute.\n '''\n\n try:\n var_space = super().__getattribute__('_rfm_var_space')\n return var_space.vars[name]\n except AttributeError:\n '''Catch early access attempt to the variable space.'''\n except KeyError:\n '''Requested name not in variable space.'''\n\n try:\n param_space = super().__getattribute__('_rfm_param_space')\n return param_space.params[name]\n except AttributeError:\n '''Catch early access attempt to the parameter space.'''\n except KeyError:\n '''Requested name not in parameter space.'''\n\n raise AttributeError(\n f'class {cls.__qualname__!r} has no attribute {name!r}'\n ) from None\n\n def setvar(cls, name, value):\n '''Set the value of a variable.\n\n :param name: The name of the variable.\n :param value: The value of the variable.\n\n :returns: :class:`True` if the variable was set.\n A variable will *not* be set, if it does not exist or when an\n attempt is made to set it with its underlying descriptor.\n This happens during the variable injection time and it should be\n delegated to the class' :func:`__setattr__` method.\n\n :raises ReframeSyntaxError: If an attempt is made to override a\n variable with a descriptor other than its underlying one.\n\n '''\n\n try:\n var_space = super().__getattribute__('_rfm_var_space')\n if name in var_space:\n if not hasattr(value, '__get__'):\n var_space[name].define(value)\n return True\n elif var_space[name].field is not value:\n desc = '.'.join([cls.__qualname__, name])\n raise ReframeSyntaxError(\n f'cannot override variable descriptor {desc!r}'\n )\n else:\n # Variable is being injected\n return False\n except AttributeError:\n '''Catch early access attempt to the variable space.'''\n return False\n\n def __setattr__(cls, name, value):\n '''Handle the special treatment required for variables and parameters.\n\n A variable's default value can be updated when accessed as a regular\n class attribute. This behavior does not apply when the assigned value\n is a descriptor object. In that case, the task of setting the value is\n delegated to the base :func:`__setattr__` (this is to comply with\n standard Python behavior). However, since the variables are already\n descriptors which are injected during class instantiation, we disallow\n any attempt to override this descriptor (since it would be silently\n re-overridden in any case).\n\n Altering the value of a parameter when accessed as a class attribute\n is not allowed. This would break the parameter space internals.\n '''\n\n # Try to treat `name` as variable\n if cls.setvar(name, value):\n return\n\n # Try to treat `name` as a parameter\n try:\n # Catch attempts to override a test parameter\n param_space = super().__getattribute__('_rfm_param_space')\n if name in param_space.params:\n raise ReframeSyntaxError(f'cannot override parameter {name!r}')\n except AttributeError:\n '''Catch early access attempt to the parameter space.'''\n\n # Treat `name` as normal class attribute\n super().__setattr__(name, value)\n\n @property\n def param_space(cls):\n ''' Make the parameter space available as read-only.'''\n return cls._rfm_param_space\n\n def is_abstract(cls):\n '''Check if the class is an abstract test.\n\n This is the case when some parameters are undefined, which results in\n the length of the parameter space being 0.\n\n :return: bool indicating whether the test has undefined parameters.\n\n :meta private:\n '''\n return len(cls.param_space) == 0\n"}}},{"rowIdx":57,"cells":{"function_name":{"kind":"string","value":"__getattr__"},"docstring":{"kind":"string","value":"Backup attribute lookup method into custom namespaces.\n\nSome ReFrame built-in types are stored under their own sub-namespaces.\nThis method will perform an attribute lookup on these sub-namespaces\nif a call to the default :func:`__getattribute__` method fails to\nretrieve the requested class attribute."},"masked_code":{"kind":"string","value":"# Copyright 2016-2021 Swiss National Supercomputing Centre (CSCS/ETH Zurich)\n# ReFrame Project Developers. See the top-level LICENSE file for details.\n#\n# SPDX-License-Identifier: BSD-3-Clause\n\n#\n# Meta-class for creating regression tests.\n#\n\nimport functools\nimport types\n\nimport reframe.core.namespaces as namespaces\nimport reframe.core.parameters as parameters\nimport reframe.core.variables as variables\nimport reframe.core.hooks as hooks\nimport reframe.utility as utils\n\nfrom reframe.core.exceptions import ReframeSyntaxError\nfrom reframe.core.deferrable import deferrable, _DeferredPerformanceExpression\n\n\nclass RegressionTestMeta(type):\n\n class MetaNamespace(namespaces.LocalNamespace):\n '''Custom namespace to control the cls attribute assignment.\n\n Regular Python class attributes can be overridden by either\n parameters or variables respecting the order of execution.\n A variable or a parameter may not be declared more than once in the\n same class body. Overriding a variable with a parameter or the other\n way around has an undefined behavior. A variable's value may be\n updated multiple times within the same class body. A parameter's\n value cannot be updated more than once within the same class body.\n '''\n\n def __setitem__(self, key, value):\n if isinstance(value, variables.TestVar):\n # Insert the attribute in the variable namespace\n try:\n self['_rfm_local_var_space'][key] = value\n value.__set_name__(self, key)\n except KeyError:\n raise ReframeSyntaxError(\n f'variable {key!r} is already declared'\n ) from None\n\n # Override the regular class attribute (if present) and return\n self._namespace.pop(key, None)\n return\n\n elif isinstance(value, parameters.TestParam):\n # Insert the attribute in the parameter namespace\n try:\n self['_rfm_local_param_space'][key] = value\n except KeyError:\n raise ReframeSyntaxError(\n f'parameter {key!r} is already declared in this class'\n ) from None\n\n # Override the regular class attribute (if present) and return\n self._namespace.pop(key, None)\n return\n\n elif key in self['_rfm_local_param_space']:\n raise ReframeSyntaxError(\n f'cannot override parameter {key!r}'\n )\n else:\n # Insert the items manually to overide the namespace clash\n # check from the base namespace.\n self._namespace[key] = value\n\n # Register functions decorated with either @sanity_function or\n # @performance_variables or @performance_function decorators.\n if hasattr(value, '_rfm_sanity_fn'):\n try:\n super().__setitem__('_rfm_sanity', value)\n except KeyError:\n raise ReframeSyntaxError(\n 'the @sanity_function decorator can only be used '\n 'once in the class body'\n ) from None\n elif hasattr(value, '_rfm_perf_key'):\n try:\n self['_rfm_perf_fns'][key] = value\n except KeyError:\n raise ReframeSyntaxError(\n f'the performance function {key!r} has already been '\n f'defined in this class'\n ) from None\n\n # Register the final methods\n if hasattr(value, '_rfm_final'):\n self['_rfm_final_methods'].add(key)\n\n # Register the hooks - if a value does not meet the conditions\n # it will be simply ignored\n self['_rfm_hook_registry'].add(value)\n\n def __getitem__(self, key):\n '''Expose and control access to the local namespaces.\n\n Variables may only be retrieved if their value has been previously\n set. Accessing a parameter in the class body is disallowed (the\n actual test parameter is set during the class instantiation).\n '''\n\n try:\n return super().__getitem__(key)\n except KeyError as err:\n try:\n # Handle variable access\n return self['_rfm_local_var_space'][key]\n\n except KeyError:\n # Handle parameter access\n if key in self['_rfm_local_param_space']:\n raise ReframeSyntaxError(\n 'accessing a test parameter from the class '\n 'body is disallowed'\n ) from None\n else:\n # As the last resource, look if key is a variable in\n # any of the base classes. If so, make its value\n # available in the current class' namespace.\n for b in self['_rfm_bases']:\n if key in b._rfm_var_space:\n # Store a deep-copy of the variable's\n # value and return.\n v = b._rfm_var_space[key].default_value\n self._namespace[key] = v\n return self._namespace[key]\n\n # If 'key' is neither a variable nor a parameter,\n # raise the exception from the base __getitem__.\n raise err from None\n\n def reset(self, key):\n '''Reset an item to rerun it through the __setitem__ logic.'''\n self[key] = self[key]\n\n class WrappedFunction:\n '''Descriptor to wrap a free function as a bound-method.\n\n The free function object is wrapped by the constructor. Instances\n of this class should be inserted into the namespace of the target class\n with the desired name for the bound-method. Since this class is a\n descriptor, the `__get__` method will return the right bound-method\n when accessed from a class instance.\n\n :meta private:\n '''\n\n __slots__ = ('fn')\n\n def __init__(self, fn, name=None):\n @functools.wraps(fn)\n def _fn(*args, **kwargs):\n return fn(*args, **kwargs)\n\n self.fn = _fn\n if name:\n self.fn.__name__ = name\n\n def __get__(self, obj, objtype=None):\n if objtype is None:\n objtype = type(obj)\n\n self.fn.__qualname__ = '.'.join(\n [objtype.__qualname__, self.fn.__name__]\n )\n if obj is None:\n return self.fn\n\n return types.MethodType(self.fn, obj)\n\n def __call__(self, *args, **kwargs):\n return self.fn(*args, **kwargs)\n\n def __getattr__(self, name):\n if name in self.__slots__:\n return super().__getattr__(name)\n else:\n return getattr(self.fn, name)\n\n def __setattr__(self, name, value):\n if name in self.__slots__:\n super().__setattr__(name, value)\n else:\n setattr(self.fn, name, value)\n\n @classmethod\n def __prepare__(metacls, name, bases, **kwargs):\n namespace = super().__prepare__(name, bases, **kwargs)\n\n # Keep reference to the bases inside the namespace\n namespace['_rfm_bases'] = [\n b for b in bases if hasattr(b, '_rfm_var_space')\n ]\n\n # Regression test parameter space defined at the class level\n local_param_space = namespaces.LocalNamespace()\n namespace['_rfm_local_param_space'] = local_param_space\n\n # Directive to insert a regression test parameter directly in the\n # class body as: `P0 = parameter([0,1,2,3])`.\n namespace['parameter'] = parameters.TestParam\n\n # Regression test var space defined at the class level\n local_var_space = namespaces.LocalNamespace()\n namespace['_rfm_local_var_space'] = local_var_space\n\n # Directives to add/modify a regression test variable\n namespace['variable'] = variables.TestVar\n namespace['required'] = variables.Undefined\n\n # Utility decorators\n namespace['_rfm_ext_bound'] = set()\n\n def bind(fn, name=None):\n '''Directive to bind a free function to a class.\n\n See online docs for more information.\n\n .. note::\n Functions bound using this directive must be re-inspected after\n the class body execution has completed. This directive attaches\n the external method into the class namespace and returns the\n associated instance of the :class:`WrappedFunction`. However,\n this instance may be further modified by other ReFrame builtins\n such as :func:`run_before`, :func:`run_after`, :func:`final` and\n so on after it was added to the namespace, which would bypass\n the logic implemented in the :func:`__setitem__` method from the\n :class:`MetaNamespace` class. Hence, we track the items set by\n this directive in the ``_rfm_ext_bound`` set, so they can be\n later re-inspected.\n '''\n\n inst = metacls.WrappedFunction(fn, name)\n namespace[inst.__name__] = inst\n\n # Track the imported external functions\n namespace['_rfm_ext_bound'].add(inst.__name__)\n return inst\n\n def final(fn):\n '''Indicate that a function is final and cannot be overridden.'''\n\n fn._rfm_final = True\n return fn\n\n namespace['bind'] = bind\n namespace['final'] = final\n namespace['_rfm_final_methods'] = set()\n\n # Hook-related functionality\n def run_before(stage):\n '''Decorator for attaching a test method to a given stage.\n\n See online docs for more information.\n '''\n return hooks.attach_to('pre_' + stage)\n\n def run_after(stage):\n '''Decorator for attaching a test method to a given stage.\n\n See online docs for more information.\n '''\n return hooks.attach_to('post_' + stage)\n\n namespace['run_before'] = run_before\n namespace['run_after'] = run_after\n namespace['require_deps'] = hooks.require_deps\n namespace['_rfm_hook_registry'] = hooks.HookRegistry()\n\n # Machinery to add a sanity function\n def sanity_function(fn):\n '''Mark a function as the test's sanity function.\n\n Decorated functions must be unary and they will be converted into\n deferred expressions.\n '''\n\n _def_fn = deferrable(fn)\n setattr(_def_fn, '_rfm_sanity_fn', True)\n return _def_fn\n\n namespace['sanity_function'] = sanity_function\n namespace['deferrable'] = deferrable\n\n # Machinery to add performance functions\n def performance_function(units, *, perf_key=None):\n '''Decorate a function to extract a performance variable.\n\n The ``units`` argument indicates the units of the performance\n variable to be extracted.\n The ``perf_key`` optional arg will be used as the name of the\n performance variable. If not provided, the function name will\n be used as the performance variable name.\n '''\n if not isinstance(units, str):\n raise TypeError('performance units must be a string')\n\n if perf_key and not isinstance(perf_key, str):\n raise TypeError(\"'perf_key' must be a string\")\n\n def _deco_wrapper(func):\n if not utils.is_trivially_callable(func, non_def_args=1):\n raise TypeError(\n f'performance function {func.__name__!r} has more '\n f'than one argument without a default value'\n )\n\n @functools.wraps(func)\n def _perf_fn(*args, **kwargs):\n return _DeferredPerformanceExpression(\n func, units, *args, **kwargs\n )\n\n _perf_key = perf_key if perf_key else func.__name__\n setattr(_perf_fn, '_rfm_perf_key', _perf_key)\n return _perf_fn\n\n return _deco_wrapper\n\n namespace['performance_function'] = performance_function\n namespace['_rfm_perf_fns'] = namespaces.LocalNamespace()\n return metacls.MetaNamespace(namespace)\n\n def __new__(metacls, name, bases, namespace, **kwargs):\n '''Remove directives from the class namespace.\n\n It does not make sense to have some directives available after the\n class was created or even at the instance level (e.g. doing\n ``self.parameter([1, 2, 3])`` does not make sense). So here, we\n intercept those directives out of the namespace before the class is\n constructed.\n '''\n\n directives = [\n 'parameter', 'variable', 'bind', 'run_before', 'run_after',\n 'require_deps', 'required', 'deferrable', 'sanity_function',\n 'final', 'performance_function'\n ]\n for b in directives:\n namespace.pop(b, None)\n\n # Reset the external functions imported through the bind directive.\n for item in namespace.pop('_rfm_ext_bound'):\n namespace.reset(item)\n\n return super().__new__(metacls, name, bases, dict(namespace), **kwargs)\n\n def __init__(cls, name, bases, namespace, **kwargs):\n super().__init__(name, bases, namespace, **kwargs)\n\n # Create a set with the attribute names already in use.\n cls._rfm_dir = set()\n for base in (b for b in bases if hasattr(b, '_rfm_dir')):\n cls._rfm_dir.update(base._rfm_dir)\n\n used_attribute_names = set(cls._rfm_dir)\n\n # Build the var space and extend the target namespace\n variables.VarSpace(cls, used_attribute_names)\n used_attribute_names.update(cls._rfm_var_space.vars)\n\n # Build the parameter space\n parameters.ParamSpace(cls, used_attribute_names)\n\n # Update used names set with the local __dict__\n cls._rfm_dir.update(cls.__dict__)\n\n # Update the hook registry with the bases\n for base in cls._rfm_bases:\n cls._rfm_hook_registry.update(\n base._rfm_hook_registry, denied_hooks=namespace\n )\n\n # Search the bases if no local sanity functions exist.\n if '_rfm_sanity' not in namespace:\n for base in cls._rfm_bases:\n if hasattr(base, '_rfm_sanity'):\n cls._rfm_sanity = getattr(base, '_rfm_sanity')\n if cls._rfm_sanity.__name__ in namespace:\n raise ReframeSyntaxError(\n f'{cls.__qualname__!r} overrides the candidate '\n f'sanity function '\n f'{cls._rfm_sanity.__qualname__!r} without '\n f'defining an alternative'\n )\n\n break\n\n # Update the performance function dict with the bases.\n for base in cls._rfm_bases:\n for k, v in base._rfm_perf_fns.items():\n if k not in namespace:\n try:\n cls._rfm_perf_fns[k] = v\n except KeyError:\n '''Performance function overridden by other class'''\n\n # Add the final functions from its parents\n cls._rfm_final_methods.update(\n *(b._rfm_final_methods for b in cls._rfm_bases)\n )\n\n if getattr(cls, '_rfm_override_final', None):\n return\n\n for b in cls._rfm_bases:\n for key in b._rfm_final_methods:\n if key in namespace and callable(namespace[key]):\n msg = (f\"'{cls.__qualname__}.{key}' attempts to \"\n f\"override final method \"\n f\"'{b.__qualname__}.{key}'; \"\n f\"you should use the pipeline hooks instead\")\n raise ReframeSyntaxError(msg)\n\n def __call__(cls, *args, **kwargs):\n '''Inject parameter and variable spaces during object construction.\n\n When a class is instantiated, this method intercepts the arguments\n associated to the parameter and variable spaces. This prevents both\n :func:`__new__` and :func:`__init__` methods from ever seing these\n arguments.\n\n The parameter and variable spaces are injected into the object after\n construction and before initialization.\n '''\n\n # Intercept constructor arguments\n _rfm_use_params = kwargs.pop('_rfm_use_params', False)\n\n obj = cls.__new__(cls, *args, **kwargs)\n\n # Insert the var & param spaces\n cls._rfm_var_space.inject(obj, cls)\n cls._rfm_param_space.inject(obj, cls, _rfm_use_params)\n\n obj.__init__(*args, **kwargs)\n return obj\n\n def __getattribute__(cls, name):\n '''Attribute lookup method for custom class attributes.\n\n ReFrame test variables are descriptors injected at the class level.\n If a variable descriptor has already been injected into the class,\n do not return the descriptor object and return the default value\n associated with that variable instead.\n\n .. warning::\n .. versionchanged:: 3.7.0\n Prior versions exposed the variable descriptor object if this\n was already present in the class, instead of returning the\n variable's default value.\n '''\n\n try:\n var_space = super().__getattribute__('_rfm_var_space')\n except AttributeError:\n var_space = None\n\n # If the variable is already injected, delegate lookup to __getattr__.\n if var_space and name in var_space.injected_vars:\n raise AttributeError('delegate variable lookup to __getattr__')\n\n # Default back to the base method if no special treatment required.\n return super().__getattribute__(name)\n\n# MASKED: __getattr__ function (lines 473-500)\n\n def setvar(cls, name, value):\n '''Set the value of a variable.\n\n :param name: The name of the variable.\n :param value: The value of the variable.\n\n :returns: :class:`True` if the variable was set.\n A variable will *not* be set, if it does not exist or when an\n attempt is made to set it with its underlying descriptor.\n This happens during the variable injection time and it should be\n delegated to the class' :func:`__setattr__` method.\n\n :raises ReframeSyntaxError: If an attempt is made to override a\n variable with a descriptor other than its underlying one.\n\n '''\n\n try:\n var_space = super().__getattribute__('_rfm_var_space')\n if name in var_space:\n if not hasattr(value, '__get__'):\n var_space[name].define(value)\n return True\n elif var_space[name].field is not value:\n desc = '.'.join([cls.__qualname__, name])\n raise ReframeSyntaxError(\n f'cannot override variable descriptor {desc!r}'\n )\n else:\n # Variable is being injected\n return False\n except AttributeError:\n '''Catch early access attempt to the variable space.'''\n return False\n\n def __setattr__(cls, name, value):\n '''Handle the special treatment required for variables and parameters.\n\n A variable's default value can be updated when accessed as a regular\n class attribute. This behavior does not apply when the assigned value\n is a descriptor object. In that case, the task of setting the value is\n delegated to the base :func:`__setattr__` (this is to comply with\n standard Python behavior). However, since the variables are already\n descriptors which are injected during class instantiation, we disallow\n any attempt to override this descriptor (since it would be silently\n re-overridden in any case).\n\n Altering the value of a parameter when accessed as a class attribute\n is not allowed. This would break the parameter space internals.\n '''\n\n # Try to treat `name` as variable\n if cls.setvar(name, value):\n return\n\n # Try to treat `name` as a parameter\n try:\n # Catch attempts to override a test parameter\n param_space = super().__getattribute__('_rfm_param_space')\n if name in param_space.params:\n raise ReframeSyntaxError(f'cannot override parameter {name!r}')\n except AttributeError:\n '''Catch early access attempt to the parameter space.'''\n\n # Treat `name` as normal class attribute\n super().__setattr__(name, value)\n\n @property\n def param_space(cls):\n ''' Make the parameter space available as read-only.'''\n return cls._rfm_param_space\n\n def is_abstract(cls):\n '''Check if the class is an abstract test.\n\n This is the case when some parameters are undefined, which results in\n the length of the parameter space being 0.\n\n :return: bool indicating whether the test has undefined parameters.\n\n :meta private:\n '''\n return len(cls.param_space) == 0"},"implementation":{"kind":"string","value":" def __getattr__(cls, name):\n '''Backup attribute lookup method into custom namespaces.\n\n Some ReFrame built-in types are stored under their own sub-namespaces.\n This method will perform an attribute lookup on these sub-namespaces\n if a call to the default :func:`__getattribute__` method fails to\n retrieve the requested class attribute.\n '''\n\n try:\n var_space = super().__getattribute__('_rfm_var_space')\n return var_space.vars[name]\n except AttributeError:\n '''Catch early access attempt to the variable space.'''\n except KeyError:\n '''Requested name not in variable space.'''\n\n try:\n param_space = super().__getattribute__('_rfm_param_space')\n return param_space.params[name]\n except AttributeError:\n '''Catch early access attempt to the parameter space.'''\n except KeyError:\n '''Requested name not in parameter space.'''\n\n raise AttributeError(\n f'class {cls.__qualname__!r} has no attribute {name!r}'\n ) from None"},"start_line":{"kind":"number","value":473,"string":"473"},"end_line":{"kind":"number","value":500,"string":"500"},"file_content":{"kind":"string","value":"# Copyright 2016-2021 Swiss National Supercomputing Centre (CSCS/ETH Zurich)\n# ReFrame Project Developers. See the top-level LICENSE file for details.\n#\n# SPDX-License-Identifier: BSD-3-Clause\n\n#\n# Meta-class for creating regression tests.\n#\n\nimport functools\nimport types\n\nimport reframe.core.namespaces as namespaces\nimport reframe.core.parameters as parameters\nimport reframe.core.variables as variables\nimport reframe.core.hooks as hooks\nimport reframe.utility as utils\n\nfrom reframe.core.exceptions import ReframeSyntaxError\nfrom reframe.core.deferrable import deferrable, _DeferredPerformanceExpression\n\n\nclass RegressionTestMeta(type):\n\n class MetaNamespace(namespaces.LocalNamespace):\n '''Custom namespace to control the cls attribute assignment.\n\n Regular Python class attributes can be overridden by either\n parameters or variables respecting the order of execution.\n A variable or a parameter may not be declared more than once in the\n same class body. Overriding a variable with a parameter or the other\n way around has an undefined behavior. A variable's value may be\n updated multiple times within the same class body. A parameter's\n value cannot be updated more than once within the same class body.\n '''\n\n def __setitem__(self, key, value):\n if isinstance(value, variables.TestVar):\n # Insert the attribute in the variable namespace\n try:\n self['_rfm_local_var_space'][key] = value\n value.__set_name__(self, key)\n except KeyError:\n raise ReframeSyntaxError(\n f'variable {key!r} is already declared'\n ) from None\n\n # Override the regular class attribute (if present) and return\n self._namespace.pop(key, None)\n return\n\n elif isinstance(value, parameters.TestParam):\n # Insert the attribute in the parameter namespace\n try:\n self['_rfm_local_param_space'][key] = value\n except KeyError:\n raise ReframeSyntaxError(\n f'parameter {key!r} is already declared in this class'\n ) from None\n\n # Override the regular class attribute (if present) and return\n self._namespace.pop(key, None)\n return\n\n elif key in self['_rfm_local_param_space']:\n raise ReframeSyntaxError(\n f'cannot override parameter {key!r}'\n )\n else:\n # Insert the items manually to overide the namespace clash\n # check from the base namespace.\n self._namespace[key] = value\n\n # Register functions decorated with either @sanity_function or\n # @performance_variables or @performance_function decorators.\n if hasattr(value, '_rfm_sanity_fn'):\n try:\n super().__setitem__('_rfm_sanity', value)\n except KeyError:\n raise ReframeSyntaxError(\n 'the @sanity_function decorator can only be used '\n 'once in the class body'\n ) from None\n elif hasattr(value, '_rfm_perf_key'):\n try:\n self['_rfm_perf_fns'][key] = value\n except KeyError:\n raise ReframeSyntaxError(\n f'the performance function {key!r} has already been '\n f'defined in this class'\n ) from None\n\n # Register the final methods\n if hasattr(value, '_rfm_final'):\n self['_rfm_final_methods'].add(key)\n\n # Register the hooks - if a value does not meet the conditions\n # it will be simply ignored\n self['_rfm_hook_registry'].add(value)\n\n def __getitem__(self, key):\n '''Expose and control access to the local namespaces.\n\n Variables may only be retrieved if their value has been previously\n set. Accessing a parameter in the class body is disallowed (the\n actual test parameter is set during the class instantiation).\n '''\n\n try:\n return super().__getitem__(key)\n except KeyError as err:\n try:\n # Handle variable access\n return self['_rfm_local_var_space'][key]\n\n except KeyError:\n # Handle parameter access\n if key in self['_rfm_local_param_space']:\n raise ReframeSyntaxError(\n 'accessing a test parameter from the class '\n 'body is disallowed'\n ) from None\n else:\n # As the last resource, look if key is a variable in\n # any of the base classes. If so, make its value\n # available in the current class' namespace.\n for b in self['_rfm_bases']:\n if key in b._rfm_var_space:\n # Store a deep-copy of the variable's\n # value and return.\n v = b._rfm_var_space[key].default_value\n self._namespace[key] = v\n return self._namespace[key]\n\n # If 'key' is neither a variable nor a parameter,\n # raise the exception from the base __getitem__.\n raise err from None\n\n def reset(self, key):\n '''Reset an item to rerun it through the __setitem__ logic.'''\n self[key] = self[key]\n\n class WrappedFunction:\n '''Descriptor to wrap a free function as a bound-method.\n\n The free function object is wrapped by the constructor. Instances\n of this class should be inserted into the namespace of the target class\n with the desired name for the bound-method. Since this class is a\n descriptor, the `__get__` method will return the right bound-method\n when accessed from a class instance.\n\n :meta private:\n '''\n\n __slots__ = ('fn')\n\n def __init__(self, fn, name=None):\n @functools.wraps(fn)\n def _fn(*args, **kwargs):\n return fn(*args, **kwargs)\n\n self.fn = _fn\n if name:\n self.fn.__name__ = name\n\n def __get__(self, obj, objtype=None):\n if objtype is None:\n objtype = type(obj)\n\n self.fn.__qualname__ = '.'.join(\n [objtype.__qualname__, self.fn.__name__]\n )\n if obj is None:\n return self.fn\n\n return types.MethodType(self.fn, obj)\n\n def __call__(self, *args, **kwargs):\n return self.fn(*args, **kwargs)\n\n def __getattr__(self, name):\n if name in self.__slots__:\n return super().__getattr__(name)\n else:\n return getattr(self.fn, name)\n\n def __setattr__(self, name, value):\n if name in self.__slots__:\n super().__setattr__(name, value)\n else:\n setattr(self.fn, name, value)\n\n @classmethod\n def __prepare__(metacls, name, bases, **kwargs):\n namespace = super().__prepare__(name, bases, **kwargs)\n\n # Keep reference to the bases inside the namespace\n namespace['_rfm_bases'] = [\n b for b in bases if hasattr(b, '_rfm_var_space')\n ]\n\n # Regression test parameter space defined at the class level\n local_param_space = namespaces.LocalNamespace()\n namespace['_rfm_local_param_space'] = local_param_space\n\n # Directive to insert a regression test parameter directly in the\n # class body as: `P0 = parameter([0,1,2,3])`.\n namespace['parameter'] = parameters.TestParam\n\n # Regression test var space defined at the class level\n local_var_space = namespaces.LocalNamespace()\n namespace['_rfm_local_var_space'] = local_var_space\n\n # Directives to add/modify a regression test variable\n namespace['variable'] = variables.TestVar\n namespace['required'] = variables.Undefined\n\n # Utility decorators\n namespace['_rfm_ext_bound'] = set()\n\n def bind(fn, name=None):\n '''Directive to bind a free function to a class.\n\n See online docs for more information.\n\n .. note::\n Functions bound using this directive must be re-inspected after\n the class body execution has completed. This directive attaches\n the external method into the class namespace and returns the\n associated instance of the :class:`WrappedFunction`. However,\n this instance may be further modified by other ReFrame builtins\n such as :func:`run_before`, :func:`run_after`, :func:`final` and\n so on after it was added to the namespace, which would bypass\n the logic implemented in the :func:`__setitem__` method from the\n :class:`MetaNamespace` class. Hence, we track the items set by\n this directive in the ``_rfm_ext_bound`` set, so they can be\n later re-inspected.\n '''\n\n inst = metacls.WrappedFunction(fn, name)\n namespace[inst.__name__] = inst\n\n # Track the imported external functions\n namespace['_rfm_ext_bound'].add(inst.__name__)\n return inst\n\n def final(fn):\n '''Indicate that a function is final and cannot be overridden.'''\n\n fn._rfm_final = True\n return fn\n\n namespace['bind'] = bind\n namespace['final'] = final\n namespace['_rfm_final_methods'] = set()\n\n # Hook-related functionality\n def run_before(stage):\n '''Decorator for attaching a test method to a given stage.\n\n See online docs for more information.\n '''\n return hooks.attach_to('pre_' + stage)\n\n def run_after(stage):\n '''Decorator for attaching a test method to a given stage.\n\n See online docs for more information.\n '''\n return hooks.attach_to('post_' + stage)\n\n namespace['run_before'] = run_before\n namespace['run_after'] = run_after\n namespace['require_deps'] = hooks.require_deps\n namespace['_rfm_hook_registry'] = hooks.HookRegistry()\n\n # Machinery to add a sanity function\n def sanity_function(fn):\n '''Mark a function as the test's sanity function.\n\n Decorated functions must be unary and they will be converted into\n deferred expressions.\n '''\n\n _def_fn = deferrable(fn)\n setattr(_def_fn, '_rfm_sanity_fn', True)\n return _def_fn\n\n namespace['sanity_function'] = sanity_function\n namespace['deferrable'] = deferrable\n\n # Machinery to add performance functions\n def performance_function(units, *, perf_key=None):\n '''Decorate a function to extract a performance variable.\n\n The ``units`` argument indicates the units of the performance\n variable to be extracted.\n The ``perf_key`` optional arg will be used as the name of the\n performance variable. If not provided, the function name will\n be used as the performance variable name.\n '''\n if not isinstance(units, str):\n raise TypeError('performance units must be a string')\n\n if perf_key and not isinstance(perf_key, str):\n raise TypeError(\"'perf_key' must be a string\")\n\n def _deco_wrapper(func):\n if not utils.is_trivially_callable(func, non_def_args=1):\n raise TypeError(\n f'performance function {func.__name__!r} has more '\n f'than one argument without a default value'\n )\n\n @functools.wraps(func)\n def _perf_fn(*args, **kwargs):\n return _DeferredPerformanceExpression(\n func, units, *args, **kwargs\n )\n\n _perf_key = perf_key if perf_key else func.__name__\n setattr(_perf_fn, '_rfm_perf_key', _perf_key)\n return _perf_fn\n\n return _deco_wrapper\n\n namespace['performance_function'] = performance_function\n namespace['_rfm_perf_fns'] = namespaces.LocalNamespace()\n return metacls.MetaNamespace(namespace)\n\n def __new__(metacls, name, bases, namespace, **kwargs):\n '''Remove directives from the class namespace.\n\n It does not make sense to have some directives available after the\n class was created or even at the instance level (e.g. doing\n ``self.parameter([1, 2, 3])`` does not make sense). So here, we\n intercept those directives out of the namespace before the class is\n constructed.\n '''\n\n directives = [\n 'parameter', 'variable', 'bind', 'run_before', 'run_after',\n 'require_deps', 'required', 'deferrable', 'sanity_function',\n 'final', 'performance_function'\n ]\n for b in directives:\n namespace.pop(b, None)\n\n # Reset the external functions imported through the bind directive.\n for item in namespace.pop('_rfm_ext_bound'):\n namespace.reset(item)\n\n return super().__new__(metacls, name, bases, dict(namespace), **kwargs)\n\n def __init__(cls, name, bases, namespace, **kwargs):\n super().__init__(name, bases, namespace, **kwargs)\n\n # Create a set with the attribute names already in use.\n cls._rfm_dir = set()\n for base in (b for b in bases if hasattr(b, '_rfm_dir')):\n cls._rfm_dir.update(base._rfm_dir)\n\n used_attribute_names = set(cls._rfm_dir)\n\n # Build the var space and extend the target namespace\n variables.VarSpace(cls, used_attribute_names)\n used_attribute_names.update(cls._rfm_var_space.vars)\n\n # Build the parameter space\n parameters.ParamSpace(cls, used_attribute_names)\n\n # Update used names set with the local __dict__\n cls._rfm_dir.update(cls.__dict__)\n\n # Update the hook registry with the bases\n for base in cls._rfm_bases:\n cls._rfm_hook_registry.update(\n base._rfm_hook_registry, denied_hooks=namespace\n )\n\n # Search the bases if no local sanity functions exist.\n if '_rfm_sanity' not in namespace:\n for base in cls._rfm_bases:\n if hasattr(base, '_rfm_sanity'):\n cls._rfm_sanity = getattr(base, '_rfm_sanity')\n if cls._rfm_sanity.__name__ in namespace:\n raise ReframeSyntaxError(\n f'{cls.__qualname__!r} overrides the candidate '\n f'sanity function '\n f'{cls._rfm_sanity.__qualname__!r} without '\n f'defining an alternative'\n )\n\n break\n\n # Update the performance function dict with the bases.\n for base in cls._rfm_bases:\n for k, v in base._rfm_perf_fns.items():\n if k not in namespace:\n try:\n cls._rfm_perf_fns[k] = v\n except KeyError:\n '''Performance function overridden by other class'''\n\n # Add the final functions from its parents\n cls._rfm_final_methods.update(\n *(b._rfm_final_methods for b in cls._rfm_bases)\n )\n\n if getattr(cls, '_rfm_override_final', None):\n return\n\n for b in cls._rfm_bases:\n for key in b._rfm_final_methods:\n if key in namespace and callable(namespace[key]):\n msg = (f\"'{cls.__qualname__}.{key}' attempts to \"\n f\"override final method \"\n f\"'{b.__qualname__}.{key}'; \"\n f\"you should use the pipeline hooks instead\")\n raise ReframeSyntaxError(msg)\n\n def __call__(cls, *args, **kwargs):\n '''Inject parameter and variable spaces during object construction.\n\n When a class is instantiated, this method intercepts the arguments\n associated to the parameter and variable spaces. This prevents both\n :func:`__new__` and :func:`__init__` methods from ever seing these\n arguments.\n\n The parameter and variable spaces are injected into the object after\n construction and before initialization.\n '''\n\n # Intercept constructor arguments\n _rfm_use_params = kwargs.pop('_rfm_use_params', False)\n\n obj = cls.__new__(cls, *args, **kwargs)\n\n # Insert the var & param spaces\n cls._rfm_var_space.inject(obj, cls)\n cls._rfm_param_space.inject(obj, cls, _rfm_use_params)\n\n obj.__init__(*args, **kwargs)\n return obj\n\n def __getattribute__(cls, name):\n '''Attribute lookup method for custom class attributes.\n\n ReFrame test variables are descriptors injected at the class level.\n If a variable descriptor has already been injected into the class,\n do not return the descriptor object and return the default value\n associated with that variable instead.\n\n .. warning::\n .. versionchanged:: 3.7.0\n Prior versions exposed the variable descriptor object if this\n was already present in the class, instead of returning the\n variable's default value.\n '''\n\n try:\n var_space = super().__getattribute__('_rfm_var_space')\n except AttributeError:\n var_space = None\n\n # If the variable is already injected, delegate lookup to __getattr__.\n if var_space and name in var_space.injected_vars:\n raise AttributeError('delegate variable lookup to __getattr__')\n\n # Default back to the base method if no special treatment required.\n return super().__getattribute__(name)\n\n def __getattr__(cls, name):\n '''Backup attribute lookup method into custom namespaces.\n\n Some ReFrame built-in types are stored under their own sub-namespaces.\n This method will perform an attribute lookup on these sub-namespaces\n if a call to the default :func:`__getattribute__` method fails to\n retrieve the requested class attribute.\n '''\n\n try:\n var_space = super().__getattribute__('_rfm_var_space')\n return var_space.vars[name]\n except AttributeError:\n '''Catch early access attempt to the variable space.'''\n except KeyError:\n '''Requested name not in variable space.'''\n\n try:\n param_space = super().__getattribute__('_rfm_param_space')\n return param_space.params[name]\n except AttributeError:\n '''Catch early access attempt to the parameter space.'''\n except KeyError:\n '''Requested name not in parameter space.'''\n\n raise AttributeError(\n f'class {cls.__qualname__!r} has no attribute {name!r}'\n ) from None\n\n def setvar(cls, name, value):\n '''Set the value of a variable.\n\n :param name: The name of the variable.\n :param value: The value of the variable.\n\n :returns: :class:`True` if the variable was set.\n A variable will *not* be set, if it does not exist or when an\n attempt is made to set it with its underlying descriptor.\n This happens during the variable injection time and it should be\n delegated to the class' :func:`__setattr__` method.\n\n :raises ReframeSyntaxError: If an attempt is made to override a\n variable with a descriptor other than its underlying one.\n\n '''\n\n try:\n var_space = super().__getattribute__('_rfm_var_space')\n if name in var_space:\n if not hasattr(value, '__get__'):\n var_space[name].define(value)\n return True\n elif var_space[name].field is not value:\n desc = '.'.join([cls.__qualname__, name])\n raise ReframeSyntaxError(\n f'cannot override variable descriptor {desc!r}'\n )\n else:\n # Variable is being injected\n return False\n except AttributeError:\n '''Catch early access attempt to the variable space.'''\n return False\n\n def __setattr__(cls, name, value):\n '''Handle the special treatment required for variables and parameters.\n\n A variable's default value can be updated when accessed as a regular\n class attribute. This behavior does not apply when the assigned value\n is a descriptor object. In that case, the task of setting the value is\n delegated to the base :func:`__setattr__` (this is to comply with\n standard Python behavior). However, since the variables are already\n descriptors which are injected during class instantiation, we disallow\n any attempt to override this descriptor (since it would be silently\n re-overridden in any case).\n\n Altering the value of a parameter when accessed as a class attribute\n is not allowed. This would break the parameter space internals.\n '''\n\n # Try to treat `name` as variable\n if cls.setvar(name, value):\n return\n\n # Try to treat `name` as a parameter\n try:\n # Catch attempts to override a test parameter\n param_space = super().__getattribute__('_rfm_param_space')\n if name in param_space.params:\n raise ReframeSyntaxError(f'cannot override parameter {name!r}')\n except AttributeError:\n '''Catch early access attempt to the parameter space.'''\n\n # Treat `name` as normal class attribute\n super().__setattr__(name, value)\n\n @property\n def param_space(cls):\n ''' Make the parameter space available as read-only.'''\n return cls._rfm_param_space\n\n def is_abstract(cls):\n '''Check if the class is an abstract test.\n\n This is the case when some parameters are undefined, which results in\n the length of the parameter space being 0.\n\n :return: bool indicating whether the test has undefined parameters.\n\n :meta private:\n '''\n return len(cls.param_space) == 0\n"}}},{"rowIdx":58,"cells":{"function_name":{"kind":"string","value":"performance_function"},"docstring":{"kind":"string","value":"Decorate a function to extract a performance variable.\n\nThe ``units`` argument indicates the units of the performance\nvariable to be extracted.\nThe ``perf_key`` optional arg will be used as the name of the\nperformance variable. If not provided, the function name will\nbe used as the performance variable name."},"masked_code":{"kind":"string","value":"# Copyright 2016-2021 Swiss National Supercomputing Centre (CSCS/ETH Zurich)\n# ReFrame Project Developers. See the top-level LICENSE file for details.\n#\n# SPDX-License-Identifier: BSD-3-Clause\n\n#\n# Meta-class for creating regression tests.\n#\n\nimport functools\nimport types\n\nimport reframe.core.namespaces as namespaces\nimport reframe.core.parameters as parameters\nimport reframe.core.variables as variables\nimport reframe.core.hooks as hooks\nimport reframe.utility as utils\n\nfrom reframe.core.exceptions import ReframeSyntaxError\nfrom reframe.core.deferrable import deferrable, _DeferredPerformanceExpression\n\n\nclass RegressionTestMeta(type):\n\n class MetaNamespace(namespaces.LocalNamespace):\n '''Custom namespace to control the cls attribute assignment.\n\n Regular Python class attributes can be overridden by either\n parameters or variables respecting the order of execution.\n A variable or a parameter may not be declared more than once in the\n same class body. Overriding a variable with a parameter or the other\n way around has an undefined behavior. A variable's value may be\n updated multiple times within the same class body. A parameter's\n value cannot be updated more than once within the same class body.\n '''\n\n def __setitem__(self, key, value):\n if isinstance(value, variables.TestVar):\n # Insert the attribute in the variable namespace\n try:\n self['_rfm_local_var_space'][key] = value\n value.__set_name__(self, key)\n except KeyError:\n raise ReframeSyntaxError(\n f'variable {key!r} is already declared'\n ) from None\n\n # Override the regular class attribute (if present) and return\n self._namespace.pop(key, None)\n return\n\n elif isinstance(value, parameters.TestParam):\n # Insert the attribute in the parameter namespace\n try:\n self['_rfm_local_param_space'][key] = value\n except KeyError:\n raise ReframeSyntaxError(\n f'parameter {key!r} is already declared in this class'\n ) from None\n\n # Override the regular class attribute (if present) and return\n self._namespace.pop(key, None)\n return\n\n elif key in self['_rfm_local_param_space']:\n raise ReframeSyntaxError(\n f'cannot override parameter {key!r}'\n )\n else:\n # Insert the items manually to overide the namespace clash\n # check from the base namespace.\n self._namespace[key] = value\n\n # Register functions decorated with either @sanity_function or\n # @performance_variables or @performance_function decorators.\n if hasattr(value, '_rfm_sanity_fn'):\n try:\n super().__setitem__('_rfm_sanity', value)\n except KeyError:\n raise ReframeSyntaxError(\n 'the @sanity_function decorator can only be used '\n 'once in the class body'\n ) from None\n elif hasattr(value, '_rfm_perf_key'):\n try:\n self['_rfm_perf_fns'][key] = value\n except KeyError:\n raise ReframeSyntaxError(\n f'the performance function {key!r} has already been '\n f'defined in this class'\n ) from None\n\n # Register the final methods\n if hasattr(value, '_rfm_final'):\n self['_rfm_final_methods'].add(key)\n\n # Register the hooks - if a value does not meet the conditions\n # it will be simply ignored\n self['_rfm_hook_registry'].add(value)\n\n def __getitem__(self, key):\n '''Expose and control access to the local namespaces.\n\n Variables may only be retrieved if their value has been previously\n set. Accessing a parameter in the class body is disallowed (the\n actual test parameter is set during the class instantiation).\n '''\n\n try:\n return super().__getitem__(key)\n except KeyError as err:\n try:\n # Handle variable access\n return self['_rfm_local_var_space'][key]\n\n except KeyError:\n # Handle parameter access\n if key in self['_rfm_local_param_space']:\n raise ReframeSyntaxError(\n 'accessing a test parameter from the class '\n 'body is disallowed'\n ) from None\n else:\n # As the last resource, look if key is a variable in\n # any of the base classes. If so, make its value\n # available in the current class' namespace.\n for b in self['_rfm_bases']:\n if key in b._rfm_var_space:\n # Store a deep-copy of the variable's\n # value and return.\n v = b._rfm_var_space[key].default_value\n self._namespace[key] = v\n return self._namespace[key]\n\n # If 'key' is neither a variable nor a parameter,\n # raise the exception from the base __getitem__.\n raise err from None\n\n def reset(self, key):\n '''Reset an item to rerun it through the __setitem__ logic.'''\n self[key] = self[key]\n\n class WrappedFunction:\n '''Descriptor to wrap a free function as a bound-method.\n\n The free function object is wrapped by the constructor. Instances\n of this class should be inserted into the namespace of the target class\n with the desired name for the bound-method. Since this class is a\n descriptor, the `__get__` method will return the right bound-method\n when accessed from a class instance.\n\n :meta private:\n '''\n\n __slots__ = ('fn')\n\n def __init__(self, fn, name=None):\n @functools.wraps(fn)\n def _fn(*args, **kwargs):\n return fn(*args, **kwargs)\n\n self.fn = _fn\n if name:\n self.fn.__name__ = name\n\n def __get__(self, obj, objtype=None):\n if objtype is None:\n objtype = type(obj)\n\n self.fn.__qualname__ = '.'.join(\n [objtype.__qualname__, self.fn.__name__]\n )\n if obj is None:\n return self.fn\n\n return types.MethodType(self.fn, obj)\n\n def __call__(self, *args, **kwargs):\n return self.fn(*args, **kwargs)\n\n def __getattr__(self, name):\n if name in self.__slots__:\n return super().__getattr__(name)\n else:\n return getattr(self.fn, name)\n\n def __setattr__(self, name, value):\n if name in self.__slots__:\n super().__setattr__(name, value)\n else:\n setattr(self.fn, name, value)\n\n @classmethod\n def __prepare__(metacls, name, bases, **kwargs):\n namespace = super().__prepare__(name, bases, **kwargs)\n\n # Keep reference to the bases inside the namespace\n namespace['_rfm_bases'] = [\n b for b in bases if hasattr(b, '_rfm_var_space')\n ]\n\n # Regression test parameter space defined at the class level\n local_param_space = namespaces.LocalNamespace()\n namespace['_rfm_local_param_space'] = local_param_space\n\n # Directive to insert a regression test parameter directly in the\n # class body as: `P0 = parameter([0,1,2,3])`.\n namespace['parameter'] = parameters.TestParam\n\n # Regression test var space defined at the class level\n local_var_space = namespaces.LocalNamespace()\n namespace['_rfm_local_var_space'] = local_var_space\n\n # Directives to add/modify a regression test variable\n namespace['variable'] = variables.TestVar\n namespace['required'] = variables.Undefined\n\n # Utility decorators\n namespace['_rfm_ext_bound'] = set()\n\n def bind(fn, name=None):\n '''Directive to bind a free function to a class.\n\n See online docs for more information.\n\n .. note::\n Functions bound using this directive must be re-inspected after\n the class body execution has completed. This directive attaches\n the external method into the class namespace and returns the\n associated instance of the :class:`WrappedFunction`. However,\n this instance may be further modified by other ReFrame builtins\n such as :func:`run_before`, :func:`run_after`, :func:`final` and\n so on after it was added to the namespace, which would bypass\n the logic implemented in the :func:`__setitem__` method from the\n :class:`MetaNamespace` class. Hence, we track the items set by\n this directive in the ``_rfm_ext_bound`` set, so they can be\n later re-inspected.\n '''\n\n inst = metacls.WrappedFunction(fn, name)\n namespace[inst.__name__] = inst\n\n # Track the imported external functions\n namespace['_rfm_ext_bound'].add(inst.__name__)\n return inst\n\n def final(fn):\n '''Indicate that a function is final and cannot be overridden.'''\n\n fn._rfm_final = True\n return fn\n\n namespace['bind'] = bind\n namespace['final'] = final\n namespace['_rfm_final_methods'] = set()\n\n # Hook-related functionality\n def run_before(stage):\n '''Decorator for attaching a test method to a given stage.\n\n See online docs for more information.\n '''\n return hooks.attach_to('pre_' + stage)\n\n def run_after(stage):\n '''Decorator for attaching a test method to a given stage.\n\n See online docs for more information.\n '''\n return hooks.attach_to('post_' + stage)\n\n namespace['run_before'] = run_before\n namespace['run_after'] = run_after\n namespace['require_deps'] = hooks.require_deps\n namespace['_rfm_hook_registry'] = hooks.HookRegistry()\n\n # Machinery to add a sanity function\n def sanity_function(fn):\n '''Mark a function as the test's sanity function.\n\n Decorated functions must be unary and they will be converted into\n deferred expressions.\n '''\n\n _def_fn = deferrable(fn)\n setattr(_def_fn, '_rfm_sanity_fn', True)\n return _def_fn\n\n namespace['sanity_function'] = sanity_function\n namespace['deferrable'] = deferrable\n\n # Machinery to add performance functions\n# MASKED: performance_function function (lines 293-325)\n\n namespace['performance_function'] = performance_function\n namespace['_rfm_perf_fns'] = namespaces.LocalNamespace()\n return metacls.MetaNamespace(namespace)\n\n def __new__(metacls, name, bases, namespace, **kwargs):\n '''Remove directives from the class namespace.\n\n It does not make sense to have some directives available after the\n class was created or even at the instance level (e.g. doing\n ``self.parameter([1, 2, 3])`` does not make sense). So here, we\n intercept those directives out of the namespace before the class is\n constructed.\n '''\n\n directives = [\n 'parameter', 'variable', 'bind', 'run_before', 'run_after',\n 'require_deps', 'required', 'deferrable', 'sanity_function',\n 'final', 'performance_function'\n ]\n for b in directives:\n namespace.pop(b, None)\n\n # Reset the external functions imported through the bind directive.\n for item in namespace.pop('_rfm_ext_bound'):\n namespace.reset(item)\n\n return super().__new__(metacls, name, bases, dict(namespace), **kwargs)\n\n def __init__(cls, name, bases, namespace, **kwargs):\n super().__init__(name, bases, namespace, **kwargs)\n\n # Create a set with the attribute names already in use.\n cls._rfm_dir = set()\n for base in (b for b in bases if hasattr(b, '_rfm_dir')):\n cls._rfm_dir.update(base._rfm_dir)\n\n used_attribute_names = set(cls._rfm_dir)\n\n # Build the var space and extend the target namespace\n variables.VarSpace(cls, used_attribute_names)\n used_attribute_names.update(cls._rfm_var_space.vars)\n\n # Build the parameter space\n parameters.ParamSpace(cls, used_attribute_names)\n\n # Update used names set with the local __dict__\n cls._rfm_dir.update(cls.__dict__)\n\n # Update the hook registry with the bases\n for base in cls._rfm_bases:\n cls._rfm_hook_registry.update(\n base._rfm_hook_registry, denied_hooks=namespace\n )\n\n # Search the bases if no local sanity functions exist.\n if '_rfm_sanity' not in namespace:\n for base in cls._rfm_bases:\n if hasattr(base, '_rfm_sanity'):\n cls._rfm_sanity = getattr(base, '_rfm_sanity')\n if cls._rfm_sanity.__name__ in namespace:\n raise ReframeSyntaxError(\n f'{cls.__qualname__!r} overrides the candidate '\n f'sanity function '\n f'{cls._rfm_sanity.__qualname__!r} without '\n f'defining an alternative'\n )\n\n break\n\n # Update the performance function dict with the bases.\n for base in cls._rfm_bases:\n for k, v in base._rfm_perf_fns.items():\n if k not in namespace:\n try:\n cls._rfm_perf_fns[k] = v\n except KeyError:\n '''Performance function overridden by other class'''\n\n # Add the final functions from its parents\n cls._rfm_final_methods.update(\n *(b._rfm_final_methods for b in cls._rfm_bases)\n )\n\n if getattr(cls, '_rfm_override_final', None):\n return\n\n for b in cls._rfm_bases:\n for key in b._rfm_final_methods:\n if key in namespace and callable(namespace[key]):\n msg = (f\"'{cls.__qualname__}.{key}' attempts to \"\n f\"override final method \"\n f\"'{b.__qualname__}.{key}'; \"\n f\"you should use the pipeline hooks instead\")\n raise ReframeSyntaxError(msg)\n\n def __call__(cls, *args, **kwargs):\n '''Inject parameter and variable spaces during object construction.\n\n When a class is instantiated, this method intercepts the arguments\n associated to the parameter and variable spaces. This prevents both\n :func:`__new__` and :func:`__init__` methods from ever seing these\n arguments.\n\n The parameter and variable spaces are injected into the object after\n construction and before initialization.\n '''\n\n # Intercept constructor arguments\n _rfm_use_params = kwargs.pop('_rfm_use_params', False)\n\n obj = cls.__new__(cls, *args, **kwargs)\n\n # Insert the var & param spaces\n cls._rfm_var_space.inject(obj, cls)\n cls._rfm_param_space.inject(obj, cls, _rfm_use_params)\n\n obj.__init__(*args, **kwargs)\n return obj\n\n def __getattribute__(cls, name):\n '''Attribute lookup method for custom class attributes.\n\n ReFrame test variables are descriptors injected at the class level.\n If a variable descriptor has already been injected into the class,\n do not return the descriptor object and return the default value\n associated with that variable instead.\n\n .. warning::\n .. versionchanged:: 3.7.0\n Prior versions exposed the variable descriptor object if this\n was already present in the class, instead of returning the\n variable's default value.\n '''\n\n try:\n var_space = super().__getattribute__('_rfm_var_space')\n except AttributeError:\n var_space = None\n\n # If the variable is already injected, delegate lookup to __getattr__.\n if var_space and name in var_space.injected_vars:\n raise AttributeError('delegate variable lookup to __getattr__')\n\n # Default back to the base method if no special treatment required.\n return super().__getattribute__(name)\n\n def __getattr__(cls, name):\n '''Backup attribute lookup method into custom namespaces.\n\n Some ReFrame built-in types are stored under their own sub-namespaces.\n This method will perform an attribute lookup on these sub-namespaces\n if a call to the default :func:`__getattribute__` method fails to\n retrieve the requested class attribute.\n '''\n\n try:\n var_space = super().__getattribute__('_rfm_var_space')\n return var_space.vars[name]\n except AttributeError:\n '''Catch early access attempt to the variable space.'''\n except KeyError:\n '''Requested name not in variable space.'''\n\n try:\n param_space = super().__getattribute__('_rfm_param_space')\n return param_space.params[name]\n except AttributeError:\n '''Catch early access attempt to the parameter space.'''\n except KeyError:\n '''Requested name not in parameter space.'''\n\n raise AttributeError(\n f'class {cls.__qualname__!r} has no attribute {name!r}'\n ) from None\n\n def setvar(cls, name, value):\n '''Set the value of a variable.\n\n :param name: The name of the variable.\n :param value: The value of the variable.\n\n :returns: :class:`True` if the variable was set.\n A variable will *not* be set, if it does not exist or when an\n attempt is made to set it with its underlying descriptor.\n This happens during the variable injection time and it should be\n delegated to the class' :func:`__setattr__` method.\n\n :raises ReframeSyntaxError: If an attempt is made to override a\n variable with a descriptor other than its underlying one.\n\n '''\n\n try:\n var_space = super().__getattribute__('_rfm_var_space')\n if name in var_space:\n if not hasattr(value, '__get__'):\n var_space[name].define(value)\n return True\n elif var_space[name].field is not value:\n desc = '.'.join([cls.__qualname__, name])\n raise ReframeSyntaxError(\n f'cannot override variable descriptor {desc!r}'\n )\n else:\n # Variable is being injected\n return False\n except AttributeError:\n '''Catch early access attempt to the variable space.'''\n return False\n\n def __setattr__(cls, name, value):\n '''Handle the special treatment required for variables and parameters.\n\n A variable's default value can be updated when accessed as a regular\n class attribute. This behavior does not apply when the assigned value\n is a descriptor object. In that case, the task of setting the value is\n delegated to the base :func:`__setattr__` (this is to comply with\n standard Python behavior). However, since the variables are already\n descriptors which are injected during class instantiation, we disallow\n any attempt to override this descriptor (since it would be silently\n re-overridden in any case).\n\n Altering the value of a parameter when accessed as a class attribute\n is not allowed. This would break the parameter space internals.\n '''\n\n # Try to treat `name` as variable\n if cls.setvar(name, value):\n return\n\n # Try to treat `name` as a parameter\n try:\n # Catch attempts to override a test parameter\n param_space = super().__getattribute__('_rfm_param_space')\n if name in param_space.params:\n raise ReframeSyntaxError(f'cannot override parameter {name!r}')\n except AttributeError:\n '''Catch early access attempt to the parameter space.'''\n\n # Treat `name` as normal class attribute\n super().__setattr__(name, value)\n\n @property\n def param_space(cls):\n ''' Make the parameter space available as read-only.'''\n return cls._rfm_param_space\n\n def is_abstract(cls):\n '''Check if the class is an abstract test.\n\n This is the case when some parameters are undefined, which results in\n the length of the parameter space being 0.\n\n :return: bool indicating whether the test has undefined parameters.\n\n :meta private:\n '''\n return len(cls.param_space) == 0"},"implementation":{"kind":"string","value":" def performance_function(units, *, perf_key=None):\n '''Decorate a function to extract a performance variable.\n\n The ``units`` argument indicates the units of the performance\n variable to be extracted.\n The ``perf_key`` optional arg will be used as the name of the\n performance variable. If not provided, the function name will\n be used as the performance variable name.\n '''\n if not isinstance(units, str):\n raise TypeError('performance units must be a string')\n\n if perf_key and not isinstance(perf_key, str):\n raise TypeError(\"'perf_key' must be a string\")\n\n def _deco_wrapper(func):\n if not utils.is_trivially_callable(func, non_def_args=1):\n raise TypeError(\n f'performance function {func.__name__!r} has more '\n f'than one argument without a default value'\n )\n\n @functools.wraps(func)\n def _perf_fn(*args, **kwargs):\n return _DeferredPerformanceExpression(\n func, units, *args, **kwargs\n )\n\n _perf_key = perf_key if perf_key else func.__name__\n setattr(_perf_fn, '_rfm_perf_key', _perf_key)\n return _perf_fn\n\n return _deco_wrapper"},"start_line":{"kind":"number","value":293,"string":"293"},"end_line":{"kind":"number","value":325,"string":"325"},"file_content":{"kind":"string","value":"# Copyright 2016-2021 Swiss National Supercomputing Centre (CSCS/ETH Zurich)\n# ReFrame Project Developers. See the top-level LICENSE file for details.\n#\n# SPDX-License-Identifier: BSD-3-Clause\n\n#\n# Meta-class for creating regression tests.\n#\n\nimport functools\nimport types\n\nimport reframe.core.namespaces as namespaces\nimport reframe.core.parameters as parameters\nimport reframe.core.variables as variables\nimport reframe.core.hooks as hooks\nimport reframe.utility as utils\n\nfrom reframe.core.exceptions import ReframeSyntaxError\nfrom reframe.core.deferrable import deferrable, _DeferredPerformanceExpression\n\n\nclass RegressionTestMeta(type):\n\n class MetaNamespace(namespaces.LocalNamespace):\n '''Custom namespace to control the cls attribute assignment.\n\n Regular Python class attributes can be overridden by either\n parameters or variables respecting the order of execution.\n A variable or a parameter may not be declared more than once in the\n same class body. Overriding a variable with a parameter or the other\n way around has an undefined behavior. A variable's value may be\n updated multiple times within the same class body. A parameter's\n value cannot be updated more than once within the same class body.\n '''\n\n def __setitem__(self, key, value):\n if isinstance(value, variables.TestVar):\n # Insert the attribute in the variable namespace\n try:\n self['_rfm_local_var_space'][key] = value\n value.__set_name__(self, key)\n except KeyError:\n raise ReframeSyntaxError(\n f'variable {key!r} is already declared'\n ) from None\n\n # Override the regular class attribute (if present) and return\n self._namespace.pop(key, None)\n return\n\n elif isinstance(value, parameters.TestParam):\n # Insert the attribute in the parameter namespace\n try:\n self['_rfm_local_param_space'][key] = value\n except KeyError:\n raise ReframeSyntaxError(\n f'parameter {key!r} is already declared in this class'\n ) from None\n\n # Override the regular class attribute (if present) and return\n self._namespace.pop(key, None)\n return\n\n elif key in self['_rfm_local_param_space']:\n raise ReframeSyntaxError(\n f'cannot override parameter {key!r}'\n )\n else:\n # Insert the items manually to overide the namespace clash\n # check from the base namespace.\n self._namespace[key] = value\n\n # Register functions decorated with either @sanity_function or\n # @performance_variables or @performance_function decorators.\n if hasattr(value, '_rfm_sanity_fn'):\n try:\n super().__setitem__('_rfm_sanity', value)\n except KeyError:\n raise ReframeSyntaxError(\n 'the @sanity_function decorator can only be used '\n 'once in the class body'\n ) from None\n elif hasattr(value, '_rfm_perf_key'):\n try:\n self['_rfm_perf_fns'][key] = value\n except KeyError:\n raise ReframeSyntaxError(\n f'the performance function {key!r} has already been '\n f'defined in this class'\n ) from None\n\n # Register the final methods\n if hasattr(value, '_rfm_final'):\n self['_rfm_final_methods'].add(key)\n\n # Register the hooks - if a value does not meet the conditions\n # it will be simply ignored\n self['_rfm_hook_registry'].add(value)\n\n def __getitem__(self, key):\n '''Expose and control access to the local namespaces.\n\n Variables may only be retrieved if their value has been previously\n set. Accessing a parameter in the class body is disallowed (the\n actual test parameter is set during the class instantiation).\n '''\n\n try:\n return super().__getitem__(key)\n except KeyError as err:\n try:\n # Handle variable access\n return self['_rfm_local_var_space'][key]\n\n except KeyError:\n # Handle parameter access\n if key in self['_rfm_local_param_space']:\n raise ReframeSyntaxError(\n 'accessing a test parameter from the class '\n 'body is disallowed'\n ) from None\n else:\n # As the last resource, look if key is a variable in\n # any of the base classes. If so, make its value\n # available in the current class' namespace.\n for b in self['_rfm_bases']:\n if key in b._rfm_var_space:\n # Store a deep-copy of the variable's\n # value and return.\n v = b._rfm_var_space[key].default_value\n self._namespace[key] = v\n return self._namespace[key]\n\n # If 'key' is neither a variable nor a parameter,\n # raise the exception from the base __getitem__.\n raise err from None\n\n def reset(self, key):\n '''Reset an item to rerun it through the __setitem__ logic.'''\n self[key] = self[key]\n\n class WrappedFunction:\n '''Descriptor to wrap a free function as a bound-method.\n\n The free function object is wrapped by the constructor. Instances\n of this class should be inserted into the namespace of the target class\n with the desired name for the bound-method. Since this class is a\n descriptor, the `__get__` method will return the right bound-method\n when accessed from a class instance.\n\n :meta private:\n '''\n\n __slots__ = ('fn')\n\n def __init__(self, fn, name=None):\n @functools.wraps(fn)\n def _fn(*args, **kwargs):\n return fn(*args, **kwargs)\n\n self.fn = _fn\n if name:\n self.fn.__name__ = name\n\n def __get__(self, obj, objtype=None):\n if objtype is None:\n objtype = type(obj)\n\n self.fn.__qualname__ = '.'.join(\n [objtype.__qualname__, self.fn.__name__]\n )\n if obj is None:\n return self.fn\n\n return types.MethodType(self.fn, obj)\n\n def __call__(self, *args, **kwargs):\n return self.fn(*args, **kwargs)\n\n def __getattr__(self, name):\n if name in self.__slots__:\n return super().__getattr__(name)\n else:\n return getattr(self.fn, name)\n\n def __setattr__(self, name, value):\n if name in self.__slots__:\n super().__setattr__(name, value)\n else:\n setattr(self.fn, name, value)\n\n @classmethod\n def __prepare__(metacls, name, bases, **kwargs):\n namespace = super().__prepare__(name, bases, **kwargs)\n\n # Keep reference to the bases inside the namespace\n namespace['_rfm_bases'] = [\n b for b in bases if hasattr(b, '_rfm_var_space')\n ]\n\n # Regression test parameter space defined at the class level\n local_param_space = namespaces.LocalNamespace()\n namespace['_rfm_local_param_space'] = local_param_space\n\n # Directive to insert a regression test parameter directly in the\n # class body as: `P0 = parameter([0,1,2,3])`.\n namespace['parameter'] = parameters.TestParam\n\n # Regression test var space defined at the class level\n local_var_space = namespaces.LocalNamespace()\n namespace['_rfm_local_var_space'] = local_var_space\n\n # Directives to add/modify a regression test variable\n namespace['variable'] = variables.TestVar\n namespace['required'] = variables.Undefined\n\n # Utility decorators\n namespace['_rfm_ext_bound'] = set()\n\n def bind(fn, name=None):\n '''Directive to bind a free function to a class.\n\n See online docs for more information.\n\n .. note::\n Functions bound using this directive must be re-inspected after\n the class body execution has completed. This directive attaches\n the external method into the class namespace and returns the\n associated instance of the :class:`WrappedFunction`. However,\n this instance may be further modified by other ReFrame builtins\n such as :func:`run_before`, :func:`run_after`, :func:`final` and\n so on after it was added to the namespace, which would bypass\n the logic implemented in the :func:`__setitem__` method from the\n :class:`MetaNamespace` class. Hence, we track the items set by\n this directive in the ``_rfm_ext_bound`` set, so they can be\n later re-inspected.\n '''\n\n inst = metacls.WrappedFunction(fn, name)\n namespace[inst.__name__] = inst\n\n # Track the imported external functions\n namespace['_rfm_ext_bound'].add(inst.__name__)\n return inst\n\n def final(fn):\n '''Indicate that a function is final and cannot be overridden.'''\n\n fn._rfm_final = True\n return fn\n\n namespace['bind'] = bind\n namespace['final'] = final\n namespace['_rfm_final_methods'] = set()\n\n # Hook-related functionality\n def run_before(stage):\n '''Decorator for attaching a test method to a given stage.\n\n See online docs for more information.\n '''\n return hooks.attach_to('pre_' + stage)\n\n def run_after(stage):\n '''Decorator for attaching a test method to a given stage.\n\n See online docs for more information.\n '''\n return hooks.attach_to('post_' + stage)\n\n namespace['run_before'] = run_before\n namespace['run_after'] = run_after\n namespace['require_deps'] = hooks.require_deps\n namespace['_rfm_hook_registry'] = hooks.HookRegistry()\n\n # Machinery to add a sanity function\n def sanity_function(fn):\n '''Mark a function as the test's sanity function.\n\n Decorated functions must be unary and they will be converted into\n deferred expressions.\n '''\n\n _def_fn = deferrable(fn)\n setattr(_def_fn, '_rfm_sanity_fn', True)\n return _def_fn\n\n namespace['sanity_function'] = sanity_function\n namespace['deferrable'] = deferrable\n\n # Machinery to add performance functions\n def performance_function(units, *, perf_key=None):\n '''Decorate a function to extract a performance variable.\n\n The ``units`` argument indicates the units of the performance\n variable to be extracted.\n The ``perf_key`` optional arg will be used as the name of the\n performance variable. If not provided, the function name will\n be used as the performance variable name.\n '''\n if not isinstance(units, str):\n raise TypeError('performance units must be a string')\n\n if perf_key and not isinstance(perf_key, str):\n raise TypeError(\"'perf_key' must be a string\")\n\n def _deco_wrapper(func):\n if not utils.is_trivially_callable(func, non_def_args=1):\n raise TypeError(\n f'performance function {func.__name__!r} has more '\n f'than one argument without a default value'\n )\n\n @functools.wraps(func)\n def _perf_fn(*args, **kwargs):\n return _DeferredPerformanceExpression(\n func, units, *args, **kwargs\n )\n\n _perf_key = perf_key if perf_key else func.__name__\n setattr(_perf_fn, '_rfm_perf_key', _perf_key)\n return _perf_fn\n\n return _deco_wrapper\n\n namespace['performance_function'] = performance_function\n namespace['_rfm_perf_fns'] = namespaces.LocalNamespace()\n return metacls.MetaNamespace(namespace)\n\n def __new__(metacls, name, bases, namespace, **kwargs):\n '''Remove directives from the class namespace.\n\n It does not make sense to have some directives available after the\n class was created or even at the instance level (e.g. doing\n ``self.parameter([1, 2, 3])`` does not make sense). So here, we\n intercept those directives out of the namespace before the class is\n constructed.\n '''\n\n directives = [\n 'parameter', 'variable', 'bind', 'run_before', 'run_after',\n 'require_deps', 'required', 'deferrable', 'sanity_function',\n 'final', 'performance_function'\n ]\n for b in directives:\n namespace.pop(b, None)\n\n # Reset the external functions imported through the bind directive.\n for item in namespace.pop('_rfm_ext_bound'):\n namespace.reset(item)\n\n return super().__new__(metacls, name, bases, dict(namespace), **kwargs)\n\n def __init__(cls, name, bases, namespace, **kwargs):\n super().__init__(name, bases, namespace, **kwargs)\n\n # Create a set with the attribute names already in use.\n cls._rfm_dir = set()\n for base in (b for b in bases if hasattr(b, '_rfm_dir')):\n cls._rfm_dir.update(base._rfm_dir)\n\n used_attribute_names = set(cls._rfm_dir)\n\n # Build the var space and extend the target namespace\n variables.VarSpace(cls, used_attribute_names)\n used_attribute_names.update(cls._rfm_var_space.vars)\n\n # Build the parameter space\n parameters.ParamSpace(cls, used_attribute_names)\n\n # Update used names set with the local __dict__\n cls._rfm_dir.update(cls.__dict__)\n\n # Update the hook registry with the bases\n for base in cls._rfm_bases:\n cls._rfm_hook_registry.update(\n base._rfm_hook_registry, denied_hooks=namespace\n )\n\n # Search the bases if no local sanity functions exist.\n if '_rfm_sanity' not in namespace:\n for base in cls._rfm_bases:\n if hasattr(base, '_rfm_sanity'):\n cls._rfm_sanity = getattr(base, '_rfm_sanity')\n if cls._rfm_sanity.__name__ in namespace:\n raise ReframeSyntaxError(\n f'{cls.__qualname__!r} overrides the candidate '\n f'sanity function '\n f'{cls._rfm_sanity.__qualname__!r} without '\n f'defining an alternative'\n )\n\n break\n\n # Update the performance function dict with the bases.\n for base in cls._rfm_bases:\n for k, v in base._rfm_perf_fns.items():\n if k not in namespace:\n try:\n cls._rfm_perf_fns[k] = v\n except KeyError:\n '''Performance function overridden by other class'''\n\n # Add the final functions from its parents\n cls._rfm_final_methods.update(\n *(b._rfm_final_methods for b in cls._rfm_bases)\n )\n\n if getattr(cls, '_rfm_override_final', None):\n return\n\n for b in cls._rfm_bases:\n for key in b._rfm_final_methods:\n if key in namespace and callable(namespace[key]):\n msg = (f\"'{cls.__qualname__}.{key}' attempts to \"\n f\"override final method \"\n f\"'{b.__qualname__}.{key}'; \"\n f\"you should use the pipeline hooks instead\")\n raise ReframeSyntaxError(msg)\n\n def __call__(cls, *args, **kwargs):\n '''Inject parameter and variable spaces during object construction.\n\n When a class is instantiated, this method intercepts the arguments\n associated to the parameter and variable spaces. This prevents both\n :func:`__new__` and :func:`__init__` methods from ever seing these\n arguments.\n\n The parameter and variable spaces are injected into the object after\n construction and before initialization.\n '''\n\n # Intercept constructor arguments\n _rfm_use_params = kwargs.pop('_rfm_use_params', False)\n\n obj = cls.__new__(cls, *args, **kwargs)\n\n # Insert the var & param spaces\n cls._rfm_var_space.inject(obj, cls)\n cls._rfm_param_space.inject(obj, cls, _rfm_use_params)\n\n obj.__init__(*args, **kwargs)\n return obj\n\n def __getattribute__(cls, name):\n '''Attribute lookup method for custom class attributes.\n\n ReFrame test variables are descriptors injected at the class level.\n If a variable descriptor has already been injected into the class,\n do not return the descriptor object and return the default value\n associated with that variable instead.\n\n .. warning::\n .. versionchanged:: 3.7.0\n Prior versions exposed the variable descriptor object if this\n was already present in the class, instead of returning the\n variable's default value.\n '''\n\n try:\n var_space = super().__getattribute__('_rfm_var_space')\n except AttributeError:\n var_space = None\n\n # If the variable is already injected, delegate lookup to __getattr__.\n if var_space and name in var_space.injected_vars:\n raise AttributeError('delegate variable lookup to __getattr__')\n\n # Default back to the base method if no special treatment required.\n return super().__getattribute__(name)\n\n def __getattr__(cls, name):\n '''Backup attribute lookup method into custom namespaces.\n\n Some ReFrame built-in types are stored under their own sub-namespaces.\n This method will perform an attribute lookup on these sub-namespaces\n if a call to the default :func:`__getattribute__` method fails to\n retrieve the requested class attribute.\n '''\n\n try:\n var_space = super().__getattribute__('_rfm_var_space')\n return var_space.vars[name]\n except AttributeError:\n '''Catch early access attempt to the variable space.'''\n except KeyError:\n '''Requested name not in variable space.'''\n\n try:\n param_space = super().__getattribute__('_rfm_param_space')\n return param_space.params[name]\n except AttributeError:\n '''Catch early access attempt to the parameter space.'''\n except KeyError:\n '''Requested name not in parameter space.'''\n\n raise AttributeError(\n f'class {cls.__qualname__!r} has no attribute {name!r}'\n ) from None\n\n def setvar(cls, name, value):\n '''Set the value of a variable.\n\n :param name: The name of the variable.\n :param value: The value of the variable.\n\n :returns: :class:`True` if the variable was set.\n A variable will *not* be set, if it does not exist or when an\n attempt is made to set it with its underlying descriptor.\n This happens during the variable injection time and it should be\n delegated to the class' :func:`__setattr__` method.\n\n :raises ReframeSyntaxError: If an attempt is made to override a\n variable with a descriptor other than its underlying one.\n\n '''\n\n try:\n var_space = super().__getattribute__('_rfm_var_space')\n if name in var_space:\n if not hasattr(value, '__get__'):\n var_space[name].define(value)\n return True\n elif var_space[name].field is not value:\n desc = '.'.join([cls.__qualname__, name])\n raise ReframeSyntaxError(\n f'cannot override variable descriptor {desc!r}'\n )\n else:\n # Variable is being injected\n return False\n except AttributeError:\n '''Catch early access attempt to the variable space.'''\n return False\n\n def __setattr__(cls, name, value):\n '''Handle the special treatment required for variables and parameters.\n\n A variable's default value can be updated when accessed as a regular\n class attribute. This behavior does not apply when the assigned value\n is a descriptor object. In that case, the task of setting the value is\n delegated to the base :func:`__setattr__` (this is to comply with\n standard Python behavior). However, since the variables are already\n descriptors which are injected during class instantiation, we disallow\n any attempt to override this descriptor (since it would be silently\n re-overridden in any case).\n\n Altering the value of a parameter when accessed as a class attribute\n is not allowed. This would break the parameter space internals.\n '''\n\n # Try to treat `name` as variable\n if cls.setvar(name, value):\n return\n\n # Try to treat `name` as a parameter\n try:\n # Catch attempts to override a test parameter\n param_space = super().__getattribute__('_rfm_param_space')\n if name in param_space.params:\n raise ReframeSyntaxError(f'cannot override parameter {name!r}')\n except AttributeError:\n '''Catch early access attempt to the parameter space.'''\n\n # Treat `name` as normal class attribute\n super().__setattr__(name, value)\n\n @property\n def param_space(cls):\n ''' Make the parameter space available as read-only.'''\n return cls._rfm_param_space\n\n def is_abstract(cls):\n '''Check if the class is an abstract test.\n\n This is the case when some parameters are undefined, which results in\n the length of the parameter space being 0.\n\n :return: bool indicating whether the test has undefined parameters.\n\n :meta private:\n '''\n return len(cls.param_space) == 0\n"}}},{"rowIdx":59,"cells":{"function_name":{"kind":"string","value":"create_test_server"},"docstring":{"kind":"string","value":"Wrapper utility that returns a test server.\n\nThis wrapper utility calls the common create test server and\nreturns a test server. The purpose of this wrapper is to minimize\nthe impact on the code of the tests already using this\nfunction.\n\n:param validatable: Whether the server will be pingable or sshable.\n:param volume_backed: Whether the instance is volume backed or not.\n:param validation_resources: Dictionary of validation resources as\n returned by `get_class_validation_resources`.\n:param clients: Client manager, defaults to os_primary.\n:param kwargs: Extra arguments are passed down to the\n `compute.create_test_server` call."},"masked_code":{"kind":"string","value":"# Copyright 2012 OpenStack Foundation\n# All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport time\n\nfrom oslo_log import log as logging\n\nfrom tempest.common import compute\nfrom tempest.common import waiters\nfrom tempest import config\nfrom tempest import exceptions\nfrom tempest.lib.common import api_microversion_fixture\nfrom tempest.lib.common import api_version_request\nfrom tempest.lib.common import api_version_utils\nfrom tempest.lib.common.utils import data_utils\nfrom tempest.lib.common.utils import test_utils\nfrom tempest.lib import exceptions as lib_exc\nimport tempest.test\n\nCONF = config.CONF\n\nLOG = logging.getLogger(__name__)\n\n\nclass BaseV2ComputeTest(api_version_utils.BaseMicroversionTest,\n tempest.test.BaseTestCase):\n \"\"\"Base test case class for all Compute API tests.\"\"\"\n\n force_tenant_isolation = False\n # Set this to True in subclasses to create a default network. See\n # https://bugs.launchpad.net/tempest/+bug/1844568\n create_default_network = False\n\n # TODO(andreaf) We should care also for the alt_manager here\n # but only once client lazy load in the manager is done\n credentials = ['primary']\n\n @classmethod\n def skip_checks(cls):\n super(BaseV2ComputeTest, cls).skip_checks()\n if not CONF.service_available.nova:\n raise cls.skipException(\"Nova is not available\")\n api_version_utils.check_skip_with_microversion(\n cls.min_microversion, cls.max_microversion,\n CONF.compute.min_microversion, CONF.compute.max_microversion)\n api_version_utils.check_skip_with_microversion(\n cls.volume_min_microversion, cls.volume_max_microversion,\n CONF.volume.min_microversion, CONF.volume.max_microversion)\n api_version_utils.check_skip_with_microversion(\n cls.placement_min_microversion, cls.placement_max_microversion,\n CONF.placement.min_microversion, CONF.placement.max_microversion)\n\n @classmethod\n def setup_credentials(cls):\n # Setting network=True, subnet=True creates a default network\n cls.set_network_resources(\n network=cls.create_default_network,\n subnet=cls.create_default_network)\n super(BaseV2ComputeTest, cls).setup_credentials()\n\n @classmethod\n def setup_clients(cls):\n super(BaseV2ComputeTest, cls).setup_clients()\n cls.servers_client = cls.os_primary.servers_client\n cls.server_groups_client = cls.os_primary.server_groups_client\n cls.flavors_client = cls.os_primary.flavors_client\n cls.compute_images_client = cls.os_primary.compute_images_client\n cls.extensions_client = cls.os_primary.extensions_client\n cls.floating_ip_pools_client = cls.os_primary.floating_ip_pools_client\n cls.floating_ips_client = cls.os_primary.compute_floating_ips_client\n cls.keypairs_client = cls.os_primary.keypairs_client\n cls.security_group_rules_client = (\n cls.os_primary.compute_security_group_rules_client)\n cls.security_groups_client =\\\n cls.os_primary.compute_security_groups_client\n cls.quotas_client = cls.os_primary.quotas_client\n cls.compute_networks_client = cls.os_primary.compute_networks_client\n cls.limits_client = cls.os_primary.limits_client\n cls.volumes_extensions_client =\\\n cls.os_primary.volumes_extensions_client\n cls.snapshots_extensions_client =\\\n cls.os_primary.snapshots_extensions_client\n cls.interfaces_client = cls.os_primary.interfaces_client\n cls.fixed_ips_client = cls.os_primary.fixed_ips_client\n cls.availability_zone_client = cls.os_primary.availability_zone_client\n cls.agents_client = cls.os_primary.agents_client\n cls.aggregates_client = cls.os_primary.aggregates_client\n cls.services_client = cls.os_primary.services_client\n cls.instance_usages_audit_log_client = (\n cls.os_primary.instance_usages_audit_log_client)\n cls.hypervisor_client = cls.os_primary.hypervisor_client\n cls.certificates_client = cls.os_primary.certificates_client\n cls.migrations_client = cls.os_primary.migrations_client\n cls.security_group_default_rules_client = (\n cls.os_primary.security_group_default_rules_client)\n cls.versions_client = cls.os_primary.compute_versions_client\n if CONF.service_available.cinder:\n cls.volumes_client = cls.os_primary.volumes_client_latest\n cls.attachments_client = cls.os_primary.attachments_client_latest\n cls.snapshots_client = cls.os_primary.snapshots_client_latest\n if CONF.service_available.glance:\n if CONF.image_feature_enabled.api_v1:\n cls.images_client = cls.os_primary.image_client\n elif CONF.image_feature_enabled.api_v2:\n cls.images_client = cls.os_primary.image_client_v2\n else:\n raise lib_exc.InvalidConfiguration(\n 'Either api_v1 or api_v2 must be True in '\n '[image-feature-enabled].')\n cls._check_depends_on_nova_network()\n\n @classmethod\n def _check_depends_on_nova_network(cls):\n # Since nova-network APIs were removed from Nova in the Rocky release,\n # determine, based on the max version from the version document, if\n # the compute API is >Queens and if so, skip tests that rely on\n # nova-network.\n if not getattr(cls, 'depends_on_nova_network', False):\n return\n versions = cls.versions_client.list_versions()['versions']\n # Find the v2.1 version which will tell us our max version for the\n # compute API we're testing against.\n for version in versions:\n if version['id'] == 'v2.1':\n max_version = api_version_request.APIVersionRequest(\n version['version'])\n break\n else:\n LOG.warning(\n 'Unable to determine max v2.1 compute API version: %s',\n versions)\n return\n\n # The max compute API version in Queens is 2.60 so we cap\n # at that version.\n queens = api_version_request.APIVersionRequest('2.60')\n if max_version > queens:\n raise cls.skipException('nova-network is gone')\n\n @classmethod\n def resource_setup(cls):\n super(BaseV2ComputeTest, cls).resource_setup()\n cls.request_microversion = (\n api_version_utils.select_request_microversion(\n cls.min_microversion,\n CONF.compute.min_microversion))\n cls.volume_request_microversion = (\n api_version_utils.select_request_microversion(\n cls.volume_min_microversion,\n CONF.volume.min_microversion))\n cls.placement_request_microversion = (\n api_version_utils.select_request_microversion(\n cls.placement_min_microversion,\n CONF.placement.min_microversion))\n cls.build_interval = CONF.compute.build_interval\n cls.build_timeout = CONF.compute.build_timeout\n cls.image_ref = CONF.compute.image_ref\n cls.image_ref_alt = CONF.compute.image_ref_alt\n cls.flavor_ref = CONF.compute.flavor_ref\n cls.flavor_ref_alt = CONF.compute.flavor_ref_alt\n cls.ssh_user = CONF.validation.image_ssh_user\n cls.ssh_alt_user = CONF.validation.image_alt_ssh_user\n cls.image_ssh_user = CONF.validation.image_ssh_user\n cls.image_alt_ssh_user = CONF.validation.image_alt_ssh_user\n cls.image_ssh_password = CONF.validation.image_ssh_password\n cls.image_alt_ssh_password = CONF.validation.image_alt_ssh_password\n\n @classmethod\n def is_requested_microversion_compatible(cls, max_version):\n \"\"\"Check the compatibility of selected request microversion\n\n This method will check if selected request microversion\n (cls.request_microversion) for test is compatible with respect\n to 'max_version'. Compatible means if selected request microversion\n is in the range(<=) of 'max_version'.\n\n :param max_version: maximum microversion to compare for compatibility.\n Example: '2.30'\n :returns: True if selected request microversion is compatible with\n 'max_version'. False in other case.\n \"\"\"\n try:\n req_version_obj = api_version_request.APIVersionRequest(\n cls.request_microversion)\n # NOTE(gmann): This is case where this method is used before calling\n # resource_setup(), where cls.request_microversion is set. There may\n # not be any such case but still we can handle this case.\n except AttributeError:\n request_microversion = (\n api_version_utils.select_request_microversion(\n cls.min_microversion,\n CONF.compute.min_microversion))\n req_version_obj = api_version_request.APIVersionRequest(\n request_microversion)\n max_version_obj = api_version_request.APIVersionRequest(max_version)\n return req_version_obj <= max_version_obj\n\n @classmethod\n def server_check_teardown(cls):\n \"\"\"Checks is the shared server clean enough for subsequent test.\n\n Method will delete the server when it's dirty.\n The setUp method is responsible for creating a new server.\n Exceptions raised in tearDown class are fails the test case,\n This method supposed to use only by tearDown methods, when\n the shared server_id is stored in the server_id of the class.\n \"\"\"\n if getattr(cls, 'server_id', None) is not None:\n try:\n waiters.wait_for_server_status(cls.servers_client,\n cls.server_id, 'ACTIVE')\n except Exception as exc:\n LOG.exception(exc)\n cls.servers_client.delete_server(cls.server_id)\n waiters.wait_for_server_termination(cls.servers_client,\n cls.server_id)\n cls.server_id = None\n raise\n\n# MASKED: create_test_server function (lines 232-284)\n\n @classmethod\n def create_security_group(cls, name=None, description=None):\n if name is None:\n name = data_utils.rand_name(cls.__name__ + \"-securitygroup\")\n if description is None:\n description = data_utils.rand_name('description')\n body = cls.security_groups_client.create_security_group(\n name=name, description=description)['security_group']\n cls.addClassResourceCleanup(\n test_utils.call_and_ignore_notfound_exc,\n cls.security_groups_client.delete_security_group,\n body['id'])\n\n return body\n\n @classmethod\n def create_test_server_group(cls, name=\"\", policy=None):\n if not name:\n name = data_utils.rand_name(cls.__name__ + \"-Server-Group\")\n if policy is None:\n policy = ['affinity']\n body = cls.server_groups_client.create_server_group(\n name=name, policies=policy)['server_group']\n cls.addClassResourceCleanup(\n test_utils.call_and_ignore_notfound_exc,\n cls.server_groups_client.delete_server_group,\n body['id'])\n return body\n\n def wait_for(self, condition):\n \"\"\"Repeatedly calls condition() until a timeout.\"\"\"\n start_time = int(time.time())\n while True:\n try:\n condition()\n except Exception:\n pass\n else:\n return\n if int(time.time()) - start_time >= self.build_timeout:\n condition()\n return\n time.sleep(self.build_interval)\n\n @classmethod\n def prepare_instance_network(cls):\n if (CONF.validation.auth_method != 'disabled' and\n CONF.validation.connect_method == 'floating'):\n cls.set_network_resources(network=True, subnet=True, router=True,\n dhcp=True)\n\n @classmethod\n def create_image_from_server(cls, server_id, **kwargs):\n \"\"\"Wrapper utility that returns an image created from the server.\n\n If compute microversion >= 2.36, the returned image response will\n be from the image service API rather than the compute image proxy API.\n \"\"\"\n name = kwargs.pop('name',\n data_utils.rand_name(cls.__name__ + \"-image\"))\n wait_until = kwargs.pop('wait_until', None)\n wait_for_server = kwargs.pop('wait_for_server', True)\n\n image = cls.compute_images_client.create_image(server_id, name=name,\n **kwargs)\n if api_version_utils.compare_version_header_to_response(\n \"OpenStack-API-Version\", \"compute 2.45\", image.response, \"lt\"):\n image_id = image['image_id']\n else:\n image_id = data_utils.parse_image_id(image.response['location'])\n\n # The compute image proxy APIs were deprecated in 2.35 so\n # use the images client directly if the API microversion being\n # used is >=2.36.\n if not cls.is_requested_microversion_compatible('2.35'):\n client = cls.images_client\n else:\n client = cls.compute_images_client\n cls.addClassResourceCleanup(test_utils.call_and_ignore_notfound_exc,\n client.delete_image, image_id)\n\n if wait_until is not None:\n try:\n wait_until = wait_until.upper()\n if not cls.is_requested_microversion_compatible('2.35'):\n wait_until = wait_until.lower()\n waiters.wait_for_image_status(client, image_id, wait_until)\n except lib_exc.NotFound:\n if wait_until.upper() == 'ACTIVE':\n # If the image is not found after create_image returned\n # that means the snapshot failed in nova-compute and nova\n # deleted the image. There should be a compute fault\n # recorded with the server in that case, so get the server\n # and dump some details.\n server = (\n cls.servers_client.show_server(server_id)['server'])\n if 'fault' in server:\n raise exceptions.SnapshotNotFoundException(\n server['fault'], image_id=image_id)\n else:\n raise exceptions.SnapshotNotFoundException(\n image_id=image_id)\n else:\n raise\n image = client.show_image(image_id)\n # Compute image client returns response wrapped in 'image' element\n # which is not the case with Glance image client.\n if 'image' in image:\n image = image['image']\n\n if wait_until.upper() == 'ACTIVE':\n if wait_for_server:\n waiters.wait_for_server_status(cls.servers_client,\n server_id, 'ACTIVE')\n return image\n\n @classmethod\n def recreate_server(cls, server_id, validatable=False, **kwargs):\n \"\"\"Destroy an existing class level server and creates a new one\n\n Some test classes use a test server that can be used by multiple\n tests. This is done to optimise runtime and test load.\n If something goes wrong with the test server, it can be rebuilt\n using this helper.\n\n This helper can also be used for the initial provisioning if no\n server_id is specified.\n\n :param server_id: UUID of the server to be rebuilt. If None is\n specified, a new server is provisioned.\n :param validatable: whether to the server needs to be\n validatable. When True, validation resources are acquired via\n the `get_class_validation_resources` helper.\n :param kwargs: extra paramaters are passed through to the\n `create_test_server` call.\n :return: the UUID of the created server.\n \"\"\"\n if server_id:\n cls.delete_server(server_id)\n\n cls.password = data_utils.rand_password()\n server = cls.create_test_server(\n validatable,\n validation_resources=cls.get_class_validation_resources(\n cls.os_primary),\n wait_until='ACTIVE',\n adminPass=cls.password,\n **kwargs)\n return server['id']\n\n @classmethod\n def delete_server(cls, server_id):\n \"\"\"Deletes an existing server and waits for it to be gone.\"\"\"\n try:\n cls.servers_client.delete_server(server_id)\n waiters.wait_for_server_termination(cls.servers_client,\n server_id)\n except Exception:\n LOG.exception('Failed to delete server %s', server_id)\n\n def resize_server(self, server_id, new_flavor_id, **kwargs):\n \"\"\"resize and confirm_resize an server, waits for it to be ACTIVE.\"\"\"\n self.servers_client.resize_server(server_id, new_flavor_id, **kwargs)\n waiters.wait_for_server_status(self.servers_client, server_id,\n 'VERIFY_RESIZE')\n self.servers_client.confirm_resize_server(server_id)\n waiters.wait_for_server_status(\n self.servers_client, server_id, 'ACTIVE')\n server = self.servers_client.show_server(server_id)['server']\n self.assert_flavor_equal(new_flavor_id, server['flavor'])\n\n @classmethod\n def delete_volume(cls, volume_id):\n \"\"\"Deletes the given volume and waits for it to be gone.\"\"\"\n try:\n cls.volumes_client.delete_volume(volume_id)\n # TODO(mriedem): We should move the wait_for_resource_deletion\n # into the delete_volume method as a convenience to the caller.\n cls.volumes_client.wait_for_resource_deletion(volume_id)\n except lib_exc.NotFound:\n LOG.warning(\"Unable to delete volume '%s' since it was not found. \"\n \"Maybe it was already deleted?\", volume_id)\n\n @classmethod\n def get_server_ip(cls, server, validation_resources=None):\n \"\"\"Get the server fixed or floating IP.\n\n Based on the configuration we're in, return a correct ip\n address for validating that a guest is up.\n\n :param server: The server dict as returned by the API\n :param validation_resources: The dict of validation resources\n provisioned for the server.\n \"\"\"\n if CONF.validation.connect_method == 'floating':\n if validation_resources:\n return validation_resources['floating_ip']['ip']\n else:\n msg = ('When validation.connect_method equals floating, '\n 'validation_resources cannot be None')\n raise lib_exc.InvalidParam(invalid_param=msg)\n elif CONF.validation.connect_method == 'fixed':\n addresses = server['addresses'][CONF.validation.network_for_ssh]\n for address in addresses:\n if address['version'] == CONF.validation.ip_version_for_ssh:\n return address['addr']\n raise exceptions.ServerUnreachable(server_id=server['id'])\n else:\n raise lib_exc.InvalidConfiguration()\n\n def setUp(self):\n super(BaseV2ComputeTest, self).setUp()\n self.useFixture(api_microversion_fixture.APIMicroversionFixture(\n compute_microversion=self.request_microversion,\n volume_microversion=self.volume_request_microversion,\n placement_microversion=self.placement_request_microversion))\n\n @classmethod\n def create_volume(cls, image_ref=None, **kwargs):\n \"\"\"Create a volume and wait for it to become 'available'.\n\n :param image_ref: Specify an image id to create a bootable volume.\n :param kwargs: other parameters to create volume.\n :returns: The available volume.\n \"\"\"\n if 'size' not in kwargs:\n kwargs['size'] = CONF.volume.volume_size\n if 'display_name' not in kwargs:\n vol_name = data_utils.rand_name(cls.__name__ + '-volume')\n kwargs['display_name'] = vol_name\n if image_ref is not None:\n kwargs['imageRef'] = image_ref\n if CONF.compute.compute_volume_common_az:\n kwargs.setdefault('availability_zone',\n CONF.compute.compute_volume_common_az)\n volume = cls.volumes_client.create_volume(**kwargs)['volume']\n cls.addClassResourceCleanup(\n cls.volumes_client.wait_for_resource_deletion, volume['id'])\n cls.addClassResourceCleanup(test_utils.call_and_ignore_notfound_exc,\n cls.volumes_client.delete_volume,\n volume['id'])\n waiters.wait_for_volume_resource_status(cls.volumes_client,\n volume['id'], 'available')\n return volume\n\n def _detach_volume(self, server, volume):\n \"\"\"Helper method to detach a volume.\n\n Ignores 404 responses if the volume or server do not exist, or the\n volume is already detached from the server.\n \"\"\"\n try:\n volume = self.volumes_client.show_volume(volume['id'])['volume']\n # Check the status. You can only detach an in-use volume, otherwise\n # the compute API will return a 400 response.\n if volume['status'] == 'in-use':\n self.servers_client.detach_volume(server['id'], volume['id'])\n except lib_exc.NotFound:\n # Ignore 404s on detach in case the server is deleted or the volume\n # is already detached.\n pass\n\n def attach_volume(self, server, volume, device=None, tag=None):\n \"\"\"Attaches volume to server and waits for 'in-use' volume status.\n\n The volume will be detached when the test tears down.\n\n :param server: The server to which the volume will be attached.\n :param volume: The volume to attach.\n :param device: Optional mountpoint for the attached volume. Note that\n this is not guaranteed for all hypervisors and is not recommended.\n :param tag: Optional device role tag to apply to the volume.\n \"\"\"\n attach_kwargs = dict(volumeId=volume['id'])\n if device:\n attach_kwargs['device'] = device\n if tag:\n attach_kwargs['tag'] = tag\n\n attachment = self.servers_client.attach_volume(\n server['id'], **attach_kwargs)['volumeAttachment']\n # On teardown detach the volume and for multiattach volumes wait for\n # the attachment to be removed. For non-multiattach volumes wait for\n # the state of the volume to change to available. This is so we don't\n # error out when trying to delete the volume during teardown.\n if volume['multiattach']:\n att = waiters.wait_for_volume_attachment_create(\n self.volumes_client, volume['id'], server['id'])\n self.addCleanup(waiters.wait_for_volume_attachment_remove,\n self.volumes_client, volume['id'],\n att['attachment_id'])\n else:\n self.addCleanup(waiters.wait_for_volume_resource_status,\n self.volumes_client, volume['id'], 'available')\n waiters.wait_for_volume_resource_status(self.volumes_client,\n volume['id'], 'in-use')\n # Ignore 404s on detach in case the server is deleted or the volume\n # is already detached.\n self.addCleanup(self._detach_volume, server, volume)\n return attachment\n\n def create_volume_snapshot(self, volume_id, name=None, description=None,\n metadata=None, force=False):\n name = name or data_utils.rand_name(\n self.__class__.__name__ + '-snapshot')\n snapshot = self.snapshots_client.create_snapshot(\n volume_id=volume_id,\n force=force,\n display_name=name,\n description=description,\n metadata=metadata)['snapshot']\n self.addCleanup(self.snapshots_client.wait_for_resource_deletion,\n snapshot['id'])\n self.addCleanup(self.snapshots_client.delete_snapshot, snapshot['id'])\n waiters.wait_for_volume_resource_status(self.snapshots_client,\n snapshot['id'], 'available')\n snapshot = self.snapshots_client.show_snapshot(\n snapshot['id'])['snapshot']\n return snapshot\n\n def assert_flavor_equal(self, flavor_id, server_flavor):\n \"\"\"Check whether server_flavor equals to flavor.\n\n :param flavor_id: flavor id\n :param server_flavor: flavor info returned by show_server.\n \"\"\"\n # Nova API > 2.46 no longer includes flavor.id, and schema check\n # will cover whether 'id' should be in flavor\n if server_flavor.get('id'):\n msg = ('server flavor is not same as flavor!')\n self.assertEqual(flavor_id, server_flavor['id'], msg)\n else:\n flavor = self.flavors_client.show_flavor(flavor_id)['flavor']\n self.assertEqual(flavor['name'], server_flavor['original_name'],\n \"original_name in server flavor is not same as \"\n \"flavor name!\")\n for key in ['ram', 'vcpus', 'disk']:\n msg = ('attribute %s in server flavor is not same as '\n 'flavor!' % key)\n self.assertEqual(flavor[key], server_flavor[key], msg)\n\n\nclass BaseV2ComputeAdminTest(BaseV2ComputeTest):\n \"\"\"Base test case class for Compute Admin API tests.\"\"\"\n\n credentials = ['primary', 'admin']\n\n @classmethod\n def setup_clients(cls):\n super(BaseV2ComputeAdminTest, cls).setup_clients()\n cls.availability_zone_admin_client = (\n cls.os_admin.availability_zone_client)\n cls.admin_flavors_client = cls.os_admin.flavors_client\n cls.admin_servers_client = cls.os_admin.servers_client\n cls.image_client = cls.os_admin.image_client_v2\n cls.admin_assisted_volume_snapshots_client = \\\n cls.os_admin.assisted_volume_snapshots_client\n\n def create_flavor(self, ram, vcpus, disk, name=None,\n is_public='True', **kwargs):\n if name is None:\n name = data_utils.rand_name(self.__class__.__name__ + \"-flavor\")\n id = kwargs.pop('id', data_utils.rand_int_id(start=1000))\n client = self.admin_flavors_client\n flavor = client.create_flavor(\n ram=ram, vcpus=vcpus, disk=disk, name=name,\n id=id, is_public=is_public, **kwargs)['flavor']\n self.addCleanup(client.wait_for_resource_deletion, flavor['id'])\n self.addCleanup(client.delete_flavor, flavor['id'])\n return flavor\n\n @classmethod\n def get_host_for_server(cls, server_id):\n server_details = cls.admin_servers_client.show_server(server_id)\n return server_details['server']['OS-EXT-SRV-ATTR:host']\n\n def get_host_other_than(self, server_id):\n source_host = self.get_host_for_server(server_id)\n\n svcs = self.os_admin.services_client.list_services(\n binary='nova-compute')['services']\n hosts = []\n for svc in svcs:\n if svc['state'] == 'up' and svc['status'] == 'enabled':\n if CONF.compute.compute_volume_common_az:\n if svc['zone'] == CONF.compute.compute_volume_common_az:\n hosts.append(svc['host'])\n else:\n hosts.append(svc['host'])\n\n for target_host in hosts:\n if source_host != target_host:\n return target_host"},"implementation":{"kind":"string","value":" @classmethod\n def create_test_server(cls, validatable=False, volume_backed=False,\n validation_resources=None, clients=None, **kwargs):\n \"\"\"Wrapper utility that returns a test server.\n\n This wrapper utility calls the common create test server and\n returns a test server. The purpose of this wrapper is to minimize\n the impact on the code of the tests already using this\n function.\n\n :param validatable: Whether the server will be pingable or sshable.\n :param volume_backed: Whether the instance is volume backed or not.\n :param validation_resources: Dictionary of validation resources as\n returned by `get_class_validation_resources`.\n :param clients: Client manager, defaults to os_primary.\n :param kwargs: Extra arguments are passed down to the\n `compute.create_test_server` call.\n \"\"\"\n if 'name' not in kwargs:\n kwargs['name'] = data_utils.rand_name(cls.__name__ + \"-server\")\n\n request_version = api_version_request.APIVersionRequest(\n cls.request_microversion)\n v2_37_version = api_version_request.APIVersionRequest('2.37')\n\n tenant_network = cls.get_tenant_network()\n # NOTE(snikitin): since microversion v2.37 'networks' field is required\n if (request_version >= v2_37_version and 'networks' not in kwargs and\n not tenant_network):\n kwargs['networks'] = 'none'\n\n if clients is None:\n clients = cls.os_primary\n\n body, servers = compute.create_test_server(\n clients,\n validatable,\n validation_resources=validation_resources,\n tenant_network=tenant_network,\n volume_backed=volume_backed,\n **kwargs)\n\n # For each server schedule wait and delete, so we first delete all\n # and then wait for all\n for server in servers:\n cls.addClassResourceCleanup(waiters.wait_for_server_termination,\n clients.servers_client, server['id'])\n for server in servers:\n cls.addClassResourceCleanup(\n test_utils.call_and_ignore_notfound_exc,\n clients.servers_client.delete_server, server['id'])\n\n return body"},"start_line":{"kind":"number","value":232,"string":"232"},"end_line":{"kind":"number","value":284,"string":"284"},"file_content":{"kind":"string","value":"# Copyright 2012 OpenStack Foundation\n# All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport time\n\nfrom oslo_log import log as logging\n\nfrom tempest.common import compute\nfrom tempest.common import waiters\nfrom tempest import config\nfrom tempest import exceptions\nfrom tempest.lib.common import api_microversion_fixture\nfrom tempest.lib.common import api_version_request\nfrom tempest.lib.common import api_version_utils\nfrom tempest.lib.common.utils import data_utils\nfrom tempest.lib.common.utils import test_utils\nfrom tempest.lib import exceptions as lib_exc\nimport tempest.test\n\nCONF = config.CONF\n\nLOG = logging.getLogger(__name__)\n\n\nclass BaseV2ComputeTest(api_version_utils.BaseMicroversionTest,\n tempest.test.BaseTestCase):\n \"\"\"Base test case class for all Compute API tests.\"\"\"\n\n force_tenant_isolation = False\n # Set this to True in subclasses to create a default network. See\n # https://bugs.launchpad.net/tempest/+bug/1844568\n create_default_network = False\n\n # TODO(andreaf) We should care also for the alt_manager here\n # but only once client lazy load in the manager is done\n credentials = ['primary']\n\n @classmethod\n def skip_checks(cls):\n super(BaseV2ComputeTest, cls).skip_checks()\n if not CONF.service_available.nova:\n raise cls.skipException(\"Nova is not available\")\n api_version_utils.check_skip_with_microversion(\n cls.min_microversion, cls.max_microversion,\n CONF.compute.min_microversion, CONF.compute.max_microversion)\n api_version_utils.check_skip_with_microversion(\n cls.volume_min_microversion, cls.volume_max_microversion,\n CONF.volume.min_microversion, CONF.volume.max_microversion)\n api_version_utils.check_skip_with_microversion(\n cls.placement_min_microversion, cls.placement_max_microversion,\n CONF.placement.min_microversion, CONF.placement.max_microversion)\n\n @classmethod\n def setup_credentials(cls):\n # Setting network=True, subnet=True creates a default network\n cls.set_network_resources(\n network=cls.create_default_network,\n subnet=cls.create_default_network)\n super(BaseV2ComputeTest, cls).setup_credentials()\n\n @classmethod\n def setup_clients(cls):\n super(BaseV2ComputeTest, cls).setup_clients()\n cls.servers_client = cls.os_primary.servers_client\n cls.server_groups_client = cls.os_primary.server_groups_client\n cls.flavors_client = cls.os_primary.flavors_client\n cls.compute_images_client = cls.os_primary.compute_images_client\n cls.extensions_client = cls.os_primary.extensions_client\n cls.floating_ip_pools_client = cls.os_primary.floating_ip_pools_client\n cls.floating_ips_client = cls.os_primary.compute_floating_ips_client\n cls.keypairs_client = cls.os_primary.keypairs_client\n cls.security_group_rules_client = (\n cls.os_primary.compute_security_group_rules_client)\n cls.security_groups_client =\\\n cls.os_primary.compute_security_groups_client\n cls.quotas_client = cls.os_primary.quotas_client\n cls.compute_networks_client = cls.os_primary.compute_networks_client\n cls.limits_client = cls.os_primary.limits_client\n cls.volumes_extensions_client =\\\n cls.os_primary.volumes_extensions_client\n cls.snapshots_extensions_client =\\\n cls.os_primary.snapshots_extensions_client\n cls.interfaces_client = cls.os_primary.interfaces_client\n cls.fixed_ips_client = cls.os_primary.fixed_ips_client\n cls.availability_zone_client = cls.os_primary.availability_zone_client\n cls.agents_client = cls.os_primary.agents_client\n cls.aggregates_client = cls.os_primary.aggregates_client\n cls.services_client = cls.os_primary.services_client\n cls.instance_usages_audit_log_client = (\n cls.os_primary.instance_usages_audit_log_client)\n cls.hypervisor_client = cls.os_primary.hypervisor_client\n cls.certificates_client = cls.os_primary.certificates_client\n cls.migrations_client = cls.os_primary.migrations_client\n cls.security_group_default_rules_client = (\n cls.os_primary.security_group_default_rules_client)\n cls.versions_client = cls.os_primary.compute_versions_client\n if CONF.service_available.cinder:\n cls.volumes_client = cls.os_primary.volumes_client_latest\n cls.attachments_client = cls.os_primary.attachments_client_latest\n cls.snapshots_client = cls.os_primary.snapshots_client_latest\n if CONF.service_available.glance:\n if CONF.image_feature_enabled.api_v1:\n cls.images_client = cls.os_primary.image_client\n elif CONF.image_feature_enabled.api_v2:\n cls.images_client = cls.os_primary.image_client_v2\n else:\n raise lib_exc.InvalidConfiguration(\n 'Either api_v1 or api_v2 must be True in '\n '[image-feature-enabled].')\n cls._check_depends_on_nova_network()\n\n @classmethod\n def _check_depends_on_nova_network(cls):\n # Since nova-network APIs were removed from Nova in the Rocky release,\n # determine, based on the max version from the version document, if\n # the compute API is >Queens and if so, skip tests that rely on\n # nova-network.\n if not getattr(cls, 'depends_on_nova_network', False):\n return\n versions = cls.versions_client.list_versions()['versions']\n # Find the v2.1 version which will tell us our max version for the\n # compute API we're testing against.\n for version in versions:\n if version['id'] == 'v2.1':\n max_version = api_version_request.APIVersionRequest(\n version['version'])\n break\n else:\n LOG.warning(\n 'Unable to determine max v2.1 compute API version: %s',\n versions)\n return\n\n # The max compute API version in Queens is 2.60 so we cap\n # at that version.\n queens = api_version_request.APIVersionRequest('2.60')\n if max_version > queens:\n raise cls.skipException('nova-network is gone')\n\n @classmethod\n def resource_setup(cls):\n super(BaseV2ComputeTest, cls).resource_setup()\n cls.request_microversion = (\n api_version_utils.select_request_microversion(\n cls.min_microversion,\n CONF.compute.min_microversion))\n cls.volume_request_microversion = (\n api_version_utils.select_request_microversion(\n cls.volume_min_microversion,\n CONF.volume.min_microversion))\n cls.placement_request_microversion = (\n api_version_utils.select_request_microversion(\n cls.placement_min_microversion,\n CONF.placement.min_microversion))\n cls.build_interval = CONF.compute.build_interval\n cls.build_timeout = CONF.compute.build_timeout\n cls.image_ref = CONF.compute.image_ref\n cls.image_ref_alt = CONF.compute.image_ref_alt\n cls.flavor_ref = CONF.compute.flavor_ref\n cls.flavor_ref_alt = CONF.compute.flavor_ref_alt\n cls.ssh_user = CONF.validation.image_ssh_user\n cls.ssh_alt_user = CONF.validation.image_alt_ssh_user\n cls.image_ssh_user = CONF.validation.image_ssh_user\n cls.image_alt_ssh_user = CONF.validation.image_alt_ssh_user\n cls.image_ssh_password = CONF.validation.image_ssh_password\n cls.image_alt_ssh_password = CONF.validation.image_alt_ssh_password\n\n @classmethod\n def is_requested_microversion_compatible(cls, max_version):\n \"\"\"Check the compatibility of selected request microversion\n\n This method will check if selected request microversion\n (cls.request_microversion) for test is compatible with respect\n to 'max_version'. Compatible means if selected request microversion\n is in the range(<=) of 'max_version'.\n\n :param max_version: maximum microversion to compare for compatibility.\n Example: '2.30'\n :returns: True if selected request microversion is compatible with\n 'max_version'. False in other case.\n \"\"\"\n try:\n req_version_obj = api_version_request.APIVersionRequest(\n cls.request_microversion)\n # NOTE(gmann): This is case where this method is used before calling\n # resource_setup(), where cls.request_microversion is set. There may\n # not be any such case but still we can handle this case.\n except AttributeError:\n request_microversion = (\n api_version_utils.select_request_microversion(\n cls.min_microversion,\n CONF.compute.min_microversion))\n req_version_obj = api_version_request.APIVersionRequest(\n request_microversion)\n max_version_obj = api_version_request.APIVersionRequest(max_version)\n return req_version_obj <= max_version_obj\n\n @classmethod\n def server_check_teardown(cls):\n \"\"\"Checks is the shared server clean enough for subsequent test.\n\n Method will delete the server when it's dirty.\n The setUp method is responsible for creating a new server.\n Exceptions raised in tearDown class are fails the test case,\n This method supposed to use only by tearDown methods, when\n the shared server_id is stored in the server_id of the class.\n \"\"\"\n if getattr(cls, 'server_id', None) is not None:\n try:\n waiters.wait_for_server_status(cls.servers_client,\n cls.server_id, 'ACTIVE')\n except Exception as exc:\n LOG.exception(exc)\n cls.servers_client.delete_server(cls.server_id)\n waiters.wait_for_server_termination(cls.servers_client,\n cls.server_id)\n cls.server_id = None\n raise\n\n @classmethod\n def create_test_server(cls, validatable=False, volume_backed=False,\n validation_resources=None, clients=None, **kwargs):\n \"\"\"Wrapper utility that returns a test server.\n\n This wrapper utility calls the common create test server and\n returns a test server. The purpose of this wrapper is to minimize\n the impact on the code of the tests already using this\n function.\n\n :param validatable: Whether the server will be pingable or sshable.\n :param volume_backed: Whether the instance is volume backed or not.\n :param validation_resources: Dictionary of validation resources as\n returned by `get_class_validation_resources`.\n :param clients: Client manager, defaults to os_primary.\n :param kwargs: Extra arguments are passed down to the\n `compute.create_test_server` call.\n \"\"\"\n if 'name' not in kwargs:\n kwargs['name'] = data_utils.rand_name(cls.__name__ + \"-server\")\n\n request_version = api_version_request.APIVersionRequest(\n cls.request_microversion)\n v2_37_version = api_version_request.APIVersionRequest('2.37')\n\n tenant_network = cls.get_tenant_network()\n # NOTE(snikitin): since microversion v2.37 'networks' field is required\n if (request_version >= v2_37_version and 'networks' not in kwargs and\n not tenant_network):\n kwargs['networks'] = 'none'\n\n if clients is None:\n clients = cls.os_primary\n\n body, servers = compute.create_test_server(\n clients,\n validatable,\n validation_resources=validation_resources,\n tenant_network=tenant_network,\n volume_backed=volume_backed,\n **kwargs)\n\n # For each server schedule wait and delete, so we first delete all\n # and then wait for all\n for server in servers:\n cls.addClassResourceCleanup(waiters.wait_for_server_termination,\n clients.servers_client, server['id'])\n for server in servers:\n cls.addClassResourceCleanup(\n test_utils.call_and_ignore_notfound_exc,\n clients.servers_client.delete_server, server['id'])\n\n return body\n\n @classmethod\n def create_security_group(cls, name=None, description=None):\n if name is None:\n name = data_utils.rand_name(cls.__name__ + \"-securitygroup\")\n if description is None:\n description = data_utils.rand_name('description')\n body = cls.security_groups_client.create_security_group(\n name=name, description=description)['security_group']\n cls.addClassResourceCleanup(\n test_utils.call_and_ignore_notfound_exc,\n cls.security_groups_client.delete_security_group,\n body['id'])\n\n return body\n\n @classmethod\n def create_test_server_group(cls, name=\"\", policy=None):\n if not name:\n name = data_utils.rand_name(cls.__name__ + \"-Server-Group\")\n if policy is None:\n policy = ['affinity']\n body = cls.server_groups_client.create_server_group(\n name=name, policies=policy)['server_group']\n cls.addClassResourceCleanup(\n test_utils.call_and_ignore_notfound_exc,\n cls.server_groups_client.delete_server_group,\n body['id'])\n return body\n\n def wait_for(self, condition):\n \"\"\"Repeatedly calls condition() until a timeout.\"\"\"\n start_time = int(time.time())\n while True:\n try:\n condition()\n except Exception:\n pass\n else:\n return\n if int(time.time()) - start_time >= self.build_timeout:\n condition()\n return\n time.sleep(self.build_interval)\n\n @classmethod\n def prepare_instance_network(cls):\n if (CONF.validation.auth_method != 'disabled' and\n CONF.validation.connect_method == 'floating'):\n cls.set_network_resources(network=True, subnet=True, router=True,\n dhcp=True)\n\n @classmethod\n def create_image_from_server(cls, server_id, **kwargs):\n \"\"\"Wrapper utility that returns an image created from the server.\n\n If compute microversion >= 2.36, the returned image response will\n be from the image service API rather than the compute image proxy API.\n \"\"\"\n name = kwargs.pop('name',\n data_utils.rand_name(cls.__name__ + \"-image\"))\n wait_until = kwargs.pop('wait_until', None)\n wait_for_server = kwargs.pop('wait_for_server', True)\n\n image = cls.compute_images_client.create_image(server_id, name=name,\n **kwargs)\n if api_version_utils.compare_version_header_to_response(\n \"OpenStack-API-Version\", \"compute 2.45\", image.response, \"lt\"):\n image_id = image['image_id']\n else:\n image_id = data_utils.parse_image_id(image.response['location'])\n\n # The compute image proxy APIs were deprecated in 2.35 so\n # use the images client directly if the API microversion being\n # used is >=2.36.\n if not cls.is_requested_microversion_compatible('2.35'):\n client = cls.images_client\n else:\n client = cls.compute_images_client\n cls.addClassResourceCleanup(test_utils.call_and_ignore_notfound_exc,\n client.delete_image, image_id)\n\n if wait_until is not None:\n try:\n wait_until = wait_until.upper()\n if not cls.is_requested_microversion_compatible('2.35'):\n wait_until = wait_until.lower()\n waiters.wait_for_image_status(client, image_id, wait_until)\n except lib_exc.NotFound:\n if wait_until.upper() == 'ACTIVE':\n # If the image is not found after create_image returned\n # that means the snapshot failed in nova-compute and nova\n # deleted the image. There should be a compute fault\n # recorded with the server in that case, so get the server\n # and dump some details.\n server = (\n cls.servers_client.show_server(server_id)['server'])\n if 'fault' in server:\n raise exceptions.SnapshotNotFoundException(\n server['fault'], image_id=image_id)\n else:\n raise exceptions.SnapshotNotFoundException(\n image_id=image_id)\n else:\n raise\n image = client.show_image(image_id)\n # Compute image client returns response wrapped in 'image' element\n # which is not the case with Glance image client.\n if 'image' in image:\n image = image['image']\n\n if wait_until.upper() == 'ACTIVE':\n if wait_for_server:\n waiters.wait_for_server_status(cls.servers_client,\n server_id, 'ACTIVE')\n return image\n\n @classmethod\n def recreate_server(cls, server_id, validatable=False, **kwargs):\n \"\"\"Destroy an existing class level server and creates a new one\n\n Some test classes use a test server that can be used by multiple\n tests. This is done to optimise runtime and test load.\n If something goes wrong with the test server, it can be rebuilt\n using this helper.\n\n This helper can also be used for the initial provisioning if no\n server_id is specified.\n\n :param server_id: UUID of the server to be rebuilt. If None is\n specified, a new server is provisioned.\n :param validatable: whether to the server needs to be\n validatable. When True, validation resources are acquired via\n the `get_class_validation_resources` helper.\n :param kwargs: extra paramaters are passed through to the\n `create_test_server` call.\n :return: the UUID of the created server.\n \"\"\"\n if server_id:\n cls.delete_server(server_id)\n\n cls.password = data_utils.rand_password()\n server = cls.create_test_server(\n validatable,\n validation_resources=cls.get_class_validation_resources(\n cls.os_primary),\n wait_until='ACTIVE',\n adminPass=cls.password,\n **kwargs)\n return server['id']\n\n @classmethod\n def delete_server(cls, server_id):\n \"\"\"Deletes an existing server and waits for it to be gone.\"\"\"\n try:\n cls.servers_client.delete_server(server_id)\n waiters.wait_for_server_termination(cls.servers_client,\n server_id)\n except Exception:\n LOG.exception('Failed to delete server %s', server_id)\n\n def resize_server(self, server_id, new_flavor_id, **kwargs):\n \"\"\"resize and confirm_resize an server, waits for it to be ACTIVE.\"\"\"\n self.servers_client.resize_server(server_id, new_flavor_id, **kwargs)\n waiters.wait_for_server_status(self.servers_client, server_id,\n 'VERIFY_RESIZE')\n self.servers_client.confirm_resize_server(server_id)\n waiters.wait_for_server_status(\n self.servers_client, server_id, 'ACTIVE')\n server = self.servers_client.show_server(server_id)['server']\n self.assert_flavor_equal(new_flavor_id, server['flavor'])\n\n @classmethod\n def delete_volume(cls, volume_id):\n \"\"\"Deletes the given volume and waits for it to be gone.\"\"\"\n try:\n cls.volumes_client.delete_volume(volume_id)\n # TODO(mriedem): We should move the wait_for_resource_deletion\n # into the delete_volume method as a convenience to the caller.\n cls.volumes_client.wait_for_resource_deletion(volume_id)\n except lib_exc.NotFound:\n LOG.warning(\"Unable to delete volume '%s' since it was not found. \"\n \"Maybe it was already deleted?\", volume_id)\n\n @classmethod\n def get_server_ip(cls, server, validation_resources=None):\n \"\"\"Get the server fixed or floating IP.\n\n Based on the configuration we're in, return a correct ip\n address for validating that a guest is up.\n\n :param server: The server dict as returned by the API\n :param validation_resources: The dict of validation resources\n provisioned for the server.\n \"\"\"\n if CONF.validation.connect_method == 'floating':\n if validation_resources:\n return validation_resources['floating_ip']['ip']\n else:\n msg = ('When validation.connect_method equals floating, '\n 'validation_resources cannot be None')\n raise lib_exc.InvalidParam(invalid_param=msg)\n elif CONF.validation.connect_method == 'fixed':\n addresses = server['addresses'][CONF.validation.network_for_ssh]\n for address in addresses:\n if address['version'] == CONF.validation.ip_version_for_ssh:\n return address['addr']\n raise exceptions.ServerUnreachable(server_id=server['id'])\n else:\n raise lib_exc.InvalidConfiguration()\n\n def setUp(self):\n super(BaseV2ComputeTest, self).setUp()\n self.useFixture(api_microversion_fixture.APIMicroversionFixture(\n compute_microversion=self.request_microversion,\n volume_microversion=self.volume_request_microversion,\n placement_microversion=self.placement_request_microversion))\n\n @classmethod\n def create_volume(cls, image_ref=None, **kwargs):\n \"\"\"Create a volume and wait for it to become 'available'.\n\n :param image_ref: Specify an image id to create a bootable volume.\n :param kwargs: other parameters to create volume.\n :returns: The available volume.\n \"\"\"\n if 'size' not in kwargs:\n kwargs['size'] = CONF.volume.volume_size\n if 'display_name' not in kwargs:\n vol_name = data_utils.rand_name(cls.__name__ + '-volume')\n kwargs['display_name'] = vol_name\n if image_ref is not None:\n kwargs['imageRef'] = image_ref\n if CONF.compute.compute_volume_common_az:\n kwargs.setdefault('availability_zone',\n CONF.compute.compute_volume_common_az)\n volume = cls.volumes_client.create_volume(**kwargs)['volume']\n cls.addClassResourceCleanup(\n cls.volumes_client.wait_for_resource_deletion, volume['id'])\n cls.addClassResourceCleanup(test_utils.call_and_ignore_notfound_exc,\n cls.volumes_client.delete_volume,\n volume['id'])\n waiters.wait_for_volume_resource_status(cls.volumes_client,\n volume['id'], 'available')\n return volume\n\n def _detach_volume(self, server, volume):\n \"\"\"Helper method to detach a volume.\n\n Ignores 404 responses if the volume or server do not exist, or the\n volume is already detached from the server.\n \"\"\"\n try:\n volume = self.volumes_client.show_volume(volume['id'])['volume']\n # Check the status. You can only detach an in-use volume, otherwise\n # the compute API will return a 400 response.\n if volume['status'] == 'in-use':\n self.servers_client.detach_volume(server['id'], volume['id'])\n except lib_exc.NotFound:\n # Ignore 404s on detach in case the server is deleted or the volume\n # is already detached.\n pass\n\n def attach_volume(self, server, volume, device=None, tag=None):\n \"\"\"Attaches volume to server and waits for 'in-use' volume status.\n\n The volume will be detached when the test tears down.\n\n :param server: The server to which the volume will be attached.\n :param volume: The volume to attach.\n :param device: Optional mountpoint for the attached volume. Note that\n this is not guaranteed for all hypervisors and is not recommended.\n :param tag: Optional device role tag to apply to the volume.\n \"\"\"\n attach_kwargs = dict(volumeId=volume['id'])\n if device:\n attach_kwargs['device'] = device\n if tag:\n attach_kwargs['tag'] = tag\n\n attachment = self.servers_client.attach_volume(\n server['id'], **attach_kwargs)['volumeAttachment']\n # On teardown detach the volume and for multiattach volumes wait for\n # the attachment to be removed. For non-multiattach volumes wait for\n # the state of the volume to change to available. This is so we don't\n # error out when trying to delete the volume during teardown.\n if volume['multiattach']:\n att = waiters.wait_for_volume_attachment_create(\n self.volumes_client, volume['id'], server['id'])\n self.addCleanup(waiters.wait_for_volume_attachment_remove,\n self.volumes_client, volume['id'],\n att['attachment_id'])\n else:\n self.addCleanup(waiters.wait_for_volume_resource_status,\n self.volumes_client, volume['id'], 'available')\n waiters.wait_for_volume_resource_status(self.volumes_client,\n volume['id'], 'in-use')\n # Ignore 404s on detach in case the server is deleted or the volume\n # is already detached.\n self.addCleanup(self._detach_volume, server, volume)\n return attachment\n\n def create_volume_snapshot(self, volume_id, name=None, description=None,\n metadata=None, force=False):\n name = name or data_utils.rand_name(\n self.__class__.__name__ + '-snapshot')\n snapshot = self.snapshots_client.create_snapshot(\n volume_id=volume_id,\n force=force,\n display_name=name,\n description=description,\n metadata=metadata)['snapshot']\n self.addCleanup(self.snapshots_client.wait_for_resource_deletion,\n snapshot['id'])\n self.addCleanup(self.snapshots_client.delete_snapshot, snapshot['id'])\n waiters.wait_for_volume_resource_status(self.snapshots_client,\n snapshot['id'], 'available')\n snapshot = self.snapshots_client.show_snapshot(\n snapshot['id'])['snapshot']\n return snapshot\n\n def assert_flavor_equal(self, flavor_id, server_flavor):\n \"\"\"Check whether server_flavor equals to flavor.\n\n :param flavor_id: flavor id\n :param server_flavor: flavor info returned by show_server.\n \"\"\"\n # Nova API > 2.46 no longer includes flavor.id, and schema check\n # will cover whether 'id' should be in flavor\n if server_flavor.get('id'):\n msg = ('server flavor is not same as flavor!')\n self.assertEqual(flavor_id, server_flavor['id'], msg)\n else:\n flavor = self.flavors_client.show_flavor(flavor_id)['flavor']\n self.assertEqual(flavor['name'], server_flavor['original_name'],\n \"original_name in server flavor is not same as \"\n \"flavor name!\")\n for key in ['ram', 'vcpus', 'disk']:\n msg = ('attribute %s in server flavor is not same as '\n 'flavor!' % key)\n self.assertEqual(flavor[key], server_flavor[key], msg)\n\n\nclass BaseV2ComputeAdminTest(BaseV2ComputeTest):\n \"\"\"Base test case class for Compute Admin API tests.\"\"\"\n\n credentials = ['primary', 'admin']\n\n @classmethod\n def setup_clients(cls):\n super(BaseV2ComputeAdminTest, cls).setup_clients()\n cls.availability_zone_admin_client = (\n cls.os_admin.availability_zone_client)\n cls.admin_flavors_client = cls.os_admin.flavors_client\n cls.admin_servers_client = cls.os_admin.servers_client\n cls.image_client = cls.os_admin.image_client_v2\n cls.admin_assisted_volume_snapshots_client = \\\n cls.os_admin.assisted_volume_snapshots_client\n\n def create_flavor(self, ram, vcpus, disk, name=None,\n is_public='True', **kwargs):\n if name is None:\n name = data_utils.rand_name(self.__class__.__name__ + \"-flavor\")\n id = kwargs.pop('id', data_utils.rand_int_id(start=1000))\n client = self.admin_flavors_client\n flavor = client.create_flavor(\n ram=ram, vcpus=vcpus, disk=disk, name=name,\n id=id, is_public=is_public, **kwargs)['flavor']\n self.addCleanup(client.wait_for_resource_deletion, flavor['id'])\n self.addCleanup(client.delete_flavor, flavor['id'])\n return flavor\n\n @classmethod\n def get_host_for_server(cls, server_id):\n server_details = cls.admin_servers_client.show_server(server_id)\n return server_details['server']['OS-EXT-SRV-ATTR:host']\n\n def get_host_other_than(self, server_id):\n source_host = self.get_host_for_server(server_id)\n\n svcs = self.os_admin.services_client.list_services(\n binary='nova-compute')['services']\n hosts = []\n for svc in svcs:\n if svc['state'] == 'up' and svc['status'] == 'enabled':\n if CONF.compute.compute_volume_common_az:\n if svc['zone'] == CONF.compute.compute_volume_common_az:\n hosts.append(svc['host'])\n else:\n hosts.append(svc['host'])\n\n for target_host in hosts:\n if source_host != target_host:\n return target_host\n"}}},{"rowIdx":60,"cells":{"function_name":{"kind":"string","value":"recreate_server"},"docstring":{"kind":"string","value":"Destroy an existing class level server and creates a new one\n\nSome test classes use a test server that can be used by multiple\ntests. This is done to optimise runtime and test load.\nIf something goes wrong with the test server, it can be rebuilt\nusing this helper.\n\nThis helper can also be used for the initial provisioning if no\nserver_id is specified.\n\n:param server_id: UUID of the server to be rebuilt. If None is\n specified, a new server is provisioned.\n:param validatable: whether to the server needs to be\n validatable. When True, validation resources are acquired via\n the `get_class_validation_resources` helper.\n:param kwargs: extra paramaters are passed through to the\n `create_test_server` call.\n:return: the UUID of the created server."},"masked_code":{"kind":"string","value":"# Copyright 2012 OpenStack Foundation\n# All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport time\n\nfrom oslo_log import log as logging\n\nfrom tempest.common import compute\nfrom tempest.common import waiters\nfrom tempest import config\nfrom tempest import exceptions\nfrom tempest.lib.common import api_microversion_fixture\nfrom tempest.lib.common import api_version_request\nfrom tempest.lib.common import api_version_utils\nfrom tempest.lib.common.utils import data_utils\nfrom tempest.lib.common.utils import test_utils\nfrom tempest.lib import exceptions as lib_exc\nimport tempest.test\n\nCONF = config.CONF\n\nLOG = logging.getLogger(__name__)\n\n\nclass BaseV2ComputeTest(api_version_utils.BaseMicroversionTest,\n tempest.test.BaseTestCase):\n \"\"\"Base test case class for all Compute API tests.\"\"\"\n\n force_tenant_isolation = False\n # Set this to True in subclasses to create a default network. See\n # https://bugs.launchpad.net/tempest/+bug/1844568\n create_default_network = False\n\n # TODO(andreaf) We should care also for the alt_manager here\n # but only once client lazy load in the manager is done\n credentials = ['primary']\n\n @classmethod\n def skip_checks(cls):\n super(BaseV2ComputeTest, cls).skip_checks()\n if not CONF.service_available.nova:\n raise cls.skipException(\"Nova is not available\")\n api_version_utils.check_skip_with_microversion(\n cls.min_microversion, cls.max_microversion,\n CONF.compute.min_microversion, CONF.compute.max_microversion)\n api_version_utils.check_skip_with_microversion(\n cls.volume_min_microversion, cls.volume_max_microversion,\n CONF.volume.min_microversion, CONF.volume.max_microversion)\n api_version_utils.check_skip_with_microversion(\n cls.placement_min_microversion, cls.placement_max_microversion,\n CONF.placement.min_microversion, CONF.placement.max_microversion)\n\n @classmethod\n def setup_credentials(cls):\n # Setting network=True, subnet=True creates a default network\n cls.set_network_resources(\n network=cls.create_default_network,\n subnet=cls.create_default_network)\n super(BaseV2ComputeTest, cls).setup_credentials()\n\n @classmethod\n def setup_clients(cls):\n super(BaseV2ComputeTest, cls).setup_clients()\n cls.servers_client = cls.os_primary.servers_client\n cls.server_groups_client = cls.os_primary.server_groups_client\n cls.flavors_client = cls.os_primary.flavors_client\n cls.compute_images_client = cls.os_primary.compute_images_client\n cls.extensions_client = cls.os_primary.extensions_client\n cls.floating_ip_pools_client = cls.os_primary.floating_ip_pools_client\n cls.floating_ips_client = cls.os_primary.compute_floating_ips_client\n cls.keypairs_client = cls.os_primary.keypairs_client\n cls.security_group_rules_client = (\n cls.os_primary.compute_security_group_rules_client)\n cls.security_groups_client =\\\n cls.os_primary.compute_security_groups_client\n cls.quotas_client = cls.os_primary.quotas_client\n cls.compute_networks_client = cls.os_primary.compute_networks_client\n cls.limits_client = cls.os_primary.limits_client\n cls.volumes_extensions_client =\\\n cls.os_primary.volumes_extensions_client\n cls.snapshots_extensions_client =\\\n cls.os_primary.snapshots_extensions_client\n cls.interfaces_client = cls.os_primary.interfaces_client\n cls.fixed_ips_client = cls.os_primary.fixed_ips_client\n cls.availability_zone_client = cls.os_primary.availability_zone_client\n cls.agents_client = cls.os_primary.agents_client\n cls.aggregates_client = cls.os_primary.aggregates_client\n cls.services_client = cls.os_primary.services_client\n cls.instance_usages_audit_log_client = (\n cls.os_primary.instance_usages_audit_log_client)\n cls.hypervisor_client = cls.os_primary.hypervisor_client\n cls.certificates_client = cls.os_primary.certificates_client\n cls.migrations_client = cls.os_primary.migrations_client\n cls.security_group_default_rules_client = (\n cls.os_primary.security_group_default_rules_client)\n cls.versions_client = cls.os_primary.compute_versions_client\n if CONF.service_available.cinder:\n cls.volumes_client = cls.os_primary.volumes_client_latest\n cls.attachments_client = cls.os_primary.attachments_client_latest\n cls.snapshots_client = cls.os_primary.snapshots_client_latest\n if CONF.service_available.glance:\n if CONF.image_feature_enabled.api_v1:\n cls.images_client = cls.os_primary.image_client\n elif CONF.image_feature_enabled.api_v2:\n cls.images_client = cls.os_primary.image_client_v2\n else:\n raise lib_exc.InvalidConfiguration(\n 'Either api_v1 or api_v2 must be True in '\n '[image-feature-enabled].')\n cls._check_depends_on_nova_network()\n\n @classmethod\n def _check_depends_on_nova_network(cls):\n # Since nova-network APIs were removed from Nova in the Rocky release,\n # determine, based on the max version from the version document, if\n # the compute API is >Queens and if so, skip tests that rely on\n # nova-network.\n if not getattr(cls, 'depends_on_nova_network', False):\n return\n versions = cls.versions_client.list_versions()['versions']\n # Find the v2.1 version which will tell us our max version for the\n # compute API we're testing against.\n for version in versions:\n if version['id'] == 'v2.1':\n max_version = api_version_request.APIVersionRequest(\n version['version'])\n break\n else:\n LOG.warning(\n 'Unable to determine max v2.1 compute API version: %s',\n versions)\n return\n\n # The max compute API version in Queens is 2.60 so we cap\n # at that version.\n queens = api_version_request.APIVersionRequest('2.60')\n if max_version > queens:\n raise cls.skipException('nova-network is gone')\n\n @classmethod\n def resource_setup(cls):\n super(BaseV2ComputeTest, cls).resource_setup()\n cls.request_microversion = (\n api_version_utils.select_request_microversion(\n cls.min_microversion,\n CONF.compute.min_microversion))\n cls.volume_request_microversion = (\n api_version_utils.select_request_microversion(\n cls.volume_min_microversion,\n CONF.volume.min_microversion))\n cls.placement_request_microversion = (\n api_version_utils.select_request_microversion(\n cls.placement_min_microversion,\n CONF.placement.min_microversion))\n cls.build_interval = CONF.compute.build_interval\n cls.build_timeout = CONF.compute.build_timeout\n cls.image_ref = CONF.compute.image_ref\n cls.image_ref_alt = CONF.compute.image_ref_alt\n cls.flavor_ref = CONF.compute.flavor_ref\n cls.flavor_ref_alt = CONF.compute.flavor_ref_alt\n cls.ssh_user = CONF.validation.image_ssh_user\n cls.ssh_alt_user = CONF.validation.image_alt_ssh_user\n cls.image_ssh_user = CONF.validation.image_ssh_user\n cls.image_alt_ssh_user = CONF.validation.image_alt_ssh_user\n cls.image_ssh_password = CONF.validation.image_ssh_password\n cls.image_alt_ssh_password = CONF.validation.image_alt_ssh_password\n\n @classmethod\n def is_requested_microversion_compatible(cls, max_version):\n \"\"\"Check the compatibility of selected request microversion\n\n This method will check if selected request microversion\n (cls.request_microversion) for test is compatible with respect\n to 'max_version'. Compatible means if selected request microversion\n is in the range(<=) of 'max_version'.\n\n :param max_version: maximum microversion to compare for compatibility.\n Example: '2.30'\n :returns: True if selected request microversion is compatible with\n 'max_version'. False in other case.\n \"\"\"\n try:\n req_version_obj = api_version_request.APIVersionRequest(\n cls.request_microversion)\n # NOTE(gmann): This is case where this method is used before calling\n # resource_setup(), where cls.request_microversion is set. There may\n # not be any such case but still we can handle this case.\n except AttributeError:\n request_microversion = (\n api_version_utils.select_request_microversion(\n cls.min_microversion,\n CONF.compute.min_microversion))\n req_version_obj = api_version_request.APIVersionRequest(\n request_microversion)\n max_version_obj = api_version_request.APIVersionRequest(max_version)\n return req_version_obj <= max_version_obj\n\n @classmethod\n def server_check_teardown(cls):\n \"\"\"Checks is the shared server clean enough for subsequent test.\n\n Method will delete the server when it's dirty.\n The setUp method is responsible for creating a new server.\n Exceptions raised in tearDown class are fails the test case,\n This method supposed to use only by tearDown methods, when\n the shared server_id is stored in the server_id of the class.\n \"\"\"\n if getattr(cls, 'server_id', None) is not None:\n try:\n waiters.wait_for_server_status(cls.servers_client,\n cls.server_id, 'ACTIVE')\n except Exception as exc:\n LOG.exception(exc)\n cls.servers_client.delete_server(cls.server_id)\n waiters.wait_for_server_termination(cls.servers_client,\n cls.server_id)\n cls.server_id = None\n raise\n\n @classmethod\n def create_test_server(cls, validatable=False, volume_backed=False,\n validation_resources=None, clients=None, **kwargs):\n \"\"\"Wrapper utility that returns a test server.\n\n This wrapper utility calls the common create test server and\n returns a test server. The purpose of this wrapper is to minimize\n the impact on the code of the tests already using this\n function.\n\n :param validatable: Whether the server will be pingable or sshable.\n :param volume_backed: Whether the instance is volume backed or not.\n :param validation_resources: Dictionary of validation resources as\n returned by `get_class_validation_resources`.\n :param clients: Client manager, defaults to os_primary.\n :param kwargs: Extra arguments are passed down to the\n `compute.create_test_server` call.\n \"\"\"\n if 'name' not in kwargs:\n kwargs['name'] = data_utils.rand_name(cls.__name__ + \"-server\")\n\n request_version = api_version_request.APIVersionRequest(\n cls.request_microversion)\n v2_37_version = api_version_request.APIVersionRequest('2.37')\n\n tenant_network = cls.get_tenant_network()\n # NOTE(snikitin): since microversion v2.37 'networks' field is required\n if (request_version >= v2_37_version and 'networks' not in kwargs and\n not tenant_network):\n kwargs['networks'] = 'none'\n\n if clients is None:\n clients = cls.os_primary\n\n body, servers = compute.create_test_server(\n clients,\n validatable,\n validation_resources=validation_resources,\n tenant_network=tenant_network,\n volume_backed=volume_backed,\n **kwargs)\n\n # For each server schedule wait and delete, so we first delete all\n # and then wait for all\n for server in servers:\n cls.addClassResourceCleanup(waiters.wait_for_server_termination,\n clients.servers_client, server['id'])\n for server in servers:\n cls.addClassResourceCleanup(\n test_utils.call_and_ignore_notfound_exc,\n clients.servers_client.delete_server, server['id'])\n\n return body\n\n @classmethod\n def create_security_group(cls, name=None, description=None):\n if name is None:\n name = data_utils.rand_name(cls.__name__ + \"-securitygroup\")\n if description is None:\n description = data_utils.rand_name('description')\n body = cls.security_groups_client.create_security_group(\n name=name, description=description)['security_group']\n cls.addClassResourceCleanup(\n test_utils.call_and_ignore_notfound_exc,\n cls.security_groups_client.delete_security_group,\n body['id'])\n\n return body\n\n @classmethod\n def create_test_server_group(cls, name=\"\", policy=None):\n if not name:\n name = data_utils.rand_name(cls.__name__ + \"-Server-Group\")\n if policy is None:\n policy = ['affinity']\n body = cls.server_groups_client.create_server_group(\n name=name, policies=policy)['server_group']\n cls.addClassResourceCleanup(\n test_utils.call_and_ignore_notfound_exc,\n cls.server_groups_client.delete_server_group,\n body['id'])\n return body\n\n def wait_for(self, condition):\n \"\"\"Repeatedly calls condition() until a timeout.\"\"\"\n start_time = int(time.time())\n while True:\n try:\n condition()\n except Exception:\n pass\n else:\n return\n if int(time.time()) - start_time >= self.build_timeout:\n condition()\n return\n time.sleep(self.build_interval)\n\n @classmethod\n def prepare_instance_network(cls):\n if (CONF.validation.auth_method != 'disabled' and\n CONF.validation.connect_method == 'floating'):\n cls.set_network_resources(network=True, subnet=True, router=True,\n dhcp=True)\n\n @classmethod\n def create_image_from_server(cls, server_id, **kwargs):\n \"\"\"Wrapper utility that returns an image created from the server.\n\n If compute microversion >= 2.36, the returned image response will\n be from the image service API rather than the compute image proxy API.\n \"\"\"\n name = kwargs.pop('name',\n data_utils.rand_name(cls.__name__ + \"-image\"))\n wait_until = kwargs.pop('wait_until', None)\n wait_for_server = kwargs.pop('wait_for_server', True)\n\n image = cls.compute_images_client.create_image(server_id, name=name,\n **kwargs)\n if api_version_utils.compare_version_header_to_response(\n \"OpenStack-API-Version\", \"compute 2.45\", image.response, \"lt\"):\n image_id = image['image_id']\n else:\n image_id = data_utils.parse_image_id(image.response['location'])\n\n # The compute image proxy APIs were deprecated in 2.35 so\n # use the images client directly if the API microversion being\n # used is >=2.36.\n if not cls.is_requested_microversion_compatible('2.35'):\n client = cls.images_client\n else:\n client = cls.compute_images_client\n cls.addClassResourceCleanup(test_utils.call_and_ignore_notfound_exc,\n client.delete_image, image_id)\n\n if wait_until is not None:\n try:\n wait_until = wait_until.upper()\n if not cls.is_requested_microversion_compatible('2.35'):\n wait_until = wait_until.lower()\n waiters.wait_for_image_status(client, image_id, wait_until)\n except lib_exc.NotFound:\n if wait_until.upper() == 'ACTIVE':\n # If the image is not found after create_image returned\n # that means the snapshot failed in nova-compute and nova\n # deleted the image. There should be a compute fault\n # recorded with the server in that case, so get the server\n # and dump some details.\n server = (\n cls.servers_client.show_server(server_id)['server'])\n if 'fault' in server:\n raise exceptions.SnapshotNotFoundException(\n server['fault'], image_id=image_id)\n else:\n raise exceptions.SnapshotNotFoundException(\n image_id=image_id)\n else:\n raise\n image = client.show_image(image_id)\n # Compute image client returns response wrapped in 'image' element\n # which is not the case with Glance image client.\n if 'image' in image:\n image = image['image']\n\n if wait_until.upper() == 'ACTIVE':\n if wait_for_server:\n waiters.wait_for_server_status(cls.servers_client,\n server_id, 'ACTIVE')\n return image\n\n# MASKED: recreate_server function (lines 402-434)\n\n @classmethod\n def delete_server(cls, server_id):\n \"\"\"Deletes an existing server and waits for it to be gone.\"\"\"\n try:\n cls.servers_client.delete_server(server_id)\n waiters.wait_for_server_termination(cls.servers_client,\n server_id)\n except Exception:\n LOG.exception('Failed to delete server %s', server_id)\n\n def resize_server(self, server_id, new_flavor_id, **kwargs):\n \"\"\"resize and confirm_resize an server, waits for it to be ACTIVE.\"\"\"\n self.servers_client.resize_server(server_id, new_flavor_id, **kwargs)\n waiters.wait_for_server_status(self.servers_client, server_id,\n 'VERIFY_RESIZE')\n self.servers_client.confirm_resize_server(server_id)\n waiters.wait_for_server_status(\n self.servers_client, server_id, 'ACTIVE')\n server = self.servers_client.show_server(server_id)['server']\n self.assert_flavor_equal(new_flavor_id, server['flavor'])\n\n @classmethod\n def delete_volume(cls, volume_id):\n \"\"\"Deletes the given volume and waits for it to be gone.\"\"\"\n try:\n cls.volumes_client.delete_volume(volume_id)\n # TODO(mriedem): We should move the wait_for_resource_deletion\n # into the delete_volume method as a convenience to the caller.\n cls.volumes_client.wait_for_resource_deletion(volume_id)\n except lib_exc.NotFound:\n LOG.warning(\"Unable to delete volume '%s' since it was not found. \"\n \"Maybe it was already deleted?\", volume_id)\n\n @classmethod\n def get_server_ip(cls, server, validation_resources=None):\n \"\"\"Get the server fixed or floating IP.\n\n Based on the configuration we're in, return a correct ip\n address for validating that a guest is up.\n\n :param server: The server dict as returned by the API\n :param validation_resources: The dict of validation resources\n provisioned for the server.\n \"\"\"\n if CONF.validation.connect_method == 'floating':\n if validation_resources:\n return validation_resources['floating_ip']['ip']\n else:\n msg = ('When validation.connect_method equals floating, '\n 'validation_resources cannot be None')\n raise lib_exc.InvalidParam(invalid_param=msg)\n elif CONF.validation.connect_method == 'fixed':\n addresses = server['addresses'][CONF.validation.network_for_ssh]\n for address in addresses:\n if address['version'] == CONF.validation.ip_version_for_ssh:\n return address['addr']\n raise exceptions.ServerUnreachable(server_id=server['id'])\n else:\n raise lib_exc.InvalidConfiguration()\n\n def setUp(self):\n super(BaseV2ComputeTest, self).setUp()\n self.useFixture(api_microversion_fixture.APIMicroversionFixture(\n compute_microversion=self.request_microversion,\n volume_microversion=self.volume_request_microversion,\n placement_microversion=self.placement_request_microversion))\n\n @classmethod\n def create_volume(cls, image_ref=None, **kwargs):\n \"\"\"Create a volume and wait for it to become 'available'.\n\n :param image_ref: Specify an image id to create a bootable volume.\n :param kwargs: other parameters to create volume.\n :returns: The available volume.\n \"\"\"\n if 'size' not in kwargs:\n kwargs['size'] = CONF.volume.volume_size\n if 'display_name' not in kwargs:\n vol_name = data_utils.rand_name(cls.__name__ + '-volume')\n kwargs['display_name'] = vol_name\n if image_ref is not None:\n kwargs['imageRef'] = image_ref\n if CONF.compute.compute_volume_common_az:\n kwargs.setdefault('availability_zone',\n CONF.compute.compute_volume_common_az)\n volume = cls.volumes_client.create_volume(**kwargs)['volume']\n cls.addClassResourceCleanup(\n cls.volumes_client.wait_for_resource_deletion, volume['id'])\n cls.addClassResourceCleanup(test_utils.call_and_ignore_notfound_exc,\n cls.volumes_client.delete_volume,\n volume['id'])\n waiters.wait_for_volume_resource_status(cls.volumes_client,\n volume['id'], 'available')\n return volume\n\n def _detach_volume(self, server, volume):\n \"\"\"Helper method to detach a volume.\n\n Ignores 404 responses if the volume or server do not exist, or the\n volume is already detached from the server.\n \"\"\"\n try:\n volume = self.volumes_client.show_volume(volume['id'])['volume']\n # Check the status. You can only detach an in-use volume, otherwise\n # the compute API will return a 400 response.\n if volume['status'] == 'in-use':\n self.servers_client.detach_volume(server['id'], volume['id'])\n except lib_exc.NotFound:\n # Ignore 404s on detach in case the server is deleted or the volume\n # is already detached.\n pass\n\n def attach_volume(self, server, volume, device=None, tag=None):\n \"\"\"Attaches volume to server and waits for 'in-use' volume status.\n\n The volume will be detached when the test tears down.\n\n :param server: The server to which the volume will be attached.\n :param volume: The volume to attach.\n :param device: Optional mountpoint for the attached volume. Note that\n this is not guaranteed for all hypervisors and is not recommended.\n :param tag: Optional device role tag to apply to the volume.\n \"\"\"\n attach_kwargs = dict(volumeId=volume['id'])\n if device:\n attach_kwargs['device'] = device\n if tag:\n attach_kwargs['tag'] = tag\n\n attachment = self.servers_client.attach_volume(\n server['id'], **attach_kwargs)['volumeAttachment']\n # On teardown detach the volume and for multiattach volumes wait for\n # the attachment to be removed. For non-multiattach volumes wait for\n # the state of the volume to change to available. This is so we don't\n # error out when trying to delete the volume during teardown.\n if volume['multiattach']:\n att = waiters.wait_for_volume_attachment_create(\n self.volumes_client, volume['id'], server['id'])\n self.addCleanup(waiters.wait_for_volume_attachment_remove,\n self.volumes_client, volume['id'],\n att['attachment_id'])\n else:\n self.addCleanup(waiters.wait_for_volume_resource_status,\n self.volumes_client, volume['id'], 'available')\n waiters.wait_for_volume_resource_status(self.volumes_client,\n volume['id'], 'in-use')\n # Ignore 404s on detach in case the server is deleted or the volume\n # is already detached.\n self.addCleanup(self._detach_volume, server, volume)\n return attachment\n\n def create_volume_snapshot(self, volume_id, name=None, description=None,\n metadata=None, force=False):\n name = name or data_utils.rand_name(\n self.__class__.__name__ + '-snapshot')\n snapshot = self.snapshots_client.create_snapshot(\n volume_id=volume_id,\n force=force,\n display_name=name,\n description=description,\n metadata=metadata)['snapshot']\n self.addCleanup(self.snapshots_client.wait_for_resource_deletion,\n snapshot['id'])\n self.addCleanup(self.snapshots_client.delete_snapshot, snapshot['id'])\n waiters.wait_for_volume_resource_status(self.snapshots_client,\n snapshot['id'], 'available')\n snapshot = self.snapshots_client.show_snapshot(\n snapshot['id'])['snapshot']\n return snapshot\n\n def assert_flavor_equal(self, flavor_id, server_flavor):\n \"\"\"Check whether server_flavor equals to flavor.\n\n :param flavor_id: flavor id\n :param server_flavor: flavor info returned by show_server.\n \"\"\"\n # Nova API > 2.46 no longer includes flavor.id, and schema check\n # will cover whether 'id' should be in flavor\n if server_flavor.get('id'):\n msg = ('server flavor is not same as flavor!')\n self.assertEqual(flavor_id, server_flavor['id'], msg)\n else:\n flavor = self.flavors_client.show_flavor(flavor_id)['flavor']\n self.assertEqual(flavor['name'], server_flavor['original_name'],\n \"original_name in server flavor is not same as \"\n \"flavor name!\")\n for key in ['ram', 'vcpus', 'disk']:\n msg = ('attribute %s in server flavor is not same as '\n 'flavor!' % key)\n self.assertEqual(flavor[key], server_flavor[key], msg)\n\n\nclass BaseV2ComputeAdminTest(BaseV2ComputeTest):\n \"\"\"Base test case class for Compute Admin API tests.\"\"\"\n\n credentials = ['primary', 'admin']\n\n @classmethod\n def setup_clients(cls):\n super(BaseV2ComputeAdminTest, cls).setup_clients()\n cls.availability_zone_admin_client = (\n cls.os_admin.availability_zone_client)\n cls.admin_flavors_client = cls.os_admin.flavors_client\n cls.admin_servers_client = cls.os_admin.servers_client\n cls.image_client = cls.os_admin.image_client_v2\n cls.admin_assisted_volume_snapshots_client = \\\n cls.os_admin.assisted_volume_snapshots_client\n\n def create_flavor(self, ram, vcpus, disk, name=None,\n is_public='True', **kwargs):\n if name is None:\n name = data_utils.rand_name(self.__class__.__name__ + \"-flavor\")\n id = kwargs.pop('id', data_utils.rand_int_id(start=1000))\n client = self.admin_flavors_client\n flavor = client.create_flavor(\n ram=ram, vcpus=vcpus, disk=disk, name=name,\n id=id, is_public=is_public, **kwargs)['flavor']\n self.addCleanup(client.wait_for_resource_deletion, flavor['id'])\n self.addCleanup(client.delete_flavor, flavor['id'])\n return flavor\n\n @classmethod\n def get_host_for_server(cls, server_id):\n server_details = cls.admin_servers_client.show_server(server_id)\n return server_details['server']['OS-EXT-SRV-ATTR:host']\n\n def get_host_other_than(self, server_id):\n source_host = self.get_host_for_server(server_id)\n\n svcs = self.os_admin.services_client.list_services(\n binary='nova-compute')['services']\n hosts = []\n for svc in svcs:\n if svc['state'] == 'up' and svc['status'] == 'enabled':\n if CONF.compute.compute_volume_common_az:\n if svc['zone'] == CONF.compute.compute_volume_common_az:\n hosts.append(svc['host'])\n else:\n hosts.append(svc['host'])\n\n for target_host in hosts:\n if source_host != target_host:\n return target_host"},"implementation":{"kind":"string","value":" @classmethod\n def recreate_server(cls, server_id, validatable=False, **kwargs):\n \"\"\"Destroy an existing class level server and creates a new one\n\n Some test classes use a test server that can be used by multiple\n tests. This is done to optimise runtime and test load.\n If something goes wrong with the test server, it can be rebuilt\n using this helper.\n\n This helper can also be used for the initial provisioning if no\n server_id is specified.\n\n :param server_id: UUID of the server to be rebuilt. If None is\n specified, a new server is provisioned.\n :param validatable: whether to the server needs to be\n validatable. When True, validation resources are acquired via\n the `get_class_validation_resources` helper.\n :param kwargs: extra paramaters are passed through to the\n `create_test_server` call.\n :return: the UUID of the created server.\n \"\"\"\n if server_id:\n cls.delete_server(server_id)\n\n cls.password = data_utils.rand_password()\n server = cls.create_test_server(\n validatable,\n validation_resources=cls.get_class_validation_resources(\n cls.os_primary),\n wait_until='ACTIVE',\n adminPass=cls.password,\n **kwargs)\n return server['id']"},"start_line":{"kind":"number","value":402,"string":"402"},"end_line":{"kind":"number","value":434,"string":"434"},"file_content":{"kind":"string","value":"# Copyright 2012 OpenStack Foundation\n# All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport time\n\nfrom oslo_log import log as logging\n\nfrom tempest.common import compute\nfrom tempest.common import waiters\nfrom tempest import config\nfrom tempest import exceptions\nfrom tempest.lib.common import api_microversion_fixture\nfrom tempest.lib.common import api_version_request\nfrom tempest.lib.common import api_version_utils\nfrom tempest.lib.common.utils import data_utils\nfrom tempest.lib.common.utils import test_utils\nfrom tempest.lib import exceptions as lib_exc\nimport tempest.test\n\nCONF = config.CONF\n\nLOG = logging.getLogger(__name__)\n\n\nclass BaseV2ComputeTest(api_version_utils.BaseMicroversionTest,\n tempest.test.BaseTestCase):\n \"\"\"Base test case class for all Compute API tests.\"\"\"\n\n force_tenant_isolation = False\n # Set this to True in subclasses to create a default network. See\n # https://bugs.launchpad.net/tempest/+bug/1844568\n create_default_network = False\n\n # TODO(andreaf) We should care also for the alt_manager here\n # but only once client lazy load in the manager is done\n credentials = ['primary']\n\n @classmethod\n def skip_checks(cls):\n super(BaseV2ComputeTest, cls).skip_checks()\n if not CONF.service_available.nova:\n raise cls.skipException(\"Nova is not available\")\n api_version_utils.check_skip_with_microversion(\n cls.min_microversion, cls.max_microversion,\n CONF.compute.min_microversion, CONF.compute.max_microversion)\n api_version_utils.check_skip_with_microversion(\n cls.volume_min_microversion, cls.volume_max_microversion,\n CONF.volume.min_microversion, CONF.volume.max_microversion)\n api_version_utils.check_skip_with_microversion(\n cls.placement_min_microversion, cls.placement_max_microversion,\n CONF.placement.min_microversion, CONF.placement.max_microversion)\n\n @classmethod\n def setup_credentials(cls):\n # Setting network=True, subnet=True creates a default network\n cls.set_network_resources(\n network=cls.create_default_network,\n subnet=cls.create_default_network)\n super(BaseV2ComputeTest, cls).setup_credentials()\n\n @classmethod\n def setup_clients(cls):\n super(BaseV2ComputeTest, cls).setup_clients()\n cls.servers_client = cls.os_primary.servers_client\n cls.server_groups_client = cls.os_primary.server_groups_client\n cls.flavors_client = cls.os_primary.flavors_client\n cls.compute_images_client = cls.os_primary.compute_images_client\n cls.extensions_client = cls.os_primary.extensions_client\n cls.floating_ip_pools_client = cls.os_primary.floating_ip_pools_client\n cls.floating_ips_client = cls.os_primary.compute_floating_ips_client\n cls.keypairs_client = cls.os_primary.keypairs_client\n cls.security_group_rules_client = (\n cls.os_primary.compute_security_group_rules_client)\n cls.security_groups_client =\\\n cls.os_primary.compute_security_groups_client\n cls.quotas_client = cls.os_primary.quotas_client\n cls.compute_networks_client = cls.os_primary.compute_networks_client\n cls.limits_client = cls.os_primary.limits_client\n cls.volumes_extensions_client =\\\n cls.os_primary.volumes_extensions_client\n cls.snapshots_extensions_client =\\\n cls.os_primary.snapshots_extensions_client\n cls.interfaces_client = cls.os_primary.interfaces_client\n cls.fixed_ips_client = cls.os_primary.fixed_ips_client\n cls.availability_zone_client = cls.os_primary.availability_zone_client\n cls.agents_client = cls.os_primary.agents_client\n cls.aggregates_client = cls.os_primary.aggregates_client\n cls.services_client = cls.os_primary.services_client\n cls.instance_usages_audit_log_client = (\n cls.os_primary.instance_usages_audit_log_client)\n cls.hypervisor_client = cls.os_primary.hypervisor_client\n cls.certificates_client = cls.os_primary.certificates_client\n cls.migrations_client = cls.os_primary.migrations_client\n cls.security_group_default_rules_client = (\n cls.os_primary.security_group_default_rules_client)\n cls.versions_client = cls.os_primary.compute_versions_client\n if CONF.service_available.cinder:\n cls.volumes_client = cls.os_primary.volumes_client_latest\n cls.attachments_client = cls.os_primary.attachments_client_latest\n cls.snapshots_client = cls.os_primary.snapshots_client_latest\n if CONF.service_available.glance:\n if CONF.image_feature_enabled.api_v1:\n cls.images_client = cls.os_primary.image_client\n elif CONF.image_feature_enabled.api_v2:\n cls.images_client = cls.os_primary.image_client_v2\n else:\n raise lib_exc.InvalidConfiguration(\n 'Either api_v1 or api_v2 must be True in '\n '[image-feature-enabled].')\n cls._check_depends_on_nova_network()\n\n @classmethod\n def _check_depends_on_nova_network(cls):\n # Since nova-network APIs were removed from Nova in the Rocky release,\n # determine, based on the max version from the version document, if\n # the compute API is >Queens and if so, skip tests that rely on\n # nova-network.\n if not getattr(cls, 'depends_on_nova_network', False):\n return\n versions = cls.versions_client.list_versions()['versions']\n # Find the v2.1 version which will tell us our max version for the\n # compute API we're testing against.\n for version in versions:\n if version['id'] == 'v2.1':\n max_version = api_version_request.APIVersionRequest(\n version['version'])\n break\n else:\n LOG.warning(\n 'Unable to determine max v2.1 compute API version: %s',\n versions)\n return\n\n # The max compute API version in Queens is 2.60 so we cap\n # at that version.\n queens = api_version_request.APIVersionRequest('2.60')\n if max_version > queens:\n raise cls.skipException('nova-network is gone')\n\n @classmethod\n def resource_setup(cls):\n super(BaseV2ComputeTest, cls).resource_setup()\n cls.request_microversion = (\n api_version_utils.select_request_microversion(\n cls.min_microversion,\n CONF.compute.min_microversion))\n cls.volume_request_microversion = (\n api_version_utils.select_request_microversion(\n cls.volume_min_microversion,\n CONF.volume.min_microversion))\n cls.placement_request_microversion = (\n api_version_utils.select_request_microversion(\n cls.placement_min_microversion,\n CONF.placement.min_microversion))\n cls.build_interval = CONF.compute.build_interval\n cls.build_timeout = CONF.compute.build_timeout\n cls.image_ref = CONF.compute.image_ref\n cls.image_ref_alt = CONF.compute.image_ref_alt\n cls.flavor_ref = CONF.compute.flavor_ref\n cls.flavor_ref_alt = CONF.compute.flavor_ref_alt\n cls.ssh_user = CONF.validation.image_ssh_user\n cls.ssh_alt_user = CONF.validation.image_alt_ssh_user\n cls.image_ssh_user = CONF.validation.image_ssh_user\n cls.image_alt_ssh_user = CONF.validation.image_alt_ssh_user\n cls.image_ssh_password = CONF.validation.image_ssh_password\n cls.image_alt_ssh_password = CONF.validation.image_alt_ssh_password\n\n @classmethod\n def is_requested_microversion_compatible(cls, max_version):\n \"\"\"Check the compatibility of selected request microversion\n\n This method will check if selected request microversion\n (cls.request_microversion) for test is compatible with respect\n to 'max_version'. Compatible means if selected request microversion\n is in the range(<=) of 'max_version'.\n\n :param max_version: maximum microversion to compare for compatibility.\n Example: '2.30'\n :returns: True if selected request microversion is compatible with\n 'max_version'. False in other case.\n \"\"\"\n try:\n req_version_obj = api_version_request.APIVersionRequest(\n cls.request_microversion)\n # NOTE(gmann): This is case where this method is used before calling\n # resource_setup(), where cls.request_microversion is set. There may\n # not be any such case but still we can handle this case.\n except AttributeError:\n request_microversion = (\n api_version_utils.select_request_microversion(\n cls.min_microversion,\n CONF.compute.min_microversion))\n req_version_obj = api_version_request.APIVersionRequest(\n request_microversion)\n max_version_obj = api_version_request.APIVersionRequest(max_version)\n return req_version_obj <= max_version_obj\n\n @classmethod\n def server_check_teardown(cls):\n \"\"\"Checks is the shared server clean enough for subsequent test.\n\n Method will delete the server when it's dirty.\n The setUp method is responsible for creating a new server.\n Exceptions raised in tearDown class are fails the test case,\n This method supposed to use only by tearDown methods, when\n the shared server_id is stored in the server_id of the class.\n \"\"\"\n if getattr(cls, 'server_id', None) is not None:\n try:\n waiters.wait_for_server_status(cls.servers_client,\n cls.server_id, 'ACTIVE')\n except Exception as exc:\n LOG.exception(exc)\n cls.servers_client.delete_server(cls.server_id)\n waiters.wait_for_server_termination(cls.servers_client,\n cls.server_id)\n cls.server_id = None\n raise\n\n @classmethod\n def create_test_server(cls, validatable=False, volume_backed=False,\n validation_resources=None, clients=None, **kwargs):\n \"\"\"Wrapper utility that returns a test server.\n\n This wrapper utility calls the common create test server and\n returns a test server. The purpose of this wrapper is to minimize\n the impact on the code of the tests already using this\n function.\n\n :param validatable: Whether the server will be pingable or sshable.\n :param volume_backed: Whether the instance is volume backed or not.\n :param validation_resources: Dictionary of validation resources as\n returned by `get_class_validation_resources`.\n :param clients: Client manager, defaults to os_primary.\n :param kwargs: Extra arguments are passed down to the\n `compute.create_test_server` call.\n \"\"\"\n if 'name' not in kwargs:\n kwargs['name'] = data_utils.rand_name(cls.__name__ + \"-server\")\n\n request_version = api_version_request.APIVersionRequest(\n cls.request_microversion)\n v2_37_version = api_version_request.APIVersionRequest('2.37')\n\n tenant_network = cls.get_tenant_network()\n # NOTE(snikitin): since microversion v2.37 'networks' field is required\n if (request_version >= v2_37_version and 'networks' not in kwargs and\n not tenant_network):\n kwargs['networks'] = 'none'\n\n if clients is None:\n clients = cls.os_primary\n\n body, servers = compute.create_test_server(\n clients,\n validatable,\n validation_resources=validation_resources,\n tenant_network=tenant_network,\n volume_backed=volume_backed,\n **kwargs)\n\n # For each server schedule wait and delete, so we first delete all\n # and then wait for all\n for server in servers:\n cls.addClassResourceCleanup(waiters.wait_for_server_termination,\n clients.servers_client, server['id'])\n for server in servers:\n cls.addClassResourceCleanup(\n test_utils.call_and_ignore_notfound_exc,\n clients.servers_client.delete_server, server['id'])\n\n return body\n\n @classmethod\n def create_security_group(cls, name=None, description=None):\n if name is None:\n name = data_utils.rand_name(cls.__name__ + \"-securitygroup\")\n if description is None:\n description = data_utils.rand_name('description')\n body = cls.security_groups_client.create_security_group(\n name=name, description=description)['security_group']\n cls.addClassResourceCleanup(\n test_utils.call_and_ignore_notfound_exc,\n cls.security_groups_client.delete_security_group,\n body['id'])\n\n return body\n\n @classmethod\n def create_test_server_group(cls, name=\"\", policy=None):\n if not name:\n name = data_utils.rand_name(cls.__name__ + \"-Server-Group\")\n if policy is None:\n policy = ['affinity']\n body = cls.server_groups_client.create_server_group(\n name=name, policies=policy)['server_group']\n cls.addClassResourceCleanup(\n test_utils.call_and_ignore_notfound_exc,\n cls.server_groups_client.delete_server_group,\n body['id'])\n return body\n\n def wait_for(self, condition):\n \"\"\"Repeatedly calls condition() until a timeout.\"\"\"\n start_time = int(time.time())\n while True:\n try:\n condition()\n except Exception:\n pass\n else:\n return\n if int(time.time()) - start_time >= self.build_timeout:\n condition()\n return\n time.sleep(self.build_interval)\n\n @classmethod\n def prepare_instance_network(cls):\n if (CONF.validation.auth_method != 'disabled' and\n CONF.validation.connect_method == 'floating'):\n cls.set_network_resources(network=True, subnet=True, router=True,\n dhcp=True)\n\n @classmethod\n def create_image_from_server(cls, server_id, **kwargs):\n \"\"\"Wrapper utility that returns an image created from the server.\n\n If compute microversion >= 2.36, the returned image response will\n be from the image service API rather than the compute image proxy API.\n \"\"\"\n name = kwargs.pop('name',\n data_utils.rand_name(cls.__name__ + \"-image\"))\n wait_until = kwargs.pop('wait_until', None)\n wait_for_server = kwargs.pop('wait_for_server', True)\n\n image = cls.compute_images_client.create_image(server_id, name=name,\n **kwargs)\n if api_version_utils.compare_version_header_to_response(\n \"OpenStack-API-Version\", \"compute 2.45\", image.response, \"lt\"):\n image_id = image['image_id']\n else:\n image_id = data_utils.parse_image_id(image.response['location'])\n\n # The compute image proxy APIs were deprecated in 2.35 so\n # use the images client directly if the API microversion being\n # used is >=2.36.\n if not cls.is_requested_microversion_compatible('2.35'):\n client = cls.images_client\n else:\n client = cls.compute_images_client\n cls.addClassResourceCleanup(test_utils.call_and_ignore_notfound_exc,\n client.delete_image, image_id)\n\n if wait_until is not None:\n try:\n wait_until = wait_until.upper()\n if not cls.is_requested_microversion_compatible('2.35'):\n wait_until = wait_until.lower()\n waiters.wait_for_image_status(client, image_id, wait_until)\n except lib_exc.NotFound:\n if wait_until.upper() == 'ACTIVE':\n # If the image is not found after create_image returned\n # that means the snapshot failed in nova-compute and nova\n # deleted the image. There should be a compute fault\n # recorded with the server in that case, so get the server\n # and dump some details.\n server = (\n cls.servers_client.show_server(server_id)['server'])\n if 'fault' in server:\n raise exceptions.SnapshotNotFoundException(\n server['fault'], image_id=image_id)\n else:\n raise exceptions.SnapshotNotFoundException(\n image_id=image_id)\n else:\n raise\n image = client.show_image(image_id)\n # Compute image client returns response wrapped in 'image' element\n # which is not the case with Glance image client.\n if 'image' in image:\n image = image['image']\n\n if wait_until.upper() == 'ACTIVE':\n if wait_for_server:\n waiters.wait_for_server_status(cls.servers_client,\n server_id, 'ACTIVE')\n return image\n\n @classmethod\n def recreate_server(cls, server_id, validatable=False, **kwargs):\n \"\"\"Destroy an existing class level server and creates a new one\n\n Some test classes use a test server that can be used by multiple\n tests. This is done to optimise runtime and test load.\n If something goes wrong with the test server, it can be rebuilt\n using this helper.\n\n This helper can also be used for the initial provisioning if no\n server_id is specified.\n\n :param server_id: UUID of the server to be rebuilt. If None is\n specified, a new server is provisioned.\n :param validatable: whether to the server needs to be\n validatable. When True, validation resources are acquired via\n the `get_class_validation_resources` helper.\n :param kwargs: extra paramaters are passed through to the\n `create_test_server` call.\n :return: the UUID of the created server.\n \"\"\"\n if server_id:\n cls.delete_server(server_id)\n\n cls.password = data_utils.rand_password()\n server = cls.create_test_server(\n validatable,\n validation_resources=cls.get_class_validation_resources(\n cls.os_primary),\n wait_until='ACTIVE',\n adminPass=cls.password,\n **kwargs)\n return server['id']\n\n @classmethod\n def delete_server(cls, server_id):\n \"\"\"Deletes an existing server and waits for it to be gone.\"\"\"\n try:\n cls.servers_client.delete_server(server_id)\n waiters.wait_for_server_termination(cls.servers_client,\n server_id)\n except Exception:\n LOG.exception('Failed to delete server %s', server_id)\n\n def resize_server(self, server_id, new_flavor_id, **kwargs):\n \"\"\"resize and confirm_resize an server, waits for it to be ACTIVE.\"\"\"\n self.servers_client.resize_server(server_id, new_flavor_id, **kwargs)\n waiters.wait_for_server_status(self.servers_client, server_id,\n 'VERIFY_RESIZE')\n self.servers_client.confirm_resize_server(server_id)\n waiters.wait_for_server_status(\n self.servers_client, server_id, 'ACTIVE')\n server = self.servers_client.show_server(server_id)['server']\n self.assert_flavor_equal(new_flavor_id, server['flavor'])\n\n @classmethod\n def delete_volume(cls, volume_id):\n \"\"\"Deletes the given volume and waits for it to be gone.\"\"\"\n try:\n cls.volumes_client.delete_volume(volume_id)\n # TODO(mriedem): We should move the wait_for_resource_deletion\n # into the delete_volume method as a convenience to the caller.\n cls.volumes_client.wait_for_resource_deletion(volume_id)\n except lib_exc.NotFound:\n LOG.warning(\"Unable to delete volume '%s' since it was not found. \"\n \"Maybe it was already deleted?\", volume_id)\n\n @classmethod\n def get_server_ip(cls, server, validation_resources=None):\n \"\"\"Get the server fixed or floating IP.\n\n Based on the configuration we're in, return a correct ip\n address for validating that a guest is up.\n\n :param server: The server dict as returned by the API\n :param validation_resources: The dict of validation resources\n provisioned for the server.\n \"\"\"\n if CONF.validation.connect_method == 'floating':\n if validation_resources:\n return validation_resources['floating_ip']['ip']\n else:\n msg = ('When validation.connect_method equals floating, '\n 'validation_resources cannot be None')\n raise lib_exc.InvalidParam(invalid_param=msg)\n elif CONF.validation.connect_method == 'fixed':\n addresses = server['addresses'][CONF.validation.network_for_ssh]\n for address in addresses:\n if address['version'] == CONF.validation.ip_version_for_ssh:\n return address['addr']\n raise exceptions.ServerUnreachable(server_id=server['id'])\n else:\n raise lib_exc.InvalidConfiguration()\n\n def setUp(self):\n super(BaseV2ComputeTest, self).setUp()\n self.useFixture(api_microversion_fixture.APIMicroversionFixture(\n compute_microversion=self.request_microversion,\n volume_microversion=self.volume_request_microversion,\n placement_microversion=self.placement_request_microversion))\n\n @classmethod\n def create_volume(cls, image_ref=None, **kwargs):\n \"\"\"Create a volume and wait for it to become 'available'.\n\n :param image_ref: Specify an image id to create a bootable volume.\n :param kwargs: other parameters to create volume.\n :returns: The available volume.\n \"\"\"\n if 'size' not in kwargs:\n kwargs['size'] = CONF.volume.volume_size\n if 'display_name' not in kwargs:\n vol_name = data_utils.rand_name(cls.__name__ + '-volume')\n kwargs['display_name'] = vol_name\n if image_ref is not None:\n kwargs['imageRef'] = image_ref\n if CONF.compute.compute_volume_common_az:\n kwargs.setdefault('availability_zone',\n CONF.compute.compute_volume_common_az)\n volume = cls.volumes_client.create_volume(**kwargs)['volume']\n cls.addClassResourceCleanup(\n cls.volumes_client.wait_for_resource_deletion, volume['id'])\n cls.addClassResourceCleanup(test_utils.call_and_ignore_notfound_exc,\n cls.volumes_client.delete_volume,\n volume['id'])\n waiters.wait_for_volume_resource_status(cls.volumes_client,\n volume['id'], 'available')\n return volume\n\n def _detach_volume(self, server, volume):\n \"\"\"Helper method to detach a volume.\n\n Ignores 404 responses if the volume or server do not exist, or the\n volume is already detached from the server.\n \"\"\"\n try:\n volume = self.volumes_client.show_volume(volume['id'])['volume']\n # Check the status. You can only detach an in-use volume, otherwise\n # the compute API will return a 400 response.\n if volume['status'] == 'in-use':\n self.servers_client.detach_volume(server['id'], volume['id'])\n except lib_exc.NotFound:\n # Ignore 404s on detach in case the server is deleted or the volume\n # is already detached.\n pass\n\n def attach_volume(self, server, volume, device=None, tag=None):\n \"\"\"Attaches volume to server and waits for 'in-use' volume status.\n\n The volume will be detached when the test tears down.\n\n :param server: The server to which the volume will be attached.\n :param volume: The volume to attach.\n :param device: Optional mountpoint for the attached volume. Note that\n this is not guaranteed for all hypervisors and is not recommended.\n :param tag: Optional device role tag to apply to the volume.\n \"\"\"\n attach_kwargs = dict(volumeId=volume['id'])\n if device:\n attach_kwargs['device'] = device\n if tag:\n attach_kwargs['tag'] = tag\n\n attachment = self.servers_client.attach_volume(\n server['id'], **attach_kwargs)['volumeAttachment']\n # On teardown detach the volume and for multiattach volumes wait for\n # the attachment to be removed. For non-multiattach volumes wait for\n # the state of the volume to change to available. This is so we don't\n # error out when trying to delete the volume during teardown.\n if volume['multiattach']:\n att = waiters.wait_for_volume_attachment_create(\n self.volumes_client, volume['id'], server['id'])\n self.addCleanup(waiters.wait_for_volume_attachment_remove,\n self.volumes_client, volume['id'],\n att['attachment_id'])\n else:\n self.addCleanup(waiters.wait_for_volume_resource_status,\n self.volumes_client, volume['id'], 'available')\n waiters.wait_for_volume_resource_status(self.volumes_client,\n volume['id'], 'in-use')\n # Ignore 404s on detach in case the server is deleted or the volume\n # is already detached.\n self.addCleanup(self._detach_volume, server, volume)\n return attachment\n\n def create_volume_snapshot(self, volume_id, name=None, description=None,\n metadata=None, force=False):\n name = name or data_utils.rand_name(\n self.__class__.__name__ + '-snapshot')\n snapshot = self.snapshots_client.create_snapshot(\n volume_id=volume_id,\n force=force,\n display_name=name,\n description=description,\n metadata=metadata)['snapshot']\n self.addCleanup(self.snapshots_client.wait_for_resource_deletion,\n snapshot['id'])\n self.addCleanup(self.snapshots_client.delete_snapshot, snapshot['id'])\n waiters.wait_for_volume_resource_status(self.snapshots_client,\n snapshot['id'], 'available')\n snapshot = self.snapshots_client.show_snapshot(\n snapshot['id'])['snapshot']\n return snapshot\n\n def assert_flavor_equal(self, flavor_id, server_flavor):\n \"\"\"Check whether server_flavor equals to flavor.\n\n :param flavor_id: flavor id\n :param server_flavor: flavor info returned by show_server.\n \"\"\"\n # Nova API > 2.46 no longer includes flavor.id, and schema check\n # will cover whether 'id' should be in flavor\n if server_flavor.get('id'):\n msg = ('server flavor is not same as flavor!')\n self.assertEqual(flavor_id, server_flavor['id'], msg)\n else:\n flavor = self.flavors_client.show_flavor(flavor_id)['flavor']\n self.assertEqual(flavor['name'], server_flavor['original_name'],\n \"original_name in server flavor is not same as \"\n \"flavor name!\")\n for key in ['ram', 'vcpus', 'disk']:\n msg = ('attribute %s in server flavor is not same as '\n 'flavor!' % key)\n self.assertEqual(flavor[key], server_flavor[key], msg)\n\n\nclass BaseV2ComputeAdminTest(BaseV2ComputeTest):\n \"\"\"Base test case class for Compute Admin API tests.\"\"\"\n\n credentials = ['primary', 'admin']\n\n @classmethod\n def setup_clients(cls):\n super(BaseV2ComputeAdminTest, cls).setup_clients()\n cls.availability_zone_admin_client = (\n cls.os_admin.availability_zone_client)\n cls.admin_flavors_client = cls.os_admin.flavors_client\n cls.admin_servers_client = cls.os_admin.servers_client\n cls.image_client = cls.os_admin.image_client_v2\n cls.admin_assisted_volume_snapshots_client = \\\n cls.os_admin.assisted_volume_snapshots_client\n\n def create_flavor(self, ram, vcpus, disk, name=None,\n is_public='True', **kwargs):\n if name is None:\n name = data_utils.rand_name(self.__class__.__name__ + \"-flavor\")\n id = kwargs.pop('id', data_utils.rand_int_id(start=1000))\n client = self.admin_flavors_client\n flavor = client.create_flavor(\n ram=ram, vcpus=vcpus, disk=disk, name=name,\n id=id, is_public=is_public, **kwargs)['flavor']\n self.addCleanup(client.wait_for_resource_deletion, flavor['id'])\n self.addCleanup(client.delete_flavor, flavor['id'])\n return flavor\n\n @classmethod\n def get_host_for_server(cls, server_id):\n server_details = cls.admin_servers_client.show_server(server_id)\n return server_details['server']['OS-EXT-SRV-ATTR:host']\n\n def get_host_other_than(self, server_id):\n source_host = self.get_host_for_server(server_id)\n\n svcs = self.os_admin.services_client.list_services(\n binary='nova-compute')['services']\n hosts = []\n for svc in svcs:\n if svc['state'] == 'up' and svc['status'] == 'enabled':\n if CONF.compute.compute_volume_common_az:\n if svc['zone'] == CONF.compute.compute_volume_common_az:\n hosts.append(svc['host'])\n else:\n hosts.append(svc['host'])\n\n for target_host in hosts:\n if source_host != target_host:\n return target_host\n"}}},{"rowIdx":61,"cells":{"function_name":{"kind":"string","value":"create_volume"},"docstring":{"kind":"string","value":"Create a volume and wait for it to become 'available'.\n\n:param image_ref: Specify an image id to create a bootable volume.\n:param kwargs: other parameters to create volume.\n:returns: The available volume."},"masked_code":{"kind":"string","value":"# Copyright 2012 OpenStack Foundation\n# All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport time\n\nfrom oslo_log import log as logging\n\nfrom tempest.common import compute\nfrom tempest.common import waiters\nfrom tempest import config\nfrom tempest import exceptions\nfrom tempest.lib.common import api_microversion_fixture\nfrom tempest.lib.common import api_version_request\nfrom tempest.lib.common import api_version_utils\nfrom tempest.lib.common.utils import data_utils\nfrom tempest.lib.common.utils import test_utils\nfrom tempest.lib import exceptions as lib_exc\nimport tempest.test\n\nCONF = config.CONF\n\nLOG = logging.getLogger(__name__)\n\n\nclass BaseV2ComputeTest(api_version_utils.BaseMicroversionTest,\n tempest.test.BaseTestCase):\n \"\"\"Base test case class for all Compute API tests.\"\"\"\n\n force_tenant_isolation = False\n # Set this to True in subclasses to create a default network. See\n # https://bugs.launchpad.net/tempest/+bug/1844568\n create_default_network = False\n\n # TODO(andreaf) We should care also for the alt_manager here\n # but only once client lazy load in the manager is done\n credentials = ['primary']\n\n @classmethod\n def skip_checks(cls):\n super(BaseV2ComputeTest, cls).skip_checks()\n if not CONF.service_available.nova:\n raise cls.skipException(\"Nova is not available\")\n api_version_utils.check_skip_with_microversion(\n cls.min_microversion, cls.max_microversion,\n CONF.compute.min_microversion, CONF.compute.max_microversion)\n api_version_utils.check_skip_with_microversion(\n cls.volume_min_microversion, cls.volume_max_microversion,\n CONF.volume.min_microversion, CONF.volume.max_microversion)\n api_version_utils.check_skip_with_microversion(\n cls.placement_min_microversion, cls.placement_max_microversion,\n CONF.placement.min_microversion, CONF.placement.max_microversion)\n\n @classmethod\n def setup_credentials(cls):\n # Setting network=True, subnet=True creates a default network\n cls.set_network_resources(\n network=cls.create_default_network,\n subnet=cls.create_default_network)\n super(BaseV2ComputeTest, cls).setup_credentials()\n\n @classmethod\n def setup_clients(cls):\n super(BaseV2ComputeTest, cls).setup_clients()\n cls.servers_client = cls.os_primary.servers_client\n cls.server_groups_client = cls.os_primary.server_groups_client\n cls.flavors_client = cls.os_primary.flavors_client\n cls.compute_images_client = cls.os_primary.compute_images_client\n cls.extensions_client = cls.os_primary.extensions_client\n cls.floating_ip_pools_client = cls.os_primary.floating_ip_pools_client\n cls.floating_ips_client = cls.os_primary.compute_floating_ips_client\n cls.keypairs_client = cls.os_primary.keypairs_client\n cls.security_group_rules_client = (\n cls.os_primary.compute_security_group_rules_client)\n cls.security_groups_client =\\\n cls.os_primary.compute_security_groups_client\n cls.quotas_client = cls.os_primary.quotas_client\n cls.compute_networks_client = cls.os_primary.compute_networks_client\n cls.limits_client = cls.os_primary.limits_client\n cls.volumes_extensions_client =\\\n cls.os_primary.volumes_extensions_client\n cls.snapshots_extensions_client =\\\n cls.os_primary.snapshots_extensions_client\n cls.interfaces_client = cls.os_primary.interfaces_client\n cls.fixed_ips_client = cls.os_primary.fixed_ips_client\n cls.availability_zone_client = cls.os_primary.availability_zone_client\n cls.agents_client = cls.os_primary.agents_client\n cls.aggregates_client = cls.os_primary.aggregates_client\n cls.services_client = cls.os_primary.services_client\n cls.instance_usages_audit_log_client = (\n cls.os_primary.instance_usages_audit_log_client)\n cls.hypervisor_client = cls.os_primary.hypervisor_client\n cls.certificates_client = cls.os_primary.certificates_client\n cls.migrations_client = cls.os_primary.migrations_client\n cls.security_group_default_rules_client = (\n cls.os_primary.security_group_default_rules_client)\n cls.versions_client = cls.os_primary.compute_versions_client\n if CONF.service_available.cinder:\n cls.volumes_client = cls.os_primary.volumes_client_latest\n cls.attachments_client = cls.os_primary.attachments_client_latest\n cls.snapshots_client = cls.os_primary.snapshots_client_latest\n if CONF.service_available.glance:\n if CONF.image_feature_enabled.api_v1:\n cls.images_client = cls.os_primary.image_client\n elif CONF.image_feature_enabled.api_v2:\n cls.images_client = cls.os_primary.image_client_v2\n else:\n raise lib_exc.InvalidConfiguration(\n 'Either api_v1 or api_v2 must be True in '\n '[image-feature-enabled].')\n cls._check_depends_on_nova_network()\n\n @classmethod\n def _check_depends_on_nova_network(cls):\n # Since nova-network APIs were removed from Nova in the Rocky release,\n # determine, based on the max version from the version document, if\n # the compute API is >Queens and if so, skip tests that rely on\n # nova-network.\n if not getattr(cls, 'depends_on_nova_network', False):\n return\n versions = cls.versions_client.list_versions()['versions']\n # Find the v2.1 version which will tell us our max version for the\n # compute API we're testing against.\n for version in versions:\n if version['id'] == 'v2.1':\n max_version = api_version_request.APIVersionRequest(\n version['version'])\n break\n else:\n LOG.warning(\n 'Unable to determine max v2.1 compute API version: %s',\n versions)\n return\n\n # The max compute API version in Queens is 2.60 so we cap\n # at that version.\n queens = api_version_request.APIVersionRequest('2.60')\n if max_version > queens:\n raise cls.skipException('nova-network is gone')\n\n @classmethod\n def resource_setup(cls):\n super(BaseV2ComputeTest, cls).resource_setup()\n cls.request_microversion = (\n api_version_utils.select_request_microversion(\n cls.min_microversion,\n CONF.compute.min_microversion))\n cls.volume_request_microversion = (\n api_version_utils.select_request_microversion(\n cls.volume_min_microversion,\n CONF.volume.min_microversion))\n cls.placement_request_microversion = (\n api_version_utils.select_request_microversion(\n cls.placement_min_microversion,\n CONF.placement.min_microversion))\n cls.build_interval = CONF.compute.build_interval\n cls.build_timeout = CONF.compute.build_timeout\n cls.image_ref = CONF.compute.image_ref\n cls.image_ref_alt = CONF.compute.image_ref_alt\n cls.flavor_ref = CONF.compute.flavor_ref\n cls.flavor_ref_alt = CONF.compute.flavor_ref_alt\n cls.ssh_user = CONF.validation.image_ssh_user\n cls.ssh_alt_user = CONF.validation.image_alt_ssh_user\n cls.image_ssh_user = CONF.validation.image_ssh_user\n cls.image_alt_ssh_user = CONF.validation.image_alt_ssh_user\n cls.image_ssh_password = CONF.validation.image_ssh_password\n cls.image_alt_ssh_password = CONF.validation.image_alt_ssh_password\n\n @classmethod\n def is_requested_microversion_compatible(cls, max_version):\n \"\"\"Check the compatibility of selected request microversion\n\n This method will check if selected request microversion\n (cls.request_microversion) for test is compatible with respect\n to 'max_version'. Compatible means if selected request microversion\n is in the range(<=) of 'max_version'.\n\n :param max_version: maximum microversion to compare for compatibility.\n Example: '2.30'\n :returns: True if selected request microversion is compatible with\n 'max_version'. False in other case.\n \"\"\"\n try:\n req_version_obj = api_version_request.APIVersionRequest(\n cls.request_microversion)\n # NOTE(gmann): This is case where this method is used before calling\n # resource_setup(), where cls.request_microversion is set. There may\n # not be any such case but still we can handle this case.\n except AttributeError:\n request_microversion = (\n api_version_utils.select_request_microversion(\n cls.min_microversion,\n CONF.compute.min_microversion))\n req_version_obj = api_version_request.APIVersionRequest(\n request_microversion)\n max_version_obj = api_version_request.APIVersionRequest(max_version)\n return req_version_obj <= max_version_obj\n\n @classmethod\n def server_check_teardown(cls):\n \"\"\"Checks is the shared server clean enough for subsequent test.\n\n Method will delete the server when it's dirty.\n The setUp method is responsible for creating a new server.\n Exceptions raised in tearDown class are fails the test case,\n This method supposed to use only by tearDown methods, when\n the shared server_id is stored in the server_id of the class.\n \"\"\"\n if getattr(cls, 'server_id', None) is not None:\n try:\n waiters.wait_for_server_status(cls.servers_client,\n cls.server_id, 'ACTIVE')\n except Exception as exc:\n LOG.exception(exc)\n cls.servers_client.delete_server(cls.server_id)\n waiters.wait_for_server_termination(cls.servers_client,\n cls.server_id)\n cls.server_id = None\n raise\n\n @classmethod\n def create_test_server(cls, validatable=False, volume_backed=False,\n validation_resources=None, clients=None, **kwargs):\n \"\"\"Wrapper utility that returns a test server.\n\n This wrapper utility calls the common create test server and\n returns a test server. The purpose of this wrapper is to minimize\n the impact on the code of the tests already using this\n function.\n\n :param validatable: Whether the server will be pingable or sshable.\n :param volume_backed: Whether the instance is volume backed or not.\n :param validation_resources: Dictionary of validation resources as\n returned by `get_class_validation_resources`.\n :param clients: Client manager, defaults to os_primary.\n :param kwargs: Extra arguments are passed down to the\n `compute.create_test_server` call.\n \"\"\"\n if 'name' not in kwargs:\n kwargs['name'] = data_utils.rand_name(cls.__name__ + \"-server\")\n\n request_version = api_version_request.APIVersionRequest(\n cls.request_microversion)\n v2_37_version = api_version_request.APIVersionRequest('2.37')\n\n tenant_network = cls.get_tenant_network()\n # NOTE(snikitin): since microversion v2.37 'networks' field is required\n if (request_version >= v2_37_version and 'networks' not in kwargs and\n not tenant_network):\n kwargs['networks'] = 'none'\n\n if clients is None:\n clients = cls.os_primary\n\n body, servers = compute.create_test_server(\n clients,\n validatable,\n validation_resources=validation_resources,\n tenant_network=tenant_network,\n volume_backed=volume_backed,\n **kwargs)\n\n # For each server schedule wait and delete, so we first delete all\n # and then wait for all\n for server in servers:\n cls.addClassResourceCleanup(waiters.wait_for_server_termination,\n clients.servers_client, server['id'])\n for server in servers:\n cls.addClassResourceCleanup(\n test_utils.call_and_ignore_notfound_exc,\n clients.servers_client.delete_server, server['id'])\n\n return body\n\n @classmethod\n def create_security_group(cls, name=None, description=None):\n if name is None:\n name = data_utils.rand_name(cls.__name__ + \"-securitygroup\")\n if description is None:\n description = data_utils.rand_name('description')\n body = cls.security_groups_client.create_security_group(\n name=name, description=description)['security_group']\n cls.addClassResourceCleanup(\n test_utils.call_and_ignore_notfound_exc,\n cls.security_groups_client.delete_security_group,\n body['id'])\n\n return body\n\n @classmethod\n def create_test_server_group(cls, name=\"\", policy=None):\n if not name:\n name = data_utils.rand_name(cls.__name__ + \"-Server-Group\")\n if policy is None:\n policy = ['affinity']\n body = cls.server_groups_client.create_server_group(\n name=name, policies=policy)['server_group']\n cls.addClassResourceCleanup(\n test_utils.call_and_ignore_notfound_exc,\n cls.server_groups_client.delete_server_group,\n body['id'])\n return body\n\n def wait_for(self, condition):\n \"\"\"Repeatedly calls condition() until a timeout.\"\"\"\n start_time = int(time.time())\n while True:\n try:\n condition()\n except Exception:\n pass\n else:\n return\n if int(time.time()) - start_time >= self.build_timeout:\n condition()\n return\n time.sleep(self.build_interval)\n\n @classmethod\n def prepare_instance_network(cls):\n if (CONF.validation.auth_method != 'disabled' and\n CONF.validation.connect_method == 'floating'):\n cls.set_network_resources(network=True, subnet=True, router=True,\n dhcp=True)\n\n @classmethod\n def create_image_from_server(cls, server_id, **kwargs):\n \"\"\"Wrapper utility that returns an image created from the server.\n\n If compute microversion >= 2.36, the returned image response will\n be from the image service API rather than the compute image proxy API.\n \"\"\"\n name = kwargs.pop('name',\n data_utils.rand_name(cls.__name__ + \"-image\"))\n wait_until = kwargs.pop('wait_until', None)\n wait_for_server = kwargs.pop('wait_for_server', True)\n\n image = cls.compute_images_client.create_image(server_id, name=name,\n **kwargs)\n if api_version_utils.compare_version_header_to_response(\n \"OpenStack-API-Version\", \"compute 2.45\", image.response, \"lt\"):\n image_id = image['image_id']\n else:\n image_id = data_utils.parse_image_id(image.response['location'])\n\n # The compute image proxy APIs were deprecated in 2.35 so\n # use the images client directly if the API microversion being\n # used is >=2.36.\n if not cls.is_requested_microversion_compatible('2.35'):\n client = cls.images_client\n else:\n client = cls.compute_images_client\n cls.addClassResourceCleanup(test_utils.call_and_ignore_notfound_exc,\n client.delete_image, image_id)\n\n if wait_until is not None:\n try:\n wait_until = wait_until.upper()\n if not cls.is_requested_microversion_compatible('2.35'):\n wait_until = wait_until.lower()\n waiters.wait_for_image_status(client, image_id, wait_until)\n except lib_exc.NotFound:\n if wait_until.upper() == 'ACTIVE':\n # If the image is not found after create_image returned\n # that means the snapshot failed in nova-compute and nova\n # deleted the image. There should be a compute fault\n # recorded with the server in that case, so get the server\n # and dump some details.\n server = (\n cls.servers_client.show_server(server_id)['server'])\n if 'fault' in server:\n raise exceptions.SnapshotNotFoundException(\n server['fault'], image_id=image_id)\n else:\n raise exceptions.SnapshotNotFoundException(\n image_id=image_id)\n else:\n raise\n image = client.show_image(image_id)\n # Compute image client returns response wrapped in 'image' element\n # which is not the case with Glance image client.\n if 'image' in image:\n image = image['image']\n\n if wait_until.upper() == 'ACTIVE':\n if wait_for_server:\n waiters.wait_for_server_status(cls.servers_client,\n server_id, 'ACTIVE')\n return image\n\n @classmethod\n def recreate_server(cls, server_id, validatable=False, **kwargs):\n \"\"\"Destroy an existing class level server and creates a new one\n\n Some test classes use a test server that can be used by multiple\n tests. This is done to optimise runtime and test load.\n If something goes wrong with the test server, it can be rebuilt\n using this helper.\n\n This helper can also be used for the initial provisioning if no\n server_id is specified.\n\n :param server_id: UUID of the server to be rebuilt. If None is\n specified, a new server is provisioned.\n :param validatable: whether to the server needs to be\n validatable. When True, validation resources are acquired via\n the `get_class_validation_resources` helper.\n :param kwargs: extra paramaters are passed through to the\n `create_test_server` call.\n :return: the UUID of the created server.\n \"\"\"\n if server_id:\n cls.delete_server(server_id)\n\n cls.password = data_utils.rand_password()\n server = cls.create_test_server(\n validatable,\n validation_resources=cls.get_class_validation_resources(\n cls.os_primary),\n wait_until='ACTIVE',\n adminPass=cls.password,\n **kwargs)\n return server['id']\n\n @classmethod\n def delete_server(cls, server_id):\n \"\"\"Deletes an existing server and waits for it to be gone.\"\"\"\n try:\n cls.servers_client.delete_server(server_id)\n waiters.wait_for_server_termination(cls.servers_client,\n server_id)\n except Exception:\n LOG.exception('Failed to delete server %s', server_id)\n\n def resize_server(self, server_id, new_flavor_id, **kwargs):\n \"\"\"resize and confirm_resize an server, waits for it to be ACTIVE.\"\"\"\n self.servers_client.resize_server(server_id, new_flavor_id, **kwargs)\n waiters.wait_for_server_status(self.servers_client, server_id,\n 'VERIFY_RESIZE')\n self.servers_client.confirm_resize_server(server_id)\n waiters.wait_for_server_status(\n self.servers_client, server_id, 'ACTIVE')\n server = self.servers_client.show_server(server_id)['server']\n self.assert_flavor_equal(new_flavor_id, server['flavor'])\n\n @classmethod\n def delete_volume(cls, volume_id):\n \"\"\"Deletes the given volume and waits for it to be gone.\"\"\"\n try:\n cls.volumes_client.delete_volume(volume_id)\n # TODO(mriedem): We should move the wait_for_resource_deletion\n # into the delete_volume method as a convenience to the caller.\n cls.volumes_client.wait_for_resource_deletion(volume_id)\n except lib_exc.NotFound:\n LOG.warning(\"Unable to delete volume '%s' since it was not found. \"\n \"Maybe it was already deleted?\", volume_id)\n\n @classmethod\n def get_server_ip(cls, server, validation_resources=None):\n \"\"\"Get the server fixed or floating IP.\n\n Based on the configuration we're in, return a correct ip\n address for validating that a guest is up.\n\n :param server: The server dict as returned by the API\n :param validation_resources: The dict of validation resources\n provisioned for the server.\n \"\"\"\n if CONF.validation.connect_method == 'floating':\n if validation_resources:\n return validation_resources['floating_ip']['ip']\n else:\n msg = ('When validation.connect_method equals floating, '\n 'validation_resources cannot be None')\n raise lib_exc.InvalidParam(invalid_param=msg)\n elif CONF.validation.connect_method == 'fixed':\n addresses = server['addresses'][CONF.validation.network_for_ssh]\n for address in addresses:\n if address['version'] == CONF.validation.ip_version_for_ssh:\n return address['addr']\n raise exceptions.ServerUnreachable(server_id=server['id'])\n else:\n raise lib_exc.InvalidConfiguration()\n\n def setUp(self):\n super(BaseV2ComputeTest, self).setUp()\n self.useFixture(api_microversion_fixture.APIMicroversionFixture(\n compute_microversion=self.request_microversion,\n volume_microversion=self.volume_request_microversion,\n placement_microversion=self.placement_request_microversion))\n\n# MASKED: create_volume function (lines 503-529)\n\n def _detach_volume(self, server, volume):\n \"\"\"Helper method to detach a volume.\n\n Ignores 404 responses if the volume or server do not exist, or the\n volume is already detached from the server.\n \"\"\"\n try:\n volume = self.volumes_client.show_volume(volume['id'])['volume']\n # Check the status. You can only detach an in-use volume, otherwise\n # the compute API will return a 400 response.\n if volume['status'] == 'in-use':\n self.servers_client.detach_volume(server['id'], volume['id'])\n except lib_exc.NotFound:\n # Ignore 404s on detach in case the server is deleted or the volume\n # is already detached.\n pass\n\n def attach_volume(self, server, volume, device=None, tag=None):\n \"\"\"Attaches volume to server and waits for 'in-use' volume status.\n\n The volume will be detached when the test tears down.\n\n :param server: The server to which the volume will be attached.\n :param volume: The volume to attach.\n :param device: Optional mountpoint for the attached volume. Note that\n this is not guaranteed for all hypervisors and is not recommended.\n :param tag: Optional device role tag to apply to the volume.\n \"\"\"\n attach_kwargs = dict(volumeId=volume['id'])\n if device:\n attach_kwargs['device'] = device\n if tag:\n attach_kwargs['tag'] = tag\n\n attachment = self.servers_client.attach_volume(\n server['id'], **attach_kwargs)['volumeAttachment']\n # On teardown detach the volume and for multiattach volumes wait for\n # the attachment to be removed. For non-multiattach volumes wait for\n # the state of the volume to change to available. This is so we don't\n # error out when trying to delete the volume during teardown.\n if volume['multiattach']:\n att = waiters.wait_for_volume_attachment_create(\n self.volumes_client, volume['id'], server['id'])\n self.addCleanup(waiters.wait_for_volume_attachment_remove,\n self.volumes_client, volume['id'],\n att['attachment_id'])\n else:\n self.addCleanup(waiters.wait_for_volume_resource_status,\n self.volumes_client, volume['id'], 'available')\n waiters.wait_for_volume_resource_status(self.volumes_client,\n volume['id'], 'in-use')\n # Ignore 404s on detach in case the server is deleted or the volume\n # is already detached.\n self.addCleanup(self._detach_volume, server, volume)\n return attachment\n\n def create_volume_snapshot(self, volume_id, name=None, description=None,\n metadata=None, force=False):\n name = name or data_utils.rand_name(\n self.__class__.__name__ + '-snapshot')\n snapshot = self.snapshots_client.create_snapshot(\n volume_id=volume_id,\n force=force,\n display_name=name,\n description=description,\n metadata=metadata)['snapshot']\n self.addCleanup(self.snapshots_client.wait_for_resource_deletion,\n snapshot['id'])\n self.addCleanup(self.snapshots_client.delete_snapshot, snapshot['id'])\n waiters.wait_for_volume_resource_status(self.snapshots_client,\n snapshot['id'], 'available')\n snapshot = self.snapshots_client.show_snapshot(\n snapshot['id'])['snapshot']\n return snapshot\n\n def assert_flavor_equal(self, flavor_id, server_flavor):\n \"\"\"Check whether server_flavor equals to flavor.\n\n :param flavor_id: flavor id\n :param server_flavor: flavor info returned by show_server.\n \"\"\"\n # Nova API > 2.46 no longer includes flavor.id, and schema check\n # will cover whether 'id' should be in flavor\n if server_flavor.get('id'):\n msg = ('server flavor is not same as flavor!')\n self.assertEqual(flavor_id, server_flavor['id'], msg)\n else:\n flavor = self.flavors_client.show_flavor(flavor_id)['flavor']\n self.assertEqual(flavor['name'], server_flavor['original_name'],\n \"original_name in server flavor is not same as \"\n \"flavor name!\")\n for key in ['ram', 'vcpus', 'disk']:\n msg = ('attribute %s in server flavor is not same as '\n 'flavor!' % key)\n self.assertEqual(flavor[key], server_flavor[key], msg)\n\n\nclass BaseV2ComputeAdminTest(BaseV2ComputeTest):\n \"\"\"Base test case class for Compute Admin API tests.\"\"\"\n\n credentials = ['primary', 'admin']\n\n @classmethod\n def setup_clients(cls):\n super(BaseV2ComputeAdminTest, cls).setup_clients()\n cls.availability_zone_admin_client = (\n cls.os_admin.availability_zone_client)\n cls.admin_flavors_client = cls.os_admin.flavors_client\n cls.admin_servers_client = cls.os_admin.servers_client\n cls.image_client = cls.os_admin.image_client_v2\n cls.admin_assisted_volume_snapshots_client = \\\n cls.os_admin.assisted_volume_snapshots_client\n\n def create_flavor(self, ram, vcpus, disk, name=None,\n is_public='True', **kwargs):\n if name is None:\n name = data_utils.rand_name(self.__class__.__name__ + \"-flavor\")\n id = kwargs.pop('id', data_utils.rand_int_id(start=1000))\n client = self.admin_flavors_client\n flavor = client.create_flavor(\n ram=ram, vcpus=vcpus, disk=disk, name=name,\n id=id, is_public=is_public, **kwargs)['flavor']\n self.addCleanup(client.wait_for_resource_deletion, flavor['id'])\n self.addCleanup(client.delete_flavor, flavor['id'])\n return flavor\n\n @classmethod\n def get_host_for_server(cls, server_id):\n server_details = cls.admin_servers_client.show_server(server_id)\n return server_details['server']['OS-EXT-SRV-ATTR:host']\n\n def get_host_other_than(self, server_id):\n source_host = self.get_host_for_server(server_id)\n\n svcs = self.os_admin.services_client.list_services(\n binary='nova-compute')['services']\n hosts = []\n for svc in svcs:\n if svc['state'] == 'up' and svc['status'] == 'enabled':\n if CONF.compute.compute_volume_common_az:\n if svc['zone'] == CONF.compute.compute_volume_common_az:\n hosts.append(svc['host'])\n else:\n hosts.append(svc['host'])\n\n for target_host in hosts:\n if source_host != target_host:\n return target_host"},"implementation":{"kind":"string","value":" @classmethod\n def create_volume(cls, image_ref=None, **kwargs):\n \"\"\"Create a volume and wait for it to become 'available'.\n\n :param image_ref: Specify an image id to create a bootable volume.\n :param kwargs: other parameters to create volume.\n :returns: The available volume.\n \"\"\"\n if 'size' not in kwargs:\n kwargs['size'] = CONF.volume.volume_size\n if 'display_name' not in kwargs:\n vol_name = data_utils.rand_name(cls.__name__ + '-volume')\n kwargs['display_name'] = vol_name\n if image_ref is not None:\n kwargs['imageRef'] = image_ref\n if CONF.compute.compute_volume_common_az:\n kwargs.setdefault('availability_zone',\n CONF.compute.compute_volume_common_az)\n volume = cls.volumes_client.create_volume(**kwargs)['volume']\n cls.addClassResourceCleanup(\n cls.volumes_client.wait_for_resource_deletion, volume['id'])\n cls.addClassResourceCleanup(test_utils.call_and_ignore_notfound_exc,\n cls.volumes_client.delete_volume,\n volume['id'])\n waiters.wait_for_volume_resource_status(cls.volumes_client,\n volume['id'], 'available')\n return volume"},"start_line":{"kind":"number","value":503,"string":"503"},"end_line":{"kind":"number","value":529,"string":"529"},"file_content":{"kind":"string","value":"# Copyright 2012 OpenStack Foundation\n# All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport time\n\nfrom oslo_log import log as logging\n\nfrom tempest.common import compute\nfrom tempest.common import waiters\nfrom tempest import config\nfrom tempest import exceptions\nfrom tempest.lib.common import api_microversion_fixture\nfrom tempest.lib.common import api_version_request\nfrom tempest.lib.common import api_version_utils\nfrom tempest.lib.common.utils import data_utils\nfrom tempest.lib.common.utils import test_utils\nfrom tempest.lib import exceptions as lib_exc\nimport tempest.test\n\nCONF = config.CONF\n\nLOG = logging.getLogger(__name__)\n\n\nclass BaseV2ComputeTest(api_version_utils.BaseMicroversionTest,\n tempest.test.BaseTestCase):\n \"\"\"Base test case class for all Compute API tests.\"\"\"\n\n force_tenant_isolation = False\n # Set this to True in subclasses to create a default network. See\n # https://bugs.launchpad.net/tempest/+bug/1844568\n create_default_network = False\n\n # TODO(andreaf) We should care also for the alt_manager here\n # but only once client lazy load in the manager is done\n credentials = ['primary']\n\n @classmethod\n def skip_checks(cls):\n super(BaseV2ComputeTest, cls).skip_checks()\n if not CONF.service_available.nova:\n raise cls.skipException(\"Nova is not available\")\n api_version_utils.check_skip_with_microversion(\n cls.min_microversion, cls.max_microversion,\n CONF.compute.min_microversion, CONF.compute.max_microversion)\n api_version_utils.check_skip_with_microversion(\n cls.volume_min_microversion, cls.volume_max_microversion,\n CONF.volume.min_microversion, CONF.volume.max_microversion)\n api_version_utils.check_skip_with_microversion(\n cls.placement_min_microversion, cls.placement_max_microversion,\n CONF.placement.min_microversion, CONF.placement.max_microversion)\n\n @classmethod\n def setup_credentials(cls):\n # Setting network=True, subnet=True creates a default network\n cls.set_network_resources(\n network=cls.create_default_network,\n subnet=cls.create_default_network)\n super(BaseV2ComputeTest, cls).setup_credentials()\n\n @classmethod\n def setup_clients(cls):\n super(BaseV2ComputeTest, cls).setup_clients()\n cls.servers_client = cls.os_primary.servers_client\n cls.server_groups_client = cls.os_primary.server_groups_client\n cls.flavors_client = cls.os_primary.flavors_client\n cls.compute_images_client = cls.os_primary.compute_images_client\n cls.extensions_client = cls.os_primary.extensions_client\n cls.floating_ip_pools_client = cls.os_primary.floating_ip_pools_client\n cls.floating_ips_client = cls.os_primary.compute_floating_ips_client\n cls.keypairs_client = cls.os_primary.keypairs_client\n cls.security_group_rules_client = (\n cls.os_primary.compute_security_group_rules_client)\n cls.security_groups_client =\\\n cls.os_primary.compute_security_groups_client\n cls.quotas_client = cls.os_primary.quotas_client\n cls.compute_networks_client = cls.os_primary.compute_networks_client\n cls.limits_client = cls.os_primary.limits_client\n cls.volumes_extensions_client =\\\n cls.os_primary.volumes_extensions_client\n cls.snapshots_extensions_client =\\\n cls.os_primary.snapshots_extensions_client\n cls.interfaces_client = cls.os_primary.interfaces_client\n cls.fixed_ips_client = cls.os_primary.fixed_ips_client\n cls.availability_zone_client = cls.os_primary.availability_zone_client\n cls.agents_client = cls.os_primary.agents_client\n cls.aggregates_client = cls.os_primary.aggregates_client\n cls.services_client = cls.os_primary.services_client\n cls.instance_usages_audit_log_client = (\n cls.os_primary.instance_usages_audit_log_client)\n cls.hypervisor_client = cls.os_primary.hypervisor_client\n cls.certificates_client = cls.os_primary.certificates_client\n cls.migrations_client = cls.os_primary.migrations_client\n cls.security_group_default_rules_client = (\n cls.os_primary.security_group_default_rules_client)\n cls.versions_client = cls.os_primary.compute_versions_client\n if CONF.service_available.cinder:\n cls.volumes_client = cls.os_primary.volumes_client_latest\n cls.attachments_client = cls.os_primary.attachments_client_latest\n cls.snapshots_client = cls.os_primary.snapshots_client_latest\n if CONF.service_available.glance:\n if CONF.image_feature_enabled.api_v1:\n cls.images_client = cls.os_primary.image_client\n elif CONF.image_feature_enabled.api_v2:\n cls.images_client = cls.os_primary.image_client_v2\n else:\n raise lib_exc.InvalidConfiguration(\n 'Either api_v1 or api_v2 must be True in '\n '[image-feature-enabled].')\n cls._check_depends_on_nova_network()\n\n @classmethod\n def _check_depends_on_nova_network(cls):\n # Since nova-network APIs were removed from Nova in the Rocky release,\n # determine, based on the max version from the version document, if\n # the compute API is >Queens and if so, skip tests that rely on\n # nova-network.\n if not getattr(cls, 'depends_on_nova_network', False):\n return\n versions = cls.versions_client.list_versions()['versions']\n # Find the v2.1 version which will tell us our max version for the\n # compute API we're testing against.\n for version in versions:\n if version['id'] == 'v2.1':\n max_version = api_version_request.APIVersionRequest(\n version['version'])\n break\n else:\n LOG.warning(\n 'Unable to determine max v2.1 compute API version: %s',\n versions)\n return\n\n # The max compute API version in Queens is 2.60 so we cap\n # at that version.\n queens = api_version_request.APIVersionRequest('2.60')\n if max_version > queens:\n raise cls.skipException('nova-network is gone')\n\n @classmethod\n def resource_setup(cls):\n super(BaseV2ComputeTest, cls).resource_setup()\n cls.request_microversion = (\n api_version_utils.select_request_microversion(\n cls.min_microversion,\n CONF.compute.min_microversion))\n cls.volume_request_microversion = (\n api_version_utils.select_request_microversion(\n cls.volume_min_microversion,\n CONF.volume.min_microversion))\n cls.placement_request_microversion = (\n api_version_utils.select_request_microversion(\n cls.placement_min_microversion,\n CONF.placement.min_microversion))\n cls.build_interval = CONF.compute.build_interval\n cls.build_timeout = CONF.compute.build_timeout\n cls.image_ref = CONF.compute.image_ref\n cls.image_ref_alt = CONF.compute.image_ref_alt\n cls.flavor_ref = CONF.compute.flavor_ref\n cls.flavor_ref_alt = CONF.compute.flavor_ref_alt\n cls.ssh_user = CONF.validation.image_ssh_user\n cls.ssh_alt_user = CONF.validation.image_alt_ssh_user\n cls.image_ssh_user = CONF.validation.image_ssh_user\n cls.image_alt_ssh_user = CONF.validation.image_alt_ssh_user\n cls.image_ssh_password = CONF.validation.image_ssh_password\n cls.image_alt_ssh_password = CONF.validation.image_alt_ssh_password\n\n @classmethod\n def is_requested_microversion_compatible(cls, max_version):\n \"\"\"Check the compatibility of selected request microversion\n\n This method will check if selected request microversion\n (cls.request_microversion) for test is compatible with respect\n to 'max_version'. Compatible means if selected request microversion\n is in the range(<=) of 'max_version'.\n\n :param max_version: maximum microversion to compare for compatibility.\n Example: '2.30'\n :returns: True if selected request microversion is compatible with\n 'max_version'. False in other case.\n \"\"\"\n try:\n req_version_obj = api_version_request.APIVersionRequest(\n cls.request_microversion)\n # NOTE(gmann): This is case where this method is used before calling\n # resource_setup(), where cls.request_microversion is set. There may\n # not be any such case but still we can handle this case.\n except AttributeError:\n request_microversion = (\n api_version_utils.select_request_microversion(\n cls.min_microversion,\n CONF.compute.min_microversion))\n req_version_obj = api_version_request.APIVersionRequest(\n request_microversion)\n max_version_obj = api_version_request.APIVersionRequest(max_version)\n return req_version_obj <= max_version_obj\n\n @classmethod\n def server_check_teardown(cls):\n \"\"\"Checks is the shared server clean enough for subsequent test.\n\n Method will delete the server when it's dirty.\n The setUp method is responsible for creating a new server.\n Exceptions raised in tearDown class are fails the test case,\n This method supposed to use only by tearDown methods, when\n the shared server_id is stored in the server_id of the class.\n \"\"\"\n if getattr(cls, 'server_id', None) is not None:\n try:\n waiters.wait_for_server_status(cls.servers_client,\n cls.server_id, 'ACTIVE')\n except Exception as exc:\n LOG.exception(exc)\n cls.servers_client.delete_server(cls.server_id)\n waiters.wait_for_server_termination(cls.servers_client,\n cls.server_id)\n cls.server_id = None\n raise\n\n @classmethod\n def create_test_server(cls, validatable=False, volume_backed=False,\n validation_resources=None, clients=None, **kwargs):\n \"\"\"Wrapper utility that returns a test server.\n\n This wrapper utility calls the common create test server and\n returns a test server. The purpose of this wrapper is to minimize\n the impact on the code of the tests already using this\n function.\n\n :param validatable: Whether the server will be pingable or sshable.\n :param volume_backed: Whether the instance is volume backed or not.\n :param validation_resources: Dictionary of validation resources as\n returned by `get_class_validation_resources`.\n :param clients: Client manager, defaults to os_primary.\n :param kwargs: Extra arguments are passed down to the\n `compute.create_test_server` call.\n \"\"\"\n if 'name' not in kwargs:\n kwargs['name'] = data_utils.rand_name(cls.__name__ + \"-server\")\n\n request_version = api_version_request.APIVersionRequest(\n cls.request_microversion)\n v2_37_version = api_version_request.APIVersionRequest('2.37')\n\n tenant_network = cls.get_tenant_network()\n # NOTE(snikitin): since microversion v2.37 'networks' field is required\n if (request_version >= v2_37_version and 'networks' not in kwargs and\n not tenant_network):\n kwargs['networks'] = 'none'\n\n if clients is None:\n clients = cls.os_primary\n\n body, servers = compute.create_test_server(\n clients,\n validatable,\n validation_resources=validation_resources,\n tenant_network=tenant_network,\n volume_backed=volume_backed,\n **kwargs)\n\n # For each server schedule wait and delete, so we first delete all\n # and then wait for all\n for server in servers:\n cls.addClassResourceCleanup(waiters.wait_for_server_termination,\n clients.servers_client, server['id'])\n for server in servers:\n cls.addClassResourceCleanup(\n test_utils.call_and_ignore_notfound_exc,\n clients.servers_client.delete_server, server['id'])\n\n return body\n\n @classmethod\n def create_security_group(cls, name=None, description=None):\n if name is None:\n name = data_utils.rand_name(cls.__name__ + \"-securitygroup\")\n if description is None:\n description = data_utils.rand_name('description')\n body = cls.security_groups_client.create_security_group(\n name=name, description=description)['security_group']\n cls.addClassResourceCleanup(\n test_utils.call_and_ignore_notfound_exc,\n cls.security_groups_client.delete_security_group,\n body['id'])\n\n return body\n\n @classmethod\n def create_test_server_group(cls, name=\"\", policy=None):\n if not name:\n name = data_utils.rand_name(cls.__name__ + \"-Server-Group\")\n if policy is None:\n policy = ['affinity']\n body = cls.server_groups_client.create_server_group(\n name=name, policies=policy)['server_group']\n cls.addClassResourceCleanup(\n test_utils.call_and_ignore_notfound_exc,\n cls.server_groups_client.delete_server_group,\n body['id'])\n return body\n\n def wait_for(self, condition):\n \"\"\"Repeatedly calls condition() until a timeout.\"\"\"\n start_time = int(time.time())\n while True:\n try:\n condition()\n except Exception:\n pass\n else:\n return\n if int(time.time()) - start_time >= self.build_timeout:\n condition()\n return\n time.sleep(self.build_interval)\n\n @classmethod\n def prepare_instance_network(cls):\n if (CONF.validation.auth_method != 'disabled' and\n CONF.validation.connect_method == 'floating'):\n cls.set_network_resources(network=True, subnet=True, router=True,\n dhcp=True)\n\n @classmethod\n def create_image_from_server(cls, server_id, **kwargs):\n \"\"\"Wrapper utility that returns an image created from the server.\n\n If compute microversion >= 2.36, the returned image response will\n be from the image service API rather than the compute image proxy API.\n \"\"\"\n name = kwargs.pop('name',\n data_utils.rand_name(cls.__name__ + \"-image\"))\n wait_until = kwargs.pop('wait_until', None)\n wait_for_server = kwargs.pop('wait_for_server', True)\n\n image = cls.compute_images_client.create_image(server_id, name=name,\n **kwargs)\n if api_version_utils.compare_version_header_to_response(\n \"OpenStack-API-Version\", \"compute 2.45\", image.response, \"lt\"):\n image_id = image['image_id']\n else:\n image_id = data_utils.parse_image_id(image.response['location'])\n\n # The compute image proxy APIs were deprecated in 2.35 so\n # use the images client directly if the API microversion being\n # used is >=2.36.\n if not cls.is_requested_microversion_compatible('2.35'):\n client = cls.images_client\n else:\n client = cls.compute_images_client\n cls.addClassResourceCleanup(test_utils.call_and_ignore_notfound_exc,\n client.delete_image, image_id)\n\n if wait_until is not None:\n try:\n wait_until = wait_until.upper()\n if not cls.is_requested_microversion_compatible('2.35'):\n wait_until = wait_until.lower()\n waiters.wait_for_image_status(client, image_id, wait_until)\n except lib_exc.NotFound:\n if wait_until.upper() == 'ACTIVE':\n # If the image is not found after create_image returned\n # that means the snapshot failed in nova-compute and nova\n # deleted the image. There should be a compute fault\n # recorded with the server in that case, so get the server\n # and dump some details.\n server = (\n cls.servers_client.show_server(server_id)['server'])\n if 'fault' in server:\n raise exceptions.SnapshotNotFoundException(\n server['fault'], image_id=image_id)\n else:\n raise exceptions.SnapshotNotFoundException(\n image_id=image_id)\n else:\n raise\n image = client.show_image(image_id)\n # Compute image client returns response wrapped in 'image' element\n # which is not the case with Glance image client.\n if 'image' in image:\n image = image['image']\n\n if wait_until.upper() == 'ACTIVE':\n if wait_for_server:\n waiters.wait_for_server_status(cls.servers_client,\n server_id, 'ACTIVE')\n return image\n\n @classmethod\n def recreate_server(cls, server_id, validatable=False, **kwargs):\n \"\"\"Destroy an existing class level server and creates a new one\n\n Some test classes use a test server that can be used by multiple\n tests. This is done to optimise runtime and test load.\n If something goes wrong with the test server, it can be rebuilt\n using this helper.\n\n This helper can also be used for the initial provisioning if no\n server_id is specified.\n\n :param server_id: UUID of the server to be rebuilt. If None is\n specified, a new server is provisioned.\n :param validatable: whether to the server needs to be\n validatable. When True, validation resources are acquired via\n the `get_class_validation_resources` helper.\n :param kwargs: extra paramaters are passed through to the\n `create_test_server` call.\n :return: the UUID of the created server.\n \"\"\"\n if server_id:\n cls.delete_server(server_id)\n\n cls.password = data_utils.rand_password()\n server = cls.create_test_server(\n validatable,\n validation_resources=cls.get_class_validation_resources(\n cls.os_primary),\n wait_until='ACTIVE',\n adminPass=cls.password,\n **kwargs)\n return server['id']\n\n @classmethod\n def delete_server(cls, server_id):\n \"\"\"Deletes an existing server and waits for it to be gone.\"\"\"\n try:\n cls.servers_client.delete_server(server_id)\n waiters.wait_for_server_termination(cls.servers_client,\n server_id)\n except Exception:\n LOG.exception('Failed to delete server %s', server_id)\n\n def resize_server(self, server_id, new_flavor_id, **kwargs):\n \"\"\"resize and confirm_resize an server, waits for it to be ACTIVE.\"\"\"\n self.servers_client.resize_server(server_id, new_flavor_id, **kwargs)\n waiters.wait_for_server_status(self.servers_client, server_id,\n 'VERIFY_RESIZE')\n self.servers_client.confirm_resize_server(server_id)\n waiters.wait_for_server_status(\n self.servers_client, server_id, 'ACTIVE')\n server = self.servers_client.show_server(server_id)['server']\n self.assert_flavor_equal(new_flavor_id, server['flavor'])\n\n @classmethod\n def delete_volume(cls, volume_id):\n \"\"\"Deletes the given volume and waits for it to be gone.\"\"\"\n try:\n cls.volumes_client.delete_volume(volume_id)\n # TODO(mriedem): We should move the wait_for_resource_deletion\n # into the delete_volume method as a convenience to the caller.\n cls.volumes_client.wait_for_resource_deletion(volume_id)\n except lib_exc.NotFound:\n LOG.warning(\"Unable to delete volume '%s' since it was not found. \"\n \"Maybe it was already deleted?\", volume_id)\n\n @classmethod\n def get_server_ip(cls, server, validation_resources=None):\n \"\"\"Get the server fixed or floating IP.\n\n Based on the configuration we're in, return a correct ip\n address for validating that a guest is up.\n\n :param server: The server dict as returned by the API\n :param validation_resources: The dict of validation resources\n provisioned for the server.\n \"\"\"\n if CONF.validation.connect_method == 'floating':\n if validation_resources:\n return validation_resources['floating_ip']['ip']\n else:\n msg = ('When validation.connect_method equals floating, '\n 'validation_resources cannot be None')\n raise lib_exc.InvalidParam(invalid_param=msg)\n elif CONF.validation.connect_method == 'fixed':\n addresses = server['addresses'][CONF.validation.network_for_ssh]\n for address in addresses:\n if address['version'] == CONF.validation.ip_version_for_ssh:\n return address['addr']\n raise exceptions.ServerUnreachable(server_id=server['id'])\n else:\n raise lib_exc.InvalidConfiguration()\n\n def setUp(self):\n super(BaseV2ComputeTest, self).setUp()\n self.useFixture(api_microversion_fixture.APIMicroversionFixture(\n compute_microversion=self.request_microversion,\n volume_microversion=self.volume_request_microversion,\n placement_microversion=self.placement_request_microversion))\n\n @classmethod\n def create_volume(cls, image_ref=None, **kwargs):\n \"\"\"Create a volume and wait for it to become 'available'.\n\n :param image_ref: Specify an image id to create a bootable volume.\n :param kwargs: other parameters to create volume.\n :returns: The available volume.\n \"\"\"\n if 'size' not in kwargs:\n kwargs['size'] = CONF.volume.volume_size\n if 'display_name' not in kwargs:\n vol_name = data_utils.rand_name(cls.__name__ + '-volume')\n kwargs['display_name'] = vol_name\n if image_ref is not None:\n kwargs['imageRef'] = image_ref\n if CONF.compute.compute_volume_common_az:\n kwargs.setdefault('availability_zone',\n CONF.compute.compute_volume_common_az)\n volume = cls.volumes_client.create_volume(**kwargs)['volume']\n cls.addClassResourceCleanup(\n cls.volumes_client.wait_for_resource_deletion, volume['id'])\n cls.addClassResourceCleanup(test_utils.call_and_ignore_notfound_exc,\n cls.volumes_client.delete_volume,\n volume['id'])\n waiters.wait_for_volume_resource_status(cls.volumes_client,\n volume['id'], 'available')\n return volume\n\n def _detach_volume(self, server, volume):\n \"\"\"Helper method to detach a volume.\n\n Ignores 404 responses if the volume or server do not exist, or the\n volume is already detached from the server.\n \"\"\"\n try:\n volume = self.volumes_client.show_volume(volume['id'])['volume']\n # Check the status. You can only detach an in-use volume, otherwise\n # the compute API will return a 400 response.\n if volume['status'] == 'in-use':\n self.servers_client.detach_volume(server['id'], volume['id'])\n except lib_exc.NotFound:\n # Ignore 404s on detach in case the server is deleted or the volume\n # is already detached.\n pass\n\n def attach_volume(self, server, volume, device=None, tag=None):\n \"\"\"Attaches volume to server and waits for 'in-use' volume status.\n\n The volume will be detached when the test tears down.\n\n :param server: The server to which the volume will be attached.\n :param volume: The volume to attach.\n :param device: Optional mountpoint for the attached volume. Note that\n this is not guaranteed for all hypervisors and is not recommended.\n :param tag: Optional device role tag to apply to the volume.\n \"\"\"\n attach_kwargs = dict(volumeId=volume['id'])\n if device:\n attach_kwargs['device'] = device\n if tag:\n attach_kwargs['tag'] = tag\n\n attachment = self.servers_client.attach_volume(\n server['id'], **attach_kwargs)['volumeAttachment']\n # On teardown detach the volume and for multiattach volumes wait for\n # the attachment to be removed. For non-multiattach volumes wait for\n # the state of the volume to change to available. This is so we don't\n # error out when trying to delete the volume during teardown.\n if volume['multiattach']:\n att = waiters.wait_for_volume_attachment_create(\n self.volumes_client, volume['id'], server['id'])\n self.addCleanup(waiters.wait_for_volume_attachment_remove,\n self.volumes_client, volume['id'],\n att['attachment_id'])\n else:\n self.addCleanup(waiters.wait_for_volume_resource_status,\n self.volumes_client, volume['id'], 'available')\n waiters.wait_for_volume_resource_status(self.volumes_client,\n volume['id'], 'in-use')\n # Ignore 404s on detach in case the server is deleted or the volume\n # is already detached.\n self.addCleanup(self._detach_volume, server, volume)\n return attachment\n\n def create_volume_snapshot(self, volume_id, name=None, description=None,\n metadata=None, force=False):\n name = name or data_utils.rand_name(\n self.__class__.__name__ + '-snapshot')\n snapshot = self.snapshots_client.create_snapshot(\n volume_id=volume_id,\n force=force,\n display_name=name,\n description=description,\n metadata=metadata)['snapshot']\n self.addCleanup(self.snapshots_client.wait_for_resource_deletion,\n snapshot['id'])\n self.addCleanup(self.snapshots_client.delete_snapshot, snapshot['id'])\n waiters.wait_for_volume_resource_status(self.snapshots_client,\n snapshot['id'], 'available')\n snapshot = self.snapshots_client.show_snapshot(\n snapshot['id'])['snapshot']\n return snapshot\n\n def assert_flavor_equal(self, flavor_id, server_flavor):\n \"\"\"Check whether server_flavor equals to flavor.\n\n :param flavor_id: flavor id\n :param server_flavor: flavor info returned by show_server.\n \"\"\"\n # Nova API > 2.46 no longer includes flavor.id, and schema check\n # will cover whether 'id' should be in flavor\n if server_flavor.get('id'):\n msg = ('server flavor is not same as flavor!')\n self.assertEqual(flavor_id, server_flavor['id'], msg)\n else:\n flavor = self.flavors_client.show_flavor(flavor_id)['flavor']\n self.assertEqual(flavor['name'], server_flavor['original_name'],\n \"original_name in server flavor is not same as \"\n \"flavor name!\")\n for key in ['ram', 'vcpus', 'disk']:\n msg = ('attribute %s in server flavor is not same as '\n 'flavor!' % key)\n self.assertEqual(flavor[key], server_flavor[key], msg)\n\n\nclass BaseV2ComputeAdminTest(BaseV2ComputeTest):\n \"\"\"Base test case class for Compute Admin API tests.\"\"\"\n\n credentials = ['primary', 'admin']\n\n @classmethod\n def setup_clients(cls):\n super(BaseV2ComputeAdminTest, cls).setup_clients()\n cls.availability_zone_admin_client = (\n cls.os_admin.availability_zone_client)\n cls.admin_flavors_client = cls.os_admin.flavors_client\n cls.admin_servers_client = cls.os_admin.servers_client\n cls.image_client = cls.os_admin.image_client_v2\n cls.admin_assisted_volume_snapshots_client = \\\n cls.os_admin.assisted_volume_snapshots_client\n\n def create_flavor(self, ram, vcpus, disk, name=None,\n is_public='True', **kwargs):\n if name is None:\n name = data_utils.rand_name(self.__class__.__name__ + \"-flavor\")\n id = kwargs.pop('id', data_utils.rand_int_id(start=1000))\n client = self.admin_flavors_client\n flavor = client.create_flavor(\n ram=ram, vcpus=vcpus, disk=disk, name=name,\n id=id, is_public=is_public, **kwargs)['flavor']\n self.addCleanup(client.wait_for_resource_deletion, flavor['id'])\n self.addCleanup(client.delete_flavor, flavor['id'])\n return flavor\n\n @classmethod\n def get_host_for_server(cls, server_id):\n server_details = cls.admin_servers_client.show_server(server_id)\n return server_details['server']['OS-EXT-SRV-ATTR:host']\n\n def get_host_other_than(self, server_id):\n source_host = self.get_host_for_server(server_id)\n\n svcs = self.os_admin.services_client.list_services(\n binary='nova-compute')['services']\n hosts = []\n for svc in svcs:\n if svc['state'] == 'up' and svc['status'] == 'enabled':\n if CONF.compute.compute_volume_common_az:\n if svc['zone'] == CONF.compute.compute_volume_common_az:\n hosts.append(svc['host'])\n else:\n hosts.append(svc['host'])\n\n for target_host in hosts:\n if source_host != target_host:\n return target_host\n"}}},{"rowIdx":62,"cells":{"function_name":{"kind":"string","value":"attach_volume"},"docstring":{"kind":"string","value":"Attaches volume to server and waits for 'in-use' volume status.\n\nThe volume will be detached when the test tears down.\n\n:param server: The server to which the volume will be attached.\n:param volume: The volume to attach.\n:param device: Optional mountpoint for the attached volume. Note that\n this is not guaranteed for all hypervisors and is not recommended.\n:param tag: Optional device role tag to apply to the volume."},"masked_code":{"kind":"string","value":"# Copyright 2012 OpenStack Foundation\n# All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport time\n\nfrom oslo_log import log as logging\n\nfrom tempest.common import compute\nfrom tempest.common import waiters\nfrom tempest import config\nfrom tempest import exceptions\nfrom tempest.lib.common import api_microversion_fixture\nfrom tempest.lib.common import api_version_request\nfrom tempest.lib.common import api_version_utils\nfrom tempest.lib.common.utils import data_utils\nfrom tempest.lib.common.utils import test_utils\nfrom tempest.lib import exceptions as lib_exc\nimport tempest.test\n\nCONF = config.CONF\n\nLOG = logging.getLogger(__name__)\n\n\nclass BaseV2ComputeTest(api_version_utils.BaseMicroversionTest,\n tempest.test.BaseTestCase):\n \"\"\"Base test case class for all Compute API tests.\"\"\"\n\n force_tenant_isolation = False\n # Set this to True in subclasses to create a default network. See\n # https://bugs.launchpad.net/tempest/+bug/1844568\n create_default_network = False\n\n # TODO(andreaf) We should care also for the alt_manager here\n # but only once client lazy load in the manager is done\n credentials = ['primary']\n\n @classmethod\n def skip_checks(cls):\n super(BaseV2ComputeTest, cls).skip_checks()\n if not CONF.service_available.nova:\n raise cls.skipException(\"Nova is not available\")\n api_version_utils.check_skip_with_microversion(\n cls.min_microversion, cls.max_microversion,\n CONF.compute.min_microversion, CONF.compute.max_microversion)\n api_version_utils.check_skip_with_microversion(\n cls.volume_min_microversion, cls.volume_max_microversion,\n CONF.volume.min_microversion, CONF.volume.max_microversion)\n api_version_utils.check_skip_with_microversion(\n cls.placement_min_microversion, cls.placement_max_microversion,\n CONF.placement.min_microversion, CONF.placement.max_microversion)\n\n @classmethod\n def setup_credentials(cls):\n # Setting network=True, subnet=True creates a default network\n cls.set_network_resources(\n network=cls.create_default_network,\n subnet=cls.create_default_network)\n super(BaseV2ComputeTest, cls).setup_credentials()\n\n @classmethod\n def setup_clients(cls):\n super(BaseV2ComputeTest, cls).setup_clients()\n cls.servers_client = cls.os_primary.servers_client\n cls.server_groups_client = cls.os_primary.server_groups_client\n cls.flavors_client = cls.os_primary.flavors_client\n cls.compute_images_client = cls.os_primary.compute_images_client\n cls.extensions_client = cls.os_primary.extensions_client\n cls.floating_ip_pools_client = cls.os_primary.floating_ip_pools_client\n cls.floating_ips_client = cls.os_primary.compute_floating_ips_client\n cls.keypairs_client = cls.os_primary.keypairs_client\n cls.security_group_rules_client = (\n cls.os_primary.compute_security_group_rules_client)\n cls.security_groups_client =\\\n cls.os_primary.compute_security_groups_client\n cls.quotas_client = cls.os_primary.quotas_client\n cls.compute_networks_client = cls.os_primary.compute_networks_client\n cls.limits_client = cls.os_primary.limits_client\n cls.volumes_extensions_client =\\\n cls.os_primary.volumes_extensions_client\n cls.snapshots_extensions_client =\\\n cls.os_primary.snapshots_extensions_client\n cls.interfaces_client = cls.os_primary.interfaces_client\n cls.fixed_ips_client = cls.os_primary.fixed_ips_client\n cls.availability_zone_client = cls.os_primary.availability_zone_client\n cls.agents_client = cls.os_primary.agents_client\n cls.aggregates_client = cls.os_primary.aggregates_client\n cls.services_client = cls.os_primary.services_client\n cls.instance_usages_audit_log_client = (\n cls.os_primary.instance_usages_audit_log_client)\n cls.hypervisor_client = cls.os_primary.hypervisor_client\n cls.certificates_client = cls.os_primary.certificates_client\n cls.migrations_client = cls.os_primary.migrations_client\n cls.security_group_default_rules_client = (\n cls.os_primary.security_group_default_rules_client)\n cls.versions_client = cls.os_primary.compute_versions_client\n if CONF.service_available.cinder:\n cls.volumes_client = cls.os_primary.volumes_client_latest\n cls.attachments_client = cls.os_primary.attachments_client_latest\n cls.snapshots_client = cls.os_primary.snapshots_client_latest\n if CONF.service_available.glance:\n if CONF.image_feature_enabled.api_v1:\n cls.images_client = cls.os_primary.image_client\n elif CONF.image_feature_enabled.api_v2:\n cls.images_client = cls.os_primary.image_client_v2\n else:\n raise lib_exc.InvalidConfiguration(\n 'Either api_v1 or api_v2 must be True in '\n '[image-feature-enabled].')\n cls._check_depends_on_nova_network()\n\n @classmethod\n def _check_depends_on_nova_network(cls):\n # Since nova-network APIs were removed from Nova in the Rocky release,\n # determine, based on the max version from the version document, if\n # the compute API is >Queens and if so, skip tests that rely on\n # nova-network.\n if not getattr(cls, 'depends_on_nova_network', False):\n return\n versions = cls.versions_client.list_versions()['versions']\n # Find the v2.1 version which will tell us our max version for the\n # compute API we're testing against.\n for version in versions:\n if version['id'] == 'v2.1':\n max_version = api_version_request.APIVersionRequest(\n version['version'])\n break\n else:\n LOG.warning(\n 'Unable to determine max v2.1 compute API version: %s',\n versions)\n return\n\n # The max compute API version in Queens is 2.60 so we cap\n # at that version.\n queens = api_version_request.APIVersionRequest('2.60')\n if max_version > queens:\n raise cls.skipException('nova-network is gone')\n\n @classmethod\n def resource_setup(cls):\n super(BaseV2ComputeTest, cls).resource_setup()\n cls.request_microversion = (\n api_version_utils.select_request_microversion(\n cls.min_microversion,\n CONF.compute.min_microversion))\n cls.volume_request_microversion = (\n api_version_utils.select_request_microversion(\n cls.volume_min_microversion,\n CONF.volume.min_microversion))\n cls.placement_request_microversion = (\n api_version_utils.select_request_microversion(\n cls.placement_min_microversion,\n CONF.placement.min_microversion))\n cls.build_interval = CONF.compute.build_interval\n cls.build_timeout = CONF.compute.build_timeout\n cls.image_ref = CONF.compute.image_ref\n cls.image_ref_alt = CONF.compute.image_ref_alt\n cls.flavor_ref = CONF.compute.flavor_ref\n cls.flavor_ref_alt = CONF.compute.flavor_ref_alt\n cls.ssh_user = CONF.validation.image_ssh_user\n cls.ssh_alt_user = CONF.validation.image_alt_ssh_user\n cls.image_ssh_user = CONF.validation.image_ssh_user\n cls.image_alt_ssh_user = CONF.validation.image_alt_ssh_user\n cls.image_ssh_password = CONF.validation.image_ssh_password\n cls.image_alt_ssh_password = CONF.validation.image_alt_ssh_password\n\n @classmethod\n def is_requested_microversion_compatible(cls, max_version):\n \"\"\"Check the compatibility of selected request microversion\n\n This method will check if selected request microversion\n (cls.request_microversion) for test is compatible with respect\n to 'max_version'. Compatible means if selected request microversion\n is in the range(<=) of 'max_version'.\n\n :param max_version: maximum microversion to compare for compatibility.\n Example: '2.30'\n :returns: True if selected request microversion is compatible with\n 'max_version'. False in other case.\n \"\"\"\n try:\n req_version_obj = api_version_request.APIVersionRequest(\n cls.request_microversion)\n # NOTE(gmann): This is case where this method is used before calling\n # resource_setup(), where cls.request_microversion is set. There may\n # not be any such case but still we can handle this case.\n except AttributeError:\n request_microversion = (\n api_version_utils.select_request_microversion(\n cls.min_microversion,\n CONF.compute.min_microversion))\n req_version_obj = api_version_request.APIVersionRequest(\n request_microversion)\n max_version_obj = api_version_request.APIVersionRequest(max_version)\n return req_version_obj <= max_version_obj\n\n @classmethod\n def server_check_teardown(cls):\n \"\"\"Checks is the shared server clean enough for subsequent test.\n\n Method will delete the server when it's dirty.\n The setUp method is responsible for creating a new server.\n Exceptions raised in tearDown class are fails the test case,\n This method supposed to use only by tearDown methods, when\n the shared server_id is stored in the server_id of the class.\n \"\"\"\n if getattr(cls, 'server_id', None) is not None:\n try:\n waiters.wait_for_server_status(cls.servers_client,\n cls.server_id, 'ACTIVE')\n except Exception as exc:\n LOG.exception(exc)\n cls.servers_client.delete_server(cls.server_id)\n waiters.wait_for_server_termination(cls.servers_client,\n cls.server_id)\n cls.server_id = None\n raise\n\n @classmethod\n def create_test_server(cls, validatable=False, volume_backed=False,\n validation_resources=None, clients=None, **kwargs):\n \"\"\"Wrapper utility that returns a test server.\n\n This wrapper utility calls the common create test server and\n returns a test server. The purpose of this wrapper is to minimize\n the impact on the code of the tests already using this\n function.\n\n :param validatable: Whether the server will be pingable or sshable.\n :param volume_backed: Whether the instance is volume backed or not.\n :param validation_resources: Dictionary of validation resources as\n returned by `get_class_validation_resources`.\n :param clients: Client manager, defaults to os_primary.\n :param kwargs: Extra arguments are passed down to the\n `compute.create_test_server` call.\n \"\"\"\n if 'name' not in kwargs:\n kwargs['name'] = data_utils.rand_name(cls.__name__ + \"-server\")\n\n request_version = api_version_request.APIVersionRequest(\n cls.request_microversion)\n v2_37_version = api_version_request.APIVersionRequest('2.37')\n\n tenant_network = cls.get_tenant_network()\n # NOTE(snikitin): since microversion v2.37 'networks' field is required\n if (request_version >= v2_37_version and 'networks' not in kwargs and\n not tenant_network):\n kwargs['networks'] = 'none'\n\n if clients is None:\n clients = cls.os_primary\n\n body, servers = compute.create_test_server(\n clients,\n validatable,\n validation_resources=validation_resources,\n tenant_network=tenant_network,\n volume_backed=volume_backed,\n **kwargs)\n\n # For each server schedule wait and delete, so we first delete all\n # and then wait for all\n for server in servers:\n cls.addClassResourceCleanup(waiters.wait_for_server_termination,\n clients.servers_client, server['id'])\n for server in servers:\n cls.addClassResourceCleanup(\n test_utils.call_and_ignore_notfound_exc,\n clients.servers_client.delete_server, server['id'])\n\n return body\n\n @classmethod\n def create_security_group(cls, name=None, description=None):\n if name is None:\n name = data_utils.rand_name(cls.__name__ + \"-securitygroup\")\n if description is None:\n description = data_utils.rand_name('description')\n body = cls.security_groups_client.create_security_group(\n name=name, description=description)['security_group']\n cls.addClassResourceCleanup(\n test_utils.call_and_ignore_notfound_exc,\n cls.security_groups_client.delete_security_group,\n body['id'])\n\n return body\n\n @classmethod\n def create_test_server_group(cls, name=\"\", policy=None):\n if not name:\n name = data_utils.rand_name(cls.__name__ + \"-Server-Group\")\n if policy is None:\n policy = ['affinity']\n body = cls.server_groups_client.create_server_group(\n name=name, policies=policy)['server_group']\n cls.addClassResourceCleanup(\n test_utils.call_and_ignore_notfound_exc,\n cls.server_groups_client.delete_server_group,\n body['id'])\n return body\n\n def wait_for(self, condition):\n \"\"\"Repeatedly calls condition() until a timeout.\"\"\"\n start_time = int(time.time())\n while True:\n try:\n condition()\n except Exception:\n pass\n else:\n return\n if int(time.time()) - start_time >= self.build_timeout:\n condition()\n return\n time.sleep(self.build_interval)\n\n @classmethod\n def prepare_instance_network(cls):\n if (CONF.validation.auth_method != 'disabled' and\n CONF.validation.connect_method == 'floating'):\n cls.set_network_resources(network=True, subnet=True, router=True,\n dhcp=True)\n\n @classmethod\n def create_image_from_server(cls, server_id, **kwargs):\n \"\"\"Wrapper utility that returns an image created from the server.\n\n If compute microversion >= 2.36, the returned image response will\n be from the image service API rather than the compute image proxy API.\n \"\"\"\n name = kwargs.pop('name',\n data_utils.rand_name(cls.__name__ + \"-image\"))\n wait_until = kwargs.pop('wait_until', None)\n wait_for_server = kwargs.pop('wait_for_server', True)\n\n image = cls.compute_images_client.create_image(server_id, name=name,\n **kwargs)\n if api_version_utils.compare_version_header_to_response(\n \"OpenStack-API-Version\", \"compute 2.45\", image.response, \"lt\"):\n image_id = image['image_id']\n else:\n image_id = data_utils.parse_image_id(image.response['location'])\n\n # The compute image proxy APIs were deprecated in 2.35 so\n # use the images client directly if the API microversion being\n # used is >=2.36.\n if not cls.is_requested_microversion_compatible('2.35'):\n client = cls.images_client\n else:\n client = cls.compute_images_client\n cls.addClassResourceCleanup(test_utils.call_and_ignore_notfound_exc,\n client.delete_image, image_id)\n\n if wait_until is not None:\n try:\n wait_until = wait_until.upper()\n if not cls.is_requested_microversion_compatible('2.35'):\n wait_until = wait_until.lower()\n waiters.wait_for_image_status(client, image_id, wait_until)\n except lib_exc.NotFound:\n if wait_until.upper() == 'ACTIVE':\n # If the image is not found after create_image returned\n # that means the snapshot failed in nova-compute and nova\n # deleted the image. There should be a compute fault\n # recorded with the server in that case, so get the server\n # and dump some details.\n server = (\n cls.servers_client.show_server(server_id)['server'])\n if 'fault' in server:\n raise exceptions.SnapshotNotFoundException(\n server['fault'], image_id=image_id)\n else:\n raise exceptions.SnapshotNotFoundException(\n image_id=image_id)\n else:\n raise\n image = client.show_image(image_id)\n # Compute image client returns response wrapped in 'image' element\n # which is not the case with Glance image client.\n if 'image' in image:\n image = image['image']\n\n if wait_until.upper() == 'ACTIVE':\n if wait_for_server:\n waiters.wait_for_server_status(cls.servers_client,\n server_id, 'ACTIVE')\n return image\n\n @classmethod\n def recreate_server(cls, server_id, validatable=False, **kwargs):\n \"\"\"Destroy an existing class level server and creates a new one\n\n Some test classes use a test server that can be used by multiple\n tests. This is done to optimise runtime and test load.\n If something goes wrong with the test server, it can be rebuilt\n using this helper.\n\n This helper can also be used for the initial provisioning if no\n server_id is specified.\n\n :param server_id: UUID of the server to be rebuilt. If None is\n specified, a new server is provisioned.\n :param validatable: whether to the server needs to be\n validatable. When True, validation resources are acquired via\n the `get_class_validation_resources` helper.\n :param kwargs: extra paramaters are passed through to the\n `create_test_server` call.\n :return: the UUID of the created server.\n \"\"\"\n if server_id:\n cls.delete_server(server_id)\n\n cls.password = data_utils.rand_password()\n server = cls.create_test_server(\n validatable,\n validation_resources=cls.get_class_validation_resources(\n cls.os_primary),\n wait_until='ACTIVE',\n adminPass=cls.password,\n **kwargs)\n return server['id']\n\n @classmethod\n def delete_server(cls, server_id):\n \"\"\"Deletes an existing server and waits for it to be gone.\"\"\"\n try:\n cls.servers_client.delete_server(server_id)\n waiters.wait_for_server_termination(cls.servers_client,\n server_id)\n except Exception:\n LOG.exception('Failed to delete server %s', server_id)\n\n def resize_server(self, server_id, new_flavor_id, **kwargs):\n \"\"\"resize and confirm_resize an server, waits for it to be ACTIVE.\"\"\"\n self.servers_client.resize_server(server_id, new_flavor_id, **kwargs)\n waiters.wait_for_server_status(self.servers_client, server_id,\n 'VERIFY_RESIZE')\n self.servers_client.confirm_resize_server(server_id)\n waiters.wait_for_server_status(\n self.servers_client, server_id, 'ACTIVE')\n server = self.servers_client.show_server(server_id)['server']\n self.assert_flavor_equal(new_flavor_id, server['flavor'])\n\n @classmethod\n def delete_volume(cls, volume_id):\n \"\"\"Deletes the given volume and waits for it to be gone.\"\"\"\n try:\n cls.volumes_client.delete_volume(volume_id)\n # TODO(mriedem): We should move the wait_for_resource_deletion\n # into the delete_volume method as a convenience to the caller.\n cls.volumes_client.wait_for_resource_deletion(volume_id)\n except lib_exc.NotFound:\n LOG.warning(\"Unable to delete volume '%s' since it was not found. \"\n \"Maybe it was already deleted?\", volume_id)\n\n @classmethod\n def get_server_ip(cls, server, validation_resources=None):\n \"\"\"Get the server fixed or floating IP.\n\n Based on the configuration we're in, return a correct ip\n address for validating that a guest is up.\n\n :param server: The server dict as returned by the API\n :param validation_resources: The dict of validation resources\n provisioned for the server.\n \"\"\"\n if CONF.validation.connect_method == 'floating':\n if validation_resources:\n return validation_resources['floating_ip']['ip']\n else:\n msg = ('When validation.connect_method equals floating, '\n 'validation_resources cannot be None')\n raise lib_exc.InvalidParam(invalid_param=msg)\n elif CONF.validation.connect_method == 'fixed':\n addresses = server['addresses'][CONF.validation.network_for_ssh]\n for address in addresses:\n if address['version'] == CONF.validation.ip_version_for_ssh:\n return address['addr']\n raise exceptions.ServerUnreachable(server_id=server['id'])\n else:\n raise lib_exc.InvalidConfiguration()\n\n def setUp(self):\n super(BaseV2ComputeTest, self).setUp()\n self.useFixture(api_microversion_fixture.APIMicroversionFixture(\n compute_microversion=self.request_microversion,\n volume_microversion=self.volume_request_microversion,\n placement_microversion=self.placement_request_microversion))\n\n @classmethod\n def create_volume(cls, image_ref=None, **kwargs):\n \"\"\"Create a volume and wait for it to become 'available'.\n\n :param image_ref: Specify an image id to create a bootable volume.\n :param kwargs: other parameters to create volume.\n :returns: The available volume.\n \"\"\"\n if 'size' not in kwargs:\n kwargs['size'] = CONF.volume.volume_size\n if 'display_name' not in kwargs:\n vol_name = data_utils.rand_name(cls.__name__ + '-volume')\n kwargs['display_name'] = vol_name\n if image_ref is not None:\n kwargs['imageRef'] = image_ref\n if CONF.compute.compute_volume_common_az:\n kwargs.setdefault('availability_zone',\n CONF.compute.compute_volume_common_az)\n volume = cls.volumes_client.create_volume(**kwargs)['volume']\n cls.addClassResourceCleanup(\n cls.volumes_client.wait_for_resource_deletion, volume['id'])\n cls.addClassResourceCleanup(test_utils.call_and_ignore_notfound_exc,\n cls.volumes_client.delete_volume,\n volume['id'])\n waiters.wait_for_volume_resource_status(cls.volumes_client,\n volume['id'], 'available')\n return volume\n\n def _detach_volume(self, server, volume):\n \"\"\"Helper method to detach a volume.\n\n Ignores 404 responses if the volume or server do not exist, or the\n volume is already detached from the server.\n \"\"\"\n try:\n volume = self.volumes_client.show_volume(volume['id'])['volume']\n # Check the status. You can only detach an in-use volume, otherwise\n # the compute API will return a 400 response.\n if volume['status'] == 'in-use':\n self.servers_client.detach_volume(server['id'], volume['id'])\n except lib_exc.NotFound:\n # Ignore 404s on detach in case the server is deleted or the volume\n # is already detached.\n pass\n\n# MASKED: attach_volume function (lines 548-585)\n\n def create_volume_snapshot(self, volume_id, name=None, description=None,\n metadata=None, force=False):\n name = name or data_utils.rand_name(\n self.__class__.__name__ + '-snapshot')\n snapshot = self.snapshots_client.create_snapshot(\n volume_id=volume_id,\n force=force,\n display_name=name,\n description=description,\n metadata=metadata)['snapshot']\n self.addCleanup(self.snapshots_client.wait_for_resource_deletion,\n snapshot['id'])\n self.addCleanup(self.snapshots_client.delete_snapshot, snapshot['id'])\n waiters.wait_for_volume_resource_status(self.snapshots_client,\n snapshot['id'], 'available')\n snapshot = self.snapshots_client.show_snapshot(\n snapshot['id'])['snapshot']\n return snapshot\n\n def assert_flavor_equal(self, flavor_id, server_flavor):\n \"\"\"Check whether server_flavor equals to flavor.\n\n :param flavor_id: flavor id\n :param server_flavor: flavor info returned by show_server.\n \"\"\"\n # Nova API > 2.46 no longer includes flavor.id, and schema check\n # will cover whether 'id' should be in flavor\n if server_flavor.get('id'):\n msg = ('server flavor is not same as flavor!')\n self.assertEqual(flavor_id, server_flavor['id'], msg)\n else:\n flavor = self.flavors_client.show_flavor(flavor_id)['flavor']\n self.assertEqual(flavor['name'], server_flavor['original_name'],\n \"original_name in server flavor is not same as \"\n \"flavor name!\")\n for key in ['ram', 'vcpus', 'disk']:\n msg = ('attribute %s in server flavor is not same as '\n 'flavor!' % key)\n self.assertEqual(flavor[key], server_flavor[key], msg)\n\n\nclass BaseV2ComputeAdminTest(BaseV2ComputeTest):\n \"\"\"Base test case class for Compute Admin API tests.\"\"\"\n\n credentials = ['primary', 'admin']\n\n @classmethod\n def setup_clients(cls):\n super(BaseV2ComputeAdminTest, cls).setup_clients()\n cls.availability_zone_admin_client = (\n cls.os_admin.availability_zone_client)\n cls.admin_flavors_client = cls.os_admin.flavors_client\n cls.admin_servers_client = cls.os_admin.servers_client\n cls.image_client = cls.os_admin.image_client_v2\n cls.admin_assisted_volume_snapshots_client = \\\n cls.os_admin.assisted_volume_snapshots_client\n\n def create_flavor(self, ram, vcpus, disk, name=None,\n is_public='True', **kwargs):\n if name is None:\n name = data_utils.rand_name(self.__class__.__name__ + \"-flavor\")\n id = kwargs.pop('id', data_utils.rand_int_id(start=1000))\n client = self.admin_flavors_client\n flavor = client.create_flavor(\n ram=ram, vcpus=vcpus, disk=disk, name=name,\n id=id, is_public=is_public, **kwargs)['flavor']\n self.addCleanup(client.wait_for_resource_deletion, flavor['id'])\n self.addCleanup(client.delete_flavor, flavor['id'])\n return flavor\n\n @classmethod\n def get_host_for_server(cls, server_id):\n server_details = cls.admin_servers_client.show_server(server_id)\n return server_details['server']['OS-EXT-SRV-ATTR:host']\n\n def get_host_other_than(self, server_id):\n source_host = self.get_host_for_server(server_id)\n\n svcs = self.os_admin.services_client.list_services(\n binary='nova-compute')['services']\n hosts = []\n for svc in svcs:\n if svc['state'] == 'up' and svc['status'] == 'enabled':\n if CONF.compute.compute_volume_common_az:\n if svc['zone'] == CONF.compute.compute_volume_common_az:\n hosts.append(svc['host'])\n else:\n hosts.append(svc['host'])\n\n for target_host in hosts:\n if source_host != target_host:\n return target_host"},"implementation":{"kind":"string","value":" def attach_volume(self, server, volume, device=None, tag=None):\n \"\"\"Attaches volume to server and waits for 'in-use' volume status.\n\n The volume will be detached when the test tears down.\n\n :param server: The server to which the volume will be attached.\n :param volume: The volume to attach.\n :param device: Optional mountpoint for the attached volume. Note that\n this is not guaranteed for all hypervisors and is not recommended.\n :param tag: Optional device role tag to apply to the volume.\n \"\"\"\n attach_kwargs = dict(volumeId=volume['id'])\n if device:\n attach_kwargs['device'] = device\n if tag:\n attach_kwargs['tag'] = tag\n\n attachment = self.servers_client.attach_volume(\n server['id'], **attach_kwargs)['volumeAttachment']\n # On teardown detach the volume and for multiattach volumes wait for\n # the attachment to be removed. For non-multiattach volumes wait for\n # the state of the volume to change to available. This is so we don't\n # error out when trying to delete the volume during teardown.\n if volume['multiattach']:\n att = waiters.wait_for_volume_attachment_create(\n self.volumes_client, volume['id'], server['id'])\n self.addCleanup(waiters.wait_for_volume_attachment_remove,\n self.volumes_client, volume['id'],\n att['attachment_id'])\n else:\n self.addCleanup(waiters.wait_for_volume_resource_status,\n self.volumes_client, volume['id'], 'available')\n waiters.wait_for_volume_resource_status(self.volumes_client,\n volume['id'], 'in-use')\n # Ignore 404s on detach in case the server is deleted or the volume\n # is already detached.\n self.addCleanup(self._detach_volume, server, volume)\n return attachment"},"start_line":{"kind":"number","value":548,"string":"548"},"end_line":{"kind":"number","value":585,"string":"585"},"file_content":{"kind":"string","value":"# Copyright 2012 OpenStack Foundation\n# All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport time\n\nfrom oslo_log import log as logging\n\nfrom tempest.common import compute\nfrom tempest.common import waiters\nfrom tempest import config\nfrom tempest import exceptions\nfrom tempest.lib.common import api_microversion_fixture\nfrom tempest.lib.common import api_version_request\nfrom tempest.lib.common import api_version_utils\nfrom tempest.lib.common.utils import data_utils\nfrom tempest.lib.common.utils import test_utils\nfrom tempest.lib import exceptions as lib_exc\nimport tempest.test\n\nCONF = config.CONF\n\nLOG = logging.getLogger(__name__)\n\n\nclass BaseV2ComputeTest(api_version_utils.BaseMicroversionTest,\n tempest.test.BaseTestCase):\n \"\"\"Base test case class for all Compute API tests.\"\"\"\n\n force_tenant_isolation = False\n # Set this to True in subclasses to create a default network. See\n # https://bugs.launchpad.net/tempest/+bug/1844568\n create_default_network = False\n\n # TODO(andreaf) We should care also for the alt_manager here\n # but only once client lazy load in the manager is done\n credentials = ['primary']\n\n @classmethod\n def skip_checks(cls):\n super(BaseV2ComputeTest, cls).skip_checks()\n if not CONF.service_available.nova:\n raise cls.skipException(\"Nova is not available\")\n api_version_utils.check_skip_with_microversion(\n cls.min_microversion, cls.max_microversion,\n CONF.compute.min_microversion, CONF.compute.max_microversion)\n api_version_utils.check_skip_with_microversion(\n cls.volume_min_microversion, cls.volume_max_microversion,\n CONF.volume.min_microversion, CONF.volume.max_microversion)\n api_version_utils.check_skip_with_microversion(\n cls.placement_min_microversion, cls.placement_max_microversion,\n CONF.placement.min_microversion, CONF.placement.max_microversion)\n\n @classmethod\n def setup_credentials(cls):\n # Setting network=True, subnet=True creates a default network\n cls.set_network_resources(\n network=cls.create_default_network,\n subnet=cls.create_default_network)\n super(BaseV2ComputeTest, cls).setup_credentials()\n\n @classmethod\n def setup_clients(cls):\n super(BaseV2ComputeTest, cls).setup_clients()\n cls.servers_client = cls.os_primary.servers_client\n cls.server_groups_client = cls.os_primary.server_groups_client\n cls.flavors_client = cls.os_primary.flavors_client\n cls.compute_images_client = cls.os_primary.compute_images_client\n cls.extensions_client = cls.os_primary.extensions_client\n cls.floating_ip_pools_client = cls.os_primary.floating_ip_pools_client\n cls.floating_ips_client = cls.os_primary.compute_floating_ips_client\n cls.keypairs_client = cls.os_primary.keypairs_client\n cls.security_group_rules_client = (\n cls.os_primary.compute_security_group_rules_client)\n cls.security_groups_client =\\\n cls.os_primary.compute_security_groups_client\n cls.quotas_client = cls.os_primary.quotas_client\n cls.compute_networks_client = cls.os_primary.compute_networks_client\n cls.limits_client = cls.os_primary.limits_client\n cls.volumes_extensions_client =\\\n cls.os_primary.volumes_extensions_client\n cls.snapshots_extensions_client =\\\n cls.os_primary.snapshots_extensions_client\n cls.interfaces_client = cls.os_primary.interfaces_client\n cls.fixed_ips_client = cls.os_primary.fixed_ips_client\n cls.availability_zone_client = cls.os_primary.availability_zone_client\n cls.agents_client = cls.os_primary.agents_client\n cls.aggregates_client = cls.os_primary.aggregates_client\n cls.services_client = cls.os_primary.services_client\n cls.instance_usages_audit_log_client = (\n cls.os_primary.instance_usages_audit_log_client)\n cls.hypervisor_client = cls.os_primary.hypervisor_client\n cls.certificates_client = cls.os_primary.certificates_client\n cls.migrations_client = cls.os_primary.migrations_client\n cls.security_group_default_rules_client = (\n cls.os_primary.security_group_default_rules_client)\n cls.versions_client = cls.os_primary.compute_versions_client\n if CONF.service_available.cinder:\n cls.volumes_client = cls.os_primary.volumes_client_latest\n cls.attachments_client = cls.os_primary.attachments_client_latest\n cls.snapshots_client = cls.os_primary.snapshots_client_latest\n if CONF.service_available.glance:\n if CONF.image_feature_enabled.api_v1:\n cls.images_client = cls.os_primary.image_client\n elif CONF.image_feature_enabled.api_v2:\n cls.images_client = cls.os_primary.image_client_v2\n else:\n raise lib_exc.InvalidConfiguration(\n 'Either api_v1 or api_v2 must be True in '\n '[image-feature-enabled].')\n cls._check_depends_on_nova_network()\n\n @classmethod\n def _check_depends_on_nova_network(cls):\n # Since nova-network APIs were removed from Nova in the Rocky release,\n # determine, based on the max version from the version document, if\n # the compute API is >Queens and if so, skip tests that rely on\n # nova-network.\n if not getattr(cls, 'depends_on_nova_network', False):\n return\n versions = cls.versions_client.list_versions()['versions']\n # Find the v2.1 version which will tell us our max version for the\n # compute API we're testing against.\n for version in versions:\n if version['id'] == 'v2.1':\n max_version = api_version_request.APIVersionRequest(\n version['version'])\n break\n else:\n LOG.warning(\n 'Unable to determine max v2.1 compute API version: %s',\n versions)\n return\n\n # The max compute API version in Queens is 2.60 so we cap\n # at that version.\n queens = api_version_request.APIVersionRequest('2.60')\n if max_version > queens:\n raise cls.skipException('nova-network is gone')\n\n @classmethod\n def resource_setup(cls):\n super(BaseV2ComputeTest, cls).resource_setup()\n cls.request_microversion = (\n api_version_utils.select_request_microversion(\n cls.min_microversion,\n CONF.compute.min_microversion))\n cls.volume_request_microversion = (\n api_version_utils.select_request_microversion(\n cls.volume_min_microversion,\n CONF.volume.min_microversion))\n cls.placement_request_microversion = (\n api_version_utils.select_request_microversion(\n cls.placement_min_microversion,\n CONF.placement.min_microversion))\n cls.build_interval = CONF.compute.build_interval\n cls.build_timeout = CONF.compute.build_timeout\n cls.image_ref = CONF.compute.image_ref\n cls.image_ref_alt = CONF.compute.image_ref_alt\n cls.flavor_ref = CONF.compute.flavor_ref\n cls.flavor_ref_alt = CONF.compute.flavor_ref_alt\n cls.ssh_user = CONF.validation.image_ssh_user\n cls.ssh_alt_user = CONF.validation.image_alt_ssh_user\n cls.image_ssh_user = CONF.validation.image_ssh_user\n cls.image_alt_ssh_user = CONF.validation.image_alt_ssh_user\n cls.image_ssh_password = CONF.validation.image_ssh_password\n cls.image_alt_ssh_password = CONF.validation.image_alt_ssh_password\n\n @classmethod\n def is_requested_microversion_compatible(cls, max_version):\n \"\"\"Check the compatibility of selected request microversion\n\n This method will check if selected request microversion\n (cls.request_microversion) for test is compatible with respect\n to 'max_version'. Compatible means if selected request microversion\n is in the range(<=) of 'max_version'.\n\n :param max_version: maximum microversion to compare for compatibility.\n Example: '2.30'\n :returns: True if selected request microversion is compatible with\n 'max_version'. False in other case.\n \"\"\"\n try:\n req_version_obj = api_version_request.APIVersionRequest(\n cls.request_microversion)\n # NOTE(gmann): This is case where this method is used before calling\n # resource_setup(), where cls.request_microversion is set. There may\n # not be any such case but still we can handle this case.\n except AttributeError:\n request_microversion = (\n api_version_utils.select_request_microversion(\n cls.min_microversion,\n CONF.compute.min_microversion))\n req_version_obj = api_version_request.APIVersionRequest(\n request_microversion)\n max_version_obj = api_version_request.APIVersionRequest(max_version)\n return req_version_obj <= max_version_obj\n\n @classmethod\n def server_check_teardown(cls):\n \"\"\"Checks is the shared server clean enough for subsequent test.\n\n Method will delete the server when it's dirty.\n The setUp method is responsible for creating a new server.\n Exceptions raised in tearDown class are fails the test case,\n This method supposed to use only by tearDown methods, when\n the shared server_id is stored in the server_id of the class.\n \"\"\"\n if getattr(cls, 'server_id', None) is not None:\n try:\n waiters.wait_for_server_status(cls.servers_client,\n cls.server_id, 'ACTIVE')\n except Exception as exc:\n LOG.exception(exc)\n cls.servers_client.delete_server(cls.server_id)\n waiters.wait_for_server_termination(cls.servers_client,\n cls.server_id)\n cls.server_id = None\n raise\n\n @classmethod\n def create_test_server(cls, validatable=False, volume_backed=False,\n validation_resources=None, clients=None, **kwargs):\n \"\"\"Wrapper utility that returns a test server.\n\n This wrapper utility calls the common create test server and\n returns a test server. The purpose of this wrapper is to minimize\n the impact on the code of the tests already using this\n function.\n\n :param validatable: Whether the server will be pingable or sshable.\n :param volume_backed: Whether the instance is volume backed or not.\n :param validation_resources: Dictionary of validation resources as\n returned by `get_class_validation_resources`.\n :param clients: Client manager, defaults to os_primary.\n :param kwargs: Extra arguments are passed down to the\n `compute.create_test_server` call.\n \"\"\"\n if 'name' not in kwargs:\n kwargs['name'] = data_utils.rand_name(cls.__name__ + \"-server\")\n\n request_version = api_version_request.APIVersionRequest(\n cls.request_microversion)\n v2_37_version = api_version_request.APIVersionRequest('2.37')\n\n tenant_network = cls.get_tenant_network()\n # NOTE(snikitin): since microversion v2.37 'networks' field is required\n if (request_version >= v2_37_version and 'networks' not in kwargs and\n not tenant_network):\n kwargs['networks'] = 'none'\n\n if clients is None:\n clients = cls.os_primary\n\n body, servers = compute.create_test_server(\n clients,\n validatable,\n validation_resources=validation_resources,\n tenant_network=tenant_network,\n volume_backed=volume_backed,\n **kwargs)\n\n # For each server schedule wait and delete, so we first delete all\n # and then wait for all\n for server in servers:\n cls.addClassResourceCleanup(waiters.wait_for_server_termination,\n clients.servers_client, server['id'])\n for server in servers:\n cls.addClassResourceCleanup(\n test_utils.call_and_ignore_notfound_exc,\n clients.servers_client.delete_server, server['id'])\n\n return body\n\n @classmethod\n def create_security_group(cls, name=None, description=None):\n if name is None:\n name = data_utils.rand_name(cls.__name__ + \"-securitygroup\")\n if description is None:\n description = data_utils.rand_name('description')\n body = cls.security_groups_client.create_security_group(\n name=name, description=description)['security_group']\n cls.addClassResourceCleanup(\n test_utils.call_and_ignore_notfound_exc,\n cls.security_groups_client.delete_security_group,\n body['id'])\n\n return body\n\n @classmethod\n def create_test_server_group(cls, name=\"\", policy=None):\n if not name:\n name = data_utils.rand_name(cls.__name__ + \"-Server-Group\")\n if policy is None:\n policy = ['affinity']\n body = cls.server_groups_client.create_server_group(\n name=name, policies=policy)['server_group']\n cls.addClassResourceCleanup(\n test_utils.call_and_ignore_notfound_exc,\n cls.server_groups_client.delete_server_group,\n body['id'])\n return body\n\n def wait_for(self, condition):\n \"\"\"Repeatedly calls condition() until a timeout.\"\"\"\n start_time = int(time.time())\n while True:\n try:\n condition()\n except Exception:\n pass\n else:\n return\n if int(time.time()) - start_time >= self.build_timeout:\n condition()\n return\n time.sleep(self.build_interval)\n\n @classmethod\n def prepare_instance_network(cls):\n if (CONF.validation.auth_method != 'disabled' and\n CONF.validation.connect_method == 'floating'):\n cls.set_network_resources(network=True, subnet=True, router=True,\n dhcp=True)\n\n @classmethod\n def create_image_from_server(cls, server_id, **kwargs):\n \"\"\"Wrapper utility that returns an image created from the server.\n\n If compute microversion >= 2.36, the returned image response will\n be from the image service API rather than the compute image proxy API.\n \"\"\"\n name = kwargs.pop('name',\n data_utils.rand_name(cls.__name__ + \"-image\"))\n wait_until = kwargs.pop('wait_until', None)\n wait_for_server = kwargs.pop('wait_for_server', True)\n\n image = cls.compute_images_client.create_image(server_id, name=name,\n **kwargs)\n if api_version_utils.compare_version_header_to_response(\n \"OpenStack-API-Version\", \"compute 2.45\", image.response, \"lt\"):\n image_id = image['image_id']\n else:\n image_id = data_utils.parse_image_id(image.response['location'])\n\n # The compute image proxy APIs were deprecated in 2.35 so\n # use the images client directly if the API microversion being\n # used is >=2.36.\n if not cls.is_requested_microversion_compatible('2.35'):\n client = cls.images_client\n else:\n client = cls.compute_images_client\n cls.addClassResourceCleanup(test_utils.call_and_ignore_notfound_exc,\n client.delete_image, image_id)\n\n if wait_until is not None:\n try:\n wait_until = wait_until.upper()\n if not cls.is_requested_microversion_compatible('2.35'):\n wait_until = wait_until.lower()\n waiters.wait_for_image_status(client, image_id, wait_until)\n except lib_exc.NotFound:\n if wait_until.upper() == 'ACTIVE':\n # If the image is not found after create_image returned\n # that means the snapshot failed in nova-compute and nova\n # deleted the image. There should be a compute fault\n # recorded with the server in that case, so get the server\n # and dump some details.\n server = (\n cls.servers_client.show_server(server_id)['server'])\n if 'fault' in server:\n raise exceptions.SnapshotNotFoundException(\n server['fault'], image_id=image_id)\n else:\n raise exceptions.SnapshotNotFoundException(\n image_id=image_id)\n else:\n raise\n image = client.show_image(image_id)\n # Compute image client returns response wrapped in 'image' element\n # which is not the case with Glance image client.\n if 'image' in image:\n image = image['image']\n\n if wait_until.upper() == 'ACTIVE':\n if wait_for_server:\n waiters.wait_for_server_status(cls.servers_client,\n server_id, 'ACTIVE')\n return image\n\n @classmethod\n def recreate_server(cls, server_id, validatable=False, **kwargs):\n \"\"\"Destroy an existing class level server and creates a new one\n\n Some test classes use a test server that can be used by multiple\n tests. This is done to optimise runtime and test load.\n If something goes wrong with the test server, it can be rebuilt\n using this helper.\n\n This helper can also be used for the initial provisioning if no\n server_id is specified.\n\n :param server_id: UUID of the server to be rebuilt. If None is\n specified, a new server is provisioned.\n :param validatable: whether to the server needs to be\n validatable. When True, validation resources are acquired via\n the `get_class_validation_resources` helper.\n :param kwargs: extra paramaters are passed through to the\n `create_test_server` call.\n :return: the UUID of the created server.\n \"\"\"\n if server_id:\n cls.delete_server(server_id)\n\n cls.password = data_utils.rand_password()\n server = cls.create_test_server(\n validatable,\n validation_resources=cls.get_class_validation_resources(\n cls.os_primary),\n wait_until='ACTIVE',\n adminPass=cls.password,\n **kwargs)\n return server['id']\n\n @classmethod\n def delete_server(cls, server_id):\n \"\"\"Deletes an existing server and waits for it to be gone.\"\"\"\n try:\n cls.servers_client.delete_server(server_id)\n waiters.wait_for_server_termination(cls.servers_client,\n server_id)\n except Exception:\n LOG.exception('Failed to delete server %s', server_id)\n\n def resize_server(self, server_id, new_flavor_id, **kwargs):\n \"\"\"resize and confirm_resize an server, waits for it to be ACTIVE.\"\"\"\n self.servers_client.resize_server(server_id, new_flavor_id, **kwargs)\n waiters.wait_for_server_status(self.servers_client, server_id,\n 'VERIFY_RESIZE')\n self.servers_client.confirm_resize_server(server_id)\n waiters.wait_for_server_status(\n self.servers_client, server_id, 'ACTIVE')\n server = self.servers_client.show_server(server_id)['server']\n self.assert_flavor_equal(new_flavor_id, server['flavor'])\n\n @classmethod\n def delete_volume(cls, volume_id):\n \"\"\"Deletes the given volume and waits for it to be gone.\"\"\"\n try:\n cls.volumes_client.delete_volume(volume_id)\n # TODO(mriedem): We should move the wait_for_resource_deletion\n # into the delete_volume method as a convenience to the caller.\n cls.volumes_client.wait_for_resource_deletion(volume_id)\n except lib_exc.NotFound:\n LOG.warning(\"Unable to delete volume '%s' since it was not found. \"\n \"Maybe it was already deleted?\", volume_id)\n\n @classmethod\n def get_server_ip(cls, server, validation_resources=None):\n \"\"\"Get the server fixed or floating IP.\n\n Based on the configuration we're in, return a correct ip\n address for validating that a guest is up.\n\n :param server: The server dict as returned by the API\n :param validation_resources: The dict of validation resources\n provisioned for the server.\n \"\"\"\n if CONF.validation.connect_method == 'floating':\n if validation_resources:\n return validation_resources['floating_ip']['ip']\n else:\n msg = ('When validation.connect_method equals floating, '\n 'validation_resources cannot be None')\n raise lib_exc.InvalidParam(invalid_param=msg)\n elif CONF.validation.connect_method == 'fixed':\n addresses = server['addresses'][CONF.validation.network_for_ssh]\n for address in addresses:\n if address['version'] == CONF.validation.ip_version_for_ssh:\n return address['addr']\n raise exceptions.ServerUnreachable(server_id=server['id'])\n else:\n raise lib_exc.InvalidConfiguration()\n\n def setUp(self):\n super(BaseV2ComputeTest, self).setUp()\n self.useFixture(api_microversion_fixture.APIMicroversionFixture(\n compute_microversion=self.request_microversion,\n volume_microversion=self.volume_request_microversion,\n placement_microversion=self.placement_request_microversion))\n\n @classmethod\n def create_volume(cls, image_ref=None, **kwargs):\n \"\"\"Create a volume and wait for it to become 'available'.\n\n :param image_ref: Specify an image id to create a bootable volume.\n :param kwargs: other parameters to create volume.\n :returns: The available volume.\n \"\"\"\n if 'size' not in kwargs:\n kwargs['size'] = CONF.volume.volume_size\n if 'display_name' not in kwargs:\n vol_name = data_utils.rand_name(cls.__name__ + '-volume')\n kwargs['display_name'] = vol_name\n if image_ref is not None:\n kwargs['imageRef'] = image_ref\n if CONF.compute.compute_volume_common_az:\n kwargs.setdefault('availability_zone',\n CONF.compute.compute_volume_common_az)\n volume = cls.volumes_client.create_volume(**kwargs)['volume']\n cls.addClassResourceCleanup(\n cls.volumes_client.wait_for_resource_deletion, volume['id'])\n cls.addClassResourceCleanup(test_utils.call_and_ignore_notfound_exc,\n cls.volumes_client.delete_volume,\n volume['id'])\n waiters.wait_for_volume_resource_status(cls.volumes_client,\n volume['id'], 'available')\n return volume\n\n def _detach_volume(self, server, volume):\n \"\"\"Helper method to detach a volume.\n\n Ignores 404 responses if the volume or server do not exist, or the\n volume is already detached from the server.\n \"\"\"\n try:\n volume = self.volumes_client.show_volume(volume['id'])['volume']\n # Check the status. You can only detach an in-use volume, otherwise\n # the compute API will return a 400 response.\n if volume['status'] == 'in-use':\n self.servers_client.detach_volume(server['id'], volume['id'])\n except lib_exc.NotFound:\n # Ignore 404s on detach in case the server is deleted or the volume\n # is already detached.\n pass\n\n def attach_volume(self, server, volume, device=None, tag=None):\n \"\"\"Attaches volume to server and waits for 'in-use' volume status.\n\n The volume will be detached when the test tears down.\n\n :param server: The server to which the volume will be attached.\n :param volume: The volume to attach.\n :param device: Optional mountpoint for the attached volume. Note that\n this is not guaranteed for all hypervisors and is not recommended.\n :param tag: Optional device role tag to apply to the volume.\n \"\"\"\n attach_kwargs = dict(volumeId=volume['id'])\n if device:\n attach_kwargs['device'] = device\n if tag:\n attach_kwargs['tag'] = tag\n\n attachment = self.servers_client.attach_volume(\n server['id'], **attach_kwargs)['volumeAttachment']\n # On teardown detach the volume and for multiattach volumes wait for\n # the attachment to be removed. For non-multiattach volumes wait for\n # the state of the volume to change to available. This is so we don't\n # error out when trying to delete the volume during teardown.\n if volume['multiattach']:\n att = waiters.wait_for_volume_attachment_create(\n self.volumes_client, volume['id'], server['id'])\n self.addCleanup(waiters.wait_for_volume_attachment_remove,\n self.volumes_client, volume['id'],\n att['attachment_id'])\n else:\n self.addCleanup(waiters.wait_for_volume_resource_status,\n self.volumes_client, volume['id'], 'available')\n waiters.wait_for_volume_resource_status(self.volumes_client,\n volume['id'], 'in-use')\n # Ignore 404s on detach in case the server is deleted or the volume\n # is already detached.\n self.addCleanup(self._detach_volume, server, volume)\n return attachment\n\n def create_volume_snapshot(self, volume_id, name=None, description=None,\n metadata=None, force=False):\n name = name or data_utils.rand_name(\n self.__class__.__name__ + '-snapshot')\n snapshot = self.snapshots_client.create_snapshot(\n volume_id=volume_id,\n force=force,\n display_name=name,\n description=description,\n metadata=metadata)['snapshot']\n self.addCleanup(self.snapshots_client.wait_for_resource_deletion,\n snapshot['id'])\n self.addCleanup(self.snapshots_client.delete_snapshot, snapshot['id'])\n waiters.wait_for_volume_resource_status(self.snapshots_client,\n snapshot['id'], 'available')\n snapshot = self.snapshots_client.show_snapshot(\n snapshot['id'])['snapshot']\n return snapshot\n\n def assert_flavor_equal(self, flavor_id, server_flavor):\n \"\"\"Check whether server_flavor equals to flavor.\n\n :param flavor_id: flavor id\n :param server_flavor: flavor info returned by show_server.\n \"\"\"\n # Nova API > 2.46 no longer includes flavor.id, and schema check\n # will cover whether 'id' should be in flavor\n if server_flavor.get('id'):\n msg = ('server flavor is not same as flavor!')\n self.assertEqual(flavor_id, server_flavor['id'], msg)\n else:\n flavor = self.flavors_client.show_flavor(flavor_id)['flavor']\n self.assertEqual(flavor['name'], server_flavor['original_name'],\n \"original_name in server flavor is not same as \"\n \"flavor name!\")\n for key in ['ram', 'vcpus', 'disk']:\n msg = ('attribute %s in server flavor is not same as '\n 'flavor!' % key)\n self.assertEqual(flavor[key], server_flavor[key], msg)\n\n\nclass BaseV2ComputeAdminTest(BaseV2ComputeTest):\n \"\"\"Base test case class for Compute Admin API tests.\"\"\"\n\n credentials = ['primary', 'admin']\n\n @classmethod\n def setup_clients(cls):\n super(BaseV2ComputeAdminTest, cls).setup_clients()\n cls.availability_zone_admin_client = (\n cls.os_admin.availability_zone_client)\n cls.admin_flavors_client = cls.os_admin.flavors_client\n cls.admin_servers_client = cls.os_admin.servers_client\n cls.image_client = cls.os_admin.image_client_v2\n cls.admin_assisted_volume_snapshots_client = \\\n cls.os_admin.assisted_volume_snapshots_client\n\n def create_flavor(self, ram, vcpus, disk, name=None,\n is_public='True', **kwargs):\n if name is None:\n name = data_utils.rand_name(self.__class__.__name__ + \"-flavor\")\n id = kwargs.pop('id', data_utils.rand_int_id(start=1000))\n client = self.admin_flavors_client\n flavor = client.create_flavor(\n ram=ram, vcpus=vcpus, disk=disk, name=name,\n id=id, is_public=is_public, **kwargs)['flavor']\n self.addCleanup(client.wait_for_resource_deletion, flavor['id'])\n self.addCleanup(client.delete_flavor, flavor['id'])\n return flavor\n\n @classmethod\n def get_host_for_server(cls, server_id):\n server_details = cls.admin_servers_client.show_server(server_id)\n return server_details['server']['OS-EXT-SRV-ATTR:host']\n\n def get_host_other_than(self, server_id):\n source_host = self.get_host_for_server(server_id)\n\n svcs = self.os_admin.services_client.list_services(\n binary='nova-compute')['services']\n hosts = []\n for svc in svcs:\n if svc['state'] == 'up' and svc['status'] == 'enabled':\n if CONF.compute.compute_volume_common_az:\n if svc['zone'] == CONF.compute.compute_volume_common_az:\n hosts.append(svc['host'])\n else:\n hosts.append(svc['host'])\n\n for target_host in hosts:\n if source_host != target_host:\n return target_host\n"}}},{"rowIdx":63,"cells":{"function_name":{"kind":"string","value":"list"},"docstring":{"kind":"string","value":"This method is a generator which yields queue objects.\n\nThis is almost the copy of list method of resource.Resource class.\nThe only difference is the request header now includes `Client-ID`\nand `X-PROJECT-ID` fields which are required by Zaqar v2 API."},"masked_code":{"kind":"string","value":"# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport uuid\n\nfrom openstack import resource\n\n\nclass Queue(resource.Resource):\n # FIXME(anyone): The name string of `location` field of Zaqar API response\n # is lower case. That is inconsistent with the guide from API-WG. This is\n # a workaround for this issue.\n location = resource.Header(\"location\")\n\n resources_key = \"queues\"\n base_path = \"/queues\"\n\n # capabilities\n allow_create = True\n allow_list = True\n allow_fetch = True\n allow_delete = True\n\n # Properties\n #: The default TTL of messages defined for a queue, which will effect for\n #: any messages posted to the queue.\n default_message_ttl = resource.Body(\"_default_message_ttl\")\n #: Description of the queue.\n description = resource.Body(\"description\")\n #: The max post size of messages defined for a queue, which will effect\n #: for any messages posted to the queue.\n max_messages_post_size = resource.Body(\"_max_messages_post_size\")\n #: Name of the queue. The name is the unique identity of a queue. It\n #: must not exceed 64 bytes in length, and it is limited to US-ASCII\n #: letters, digits, underscores, and hyphens.\n name = resource.Body(\"name\", alternate_id=True)\n #: The ID to identify the client accessing Zaqar API. Must be specified\n #: in header for each API request.\n client_id = resource.Header(\"Client-ID\")\n #: The ID to identify the project accessing Zaqar API. Must be specified\n #: in case keystone auth is not enabled in Zaqar service.\n project_id = resource.Header(\"X-PROJECT-ID\")\n\n def create(self, session, prepend_key=True):\n request = self._prepare_request(requires_id=True,\n prepend_key=prepend_key)\n headers = {\n \"Client-ID\": self.client_id or str(uuid.uuid4()),\n \"X-PROJECT-ID\": self.project_id or session.get_project_id()\n }\n request.headers.update(headers)\n response = session.put(request.url,\n json=request.body, headers=request.headers)\n\n self._translate_response(response, has_body=False)\n return self\n\n# MASKED: list function (lines 67-106)\n\n def fetch(self, session, requires_id=True, error_message=None):\n request = self._prepare_request(requires_id=requires_id)\n headers = {\n \"Client-ID\": self.client_id or str(uuid.uuid4()),\n \"X-PROJECT-ID\": self.project_id or session.get_project_id()\n }\n request.headers.update(headers)\n response = session.get(request.url,\n headers=headers)\n self._translate_response(response)\n\n return self\n\n def delete(self, session):\n request = self._prepare_request()\n headers = {\n \"Client-ID\": self.client_id or str(uuid.uuid4()),\n \"X-PROJECT-ID\": self.project_id or session.get_project_id()\n }\n request.headers.update(headers)\n response = session.delete(request.url,\n headers=headers)\n\n self._translate_response(response, has_body=False)\n return self"},"implementation":{"kind":"string","value":" @classmethod\n def list(cls, session, paginated=False, **params):\n \"\"\"This method is a generator which yields queue objects.\n\n This is almost the copy of list method of resource.Resource class.\n The only difference is the request header now includes `Client-ID`\n and `X-PROJECT-ID` fields which are required by Zaqar v2 API.\n \"\"\"\n more_data = True\n query_params = cls._query_mapping._transpose(params)\n uri = cls.base_path % params\n headers = {\n \"Client-ID\": params.get('client_id', None) or str(uuid.uuid4()),\n \"X-PROJECT-ID\": params.get('project_id', None\n ) or session.get_project_id()\n }\n\n while more_data:\n resp = session.get(uri,\n headers=headers, params=query_params)\n resp = resp.json()\n resp = resp[cls.resources_key]\n\n if not resp:\n more_data = False\n\n yielded = 0\n new_marker = None\n for data in resp:\n value = cls.existing(**data)\n new_marker = value.id\n yielded += 1\n yield value\n\n if not paginated:\n return\n if \"limit\" in query_params and yielded < query_params[\"limit\"]:\n return\n query_params[\"limit\"] = yielded\n query_params[\"marker\"] = new_marker"},"start_line":{"kind":"number","value":67,"string":"67"},"end_line":{"kind":"number","value":106,"string":"106"},"file_content":{"kind":"string","value":"# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport uuid\n\nfrom openstack import resource\n\n\nclass Queue(resource.Resource):\n # FIXME(anyone): The name string of `location` field of Zaqar API response\n # is lower case. That is inconsistent with the guide from API-WG. This is\n # a workaround for this issue.\n location = resource.Header(\"location\")\n\n resources_key = \"queues\"\n base_path = \"/queues\"\n\n # capabilities\n allow_create = True\n allow_list = True\n allow_fetch = True\n allow_delete = True\n\n # Properties\n #: The default TTL of messages defined for a queue, which will effect for\n #: any messages posted to the queue.\n default_message_ttl = resource.Body(\"_default_message_ttl\")\n #: Description of the queue.\n description = resource.Body(\"description\")\n #: The max post size of messages defined for a queue, which will effect\n #: for any messages posted to the queue.\n max_messages_post_size = resource.Body(\"_max_messages_post_size\")\n #: Name of the queue. The name is the unique identity of a queue. It\n #: must not exceed 64 bytes in length, and it is limited to US-ASCII\n #: letters, digits, underscores, and hyphens.\n name = resource.Body(\"name\", alternate_id=True)\n #: The ID to identify the client accessing Zaqar API. Must be specified\n #: in header for each API request.\n client_id = resource.Header(\"Client-ID\")\n #: The ID to identify the project accessing Zaqar API. Must be specified\n #: in case keystone auth is not enabled in Zaqar service.\n project_id = resource.Header(\"X-PROJECT-ID\")\n\n def create(self, session, prepend_key=True):\n request = self._prepare_request(requires_id=True,\n prepend_key=prepend_key)\n headers = {\n \"Client-ID\": self.client_id or str(uuid.uuid4()),\n \"X-PROJECT-ID\": self.project_id or session.get_project_id()\n }\n request.headers.update(headers)\n response = session.put(request.url,\n json=request.body, headers=request.headers)\n\n self._translate_response(response, has_body=False)\n return self\n\n @classmethod\n def list(cls, session, paginated=False, **params):\n \"\"\"This method is a generator which yields queue objects.\n\n This is almost the copy of list method of resource.Resource class.\n The only difference is the request header now includes `Client-ID`\n and `X-PROJECT-ID` fields which are required by Zaqar v2 API.\n \"\"\"\n more_data = True\n query_params = cls._query_mapping._transpose(params)\n uri = cls.base_path % params\n headers = {\n \"Client-ID\": params.get('client_id', None) or str(uuid.uuid4()),\n \"X-PROJECT-ID\": params.get('project_id', None\n ) or session.get_project_id()\n }\n\n while more_data:\n resp = session.get(uri,\n headers=headers, params=query_params)\n resp = resp.json()\n resp = resp[cls.resources_key]\n\n if not resp:\n more_data = False\n\n yielded = 0\n new_marker = None\n for data in resp:\n value = cls.existing(**data)\n new_marker = value.id\n yielded += 1\n yield value\n\n if not paginated:\n return\n if \"limit\" in query_params and yielded < query_params[\"limit\"]:\n return\n query_params[\"limit\"] = yielded\n query_params[\"marker\"] = new_marker\n\n def fetch(self, session, requires_id=True, error_message=None):\n request = self._prepare_request(requires_id=requires_id)\n headers = {\n \"Client-ID\": self.client_id or str(uuid.uuid4()),\n \"X-PROJECT-ID\": self.project_id or session.get_project_id()\n }\n request.headers.update(headers)\n response = session.get(request.url,\n headers=headers)\n self._translate_response(response)\n\n return self\n\n def delete(self, session):\n request = self._prepare_request()\n headers = {\n \"Client-ID\": self.client_id or str(uuid.uuid4()),\n \"X-PROJECT-ID\": self.project_id or session.get_project_id()\n }\n request.headers.update(headers)\n response = session.delete(request.url,\n headers=headers)\n\n self._translate_response(response, has_body=False)\n return self\n"}}},{"rowIdx":64,"cells":{"function_name":{"kind":"string","value":"ComputeConvOutputShape"},"docstring":{"kind":"string","value":"Computes output shape for convolution and pooling layers.\n\nIf `in_shape` is a dynamic shape, the output will be Tensors, while if\n`in_shape` is a list of ints then the output will also be a list of ints.\n\nArgs:\n in_shape: A length 4 Tensor or list representing the input shape.\n t_stride: The stride along the time dimension.\n f_stride: The stride along the frequency dimension.\n outc: The expected output channel. If None, will use the input channel.\n padding: 'SAME' or 'VALID'.\n\nReturns:\n The expected output shape."},"masked_code":{"kind":"string","value":"# Lint as: python2, python3\n# Copyright 2018 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\"\"\"Common conv layers.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\n\nfrom lingvo.core import base_layer\nfrom lingvo.core import bn_layers\nfrom lingvo.core import py_utils\nfrom lingvo.core import tshape\n\n\n# MASKED: ComputeConvOutputShape function (lines 30-67)\n\n\ndef ComputeConvOutputPadding(paddings, window, stride,\n padding_algorithm='SAME'):\n \"\"\"Computes paddings for convolution and pooling output.\n\n out_padding[i] == 1 iff any in_padding corresponding to that output is 1.\n\n Args:\n paddings: The paddings tensor. It is expected to be of shape [batch, time].\n window: The size of the windows.\n stride: The time-stride between adjacent windows.\n padding_algorithm: 'SAME' or 'VALID'.\n\n Returns:\n out_padding, The new padding tensor of size [batch, ceil(time / stride)].\n \"\"\"\n if stride == 1:\n return paddings\n\n # Pad so input_length divides stride.\n input_length = py_utils.GetShape(paddings)[1]\n pad_len = (input_length + stride - 1) // stride * stride - input_length\n paddings = tf.pad(paddings, [[0, 0], [0, pad_len]], constant_values=1.0)\n out_padding = tf.nn.pool(\n tf.expand_dims(paddings, -1),\n [window],\n 'MAX',\n padding_algorithm,\n strides=[stride],\n )\n return tf.squeeze(out_padding, -1)\n\n\nclass BaseConv2DLayerWithPadding(base_layer.BaseLayer):\n \"\"\"Base class for 2D convolution layers.\"\"\"\n\n @classmethod\n def Params(cls):\n p = super(BaseConv2DLayerWithPadding, cls).Params()\n p.Define(\n 'filter_shape', (0, 0, 0, 0),\n 'Filter shape. Must be a sequence of length 4. Elements are in'\n ' the order of height (time), width (frequency), in_channel,'\n ' out_channel. For causal convolution, filter_shape[0]'\n ' is the actual number of trained weights in the time dimension'\n ' of the kernel.')\n p.Define(\n 'filter_stride', (1, 1),\n 'Filter stride to use. Must be a pair of ints. The first int'\n ' specifies the stride on the time dimension. The second int'\n ' specifies the stride on the frequency dimension.')\n p.Define(\n 'dilation_rate', (1, 1),\n 'If > 1, dilation rate for atrous convolution. '\n 'Must be a pair of ints. '\n 'The first int specifies the dilation rate on the time dimension. '\n 'The second int specifies the dilation rate on the frequency '\n 'dimension. '\n 'If any value of dilation_rate is > 1, then all values of strides '\n 'must be 1.')\n p.Define(\n 'weight_norm', False,\n 'If true, apply weight normalization to weights as proposed by'\n ' Salimans and Kingma, 2016: https://arxiv.org/abs/1602.07868')\n return p\n\n @base_layer.initializer\n def __init__(self, params):\n super(BaseConv2DLayerWithPadding, self).__init__(params)\n p = self.params\n assert p.name\n assert len(p.filter_shape) == 4\n assert len(p.filter_stride) == 2\n assert all(x > 0 for x in p.filter_shape)\n assert all(x > 0 for x in p.filter_stride)\n assert len(p.dilation_rate) == 2\n assert all(x > 0 for x in p.dilation_rate)\n # Dilation and stride can't be combined.\n if any(x > 1 for x in p.dilation_rate):\n assert all(x == 1 for x in p.filter_stride)\n\n @property\n def output_channels(self):\n \"\"\"The number of output channels for this conv layer.\"\"\"\n raise NotImplementedError()\n\n @property\n def input_channels(self):\n \"\"\"The number of input channels for this conv layer.\"\"\"\n return self.params.filter_shape[2]\n\n def OutShape(self, in_shape):\n \"\"\"Compute the output shape given the input shape.\"\"\"\n p = self.params\n return ComputeConvOutputShape(in_shape, p.filter_stride[0],\n p.filter_stride[1], self.output_channels)\n\n def FProp(self, theta, inputs, paddings):\n \"\"\"Apply convolution to inputs.\n\n Args:\n theta: A `.NestedMap` object containing weights' values of this layer and\n its children layers.\n inputs: The inputs tensor. It is expected to be of shape [batch, time,\n frequency, channel]. The time dimension corresponds to the height\n dimension as in images and the frequency dimension corresponds to the\n width dimension as in images.\n paddings: The paddings tensor, expected to be of shape [batch, time].\n\n Returns:\n outputs, out_paddings pair.\n \"\"\"\n p = self.params\n with tf.name_scope(p.name):\n inputs = py_utils.with_dependencies([\n py_utils.assert_shape_match(tf.shape(paddings), [-1, -1]),\n py_utils.assert_shape_match(\n tf.shape(inputs),\n tf.concat([tf.shape(paddings), [-1, self.input_channels]], 0))\n ], inputs)\n\n def _ApplyPadding(tensor_in, padding_in):\n padding_expanded = tf.expand_dims(tf.expand_dims(padding_in, -1), -1)\n return tensor_in * (1.0 - padding_expanded)\n\n # Zeroing out padded inputs.\n inputs = _ApplyPadding(inputs, paddings)\n\n # Evaluate the conv kernel on 'inputs'.\n out = self._EvaluateConvKernel(theta, inputs)\n\n # NOTE: this may be slightly inaccurate when p.dilation_rate[0] > 1.\n # But there's likely no real problems. Trying to set it gives an error:\n # pooling with SAME padding is not implemented for dilation_rate > 1.\n # NOTE: we use window=p.filter_stride[0] to be compatible with legacy\n # implementation. Consider updating it to be the actual shape.\n conv_padding = ComputeConvOutputPadding(\n paddings, window=p.filter_stride[0], stride=p.filter_stride[0])\n # Assuming padded nodes will be properly zero-ed out if necessary by\n # sub-sequent layers.\n # out = _ApplyPadding(out, conv_padding)\n out = py_utils.HasShape(out, self.OutShape(tf.shape(inputs)))\n return out, conv_padding\n\n def _EvaluateConvKernel(self, theta, conv_input):\n \"\"\"Evaluate the convolution kernel on input 'conv_input'.\"\"\"\n raise NotImplementedError\n\n\nclass Conv2DLayerWithPadding(BaseConv2DLayerWithPadding):\n \"\"\"Conv2D layer.\"\"\"\n\n @base_layer.initializer\n def __init__(self, params):\n super(Conv2DLayerWithPadding, self).__init__(params)\n p = self.params\n assert p.name\n w_pc = py_utils.WeightParams(\n shape=p.filter_shape,\n init=p.params_init,\n dtype=p.dtype,\n collections=[self.__class__.__name__ + '_vars'])\n with tf.variable_scope(p.name):\n self.CreateVariable('w', w_pc)\n if p.weight_norm:\n self.CreateVariable(\n 'g',\n py_utils.WeightParams(\n shape=[p.filter_shape[-1]],\n init=py_utils.WeightInit.Constant(0.0),\n dtype=p.dtype,\n collections=[self.__class__.__name__ + '_vars']))\n\n @property\n def output_channels(self):\n \"\"\"The number of output channels for this conv layer.\"\"\"\n p = self.params\n return p.filter_shape[-1]\n\n def _GetWeight(self, theta):\n p = self.params\n if p.weight_norm:\n # Normalize along the last dim (standard conv).\n filter_w = tf.nn.l2_normalize(theta.w, [0, 1, 2]) * tf.reshape(\n (theta.g + 1.0), [1, 1, 1, p.filter_shape[-1]])\n else:\n filter_w = theta.w\n return filter_w\n\n def _EvaluateConvKernel(self, theta, inputs):\n \"\"\"Apply convolution to inputs.\"\"\"\n p = self.params\n filter_w = self._GetWeight(theta)\n return tf.nn.convolution(\n inputs,\n filter_w,\n strides=p.filter_stride,\n dilation_rate=p.dilation_rate,\n data_format='NHWC',\n padding='SAME')\n\n\nclass CausalConv2DLayerWithPadding(Conv2DLayerWithPadding):\n \"\"\"2D conv layer with causal dependency on the time axis.\"\"\"\n\n @base_layer.initializer\n def __init__(self, params):\n super(CausalConv2DLayerWithPadding, self).__init__(params)\n p = self.params\n assert p.filter_shape[1] == 1, 'Only 1d causal convolution is supported.'\n\n def _EvaluateConvKernel(self, theta, inputs):\n \"\"\"Apply convolution to inputs.\"\"\"\n p = self.params\n assert p.filter_shape[1] == 1, 'Only 1D causal convolutions supported.'\n # Use VALID padding and shift the inputs to the right to ensure that the\n # first output only depends on the first input and so on. The output is\n # the same size as the input, as if the convolution used SAME padding.\n padding_algorithm = 'VALID'\n # The effective spatial filter width for dilated convolutions is\n # (kernel_width - 1) * dilation_rate + 1 as according to\n # https://www.tensorflow.org/api_docs/python/tf/nn/convolution.\n causal_pad_size = (p.filter_shape[0] - 1) * p.dilation_rate[0]\n inputs = tf.pad(inputs, [[0, 0], [causal_pad_size, 0], [0, 0], [0, 0]])\n\n filter_w = self._GetWeight(theta)\n return tf.nn.convolution(\n inputs,\n filter_w,\n strides=p.filter_stride,\n dilation_rate=p.dilation_rate,\n data_format='NHWC',\n padding=padding_algorithm)\n\n\nclass DepthwiseConv2DLayer(BaseConv2DLayerWithPadding):\n \"\"\"Depthwise conv 2D layer.\n\n paper: https://arxiv.org/abs/1610.02357\n \"\"\"\n\n @classmethod\n def Params(cls):\n p = super(DepthwiseConv2DLayer, cls).Params()\n # Redefine 'filter_shape' since the semantic of shape elements is different\n # from regular Conv2D.\n p.Delete('filter_shape')\n p.Define(\n 'filter_shape', (0, 0, 0, 0),\n 'Filter shape. Must be a sequence of length 4. Elements are in'\n ' the order of height (time), width (frequency), in_channel,'\n ' channel_multipliers. ')\n return p\n\n @base_layer.initializer\n def __init__(self, params):\n super(DepthwiseConv2DLayer, self).__init__(params)\n p = self.params\n assert p.name\n w_pc = py_utils.WeightParams(\n shape=p.filter_shape,\n init=p.params_init,\n dtype=p.dtype,\n collections=[self.__class__.__name__ + '_vars'])\n\n with tf.variable_scope(p.name):\n self.CreateVariable('w', w_pc)\n if p.weight_norm:\n self.CreateVariable(\n 'g',\n py_utils.WeightParams(\n shape=[p.filter_shape[2], p.filter_shape[3]],\n init=py_utils.WeightInit.Constant(0.0),\n dtype=p.dtype,\n collections=[self.__class__.__name__ + '_vars']))\n\n @property\n def output_channels(self):\n \"\"\"The number of output channels for this conv layer.\"\"\"\n p = self.params\n # Depthwise convolution filter shape is:\n # [..., in_channels, channel_multiplier].\n return p.filter_shape[2] * p.filter_shape[3]\n\n def _GetWeight(self, theta):\n p = self.params\n if p.weight_norm:\n # Normalize along the last two dims.\n filter_w = tf.nn.l2_normalize(theta.w, [0, 1]) * tf.reshape(\n (theta.g + 1.0), [1, 1, p.filter_shape[2], p.filter_shape[3]])\n else:\n filter_w = theta.w\n return filter_w\n\n def _EvaluateConvKernel(self, theta, inputs):\n \"\"\"Apply convolution to inputs.\"\"\"\n p = self.params\n filter_w = self._GetWeight(theta)\n return tf.nn.depthwise_conv2d(\n inputs,\n filter_w,\n strides=[1, p.filter_stride[0], p.filter_stride[1], 1],\n rate=p.dilation_rate,\n data_format='NHWC',\n padding='SAME')\n\n\nclass CausalDepthwiseConv2DLayer(DepthwiseConv2DLayer):\n \"\"\"Depthwise conv layer with causal dependency on the time axis.\"\"\"\n\n @base_layer.initializer\n def __init__(self, params):\n super(CausalDepthwiseConv2DLayer, self).__init__(params)\n p = self.params\n assert p.filter_shape[1] == 1, 'Only 1d causal convolution is supported.'\n\n def _EvaluateConvKernel(self, theta, inputs):\n \"\"\"Apply convolution to inputs.\"\"\"\n p = self.params\n assert p.filter_shape[1] == 1, 'Only 1D causal convolutions supported.'\n # Use VALID padding and shift the inputs to the right to ensure that the\n # first output only depends on the first input and so on. The output is\n # the same size as the input, as if the convolution used SAME padding.\n padding_algorithm = 'VALID'\n # The effective spatial filter width for dilated convolutions is\n # (kernel_width - 1) * dilation_rate + 1 as according to\n # https://www.tensorflow.org/api_docs/python/tf/nn/convolution.\n causal_pad_size = (p.filter_shape[0] - 1) * p.dilation_rate[0]\n inputs = tf.pad(inputs, [[0, 0], [causal_pad_size, 0], [0, 0], [0, 0]])\n filter_w = self._GetWeight(theta)\n return tf.nn.depthwise_conv2d(\n inputs,\n filter_w,\n strides=[1, p.filter_stride[0], p.filter_stride[1], 1],\n rate=p.dilation_rate,\n data_format='NHWC',\n padding=padding_algorithm)\n\n\nclass NormalizedDepthwiseConv2DLayer(DepthwiseConv2DLayer):\n \"\"\"DepthwiseConv2DLayer where weights are normalized over the time dim.\n\n https://arxiv.org/abs/1901.10430\n \"\"\"\n\n @classmethod\n def Params(cls):\n p = super(NormalizedDepthwiseConv2DLayer, cls).Params()\n p.Define('dropconnect_prob', 0.0,\n 'Prob at which DropConnect regularization is performed.')\n p.Define('deterministic_dropout', False, 'Use determnisitc dropout or not.')\n p.Define('temperature', 1.0,\n 'Temperature for the softmax normalization of the weights.')\n p.Define('weight_tiling_factor', 1,\n 'Number of times weights are tiled over the input channels.')\n return p\n\n @base_layer.initializer\n def __init__(self, params):\n super(NormalizedDepthwiseConv2DLayer, self).__init__(params)\n p = self.params\n assert p.filter_shape[1] == 1, 'Only 1d convolution is supported.'\n assert p.temperature > 0.0, 'Absolute zero temperature is not possible.'\n\n @property\n def output_channels(self):\n \"\"\"The number of output channels for this conv layer.\"\"\"\n p = self.params\n # Depthwise convolution filter shape is:\n # [kernel_size, 1, in_channels, channel_multiplier].\n return p.filter_shape[2] * p.filter_shape[3] * p.weight_tiling_factor\n\n @property\n def input_channels(self):\n \"\"\"The number of output channels for this conv layer.\"\"\"\n p = self.params\n return p.filter_shape[2] * p.weight_tiling_factor\n\n def _GetWeight(self, theta):\n p = self.params\n filter_w = theta.w\n\n # First normalize filter_w over the temporal dimension here.\n filter_w = tf.nn.softmax(filter_w / p.temperature, axis=0)\n\n # Add dropconnect on the weights for regularization.\n if p.dropconnect_prob > 0.0 and not p.is_eval:\n if p.deterministic_dropout:\n filter_w = py_utils.DeterministicDropout(\n filter_w, 1.0 - p.dropconnect_prob,\n py_utils.GenerateStepSeedPair(p, theta.global_step))\n else:\n filter_w = tf.nn.dropout(\n filter_w, 1.0 - p.dropconnect_prob, seed=p.random_seed)\n\n # Tie the parameters of every subsequent number of weight_tiling_factor\n # channels.\n filter_w = tf.tile(filter_w, [1, 1, p.weight_tiling_factor, 1])\n return filter_w\n\n @classmethod\n def FPropMeta(cls, p, inputs, paddings):\n py_utils.CheckShapes((inputs, paddings))\n b, t, f, ic = inputs\n assert f == 1\n oc = p.filter_shape[2] * p.filter_shape[3] * p.weight_tiling_factor\n outputs = tshape.Shape([b, t, f, oc])\n flops = b * t * f * p.filter_shape[0] * ic * oc * 5\n return py_utils.NestedMap(flops=flops, out_shapes=(outputs, paddings))\n\n\nclass CausalNormalizedDepthwiseConv2DLayer(NormalizedDepthwiseConv2DLayer):\n \"\"\"Depthwise conv layer with causal dependency on the time axis.\"\"\"\n\n def _EvaluateConvKernel(self, theta, inputs):\n \"\"\"Apply convolution to inputs.\"\"\"\n # Same as CausalDepthwiseConv2DLayer.\n p = self.params\n assert p.filter_shape[1] == 1, 'Only 1D causal convolutions supported.'\n padding_algorithm = 'VALID'\n causal_pad_size = (p.filter_shape[0] - 1) * p.dilation_rate[0]\n inputs = tf.pad(inputs, [[0, 0], [causal_pad_size, 0], [0, 0], [0, 0]])\n filter_w = self._GetWeight(theta)\n return tf.nn.depthwise_conv2d(\n inputs,\n filter_w,\n strides=[1, p.filter_stride[0], p.filter_stride[1], 1],\n rate=p.dilation_rate,\n data_format='NHWC',\n padding=padding_algorithm)\n\n\nclass ConvBatchNormLayer(bn_layers.BatchNormLayer):\n \"\"\"A wrapper around regular BatchNormLayer that pass around the ...\n\n paddings layers.\n \"\"\"\n\n def FProp(self, theta, inputs, paddings):\n paddings_expanded = tf.expand_dims(tf.expand_dims(paddings, -1), -1)\n bned = super(ConvBatchNormLayer, self).FProp(\n theta, inputs, paddings_expanded)\n return bned, paddings\n\n\n# Supported activation functions.\n_ACTIVATIONS = {\n 'RELU': tf.nn.relu,\n 'RELU6': tf.nn.relu6,\n 'SIGMOID': tf.sigmoid,\n 'TANH': tf.tanh,\n 'SWISH': tf.nn.swish,\n 'NONE': tf.identity,\n}\n\n\nclass ActivationLayer(base_layer.BaseLayer):\n \"\"\"Applies activation function to the inputs.\"\"\"\n\n @classmethod\n def Params(cls):\n p = super(ActivationLayer, cls).Params()\n p.Define('activation', 'RELU',\n 'The activation function to apply')\n return p\n\n def FProp(self, theta, inputs, paddings):\n p = self.params\n out = _ACTIVATIONS[p.activation](inputs)\n return out, paddings\n\n\nclass PaddingLayer(base_layer.BaseLayer):\n \"\"\"Zeros out padded positions.\"\"\"\n\n def FProp(self, theta, inputs, paddings):\n paddings_expanded = tf.expand_dims(tf.expand_dims(paddings, -1), -1)\n return inputs * (1.0 - paddings_expanded), paddings"},"implementation":{"kind":"string","value":"def ComputeConvOutputShape(in_shape,\n t_stride,\n f_stride,\n outc=None,\n padding='SAME'):\n \"\"\"Computes output shape for convolution and pooling layers.\n\n If `in_shape` is a dynamic shape, the output will be Tensors, while if\n `in_shape` is a list of ints then the output will also be a list of ints.\n\n Args:\n in_shape: A length 4 Tensor or list representing the input shape.\n t_stride: The stride along the time dimension.\n f_stride: The stride along the frequency dimension.\n outc: The expected output channel. If None, will use the input channel.\n padding: 'SAME' or 'VALID'.\n\n Returns:\n The expected output shape.\n \"\"\"\n # In the order of batch, time, frequency, channel\n n = in_shape[0]\n t = in_shape[1]\n f = in_shape[2]\n c = in_shape[3]\n # Last two dimensions has to be specified.\n assert f is not None and c is not None\n if padding == 'VALID':\n if t:\n t -= t_stride - 1\n f -= f_stride - 1\n ot = t\n if ot is not None:\n ot = (ot + t_stride - 1) // t_stride\n of = (f + f_stride - 1) // f_stride\n if outc is None:\n outc = c\n return [n, ot, of, outc]"},"start_line":{"kind":"number","value":30,"string":"30"},"end_line":{"kind":"number","value":67,"string":"67"},"file_content":{"kind":"string","value":"# Lint as: python2, python3\n# Copyright 2018 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\"\"\"Common conv layers.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\n\nfrom lingvo.core import base_layer\nfrom lingvo.core import bn_layers\nfrom lingvo.core import py_utils\nfrom lingvo.core import tshape\n\n\ndef ComputeConvOutputShape(in_shape,\n t_stride,\n f_stride,\n outc=None,\n padding='SAME'):\n \"\"\"Computes output shape for convolution and pooling layers.\n\n If `in_shape` is a dynamic shape, the output will be Tensors, while if\n `in_shape` is a list of ints then the output will also be a list of ints.\n\n Args:\n in_shape: A length 4 Tensor or list representing the input shape.\n t_stride: The stride along the time dimension.\n f_stride: The stride along the frequency dimension.\n outc: The expected output channel. If None, will use the input channel.\n padding: 'SAME' or 'VALID'.\n\n Returns:\n The expected output shape.\n \"\"\"\n # In the order of batch, time, frequency, channel\n n = in_shape[0]\n t = in_shape[1]\n f = in_shape[2]\n c = in_shape[3]\n # Last two dimensions has to be specified.\n assert f is not None and c is not None\n if padding == 'VALID':\n if t:\n t -= t_stride - 1\n f -= f_stride - 1\n ot = t\n if ot is not None:\n ot = (ot + t_stride - 1) // t_stride\n of = (f + f_stride - 1) // f_stride\n if outc is None:\n outc = c\n return [n, ot, of, outc]\n\n\ndef ComputeConvOutputPadding(paddings, window, stride,\n padding_algorithm='SAME'):\n \"\"\"Computes paddings for convolution and pooling output.\n\n out_padding[i] == 1 iff any in_padding corresponding to that output is 1.\n\n Args:\n paddings: The paddings tensor. It is expected to be of shape [batch, time].\n window: The size of the windows.\n stride: The time-stride between adjacent windows.\n padding_algorithm: 'SAME' or 'VALID'.\n\n Returns:\n out_padding, The new padding tensor of size [batch, ceil(time / stride)].\n \"\"\"\n if stride == 1:\n return paddings\n\n # Pad so input_length divides stride.\n input_length = py_utils.GetShape(paddings)[1]\n pad_len = (input_length + stride - 1) // stride * stride - input_length\n paddings = tf.pad(paddings, [[0, 0], [0, pad_len]], constant_values=1.0)\n out_padding = tf.nn.pool(\n tf.expand_dims(paddings, -1),\n [window],\n 'MAX',\n padding_algorithm,\n strides=[stride],\n )\n return tf.squeeze(out_padding, -1)\n\n\nclass BaseConv2DLayerWithPadding(base_layer.BaseLayer):\n \"\"\"Base class for 2D convolution layers.\"\"\"\n\n @classmethod\n def Params(cls):\n p = super(BaseConv2DLayerWithPadding, cls).Params()\n p.Define(\n 'filter_shape', (0, 0, 0, 0),\n 'Filter shape. Must be a sequence of length 4. Elements are in'\n ' the order of height (time), width (frequency), in_channel,'\n ' out_channel. For causal convolution, filter_shape[0]'\n ' is the actual number of trained weights in the time dimension'\n ' of the kernel.')\n p.Define(\n 'filter_stride', (1, 1),\n 'Filter stride to use. Must be a pair of ints. The first int'\n ' specifies the stride on the time dimension. The second int'\n ' specifies the stride on the frequency dimension.')\n p.Define(\n 'dilation_rate', (1, 1),\n 'If > 1, dilation rate for atrous convolution. '\n 'Must be a pair of ints. '\n 'The first int specifies the dilation rate on the time dimension. '\n 'The second int specifies the dilation rate on the frequency '\n 'dimension. '\n 'If any value of dilation_rate is > 1, then all values of strides '\n 'must be 1.')\n p.Define(\n 'weight_norm', False,\n 'If true, apply weight normalization to weights as proposed by'\n ' Salimans and Kingma, 2016: https://arxiv.org/abs/1602.07868')\n return p\n\n @base_layer.initializer\n def __init__(self, params):\n super(BaseConv2DLayerWithPadding, self).__init__(params)\n p = self.params\n assert p.name\n assert len(p.filter_shape) == 4\n assert len(p.filter_stride) == 2\n assert all(x > 0 for x in p.filter_shape)\n assert all(x > 0 for x in p.filter_stride)\n assert len(p.dilation_rate) == 2\n assert all(x > 0 for x in p.dilation_rate)\n # Dilation and stride can't be combined.\n if any(x > 1 for x in p.dilation_rate):\n assert all(x == 1 for x in p.filter_stride)\n\n @property\n def output_channels(self):\n \"\"\"The number of output channels for this conv layer.\"\"\"\n raise NotImplementedError()\n\n @property\n def input_channels(self):\n \"\"\"The number of input channels for this conv layer.\"\"\"\n return self.params.filter_shape[2]\n\n def OutShape(self, in_shape):\n \"\"\"Compute the output shape given the input shape.\"\"\"\n p = self.params\n return ComputeConvOutputShape(in_shape, p.filter_stride[0],\n p.filter_stride[1], self.output_channels)\n\n def FProp(self, theta, inputs, paddings):\n \"\"\"Apply convolution to inputs.\n\n Args:\n theta: A `.NestedMap` object containing weights' values of this layer and\n its children layers.\n inputs: The inputs tensor. It is expected to be of shape [batch, time,\n frequency, channel]. The time dimension corresponds to the height\n dimension as in images and the frequency dimension corresponds to the\n width dimension as in images.\n paddings: The paddings tensor, expected to be of shape [batch, time].\n\n Returns:\n outputs, out_paddings pair.\n \"\"\"\n p = self.params\n with tf.name_scope(p.name):\n inputs = py_utils.with_dependencies([\n py_utils.assert_shape_match(tf.shape(paddings), [-1, -1]),\n py_utils.assert_shape_match(\n tf.shape(inputs),\n tf.concat([tf.shape(paddings), [-1, self.input_channels]], 0))\n ], inputs)\n\n def _ApplyPadding(tensor_in, padding_in):\n padding_expanded = tf.expand_dims(tf.expand_dims(padding_in, -1), -1)\n return tensor_in * (1.0 - padding_expanded)\n\n # Zeroing out padded inputs.\n inputs = _ApplyPadding(inputs, paddings)\n\n # Evaluate the conv kernel on 'inputs'.\n out = self._EvaluateConvKernel(theta, inputs)\n\n # NOTE: this may be slightly inaccurate when p.dilation_rate[0] > 1.\n # But there's likely no real problems. Trying to set it gives an error:\n # pooling with SAME padding is not implemented for dilation_rate > 1.\n # NOTE: we use window=p.filter_stride[0] to be compatible with legacy\n # implementation. Consider updating it to be the actual shape.\n conv_padding = ComputeConvOutputPadding(\n paddings, window=p.filter_stride[0], stride=p.filter_stride[0])\n # Assuming padded nodes will be properly zero-ed out if necessary by\n # sub-sequent layers.\n # out = _ApplyPadding(out, conv_padding)\n out = py_utils.HasShape(out, self.OutShape(tf.shape(inputs)))\n return out, conv_padding\n\n def _EvaluateConvKernel(self, theta, conv_input):\n \"\"\"Evaluate the convolution kernel on input 'conv_input'.\"\"\"\n raise NotImplementedError\n\n\nclass Conv2DLayerWithPadding(BaseConv2DLayerWithPadding):\n \"\"\"Conv2D layer.\"\"\"\n\n @base_layer.initializer\n def __init__(self, params):\n super(Conv2DLayerWithPadding, self).__init__(params)\n p = self.params\n assert p.name\n w_pc = py_utils.WeightParams(\n shape=p.filter_shape,\n init=p.params_init,\n dtype=p.dtype,\n collections=[self.__class__.__name__ + '_vars'])\n with tf.variable_scope(p.name):\n self.CreateVariable('w', w_pc)\n if p.weight_norm:\n self.CreateVariable(\n 'g',\n py_utils.WeightParams(\n shape=[p.filter_shape[-1]],\n init=py_utils.WeightInit.Constant(0.0),\n dtype=p.dtype,\n collections=[self.__class__.__name__ + '_vars']))\n\n @property\n def output_channels(self):\n \"\"\"The number of output channels for this conv layer.\"\"\"\n p = self.params\n return p.filter_shape[-1]\n\n def _GetWeight(self, theta):\n p = self.params\n if p.weight_norm:\n # Normalize along the last dim (standard conv).\n filter_w = tf.nn.l2_normalize(theta.w, [0, 1, 2]) * tf.reshape(\n (theta.g + 1.0), [1, 1, 1, p.filter_shape[-1]])\n else:\n filter_w = theta.w\n return filter_w\n\n def _EvaluateConvKernel(self, theta, inputs):\n \"\"\"Apply convolution to inputs.\"\"\"\n p = self.params\n filter_w = self._GetWeight(theta)\n return tf.nn.convolution(\n inputs,\n filter_w,\n strides=p.filter_stride,\n dilation_rate=p.dilation_rate,\n data_format='NHWC',\n padding='SAME')\n\n\nclass CausalConv2DLayerWithPadding(Conv2DLayerWithPadding):\n \"\"\"2D conv layer with causal dependency on the time axis.\"\"\"\n\n @base_layer.initializer\n def __init__(self, params):\n super(CausalConv2DLayerWithPadding, self).__init__(params)\n p = self.params\n assert p.filter_shape[1] == 1, 'Only 1d causal convolution is supported.'\n\n def _EvaluateConvKernel(self, theta, inputs):\n \"\"\"Apply convolution to inputs.\"\"\"\n p = self.params\n assert p.filter_shape[1] == 1, 'Only 1D causal convolutions supported.'\n # Use VALID padding and shift the inputs to the right to ensure that the\n # first output only depends on the first input and so on. The output is\n # the same size as the input, as if the convolution used SAME padding.\n padding_algorithm = 'VALID'\n # The effective spatial filter width for dilated convolutions is\n # (kernel_width - 1) * dilation_rate + 1 as according to\n # https://www.tensorflow.org/api_docs/python/tf/nn/convolution.\n causal_pad_size = (p.filter_shape[0] - 1) * p.dilation_rate[0]\n inputs = tf.pad(inputs, [[0, 0], [causal_pad_size, 0], [0, 0], [0, 0]])\n\n filter_w = self._GetWeight(theta)\n return tf.nn.convolution(\n inputs,\n filter_w,\n strides=p.filter_stride,\n dilation_rate=p.dilation_rate,\n data_format='NHWC',\n padding=padding_algorithm)\n\n\nclass DepthwiseConv2DLayer(BaseConv2DLayerWithPadding):\n \"\"\"Depthwise conv 2D layer.\n\n paper: https://arxiv.org/abs/1610.02357\n \"\"\"\n\n @classmethod\n def Params(cls):\n p = super(DepthwiseConv2DLayer, cls).Params()\n # Redefine 'filter_shape' since the semantic of shape elements is different\n # from regular Conv2D.\n p.Delete('filter_shape')\n p.Define(\n 'filter_shape', (0, 0, 0, 0),\n 'Filter shape. Must be a sequence of length 4. Elements are in'\n ' the order of height (time), width (frequency), in_channel,'\n ' channel_multipliers. ')\n return p\n\n @base_layer.initializer\n def __init__(self, params):\n super(DepthwiseConv2DLayer, self).__init__(params)\n p = self.params\n assert p.name\n w_pc = py_utils.WeightParams(\n shape=p.filter_shape,\n init=p.params_init,\n dtype=p.dtype,\n collections=[self.__class__.__name__ + '_vars'])\n\n with tf.variable_scope(p.name):\n self.CreateVariable('w', w_pc)\n if p.weight_norm:\n self.CreateVariable(\n 'g',\n py_utils.WeightParams(\n shape=[p.filter_shape[2], p.filter_shape[3]],\n init=py_utils.WeightInit.Constant(0.0),\n dtype=p.dtype,\n collections=[self.__class__.__name__ + '_vars']))\n\n @property\n def output_channels(self):\n \"\"\"The number of output channels for this conv layer.\"\"\"\n p = self.params\n # Depthwise convolution filter shape is:\n # [..., in_channels, channel_multiplier].\n return p.filter_shape[2] * p.filter_shape[3]\n\n def _GetWeight(self, theta):\n p = self.params\n if p.weight_norm:\n # Normalize along the last two dims.\n filter_w = tf.nn.l2_normalize(theta.w, [0, 1]) * tf.reshape(\n (theta.g + 1.0), [1, 1, p.filter_shape[2], p.filter_shape[3]])\n else:\n filter_w = theta.w\n return filter_w\n\n def _EvaluateConvKernel(self, theta, inputs):\n \"\"\"Apply convolution to inputs.\"\"\"\n p = self.params\n filter_w = self._GetWeight(theta)\n return tf.nn.depthwise_conv2d(\n inputs,\n filter_w,\n strides=[1, p.filter_stride[0], p.filter_stride[1], 1],\n rate=p.dilation_rate,\n data_format='NHWC',\n padding='SAME')\n\n\nclass CausalDepthwiseConv2DLayer(DepthwiseConv2DLayer):\n \"\"\"Depthwise conv layer with causal dependency on the time axis.\"\"\"\n\n @base_layer.initializer\n def __init__(self, params):\n super(CausalDepthwiseConv2DLayer, self).__init__(params)\n p = self.params\n assert p.filter_shape[1] == 1, 'Only 1d causal convolution is supported.'\n\n def _EvaluateConvKernel(self, theta, inputs):\n \"\"\"Apply convolution to inputs.\"\"\"\n p = self.params\n assert p.filter_shape[1] == 1, 'Only 1D causal convolutions supported.'\n # Use VALID padding and shift the inputs to the right to ensure that the\n # first output only depends on the first input and so on. The output is\n # the same size as the input, as if the convolution used SAME padding.\n padding_algorithm = 'VALID'\n # The effective spatial filter width for dilated convolutions is\n # (kernel_width - 1) * dilation_rate + 1 as according to\n # https://www.tensorflow.org/api_docs/python/tf/nn/convolution.\n causal_pad_size = (p.filter_shape[0] - 1) * p.dilation_rate[0]\n inputs = tf.pad(inputs, [[0, 0], [causal_pad_size, 0], [0, 0], [0, 0]])\n filter_w = self._GetWeight(theta)\n return tf.nn.depthwise_conv2d(\n inputs,\n filter_w,\n strides=[1, p.filter_stride[0], p.filter_stride[1], 1],\n rate=p.dilation_rate,\n data_format='NHWC',\n padding=padding_algorithm)\n\n\nclass NormalizedDepthwiseConv2DLayer(DepthwiseConv2DLayer):\n \"\"\"DepthwiseConv2DLayer where weights are normalized over the time dim.\n\n https://arxiv.org/abs/1901.10430\n \"\"\"\n\n @classmethod\n def Params(cls):\n p = super(NormalizedDepthwiseConv2DLayer, cls).Params()\n p.Define('dropconnect_prob', 0.0,\n 'Prob at which DropConnect regularization is performed.')\n p.Define('deterministic_dropout', False, 'Use determnisitc dropout or not.')\n p.Define('temperature', 1.0,\n 'Temperature for the softmax normalization of the weights.')\n p.Define('weight_tiling_factor', 1,\n 'Number of times weights are tiled over the input channels.')\n return p\n\n @base_layer.initializer\n def __init__(self, params):\n super(NormalizedDepthwiseConv2DLayer, self).__init__(params)\n p = self.params\n assert p.filter_shape[1] == 1, 'Only 1d convolution is supported.'\n assert p.temperature > 0.0, 'Absolute zero temperature is not possible.'\n\n @property\n def output_channels(self):\n \"\"\"The number of output channels for this conv layer.\"\"\"\n p = self.params\n # Depthwise convolution filter shape is:\n # [kernel_size, 1, in_channels, channel_multiplier].\n return p.filter_shape[2] * p.filter_shape[3] * p.weight_tiling_factor\n\n @property\n def input_channels(self):\n \"\"\"The number of output channels for this conv layer.\"\"\"\n p = self.params\n return p.filter_shape[2] * p.weight_tiling_factor\n\n def _GetWeight(self, theta):\n p = self.params\n filter_w = theta.w\n\n # First normalize filter_w over the temporal dimension here.\n filter_w = tf.nn.softmax(filter_w / p.temperature, axis=0)\n\n # Add dropconnect on the weights for regularization.\n if p.dropconnect_prob > 0.0 and not p.is_eval:\n if p.deterministic_dropout:\n filter_w = py_utils.DeterministicDropout(\n filter_w, 1.0 - p.dropconnect_prob,\n py_utils.GenerateStepSeedPair(p, theta.global_step))\n else:\n filter_w = tf.nn.dropout(\n filter_w, 1.0 - p.dropconnect_prob, seed=p.random_seed)\n\n # Tie the parameters of every subsequent number of weight_tiling_factor\n # channels.\n filter_w = tf.tile(filter_w, [1, 1, p.weight_tiling_factor, 1])\n return filter_w\n\n @classmethod\n def FPropMeta(cls, p, inputs, paddings):\n py_utils.CheckShapes((inputs, paddings))\n b, t, f, ic = inputs\n assert f == 1\n oc = p.filter_shape[2] * p.filter_shape[3] * p.weight_tiling_factor\n outputs = tshape.Shape([b, t, f, oc])\n flops = b * t * f * p.filter_shape[0] * ic * oc * 5\n return py_utils.NestedMap(flops=flops, out_shapes=(outputs, paddings))\n\n\nclass CausalNormalizedDepthwiseConv2DLayer(NormalizedDepthwiseConv2DLayer):\n \"\"\"Depthwise conv layer with causal dependency on the time axis.\"\"\"\n\n def _EvaluateConvKernel(self, theta, inputs):\n \"\"\"Apply convolution to inputs.\"\"\"\n # Same as CausalDepthwiseConv2DLayer.\n p = self.params\n assert p.filter_shape[1] == 1, 'Only 1D causal convolutions supported.'\n padding_algorithm = 'VALID'\n causal_pad_size = (p.filter_shape[0] - 1) * p.dilation_rate[0]\n inputs = tf.pad(inputs, [[0, 0], [causal_pad_size, 0], [0, 0], [0, 0]])\n filter_w = self._GetWeight(theta)\n return tf.nn.depthwise_conv2d(\n inputs,\n filter_w,\n strides=[1, p.filter_stride[0], p.filter_stride[1], 1],\n rate=p.dilation_rate,\n data_format='NHWC',\n padding=padding_algorithm)\n\n\nclass ConvBatchNormLayer(bn_layers.BatchNormLayer):\n \"\"\"A wrapper around regular BatchNormLayer that pass around the ...\n\n paddings layers.\n \"\"\"\n\n def FProp(self, theta, inputs, paddings):\n paddings_expanded = tf.expand_dims(tf.expand_dims(paddings, -1), -1)\n bned = super(ConvBatchNormLayer, self).FProp(\n theta, inputs, paddings_expanded)\n return bned, paddings\n\n\n# Supported activation functions.\n_ACTIVATIONS = {\n 'RELU': tf.nn.relu,\n 'RELU6': tf.nn.relu6,\n 'SIGMOID': tf.sigmoid,\n 'TANH': tf.tanh,\n 'SWISH': tf.nn.swish,\n 'NONE': tf.identity,\n}\n\n\nclass ActivationLayer(base_layer.BaseLayer):\n \"\"\"Applies activation function to the inputs.\"\"\"\n\n @classmethod\n def Params(cls):\n p = super(ActivationLayer, cls).Params()\n p.Define('activation', 'RELU',\n 'The activation function to apply')\n return p\n\n def FProp(self, theta, inputs, paddings):\n p = self.params\n out = _ACTIVATIONS[p.activation](inputs)\n return out, paddings\n\n\nclass PaddingLayer(base_layer.BaseLayer):\n \"\"\"Zeros out padded positions.\"\"\"\n\n def FProp(self, theta, inputs, paddings):\n paddings_expanded = tf.expand_dims(tf.expand_dims(paddings, -1), -1)\n return inputs * (1.0 - paddings_expanded), paddings\n"}}},{"rowIdx":65,"cells":{"function_name":{"kind":"string","value":"ComputeConvOutputPadding"},"docstring":{"kind":"string","value":"Computes paddings for convolution and pooling output.\n\nout_padding[i] == 1 iff any in_padding corresponding to that output is 1.\n\nArgs:\n paddings: The paddings tensor. It is expected to be of shape [batch, time].\n window: The size of the windows.\n stride: The time-stride between adjacent windows.\n padding_algorithm: 'SAME' or 'VALID'.\n\nReturns:\n out_padding, The new padding tensor of size [batch, ceil(time / stride)]."},"masked_code":{"kind":"string","value":"# Lint as: python2, python3\n# Copyright 2018 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\"\"\"Common conv layers.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\n\nfrom lingvo.core import base_layer\nfrom lingvo.core import bn_layers\nfrom lingvo.core import py_utils\nfrom lingvo.core import tshape\n\n\ndef ComputeConvOutputShape(in_shape,\n t_stride,\n f_stride,\n outc=None,\n padding='SAME'):\n \"\"\"Computes output shape for convolution and pooling layers.\n\n If `in_shape` is a dynamic shape, the output will be Tensors, while if\n `in_shape` is a list of ints then the output will also be a list of ints.\n\n Args:\n in_shape: A length 4 Tensor or list representing the input shape.\n t_stride: The stride along the time dimension.\n f_stride: The stride along the frequency dimension.\n outc: The expected output channel. If None, will use the input channel.\n padding: 'SAME' or 'VALID'.\n\n Returns:\n The expected output shape.\n \"\"\"\n # In the order of batch, time, frequency, channel\n n = in_shape[0]\n t = in_shape[1]\n f = in_shape[2]\n c = in_shape[3]\n # Last two dimensions has to be specified.\n assert f is not None and c is not None\n if padding == 'VALID':\n if t:\n t -= t_stride - 1\n f -= f_stride - 1\n ot = t\n if ot is not None:\n ot = (ot + t_stride - 1) // t_stride\n of = (f + f_stride - 1) // f_stride\n if outc is None:\n outc = c\n return [n, ot, of, outc]\n\n\n# MASKED: ComputeConvOutputPadding function (lines 70-99)\n\n\nclass BaseConv2DLayerWithPadding(base_layer.BaseLayer):\n \"\"\"Base class for 2D convolution layers.\"\"\"\n\n @classmethod\n def Params(cls):\n p = super(BaseConv2DLayerWithPadding, cls).Params()\n p.Define(\n 'filter_shape', (0, 0, 0, 0),\n 'Filter shape. Must be a sequence of length 4. Elements are in'\n ' the order of height (time), width (frequency), in_channel,'\n ' out_channel. For causal convolution, filter_shape[0]'\n ' is the actual number of trained weights in the time dimension'\n ' of the kernel.')\n p.Define(\n 'filter_stride', (1, 1),\n 'Filter stride to use. Must be a pair of ints. The first int'\n ' specifies the stride on the time dimension. The second int'\n ' specifies the stride on the frequency dimension.')\n p.Define(\n 'dilation_rate', (1, 1),\n 'If > 1, dilation rate for atrous convolution. '\n 'Must be a pair of ints. '\n 'The first int specifies the dilation rate on the time dimension. '\n 'The second int specifies the dilation rate on the frequency '\n 'dimension. '\n 'If any value of dilation_rate is > 1, then all values of strides '\n 'must be 1.')\n p.Define(\n 'weight_norm', False,\n 'If true, apply weight normalization to weights as proposed by'\n ' Salimans and Kingma, 2016: https://arxiv.org/abs/1602.07868')\n return p\n\n @base_layer.initializer\n def __init__(self, params):\n super(BaseConv2DLayerWithPadding, self).__init__(params)\n p = self.params\n assert p.name\n assert len(p.filter_shape) == 4\n assert len(p.filter_stride) == 2\n assert all(x > 0 for x in p.filter_shape)\n assert all(x > 0 for x in p.filter_stride)\n assert len(p.dilation_rate) == 2\n assert all(x > 0 for x in p.dilation_rate)\n # Dilation and stride can't be combined.\n if any(x > 1 for x in p.dilation_rate):\n assert all(x == 1 for x in p.filter_stride)\n\n @property\n def output_channels(self):\n \"\"\"The number of output channels for this conv layer.\"\"\"\n raise NotImplementedError()\n\n @property\n def input_channels(self):\n \"\"\"The number of input channels for this conv layer.\"\"\"\n return self.params.filter_shape[2]\n\n def OutShape(self, in_shape):\n \"\"\"Compute the output shape given the input shape.\"\"\"\n p = self.params\n return ComputeConvOutputShape(in_shape, p.filter_stride[0],\n p.filter_stride[1], self.output_channels)\n\n def FProp(self, theta, inputs, paddings):\n \"\"\"Apply convolution to inputs.\n\n Args:\n theta: A `.NestedMap` object containing weights' values of this layer and\n its children layers.\n inputs: The inputs tensor. It is expected to be of shape [batch, time,\n frequency, channel]. The time dimension corresponds to the height\n dimension as in images and the frequency dimension corresponds to the\n width dimension as in images.\n paddings: The paddings tensor, expected to be of shape [batch, time].\n\n Returns:\n outputs, out_paddings pair.\n \"\"\"\n p = self.params\n with tf.name_scope(p.name):\n inputs = py_utils.with_dependencies([\n py_utils.assert_shape_match(tf.shape(paddings), [-1, -1]),\n py_utils.assert_shape_match(\n tf.shape(inputs),\n tf.concat([tf.shape(paddings), [-1, self.input_channels]], 0))\n ], inputs)\n\n def _ApplyPadding(tensor_in, padding_in):\n padding_expanded = tf.expand_dims(tf.expand_dims(padding_in, -1), -1)\n return tensor_in * (1.0 - padding_expanded)\n\n # Zeroing out padded inputs.\n inputs = _ApplyPadding(inputs, paddings)\n\n # Evaluate the conv kernel on 'inputs'.\n out = self._EvaluateConvKernel(theta, inputs)\n\n # NOTE: this may be slightly inaccurate when p.dilation_rate[0] > 1.\n # But there's likely no real problems. Trying to set it gives an error:\n # pooling with SAME padding is not implemented for dilation_rate > 1.\n # NOTE: we use window=p.filter_stride[0] to be compatible with legacy\n # implementation. Consider updating it to be the actual shape.\n conv_padding = ComputeConvOutputPadding(\n paddings, window=p.filter_stride[0], stride=p.filter_stride[0])\n # Assuming padded nodes will be properly zero-ed out if necessary by\n # sub-sequent layers.\n # out = _ApplyPadding(out, conv_padding)\n out = py_utils.HasShape(out, self.OutShape(tf.shape(inputs)))\n return out, conv_padding\n\n def _EvaluateConvKernel(self, theta, conv_input):\n \"\"\"Evaluate the convolution kernel on input 'conv_input'.\"\"\"\n raise NotImplementedError\n\n\nclass Conv2DLayerWithPadding(BaseConv2DLayerWithPadding):\n \"\"\"Conv2D layer.\"\"\"\n\n @base_layer.initializer\n def __init__(self, params):\n super(Conv2DLayerWithPadding, self).__init__(params)\n p = self.params\n assert p.name\n w_pc = py_utils.WeightParams(\n shape=p.filter_shape,\n init=p.params_init,\n dtype=p.dtype,\n collections=[self.__class__.__name__ + '_vars'])\n with tf.variable_scope(p.name):\n self.CreateVariable('w', w_pc)\n if p.weight_norm:\n self.CreateVariable(\n 'g',\n py_utils.WeightParams(\n shape=[p.filter_shape[-1]],\n init=py_utils.WeightInit.Constant(0.0),\n dtype=p.dtype,\n collections=[self.__class__.__name__ + '_vars']))\n\n @property\n def output_channels(self):\n \"\"\"The number of output channels for this conv layer.\"\"\"\n p = self.params\n return p.filter_shape[-1]\n\n def _GetWeight(self, theta):\n p = self.params\n if p.weight_norm:\n # Normalize along the last dim (standard conv).\n filter_w = tf.nn.l2_normalize(theta.w, [0, 1, 2]) * tf.reshape(\n (theta.g + 1.0), [1, 1, 1, p.filter_shape[-1]])\n else:\n filter_w = theta.w\n return filter_w\n\n def _EvaluateConvKernel(self, theta, inputs):\n \"\"\"Apply convolution to inputs.\"\"\"\n p = self.params\n filter_w = self._GetWeight(theta)\n return tf.nn.convolution(\n inputs,\n filter_w,\n strides=p.filter_stride,\n dilation_rate=p.dilation_rate,\n data_format='NHWC',\n padding='SAME')\n\n\nclass CausalConv2DLayerWithPadding(Conv2DLayerWithPadding):\n \"\"\"2D conv layer with causal dependency on the time axis.\"\"\"\n\n @base_layer.initializer\n def __init__(self, params):\n super(CausalConv2DLayerWithPadding, self).__init__(params)\n p = self.params\n assert p.filter_shape[1] == 1, 'Only 1d causal convolution is supported.'\n\n def _EvaluateConvKernel(self, theta, inputs):\n \"\"\"Apply convolution to inputs.\"\"\"\n p = self.params\n assert p.filter_shape[1] == 1, 'Only 1D causal convolutions supported.'\n # Use VALID padding and shift the inputs to the right to ensure that the\n # first output only depends on the first input and so on. The output is\n # the same size as the input, as if the convolution used SAME padding.\n padding_algorithm = 'VALID'\n # The effective spatial filter width for dilated convolutions is\n # (kernel_width - 1) * dilation_rate + 1 as according to\n # https://www.tensorflow.org/api_docs/python/tf/nn/convolution.\n causal_pad_size = (p.filter_shape[0] - 1) * p.dilation_rate[0]\n inputs = tf.pad(inputs, [[0, 0], [causal_pad_size, 0], [0, 0], [0, 0]])\n\n filter_w = self._GetWeight(theta)\n return tf.nn.convolution(\n inputs,\n filter_w,\n strides=p.filter_stride,\n dilation_rate=p.dilation_rate,\n data_format='NHWC',\n padding=padding_algorithm)\n\n\nclass DepthwiseConv2DLayer(BaseConv2DLayerWithPadding):\n \"\"\"Depthwise conv 2D layer.\n\n paper: https://arxiv.org/abs/1610.02357\n \"\"\"\n\n @classmethod\n def Params(cls):\n p = super(DepthwiseConv2DLayer, cls).Params()\n # Redefine 'filter_shape' since the semantic of shape elements is different\n # from regular Conv2D.\n p.Delete('filter_shape')\n p.Define(\n 'filter_shape', (0, 0, 0, 0),\n 'Filter shape. Must be a sequence of length 4. Elements are in'\n ' the order of height (time), width (frequency), in_channel,'\n ' channel_multipliers. ')\n return p\n\n @base_layer.initializer\n def __init__(self, params):\n super(DepthwiseConv2DLayer, self).__init__(params)\n p = self.params\n assert p.name\n w_pc = py_utils.WeightParams(\n shape=p.filter_shape,\n init=p.params_init,\n dtype=p.dtype,\n collections=[self.__class__.__name__ + '_vars'])\n\n with tf.variable_scope(p.name):\n self.CreateVariable('w', w_pc)\n if p.weight_norm:\n self.CreateVariable(\n 'g',\n py_utils.WeightParams(\n shape=[p.filter_shape[2], p.filter_shape[3]],\n init=py_utils.WeightInit.Constant(0.0),\n dtype=p.dtype,\n collections=[self.__class__.__name__ + '_vars']))\n\n @property\n def output_channels(self):\n \"\"\"The number of output channels for this conv layer.\"\"\"\n p = self.params\n # Depthwise convolution filter shape is:\n # [..., in_channels, channel_multiplier].\n return p.filter_shape[2] * p.filter_shape[3]\n\n def _GetWeight(self, theta):\n p = self.params\n if p.weight_norm:\n # Normalize along the last two dims.\n filter_w = tf.nn.l2_normalize(theta.w, [0, 1]) * tf.reshape(\n (theta.g + 1.0), [1, 1, p.filter_shape[2], p.filter_shape[3]])\n else:\n filter_w = theta.w\n return filter_w\n\n def _EvaluateConvKernel(self, theta, inputs):\n \"\"\"Apply convolution to inputs.\"\"\"\n p = self.params\n filter_w = self._GetWeight(theta)\n return tf.nn.depthwise_conv2d(\n inputs,\n filter_w,\n strides=[1, p.filter_stride[0], p.filter_stride[1], 1],\n rate=p.dilation_rate,\n data_format='NHWC',\n padding='SAME')\n\n\nclass CausalDepthwiseConv2DLayer(DepthwiseConv2DLayer):\n \"\"\"Depthwise conv layer with causal dependency on the time axis.\"\"\"\n\n @base_layer.initializer\n def __init__(self, params):\n super(CausalDepthwiseConv2DLayer, self).__init__(params)\n p = self.params\n assert p.filter_shape[1] == 1, 'Only 1d causal convolution is supported.'\n\n def _EvaluateConvKernel(self, theta, inputs):\n \"\"\"Apply convolution to inputs.\"\"\"\n p = self.params\n assert p.filter_shape[1] == 1, 'Only 1D causal convolutions supported.'\n # Use VALID padding and shift the inputs to the right to ensure that the\n # first output only depends on the first input and so on. The output is\n # the same size as the input, as if the convolution used SAME padding.\n padding_algorithm = 'VALID'\n # The effective spatial filter width for dilated convolutions is\n # (kernel_width - 1) * dilation_rate + 1 as according to\n # https://www.tensorflow.org/api_docs/python/tf/nn/convolution.\n causal_pad_size = (p.filter_shape[0] - 1) * p.dilation_rate[0]\n inputs = tf.pad(inputs, [[0, 0], [causal_pad_size, 0], [0, 0], [0, 0]])\n filter_w = self._GetWeight(theta)\n return tf.nn.depthwise_conv2d(\n inputs,\n filter_w,\n strides=[1, p.filter_stride[0], p.filter_stride[1], 1],\n rate=p.dilation_rate,\n data_format='NHWC',\n padding=padding_algorithm)\n\n\nclass NormalizedDepthwiseConv2DLayer(DepthwiseConv2DLayer):\n \"\"\"DepthwiseConv2DLayer where weights are normalized over the time dim.\n\n https://arxiv.org/abs/1901.10430\n \"\"\"\n\n @classmethod\n def Params(cls):\n p = super(NormalizedDepthwiseConv2DLayer, cls).Params()\n p.Define('dropconnect_prob', 0.0,\n 'Prob at which DropConnect regularization is performed.')\n p.Define('deterministic_dropout', False, 'Use determnisitc dropout or not.')\n p.Define('temperature', 1.0,\n 'Temperature for the softmax normalization of the weights.')\n p.Define('weight_tiling_factor', 1,\n 'Number of times weights are tiled over the input channels.')\n return p\n\n @base_layer.initializer\n def __init__(self, params):\n super(NormalizedDepthwiseConv2DLayer, self).__init__(params)\n p = self.params\n assert p.filter_shape[1] == 1, 'Only 1d convolution is supported.'\n assert p.temperature > 0.0, 'Absolute zero temperature is not possible.'\n\n @property\n def output_channels(self):\n \"\"\"The number of output channels for this conv layer.\"\"\"\n p = self.params\n # Depthwise convolution filter shape is:\n # [kernel_size, 1, in_channels, channel_multiplier].\n return p.filter_shape[2] * p.filter_shape[3] * p.weight_tiling_factor\n\n @property\n def input_channels(self):\n \"\"\"The number of output channels for this conv layer.\"\"\"\n p = self.params\n return p.filter_shape[2] * p.weight_tiling_factor\n\n def _GetWeight(self, theta):\n p = self.params\n filter_w = theta.w\n\n # First normalize filter_w over the temporal dimension here.\n filter_w = tf.nn.softmax(filter_w / p.temperature, axis=0)\n\n # Add dropconnect on the weights for regularization.\n if p.dropconnect_prob > 0.0 and not p.is_eval:\n if p.deterministic_dropout:\n filter_w = py_utils.DeterministicDropout(\n filter_w, 1.0 - p.dropconnect_prob,\n py_utils.GenerateStepSeedPair(p, theta.global_step))\n else:\n filter_w = tf.nn.dropout(\n filter_w, 1.0 - p.dropconnect_prob, seed=p.random_seed)\n\n # Tie the parameters of every subsequent number of weight_tiling_factor\n # channels.\n filter_w = tf.tile(filter_w, [1, 1, p.weight_tiling_factor, 1])\n return filter_w\n\n @classmethod\n def FPropMeta(cls, p, inputs, paddings):\n py_utils.CheckShapes((inputs, paddings))\n b, t, f, ic = inputs\n assert f == 1\n oc = p.filter_shape[2] * p.filter_shape[3] * p.weight_tiling_factor\n outputs = tshape.Shape([b, t, f, oc])\n flops = b * t * f * p.filter_shape[0] * ic * oc * 5\n return py_utils.NestedMap(flops=flops, out_shapes=(outputs, paddings))\n\n\nclass CausalNormalizedDepthwiseConv2DLayer(NormalizedDepthwiseConv2DLayer):\n \"\"\"Depthwise conv layer with causal dependency on the time axis.\"\"\"\n\n def _EvaluateConvKernel(self, theta, inputs):\n \"\"\"Apply convolution to inputs.\"\"\"\n # Same as CausalDepthwiseConv2DLayer.\n p = self.params\n assert p.filter_shape[1] == 1, 'Only 1D causal convolutions supported.'\n padding_algorithm = 'VALID'\n causal_pad_size = (p.filter_shape[0] - 1) * p.dilation_rate[0]\n inputs = tf.pad(inputs, [[0, 0], [causal_pad_size, 0], [0, 0], [0, 0]])\n filter_w = self._GetWeight(theta)\n return tf.nn.depthwise_conv2d(\n inputs,\n filter_w,\n strides=[1, p.filter_stride[0], p.filter_stride[1], 1],\n rate=p.dilation_rate,\n data_format='NHWC',\n padding=padding_algorithm)\n\n\nclass ConvBatchNormLayer(bn_layers.BatchNormLayer):\n \"\"\"A wrapper around regular BatchNormLayer that pass around the ...\n\n paddings layers.\n \"\"\"\n\n def FProp(self, theta, inputs, paddings):\n paddings_expanded = tf.expand_dims(tf.expand_dims(paddings, -1), -1)\n bned = super(ConvBatchNormLayer, self).FProp(\n theta, inputs, paddings_expanded)\n return bned, paddings\n\n\n# Supported activation functions.\n_ACTIVATIONS = {\n 'RELU': tf.nn.relu,\n 'RELU6': tf.nn.relu6,\n 'SIGMOID': tf.sigmoid,\n 'TANH': tf.tanh,\n 'SWISH': tf.nn.swish,\n 'NONE': tf.identity,\n}\n\n\nclass ActivationLayer(base_layer.BaseLayer):\n \"\"\"Applies activation function to the inputs.\"\"\"\n\n @classmethod\n def Params(cls):\n p = super(ActivationLayer, cls).Params()\n p.Define('activation', 'RELU',\n 'The activation function to apply')\n return p\n\n def FProp(self, theta, inputs, paddings):\n p = self.params\n out = _ACTIVATIONS[p.activation](inputs)\n return out, paddings\n\n\nclass PaddingLayer(base_layer.BaseLayer):\n \"\"\"Zeros out padded positions.\"\"\"\n\n def FProp(self, theta, inputs, paddings):\n paddings_expanded = tf.expand_dims(tf.expand_dims(paddings, -1), -1)\n return inputs * (1.0 - paddings_expanded), paddings"},"implementation":{"kind":"string","value":"def ComputeConvOutputPadding(paddings, window, stride,\n padding_algorithm='SAME'):\n \"\"\"Computes paddings for convolution and pooling output.\n\n out_padding[i] == 1 iff any in_padding corresponding to that output is 1.\n\n Args:\n paddings: The paddings tensor. It is expected to be of shape [batch, time].\n window: The size of the windows.\n stride: The time-stride between adjacent windows.\n padding_algorithm: 'SAME' or 'VALID'.\n\n Returns:\n out_padding, The new padding tensor of size [batch, ceil(time / stride)].\n \"\"\"\n if stride == 1:\n return paddings\n\n # Pad so input_length divides stride.\n input_length = py_utils.GetShape(paddings)[1]\n pad_len = (input_length + stride - 1) // stride * stride - input_length\n paddings = tf.pad(paddings, [[0, 0], [0, pad_len]], constant_values=1.0)\n out_padding = tf.nn.pool(\n tf.expand_dims(paddings, -1),\n [window],\n 'MAX',\n padding_algorithm,\n strides=[stride],\n )\n return tf.squeeze(out_padding, -1)"},"start_line":{"kind":"number","value":70,"string":"70"},"end_line":{"kind":"number","value":99,"string":"99"},"file_content":{"kind":"string","value":"# Lint as: python2, python3\n# Copyright 2018 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\"\"\"Common conv layers.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport tensorflow as tf\n\nfrom lingvo.core import base_layer\nfrom lingvo.core import bn_layers\nfrom lingvo.core import py_utils\nfrom lingvo.core import tshape\n\n\ndef ComputeConvOutputShape(in_shape,\n t_stride,\n f_stride,\n outc=None,\n padding='SAME'):\n \"\"\"Computes output shape for convolution and pooling layers.\n\n If `in_shape` is a dynamic shape, the output will be Tensors, while if\n `in_shape` is a list of ints then the output will also be a list of ints.\n\n Args:\n in_shape: A length 4 Tensor or list representing the input shape.\n t_stride: The stride along the time dimension.\n f_stride: The stride along the frequency dimension.\n outc: The expected output channel. If None, will use the input channel.\n padding: 'SAME' or 'VALID'.\n\n Returns:\n The expected output shape.\n \"\"\"\n # In the order of batch, time, frequency, channel\n n = in_shape[0]\n t = in_shape[1]\n f = in_shape[2]\n c = in_shape[3]\n # Last two dimensions has to be specified.\n assert f is not None and c is not None\n if padding == 'VALID':\n if t:\n t -= t_stride - 1\n f -= f_stride - 1\n ot = t\n if ot is not None:\n ot = (ot + t_stride - 1) // t_stride\n of = (f + f_stride - 1) // f_stride\n if outc is None:\n outc = c\n return [n, ot, of, outc]\n\n\ndef ComputeConvOutputPadding(paddings, window, stride,\n padding_algorithm='SAME'):\n \"\"\"Computes paddings for convolution and pooling output.\n\n out_padding[i] == 1 iff any in_padding corresponding to that output is 1.\n\n Args:\n paddings: The paddings tensor. It is expected to be of shape [batch, time].\n window: The size of the windows.\n stride: The time-stride between adjacent windows.\n padding_algorithm: 'SAME' or 'VALID'.\n\n Returns:\n out_padding, The new padding tensor of size [batch, ceil(time / stride)].\n \"\"\"\n if stride == 1:\n return paddings\n\n # Pad so input_length divides stride.\n input_length = py_utils.GetShape(paddings)[1]\n pad_len = (input_length + stride - 1) // stride * stride - input_length\n paddings = tf.pad(paddings, [[0, 0], [0, pad_len]], constant_values=1.0)\n out_padding = tf.nn.pool(\n tf.expand_dims(paddings, -1),\n [window],\n 'MAX',\n padding_algorithm,\n strides=[stride],\n )\n return tf.squeeze(out_padding, -1)\n\n\nclass BaseConv2DLayerWithPadding(base_layer.BaseLayer):\n \"\"\"Base class for 2D convolution layers.\"\"\"\n\n @classmethod\n def Params(cls):\n p = super(BaseConv2DLayerWithPadding, cls).Params()\n p.Define(\n 'filter_shape', (0, 0, 0, 0),\n 'Filter shape. Must be a sequence of length 4. Elements are in'\n ' the order of height (time), width (frequency), in_channel,'\n ' out_channel. For causal convolution, filter_shape[0]'\n ' is the actual number of trained weights in the time dimension'\n ' of the kernel.')\n p.Define(\n 'filter_stride', (1, 1),\n 'Filter stride to use. Must be a pair of ints. The first int'\n ' specifies the stride on the time dimension. The second int'\n ' specifies the stride on the frequency dimension.')\n p.Define(\n 'dilation_rate', (1, 1),\n 'If > 1, dilation rate for atrous convolution. '\n 'Must be a pair of ints. '\n 'The first int specifies the dilation rate on the time dimension. '\n 'The second int specifies the dilation rate on the frequency '\n 'dimension. '\n 'If any value of dilation_rate is > 1, then all values of strides '\n 'must be 1.')\n p.Define(\n 'weight_norm', False,\n 'If true, apply weight normalization to weights as proposed by'\n ' Salimans and Kingma, 2016: https://arxiv.org/abs/1602.07868')\n return p\n\n @base_layer.initializer\n def __init__(self, params):\n super(BaseConv2DLayerWithPadding, self).__init__(params)\n p = self.params\n assert p.name\n assert len(p.filter_shape) == 4\n assert len(p.filter_stride) == 2\n assert all(x > 0 for x in p.filter_shape)\n assert all(x > 0 for x in p.filter_stride)\n assert len(p.dilation_rate) == 2\n assert all(x > 0 for x in p.dilation_rate)\n # Dilation and stride can't be combined.\n if any(x > 1 for x in p.dilation_rate):\n assert all(x == 1 for x in p.filter_stride)\n\n @property\n def output_channels(self):\n \"\"\"The number of output channels for this conv layer.\"\"\"\n raise NotImplementedError()\n\n @property\n def input_channels(self):\n \"\"\"The number of input channels for this conv layer.\"\"\"\n return self.params.filter_shape[2]\n\n def OutShape(self, in_shape):\n \"\"\"Compute the output shape given the input shape.\"\"\"\n p = self.params\n return ComputeConvOutputShape(in_shape, p.filter_stride[0],\n p.filter_stride[1], self.output_channels)\n\n def FProp(self, theta, inputs, paddings):\n \"\"\"Apply convolution to inputs.\n\n Args:\n theta: A `.NestedMap` object containing weights' values of this layer and\n its children layers.\n inputs: The inputs tensor. It is expected to be of shape [batch, time,\n frequency, channel]. The time dimension corresponds to the height\n dimension as in images and the frequency dimension corresponds to the\n width dimension as in images.\n paddings: The paddings tensor, expected to be of shape [batch, time].\n\n Returns:\n outputs, out_paddings pair.\n \"\"\"\n p = self.params\n with tf.name_scope(p.name):\n inputs = py_utils.with_dependencies([\n py_utils.assert_shape_match(tf.shape(paddings), [-1, -1]),\n py_utils.assert_shape_match(\n tf.shape(inputs),\n tf.concat([tf.shape(paddings), [-1, self.input_channels]], 0))\n ], inputs)\n\n def _ApplyPadding(tensor_in, padding_in):\n padding_expanded = tf.expand_dims(tf.expand_dims(padding_in, -1), -1)\n return tensor_in * (1.0 - padding_expanded)\n\n # Zeroing out padded inputs.\n inputs = _ApplyPadding(inputs, paddings)\n\n # Evaluate the conv kernel on 'inputs'.\n out = self._EvaluateConvKernel(theta, inputs)\n\n # NOTE: this may be slightly inaccurate when p.dilation_rate[0] > 1.\n # But there's likely no real problems. Trying to set it gives an error:\n # pooling with SAME padding is not implemented for dilation_rate > 1.\n # NOTE: we use window=p.filter_stride[0] to be compatible with legacy\n # implementation. Consider updating it to be the actual shape.\n conv_padding = ComputeConvOutputPadding(\n paddings, window=p.filter_stride[0], stride=p.filter_stride[0])\n # Assuming padded nodes will be properly zero-ed out if necessary by\n # sub-sequent layers.\n # out = _ApplyPadding(out, conv_padding)\n out = py_utils.HasShape(out, self.OutShape(tf.shape(inputs)))\n return out, conv_padding\n\n def _EvaluateConvKernel(self, theta, conv_input):\n \"\"\"Evaluate the convolution kernel on input 'conv_input'.\"\"\"\n raise NotImplementedError\n\n\nclass Conv2DLayerWithPadding(BaseConv2DLayerWithPadding):\n \"\"\"Conv2D layer.\"\"\"\n\n @base_layer.initializer\n def __init__(self, params):\n super(Conv2DLayerWithPadding, self).__init__(params)\n p = self.params\n assert p.name\n w_pc = py_utils.WeightParams(\n shape=p.filter_shape,\n init=p.params_init,\n dtype=p.dtype,\n collections=[self.__class__.__name__ + '_vars'])\n with tf.variable_scope(p.name):\n self.CreateVariable('w', w_pc)\n if p.weight_norm:\n self.CreateVariable(\n 'g',\n py_utils.WeightParams(\n shape=[p.filter_shape[-1]],\n init=py_utils.WeightInit.Constant(0.0),\n dtype=p.dtype,\n collections=[self.__class__.__name__ + '_vars']))\n\n @property\n def output_channels(self):\n \"\"\"The number of output channels for this conv layer.\"\"\"\n p = self.params\n return p.filter_shape[-1]\n\n def _GetWeight(self, theta):\n p = self.params\n if p.weight_norm:\n # Normalize along the last dim (standard conv).\n filter_w = tf.nn.l2_normalize(theta.w, [0, 1, 2]) * tf.reshape(\n (theta.g + 1.0), [1, 1, 1, p.filter_shape[-1]])\n else:\n filter_w = theta.w\n return filter_w\n\n def _EvaluateConvKernel(self, theta, inputs):\n \"\"\"Apply convolution to inputs.\"\"\"\n p = self.params\n filter_w = self._GetWeight(theta)\n return tf.nn.convolution(\n inputs,\n filter_w,\n strides=p.filter_stride,\n dilation_rate=p.dilation_rate,\n data_format='NHWC',\n padding='SAME')\n\n\nclass CausalConv2DLayerWithPadding(Conv2DLayerWithPadding):\n \"\"\"2D conv layer with causal dependency on the time axis.\"\"\"\n\n @base_layer.initializer\n def __init__(self, params):\n super(CausalConv2DLayerWithPadding, self).__init__(params)\n p = self.params\n assert p.filter_shape[1] == 1, 'Only 1d causal convolution is supported.'\n\n def _EvaluateConvKernel(self, theta, inputs):\n \"\"\"Apply convolution to inputs.\"\"\"\n p = self.params\n assert p.filter_shape[1] == 1, 'Only 1D causal convolutions supported.'\n # Use VALID padding and shift the inputs to the right to ensure that the\n # first output only depends on the first input and so on. The output is\n # the same size as the input, as if the convolution used SAME padding.\n padding_algorithm = 'VALID'\n # The effective spatial filter width for dilated convolutions is\n # (kernel_width - 1) * dilation_rate + 1 as according to\n # https://www.tensorflow.org/api_docs/python/tf/nn/convolution.\n causal_pad_size = (p.filter_shape[0] - 1) * p.dilation_rate[0]\n inputs = tf.pad(inputs, [[0, 0], [causal_pad_size, 0], [0, 0], [0, 0]])\n\n filter_w = self._GetWeight(theta)\n return tf.nn.convolution(\n inputs,\n filter_w,\n strides=p.filter_stride,\n dilation_rate=p.dilation_rate,\n data_format='NHWC',\n padding=padding_algorithm)\n\n\nclass DepthwiseConv2DLayer(BaseConv2DLayerWithPadding):\n \"\"\"Depthwise conv 2D layer.\n\n paper: https://arxiv.org/abs/1610.02357\n \"\"\"\n\n @classmethod\n def Params(cls):\n p = super(DepthwiseConv2DLayer, cls).Params()\n # Redefine 'filter_shape' since the semantic of shape elements is different\n # from regular Conv2D.\n p.Delete('filter_shape')\n p.Define(\n 'filter_shape', (0, 0, 0, 0),\n 'Filter shape. Must be a sequence of length 4. Elements are in'\n ' the order of height (time), width (frequency), in_channel,'\n ' channel_multipliers. ')\n return p\n\n @base_layer.initializer\n def __init__(self, params):\n super(DepthwiseConv2DLayer, self).__init__(params)\n p = self.params\n assert p.name\n w_pc = py_utils.WeightParams(\n shape=p.filter_shape,\n init=p.params_init,\n dtype=p.dtype,\n collections=[self.__class__.__name__ + '_vars'])\n\n with tf.variable_scope(p.name):\n self.CreateVariable('w', w_pc)\n if p.weight_norm:\n self.CreateVariable(\n 'g',\n py_utils.WeightParams(\n shape=[p.filter_shape[2], p.filter_shape[3]],\n init=py_utils.WeightInit.Constant(0.0),\n dtype=p.dtype,\n collections=[self.__class__.__name__ + '_vars']))\n\n @property\n def output_channels(self):\n \"\"\"The number of output channels for this conv layer.\"\"\"\n p = self.params\n # Depthwise convolution filter shape is:\n # [..., in_channels, channel_multiplier].\n return p.filter_shape[2] * p.filter_shape[3]\n\n def _GetWeight(self, theta):\n p = self.params\n if p.weight_norm:\n # Normalize along the last two dims.\n filter_w = tf.nn.l2_normalize(theta.w, [0, 1]) * tf.reshape(\n (theta.g + 1.0), [1, 1, p.filter_shape[2], p.filter_shape[3]])\n else:\n filter_w = theta.w\n return filter_w\n\n def _EvaluateConvKernel(self, theta, inputs):\n \"\"\"Apply convolution to inputs.\"\"\"\n p = self.params\n filter_w = self._GetWeight(theta)\n return tf.nn.depthwise_conv2d(\n inputs,\n filter_w,\n strides=[1, p.filter_stride[0], p.filter_stride[1], 1],\n rate=p.dilation_rate,\n data_format='NHWC',\n padding='SAME')\n\n\nclass CausalDepthwiseConv2DLayer(DepthwiseConv2DLayer):\n \"\"\"Depthwise conv layer with causal dependency on the time axis.\"\"\"\n\n @base_layer.initializer\n def __init__(self, params):\n super(CausalDepthwiseConv2DLayer, self).__init__(params)\n p = self.params\n assert p.filter_shape[1] == 1, 'Only 1d causal convolution is supported.'\n\n def _EvaluateConvKernel(self, theta, inputs):\n \"\"\"Apply convolution to inputs.\"\"\"\n p = self.params\n assert p.filter_shape[1] == 1, 'Only 1D causal convolutions supported.'\n # Use VALID padding and shift the inputs to the right to ensure that the\n # first output only depends on the first input and so on. The output is\n # the same size as the input, as if the convolution used SAME padding.\n padding_algorithm = 'VALID'\n # The effective spatial filter width for dilated convolutions is\n # (kernel_width - 1) * dilation_rate + 1 as according to\n # https://www.tensorflow.org/api_docs/python/tf/nn/convolution.\n causal_pad_size = (p.filter_shape[0] - 1) * p.dilation_rate[0]\n inputs = tf.pad(inputs, [[0, 0], [causal_pad_size, 0], [0, 0], [0, 0]])\n filter_w = self._GetWeight(theta)\n return tf.nn.depthwise_conv2d(\n inputs,\n filter_w,\n strides=[1, p.filter_stride[0], p.filter_stride[1], 1],\n rate=p.dilation_rate,\n data_format='NHWC',\n padding=padding_algorithm)\n\n\nclass NormalizedDepthwiseConv2DLayer(DepthwiseConv2DLayer):\n \"\"\"DepthwiseConv2DLayer where weights are normalized over the time dim.\n\n https://arxiv.org/abs/1901.10430\n \"\"\"\n\n @classmethod\n def Params(cls):\n p = super(NormalizedDepthwiseConv2DLayer, cls).Params()\n p.Define('dropconnect_prob', 0.0,\n 'Prob at which DropConnect regularization is performed.')\n p.Define('deterministic_dropout', False, 'Use determnisitc dropout or not.')\n p.Define('temperature', 1.0,\n 'Temperature for the softmax normalization of the weights.')\n p.Define('weight_tiling_factor', 1,\n 'Number of times weights are tiled over the input channels.')\n return p\n\n @base_layer.initializer\n def __init__(self, params):\n super(NormalizedDepthwiseConv2DLayer, self).__init__(params)\n p = self.params\n assert p.filter_shape[1] == 1, 'Only 1d convolution is supported.'\n assert p.temperature > 0.0, 'Absolute zero temperature is not possible.'\n\n @property\n def output_channels(self):\n \"\"\"The number of output channels for this conv layer.\"\"\"\n p = self.params\n # Depthwise convolution filter shape is:\n # [kernel_size, 1, in_channels, channel_multiplier].\n return p.filter_shape[2] * p.filter_shape[3] * p.weight_tiling_factor\n\n @property\n def input_channels(self):\n \"\"\"The number of output channels for this conv layer.\"\"\"\n p = self.params\n return p.filter_shape[2] * p.weight_tiling_factor\n\n def _GetWeight(self, theta):\n p = self.params\n filter_w = theta.w\n\n # First normalize filter_w over the temporal dimension here.\n filter_w = tf.nn.softmax(filter_w / p.temperature, axis=0)\n\n # Add dropconnect on the weights for regularization.\n if p.dropconnect_prob > 0.0 and not p.is_eval:\n if p.deterministic_dropout:\n filter_w = py_utils.DeterministicDropout(\n filter_w, 1.0 - p.dropconnect_prob,\n py_utils.GenerateStepSeedPair(p, theta.global_step))\n else:\n filter_w = tf.nn.dropout(\n filter_w, 1.0 - p.dropconnect_prob, seed=p.random_seed)\n\n # Tie the parameters of every subsequent number of weight_tiling_factor\n # channels.\n filter_w = tf.tile(filter_w, [1, 1, p.weight_tiling_factor, 1])\n return filter_w\n\n @classmethod\n def FPropMeta(cls, p, inputs, paddings):\n py_utils.CheckShapes((inputs, paddings))\n b, t, f, ic = inputs\n assert f == 1\n oc = p.filter_shape[2] * p.filter_shape[3] * p.weight_tiling_factor\n outputs = tshape.Shape([b, t, f, oc])\n flops = b * t * f * p.filter_shape[0] * ic * oc * 5\n return py_utils.NestedMap(flops=flops, out_shapes=(outputs, paddings))\n\n\nclass CausalNormalizedDepthwiseConv2DLayer(NormalizedDepthwiseConv2DLayer):\n \"\"\"Depthwise conv layer with causal dependency on the time axis.\"\"\"\n\n def _EvaluateConvKernel(self, theta, inputs):\n \"\"\"Apply convolution to inputs.\"\"\"\n # Same as CausalDepthwiseConv2DLayer.\n p = self.params\n assert p.filter_shape[1] == 1, 'Only 1D causal convolutions supported.'\n padding_algorithm = 'VALID'\n causal_pad_size = (p.filter_shape[0] - 1) * p.dilation_rate[0]\n inputs = tf.pad(inputs, [[0, 0], [causal_pad_size, 0], [0, 0], [0, 0]])\n filter_w = self._GetWeight(theta)\n return tf.nn.depthwise_conv2d(\n inputs,\n filter_w,\n strides=[1, p.filter_stride[0], p.filter_stride[1], 1],\n rate=p.dilation_rate,\n data_format='NHWC',\n padding=padding_algorithm)\n\n\nclass ConvBatchNormLayer(bn_layers.BatchNormLayer):\n \"\"\"A wrapper around regular BatchNormLayer that pass around the ...\n\n paddings layers.\n \"\"\"\n\n def FProp(self, theta, inputs, paddings):\n paddings_expanded = tf.expand_dims(tf.expand_dims(paddings, -1), -1)\n bned = super(ConvBatchNormLayer, self).FProp(\n theta, inputs, paddings_expanded)\n return bned, paddings\n\n\n# Supported activation functions.\n_ACTIVATIONS = {\n 'RELU': tf.nn.relu,\n 'RELU6': tf.nn.relu6,\n 'SIGMOID': tf.sigmoid,\n 'TANH': tf.tanh,\n 'SWISH': tf.nn.swish,\n 'NONE': tf.identity,\n}\n\n\nclass ActivationLayer(base_layer.BaseLayer):\n \"\"\"Applies activation function to the inputs.\"\"\"\n\n @classmethod\n def Params(cls):\n p = super(ActivationLayer, cls).Params()\n p.Define('activation', 'RELU',\n 'The activation function to apply')\n return p\n\n def FProp(self, theta, inputs, paddings):\n p = self.params\n out = _ACTIVATIONS[p.activation](inputs)\n return out, paddings\n\n\nclass PaddingLayer(base_layer.BaseLayer):\n \"\"\"Zeros out padded positions.\"\"\"\n\n def FProp(self, theta, inputs, paddings):\n paddings_expanded = tf.expand_dims(tf.expand_dims(paddings, -1), -1)\n return inputs * (1.0 - paddings_expanded), paddings\n"}}},{"rowIdx":66,"cells":{"function_name":{"kind":"string","value":"list_topic_keys"},"docstring":{"kind":"string","value":"Namespace/ServiceBus Connection String\nAPI Version: 2017-04-01.\n\n\n:param str authorization_rule_name: The authorization rule name.\n:param str namespace_name: The namespace name\n:param str resource_group_name: Name of the Resource group within the Azure subscription.\n:param str topic_name: The topic name."},"masked_code":{"kind":"string","value":"# coding=utf-8\n# *** WARNING: this file was generated by the Pulumi SDK Generator. ***\n# *** Do not edit by hand unless you're certain you know what you are doing! ***\n\nimport warnings\nimport pulumi\nimport pulumi.runtime\nfrom typing import Any, Mapping, Optional, Sequence, Union\nfrom .. import _utilities, _tables\n\n__all__ = [\n 'ListTopicKeysResult',\n 'AwaitableListTopicKeysResult',\n 'list_topic_keys',\n]\n\n@pulumi.output_type\nclass ListTopicKeysResult:\n \"\"\"\n Namespace/ServiceBus Connection String\n \"\"\"\n def __init__(__self__, alias_primary_connection_string=None, alias_secondary_connection_string=None, key_name=None, primary_connection_string=None, primary_key=None, secondary_connection_string=None, secondary_key=None):\n if alias_primary_connection_string and not isinstance(alias_primary_connection_string, str):\n raise TypeError(\"Expected argument 'alias_primary_connection_string' to be a str\")\n pulumi.set(__self__, \"alias_primary_connection_string\", alias_primary_connection_string)\n if alias_secondary_connection_string and not isinstance(alias_secondary_connection_string, str):\n raise TypeError(\"Expected argument 'alias_secondary_connection_string' to be a str\")\n pulumi.set(__self__, \"alias_secondary_connection_string\", alias_secondary_connection_string)\n if key_name and not isinstance(key_name, str):\n raise TypeError(\"Expected argument 'key_name' to be a str\")\n pulumi.set(__self__, \"key_name\", key_name)\n if primary_connection_string and not isinstance(primary_connection_string, str):\n raise TypeError(\"Expected argument 'primary_connection_string' to be a str\")\n pulumi.set(__self__, \"primary_connection_string\", primary_connection_string)\n if primary_key and not isinstance(primary_key, str):\n raise TypeError(\"Expected argument 'primary_key' to be a str\")\n pulumi.set(__self__, \"primary_key\", primary_key)\n if secondary_connection_string and not isinstance(secondary_connection_string, str):\n raise TypeError(\"Expected argument 'secondary_connection_string' to be a str\")\n pulumi.set(__self__, \"secondary_connection_string\", secondary_connection_string)\n if secondary_key and not isinstance(secondary_key, str):\n raise TypeError(\"Expected argument 'secondary_key' to be a str\")\n pulumi.set(__self__, \"secondary_key\", secondary_key)\n\n @property\n @pulumi.getter(name=\"aliasPrimaryConnectionString\")\n def alias_primary_connection_string(self) -> str:\n \"\"\"\n Primary connection string of the alias if GEO DR is enabled\n \"\"\"\n return pulumi.get(self, \"alias_primary_connection_string\")\n\n @property\n @pulumi.getter(name=\"aliasSecondaryConnectionString\")\n def alias_secondary_connection_string(self) -> str:\n \"\"\"\n Secondary connection string of the alias if GEO DR is enabled\n \"\"\"\n return pulumi.get(self, \"alias_secondary_connection_string\")\n\n @property\n @pulumi.getter(name=\"keyName\")\n def key_name(self) -> str:\n \"\"\"\n A string that describes the authorization rule.\n \"\"\"\n return pulumi.get(self, \"key_name\")\n\n @property\n @pulumi.getter(name=\"primaryConnectionString\")\n def primary_connection_string(self) -> str:\n \"\"\"\n Primary connection string of the created namespace authorization rule.\n \"\"\"\n return pulumi.get(self, \"primary_connection_string\")\n\n @property\n @pulumi.getter(name=\"primaryKey\")\n def primary_key(self) -> str:\n \"\"\"\n A base64-encoded 256-bit primary key for signing and validating the SAS token.\n \"\"\"\n return pulumi.get(self, \"primary_key\")\n\n @property\n @pulumi.getter(name=\"secondaryConnectionString\")\n def secondary_connection_string(self) -> str:\n \"\"\"\n Secondary connection string of the created namespace authorization rule.\n \"\"\"\n return pulumi.get(self, \"secondary_connection_string\")\n\n @property\n @pulumi.getter(name=\"secondaryKey\")\n def secondary_key(self) -> str:\n \"\"\"\n A base64-encoded 256-bit primary key for signing and validating the SAS token.\n \"\"\"\n return pulumi.get(self, \"secondary_key\")\n\n\nclass AwaitableListTopicKeysResult(ListTopicKeysResult):\n # pylint: disable=using-constant-test\n def __await__(self):\n if False:\n yield self\n return ListTopicKeysResult(\n alias_primary_connection_string=self.alias_primary_connection_string,\n alias_secondary_connection_string=self.alias_secondary_connection_string,\n key_name=self.key_name,\n primary_connection_string=self.primary_connection_string,\n primary_key=self.primary_key,\n secondary_connection_string=self.secondary_connection_string,\n secondary_key=self.secondary_key)\n\n\n# MASKED: list_topic_keys function (lines 117-150)"},"implementation":{"kind":"string","value":"def list_topic_keys(authorization_rule_name: Optional[str] = None,\n namespace_name: Optional[str] = None,\n resource_group_name: Optional[str] = None,\n topic_name: Optional[str] = None,\n opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableListTopicKeysResult:\n \"\"\"\n Namespace/ServiceBus Connection String\n API Version: 2017-04-01.\n\n\n :param str authorization_rule_name: The authorization rule name.\n :param str namespace_name: The namespace name\n :param str resource_group_name: Name of the Resource group within the Azure subscription.\n :param str topic_name: The topic name.\n \"\"\"\n __args__ = dict()\n __args__['authorizationRuleName'] = authorization_rule_name\n __args__['namespaceName'] = namespace_name\n __args__['resourceGroupName'] = resource_group_name\n __args__['topicName'] = topic_name\n if opts is None:\n opts = pulumi.InvokeOptions()\n if opts.version is None:\n opts.version = _utilities.get_version()\n __ret__ = pulumi.runtime.invoke('azure-nextgen:servicebus:listTopicKeys', __args__, opts=opts, typ=ListTopicKeysResult).value\n\n return AwaitableListTopicKeysResult(\n alias_primary_connection_string=__ret__.alias_primary_connection_string,\n alias_secondary_connection_string=__ret__.alias_secondary_connection_string,\n key_name=__ret__.key_name,\n primary_connection_string=__ret__.primary_connection_string,\n primary_key=__ret__.primary_key,\n secondary_connection_string=__ret__.secondary_connection_string,\n secondary_key=__ret__.secondary_key)"},"start_line":{"kind":"number","value":117,"string":"117"},"end_line":{"kind":"number","value":150,"string":"150"},"file_content":{"kind":"string","value":"# coding=utf-8\n# *** WARNING: this file was generated by the Pulumi SDK Generator. ***\n# *** Do not edit by hand unless you're certain you know what you are doing! ***\n\nimport warnings\nimport pulumi\nimport pulumi.runtime\nfrom typing import Any, Mapping, Optional, Sequence, Union\nfrom .. import _utilities, _tables\n\n__all__ = [\n 'ListTopicKeysResult',\n 'AwaitableListTopicKeysResult',\n 'list_topic_keys',\n]\n\n@pulumi.output_type\nclass ListTopicKeysResult:\n \"\"\"\n Namespace/ServiceBus Connection String\n \"\"\"\n def __init__(__self__, alias_primary_connection_string=None, alias_secondary_connection_string=None, key_name=None, primary_connection_string=None, primary_key=None, secondary_connection_string=None, secondary_key=None):\n if alias_primary_connection_string and not isinstance(alias_primary_connection_string, str):\n raise TypeError(\"Expected argument 'alias_primary_connection_string' to be a str\")\n pulumi.set(__self__, \"alias_primary_connection_string\", alias_primary_connection_string)\n if alias_secondary_connection_string and not isinstance(alias_secondary_connection_string, str):\n raise TypeError(\"Expected argument 'alias_secondary_connection_string' to be a str\")\n pulumi.set(__self__, \"alias_secondary_connection_string\", alias_secondary_connection_string)\n if key_name and not isinstance(key_name, str):\n raise TypeError(\"Expected argument 'key_name' to be a str\")\n pulumi.set(__self__, \"key_name\", key_name)\n if primary_connection_string and not isinstance(primary_connection_string, str):\n raise TypeError(\"Expected argument 'primary_connection_string' to be a str\")\n pulumi.set(__self__, \"primary_connection_string\", primary_connection_string)\n if primary_key and not isinstance(primary_key, str):\n raise TypeError(\"Expected argument 'primary_key' to be a str\")\n pulumi.set(__self__, \"primary_key\", primary_key)\n if secondary_connection_string and not isinstance(secondary_connection_string, str):\n raise TypeError(\"Expected argument 'secondary_connection_string' to be a str\")\n pulumi.set(__self__, \"secondary_connection_string\", secondary_connection_string)\n if secondary_key and not isinstance(secondary_key, str):\n raise TypeError(\"Expected argument 'secondary_key' to be a str\")\n pulumi.set(__self__, \"secondary_key\", secondary_key)\n\n @property\n @pulumi.getter(name=\"aliasPrimaryConnectionString\")\n def alias_primary_connection_string(self) -> str:\n \"\"\"\n Primary connection string of the alias if GEO DR is enabled\n \"\"\"\n return pulumi.get(self, \"alias_primary_connection_string\")\n\n @property\n @pulumi.getter(name=\"aliasSecondaryConnectionString\")\n def alias_secondary_connection_string(self) -> str:\n \"\"\"\n Secondary connection string of the alias if GEO DR is enabled\n \"\"\"\n return pulumi.get(self, \"alias_secondary_connection_string\")\n\n @property\n @pulumi.getter(name=\"keyName\")\n def key_name(self) -> str:\n \"\"\"\n A string that describes the authorization rule.\n \"\"\"\n return pulumi.get(self, \"key_name\")\n\n @property\n @pulumi.getter(name=\"primaryConnectionString\")\n def primary_connection_string(self) -> str:\n \"\"\"\n Primary connection string of the created namespace authorization rule.\n \"\"\"\n return pulumi.get(self, \"primary_connection_string\")\n\n @property\n @pulumi.getter(name=\"primaryKey\")\n def primary_key(self) -> str:\n \"\"\"\n A base64-encoded 256-bit primary key for signing and validating the SAS token.\n \"\"\"\n return pulumi.get(self, \"primary_key\")\n\n @property\n @pulumi.getter(name=\"secondaryConnectionString\")\n def secondary_connection_string(self) -> str:\n \"\"\"\n Secondary connection string of the created namespace authorization rule.\n \"\"\"\n return pulumi.get(self, \"secondary_connection_string\")\n\n @property\n @pulumi.getter(name=\"secondaryKey\")\n def secondary_key(self) -> str:\n \"\"\"\n A base64-encoded 256-bit primary key for signing and validating the SAS token.\n \"\"\"\n return pulumi.get(self, \"secondary_key\")\n\n\nclass AwaitableListTopicKeysResult(ListTopicKeysResult):\n # pylint: disable=using-constant-test\n def __await__(self):\n if False:\n yield self\n return ListTopicKeysResult(\n alias_primary_connection_string=self.alias_primary_connection_string,\n alias_secondary_connection_string=self.alias_secondary_connection_string,\n key_name=self.key_name,\n primary_connection_string=self.primary_connection_string,\n primary_key=self.primary_key,\n secondary_connection_string=self.secondary_connection_string,\n secondary_key=self.secondary_key)\n\n\ndef list_topic_keys(authorization_rule_name: Optional[str] = None,\n namespace_name: Optional[str] = None,\n resource_group_name: Optional[str] = None,\n topic_name: Optional[str] = None,\n opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableListTopicKeysResult:\n \"\"\"\n Namespace/ServiceBus Connection String\n API Version: 2017-04-01.\n\n\n :param str authorization_rule_name: The authorization rule name.\n :param str namespace_name: The namespace name\n :param str resource_group_name: Name of the Resource group within the Azure subscription.\n :param str topic_name: The topic name.\n \"\"\"\n __args__ = dict()\n __args__['authorizationRuleName'] = authorization_rule_name\n __args__['namespaceName'] = namespace_name\n __args__['resourceGroupName'] = resource_group_name\n __args__['topicName'] = topic_name\n if opts is None:\n opts = pulumi.InvokeOptions()\n if opts.version is None:\n opts.version = _utilities.get_version()\n __ret__ = pulumi.runtime.invoke('azure-nextgen:servicebus:listTopicKeys', __args__, opts=opts, typ=ListTopicKeysResult).value\n\n return AwaitableListTopicKeysResult(\n alias_primary_connection_string=__ret__.alias_primary_connection_string,\n alias_secondary_connection_string=__ret__.alias_secondary_connection_string,\n key_name=__ret__.key_name,\n primary_connection_string=__ret__.primary_connection_string,\n primary_key=__ret__.primary_key,\n secondary_connection_string=__ret__.secondary_connection_string,\n secondary_key=__ret__.secondary_key)\n"}}},{"rowIdx":67,"cells":{"function_name":{"kind":"string","value":"__init__"},"docstring":{"kind":"string","value":"Instantiate the pager.\n\nArgs:\n method (Callable): The method that was originally called, and\n which instantiated this pager.\n request (google.cloud.compute_v1.types.AggregatedListTargetInstancesRequest):\n The initial request object.\n response (google.cloud.compute_v1.types.TargetInstanceAggregatedList):\n The initial response object.\n metadata (Sequence[Tuple[str, str]]): Strings which should be\n sent along with the request as metadata."},"masked_code":{"kind":"string","value":"# -*- coding: utf-8 -*-\n# Copyright 2020 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\nfrom typing import (\n Any,\n AsyncIterable,\n Awaitable,\n Callable,\n Iterable,\n Sequence,\n Tuple,\n Optional,\n)\n\nfrom google.cloud.compute_v1.types import compute\n\n\nclass AggregatedListPager:\n \"\"\"A pager for iterating through ``aggregated_list`` requests.\n\n This class thinly wraps an initial\n :class:`google.cloud.compute_v1.types.TargetInstanceAggregatedList` object, and\n provides an ``__iter__`` method to iterate through its\n ``items`` field.\n\n If there are more pages, the ``__iter__`` method will make additional\n ``AggregatedList`` requests and continue to iterate\n through the ``items`` field on the\n corresponding responses.\n\n All the usual :class:`google.cloud.compute_v1.types.TargetInstanceAggregatedList`\n attributes are available on the pager. If multiple requests are made, only\n the most recent response is retained, and thus used for attribute lookup.\n \"\"\"\n\n# MASKED: __init__ function (lines 48-71)\n\n def __getattr__(self, name: str) -> Any:\n return getattr(self._response, name)\n\n @property\n def pages(self) -> Iterable[compute.TargetInstanceAggregatedList]:\n yield self._response\n while self._response.next_page_token:\n self._request.page_token = self._response.next_page_token\n self._response = self._method(self._request, metadata=self._metadata)\n yield self._response\n\n def __iter__(self) -> Iterable[Tuple[str, compute.TargetInstancesScopedList]]:\n for page in self.pages:\n yield from page.items.items()\n\n def get(self, key: str) -> Optional[compute.TargetInstancesScopedList]:\n return self._response.items.get(key)\n\n def __repr__(self) -> str:\n return \"{0}<{1!r}>\".format(self.__class__.__name__, self._response)\n\n\nclass ListPager:\n \"\"\"A pager for iterating through ``list`` requests.\n\n This class thinly wraps an initial\n :class:`google.cloud.compute_v1.types.TargetInstanceList` object, and\n provides an ``__iter__`` method to iterate through its\n ``items`` field.\n\n If there are more pages, the ``__iter__`` method will make additional\n ``List`` requests and continue to iterate\n through the ``items`` field on the\n corresponding responses.\n\n All the usual :class:`google.cloud.compute_v1.types.TargetInstanceList`\n attributes are available on the pager. If multiple requests are made, only\n the most recent response is retained, and thus used for attribute lookup.\n \"\"\"\n\n def __init__(\n self,\n method: Callable[..., compute.TargetInstanceList],\n request: compute.ListTargetInstancesRequest,\n response: compute.TargetInstanceList,\n *,\n metadata: Sequence[Tuple[str, str]] = ()\n ):\n \"\"\"Instantiate the pager.\n\n Args:\n method (Callable): The method that was originally called, and\n which instantiated this pager.\n request (google.cloud.compute_v1.types.ListTargetInstancesRequest):\n The initial request object.\n response (google.cloud.compute_v1.types.TargetInstanceList):\n The initial response object.\n metadata (Sequence[Tuple[str, str]]): Strings which should be\n sent along with the request as metadata.\n \"\"\"\n self._method = method\n self._request = compute.ListTargetInstancesRequest(request)\n self._response = response\n self._metadata = metadata\n\n def __getattr__(self, name: str) -> Any:\n return getattr(self._response, name)\n\n @property\n def pages(self) -> Iterable[compute.TargetInstanceList]:\n yield self._response\n while self._response.next_page_token:\n self._request.page_token = self._response.next_page_token\n self._response = self._method(self._request, metadata=self._metadata)\n yield self._response\n\n def __iter__(self) -> Iterable[compute.TargetInstance]:\n for page in self.pages:\n yield from page.items\n\n def __repr__(self) -> str:\n return \"{0}<{1!r}>\".format(self.__class__.__name__, self._response)"},"implementation":{"kind":"string","value":" def __init__(\n self,\n method: Callable[..., compute.TargetInstanceAggregatedList],\n request: compute.AggregatedListTargetInstancesRequest,\n response: compute.TargetInstanceAggregatedList,\n *,\n metadata: Sequence[Tuple[str, str]] = ()\n ):\n \"\"\"Instantiate the pager.\n\n Args:\n method (Callable): The method that was originally called, and\n which instantiated this pager.\n request (google.cloud.compute_v1.types.AggregatedListTargetInstancesRequest):\n The initial request object.\n response (google.cloud.compute_v1.types.TargetInstanceAggregatedList):\n The initial response object.\n metadata (Sequence[Tuple[str, str]]): Strings which should be\n sent along with the request as metadata.\n \"\"\"\n self._method = method\n self._request = compute.AggregatedListTargetInstancesRequest(request)\n self._response = response\n self._metadata = metadata"},"start_line":{"kind":"number","value":48,"string":"48"},"end_line":{"kind":"number","value":71,"string":"71"},"file_content":{"kind":"string","value":"# -*- coding: utf-8 -*-\n# Copyright 2020 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\nfrom typing import (\n Any,\n AsyncIterable,\n Awaitable,\n Callable,\n Iterable,\n Sequence,\n Tuple,\n Optional,\n)\n\nfrom google.cloud.compute_v1.types import compute\n\n\nclass AggregatedListPager:\n \"\"\"A pager for iterating through ``aggregated_list`` requests.\n\n This class thinly wraps an initial\n :class:`google.cloud.compute_v1.types.TargetInstanceAggregatedList` object, and\n provides an ``__iter__`` method to iterate through its\n ``items`` field.\n\n If there are more pages, the ``__iter__`` method will make additional\n ``AggregatedList`` requests and continue to iterate\n through the ``items`` field on the\n corresponding responses.\n\n All the usual :class:`google.cloud.compute_v1.types.TargetInstanceAggregatedList`\n attributes are available on the pager. If multiple requests are made, only\n the most recent response is retained, and thus used for attribute lookup.\n \"\"\"\n\n def __init__(\n self,\n method: Callable[..., compute.TargetInstanceAggregatedList],\n request: compute.AggregatedListTargetInstancesRequest,\n response: compute.TargetInstanceAggregatedList,\n *,\n metadata: Sequence[Tuple[str, str]] = ()\n ):\n \"\"\"Instantiate the pager.\n\n Args:\n method (Callable): The method that was originally called, and\n which instantiated this pager.\n request (google.cloud.compute_v1.types.AggregatedListTargetInstancesRequest):\n The initial request object.\n response (google.cloud.compute_v1.types.TargetInstanceAggregatedList):\n The initial response object.\n metadata (Sequence[Tuple[str, str]]): Strings which should be\n sent along with the request as metadata.\n \"\"\"\n self._method = method\n self._request = compute.AggregatedListTargetInstancesRequest(request)\n self._response = response\n self._metadata = metadata\n\n def __getattr__(self, name: str) -> Any:\n return getattr(self._response, name)\n\n @property\n def pages(self) -> Iterable[compute.TargetInstanceAggregatedList]:\n yield self._response\n while self._response.next_page_token:\n self._request.page_token = self._response.next_page_token\n self._response = self._method(self._request, metadata=self._metadata)\n yield self._response\n\n def __iter__(self) -> Iterable[Tuple[str, compute.TargetInstancesScopedList]]:\n for page in self.pages:\n yield from page.items.items()\n\n def get(self, key: str) -> Optional[compute.TargetInstancesScopedList]:\n return self._response.items.get(key)\n\n def __repr__(self) -> str:\n return \"{0}<{1!r}>\".format(self.__class__.__name__, self._response)\n\n\nclass ListPager:\n \"\"\"A pager for iterating through ``list`` requests.\n\n This class thinly wraps an initial\n :class:`google.cloud.compute_v1.types.TargetInstanceList` object, and\n provides an ``__iter__`` method to iterate through its\n ``items`` field.\n\n If there are more pages, the ``__iter__`` method will make additional\n ``List`` requests and continue to iterate\n through the ``items`` field on the\n corresponding responses.\n\n All the usual :class:`google.cloud.compute_v1.types.TargetInstanceList`\n attributes are available on the pager. If multiple requests are made, only\n the most recent response is retained, and thus used for attribute lookup.\n \"\"\"\n\n def __init__(\n self,\n method: Callable[..., compute.TargetInstanceList],\n request: compute.ListTargetInstancesRequest,\n response: compute.TargetInstanceList,\n *,\n metadata: Sequence[Tuple[str, str]] = ()\n ):\n \"\"\"Instantiate the pager.\n\n Args:\n method (Callable): The method that was originally called, and\n which instantiated this pager.\n request (google.cloud.compute_v1.types.ListTargetInstancesRequest):\n The initial request object.\n response (google.cloud.compute_v1.types.TargetInstanceList):\n The initial response object.\n metadata (Sequence[Tuple[str, str]]): Strings which should be\n sent along with the request as metadata.\n \"\"\"\n self._method = method\n self._request = compute.ListTargetInstancesRequest(request)\n self._response = response\n self._metadata = metadata\n\n def __getattr__(self, name: str) -> Any:\n return getattr(self._response, name)\n\n @property\n def pages(self) -> Iterable[compute.TargetInstanceList]:\n yield self._response\n while self._response.next_page_token:\n self._request.page_token = self._response.next_page_token\n self._response = self._method(self._request, metadata=self._metadata)\n yield self._response\n\n def __iter__(self) -> Iterable[compute.TargetInstance]:\n for page in self.pages:\n yield from page.items\n\n def __repr__(self) -> str:\n return \"{0}<{1!r}>\".format(self.__class__.__name__, self._response)\n"}}},{"rowIdx":68,"cells":{"function_name":{"kind":"string","value":"__init__"},"docstring":{"kind":"string","value":"Instantiate the pager.\n\nArgs:\n method (Callable): The method that was originally called, and\n which instantiated this pager.\n request (google.cloud.compute_v1.types.ListTargetInstancesRequest):\n The initial request object.\n response (google.cloud.compute_v1.types.TargetInstanceList):\n The initial response object.\n metadata (Sequence[Tuple[str, str]]): Strings which should be\n sent along with the request as metadata."},"masked_code":{"kind":"string","value":"# -*- coding: utf-8 -*-\n# Copyright 2020 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\nfrom typing import (\n Any,\n AsyncIterable,\n Awaitable,\n Callable,\n Iterable,\n Sequence,\n Tuple,\n Optional,\n)\n\nfrom google.cloud.compute_v1.types import compute\n\n\nclass AggregatedListPager:\n \"\"\"A pager for iterating through ``aggregated_list`` requests.\n\n This class thinly wraps an initial\n :class:`google.cloud.compute_v1.types.TargetInstanceAggregatedList` object, and\n provides an ``__iter__`` method to iterate through its\n ``items`` field.\n\n If there are more pages, the ``__iter__`` method will make additional\n ``AggregatedList`` requests and continue to iterate\n through the ``items`` field on the\n corresponding responses.\n\n All the usual :class:`google.cloud.compute_v1.types.TargetInstanceAggregatedList`\n attributes are available on the pager. If multiple requests are made, only\n the most recent response is retained, and thus used for attribute lookup.\n \"\"\"\n\n def __init__(\n self,\n method: Callable[..., compute.TargetInstanceAggregatedList],\n request: compute.AggregatedListTargetInstancesRequest,\n response: compute.TargetInstanceAggregatedList,\n *,\n metadata: Sequence[Tuple[str, str]] = ()\n ):\n \"\"\"Instantiate the pager.\n\n Args:\n method (Callable): The method that was originally called, and\n which instantiated this pager.\n request (google.cloud.compute_v1.types.AggregatedListTargetInstancesRequest):\n The initial request object.\n response (google.cloud.compute_v1.types.TargetInstanceAggregatedList):\n The initial response object.\n metadata (Sequence[Tuple[str, str]]): Strings which should be\n sent along with the request as metadata.\n \"\"\"\n self._method = method\n self._request = compute.AggregatedListTargetInstancesRequest(request)\n self._response = response\n self._metadata = metadata\n\n def __getattr__(self, name: str) -> Any:\n return getattr(self._response, name)\n\n @property\n def pages(self) -> Iterable[compute.TargetInstanceAggregatedList]:\n yield self._response\n while self._response.next_page_token:\n self._request.page_token = self._response.next_page_token\n self._response = self._method(self._request, metadata=self._metadata)\n yield self._response\n\n def __iter__(self) -> Iterable[Tuple[str, compute.TargetInstancesScopedList]]:\n for page in self.pages:\n yield from page.items.items()\n\n def get(self, key: str) -> Optional[compute.TargetInstancesScopedList]:\n return self._response.items.get(key)\n\n def __repr__(self) -> str:\n return \"{0}<{1!r}>\".format(self.__class__.__name__, self._response)\n\n\nclass ListPager:\n \"\"\"A pager for iterating through ``list`` requests.\n\n This class thinly wraps an initial\n :class:`google.cloud.compute_v1.types.TargetInstanceList` object, and\n provides an ``__iter__`` method to iterate through its\n ``items`` field.\n\n If there are more pages, the ``__iter__`` method will make additional\n ``List`` requests and continue to iterate\n through the ``items`` field on the\n corresponding responses.\n\n All the usual :class:`google.cloud.compute_v1.types.TargetInstanceList`\n attributes are available on the pager. If multiple requests are made, only\n the most recent response is retained, and thus used for attribute lookup.\n \"\"\"\n\n# MASKED: __init__ function (lines 113-136)\n\n def __getattr__(self, name: str) -> Any:\n return getattr(self._response, name)\n\n @property\n def pages(self) -> Iterable[compute.TargetInstanceList]:\n yield self._response\n while self._response.next_page_token:\n self._request.page_token = self._response.next_page_token\n self._response = self._method(self._request, metadata=self._metadata)\n yield self._response\n\n def __iter__(self) -> Iterable[compute.TargetInstance]:\n for page in self.pages:\n yield from page.items\n\n def __repr__(self) -> str:\n return \"{0}<{1!r}>\".format(self.__class__.__name__, self._response)"},"implementation":{"kind":"string","value":" def __init__(\n self,\n method: Callable[..., compute.TargetInstanceList],\n request: compute.ListTargetInstancesRequest,\n response: compute.TargetInstanceList,\n *,\n metadata: Sequence[Tuple[str, str]] = ()\n ):\n \"\"\"Instantiate the pager.\n\n Args:\n method (Callable): The method that was originally called, and\n which instantiated this pager.\n request (google.cloud.compute_v1.types.ListTargetInstancesRequest):\n The initial request object.\n response (google.cloud.compute_v1.types.TargetInstanceList):\n The initial response object.\n metadata (Sequence[Tuple[str, str]]): Strings which should be\n sent along with the request as metadata.\n \"\"\"\n self._method = method\n self._request = compute.ListTargetInstancesRequest(request)\n self._response = response\n self._metadata = metadata"},"start_line":{"kind":"number","value":113,"string":"113"},"end_line":{"kind":"number","value":136,"string":"136"},"file_content":{"kind":"string","value":"# -*- coding: utf-8 -*-\n# Copyright 2020 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\nfrom typing import (\n Any,\n AsyncIterable,\n Awaitable,\n Callable,\n Iterable,\n Sequence,\n Tuple,\n Optional,\n)\n\nfrom google.cloud.compute_v1.types import compute\n\n\nclass AggregatedListPager:\n \"\"\"A pager for iterating through ``aggregated_list`` requests.\n\n This class thinly wraps an initial\n :class:`google.cloud.compute_v1.types.TargetInstanceAggregatedList` object, and\n provides an ``__iter__`` method to iterate through its\n ``items`` field.\n\n If there are more pages, the ``__iter__`` method will make additional\n ``AggregatedList`` requests and continue to iterate\n through the ``items`` field on the\n corresponding responses.\n\n All the usual :class:`google.cloud.compute_v1.types.TargetInstanceAggregatedList`\n attributes are available on the pager. If multiple requests are made, only\n the most recent response is retained, and thus used for attribute lookup.\n \"\"\"\n\n def __init__(\n self,\n method: Callable[..., compute.TargetInstanceAggregatedList],\n request: compute.AggregatedListTargetInstancesRequest,\n response: compute.TargetInstanceAggregatedList,\n *,\n metadata: Sequence[Tuple[str, str]] = ()\n ):\n \"\"\"Instantiate the pager.\n\n Args:\n method (Callable): The method that was originally called, and\n which instantiated this pager.\n request (google.cloud.compute_v1.types.AggregatedListTargetInstancesRequest):\n The initial request object.\n response (google.cloud.compute_v1.types.TargetInstanceAggregatedList):\n The initial response object.\n metadata (Sequence[Tuple[str, str]]): Strings which should be\n sent along with the request as metadata.\n \"\"\"\n self._method = method\n self._request = compute.AggregatedListTargetInstancesRequest(request)\n self._response = response\n self._metadata = metadata\n\n def __getattr__(self, name: str) -> Any:\n return getattr(self._response, name)\n\n @property\n def pages(self) -> Iterable[compute.TargetInstanceAggregatedList]:\n yield self._response\n while self._response.next_page_token:\n self._request.page_token = self._response.next_page_token\n self._response = self._method(self._request, metadata=self._metadata)\n yield self._response\n\n def __iter__(self) -> Iterable[Tuple[str, compute.TargetInstancesScopedList]]:\n for page in self.pages:\n yield from page.items.items()\n\n def get(self, key: str) -> Optional[compute.TargetInstancesScopedList]:\n return self._response.items.get(key)\n\n def __repr__(self) -> str:\n return \"{0}<{1!r}>\".format(self.__class__.__name__, self._response)\n\n\nclass ListPager:\n \"\"\"A pager for iterating through ``list`` requests.\n\n This class thinly wraps an initial\n :class:`google.cloud.compute_v1.types.TargetInstanceList` object, and\n provides an ``__iter__`` method to iterate through its\n ``items`` field.\n\n If there are more pages, the ``__iter__`` method will make additional\n ``List`` requests and continue to iterate\n through the ``items`` field on the\n corresponding responses.\n\n All the usual :class:`google.cloud.compute_v1.types.TargetInstanceList`\n attributes are available on the pager. If multiple requests are made, only\n the most recent response is retained, and thus used for attribute lookup.\n \"\"\"\n\n def __init__(\n self,\n method: Callable[..., compute.TargetInstanceList],\n request: compute.ListTargetInstancesRequest,\n response: compute.TargetInstanceList,\n *,\n metadata: Sequence[Tuple[str, str]] = ()\n ):\n \"\"\"Instantiate the pager.\n\n Args:\n method (Callable): The method that was originally called, and\n which instantiated this pager.\n request (google.cloud.compute_v1.types.ListTargetInstancesRequest):\n The initial request object.\n response (google.cloud.compute_v1.types.TargetInstanceList):\n The initial response object.\n metadata (Sequence[Tuple[str, str]]): Strings which should be\n sent along with the request as metadata.\n \"\"\"\n self._method = method\n self._request = compute.ListTargetInstancesRequest(request)\n self._response = response\n self._metadata = metadata\n\n def __getattr__(self, name: str) -> Any:\n return getattr(self._response, name)\n\n @property\n def pages(self) -> Iterable[compute.TargetInstanceList]:\n yield self._response\n while self._response.next_page_token:\n self._request.page_token = self._response.next_page_token\n self._response = self._method(self._request, metadata=self._metadata)\n yield self._response\n\n def __iter__(self) -> Iterable[compute.TargetInstance]:\n for page in self.pages:\n yield from page.items\n\n def __repr__(self) -> str:\n return \"{0}<{1!r}>\".format(self.__class__.__name__, self._response)\n"}}},{"rowIdx":69,"cells":{"function_name":{"kind":"string","value":"__init__"},"docstring":{"kind":"string","value":"Dataset that loads tensors via a csv containing file paths to audio files and transcripts separated by\na comma. Each new line is a different sample. Example below:\n\n/path/to/audio.wav,/path/to/audio.txt\n...\n\n:param audio_conf: Dictionary containing the sample rate, window and the window length/stride in seconds\n:param manifest_filepath: Path to manifest csv as describe above\n:param labels: String containing all the possible characters to map to\n:param normalize: Apply standard mean and deviation normalization to audio tensor\n:param speed_volume_perturb(default False): Apply random tempo and gain perturbations\n:param spec_augment(default False): Apply simple spectral augmentation to mel spectograms"},"masked_code":{"kind":"string","value":"import os\nimport subprocess\nfrom tempfile import NamedTemporaryFile\n\nfrom torch.distributed import get_rank\nfrom torch.distributed import get_world_size\nfrom torch.utils.data.sampler import Sampler\n\nimport librosa\nimport numpy as np\nimport scipy.signal\nimport torch\nfrom scipy.io.wavfile import read\nimport math\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data import Dataset\nfrom .spec_augment import spec_augment\n\nfrom hangul_utils import split_syllable_char, split_syllables, join_jamos\n\nwindows = {'hamming': scipy.signal.hamming, 'hann': scipy.signal.hann, 'blackman': scipy.signal.blackman,\n 'bartlett': scipy.signal.bartlett}\n\n\ndef load_audio(path):\n# sample_rate, sound = read(path)\n sound, sr = librosa.load(path, sr=16000)\n \n# librosa.output.write_wav('org.wav', sound, sr)\n# print('save 1')\n \n# sound = sound.astype('float32') / 32767 # normalize audio\n sound = librosa.util.normalize(sound) # normalize audio\n sound = sound.astype('float32')\n \n# librosa.output.write_wav('norm.wav', sound, sr)\n# print('save 2')\n \n if len(sound.shape) > 1:\n if sound.shape[1] == 1:\n sound = sound.squeeze()\n else:\n sound = sound.mean(axis=1) # multiple channels, average\n return sound\n\n\nclass AudioParser(object):\n def parse_transcript(self, transcript_path):\n \"\"\"\n :param transcript_path: Path where transcript is stored from the manifest file\n :return: Transcript in training/testing format\n \"\"\"\n raise NotImplementedError\n\n def parse_audio(self, audio_path):\n \"\"\"\n :param audio_path: Path where audio is stored from the manifest file\n :return: Audio in training/testing format\n \"\"\"\n raise NotImplementedError\n\n\nclass NoiseInjection(object):\n def __init__(self,\n path=None,\n sample_rate=16000,\n noise_levels=(0, 0.5)):\n \"\"\"\n Adds noise to an input signal with specific SNR. Higher the noise level, the more noise added.\n Modified code from https://github.com/willfrey/audio/blob/master/torchaudio/transforms.py\n \"\"\"\n if path is not None and not os.path.exists(path):\n print(\"Directory doesn't exist: {}\".format(path))\n raise IOError\n self.paths = path is not None and librosa.util.find_files(path)\n self.sample_rate = sample_rate\n self.noise_levels = noise_levels\n\n def inject_noise(self, data):\n noise_path = np.random.choice(self.paths)\n noise_level = np.random.uniform(*self.noise_levels)\n return self.inject_noise_sample(data, noise_path, noise_level)\n\n def inject_noise_sample(self, data, noise_path, noise_level):\n noise_len = get_audio_length(noise_path)\n data_len = len(data) / self.sample_rate\n noise_start = np.random.rand() * (noise_len - data_len)\n noise_end = noise_start + data_len\n noise_dst = audio_with_sox(noise_path, self.sample_rate, noise_start, noise_end)\n assert len(data) == len(noise_dst)\n noise_energy = np.sqrt(noise_dst.dot(noise_dst) / noise_dst.size)\n data_energy = np.sqrt(data.dot(data) / data.size)\n data += noise_level * noise_dst * data_energy / noise_energy\n return data\n\n\nclass SpectrogramParser(AudioParser):\n def __init__(self, audio_conf, normalize=False, speed_volume_perturb=False, spec_augment=False):\n \"\"\"\n Parses audio file into spectrogram with optional normalization and various augmentations\n :param audio_conf: Dictionary containing the sample rate, window and the window length/stride in seconds\n :param normalize(default False): Apply standard mean and deviation normalization to audio tensor\n :param speed_volume_perturb(default False): Apply random tempo and gain perturbations\n :param spec_augment(default False): Apply simple spectral augmentation to mel spectograms\n \"\"\"\n super(SpectrogramParser, self).__init__()\n self.window_stride = audio_conf['window_stride']\n self.window_size = audio_conf['window_size']\n self.sample_rate = audio_conf['sample_rate']\n self.window = windows.get(audio_conf['window'], windows['hamming'])\n self.normalize = normalize\n self.speed_volume_perturb = speed_volume_perturb\n self.spec_augment = spec_augment\n self.noiseInjector = NoiseInjection(audio_conf['noise_dir'], self.sample_rate,\n audio_conf['noise_levels']) if audio_conf.get(\n 'noise_dir') is not None else None\n self.noise_prob = audio_conf.get('noise_prob')\n\n def parse_audio(self, audio_path,audio=None,change_speed=None):\n if audio is not None:\n y = audio\n elif self.speed_volume_perturb:\n y = load_randomly_augmented_audio(audio_path, self.sample_rate)\n# librosa.output.write_wav('test.wav', y, sr=16000, norm=False)\n# print('test')\n else:\n y = load_audio(audio_path)\n \n# librosa.output.write_wav('y1.wav', y, sr=16000)\n# print('save@@@@@@@@@@@@') \n \n # change audio speed\n if change_speed is not None:\n y = librosa.effects.time_stretch(y, change_speed)\n \n if self.noiseInjector:\n add_noise = np.random.binomial(1, self.noise_prob)\n if add_noise:\n y = self.noiseInjector.inject_noise(y)\n \n# librosa.output.write_wav('y2.wav', y, sr=16000)\n# print('save@@@@@@@@@@@@') \n# import sys\n# sys.exit()\n \n n_fft = int(self.sample_rate * self.window_size)\n win_length = n_fft\n hop_length = int(self.sample_rate * self.window_stride)\n # STFT\n D = librosa.stft(y, n_fft=n_fft, hop_length=hop_length,\n win_length=win_length, window=self.window)\n spect, phase = librosa.magphase(D)\n # S = log(S+1)\n spect = np.log1p(spect)\n spect = torch.FloatTensor(spect)\n if self.normalize:\n mean = spect.mean()\n std = spect.std()\n spect.add_(-mean)\n spect.div_(std)\n\n if self.spec_augment:\n spect = spec_augment(spect)\n\n return spect\n\n def parse_transcript(self, transcript_path):\n raise NotImplementedError\n\n\nclass SpectrogramDataset(Dataset, SpectrogramParser):\n# MASKED: __init__ function (lines 172-199)\n\n def __getitem__(self, index):\n sample = self.ids[index]\n audio_path, transcript_path = sample[0], sample[1]\n spect = self.parse_audio(audio_path)\n transcript = self.parse_transcript(transcript_path)\n \n return spect, transcript\n\n def parse_transcript(self, transcript_path):\n with open(transcript_path, 'r', encoding='utf8') as transcript_file:\n# with open(transcript_path, 'r', encoding='utf-16') as transcript_file:\n transcript = transcript_file.read().replace('\\n', '')\n \n if self.use_jamo:\n transcript = split_syllables(transcript)\n \n transcript = list(filter(None, [self.labels_map.get(x) for x in list(transcript)]))\n return transcript\n\n def __len__(self):\n return self.size\n\n\ndef _collate_fn(batch):\n def func(p):\n return p[0].size(1)\n\n batch = sorted(batch, key=lambda sample: sample[0].size(1), reverse=True)\n longest_sample = max(batch, key=func)[0]\n freq_size = longest_sample.size(0)\n minibatch_size = len(batch)\n max_seqlength = longest_sample.size(1)\n inputs = torch.zeros(minibatch_size, 1, freq_size, max_seqlength)\n input_percentages = torch.FloatTensor(minibatch_size)\n target_sizes = torch.IntTensor(minibatch_size)\n targets = []\n for x in range(minibatch_size):\n sample = batch[x]\n tensor = sample[0]\n target = sample[1]\n seq_length = tensor.size(1)\n inputs[x][0].narrow(1, 0, seq_length).copy_(tensor)\n input_percentages[x] = seq_length / float(max_seqlength)\n target_sizes[x] = len(target)\n targets.extend(target)\n targets = torch.IntTensor(targets)\n return inputs, targets, input_percentages, target_sizes\n\n\nclass AudioDataLoader(DataLoader):\n def __init__(self, *args, **kwargs):\n \"\"\"\n Creates a data loader for AudioDatasets.\n \"\"\"\n super(AudioDataLoader, self).__init__(*args, **kwargs)\n self.collate_fn = _collate_fn\n\n\nclass BucketingSampler(Sampler):\n def __init__(self, data_source, batch_size=1):\n \"\"\"\n Samples batches assuming they are in order of size to batch similarly sized samples together.\n \"\"\"\n super(BucketingSampler, self).__init__(data_source)\n self.data_source = data_source\n ids = list(range(0, len(data_source)))\n self.bins = [ids[i:i + batch_size] for i in range(0, len(ids), batch_size)]\n\n def __iter__(self):\n for ids in self.bins:\n np.random.shuffle(ids)\n yield ids\n\n def __len__(self):\n return len(self.bins)\n\n def shuffle(self, epoch):\n np.random.shuffle(self.bins)\n\n\nclass DistributedBucketingSampler(Sampler):\n def __init__(self, data_source, batch_size=1, num_replicas=None, rank=None):\n \"\"\"\n Samples batches assuming they are in order of size to batch similarly sized samples together.\n \"\"\"\n super(DistributedBucketingSampler, self).__init__(data_source)\n if num_replicas is None:\n num_replicas = get_world_size()\n if rank is None:\n rank = get_rank()\n self.data_source = data_source\n self.ids = list(range(0, len(data_source)))\n self.batch_size = batch_size\n self.bins = [self.ids[i:i + batch_size] for i in range(0, len(self.ids), batch_size)]\n self.num_replicas = num_replicas\n self.rank = rank\n self.num_samples = int(math.ceil(len(self.bins) * 1.0 / self.num_replicas))\n self.total_size = self.num_samples * self.num_replicas\n\n def __iter__(self):\n offset = self.rank\n # add extra samples to make it evenly divisible\n bins = self.bins + self.bins[:(self.total_size - len(self.bins))]\n assert len(bins) == self.total_size\n samples = bins[offset::self.num_replicas] # Get every Nth bin, starting from rank\n return iter(samples)\n\n def __len__(self):\n return self.num_samples\n\n def shuffle(self, epoch):\n # deterministically shuffle based on epoch\n g = torch.Generator()\n g.manual_seed(epoch)\n bin_ids = list(torch.randperm(len(self.bins), generator=g))\n self.bins = [self.bins[i] for i in bin_ids]\n\n\ndef get_audio_length(path):\n output = subprocess.check_output(['soxi -D \\\"%s\\\"' % path.strip()], shell=True)\n return float(output)\n\n\ndef audio_with_sox(path, sample_rate, start_time, end_time):\n \"\"\"\n crop and resample the recording with sox and loads it.\n \"\"\"\n with NamedTemporaryFile(suffix=\".wav\") as tar_file:\n tar_filename = tar_file.name\n sox_params = \"sox \\\"{}\\\" -r {} -c 1 -b 16 -e si {} trim {} ={} >/dev/null 2>&1\".format(path, sample_rate,\n tar_filename, start_time,\n end_time)\n os.system(sox_params)\n y = load_audio(tar_filename)\n return y\n\n\ndef augment_audio_with_sox(path, sample_rate, tempo, gain):\n \"\"\"\n Changes tempo and gain of the recording with sox and loads it.\n \"\"\"\n with NamedTemporaryFile(suffix=\".wav\") as augmented_file:\n augmented_filename = augmented_file.name\n sox_augment_params = [\"tempo\", \"{:.3f}\".format(tempo), \"gain\", \"{:.3f}\".format(gain)]\n sox_params = \"sox \\\"{}\\\" -r {} -c 1 -b 16 -e si {} {} >/dev/null 2>&1\".format(path, sample_rate,\n augmented_filename,\n \" \".join(sox_augment_params))\n os.system(sox_params)\n y = load_audio(augmented_filename)\n return y\n\n\n# original tempo_range=(0.85,1.15)\n# original gain_range=(-6,8)\ndef load_randomly_augmented_audio(path, sample_rate=16000, tempo_range=(0.85,1.15),\n gain_range=(-6, 8)):\n \"\"\"\n Picks tempo and gain uniformly, applies it to the utterance by using sox utility.\n Returns the augmented utterance.\n \"\"\"\n low_tempo, high_tempo = tempo_range\n tempo_value = np.random.uniform(low=low_tempo, high=high_tempo)\n low_gain, high_gain = gain_range\n gain_value = np.random.uniform(low=low_gain, high=high_gain)\n audio = augment_audio_with_sox(path=path, sample_rate=sample_rate,\n tempo=tempo_value, gain=gain_value)\n \n return audio"},"implementation":{"kind":"string","value":" def __init__(self, audio_conf, manifest_filepath, labels, normalize=False, speed_volume_perturb=False, spec_augment=False):\n \"\"\"\n Dataset that loads tensors via a csv containing file paths to audio files and transcripts separated by\n a comma. Each new line is a different sample. Example below:\n\n /path/to/audio.wav,/path/to/audio.txt\n ...\n\n :param audio_conf: Dictionary containing the sample rate, window and the window length/stride in seconds\n :param manifest_filepath: Path to manifest csv as describe above\n :param labels: String containing all the possible characters to map to\n :param normalize: Apply standard mean and deviation normalization to audio tensor\n :param speed_volume_perturb(default False): Apply random tempo and gain perturbations\n :param spec_augment(default False): Apply simple spectral augmentation to mel spectograms\n \"\"\"\n with open(manifest_filepath) as f:\n ids = f.readlines()\n ids = [x.strip().split(',') for x in ids]\n self.ids = ids\n self.size = len(ids)\n self.labels_map = dict([(labels[i], i) for i in range(len(labels))])\n\n try:\n self.use_jamo = audio_conf['use_jamo']\n except:\n self.use_jamo = False\n \n super(SpectrogramDataset, self).__init__(audio_conf, normalize, speed_volume_perturb, spec_augment)"},"start_line":{"kind":"number","value":172,"string":"172"},"end_line":{"kind":"number","value":199,"string":"199"},"file_content":{"kind":"string","value":"import os\nimport subprocess\nfrom tempfile import NamedTemporaryFile\n\nfrom torch.distributed import get_rank\nfrom torch.distributed import get_world_size\nfrom torch.utils.data.sampler import Sampler\n\nimport librosa\nimport numpy as np\nimport scipy.signal\nimport torch\nfrom scipy.io.wavfile import read\nimport math\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data import Dataset\nfrom .spec_augment import spec_augment\n\nfrom hangul_utils import split_syllable_char, split_syllables, join_jamos\n\nwindows = {'hamming': scipy.signal.hamming, 'hann': scipy.signal.hann, 'blackman': scipy.signal.blackman,\n 'bartlett': scipy.signal.bartlett}\n\n\ndef load_audio(path):\n# sample_rate, sound = read(path)\n sound, sr = librosa.load(path, sr=16000)\n \n# librosa.output.write_wav('org.wav', sound, sr)\n# print('save 1')\n \n# sound = sound.astype('float32') / 32767 # normalize audio\n sound = librosa.util.normalize(sound) # normalize audio\n sound = sound.astype('float32')\n \n# librosa.output.write_wav('norm.wav', sound, sr)\n# print('save 2')\n \n if len(sound.shape) > 1:\n if sound.shape[1] == 1:\n sound = sound.squeeze()\n else:\n sound = sound.mean(axis=1) # multiple channels, average\n return sound\n\n\nclass AudioParser(object):\n def parse_transcript(self, transcript_path):\n \"\"\"\n :param transcript_path: Path where transcript is stored from the manifest file\n :return: Transcript in training/testing format\n \"\"\"\n raise NotImplementedError\n\n def parse_audio(self, audio_path):\n \"\"\"\n :param audio_path: Path where audio is stored from the manifest file\n :return: Audio in training/testing format\n \"\"\"\n raise NotImplementedError\n\n\nclass NoiseInjection(object):\n def __init__(self,\n path=None,\n sample_rate=16000,\n noise_levels=(0, 0.5)):\n \"\"\"\n Adds noise to an input signal with specific SNR. Higher the noise level, the more noise added.\n Modified code from https://github.com/willfrey/audio/blob/master/torchaudio/transforms.py\n \"\"\"\n if path is not None and not os.path.exists(path):\n print(\"Directory doesn't exist: {}\".format(path))\n raise IOError\n self.paths = path is not None and librosa.util.find_files(path)\n self.sample_rate = sample_rate\n self.noise_levels = noise_levels\n\n def inject_noise(self, data):\n noise_path = np.random.choice(self.paths)\n noise_level = np.random.uniform(*self.noise_levels)\n return self.inject_noise_sample(data, noise_path, noise_level)\n\n def inject_noise_sample(self, data, noise_path, noise_level):\n noise_len = get_audio_length(noise_path)\n data_len = len(data) / self.sample_rate\n noise_start = np.random.rand() * (noise_len - data_len)\n noise_end = noise_start + data_len\n noise_dst = audio_with_sox(noise_path, self.sample_rate, noise_start, noise_end)\n assert len(data) == len(noise_dst)\n noise_energy = np.sqrt(noise_dst.dot(noise_dst) / noise_dst.size)\n data_energy = np.sqrt(data.dot(data) / data.size)\n data += noise_level * noise_dst * data_energy / noise_energy\n return data\n\n\nclass SpectrogramParser(AudioParser):\n def __init__(self, audio_conf, normalize=False, speed_volume_perturb=False, spec_augment=False):\n \"\"\"\n Parses audio file into spectrogram with optional normalization and various augmentations\n :param audio_conf: Dictionary containing the sample rate, window and the window length/stride in seconds\n :param normalize(default False): Apply standard mean and deviation normalization to audio tensor\n :param speed_volume_perturb(default False): Apply random tempo and gain perturbations\n :param spec_augment(default False): Apply simple spectral augmentation to mel spectograms\n \"\"\"\n super(SpectrogramParser, self).__init__()\n self.window_stride = audio_conf['window_stride']\n self.window_size = audio_conf['window_size']\n self.sample_rate = audio_conf['sample_rate']\n self.window = windows.get(audio_conf['window'], windows['hamming'])\n self.normalize = normalize\n self.speed_volume_perturb = speed_volume_perturb\n self.spec_augment = spec_augment\n self.noiseInjector = NoiseInjection(audio_conf['noise_dir'], self.sample_rate,\n audio_conf['noise_levels']) if audio_conf.get(\n 'noise_dir') is not None else None\n self.noise_prob = audio_conf.get('noise_prob')\n\n def parse_audio(self, audio_path,audio=None,change_speed=None):\n if audio is not None:\n y = audio\n elif self.speed_volume_perturb:\n y = load_randomly_augmented_audio(audio_path, self.sample_rate)\n# librosa.output.write_wav('test.wav', y, sr=16000, norm=False)\n# print('test')\n else:\n y = load_audio(audio_path)\n \n# librosa.output.write_wav('y1.wav', y, sr=16000)\n# print('save@@@@@@@@@@@@') \n \n # change audio speed\n if change_speed is not None:\n y = librosa.effects.time_stretch(y, change_speed)\n \n if self.noiseInjector:\n add_noise = np.random.binomial(1, self.noise_prob)\n if add_noise:\n y = self.noiseInjector.inject_noise(y)\n \n# librosa.output.write_wav('y2.wav', y, sr=16000)\n# print('save@@@@@@@@@@@@') \n# import sys\n# sys.exit()\n \n n_fft = int(self.sample_rate * self.window_size)\n win_length = n_fft\n hop_length = int(self.sample_rate * self.window_stride)\n # STFT\n D = librosa.stft(y, n_fft=n_fft, hop_length=hop_length,\n win_length=win_length, window=self.window)\n spect, phase = librosa.magphase(D)\n # S = log(S+1)\n spect = np.log1p(spect)\n spect = torch.FloatTensor(spect)\n if self.normalize:\n mean = spect.mean()\n std = spect.std()\n spect.add_(-mean)\n spect.div_(std)\n\n if self.spec_augment:\n spect = spec_augment(spect)\n\n return spect\n\n def parse_transcript(self, transcript_path):\n raise NotImplementedError\n\n\nclass SpectrogramDataset(Dataset, SpectrogramParser):\n def __init__(self, audio_conf, manifest_filepath, labels, normalize=False, speed_volume_perturb=False, spec_augment=False):\n \"\"\"\n Dataset that loads tensors via a csv containing file paths to audio files and transcripts separated by\n a comma. Each new line is a different sample. Example below:\n\n /path/to/audio.wav,/path/to/audio.txt\n ...\n\n :param audio_conf: Dictionary containing the sample rate, window and the window length/stride in seconds\n :param manifest_filepath: Path to manifest csv as describe above\n :param labels: String containing all the possible characters to map to\n :param normalize: Apply standard mean and deviation normalization to audio tensor\n :param speed_volume_perturb(default False): Apply random tempo and gain perturbations\n :param spec_augment(default False): Apply simple spectral augmentation to mel spectograms\n \"\"\"\n with open(manifest_filepath) as f:\n ids = f.readlines()\n ids = [x.strip().split(',') for x in ids]\n self.ids = ids\n self.size = len(ids)\n self.labels_map = dict([(labels[i], i) for i in range(len(labels))])\n\n try:\n self.use_jamo = audio_conf['use_jamo']\n except:\n self.use_jamo = False\n \n super(SpectrogramDataset, self).__init__(audio_conf, normalize, speed_volume_perturb, spec_augment)\n\n def __getitem__(self, index):\n sample = self.ids[index]\n audio_path, transcript_path = sample[0], sample[1]\n spect = self.parse_audio(audio_path)\n transcript = self.parse_transcript(transcript_path)\n \n return spect, transcript\n\n def parse_transcript(self, transcript_path):\n with open(transcript_path, 'r', encoding='utf8') as transcript_file:\n# with open(transcript_path, 'r', encoding='utf-16') as transcript_file:\n transcript = transcript_file.read().replace('\\n', '')\n \n if self.use_jamo:\n transcript = split_syllables(transcript)\n \n transcript = list(filter(None, [self.labels_map.get(x) for x in list(transcript)]))\n return transcript\n\n def __len__(self):\n return self.size\n\n\ndef _collate_fn(batch):\n def func(p):\n return p[0].size(1)\n\n batch = sorted(batch, key=lambda sample: sample[0].size(1), reverse=True)\n longest_sample = max(batch, key=func)[0]\n freq_size = longest_sample.size(0)\n minibatch_size = len(batch)\n max_seqlength = longest_sample.size(1)\n inputs = torch.zeros(minibatch_size, 1, freq_size, max_seqlength)\n input_percentages = torch.FloatTensor(minibatch_size)\n target_sizes = torch.IntTensor(minibatch_size)\n targets = []\n for x in range(minibatch_size):\n sample = batch[x]\n tensor = sample[0]\n target = sample[1]\n seq_length = tensor.size(1)\n inputs[x][0].narrow(1, 0, seq_length).copy_(tensor)\n input_percentages[x] = seq_length / float(max_seqlength)\n target_sizes[x] = len(target)\n targets.extend(target)\n targets = torch.IntTensor(targets)\n return inputs, targets, input_percentages, target_sizes\n\n\nclass AudioDataLoader(DataLoader):\n def __init__(self, *args, **kwargs):\n \"\"\"\n Creates a data loader for AudioDatasets.\n \"\"\"\n super(AudioDataLoader, self).__init__(*args, **kwargs)\n self.collate_fn = _collate_fn\n\n\nclass BucketingSampler(Sampler):\n def __init__(self, data_source, batch_size=1):\n \"\"\"\n Samples batches assuming they are in order of size to batch similarly sized samples together.\n \"\"\"\n super(BucketingSampler, self).__init__(data_source)\n self.data_source = data_source\n ids = list(range(0, len(data_source)))\n self.bins = [ids[i:i + batch_size] for i in range(0, len(ids), batch_size)]\n\n def __iter__(self):\n for ids in self.bins:\n np.random.shuffle(ids)\n yield ids\n\n def __len__(self):\n return len(self.bins)\n\n def shuffle(self, epoch):\n np.random.shuffle(self.bins)\n\n\nclass DistributedBucketingSampler(Sampler):\n def __init__(self, data_source, batch_size=1, num_replicas=None, rank=None):\n \"\"\"\n Samples batches assuming they are in order of size to batch similarly sized samples together.\n \"\"\"\n super(DistributedBucketingSampler, self).__init__(data_source)\n if num_replicas is None:\n num_replicas = get_world_size()\n if rank is None:\n rank = get_rank()\n self.data_source = data_source\n self.ids = list(range(0, len(data_source)))\n self.batch_size = batch_size\n self.bins = [self.ids[i:i + batch_size] for i in range(0, len(self.ids), batch_size)]\n self.num_replicas = num_replicas\n self.rank = rank\n self.num_samples = int(math.ceil(len(self.bins) * 1.0 / self.num_replicas))\n self.total_size = self.num_samples * self.num_replicas\n\n def __iter__(self):\n offset = self.rank\n # add extra samples to make it evenly divisible\n bins = self.bins + self.bins[:(self.total_size - len(self.bins))]\n assert len(bins) == self.total_size\n samples = bins[offset::self.num_replicas] # Get every Nth bin, starting from rank\n return iter(samples)\n\n def __len__(self):\n return self.num_samples\n\n def shuffle(self, epoch):\n # deterministically shuffle based on epoch\n g = torch.Generator()\n g.manual_seed(epoch)\n bin_ids = list(torch.randperm(len(self.bins), generator=g))\n self.bins = [self.bins[i] for i in bin_ids]\n\n\ndef get_audio_length(path):\n output = subprocess.check_output(['soxi -D \\\"%s\\\"' % path.strip()], shell=True)\n return float(output)\n\n\ndef audio_with_sox(path, sample_rate, start_time, end_time):\n \"\"\"\n crop and resample the recording with sox and loads it.\n \"\"\"\n with NamedTemporaryFile(suffix=\".wav\") as tar_file:\n tar_filename = tar_file.name\n sox_params = \"sox \\\"{}\\\" -r {} -c 1 -b 16 -e si {} trim {} ={} >/dev/null 2>&1\".format(path, sample_rate,\n tar_filename, start_time,\n end_time)\n os.system(sox_params)\n y = load_audio(tar_filename)\n return y\n\n\ndef augment_audio_with_sox(path, sample_rate, tempo, gain):\n \"\"\"\n Changes tempo and gain of the recording with sox and loads it.\n \"\"\"\n with NamedTemporaryFile(suffix=\".wav\") as augmented_file:\n augmented_filename = augmented_file.name\n sox_augment_params = [\"tempo\", \"{:.3f}\".format(tempo), \"gain\", \"{:.3f}\".format(gain)]\n sox_params = \"sox \\\"{}\\\" -r {} -c 1 -b 16 -e si {} {} >/dev/null 2>&1\".format(path, sample_rate,\n augmented_filename,\n \" \".join(sox_augment_params))\n os.system(sox_params)\n y = load_audio(augmented_filename)\n return y\n\n\n# original tempo_range=(0.85,1.15)\n# original gain_range=(-6,8)\ndef load_randomly_augmented_audio(path, sample_rate=16000, tempo_range=(0.85,1.15),\n gain_range=(-6, 8)):\n \"\"\"\n Picks tempo and gain uniformly, applies it to the utterance by using sox utility.\n Returns the augmented utterance.\n \"\"\"\n low_tempo, high_tempo = tempo_range\n tempo_value = np.random.uniform(low=low_tempo, high=high_tempo)\n low_gain, high_gain = gain_range\n gain_value = np.random.uniform(low=low_gain, high=high_gain)\n audio = augment_audio_with_sox(path=path, sample_rate=sample_rate,\n tempo=tempo_value, gain=gain_value)\n \n return audio\n"}}},{"rowIdx":70,"cells":{"function_name":{"kind":"string","value":"validate_meta_info"},"docstring":{"kind":"string","value":"Validate meta information\n\nAdds 'BIDS.NA' if no BIDS info present\nAdds 'BIDS.valid' and 'BIDS.error_message'\n to communicate to user if values are valid\n\nCurrently, validation is only checking if\n mandatory properties are non-empty strings\n\nCould add the following checks:\n Are the values alpha numeric?"},"masked_code":{"kind":"string","value":"import argparse\nimport logging\nimport json\nimport os\nimport tempfile\nimport sys\nimport re\n\nimport flywheel\n\nfrom .supporting_files import bidsify_flywheel, utils, templates\nfrom .supporting_files.project_tree import get_project_tree\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger('curate-bids')\n\ndef clear_meta_info(context, template):\n if 'info' in context and template.namespace in context['info']:\n del context['info'][template.namespace]\n\ndef format_validation_error(err):\n path = '/'.join(err.path)\n if path:\n return path + ' ' + err.message\n return err.message\n\n# MASKED: validate_meta_info function (lines 27-78)\n\ndef update_meta_info(fw, context):\n \"\"\" Update file information\n\n \"\"\"\n # Modify file\n if context['container_type'] == 'file':\n # Modify acquisition file\n if context['parent_container_type'] == 'acquisition':\n fw.set_acquisition_file_info(\n context['acquisition']['id'],\n context['file']['name'],\n context['file']['info']\n )\n # Modify project file\n elif context['parent_container_type'] == 'project':\n fw.set_project_file_info(\n context['project']['id'],\n context['file']['name'],\n context['file']['info']\n )\n # Modify session file\n elif context['parent_container_type'] == 'session':\n fw.set_session_file_info(\n context['session']['id'],\n context['file']['name'],\n context['file']['info']\n )\n else:\n logger.info('Cannot determine file parent container type: ' + context['parent_container_type'])\n # Modify project\n elif context['container_type'] == 'project':\n fw.replace_project_info(context['project']['id'], context['project']['info'])\n # Modify session\n elif context['container_type'] == 'session':\n fw.replace_session_info(context['session']['id'], context['session']['info'])\n # Modify acquisition\n elif context['container_type'] == 'acquisition':\n fw.replace_acquisition_info(context['acquisition']['id'], context['acquisition']['info'])\n # Cannot determine container type\n else:\n logger.info('Cannot determine container type: ' + context['container_type'])\n\ndef curate_bids_dir(fw, project_id, session_id=None, reset=False, template_file=None, session_only=False):\n \"\"\"\n\n fw: Flywheel client\n project_id: project id of project to curate\n session_id: The optional session id to curate\n reset: Whether or not to reset bids info before curation\n template_file: The template file to use\n session_only: If true, then only curate the provided session\n\n \"\"\"\n project = get_project_tree(fw, project_id, session_id=session_id, session_only=session_only)\n curate_bids_tree(fw, project, reset, template_file, True)\n\ndef curate_bids_tree(fw, project, reset=False, template_file=None, update=True):\n # Get project\n project_files = project.get('files', [])\n\n # Get template (for now, just use default)\n template = templates.DEFAULT_TEMPLATE\n\n # Check for project file\n if not template_file:\n template_filename = utils.find_custom_template(project_files)\n\n if template_filename:\n fd, path = tempfile.mkstemp('.json')\n os.close(fd)\n\n logger.info('Using project template: {0}'.format(template_filename))\n fw.download_file_from_project(project['id'], template_filename, path)\n template_file = path\n if template_file:\n template = templates.loadTemplate(template_file)\n\n ##\n # Curation is now a 3-pass process\n # 1. Do initial template matching and updating\n # 2. Perform any path resolutions\n # 3. Send updates to server\n ##\n\n # 1. Do initial template matching and updating\n for context in project.context_iter():\n ctype = context['container_type']\n parent_ctype = context['parent_container_type']\n\n if reset:\n clear_meta_info(context[ctype], template)\n\n elif context[ctype].get('info',{}).get('BIDS') == 'NA':\n continue\n\n if ctype == 'project':\n bidsify_flywheel.process_matching_templates(context, template)\n # Validate meta information\n # TODO: Improve the validator to understand what is valid for dataset_description file...\n # validate_meta_info(context['project'])\n\n elif ctype == 'session':\n bidsify_flywheel.process_matching_templates(context, template)\n\n # Add run_counter\n context['run_counters'] = utils.RunCounterMap()\n\n elif ctype == 'acquisition':\n bidsify_flywheel.process_matching_templates(context, template)\n\n elif ctype == 'file':\n if parent_ctype == 'project' and PROJECT_TEMPLATE_FILE_NAME_REGEX.search(context['file']['name']):\n # Don't BIDSIFY project template\n continue\n\n # Process matching\n context['file'] = bidsify_flywheel.process_matching_templates(context, template)\n # Validate meta information\n validate_meta_info(context['file'], template)\n\n # 2. Perform any path resolutions\n session = None\n for context in project.context_iter():\n # Resolution\n bidsify_flywheel.process_resolvers(context, template)\n\n # 3. Send updates to server\n if update:\n for context in project.context_iter():\n ctype = context['container_type']\n node = context[ctype]\n if node.is_dirty():\n update_meta_info(fw, context)\n\ndef main_with_args(api_key, session_id, reset, session_only):\n\n ### Prep\n # Check API key - raises Error if key is invalid\n fw = flywheel.Flywheel(api_key)\n if session_id:\n project_id = utils.get_project_id_from_session_id(fw, session_id)\n else:\n print('Session id is required!')\n sys.exit(1)\n\n ### Curate BIDS project\n curate_bids_dir(fw, project_id, session_id, reset=reset, session_only=session_only)\n\n\ndef main():\n ### Read in arguments\n parser = argparse.ArgumentParser(description='BIDS Curation')\n parser.add_argument('--api-key', dest='api_key', action='store',\n required=True, help='API key')\n parser.add_argument('-p', dest='project_label', action='store',\n required=False, default=None, help='Project Label on Flywheel instance')\n parser.add_argument('--session', dest='session_id', action='store',\n required=False, default=None, help='Session ID, used to look up project if project label is not readily available')\n parser.add_argument('--reset', dest='reset', action='store_true',\n default=False, help='Reset BIDS data before running')\n parser.add_argument('--session-only', dest='session_only', action='store_true',\n default=False, help='Only curate the session identified by --session')\n parser.add_argument('--template-file', dest='template_file', action='store',\n default=None, help='Template file to use')\n args = parser.parse_args()\n\n ### Prep\n # Check API key - raises Error if key is invalid\n fw = flywheel.Flywheel(args.api_key)\n # Get project id from label\n if args.project_label:\n project_id = utils.validate_project_label(fw, args.project_label)\n elif args.session_id:\n project_id = utils.get_project_id_from_session_id(fw, args.session_id)\n else:\n print('Either project label or session id is required!')\n sys.exit(1)\n\n ### Curate BIDS project\n curate_bids_dir(fw, project_id, args.session_id, reset=args.reset, template_file=args.template_file, session_only=args.session_only)\n\nif __name__ == '__main__':\n main()\n"},"implementation":{"kind":"string","value":"def validate_meta_info(container, template):\n \"\"\" Validate meta information\n\n Adds 'BIDS.NA' if no BIDS info present\n Adds 'BIDS.valid' and 'BIDS.error_message'\n to communicate to user if values are valid\n\n Currently, validation is only checking if\n mandatory properties are non-empty strings\n\n Could add the following checks:\n Are the values alpha numeric?\n\n\n \"\"\"\n # Get namespace\n namespace = template.namespace\n\n # If 'info' is NOT in container, then must not\n # have matched to a template, create 'info'\n # field with object {'BIDS': 'NA'}\n if 'info' not in container:\n container['info'] = {namespace: 'NA'}\n # if the namespace ('BIDS') is NOT in 'info',\n # then must not have matched to a template,\n # add {'BIDS': 'NA'} to the meta info\n elif namespace not in container['info']:\n container['info'][namespace] = 'NA'\n # If already assigned BIDS 'NA', then break\n elif container['info'][namespace] == 'NA':\n pass\n # Otherwise, iterate over keys within container\n else:\n valid = True\n error_message = ''\n\n # Find template\n templateName = container['info'][namespace].get('template')\n if templateName:\n templateDef = template.definitions.get(templateName)\n if templateDef:\n errors = template.validate(templateDef, container['info'][namespace])\n if errors:\n valid = False\n error_message = '\\n'.join([format_validation_error(err) for err in errors])\n else:\n valid = False\n error_message += 'Unknown template: %s. ' % templateName\n\n # Assign 'valid' and 'error_message' values\n container['info'][namespace]['valid'] = valid\n container['info'][namespace]['error_message'] = error_message"},"start_line":{"kind":"number","value":27,"string":"27"},"end_line":{"kind":"number","value":78,"string":"78"},"file_content":{"kind":"string","value":"import argparse\nimport logging\nimport json\nimport os\nimport tempfile\nimport sys\nimport re\n\nimport flywheel\n\nfrom .supporting_files import bidsify_flywheel, utils, templates\nfrom .supporting_files.project_tree import get_project_tree\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger('curate-bids')\n\ndef clear_meta_info(context, template):\n if 'info' in context and template.namespace in context['info']:\n del context['info'][template.namespace]\n\ndef format_validation_error(err):\n path = '/'.join(err.path)\n if path:\n return path + ' ' + err.message\n return err.message\n\ndef validate_meta_info(container, template):\n \"\"\" Validate meta information\n\n Adds 'BIDS.NA' if no BIDS info present\n Adds 'BIDS.valid' and 'BIDS.error_message'\n to communicate to user if values are valid\n\n Currently, validation is only checking if\n mandatory properties are non-empty strings\n\n Could add the following checks:\n Are the values alpha numeric?\n\n\n \"\"\"\n # Get namespace\n namespace = template.namespace\n\n # If 'info' is NOT in container, then must not\n # have matched to a template, create 'info'\n # field with object {'BIDS': 'NA'}\n if 'info' not in container:\n container['info'] = {namespace: 'NA'}\n # if the namespace ('BIDS') is NOT in 'info',\n # then must not have matched to a template,\n # add {'BIDS': 'NA'} to the meta info\n elif namespace not in container['info']:\n container['info'][namespace] = 'NA'\n # If already assigned BIDS 'NA', then break\n elif container['info'][namespace] == 'NA':\n pass\n # Otherwise, iterate over keys within container\n else:\n valid = True\n error_message = ''\n\n # Find template\n templateName = container['info'][namespace].get('template')\n if templateName:\n templateDef = template.definitions.get(templateName)\n if templateDef:\n errors = template.validate(templateDef, container['info'][namespace])\n if errors:\n valid = False\n error_message = '\\n'.join([format_validation_error(err) for err in errors])\n else:\n valid = False\n error_message += 'Unknown template: %s. ' % templateName\n\n # Assign 'valid' and 'error_message' values\n container['info'][namespace]['valid'] = valid\n container['info'][namespace]['error_message'] = error_message\n\ndef update_meta_info(fw, context):\n \"\"\" Update file information\n\n \"\"\"\n # Modify file\n if context['container_type'] == 'file':\n # Modify acquisition file\n if context['parent_container_type'] == 'acquisition':\n fw.set_acquisition_file_info(\n context['acquisition']['id'],\n context['file']['name'],\n context['file']['info']\n )\n # Modify project file\n elif context['parent_container_type'] == 'project':\n fw.set_project_file_info(\n context['project']['id'],\n context['file']['name'],\n context['file']['info']\n )\n # Modify session file\n elif context['parent_container_type'] == 'session':\n fw.set_session_file_info(\n context['session']['id'],\n context['file']['name'],\n context['file']['info']\n )\n else:\n logger.info('Cannot determine file parent container type: ' + context['parent_container_type'])\n # Modify project\n elif context['container_type'] == 'project':\n fw.replace_project_info(context['project']['id'], context['project']['info'])\n # Modify session\n elif context['container_type'] == 'session':\n fw.replace_session_info(context['session']['id'], context['session']['info'])\n # Modify acquisition\n elif context['container_type'] == 'acquisition':\n fw.replace_acquisition_info(context['acquisition']['id'], context['acquisition']['info'])\n # Cannot determine container type\n else:\n logger.info('Cannot determine container type: ' + context['container_type'])\n\ndef curate_bids_dir(fw, project_id, session_id=None, reset=False, template_file=None, session_only=False):\n \"\"\"\n\n fw: Flywheel client\n project_id: project id of project to curate\n session_id: The optional session id to curate\n reset: Whether or not to reset bids info before curation\n template_file: The template file to use\n session_only: If true, then only curate the provided session\n\n \"\"\"\n project = get_project_tree(fw, project_id, session_id=session_id, session_only=session_only)\n curate_bids_tree(fw, project, reset, template_file, True)\n\ndef curate_bids_tree(fw, project, reset=False, template_file=None, update=True):\n # Get project\n project_files = project.get('files', [])\n\n # Get template (for now, just use default)\n template = templates.DEFAULT_TEMPLATE\n\n # Check for project file\n if not template_file:\n template_filename = utils.find_custom_template(project_files)\n\n if template_filename:\n fd, path = tempfile.mkstemp('.json')\n os.close(fd)\n\n logger.info('Using project template: {0}'.format(template_filename))\n fw.download_file_from_project(project['id'], template_filename, path)\n template_file = path\n if template_file:\n template = templates.loadTemplate(template_file)\n\n ##\n # Curation is now a 3-pass process\n # 1. Do initial template matching and updating\n # 2. Perform any path resolutions\n # 3. Send updates to server\n ##\n\n # 1. Do initial template matching and updating\n for context in project.context_iter():\n ctype = context['container_type']\n parent_ctype = context['parent_container_type']\n\n if reset:\n clear_meta_info(context[ctype], template)\n\n elif context[ctype].get('info',{}).get('BIDS') == 'NA':\n continue\n\n if ctype == 'project':\n bidsify_flywheel.process_matching_templates(context, template)\n # Validate meta information\n # TODO: Improve the validator to understand what is valid for dataset_description file...\n # validate_meta_info(context['project'])\n\n elif ctype == 'session':\n bidsify_flywheel.process_matching_templates(context, template)\n\n # Add run_counter\n context['run_counters'] = utils.RunCounterMap()\n\n elif ctype == 'acquisition':\n bidsify_flywheel.process_matching_templates(context, template)\n\n elif ctype == 'file':\n if parent_ctype == 'project' and PROJECT_TEMPLATE_FILE_NAME_REGEX.search(context['file']['name']):\n # Don't BIDSIFY project template\n continue\n\n # Process matching\n context['file'] = bidsify_flywheel.process_matching_templates(context, template)\n # Validate meta information\n validate_meta_info(context['file'], template)\n\n # 2. Perform any path resolutions\n session = None\n for context in project.context_iter():\n # Resolution\n bidsify_flywheel.process_resolvers(context, template)\n\n # 3. Send updates to server\n if update:\n for context in project.context_iter():\n ctype = context['container_type']\n node = context[ctype]\n if node.is_dirty():\n update_meta_info(fw, context)\n\ndef main_with_args(api_key, session_id, reset, session_only):\n\n ### Prep\n # Check API key - raises Error if key is invalid\n fw = flywheel.Flywheel(api_key)\n if session_id:\n project_id = utils.get_project_id_from_session_id(fw, session_id)\n else:\n print('Session id is required!')\n sys.exit(1)\n\n ### Curate BIDS project\n curate_bids_dir(fw, project_id, session_id, reset=reset, session_only=session_only)\n\n\ndef main():\n ### Read in arguments\n parser = argparse.ArgumentParser(description='BIDS Curation')\n parser.add_argument('--api-key', dest='api_key', action='store',\n required=True, help='API key')\n parser.add_argument('-p', dest='project_label', action='store',\n required=False, default=None, help='Project Label on Flywheel instance')\n parser.add_argument('--session', dest='session_id', action='store',\n required=False, default=None, help='Session ID, used to look up project if project label is not readily available')\n parser.add_argument('--reset', dest='reset', action='store_true',\n default=False, help='Reset BIDS data before running')\n parser.add_argument('--session-only', dest='session_only', action='store_true',\n default=False, help='Only curate the session identified by --session')\n parser.add_argument('--template-file', dest='template_file', action='store',\n default=None, help='Template file to use')\n args = parser.parse_args()\n\n ### Prep\n # Check API key - raises Error if key is invalid\n fw = flywheel.Flywheel(args.api_key)\n # Get project id from label\n if args.project_label:\n project_id = utils.validate_project_label(fw, args.project_label)\n elif args.session_id:\n project_id = utils.get_project_id_from_session_id(fw, args.session_id)\n else:\n print('Either project label or session id is required!')\n sys.exit(1)\n\n ### Curate BIDS project\n curate_bids_dir(fw, project_id, args.session_id, reset=args.reset, template_file=args.template_file, session_only=args.session_only)\n\nif __name__ == '__main__':\n main()\n\n"}}},{"rowIdx":71,"cells":{"function_name":{"kind":"string","value":"__add"},"docstring":{"kind":"string","value":":param name:\n 系列名称，用于 tooltip 的显示，legend 的图例筛选。\n:param x_axis:\n x 坐标轴数据。\n:param y_axis:\n y 坐标轴数据。数据中，每一行是一个『数据项』，每一列属于一个『维度』。\n 数据项具体为 [open, close, lowest, highest] （即：[开盘值, 收盘值,\n 最低值, 最高值]）。\n:param kwargs:"},"masked_code":{"kind":"string","value":"# coding=utf-8\n\nfrom pyecharts.chart import Chart\n\n\ndef kline_tooltip_formatter(params):\n text = (\n params[0].seriesName\n + \"<br/>\"\n + \"- open:\"\n + params[0].data[1]\n + \"<br/>\"\n + \"- close:\"\n + params[0].data[2]\n + \"<br/>\"\n + \"- lowest:\"\n + params[0].data[3]\n + \"<br/>\"\n + \"- highest:\"\n + params[0].data[4]\n )\n return text\n\n\nclass Kline(Chart):\n \"\"\"\n <<< K 线图 >>>\n\n 红涨蓝跌\n \"\"\"\n\n def __init__(self, title=\"\", subtitle=\"\", **kwargs):\n super(Kline, self).__init__(title, subtitle, **kwargs)\n\n def add(self, *args, **kwargs):\n self.__add(*args, **kwargs)\n return self\n\n# MASKED: __add function (lines 39-77)"},"implementation":{"kind":"string","value":" def __add(self, name, x_axis, y_axis, **kwargs):\n \"\"\"\n\n :param name:\n 系列名称，用于 tooltip 的显示，legend 的图例筛选。\n :param x_axis:\n x 坐标轴数据。\n :param y_axis:\n y 坐标轴数据。数据中，每一行是一个『数据项』，每一列属于一个『维度』。\n 数据项具体为 [open, close, lowest, highest] （即：[开盘值, 收盘值,\n 最低值, 最高值]）。\n :param kwargs:\n \"\"\"\n kwargs.update(type=\"candlestick\", x_axis=x_axis)\n if \"tooltip_formatter\" not in kwargs:\n kwargs[\"tooltip_formatter\"] = kline_tooltip_formatter\n if \"tooltip_trigger\" not in kwargs:\n kwargs[\"tooltip_trigger\"] = \"axis\"\n chart = self._get_all_options(**kwargs)\n\n xaxis, yaxis = chart[\"xy_axis\"]\n self._option.update(xAxis=xaxis, yAxis=yaxis)\n self._option.get(\"xAxis\")[0][\"scale\"] = True\n self._option.get(\"yAxis\")[0][\"scale\"] = True\n self._option.get(\"yAxis\")[0][\"splitArea\"] = {\"show\": True}\n\n self._option.get(\"legend\")[0].get(\"data\").append(name)\n\n self._option.get(\"series\").append(\n {\n \"type\": \"candlestick\",\n \"name\": name,\n \"data\": y_axis,\n \"markPoint\": chart[\"mark_point\"],\n \"markLine\": chart[\"mark_line\"],\n \"seriesId\": self._option.get(\"series_id\"),\n }\n )\n self._config_components(**kwargs)"},"start_line":{"kind":"number","value":39,"string":"39"},"end_line":{"kind":"number","value":77,"string":"77"},"file_content":{"kind":"string","value":"# coding=utf-8\r\n\r\nfrom pyecharts.chart import Chart\r\n\r\n\r\ndef kline_tooltip_formatter(params):\r\n text = (\r\n params[0].seriesName\r\n + \"<br/>\"\r\n + \"- open:\"\r\n + params[0].data[1]\r\n + \"<br/>\"\r\n + \"- close:\"\r\n + params[0].data[2]\r\n + \"<br/>\"\r\n + \"- lowest:\"\r\n + params[0].data[3]\r\n + \"<br/>\"\r\n + \"- highest:\"\r\n + params[0].data[4]\r\n )\r\n return text\r\n\r\n\r\nclass Kline(Chart):\r\n \"\"\"\r\n <<< K 线图 >>>\r\n\r\n 红涨蓝跌\r\n \"\"\"\r\n\r\n def __init__(self, title=\"\", subtitle=\"\", **kwargs):\r\n super(Kline, self).__init__(title, subtitle, **kwargs)\r\n\r\n def add(self, *args, **kwargs):\r\n self.__add(*args, **kwargs)\r\n return self\r\n\r\n def __add(self, name, x_axis, y_axis, **kwargs):\r\n \"\"\"\r\n\r\n :param name:\r\n 系列名称，用于 tooltip 的显示，legend 的图例筛选。\r\n :param x_axis:\r\n x 坐标轴数据。\r\n :param y_axis:\r\n y 坐标轴数据。数据中，每一行是一个『数据项』，每一列属于一个『维度』。\r\n 数据项具体为 [open, close, lowest, highest] （即：[开盘值, 收盘值,\r\n 最低值, 最高值]）。\r\n :param kwargs:\r\n \"\"\"\r\n kwargs.update(type=\"candlestick\", x_axis=x_axis)\r\n if \"tooltip_formatter\" not in kwargs:\r\n kwargs[\"tooltip_formatter\"] = kline_tooltip_formatter\r\n if \"tooltip_trigger\" not in kwargs:\r\n kwargs[\"tooltip_trigger\"] = \"axis\"\r\n chart = self._get_all_options(**kwargs)\r\n\r\n xaxis, yaxis = chart[\"xy_axis\"]\r\n self._option.update(xAxis=xaxis, yAxis=yaxis)\r\n self._option.get(\"xAxis\")[0][\"scale\"] = True\r\n self._option.get(\"yAxis\")[0][\"scale\"] = True\r\n self._option.get(\"yAxis\")[0][\"splitArea\"] = {\"show\": True}\r\n\r\n self._option.get(\"legend\")[0].get(\"data\").append(name)\r\n\r\n self._option.get(\"series\").append(\r\n {\r\n \"type\": \"candlestick\",\r\n \"name\": name,\r\n \"data\": y_axis,\r\n \"markPoint\": chart[\"mark_point\"],\r\n \"markLine\": chart[\"mark_line\"],\r\n \"seriesId\": self._option.get(\"series_id\"),\r\n }\r\n )\r\n self._config_components(**kwargs)\r\n"}}},{"rowIdx":72,"cells":{"function_name":{"kind":"string","value":"PsdArray2Noise_1d"},"docstring":{"kind":"string","value":"Generates a noise pattern whose Power Spectral density is given by Psd.\n\nParameters\n---------------------\nPsd : 1d array\n Contains the numeric Psd (treated as evenly spaced array)\n\nSemiaxis :\n 0 : does nothing\n 1 : halvens Pds, then replicates the halven part for left frequencies,\n producing an output as long as Psd\n 2 : replicates all Pds for lef frequencies as well, producing an output\n twice as long as Psd\nReal : boolean\n If True, the real part of the output is returned (default)\n\nReturns:\n---------------------\n An array of the same length of Psd"},"masked_code":{"kind":"string","value":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jul 07 14:08:31 2016\n\n@author: Mic\n\"\"\"\nfrom __future__ import division\nfrom wiselib2.must import *\nimport numpy as np\nimport wiselib2.Rayman as rm\nGauss1d = lambda x ,y : None\nfrom scipy import interpolate as interpolate\n\nfrom matplotlib import pyplot as plt\n\nclass PsdFuns:\n\t'''\n\tEnsemble of possible Psd Functions.\n\tEach element is a callable Psd.\n\tMost used are\n\t\tPsdFuns.PowerLaw(x,a,b)\n\t\tPsdFuns.Interp(x, xData, yData)\n\t'''\n\t@staticmethod\n\tdef Flat(x, *args):\n\t\tN = len(x)\n\t\treturn np.zeros([1,N]) +1\n\t@staticmethod\n\tdef PowerLaw(x,a,b):\n\t\treturn a*x**b\n\t@staticmethod\n\tdef Gaussian(x,sigma, x0=0):\n\t\treturn np.exp(-0.5 * (x-x0)**2/sigma**2)\n\t@staticmethod\n\tdef Interp(x, xData, yData):\n\t\tf = interpolate.interp1d(xData, yData)\n\n\t\treturn f(x)\n\n\ndef PsdFun2Noise_1d(N,dx, PsdFun, PsdArgs):\n\t'''\n\t\tGenerates a noise pattern based an the Power spectral density returned\n\t\tby PsdFun\n\t'''\n\tx = np.arange(0,N//2+1, dx)\n\tyHalf = PsdFun(x, *PsdArgs)\n\ty = Psd2NoisePattern_1d(yHalf, Semiaxis = True \t)\n\treturn x,y\n\n\n#============================================================================\n#\tFUN: \tPsdArray2Noise_1d_v2\n#============================================================================\ndef PsdArray2Noise_1d_v2(f_in, Psd_in, L_mm,N):\n\t'''\n\t\tReturns meters\n\t'''\n\tfrom scipy import interpolate\n\tlog=np.log\n\tfft = np.fft.fft\n\tfftshift = np.fft.fftshift\n\n\tff = f_in\n\tyy = Psd_in\n\tL = L_mm\n\tN = int(N)\n\tN2 = int(N//2)\n\tL =300 # (mm)\n\tL_um = L*1e3\n\tL_nm = L*1e6\n\n\tfMin = 1/L_um\n\n\t##vecchia riga\n\t##fSpline = (np.array(range(N2))+1)/L_um # um^-1\n\tfSpline = np.arange(N2)/N2 * (max(ff) - min(ff)) + min(ff)\n\n\tfun = interpolate.splrep(log(ff), log(yy), s=2)\n\tyPsd_log = interpolate.splev(log(fSpline), fun)\n\tySpline = np.exp(yPsd_log)\n\tyPsd = ySpline\n\n\t# tolgo\n\tyPsd[fSpline<ff[0]] = 200\n\tn = len(yPsd)\n\n\n\tplt.plot(fSpline, yPsd,'-')\n\tplt.plot(ff, yy,'x')\n\tplt.legend(['ySpline','Data'])\n\tax = plt.axes()\n\t#ax.set_yscale('log')\n\t#ax.set_xscale('log')\n\n\t#% controllo RMS integrando la yPsd\n\timport scipy.integrate as integrate\n\n\tRMS = np.sqrt(integrate.trapz(yPsd, fSpline/1000))\n\n\t#% Modo Manfredda style\n\n\t#yPsdNorm = np.sqrt(yPsd/L_um/1000)\n\t#yPsdNorm_reverse = yPsdNorm[::-1]\n\tyPsd_reverse = yPsd[::-1]\n\tell= 1/(fSpline[1] - fSpline[0])\n\n\tif N%2 == 0:\n\t\tyPsd2 = np.hstack((yPsd_reverse ,0,yPsd[0:-1]))\n\telse:\n\t\tyPsd2 = np.hstack((yPsd_reverse ,0,yPsd))\n\t##yPsd2Norm = np.sqrt(yPsd2/ell/1000/2)\n\tyPsd2Norm = np.sqrt(yPsd2/ell/1000)\n\tn_ = len(yPsd2)\n\tprint('len(yPsd2) = %0.2d' % len(yPsd2Norm))\n\tphi = 2*np.pi * np.random.rand(n_)\n\tr = np.exp(1j*phi)\n\n\tyPsd2Norm_ = fftshift(yPsd2Norm)\n\t#yPsd2Norm_[len(yPsd2Norm_)//2] = 0\n\n\tyRaf = np.fft.fft(r*yPsd2Norm_)\n\tyRaf = np.real(yRaf)\n\tprint('Rms = %0.2e nm' % np.std(yRaf))\n\n\tplt.plot(yPsd2Norm_)\n\n\tprint('max yPsd_ = %d nm' % max(yPsd2))\n\tprint('max yPsd2Norm = %0.4f nm' % max(yPsd2Norm))\n\tprint('Rms yRaf2 = %0.2e nm' % np.std(yRaf))\n\treturn yRaf * 1e-9\n\n#============================================================================\n#\tFUN: \tPsd2Noise\n#============================================================================\n# MASKED: PsdArray2Noise_1d function (lines 136-177)\nPsd2Noise_1d = PsdArray2Noise_1d\n#============================================================================\n#\tFUN: \tNoNoise_1d\n#============================================================================\ndef NoNoise_1d(N, *args):\n\treturn np.zeros([1,N])\n\n#============================================================================\n#\tFUN: \tGaussianNoise_1d\n#============================================================================\ndef GaussianNoise_1d(N,dx, Sigma):\n\t'''\n\tPSD(f) = np.exp(-0.5^f/Sigma^2)\n\t'''\n\tx = np.linspace( - N//2 *dx, N//2-1 * dx,N)\n\ty = np.exp(-0.5*x**2/Sigma**2)\n\treturn Psd2NoisePattern_1d(y)\n\n\n#============================================================================\n#\tFUN: \tPowerLawNoise_1d\n#============================================================================\ndef PowerLawNoise_1d(N, dx, a, b):\n\t'''\n\tPSD(x) = a*x^b\n\t'''\n\tx = np.arange(0,N//2+1, dx)\n\tyHalf = a * x**b\n#\ty = np.hstack((yHalf[-1:0:-1], 0, yHalf[1:-1]))\n\treturn Psd2NoisePattern_1d(y, Semiaxis = True)\n\n#============================================================================\n#\tFUN: \tCustomNoise_1d\n#============================================================================\ndef CustomNoise_1d(N, dx, xPsd, yPsd):\n\txPsd_, yPsd_ = rm.FastResample1d(xPsd, yPsd,N)\n\treturn Psd2NoisePattern_1d(yPsd_, Semiaxis = True)\n\n#============================================================================\n#\tCLASS: \tNoiseGenerator\n#============================================================================\nclass PsdGenerator:\n\tNoNoise = staticmethod(NoNoise_1d)\n\tGauss = staticmethod(GaussianNoise_1d)\n\tPowerLaw = staticmethod(PowerLawNoise_1d)\n\tNumericArray = staticmethod(CustomNoise_1d)\n\n\n\n#============================================================================\n#\tFUN: \tFitPowerLaw\n#============================================================================\ndef FitPowerLaw(x,y):\n\t'''\n\tFits the input data in the form\n\t\ty = a*x^b\n\treturns a,b\n\t'''\n\timport scipy.optimize as optimize\n\n\tfFit = lambda p, x: p[0] * x ** p[1]\n\tfErr = lambda p, x, y: (y - fFit(p, x))\n\n\tp0 = [max(y), -1.0]\n\tout = optimize.leastsq(fErr, p0, args=(x, y), full_output=1)\n\n\tpOut = out[0]\n\n\tb = pOut[1]\n\ta = pOut[0]\n\n#\tindexErr = np.np.sqrt( covar[0][0] )\n#\tampErr = np.np.sqrt( covar[1][1] ) * amp\n\n\treturn a,b\n\n#==============================================================================\n# \tCLASS: RoughnessMaker\n#==============================================================================\n\nclass RoughnessMaker(object):\n\n\tclass Options():\n\t\tFIT_NUMERIC_DATA_WITH_POWER_LAW = True\n\t\tAUTO_ZERO_MEAN_FOR_NUMERIC_DATA = True\n\t\tAUTO_FILL_NUMERIC_DATA_WITH_ZERO = True\n\t\tAUTO_RESET_CUTOFF_ON_PSDTYPE_CHANGE = True\n\n\tdef __init__(self):\n\t\tself.PsdType = PsdFuns.PowerLaw\n\t\tself.PsdParams = np.array([1,1])\n\t\tself._IsNumericPsdInFreq = None\n\t\tself.CutoffLowHigh = [None, None]\n\t\tself.ProfileScaling = 1\n\t\treturn None\n\n\t@property\n\tdef PsdType(self):\n\t\treturn self._PsdType\n\t@PsdType.setter\n\tdef PsdType(self, Val):\n\t\t'''\n\t\tNote: each time that the Property value is set, self.CutoffLowHigh is\n\t\treset, is specified by options\n\t\t'''\n\t\tself. _PsdType = Val\n\t\tif self.Options.AUTO_RESET_CUTOFF_ON_PSDTYPE_CHANGE == True:\n\t\t\tself.PsdCutoffLowHigh = [None, None]\n\n\t#======================================================================\n\t# \tFUN: PdfEval\n\t#======================================================================\n\tdef PsdEval(self, N, df, CutoffLowHigh = [None, None]):\n\t\t'''\n\t\tEvals the PSD in the range [0 - N*df]\n\t\tIt's good custom to have PSD[0] = 0, so that the noise pattern is\n\t\tzero-mean.\n\n\t\tParameters:\n\t\t----------------------\n\t\t\tN : int\n\t\t\t\t#of samples\n\t\t\tdf : float\n\t\t\t\tspacing of spatial frequencies (df=1/TotalLength)\n\t\t\tCutoffLowHigh : [LowCutoff, HighCutoff]\n\t\t\t\tif >0, then Psd(f<Cutoff) is set to 0.\n\t\t\t\t\t\tif None, then LowCutoff = min()\n\t\tReturns : fAll, yPsdAll\n\t\t----------------------\n\t\t\tfAll : 1d array\n\t\t\t\tcontains the spatial frequencies\n\t\t\tyPsd : 1d array\n\t\t\t\tcontains the Psd\n\t\t'''\n\n\t\t'''\n\t\tThe Pdf is evaluated only within LowCutoff and HoghCutoff\n\t\tIf the Pdf is PsdFuns.Interp, then LowCutoff and HighCutoff are\n\t\tautomatically set to min and max values of the experimental data\n\t\t'''\n\t\tStrMessage = ''\n\t\tdef GetInRange(fAll, LowCutoff, HighCutoff):\n\t\t\t_tmpa = fAll >= LowCutoff\n\t\t\t_tmpb = fAll <= HighCutoff\n\t\t\tfMid_Pos = np.all([_tmpa, _tmpb],0)\n\t\t\tfMid = fAll[fMid_Pos]\n\t\t\treturn fMid_Pos, fMid\n\n\t\tLowCutoff, HighCutoff = CutoffLowHigh\n\t\tfMin = 0\n\t\tfMax = (N-1)*df\n\t\tfAll = np.linspace(0, fMax, N)\n\t\tyPsdAll = fAll* 0 # init\n\n\t\tLowCutoff = 0 if LowCutoff is None else LowCutoff\n\t\tHighCutoff = N*df if HighCutoff is None else HighCutoff\n\n\n\n\t\t# Numeric PSD\n\t\t# Note: by default returned yPsd is always 0 outside the input data range\n\t\tif self.PsdType == PsdFuns.Interp:\n\t\t\t# Use Auto-Fit + PowerLaw\n\t\t\tif self.Options.FIT_NUMERIC_DATA_WITH_POWER_LAW == True:\n\t\t\t\t\txFreq,y = self.NumericPsdGetXY()\n\t\t\t\t\tp = FitPowerLaw(1/xFreq,y)\n\t\t\t\t\t_PsdParams = p[0], -p[1]\n\t\t\t\t\tLowCutoff = np.amin(self._PsdNumericX)\n\t\t\t\t\tHighCutoff = np.amin(self._PsdNumericX)\n\t\t\t\t\tfMid_Pos, fMid = GetInRange(fAll, LowCutoff, HighCutoff)\n\t\t\t\t\tyPsd = PsdFuns.PowerLaw(fMid, *_PsdParams )\n\t\t\t# Use Interpolation\n\t\t\telse:\n\t\t\t\t# check Cutoff\n\t\t\t\tLowVal = np.amin(self._PsdNumericX)\n\t\t\t\tHighVal = np.amax(self._PsdNumericX)\n\t\t\t\tLowCutoff = LowVal if LowCutoff <= LowVal else LowCutoff\n\t\t\t\tHighCutoff = HighVal if HighCutoff >= HighVal else HighCutoff\n\n\n\t\t\t\t# Get the list of good frequency values (fMid) and their positions\n\t\t\t\t# (fMid_Pos)\n\t\t\t\tfMid_Pos, fMid = GetInRange(fAll, LowCutoff, HighCutoff)\n\n\t\t\t\t##yPsd = self.PsdType(fMid, *self.PsdParams)\n\t\t\t\t## non funziona, rimpiazzo a mano\n\t\t\t\tyPsd = PsdFuns.Interp(fMid, self._PsdNumericX, self._PsdNumericY)\n\n\t\t# Analytical Psd\n\t\telse:\n\t\t\tfMid_Pos, fMid = GetInRange(fAll, LowCutoff, HighCutoff)\n\t\t\tyPsd = self.PsdType(fMid, *self.PsdParams)\n\n\t\t# copying array subset\n\t\tyPsdAll[fMid_Pos] = yPsd\n\n\t\treturn fAll, yPsdAll\n\n\t#======================================================================\n\t# \tFUN: _FitNumericPsdWithPowerLaw\n\t#======================================================================\n# in disusos\n\tdef _FitNumericPsdWithPowerLaw(self):\n\t\tx,y = self.NumericPsdGetXY()\n\t\tif self._IsNumericPsdInFreq == True:\n\t\t\tp = FitPowerLaw(1/x,y)\n\t\t\tself.PsdParams = p[0], -p[1]\n\t\telse:\n\t\t\tp = FitPowerLaw(x,y)\n\t\t\tself.PsdParams = p[0], p[1]\n\n\n\t#======================================================================\n\t# \tFUN: MakeProfile\n\t#======================================================================\n\tdef MakeProfile(self, L,N):\n\t\t'''\n\t\t\tEvaluates the psd according to .PsdType, .PsdParams and .Options directives\n\t\t\tReturns an evenly-spaced array.\n\t\t\tIf PsdType = NumericArray, linear interpolation is performed.\n\n\t\t\t:PARAM: N: # of samples\n\t\t\t:PARAM: dx: grid spacing (spatial frequency)\n\n\t\t\treturns:\n\t\t\t\t1d arr\n\t\t'''\n\n\n\t\tif self.PsdType == PsdFuns.Interp:\n\t\t\t# chiama codice ad hoc\n\t\t\tL_mm = L*1e3\n\t\t\tyRoughness = PsdArray2Noise_1d_v2(self._PsdNumericX, self._PsdNumericY, L_mm, N)\n\t\telse:\n\t\t\tprint('Irreversible error. The code was not completed to handle this instance')\n\t\treturn yRoughness * self.ProfileScaling\n#\t\tf, yPsd = self.PsdEval(N//2 + 1,df)\n\n\n\t\t# Special case\n#\t\tif self.Options.FIT_NUMERIC_DATA_WITH_POWER_LAW == True:\n#\t\t\tself.PsdParams = list(FitPowerLaw(*self.NumericPsdGetXY()))\n#\t\t\tyPsd = PsdFuns.PowerLaw(x, *self.PsdParams)\n#\t\telse: # general calse\n#\t\t\tyPsd = self.PsdType(x, *self.PsdParams)\n\n#\t\tyRoughness = Psd2Noise_1d(yPsd, N, Semiaxis = True)\n\n\n#\t\tx = np.linspace(0, N*dx,N)\n#\t\t# Special case\n#\t\tif self.Options.FIT_NUMERIC_DATA_WITH_POWER_LAW == True:\n#\t\t\tself.PsdParams = list(FitPowerLaw(*self.NumericPsdGetXY()))\n#\t\t\ty = PowerLawNoise_1d(N, dx, *self.PsdParams)\n#\t\telse: # general calse\n#\t\t\ty = self.PsdType(N,dx, *self.PsdParams)\n#\t\treturn y\n\tGenerate = MakeProfile\n\t#======================================================================\n\t# \tFUN: NumericPsdSetXY\n\t#======================================================================\n\tdef NumericPsdSetXY(self,x,y):\n\t\tself._PsdNumericX = x\n\t\tself._PsdNumericY = y\n\n\n\t#======================================================================\n\t# \tFUN: NumericPsdGetXY\n\t#======================================================================\n\n\tdef NumericPsdGetXY(self):\n\t\ttry:\n\t\t\treturn self._PsdNumericX, self._PsdNumericY\n\t\texcept:\n\t\t\tprint('Error in RoughnessMaker.NumericPsdGetXY. Maybe the data file was not properly loaded')\n\t#======================================================================\n\t# \tFUN: NumericPsdLoadXY\n\t#======================================================================\n\tdef NumericPsdLoadXY(self, FilePath, xScaling = 1, yScaling = 1 , xIsSpatialFreq = True):\n\t\t''' @TODO: specificare formati e tipi di file\n\n\t\tParameters\n\t\t----------------------------\n\t\txIsSpatialFreq : bool\n\t\t\t\t\t\ttrue If the first column (Read_x_values) contains spatial\n\t\t\t\t\t\tfrequencies. False if it contains lenghts. Default = True\n\t\txScaling, yScaling: floats\n\t\t\t\t\t\tRead_x_values => Read_x_values * xScaling\n\n\t\t\t\t\t\tRead_y_values => Read_y_values * yScaling\n\n\t\t\t\t\t\tSometimes, properly setting the x and y scaling values may be confusing (although just matter of high-school considerations). On this purpose, the property .RoughnessMaker.ProfileScaling property can be used also..ProfileScaling is the scale factor that acts on the output of MakeProfile() function only.\n\n\t\tremarks\n\t\t\t\t\t\t--------\n\t\t\t\t\t\tpippo\n\n\t\t'''\n\n\t\ttry:\n\t\t\tself._IsNumericPsdInFreq = xIsSpatialFreq\n\n\t\t\ts = np.loadtxt(FilePath)\n\t\t\tx = s[:,0]\n\t\t\ty = s[:,1]\n\n\t\t\tx = x * xScaling\n\t\t\ty = y * yScaling\n\n\t\t\t# inversion of x-axis if not spatial frequencies\n\t\t\tif xIsSpatialFreq == False:\n\t\t\t\tf = 1/x\n\t\t\telse:\n\t\t\t\tf = x\n\t\t\t# array sorting\n\t\t\ti = np.argsort(f)\n\t\t\tf = f[i]\n\t\t\ty = y[i]\n\t\t\t# I set the Cutoff value of the class according to available data\n\t\t\tself.PsdCutoffLowHigh = [np.amin, np.amax(f)]\n\n\t\t\t# I set class operating variables\n\t\t\tself.PsdType = PsdFuns.Interp\n\t\t\tself.PsdParams = [f,y]\n\n\n\t\t\t# Auto-set\n\t\t\t# fill 0-value (DC Component)\n\t#\t\tif self.Options.AUTO_FILL_NUMERIC_DATA_WITH_ZERO == True:\n\t#\t\t\tif np.amin(x >0):\n\t#\t\t\t\tx = np.insert(x,0,0)\n\t#\t\t\t\ty = np.insert(y,0,0)\t# 0 in psd => 0-mean value in the noise pattern\n\n\n\t\t\t# sync other class values\n\t\t\tself.NumericPsdSetXY(f, y)\n\t\texcept:\n\t\t\tpass\n\n\t\tdef Generate(self, N = None, dx = None, CutoffLowHigh = [None, None]):\n\t\t\t'''\n\t\t\tParameters\n\t\t\t\tN: # of output samples\n\t\t\t\tdx: step of the x axis\n\t\t\tNote: generates an evenly spaced array\n\t\t\t'''\n\t\t\tL = dx * N\n\t\t\tdf = 1/L\n\t\t\tfPsd, yPsd = self.PsdEval(N//2 +1 , df = df,\n\t\t\t\t\t\t\t\t\t\t\tCutoffLowHigh = CutoffLowHigh )\n\t\t\th = Psd2Noise_1d(yPsd, Semiaxis = True)\n\n\t\t\treturn h\n\n\t#======================================================================\n\t# \tFUN: NumericPsdCheck\n\t#======================================================================\n\tdef NumericPsdCheck(self, N, L):\n\n\t\tdf = 1/L\n\t\t# Stored data\n\t\tff,yy = self.NumericPsdGetXY()\n\t\t# Evaluated data\n\t\tfPsd, yPsd = self.PsdEval(N, df)\n\n\n\t\tplt.plot(fPsd, np.log10(yPsd),'x')\n\t\tplt.plot(ff, np.log10(yy),'.r')\n\t\tplt.legend(['Evaluated data', 'Stored data'])\n\t\tplt.suptitle('Usage of stored data (PSD)')\n\n\t\tfMax = df*(N//2)\n\t\tfMin = df\n\t\tStrMsg = ''\n\t\t_max = np.max(ff)\n\t\t_min = np.min(ff)\n\t\tprint('fMax query = %0.1e m^-1' % fMax )\n\t\tprint('fMax data= %0.1e m^-1 = %0.2e um^-1' % (_max, (_max * 1e6) ))\n\t\tprint('fMin query= %0.1e m^-1' % fMin )\n\t\tprint('fMin data= %0.1e m^-1 = %0.2e um^-1' % (_min, (_min * 1e6) ))\n\n\t\treturn StrMsg"},"implementation":{"kind":"string","value":"def PsdArray2Noise_1d(PsdArray, N, Semiaxis = True, Real = True):\n\t'''\n\tGenerates a noise pattern whose Power Spectral density is given by Psd.\n\n\tParameters\n\t---------------------\n\tPsd : 1d array\n\t\tContains the numeric Psd (treated as evenly spaced array)\n\n\tSemiaxis :\n\t\t0 : does nothing\n\t\t1 : halvens Pds, then replicates the halven part for left frequencies,\n\t\t\tproducing an output as long as Psd\n\t\t2 : replicates all Pds for lef frequencies as well, producing an output\n\t\t\ttwice as long as Psd\n\tReal : boolean\n\t\tIf True, the real part of the output is returned (default)\n\n\tReturns:\n\t---------------------\n\t\tAn array of the same length of Psd\n\t'''\n\n\tif Semiaxis == True:\n\t\tyHalf = PsdArray\n\t\tPsdArrayNew = np.hstack((yHalf[-1:0:-1], yHalf))\n\t\tidelta = len(PsdArrayNew) - N\n\t\tif idelta == 1:# piu lungo\n\t\t\tPsdArrayNew = PsdArrayNew[0:-1] # uguale\n\t\telif idelta == 0:\n\t\t\tpass\n\t\telse:\n\t\t\tprint('Error! len(PsdArrayNew) - len(PsdArray) = %0d' % idelta)\n\ty = np.fft.fftshift(PsdArrayNew)\n\tr = 2*np.pi * np.random.rand(len(PsdArrayNew))\n\n\tf = np.fft.ifft(y * np.exp(1j*r))\n\n\tif Real:\n\t\treturn np.real(f)\n\telse:\n\t\treturn f"},"start_line":{"kind":"number","value":136,"string":"136"},"end_line":{"kind":"number","value":177,"string":"177"},"file_content":{"kind":"string","value":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jul 07 14:08:31 2016\n\n@author: Mic\n\"\"\"\nfrom __future__ import division\nfrom wiselib2.must import *\nimport numpy as np\nimport wiselib2.Rayman as rm\nGauss1d = lambda x ,y : None\nfrom scipy import interpolate as interpolate\n\nfrom matplotlib import pyplot as plt\n\nclass PsdFuns:\n\t'''\n\tEnsemble of possible Psd Functions.\n\tEach element is a callable Psd.\n\tMost used are\n\t\tPsdFuns.PowerLaw(x,a,b)\n\t\tPsdFuns.Interp(x, xData, yData)\n\t'''\n\t@staticmethod\n\tdef Flat(x, *args):\n\t\tN = len(x)\n\t\treturn np.zeros([1,N]) +1\n\t@staticmethod\n\tdef PowerLaw(x,a,b):\n\t\treturn a*x**b\n\t@staticmethod\n\tdef Gaussian(x,sigma, x0=0):\n\t\treturn np.exp(-0.5 * (x-x0)**2/sigma**2)\n\t@staticmethod\n\tdef Interp(x, xData, yData):\n\t\tf = interpolate.interp1d(xData, yData)\n\n\t\treturn f(x)\n\n\ndef PsdFun2Noise_1d(N,dx, PsdFun, PsdArgs):\n\t'''\n\t\tGenerates a noise pattern based an the Power spectral density returned\n\t\tby PsdFun\n\t'''\n\tx = np.arange(0,N//2+1, dx)\n\tyHalf = PsdFun(x, *PsdArgs)\n\ty = Psd2NoisePattern_1d(yHalf, Semiaxis = True \t)\n\treturn x,y\n\n\n#============================================================================\n#\tFUN: \tPsdArray2Noise_1d_v2\n#============================================================================\ndef PsdArray2Noise_1d_v2(f_in, Psd_in, L_mm,N):\n\t'''\n\t\tReturns meters\n\t'''\n\tfrom scipy import interpolate\n\tlog=np.log\n\tfft = np.fft.fft\n\tfftshift = np.fft.fftshift\n\n\tff = f_in\n\tyy = Psd_in\n\tL = L_mm\n\tN = int(N)\n\tN2 = int(N//2)\n\tL =300 # (mm)\n\tL_um = L*1e3\n\tL_nm = L*1e6\n\n\tfMin = 1/L_um\n\n\t##vecchia riga\n\t##fSpline = (np.array(range(N2))+1)/L_um # um^-1\n\tfSpline = np.arange(N2)/N2 * (max(ff) - min(ff)) + min(ff)\n\n\tfun = interpolate.splrep(log(ff), log(yy), s=2)\n\tyPsd_log = interpolate.splev(log(fSpline), fun)\n\tySpline = np.exp(yPsd_log)\n\tyPsd = ySpline\n\n\t# tolgo\n\tyPsd[fSpline<ff[0]] = 200\n\tn = len(yPsd)\n\n\n\tplt.plot(fSpline, yPsd,'-')\n\tplt.plot(ff, yy,'x')\n\tplt.legend(['ySpline','Data'])\n\tax = plt.axes()\n\t#ax.set_yscale('log')\n\t#ax.set_xscale('log')\n\n\t#% controllo RMS integrando la yPsd\n\timport scipy.integrate as integrate\n\n\tRMS = np.sqrt(integrate.trapz(yPsd, fSpline/1000))\n\n\t#% Modo Manfredda style\n\n\t#yPsdNorm = np.sqrt(yPsd/L_um/1000)\n\t#yPsdNorm_reverse = yPsdNorm[::-1]\n\tyPsd_reverse = yPsd[::-1]\n\tell= 1/(fSpline[1] - fSpline[0])\n\n\tif N%2 == 0:\n\t\tyPsd2 = np.hstack((yPsd_reverse ,0,yPsd[0:-1]))\n\telse:\n\t\tyPsd2 = np.hstack((yPsd_reverse ,0,yPsd))\n\t##yPsd2Norm = np.sqrt(yPsd2/ell/1000/2)\n\tyPsd2Norm = np.sqrt(yPsd2/ell/1000)\n\tn_ = len(yPsd2)\n\tprint('len(yPsd2) = %0.2d' % len(yPsd2Norm))\n\tphi = 2*np.pi * np.random.rand(n_)\n\tr = np.exp(1j*phi)\n\n\tyPsd2Norm_ = fftshift(yPsd2Norm)\n\t#yPsd2Norm_[len(yPsd2Norm_)//2] = 0\n\n\tyRaf = np.fft.fft(r*yPsd2Norm_)\n\tyRaf = np.real(yRaf)\n\tprint('Rms = %0.2e nm' % np.std(yRaf))\n\n\tplt.plot(yPsd2Norm_)\n\n\tprint('max yPsd_ = %d nm' % max(yPsd2))\n\tprint('max yPsd2Norm = %0.4f nm' % max(yPsd2Norm))\n\tprint('Rms yRaf2 = %0.2e nm' % np.std(yRaf))\n\treturn yRaf * 1e-9\n\n#============================================================================\n#\tFUN: \tPsd2Noise\n#============================================================================\ndef PsdArray2Noise_1d(PsdArray, N, Semiaxis = True, Real = True):\n\t'''\n\tGenerates a noise pattern whose Power Spectral density is given by Psd.\n\n\tParameters\n\t---------------------\n\tPsd : 1d array\n\t\tContains the numeric Psd (treated as evenly spaced array)\n\n\tSemiaxis :\n\t\t0 : does nothing\n\t\t1 : halvens Pds, then replicates the halven part for left frequencies,\n\t\t\tproducing an output as long as Psd\n\t\t2 : replicates all Pds for lef frequencies as well, producing an output\n\t\t\ttwice as long as Psd\n\tReal : boolean\n\t\tIf True, the real part of the output is returned (default)\n\n\tReturns:\n\t---------------------\n\t\tAn array of the same length of Psd\n\t'''\n\n\tif Semiaxis == True:\n\t\tyHalf = PsdArray\n\t\tPsdArrayNew = np.hstack((yHalf[-1:0:-1], yHalf))\n\t\tidelta = len(PsdArrayNew) - N\n\t\tif idelta == 1:# piu lungo\n\t\t\tPsdArrayNew = PsdArrayNew[0:-1] # uguale\n\t\telif idelta == 0:\n\t\t\tpass\n\t\telse:\n\t\t\tprint('Error! len(PsdArrayNew) - len(PsdArray) = %0d' % idelta)\n\ty = np.fft.fftshift(PsdArrayNew)\n\tr = 2*np.pi * np.random.rand(len(PsdArrayNew))\n\n\tf = np.fft.ifft(y * np.exp(1j*r))\n\n\tif Real:\n\t\treturn np.real(f)\n\telse:\n\t\treturn f\nPsd2Noise_1d = PsdArray2Noise_1d\n#============================================================================\n#\tFUN: \tNoNoise_1d\n#============================================================================\ndef NoNoise_1d(N, *args):\n\treturn np.zeros([1,N])\n\n#============================================================================\n#\tFUN: \tGaussianNoise_1d\n#============================================================================\ndef GaussianNoise_1d(N,dx, Sigma):\n\t'''\n\tPSD(f) = np.exp(-0.5^f/Sigma^2)\n\t'''\n\tx = np.linspace( - N//2 *dx, N//2-1 * dx,N)\n\ty = np.exp(-0.5*x**2/Sigma**2)\n\treturn Psd2NoisePattern_1d(y)\n\n\n#============================================================================\n#\tFUN: \tPowerLawNoise_1d\n#============================================================================\ndef PowerLawNoise_1d(N, dx, a, b):\n\t'''\n\tPSD(x) = a*x^b\n\t'''\n\tx = np.arange(0,N//2+1, dx)\n\tyHalf = a * x**b\n#\ty = np.hstack((yHalf[-1:0:-1], 0, yHalf[1:-1]))\n\treturn Psd2NoisePattern_1d(y, Semiaxis = True)\n\n#============================================================================\n#\tFUN: \tCustomNoise_1d\n#============================================================================\ndef CustomNoise_1d(N, dx, xPsd, yPsd):\n\txPsd_, yPsd_ = rm.FastResample1d(xPsd, yPsd,N)\n\treturn Psd2NoisePattern_1d(yPsd_, Semiaxis = True)\n\n#============================================================================\n#\tCLASS: \tNoiseGenerator\n#============================================================================\nclass PsdGenerator:\n\tNoNoise = staticmethod(NoNoise_1d)\n\tGauss = staticmethod(GaussianNoise_1d)\n\tPowerLaw = staticmethod(PowerLawNoise_1d)\n\tNumericArray = staticmethod(CustomNoise_1d)\n\n\n\n#============================================================================\n#\tFUN: \tFitPowerLaw\n#============================================================================\ndef FitPowerLaw(x,y):\n\t'''\n\tFits the input data in the form\n\t\ty = a*x^b\n\treturns a,b\n\t'''\n\timport scipy.optimize as optimize\n\n\tfFit = lambda p, x: p[0] * x ** p[1]\n\tfErr = lambda p, x, y: (y - fFit(p, x))\n\n\tp0 = [max(y), -1.0]\n\tout = optimize.leastsq(fErr, p0, args=(x, y), full_output=1)\n\n\tpOut = out[0]\n\n\tb = pOut[1]\n\ta = pOut[0]\n\n#\tindexErr = np.np.sqrt( covar[0][0] )\n#\tampErr = np.np.sqrt( covar[1][1] ) * amp\n\n\treturn a,b\n\n#==============================================================================\n# \tCLASS: RoughnessMaker\n#==============================================================================\n\nclass RoughnessMaker(object):\n\n\tclass Options():\n\t\tFIT_NUMERIC_DATA_WITH_POWER_LAW = True\n\t\tAUTO_ZERO_MEAN_FOR_NUMERIC_DATA = True\n\t\tAUTO_FILL_NUMERIC_DATA_WITH_ZERO = True\n\t\tAUTO_RESET_CUTOFF_ON_PSDTYPE_CHANGE = True\n\n\tdef __init__(self):\n\t\tself.PsdType = PsdFuns.PowerLaw\n\t\tself.PsdParams = np.array([1,1])\n\t\tself._IsNumericPsdInFreq = None\n\t\tself.CutoffLowHigh = [None, None]\n\t\tself.ProfileScaling = 1\n\t\treturn None\n\n\t@property\n\tdef PsdType(self):\n\t\treturn self._PsdType\n\t@PsdType.setter\n\tdef PsdType(self, Val):\n\t\t'''\n\t\tNote: each time that the Property value is set, self.CutoffLowHigh is\n\t\treset, is specified by options\n\t\t'''\n\t\tself. _PsdType = Val\n\t\tif self.Options.AUTO_RESET_CUTOFF_ON_PSDTYPE_CHANGE == True:\n\t\t\tself.PsdCutoffLowHigh = [None, None]\n\n\t#======================================================================\n\t# \tFUN: PdfEval\n\t#======================================================================\n\tdef PsdEval(self, N, df, CutoffLowHigh = [None, None]):\n\t\t'''\n\t\tEvals the PSD in the range [0 - N*df]\n\t\tIt's good custom to have PSD[0] = 0, so that the noise pattern is\n\t\tzero-mean.\n\n\t\tParameters:\n\t\t----------------------\n\t\t\tN : int\n\t\t\t\t#of samples\n\t\t\tdf : float\n\t\t\t\tspacing of spatial frequencies (df=1/TotalLength)\n\t\t\tCutoffLowHigh : [LowCutoff, HighCutoff]\n\t\t\t\tif >0, then Psd(f<Cutoff) is set to 0.\n\t\t\t\t\t\tif None, then LowCutoff = min()\n\t\tReturns : fAll, yPsdAll\n\t\t----------------------\n\t\t\tfAll : 1d array\n\t\t\t\tcontains the spatial frequencies\n\t\t\tyPsd : 1d array\n\t\t\t\tcontains the Psd\n\t\t'''\n\n\t\t'''\n\t\tThe Pdf is evaluated only within LowCutoff and HoghCutoff\n\t\tIf the Pdf is PsdFuns.Interp, then LowCutoff and HighCutoff are\n\t\tautomatically set to min and max values of the experimental data\n\t\t'''\n\t\tStrMessage = ''\n\t\tdef GetInRange(fAll, LowCutoff, HighCutoff):\n\t\t\t_tmpa = fAll >= LowCutoff\n\t\t\t_tmpb = fAll <= HighCutoff\n\t\t\tfMid_Pos = np.all([_tmpa, _tmpb],0)\n\t\t\tfMid = fAll[fMid_Pos]\n\t\t\treturn fMid_Pos, fMid\n\n\t\tLowCutoff, HighCutoff = CutoffLowHigh\n\t\tfMin = 0\n\t\tfMax = (N-1)*df\n\t\tfAll = np.linspace(0, fMax, N)\n\t\tyPsdAll = fAll* 0 # init\n\n\t\tLowCutoff = 0 if LowCutoff is None else LowCutoff\n\t\tHighCutoff = N*df if HighCutoff is None else HighCutoff\n\n\n\n\t\t# Numeric PSD\n\t\t# Note: by default returned yPsd is always 0 outside the input data range\n\t\tif self.PsdType == PsdFuns.Interp:\n\t\t\t# Use Auto-Fit + PowerLaw\n\t\t\tif self.Options.FIT_NUMERIC_DATA_WITH_POWER_LAW == True:\n\t\t\t\t\txFreq,y = self.NumericPsdGetXY()\n\t\t\t\t\tp = FitPowerLaw(1/xFreq,y)\n\t\t\t\t\t_PsdParams = p[0], -p[1]\n\t\t\t\t\tLowCutoff = np.amin(self._PsdNumericX)\n\t\t\t\t\tHighCutoff = np.amin(self._PsdNumericX)\n\t\t\t\t\tfMid_Pos, fMid = GetInRange(fAll, LowCutoff, HighCutoff)\n\t\t\t\t\tyPsd = PsdFuns.PowerLaw(fMid, *_PsdParams )\n\t\t\t# Use Interpolation\n\t\t\telse:\n\t\t\t\t# check Cutoff\n\t\t\t\tLowVal = np.amin(self._PsdNumericX)\n\t\t\t\tHighVal = np.amax(self._PsdNumericX)\n\t\t\t\tLowCutoff = LowVal if LowCutoff <= LowVal else LowCutoff\n\t\t\t\tHighCutoff = HighVal if HighCutoff >= HighVal else HighCutoff\n\n\n\t\t\t\t# Get the list of good frequency values (fMid) and their positions\n\t\t\t\t# (fMid_Pos)\n\t\t\t\tfMid_Pos, fMid = GetInRange(fAll, LowCutoff, HighCutoff)\n\n\t\t\t\t##yPsd = self.PsdType(fMid, *self.PsdParams)\n\t\t\t\t## non funziona, rimpiazzo a mano\n\t\t\t\tyPsd = PsdFuns.Interp(fMid, self._PsdNumericX, self._PsdNumericY)\n\n\t\t# Analytical Psd\n\t\telse:\n\t\t\tfMid_Pos, fMid = GetInRange(fAll, LowCutoff, HighCutoff)\n\t\t\tyPsd = self.PsdType(fMid, *self.PsdParams)\n\n\t\t# copying array subset\n\t\tyPsdAll[fMid_Pos] = yPsd\n\n\t\treturn fAll, yPsdAll\n\n\t#======================================================================\n\t# \tFUN: _FitNumericPsdWithPowerLaw\n\t#======================================================================\n# in disusos\n\tdef _FitNumericPsdWithPowerLaw(self):\n\t\tx,y = self.NumericPsdGetXY()\n\t\tif self._IsNumericPsdInFreq == True:\n\t\t\tp = FitPowerLaw(1/x,y)\n\t\t\tself.PsdParams = p[0], -p[1]\n\t\telse:\n\t\t\tp = FitPowerLaw(x,y)\n\t\t\tself.PsdParams = p[0], p[1]\n\n\n\t#======================================================================\n\t# \tFUN: MakeProfile\n\t#======================================================================\n\tdef MakeProfile(self, L,N):\n\t\t'''\n\t\t\tEvaluates the psd according to .PsdType, .PsdParams and .Options directives\n\t\t\tReturns an evenly-spaced array.\n\t\t\tIf PsdType = NumericArray, linear interpolation is performed.\n\n\t\t\t:PARAM: N: # of samples\n\t\t\t:PARAM: dx: grid spacing (spatial frequency)\n\n\t\t\treturns:\n\t\t\t\t1d arr\n\t\t'''\n\n\n\t\tif self.PsdType == PsdFuns.Interp:\n\t\t\t# chiama codice ad hoc\n\t\t\tL_mm = L*1e3\n\t\t\tyRoughness = PsdArray2Noise_1d_v2(self._PsdNumericX, self._PsdNumericY, L_mm, N)\n\t\telse:\n\t\t\tprint('Irreversible error. The code was not completed to handle this instance')\n\t\treturn yRoughness * self.ProfileScaling\n#\t\tf, yPsd = self.PsdEval(N//2 + 1,df)\n\n\n\t\t# Special case\n#\t\tif self.Options.FIT_NUMERIC_DATA_WITH_POWER_LAW == True:\n#\t\t\tself.PsdParams = list(FitPowerLaw(*self.NumericPsdGetXY()))\n#\t\t\tyPsd = PsdFuns.PowerLaw(x, *self.PsdParams)\n#\t\telse: # general calse\n#\t\t\tyPsd = self.PsdType(x, *self.PsdParams)\n\n#\t\tyRoughness = Psd2Noise_1d(yPsd, N, Semiaxis = True)\n\n\n#\t\tx = np.linspace(0, N*dx,N)\n#\t\t# Special case\n#\t\tif self.Options.FIT_NUMERIC_DATA_WITH_POWER_LAW == True:\n#\t\t\tself.PsdParams = list(FitPowerLaw(*self.NumericPsdGetXY()))\n#\t\t\ty = PowerLawNoise_1d(N, dx, *self.PsdParams)\n#\t\telse: # general calse\n#\t\t\ty = self.PsdType(N,dx, *self.PsdParams)\n#\t\treturn y\n\tGenerate = MakeProfile\n\t#======================================================================\n\t# \tFUN: NumericPsdSetXY\n\t#======================================================================\n\tdef NumericPsdSetXY(self,x,y):\n\t\tself._PsdNumericX = x\n\t\tself._PsdNumericY = y\n\n\n\t#======================================================================\n\t# \tFUN: NumericPsdGetXY\n\t#======================================================================\n\n\tdef NumericPsdGetXY(self):\n\t\ttry:\n\t\t\treturn self._PsdNumericX, self._PsdNumericY\n\t\texcept:\n\t\t\tprint('Error in RoughnessMaker.NumericPsdGetXY. Maybe the data file was not properly loaded')\n\t#======================================================================\n\t# \tFUN: NumericPsdLoadXY\n\t#======================================================================\n\tdef NumericPsdLoadXY(self, FilePath, xScaling = 1, yScaling = 1 , xIsSpatialFreq = True):\n\t\t''' @TODO: specificare formati e tipi di file\n\n\t\tParameters\n\t\t----------------------------\n\t\txIsSpatialFreq : bool\n\t\t\t\t\t\ttrue If the first column (Read_x_values) contains spatial\n\t\t\t\t\t\tfrequencies. False if it contains lenghts. Default = True\n\t\txScaling, yScaling: floats\n\t\t\t\t\t\tRead_x_values => Read_x_values * xScaling\n\n\t\t\t\t\t\tRead_y_values => Read_y_values * yScaling\n\n\t\t\t\t\t\tSometimes, properly setting the x and y scaling values may be confusing (although just matter of high-school considerations). On this purpose, the property .RoughnessMaker.ProfileScaling property can be used also..ProfileScaling is the scale factor that acts on the output of MakeProfile() function only.\n\n\t\tremarks\n\t\t\t\t\t\t--------\n\t\t\t\t\t\tpippo\n\n\t\t'''\n\n\t\ttry:\n\t\t\tself._IsNumericPsdInFreq = xIsSpatialFreq\n\n\t\t\ts = np.loadtxt(FilePath)\n\t\t\tx = s[:,0]\n\t\t\ty = s[:,1]\n\n\t\t\tx = x * xScaling\n\t\t\ty = y * yScaling\n\n\t\t\t# inversion of x-axis if not spatial frequencies\n\t\t\tif xIsSpatialFreq == False:\n\t\t\t\tf = 1/x\n\t\t\telse:\n\t\t\t\tf = x\n\t\t\t# array sorting\n\t\t\ti = np.argsort(f)\n\t\t\tf = f[i]\n\t\t\ty = y[i]\n\t\t\t# I set the Cutoff value of the class according to available data\n\t\t\tself.PsdCutoffLowHigh = [np.amin, np.amax(f)]\n\n\t\t\t# I set class operating variables\n\t\t\tself.PsdType = PsdFuns.Interp\n\t\t\tself.PsdParams = [f,y]\n\n\n\t\t\t# Auto-set\n\t\t\t# fill 0-value (DC Component)\n\t#\t\tif self.Options.AUTO_FILL_NUMERIC_DATA_WITH_ZERO == True:\n\t#\t\t\tif np.amin(x >0):\n\t#\t\t\t\tx = np.insert(x,0,0)\n\t#\t\t\t\ty = np.insert(y,0,0)\t# 0 in psd => 0-mean value in the noise pattern\n\n\n\t\t\t# sync other class values\n\t\t\tself.NumericPsdSetXY(f, y)\n\t\texcept:\n\t\t\tpass\n\n\t\tdef Generate(self, N = None, dx = None, CutoffLowHigh = [None, None]):\n\t\t\t'''\n\t\t\tParameters\n\t\t\t\tN: # of output samples\n\t\t\t\tdx: step of the x axis\n\t\t\tNote: generates an evenly spaced array\n\t\t\t'''\n\t\t\tL = dx * N\n\t\t\tdf = 1/L\n\t\t\tfPsd, yPsd = self.PsdEval(N//2 +1 , df = df,\n\t\t\t\t\t\t\t\t\t\t\tCutoffLowHigh = CutoffLowHigh )\n\t\t\th = Psd2Noise_1d(yPsd, Semiaxis = True)\n\n\t\t\treturn h\n\n\t#======================================================================\n\t# \tFUN: NumericPsdCheck\n\t#======================================================================\n\tdef NumericPsdCheck(self, N, L):\n\n\t\tdf = 1/L\n\t\t# Stored data\n\t\tff,yy = self.NumericPsdGetXY()\n\t\t# Evaluated data\n\t\tfPsd, yPsd = self.PsdEval(N, df)\n\n\n\t\tplt.plot(fPsd, np.log10(yPsd),'x')\n\t\tplt.plot(ff, np.log10(yy),'.r')\n\t\tplt.legend(['Evaluated data', 'Stored data'])\n\t\tplt.suptitle('Usage of stored data (PSD)')\n\n\t\tfMax = df*(N//2)\n\t\tfMin = df\n\t\tStrMsg = ''\n\t\t_max = np.max(ff)\n\t\t_min = np.min(ff)\n\t\tprint('fMax query = %0.1e m^-1' % fMax )\n\t\tprint('fMax data= %0.1e m^-1 = %0.2e um^-1' % (_max, (_max * 1e6) ))\n\t\tprint('fMin query= %0.1e m^-1' % fMin )\n\t\tprint('fMin data= %0.1e m^-1 = %0.2e um^-1' % (_min, (_min * 1e6) ))\n\n\t\treturn StrMsg"}}},{"rowIdx":73,"cells":{"function_name":{"kind":"string","value":"MakeProfile"},"docstring":{"kind":"string","value":"Evaluates the psd according to .PsdType, .PsdParams and .Options directives\nReturns an evenly-spaced array.\nIf PsdType = NumericArray, linear interpolation is performed.\n\n:PARAM: N: # of samples\n:PARAM: dx: grid spacing (spatial frequency)\n\nreturns:\n 1d arr"},"masked_code":{"kind":"string","value":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jul 07 14:08:31 2016\n\n@author: Mic\n\"\"\"\nfrom __future__ import division\nfrom wiselib2.must import *\nimport numpy as np\nimport wiselib2.Rayman as rm\nGauss1d = lambda x ,y : None\nfrom scipy import interpolate as interpolate\n\nfrom matplotlib import pyplot as plt\n\nclass PsdFuns:\n\t'''\n\tEnsemble of possible Psd Functions.\n\tEach element is a callable Psd.\n\tMost used are\n\t\tPsdFuns.PowerLaw(x,a,b)\n\t\tPsdFuns.Interp(x, xData, yData)\n\t'''\n\t@staticmethod\n\tdef Flat(x, *args):\n\t\tN = len(x)\n\t\treturn np.zeros([1,N]) +1\n\t@staticmethod\n\tdef PowerLaw(x,a,b):\n\t\treturn a*x**b\n\t@staticmethod\n\tdef Gaussian(x,sigma, x0=0):\n\t\treturn np.exp(-0.5 * (x-x0)**2/sigma**2)\n\t@staticmethod\n\tdef Interp(x, xData, yData):\n\t\tf = interpolate.interp1d(xData, yData)\n\n\t\treturn f(x)\n\n\ndef PsdFun2Noise_1d(N,dx, PsdFun, PsdArgs):\n\t'''\n\t\tGenerates a noise pattern based an the Power spectral density returned\n\t\tby PsdFun\n\t'''\n\tx = np.arange(0,N//2+1, dx)\n\tyHalf = PsdFun(x, *PsdArgs)\n\ty = Psd2NoisePattern_1d(yHalf, Semiaxis = True \t)\n\treturn x,y\n\n\n#============================================================================\n#\tFUN: \tPsdArray2Noise_1d_v2\n#============================================================================\ndef PsdArray2Noise_1d_v2(f_in, Psd_in, L_mm,N):\n\t'''\n\t\tReturns meters\n\t'''\n\tfrom scipy import interpolate\n\tlog=np.log\n\tfft = np.fft.fft\n\tfftshift = np.fft.fftshift\n\n\tff = f_in\n\tyy = Psd_in\n\tL = L_mm\n\tN = int(N)\n\tN2 = int(N//2)\n\tL =300 # (mm)\n\tL_um = L*1e3\n\tL_nm = L*1e6\n\n\tfMin = 1/L_um\n\n\t##vecchia riga\n\t##fSpline = (np.array(range(N2))+1)/L_um # um^-1\n\tfSpline = np.arange(N2)/N2 * (max(ff) - min(ff)) + min(ff)\n\n\tfun = interpolate.splrep(log(ff), log(yy), s=2)\n\tyPsd_log = interpolate.splev(log(fSpline), fun)\n\tySpline = np.exp(yPsd_log)\n\tyPsd = ySpline\n\n\t# tolgo\n\tyPsd[fSpline<ff[0]] = 200\n\tn = len(yPsd)\n\n\n\tplt.plot(fSpline, yPsd,'-')\n\tplt.plot(ff, yy,'x')\n\tplt.legend(['ySpline','Data'])\n\tax = plt.axes()\n\t#ax.set_yscale('log')\n\t#ax.set_xscale('log')\n\n\t#% controllo RMS integrando la yPsd\n\timport scipy.integrate as integrate\n\n\tRMS = np.sqrt(integrate.trapz(yPsd, fSpline/1000))\n\n\t#% Modo Manfredda style\n\n\t#yPsdNorm = np.sqrt(yPsd/L_um/1000)\n\t#yPsdNorm_reverse = yPsdNorm[::-1]\n\tyPsd_reverse = yPsd[::-1]\n\tell= 1/(fSpline[1] - fSpline[0])\n\n\tif N%2 == 0:\n\t\tyPsd2 = np.hstack((yPsd_reverse ,0,yPsd[0:-1]))\n\telse:\n\t\tyPsd2 = np.hstack((yPsd_reverse ,0,yPsd))\n\t##yPsd2Norm = np.sqrt(yPsd2/ell/1000/2)\n\tyPsd2Norm = np.sqrt(yPsd2/ell/1000)\n\tn_ = len(yPsd2)\n\tprint('len(yPsd2) = %0.2d' % len(yPsd2Norm))\n\tphi = 2*np.pi * np.random.rand(n_)\n\tr = np.exp(1j*phi)\n\n\tyPsd2Norm_ = fftshift(yPsd2Norm)\n\t#yPsd2Norm_[len(yPsd2Norm_)//2] = 0\n\n\tyRaf = np.fft.fft(r*yPsd2Norm_)\n\tyRaf = np.real(yRaf)\n\tprint('Rms = %0.2e nm' % np.std(yRaf))\n\n\tplt.plot(yPsd2Norm_)\n\n\tprint('max yPsd_ = %d nm' % max(yPsd2))\n\tprint('max yPsd2Norm = %0.4f nm' % max(yPsd2Norm))\n\tprint('Rms yRaf2 = %0.2e nm' % np.std(yRaf))\n\treturn yRaf * 1e-9\n\n#============================================================================\n#\tFUN: \tPsd2Noise\n#============================================================================\ndef PsdArray2Noise_1d(PsdArray, N, Semiaxis = True, Real = True):\n\t'''\n\tGenerates a noise pattern whose Power Spectral density is given by Psd.\n\n\tParameters\n\t---------------------\n\tPsd : 1d array\n\t\tContains the numeric Psd (treated as evenly spaced array)\n\n\tSemiaxis :\n\t\t0 : does nothing\n\t\t1 : halvens Pds, then replicates the halven part for left frequencies,\n\t\t\tproducing an output as long as Psd\n\t\t2 : replicates all Pds for lef frequencies as well, producing an output\n\t\t\ttwice as long as Psd\n\tReal : boolean\n\t\tIf True, the real part of the output is returned (default)\n\n\tReturns:\n\t---------------------\n\t\tAn array of the same length of Psd\n\t'''\n\n\tif Semiaxis == True:\n\t\tyHalf = PsdArray\n\t\tPsdArrayNew = np.hstack((yHalf[-1:0:-1], yHalf))\n\t\tidelta = len(PsdArrayNew) - N\n\t\tif idelta == 1:# piu lungo\n\t\t\tPsdArrayNew = PsdArrayNew[0:-1] # uguale\n\t\telif idelta == 0:\n\t\t\tpass\n\t\telse:\n\t\t\tprint('Error! len(PsdArrayNew) - len(PsdArray) = %0d' % idelta)\n\ty = np.fft.fftshift(PsdArrayNew)\n\tr = 2*np.pi * np.random.rand(len(PsdArrayNew))\n\n\tf = np.fft.ifft(y * np.exp(1j*r))\n\n\tif Real:\n\t\treturn np.real(f)\n\telse:\n\t\treturn f\nPsd2Noise_1d = PsdArray2Noise_1d\n#============================================================================\n#\tFUN: \tNoNoise_1d\n#============================================================================\ndef NoNoise_1d(N, *args):\n\treturn np.zeros([1,N])\n\n#============================================================================\n#\tFUN: \tGaussianNoise_1d\n#============================================================================\ndef GaussianNoise_1d(N,dx, Sigma):\n\t'''\n\tPSD(f) = np.exp(-0.5^f/Sigma^2)\n\t'''\n\tx = np.linspace( - N//2 *dx, N//2-1 * dx,N)\n\ty = np.exp(-0.5*x**2/Sigma**2)\n\treturn Psd2NoisePattern_1d(y)\n\n\n#============================================================================\n#\tFUN: \tPowerLawNoise_1d\n#============================================================================\ndef PowerLawNoise_1d(N, dx, a, b):\n\t'''\n\tPSD(x) = a*x^b\n\t'''\n\tx = np.arange(0,N//2+1, dx)\n\tyHalf = a * x**b\n#\ty = np.hstack((yHalf[-1:0:-1], 0, yHalf[1:-1]))\n\treturn Psd2NoisePattern_1d(y, Semiaxis = True)\n\n#============================================================================\n#\tFUN: \tCustomNoise_1d\n#============================================================================\ndef CustomNoise_1d(N, dx, xPsd, yPsd):\n\txPsd_, yPsd_ = rm.FastResample1d(xPsd, yPsd,N)\n\treturn Psd2NoisePattern_1d(yPsd_, Semiaxis = True)\n\n#============================================================================\n#\tCLASS: \tNoiseGenerator\n#============================================================================\nclass PsdGenerator:\n\tNoNoise = staticmethod(NoNoise_1d)\n\tGauss = staticmethod(GaussianNoise_1d)\n\tPowerLaw = staticmethod(PowerLawNoise_1d)\n\tNumericArray = staticmethod(CustomNoise_1d)\n\n\n\n#============================================================================\n#\tFUN: \tFitPowerLaw\n#============================================================================\ndef FitPowerLaw(x,y):\n\t'''\n\tFits the input data in the form\n\t\ty = a*x^b\n\treturns a,b\n\t'''\n\timport scipy.optimize as optimize\n\n\tfFit = lambda p, x: p[0] * x ** p[1]\n\tfErr = lambda p, x, y: (y - fFit(p, x))\n\n\tp0 = [max(y), -1.0]\n\tout = optimize.leastsq(fErr, p0, args=(x, y), full_output=1)\n\n\tpOut = out[0]\n\n\tb = pOut[1]\n\ta = pOut[0]\n\n#\tindexErr = np.np.sqrt( covar[0][0] )\n#\tampErr = np.np.sqrt( covar[1][1] ) * amp\n\n\treturn a,b\n\n#==============================================================================\n# \tCLASS: RoughnessMaker\n#==============================================================================\n\nclass RoughnessMaker(object):\n\n\tclass Options():\n\t\tFIT_NUMERIC_DATA_WITH_POWER_LAW = True\n\t\tAUTO_ZERO_MEAN_FOR_NUMERIC_DATA = True\n\t\tAUTO_FILL_NUMERIC_DATA_WITH_ZERO = True\n\t\tAUTO_RESET_CUTOFF_ON_PSDTYPE_CHANGE = True\n\n\tdef __init__(self):\n\t\tself.PsdType = PsdFuns.PowerLaw\n\t\tself.PsdParams = np.array([1,1])\n\t\tself._IsNumericPsdInFreq = None\n\t\tself.CutoffLowHigh = [None, None]\n\t\tself.ProfileScaling = 1\n\t\treturn None\n\n\t@property\n\tdef PsdType(self):\n\t\treturn self._PsdType\n\t@PsdType.setter\n\tdef PsdType(self, Val):\n\t\t'''\n\t\tNote: each time that the Property value is set, self.CutoffLowHigh is\n\t\treset, is specified by options\n\t\t'''\n\t\tself. _PsdType = Val\n\t\tif self.Options.AUTO_RESET_CUTOFF_ON_PSDTYPE_CHANGE == True:\n\t\t\tself.PsdCutoffLowHigh = [None, None]\n\n\t#======================================================================\n\t# \tFUN: PdfEval\n\t#======================================================================\n\tdef PsdEval(self, N, df, CutoffLowHigh = [None, None]):\n\t\t'''\n\t\tEvals the PSD in the range [0 - N*df]\n\t\tIt's good custom to have PSD[0] = 0, so that the noise pattern is\n\t\tzero-mean.\n\n\t\tParameters:\n\t\t----------------------\n\t\t\tN : int\n\t\t\t\t#of samples\n\t\t\tdf : float\n\t\t\t\tspacing of spatial frequencies (df=1/TotalLength)\n\t\t\tCutoffLowHigh : [LowCutoff, HighCutoff]\n\t\t\t\tif >0, then Psd(f<Cutoff) is set to 0.\n\t\t\t\t\t\tif None, then LowCutoff = min()\n\t\tReturns : fAll, yPsdAll\n\t\t----------------------\n\t\t\tfAll : 1d array\n\t\t\t\tcontains the spatial frequencies\n\t\t\tyPsd : 1d array\n\t\t\t\tcontains the Psd\n\t\t'''\n\n\t\t'''\n\t\tThe Pdf is evaluated only within LowCutoff and HoghCutoff\n\t\tIf the Pdf is PsdFuns.Interp, then LowCutoff and HighCutoff are\n\t\tautomatically set to min and max values of the experimental data\n\t\t'''\n\t\tStrMessage = ''\n\t\tdef GetInRange(fAll, LowCutoff, HighCutoff):\n\t\t\t_tmpa = fAll >= LowCutoff\n\t\t\t_tmpb = fAll <= HighCutoff\n\t\t\tfMid_Pos = np.all([_tmpa, _tmpb],0)\n\t\t\tfMid = fAll[fMid_Pos]\n\t\t\treturn fMid_Pos, fMid\n\n\t\tLowCutoff, HighCutoff = CutoffLowHigh\n\t\tfMin = 0\n\t\tfMax = (N-1)*df\n\t\tfAll = np.linspace(0, fMax, N)\n\t\tyPsdAll = fAll* 0 # init\n\n\t\tLowCutoff = 0 if LowCutoff is None else LowCutoff\n\t\tHighCutoff = N*df if HighCutoff is None else HighCutoff\n\n\n\n\t\t# Numeric PSD\n\t\t# Note: by default returned yPsd is always 0 outside the input data range\n\t\tif self.PsdType == PsdFuns.Interp:\n\t\t\t# Use Auto-Fit + PowerLaw\n\t\t\tif self.Options.FIT_NUMERIC_DATA_WITH_POWER_LAW == True:\n\t\t\t\t\txFreq,y = self.NumericPsdGetXY()\n\t\t\t\t\tp = FitPowerLaw(1/xFreq,y)\n\t\t\t\t\t_PsdParams = p[0], -p[1]\n\t\t\t\t\tLowCutoff = np.amin(self._PsdNumericX)\n\t\t\t\t\tHighCutoff = np.amin(self._PsdNumericX)\n\t\t\t\t\tfMid_Pos, fMid = GetInRange(fAll, LowCutoff, HighCutoff)\n\t\t\t\t\tyPsd = PsdFuns.PowerLaw(fMid, *_PsdParams )\n\t\t\t# Use Interpolation\n\t\t\telse:\n\t\t\t\t# check Cutoff\n\t\t\t\tLowVal = np.amin(self._PsdNumericX)\n\t\t\t\tHighVal = np.amax(self._PsdNumericX)\n\t\t\t\tLowCutoff = LowVal if LowCutoff <= LowVal else LowCutoff\n\t\t\t\tHighCutoff = HighVal if HighCutoff >= HighVal else HighCutoff\n\n\n\t\t\t\t# Get the list of good frequency values (fMid) and their positions\n\t\t\t\t# (fMid_Pos)\n\t\t\t\tfMid_Pos, fMid = GetInRange(fAll, LowCutoff, HighCutoff)\n\n\t\t\t\t##yPsd = self.PsdType(fMid, *self.PsdParams)\n\t\t\t\t## non funziona, rimpiazzo a mano\n\t\t\t\tyPsd = PsdFuns.Interp(fMid, self._PsdNumericX, self._PsdNumericY)\n\n\t\t# Analytical Psd\n\t\telse:\n\t\t\tfMid_Pos, fMid = GetInRange(fAll, LowCutoff, HighCutoff)\n\t\t\tyPsd = self.PsdType(fMid, *self.PsdParams)\n\n\t\t# copying array subset\n\t\tyPsdAll[fMid_Pos] = yPsd\n\n\t\treturn fAll, yPsdAll\n\n\t#======================================================================\n\t# \tFUN: _FitNumericPsdWithPowerLaw\n\t#======================================================================\n# in disusos\n\tdef _FitNumericPsdWithPowerLaw(self):\n\t\tx,y = self.NumericPsdGetXY()\n\t\tif self._IsNumericPsdInFreq == True:\n\t\t\tp = FitPowerLaw(1/x,y)\n\t\t\tself.PsdParams = p[0], -p[1]\n\t\telse:\n\t\t\tp = FitPowerLaw(x,y)\n\t\t\tself.PsdParams = p[0], p[1]\n\n\n\t#======================================================================\n\t# \tFUN: MakeProfile\n\t#======================================================================\n# MASKED: MakeProfile function (lines 393-413)\n#\t\tf, yPsd = self.PsdEval(N//2 + 1,df)\n\n\n\t\t# Special case\n#\t\tif self.Options.FIT_NUMERIC_DATA_WITH_POWER_LAW == True:\n#\t\t\tself.PsdParams = list(FitPowerLaw(*self.NumericPsdGetXY()))\n#\t\t\tyPsd = PsdFuns.PowerLaw(x, *self.PsdParams)\n#\t\telse: # general calse\n#\t\t\tyPsd = self.PsdType(x, *self.PsdParams)\n\n#\t\tyRoughness = Psd2Noise_1d(yPsd, N, Semiaxis = True)\n\n\n#\t\tx = np.linspace(0, N*dx,N)\n#\t\t# Special case\n#\t\tif self.Options.FIT_NUMERIC_DATA_WITH_POWER_LAW == True:\n#\t\t\tself.PsdParams = list(FitPowerLaw(*self.NumericPsdGetXY()))\n#\t\t\ty = PowerLawNoise_1d(N, dx, *self.PsdParams)\n#\t\telse: # general calse\n#\t\t\ty = self.PsdType(N,dx, *self.PsdParams)\n#\t\treturn y\n\tGenerate = MakeProfile\n\t#======================================================================\n\t# \tFUN: NumericPsdSetXY\n\t#======================================================================\n\tdef NumericPsdSetXY(self,x,y):\n\t\tself._PsdNumericX = x\n\t\tself._PsdNumericY = y\n\n\n\t#======================================================================\n\t# \tFUN: NumericPsdGetXY\n\t#======================================================================\n\n\tdef NumericPsdGetXY(self):\n\t\ttry:\n\t\t\treturn self._PsdNumericX, self._PsdNumericY\n\t\texcept:\n\t\t\tprint('Error in RoughnessMaker.NumericPsdGetXY. Maybe the data file was not properly loaded')\n\t#======================================================================\n\t# \tFUN: NumericPsdLoadXY\n\t#======================================================================\n\tdef NumericPsdLoadXY(self, FilePath, xScaling = 1, yScaling = 1 , xIsSpatialFreq = True):\n\t\t''' @TODO: specificare formati e tipi di file\n\n\t\tParameters\n\t\t----------------------------\n\t\txIsSpatialFreq : bool\n\t\t\t\t\t\ttrue If the first column (Read_x_values) contains spatial\n\t\t\t\t\t\tfrequencies. False if it contains lenghts. Default = True\n\t\txScaling, yScaling: floats\n\t\t\t\t\t\tRead_x_values => Read_x_values * xScaling\n\n\t\t\t\t\t\tRead_y_values => Read_y_values * yScaling\n\n\t\t\t\t\t\tSometimes, properly setting the x and y scaling values may be confusing (although just matter of high-school considerations). On this purpose, the property .RoughnessMaker.ProfileScaling property can be used also..ProfileScaling is the scale factor that acts on the output of MakeProfile() function only.\n\n\t\tremarks\n\t\t\t\t\t\t--------\n\t\t\t\t\t\tpippo\n\n\t\t'''\n\n\t\ttry:\n\t\t\tself._IsNumericPsdInFreq = xIsSpatialFreq\n\n\t\t\ts = np.loadtxt(FilePath)\n\t\t\tx = s[:,0]\n\t\t\ty = s[:,1]\n\n\t\t\tx = x * xScaling\n\t\t\ty = y * yScaling\n\n\t\t\t# inversion of x-axis if not spatial frequencies\n\t\t\tif xIsSpatialFreq == False:\n\t\t\t\tf = 1/x\n\t\t\telse:\n\t\t\t\tf = x\n\t\t\t# array sorting\n\t\t\ti = np.argsort(f)\n\t\t\tf = f[i]\n\t\t\ty = y[i]\n\t\t\t# I set the Cutoff value of the class according to available data\n\t\t\tself.PsdCutoffLowHigh = [np.amin, np.amax(f)]\n\n\t\t\t# I set class operating variables\n\t\t\tself.PsdType = PsdFuns.Interp\n\t\t\tself.PsdParams = [f,y]\n\n\n\t\t\t# Auto-set\n\t\t\t# fill 0-value (DC Component)\n\t#\t\tif self.Options.AUTO_FILL_NUMERIC_DATA_WITH_ZERO == True:\n\t#\t\t\tif np.amin(x >0):\n\t#\t\t\t\tx = np.insert(x,0,0)\n\t#\t\t\t\ty = np.insert(y,0,0)\t# 0 in psd => 0-mean value in the noise pattern\n\n\n\t\t\t# sync other class values\n\t\t\tself.NumericPsdSetXY(f, y)\n\t\texcept:\n\t\t\tpass\n\n\t\tdef Generate(self, N = None, dx = None, CutoffLowHigh = [None, None]):\n\t\t\t'''\n\t\t\tParameters\n\t\t\t\tN: # of output samples\n\t\t\t\tdx: step of the x axis\n\t\t\tNote: generates an evenly spaced array\n\t\t\t'''\n\t\t\tL = dx * N\n\t\t\tdf = 1/L\n\t\t\tfPsd, yPsd = self.PsdEval(N//2 +1 , df = df,\n\t\t\t\t\t\t\t\t\t\t\tCutoffLowHigh = CutoffLowHigh )\n\t\t\th = Psd2Noise_1d(yPsd, Semiaxis = True)\n\n\t\t\treturn h\n\n\t#======================================================================\n\t# \tFUN: NumericPsdCheck\n\t#======================================================================\n\tdef NumericPsdCheck(self, N, L):\n\n\t\tdf = 1/L\n\t\t# Stored data\n\t\tff,yy = self.NumericPsdGetXY()\n\t\t# Evaluated data\n\t\tfPsd, yPsd = self.PsdEval(N, df)\n\n\n\t\tplt.plot(fPsd, np.log10(yPsd),'x')\n\t\tplt.plot(ff, np.log10(yy),'.r')\n\t\tplt.legend(['Evaluated data', 'Stored data'])\n\t\tplt.suptitle('Usage of stored data (PSD)')\n\n\t\tfMax = df*(N//2)\n\t\tfMin = df\n\t\tStrMsg = ''\n\t\t_max = np.max(ff)\n\t\t_min = np.min(ff)\n\t\tprint('fMax query = %0.1e m^-1' % fMax )\n\t\tprint('fMax data= %0.1e m^-1 = %0.2e um^-1' % (_max, (_max * 1e6) ))\n\t\tprint('fMin query= %0.1e m^-1' % fMin )\n\t\tprint('fMin data= %0.1e m^-1 = %0.2e um^-1' % (_min, (_min * 1e6) ))\n\n\t\treturn StrMsg"},"implementation":{"kind":"string","value":"\tdef MakeProfile(self, L,N):\n\t\t'''\n\t\t\tEvaluates the psd according to .PsdType, .PsdParams and .Options directives\n\t\t\tReturns an evenly-spaced array.\n\t\t\tIf PsdType = NumericArray, linear interpolation is performed.\n\n\t\t\t:PARAM: N: # of samples\n\t\t\t:PARAM: dx: grid spacing (spatial frequency)\n\n\t\t\treturns:\n\t\t\t\t1d arr\n\t\t'''\n\n\n\t\tif self.PsdType == PsdFuns.Interp:\n\t\t\t# chiama codice ad hoc\n\t\t\tL_mm = L*1e3\n\t\t\tyRoughness = PsdArray2Noise_1d_v2(self._PsdNumericX, self._PsdNumericY, L_mm, N)\n\t\telse:\n\t\t\tprint('Irreversible error. The code was not completed to handle this instance')\n\t\treturn yRoughness * self.ProfileScaling"},"start_line":{"kind":"number","value":393,"string":"393"},"end_line":{"kind":"number","value":413,"string":"413"},"file_content":{"kind":"string","value":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jul 07 14:08:31 2016\n\n@author: Mic\n\"\"\"\nfrom __future__ import division\nfrom wiselib2.must import *\nimport numpy as np\nimport wiselib2.Rayman as rm\nGauss1d = lambda x ,y : None\nfrom scipy import interpolate as interpolate\n\nfrom matplotlib import pyplot as plt\n\nclass PsdFuns:\n\t'''\n\tEnsemble of possible Psd Functions.\n\tEach element is a callable Psd.\n\tMost used are\n\t\tPsdFuns.PowerLaw(x,a,b)\n\t\tPsdFuns.Interp(x, xData, yData)\n\t'''\n\t@staticmethod\n\tdef Flat(x, *args):\n\t\tN = len(x)\n\t\treturn np.zeros([1,N]) +1\n\t@staticmethod\n\tdef PowerLaw(x,a,b):\n\t\treturn a*x**b\n\t@staticmethod\n\tdef Gaussian(x,sigma, x0=0):\n\t\treturn np.exp(-0.5 * (x-x0)**2/sigma**2)\n\t@staticmethod\n\tdef Interp(x, xData, yData):\n\t\tf = interpolate.interp1d(xData, yData)\n\n\t\treturn f(x)\n\n\ndef PsdFun2Noise_1d(N,dx, PsdFun, PsdArgs):\n\t'''\n\t\tGenerates a noise pattern based an the Power spectral density returned\n\t\tby PsdFun\n\t'''\n\tx = np.arange(0,N//2+1, dx)\n\tyHalf = PsdFun(x, *PsdArgs)\n\ty = Psd2NoisePattern_1d(yHalf, Semiaxis = True \t)\n\treturn x,y\n\n\n#============================================================================\n#\tFUN: \tPsdArray2Noise_1d_v2\n#============================================================================\ndef PsdArray2Noise_1d_v2(f_in, Psd_in, L_mm,N):\n\t'''\n\t\tReturns meters\n\t'''\n\tfrom scipy import interpolate\n\tlog=np.log\n\tfft = np.fft.fft\n\tfftshift = np.fft.fftshift\n\n\tff = f_in\n\tyy = Psd_in\n\tL = L_mm\n\tN = int(N)\n\tN2 = int(N//2)\n\tL =300 # (mm)\n\tL_um = L*1e3\n\tL_nm = L*1e6\n\n\tfMin = 1/L_um\n\n\t##vecchia riga\n\t##fSpline = (np.array(range(N2))+1)/L_um # um^-1\n\tfSpline = np.arange(N2)/N2 * (max(ff) - min(ff)) + min(ff)\n\n\tfun = interpolate.splrep(log(ff), log(yy), s=2)\n\tyPsd_log = interpolate.splev(log(fSpline), fun)\n\tySpline = np.exp(yPsd_log)\n\tyPsd = ySpline\n\n\t# tolgo\n\tyPsd[fSpline<ff[0]] = 200\n\tn = len(yPsd)\n\n\n\tplt.plot(fSpline, yPsd,'-')\n\tplt.plot(ff, yy,'x')\n\tplt.legend(['ySpline','Data'])\n\tax = plt.axes()\n\t#ax.set_yscale('log')\n\t#ax.set_xscale('log')\n\n\t#% controllo RMS integrando la yPsd\n\timport scipy.integrate as integrate\n\n\tRMS = np.sqrt(integrate.trapz(yPsd, fSpline/1000))\n\n\t#% Modo Manfredda style\n\n\t#yPsdNorm = np.sqrt(yPsd/L_um/1000)\n\t#yPsdNorm_reverse = yPsdNorm[::-1]\n\tyPsd_reverse = yPsd[::-1]\n\tell= 1/(fSpline[1] - fSpline[0])\n\n\tif N%2 == 0:\n\t\tyPsd2 = np.hstack((yPsd_reverse ,0,yPsd[0:-1]))\n\telse:\n\t\tyPsd2 = np.hstack((yPsd_reverse ,0,yPsd))\n\t##yPsd2Norm = np.sqrt(yPsd2/ell/1000/2)\n\tyPsd2Norm = np.sqrt(yPsd2/ell/1000)\n\tn_ = len(yPsd2)\n\tprint('len(yPsd2) = %0.2d' % len(yPsd2Norm))\n\tphi = 2*np.pi * np.random.rand(n_)\n\tr = np.exp(1j*phi)\n\n\tyPsd2Norm_ = fftshift(yPsd2Norm)\n\t#yPsd2Norm_[len(yPsd2Norm_)//2] = 0\n\n\tyRaf = np.fft.fft(r*yPsd2Norm_)\n\tyRaf = np.real(yRaf)\n\tprint('Rms = %0.2e nm' % np.std(yRaf))\n\n\tplt.plot(yPsd2Norm_)\n\n\tprint('max yPsd_ = %d nm' % max(yPsd2))\n\tprint('max yPsd2Norm = %0.4f nm' % max(yPsd2Norm))\n\tprint('Rms yRaf2 = %0.2e nm' % np.std(yRaf))\n\treturn yRaf * 1e-9\n\n#============================================================================\n#\tFUN: \tPsd2Noise\n#============================================================================\ndef PsdArray2Noise_1d(PsdArray, N, Semiaxis = True, Real = True):\n\t'''\n\tGenerates a noise pattern whose Power Spectral density is given by Psd.\n\n\tParameters\n\t---------------------\n\tPsd : 1d array\n\t\tContains the numeric Psd (treated as evenly spaced array)\n\n\tSemiaxis :\n\t\t0 : does nothing\n\t\t1 : halvens Pds, then replicates the halven part for left frequencies,\n\t\t\tproducing an output as long as Psd\n\t\t2 : replicates all Pds for lef frequencies as well, producing an output\n\t\t\ttwice as long as Psd\n\tReal : boolean\n\t\tIf True, the real part of the output is returned (default)\n\n\tReturns:\n\t---------------------\n\t\tAn array of the same length of Psd\n\t'''\n\n\tif Semiaxis == True:\n\t\tyHalf = PsdArray\n\t\tPsdArrayNew = np.hstack((yHalf[-1:0:-1], yHalf))\n\t\tidelta = len(PsdArrayNew) - N\n\t\tif idelta == 1:# piu lungo\n\t\t\tPsdArrayNew = PsdArrayNew[0:-1] # uguale\n\t\telif idelta == 0:\n\t\t\tpass\n\t\telse:\n\t\t\tprint('Error! len(PsdArrayNew) - len(PsdArray) = %0d' % idelta)\n\ty = np.fft.fftshift(PsdArrayNew)\n\tr = 2*np.pi * np.random.rand(len(PsdArrayNew))\n\n\tf = np.fft.ifft(y * np.exp(1j*r))\n\n\tif Real:\n\t\treturn np.real(f)\n\telse:\n\t\treturn f\nPsd2Noise_1d = PsdArray2Noise_1d\n#============================================================================\n#\tFUN: \tNoNoise_1d\n#============================================================================\ndef NoNoise_1d(N, *args):\n\treturn np.zeros([1,N])\n\n#============================================================================\n#\tFUN: \tGaussianNoise_1d\n#============================================================================\ndef GaussianNoise_1d(N,dx, Sigma):\n\t'''\n\tPSD(f) = np.exp(-0.5^f/Sigma^2)\n\t'''\n\tx = np.linspace( - N//2 *dx, N//2-1 * dx,N)\n\ty = np.exp(-0.5*x**2/Sigma**2)\n\treturn Psd2NoisePattern_1d(y)\n\n\n#============================================================================\n#\tFUN: \tPowerLawNoise_1d\n#============================================================================\ndef PowerLawNoise_1d(N, dx, a, b):\n\t'''\n\tPSD(x) = a*x^b\n\t'''\n\tx = np.arange(0,N//2+1, dx)\n\tyHalf = a * x**b\n#\ty = np.hstack((yHalf[-1:0:-1], 0, yHalf[1:-1]))\n\treturn Psd2NoisePattern_1d(y, Semiaxis = True)\n\n#============================================================================\n#\tFUN: \tCustomNoise_1d\n#============================================================================\ndef CustomNoise_1d(N, dx, xPsd, yPsd):\n\txPsd_, yPsd_ = rm.FastResample1d(xPsd, yPsd,N)\n\treturn Psd2NoisePattern_1d(yPsd_, Semiaxis = True)\n\n#============================================================================\n#\tCLASS: \tNoiseGenerator\n#============================================================================\nclass PsdGenerator:\n\tNoNoise = staticmethod(NoNoise_1d)\n\tGauss = staticmethod(GaussianNoise_1d)\n\tPowerLaw = staticmethod(PowerLawNoise_1d)\n\tNumericArray = staticmethod(CustomNoise_1d)\n\n\n\n#============================================================================\n#\tFUN: \tFitPowerLaw\n#============================================================================\ndef FitPowerLaw(x,y):\n\t'''\n\tFits the input data in the form\n\t\ty = a*x^b\n\treturns a,b\n\t'''\n\timport scipy.optimize as optimize\n\n\tfFit = lambda p, x: p[0] * x ** p[1]\n\tfErr = lambda p, x, y: (y - fFit(p, x))\n\n\tp0 = [max(y), -1.0]\n\tout = optimize.leastsq(fErr, p0, args=(x, y), full_output=1)\n\n\tpOut = out[0]\n\n\tb = pOut[1]\n\ta = pOut[0]\n\n#\tindexErr = np.np.sqrt( covar[0][0] )\n#\tampErr = np.np.sqrt( covar[1][1] ) * amp\n\n\treturn a,b\n\n#==============================================================================\n# \tCLASS: RoughnessMaker\n#==============================================================================\n\nclass RoughnessMaker(object):\n\n\tclass Options():\n\t\tFIT_NUMERIC_DATA_WITH_POWER_LAW = True\n\t\tAUTO_ZERO_MEAN_FOR_NUMERIC_DATA = True\n\t\tAUTO_FILL_NUMERIC_DATA_WITH_ZERO = True\n\t\tAUTO_RESET_CUTOFF_ON_PSDTYPE_CHANGE = True\n\n\tdef __init__(self):\n\t\tself.PsdType = PsdFuns.PowerLaw\n\t\tself.PsdParams = np.array([1,1])\n\t\tself._IsNumericPsdInFreq = None\n\t\tself.CutoffLowHigh = [None, None]\n\t\tself.ProfileScaling = 1\n\t\treturn None\n\n\t@property\n\tdef PsdType(self):\n\t\treturn self._PsdType\n\t@PsdType.setter\n\tdef PsdType(self, Val):\n\t\t'''\n\t\tNote: each time that the Property value is set, self.CutoffLowHigh is\n\t\treset, is specified by options\n\t\t'''\n\t\tself. _PsdType = Val\n\t\tif self.Options.AUTO_RESET_CUTOFF_ON_PSDTYPE_CHANGE == True:\n\t\t\tself.PsdCutoffLowHigh = [None, None]\n\n\t#======================================================================\n\t# \tFUN: PdfEval\n\t#======================================================================\n\tdef PsdEval(self, N, df, CutoffLowHigh = [None, None]):\n\t\t'''\n\t\tEvals the PSD in the range [0 - N*df]\n\t\tIt's good custom to have PSD[0] = 0, so that the noise pattern is\n\t\tzero-mean.\n\n\t\tParameters:\n\t\t----------------------\n\t\t\tN : int\n\t\t\t\t#of samples\n\t\t\tdf : float\n\t\t\t\tspacing of spatial frequencies (df=1/TotalLength)\n\t\t\tCutoffLowHigh : [LowCutoff, HighCutoff]\n\t\t\t\tif >0, then Psd(f<Cutoff) is set to 0.\n\t\t\t\t\t\tif None, then LowCutoff = min()\n\t\tReturns : fAll, yPsdAll\n\t\t----------------------\n\t\t\tfAll : 1d array\n\t\t\t\tcontains the spatial frequencies\n\t\t\tyPsd : 1d array\n\t\t\t\tcontains the Psd\n\t\t'''\n\n\t\t'''\n\t\tThe Pdf is evaluated only within LowCutoff and HoghCutoff\n\t\tIf the Pdf is PsdFuns.Interp, then LowCutoff and HighCutoff are\n\t\tautomatically set to min and max values of the experimental data\n\t\t'''\n\t\tStrMessage = ''\n\t\tdef GetInRange(fAll, LowCutoff, HighCutoff):\n\t\t\t_tmpa = fAll >= LowCutoff\n\t\t\t_tmpb = fAll <= HighCutoff\n\t\t\tfMid_Pos = np.all([_tmpa, _tmpb],0)\n\t\t\tfMid = fAll[fMid_Pos]\n\t\t\treturn fMid_Pos, fMid\n\n\t\tLowCutoff, HighCutoff = CutoffLowHigh\n\t\tfMin = 0\n\t\tfMax = (N-1)*df\n\t\tfAll = np.linspace(0, fMax, N)\n\t\tyPsdAll = fAll* 0 # init\n\n\t\tLowCutoff = 0 if LowCutoff is None else LowCutoff\n\t\tHighCutoff = N*df if HighCutoff is None else HighCutoff\n\n\n\n\t\t# Numeric PSD\n\t\t# Note: by default returned yPsd is always 0 outside the input data range\n\t\tif self.PsdType == PsdFuns.Interp:\n\t\t\t# Use Auto-Fit + PowerLaw\n\t\t\tif self.Options.FIT_NUMERIC_DATA_WITH_POWER_LAW == True:\n\t\t\t\t\txFreq,y = self.NumericPsdGetXY()\n\t\t\t\t\tp = FitPowerLaw(1/xFreq,y)\n\t\t\t\t\t_PsdParams = p[0], -p[1]\n\t\t\t\t\tLowCutoff = np.amin(self._PsdNumericX)\n\t\t\t\t\tHighCutoff = np.amin(self._PsdNumericX)\n\t\t\t\t\tfMid_Pos, fMid = GetInRange(fAll, LowCutoff, HighCutoff)\n\t\t\t\t\tyPsd = PsdFuns.PowerLaw(fMid, *_PsdParams )\n\t\t\t# Use Interpolation\n\t\t\telse:\n\t\t\t\t# check Cutoff\n\t\t\t\tLowVal = np.amin(self._PsdNumericX)\n\t\t\t\tHighVal = np.amax(self._PsdNumericX)\n\t\t\t\tLowCutoff = LowVal if LowCutoff <= LowVal else LowCutoff\n\t\t\t\tHighCutoff = HighVal if HighCutoff >= HighVal else HighCutoff\n\n\n\t\t\t\t# Get the list of good frequency values (fMid) and their positions\n\t\t\t\t# (fMid_Pos)\n\t\t\t\tfMid_Pos, fMid = GetInRange(fAll, LowCutoff, HighCutoff)\n\n\t\t\t\t##yPsd = self.PsdType(fMid, *self.PsdParams)\n\t\t\t\t## non funziona, rimpiazzo a mano\n\t\t\t\tyPsd = PsdFuns.Interp(fMid, self._PsdNumericX, self._PsdNumericY)\n\n\t\t# Analytical Psd\n\t\telse:\n\t\t\tfMid_Pos, fMid = GetInRange(fAll, LowCutoff, HighCutoff)\n\t\t\tyPsd = self.PsdType(fMid, *self.PsdParams)\n\n\t\t# copying array subset\n\t\tyPsdAll[fMid_Pos] = yPsd\n\n\t\treturn fAll, yPsdAll\n\n\t#======================================================================\n\t# \tFUN: _FitNumericPsdWithPowerLaw\n\t#======================================================================\n# in disusos\n\tdef _FitNumericPsdWithPowerLaw(self):\n\t\tx,y = self.NumericPsdGetXY()\n\t\tif self._IsNumericPsdInFreq == True:\n\t\t\tp = FitPowerLaw(1/x,y)\n\t\t\tself.PsdParams = p[0], -p[1]\n\t\telse:\n\t\t\tp = FitPowerLaw(x,y)\n\t\t\tself.PsdParams = p[0], p[1]\n\n\n\t#======================================================================\n\t# \tFUN: MakeProfile\n\t#======================================================================\n\tdef MakeProfile(self, L,N):\n\t\t'''\n\t\t\tEvaluates the psd according to .PsdType, .PsdParams and .Options directives\n\t\t\tReturns an evenly-spaced array.\n\t\t\tIf PsdType = NumericArray, linear interpolation is performed.\n\n\t\t\t:PARAM: N: # of samples\n\t\t\t:PARAM: dx: grid spacing (spatial frequency)\n\n\t\t\treturns:\n\t\t\t\t1d arr\n\t\t'''\n\n\n\t\tif self.PsdType == PsdFuns.Interp:\n\t\t\t# chiama codice ad hoc\n\t\t\tL_mm = L*1e3\n\t\t\tyRoughness = PsdArray2Noise_1d_v2(self._PsdNumericX, self._PsdNumericY, L_mm, N)\n\t\telse:\n\t\t\tprint('Irreversible error. The code was not completed to handle this instance')\n\t\treturn yRoughness * self.ProfileScaling\n#\t\tf, yPsd = self.PsdEval(N//2 + 1,df)\n\n\n\t\t# Special case\n#\t\tif self.Options.FIT_NUMERIC_DATA_WITH_POWER_LAW == True:\n#\t\t\tself.PsdParams = list(FitPowerLaw(*self.NumericPsdGetXY()))\n#\t\t\tyPsd = PsdFuns.PowerLaw(x, *self.PsdParams)\n#\t\telse: # general calse\n#\t\t\tyPsd = self.PsdType(x, *self.PsdParams)\n\n#\t\tyRoughness = Psd2Noise_1d(yPsd, N, Semiaxis = True)\n\n\n#\t\tx = np.linspace(0, N*dx,N)\n#\t\t# Special case\n#\t\tif self.Options.FIT_NUMERIC_DATA_WITH_POWER_LAW == True:\n#\t\t\tself.PsdParams = list(FitPowerLaw(*self.NumericPsdGetXY()))\n#\t\t\ty = PowerLawNoise_1d(N, dx, *self.PsdParams)\n#\t\telse: # general calse\n#\t\t\ty = self.PsdType(N,dx, *self.PsdParams)\n#\t\treturn y\n\tGenerate = MakeProfile\n\t#======================================================================\n\t# \tFUN: NumericPsdSetXY\n\t#======================================================================\n\tdef NumericPsdSetXY(self,x,y):\n\t\tself._PsdNumericX = x\n\t\tself._PsdNumericY = y\n\n\n\t#======================================================================\n\t# \tFUN: NumericPsdGetXY\n\t#======================================================================\n\n\tdef NumericPsdGetXY(self):\n\t\ttry:\n\t\t\treturn self._PsdNumericX, self._PsdNumericY\n\t\texcept:\n\t\t\tprint('Error in RoughnessMaker.NumericPsdGetXY. Maybe the data file was not properly loaded')\n\t#======================================================================\n\t# \tFUN: NumericPsdLoadXY\n\t#======================================================================\n\tdef NumericPsdLoadXY(self, FilePath, xScaling = 1, yScaling = 1 , xIsSpatialFreq = True):\n\t\t''' @TODO: specificare formati e tipi di file\n\n\t\tParameters\n\t\t----------------------------\n\t\txIsSpatialFreq : bool\n\t\t\t\t\t\ttrue If the first column (Read_x_values) contains spatial\n\t\t\t\t\t\tfrequencies. False if it contains lenghts. Default = True\n\t\txScaling, yScaling: floats\n\t\t\t\t\t\tRead_x_values => Read_x_values * xScaling\n\n\t\t\t\t\t\tRead_y_values => Read_y_values * yScaling\n\n\t\t\t\t\t\tSometimes, properly setting the x and y scaling values may be confusing (although just matter of high-school considerations). On this purpose, the property .RoughnessMaker.ProfileScaling property can be used also..ProfileScaling is the scale factor that acts on the output of MakeProfile() function only.\n\n\t\tremarks\n\t\t\t\t\t\t--------\n\t\t\t\t\t\tpippo\n\n\t\t'''\n\n\t\ttry:\n\t\t\tself._IsNumericPsdInFreq = xIsSpatialFreq\n\n\t\t\ts = np.loadtxt(FilePath)\n\t\t\tx = s[:,0]\n\t\t\ty = s[:,1]\n\n\t\t\tx = x * xScaling\n\t\t\ty = y * yScaling\n\n\t\t\t# inversion of x-axis if not spatial frequencies\n\t\t\tif xIsSpatialFreq == False:\n\t\t\t\tf = 1/x\n\t\t\telse:\n\t\t\t\tf = x\n\t\t\t# array sorting\n\t\t\ti = np.argsort(f)\n\t\t\tf = f[i]\n\t\t\ty = y[i]\n\t\t\t# I set the Cutoff value of the class according to available data\n\t\t\tself.PsdCutoffLowHigh = [np.amin, np.amax(f)]\n\n\t\t\t# I set class operating variables\n\t\t\tself.PsdType = PsdFuns.Interp\n\t\t\tself.PsdParams = [f,y]\n\n\n\t\t\t# Auto-set\n\t\t\t# fill 0-value (DC Component)\n\t#\t\tif self.Options.AUTO_FILL_NUMERIC_DATA_WITH_ZERO == True:\n\t#\t\t\tif np.amin(x >0):\n\t#\t\t\t\tx = np.insert(x,0,0)\n\t#\t\t\t\ty = np.insert(y,0,0)\t# 0 in psd => 0-mean value in the noise pattern\n\n\n\t\t\t# sync other class values\n\t\t\tself.NumericPsdSetXY(f, y)\n\t\texcept:\n\t\t\tpass\n\n\t\tdef Generate(self, N = None, dx = None, CutoffLowHigh = [None, None]):\n\t\t\t'''\n\t\t\tParameters\n\t\t\t\tN: # of output samples\n\t\t\t\tdx: step of the x axis\n\t\t\tNote: generates an evenly spaced array\n\t\t\t'''\n\t\t\tL = dx * N\n\t\t\tdf = 1/L\n\t\t\tfPsd, yPsd = self.PsdEval(N//2 +1 , df = df,\n\t\t\t\t\t\t\t\t\t\t\tCutoffLowHigh = CutoffLowHigh )\n\t\t\th = Psd2Noise_1d(yPsd, Semiaxis = True)\n\n\t\t\treturn h\n\n\t#======================================================================\n\t# \tFUN: NumericPsdCheck\n\t#======================================================================\n\tdef NumericPsdCheck(self, N, L):\n\n\t\tdf = 1/L\n\t\t# Stored data\n\t\tff,yy = self.NumericPsdGetXY()\n\t\t# Evaluated data\n\t\tfPsd, yPsd = self.PsdEval(N, df)\n\n\n\t\tplt.plot(fPsd, np.log10(yPsd),'x')\n\t\tplt.plot(ff, np.log10(yy),'.r')\n\t\tplt.legend(['Evaluated data', 'Stored data'])\n\t\tplt.suptitle('Usage of stored data (PSD)')\n\n\t\tfMax = df*(N//2)\n\t\tfMin = df\n\t\tStrMsg = ''\n\t\t_max = np.max(ff)\n\t\t_min = np.min(ff)\n\t\tprint('fMax query = %0.1e m^-1' % fMax )\n\t\tprint('fMax data= %0.1e m^-1 = %0.2e um^-1' % (_max, (_max * 1e6) ))\n\t\tprint('fMin query= %0.1e m^-1' % fMin )\n\t\tprint('fMin data= %0.1e m^-1 = %0.2e um^-1' % (_min, (_min * 1e6) ))\n\n\t\treturn StrMsg"}}},{"rowIdx":74,"cells":{"function_name":{"kind":"string","value":"__getitem__"},"docstring":{"kind":"string","value":"Get the procedure params according to the index.\n\nCreate the register when it does not exist.\n\n:param index:\n:return: ProcedureParamStorage"},"masked_code":{"kind":"string","value":"#!/usr/bin/python3\n# -*- coding: utf8 -*-\n\n# Copyright (c) 2020 Baidu, Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"\nProcedure Params\n\"\"\"\n\n\nclass ProcedureParams:\n \"\"\"\n The procedure params dict\n \"\"\"\n\n def __init__(self):\n \"\"\"\n The constructor of the ProcedureParams class\n \"\"\"\n\n self.paramsDict = {} # the inner data for procedure params dict\n\n# MASKED: __getitem__ function (lines 35-50)\n\n\nclass ProcedureParamStorage:\n \"\"\"\n The storage for procedure param\n \"\"\"\n\n def __init__(self, index):\n \"\"\"\n The quantum param object needs to know its index.\n :param index: the quantum register index\n \"\"\"\n\n self.index = index"},"implementation":{"kind":"string","value":" def __getitem__(self, index):\n \"\"\"\n Get the procedure params according to the index.\n\n Create the register when it does not exist.\n\n :param index:\n :return: ProcedureParamStorage\n \"\"\"\n\n value = self.paramsDict.get(index)\n if value is not None:\n return value\n value = ProcedureParamStorage(index)\n self.paramsDict[index] = value\n return value"},"start_line":{"kind":"number","value":35,"string":"35"},"end_line":{"kind":"number","value":50,"string":"50"},"file_content":{"kind":"string","value":"#!/usr/bin/python3\n# -*- coding: utf8 -*-\n\n# Copyright (c) 2020 Baidu, Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"\nProcedure Params\n\"\"\"\n\n\nclass ProcedureParams:\n \"\"\"\n The procedure params dict\n \"\"\"\n\n def __init__(self):\n \"\"\"\n The constructor of the ProcedureParams class\n \"\"\"\n\n self.paramsDict = {} # the inner data for procedure params dict\n\n def __getitem__(self, index):\n \"\"\"\n Get the procedure params according to the index.\n\n Create the register when it does not exist.\n\n :param index:\n :return: ProcedureParamStorage\n \"\"\"\n\n value = self.paramsDict.get(index)\n if value is not None:\n return value\n value = ProcedureParamStorage(index)\n self.paramsDict[index] = value\n return value\n\n\nclass ProcedureParamStorage:\n \"\"\"\n The storage for procedure param\n \"\"\"\n\n def __init__(self, index):\n \"\"\"\n The quantum param object needs to know its index.\n :param index: the quantum register index\n \"\"\"\n\n self.index = index"}}},{"rowIdx":75,"cells":{"function_name":{"kind":"string","value":"build_model"},"docstring":{"kind":"string","value":"Build DCE using the initialized attributes\n\nArgs:\n norm: boolean, wheher to add a normalization layer at the begining\n of the autoencoder\n act: string, keras activation function name for autoencoder"},"masked_code":{"kind":"string","value":"\"\"\"\nDeepChEmbed （DCE) Models\n\"\"\"\nfrom dimreducer import DeepAutoEncoder\nfrom cluster import KMeansLayer\nfrom cluster import KMeans\nfrom keras import Model\nfrom keras import optimizers\nfrom keras.utils import normalize\nimport numpy as np\n\nclass DCE():\n \"\"\"\n The class to build a deep chemical embedding model.\n\n Attributes:\n autoencoder_dims: a list of dimensions for encoder, the first\n element as input dimension, and the last one as\n hidden layer dimension.\n n_clusters: int, number of clusters for clustering layer.\n alpha: float, parameters for soft label assigning.\n update_interval: int, indicating every number of epoches, the harhened\n labels will be upadated and/or convergence cretia will\n be examed.\n max_iteration: int, maximum iteration for the combined training\n clustering_tol: float, convergence cretia for clustering layer\n model: keras Model variable\n HARDENING_FUNCS: smoothsetp hardening functions for unsupervised DCE\n training, up to 9th order\n \"\"\"\n\n HARDENING_FUNCS = {\n 1: lambda x: x,\n 3: lambda x: (-2*x + 3) * x**2,\n 5: lambda x: ((6*x - 15)*x + 10) * x**3,\n 7: lambda x: (((-20*x + 70)*x - 84)*x + 35) * x**4,\n 9: lambda x: ((((70*x - 315)*x + 540)*x -420)*x + 126) * x**5}\n\n def __init__(self, autoencoder_dims, n_clusters, update_interval=50,\n max_iteration=1e4, clustering_tol=1e-4, alpha=1.0):\n \"\"\"Construtor of DCE. \"\"\"\n self.autoencoder_dims = autoencoder_dims\n self.n_clusters = n_clusters\n self.alpha = alpha\n self.update_interval = update_interval\n self.max_iteration = max_iteration\n self.clustering_tol = clustering_tol\n self.model = None\n\n return\n\n# MASKED: build_model function (lines 52-68)\n\n def train_model(self, data_train,\n labels_train=None, data_test=None, labels_test=None,\n verbose=1,\n compiled=False, clustering_loss='kld',\n decoder_loss='mse',clustering_loss_weight=0.5,\n hardening_order=1, hardening_strength=2.0,\n compiled=False,\n optimizer='adam', lr=0.001, decay=0.0):\n \"\"\"Train DCE Model:\n\n If labels_train are not present, train DCE model in a unsupervised\n learning process; otherwise, train DCE model in a supervised learning\n process.\n\n Args:\n data_train: input training data\n labels_train: true labels of traning data\n data_test: input test data\n labels_test: true lables of testing data\n verbose: 0, turn off the screen prints\n clustering_loss: string, clustering layer loss function\n decoder_loss:, string, decoder loss function\n clustering_loss_weight: float in [0,1], w_c,\n harderning_order: odd int, the order of hardening function\n harderning_strength: float >=1.0, the streng of the harderning\n compiled: boolean, indicating if the model is compiled or not\n optmizer: string, keras optimizers\n lr: learning rate\n dacay: learning rate dacay\n\n Returns:\n train_loss: training loss\n test_loss: only if data_test and labels_test are not None in\n supervised learning process\n \"\"\"\n if (not compiled):\n assert clustering_loss_weight <= 1 and clustering_loss_weight >= 0\n\n if optimizer == 'adam':\n dce_optimizer = optimizers.Adam(lr=lr,decay=decay)\n elif optimizer == 'sgd':\n dce_optimizer = optimizers.sgd(lr=lr,decay=decay)\n else:\n raise Exception('Input optimizer was not found')\n\n self.model.compile(loss={'clustering': clustering_loss,\n 'decoder_output': decoder_loss},\n loss_weights=[clustering_loss_weight,\n 1 - clustering_loss_weight],\n optimizer=dce_optimizer)\n\n if (labels_train is not None):\n supervised_learning = True\n if verbose >= 1: print('Starting supervised learning')\n else:\n supervised_learning = False\n if verbose >= 1: print('Starting unsupervised learning')\n\n # initializing model by using sklean-Kmeans as guess\n kmeans_init = KMeans(n_clusters=self.n_clusters)\n kmeans_init.build_model()\n encoder = Model(inputs=self.model.input,\n outputs=self.model.get_layer(\\\n name='embedding_layer').output)\n kmeans_init.model.fit(encoder.predict(data_train))\n y_pred_last = kmeans_init.model.labels_\n self.model.get_layer(name='clustering').\\\n set_weights([kmeans_init.model.cluster_centers_])\n\n # Prepare training: p disctribution methods\n if not supervised_learning:\n # Unsupervised Learning\n assert hardening_order in DCE.HARDENING_FUNCS.keys()\n assert hardening_strength >= 1.0\n h_func = DCE.HARDENING_FUNCS[hardening_order]\n else:\n # Supervised Learning\n assert len(labels_train) == len(data_train)\n assert len(np.unique(labels_train)) == self.n_clusters\n p = np.zeros(shape=(len(labels_train), self.n_clusters))\n for i in range(len(labels_train)):\n p[i][labels_train[i]] = 1.0\n\n if data_test is not None:\n assert len(labels_test) == len(data_test)\n assert len(np.unique(labels_test)) == self.n_clusters\n p_test = np.zeros(shape=(len(labels_test), self.n_clusters))\n for i in range(len(labels_test)):\n p_test[i][labels_test[i]] = 1.0\n\n validation_loss = []\n\n # training start:\n loss = []\n\n for iteration in range(int(self.max_iteration)):\n\n if iteration % self.update_interval == 0:\n # updating p for unsupervised learning process\n q, _ = self.model.predict(data_train)\n if not supervised_learning:\n p = DCE.hardening(q, h_func, hardening_strength)\n\n # get label change i\n y_pred = q.argmax(1)\n delta_label_i = np.sum(y_pred != y_pred_last).\\\n astype(np.float32) / y_pred.shape[0]\n y_pred_last = y_pred\n\n # exam convergence\n if iteration > 0 and delta_label_i < self.clustering_tol:\n print(str(delta_label_i) +' < ' + str(self.clustering_tol))\n print('Reached tolerance threshold. Stopping training.')\n break\n\n loss.append(self.model.train_on_batch(x=data_train,\n y=[p,data_train]))\n if supervised_learning and data_test is not None:\n validation_loss.append(self.model.test_on_batch(\n x=data_test, y=[p_test,data_test]))\n\n if verbose > 0 and iteration % self.update_interval == 0:\n print('Epoch: ' + str(iteration))\n if verbose == 1:\n print(' Total_loss = ' + str(loss[iteration][0]) +\n ';Delta_label = ' + str(delta_label_i))\n print(' Clustering_loss = ' + str(loss[iteration][1]) +\n '; Decoder_loss = ' + str(loss[iteration][2]))\n\n if iteration == self.max_iteration - 1:\n print('Reached maximum iteration. Stopping training.')\n\n if data_test is None:\n return np.array(loss).T\n else:\n return [np.array(loss).T, np.array(validation_loss).T]\n\n @staticmethod\n def hardening(q, h_func, stength):\n \"\"\"hardening distribution P and return Q\n\n Args:\n q: input distributions.\n h_func: input harderning function.\n strength: hardening strength.\n\n returns:\n p: hardened and normatlized distributions.\n\n \"\"\"\n q = h_func(q)\n weight = q ** stength / q.sum(0)\n return (weight.T / weight.sum(1)).T"},"implementation":{"kind":"string","value":" def build_model(self, norm=True, act='relu'):\n \"\"\"Build DCE using the initialized attributes\n\n Args:\n norm: boolean, wheher to add a normalization layer at the begining\n of the autoencoder\n act: string, keras activation function name for autoencoder\n \"\"\"\n autoencoder = DeepAutoEncoder(self.autoencoder_dims, act)\n autoencoder.build_model(norm=norm)\n embeding = autoencoder.model.get_layer(name='embedding_layer').output\n clustering = KMeansLayer(self.n_clusters, alpha=self.alpha,\n name='clustering')(embeding)\n self.model = Model(inputs=autoencoder.model.input,\n outputs=[clustering,autoencoder.model.output])\n\n return"},"start_line":{"kind":"number","value":52,"string":"52"},"end_line":{"kind":"number","value":68,"string":"68"},"file_content":{"kind":"string","value":"\"\"\"\nDeepChEmbed （DCE) Models\n\"\"\"\nfrom dimreducer import DeepAutoEncoder\nfrom cluster import KMeansLayer\nfrom cluster import KMeans\nfrom keras import Model\nfrom keras import optimizers\nfrom keras.utils import normalize\nimport numpy as np\n\nclass DCE():\n \"\"\"\n The class to build a deep chemical embedding model.\n\n Attributes:\n autoencoder_dims: a list of dimensions for encoder, the first\n element as input dimension, and the last one as\n hidden layer dimension.\n n_clusters: int, number of clusters for clustering layer.\n alpha: float, parameters for soft label assigning.\n update_interval: int, indicating every number of epoches, the harhened\n labels will be upadated and/or convergence cretia will\n be examed.\n max_iteration: int, maximum iteration for the combined training\n clustering_tol: float, convergence cretia for clustering layer\n model: keras Model variable\n HARDENING_FUNCS: smoothsetp hardening functions for unsupervised DCE\n training, up to 9th order\n \"\"\"\n\n HARDENING_FUNCS = {\n 1: lambda x: x,\n 3: lambda x: (-2*x + 3) * x**2,\n 5: lambda x: ((6*x - 15)*x + 10) * x**3,\n 7: lambda x: (((-20*x + 70)*x - 84)*x + 35) * x**4,\n 9: lambda x: ((((70*x - 315)*x + 540)*x -420)*x + 126) * x**5}\n\n def __init__(self, autoencoder_dims, n_clusters, update_interval=50,\n max_iteration=1e4, clustering_tol=1e-4, alpha=1.0):\n \"\"\"Construtor of DCE. \"\"\"\n self.autoencoder_dims = autoencoder_dims\n self.n_clusters = n_clusters\n self.alpha = alpha\n self.update_interval = update_interval\n self.max_iteration = max_iteration\n self.clustering_tol = clustering_tol\n self.model = None\n\n return\n\n def build_model(self, norm=True, act='relu'):\n \"\"\"Build DCE using the initialized attributes\n\n Args:\n norm: boolean, wheher to add a normalization layer at the begining\n of the autoencoder\n act: string, keras activation function name for autoencoder\n \"\"\"\n autoencoder = DeepAutoEncoder(self.autoencoder_dims, act)\n autoencoder.build_model(norm=norm)\n embeding = autoencoder.model.get_layer(name='embedding_layer').output\n clustering = KMeansLayer(self.n_clusters, alpha=self.alpha,\n name='clustering')(embeding)\n self.model = Model(inputs=autoencoder.model.input,\n outputs=[clustering,autoencoder.model.output])\n\n return\n\n def train_model(self, data_train,\n labels_train=None, data_test=None, labels_test=None,\n verbose=1,\n compiled=False, clustering_loss='kld',\n decoder_loss='mse',clustering_loss_weight=0.5,\n hardening_order=1, hardening_strength=2.0,\n compiled=False,\n optimizer='adam', lr=0.001, decay=0.0):\n \"\"\"Train DCE Model:\n\n If labels_train are not present, train DCE model in a unsupervised\n learning process; otherwise, train DCE model in a supervised learning\n process.\n\n Args:\n data_train: input training data\n labels_train: true labels of traning data\n data_test: input test data\n labels_test: true lables of testing data\n verbose: 0, turn off the screen prints\n clustering_loss: string, clustering layer loss function\n decoder_loss:, string, decoder loss function\n clustering_loss_weight: float in [0,1], w_c,\n harderning_order: odd int, the order of hardening function\n harderning_strength: float >=1.0, the streng of the harderning\n compiled: boolean, indicating if the model is compiled or not\n optmizer: string, keras optimizers\n lr: learning rate\n dacay: learning rate dacay\n\n Returns:\n train_loss: training loss\n test_loss: only if data_test and labels_test are not None in\n supervised learning process\n \"\"\"\n if (not compiled):\n assert clustering_loss_weight <= 1 and clustering_loss_weight >= 0\n\n if optimizer == 'adam':\n dce_optimizer = optimizers.Adam(lr=lr,decay=decay)\n elif optimizer == 'sgd':\n dce_optimizer = optimizers.sgd(lr=lr,decay=decay)\n else:\n raise Exception('Input optimizer was not found')\n\n self.model.compile(loss={'clustering': clustering_loss,\n 'decoder_output': decoder_loss},\n loss_weights=[clustering_loss_weight,\n 1 - clustering_loss_weight],\n optimizer=dce_optimizer)\n\n if (labels_train is not None):\n supervised_learning = True\n if verbose >= 1: print('Starting supervised learning')\n else:\n supervised_learning = False\n if verbose >= 1: print('Starting unsupervised learning')\n\n # initializing model by using sklean-Kmeans as guess\n kmeans_init = KMeans(n_clusters=self.n_clusters)\n kmeans_init.build_model()\n encoder = Model(inputs=self.model.input,\n outputs=self.model.get_layer(\\\n name='embedding_layer').output)\n kmeans_init.model.fit(encoder.predict(data_train))\n y_pred_last = kmeans_init.model.labels_\n self.model.get_layer(name='clustering').\\\n set_weights([kmeans_init.model.cluster_centers_])\n\n # Prepare training: p disctribution methods\n if not supervised_learning:\n # Unsupervised Learning\n assert hardening_order in DCE.HARDENING_FUNCS.keys()\n assert hardening_strength >= 1.0\n h_func = DCE.HARDENING_FUNCS[hardening_order]\n else:\n # Supervised Learning\n assert len(labels_train) == len(data_train)\n assert len(np.unique(labels_train)) == self.n_clusters\n p = np.zeros(shape=(len(labels_train), self.n_clusters))\n for i in range(len(labels_train)):\n p[i][labels_train[i]] = 1.0\n\n if data_test is not None:\n assert len(labels_test) == len(data_test)\n assert len(np.unique(labels_test)) == self.n_clusters\n p_test = np.zeros(shape=(len(labels_test), self.n_clusters))\n for i in range(len(labels_test)):\n p_test[i][labels_test[i]] = 1.0\n\n validation_loss = []\n\n # training start:\n loss = []\n\n for iteration in range(int(self.max_iteration)):\n\n if iteration % self.update_interval == 0:\n # updating p for unsupervised learning process\n q, _ = self.model.predict(data_train)\n if not supervised_learning:\n p = DCE.hardening(q, h_func, hardening_strength)\n\n # get label change i\n y_pred = q.argmax(1)\n delta_label_i = np.sum(y_pred != y_pred_last).\\\n astype(np.float32) / y_pred.shape[0]\n y_pred_last = y_pred\n\n # exam convergence\n if iteration > 0 and delta_label_i < self.clustering_tol:\n print(str(delta_label_i) +' < ' + str(self.clustering_tol))\n print('Reached tolerance threshold. Stopping training.')\n break\n\n loss.append(self.model.train_on_batch(x=data_train,\n y=[p,data_train]))\n if supervised_learning and data_test is not None:\n validation_loss.append(self.model.test_on_batch(\n x=data_test, y=[p_test,data_test]))\n\n if verbose > 0 and iteration % self.update_interval == 0:\n print('Epoch: ' + str(iteration))\n if verbose == 1:\n print(' Total_loss = ' + str(loss[iteration][0]) +\n ';Delta_label = ' + str(delta_label_i))\n print(' Clustering_loss = ' + str(loss[iteration][1]) +\n '; Decoder_loss = ' + str(loss[iteration][2]))\n\n if iteration == self.max_iteration - 1:\n print('Reached maximum iteration. Stopping training.')\n\n if data_test is None:\n return np.array(loss).T\n else:\n return [np.array(loss).T, np.array(validation_loss).T]\n\n @staticmethod\n def hardening(q, h_func, stength):\n \"\"\"hardening distribution P and return Q\n\n Args:\n q: input distributions.\n h_func: input harderning function.\n strength: hardening strength.\n\n returns:\n p: hardened and normatlized distributions.\n\n \"\"\"\n q = h_func(q)\n weight = q ** stength / q.sum(0)\n return (weight.T / weight.sum(1)).T\n"}}},{"rowIdx":76,"cells":{"function_name":{"kind":"string","value":"hardening"},"docstring":{"kind":"string","value":"hardening distribution P and return Q\n\nArgs:\n q: input distributions.\n h_func: input harderning function.\n strength: hardening strength.\n\nreturns:\n p: hardened and normatlized distributions."},"masked_code":{"kind":"string","value":"\"\"\"\nDeepChEmbed （DCE) Models\n\"\"\"\nfrom dimreducer import DeepAutoEncoder\nfrom cluster import KMeansLayer\nfrom cluster import KMeans\nfrom keras import Model\nfrom keras import optimizers\nfrom keras.utils import normalize\nimport numpy as np\n\nclass DCE():\n \"\"\"\n The class to build a deep chemical embedding model.\n\n Attributes:\n autoencoder_dims: a list of dimensions for encoder, the first\n element as input dimension, and the last one as\n hidden layer dimension.\n n_clusters: int, number of clusters for clustering layer.\n alpha: float, parameters for soft label assigning.\n update_interval: int, indicating every number of epoches, the harhened\n labels will be upadated and/or convergence cretia will\n be examed.\n max_iteration: int, maximum iteration for the combined training\n clustering_tol: float, convergence cretia for clustering layer\n model: keras Model variable\n HARDENING_FUNCS: smoothsetp hardening functions for unsupervised DCE\n training, up to 9th order\n \"\"\"\n\n HARDENING_FUNCS = {\n 1: lambda x: x,\n 3: lambda x: (-2*x + 3) * x**2,\n 5: lambda x: ((6*x - 15)*x + 10) * x**3,\n 7: lambda x: (((-20*x + 70)*x - 84)*x + 35) * x**4,\n 9: lambda x: ((((70*x - 315)*x + 540)*x -420)*x + 126) * x**5}\n\n def __init__(self, autoencoder_dims, n_clusters, update_interval=50,\n max_iteration=1e4, clustering_tol=1e-4, alpha=1.0):\n \"\"\"Construtor of DCE. \"\"\"\n self.autoencoder_dims = autoencoder_dims\n self.n_clusters = n_clusters\n self.alpha = alpha\n self.update_interval = update_interval\n self.max_iteration = max_iteration\n self.clustering_tol = clustering_tol\n self.model = None\n\n return\n\n def build_model(self, norm=True, act='relu'):\n \"\"\"Build DCE using the initialized attributes\n\n Args:\n norm: boolean, wheher to add a normalization layer at the begining\n of the autoencoder\n act: string, keras activation function name for autoencoder\n \"\"\"\n autoencoder = DeepAutoEncoder(self.autoencoder_dims, act)\n autoencoder.build_model(norm=norm)\n embeding = autoencoder.model.get_layer(name='embedding_layer').output\n clustering = KMeansLayer(self.n_clusters, alpha=self.alpha,\n name='clustering')(embeding)\n self.model = Model(inputs=autoencoder.model.input,\n outputs=[clustering,autoencoder.model.output])\n\n return\n\n def train_model(self, data_train,\n labels_train=None, data_test=None, labels_test=None,\n verbose=1,\n compiled=False, clustering_loss='kld',\n decoder_loss='mse',clustering_loss_weight=0.5,\n hardening_order=1, hardening_strength=2.0,\n compiled=False,\n optimizer='adam', lr=0.001, decay=0.0):\n \"\"\"Train DCE Model:\n\n If labels_train are not present, train DCE model in a unsupervised\n learning process; otherwise, train DCE model in a supervised learning\n process.\n\n Args:\n data_train: input training data\n labels_train: true labels of traning data\n data_test: input test data\n labels_test: true lables of testing data\n verbose: 0, turn off the screen prints\n clustering_loss: string, clustering layer loss function\n decoder_loss:, string, decoder loss function\n clustering_loss_weight: float in [0,1], w_c,\n harderning_order: odd int, the order of hardening function\n harderning_strength: float >=1.0, the streng of the harderning\n compiled: boolean, indicating if the model is compiled or not\n optmizer: string, keras optimizers\n lr: learning rate\n dacay: learning rate dacay\n\n Returns:\n train_loss: training loss\n test_loss: only if data_test and labels_test are not None in\n supervised learning process\n \"\"\"\n if (not compiled):\n assert clustering_loss_weight <= 1 and clustering_loss_weight >= 0\n\n if optimizer == 'adam':\n dce_optimizer = optimizers.Adam(lr=lr,decay=decay)\n elif optimizer == 'sgd':\n dce_optimizer = optimizers.sgd(lr=lr,decay=decay)\n else:\n raise Exception('Input optimizer was not found')\n\n self.model.compile(loss={'clustering': clustering_loss,\n 'decoder_output': decoder_loss},\n loss_weights=[clustering_loss_weight,\n 1 - clustering_loss_weight],\n optimizer=dce_optimizer)\n\n if (labels_train is not None):\n supervised_learning = True\n if verbose >= 1: print('Starting supervised learning')\n else:\n supervised_learning = False\n if verbose >= 1: print('Starting unsupervised learning')\n\n # initializing model by using sklean-Kmeans as guess\n kmeans_init = KMeans(n_clusters=self.n_clusters)\n kmeans_init.build_model()\n encoder = Model(inputs=self.model.input,\n outputs=self.model.get_layer(\\\n name='embedding_layer').output)\n kmeans_init.model.fit(encoder.predict(data_train))\n y_pred_last = kmeans_init.model.labels_\n self.model.get_layer(name='clustering').\\\n set_weights([kmeans_init.model.cluster_centers_])\n\n # Prepare training: p disctribution methods\n if not supervised_learning:\n # Unsupervised Learning\n assert hardening_order in DCE.HARDENING_FUNCS.keys()\n assert hardening_strength >= 1.0\n h_func = DCE.HARDENING_FUNCS[hardening_order]\n else:\n # Supervised Learning\n assert len(labels_train) == len(data_train)\n assert len(np.unique(labels_train)) == self.n_clusters\n p = np.zeros(shape=(len(labels_train), self.n_clusters))\n for i in range(len(labels_train)):\n p[i][labels_train[i]] = 1.0\n\n if data_test is not None:\n assert len(labels_test) == len(data_test)\n assert len(np.unique(labels_test)) == self.n_clusters\n p_test = np.zeros(shape=(len(labels_test), self.n_clusters))\n for i in range(len(labels_test)):\n p_test[i][labels_test[i]] = 1.0\n\n validation_loss = []\n\n # training start:\n loss = []\n\n for iteration in range(int(self.max_iteration)):\n\n if iteration % self.update_interval == 0:\n # updating p for unsupervised learning process\n q, _ = self.model.predict(data_train)\n if not supervised_learning:\n p = DCE.hardening(q, h_func, hardening_strength)\n\n # get label change i\n y_pred = q.argmax(1)\n delta_label_i = np.sum(y_pred != y_pred_last).\\\n astype(np.float32) / y_pred.shape[0]\n y_pred_last = y_pred\n\n # exam convergence\n if iteration > 0 and delta_label_i < self.clustering_tol:\n print(str(delta_label_i) +' < ' + str(self.clustering_tol))\n print('Reached tolerance threshold. Stopping training.')\n break\n\n loss.append(self.model.train_on_batch(x=data_train,\n y=[p,data_train]))\n if supervised_learning and data_test is not None:\n validation_loss.append(self.model.test_on_batch(\n x=data_test, y=[p_test,data_test]))\n\n if verbose > 0 and iteration % self.update_interval == 0:\n print('Epoch: ' + str(iteration))\n if verbose == 1:\n print(' Total_loss = ' + str(loss[iteration][0]) +\n ';Delta_label = ' + str(delta_label_i))\n print(' Clustering_loss = ' + str(loss[iteration][1]) +\n '; Decoder_loss = ' + str(loss[iteration][2]))\n\n if iteration == self.max_iteration - 1:\n print('Reached maximum iteration. Stopping training.')\n\n if data_test is None:\n return np.array(loss).T\n else:\n return [np.array(loss).T, np.array(validation_loss).T]\n\n# MASKED: hardening function (lines 207-222)"},"implementation":{"kind":"string","value":" @staticmethod\n def hardening(q, h_func, stength):\n \"\"\"hardening distribution P and return Q\n\n Args:\n q: input distributions.\n h_func: input harderning function.\n strength: hardening strength.\n\n returns:\n p: hardened and normatlized distributions.\n\n \"\"\"\n q = h_func(q)\n weight = q ** stength / q.sum(0)\n return (weight.T / weight.sum(1)).T"},"start_line":{"kind":"number","value":207,"string":"207"},"end_line":{"kind":"number","value":222,"string":"222"},"file_content":{"kind":"string","value":"\"\"\"\nDeepChEmbed （DCE) Models\n\"\"\"\nfrom dimreducer import DeepAutoEncoder\nfrom cluster import KMeansLayer\nfrom cluster import KMeans\nfrom keras import Model\nfrom keras import optimizers\nfrom keras.utils import normalize\nimport numpy as np\n\nclass DCE():\n \"\"\"\n The class to build a deep chemical embedding model.\n\n Attributes:\n autoencoder_dims: a list of dimensions for encoder, the first\n element as input dimension, and the last one as\n hidden layer dimension.\n n_clusters: int, number of clusters for clustering layer.\n alpha: float, parameters for soft label assigning.\n update_interval: int, indicating every number of epoches, the harhened\n labels will be upadated and/or convergence cretia will\n be examed.\n max_iteration: int, maximum iteration for the combined training\n clustering_tol: float, convergence cretia for clustering layer\n model: keras Model variable\n HARDENING_FUNCS: smoothsetp hardening functions for unsupervised DCE\n training, up to 9th order\n \"\"\"\n\n HARDENING_FUNCS = {\n 1: lambda x: x,\n 3: lambda x: (-2*x + 3) * x**2,\n 5: lambda x: ((6*x - 15)*x + 10) * x**3,\n 7: lambda x: (((-20*x + 70)*x - 84)*x + 35) * x**4,\n 9: lambda x: ((((70*x - 315)*x + 540)*x -420)*x + 126) * x**5}\n\n def __init__(self, autoencoder_dims, n_clusters, update_interval=50,\n max_iteration=1e4, clustering_tol=1e-4, alpha=1.0):\n \"\"\"Construtor of DCE. \"\"\"\n self.autoencoder_dims = autoencoder_dims\n self.n_clusters = n_clusters\n self.alpha = alpha\n self.update_interval = update_interval\n self.max_iteration = max_iteration\n self.clustering_tol = clustering_tol\n self.model = None\n\n return\n\n def build_model(self, norm=True, act='relu'):\n \"\"\"Build DCE using the initialized attributes\n\n Args:\n norm: boolean, wheher to add a normalization layer at the begining\n of the autoencoder\n act: string, keras activation function name for autoencoder\n \"\"\"\n autoencoder = DeepAutoEncoder(self.autoencoder_dims, act)\n autoencoder.build_model(norm=norm)\n embeding = autoencoder.model.get_layer(name='embedding_layer').output\n clustering = KMeansLayer(self.n_clusters, alpha=self.alpha,\n name='clustering')(embeding)\n self.model = Model(inputs=autoencoder.model.input,\n outputs=[clustering,autoencoder.model.output])\n\n return\n\n def train_model(self, data_train,\n labels_train=None, data_test=None, labels_test=None,\n verbose=1,\n compiled=False, clustering_loss='kld',\n decoder_loss='mse',clustering_loss_weight=0.5,\n hardening_order=1, hardening_strength=2.0,\n compiled=False,\n optimizer='adam', lr=0.001, decay=0.0):\n \"\"\"Train DCE Model:\n\n If labels_train are not present, train DCE model in a unsupervised\n learning process; otherwise, train DCE model in a supervised learning\n process.\n\n Args:\n data_train: input training data\n labels_train: true labels of traning data\n data_test: input test data\n labels_test: true lables of testing data\n verbose: 0, turn off the screen prints\n clustering_loss: string, clustering layer loss function\n decoder_loss:, string, decoder loss function\n clustering_loss_weight: float in [0,1], w_c,\n harderning_order: odd int, the order of hardening function\n harderning_strength: float >=1.0, the streng of the harderning\n compiled: boolean, indicating if the model is compiled or not\n optmizer: string, keras optimizers\n lr: learning rate\n dacay: learning rate dacay\n\n Returns:\n train_loss: training loss\n test_loss: only if data_test and labels_test are not None in\n supervised learning process\n \"\"\"\n if (not compiled):\n assert clustering_loss_weight <= 1 and clustering_loss_weight >= 0\n\n if optimizer == 'adam':\n dce_optimizer = optimizers.Adam(lr=lr,decay=decay)\n elif optimizer == 'sgd':\n dce_optimizer = optimizers.sgd(lr=lr,decay=decay)\n else:\n raise Exception('Input optimizer was not found')\n\n self.model.compile(loss={'clustering': clustering_loss,\n 'decoder_output': decoder_loss},\n loss_weights=[clustering_loss_weight,\n 1 - clustering_loss_weight],\n optimizer=dce_optimizer)\n\n if (labels_train is not None):\n supervised_learning = True\n if verbose >= 1: print('Starting supervised learning')\n else:\n supervised_learning = False\n if verbose >= 1: print('Starting unsupervised learning')\n\n # initializing model by using sklean-Kmeans as guess\n kmeans_init = KMeans(n_clusters=self.n_clusters)\n kmeans_init.build_model()\n encoder = Model(inputs=self.model.input,\n outputs=self.model.get_layer(\\\n name='embedding_layer').output)\n kmeans_init.model.fit(encoder.predict(data_train))\n y_pred_last = kmeans_init.model.labels_\n self.model.get_layer(name='clustering').\\\n set_weights([kmeans_init.model.cluster_centers_])\n\n # Prepare training: p disctribution methods\n if not supervised_learning:\n # Unsupervised Learning\n assert hardening_order in DCE.HARDENING_FUNCS.keys()\n assert hardening_strength >= 1.0\n h_func = DCE.HARDENING_FUNCS[hardening_order]\n else:\n # Supervised Learning\n assert len(labels_train) == len(data_train)\n assert len(np.unique(labels_train)) == self.n_clusters\n p = np.zeros(shape=(len(labels_train), self.n_clusters))\n for i in range(len(labels_train)):\n p[i][labels_train[i]] = 1.0\n\n if data_test is not None:\n assert len(labels_test) == len(data_test)\n assert len(np.unique(labels_test)) == self.n_clusters\n p_test = np.zeros(shape=(len(labels_test), self.n_clusters))\n for i in range(len(labels_test)):\n p_test[i][labels_test[i]] = 1.0\n\n validation_loss = []\n\n # training start:\n loss = []\n\n for iteration in range(int(self.max_iteration)):\n\n if iteration % self.update_interval == 0:\n # updating p for unsupervised learning process\n q, _ = self.model.predict(data_train)\n if not supervised_learning:\n p = DCE.hardening(q, h_func, hardening_strength)\n\n # get label change i\n y_pred = q.argmax(1)\n delta_label_i = np.sum(y_pred != y_pred_last).\\\n astype(np.float32) / y_pred.shape[0]\n y_pred_last = y_pred\n\n # exam convergence\n if iteration > 0 and delta_label_i < self.clustering_tol:\n print(str(delta_label_i) +' < ' + str(self.clustering_tol))\n print('Reached tolerance threshold. Stopping training.')\n break\n\n loss.append(self.model.train_on_batch(x=data_train,\n y=[p,data_train]))\n if supervised_learning and data_test is not None:\n validation_loss.append(self.model.test_on_batch(\n x=data_test, y=[p_test,data_test]))\n\n if verbose > 0 and iteration % self.update_interval == 0:\n print('Epoch: ' + str(iteration))\n if verbose == 1:\n print(' Total_loss = ' + str(loss[iteration][0]) +\n ';Delta_label = ' + str(delta_label_i))\n print(' Clustering_loss = ' + str(loss[iteration][1]) +\n '; Decoder_loss = ' + str(loss[iteration][2]))\n\n if iteration == self.max_iteration - 1:\n print('Reached maximum iteration. Stopping training.')\n\n if data_test is None:\n return np.array(loss).T\n else:\n return [np.array(loss).T, np.array(validation_loss).T]\n\n @staticmethod\n def hardening(q, h_func, stength):\n \"\"\"hardening distribution P and return Q\n\n Args:\n q: input distributions.\n h_func: input harderning function.\n strength: hardening strength.\n\n returns:\n p: hardened and normatlized distributions.\n\n \"\"\"\n q = h_func(q)\n weight = q ** stength / q.sum(0)\n return (weight.T / weight.sum(1)).T\n"}}},{"rowIdx":77,"cells":{"function_name":{"kind":"string","value":"authenticate_active"},"docstring":{"kind":"string","value":"Generate a WLS 'success' response based on interaction with the user\n\nThis function creates a WLS response specifying that the principal was\nauthenticated based on 'fresh' interaction with the user (e.g. input of\na username and password).\n\nArgs:\n request (AuthRequest): the original WAA request\n principal (AuthPrincipal): the principal authenticated by the WLS\n auth (str): the authentication method used by the principal.\n life (int): if specified, the validity (in seconds) of the\n principal's session with the WLS.\n sign (bool): whether to sign the response or not. Recommended to\n leave this at the default value of `True` (see warning below).\n\n *args: passed to `AuthResponse.respond_to_request`\n **kwargs: passed to `AuthResponse.respond_to_request`\n\nReturns:\n An `AuthResponse` instance matching the given arguments.\n\nWarning:\n Responses indicating successful authentication *MUST* be signed by\n the WLS. It is recommended that you leave `sign` set to `True`, or\n make sure to sign the response manually afterwards."},"masked_code":{"kind":"string","value":"import datetime\n\nfrom . import status\nfrom .errors import InvalidAuthRequest, ProtocolVersionUnsupported, NoMutualAuthType\nfrom .signing import Key\nfrom .response import AuthResponse\n\nclass AuthPrincipal:\n def __init__(self, userid, auth_methods, ptags=None, session_expiry=None):\n self.userid = userid\n self.auth_methods = auth_methods\n if ptags is None:\n ptags = []\n self.ptags = ptags\n self.session_expiry = session_expiry\n\n\nclass LoginService:\n \"\"\"High-level interface to implement a web login service (WLS).\n\n This class provides a convenient interface for implementing a WLS with any\n authentication backend. It is intended to be instantiated with a single\n private key, which is used to sign the responses it generates.\n\n Mechanisms deemed useful for WLS implementation are provided:\n - storing the list of supported authentication methods, and checking\n whether the WLS and a WAA's request have an method in common\n - checking whether the protocol version specified in the WAA request is\n supported by `ucam_wls`\n\n These mechanisms can optionally be turned off.\n\n Attributes:\n key (ucam_wls.signing.Key): a private key to be used to sign responses\n auth_methods (list): a list of supported authentication methods\n \"\"\"\n def __init__(self, key, auth_methods):\n if not isinstance(key, Key):\n raise TypeError(\"key must be a ucam_wls.signing.Key instance\")\n self.key = key\n self.auth_methods = auth_methods\n\n def have_mutual_auth_type(self, request):\n if request.aauth and any(request.aauth):\n return set(request.aauth) & set(self.auth_methods) != set()\n else:\n return True\n\n def _pre_response(self, request, skip_handling_check, check_auth_types=True):\n if not skip_handling_check:\n if not request.data_valid:\n raise InvalidAuthRequest\n if check_auth_types and not self.have_mutual_auth_type(request):\n raise NoMutualAuthType(\n \"WLS supports %s; WAA wants one of %s\" % (\n self.auth_methods, request.aauth\n )\n )\n if not request.version_supported:\n raise ProtocolVersionUnsupported(request.ver)\n\n def _finish_response(self, response, sign=True, force_signature=False):\n if sign or response.requires_signature:\n if not response.is_signed or force_signature:\n self.key.sign(response)\n return response\n\n# MASKED: authenticate_active function (lines 68-108)\n\n def authenticate_passive(self, request, principal, sso=[], sign=True,\n skip_handling_check=False, *args, **kwargs):\n \"\"\"Generate a WLS 'success' response based on a pre-existing identity\n\n This function creates a WLS response specifying that the principal was\n authenticated based on previous successful authentication (e.g. an\n existing WLS session cookie).\n\n Args:\n request (AuthRequest): the original WAA request\n principal (AuthPrincipal): the principal authenticated by the WLS\n sso (list): a list of strings indicating the authentication methods\n previously used for authentication by the principal. If an\n empty list is passed, `principal.auth_methods` will be used.\n sign (bool): whether to sign the response or not. Recommended to\n leave this at the default value of `True` (see warning below).\n\n *args: passed to `AuthResponse.respond_to_request`\n **kwargs: passed to `AuthResponse.respond_to_request`\n\n Returns:\n An `AuthResponse` instance matching the given arguments.\n\n Warning:\n Responses indicating successful authentication *MUST* be signed by\n the WLS. It is recommended that you leave `sign` set to `True`, or\n make sure to sign the response manually afterwards.\n \"\"\"\n self._pre_response(request, skip_handling_check)\n\n if request.iact == True:\n raise ValueError(\"WAA demanded active authentication (iact == 'yes')\")\n\n if len(sso) == 0:\n sso = principal.auth_methods\n\n if len(sso) == 0:\n raise ValueError(\"no authentication methods specified for `sso`\")\n\n if principal.session_expiry is not None:\n life = int((principal.session_expiry - datetime.datetime.utcnow()).total_seconds())\n else:\n life = None\n\n response = AuthResponse.respond_to_request(\n request=request, code=status.SUCCESS, principal=principal.userid,\n sso=sso, ptags=principal.ptags, life=life, *args, **kwargs\n )\n return self._finish_response(response=response, sign=sign)\n\n def generate_failure(self, code, request, msg='', sign=True,\n skip_handling_check=False, *args, **kwargs):\n \"\"\"Generate a response indicating failure.\n\n This is to be used in all cases where the outcome of user interaction\n is not success. This function will refuse to handle a request where\n the 'fail' parameter is 'yes' (in which case the WLS must not redirect\n back to the WAA).\n\n Args:\n code (int): the response status code. Values specified in the\n protocol are available as constants under `ucam_wls.status`.\n request (AuthRequest): the original WAA request\n msg (str): an optional message that could be shown to the end user\n by the WAA\n sign (bool): whether to sign the response or not.\n\n *args: passed to `AuthResponse.respond_to_request`\n **kwargs: passed to `AuthResponse.respond_to_request`\n\n Returns:\n An `AuthResponse` instance matching the given arguments.\n\n Note:\n Signatures on WLS responses indicating a non-success can optionally\n be signed. In the interests of security, the default in this\n function is to go ahead and sign anyway, but this can be turned off\n if really desired.\n \"\"\"\n self._pre_response(request, skip_handling_check, check_auth_types=False)\n\n if request.fail:\n raise ValueError(\"WAA specified that WLS must not redirect \"\n \"back to it on failure\")\n\n if code == status.SUCCESS:\n raise ValueError(\"Failure responses must not have success status\")\n\n response = AuthResponse.respond_to_request(\n request=request, code=code, *args, **kwargs\n )\n return self._finish_response(response=response, sign=sign)"},"implementation":{"kind":"string","value":" def authenticate_active(self, request, principal, auth, life=None,\n sign=True, skip_handling_check=False, *args, **kwargs):\n \"\"\"Generate a WLS 'success' response based on interaction with the user\n\n This function creates a WLS response specifying that the principal was\n authenticated based on 'fresh' interaction with the user (e.g. input of\n a username and password).\n\n Args:\n request (AuthRequest): the original WAA request\n principal (AuthPrincipal): the principal authenticated by the WLS\n auth (str): the authentication method used by the principal.\n life (int): if specified, the validity (in seconds) of the\n principal's session with the WLS.\n sign (bool): whether to sign the response or not. Recommended to\n leave this at the default value of `True` (see warning below).\n\n *args: passed to `AuthResponse.respond_to_request`\n **kwargs: passed to `AuthResponse.respond_to_request`\n\n Returns:\n An `AuthResponse` instance matching the given arguments.\n\n Warning:\n Responses indicating successful authentication *MUST* be signed by\n the WLS. It is recommended that you leave `sign` set to `True`, or\n make sure to sign the response manually afterwards.\n \"\"\"\n self._pre_response(request, skip_handling_check)\n\n if request.iact == False:\n raise ValueError(\"WAA demanded passive authentication (iact == 'no')\")\n\n if life is None and principal.session_expiry is not None:\n life = int((principal.session_expiry - datetime.datetime.utcnow()).total_seconds())\n\n response = AuthResponse.respond_to_request(\n request=request, code=status.SUCCESS, principal=principal.userid,\n auth=auth, ptags=principal.ptags, life=life, *args, **kwargs\n )\n return self._finish_response(response=response, sign=sign)"},"start_line":{"kind":"number","value":68,"string":"68"},"end_line":{"kind":"number","value":108,"string":"108"},"file_content":{"kind":"string","value":"import datetime\n\nfrom . import status\nfrom .errors import InvalidAuthRequest, ProtocolVersionUnsupported, NoMutualAuthType\nfrom .signing import Key\nfrom .response import AuthResponse\n\nclass AuthPrincipal:\n def __init__(self, userid, auth_methods, ptags=None, session_expiry=None):\n self.userid = userid\n self.auth_methods = auth_methods\n if ptags is None:\n ptags = []\n self.ptags = ptags\n self.session_expiry = session_expiry\n\n\nclass LoginService:\n \"\"\"High-level interface to implement a web login service (WLS).\n\n This class provides a convenient interface for implementing a WLS with any\n authentication backend. It is intended to be instantiated with a single\n private key, which is used to sign the responses it generates.\n\n Mechanisms deemed useful for WLS implementation are provided:\n - storing the list of supported authentication methods, and checking\n whether the WLS and a WAA's request have an method in common\n - checking whether the protocol version specified in the WAA request is\n supported by `ucam_wls`\n\n These mechanisms can optionally be turned off.\n\n Attributes:\n key (ucam_wls.signing.Key): a private key to be used to sign responses\n auth_methods (list): a list of supported authentication methods\n \"\"\"\n def __init__(self, key, auth_methods):\n if not isinstance(key, Key):\n raise TypeError(\"key must be a ucam_wls.signing.Key instance\")\n self.key = key\n self.auth_methods = auth_methods\n\n def have_mutual_auth_type(self, request):\n if request.aauth and any(request.aauth):\n return set(request.aauth) & set(self.auth_methods) != set()\n else:\n return True\n\n def _pre_response(self, request, skip_handling_check, check_auth_types=True):\n if not skip_handling_check:\n if not request.data_valid:\n raise InvalidAuthRequest\n if check_auth_types and not self.have_mutual_auth_type(request):\n raise NoMutualAuthType(\n \"WLS supports %s; WAA wants one of %s\" % (\n self.auth_methods, request.aauth\n )\n )\n if not request.version_supported:\n raise ProtocolVersionUnsupported(request.ver)\n\n def _finish_response(self, response, sign=True, force_signature=False):\n if sign or response.requires_signature:\n if not response.is_signed or force_signature:\n self.key.sign(response)\n return response\n\n def authenticate_active(self, request, principal, auth, life=None,\n sign=True, skip_handling_check=False, *args, **kwargs):\n \"\"\"Generate a WLS 'success' response based on interaction with the user\n\n This function creates a WLS response specifying that the principal was\n authenticated based on 'fresh' interaction with the user (e.g. input of\n a username and password).\n\n Args:\n request (AuthRequest): the original WAA request\n principal (AuthPrincipal): the principal authenticated by the WLS\n auth (str): the authentication method used by the principal.\n life (int): if specified, the validity (in seconds) of the\n principal's session with the WLS.\n sign (bool): whether to sign the response or not. Recommended to\n leave this at the default value of `True` (see warning below).\n\n *args: passed to `AuthResponse.respond_to_request`\n **kwargs: passed to `AuthResponse.respond_to_request`\n\n Returns:\n An `AuthResponse` instance matching the given arguments.\n\n Warning:\n Responses indicating successful authentication *MUST* be signed by\n the WLS. It is recommended that you leave `sign` set to `True`, or\n make sure to sign the response manually afterwards.\n \"\"\"\n self._pre_response(request, skip_handling_check)\n\n if request.iact == False:\n raise ValueError(\"WAA demanded passive authentication (iact == 'no')\")\n\n if life is None and principal.session_expiry is not None:\n life = int((principal.session_expiry - datetime.datetime.utcnow()).total_seconds())\n\n response = AuthResponse.respond_to_request(\n request=request, code=status.SUCCESS, principal=principal.userid,\n auth=auth, ptags=principal.ptags, life=life, *args, **kwargs\n )\n return self._finish_response(response=response, sign=sign)\n\n def authenticate_passive(self, request, principal, sso=[], sign=True,\n skip_handling_check=False, *args, **kwargs):\n \"\"\"Generate a WLS 'success' response based on a pre-existing identity\n\n This function creates a WLS response specifying that the principal was\n authenticated based on previous successful authentication (e.g. an\n existing WLS session cookie).\n\n Args:\n request (AuthRequest): the original WAA request\n principal (AuthPrincipal): the principal authenticated by the WLS\n sso (list): a list of strings indicating the authentication methods\n previously used for authentication by the principal. If an\n empty list is passed, `principal.auth_methods` will be used.\n sign (bool): whether to sign the response or not. Recommended to\n leave this at the default value of `True` (see warning below).\n\n *args: passed to `AuthResponse.respond_to_request`\n **kwargs: passed to `AuthResponse.respond_to_request`\n\n Returns:\n An `AuthResponse` instance matching the given arguments.\n\n Warning:\n Responses indicating successful authentication *MUST* be signed by\n the WLS. It is recommended that you leave `sign` set to `True`, or\n make sure to sign the response manually afterwards.\n \"\"\"\n self._pre_response(request, skip_handling_check)\n\n if request.iact == True:\n raise ValueError(\"WAA demanded active authentication (iact == 'yes')\")\n\n if len(sso) == 0:\n sso = principal.auth_methods\n\n if len(sso) == 0:\n raise ValueError(\"no authentication methods specified for `sso`\")\n\n if principal.session_expiry is not None:\n life = int((principal.session_expiry - datetime.datetime.utcnow()).total_seconds())\n else:\n life = None\n\n response = AuthResponse.respond_to_request(\n request=request, code=status.SUCCESS, principal=principal.userid,\n sso=sso, ptags=principal.ptags, life=life, *args, **kwargs\n )\n return self._finish_response(response=response, sign=sign)\n\n def generate_failure(self, code, request, msg='', sign=True,\n skip_handling_check=False, *args, **kwargs):\n \"\"\"Generate a response indicating failure.\n\n This is to be used in all cases where the outcome of user interaction\n is not success. This function will refuse to handle a request where\n the 'fail' parameter is 'yes' (in which case the WLS must not redirect\n back to the WAA).\n\n Args:\n code (int): the response status code. Values specified in the\n protocol are available as constants under `ucam_wls.status`.\n request (AuthRequest): the original WAA request\n msg (str): an optional message that could be shown to the end user\n by the WAA\n sign (bool): whether to sign the response or not.\n\n *args: passed to `AuthResponse.respond_to_request`\n **kwargs: passed to `AuthResponse.respond_to_request`\n\n Returns:\n An `AuthResponse` instance matching the given arguments.\n\n Note:\n Signatures on WLS responses indicating a non-success can optionally\n be signed. In the interests of security, the default in this\n function is to go ahead and sign anyway, but this can be turned off\n if really desired.\n \"\"\"\n self._pre_response(request, skip_handling_check, check_auth_types=False)\n\n if request.fail:\n raise ValueError(\"WAA specified that WLS must not redirect \"\n \"back to it on failure\")\n\n if code == status.SUCCESS:\n raise ValueError(\"Failure responses must not have success status\")\n\n response = AuthResponse.respond_to_request(\n request=request, code=code, *args, **kwargs\n )\n return self._finish_response(response=response, sign=sign)\n"}}},{"rowIdx":78,"cells":{"function_name":{"kind":"string","value":"authenticate_passive"},"docstring":{"kind":"string","value":"Generate a WLS 'success' response based on a pre-existing identity\n\nThis function creates a WLS response specifying that the principal was\nauthenticated based on previous successful authentication (e.g. an\nexisting WLS session cookie).\n\nArgs:\n request (AuthRequest): the original WAA request\n principal (AuthPrincipal): the principal authenticated by the WLS\n sso (list): a list of strings indicating the authentication methods\n previously used for authentication by the principal. If an\n empty list is passed, `principal.auth_methods` will be used.\n sign (bool): whether to sign the response or not. Recommended to\n leave this at the default value of `True` (see warning below).\n\n *args: passed to `AuthResponse.respond_to_request`\n **kwargs: passed to `AuthResponse.respond_to_request`\n\nReturns:\n An `AuthResponse` instance matching the given arguments.\n\nWarning:\n Responses indicating successful authentication *MUST* be signed by\n the WLS. It is recommended that you leave `sign` set to `True`, or\n make sure to sign the response manually afterwards."},"masked_code":{"kind":"string","value":"import datetime\n\nfrom . import status\nfrom .errors import InvalidAuthRequest, ProtocolVersionUnsupported, NoMutualAuthType\nfrom .signing import Key\nfrom .response import AuthResponse\n\nclass AuthPrincipal:\n def __init__(self, userid, auth_methods, ptags=None, session_expiry=None):\n self.userid = userid\n self.auth_methods = auth_methods\n if ptags is None:\n ptags = []\n self.ptags = ptags\n self.session_expiry = session_expiry\n\n\nclass LoginService:\n \"\"\"High-level interface to implement a web login service (WLS).\n\n This class provides a convenient interface for implementing a WLS with any\n authentication backend. It is intended to be instantiated with a single\n private key, which is used to sign the responses it generates.\n\n Mechanisms deemed useful for WLS implementation are provided:\n - storing the list of supported authentication methods, and checking\n whether the WLS and a WAA's request have an method in common\n - checking whether the protocol version specified in the WAA request is\n supported by `ucam_wls`\n\n These mechanisms can optionally be turned off.\n\n Attributes:\n key (ucam_wls.signing.Key): a private key to be used to sign responses\n auth_methods (list): a list of supported authentication methods\n \"\"\"\n def __init__(self, key, auth_methods):\n if not isinstance(key, Key):\n raise TypeError(\"key must be a ucam_wls.signing.Key instance\")\n self.key = key\n self.auth_methods = auth_methods\n\n def have_mutual_auth_type(self, request):\n if request.aauth and any(request.aauth):\n return set(request.aauth) & set(self.auth_methods) != set()\n else:\n return True\n\n def _pre_response(self, request, skip_handling_check, check_auth_types=True):\n if not skip_handling_check:\n if not request.data_valid:\n raise InvalidAuthRequest\n if check_auth_types and not self.have_mutual_auth_type(request):\n raise NoMutualAuthType(\n \"WLS supports %s; WAA wants one of %s\" % (\n self.auth_methods, request.aauth\n )\n )\n if not request.version_supported:\n raise ProtocolVersionUnsupported(request.ver)\n\n def _finish_response(self, response, sign=True, force_signature=False):\n if sign or response.requires_signature:\n if not response.is_signed or force_signature:\n self.key.sign(response)\n return response\n\n def authenticate_active(self, request, principal, auth, life=None,\n sign=True, skip_handling_check=False, *args, **kwargs):\n \"\"\"Generate a WLS 'success' response based on interaction with the user\n\n This function creates a WLS response specifying that the principal was\n authenticated based on 'fresh' interaction with the user (e.g. input of\n a username and password).\n\n Args:\n request (AuthRequest): the original WAA request\n principal (AuthPrincipal): the principal authenticated by the WLS\n auth (str): the authentication method used by the principal.\n life (int): if specified, the validity (in seconds) of the\n principal's session with the WLS.\n sign (bool): whether to sign the response or not. Recommended to\n leave this at the default value of `True` (see warning below).\n\n *args: passed to `AuthResponse.respond_to_request`\n **kwargs: passed to `AuthResponse.respond_to_request`\n\n Returns:\n An `AuthResponse` instance matching the given arguments.\n\n Warning:\n Responses indicating successful authentication *MUST* be signed by\n the WLS. It is recommended that you leave `sign` set to `True`, or\n make sure to sign the response manually afterwards.\n \"\"\"\n self._pre_response(request, skip_handling_check)\n\n if request.iact == False:\n raise ValueError(\"WAA demanded passive authentication (iact == 'no')\")\n\n if life is None and principal.session_expiry is not None:\n life = int((principal.session_expiry - datetime.datetime.utcnow()).total_seconds())\n\n response = AuthResponse.respond_to_request(\n request=request, code=status.SUCCESS, principal=principal.userid,\n auth=auth, ptags=principal.ptags, life=life, *args, **kwargs\n )\n return self._finish_response(response=response, sign=sign)\n\n# MASKED: authenticate_passive function (lines 110-158)\n\n def generate_failure(self, code, request, msg='', sign=True,\n skip_handling_check=False, *args, **kwargs):\n \"\"\"Generate a response indicating failure.\n\n This is to be used in all cases where the outcome of user interaction\n is not success. This function will refuse to handle a request where\n the 'fail' parameter is 'yes' (in which case the WLS must not redirect\n back to the WAA).\n\n Args:\n code (int): the response status code. Values specified in the\n protocol are available as constants under `ucam_wls.status`.\n request (AuthRequest): the original WAA request\n msg (str): an optional message that could be shown to the end user\n by the WAA\n sign (bool): whether to sign the response or not.\n\n *args: passed to `AuthResponse.respond_to_request`\n **kwargs: passed to `AuthResponse.respond_to_request`\n\n Returns:\n An `AuthResponse` instance matching the given arguments.\n\n Note:\n Signatures on WLS responses indicating a non-success can optionally\n be signed. In the interests of security, the default in this\n function is to go ahead and sign anyway, but this can be turned off\n if really desired.\n \"\"\"\n self._pre_response(request, skip_handling_check, check_auth_types=False)\n\n if request.fail:\n raise ValueError(\"WAA specified that WLS must not redirect \"\n \"back to it on failure\")\n\n if code == status.SUCCESS:\n raise ValueError(\"Failure responses must not have success status\")\n\n response = AuthResponse.respond_to_request(\n request=request, code=code, *args, **kwargs\n )\n return self._finish_response(response=response, sign=sign)"},"implementation":{"kind":"string","value":" def authenticate_passive(self, request, principal, sso=[], sign=True,\n skip_handling_check=False, *args, **kwargs):\n \"\"\"Generate a WLS 'success' response based on a pre-existing identity\n\n This function creates a WLS response specifying that the principal was\n authenticated based on previous successful authentication (e.g. an\n existing WLS session cookie).\n\n Args:\n request (AuthRequest): the original WAA request\n principal (AuthPrincipal): the principal authenticated by the WLS\n sso (list): a list of strings indicating the authentication methods\n previously used for authentication by the principal. If an\n empty list is passed, `principal.auth_methods` will be used.\n sign (bool): whether to sign the response or not. Recommended to\n leave this at the default value of `True` (see warning below).\n\n *args: passed to `AuthResponse.respond_to_request`\n **kwargs: passed to `AuthResponse.respond_to_request`\n\n Returns:\n An `AuthResponse` instance matching the given arguments.\n\n Warning:\n Responses indicating successful authentication *MUST* be signed by\n the WLS. It is recommended that you leave `sign` set to `True`, or\n make sure to sign the response manually afterwards.\n \"\"\"\n self._pre_response(request, skip_handling_check)\n\n if request.iact == True:\n raise ValueError(\"WAA demanded active authentication (iact == 'yes')\")\n\n if len(sso) == 0:\n sso = principal.auth_methods\n\n if len(sso) == 0:\n raise ValueError(\"no authentication methods specified for `sso`\")\n\n if principal.session_expiry is not None:\n life = int((principal.session_expiry - datetime.datetime.utcnow()).total_seconds())\n else:\n life = None\n\n response = AuthResponse.respond_to_request(\n request=request, code=status.SUCCESS, principal=principal.userid,\n sso=sso, ptags=principal.ptags, life=life, *args, **kwargs\n )\n return self._finish_response(response=response, sign=sign)"},"start_line":{"kind":"number","value":110,"string":"110"},"end_line":{"kind":"number","value":158,"string":"158"},"file_content":{"kind":"string","value":"import datetime\n\nfrom . import status\nfrom .errors import InvalidAuthRequest, ProtocolVersionUnsupported, NoMutualAuthType\nfrom .signing import Key\nfrom .response import AuthResponse\n\nclass AuthPrincipal:\n def __init__(self, userid, auth_methods, ptags=None, session_expiry=None):\n self.userid = userid\n self.auth_methods = auth_methods\n if ptags is None:\n ptags = []\n self.ptags = ptags\n self.session_expiry = session_expiry\n\n\nclass LoginService:\n \"\"\"High-level interface to implement a web login service (WLS).\n\n This class provides a convenient interface for implementing a WLS with any\n authentication backend. It is intended to be instantiated with a single\n private key, which is used to sign the responses it generates.\n\n Mechanisms deemed useful for WLS implementation are provided:\n - storing the list of supported authentication methods, and checking\n whether the WLS and a WAA's request have an method in common\n - checking whether the protocol version specified in the WAA request is\n supported by `ucam_wls`\n\n These mechanisms can optionally be turned off.\n\n Attributes:\n key (ucam_wls.signing.Key): a private key to be used to sign responses\n auth_methods (list): a list of supported authentication methods\n \"\"\"\n def __init__(self, key, auth_methods):\n if not isinstance(key, Key):\n raise TypeError(\"key must be a ucam_wls.signing.Key instance\")\n self.key = key\n self.auth_methods = auth_methods\n\n def have_mutual_auth_type(self, request):\n if request.aauth and any(request.aauth):\n return set(request.aauth) & set(self.auth_methods) != set()\n else:\n return True\n\n def _pre_response(self, request, skip_handling_check, check_auth_types=True):\n if not skip_handling_check:\n if not request.data_valid:\n raise InvalidAuthRequest\n if check_auth_types and not self.have_mutual_auth_type(request):\n raise NoMutualAuthType(\n \"WLS supports %s; WAA wants one of %s\" % (\n self.auth_methods, request.aauth\n )\n )\n if not request.version_supported:\n raise ProtocolVersionUnsupported(request.ver)\n\n def _finish_response(self, response, sign=True, force_signature=False):\n if sign or response.requires_signature:\n if not response.is_signed or force_signature:\n self.key.sign(response)\n return response\n\n def authenticate_active(self, request, principal, auth, life=None,\n sign=True, skip_handling_check=False, *args, **kwargs):\n \"\"\"Generate a WLS 'success' response based on interaction with the user\n\n This function creates a WLS response specifying that the principal was\n authenticated based on 'fresh' interaction with the user (e.g. input of\n a username and password).\n\n Args:\n request (AuthRequest): the original WAA request\n principal (AuthPrincipal): the principal authenticated by the WLS\n auth (str): the authentication method used by the principal.\n life (int): if specified, the validity (in seconds) of the\n principal's session with the WLS.\n sign (bool): whether to sign the response or not. Recommended to\n leave this at the default value of `True` (see warning below).\n\n *args: passed to `AuthResponse.respond_to_request`\n **kwargs: passed to `AuthResponse.respond_to_request`\n\n Returns:\n An `AuthResponse` instance matching the given arguments.\n\n Warning:\n Responses indicating successful authentication *MUST* be signed by\n the WLS. It is recommended that you leave `sign` set to `True`, or\n make sure to sign the response manually afterwards.\n \"\"\"\n self._pre_response(request, skip_handling_check)\n\n if request.iact == False:\n raise ValueError(\"WAA demanded passive authentication (iact == 'no')\")\n\n if life is None and principal.session_expiry is not None:\n life = int((principal.session_expiry - datetime.datetime.utcnow()).total_seconds())\n\n response = AuthResponse.respond_to_request(\n request=request, code=status.SUCCESS, principal=principal.userid,\n auth=auth, ptags=principal.ptags, life=life, *args, **kwargs\n )\n return self._finish_response(response=response, sign=sign)\n\n def authenticate_passive(self, request, principal, sso=[], sign=True,\n skip_handling_check=False, *args, **kwargs):\n \"\"\"Generate a WLS 'success' response based on a pre-existing identity\n\n This function creates a WLS response specifying that the principal was\n authenticated based on previous successful authentication (e.g. an\n existing WLS session cookie).\n\n Args:\n request (AuthRequest): the original WAA request\n principal (AuthPrincipal): the principal authenticated by the WLS\n sso (list): a list of strings indicating the authentication methods\n previously used for authentication by the principal. If an\n empty list is passed, `principal.auth_methods` will be used.\n sign (bool): whether to sign the response or not. Recommended to\n leave this at the default value of `True` (see warning below).\n\n *args: passed to `AuthResponse.respond_to_request`\n **kwargs: passed to `AuthResponse.respond_to_request`\n\n Returns:\n An `AuthResponse` instance matching the given arguments.\n\n Warning:\n Responses indicating successful authentication *MUST* be signed by\n the WLS. It is recommended that you leave `sign` set to `True`, or\n make sure to sign the response manually afterwards.\n \"\"\"\n self._pre_response(request, skip_handling_check)\n\n if request.iact == True:\n raise ValueError(\"WAA demanded active authentication (iact == 'yes')\")\n\n if len(sso) == 0:\n sso = principal.auth_methods\n\n if len(sso) == 0:\n raise ValueError(\"no authentication methods specified for `sso`\")\n\n if principal.session_expiry is not None:\n life = int((principal.session_expiry - datetime.datetime.utcnow()).total_seconds())\n else:\n life = None\n\n response = AuthResponse.respond_to_request(\n request=request, code=status.SUCCESS, principal=principal.userid,\n sso=sso, ptags=principal.ptags, life=life, *args, **kwargs\n )\n return self._finish_response(response=response, sign=sign)\n\n def generate_failure(self, code, request, msg='', sign=True,\n skip_handling_check=False, *args, **kwargs):\n \"\"\"Generate a response indicating failure.\n\n This is to be used in all cases where the outcome of user interaction\n is not success. This function will refuse to handle a request where\n the 'fail' parameter is 'yes' (in which case the WLS must not redirect\n back to the WAA).\n\n Args:\n code (int): the response status code. Values specified in the\n protocol are available as constants under `ucam_wls.status`.\n request (AuthRequest): the original WAA request\n msg (str): an optional message that could be shown to the end user\n by the WAA\n sign (bool): whether to sign the response or not.\n\n *args: passed to `AuthResponse.respond_to_request`\n **kwargs: passed to `AuthResponse.respond_to_request`\n\n Returns:\n An `AuthResponse` instance matching the given arguments.\n\n Note:\n Signatures on WLS responses indicating a non-success can optionally\n be signed. In the interests of security, the default in this\n function is to go ahead and sign anyway, but this can be turned off\n if really desired.\n \"\"\"\n self._pre_response(request, skip_handling_check, check_auth_types=False)\n\n if request.fail:\n raise ValueError(\"WAA specified that WLS must not redirect \"\n \"back to it on failure\")\n\n if code == status.SUCCESS:\n raise ValueError(\"Failure responses must not have success status\")\n\n response = AuthResponse.respond_to_request(\n request=request, code=code, *args, **kwargs\n )\n return self._finish_response(response=response, sign=sign)\n"}}},{"rowIdx":79,"cells":{"function_name":{"kind":"string","value":"__init__"},"docstring":{"kind":"string","value":":param bool enable_magnetic_store_writes: A flag to enable magnetic store writes.\n:param 'TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationArgs' magnetic_store_rejected_data_location: The location to write error reports for records rejected asynchronously during magnetic store writes. See Magnetic Store Rejected Data Location below for more details."},"masked_code":{"kind":"string","value":"# coding=utf-8\n# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***\n# *** Do not edit by hand unless you're certain you know what you are doing! ***\n\nimport warnings\nimport pulumi\nimport pulumi.runtime\nfrom typing import Any, Mapping, Optional, Sequence, Union, overload\nfrom .. import _utilities\nfrom . import outputs\n\n__all__ = [\n 'TableMagneticStoreWriteProperties',\n 'TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation',\n 'TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration',\n 'TableRetentionProperties',\n]\n\n@pulumi.output_type\nclass TableMagneticStoreWriteProperties(dict):\n @staticmethod\n def __key_warning(key: str):\n suggest = None\n if key == \"enableMagneticStoreWrites\":\n suggest = \"enable_magnetic_store_writes\"\n elif key == \"magneticStoreRejectedDataLocation\":\n suggest = \"magnetic_store_rejected_data_location\"\n\n if suggest:\n pulumi.log.warn(f\"Key '{key}' not found in TableMagneticStoreWriteProperties. Access the value via the '{suggest}' property getter instead.\")\n\n def __getitem__(self, key: str) -> Any:\n TableMagneticStoreWriteProperties.__key_warning(key)\n return super().__getitem__(key)\n\n def get(self, key: str, default = None) -> Any:\n TableMagneticStoreWriteProperties.__key_warning(key)\n return super().get(key, default)\n\n# MASKED: __init__ function (lines 40-50)\n\n @property\n @pulumi.getter(name=\"enableMagneticStoreWrites\")\n def enable_magnetic_store_writes(self) -> Optional[bool]:\n \"\"\"\n A flag to enable magnetic store writes.\n \"\"\"\n return pulumi.get(self, \"enable_magnetic_store_writes\")\n\n @property\n @pulumi.getter(name=\"magneticStoreRejectedDataLocation\")\n def magnetic_store_rejected_data_location(self) -> Optional['outputs.TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation']:\n \"\"\"\n The location to write error reports for records rejected asynchronously during magnetic store writes. See Magnetic Store Rejected Data Location below for more details.\n \"\"\"\n return pulumi.get(self, \"magnetic_store_rejected_data_location\")\n\n\n@pulumi.output_type\nclass TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation(dict):\n @staticmethod\n def __key_warning(key: str):\n suggest = None\n if key == \"s3Configuration\":\n suggest = \"s3_configuration\"\n\n if suggest:\n pulumi.log.warn(f\"Key '{key}' not found in TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation. Access the value via the '{suggest}' property getter instead.\")\n\n def __getitem__(self, key: str) -> Any:\n TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation.__key_warning(key)\n return super().__getitem__(key)\n\n def get(self, key: str, default = None) -> Any:\n TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation.__key_warning(key)\n return super().get(key, default)\n\n def __init__(__self__, *,\n s3_configuration: Optional['outputs.TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration'] = None):\n \"\"\"\n :param 'TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3ConfigurationArgs' s3_configuration: Configuration of an S3 location to write error reports for records rejected, asynchronously, during magnetic store writes. See S3 Configuration below for more details.\n \"\"\"\n if s3_configuration is not None:\n pulumi.set(__self__, \"s3_configuration\", s3_configuration)\n\n @property\n @pulumi.getter(name=\"s3Configuration\")\n def s3_configuration(self) -> Optional['outputs.TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration']:\n \"\"\"\n Configuration of an S3 location to write error reports for records rejected, asynchronously, during magnetic store writes. See S3 Configuration below for more details.\n \"\"\"\n return pulumi.get(self, \"s3_configuration\")\n\n\n@pulumi.output_type\nclass TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration(dict):\n @staticmethod\n def __key_warning(key: str):\n suggest = None\n if key == \"bucketName\":\n suggest = \"bucket_name\"\n elif key == \"encryptionOption\":\n suggest = \"encryption_option\"\n elif key == \"kmsKeyId\":\n suggest = \"kms_key_id\"\n elif key == \"objectKeyPrefix\":\n suggest = \"object_key_prefix\"\n\n if suggest:\n pulumi.log.warn(f\"Key '{key}' not found in TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration. Access the value via the '{suggest}' property getter instead.\")\n\n def __getitem__(self, key: str) -> Any:\n TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration.__key_warning(key)\n return super().__getitem__(key)\n\n def get(self, key: str, default = None) -> Any:\n TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration.__key_warning(key)\n return super().get(key, default)\n\n def __init__(__self__, *,\n bucket_name: Optional[str] = None,\n encryption_option: Optional[str] = None,\n kms_key_id: Optional[str] = None,\n object_key_prefix: Optional[str] = None):\n \"\"\"\n :param str bucket_name: Bucket name of the customer S3 bucket.\n :param str encryption_option: Encryption option for the customer s3 location. Options are S3 server side encryption with an S3-managed key or KMS managed key. Valid values are `SSE_KMS` and `SSE_S3`.\n :param str kms_key_id: KMS key arn for the customer s3 location when encrypting with a KMS managed key.\n :param str object_key_prefix: Object key prefix for the customer S3 location.\n \"\"\"\n if bucket_name is not None:\n pulumi.set(__self__, \"bucket_name\", bucket_name)\n if encryption_option is not None:\n pulumi.set(__self__, \"encryption_option\", encryption_option)\n if kms_key_id is not None:\n pulumi.set(__self__, \"kms_key_id\", kms_key_id)\n if object_key_prefix is not None:\n pulumi.set(__self__, \"object_key_prefix\", object_key_prefix)\n\n @property\n @pulumi.getter(name=\"bucketName\")\n def bucket_name(self) -> Optional[str]:\n \"\"\"\n Bucket name of the customer S3 bucket.\n \"\"\"\n return pulumi.get(self, \"bucket_name\")\n\n @property\n @pulumi.getter(name=\"encryptionOption\")\n def encryption_option(self) -> Optional[str]:\n \"\"\"\n Encryption option for the customer s3 location. Options are S3 server side encryption with an S3-managed key or KMS managed key. Valid values are `SSE_KMS` and `SSE_S3`.\n \"\"\"\n return pulumi.get(self, \"encryption_option\")\n\n @property\n @pulumi.getter(name=\"kmsKeyId\")\n def kms_key_id(self) -> Optional[str]:\n \"\"\"\n KMS key arn for the customer s3 location when encrypting with a KMS managed key.\n \"\"\"\n return pulumi.get(self, \"kms_key_id\")\n\n @property\n @pulumi.getter(name=\"objectKeyPrefix\")\n def object_key_prefix(self) -> Optional[str]:\n \"\"\"\n Object key prefix for the customer S3 location.\n \"\"\"\n return pulumi.get(self, \"object_key_prefix\")\n\n\n@pulumi.output_type\nclass TableRetentionProperties(dict):\n @staticmethod\n def __key_warning(key: str):\n suggest = None\n if key == \"magneticStoreRetentionPeriodInDays\":\n suggest = \"magnetic_store_retention_period_in_days\"\n elif key == \"memoryStoreRetentionPeriodInHours\":\n suggest = \"memory_store_retention_period_in_hours\"\n\n if suggest:\n pulumi.log.warn(f\"Key '{key}' not found in TableRetentionProperties. Access the value via the '{suggest}' property getter instead.\")\n\n def __getitem__(self, key: str) -> Any:\n TableRetentionProperties.__key_warning(key)\n return super().__getitem__(key)\n\n def get(self, key: str, default = None) -> Any:\n TableRetentionProperties.__key_warning(key)\n return super().get(key, default)\n\n def __init__(__self__, *,\n magnetic_store_retention_period_in_days: int,\n memory_store_retention_period_in_hours: int):\n \"\"\"\n :param int magnetic_store_retention_period_in_days: The duration for which data must be stored in the magnetic store. Minimum value of 1. Maximum value of 73000.\n :param int memory_store_retention_period_in_hours: The duration for which data must be stored in the memory store. Minimum value of 1. Maximum value of 8766.\n \"\"\"\n pulumi.set(__self__, \"magnetic_store_retention_period_in_days\", magnetic_store_retention_period_in_days)\n pulumi.set(__self__, \"memory_store_retention_period_in_hours\", memory_store_retention_period_in_hours)\n\n @property\n @pulumi.getter(name=\"magneticStoreRetentionPeriodInDays\")\n def magnetic_store_retention_period_in_days(self) -> int:\n \"\"\"\n The duration for which data must be stored in the magnetic store. Minimum value of 1. Maximum value of 73000.\n \"\"\"\n return pulumi.get(self, \"magnetic_store_retention_period_in_days\")\n\n @property\n @pulumi.getter(name=\"memoryStoreRetentionPeriodInHours\")\n def memory_store_retention_period_in_hours(self) -> int:\n \"\"\"\n The duration for which data must be stored in the memory store. Minimum value of 1. Maximum value of 8766.\n \"\"\"\n return pulumi.get(self, \"memory_store_retention_period_in_hours\")\n\n"},"implementation":{"kind":"string","value":" def __init__(__self__, *,\n enable_magnetic_store_writes: Optional[bool] = None,\n magnetic_store_rejected_data_location: Optional['outputs.TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation'] = None):\n \"\"\"\n :param bool enable_magnetic_store_writes: A flag to enable magnetic store writes.\n :param 'TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationArgs' magnetic_store_rejected_data_location: The location to write error reports for records rejected asynchronously during magnetic store writes. See Magnetic Store Rejected Data Location below for more details.\n \"\"\"\n if enable_magnetic_store_writes is not None:\n pulumi.set(__self__, \"enable_magnetic_store_writes\", enable_magnetic_store_writes)\n if magnetic_store_rejected_data_location is not None:\n pulumi.set(__self__, \"magnetic_store_rejected_data_location\", magnetic_store_rejected_data_location)"},"start_line":{"kind":"number","value":40,"string":"40"},"end_line":{"kind":"number","value":50,"string":"50"},"file_content":{"kind":"string","value":"# coding=utf-8\n# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***\n# *** Do not edit by hand unless you're certain you know what you are doing! ***\n\nimport warnings\nimport pulumi\nimport pulumi.runtime\nfrom typing import Any, Mapping, Optional, Sequence, Union, overload\nfrom .. import _utilities\nfrom . import outputs\n\n__all__ = [\n 'TableMagneticStoreWriteProperties',\n 'TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation',\n 'TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration',\n 'TableRetentionProperties',\n]\n\n@pulumi.output_type\nclass TableMagneticStoreWriteProperties(dict):\n @staticmethod\n def __key_warning(key: str):\n suggest = None\n if key == \"enableMagneticStoreWrites\":\n suggest = \"enable_magnetic_store_writes\"\n elif key == \"magneticStoreRejectedDataLocation\":\n suggest = \"magnetic_store_rejected_data_location\"\n\n if suggest:\n pulumi.log.warn(f\"Key '{key}' not found in TableMagneticStoreWriteProperties. Access the value via the '{suggest}' property getter instead.\")\n\n def __getitem__(self, key: str) -> Any:\n TableMagneticStoreWriteProperties.__key_warning(key)\n return super().__getitem__(key)\n\n def get(self, key: str, default = None) -> Any:\n TableMagneticStoreWriteProperties.__key_warning(key)\n return super().get(key, default)\n\n def __init__(__self__, *,\n enable_magnetic_store_writes: Optional[bool] = None,\n magnetic_store_rejected_data_location: Optional['outputs.TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation'] = None):\n \"\"\"\n :param bool enable_magnetic_store_writes: A flag to enable magnetic store writes.\n :param 'TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationArgs' magnetic_store_rejected_data_location: The location to write error reports for records rejected asynchronously during magnetic store writes. See Magnetic Store Rejected Data Location below for more details.\n \"\"\"\n if enable_magnetic_store_writes is not None:\n pulumi.set(__self__, \"enable_magnetic_store_writes\", enable_magnetic_store_writes)\n if magnetic_store_rejected_data_location is not None:\n pulumi.set(__self__, \"magnetic_store_rejected_data_location\", magnetic_store_rejected_data_location)\n\n @property\n @pulumi.getter(name=\"enableMagneticStoreWrites\")\n def enable_magnetic_store_writes(self) -> Optional[bool]:\n \"\"\"\n A flag to enable magnetic store writes.\n \"\"\"\n return pulumi.get(self, \"enable_magnetic_store_writes\")\n\n @property\n @pulumi.getter(name=\"magneticStoreRejectedDataLocation\")\n def magnetic_store_rejected_data_location(self) -> Optional['outputs.TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation']:\n \"\"\"\n The location to write error reports for records rejected asynchronously during magnetic store writes. See Magnetic Store Rejected Data Location below for more details.\n \"\"\"\n return pulumi.get(self, \"magnetic_store_rejected_data_location\")\n\n\n@pulumi.output_type\nclass TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation(dict):\n @staticmethod\n def __key_warning(key: str):\n suggest = None\n if key == \"s3Configuration\":\n suggest = \"s3_configuration\"\n\n if suggest:\n pulumi.log.warn(f\"Key '{key}' not found in TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation. Access the value via the '{suggest}' property getter instead.\")\n\n def __getitem__(self, key: str) -> Any:\n TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation.__key_warning(key)\n return super().__getitem__(key)\n\n def get(self, key: str, default = None) -> Any:\n TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation.__key_warning(key)\n return super().get(key, default)\n\n def __init__(__self__, *,\n s3_configuration: Optional['outputs.TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration'] = None):\n \"\"\"\n :param 'TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3ConfigurationArgs' s3_configuration: Configuration of an S3 location to write error reports for records rejected, asynchronously, during magnetic store writes. See S3 Configuration below for more details.\n \"\"\"\n if s3_configuration is not None:\n pulumi.set(__self__, \"s3_configuration\", s3_configuration)\n\n @property\n @pulumi.getter(name=\"s3Configuration\")\n def s3_configuration(self) -> Optional['outputs.TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration']:\n \"\"\"\n Configuration of an S3 location to write error reports for records rejected, asynchronously, during magnetic store writes. See S3 Configuration below for more details.\n \"\"\"\n return pulumi.get(self, \"s3_configuration\")\n\n\n@pulumi.output_type\nclass TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration(dict):\n @staticmethod\n def __key_warning(key: str):\n suggest = None\n if key == \"bucketName\":\n suggest = \"bucket_name\"\n elif key == \"encryptionOption\":\n suggest = \"encryption_option\"\n elif key == \"kmsKeyId\":\n suggest = \"kms_key_id\"\n elif key == \"objectKeyPrefix\":\n suggest = \"object_key_prefix\"\n\n if suggest:\n pulumi.log.warn(f\"Key '{key}' not found in TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration. Access the value via the '{suggest}' property getter instead.\")\n\n def __getitem__(self, key: str) -> Any:\n TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration.__key_warning(key)\n return super().__getitem__(key)\n\n def get(self, key: str, default = None) -> Any:\n TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration.__key_warning(key)\n return super().get(key, default)\n\n def __init__(__self__, *,\n bucket_name: Optional[str] = None,\n encryption_option: Optional[str] = None,\n kms_key_id: Optional[str] = None,\n object_key_prefix: Optional[str] = None):\n \"\"\"\n :param str bucket_name: Bucket name of the customer S3 bucket.\n :param str encryption_option: Encryption option for the customer s3 location. Options are S3 server side encryption with an S3-managed key or KMS managed key. Valid values are `SSE_KMS` and `SSE_S3`.\n :param str kms_key_id: KMS key arn for the customer s3 location when encrypting with a KMS managed key.\n :param str object_key_prefix: Object key prefix for the customer S3 location.\n \"\"\"\n if bucket_name is not None:\n pulumi.set(__self__, \"bucket_name\", bucket_name)\n if encryption_option is not None:\n pulumi.set(__self__, \"encryption_option\", encryption_option)\n if kms_key_id is not None:\n pulumi.set(__self__, \"kms_key_id\", kms_key_id)\n if object_key_prefix is not None:\n pulumi.set(__self__, \"object_key_prefix\", object_key_prefix)\n\n @property\n @pulumi.getter(name=\"bucketName\")\n def bucket_name(self) -> Optional[str]:\n \"\"\"\n Bucket name of the customer S3 bucket.\n \"\"\"\n return pulumi.get(self, \"bucket_name\")\n\n @property\n @pulumi.getter(name=\"encryptionOption\")\n def encryption_option(self) -> Optional[str]:\n \"\"\"\n Encryption option for the customer s3 location. Options are S3 server side encryption with an S3-managed key or KMS managed key. Valid values are `SSE_KMS` and `SSE_S3`.\n \"\"\"\n return pulumi.get(self, \"encryption_option\")\n\n @property\n @pulumi.getter(name=\"kmsKeyId\")\n def kms_key_id(self) -> Optional[str]:\n \"\"\"\n KMS key arn for the customer s3 location when encrypting with a KMS managed key.\n \"\"\"\n return pulumi.get(self, \"kms_key_id\")\n\n @property\n @pulumi.getter(name=\"objectKeyPrefix\")\n def object_key_prefix(self) -> Optional[str]:\n \"\"\"\n Object key prefix for the customer S3 location.\n \"\"\"\n return pulumi.get(self, \"object_key_prefix\")\n\n\n@pulumi.output_type\nclass TableRetentionProperties(dict):\n @staticmethod\n def __key_warning(key: str):\n suggest = None\n if key == \"magneticStoreRetentionPeriodInDays\":\n suggest = \"magnetic_store_retention_period_in_days\"\n elif key == \"memoryStoreRetentionPeriodInHours\":\n suggest = \"memory_store_retention_period_in_hours\"\n\n if suggest:\n pulumi.log.warn(f\"Key '{key}' not found in TableRetentionProperties. Access the value via the '{suggest}' property getter instead.\")\n\n def __getitem__(self, key: str) -> Any:\n TableRetentionProperties.__key_warning(key)\n return super().__getitem__(key)\n\n def get(self, key: str, default = None) -> Any:\n TableRetentionProperties.__key_warning(key)\n return super().get(key, default)\n\n def __init__(__self__, *,\n magnetic_store_retention_period_in_days: int,\n memory_store_retention_period_in_hours: int):\n \"\"\"\n :param int magnetic_store_retention_period_in_days: The duration for which data must be stored in the magnetic store. Minimum value of 1. Maximum value of 73000.\n :param int memory_store_retention_period_in_hours: The duration for which data must be stored in the memory store. Minimum value of 1. Maximum value of 8766.\n \"\"\"\n pulumi.set(__self__, \"magnetic_store_retention_period_in_days\", magnetic_store_retention_period_in_days)\n pulumi.set(__self__, \"memory_store_retention_period_in_hours\", memory_store_retention_period_in_hours)\n\n @property\n @pulumi.getter(name=\"magneticStoreRetentionPeriodInDays\")\n def magnetic_store_retention_period_in_days(self) -> int:\n \"\"\"\n The duration for which data must be stored in the magnetic store. Minimum value of 1. Maximum value of 73000.\n \"\"\"\n return pulumi.get(self, \"magnetic_store_retention_period_in_days\")\n\n @property\n @pulumi.getter(name=\"memoryStoreRetentionPeriodInHours\")\n def memory_store_retention_period_in_hours(self) -> int:\n \"\"\"\n The duration for which data must be stored in the memory store. Minimum value of 1. Maximum value of 8766.\n \"\"\"\n return pulumi.get(self, \"memory_store_retention_period_in_hours\")\n\n\n"}}},{"rowIdx":80,"cells":{"function_name":{"kind":"string","value":"__init__"},"docstring":{"kind":"string","value":":param str bucket_name: Bucket name of the customer S3 bucket.\n:param str encryption_option: Encryption option for the customer s3 location. Options are S3 server side encryption with an S3-managed key or KMS managed key. Valid values are `SSE_KMS` and `SSE_S3`.\n:param str kms_key_id: KMS key arn for the customer s3 location when encrypting with a KMS managed key.\n:param str object_key_prefix: Object key prefix for the customer S3 location."},"masked_code":{"kind":"string","value":"# coding=utf-8\n# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***\n# *** Do not edit by hand unless you're certain you know what you are doing! ***\n\nimport warnings\nimport pulumi\nimport pulumi.runtime\nfrom typing import Any, Mapping, Optional, Sequence, Union, overload\nfrom .. import _utilities\nfrom . import outputs\n\n__all__ = [\n 'TableMagneticStoreWriteProperties',\n 'TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation',\n 'TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration',\n 'TableRetentionProperties',\n]\n\n@pulumi.output_type\nclass TableMagneticStoreWriteProperties(dict):\n @staticmethod\n def __key_warning(key: str):\n suggest = None\n if key == \"enableMagneticStoreWrites\":\n suggest = \"enable_magnetic_store_writes\"\n elif key == \"magneticStoreRejectedDataLocation\":\n suggest = \"magnetic_store_rejected_data_location\"\n\n if suggest:\n pulumi.log.warn(f\"Key '{key}' not found in TableMagneticStoreWriteProperties. Access the value via the '{suggest}' property getter instead.\")\n\n def __getitem__(self, key: str) -> Any:\n TableMagneticStoreWriteProperties.__key_warning(key)\n return super().__getitem__(key)\n\n def get(self, key: str, default = None) -> Any:\n TableMagneticStoreWriteProperties.__key_warning(key)\n return super().get(key, default)\n\n def __init__(__self__, *,\n enable_magnetic_store_writes: Optional[bool] = None,\n magnetic_store_rejected_data_location: Optional['outputs.TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation'] = None):\n \"\"\"\n :param bool enable_magnetic_store_writes: A flag to enable magnetic store writes.\n :param 'TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationArgs' magnetic_store_rejected_data_location: The location to write error reports for records rejected asynchronously during magnetic store writes. See Magnetic Store Rejected Data Location below for more details.\n \"\"\"\n if enable_magnetic_store_writes is not None:\n pulumi.set(__self__, \"enable_magnetic_store_writes\", enable_magnetic_store_writes)\n if magnetic_store_rejected_data_location is not None:\n pulumi.set(__self__, \"magnetic_store_rejected_data_location\", magnetic_store_rejected_data_location)\n\n @property\n @pulumi.getter(name=\"enableMagneticStoreWrites\")\n def enable_magnetic_store_writes(self) -> Optional[bool]:\n \"\"\"\n A flag to enable magnetic store writes.\n \"\"\"\n return pulumi.get(self, \"enable_magnetic_store_writes\")\n\n @property\n @pulumi.getter(name=\"magneticStoreRejectedDataLocation\")\n def magnetic_store_rejected_data_location(self) -> Optional['outputs.TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation']:\n \"\"\"\n The location to write error reports for records rejected asynchronously during magnetic store writes. See Magnetic Store Rejected Data Location below for more details.\n \"\"\"\n return pulumi.get(self, \"magnetic_store_rejected_data_location\")\n\n\n@pulumi.output_type\nclass TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation(dict):\n @staticmethod\n def __key_warning(key: str):\n suggest = None\n if key == \"s3Configuration\":\n suggest = \"s3_configuration\"\n\n if suggest:\n pulumi.log.warn(f\"Key '{key}' not found in TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation. Access the value via the '{suggest}' property getter instead.\")\n\n def __getitem__(self, key: str) -> Any:\n TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation.__key_warning(key)\n return super().__getitem__(key)\n\n def get(self, key: str, default = None) -> Any:\n TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation.__key_warning(key)\n return super().get(key, default)\n\n def __init__(__self__, *,\n s3_configuration: Optional['outputs.TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration'] = None):\n \"\"\"\n :param 'TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3ConfigurationArgs' s3_configuration: Configuration of an S3 location to write error reports for records rejected, asynchronously, during magnetic store writes. See S3 Configuration below for more details.\n \"\"\"\n if s3_configuration is not None:\n pulumi.set(__self__, \"s3_configuration\", s3_configuration)\n\n @property\n @pulumi.getter(name=\"s3Configuration\")\n def s3_configuration(self) -> Optional['outputs.TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration']:\n \"\"\"\n Configuration of an S3 location to write error reports for records rejected, asynchronously, during magnetic store writes. See S3 Configuration below for more details.\n \"\"\"\n return pulumi.get(self, \"s3_configuration\")\n\n\n@pulumi.output_type\nclass TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration(dict):\n @staticmethod\n def __key_warning(key: str):\n suggest = None\n if key == \"bucketName\":\n suggest = \"bucket_name\"\n elif key == \"encryptionOption\":\n suggest = \"encryption_option\"\n elif key == \"kmsKeyId\":\n suggest = \"kms_key_id\"\n elif key == \"objectKeyPrefix\":\n suggest = \"object_key_prefix\"\n\n if suggest:\n pulumi.log.warn(f\"Key '{key}' not found in TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration. Access the value via the '{suggest}' property getter instead.\")\n\n def __getitem__(self, key: str) -> Any:\n TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration.__key_warning(key)\n return super().__getitem__(key)\n\n def get(self, key: str, default = None) -> Any:\n TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration.__key_warning(key)\n return super().get(key, default)\n\n# MASKED: __init__ function (lines 130-148)\n\n @property\n @pulumi.getter(name=\"bucketName\")\n def bucket_name(self) -> Optional[str]:\n \"\"\"\n Bucket name of the customer S3 bucket.\n \"\"\"\n return pulumi.get(self, \"bucket_name\")\n\n @property\n @pulumi.getter(name=\"encryptionOption\")\n def encryption_option(self) -> Optional[str]:\n \"\"\"\n Encryption option for the customer s3 location. Options are S3 server side encryption with an S3-managed key or KMS managed key. Valid values are `SSE_KMS` and `SSE_S3`.\n \"\"\"\n return pulumi.get(self, \"encryption_option\")\n\n @property\n @pulumi.getter(name=\"kmsKeyId\")\n def kms_key_id(self) -> Optional[str]:\n \"\"\"\n KMS key arn for the customer s3 location when encrypting with a KMS managed key.\n \"\"\"\n return pulumi.get(self, \"kms_key_id\")\n\n @property\n @pulumi.getter(name=\"objectKeyPrefix\")\n def object_key_prefix(self) -> Optional[str]:\n \"\"\"\n Object key prefix for the customer S3 location.\n \"\"\"\n return pulumi.get(self, \"object_key_prefix\")\n\n\n@pulumi.output_type\nclass TableRetentionProperties(dict):\n @staticmethod\n def __key_warning(key: str):\n suggest = None\n if key == \"magneticStoreRetentionPeriodInDays\":\n suggest = \"magnetic_store_retention_period_in_days\"\n elif key == \"memoryStoreRetentionPeriodInHours\":\n suggest = \"memory_store_retention_period_in_hours\"\n\n if suggest:\n pulumi.log.warn(f\"Key '{key}' not found in TableRetentionProperties. Access the value via the '{suggest}' property getter instead.\")\n\n def __getitem__(self, key: str) -> Any:\n TableRetentionProperties.__key_warning(key)\n return super().__getitem__(key)\n\n def get(self, key: str, default = None) -> Any:\n TableRetentionProperties.__key_warning(key)\n return super().get(key, default)\n\n def __init__(__self__, *,\n magnetic_store_retention_period_in_days: int,\n memory_store_retention_period_in_hours: int):\n \"\"\"\n :param int magnetic_store_retention_period_in_days: The duration for which data must be stored in the magnetic store. Minimum value of 1. Maximum value of 73000.\n :param int memory_store_retention_period_in_hours: The duration for which data must be stored in the memory store. Minimum value of 1. Maximum value of 8766.\n \"\"\"\n pulumi.set(__self__, \"magnetic_store_retention_period_in_days\", magnetic_store_retention_period_in_days)\n pulumi.set(__self__, \"memory_store_retention_period_in_hours\", memory_store_retention_period_in_hours)\n\n @property\n @pulumi.getter(name=\"magneticStoreRetentionPeriodInDays\")\n def magnetic_store_retention_period_in_days(self) -> int:\n \"\"\"\n The duration for which data must be stored in the magnetic store. Minimum value of 1. Maximum value of 73000.\n \"\"\"\n return pulumi.get(self, \"magnetic_store_retention_period_in_days\")\n\n @property\n @pulumi.getter(name=\"memoryStoreRetentionPeriodInHours\")\n def memory_store_retention_period_in_hours(self) -> int:\n \"\"\"\n The duration for which data must be stored in the memory store. Minimum value of 1. Maximum value of 8766.\n \"\"\"\n return pulumi.get(self, \"memory_store_retention_period_in_hours\")\n\n"},"implementation":{"kind":"string","value":" def __init__(__self__, *,\n bucket_name: Optional[str] = None,\n encryption_option: Optional[str] = None,\n kms_key_id: Optional[str] = None,\n object_key_prefix: Optional[str] = None):\n \"\"\"\n :param str bucket_name: Bucket name of the customer S3 bucket.\n :param str encryption_option: Encryption option for the customer s3 location. Options are S3 server side encryption with an S3-managed key or KMS managed key. Valid values are `SSE_KMS` and `SSE_S3`.\n :param str kms_key_id: KMS key arn for the customer s3 location when encrypting with a KMS managed key.\n :param str object_key_prefix: Object key prefix for the customer S3 location.\n \"\"\"\n if bucket_name is not None:\n pulumi.set(__self__, \"bucket_name\", bucket_name)\n if encryption_option is not None:\n pulumi.set(__self__, \"encryption_option\", encryption_option)\n if kms_key_id is not None:\n pulumi.set(__self__, \"kms_key_id\", kms_key_id)\n if object_key_prefix is not None:\n pulumi.set(__self__, \"object_key_prefix\", object_key_prefix)"},"start_line":{"kind":"number","value":130,"string":"130"},"end_line":{"kind":"number","value":148,"string":"148"},"file_content":{"kind":"string","value":"# coding=utf-8\n# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***\n# *** Do not edit by hand unless you're certain you know what you are doing! ***\n\nimport warnings\nimport pulumi\nimport pulumi.runtime\nfrom typing import Any, Mapping, Optional, Sequence, Union, overload\nfrom .. import _utilities\nfrom . import outputs\n\n__all__ = [\n 'TableMagneticStoreWriteProperties',\n 'TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation',\n 'TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration',\n 'TableRetentionProperties',\n]\n\n@pulumi.output_type\nclass TableMagneticStoreWriteProperties(dict):\n @staticmethod\n def __key_warning(key: str):\n suggest = None\n if key == \"enableMagneticStoreWrites\":\n suggest = \"enable_magnetic_store_writes\"\n elif key == \"magneticStoreRejectedDataLocation\":\n suggest = \"magnetic_store_rejected_data_location\"\n\n if suggest:\n pulumi.log.warn(f\"Key '{key}' not found in TableMagneticStoreWriteProperties. Access the value via the '{suggest}' property getter instead.\")\n\n def __getitem__(self, key: str) -> Any:\n TableMagneticStoreWriteProperties.__key_warning(key)\n return super().__getitem__(key)\n\n def get(self, key: str, default = None) -> Any:\n TableMagneticStoreWriteProperties.__key_warning(key)\n return super().get(key, default)\n\n def __init__(__self__, *,\n enable_magnetic_store_writes: Optional[bool] = None,\n magnetic_store_rejected_data_location: Optional['outputs.TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation'] = None):\n \"\"\"\n :param bool enable_magnetic_store_writes: A flag to enable magnetic store writes.\n :param 'TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationArgs' magnetic_store_rejected_data_location: The location to write error reports for records rejected asynchronously during magnetic store writes. See Magnetic Store Rejected Data Location below for more details.\n \"\"\"\n if enable_magnetic_store_writes is not None:\n pulumi.set(__self__, \"enable_magnetic_store_writes\", enable_magnetic_store_writes)\n if magnetic_store_rejected_data_location is not None:\n pulumi.set(__self__, \"magnetic_store_rejected_data_location\", magnetic_store_rejected_data_location)\n\n @property\n @pulumi.getter(name=\"enableMagneticStoreWrites\")\n def enable_magnetic_store_writes(self) -> Optional[bool]:\n \"\"\"\n A flag to enable magnetic store writes.\n \"\"\"\n return pulumi.get(self, \"enable_magnetic_store_writes\")\n\n @property\n @pulumi.getter(name=\"magneticStoreRejectedDataLocation\")\n def magnetic_store_rejected_data_location(self) -> Optional['outputs.TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation']:\n \"\"\"\n The location to write error reports for records rejected asynchronously during magnetic store writes. See Magnetic Store Rejected Data Location below for more details.\n \"\"\"\n return pulumi.get(self, \"magnetic_store_rejected_data_location\")\n\n\n@pulumi.output_type\nclass TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation(dict):\n @staticmethod\n def __key_warning(key: str):\n suggest = None\n if key == \"s3Configuration\":\n suggest = \"s3_configuration\"\n\n if suggest:\n pulumi.log.warn(f\"Key '{key}' not found in TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation. Access the value via the '{suggest}' property getter instead.\")\n\n def __getitem__(self, key: str) -> Any:\n TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation.__key_warning(key)\n return super().__getitem__(key)\n\n def get(self, key: str, default = None) -> Any:\n TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation.__key_warning(key)\n return super().get(key, default)\n\n def __init__(__self__, *,\n s3_configuration: Optional['outputs.TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration'] = None):\n \"\"\"\n :param 'TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3ConfigurationArgs' s3_configuration: Configuration of an S3 location to write error reports for records rejected, asynchronously, during magnetic store writes. See S3 Configuration below for more details.\n \"\"\"\n if s3_configuration is not None:\n pulumi.set(__self__, \"s3_configuration\", s3_configuration)\n\n @property\n @pulumi.getter(name=\"s3Configuration\")\n def s3_configuration(self) -> Optional['outputs.TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration']:\n \"\"\"\n Configuration of an S3 location to write error reports for records rejected, asynchronously, during magnetic store writes. See S3 Configuration below for more details.\n \"\"\"\n return pulumi.get(self, \"s3_configuration\")\n\n\n@pulumi.output_type\nclass TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration(dict):\n @staticmethod\n def __key_warning(key: str):\n suggest = None\n if key == \"bucketName\":\n suggest = \"bucket_name\"\n elif key == \"encryptionOption\":\n suggest = \"encryption_option\"\n elif key == \"kmsKeyId\":\n suggest = \"kms_key_id\"\n elif key == \"objectKeyPrefix\":\n suggest = \"object_key_prefix\"\n\n if suggest:\n pulumi.log.warn(f\"Key '{key}' not found in TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration. Access the value via the '{suggest}' property getter instead.\")\n\n def __getitem__(self, key: str) -> Any:\n TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration.__key_warning(key)\n return super().__getitem__(key)\n\n def get(self, key: str, default = None) -> Any:\n TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationS3Configuration.__key_warning(key)\n return super().get(key, default)\n\n def __init__(__self__, *,\n bucket_name: Optional[str] = None,\n encryption_option: Optional[str] = None,\n kms_key_id: Optional[str] = None,\n object_key_prefix: Optional[str] = None):\n \"\"\"\n :param str bucket_name: Bucket name of the customer S3 bucket.\n :param str encryption_option: Encryption option for the customer s3 location. Options are S3 server side encryption with an S3-managed key or KMS managed key. Valid values are `SSE_KMS` and `SSE_S3`.\n :param str kms_key_id: KMS key arn for the customer s3 location when encrypting with a KMS managed key.\n :param str object_key_prefix: Object key prefix for the customer S3 location.\n \"\"\"\n if bucket_name is not None:\n pulumi.set(__self__, \"bucket_name\", bucket_name)\n if encryption_option is not None:\n pulumi.set(__self__, \"encryption_option\", encryption_option)\n if kms_key_id is not None:\n pulumi.set(__self__, \"kms_key_id\", kms_key_id)\n if object_key_prefix is not None:\n pulumi.set(__self__, \"object_key_prefix\", object_key_prefix)\n\n @property\n @pulumi.getter(name=\"bucketName\")\n def bucket_name(self) -> Optional[str]:\n \"\"\"\n Bucket name of the customer S3 bucket.\n \"\"\"\n return pulumi.get(self, \"bucket_name\")\n\n @property\n @pulumi.getter(name=\"encryptionOption\")\n def encryption_option(self) -> Optional[str]:\n \"\"\"\n Encryption option for the customer s3 location. Options are S3 server side encryption with an S3-managed key or KMS managed key. Valid values are `SSE_KMS` and `SSE_S3`.\n \"\"\"\n return pulumi.get(self, \"encryption_option\")\n\n @property\n @pulumi.getter(name=\"kmsKeyId\")\n def kms_key_id(self) -> Optional[str]:\n \"\"\"\n KMS key arn for the customer s3 location when encrypting with a KMS managed key.\n \"\"\"\n return pulumi.get(self, \"kms_key_id\")\n\n @property\n @pulumi.getter(name=\"objectKeyPrefix\")\n def object_key_prefix(self) -> Optional[str]:\n \"\"\"\n Object key prefix for the customer S3 location.\n \"\"\"\n return pulumi.get(self, \"object_key_prefix\")\n\n\n@pulumi.output_type\nclass TableRetentionProperties(dict):\n @staticmethod\n def __key_warning(key: str):\n suggest = None\n if key == \"magneticStoreRetentionPeriodInDays\":\n suggest = \"magnetic_store_retention_period_in_days\"\n elif key == \"memoryStoreRetentionPeriodInHours\":\n suggest = \"memory_store_retention_period_in_hours\"\n\n if suggest:\n pulumi.log.warn(f\"Key '{key}' not found in TableRetentionProperties. Access the value via the '{suggest}' property getter instead.\")\n\n def __getitem__(self, key: str) -> Any:\n TableRetentionProperties.__key_warning(key)\n return super().__getitem__(key)\n\n def get(self, key: str, default = None) -> Any:\n TableRetentionProperties.__key_warning(key)\n return super().get(key, default)\n\n def __init__(__self__, *,\n magnetic_store_retention_period_in_days: int,\n memory_store_retention_period_in_hours: int):\n \"\"\"\n :param int magnetic_store_retention_period_in_days: The duration for which data must be stored in the magnetic store. Minimum value of 1. Maximum value of 73000.\n :param int memory_store_retention_period_in_hours: The duration for which data must be stored in the memory store. Minimum value of 1. Maximum value of 8766.\n \"\"\"\n pulumi.set(__self__, \"magnetic_store_retention_period_in_days\", magnetic_store_retention_period_in_days)\n pulumi.set(__self__, \"memory_store_retention_period_in_hours\", memory_store_retention_period_in_hours)\n\n @property\n @pulumi.getter(name=\"magneticStoreRetentionPeriodInDays\")\n def magnetic_store_retention_period_in_days(self) -> int:\n \"\"\"\n The duration for which data must be stored in the magnetic store. Minimum value of 1. Maximum value of 73000.\n \"\"\"\n return pulumi.get(self, \"magnetic_store_retention_period_in_days\")\n\n @property\n @pulumi.getter(name=\"memoryStoreRetentionPeriodInHours\")\n def memory_store_retention_period_in_hours(self) -> int:\n \"\"\"\n The duration for which data must be stored in the memory store. Minimum value of 1. Maximum value of 8766.\n \"\"\"\n return pulumi.get(self, \"memory_store_retention_period_in_hours\")\n\n\n"}}},{"rowIdx":81,"cells":{"function_name":{"kind":"string","value":"post"},"docstring":{"kind":"string","value":"Handle the POST request and sign up the user if form validation passes\n\nReturns:\n A redirect or a template with the validation errors"},"masked_code":{"kind":"string","value":"from flask import render_template, flash, redirect, url_for, request\nfrom flask.views import MethodView\n\nfrom app.middleware import auth\nfrom app.models.user import User\nfrom app.validators.register_form import RegisterForm\nfrom app.services import avatar_service\n\n\nclass RegisterController(MethodView):\n @auth.optional\n def get(self):\n \"\"\"\n Show register form\n\n Returns:\n Register template with form\n \"\"\"\n return render_template('auth/register.html', form=RegisterForm())\n\n# MASKED: post function (lines 21-45)"},"implementation":{"kind":"string","value":" @auth.optional\n def post(self):\n \"\"\"\n Handle the POST request and sign up the user if form validation passes\n\n Returns:\n A redirect or a template with the validation errors\n \"\"\"\n form = RegisterForm()\n\n if form.validate_on_submit():\n form.validate_username(form.username)\n\n avatar = 'no-image.png'\n\n if 'avatar' in request.files and request.files['avatar']:\n avatar = avatar_service.save(form.avatar.data)\n\n User.create(form.username.data, form.password.data, avatar)\n\n flash('Your account has been created. You may now login.', 'info')\n\n return redirect(url_for('login'))\n\n return render_template('auth/register.html', form=form)"},"start_line":{"kind":"number","value":21,"string":"21"},"end_line":{"kind":"number","value":45,"string":"45"},"file_content":{"kind":"string","value":"from flask import render_template, flash, redirect, url_for, request\nfrom flask.views import MethodView\n\nfrom app.middleware import auth\nfrom app.models.user import User\nfrom app.validators.register_form import RegisterForm\nfrom app.services import avatar_service\n\n\nclass RegisterController(MethodView):\n @auth.optional\n def get(self):\n \"\"\"\n Show register form\n\n Returns:\n Register template with form\n \"\"\"\n return render_template('auth/register.html', form=RegisterForm())\n\n @auth.optional\n def post(self):\n \"\"\"\n Handle the POST request and sign up the user if form validation passes\n\n Returns:\n A redirect or a template with the validation errors\n \"\"\"\n form = RegisterForm()\n\n if form.validate_on_submit():\n form.validate_username(form.username)\n\n avatar = 'no-image.png'\n\n if 'avatar' in request.files and request.files['avatar']:\n avatar = avatar_service.save(form.avatar.data)\n\n User.create(form.username.data, form.password.data, avatar)\n\n flash('Your account has been created. You may now login.', 'info')\n\n return redirect(url_for('login'))\n\n return render_template('auth/register.html', form=form)\n"}}},{"rowIdx":82,"cells":{"function_name":{"kind":"string","value":"__init__"},"docstring":{"kind":"string","value":"Initializer for linear model.\n\nArgs:\n num_inputs: the dimension of input data.\n num_hidden_layers: the number of hidden layers.\n num_inner_features: the number of features in the hidden layers"},"masked_code":{"kind":"string","value":"\"\"\"Polynomial model class used by agents for building stuff.\n\"\"\"\nfrom torch import nn, optim\n\nimport torch\nimport torch.nn.functional as F\n\nfrom stock_trading_backend.agent.model import Model\n\n\nclass NNModel(nn.Module):\n \"\"\"Torch neural network model.\n \"\"\"\n# MASKED: __init__ function (lines 14-29)\n\n def forward(self, input_tensor):\n \"\"\"Forward pass on the neural network model.\n\n Args:\n input_tensor: the input tensor.\n\n Returns:\n Tensor with model results.\n \"\"\"\n output = F.relu(self.input_layer(input_tensor))\n output = self.hidden_layers(output)\n output = self.output_layer(output)\n return output\n\n\nclass NeuralNetworkModel(Model):\n \"\"\"Neural netowrk model class.\n \"\"\"\n name = \"neural_network_model\"\n\n def __init__(self, learning_rate=1e-3, num_hidden_layers=1, num_inner_features=100):\n \"\"\"Initializer for model class.\n\n Args:\n learning_rate: the learning rate of the model.\n num_hidden_layers: number of hidden layers in the network.\n num_inner_features: number of features in the hidden layers.\n \"\"\"\n super(NeuralNetworkModel, self).__init__()\n self.model = None\n self.optimizer = None\n self.criterion = nn.MSELoss()\n self.learning_rate = learning_rate\n self.num_hidden_layers = num_hidden_layers\n self.num_inner_features = num_inner_features\n self.id_str = \"{}_{}_{}_{}\".format(self.name, learning_rate, num_hidden_layers,\n num_inner_features)\n\n def _init_model(self, num_inputs):\n \"\"\"Initializes internal linear model.\n\n Args:\n num_inputs: number of inputs that model will have.\n \"\"\"\n self.model = NNModel(num_inputs, self.num_hidden_layers, self.num_inner_features)\n self.optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate)\n\n def _predict(self, state_action_tensor):\n \"\"\"Use provided information to make a prediction.\n\n Args:\n state_action_tensor: pytorch tensor with state-action values.\n\n Returns:\n Predicted values for observation-action tensors.\n \"\"\"\n if self.model is None:\n self._init_model(state_action_tensor.shape[1])\n return self.model(state_action_tensor).detach().reshape(-1)\n\n def _train(self, state_action_tensor, expected_values_tensor):\n \"\"\"Train the model for 1 epoch.\n\n Args:\n state_action_tensor: pytorch tensor with state-action expected_values.\n expected_values: pytorch tensor with expected values for each state-action.\n\n Returns:\n The loss before trainig.\n \"\"\"\n if self.model is None:\n self._init_model(state_action_tensor.shape[1])\n\n self.optimizer.zero_grad()\n output = self.model(state_action_tensor)\n loss = self.criterion(output, expected_values_tensor)\n loss_value = loss.data.item()\n loss.backward()\n self.optimizer.step()\n return loss_value"},"implementation":{"kind":"string","value":" def __init__(self, num_inputs, num_hidden_layers, num_inner_features):\n \"\"\"Initializer for linear model.\n\n Args:\n num_inputs: the dimension of input data.\n num_hidden_layers: the number of hidden layers.\n num_inner_features: the number of features in the hidden layers\n \"\"\"\n super(NNModel, self).__init__()\n self.input_layer = nn.Linear(num_inputs, num_inner_features)\n hidden_layers = []\n for _ in range(num_hidden_layers):\n hidden_layers.append(nn.Linear(num_inner_features, num_inner_features))\n hidden_layers.append(nn.ReLU())\n self.hidden_layers = nn.Sequential(*hidden_layers)\n self.output_layer = nn.Linear(num_inner_features, 1)"},"start_line":{"kind":"number","value":14,"string":"14"},"end_line":{"kind":"number","value":29,"string":"29"},"file_content":{"kind":"string","value":"\"\"\"Polynomial model class used by agents for building stuff.\n\"\"\"\nfrom torch import nn, optim\n\nimport torch\nimport torch.nn.functional as F\n\nfrom stock_trading_backend.agent.model import Model\n\n\nclass NNModel(nn.Module):\n \"\"\"Torch neural network model.\n \"\"\"\n def __init__(self, num_inputs, num_hidden_layers, num_inner_features):\n \"\"\"Initializer for linear model.\n\n Args:\n num_inputs: the dimension of input data.\n num_hidden_layers: the number of hidden layers.\n num_inner_features: the number of features in the hidden layers\n \"\"\"\n super(NNModel, self).__init__()\n self.input_layer = nn.Linear(num_inputs, num_inner_features)\n hidden_layers = []\n for _ in range(num_hidden_layers):\n hidden_layers.append(nn.Linear(num_inner_features, num_inner_features))\n hidden_layers.append(nn.ReLU())\n self.hidden_layers = nn.Sequential(*hidden_layers)\n self.output_layer = nn.Linear(num_inner_features, 1)\n\n def forward(self, input_tensor):\n \"\"\"Forward pass on the neural network model.\n\n Args:\n input_tensor: the input tensor.\n\n Returns:\n Tensor with model results.\n \"\"\"\n output = F.relu(self.input_layer(input_tensor))\n output = self.hidden_layers(output)\n output = self.output_layer(output)\n return output\n\n\nclass NeuralNetworkModel(Model):\n \"\"\"Neural netowrk model class.\n \"\"\"\n name = \"neural_network_model\"\n\n def __init__(self, learning_rate=1e-3, num_hidden_layers=1, num_inner_features=100):\n \"\"\"Initializer for model class.\n\n Args:\n learning_rate: the learning rate of the model.\n num_hidden_layers: number of hidden layers in the network.\n num_inner_features: number of features in the hidden layers.\n \"\"\"\n super(NeuralNetworkModel, self).__init__()\n self.model = None\n self.optimizer = None\n self.criterion = nn.MSELoss()\n self.learning_rate = learning_rate\n self.num_hidden_layers = num_hidden_layers\n self.num_inner_features = num_inner_features\n self.id_str = \"{}_{}_{}_{}\".format(self.name, learning_rate, num_hidden_layers,\n num_inner_features)\n\n def _init_model(self, num_inputs):\n \"\"\"Initializes internal linear model.\n\n Args:\n num_inputs: number of inputs that model will have.\n \"\"\"\n self.model = NNModel(num_inputs, self.num_hidden_layers, self.num_inner_features)\n self.optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate)\n\n def _predict(self, state_action_tensor):\n \"\"\"Use provided information to make a prediction.\n\n Args:\n state_action_tensor: pytorch tensor with state-action values.\n\n Returns:\n Predicted values for observation-action tensors.\n \"\"\"\n if self.model is None:\n self._init_model(state_action_tensor.shape[1])\n return self.model(state_action_tensor).detach().reshape(-1)\n\n def _train(self, state_action_tensor, expected_values_tensor):\n \"\"\"Train the model for 1 epoch.\n\n Args:\n state_action_tensor: pytorch tensor with state-action expected_values.\n expected_values: pytorch tensor with expected values for each state-action.\n\n Returns:\n The loss before trainig.\n \"\"\"\n if self.model is None:\n self._init_model(state_action_tensor.shape[1])\n\n self.optimizer.zero_grad()\n output = self.model(state_action_tensor)\n loss = self.criterion(output, expected_values_tensor)\n loss_value = loss.data.item()\n loss.backward()\n self.optimizer.step()\n return loss_value\n"}}},{"rowIdx":83,"cells":{"function_name":{"kind":"string","value":"forward"},"docstring":{"kind":"string","value":"Forward pass on the neural network model.\n\nArgs:\n input_tensor: the input tensor.\n\nReturns:\n Tensor with model results."},"masked_code":{"kind":"string","value":"\"\"\"Polynomial model class used by agents for building stuff.\n\"\"\"\nfrom torch import nn, optim\n\nimport torch\nimport torch.nn.functional as F\n\nfrom stock_trading_backend.agent.model import Model\n\n\nclass NNModel(nn.Module):\n \"\"\"Torch neural network model.\n \"\"\"\n def __init__(self, num_inputs, num_hidden_layers, num_inner_features):\n \"\"\"Initializer for linear model.\n\n Args:\n num_inputs: the dimension of input data.\n num_hidden_layers: the number of hidden layers.\n num_inner_features: the number of features in the hidden layers\n \"\"\"\n super(NNModel, self).__init__()\n self.input_layer = nn.Linear(num_inputs, num_inner_features)\n hidden_layers = []\n for _ in range(num_hidden_layers):\n hidden_layers.append(nn.Linear(num_inner_features, num_inner_features))\n hidden_layers.append(nn.ReLU())\n self.hidden_layers = nn.Sequential(*hidden_layers)\n self.output_layer = nn.Linear(num_inner_features, 1)\n\n# MASKED: forward function (lines 31-43)\n\n\nclass NeuralNetworkModel(Model):\n \"\"\"Neural netowrk model class.\n \"\"\"\n name = \"neural_network_model\"\n\n def __init__(self, learning_rate=1e-3, num_hidden_layers=1, num_inner_features=100):\n \"\"\"Initializer for model class.\n\n Args:\n learning_rate: the learning rate of the model.\n num_hidden_layers: number of hidden layers in the network.\n num_inner_features: number of features in the hidden layers.\n \"\"\"\n super(NeuralNetworkModel, self).__init__()\n self.model = None\n self.optimizer = None\n self.criterion = nn.MSELoss()\n self.learning_rate = learning_rate\n self.num_hidden_layers = num_hidden_layers\n self.num_inner_features = num_inner_features\n self.id_str = \"{}_{}_{}_{}\".format(self.name, learning_rate, num_hidden_layers,\n num_inner_features)\n\n def _init_model(self, num_inputs):\n \"\"\"Initializes internal linear model.\n\n Args:\n num_inputs: number of inputs that model will have.\n \"\"\"\n self.model = NNModel(num_inputs, self.num_hidden_layers, self.num_inner_features)\n self.optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate)\n\n def _predict(self, state_action_tensor):\n \"\"\"Use provided information to make a prediction.\n\n Args:\n state_action_tensor: pytorch tensor with state-action values.\n\n Returns:\n Predicted values for observation-action tensors.\n \"\"\"\n if self.model is None:\n self._init_model(state_action_tensor.shape[1])\n return self.model(state_action_tensor).detach().reshape(-1)\n\n def _train(self, state_action_tensor, expected_values_tensor):\n \"\"\"Train the model for 1 epoch.\n\n Args:\n state_action_tensor: pytorch tensor with state-action expected_values.\n expected_values: pytorch tensor with expected values for each state-action.\n\n Returns:\n The loss before trainig.\n \"\"\"\n if self.model is None:\n self._init_model(state_action_tensor.shape[1])\n\n self.optimizer.zero_grad()\n output = self.model(state_action_tensor)\n loss = self.criterion(output, expected_values_tensor)\n loss_value = loss.data.item()\n loss.backward()\n self.optimizer.step()\n return loss_value"},"implementation":{"kind":"string","value":" def forward(self, input_tensor):\n \"\"\"Forward pass on the neural network model.\n\n Args:\n input_tensor: the input tensor.\n\n Returns:\n Tensor with model results.\n \"\"\"\n output = F.relu(self.input_layer(input_tensor))\n output = self.hidden_layers(output)\n output = self.output_layer(output)\n return output"},"start_line":{"kind":"number","value":31,"string":"31"},"end_line":{"kind":"number","value":43,"string":"43"},"file_content":{"kind":"string","value":"\"\"\"Polynomial model class used by agents for building stuff.\n\"\"\"\nfrom torch import nn, optim\n\nimport torch\nimport torch.nn.functional as F\n\nfrom stock_trading_backend.agent.model import Model\n\n\nclass NNModel(nn.Module):\n \"\"\"Torch neural network model.\n \"\"\"\n def __init__(self, num_inputs, num_hidden_layers, num_inner_features):\n \"\"\"Initializer for linear model.\n\n Args:\n num_inputs: the dimension of input data.\n num_hidden_layers: the number of hidden layers.\n num_inner_features: the number of features in the hidden layers\n \"\"\"\n super(NNModel, self).__init__()\n self.input_layer = nn.Linear(num_inputs, num_inner_features)\n hidden_layers = []\n for _ in range(num_hidden_layers):\n hidden_layers.append(nn.Linear(num_inner_features, num_inner_features))\n hidden_layers.append(nn.ReLU())\n self.hidden_layers = nn.Sequential(*hidden_layers)\n self.output_layer = nn.Linear(num_inner_features, 1)\n\n def forward(self, input_tensor):\n \"\"\"Forward pass on the neural network model.\n\n Args:\n input_tensor: the input tensor.\n\n Returns:\n Tensor with model results.\n \"\"\"\n output = F.relu(self.input_layer(input_tensor))\n output = self.hidden_layers(output)\n output = self.output_layer(output)\n return output\n\n\nclass NeuralNetworkModel(Model):\n \"\"\"Neural netowrk model class.\n \"\"\"\n name = \"neural_network_model\"\n\n def __init__(self, learning_rate=1e-3, num_hidden_layers=1, num_inner_features=100):\n \"\"\"Initializer for model class.\n\n Args:\n learning_rate: the learning rate of the model.\n num_hidden_layers: number of hidden layers in the network.\n num_inner_features: number of features in the hidden layers.\n \"\"\"\n super(NeuralNetworkModel, self).__init__()\n self.model = None\n self.optimizer = None\n self.criterion = nn.MSELoss()\n self.learning_rate = learning_rate\n self.num_hidden_layers = num_hidden_layers\n self.num_inner_features = num_inner_features\n self.id_str = \"{}_{}_{}_{}\".format(self.name, learning_rate, num_hidden_layers,\n num_inner_features)\n\n def _init_model(self, num_inputs):\n \"\"\"Initializes internal linear model.\n\n Args:\n num_inputs: number of inputs that model will have.\n \"\"\"\n self.model = NNModel(num_inputs, self.num_hidden_layers, self.num_inner_features)\n self.optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate)\n\n def _predict(self, state_action_tensor):\n \"\"\"Use provided information to make a prediction.\n\n Args:\n state_action_tensor: pytorch tensor with state-action values.\n\n Returns:\n Predicted values for observation-action tensors.\n \"\"\"\n if self.model is None:\n self._init_model(state_action_tensor.shape[1])\n return self.model(state_action_tensor).detach().reshape(-1)\n\n def _train(self, state_action_tensor, expected_values_tensor):\n \"\"\"Train the model for 1 epoch.\n\n Args:\n state_action_tensor: pytorch tensor with state-action expected_values.\n expected_values: pytorch tensor with expected values for each state-action.\n\n Returns:\n The loss before trainig.\n \"\"\"\n if self.model is None:\n self._init_model(state_action_tensor.shape[1])\n\n self.optimizer.zero_grad()\n output = self.model(state_action_tensor)\n loss = self.criterion(output, expected_values_tensor)\n loss_value = loss.data.item()\n loss.backward()\n self.optimizer.step()\n return loss_value\n"}}},{"rowIdx":84,"cells":{"function_name":{"kind":"string","value":"__init__"},"docstring":{"kind":"string","value":"Initializer for model class.\n\nArgs:\n learning_rate: the learning rate of the model.\n num_hidden_layers: number of hidden layers in the network.\n num_inner_features: number of features in the hidden layers."},"masked_code":{"kind":"string","value":"\"\"\"Polynomial model class used by agents for building stuff.\n\"\"\"\nfrom torch import nn, optim\n\nimport torch\nimport torch.nn.functional as F\n\nfrom stock_trading_backend.agent.model import Model\n\n\nclass NNModel(nn.Module):\n \"\"\"Torch neural network model.\n \"\"\"\n def __init__(self, num_inputs, num_hidden_layers, num_inner_features):\n \"\"\"Initializer for linear model.\n\n Args:\n num_inputs: the dimension of input data.\n num_hidden_layers: the number of hidden layers.\n num_inner_features: the number of features in the hidden layers\n \"\"\"\n super(NNModel, self).__init__()\n self.input_layer = nn.Linear(num_inputs, num_inner_features)\n hidden_layers = []\n for _ in range(num_hidden_layers):\n hidden_layers.append(nn.Linear(num_inner_features, num_inner_features))\n hidden_layers.append(nn.ReLU())\n self.hidden_layers = nn.Sequential(*hidden_layers)\n self.output_layer = nn.Linear(num_inner_features, 1)\n\n def forward(self, input_tensor):\n \"\"\"Forward pass on the neural network model.\n\n Args:\n input_tensor: the input tensor.\n\n Returns:\n Tensor with model results.\n \"\"\"\n output = F.relu(self.input_layer(input_tensor))\n output = self.hidden_layers(output)\n output = self.output_layer(output)\n return output\n\n\nclass NeuralNetworkModel(Model):\n \"\"\"Neural netowrk model class.\n \"\"\"\n name = \"neural_network_model\"\n\n# MASKED: __init__ function (lines 51-67)\n\n def _init_model(self, num_inputs):\n \"\"\"Initializes internal linear model.\n\n Args:\n num_inputs: number of inputs that model will have.\n \"\"\"\n self.model = NNModel(num_inputs, self.num_hidden_layers, self.num_inner_features)\n self.optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate)\n\n def _predict(self, state_action_tensor):\n \"\"\"Use provided information to make a prediction.\n\n Args:\n state_action_tensor: pytorch tensor with state-action values.\n\n Returns:\n Predicted values for observation-action tensors.\n \"\"\"\n if self.model is None:\n self._init_model(state_action_tensor.shape[1])\n return self.model(state_action_tensor).detach().reshape(-1)\n\n def _train(self, state_action_tensor, expected_values_tensor):\n \"\"\"Train the model for 1 epoch.\n\n Args:\n state_action_tensor: pytorch tensor with state-action expected_values.\n expected_values: pytorch tensor with expected values for each state-action.\n\n Returns:\n The loss before trainig.\n \"\"\"\n if self.model is None:\n self._init_model(state_action_tensor.shape[1])\n\n self.optimizer.zero_grad()\n output = self.model(state_action_tensor)\n loss = self.criterion(output, expected_values_tensor)\n loss_value = loss.data.item()\n loss.backward()\n self.optimizer.step()\n return loss_value"},"implementation":{"kind":"string","value":" def __init__(self, learning_rate=1e-3, num_hidden_layers=1, num_inner_features=100):\n \"\"\"Initializer for model class.\n\n Args:\n learning_rate: the learning rate of the model.\n num_hidden_layers: number of hidden layers in the network.\n num_inner_features: number of features in the hidden layers.\n \"\"\"\n super(NeuralNetworkModel, self).__init__()\n self.model = None\n self.optimizer = None\n self.criterion = nn.MSELoss()\n self.learning_rate = learning_rate\n self.num_hidden_layers = num_hidden_layers\n self.num_inner_features = num_inner_features\n self.id_str = \"{}_{}_{}_{}\".format(self.name, learning_rate, num_hidden_layers,\n num_inner_features)"},"start_line":{"kind":"number","value":51,"string":"51"},"end_line":{"kind":"number","value":67,"string":"67"},"file_content":{"kind":"string","value":"\"\"\"Polynomial model class used by agents for building stuff.\n\"\"\"\nfrom torch import nn, optim\n\nimport torch\nimport torch.nn.functional as F\n\nfrom stock_trading_backend.agent.model import Model\n\n\nclass NNModel(nn.Module):\n \"\"\"Torch neural network model.\n \"\"\"\n def __init__(self, num_inputs, num_hidden_layers, num_inner_features):\n \"\"\"Initializer for linear model.\n\n Args:\n num_inputs: the dimension of input data.\n num_hidden_layers: the number of hidden layers.\n num_inner_features: the number of features in the hidden layers\n \"\"\"\n super(NNModel, self).__init__()\n self.input_layer = nn.Linear(num_inputs, num_inner_features)\n hidden_layers = []\n for _ in range(num_hidden_layers):\n hidden_layers.append(nn.Linear(num_inner_features, num_inner_features))\n hidden_layers.append(nn.ReLU())\n self.hidden_layers = nn.Sequential(*hidden_layers)\n self.output_layer = nn.Linear(num_inner_features, 1)\n\n def forward(self, input_tensor):\n \"\"\"Forward pass on the neural network model.\n\n Args:\n input_tensor: the input tensor.\n\n Returns:\n Tensor with model results.\n \"\"\"\n output = F.relu(self.input_layer(input_tensor))\n output = self.hidden_layers(output)\n output = self.output_layer(output)\n return output\n\n\nclass NeuralNetworkModel(Model):\n \"\"\"Neural netowrk model class.\n \"\"\"\n name = \"neural_network_model\"\n\n def __init__(self, learning_rate=1e-3, num_hidden_layers=1, num_inner_features=100):\n \"\"\"Initializer for model class.\n\n Args:\n learning_rate: the learning rate of the model.\n num_hidden_layers: number of hidden layers in the network.\n num_inner_features: number of features in the hidden layers.\n \"\"\"\n super(NeuralNetworkModel, self).__init__()\n self.model = None\n self.optimizer = None\n self.criterion = nn.MSELoss()\n self.learning_rate = learning_rate\n self.num_hidden_layers = num_hidden_layers\n self.num_inner_features = num_inner_features\n self.id_str = \"{}_{}_{}_{}\".format(self.name, learning_rate, num_hidden_layers,\n num_inner_features)\n\n def _init_model(self, num_inputs):\n \"\"\"Initializes internal linear model.\n\n Args:\n num_inputs: number of inputs that model will have.\n \"\"\"\n self.model = NNModel(num_inputs, self.num_hidden_layers, self.num_inner_features)\n self.optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate)\n\n def _predict(self, state_action_tensor):\n \"\"\"Use provided information to make a prediction.\n\n Args:\n state_action_tensor: pytorch tensor with state-action values.\n\n Returns:\n Predicted values for observation-action tensors.\n \"\"\"\n if self.model is None:\n self._init_model(state_action_tensor.shape[1])\n return self.model(state_action_tensor).detach().reshape(-1)\n\n def _train(self, state_action_tensor, expected_values_tensor):\n \"\"\"Train the model for 1 epoch.\n\n Args:\n state_action_tensor: pytorch tensor with state-action expected_values.\n expected_values: pytorch tensor with expected values for each state-action.\n\n Returns:\n The loss before trainig.\n \"\"\"\n if self.model is None:\n self._init_model(state_action_tensor.shape[1])\n\n self.optimizer.zero_grad()\n output = self.model(state_action_tensor)\n loss = self.criterion(output, expected_values_tensor)\n loss_value = loss.data.item()\n loss.backward()\n self.optimizer.step()\n return loss_value\n"}}},{"rowIdx":85,"cells":{"function_name":{"kind":"string","value":"_predict"},"docstring":{"kind":"string","value":"Use provided information to make a prediction.\n\nArgs:\n state_action_tensor: pytorch tensor with state-action values.\n\nReturns:\n Predicted values for observation-action tensors."},"masked_code":{"kind":"string","value":"\"\"\"Polynomial model class used by agents for building stuff.\n\"\"\"\nfrom torch import nn, optim\n\nimport torch\nimport torch.nn.functional as F\n\nfrom stock_trading_backend.agent.model import Model\n\n\nclass NNModel(nn.Module):\n \"\"\"Torch neural network model.\n \"\"\"\n def __init__(self, num_inputs, num_hidden_layers, num_inner_features):\n \"\"\"Initializer for linear model.\n\n Args:\n num_inputs: the dimension of input data.\n num_hidden_layers: the number of hidden layers.\n num_inner_features: the number of features in the hidden layers\n \"\"\"\n super(NNModel, self).__init__()\n self.input_layer = nn.Linear(num_inputs, num_inner_features)\n hidden_layers = []\n for _ in range(num_hidden_layers):\n hidden_layers.append(nn.Linear(num_inner_features, num_inner_features))\n hidden_layers.append(nn.ReLU())\n self.hidden_layers = nn.Sequential(*hidden_layers)\n self.output_layer = nn.Linear(num_inner_features, 1)\n\n def forward(self, input_tensor):\n \"\"\"Forward pass on the neural network model.\n\n Args:\n input_tensor: the input tensor.\n\n Returns:\n Tensor with model results.\n \"\"\"\n output = F.relu(self.input_layer(input_tensor))\n output = self.hidden_layers(output)\n output = self.output_layer(output)\n return output\n\n\nclass NeuralNetworkModel(Model):\n \"\"\"Neural netowrk model class.\n \"\"\"\n name = \"neural_network_model\"\n\n def __init__(self, learning_rate=1e-3, num_hidden_layers=1, num_inner_features=100):\n \"\"\"Initializer for model class.\n\n Args:\n learning_rate: the learning rate of the model.\n num_hidden_layers: number of hidden layers in the network.\n num_inner_features: number of features in the hidden layers.\n \"\"\"\n super(NeuralNetworkModel, self).__init__()\n self.model = None\n self.optimizer = None\n self.criterion = nn.MSELoss()\n self.learning_rate = learning_rate\n self.num_hidden_layers = num_hidden_layers\n self.num_inner_features = num_inner_features\n self.id_str = \"{}_{}_{}_{}\".format(self.name, learning_rate, num_hidden_layers,\n num_inner_features)\n\n def _init_model(self, num_inputs):\n \"\"\"Initializes internal linear model.\n\n Args:\n num_inputs: number of inputs that model will have.\n \"\"\"\n self.model = NNModel(num_inputs, self.num_hidden_layers, self.num_inner_features)\n self.optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate)\n\n# MASKED: _predict function (lines 78-89)\n\n def _train(self, state_action_tensor, expected_values_tensor):\n \"\"\"Train the model for 1 epoch.\n\n Args:\n state_action_tensor: pytorch tensor with state-action expected_values.\n expected_values: pytorch tensor with expected values for each state-action.\n\n Returns:\n The loss before trainig.\n \"\"\"\n if self.model is None:\n self._init_model(state_action_tensor.shape[1])\n\n self.optimizer.zero_grad()\n output = self.model(state_action_tensor)\n loss = self.criterion(output, expected_values_tensor)\n loss_value = loss.data.item()\n loss.backward()\n self.optimizer.step()\n return loss_value"},"implementation":{"kind":"string","value":" def _predict(self, state_action_tensor):\n \"\"\"Use provided information to make a prediction.\n\n Args:\n state_action_tensor: pytorch tensor with state-action values.\n\n Returns:\n Predicted values for observation-action tensors.\n \"\"\"\n if self.model is None:\n self._init_model(state_action_tensor.shape[1])\n return self.model(state_action_tensor).detach().reshape(-1)"},"start_line":{"kind":"number","value":78,"string":"78"},"end_line":{"kind":"number","value":89,"string":"89"},"file_content":{"kind":"string","value":"\"\"\"Polynomial model class used by agents for building stuff.\n\"\"\"\nfrom torch import nn, optim\n\nimport torch\nimport torch.nn.functional as F\n\nfrom stock_trading_backend.agent.model import Model\n\n\nclass NNModel(nn.Module):\n \"\"\"Torch neural network model.\n \"\"\"\n def __init__(self, num_inputs, num_hidden_layers, num_inner_features):\n \"\"\"Initializer for linear model.\n\n Args:\n num_inputs: the dimension of input data.\n num_hidden_layers: the number of hidden layers.\n num_inner_features: the number of features in the hidden layers\n \"\"\"\n super(NNModel, self).__init__()\n self.input_layer = nn.Linear(num_inputs, num_inner_features)\n hidden_layers = []\n for _ in range(num_hidden_layers):\n hidden_layers.append(nn.Linear(num_inner_features, num_inner_features))\n hidden_layers.append(nn.ReLU())\n self.hidden_layers = nn.Sequential(*hidden_layers)\n self.output_layer = nn.Linear(num_inner_features, 1)\n\n def forward(self, input_tensor):\n \"\"\"Forward pass on the neural network model.\n\n Args:\n input_tensor: the input tensor.\n\n Returns:\n Tensor with model results.\n \"\"\"\n output = F.relu(self.input_layer(input_tensor))\n output = self.hidden_layers(output)\n output = self.output_layer(output)\n return output\n\n\nclass NeuralNetworkModel(Model):\n \"\"\"Neural netowrk model class.\n \"\"\"\n name = \"neural_network_model\"\n\n def __init__(self, learning_rate=1e-3, num_hidden_layers=1, num_inner_features=100):\n \"\"\"Initializer for model class.\n\n Args:\n learning_rate: the learning rate of the model.\n num_hidden_layers: number of hidden layers in the network.\n num_inner_features: number of features in the hidden layers.\n \"\"\"\n super(NeuralNetworkModel, self).__init__()\n self.model = None\n self.optimizer = None\n self.criterion = nn.MSELoss()\n self.learning_rate = learning_rate\n self.num_hidden_layers = num_hidden_layers\n self.num_inner_features = num_inner_features\n self.id_str = \"{}_{}_{}_{}\".format(self.name, learning_rate, num_hidden_layers,\n num_inner_features)\n\n def _init_model(self, num_inputs):\n \"\"\"Initializes internal linear model.\n\n Args:\n num_inputs: number of inputs that model will have.\n \"\"\"\n self.model = NNModel(num_inputs, self.num_hidden_layers, self.num_inner_features)\n self.optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate)\n\n def _predict(self, state_action_tensor):\n \"\"\"Use provided information to make a prediction.\n\n Args:\n state_action_tensor: pytorch tensor with state-action values.\n\n Returns:\n Predicted values for observation-action tensors.\n \"\"\"\n if self.model is None:\n self._init_model(state_action_tensor.shape[1])\n return self.model(state_action_tensor).detach().reshape(-1)\n\n def _train(self, state_action_tensor, expected_values_tensor):\n \"\"\"Train the model for 1 epoch.\n\n Args:\n state_action_tensor: pytorch tensor with state-action expected_values.\n expected_values: pytorch tensor with expected values for each state-action.\n\n Returns:\n The loss before trainig.\n \"\"\"\n if self.model is None:\n self._init_model(state_action_tensor.shape[1])\n\n self.optimizer.zero_grad()\n output = self.model(state_action_tensor)\n loss = self.criterion(output, expected_values_tensor)\n loss_value = loss.data.item()\n loss.backward()\n self.optimizer.step()\n return loss_value\n"}}},{"rowIdx":86,"cells":{"function_name":{"kind":"string","value":"_train"},"docstring":{"kind":"string","value":"Train the model for 1 epoch.\n\nArgs:\n state_action_tensor: pytorch tensor with state-action expected_values.\n expected_values: pytorch tensor with expected values for each state-action.\n\nReturns:\n The loss before trainig."},"masked_code":{"kind":"string","value":"\"\"\"Polynomial model class used by agents for building stuff.\n\"\"\"\nfrom torch import nn, optim\n\nimport torch\nimport torch.nn.functional as F\n\nfrom stock_trading_backend.agent.model import Model\n\n\nclass NNModel(nn.Module):\n \"\"\"Torch neural network model.\n \"\"\"\n def __init__(self, num_inputs, num_hidden_layers, num_inner_features):\n \"\"\"Initializer for linear model.\n\n Args:\n num_inputs: the dimension of input data.\n num_hidden_layers: the number of hidden layers.\n num_inner_features: the number of features in the hidden layers\n \"\"\"\n super(NNModel, self).__init__()\n self.input_layer = nn.Linear(num_inputs, num_inner_features)\n hidden_layers = []\n for _ in range(num_hidden_layers):\n hidden_layers.append(nn.Linear(num_inner_features, num_inner_features))\n hidden_layers.append(nn.ReLU())\n self.hidden_layers = nn.Sequential(*hidden_layers)\n self.output_layer = nn.Linear(num_inner_features, 1)\n\n def forward(self, input_tensor):\n \"\"\"Forward pass on the neural network model.\n\n Args:\n input_tensor: the input tensor.\n\n Returns:\n Tensor with model results.\n \"\"\"\n output = F.relu(self.input_layer(input_tensor))\n output = self.hidden_layers(output)\n output = self.output_layer(output)\n return output\n\n\nclass NeuralNetworkModel(Model):\n \"\"\"Neural netowrk model class.\n \"\"\"\n name = \"neural_network_model\"\n\n def __init__(self, learning_rate=1e-3, num_hidden_layers=1, num_inner_features=100):\n \"\"\"Initializer for model class.\n\n Args:\n learning_rate: the learning rate of the model.\n num_hidden_layers: number of hidden layers in the network.\n num_inner_features: number of features in the hidden layers.\n \"\"\"\n super(NeuralNetworkModel, self).__init__()\n self.model = None\n self.optimizer = None\n self.criterion = nn.MSELoss()\n self.learning_rate = learning_rate\n self.num_hidden_layers = num_hidden_layers\n self.num_inner_features = num_inner_features\n self.id_str = \"{}_{}_{}_{}\".format(self.name, learning_rate, num_hidden_layers,\n num_inner_features)\n\n def _init_model(self, num_inputs):\n \"\"\"Initializes internal linear model.\n\n Args:\n num_inputs: number of inputs that model will have.\n \"\"\"\n self.model = NNModel(num_inputs, self.num_hidden_layers, self.num_inner_features)\n self.optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate)\n\n def _predict(self, state_action_tensor):\n \"\"\"Use provided information to make a prediction.\n\n Args:\n state_action_tensor: pytorch tensor with state-action values.\n\n Returns:\n Predicted values for observation-action tensors.\n \"\"\"\n if self.model is None:\n self._init_model(state_action_tensor.shape[1])\n return self.model(state_action_tensor).detach().reshape(-1)\n\n# MASKED: _train function (lines 91-110)"},"implementation":{"kind":"string","value":" def _train(self, state_action_tensor, expected_values_tensor):\n \"\"\"Train the model for 1 epoch.\n\n Args:\n state_action_tensor: pytorch tensor with state-action expected_values.\n expected_values: pytorch tensor with expected values for each state-action.\n\n Returns:\n The loss before trainig.\n \"\"\"\n if self.model is None:\n self._init_model(state_action_tensor.shape[1])\n\n self.optimizer.zero_grad()\n output = self.model(state_action_tensor)\n loss = self.criterion(output, expected_values_tensor)\n loss_value = loss.data.item()\n loss.backward()\n self.optimizer.step()\n return loss_value"},"start_line":{"kind":"number","value":91,"string":"91"},"end_line":{"kind":"number","value":110,"string":"110"},"file_content":{"kind":"string","value":"\"\"\"Polynomial model class used by agents for building stuff.\n\"\"\"\nfrom torch import nn, optim\n\nimport torch\nimport torch.nn.functional as F\n\nfrom stock_trading_backend.agent.model import Model\n\n\nclass NNModel(nn.Module):\n \"\"\"Torch neural network model.\n \"\"\"\n def __init__(self, num_inputs, num_hidden_layers, num_inner_features):\n \"\"\"Initializer for linear model.\n\n Args:\n num_inputs: the dimension of input data.\n num_hidden_layers: the number of hidden layers.\n num_inner_features: the number of features in the hidden layers\n \"\"\"\n super(NNModel, self).__init__()\n self.input_layer = nn.Linear(num_inputs, num_inner_features)\n hidden_layers = []\n for _ in range(num_hidden_layers):\n hidden_layers.append(nn.Linear(num_inner_features, num_inner_features))\n hidden_layers.append(nn.ReLU())\n self.hidden_layers = nn.Sequential(*hidden_layers)\n self.output_layer = nn.Linear(num_inner_features, 1)\n\n def forward(self, input_tensor):\n \"\"\"Forward pass on the neural network model.\n\n Args:\n input_tensor: the input tensor.\n\n Returns:\n Tensor with model results.\n \"\"\"\n output = F.relu(self.input_layer(input_tensor))\n output = self.hidden_layers(output)\n output = self.output_layer(output)\n return output\n\n\nclass NeuralNetworkModel(Model):\n \"\"\"Neural netowrk model class.\n \"\"\"\n name = \"neural_network_model\"\n\n def __init__(self, learning_rate=1e-3, num_hidden_layers=1, num_inner_features=100):\n \"\"\"Initializer for model class.\n\n Args:\n learning_rate: the learning rate of the model.\n num_hidden_layers: number of hidden layers in the network.\n num_inner_features: number of features in the hidden layers.\n \"\"\"\n super(NeuralNetworkModel, self).__init__()\n self.model = None\n self.optimizer = None\n self.criterion = nn.MSELoss()\n self.learning_rate = learning_rate\n self.num_hidden_layers = num_hidden_layers\n self.num_inner_features = num_inner_features\n self.id_str = \"{}_{}_{}_{}\".format(self.name, learning_rate, num_hidden_layers,\n num_inner_features)\n\n def _init_model(self, num_inputs):\n \"\"\"Initializes internal linear model.\n\n Args:\n num_inputs: number of inputs that model will have.\n \"\"\"\n self.model = NNModel(num_inputs, self.num_hidden_layers, self.num_inner_features)\n self.optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate)\n\n def _predict(self, state_action_tensor):\n \"\"\"Use provided information to make a prediction.\n\n Args:\n state_action_tensor: pytorch tensor with state-action values.\n\n Returns:\n Predicted values for observation-action tensors.\n \"\"\"\n if self.model is None:\n self._init_model(state_action_tensor.shape[1])\n return self.model(state_action_tensor).detach().reshape(-1)\n\n def _train(self, state_action_tensor, expected_values_tensor):\n \"\"\"Train the model for 1 epoch.\n\n Args:\n state_action_tensor: pytorch tensor with state-action expected_values.\n expected_values: pytorch tensor with expected values for each state-action.\n\n Returns:\n The loss before trainig.\n \"\"\"\n if self.model is None:\n self._init_model(state_action_tensor.shape[1])\n\n self.optimizer.zero_grad()\n output = self.model(state_action_tensor)\n loss = self.criterion(output, expected_values_tensor)\n loss_value = loss.data.item()\n loss.backward()\n self.optimizer.step()\n return loss_value\n"}}},{"rowIdx":87,"cells":{"function_name":{"kind":"string","value":"getNetworkCellularGatewaySettingsDhcp"},"docstring":{"kind":"string","value":"**List common DHCP settings of MGs**\nhttps://developer.cisco.com/meraki/api/#!get-network-cellular-gateway-settings-dhcp\n\n- networkId (string)"},"masked_code":{"kind":"string","value":"class MGDHCPSettings(object):\n def __init__(self, session):\n super(MGDHCPSettings, self).__init__()\n self._session = session\n \n# MASKED: getNetworkCellularGatewaySettingsDhcp function (lines 6-20)\n\n def updateNetworkCellularGatewaySettingsDhcp(self, networkId: str, **kwargs):\n \"\"\"\n **Update common DHCP settings of MGs**\n https://developer.cisco.com/meraki/api/#!update-network-cellular-gateway-settings-dhcp\n \n - networkId (string)\n - dhcpLeaseTime (string): DHCP Lease time for all MG of the network. It can be '30 minutes', '1 hour', '4 hours', '12 hours', '1 day' or '1 week'.\n - dnsNameservers (string): DNS name servers mode for all MG of the network. It can take 4 different values: 'upstream_dns', 'google_dns', 'opendns', 'custom'.\n - dnsCustomNameservers (array): list of fixed IP representing the the DNS Name servers when the mode is 'custom'\n \"\"\"\n\n kwargs.update(locals())\n\n metadata = {\n 'tags': ['MG DHCP settings'],\n 'operation': 'updateNetworkCellularGatewaySettingsDhcp',\n }\n resource = f'/networks/{networkId}/cellularGateway/settings/dhcp'\n\n body_params = ['dhcpLeaseTime', 'dnsNameservers', 'dnsCustomNameservers']\n payload = {k: v for (k, v) in kwargs.items() if k in body_params}\n\n return self._session.put(metadata, resource, payload)\n"},"implementation":{"kind":"string","value":" def getNetworkCellularGatewaySettingsDhcp(self, networkId: str):\n \"\"\"\n **List common DHCP settings of MGs**\n https://developer.cisco.com/meraki/api/#!get-network-cellular-gateway-settings-dhcp\n \n - networkId (string)\n \"\"\"\n\n metadata = {\n 'tags': ['MG DHCP settings'],\n 'operation': 'getNetworkCellularGatewaySettingsDhcp',\n }\n resource = f'/networks/{networkId}/cellularGateway/settings/dhcp'\n\n return self._session.get(metadata, resource)"},"start_line":{"kind":"number","value":6,"string":"6"},"end_line":{"kind":"number","value":20,"string":"20"},"file_content":{"kind":"string","value":"class MGDHCPSettings(object):\n def __init__(self, session):\n super(MGDHCPSettings, self).__init__()\n self._session = session\n \n def getNetworkCellularGatewaySettingsDhcp(self, networkId: str):\n \"\"\"\n **List common DHCP settings of MGs**\n https://developer.cisco.com/meraki/api/#!get-network-cellular-gateway-settings-dhcp\n \n - networkId (string)\n \"\"\"\n\n metadata = {\n 'tags': ['MG DHCP settings'],\n 'operation': 'getNetworkCellularGatewaySettingsDhcp',\n }\n resource = f'/networks/{networkId}/cellularGateway/settings/dhcp'\n\n return self._session.get(metadata, resource)\n\n def updateNetworkCellularGatewaySettingsDhcp(self, networkId: str, **kwargs):\n \"\"\"\n **Update common DHCP settings of MGs**\n https://developer.cisco.com/meraki/api/#!update-network-cellular-gateway-settings-dhcp\n \n - networkId (string)\n - dhcpLeaseTime (string): DHCP Lease time for all MG of the network. It can be '30 minutes', '1 hour', '4 hours', '12 hours', '1 day' or '1 week'.\n - dnsNameservers (string): DNS name servers mode for all MG of the network. It can take 4 different values: 'upstream_dns', 'google_dns', 'opendns', 'custom'.\n - dnsCustomNameservers (array): list of fixed IP representing the the DNS Name servers when the mode is 'custom'\n \"\"\"\n\n kwargs.update(locals())\n\n metadata = {\n 'tags': ['MG DHCP settings'],\n 'operation': 'updateNetworkCellularGatewaySettingsDhcp',\n }\n resource = f'/networks/{networkId}/cellularGateway/settings/dhcp'\n\n body_params = ['dhcpLeaseTime', 'dnsNameservers', 'dnsCustomNameservers']\n payload = {k: v for (k, v) in kwargs.items() if k in body_params}\n\n return self._session.put(metadata, resource, payload)\n\n"}}},{"rowIdx":88,"cells":{"function_name":{"kind":"string","value":"blend"},"docstring":{"kind":"string","value":"Blend image1 and image2 using 'factor'.\n\nA value of factor 0.0 means only image1 is used.\nA value of 1.0 means only image2 is used. A value between 0.0 and\n1.0 means we linearly interpolate the pixel values between the two\nimages. A value greater than 1.0 \"extrapolates\" the difference\nbetween the two pixel values, and we clip the results to values\nbetween 0 and 255.\n\nArgs:\n image1: An image Tensor.\n image2: An image Tensor.\n factor: A floating point value above 0.0.\n\nReturns:\n A blended image Tensor."},"masked_code":{"kind":"string","value":"# Copyright 2019 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Various ops for augmentation.\"\"\"\n\nimport math\n\nimport tensorflow as tf\nfrom tensorflow_addons import image as tfa_image\n\n# Default replace value\nREPLACE_VALUE = 128\n\n\n# MASKED: blend function (lines 25-45)\n\n\ndef wrap(image):\n \"\"\"Returns 'image' with an extra channel set to all 1s.\"\"\"\n shape = tf.shape(image)\n extended_channel = tf.ones([shape[0], shape[1], 1], image.dtype)\n extended = tf.concat([image, extended_channel], 2)\n return extended\n\n\ndef unwrap(image):\n \"\"\"Unwraps an image produced by wrap.\n\n Where there is a 0 in the last channel for every spatial position,\n the rest of the three channels in that spatial dimension are grayed\n (set to 128). Operations like translate and shear on a wrapped\n Tensor will leave 0s in empty locations. Some transformations look\n at the intensity of values to do preprocessing, and we want these\n empty pixels to assume the 'average' value, rather than pure black.\n\n\n Args:\n image: A 3D Image Tensor with 4 channels.\n\n Returns:\n image: A 3D image Tensor with 3 channels.\n \"\"\"\n image_shape = tf.shape(image)\n # Flatten the spatial dimensions.\n flattened_image = tf.reshape(image, [-1, image_shape[2]])\n\n # Find all pixels where the last channel is zero.\n alpha_channel = tf.expand_dims(flattened_image[:, image_shape[2] - 1], 1)\n\n replace = tf.constant([REPLACE_VALUE, REPLACE_VALUE, REPLACE_VALUE, 1],\n image.dtype)\n\n # Where they are zero, fill them in with 'replace'.\n flattened_image = tf.where(\n tf.equal(alpha_channel, 0),\n tf.ones_like(flattened_image, dtype=image.dtype) * replace,\n flattened_image)\n\n image = tf.reshape(flattened_image, image_shape)\n image = tf.slice(image, [0, 0, 0],\n [image_shape[0], image_shape[1], image_shape[2] - 1])\n return image\n\n\ndef solarize(image, threshold=128):\n # For each pixel in the image, select the pixel\n # if the value is less than the threshold.\n # Otherwise, subtract 255 from the pixel.\n threshold = tf.saturate_cast(threshold, image.dtype)\n return tf.where(image < threshold, image, 255 - image)\n\n\ndef solarize_add(image, addition=0, threshold=128):\n # For each pixel in the image less than threshold\n # we add 'addition' amount to it and then clip the\n # pixel value to be between 0 and 255. The value\n # of 'addition' is between -128 and 128\n threshold = tf.saturate_cast(threshold, image.dtype)\n added_im = tf.cast(image, tf.int32) + tf.cast(addition, tf.int32)\n added_im = tf.saturate_cast(added_im, tf.uint8)\n return tf.where(image < threshold, added_im, image)\n\n\ndef invert(image):\n \"\"\"Inverts the image pixels.\"\"\"\n return 255 - tf.convert_to_tensor(image)\n\n\ndef invert_blend(image, factor):\n \"\"\"Implements blend of invert with original image.\"\"\"\n return blend(invert(image), image, factor)\n\n\ndef color(image, factor):\n \"\"\"Equivalent of PIL Color.\"\"\"\n degenerate = tf.image.grayscale_to_rgb(tf.image.rgb_to_grayscale(image))\n return blend(degenerate, image, factor)\n\n\ndef contrast(image, factor):\n \"\"\"Equivalent of PIL Contrast.\"\"\"\n grayscale_im = tf.image.rgb_to_grayscale(image)\n mean = tf.reduce_mean(tf.cast(grayscale_im, tf.float32))\n mean = tf.saturate_cast(mean + 0.5, tf.uint8)\n\n degenerate = tf.ones_like(grayscale_im, dtype=tf.uint8) * mean\n degenerate = tf.image.grayscale_to_rgb(degenerate)\n\n return blend(degenerate, image, factor)\n\n\ndef brightness(image, factor):\n \"\"\"Equivalent of PIL Brightness.\"\"\"\n degenerate = tf.zeros_like(image)\n return blend(degenerate, image, factor)\n\n\ndef posterize(image, bits):\n \"\"\"Equivalent of PIL Posterize.\"\"\"\n shift = tf.cast(8 - bits, image.dtype)\n return tf.bitwise.left_shift(tf.bitwise.right_shift(image, shift), shift)\n\n\ndef rotate(image, degrees):\n \"\"\"Equivalent of PIL Rotation.\"\"\"\n # Convert from degrees to radians\n degrees_to_radians = math.pi / 180.0\n radians = degrees * degrees_to_radians\n\n # In practice, we should randomize the rotation degrees by flipping\n # it negatively half the time, but that's done on 'degrees' outside\n # of the function.\n image = tfa_image.transform_ops.rotate(wrap(image), radians)\n return unwrap(image)\n\n\ndef translate_x(image, pixels):\n \"\"\"Equivalent of PIL Translate in X dimension.\"\"\"\n image = tfa_image.translate_ops.translate(wrap(image), [-pixels, 0])\n return unwrap(image)\n\n\ndef translate_y(image, pixels):\n \"\"\"Equivalent of PIL Translate in Y dimension.\"\"\"\n image = tfa_image.translate_ops.translate(wrap(image), [0, -pixels])\n return unwrap(image)\n\n\ndef shear_x(image, level):\n \"\"\"Equivalent of PIL Shearing in X dimension.\"\"\"\n # Shear parallel to x axis is a projective transform\n # with a matrix form of:\n # [1 level\n # 0 1]\n image = tfa_image.transform_ops.transform(\n wrap(image), [1., level, 0., 0., 1., 0., 0., 0.])\n return unwrap(image)\n\n\ndef shear_y(image, level):\n \"\"\"Equivalent of PIL Shearing in Y dimension.\"\"\"\n # Shear parallel to y axis is a projective transform\n # with a matrix form of:\n # [1 0\n # level 1]\n image = tfa_image.transform_ops.transform(\n wrap(image), [1., 0., 0., level, 1., 0., 0., 0.])\n return unwrap(image)\n\n\ndef autocontrast(image):\n \"\"\"Implements Autocontrast function from PIL using TF ops.\"\"\"\n\n def scale_channel(channel):\n \"\"\"Scale the 2D image using the autocontrast rule.\"\"\"\n # A possibly cheaper version can be done using cumsum/unique_with_counts\n # over the histogram values, rather than iterating over the entire image.\n # to compute mins and maxes.\n lo = tf.cast(tf.reduce_min(channel), tf.float32)\n hi = tf.cast(tf.reduce_max(channel), tf.float32)\n\n # Scale the image, making the lowest value 0 and the highest value 255.\n def scale_values(im):\n scale = 255.0 / (hi - lo)\n offset = -lo * scale\n im = tf.cast(im, tf.float32) * scale + offset\n return tf.saturate_cast(im, tf.uint8)\n\n result = tf.cond(hi > lo, lambda: scale_values(channel), lambda: channel)\n return result\n\n # Assumes RGB for now. Scales each channel independently\n # and then stacks the result.\n s1 = scale_channel(image[:, :, 0])\n s2 = scale_channel(image[:, :, 1])\n s3 = scale_channel(image[:, :, 2])\n image = tf.stack([s1, s2, s3], 2)\n return image\n\n\ndef autocontrast_blend(image, factor):\n \"\"\"Implements blend of autocontrast with original image.\"\"\"\n return blend(autocontrast(image), image, factor)\n\n\ndef sharpness(image, factor):\n \"\"\"Implements Sharpness function from PIL using TF ops.\"\"\"\n orig_im = image\n image = tf.cast(image, tf.float32)\n # Make image 4D for conv operation\n image = tf.expand_dims(image, 0)\n # SMOOTH PIL Kernel\n kernel = tf.constant([[1, 1, 1], [1, 5, 1], [1, 1, 1]],\n dtype=tf.float32,\n shape=[3, 3, 1, 1]) / 13.\n # Tile across channel dimension\n kernel = tf.tile(kernel, [1, 1, 3, 1])\n strides = [1, 1, 1, 1]\n degenerate = tf.nn.depthwise_conv2d(\n image, kernel, strides, padding='VALID', dilations=[1, 1])\n degenerate = tf.squeeze(tf.saturate_cast(degenerate, tf.uint8), [0])\n\n # For the borders of the resulting image, fill in the values of the\n # original image.\n mask = tf.ones_like(degenerate)\n padded_mask = tf.pad(mask, [[1, 1], [1, 1], [0, 0]])\n padded_degenerate = tf.pad(degenerate, [[1, 1], [1, 1], [0, 0]])\n result = tf.where(tf.equal(padded_mask, 1), padded_degenerate, orig_im)\n\n # Blend the final result\n return blend(result, orig_im, factor)\n\n\ndef equalize(image):\n \"\"\"Implements Equalize function from PIL using TF ops.\"\"\"\n\n def scale_channel(im, c):\n \"\"\"Scale the data in the channel to implement equalize.\"\"\"\n im = tf.cast(im[:, :, c], tf.int32)\n # Compute the histogram of the image channel.\n histo = tf.histogram_fixed_width(im, [0, 255], nbins=256)\n\n # For the purposes of computing the step, filter out the nonzeros.\n nonzero = tf.where(tf.not_equal(histo, 0))\n nonzero_histo = tf.reshape(tf.gather(histo, nonzero), [-1])\n step = (tf.reduce_sum(nonzero_histo) - nonzero_histo[-1]) // 255\n\n def build_lut(histo, step):\n # Compute the cumulative sum, shifting by step // 2\n # and then normalization by step.\n lut = (tf.cumsum(histo) + (step // 2)) // step\n # Shift lut, prepending with 0.\n lut = tf.concat([[0], lut[:-1]], 0)\n # Clip the counts to be in range. This is done\n # in the C code for image.point.\n return tf.clip_by_value(lut, 0, 255)\n\n # If step is zero, return the original image. Otherwise, build\n # lut from the full histogram and step and then index from it.\n result = tf.cond(\n tf.equal(step, 0), lambda: im,\n lambda: tf.gather(build_lut(histo, step), im))\n\n return tf.cast(result, tf.uint8)\n\n # Assumes RGB for now. Scales each channel independently\n # and then stacks the result.\n s1 = scale_channel(image, 0)\n s2 = scale_channel(image, 1)\n s3 = scale_channel(image, 2)\n image = tf.stack([s1, s2, s3], 2)\n return image\n\n\ndef equalize_blend(image, factor):\n \"\"\"Implements blend of equalize with original image.\"\"\"\n return blend(equalize(image), image, factor)\n\n\ndef _convolve_image_with_kernel(image, kernel):\n num_channels = tf.shape(image)[-1]\n kernel = tf.tile(kernel, [1, 1, num_channels, 1])\n image = tf.expand_dims(image, axis=0)\n convolved_im = tf.nn.depthwise_conv2d(\n tf.cast(image, tf.float32), kernel, strides=[1, 1, 1, 1], padding='SAME')\n # adding 0.5 for future rounding, same as in PIL:\n # https://github.com/python-pillow/Pillow/blob/555e305a60d7fcefd1ad4aa6c8fd879e2f474192/src/libImaging/Filter.c#L101 # pylint: disable=line-too-long\n convolved_im = convolved_im + 0.5\n return tf.squeeze(convolved_im, axis=0)\n\n\ndef blur(image, factor):\n \"\"\"Blur with the same kernel as ImageFilter.BLUR.\"\"\"\n # See https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageFilter.py # pylint: disable=line-too-long\n # class BLUR(BuiltinFilter):\n # name = \"Blur\"\n # # fmt: off\n # filterargs = (5, 5), 16, 0, (\n # 1, 1, 1, 1, 1,\n # 1, 0, 0, 0, 1,\n # 1, 0, 0, 0, 1,\n # 1, 0, 0, 0, 1,\n # 1, 1, 1, 1, 1,\n # )\n # # fmt: on\n #\n # filterargs are following:\n # (kernel_size_x, kernel_size_y), divisor, offset, kernel\n #\n blur_kernel = tf.constant(\n [[1., 1., 1., 1., 1.], [1., 0., 0., 0., 1.], [1., 0., 0., 0., 1.],\n [1., 0., 0., 0., 1.], [1., 1., 1., 1., 1.]],\n dtype=tf.float32,\n shape=[5, 5, 1, 1]) / 16.0\n blurred_im = _convolve_image_with_kernel(image, blur_kernel)\n return blend(image, blurred_im, factor)\n\n\ndef smooth(image, factor):\n \"\"\"Smooth with the same kernel as ImageFilter.SMOOTH.\"\"\"\n # See https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageFilter.py # pylint: disable=line-too-long\n # class SMOOTH(BuiltinFilter):\n # name = \"Smooth\"\n # # fmt: off\n # filterargs = (3, 3), 13, 0, (\n # 1, 1, 1,\n # 1, 5, 1,\n # 1, 1, 1,\n # )\n # # fmt: on\n #\n # filterargs are following:\n # (kernel_size_x, kernel_size_y), divisor, offset, kernel\n #\n smooth_kernel = tf.constant([[1., 1., 1.], [1., 5., 1.], [1., 1., 1.]],\n dtype=tf.float32,\n shape=[3, 3, 1, 1]) / 13.0\n smoothed_im = _convolve_image_with_kernel(image, smooth_kernel)\n return blend(image, smoothed_im, factor)\n\n\ndef rescale(image, level):\n \"\"\"Rescales image and enlarged cornet.\"\"\"\n # See tf.image.ResizeMethod for full list\n size = image.shape[:2]\n scale = level * 0.25\n scale_height = tf.cast(scale * size[0], tf.int32)\n scale_width = tf.cast(scale * size[1], tf.int32)\n cropped_image = tf.image.crop_to_bounding_box(\n image,\n offset_height=scale_height,\n offset_width=scale_width,\n target_height=size[0] - scale_height,\n target_width=size[1] - scale_width)\n rescaled = tf.image.resize(cropped_image, size, tf.image.ResizeMethod.BICUBIC)\n return tf.saturate_cast(rescaled, tf.uint8)\n\n\nNAME_TO_FUNC = {\n 'Identity': tf.identity,\n 'AutoContrast': autocontrast,\n 'AutoContrastBlend': autocontrast_blend,\n 'Equalize': equalize,\n 'EqualizeBlend': equalize_blend,\n 'Invert': invert,\n 'InvertBlend': invert_blend,\n 'Rotate': rotate,\n 'Posterize': posterize,\n 'Solarize': solarize,\n 'SolarizeAdd': solarize_add,\n 'Color': color,\n 'Contrast': contrast,\n 'Brightness': brightness,\n 'Sharpness': sharpness,\n 'ShearX': shear_x,\n 'ShearY': shear_y,\n 'TranslateX': translate_x,\n 'TranslateY': translate_y,\n 'Blur': blur,\n 'Smooth': smooth,\n 'Rescale': rescale,\n}"},"implementation":{"kind":"string","value":"def blend(image1, image2, factor):\n \"\"\"Blend image1 and image2 using 'factor'.\n\n A value of factor 0.0 means only image1 is used.\n A value of 1.0 means only image2 is used. A value between 0.0 and\n 1.0 means we linearly interpolate the pixel values between the two\n images. A value greater than 1.0 \"extrapolates\" the difference\n between the two pixel values, and we clip the results to values\n between 0 and 255.\n\n Args:\n image1: An image Tensor.\n image2: An image Tensor.\n factor: A floating point value above 0.0.\n\n Returns:\n A blended image Tensor.\n \"\"\"\n image1 = tf.cast(image1, tf.float32)\n image2 = tf.cast(image2, tf.float32)\n return tf.saturate_cast(image1 + factor * (image2 - image1), tf.uint8)"},"start_line":{"kind":"number","value":25,"string":"25"},"end_line":{"kind":"number","value":45,"string":"45"},"file_content":{"kind":"string","value":"# Copyright 2019 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\"\"\"Various ops for augmentation.\"\"\"\n\nimport math\n\nimport tensorflow as tf\nfrom tensorflow_addons import image as tfa_image\n\n# Default replace value\nREPLACE_VALUE = 128\n\n\ndef blend(image1, image2, factor):\n \"\"\"Blend image1 and image2 using 'factor'.\n\n A value of factor 0.0 means only image1 is used.\n A value of 1.0 means only image2 is used. A value between 0.0 and\n 1.0 means we linearly interpolate the pixel values between the two\n images. A value greater than 1.0 \"extrapolates\" the difference\n between the two pixel values, and we clip the results to values\n between 0 and 255.\n\n Args:\n image1: An image Tensor.\n image2: An image Tensor.\n factor: A floating point value above 0.0.\n\n Returns:\n A blended image Tensor.\n \"\"\"\n image1 = tf.cast(image1, tf.float32)\n image2 = tf.cast(image2, tf.float32)\n return tf.saturate_cast(image1 + factor * (image2 - image1), tf.uint8)\n\n\ndef wrap(image):\n \"\"\"Returns 'image' with an extra channel set to all 1s.\"\"\"\n shape = tf.shape(image)\n extended_channel = tf.ones([shape[0], shape[1], 1], image.dtype)\n extended = tf.concat([image, extended_channel], 2)\n return extended\n\n\ndef unwrap(image):\n \"\"\"Unwraps an image produced by wrap.\n\n Where there is a 0 in the last channel for every spatial position,\n the rest of the three channels in that spatial dimension are grayed\n (set to 128). Operations like translate and shear on a wrapped\n Tensor will leave 0s in empty locations. Some transformations look\n at the intensity of values to do preprocessing, and we want these\n empty pixels to assume the 'average' value, rather than pure black.\n\n\n Args:\n image: A 3D Image Tensor with 4 channels.\n\n Returns:\n image: A 3D image Tensor with 3 channels.\n \"\"\"\n image_shape = tf.shape(image)\n # Flatten the spatial dimensions.\n flattened_image = tf.reshape(image, [-1, image_shape[2]])\n\n # Find all pixels where the last channel is zero.\n alpha_channel = tf.expand_dims(flattened_image[:, image_shape[2] - 1], 1)\n\n replace = tf.constant([REPLACE_VALUE, REPLACE_VALUE, REPLACE_VALUE, 1],\n image.dtype)\n\n # Where they are zero, fill them in with 'replace'.\n flattened_image = tf.where(\n tf.equal(alpha_channel, 0),\n tf.ones_like(flattened_image, dtype=image.dtype) * replace,\n flattened_image)\n\n image = tf.reshape(flattened_image, image_shape)\n image = tf.slice(image, [0, 0, 0],\n [image_shape[0], image_shape[1], image_shape[2] - 1])\n return image\n\n\ndef solarize(image, threshold=128):\n # For each pixel in the image, select the pixel\n # if the value is less than the threshold.\n # Otherwise, subtract 255 from the pixel.\n threshold = tf.saturate_cast(threshold, image.dtype)\n return tf.where(image < threshold, image, 255 - image)\n\n\ndef solarize_add(image, addition=0, threshold=128):\n # For each pixel in the image less than threshold\n # we add 'addition' amount to it and then clip the\n # pixel value to be between 0 and 255. The value\n # of 'addition' is between -128 and 128\n threshold = tf.saturate_cast(threshold, image.dtype)\n added_im = tf.cast(image, tf.int32) + tf.cast(addition, tf.int32)\n added_im = tf.saturate_cast(added_im, tf.uint8)\n return tf.where(image < threshold, added_im, image)\n\n\ndef invert(image):\n \"\"\"Inverts the image pixels.\"\"\"\n return 255 - tf.convert_to_tensor(image)\n\n\ndef invert_blend(image, factor):\n \"\"\"Implements blend of invert with original image.\"\"\"\n return blend(invert(image), image, factor)\n\n\ndef color(image, factor):\n \"\"\"Equivalent of PIL Color.\"\"\"\n degenerate = tf.image.grayscale_to_rgb(tf.image.rgb_to_grayscale(image))\n return blend(degenerate, image, factor)\n\n\ndef contrast(image, factor):\n \"\"\"Equivalent of PIL Contrast.\"\"\"\n grayscale_im = tf.image.rgb_to_grayscale(image)\n mean = tf.reduce_mean(tf.cast(grayscale_im, tf.float32))\n mean = tf.saturate_cast(mean + 0.5, tf.uint8)\n\n degenerate = tf.ones_like(grayscale_im, dtype=tf.uint8) * mean\n degenerate = tf.image.grayscale_to_rgb(degenerate)\n\n return blend(degenerate, image, factor)\n\n\ndef brightness(image, factor):\n \"\"\"Equivalent of PIL Brightness.\"\"\"\n degenerate = tf.zeros_like(image)\n return blend(degenerate, image, factor)\n\n\ndef posterize(image, bits):\n \"\"\"Equivalent of PIL Posterize.\"\"\"\n shift = tf.cast(8 - bits, image.dtype)\n return tf.bitwise.left_shift(tf.bitwise.right_shift(image, shift), shift)\n\n\ndef rotate(image, degrees):\n \"\"\"Equivalent of PIL Rotation.\"\"\"\n # Convert from degrees to radians\n degrees_to_radians = math.pi / 180.0\n radians = degrees * degrees_to_radians\n\n # In practice, we should randomize the rotation degrees by flipping\n # it negatively half the time, but that's done on 'degrees' outside\n # of the function.\n image = tfa_image.transform_ops.rotate(wrap(image), radians)\n return unwrap(image)\n\n\ndef translate_x(image, pixels):\n \"\"\"Equivalent of PIL Translate in X dimension.\"\"\"\n image = tfa_image.translate_ops.translate(wrap(image), [-pixels, 0])\n return unwrap(image)\n\n\ndef translate_y(image, pixels):\n \"\"\"Equivalent of PIL Translate in Y dimension.\"\"\"\n image = tfa_image.translate_ops.translate(wrap(image), [0, -pixels])\n return unwrap(image)\n\n\ndef shear_x(image, level):\n \"\"\"Equivalent of PIL Shearing in X dimension.\"\"\"\n # Shear parallel to x axis is a projective transform\n # with a matrix form of:\n # [1 level\n # 0 1]\n image = tfa_image.transform_ops.transform(\n wrap(image), [1., level, 0., 0., 1., 0., 0., 0.])\n return unwrap(image)\n\n\ndef shear_y(image, level):\n \"\"\"Equivalent of PIL Shearing in Y dimension.\"\"\"\n # Shear parallel to y axis is a projective transform\n # with a matrix form of:\n # [1 0\n # level 1]\n image = tfa_image.transform_ops.transform(\n wrap(image), [1., 0., 0., level, 1., 0., 0., 0.])\n return unwrap(image)\n\n\ndef autocontrast(image):\n \"\"\"Implements Autocontrast function from PIL using TF ops.\"\"\"\n\n def scale_channel(channel):\n \"\"\"Scale the 2D image using the autocontrast rule.\"\"\"\n # A possibly cheaper version can be done using cumsum/unique_with_counts\n # over the histogram values, rather than iterating over the entire image.\n # to compute mins and maxes.\n lo = tf.cast(tf.reduce_min(channel), tf.float32)\n hi = tf.cast(tf.reduce_max(channel), tf.float32)\n\n # Scale the image, making the lowest value 0 and the highest value 255.\n def scale_values(im):\n scale = 255.0 / (hi - lo)\n offset = -lo * scale\n im = tf.cast(im, tf.float32) * scale + offset\n return tf.saturate_cast(im, tf.uint8)\n\n result = tf.cond(hi > lo, lambda: scale_values(channel), lambda: channel)\n return result\n\n # Assumes RGB for now. Scales each channel independently\n # and then stacks the result.\n s1 = scale_channel(image[:, :, 0])\n s2 = scale_channel(image[:, :, 1])\n s3 = scale_channel(image[:, :, 2])\n image = tf.stack([s1, s2, s3], 2)\n return image\n\n\ndef autocontrast_blend(image, factor):\n \"\"\"Implements blend of autocontrast with original image.\"\"\"\n return blend(autocontrast(image), image, factor)\n\n\ndef sharpness(image, factor):\n \"\"\"Implements Sharpness function from PIL using TF ops.\"\"\"\n orig_im = image\n image = tf.cast(image, tf.float32)\n # Make image 4D for conv operation\n image = tf.expand_dims(image, 0)\n # SMOOTH PIL Kernel\n kernel = tf.constant([[1, 1, 1], [1, 5, 1], [1, 1, 1]],\n dtype=tf.float32,\n shape=[3, 3, 1, 1]) / 13.\n # Tile across channel dimension\n kernel = tf.tile(kernel, [1, 1, 3, 1])\n strides = [1, 1, 1, 1]\n degenerate = tf.nn.depthwise_conv2d(\n image, kernel, strides, padding='VALID', dilations=[1, 1])\n degenerate = tf.squeeze(tf.saturate_cast(degenerate, tf.uint8), [0])\n\n # For the borders of the resulting image, fill in the values of the\n # original image.\n mask = tf.ones_like(degenerate)\n padded_mask = tf.pad(mask, [[1, 1], [1, 1], [0, 0]])\n padded_degenerate = tf.pad(degenerate, [[1, 1], [1, 1], [0, 0]])\n result = tf.where(tf.equal(padded_mask, 1), padded_degenerate, orig_im)\n\n # Blend the final result\n return blend(result, orig_im, factor)\n\n\ndef equalize(image):\n \"\"\"Implements Equalize function from PIL using TF ops.\"\"\"\n\n def scale_channel(im, c):\n \"\"\"Scale the data in the channel to implement equalize.\"\"\"\n im = tf.cast(im[:, :, c], tf.int32)\n # Compute the histogram of the image channel.\n histo = tf.histogram_fixed_width(im, [0, 255], nbins=256)\n\n # For the purposes of computing the step, filter out the nonzeros.\n nonzero = tf.where(tf.not_equal(histo, 0))\n nonzero_histo = tf.reshape(tf.gather(histo, nonzero), [-1])\n step = (tf.reduce_sum(nonzero_histo) - nonzero_histo[-1]) // 255\n\n def build_lut(histo, step):\n # Compute the cumulative sum, shifting by step // 2\n # and then normalization by step.\n lut = (tf.cumsum(histo) + (step // 2)) // step\n # Shift lut, prepending with 0.\n lut = tf.concat([[0], lut[:-1]], 0)\n # Clip the counts to be in range. This is done\n # in the C code for image.point.\n return tf.clip_by_value(lut, 0, 255)\n\n # If step is zero, return the original image. Otherwise, build\n # lut from the full histogram and step and then index from it.\n result = tf.cond(\n tf.equal(step, 0), lambda: im,\n lambda: tf.gather(build_lut(histo, step), im))\n\n return tf.cast(result, tf.uint8)\n\n # Assumes RGB for now. Scales each channel independently\n # and then stacks the result.\n s1 = scale_channel(image, 0)\n s2 = scale_channel(image, 1)\n s3 = scale_channel(image, 2)\n image = tf.stack([s1, s2, s3], 2)\n return image\n\n\ndef equalize_blend(image, factor):\n \"\"\"Implements blend of equalize with original image.\"\"\"\n return blend(equalize(image), image, factor)\n\n\ndef _convolve_image_with_kernel(image, kernel):\n num_channels = tf.shape(image)[-1]\n kernel = tf.tile(kernel, [1, 1, num_channels, 1])\n image = tf.expand_dims(image, axis=0)\n convolved_im = tf.nn.depthwise_conv2d(\n tf.cast(image, tf.float32), kernel, strides=[1, 1, 1, 1], padding='SAME')\n # adding 0.5 for future rounding, same as in PIL:\n # https://github.com/python-pillow/Pillow/blob/555e305a60d7fcefd1ad4aa6c8fd879e2f474192/src/libImaging/Filter.c#L101 # pylint: disable=line-too-long\n convolved_im = convolved_im + 0.5\n return tf.squeeze(convolved_im, axis=0)\n\n\ndef blur(image, factor):\n \"\"\"Blur with the same kernel as ImageFilter.BLUR.\"\"\"\n # See https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageFilter.py # pylint: disable=line-too-long\n # class BLUR(BuiltinFilter):\n # name = \"Blur\"\n # # fmt: off\n # filterargs = (5, 5), 16, 0, (\n # 1, 1, 1, 1, 1,\n # 1, 0, 0, 0, 1,\n # 1, 0, 0, 0, 1,\n # 1, 0, 0, 0, 1,\n # 1, 1, 1, 1, 1,\n # )\n # # fmt: on\n #\n # filterargs are following:\n # (kernel_size_x, kernel_size_y), divisor, offset, kernel\n #\n blur_kernel = tf.constant(\n [[1., 1., 1., 1., 1.], [1., 0., 0., 0., 1.], [1., 0., 0., 0., 1.],\n [1., 0., 0., 0., 1.], [1., 1., 1., 1., 1.]],\n dtype=tf.float32,\n shape=[5, 5, 1, 1]) / 16.0\n blurred_im = _convolve_image_with_kernel(image, blur_kernel)\n return blend(image, blurred_im, factor)\n\n\ndef smooth(image, factor):\n \"\"\"Smooth with the same kernel as ImageFilter.SMOOTH.\"\"\"\n # See https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageFilter.py # pylint: disable=line-too-long\n # class SMOOTH(BuiltinFilter):\n # name = \"Smooth\"\n # # fmt: off\n # filterargs = (3, 3), 13, 0, (\n # 1, 1, 1,\n # 1, 5, 1,\n # 1, 1, 1,\n # )\n # # fmt: on\n #\n # filterargs are following:\n # (kernel_size_x, kernel_size_y), divisor, offset, kernel\n #\n smooth_kernel = tf.constant([[1., 1., 1.], [1., 5., 1.], [1., 1., 1.]],\n dtype=tf.float32,\n shape=[3, 3, 1, 1]) / 13.0\n smoothed_im = _convolve_image_with_kernel(image, smooth_kernel)\n return blend(image, smoothed_im, factor)\n\n\ndef rescale(image, level):\n \"\"\"Rescales image and enlarged cornet.\"\"\"\n # See tf.image.ResizeMethod for full list\n size = image.shape[:2]\n scale = level * 0.25\n scale_height = tf.cast(scale * size[0], tf.int32)\n scale_width = tf.cast(scale * size[1], tf.int32)\n cropped_image = tf.image.crop_to_bounding_box(\n image,\n offset_height=scale_height,\n offset_width=scale_width,\n target_height=size[0] - scale_height,\n target_width=size[1] - scale_width)\n rescaled = tf.image.resize(cropped_image, size, tf.image.ResizeMethod.BICUBIC)\n return tf.saturate_cast(rescaled, tf.uint8)\n\n\nNAME_TO_FUNC = {\n 'Identity': tf.identity,\n 'AutoContrast': autocontrast,\n 'AutoContrastBlend': autocontrast_blend,\n 'Equalize': equalize,\n 'EqualizeBlend': equalize_blend,\n 'Invert': invert,\n 'InvertBlend': invert_blend,\n 'Rotate': rotate,\n 'Posterize': posterize,\n 'Solarize': solarize,\n 'SolarizeAdd': solarize_add,\n 'Color': color,\n 'Contrast': contrast,\n 'Brightness': brightness,\n 'Sharpness': sharpness,\n 'ShearX': shear_x,\n 'ShearY': shear_y,\n 'TranslateX': translate_x,\n 'TranslateY': translate_y,\n 'Blur': blur,\n 'Smooth': smooth,\n 'Rescale': rescale,\n}\n"}}},{"rowIdx":89,"cells":{"function_name":{"kind":"string","value":"get_book_quote_page"},"docstring":{"kind":"string","value":"Find the ``<a>`` element pointing to the quote page of a book.\n\nArgs:\n soup (bs4.element.Tag):\n\nReturns:"},"masked_code":{"kind":"string","value":"\"\"\"\nScrape quotes, books and authors from ``Good Reads`` website.\n\"\"\"\n\nimport bs4\nfrom .utils import *\n\n\ndef get_author_name(soup):\n \"\"\"Get the author's name from its main page.\n\n Args:\n soup (bs4.element.Tag): connection to the author page.\n\n Returns:\n string: name of the author.\n\n Examples::\n >>> from scrapereads import connect\n >>> url = 'https://www.goodreads.com/author/show/1077326'\n >>> soup = connect(url)\n >>> get_author_name(soup)\n J.K. Rowling\n\n \"\"\"\n author_h1 = soup.find('h1', attrs={'class': 'authorName'})\n return author_h1.find('span').text\n\n\ndef get_author_desc(soup):\n \"\"\"Get the author description / biography.\n\n Args:\n soup (bs4.element.Tag): connection to the author page.\n\n Returns:\n str: long description of the author.\n\n Examples::\n >>> from scrapereads import connect\n >>> url = 'https://www.goodreads.com/author/show/1077326'\n >>> soup = connect(url)\n >>> get_author_desc(soup)\n See also: Robert Galbraith\n Although she writes under the pen name J.K. Rowling, pronounced like rolling,\n her name when her first Harry Potter book was published was simply Joanne Rowling.\n ...\n\n \"\"\"\n author_info_desc = soup.find('div', attrs={'class': 'aboutAuthorInfo'})\n author_info_long = author_info_desc.findAll('span')[-1]\n long_desc = \"\"\n for sentence in author_info_long.children:\n if isinstance(sentence, bs4.element.Tag):\n if sentence.name == 'br':\n long_desc += '\\n'\n else:\n long_desc += sentence.text\n else:\n long_desc += sentence\n long_desc = long_desc.replace('’', \"'\")\n return long_desc\n\n\ndef get_author_info(soup):\n \"\"\"Get all information from an author (genres, influences, website etc.).\n\n Args:\n soup (bs4.element.Tag): author page connection.\n\n Returns:\n dict\n\n \"\"\"\n container = soup.find('div', attrs={'class': 'rightContainer'})\n author_info = {}\n data_div = container.find('br', attrs={'class': 'clear'})\n while data_div:\n if data_div.name:\n data_class = data_div.get('class')[0]\n # Information section is finished\n if data_class == 'aboutAuthorInfo':\n break\n # Key elements\n elif data_class == 'dataTitle':\n key = data_div.text.strip()\n author_info[key] = []\n # Born section\n if data_div.text == 'Born':\n data_div = data_div.next_sibling\n author_info[key].append(data_div.strip())\n # Influences section\n elif data_div.text == 'Influences':\n data_div = data_div.next_sibling.next_sibling\n data_items = data_div.findAll('span')[-1].findAll('a')\n for data_a in data_items:\n author_info[key].append(data_a.text.strip())\n # Member since section\n elif data_div.text == 'Member Since':\n data_div = data_div.next_sibling.next_sibling\n author_info[key].append(data_div.text.strip())\n # Genre, website and other sections\n else:\n data_items = data_div.findAll('a')\n for data_a in data_items:\n author_info[key].append(data_a.text.strip())\n data_div = data_div.next_sibling\n author_info.update({'Description': get_author_desc(soup)})\n return author_info\n\n\ndef scrape_quotes_container(soup):\n \"\"\"Get the quote container from a quote page.\n\n Args:\n soup (bs4.element.Tag): connection to the quote page.\n\n Returns:\n bs4.element.Tag\n\n \"\"\"\n return soup.findAll('div', attrs={'class': 'quotes'})\n\n\ndef scrape_quotes(soup):\n \"\"\"Retrieve all ``<div>`` quote element from a quote page.\n\n Args:\n soup (bs4.element.Tag): connection to the quote page.\n\n Returns:\n yield bs4.element.Tag\n\n \"\"\"\n for container_div in scrape_quotes_container(soup):\n quote_div = container_div.find('div', attrs={'class': 'quote'})\n while quote_div:\n if quote_div.name == 'div' and quote_div.get('class') and 'quote' in quote_div.get('class'):\n yield quote_div\n quote_div = quote_div.next_sibling\n\n\ndef get_quote_text(quote_div):\n \"\"\"Get the text from a ``<div>`` quote element.\n\n Args:\n quote_div (bs4.element.Tag): ``<div>`` quote element to extract the text.\n\n Returns:\n string\n\n \"\"\"\n quote_text = ''\n text_iterator = quote_div.find('div', attrs={'class': 'quoteText'}).children\n for text in text_iterator:\n if text.name == 'br':\n quote_text += '\\n'\n elif not text.name:\n quote_text += text.strip()\n quote_text = process_quote_text(quote_text)\n return quote_text\n\n\ndef scrape_quote_tags(quote_div):\n \"\"\"Scrape tags from a ``<div>`` quote element.\n\n Args:\n quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page.\n\n Returns:\n yield ``<a>`` tags\n\n \"\"\"\n tags_container = quote_div.find('div', attrs={'class': 'greyText smallText left'})\n if tags_container:\n for tag in tags_container.children:\n if tag.name == 'a':\n yield tag\n return None\n\n\ndef get_quote_book(quote_div):\n \"\"\"Get the reference (book) from a ``<div>`` quote element.\n\n Args:\n quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page.\n\n Returns:\n bs4.element.Tag\n\n \"\"\"\n quote_details = quote_div.find('div', attrs={'class': 'quoteText'})\n return quote_details.find('a', attrs={'class': 'authorOrTitle'})\n\n\ndef get_quote_author_name(quote_div):\n \"\"\"Get the author's name from a ``<div>`` quote element.\n\n Args:\n quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page.\n\n Returns:\n string\n\n \"\"\"\n quote_text = quote_div.find('div', attrs={'class': 'quoteText '})\n author_name = quote_text.find('span', attrs={'class': 'authorOrTitle'}).text\n return remove_punctuation(author_name).title()\n\n\ndef get_quote_likes(quote_div):\n \"\"\"Get the likes ``<a>`` tag from a ``<div>`` quote element.\n\n Args:\n quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page.\n\n Returns:\n bs4.element.Tag: ``<a>`` tag for likes.\n\n \"\"\"\n quote_footer = quote_div.find('div', attrs={'class': 'quoteFooter'})\n return quote_footer.find('a', attrs={'class': 'smallText'})\n\n\n# TODO: deprecate this\ndef get_quote_name_id(quote_div):\n \"\"\"Get the name and id of a ``<div>`` quote element.\n\n Args:\n quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page.\n\n Returns:\n tuple: id and name.\n\n \"\"\"\n quote_href = get_quote_likes(quote_div).get('href')\n quote_id = quote_href.split('/')[-1].split('-')[0]\n quote_name = '-'.join(quote_href.split('/')[-1].split('-')[1:])\n return quote_id, quote_name\n\n\ndef scrape_author_books(soup):\n \"\"\"Retrieve books from an author's page.\n\n Args:\n soup (bs4.element.Tag): connection to an author books page.\n\n Returns:\n yield bs4.element.Tag: ``<tr>`` element.\n\n \"\"\"\n table_tr = soup.find('tr')\n while table_tr:\n if table_tr.name == 'tr':\n yield table_tr\n table_tr = table_tr.next_sibling\n\n\ndef get_author_book_title(book_tr):\n \"\"\"Get the book title ``<a>`` element from a table ``<tr>`` element from an author page.\n\n Args:\n book_tr (bs4.element.Tag): ``<tr>`` book element.\n\n Returns:\n bs4.element.Tag: book title ``<a>`` element.\n\n Examples::\n >>> for book_tr in scrape_author_books(soup):\n ... book_title = get_author_book_title(book_tr)\n ... print(book_title.text.strip(), book_title.get('href'))\n The Bell Jar /book/show/6514.The_Bell_Jar\n Ariel /book/show/395090.Ariel\n The Collected Poems /book/show/31426.The_Collected_Poems\n The Unabridged Journals of Sylvia Plath /book/show/11623.The_Unabridged_Journals_of_Sylvia_Plath\n\n \"\"\"\n return book_tr.find('a', attrs={'class': 'bookTitle'})\n\n\ndef get_author_book_author(book_tr):\n \"\"\"Get the author ``<a>`` element from a table ``<tr>`` element.\n\n Args:\n book_tr (bs4.element.Tag): ``<tr>`` book element.\n\n Returns:\n bs4.element.Tag: author name ``<a>`` element.\n\n Examples::\n >>> for book_tr in scrape_author_books(soup):\n ... book_author = get_author_book_author(book_tr)\n ... print(book_author.text, book_author.get('href'))\n Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath\n Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath\n Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath\n Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath\n Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath\n\n \"\"\"\n return book_tr.find('a', attrs={'class': 'authorName'})\n\n\ndef get_author_book_ratings(book_tr):\n \"\"\"Get the ratings ``<span>`` element from a table ``<tr>`` element from an author page.\n\n Args:\n book_tr (bs4.element.Tag): ``<tr>`` book element.\n\n Returns:\n bs4.element.Tag: ratings ``<span>`` element.\n\n Examples::\n >>> for book_tr in scrape_author_books(soup):\n ... ratings_span = get_author_book_ratings(book_tr)\n ... print(ratings_span.contents[-1])\n 4.55 avg rating — 2,414 ratings\n 3.77 avg rating — 1,689 ratings\n 4.28 avg rating — 892 ratings\n 4.54 avg rating — 490 ratings\n ...\n\n \"\"\"\n return book_tr.find('span', attrs={'class': 'minirating'})\n\n\ndef get_author_book_edition(book_tr):\n \"\"\"Get the edition ``<a>`` element from a table ``<tr>`` element from an author page.\n\n Args:\n book_tr (bs4.element.Tag): ``<tr>`` book element.\n\n Returns:\n bs4.element.Tag: book edition ``<a>`` element.\n\n Examples::\n >>> for book_tr in scrape_author_books(soup):\n ... book_edition = get_author_book_edition(book_tr)\n ... if book_edition:\n ... print(book_edition.text, book_edition.get('href'))\n ... print()\n 493 editions /work/editions/1385044-the-bell-jar\n 80 editions /work/editions/1185316-ariel\n 30 editions /work/editions/1003095-the-collected-poems\n 45 editions /work/editions/3094683-the-unabridged-journals-of-sylvia-plath\n ...\n\n \"\"\"\n book_details = book_tr.find('span', attrs={'class': 'greyText smallText uitext'})\n return book_details.find('a', attrs={'class': 'greyText'})\n\n\ndef get_author_book_date(book_tr):\n \"\"\"Get the published date from a table ``<tr>`` element from an author page.\n\n Args:\n book_tr (bs4.element.Tag): ``<tr>`` book element.\n\n Returns:\n int: date of publication\n\n Examples::\n >>> for book_tr in scrape_author_books(soup):\n ... book_date = get_author_book_date(book_tr)\n ... print(book_date)\n None\n None\n 1958\n 2009\n ...\n\n \"\"\"\n book_details = book_tr.find('span', attrs={'class': 'greyText smallText uitext'})\n book_publish = book_details.contents[-1].replace('—', '').replace('\\n', '')\n book_date = book_publish.replace('published', '').strip()\n book_date = eval(book_date) if book_date != '' else None\n return book_date\n\n\n# MASKED: get_book_quote_page function (lines 380-392)"},"implementation":{"kind":"string","value":"def get_book_quote_page(soup):\n \"\"\"Find the ``<a>`` element pointing to the quote page of a book.\n\n Args:\n soup (bs4.element.Tag):\n\n Returns:\n\n \"\"\"\n quote_div = soup.findAll('div', attrs={'class': ' clearFloats bigBox'})\n if quote_div:\n return quote_div[-1].find('a')\n return None"},"start_line":{"kind":"number","value":380,"string":"380"},"end_line":{"kind":"number","value":392,"string":"392"},"file_content":{"kind":"string","value":"\"\"\"\nScrape quotes, books and authors from ``Good Reads`` website.\n\"\"\"\n\nimport bs4\nfrom .utils import *\n\n\ndef get_author_name(soup):\n \"\"\"Get the author's name from its main page.\n\n Args:\n soup (bs4.element.Tag): connection to the author page.\n\n Returns:\n string: name of the author.\n\n Examples::\n >>> from scrapereads import connect\n >>> url = 'https://www.goodreads.com/author/show/1077326'\n >>> soup = connect(url)\n >>> get_author_name(soup)\n J.K. Rowling\n\n \"\"\"\n author_h1 = soup.find('h1', attrs={'class': 'authorName'})\n return author_h1.find('span').text\n\n\ndef get_author_desc(soup):\n \"\"\"Get the author description / biography.\n\n Args:\n soup (bs4.element.Tag): connection to the author page.\n\n Returns:\n str: long description of the author.\n\n Examples::\n >>> from scrapereads import connect\n >>> url = 'https://www.goodreads.com/author/show/1077326'\n >>> soup = connect(url)\n >>> get_author_desc(soup)\n See also: Robert Galbraith\n Although she writes under the pen name J.K. Rowling, pronounced like rolling,\n her name when her first Harry Potter book was published was simply Joanne Rowling.\n ...\n\n \"\"\"\n author_info_desc = soup.find('div', attrs={'class': 'aboutAuthorInfo'})\n author_info_long = author_info_desc.findAll('span')[-1]\n long_desc = \"\"\n for sentence in author_info_long.children:\n if isinstance(sentence, bs4.element.Tag):\n if sentence.name == 'br':\n long_desc += '\\n'\n else:\n long_desc += sentence.text\n else:\n long_desc += sentence\n long_desc = long_desc.replace('’', \"'\")\n return long_desc\n\n\ndef get_author_info(soup):\n \"\"\"Get all information from an author (genres, influences, website etc.).\n\n Args:\n soup (bs4.element.Tag): author page connection.\n\n Returns:\n dict\n\n \"\"\"\n container = soup.find('div', attrs={'class': 'rightContainer'})\n author_info = {}\n data_div = container.find('br', attrs={'class': 'clear'})\n while data_div:\n if data_div.name:\n data_class = data_div.get('class')[0]\n # Information section is finished\n if data_class == 'aboutAuthorInfo':\n break\n # Key elements\n elif data_class == 'dataTitle':\n key = data_div.text.strip()\n author_info[key] = []\n # Born section\n if data_div.text == 'Born':\n data_div = data_div.next_sibling\n author_info[key].append(data_div.strip())\n # Influences section\n elif data_div.text == 'Influences':\n data_div = data_div.next_sibling.next_sibling\n data_items = data_div.findAll('span')[-1].findAll('a')\n for data_a in data_items:\n author_info[key].append(data_a.text.strip())\n # Member since section\n elif data_div.text == 'Member Since':\n data_div = data_div.next_sibling.next_sibling\n author_info[key].append(data_div.text.strip())\n # Genre, website and other sections\n else:\n data_items = data_div.findAll('a')\n for data_a in data_items:\n author_info[key].append(data_a.text.strip())\n data_div = data_div.next_sibling\n author_info.update({'Description': get_author_desc(soup)})\n return author_info\n\n\ndef scrape_quotes_container(soup):\n \"\"\"Get the quote container from a quote page.\n\n Args:\n soup (bs4.element.Tag): connection to the quote page.\n\n Returns:\n bs4.element.Tag\n\n \"\"\"\n return soup.findAll('div', attrs={'class': 'quotes'})\n\n\ndef scrape_quotes(soup):\n \"\"\"Retrieve all ``<div>`` quote element from a quote page.\n\n Args:\n soup (bs4.element.Tag): connection to the quote page.\n\n Returns:\n yield bs4.element.Tag\n\n \"\"\"\n for container_div in scrape_quotes_container(soup):\n quote_div = container_div.find('div', attrs={'class': 'quote'})\n while quote_div:\n if quote_div.name == 'div' and quote_div.get('class') and 'quote' in quote_div.get('class'):\n yield quote_div\n quote_div = quote_div.next_sibling\n\n\ndef get_quote_text(quote_div):\n \"\"\"Get the text from a ``<div>`` quote element.\n\n Args:\n quote_div (bs4.element.Tag): ``<div>`` quote element to extract the text.\n\n Returns:\n string\n\n \"\"\"\n quote_text = ''\n text_iterator = quote_div.find('div', attrs={'class': 'quoteText'}).children\n for text in text_iterator:\n if text.name == 'br':\n quote_text += '\\n'\n elif not text.name:\n quote_text += text.strip()\n quote_text = process_quote_text(quote_text)\n return quote_text\n\n\ndef scrape_quote_tags(quote_div):\n \"\"\"Scrape tags from a ``<div>`` quote element.\n\n Args:\n quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page.\n\n Returns:\n yield ``<a>`` tags\n\n \"\"\"\n tags_container = quote_div.find('div', attrs={'class': 'greyText smallText left'})\n if tags_container:\n for tag in tags_container.children:\n if tag.name == 'a':\n yield tag\n return None\n\n\ndef get_quote_book(quote_div):\n \"\"\"Get the reference (book) from a ``<div>`` quote element.\n\n Args:\n quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page.\n\n Returns:\n bs4.element.Tag\n\n \"\"\"\n quote_details = quote_div.find('div', attrs={'class': 'quoteText'})\n return quote_details.find('a', attrs={'class': 'authorOrTitle'})\n\n\ndef get_quote_author_name(quote_div):\n \"\"\"Get the author's name from a ``<div>`` quote element.\n\n Args:\n quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page.\n\n Returns:\n string\n\n \"\"\"\n quote_text = quote_div.find('div', attrs={'class': 'quoteText '})\n author_name = quote_text.find('span', attrs={'class': 'authorOrTitle'}).text\n return remove_punctuation(author_name).title()\n\n\ndef get_quote_likes(quote_div):\n \"\"\"Get the likes ``<a>`` tag from a ``<div>`` quote element.\n\n Args:\n quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page.\n\n Returns:\n bs4.element.Tag: ``<a>`` tag for likes.\n\n \"\"\"\n quote_footer = quote_div.find('div', attrs={'class': 'quoteFooter'})\n return quote_footer.find('a', attrs={'class': 'smallText'})\n\n\n# TODO: deprecate this\ndef get_quote_name_id(quote_div):\n \"\"\"Get the name and id of a ``<div>`` quote element.\n\n Args:\n quote_div (bs4.element.Tag): ``<div>`` quote element from a quote page.\n\n Returns:\n tuple: id and name.\n\n \"\"\"\n quote_href = get_quote_likes(quote_div).get('href')\n quote_id = quote_href.split('/')[-1].split('-')[0]\n quote_name = '-'.join(quote_href.split('/')[-1].split('-')[1:])\n return quote_id, quote_name\n\n\ndef scrape_author_books(soup):\n \"\"\"Retrieve books from an author's page.\n\n Args:\n soup (bs4.element.Tag): connection to an author books page.\n\n Returns:\n yield bs4.element.Tag: ``<tr>`` element.\n\n \"\"\"\n table_tr = soup.find('tr')\n while table_tr:\n if table_tr.name == 'tr':\n yield table_tr\n table_tr = table_tr.next_sibling\n\n\ndef get_author_book_title(book_tr):\n \"\"\"Get the book title ``<a>`` element from a table ``<tr>`` element from an author page.\n\n Args:\n book_tr (bs4.element.Tag): ``<tr>`` book element.\n\n Returns:\n bs4.element.Tag: book title ``<a>`` element.\n\n Examples::\n >>> for book_tr in scrape_author_books(soup):\n ... book_title = get_author_book_title(book_tr)\n ... print(book_title.text.strip(), book_title.get('href'))\n The Bell Jar /book/show/6514.The_Bell_Jar\n Ariel /book/show/395090.Ariel\n The Collected Poems /book/show/31426.The_Collected_Poems\n The Unabridged Journals of Sylvia Plath /book/show/11623.The_Unabridged_Journals_of_Sylvia_Plath\n\n \"\"\"\n return book_tr.find('a', attrs={'class': 'bookTitle'})\n\n\ndef get_author_book_author(book_tr):\n \"\"\"Get the author ``<a>`` element from a table ``<tr>`` element.\n\n Args:\n book_tr (bs4.element.Tag): ``<tr>`` book element.\n\n Returns:\n bs4.element.Tag: author name ``<a>`` element.\n\n Examples::\n >>> for book_tr in scrape_author_books(soup):\n ... book_author = get_author_book_author(book_tr)\n ... print(book_author.text, book_author.get('href'))\n Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath\n Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath\n Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath\n Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath\n Sylvia Plath https://www.goodreads.com/author/show/4379.Sylvia_Plath\n\n \"\"\"\n return book_tr.find('a', attrs={'class': 'authorName'})\n\n\ndef get_author_book_ratings(book_tr):\n \"\"\"Get the ratings ``<span>`` element from a table ``<tr>`` element from an author page.\n\n Args:\n book_tr (bs4.element.Tag): ``<tr>`` book element.\n\n Returns:\n bs4.element.Tag: ratings ``<span>`` element.\n\n Examples::\n >>> for book_tr in scrape_author_books(soup):\n ... ratings_span = get_author_book_ratings(book_tr)\n ... print(ratings_span.contents[-1])\n 4.55 avg rating — 2,414 ratings\n 3.77 avg rating — 1,689 ratings\n 4.28 avg rating — 892 ratings\n 4.54 avg rating — 490 ratings\n ...\n\n \"\"\"\n return book_tr.find('span', attrs={'class': 'minirating'})\n\n\ndef get_author_book_edition(book_tr):\n \"\"\"Get the edition ``<a>`` element from a table ``<tr>`` element from an author page.\n\n Args:\n book_tr (bs4.element.Tag): ``<tr>`` book element.\n\n Returns:\n bs4.element.Tag: book edition ``<a>`` element.\n\n Examples::\n >>> for book_tr in scrape_author_books(soup):\n ... book_edition = get_author_book_edition(book_tr)\n ... if book_edition:\n ... print(book_edition.text, book_edition.get('href'))\n ... print()\n 493 editions /work/editions/1385044-the-bell-jar\n 80 editions /work/editions/1185316-ariel\n 30 editions /work/editions/1003095-the-collected-poems\n 45 editions /work/editions/3094683-the-unabridged-journals-of-sylvia-plath\n ...\n\n \"\"\"\n book_details = book_tr.find('span', attrs={'class': 'greyText smallText uitext'})\n return book_details.find('a', attrs={'class': 'greyText'})\n\n\ndef get_author_book_date(book_tr):\n \"\"\"Get the published date from a table ``<tr>`` element from an author page.\n\n Args:\n book_tr (bs4.element.Tag): ``<tr>`` book element.\n\n Returns:\n int: date of publication\n\n Examples::\n >>> for book_tr in scrape_author_books(soup):\n ... book_date = get_author_book_date(book_tr)\n ... print(book_date)\n None\n None\n 1958\n 2009\n ...\n\n \"\"\"\n book_details = book_tr.find('span', attrs={'class': 'greyText smallText uitext'})\n book_publish = book_details.contents[-1].replace('—', '').replace('\\n', '')\n book_date = book_publish.replace('published', '').strip()\n book_date = eval(book_date) if book_date != '' else None\n return book_date\n\n\ndef get_book_quote_page(soup):\n \"\"\"Find the ``<a>`` element pointing to the quote page of a book.\n\n Args:\n soup (bs4.element.Tag):\n\n Returns:\n\n \"\"\"\n quote_div = soup.findAll('div', attrs={'class': ' clearFloats bigBox'})\n if quote_div:\n return quote_div[-1].find('a')\n return None\n"}}},{"rowIdx":90,"cells":{"function_name":{"kind":"string","value":"find_generator"},"docstring":{"kind":"string","value":"The order of any element in a group can be divided by p-1.\nStep 1: Calculate all Divisors.\nStep 2: Test for a random element e of G wether e to the power of a Divisor is 1.\n if neither is one but e to the power of p-1, a generator is found."},"masked_code":{"kind":"string","value":"\"\"\"\nThis file implements the signature scheme from \"Unique Ring Signatures: A Practical\nConstruction\" by Matthew Franklin and Haibin Zhang\n\"\"\"\n\nimport sys\nimport math\nfrom random import randint\nimport hashlib\nfrom libsig.AbstractRingSignatureScheme import AbstractRingSignatureScheme\n#from AbstractRingSignatureScheme import AbstractRingSignatureScheme\n#from libsig import primes\n\n\n# ----------- HELPER FUNCTIONS ----------- \n\n# function to find divisors in order to find generators\ndef find_divisors(x):\n \"\"\"\n This is the \"function to find divisors in order to find generators\" module.\n This DocTest verifies that the module is correctly calculating all divisors\n of a number x.\n\n >>> find_divisors(10)\n [1, 2, 5, 10]\n\n >>> find_divisors(112)\n [1, 2, 4, 7, 8, 14, 16, 28, 56, 112]\n \"\"\"\n divisors = [ i for i in range(1,x+1) if x % i == 0]\n return divisors\n\n# function to find random generator of G\n# MASKED: find_generator function (lines 34-69)\n\ndef list_to_string(input_list):\n '''\n convert a list into a concatenated string of all its elements\n '''\n\n result = ''.join(map(str,input_list))\n return result\n\n\n# ----------- HELPER FUNCTIONS END ----------- \n\n\nclass UniqueRingSignature(AbstractRingSignatureScheme):\n '''\n | output: pp = (lamdba, q, G, H, H2) with,\n | q is prime,\n | g is generator of G,\n | G is multiplicative Group with prime order q,\n | H1 and H2 are two Hash functions H1: {0,1}* -> G,\n | (as well as H2: {0,1}* -> Zq which is the same).\n '''\n\n # set prime p (Sophie-Germain and therefore save)\n #q = 53\n q = 59\n # find random generator of G\n g = find_generator(q-1)\n\n # hash functions with desired range and the usage of secure hashes\n h1 = lambda x: int(hashlib.sha256(str(x).encode()).hexdigest(),16)%(UniqueRingSignature.q)\n # this way to share the information should be improved\n h2 = lambda x: int(hashlib.sha512(str(x).encode()).hexdigest(),16)%(UniqueRingSignature.q)\n\n # list of public keys\n Rp = list()\n\n @staticmethod\n def keygen(verbose=False):\n #print(\"---- KeyGen Started ---- \\n\")\n r = randint(1,UniqueRingSignature.q)\n # x = g**r % q\n x = pow(UniqueRingSignature.g, r,UniqueRingSignature.q)\n\n # y = g**x\n y = pow(UniqueRingSignature.g, x, UniqueRingSignature.q)\n\n if verbose == True:\n print(\"KeyGen Config: public key y=\" + str(y) + \", private key x=\" + str(x) + \"\\n\")\n print(\"---- KeyGen Completed ---- \\n\")\n # Caution! I know, keygen should NOT return the private key, but this is needed to \"play\" through a whole signature - validation process\n return x,y\n\n @staticmethod\n def ringsign(x, pubkey, message,verbose=False):\n '''\n input: x is the privkey from user i, \n | all public keys: pubkeys,\n | the message\n \n output: (R,m, (H(mR)^xi), c1,t1,...,cn,tn),\n | R: all the pubkeys concatenated,\n | cj,tj: random number within Zq\n ''' \n\n # calculate R = pk1,pk2,..,pkn\n R = list_to_string(pubkey)\n\n g = UniqueRingSignature.g\n q = UniqueRingSignature.q\n h1 = UniqueRingSignature.h1\n h2 = UniqueRingSignature.h2\n\n # message + pubkeys concatenated\n mR = message + str(R)\n\n C = list()\n T = list()\n A = list()\n B = list()\n ri = -1\n\n # simulation step\n #\n for i in pubkey:\n # Step 1:\n # \n a = 0 \n b = 0\n c = 0\n t = 0\n if pow(g,x,q) != i:\n c, t = randint(1,q), randint(1,q)\n a = (pow(g, t) * pow(int(i), c)) % q\n b = (pow(h1(mR), t) * pow(pow(h1(mR),x),c)) % q\n else:\n # Step 2:\n # \n ri = randint(1, q)\n a = pow(g, ri, q)\n b = pow(h1(mR), ri, q)\n\n # insert to allocate place\n c = -1\n t = -1\n\n A.append(a)\n B.append(b)\n C.append(c)\n T.append(t)\n # for end\n\n # Step 3:\n # \n cj = 0\n\n # list count from 0\n ab = ''.join('{}{}'.format(*t) for t in zip(A,B))\n\n usernr = 0\n for i in range(len(pubkey)):\n if pubkey[i] != (pow(g,x,q)):\n cj = (cj + C[i]) % q\n else: \n usernr = i\n\n ci = h2(message + R + ab) - (cj % (q-1))\n\n # update ci, this was initialized with -1\n C[usernr] = ci\n\n ti = ((ri - (C[usernr]*x)) % (q-1))\n if ti < 0:\n ti = (q-1) + ti\n\n # update ti, this was initialized with -1\n T[usernr] = ti\n\n # Step 4:\n # \n # concatenate ct: c1,t1,c2,t2,...,cn,tn\n ct = ','.join('{},{}'.format(*t) for t in zip(C,T))\n\n # returning result\n result = R + \",\"+message+\",\"+str(pow(h1(mR),x, q))+\",\" + ct \n if verbose == True:\n print(\"RingSign Result: \"+ result)\n print(\"---- RingSign Completed ---- \\n\")\n return result\n\n\n @staticmethod\n def verify(R, message, signature,verbose=False):\n '''\n Input: the public keys R\n | the message\n | the signature computed with ringsign\n\n Output: whether the message was signed by R or not\n '''\n\n g = UniqueRingSignature.g\n q = UniqueRingSignature.q\n h1 = UniqueRingSignature.h1\n h2 = UniqueRingSignature.h2\n\n # parse the signature\n parsed = signature.split(\",\")\n tt = int(parsed[2])\n cjs = list()\n tjs = list()\n for i in range(0,int(((len(parsed))/2)-1)):\n cjs.append(int(parsed[3+2*i]))\n tjs.append(int(parsed[4+2*i]))\n\n #print(str(cjs)+\" \"+str(tjs) + \" \"+ str(tt))\n\n # check signature\n # sum of all cjs\n # =?\n # self.pp['h2'](message + R + gyh1)\n\n mR = list_to_string(R)\n\n val1 = sum(cjs) % q\n # for all users in R:\n # g**tj * yj ** cj , h1(m||R)**tj * tt**cj\n gyh1 = \"\"\n for i in range(len(tjs)):\n if tjs[i] < 0:\n tjs[i] = (q-1) + tjs[i]\n if cjs[i] < 0:\n cjs[i] = (q-1) + cjs[i]\n\n gy = (pow(g,(tjs[i]),q) * (pow((R[i]),(cjs[i]),q))) % q\n h = (pow(int(h1(message + mR)), int(tjs[i])) * pow(tt,int(cjs[i]))) % q\n gyh1 = gyh1 + str( gy) + str( h)\n\n val2 = str(h2(message + list_to_string(R) + gyh1))\n if int(val1) == int(val2):\n if verbose == True:\n print(\"Signature is valid!\\n\")\n print(\"Common Result: \" + str(val1))\n print(\"---- Validation Completed ---- \\n\")\n return True\n else:\n if verbose == True:\n print(\"Signature is not valid!\\n\")\n print(str(val1) + \" != \" + str(val2))\n print(\"---- Validation Completed ---- \\n\")\n return False \n\ndef local_test(verbose=True):\n # verbose output\n print(verbose)\n \n # user 1 will signate and validate later,\n # therefore his private key is saved for test purposes\n privKey1,pubkey = UniqueRingSignature.keygen(verbose)\n UniqueRingSignature.Rp.append(pubkey)\n a,pubkey = UniqueRingSignature.keygen(verbose)\n UniqueRingSignature.Rp.append(pubkey)\n\n\n # usernr start from 0\n # ringsign(self, privkey, usernr, pubkeys, message)\n ring = UniqueRingSignature.ringsign(privKey1, UniqueRingSignature.Rp, \"asdf\", verbose)\n if verbose:\n print(\"Result of Signature Validation:\")\n # verify(pubkeys, message, signature):\n UniqueRingSignature.verify(UniqueRingSignature.Rp, \"asdf\", ring, verbose)\n\nif __name__ == '__main__':\n # doctest start\n import doctest\n doctest.testmod()\n\n if len(sys.argv) > 1:\n verbose = False\n if sys.argv[1] == \"True\":\n verbose = True\n # run a local test\n local_test(verbose)\n "},"implementation":{"kind":"string","value":"def find_generator(p):\n '''\n The order of any element in a group can be divided by p-1.\n Step 1: Calculate all Divisors.\n Step 2: Test for a random element e of G wether e to the power of a Divisor is 1.\n if neither is one but e to the power of p-1, a generator is found.\n '''\n\n # Init\n # Generate element which is tested for generator characteristics.\n # Saved in list to prevent checking the same element twice.\n testGen = randint(1,p)\n listTested = []\n listTested.append(testGen)\n\n # Step 1.\n divisors = find_divisors(p)\n\n # try for all random numbers\n # Caution: this leads to a truly random generator but is not very efficient.\n while len(listTested) < p-1:\n # only test each possible generator once\n if testGen in listTested:\n # Step 2.\n for div in divisors:\n testPotency = math.pow(testGen,div) % (p+1)\n if testPotency == 1.0 and div != divisors[-1]:\n # element does not have the same order like the group,\n # therefore try next element\n break\n elif testPotency == 1.0 and div == divisors[-1]:\n # generator is found\n return testGen\n # try new element\n testGen = randint(1,p)\n listTested.append(testGen)"},"start_line":{"kind":"number","value":34,"string":"34"},"end_line":{"kind":"number","value":69,"string":"69"},"file_content":{"kind":"string","value":"\"\"\"\nThis file implements the signature scheme from \"Unique Ring Signatures: A Practical\nConstruction\" by Matthew Franklin and Haibin Zhang\n\"\"\"\n\nimport sys\nimport math\nfrom random import randint\nimport hashlib\nfrom libsig.AbstractRingSignatureScheme import AbstractRingSignatureScheme\n#from AbstractRingSignatureScheme import AbstractRingSignatureScheme\n#from libsig import primes\n\n\n# ----------- HELPER FUNCTIONS ----------- \n\n# function to find divisors in order to find generators\ndef find_divisors(x):\n \"\"\"\n This is the \"function to find divisors in order to find generators\" module.\n This DocTest verifies that the module is correctly calculating all divisors\n of a number x.\n\n >>> find_divisors(10)\n [1, 2, 5, 10]\n\n >>> find_divisors(112)\n [1, 2, 4, 7, 8, 14, 16, 28, 56, 112]\n \"\"\"\n divisors = [ i for i in range(1,x+1) if x % i == 0]\n return divisors\n\n# function to find random generator of G\ndef find_generator(p):\n '''\n The order of any element in a group can be divided by p-1.\n Step 1: Calculate all Divisors.\n Step 2: Test for a random element e of G wether e to the power of a Divisor is 1.\n if neither is one but e to the power of p-1, a generator is found.\n '''\n\n # Init\n # Generate element which is tested for generator characteristics.\n # Saved in list to prevent checking the same element twice.\n testGen = randint(1,p)\n listTested = []\n listTested.append(testGen)\n\n # Step 1.\n divisors = find_divisors(p)\n\n # try for all random numbers\n # Caution: this leads to a truly random generator but is not very efficient.\n while len(listTested) < p-1:\n # only test each possible generator once\n if testGen in listTested:\n # Step 2.\n for div in divisors:\n testPotency = math.pow(testGen,div) % (p+1)\n if testPotency == 1.0 and div != divisors[-1]:\n # element does not have the same order like the group,\n # therefore try next element\n break\n elif testPotency == 1.0 and div == divisors[-1]:\n # generator is found\n return testGen\n # try new element\n testGen = randint(1,p)\n listTested.append(testGen)\n\ndef list_to_string(input_list):\n '''\n convert a list into a concatenated string of all its elements\n '''\n\n result = ''.join(map(str,input_list))\n return result\n\n\n# ----------- HELPER FUNCTIONS END ----------- \n\n\nclass UniqueRingSignature(AbstractRingSignatureScheme):\n '''\n | output: pp = (lamdba, q, G, H, H2) with,\n | q is prime,\n | g is generator of G,\n | G is multiplicative Group with prime order q,\n | H1 and H2 are two Hash functions H1: {0,1}* -> G,\n | (as well as H2: {0,1}* -> Zq which is the same).\n '''\n\n # set prime p (Sophie-Germain and therefore save)\n #q = 53\n q = 59\n # find random generator of G\n g = find_generator(q-1)\n\n # hash functions with desired range and the usage of secure hashes\n h1 = lambda x: int(hashlib.sha256(str(x).encode()).hexdigest(),16)%(UniqueRingSignature.q)\n # this way to share the information should be improved\n h2 = lambda x: int(hashlib.sha512(str(x).encode()).hexdigest(),16)%(UniqueRingSignature.q)\n\n # list of public keys\n Rp = list()\n\n @staticmethod\n def keygen(verbose=False):\n #print(\"---- KeyGen Started ---- \\n\")\n r = randint(1,UniqueRingSignature.q)\n # x = g**r % q\n x = pow(UniqueRingSignature.g, r,UniqueRingSignature.q)\n\n # y = g**x\n y = pow(UniqueRingSignature.g, x, UniqueRingSignature.q)\n\n if verbose == True:\n print(\"KeyGen Config: public key y=\" + str(y) + \", private key x=\" + str(x) + \"\\n\")\n print(\"---- KeyGen Completed ---- \\n\")\n # Caution! I know, keygen should NOT return the private key, but this is needed to \"play\" through a whole signature - validation process\n return x,y\n\n @staticmethod\n def ringsign(x, pubkey, message,verbose=False):\n '''\n input: x is the privkey from user i, \n | all public keys: pubkeys,\n | the message\n \n output: (R,m, (H(mR)^xi), c1,t1,...,cn,tn),\n | R: all the pubkeys concatenated,\n | cj,tj: random number within Zq\n ''' \n\n # calculate R = pk1,pk2,..,pkn\n R = list_to_string(pubkey)\n\n g = UniqueRingSignature.g\n q = UniqueRingSignature.q\n h1 = UniqueRingSignature.h1\n h2 = UniqueRingSignature.h2\n\n # message + pubkeys concatenated\n mR = message + str(R)\n\n C = list()\n T = list()\n A = list()\n B = list()\n ri = -1\n\n # simulation step\n #\n for i in pubkey:\n # Step 1:\n # \n a = 0 \n b = 0\n c = 0\n t = 0\n if pow(g,x,q) != i:\n c, t = randint(1,q), randint(1,q)\n a = (pow(g, t) * pow(int(i), c)) % q\n b = (pow(h1(mR), t) * pow(pow(h1(mR),x),c)) % q\n else:\n # Step 2:\n # \n ri = randint(1, q)\n a = pow(g, ri, q)\n b = pow(h1(mR), ri, q)\n\n # insert to allocate place\n c = -1\n t = -1\n\n A.append(a)\n B.append(b)\n C.append(c)\n T.append(t)\n # for end\n\n # Step 3:\n # \n cj = 0\n\n # list count from 0\n ab = ''.join('{}{}'.format(*t) for t in zip(A,B))\n\n usernr = 0\n for i in range(len(pubkey)):\n if pubkey[i] != (pow(g,x,q)):\n cj = (cj + C[i]) % q\n else: \n usernr = i\n\n ci = h2(message + R + ab) - (cj % (q-1))\n\n # update ci, this was initialized with -1\n C[usernr] = ci\n\n ti = ((ri - (C[usernr]*x)) % (q-1))\n if ti < 0:\n ti = (q-1) + ti\n\n # update ti, this was initialized with -1\n T[usernr] = ti\n\n # Step 4:\n # \n # concatenate ct: c1,t1,c2,t2,...,cn,tn\n ct = ','.join('{},{}'.format(*t) for t in zip(C,T))\n\n # returning result\n result = R + \",\"+message+\",\"+str(pow(h1(mR),x, q))+\",\" + ct \n if verbose == True:\n print(\"RingSign Result: \"+ result)\n print(\"---- RingSign Completed ---- \\n\")\n return result\n\n\n @staticmethod\n def verify(R, message, signature,verbose=False):\n '''\n Input: the public keys R\n | the message\n | the signature computed with ringsign\n\n Output: whether the message was signed by R or not\n '''\n\n g = UniqueRingSignature.g\n q = UniqueRingSignature.q\n h1 = UniqueRingSignature.h1\n h2 = UniqueRingSignature.h2\n\n # parse the signature\n parsed = signature.split(\",\")\n tt = int(parsed[2])\n cjs = list()\n tjs = list()\n for i in range(0,int(((len(parsed))/2)-1)):\n cjs.append(int(parsed[3+2*i]))\n tjs.append(int(parsed[4+2*i]))\n\n #print(str(cjs)+\" \"+str(tjs) + \" \"+ str(tt))\n\n # check signature\n # sum of all cjs\n # =?\n # self.pp['h2'](message + R + gyh1)\n\n mR = list_to_string(R)\n\n val1 = sum(cjs) % q\n # for all users in R:\n # g**tj * yj ** cj , h1(m||R)**tj * tt**cj\n gyh1 = \"\"\n for i in range(len(tjs)):\n if tjs[i] < 0:\n tjs[i] = (q-1) + tjs[i]\n if cjs[i] < 0:\n cjs[i] = (q-1) + cjs[i]\n\n gy = (pow(g,(tjs[i]),q) * (pow((R[i]),(cjs[i]),q))) % q\n h = (pow(int(h1(message + mR)), int(tjs[i])) * pow(tt,int(cjs[i]))) % q\n gyh1 = gyh1 + str( gy) + str( h)\n\n val2 = str(h2(message + list_to_string(R) + gyh1))\n if int(val1) == int(val2):\n if verbose == True:\n print(\"Signature is valid!\\n\")\n print(\"Common Result: \" + str(val1))\n print(\"---- Validation Completed ---- \\n\")\n return True\n else:\n if verbose == True:\n print(\"Signature is not valid!\\n\")\n print(str(val1) + \" != \" + str(val2))\n print(\"---- Validation Completed ---- \\n\")\n return False \n\ndef local_test(verbose=True):\n # verbose output\n print(verbose)\n \n # user 1 will signate and validate later,\n # therefore his private key is saved for test purposes\n privKey1,pubkey = UniqueRingSignature.keygen(verbose)\n UniqueRingSignature.Rp.append(pubkey)\n a,pubkey = UniqueRingSignature.keygen(verbose)\n UniqueRingSignature.Rp.append(pubkey)\n\n\n # usernr start from 0\n # ringsign(self, privkey, usernr, pubkeys, message)\n ring = UniqueRingSignature.ringsign(privKey1, UniqueRingSignature.Rp, \"asdf\", verbose)\n if verbose:\n print(\"Result of Signature Validation:\")\n # verify(pubkeys, message, signature):\n UniqueRingSignature.verify(UniqueRingSignature.Rp, \"asdf\", ring, verbose)\n\nif __name__ == '__main__':\n # doctest start\n import doctest\n doctest.testmod()\n\n if len(sys.argv) > 1:\n verbose = False\n if sys.argv[1] == \"True\":\n verbose = True\n # run a local test\n local_test(verbose)\n \n"}}},{"rowIdx":91,"cells":{"function_name":{"kind":"string","value":"children"},"docstring":{"kind":"string","value":"Get childen nodes of this node.\n\nArguments:\n depth: Number of levels of children to traverse. 0 returns only this node.\n include_self: Includes this node in the results.\n include_parents: Includes nodes that match in the results, when they also have\n child nodes that match.\n include_children: If True, as soon as a match is found it's children will not\n be included in the search.\n required_offset: Only match nodes with a source offset that contains this offset.\n offset_limits: Only match nodes when their source offset is contained inside\n this source offset.\n filters: Dictionary of {attribute: value} that children must match. Can also\n be given as a list of dicts, children that match one of the dicts\n will be returned.\n exclude_filter: Dictionary of {attribute:value} that children cannot match.\n\nReturns:\n List of node objects."},"masked_code":{"kind":"string","value":"#!/usr/bin/python3\n\nimport functools\nfrom copy import deepcopy\n\nfrom .grammar import BASE_NODE_TYPES\n\n\nclass NodeBase:\n \"\"\"Represents a node within the solidity AST.\n\n Attributes:\n depth: Number of nodes between this node and the SourceUnit\n offset: Absolute source offsets as a (start, stop) tuple\n contract_id: Contract ID as given by the standard compiler JSON\n fields: List of attributes for this node\n \"\"\"\n\n def __init__(self, ast, parent):\n self.depth = parent.depth + 1 if parent is not None else 0\n self._parent = parent\n self._children = set()\n src = [int(i) for i in ast[\"src\"].split(\":\")]\n self.offset = (src[0], src[0] + src[1])\n self.contract_id = src[2]\n self.fields = sorted(ast.keys())\n\n for key, value in ast.items():\n if isinstance(value, dict) and value.get(\"nodeType\") == \"Block\":\n value = value[\"statements\"]\n elif key == \"body\" and not value:\n value = []\n if isinstance(value, dict):\n item = node_class_factory(value, self)\n if isinstance(item, NodeBase):\n self._children.add(item)\n setattr(self, key, item)\n elif isinstance(value, list):\n items = [node_class_factory(i, self) for i in value]\n setattr(self, key, items)\n self._children.update(i for i in items if isinstance(i, NodeBase))\n else:\n setattr(self, key, value)\n\n def __hash__(self):\n return hash(f\"{self.nodeType}{self.depth}{self.offset}\")\n\n def __repr__(self):\n repr_str = f\"<{self.nodeType}\"\n if hasattr(self, \"nodes\"):\n repr_str += \" iterable\"\n if hasattr(self, \"type\"):\n if isinstance(self.type, str):\n repr_str += f\" {self.type}\"\n else:\n repr_str += f\" {self.type._display()}\"\n if self._display():\n repr_str += f\" '{self._display()}'\"\n else:\n repr_str += \" object\"\n return f\"{repr_str}>\"\n\n def _display(self):\n if hasattr(self, \"name\") and hasattr(self, \"value\"):\n return f\"{self.name} = {self.value}\"\n for attr in (\"name\", \"value\", \"absolutePath\"):\n if hasattr(self, attr):\n return f\"{getattr(self, attr)}\"\n return \"\"\n\n# MASKED: children function (lines 71-114)\n\n def parents(self, depth=-1, filters=None):\n \"\"\"Get parent nodes of this node.\n\n Arguments:\n depth: Depth limit. If given as a negative value, it will be subtracted\n from this object's depth.\n filters: Dictionary of {attribute: value} that parents must match.\n\n Returns: list of nodes\"\"\"\n if filters and not isinstance(filters, dict):\n raise TypeError(\"Filters must be a dict\")\n if depth < 0:\n depth = self.depth + depth\n if depth >= self.depth or depth < 0:\n raise IndexError(\"Given depth exceeds node depth\")\n node_list = []\n parent = self\n while True:\n parent = parent._parent\n if not filters or _check_filter(parent, filters, {}):\n node_list.append(parent)\n if parent.depth == depth:\n return node_list\n\n def parent(self, depth=-1, filters=None):\n \"\"\"Get a parent node of this node.\n\n Arguments:\n depth: Depth limit. If given as a negative value, it will be subtracted\n from this object's depth. The parent at this exact depth is returned.\n filters: Dictionary of {attribute: value} that the parent must match.\n\n If a filter value is given, will return the first parent that meets the filters\n up to the given depth. If none is found, returns None.\n\n If no filter is given, returns the parent at the given depth.\"\"\"\n if filters and not isinstance(filters, dict):\n raise TypeError(\"Filters must be a dict\")\n if depth < 0:\n depth = self.depth + depth\n if depth >= self.depth or depth < 0:\n raise IndexError(\"Given depth exceeds node depth\")\n parent = self\n while parent.depth > depth:\n parent = parent._parent\n if parent.depth == depth and not filters:\n return parent\n if filters and _check_filter(parent, filters, {}):\n return parent\n return None\n\n def is_child_of(self, node):\n \"\"\"Checks if this object is a child of the given node object.\"\"\"\n if node.depth >= self.depth:\n return False\n return self.parent(node.depth) == node\n\n def is_parent_of(self, node):\n \"\"\"Checks if this object is a parent of the given node object.\"\"\"\n if node.depth <= self.depth:\n return False\n return node.parent(self.depth) == self\n\n def get(self, key, default=None):\n \"\"\"\n Gets an attribute from this node, if that attribute exists.\n\n Arguments:\n key: Field name to return. May contain decimals to return a value\n from a child node.\n default: Default value to return.\n\n Returns: Field value if it exists. Default value if not.\n \"\"\"\n if key is None:\n raise TypeError(\"Cannot match against None\")\n obj = self\n for k in key.split(\".\"):\n if isinstance(obj, dict):\n obj = obj.get(k)\n else:\n obj = getattr(obj, k, None)\n return obj or default\n\n\nclass IterableNodeBase(NodeBase):\n def __getitem__(self, key):\n if isinstance(key, str):\n try:\n return next(i for i in self.nodes if getattr(i, \"name\", None) == key)\n except StopIteration:\n raise KeyError(key)\n return self.nodes[key]\n\n def __iter__(self):\n return iter(self.nodes)\n\n def __len__(self):\n return len(self.nodes)\n\n def __contains__(self, obj):\n return obj in self.nodes\n\n\ndef node_class_factory(ast, parent):\n ast = deepcopy(ast)\n if not isinstance(ast, dict) or \"nodeType\" not in ast:\n return ast\n if \"body\" in ast:\n ast[\"nodes\"] = ast.pop(\"body\")\n base_class = IterableNodeBase if \"nodes\" in ast else NodeBase\n base_type = next((k for k, v in BASE_NODE_TYPES.items() if ast[\"nodeType\"] in v), None)\n if base_type:\n ast[\"baseNodeType\"] = base_type\n return type(ast[\"nodeType\"], (base_class,), {})(ast, parent)\n\n\ndef _check_filters(required_offset, offset_limits, filters, exclude, node):\n if required_offset and not is_inside_offset(required_offset, node.offset):\n return False\n if offset_limits and not is_inside_offset(node.offset, offset_limits):\n return False\n for f in filters:\n if _check_filter(node, f, exclude):\n return True\n return False\n\n\ndef _check_filter(node, filters, exclude):\n for key, value in filters.items():\n if node.get(key) != value:\n return False\n for key, value in exclude.items():\n if node.get(key) == value:\n return False\n return True\n\n\ndef _find_children(filter_fn, include_parents, include_children, find_fn, depth, node):\n if depth is not None:\n depth -= 1\n if depth < 0:\n return [node] if filter_fn(node) else []\n if not include_children and filter_fn(node):\n return [node]\n node_list = []\n for child in node._children:\n node_list.extend(find_fn(find_fn, depth, child))\n if (include_parents or not node_list) and filter_fn(node):\n node_list.insert(0, node)\n return node_list\n\n\ndef is_inside_offset(inner, outer):\n \"\"\"Checks if the first offset is contained in the second offset\n\n Args:\n inner: inner offset tuple\n outer: outer offset tuple\n\n Returns: bool\"\"\"\n return outer[0] <= inner[0] <= inner[1] <= outer[1]"},"implementation":{"kind":"string","value":" def children(\n self,\n depth=None,\n include_self=False,\n include_parents=True,\n include_children=True,\n required_offset=None,\n offset_limits=None,\n filters=None,\n exclude_filter=None,\n ):\n \"\"\"Get childen nodes of this node.\n\n Arguments:\n depth: Number of levels of children to traverse. 0 returns only this node.\n include_self: Includes this node in the results.\n include_parents: Includes nodes that match in the results, when they also have\n child nodes that match.\n include_children: If True, as soon as a match is found it's children will not\n be included in the search.\n required_offset: Only match nodes with a source offset that contains this offset.\n offset_limits: Only match nodes when their source offset is contained inside\n this source offset.\n filters: Dictionary of {attribute: value} that children must match. Can also\n be given as a list of dicts, children that match one of the dicts\n will be returned.\n exclude_filter: Dictionary of {attribute:value} that children cannot match.\n\n Returns:\n List of node objects.\"\"\"\n if filters is None:\n filters = {}\n if exclude_filter is None:\n exclude_filter = {}\n if isinstance(filters, dict):\n filters = [filters]\n filter_fn = functools.partial(\n _check_filters, required_offset, offset_limits, filters, exclude_filter\n )\n find_fn = functools.partial(_find_children, filter_fn, include_parents, include_children)\n result = find_fn(find_fn, depth, self)\n if include_self or not result or result[0] != self:\n return result\n return result[1:]"},"start_line":{"kind":"number","value":71,"string":"71"},"end_line":{"kind":"number","value":114,"string":"114"},"file_content":{"kind":"string","value":"#!/usr/bin/python3\n\nimport functools\nfrom copy import deepcopy\n\nfrom .grammar import BASE_NODE_TYPES\n\n\nclass NodeBase:\n \"\"\"Represents a node within the solidity AST.\n\n Attributes:\n depth: Number of nodes between this node and the SourceUnit\n offset: Absolute source offsets as a (start, stop) tuple\n contract_id: Contract ID as given by the standard compiler JSON\n fields: List of attributes for this node\n \"\"\"\n\n def __init__(self, ast, parent):\n self.depth = parent.depth + 1 if parent is not None else 0\n self._parent = parent\n self._children = set()\n src = [int(i) for i in ast[\"src\"].split(\":\")]\n self.offset = (src[0], src[0] + src[1])\n self.contract_id = src[2]\n self.fields = sorted(ast.keys())\n\n for key, value in ast.items():\n if isinstance(value, dict) and value.get(\"nodeType\") == \"Block\":\n value = value[\"statements\"]\n elif key == \"body\" and not value:\n value = []\n if isinstance(value, dict):\n item = node_class_factory(value, self)\n if isinstance(item, NodeBase):\n self._children.add(item)\n setattr(self, key, item)\n elif isinstance(value, list):\n items = [node_class_factory(i, self) for i in value]\n setattr(self, key, items)\n self._children.update(i for i in items if isinstance(i, NodeBase))\n else:\n setattr(self, key, value)\n\n def __hash__(self):\n return hash(f\"{self.nodeType}{self.depth}{self.offset}\")\n\n def __repr__(self):\n repr_str = f\"<{self.nodeType}\"\n if hasattr(self, \"nodes\"):\n repr_str += \" iterable\"\n if hasattr(self, \"type\"):\n if isinstance(self.type, str):\n repr_str += f\" {self.type}\"\n else:\n repr_str += f\" {self.type._display()}\"\n if self._display():\n repr_str += f\" '{self._display()}'\"\n else:\n repr_str += \" object\"\n return f\"{repr_str}>\"\n\n def _display(self):\n if hasattr(self, \"name\") and hasattr(self, \"value\"):\n return f\"{self.name} = {self.value}\"\n for attr in (\"name\", \"value\", \"absolutePath\"):\n if hasattr(self, attr):\n return f\"{getattr(self, attr)}\"\n return \"\"\n\n def children(\n self,\n depth=None,\n include_self=False,\n include_parents=True,\n include_children=True,\n required_offset=None,\n offset_limits=None,\n filters=None,\n exclude_filter=None,\n ):\n \"\"\"Get childen nodes of this node.\n\n Arguments:\n depth: Number of levels of children to traverse. 0 returns only this node.\n include_self: Includes this node in the results.\n include_parents: Includes nodes that match in the results, when they also have\n child nodes that match.\n include_children: If True, as soon as a match is found it's children will not\n be included in the search.\n required_offset: Only match nodes with a source offset that contains this offset.\n offset_limits: Only match nodes when their source offset is contained inside\n this source offset.\n filters: Dictionary of {attribute: value} that children must match. Can also\n be given as a list of dicts, children that match one of the dicts\n will be returned.\n exclude_filter: Dictionary of {attribute:value} that children cannot match.\n\n Returns:\n List of node objects.\"\"\"\n if filters is None:\n filters = {}\n if exclude_filter is None:\n exclude_filter = {}\n if isinstance(filters, dict):\n filters = [filters]\n filter_fn = functools.partial(\n _check_filters, required_offset, offset_limits, filters, exclude_filter\n )\n find_fn = functools.partial(_find_children, filter_fn, include_parents, include_children)\n result = find_fn(find_fn, depth, self)\n if include_self or not result or result[0] != self:\n return result\n return result[1:]\n\n def parents(self, depth=-1, filters=None):\n \"\"\"Get parent nodes of this node.\n\n Arguments:\n depth: Depth limit. If given as a negative value, it will be subtracted\n from this object's depth.\n filters: Dictionary of {attribute: value} that parents must match.\n\n Returns: list of nodes\"\"\"\n if filters and not isinstance(filters, dict):\n raise TypeError(\"Filters must be a dict\")\n if depth < 0:\n depth = self.depth + depth\n if depth >= self.depth or depth < 0:\n raise IndexError(\"Given depth exceeds node depth\")\n node_list = []\n parent = self\n while True:\n parent = parent._parent\n if not filters or _check_filter(parent, filters, {}):\n node_list.append(parent)\n if parent.depth == depth:\n return node_list\n\n def parent(self, depth=-1, filters=None):\n \"\"\"Get a parent node of this node.\n\n Arguments:\n depth: Depth limit. If given as a negative value, it will be subtracted\n from this object's depth. The parent at this exact depth is returned.\n filters: Dictionary of {attribute: value} that the parent must match.\n\n If a filter value is given, will return the first parent that meets the filters\n up to the given depth. If none is found, returns None.\n\n If no filter is given, returns the parent at the given depth.\"\"\"\n if filters and not isinstance(filters, dict):\n raise TypeError(\"Filters must be a dict\")\n if depth < 0:\n depth = self.depth + depth\n if depth >= self.depth or depth < 0:\n raise IndexError(\"Given depth exceeds node depth\")\n parent = self\n while parent.depth > depth:\n parent = parent._parent\n if parent.depth == depth and not filters:\n return parent\n if filters and _check_filter(parent, filters, {}):\n return parent\n return None\n\n def is_child_of(self, node):\n \"\"\"Checks if this object is a child of the given node object.\"\"\"\n if node.depth >= self.depth:\n return False\n return self.parent(node.depth) == node\n\n def is_parent_of(self, node):\n \"\"\"Checks if this object is a parent of the given node object.\"\"\"\n if node.depth <= self.depth:\n return False\n return node.parent(self.depth) == self\n\n def get(self, key, default=None):\n \"\"\"\n Gets an attribute from this node, if that attribute exists.\n\n Arguments:\n key: Field name to return. May contain decimals to return a value\n from a child node.\n default: Default value to return.\n\n Returns: Field value if it exists. Default value if not.\n \"\"\"\n if key is None:\n raise TypeError(\"Cannot match against None\")\n obj = self\n for k in key.split(\".\"):\n if isinstance(obj, dict):\n obj = obj.get(k)\n else:\n obj = getattr(obj, k, None)\n return obj or default\n\n\nclass IterableNodeBase(NodeBase):\n def __getitem__(self, key):\n if isinstance(key, str):\n try:\n return next(i for i in self.nodes if getattr(i, \"name\", None) == key)\n except StopIteration:\n raise KeyError(key)\n return self.nodes[key]\n\n def __iter__(self):\n return iter(self.nodes)\n\n def __len__(self):\n return len(self.nodes)\n\n def __contains__(self, obj):\n return obj in self.nodes\n\n\ndef node_class_factory(ast, parent):\n ast = deepcopy(ast)\n if not isinstance(ast, dict) or \"nodeType\" not in ast:\n return ast\n if \"body\" in ast:\n ast[\"nodes\"] = ast.pop(\"body\")\n base_class = IterableNodeBase if \"nodes\" in ast else NodeBase\n base_type = next((k for k, v in BASE_NODE_TYPES.items() if ast[\"nodeType\"] in v), None)\n if base_type:\n ast[\"baseNodeType\"] = base_type\n return type(ast[\"nodeType\"], (base_class,), {})(ast, parent)\n\n\ndef _check_filters(required_offset, offset_limits, filters, exclude, node):\n if required_offset and not is_inside_offset(required_offset, node.offset):\n return False\n if offset_limits and not is_inside_offset(node.offset, offset_limits):\n return False\n for f in filters:\n if _check_filter(node, f, exclude):\n return True\n return False\n\n\ndef _check_filter(node, filters, exclude):\n for key, value in filters.items():\n if node.get(key) != value:\n return False\n for key, value in exclude.items():\n if node.get(key) == value:\n return False\n return True\n\n\ndef _find_children(filter_fn, include_parents, include_children, find_fn, depth, node):\n if depth is not None:\n depth -= 1\n if depth < 0:\n return [node] if filter_fn(node) else []\n if not include_children and filter_fn(node):\n return [node]\n node_list = []\n for child in node._children:\n node_list.extend(find_fn(find_fn, depth, child))\n if (include_parents or not node_list) and filter_fn(node):\n node_list.insert(0, node)\n return node_list\n\n\ndef is_inside_offset(inner, outer):\n \"\"\"Checks if the first offset is contained in the second offset\n\n Args:\n inner: inner offset tuple\n outer: outer offset tuple\n\n Returns: bool\"\"\"\n return outer[0] <= inner[0] <= inner[1] <= outer[1]\n"}}},{"rowIdx":92,"cells":{"function_name":{"kind":"string","value":"pqcost"},"docstring":{"kind":"string","value":"Splits the gencost variable into two pieces if costs are given for Qg.\n\nChecks whether C{gencost} has cost information for reactive power\ngeneration (rows C{ng+1} to C{2*ng}). If so, it returns the first C{ng}\nrows in C{pcost} and the last C{ng} rows in C{qcost}. Otherwise, leaves\nC{qcost} empty. Also does some error checking.\nIf C{on} is specified (list of indices of generators which are on line)\nit only returns the rows corresponding to these generators.\n\n@author: Ray Zimmerman (PSERC Cornell)"},"masked_code":{"kind":"string","value":"# Copyright (c) 1996-2015 PSERC. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\n\"\"\"Splits the gencost variable into two pieces if costs are given for Qg.\n\"\"\"\n\nfrom sys import stderr\n\nfrom numpy import array, arange\n\n\n# MASKED: pqcost function (lines 13-37)"},"implementation":{"kind":"string","value":"def pqcost(gencost, ng, on=None):\n \"\"\"Splits the gencost variable into two pieces if costs are given for Qg.\n\n Checks whether C{gencost} has cost information for reactive power\n generation (rows C{ng+1} to C{2*ng}). If so, it returns the first C{ng}\n rows in C{pcost} and the last C{ng} rows in C{qcost}. Otherwise, leaves\n C{qcost} empty. Also does some error checking.\n If C{on} is specified (list of indices of generators which are on line)\n it only returns the rows corresponding to these generators.\n\n @author: Ray Zimmerman (PSERC Cornell)\n \"\"\"\n if on is None:\n on = arange(ng)\n\n if gencost.shape[0] == ng:\n pcost = gencost[on, :]\n qcost = array([])\n elif gencost.shape[0] == 2 * ng:\n pcost = gencost[on, :]\n qcost = gencost[on + ng, :]\n else:\n stderr.write('pqcost: gencost has wrong number of rows\\n')\n\n return pcost, qcost"},"start_line":{"kind":"number","value":13,"string":"13"},"end_line":{"kind":"number","value":37,"string":"37"},"file_content":{"kind":"string","value":"# Copyright (c) 1996-2015 PSERC. All rights reserved.\n# Use of this source code is governed by a BSD-style\n# license that can be found in the LICENSE file.\n\n\"\"\"Splits the gencost variable into two pieces if costs are given for Qg.\n\"\"\"\n\nfrom sys import stderr\n\nfrom numpy import array, arange\n\n\ndef pqcost(gencost, ng, on=None):\n \"\"\"Splits the gencost variable into two pieces if costs are given for Qg.\n\n Checks whether C{gencost} has cost information for reactive power\n generation (rows C{ng+1} to C{2*ng}). If so, it returns the first C{ng}\n rows in C{pcost} and the last C{ng} rows in C{qcost}. Otherwise, leaves\n C{qcost} empty. Also does some error checking.\n If C{on} is specified (list of indices of generators which are on line)\n it only returns the rows corresponding to these generators.\n\n @author: Ray Zimmerman (PSERC Cornell)\n \"\"\"\n if on is None:\n on = arange(ng)\n\n if gencost.shape[0] == ng:\n pcost = gencost[on, :]\n qcost = array([])\n elif gencost.shape[0] == 2 * ng:\n pcost = gencost[on, :]\n qcost = gencost[on + ng, :]\n else:\n stderr.write('pqcost: gencost has wrong number of rows\\n')\n\n return pcost, qcost\n"}}},{"rowIdx":93,"cells":{"function_name":{"kind":"string","value":"thumbIncrementCheck"},"docstring":{"kind":"string","value":"Checks whether your thumb is up or not.\nNo matter what hand you use.\nreturns 1 if thumb is up else 0"},"masked_code":{"kind":"string","value":"\"\"\"Python 3.9.5\"\"\"\nimport cv2\n\nimport HandTrackingModule as htm\n\n\n# MASKED: thumbIncrementCheck function (lines 7-20)\n\n\ndef textOutput(count, cc) -> str:\n \"\"\"Returns an appropriate text output depending on\n `count` and `cc`.\"\"\"\n text = \"NOTHING\"\n if (count, cc) == (2, 2):\n text = \"SCISSOR\"\n elif count == 0:\n text = \"ROCK\"\n elif count == 5:\n text = \"PAPER\"\n else:\n pass\n return text\n\n\ndef main():\n # cap = cv2.VideoCapture(0) # opens the camera\n detector = htm.HandDetector()\n\n while True:\n success, img = cv2.imread(\"/home/laughinglouds/Pictures/Webcam/2021-04-13-133250.jpg\")\n img = detector.findHands(img)\n lmlist = detector.findPosition(img, draw=True)\n\n # If a hand is not detected value will be 0\n # else non-zero (21)\n hand_exists = len(lmlist)\n\n tipIDs = [4, 8, 12, 16, 20] # Represents fingertips\n dipIDs = [2, 7, 11, 15, 19] # Represents landmarks below the tips\n count = 0 # keeps count of how many fingers are up\n cc = 0 # for later checking if `Scissor` or not\n if hand_exists:\n # Looping for the five fingers\n for i in range(0, 5):\n if i == 0:\n count += thumbIncrementCheck(lmlist)\n else:\n # 8: Index finger\n # 12: Middle finger\n if (lmlist[tipIDs[i]][2] < lmlist[dipIDs[i]][2]) and (\n tipIDs[i] in (8, 12) # if either index or middle\n ):\n count += 1\n cc += 1\n elif lmlist[tipIDs[i]][2] < lmlist[dipIDs[i]][2]:\n count += 1\n # print(cc)\n else:\n count = -1\n\n txt = textOutput(count, cc)\n\n # (10, 140) is coordinate of txt on the screen\n cv2.putText(img, str(txt), (10, 140), cv2.FONT_HERSHEY_PLAIN, 3, (0, 0, 255), 3)\n \n cv2.imshow(\"Image\", img)\n # close key isn't working for me\n # os: linux mint 20.1\n if cv2.waitKey(1) & 0xFF == ord(\"q\"):\n break\n\nif __name__ == \"__main__\":\n main()"},"implementation":{"kind":"string","value":"def thumbIncrementCheck(lmList: list[list[int]]) -> int:\n \"\"\"Checks whether your thumb is up or not.\n No matter what hand you use.\n returns 1 if thumb is up else 0\"\"\"\n count = 0\n t_x = lmList[4][1]\n p_x = lmList[17][1]\n if t_x > p_x: # If true: RIGHT hand\n if lmList[4][1] >= lmList[2][1]:\n count += 1\n else: # ELse: LEFT hand\n if lmList[4][1] <= lmList[2][1]:\n count += 1\n return count"},"start_line":{"kind":"number","value":7,"string":"7"},"end_line":{"kind":"number","value":20,"string":"20"},"file_content":{"kind":"string","value":"\"\"\"Python 3.9.5\"\"\"\nimport cv2\n\nimport HandTrackingModule as htm\n\n\ndef thumbIncrementCheck(lmList: list[list[int]]) -> int:\n \"\"\"Checks whether your thumb is up or not.\n No matter what hand you use.\n returns 1 if thumb is up else 0\"\"\"\n count = 0\n t_x = lmList[4][1]\n p_x = lmList[17][1]\n if t_x > p_x: # If true: RIGHT hand\n if lmList[4][1] >= lmList[2][1]:\n count += 1\n else: # ELse: LEFT hand\n if lmList[4][1] <= lmList[2][1]:\n count += 1\n return count\n\n\ndef textOutput(count, cc) -> str:\n \"\"\"Returns an appropriate text output depending on\n `count` and `cc`.\"\"\"\n text = \"NOTHING\"\n if (count, cc) == (2, 2):\n text = \"SCISSOR\"\n elif count == 0:\n text = \"ROCK\"\n elif count == 5:\n text = \"PAPER\"\n else:\n pass\n return text\n\n\ndef main():\n # cap = cv2.VideoCapture(0) # opens the camera\n detector = htm.HandDetector()\n\n while True:\n success, img = cv2.imread(\"/home/laughinglouds/Pictures/Webcam/2021-04-13-133250.jpg\")\n img = detector.findHands(img)\n lmlist = detector.findPosition(img, draw=True)\n\n # If a hand is not detected value will be 0\n # else non-zero (21)\n hand_exists = len(lmlist)\n\n tipIDs = [4, 8, 12, 16, 20] # Represents fingertips\n dipIDs = [2, 7, 11, 15, 19] # Represents landmarks below the tips\n count = 0 # keeps count of how many fingers are up\n cc = 0 # for later checking if `Scissor` or not\n if hand_exists:\n # Looping for the five fingers\n for i in range(0, 5):\n if i == 0:\n count += thumbIncrementCheck(lmlist)\n else:\n # 8: Index finger\n # 12: Middle finger\n if (lmlist[tipIDs[i]][2] < lmlist[dipIDs[i]][2]) and (\n tipIDs[i] in (8, 12) # if either index or middle\n ):\n count += 1\n cc += 1\n elif lmlist[tipIDs[i]][2] < lmlist[dipIDs[i]][2]:\n count += 1\n # print(cc)\n else:\n count = -1\n\n txt = textOutput(count, cc)\n\n # (10, 140) is coordinate of txt on the screen\n cv2.putText(img, str(txt), (10, 140), cv2.FONT_HERSHEY_PLAIN, 3, (0, 0, 255), 3)\n \n cv2.imshow(\"Image\", img)\n # close key isn't working for me\n # os: linux mint 20.1\n if cv2.waitKey(1) & 0xFF == ord(\"q\"):\n break\n\nif __name__ == \"__main__\":\n main()\n"}}},{"rowIdx":94,"cells":{"function_name":{"kind":"string","value":"_read_kit_data"},"docstring":{"kind":"string","value":"Read epochs data.\n\nReturns\n-------\ndata : array, [channels x samples]\n the data matrix (channels x samples).\ntimes : array, [samples]\n returns the time values corresponding to the samples."},"masked_code":{"kind":"string","value":"\"\"\"Conversion tool from SQD to FIF.\n\nRawKIT class is adapted from Denis Engemann et al.'s mne_bti2fiff.py.\n\"\"\"\n\n# Authors: Teon Brooks <teon.brooks@gmail.com>\n# Joan Massich <mailsik@gmail.com>\n# Christian Brodbeck <christianbrodbeck@nyu.edu>\n#\n# License: BSD (3-clause)\n\nfrom collections import defaultdict, OrderedDict\nfrom math import sin, cos\nfrom os import SEEK_CUR, path as op\nfrom struct import unpack\n\nimport numpy as np\nfrom scipy import linalg\n\nfrom ..pick import pick_types\nfrom ...utils import (verbose, logger, warn, fill_doc, _check_option,\n _stamp_to_dt)\nfrom ...transforms import apply_trans, als_ras_trans\nfrom ..base import BaseRaw\nfrom ..utils import _mult_cal_one\nfrom ...epochs import BaseEpochs\nfrom ..constants import FIFF\nfrom ..meas_info import _empty_info\nfrom .constants import KIT, LEGACY_AMP_PARAMS\nfrom .coreg import read_mrk\nfrom ...event import read_events\n\nfrom .._digitization import _set_dig_kit\n\n\ndef _call_digitization(info, mrk, elp, hsp, kit_info):\n # Use values from kit_info only if all others are None\n if mrk is None and elp is None and hsp is None:\n mrk = kit_info.get('mrk', None)\n elp = kit_info.get('elp', None)\n hsp = kit_info.get('hsp', None)\n\n # prepare mrk\n if isinstance(mrk, list):\n mrk = [read_mrk(marker) if isinstance(marker, str)\n else marker for marker in mrk]\n mrk = np.mean(mrk, axis=0)\n\n # setup digitization\n if mrk is not None and elp is not None and hsp is not None:\n dig_points, dev_head_t = _set_dig_kit(\n mrk, elp, hsp, kit_info['eeg_dig'])\n info['dig'] = dig_points\n info['dev_head_t'] = dev_head_t\n elif mrk is not None or elp is not None or hsp is not None:\n raise ValueError(\"mrk, elp and hsp need to be provided as a group \"\n \"(all or none)\")\n\n return info\n\n\nclass UnsupportedKITFormat(ValueError):\n \"\"\"Our reader is not guaranteed to work with old files.\"\"\"\n\n def __init__(self, sqd_version, *args, **kwargs): # noqa: D102\n self.sqd_version = sqd_version\n ValueError.__init__(self, *args, **kwargs)\n\n\n@fill_doc\nclass RawKIT(BaseRaw):\n \"\"\"Raw object from KIT SQD file.\n\n Parameters\n ----------\n input_fname : str\n Path to the sqd file.\n mrk : None | str | array_like, shape (5, 3) | list of str or array_like\n Marker points representing the location of the marker coils with\n respect to the MEG Sensors, or path to a marker file.\n If list, all of the markers will be averaged together.\n elp : None | str | array_like, shape (8, 3)\n Digitizer points representing the location of the fiducials and the\n marker coils with respect to the digitized head shape, or path to a\n file containing these points.\n hsp : None | str | array, shape (n_points, 3)\n Digitizer head shape points, or path to head shape file. If more than\n 10,000 points are in the head shape, they are automatically decimated.\n stim : list of int | '<' | '>' | None\n Channel-value correspondence when converting KIT trigger channels to a\n Neuromag-style stim channel. For '<', the largest values are assigned\n to the first channel (default). For '>', the largest values are\n assigned to the last channel. Can also be specified as a list of\n trigger channel indexes. If None, no synthesized channel is generated.\n slope : '+' | '-'\n How to interpret values on KIT trigger channels when synthesizing a\n Neuromag-style stim channel. With '+', a positive slope (low-to-high)\n is interpreted as an event. With '-', a negative slope (high-to-low)\n is interpreted as an event.\n stimthresh : float\n The threshold level for accepting voltage changes in KIT trigger\n channels as a trigger event. If None, stim must also be set to None.\n %(preload)s\n stim_code : 'binary' | 'channel'\n How to decode trigger values from stim channels. 'binary' read stim\n channel events as binary code, 'channel' encodes channel number.\n allow_unknown_format : bool\n Force reading old data that is not officially supported. Alternatively,\n read and re-save the data with the KIT MEG Laboratory application.\n %(standardize_names)s\n %(verbose)s\n\n Notes\n -----\n ``elp`` and ``hsp`` are usually the exported text files (*.txt) from the\n Polhemus FastScan system. hsp refers to the headshape surface points. elp\n refers to the points in head-space that corresponds to the HPI points.\n Currently, '*.elp' and '*.hsp' files are NOT supported.\n\n See Also\n --------\n mne.io.Raw : Documentation of attribute and methods.\n \"\"\"\n\n @verbose\n def __init__(self, input_fname, mrk=None, elp=None, hsp=None, stim='>',\n slope='-', stimthresh=1, preload=False, stim_code='binary',\n allow_unknown_format=False, standardize_names=None,\n verbose=None): # noqa: D102\n logger.info('Extracting SQD Parameters from %s...' % input_fname)\n input_fname = op.abspath(input_fname)\n self.preload = False\n logger.info('Creating Raw.info structure...')\n info, kit_info = get_kit_info(\n input_fname, allow_unknown_format, standardize_names)\n kit_info['slope'] = slope\n kit_info['stimthresh'] = stimthresh\n if kit_info['acq_type'] != KIT.CONTINUOUS:\n raise TypeError('SQD file contains epochs, not raw data. Wrong '\n 'reader.')\n logger.info('Creating Info structure...')\n\n last_samps = [kit_info['n_samples'] - 1]\n self._raw_extras = [kit_info]\n self._set_stimchannels(info, stim, stim_code)\n super(RawKIT, self).__init__(\n info, preload, last_samps=last_samps, filenames=[input_fname],\n raw_extras=self._raw_extras, verbose=verbose)\n self.info = _call_digitization(\n info=self.info, mrk=mrk, elp=elp, hsp=hsp, kit_info=kit_info)\n logger.info('Ready.')\n\n def read_stim_ch(self, buffer_size=1e5):\n \"\"\"Read events from data.\n\n Parameter\n ---------\n buffer_size : int\n The size of chunk to by which the data are scanned.\n\n Returns\n -------\n events : array, [samples]\n The event vector (1 x samples).\n \"\"\"\n buffer_size = int(buffer_size)\n start = int(self.first_samp)\n stop = int(self.last_samp + 1)\n\n pick = pick_types(self.info, meg=False, ref_meg=False,\n stim=True, exclude=[])\n stim_ch = np.empty((1, stop), dtype=np.int64)\n for b_start in range(start, stop, buffer_size):\n b_stop = b_start + buffer_size\n x = self[pick, b_start:b_stop][0]\n stim_ch[:, b_start:b_start + x.shape[1]] = x\n\n return stim_ch\n\n def _set_stimchannels(self, info, stim, stim_code):\n \"\"\"Specify how the trigger channel is synthesized from analog channels.\n\n Has to be done before loading data. For a RawKIT instance that has been\n created with preload=True, this method will raise a\n NotImplementedError.\n\n Parameters\n ----------\n info : instance of MeasInfo\n The measurement info.\n stim : list of int | '<' | '>'\n Can be submitted as list of trigger channels.\n If a list is not specified, the default triggers extracted from\n misc channels will be used with specified directionality.\n '<' means that largest values assigned to the first channel\n in sequence.\n '>' means the largest trigger assigned to the last channel\n in sequence.\n stim_code : 'binary' | 'channel'\n How to decode trigger values from stim channels. 'binary' read stim\n channel events as binary code, 'channel' encodes channel number.\n \"\"\"\n if self.preload:\n raise NotImplementedError(\"Can't change stim channel after \"\n \"loading data\")\n _check_option('stim_code', stim_code, ['binary', 'channel'])\n\n if stim is not None:\n if isinstance(stim, str):\n picks = _default_stim_chs(info)\n if stim == '<':\n stim = picks[::-1]\n elif stim == '>':\n stim = picks\n else:\n raise ValueError(\"stim needs to be list of int, '>' or \"\n \"'<', not %r\" % str(stim))\n else:\n stim = np.asarray(stim, int)\n if stim.max() >= self._raw_extras[0]['nchan']:\n raise ValueError(\n 'Got stim=%s, but sqd file only has %i channels' %\n (stim, self._raw_extras[0]['nchan']))\n\n # modify info\n nchan = self._raw_extras[0]['nchan'] + 1\n info['chs'].append(dict(\n cal=KIT.CALIB_FACTOR, logno=nchan, scanno=nchan, range=1.0,\n unit=FIFF.FIFF_UNIT_NONE, unit_mul=FIFF.FIFF_UNITM_NONE,\n ch_name='STI 014',\n coil_type=FIFF.FIFFV_COIL_NONE, loc=np.full(12, np.nan),\n kind=FIFF.FIFFV_STIM_CH, coord_frame=FIFF.FIFFV_COORD_UNKNOWN))\n info._update_redundant()\n\n self._raw_extras[0]['stim'] = stim\n self._raw_extras[0]['stim_code'] = stim_code\n\n def _read_segment_file(self, data, idx, fi, start, stop, cals, mult):\n \"\"\"Read a chunk of raw data.\"\"\"\n sqd = self._raw_extras[fi]\n nchan = sqd['nchan']\n data_left = (stop - start) * nchan\n conv_factor = sqd['conv_factor']\n\n n_bytes = sqd['dtype'].itemsize\n assert n_bytes in (2, 4)\n # Read up to 100 MB of data at a time.\n blk_size = min(data_left, (100000000 // n_bytes // nchan) * nchan)\n with open(self._filenames[fi], 'rb', buffering=0) as fid:\n # extract data\n pointer = start * nchan * n_bytes\n fid.seek(sqd['dirs'][KIT.DIR_INDEX_RAW_DATA]['offset'] + pointer)\n stim = sqd['stim']\n for blk_start in np.arange(0, data_left, blk_size) // nchan:\n blk_size = min(blk_size, data_left - blk_start * nchan)\n block = np.fromfile(fid, dtype=sqd['dtype'], count=blk_size)\n block = block.reshape(nchan, -1, order='F').astype(float)\n blk_stop = blk_start + block.shape[1]\n data_view = data[:, blk_start:blk_stop]\n block *= conv_factor\n\n # Create a synthetic stim channel\n if stim is not None:\n stim_ch = _make_stim_channel(\n block[stim, :], sqd['slope'], sqd['stimthresh'],\n sqd['stim_code'], stim)\n block = np.vstack((block, stim_ch))\n\n _mult_cal_one(data_view, block, idx, cals, mult)\n # cals are all unity, so can be ignored\n\n\ndef _default_stim_chs(info):\n \"\"\"Return default stim channels for SQD files.\"\"\"\n return pick_types(info, meg=False, ref_meg=False, misc=True,\n exclude=[])[:8]\n\n\ndef _make_stim_channel(trigger_chs, slope, threshold, stim_code,\n trigger_values):\n \"\"\"Create synthetic stim channel from multiple trigger channels.\"\"\"\n if slope == '+':\n trig_chs_bin = trigger_chs > threshold\n elif slope == '-':\n trig_chs_bin = trigger_chs < threshold\n else:\n raise ValueError(\"slope needs to be '+' or '-'\")\n # trigger value\n if stim_code == 'binary':\n trigger_values = 2 ** np.arange(len(trigger_chs))\n elif stim_code != 'channel':\n raise ValueError(\"stim_code must be 'binary' or 'channel', got %s\" %\n repr(stim_code))\n trig_chs = trig_chs_bin * trigger_values[:, np.newaxis]\n return np.array(trig_chs.sum(axis=0), ndmin=2)\n\n\nclass EpochsKIT(BaseEpochs):\n \"\"\"Epochs Array object from KIT SQD file.\n\n Parameters\n ----------\n input_fname : str\n Path to the sqd file.\n events : str | array, shape (n_events, 3)\n Path to events file. If array, it is the events typically returned\n by the read_events function. If some events don't match the events\n of interest as specified by event_id,they will be marked as 'IGNORED'\n in the drop log.\n event_id : int | list of int | dict | None\n The id of the event to consider. If dict,\n the keys can later be used to access associated events. Example:\n dict(auditory=1, visual=3). If int, a dict will be created with\n the id as string. If a list, all events with the IDs specified\n in the list are used. If None, all events will be used with\n and a dict is created with string integer names corresponding\n to the event id integers.\n tmin : float\n Start time before event.\n baseline : None or tuple of length 2 (default (None, 0))\n The time interval to apply baseline correction.\n If None do not apply it. If baseline is (a, b)\n the interval is between \"a (s)\" and \"b (s)\".\n If a is None the beginning of the data is used\n and if b is None then b is set to the end of the interval.\n If baseline is equal to (None, None) all the time\n interval is used.\n The baseline (a, b) includes both endpoints, i.e. all\n timepoints t such that a <= t <= b.\n reject : dict | None\n Rejection parameters based on peak-to-peak amplitude.\n Valid keys are 'grad' | 'mag' | 'eeg' | 'eog' | 'ecg'.\n If reject is None then no rejection is done. Example::\n\n reject = dict(grad=4000e-13, # T / m (gradiometers)\n mag=4e-12, # T (magnetometers)\n eeg=40e-6, # V (EEG channels)\n eog=250e-6 # V (EOG channels)\n )\n flat : dict | None\n Rejection parameters based on flatness of signal.\n Valid keys are 'grad' | 'mag' | 'eeg' | 'eog' | 'ecg', and values\n are floats that set the minimum acceptable peak-to-peak amplitude.\n If flat is None then no rejection is done.\n reject_tmin : scalar | None\n Start of the time window used to reject epochs (with the default None,\n the window will start with tmin).\n reject_tmax : scalar | None\n End of the time window used to reject epochs (with the default None,\n the window will end with tmax).\n mrk : None | str | array_like, shape = (5, 3) | list of str or array_like\n Marker points representing the location of the marker coils with\n respect to the MEG Sensors, or path to a marker file.\n If list, all of the markers will be averaged together.\n elp : None | str | array_like, shape = (8, 3)\n Digitizer points representing the location of the fiducials and the\n marker coils with respect to the digitized head shape, or path to a\n file containing these points.\n hsp : None | str | array, shape = (n_points, 3)\n Digitizer head shape points, or path to head shape file. If more than\n 10`000 points are in the head shape, they are automatically decimated.\n allow_unknown_format : bool\n Force reading old data that is not officially supported. Alternatively,\n read and re-save the data with the KIT MEG Laboratory application.\n %(standardize_names)s\n %(verbose)s\n\n Notes\n -----\n ``elp`` and ``hsp`` are usually the exported text files (*.txt) from the\n Polhemus FastScan system. hsp refers to the headshape surface points. elp\n refers to the points in head-space that corresponds to the HPI points.\n Currently, '*.elp' and '*.hsp' files are NOT supported.\n\n See Also\n --------\n mne.Epochs : Documentation of attribute and methods.\n \"\"\"\n\n @verbose\n def __init__(self, input_fname, events, event_id=None, tmin=0,\n baseline=None, reject=None, flat=None, reject_tmin=None,\n reject_tmax=None, mrk=None, elp=None, hsp=None,\n allow_unknown_format=False, standardize_names=None,\n verbose=None): # noqa: D102\n\n if isinstance(events, str):\n events = read_events(events)\n\n logger.info('Extracting KIT Parameters from %s...' % input_fname)\n input_fname = op.abspath(input_fname)\n self.info, kit_info = get_kit_info(\n input_fname, allow_unknown_format, standardize_names)\n kit_info.update(filename=input_fname)\n self._raw_extras = [kit_info]\n self._filenames = []\n if len(events) != self._raw_extras[0]['n_epochs']:\n raise ValueError('Event list does not match number of epochs.')\n\n if self._raw_extras[0]['acq_type'] == KIT.EPOCHS:\n self._raw_extras[0]['data_length'] = KIT.INT\n else:\n raise TypeError('SQD file contains raw data, not epochs or '\n 'average. Wrong reader.')\n\n if event_id is None: # convert to int to make typing-checks happy\n event_id = {str(e): int(e) for e in np.unique(events[:, 2])}\n\n for key, val in event_id.items():\n if val not in events[:, 2]:\n raise ValueError('No matching events found for %s '\n '(event id %i)' % (key, val))\n\n data = self._read_kit_data()\n assert data.shape == (self._raw_extras[0]['n_epochs'],\n self.info['nchan'],\n self._raw_extras[0]['frame_length'])\n tmax = ((data.shape[2] - 1) / self.info['sfreq']) + tmin\n super(EpochsKIT, self).__init__(\n self.info, data, events, event_id, tmin, tmax, baseline,\n reject=reject, flat=flat, reject_tmin=reject_tmin,\n reject_tmax=reject_tmax, filename=input_fname, verbose=verbose)\n self.info = _call_digitization(\n info=self.info, mrk=mrk, elp=elp, hsp=hsp, kit_info=kit_info)\n logger.info('Ready.')\n\n# MASKED: _read_kit_data function (lines 427-454)\n\n\ndef _read_dir(fid):\n return dict(offset=np.fromfile(fid, np.uint32, 1)[0],\n size=np.fromfile(fid, np.int32, 1)[0],\n max_count=np.fromfile(fid, np.int32, 1)[0],\n count=np.fromfile(fid, np.int32, 1)[0])\n\n\n@verbose\ndef get_kit_info(rawfile, allow_unknown_format, standardize_names=None,\n verbose=None):\n \"\"\"Extract all the information from the sqd/con file.\n\n Parameters\n ----------\n rawfile : str\n KIT file to be read.\n allow_unknown_format : bool\n Force reading old data that is not officially supported. Alternatively,\n read and re-save the data with the KIT MEG Laboratory application.\n %(standardize_names)s\n %(verbose)s\n\n Returns\n -------\n info : instance of Info\n An Info for the instance.\n sqd : dict\n A dict containing all the sqd parameter settings.\n \"\"\"\n sqd = dict()\n sqd['rawfile'] = rawfile\n unsupported_format = False\n sqd['dirs'] = dirs = list()\n with open(rawfile, 'rb', buffering=0) as fid: # buffering=0 for np bug\n #\n # directories (0)\n #\n dirs.append(_read_dir(fid))\n dirs.extend(_read_dir(fid) for _ in range(dirs[0]['count'] - 1))\n assert len(dirs) == dirs[KIT.DIR_INDEX_DIR]['count']\n\n #\n # system (1)\n #\n fid.seek(dirs[KIT.DIR_INDEX_SYSTEM]['offset'])\n # check file format version\n version, revision = unpack('2i', fid.read(2 * KIT.INT))\n if version < 2 or (version == 2 and revision < 3):\n version_string = \"V%iR%03i\" % (version, revision)\n if allow_unknown_format:\n unsupported_format = True\n logger.warning(\"Force loading KIT format %s\", version_string)\n else:\n raise UnsupportedKITFormat(\n version_string,\n \"SQD file format %s is not officially supported. \"\n \"Set allow_unknown_format=True to load it anyways.\" %\n (version_string,))\n\n sysid = unpack('i', fid.read(KIT.INT))[0]\n # basic info\n system_name = unpack('128s', fid.read(128))[0].decode()\n # model name\n model_name = unpack('128s', fid.read(128))[0].decode()\n # channels\n sqd['nchan'] = channel_count = unpack('i', fid.read(KIT.INT))[0]\n comment = unpack('256s', fid.read(256))[0].decode()\n create_time, last_modified_time = unpack('2i', fid.read(2 * KIT.INT))\n fid.seek(KIT.INT * 3, SEEK_CUR) # reserved\n dewar_style = unpack('i', fid.read(KIT.INT))[0]\n fid.seek(KIT.INT * 3, SEEK_CUR) # spare\n fll_type = unpack('i', fid.read(KIT.INT))[0]\n fid.seek(KIT.INT * 3, SEEK_CUR) # spare\n trigger_type = unpack('i', fid.read(KIT.INT))[0]\n fid.seek(KIT.INT * 3, SEEK_CUR) # spare\n adboard_type = unpack('i', fid.read(KIT.INT))[0]\n fid.seek(KIT.INT * 29, SEEK_CUR) # reserved\n\n if version < 2 or (version == 2 and revision <= 3):\n adc_range = float(unpack('i', fid.read(KIT.INT))[0])\n else:\n adc_range = unpack('d', fid.read(KIT.DOUBLE))[0]\n adc_polarity, adc_allocated, adc_stored = unpack('3i',\n fid.read(3 * KIT.INT))\n system_name = system_name.replace('\\x00', '')\n system_name = system_name.strip().replace('\\n', '/')\n model_name = model_name.replace('\\x00', '')\n model_name = model_name.strip().replace('\\n', '/')\n\n full_version = f'V{version:d}R{revision:03d}'\n logger.debug(\"SQD file basic information:\")\n logger.debug(\"Meg160 version = %s\", full_version)\n logger.debug(\"System ID = %i\", sysid)\n logger.debug(\"System name = %s\", system_name)\n logger.debug(\"Model name = %s\", model_name)\n logger.debug(\"Channel count = %i\", channel_count)\n logger.debug(\"Comment = %s\", comment)\n logger.debug(\"Dewar style = %i\", dewar_style)\n logger.debug(\"FLL type = %i\", fll_type)\n logger.debug(\"Trigger type = %i\", trigger_type)\n logger.debug(\"A/D board type = %i\", adboard_type)\n logger.debug(\"ADC range = +/-%s[V]\", adc_range / 2.)\n logger.debug(\"ADC allocate = %i[bit]\", adc_allocated)\n logger.debug(\"ADC bit = %i[bit]\", adc_stored)\n # MGH description: 'acquisition (megacq) VectorView system at NMR-MGH'\n description = \\\n f'{system_name} ({sysid}) {full_version} {model_name}'\n sqd['dtype'] = np.dtype(getattr(np, f'int{adc_allocated}'))\n\n # check that we can read this file\n if fll_type not in KIT.FLL_SETTINGS:\n fll_types = sorted(KIT.FLL_SETTINGS.keys())\n use_fll_type = fll_types[\n np.searchsorted(fll_types, fll_type) - 1]\n warn('Unknown site filter settings (FLL) for system '\n '\"%s\" model \"%s\" (ID %s), will assume FLL %d->%d, check '\n 'your data for correctness, including channel scales and '\n 'filter settings!'\n % (system_name, model_name, sysid, fll_type, use_fll_type))\n fll_type = use_fll_type\n\n #\n # channel information (4)\n #\n chan_dir = dirs[KIT.DIR_INDEX_CHANNELS]\n chan_offset, chan_size = chan_dir['offset'], chan_dir['size']\n sqd['channels'] = channels = []\n exg_gains = list()\n for i in range(channel_count):\n fid.seek(chan_offset + chan_size * i)\n channel_type, = unpack('i', fid.read(KIT.INT))\n # System 52 mislabeled reference channels as NULL. This was fixed\n # in system 53; not sure about 51...\n if sysid == 52 and i < 160 and channel_type == KIT.CHANNEL_NULL:\n channel_type = KIT.CHANNEL_MAGNETOMETER_REFERENCE\n\n if channel_type in KIT.CHANNELS_MEG:\n if channel_type not in KIT.CH_TO_FIFF_COIL:\n raise NotImplementedError(\n \"KIT channel type %i can not be read. Please contact \"\n \"the mne-python developers.\" % channel_type)\n channels.append({\n 'type': channel_type,\n # (x, y, z, theta, phi) for all MEG channels. Some channel\n # types have additional information which we're not using.\n 'loc': np.fromfile(fid, dtype='d', count=5),\n })\n if channel_type in KIT.CHANNEL_NAME_NCHAR:\n fid.seek(16, SEEK_CUR) # misc fields\n channels[-1]['name'] = _read_name(fid, channel_type)\n elif channel_type in KIT.CHANNELS_MISC:\n channel_no, = unpack('i', fid.read(KIT.INT))\n fid.seek(4, SEEK_CUR)\n name = _read_name(fid, channel_type)\n channels.append({\n 'type': channel_type,\n 'no': channel_no,\n 'name': name,\n })\n if channel_type in (KIT.CHANNEL_EEG, KIT.CHANNEL_ECG):\n offset = 6 if channel_type == KIT.CHANNEL_EEG else 8\n fid.seek(offset, SEEK_CUR)\n exg_gains.append(np.fromfile(fid, 'd', 1)[0])\n elif channel_type == KIT.CHANNEL_NULL:\n channels.append({'type': channel_type})\n else:\n raise IOError(\"Unknown KIT channel type: %i\" % channel_type)\n exg_gains = np.array(exg_gains)\n\n #\n # Channel sensitivity information: (5)\n #\n\n # only sensor channels requires gain. the additional misc channels\n # (trigger channels, audio and voice channels) are passed\n # through unaffected\n fid.seek(dirs[KIT.DIR_INDEX_CALIBRATION]['offset'])\n # (offset [Volt], gain [Tesla/Volt]) for each channel\n sensitivity = np.fromfile(fid, dtype='d', count=channel_count * 2)\n sensitivity.shape = (channel_count, 2)\n channel_offset, channel_gain = sensitivity.T\n assert (channel_offset == 0).all() # otherwise we have a problem\n\n #\n # amplifier gain (7)\n #\n fid.seek(dirs[KIT.DIR_INDEX_AMP_FILTER]['offset'])\n amp_data = unpack('i', fid.read(KIT.INT))[0]\n if fll_type >= 100: # Kapper Type\n # gain: mask bit\n gain1 = (amp_data & 0x00007000) >> 12\n gain2 = (amp_data & 0x70000000) >> 28\n gain3 = (amp_data & 0x07000000) >> 24\n amp_gain = (KIT.GAINS[gain1] * KIT.GAINS[gain2] * KIT.GAINS[gain3])\n # filter settings\n hpf = (amp_data & 0x00000700) >> 8\n lpf = (amp_data & 0x00070000) >> 16\n bef = (amp_data & 0x00000003) >> 0\n else: # Hanger Type\n # gain\n input_gain = (amp_data & 0x1800) >> 11\n output_gain = (amp_data & 0x0007) >> 0\n amp_gain = KIT.GAINS[input_gain] * KIT.GAINS[output_gain]\n # filter settings\n hpf = (amp_data & 0x007) >> 4\n lpf = (amp_data & 0x0700) >> 8\n bef = (amp_data & 0xc000) >> 14\n hpf_options, lpf_options, bef_options = KIT.FLL_SETTINGS[fll_type]\n sqd['highpass'] = KIT.HPFS[hpf_options][hpf]\n sqd['lowpass'] = KIT.LPFS[lpf_options][lpf]\n sqd['notch'] = KIT.BEFS[bef_options][bef]\n\n #\n # Acquisition Parameters (8)\n #\n fid.seek(dirs[KIT.DIR_INDEX_ACQ_COND]['offset'])\n sqd['acq_type'], = acq_type, = unpack('i', fid.read(KIT.INT))\n sqd['sfreq'], = unpack('d', fid.read(KIT.DOUBLE))\n if acq_type == KIT.CONTINUOUS:\n # samples_count, = unpack('i', fid.read(KIT.INT))\n fid.seek(KIT.INT, SEEK_CUR)\n sqd['n_samples'], = unpack('i', fid.read(KIT.INT))\n elif acq_type == KIT.EVOKED or acq_type == KIT.EPOCHS:\n sqd['frame_length'], = unpack('i', fid.read(KIT.INT))\n sqd['pretrigger_length'], = unpack('i', fid.read(KIT.INT))\n sqd['average_count'], = unpack('i', fid.read(KIT.INT))\n sqd['n_epochs'], = unpack('i', fid.read(KIT.INT))\n if acq_type == KIT.EVOKED:\n sqd['n_samples'] = sqd['frame_length']\n else:\n sqd['n_samples'] = sqd['frame_length'] * sqd['n_epochs']\n else:\n raise IOError(\"Invalid acquisition type: %i. Your file is neither \"\n \"continuous nor epoched data.\" % (acq_type,))\n\n #\n # digitization information (12 and 26)\n #\n dig_dir = dirs[KIT.DIR_INDEX_DIG_POINTS]\n cor_dir = dirs[KIT.DIR_INDEX_COREG]\n dig = dict()\n hsp = list()\n if dig_dir['count'] > 0 and cor_dir['count'] > 0:\n # directories (0)\n fid.seek(dig_dir['offset'])\n for _ in range(dig_dir['count']):\n name = _read_name(fid, n=8).strip()\n # Sometimes there are mismatches (e.g., AFz vs AFZ) between\n # the channel name and its digitized, name, so let's be case\n # insensitive. It will also prevent collisions with HSP\n name = name.lower()\n rr = np.fromfile(fid, 'd', 3)\n if name:\n assert name not in dig\n dig[name] = rr\n else:\n hsp.append(rr)\n\n # nasion, lpa, rpa, HPI in native space\n elp = [dig.pop(key) for key in (\n 'fidnz', 'fidt9', 'fidt10',\n 'hpi_1', 'hpi_2', 'hpi_3', 'hpi_4')]\n if 'hpi_5' in dig and dig['hpi_5'].any():\n elp.append(dig.pop('hpi_5'))\n elp = np.array(elp)\n hsp = np.array(hsp, float).reshape(-1, 3)\n assert elp.shape in ((7, 3), (8, 3))\n # coregistration\n fid.seek(cor_dir['offset'])\n mrk = np.zeros((elp.shape[0] - 3, 3))\n for _ in range(cor_dir['count']):\n done = np.fromfile(fid, np.int32, 1)[0]\n fid.seek(16 * KIT.DOUBLE + # meg_to_mri\n 16 * KIT.DOUBLE, # mri_to_meg\n SEEK_CUR)\n marker_count = np.fromfile(fid, np.int32, 1)[0]\n if not done:\n continue\n assert marker_count >= len(mrk)\n for mi in range(len(mrk)):\n mri_type, meg_type, mri_done, meg_done = \\\n np.fromfile(fid, np.int32, 4)\n assert meg_done\n fid.seek(3 * KIT.DOUBLE, SEEK_CUR) # mri_pos\n mrk[mi] = np.fromfile(fid, 'd', 3)\n fid.seek(256, SEEK_CUR) # marker_file (char)\n sqd.update(hsp=hsp, elp=elp, mrk=mrk)\n\n all_names = set(ch.get('name', '') for ch in channels)\n if standardize_names is None and all_names.difference({'', 'EEG'}):\n standardize_names = True\n warn('standardize_names defaults to True in 0.21 but will change '\n 'to False in 0.22', DeprecationWarning)\n\n # precompute conversion factor for reading data\n if unsupported_format:\n if sysid not in LEGACY_AMP_PARAMS:\n raise IOError(\"Legacy parameters for system ID %i unavailable\" %\n (sysid,))\n adc_range, adc_stored = LEGACY_AMP_PARAMS[sysid]\n is_meg = np.array([ch['type'] in KIT.CHANNELS_MEG for ch in channels])\n ad_to_volt = adc_range / (2 ** adc_stored)\n ad_to_tesla = ad_to_volt / amp_gain * channel_gain\n conv_factor = np.where(is_meg, ad_to_tesla, ad_to_volt)\n # XXX this is a bit of a hack. Should probably do this more cleanly at\n # some point... the 2 ** (adc_stored - 14) was emperically determined using\n # the test files with known amplitudes. The conv_factors need to be\n # replaced by these values otherwise we're off by a factor off 5000.0\n # for the EEG data.\n is_exg = [ch['type'] in (KIT.CHANNEL_EEG, KIT.CHANNEL_ECG)\n for ch in channels]\n exg_gains /= 2 ** (adc_stored - 14)\n conv_factor[is_exg] = exg_gains\n sqd['conv_factor'] = conv_factor[:, np.newaxis]\n\n # Create raw.info dict for raw fif object with SQD data\n info = _empty_info(float(sqd['sfreq']))\n info.update(meas_date=_stamp_to_dt((create_time, 0)),\n lowpass=sqd['lowpass'],\n highpass=sqd['highpass'], kit_system_id=sysid,\n description=description)\n\n # Creates a list of dicts of meg channels for raw.info\n logger.info('Setting channel info structure...')\n info['chs'] = fiff_channels = []\n channel_index = defaultdict(lambda: 0)\n sqd['eeg_dig'] = OrderedDict()\n for idx, ch in enumerate(channels, 1):\n if ch['type'] in KIT.CHANNELS_MEG:\n ch_name = ch.get('name', '')\n if ch_name == '' or standardize_names:\n ch_name = 'MEG %03d' % idx\n # create three orthogonal vector\n # ch_angles[0]: theta, ch_angles[1]: phi\n theta, phi = np.radians(ch['loc'][3:])\n x = sin(theta) * cos(phi)\n y = sin(theta) * sin(phi)\n z = cos(theta)\n vec_z = np.array([x, y, z])\n vec_z /= linalg.norm(vec_z)\n vec_x = np.zeros(vec_z.size, dtype=np.float64)\n if vec_z[1] < vec_z[2]:\n if vec_z[0] < vec_z[1]:\n vec_x[0] = 1.0\n else:\n vec_x[1] = 1.0\n elif vec_z[0] < vec_z[2]:\n vec_x[0] = 1.0\n else:\n vec_x[2] = 1.0\n vec_x -= np.sum(vec_x * vec_z) * vec_z\n vec_x /= linalg.norm(vec_x)\n vec_y = np.cross(vec_z, vec_x)\n # transform to Neuromag like coordinate space\n vecs = np.vstack((ch['loc'][:3], vec_x, vec_y, vec_z))\n vecs = apply_trans(als_ras_trans, vecs)\n unit = FIFF.FIFF_UNIT_T\n loc = vecs.ravel()\n else:\n ch_type_label = KIT.CH_LABEL[ch['type']]\n channel_index[ch_type_label] += 1\n ch_type_index = channel_index[ch_type_label]\n ch_name = ch.get('name', '')\n eeg_name = ch_name.lower()\n # some files have all EEG labeled as EEG\n if ch_name in ('', 'EEG') or standardize_names:\n ch_name = '%s %03i' % (ch_type_label, ch_type_index)\n unit = FIFF.FIFF_UNIT_V\n loc = np.zeros(12)\n if eeg_name and eeg_name in dig:\n loc[:3] = sqd['eeg_dig'][eeg_name] = dig[eeg_name]\n fiff_channels.append(dict(\n cal=KIT.CALIB_FACTOR, logno=idx, scanno=idx, range=KIT.RANGE,\n unit=unit, unit_mul=KIT.UNIT_MUL, ch_name=ch_name,\n coord_frame=FIFF.FIFFV_COORD_DEVICE,\n coil_type=KIT.CH_TO_FIFF_COIL[ch['type']],\n kind=KIT.CH_TO_FIFF_KIND[ch['type']], loc=loc))\n info._update_redundant()\n return info, sqd\n\n\ndef _read_name(fid, ch_type=None, n=None):\n n = n if ch_type is None else KIT.CHANNEL_NAME_NCHAR[ch_type]\n return fid.read(n).split(b'\\x00')[0].decode('utf-8')\n\n\n@fill_doc\ndef read_raw_kit(input_fname, mrk=None, elp=None, hsp=None, stim='>',\n slope='-', stimthresh=1, preload=False, stim_code='binary',\n allow_unknown_format=False, standardize_names=None,\n verbose=None):\n \"\"\"Reader function for Ricoh/KIT conversion to FIF.\n\n Parameters\n ----------\n input_fname : str\n Path to the sqd file.\n mrk : None | str | array_like, shape (5, 3) | list of str or array_like\n Marker points representing the location of the marker coils with\n respect to the MEG Sensors, or path to a marker file.\n If list, all of the markers will be averaged together.\n elp : None | str | array_like, shape (8, 3)\n Digitizer points representing the location of the fiducials and the\n marker coils with respect to the digitized head shape, or path to a\n file containing these points.\n hsp : None | str | array, shape (n_points, 3)\n Digitizer head shape points, or path to head shape file. If more than\n 10,000 points are in the head shape, they are automatically decimated.\n stim : list of int | '<' | '>'\n Channel-value correspondence when converting KIT trigger channels to a\n Neuromag-style stim channel. For '<', the largest values are assigned\n to the first channel (default). For '>', the largest values are\n assigned to the last channel. Can also be specified as a list of\n trigger channel indexes.\n slope : '+' | '-'\n How to interpret values on KIT trigger channels when synthesizing a\n Neuromag-style stim channel. With '+', a positive slope (low-to-high)\n is interpreted as an event. With '-', a negative slope (high-to-low)\n is interpreted as an event.\n stimthresh : float\n The threshold level for accepting voltage changes in KIT trigger\n channels as a trigger event.\n %(preload)s\n stim_code : 'binary' | 'channel'\n How to decode trigger values from stim channels. 'binary' read stim\n channel events as binary code, 'channel' encodes channel number.\n allow_unknown_format : bool\n Force reading old data that is not officially supported. Alternatively,\n read and re-save the data with the KIT MEG Laboratory application.\n %(standardize_names)s\n %(verbose)s\n\n Returns\n -------\n raw : instance of RawKIT\n A Raw object containing KIT data.\n\n See Also\n --------\n mne.io.Raw : Documentation of attribute and methods.\n\n Notes\n -----\n If mrk, hsp or elp are array_like inputs, then the numbers in xyz\n coordinates should be in units of meters.\n \"\"\"\n return RawKIT(input_fname=input_fname, mrk=mrk, elp=elp, hsp=hsp,\n stim=stim, slope=slope, stimthresh=stimthresh,\n preload=preload, stim_code=stim_code,\n allow_unknown_format=allow_unknown_format,\n standardize_names=standardize_names, verbose=verbose)\n\n\n@fill_doc\ndef read_epochs_kit(input_fname, events, event_id=None, mrk=None, elp=None,\n hsp=None, allow_unknown_format=False,\n standardize_names=None, verbose=None):\n \"\"\"Reader function for Ricoh/KIT epochs files.\n\n Parameters\n ----------\n input_fname : str\n Path to the sqd file.\n events : array, shape (n_events, 3)\n The events typically returned by the read_events function.\n If some events don't match the events of interest as specified\n by event_id, they will be marked as 'IGNORED' in the drop log.\n event_id : int | list of int | dict | None\n The id of the event to consider. If dict,\n the keys can later be used to access associated events. Example:\n dict(auditory=1, visual=3). If int, a dict will be created with\n the id as string. If a list, all events with the IDs specified\n in the list are used. If None, all events will be used with\n and a dict is created with string integer names corresponding\n to the event id integers.\n mrk : None | str | array_like, shape (5, 3) | list of str or array_like\n Marker points representing the location of the marker coils with\n respect to the MEG Sensors, or path to a marker file.\n If list, all of the markers will be averaged together.\n elp : None | str | array_like, shape (8, 3)\n Digitizer points representing the location of the fiducials and the\n marker coils with respect to the digitized head shape, or path to a\n file containing these points.\n hsp : None | str | array, shape (n_points, 3)\n Digitizer head shape points, or path to head shape file. If more than\n 10,000 points are in the head shape, they are automatically decimated.\n allow_unknown_format : bool\n Force reading old data that is not officially supported. Alternatively,\n read and re-save the data with the KIT MEG Laboratory application.\n %(standardize_names)s\n %(verbose)s\n\n Returns\n -------\n epochs : instance of Epochs\n The epochs.\n\n Notes\n -----\n .. versionadded:: 0.9.0\n \"\"\"\n epochs = EpochsKIT(input_fname=input_fname, events=events,\n event_id=event_id, mrk=mrk, elp=elp, hsp=hsp,\n allow_unknown_format=allow_unknown_format,\n standardize_names=standardize_names,\n verbose=verbose)\n return epochs"},"implementation":{"kind":"string","value":" def _read_kit_data(self):\n \"\"\"Read epochs data.\n\n Returns\n -------\n data : array, [channels x samples]\n the data matrix (channels x samples).\n times : array, [samples]\n returns the time values corresponding to the samples.\n \"\"\"\n info = self._raw_extras[0]\n epoch_length = info['frame_length']\n n_epochs = info['n_epochs']\n n_samples = info['n_samples']\n filename = info['filename']\n dtype = info['dtype']\n nchan = info['nchan']\n\n with open(filename, 'rb', buffering=0) as fid:\n fid.seek(info['dirs'][KIT.DIR_INDEX_RAW_DATA]['offset'])\n count = n_samples * nchan\n data = np.fromfile(fid, dtype=dtype, count=count)\n data = data.reshape((n_samples, nchan)).T\n data = data * info['conv_factor']\n data = data.reshape((nchan, n_epochs, epoch_length))\n data = data.transpose((1, 0, 2))\n\n return data"},"start_line":{"kind":"number","value":427,"string":"427"},"end_line":{"kind":"number","value":454,"string":"454"},"file_content":{"kind":"string","value":"\"\"\"Conversion tool from SQD to FIF.\n\nRawKIT class is adapted from Denis Engemann et al.'s mne_bti2fiff.py.\n\"\"\"\n\n# Authors: Teon Brooks <teon.brooks@gmail.com>\n# Joan Massich <mailsik@gmail.com>\n# Christian Brodbeck <christianbrodbeck@nyu.edu>\n#\n# License: BSD (3-clause)\n\nfrom collections import defaultdict, OrderedDict\nfrom math import sin, cos\nfrom os import SEEK_CUR, path as op\nfrom struct import unpack\n\nimport numpy as np\nfrom scipy import linalg\n\nfrom ..pick import pick_types\nfrom ...utils import (verbose, logger, warn, fill_doc, _check_option,\n _stamp_to_dt)\nfrom ...transforms import apply_trans, als_ras_trans\nfrom ..base import BaseRaw\nfrom ..utils import _mult_cal_one\nfrom ...epochs import BaseEpochs\nfrom ..constants import FIFF\nfrom ..meas_info import _empty_info\nfrom .constants import KIT, LEGACY_AMP_PARAMS\nfrom .coreg import read_mrk\nfrom ...event import read_events\n\nfrom .._digitization import _set_dig_kit\n\n\ndef _call_digitization(info, mrk, elp, hsp, kit_info):\n # Use values from kit_info only if all others are None\n if mrk is None and elp is None and hsp is None:\n mrk = kit_info.get('mrk', None)\n elp = kit_info.get('elp', None)\n hsp = kit_info.get('hsp', None)\n\n # prepare mrk\n if isinstance(mrk, list):\n mrk = [read_mrk(marker) if isinstance(marker, str)\n else marker for marker in mrk]\n mrk = np.mean(mrk, axis=0)\n\n # setup digitization\n if mrk is not None and elp is not None and hsp is not None:\n dig_points, dev_head_t = _set_dig_kit(\n mrk, elp, hsp, kit_info['eeg_dig'])\n info['dig'] = dig_points\n info['dev_head_t'] = dev_head_t\n elif mrk is not None or elp is not None or hsp is not None:\n raise ValueError(\"mrk, elp and hsp need to be provided as a group \"\n \"(all or none)\")\n\n return info\n\n\nclass UnsupportedKITFormat(ValueError):\n \"\"\"Our reader is not guaranteed to work with old files.\"\"\"\n\n def __init__(self, sqd_version, *args, **kwargs): # noqa: D102\n self.sqd_version = sqd_version\n ValueError.__init__(self, *args, **kwargs)\n\n\n@fill_doc\nclass RawKIT(BaseRaw):\n \"\"\"Raw object from KIT SQD file.\n\n Parameters\n ----------\n input_fname : str\n Path to the sqd file.\n mrk : None | str | array_like, shape (5, 3) | list of str or array_like\n Marker points representing the location of the marker coils with\n respect to the MEG Sensors, or path to a marker file.\n If list, all of the markers will be averaged together.\n elp : None | str | array_like, shape (8, 3)\n Digitizer points representing the location of the fiducials and the\n marker coils with respect to the digitized head shape, or path to a\n file containing these points.\n hsp : None | str | array, shape (n_points, 3)\n Digitizer head shape points, or path to head shape file. If more than\n 10,000 points are in the head shape, they are automatically decimated.\n stim : list of int | '<' | '>' | None\n Channel-value correspondence when converting KIT trigger channels to a\n Neuromag-style stim channel. For '<', the largest values are assigned\n to the first channel (default). For '>', the largest values are\n assigned to the last channel. Can also be specified as a list of\n trigger channel indexes. If None, no synthesized channel is generated.\n slope : '+' | '-'\n How to interpret values on KIT trigger channels when synthesizing a\n Neuromag-style stim channel. With '+', a positive slope (low-to-high)\n is interpreted as an event. With '-', a negative slope (high-to-low)\n is interpreted as an event.\n stimthresh : float\n The threshold level for accepting voltage changes in KIT trigger\n channels as a trigger event. If None, stim must also be set to None.\n %(preload)s\n stim_code : 'binary' | 'channel'\n How to decode trigger values from stim channels. 'binary' read stim\n channel events as binary code, 'channel' encodes channel number.\n allow_unknown_format : bool\n Force reading old data that is not officially supported. Alternatively,\n read and re-save the data with the KIT MEG Laboratory application.\n %(standardize_names)s\n %(verbose)s\n\n Notes\n -----\n ``elp`` and ``hsp`` are usually the exported text files (*.txt) from the\n Polhemus FastScan system. hsp refers to the headshape surface points. elp\n refers to the points in head-space that corresponds to the HPI points.\n Currently, '*.elp' and '*.hsp' files are NOT supported.\n\n See Also\n --------\n mne.io.Raw : Documentation of attribute and methods.\n \"\"\"\n\n @verbose\n def __init__(self, input_fname, mrk=None, elp=None, hsp=None, stim='>',\n slope='-', stimthresh=1, preload=False, stim_code='binary',\n allow_unknown_format=False, standardize_names=None,\n verbose=None): # noqa: D102\n logger.info('Extracting SQD Parameters from %s...' % input_fname)\n input_fname = op.abspath(input_fname)\n self.preload = False\n logger.info('Creating Raw.info structure...')\n info, kit_info = get_kit_info(\n input_fname, allow_unknown_format, standardize_names)\n kit_info['slope'] = slope\n kit_info['stimthresh'] = stimthresh\n if kit_info['acq_type'] != KIT.CONTINUOUS:\n raise TypeError('SQD file contains epochs, not raw data. Wrong '\n 'reader.')\n logger.info('Creating Info structure...')\n\n last_samps = [kit_info['n_samples'] - 1]\n self._raw_extras = [kit_info]\n self._set_stimchannels(info, stim, stim_code)\n super(RawKIT, self).__init__(\n info, preload, last_samps=last_samps, filenames=[input_fname],\n raw_extras=self._raw_extras, verbose=verbose)\n self.info = _call_digitization(\n info=self.info, mrk=mrk, elp=elp, hsp=hsp, kit_info=kit_info)\n logger.info('Ready.')\n\n def read_stim_ch(self, buffer_size=1e5):\n \"\"\"Read events from data.\n\n Parameter\n ---------\n buffer_size : int\n The size of chunk to by which the data are scanned.\n\n Returns\n -------\n events : array, [samples]\n The event vector (1 x samples).\n \"\"\"\n buffer_size = int(buffer_size)\n start = int(self.first_samp)\n stop = int(self.last_samp + 1)\n\n pick = pick_types(self.info, meg=False, ref_meg=False,\n stim=True, exclude=[])\n stim_ch = np.empty((1, stop), dtype=np.int64)\n for b_start in range(start, stop, buffer_size):\n b_stop = b_start + buffer_size\n x = self[pick, b_start:b_stop][0]\n stim_ch[:, b_start:b_start + x.shape[1]] = x\n\n return stim_ch\n\n def _set_stimchannels(self, info, stim, stim_code):\n \"\"\"Specify how the trigger channel is synthesized from analog channels.\n\n Has to be done before loading data. For a RawKIT instance that has been\n created with preload=True, this method will raise a\n NotImplementedError.\n\n Parameters\n ----------\n info : instance of MeasInfo\n The measurement info.\n stim : list of int | '<' | '>'\n Can be submitted as list of trigger channels.\n If a list is not specified, the default triggers extracted from\n misc channels will be used with specified directionality.\n '<' means that largest values assigned to the first channel\n in sequence.\n '>' means the largest trigger assigned to the last channel\n in sequence.\n stim_code : 'binary' | 'channel'\n How to decode trigger values from stim channels. 'binary' read stim\n channel events as binary code, 'channel' encodes channel number.\n \"\"\"\n if self.preload:\n raise NotImplementedError(\"Can't change stim channel after \"\n \"loading data\")\n _check_option('stim_code', stim_code, ['binary', 'channel'])\n\n if stim is not None:\n if isinstance(stim, str):\n picks = _default_stim_chs(info)\n if stim == '<':\n stim = picks[::-1]\n elif stim == '>':\n stim = picks\n else:\n raise ValueError(\"stim needs to be list of int, '>' or \"\n \"'<', not %r\" % str(stim))\n else:\n stim = np.asarray(stim, int)\n if stim.max() >= self._raw_extras[0]['nchan']:\n raise ValueError(\n 'Got stim=%s, but sqd file only has %i channels' %\n (stim, self._raw_extras[0]['nchan']))\n\n # modify info\n nchan = self._raw_extras[0]['nchan'] + 1\n info['chs'].append(dict(\n cal=KIT.CALIB_FACTOR, logno=nchan, scanno=nchan, range=1.0,\n unit=FIFF.FIFF_UNIT_NONE, unit_mul=FIFF.FIFF_UNITM_NONE,\n ch_name='STI 014',\n coil_type=FIFF.FIFFV_COIL_NONE, loc=np.full(12, np.nan),\n kind=FIFF.FIFFV_STIM_CH, coord_frame=FIFF.FIFFV_COORD_UNKNOWN))\n info._update_redundant()\n\n self._raw_extras[0]['stim'] = stim\n self._raw_extras[0]['stim_code'] = stim_code\n\n def _read_segment_file(self, data, idx, fi, start, stop, cals, mult):\n \"\"\"Read a chunk of raw data.\"\"\"\n sqd = self._raw_extras[fi]\n nchan = sqd['nchan']\n data_left = (stop - start) * nchan\n conv_factor = sqd['conv_factor']\n\n n_bytes = sqd['dtype'].itemsize\n assert n_bytes in (2, 4)\n # Read up to 100 MB of data at a time.\n blk_size = min(data_left, (100000000 // n_bytes // nchan) * nchan)\n with open(self._filenames[fi], 'rb', buffering=0) as fid:\n # extract data\n pointer = start * nchan * n_bytes\n fid.seek(sqd['dirs'][KIT.DIR_INDEX_RAW_DATA]['offset'] + pointer)\n stim = sqd['stim']\n for blk_start in np.arange(0, data_left, blk_size) // nchan:\n blk_size = min(blk_size, data_left - blk_start * nchan)\n block = np.fromfile(fid, dtype=sqd['dtype'], count=blk_size)\n block = block.reshape(nchan, -1, order='F').astype(float)\n blk_stop = blk_start + block.shape[1]\n data_view = data[:, blk_start:blk_stop]\n block *= conv_factor\n\n # Create a synthetic stim channel\n if stim is not None:\n stim_ch = _make_stim_channel(\n block[stim, :], sqd['slope'], sqd['stimthresh'],\n sqd['stim_code'], stim)\n block = np.vstack((block, stim_ch))\n\n _mult_cal_one(data_view, block, idx, cals, mult)\n # cals are all unity, so can be ignored\n\n\ndef _default_stim_chs(info):\n \"\"\"Return default stim channels for SQD files.\"\"\"\n return pick_types(info, meg=False, ref_meg=False, misc=True,\n exclude=[])[:8]\n\n\ndef _make_stim_channel(trigger_chs, slope, threshold, stim_code,\n trigger_values):\n \"\"\"Create synthetic stim channel from multiple trigger channels.\"\"\"\n if slope == '+':\n trig_chs_bin = trigger_chs > threshold\n elif slope == '-':\n trig_chs_bin = trigger_chs < threshold\n else:\n raise ValueError(\"slope needs to be '+' or '-'\")\n # trigger value\n if stim_code == 'binary':\n trigger_values = 2 ** np.arange(len(trigger_chs))\n elif stim_code != 'channel':\n raise ValueError(\"stim_code must be 'binary' or 'channel', got %s\" %\n repr(stim_code))\n trig_chs = trig_chs_bin * trigger_values[:, np.newaxis]\n return np.array(trig_chs.sum(axis=0), ndmin=2)\n\n\nclass EpochsKIT(BaseEpochs):\n \"\"\"Epochs Array object from KIT SQD file.\n\n Parameters\n ----------\n input_fname : str\n Path to the sqd file.\n events : str | array, shape (n_events, 3)\n Path to events file. If array, it is the events typically returned\n by the read_events function. If some events don't match the events\n of interest as specified by event_id,they will be marked as 'IGNORED'\n in the drop log.\n event_id : int | list of int | dict | None\n The id of the event to consider. If dict,\n the keys can later be used to access associated events. Example:\n dict(auditory=1, visual=3). If int, a dict will be created with\n the id as string. If a list, all events with the IDs specified\n in the list are used. If None, all events will be used with\n and a dict is created with string integer names corresponding\n to the event id integers.\n tmin : float\n Start time before event.\n baseline : None or tuple of length 2 (default (None, 0))\n The time interval to apply baseline correction.\n If None do not apply it. If baseline is (a, b)\n the interval is between \"a (s)\" and \"b (s)\".\n If a is None the beginning of the data is used\n and if b is None then b is set to the end of the interval.\n If baseline is equal to (None, None) all the time\n interval is used.\n The baseline (a, b) includes both endpoints, i.e. all\n timepoints t such that a <= t <= b.\n reject : dict | None\n Rejection parameters based on peak-to-peak amplitude.\n Valid keys are 'grad' | 'mag' | 'eeg' | 'eog' | 'ecg'.\n If reject is None then no rejection is done. Example::\n\n reject = dict(grad=4000e-13, # T / m (gradiometers)\n mag=4e-12, # T (magnetometers)\n eeg=40e-6, # V (EEG channels)\n eog=250e-6 # V (EOG channels)\n )\n flat : dict | None\n Rejection parameters based on flatness of signal.\n Valid keys are 'grad' | 'mag' | 'eeg' | 'eog' | 'ecg', and values\n are floats that set the minimum acceptable peak-to-peak amplitude.\n If flat is None then no rejection is done.\n reject_tmin : scalar | None\n Start of the time window used to reject epochs (with the default None,\n the window will start with tmin).\n reject_tmax : scalar | None\n End of the time window used to reject epochs (with the default None,\n the window will end with tmax).\n mrk : None | str | array_like, shape = (5, 3) | list of str or array_like\n Marker points representing the location of the marker coils with\n respect to the MEG Sensors, or path to a marker file.\n If list, all of the markers will be averaged together.\n elp : None | str | array_like, shape = (8, 3)\n Digitizer points representing the location of the fiducials and the\n marker coils with respect to the digitized head shape, or path to a\n file containing these points.\n hsp : None | str | array, shape = (n_points, 3)\n Digitizer head shape points, or path to head shape file. If more than\n 10`000 points are in the head shape, they are automatically decimated.\n allow_unknown_format : bool\n Force reading old data that is not officially supported. Alternatively,\n read and re-save the data with the KIT MEG Laboratory application.\n %(standardize_names)s\n %(verbose)s\n\n Notes\n -----\n ``elp`` and ``hsp`` are usually the exported text files (*.txt) from the\n Polhemus FastScan system. hsp refers to the headshape surface points. elp\n refers to the points in head-space that corresponds to the HPI points.\n Currently, '*.elp' and '*.hsp' files are NOT supported.\n\n See Also\n --------\n mne.Epochs : Documentation of attribute and methods.\n \"\"\"\n\n @verbose\n def __init__(self, input_fname, events, event_id=None, tmin=0,\n baseline=None, reject=None, flat=None, reject_tmin=None,\n reject_tmax=None, mrk=None, elp=None, hsp=None,\n allow_unknown_format=False, standardize_names=None,\n verbose=None): # noqa: D102\n\n if isinstance(events, str):\n events = read_events(events)\n\n logger.info('Extracting KIT Parameters from %s...' % input_fname)\n input_fname = op.abspath(input_fname)\n self.info, kit_info = get_kit_info(\n input_fname, allow_unknown_format, standardize_names)\n kit_info.update(filename=input_fname)\n self._raw_extras = [kit_info]\n self._filenames = []\n if len(events) != self._raw_extras[0]['n_epochs']:\n raise ValueError('Event list does not match number of epochs.')\n\n if self._raw_extras[0]['acq_type'] == KIT.EPOCHS:\n self._raw_extras[0]['data_length'] = KIT.INT\n else:\n raise TypeError('SQD file contains raw data, not epochs or '\n 'average. Wrong reader.')\n\n if event_id is None: # convert to int to make typing-checks happy\n event_id = {str(e): int(e) for e in np.unique(events[:, 2])}\n\n for key, val in event_id.items():\n if val not in events[:, 2]:\n raise ValueError('No matching events found for %s '\n '(event id %i)' % (key, val))\n\n data = self._read_kit_data()\n assert data.shape == (self._raw_extras[0]['n_epochs'],\n self.info['nchan'],\n self._raw_extras[0]['frame_length'])\n tmax = ((data.shape[2] - 1) / self.info['sfreq']) + tmin\n super(EpochsKIT, self).__init__(\n self.info, data, events, event_id, tmin, tmax, baseline,\n reject=reject, flat=flat, reject_tmin=reject_tmin,\n reject_tmax=reject_tmax, filename=input_fname, verbose=verbose)\n self.info = _call_digitization(\n info=self.info, mrk=mrk, elp=elp, hsp=hsp, kit_info=kit_info)\n logger.info('Ready.')\n\n def _read_kit_data(self):\n \"\"\"Read epochs data.\n\n Returns\n -------\n data : array, [channels x samples]\n the data matrix (channels x samples).\n times : array, [samples]\n returns the time values corresponding to the samples.\n \"\"\"\n info = self._raw_extras[0]\n epoch_length = info['frame_length']\n n_epochs = info['n_epochs']\n n_samples = info['n_samples']\n filename = info['filename']\n dtype = info['dtype']\n nchan = info['nchan']\n\n with open(filename, 'rb', buffering=0) as fid:\n fid.seek(info['dirs'][KIT.DIR_INDEX_RAW_DATA]['offset'])\n count = n_samples * nchan\n data = np.fromfile(fid, dtype=dtype, count=count)\n data = data.reshape((n_samples, nchan)).T\n data = data * info['conv_factor']\n data = data.reshape((nchan, n_epochs, epoch_length))\n data = data.transpose((1, 0, 2))\n\n return data\n\n\ndef _read_dir(fid):\n return dict(offset=np.fromfile(fid, np.uint32, 1)[0],\n size=np.fromfile(fid, np.int32, 1)[0],\n max_count=np.fromfile(fid, np.int32, 1)[0],\n count=np.fromfile(fid, np.int32, 1)[0])\n\n\n@verbose\ndef get_kit_info(rawfile, allow_unknown_format, standardize_names=None,\n verbose=None):\n \"\"\"Extract all the information from the sqd/con file.\n\n Parameters\n ----------\n rawfile : str\n KIT file to be read.\n allow_unknown_format : bool\n Force reading old data that is not officially supported. Alternatively,\n read and re-save the data with the KIT MEG Laboratory application.\n %(standardize_names)s\n %(verbose)s\n\n Returns\n -------\n info : instance of Info\n An Info for the instance.\n sqd : dict\n A dict containing all the sqd parameter settings.\n \"\"\"\n sqd = dict()\n sqd['rawfile'] = rawfile\n unsupported_format = False\n sqd['dirs'] = dirs = list()\n with open(rawfile, 'rb', buffering=0) as fid: # buffering=0 for np bug\n #\n # directories (0)\n #\n dirs.append(_read_dir(fid))\n dirs.extend(_read_dir(fid) for _ in range(dirs[0]['count'] - 1))\n assert len(dirs) == dirs[KIT.DIR_INDEX_DIR]['count']\n\n #\n # system (1)\n #\n fid.seek(dirs[KIT.DIR_INDEX_SYSTEM]['offset'])\n # check file format version\n version, revision = unpack('2i', fid.read(2 * KIT.INT))\n if version < 2 or (version == 2 and revision < 3):\n version_string = \"V%iR%03i\" % (version, revision)\n if allow_unknown_format:\n unsupported_format = True\n logger.warning(\"Force loading KIT format %s\", version_string)\n else:\n raise UnsupportedKITFormat(\n version_string,\n \"SQD file format %s is not officially supported. \"\n \"Set allow_unknown_format=True to load it anyways.\" %\n (version_string,))\n\n sysid = unpack('i', fid.read(KIT.INT))[0]\n # basic info\n system_name = unpack('128s', fid.read(128))[0].decode()\n # model name\n model_name = unpack('128s', fid.read(128))[0].decode()\n # channels\n sqd['nchan'] = channel_count = unpack('i', fid.read(KIT.INT))[0]\n comment = unpack('256s', fid.read(256))[0].decode()\n create_time, last_modified_time = unpack('2i', fid.read(2 * KIT.INT))\n fid.seek(KIT.INT * 3, SEEK_CUR) # reserved\n dewar_style = unpack('i', fid.read(KIT.INT))[0]\n fid.seek(KIT.INT * 3, SEEK_CUR) # spare\n fll_type = unpack('i', fid.read(KIT.INT))[0]\n fid.seek(KIT.INT * 3, SEEK_CUR) # spare\n trigger_type = unpack('i', fid.read(KIT.INT))[0]\n fid.seek(KIT.INT * 3, SEEK_CUR) # spare\n adboard_type = unpack('i', fid.read(KIT.INT))[0]\n fid.seek(KIT.INT * 29, SEEK_CUR) # reserved\n\n if version < 2 or (version == 2 and revision <= 3):\n adc_range = float(unpack('i', fid.read(KIT.INT))[0])\n else:\n adc_range = unpack('d', fid.read(KIT.DOUBLE))[0]\n adc_polarity, adc_allocated, adc_stored = unpack('3i',\n fid.read(3 * KIT.INT))\n system_name = system_name.replace('\\x00', '')\n system_name = system_name.strip().replace('\\n', '/')\n model_name = model_name.replace('\\x00', '')\n model_name = model_name.strip().replace('\\n', '/')\n\n full_version = f'V{version:d}R{revision:03d}'\n logger.debug(\"SQD file basic information:\")\n logger.debug(\"Meg160 version = %s\", full_version)\n logger.debug(\"System ID = %i\", sysid)\n logger.debug(\"System name = %s\", system_name)\n logger.debug(\"Model name = %s\", model_name)\n logger.debug(\"Channel count = %i\", channel_count)\n logger.debug(\"Comment = %s\", comment)\n logger.debug(\"Dewar style = %i\", dewar_style)\n logger.debug(\"FLL type = %i\", fll_type)\n logger.debug(\"Trigger type = %i\", trigger_type)\n logger.debug(\"A/D board type = %i\", adboard_type)\n logger.debug(\"ADC range = +/-%s[V]\", adc_range / 2.)\n logger.debug(\"ADC allocate = %i[bit]\", adc_allocated)\n logger.debug(\"ADC bit = %i[bit]\", adc_stored)\n # MGH description: 'acquisition (megacq) VectorView system at NMR-MGH'\n description = \\\n f'{system_name} ({sysid}) {full_version} {model_name}'\n sqd['dtype'] = np.dtype(getattr(np, f'int{adc_allocated}'))\n\n # check that we can read this file\n if fll_type not in KIT.FLL_SETTINGS:\n fll_types = sorted(KIT.FLL_SETTINGS.keys())\n use_fll_type = fll_types[\n np.searchsorted(fll_types, fll_type) - 1]\n warn('Unknown site filter settings (FLL) for system '\n '\"%s\" model \"%s\" (ID %s), will assume FLL %d->%d, check '\n 'your data for correctness, including channel scales and '\n 'filter settings!'\n % (system_name, model_name, sysid, fll_type, use_fll_type))\n fll_type = use_fll_type\n\n #\n # channel information (4)\n #\n chan_dir = dirs[KIT.DIR_INDEX_CHANNELS]\n chan_offset, chan_size = chan_dir['offset'], chan_dir['size']\n sqd['channels'] = channels = []\n exg_gains = list()\n for i in range(channel_count):\n fid.seek(chan_offset + chan_size * i)\n channel_type, = unpack('i', fid.read(KIT.INT))\n # System 52 mislabeled reference channels as NULL. This was fixed\n # in system 53; not sure about 51...\n if sysid == 52 and i < 160 and channel_type == KIT.CHANNEL_NULL:\n channel_type = KIT.CHANNEL_MAGNETOMETER_REFERENCE\n\n if channel_type in KIT.CHANNELS_MEG:\n if channel_type not in KIT.CH_TO_FIFF_COIL:\n raise NotImplementedError(\n \"KIT channel type %i can not be read. Please contact \"\n \"the mne-python developers.\" % channel_type)\n channels.append({\n 'type': channel_type,\n # (x, y, z, theta, phi) for all MEG channels. Some channel\n # types have additional information which we're not using.\n 'loc': np.fromfile(fid, dtype='d', count=5),\n })\n if channel_type in KIT.CHANNEL_NAME_NCHAR:\n fid.seek(16, SEEK_CUR) # misc fields\n channels[-1]['name'] = _read_name(fid, channel_type)\n elif channel_type in KIT.CHANNELS_MISC:\n channel_no, = unpack('i', fid.read(KIT.INT))\n fid.seek(4, SEEK_CUR)\n name = _read_name(fid, channel_type)\n channels.append({\n 'type': channel_type,\n 'no': channel_no,\n 'name': name,\n })\n if channel_type in (KIT.CHANNEL_EEG, KIT.CHANNEL_ECG):\n offset = 6 if channel_type == KIT.CHANNEL_EEG else 8\n fid.seek(offset, SEEK_CUR)\n exg_gains.append(np.fromfile(fid, 'd', 1)[0])\n elif channel_type == KIT.CHANNEL_NULL:\n channels.append({'type': channel_type})\n else:\n raise IOError(\"Unknown KIT channel type: %i\" % channel_type)\n exg_gains = np.array(exg_gains)\n\n #\n # Channel sensitivity information: (5)\n #\n\n # only sensor channels requires gain. the additional misc channels\n # (trigger channels, audio and voice channels) are passed\n # through unaffected\n fid.seek(dirs[KIT.DIR_INDEX_CALIBRATION]['offset'])\n # (offset [Volt], gain [Tesla/Volt]) for each channel\n sensitivity = np.fromfile(fid, dtype='d', count=channel_count * 2)\n sensitivity.shape = (channel_count, 2)\n channel_offset, channel_gain = sensitivity.T\n assert (channel_offset == 0).all() # otherwise we have a problem\n\n #\n # amplifier gain (7)\n #\n fid.seek(dirs[KIT.DIR_INDEX_AMP_FILTER]['offset'])\n amp_data = unpack('i', fid.read(KIT.INT))[0]\n if fll_type >= 100: # Kapper Type\n # gain: mask bit\n gain1 = (amp_data & 0x00007000) >> 12\n gain2 = (amp_data & 0x70000000) >> 28\n gain3 = (amp_data & 0x07000000) >> 24\n amp_gain = (KIT.GAINS[gain1] * KIT.GAINS[gain2] * KIT.GAINS[gain3])\n # filter settings\n hpf = (amp_data & 0x00000700) >> 8\n lpf = (amp_data & 0x00070000) >> 16\n bef = (amp_data & 0x00000003) >> 0\n else: # Hanger Type\n # gain\n input_gain = (amp_data & 0x1800) >> 11\n output_gain = (amp_data & 0x0007) >> 0\n amp_gain = KIT.GAINS[input_gain] * KIT.GAINS[output_gain]\n # filter settings\n hpf = (amp_data & 0x007) >> 4\n lpf = (amp_data & 0x0700) >> 8\n bef = (amp_data & 0xc000) >> 14\n hpf_options, lpf_options, bef_options = KIT.FLL_SETTINGS[fll_type]\n sqd['highpass'] = KIT.HPFS[hpf_options][hpf]\n sqd['lowpass'] = KIT.LPFS[lpf_options][lpf]\n sqd['notch'] = KIT.BEFS[bef_options][bef]\n\n #\n # Acquisition Parameters (8)\n #\n fid.seek(dirs[KIT.DIR_INDEX_ACQ_COND]['offset'])\n sqd['acq_type'], = acq_type, = unpack('i', fid.read(KIT.INT))\n sqd['sfreq'], = unpack('d', fid.read(KIT.DOUBLE))\n if acq_type == KIT.CONTINUOUS:\n # samples_count, = unpack('i', fid.read(KIT.INT))\n fid.seek(KIT.INT, SEEK_CUR)\n sqd['n_samples'], = unpack('i', fid.read(KIT.INT))\n elif acq_type == KIT.EVOKED or acq_type == KIT.EPOCHS:\n sqd['frame_length'], = unpack('i', fid.read(KIT.INT))\n sqd['pretrigger_length'], = unpack('i', fid.read(KIT.INT))\n sqd['average_count'], = unpack('i', fid.read(KIT.INT))\n sqd['n_epochs'], = unpack('i', fid.read(KIT.INT))\n if acq_type == KIT.EVOKED:\n sqd['n_samples'] = sqd['frame_length']\n else:\n sqd['n_samples'] = sqd['frame_length'] * sqd['n_epochs']\n else:\n raise IOError(\"Invalid acquisition type: %i. Your file is neither \"\n \"continuous nor epoched data.\" % (acq_type,))\n\n #\n # digitization information (12 and 26)\n #\n dig_dir = dirs[KIT.DIR_INDEX_DIG_POINTS]\n cor_dir = dirs[KIT.DIR_INDEX_COREG]\n dig = dict()\n hsp = list()\n if dig_dir['count'] > 0 and cor_dir['count'] > 0:\n # directories (0)\n fid.seek(dig_dir['offset'])\n for _ in range(dig_dir['count']):\n name = _read_name(fid, n=8).strip()\n # Sometimes there are mismatches (e.g., AFz vs AFZ) between\n # the channel name and its digitized, name, so let's be case\n # insensitive. It will also prevent collisions with HSP\n name = name.lower()\n rr = np.fromfile(fid, 'd', 3)\n if name:\n assert name not in dig\n dig[name] = rr\n else:\n hsp.append(rr)\n\n # nasion, lpa, rpa, HPI in native space\n elp = [dig.pop(key) for key in (\n 'fidnz', 'fidt9', 'fidt10',\n 'hpi_1', 'hpi_2', 'hpi_3', 'hpi_4')]\n if 'hpi_5' in dig and dig['hpi_5'].any():\n elp.append(dig.pop('hpi_5'))\n elp = np.array(elp)\n hsp = np.array(hsp, float).reshape(-1, 3)\n assert elp.shape in ((7, 3), (8, 3))\n # coregistration\n fid.seek(cor_dir['offset'])\n mrk = np.zeros((elp.shape[0] - 3, 3))\n for _ in range(cor_dir['count']):\n done = np.fromfile(fid, np.int32, 1)[0]\n fid.seek(16 * KIT.DOUBLE + # meg_to_mri\n 16 * KIT.DOUBLE, # mri_to_meg\n SEEK_CUR)\n marker_count = np.fromfile(fid, np.int32, 1)[0]\n if not done:\n continue\n assert marker_count >= len(mrk)\n for mi in range(len(mrk)):\n mri_type, meg_type, mri_done, meg_done = \\\n np.fromfile(fid, np.int32, 4)\n assert meg_done\n fid.seek(3 * KIT.DOUBLE, SEEK_CUR) # mri_pos\n mrk[mi] = np.fromfile(fid, 'd', 3)\n fid.seek(256, SEEK_CUR) # marker_file (char)\n sqd.update(hsp=hsp, elp=elp, mrk=mrk)\n\n all_names = set(ch.get('name', '') for ch in channels)\n if standardize_names is None and all_names.difference({'', 'EEG'}):\n standardize_names = True\n warn('standardize_names defaults to True in 0.21 but will change '\n 'to False in 0.22', DeprecationWarning)\n\n # precompute conversion factor for reading data\n if unsupported_format:\n if sysid not in LEGACY_AMP_PARAMS:\n raise IOError(\"Legacy parameters for system ID %i unavailable\" %\n (sysid,))\n adc_range, adc_stored = LEGACY_AMP_PARAMS[sysid]\n is_meg = np.array([ch['type'] in KIT.CHANNELS_MEG for ch in channels])\n ad_to_volt = adc_range / (2 ** adc_stored)\n ad_to_tesla = ad_to_volt / amp_gain * channel_gain\n conv_factor = np.where(is_meg, ad_to_tesla, ad_to_volt)\n # XXX this is a bit of a hack. Should probably do this more cleanly at\n # some point... the 2 ** (adc_stored - 14) was emperically determined using\n # the test files with known amplitudes. The conv_factors need to be\n # replaced by these values otherwise we're off by a factor off 5000.0\n # for the EEG data.\n is_exg = [ch['type'] in (KIT.CHANNEL_EEG, KIT.CHANNEL_ECG)\n for ch in channels]\n exg_gains /= 2 ** (adc_stored - 14)\n conv_factor[is_exg] = exg_gains\n sqd['conv_factor'] = conv_factor[:, np.newaxis]\n\n # Create raw.info dict for raw fif object with SQD data\n info = _empty_info(float(sqd['sfreq']))\n info.update(meas_date=_stamp_to_dt((create_time, 0)),\n lowpass=sqd['lowpass'],\n highpass=sqd['highpass'], kit_system_id=sysid,\n description=description)\n\n # Creates a list of dicts of meg channels for raw.info\n logger.info('Setting channel info structure...')\n info['chs'] = fiff_channels = []\n channel_index = defaultdict(lambda: 0)\n sqd['eeg_dig'] = OrderedDict()\n for idx, ch in enumerate(channels, 1):\n if ch['type'] in KIT.CHANNELS_MEG:\n ch_name = ch.get('name', '')\n if ch_name == '' or standardize_names:\n ch_name = 'MEG %03d' % idx\n # create three orthogonal vector\n # ch_angles[0]: theta, ch_angles[1]: phi\n theta, phi = np.radians(ch['loc'][3:])\n x = sin(theta) * cos(phi)\n y = sin(theta) * sin(phi)\n z = cos(theta)\n vec_z = np.array([x, y, z])\n vec_z /= linalg.norm(vec_z)\n vec_x = np.zeros(vec_z.size, dtype=np.float64)\n if vec_z[1] < vec_z[2]:\n if vec_z[0] < vec_z[1]:\n vec_x[0] = 1.0\n else:\n vec_x[1] = 1.0\n elif vec_z[0] < vec_z[2]:\n vec_x[0] = 1.0\n else:\n vec_x[2] = 1.0\n vec_x -= np.sum(vec_x * vec_z) * vec_z\n vec_x /= linalg.norm(vec_x)\n vec_y = np.cross(vec_z, vec_x)\n # transform to Neuromag like coordinate space\n vecs = np.vstack((ch['loc'][:3], vec_x, vec_y, vec_z))\n vecs = apply_trans(als_ras_trans, vecs)\n unit = FIFF.FIFF_UNIT_T\n loc = vecs.ravel()\n else:\n ch_type_label = KIT.CH_LABEL[ch['type']]\n channel_index[ch_type_label] += 1\n ch_type_index = channel_index[ch_type_label]\n ch_name = ch.get('name', '')\n eeg_name = ch_name.lower()\n # some files have all EEG labeled as EEG\n if ch_name in ('', 'EEG') or standardize_names:\n ch_name = '%s %03i' % (ch_type_label, ch_type_index)\n unit = FIFF.FIFF_UNIT_V\n loc = np.zeros(12)\n if eeg_name and eeg_name in dig:\n loc[:3] = sqd['eeg_dig'][eeg_name] = dig[eeg_name]\n fiff_channels.append(dict(\n cal=KIT.CALIB_FACTOR, logno=idx, scanno=idx, range=KIT.RANGE,\n unit=unit, unit_mul=KIT.UNIT_MUL, ch_name=ch_name,\n coord_frame=FIFF.FIFFV_COORD_DEVICE,\n coil_type=KIT.CH_TO_FIFF_COIL[ch['type']],\n kind=KIT.CH_TO_FIFF_KIND[ch['type']], loc=loc))\n info._update_redundant()\n return info, sqd\n\n\ndef _read_name(fid, ch_type=None, n=None):\n n = n if ch_type is None else KIT.CHANNEL_NAME_NCHAR[ch_type]\n return fid.read(n).split(b'\\x00')[0].decode('utf-8')\n\n\n@fill_doc\ndef read_raw_kit(input_fname, mrk=None, elp=None, hsp=None, stim='>',\n slope='-', stimthresh=1, preload=False, stim_code='binary',\n allow_unknown_format=False, standardize_names=None,\n verbose=None):\n \"\"\"Reader function for Ricoh/KIT conversion to FIF.\n\n Parameters\n ----------\n input_fname : str\n Path to the sqd file.\n mrk : None | str | array_like, shape (5, 3) | list of str or array_like\n Marker points representing the location of the marker coils with\n respect to the MEG Sensors, or path to a marker file.\n If list, all of the markers will be averaged together.\n elp : None | str | array_like, shape (8, 3)\n Digitizer points representing the location of the fiducials and the\n marker coils with respect to the digitized head shape, or path to a\n file containing these points.\n hsp : None | str | array, shape (n_points, 3)\n Digitizer head shape points, or path to head shape file. If more than\n 10,000 points are in the head shape, they are automatically decimated.\n stim : list of int | '<' | '>'\n Channel-value correspondence when converting KIT trigger channels to a\n Neuromag-style stim channel. For '<', the largest values are assigned\n to the first channel (default). For '>', the largest values are\n assigned to the last channel. Can also be specified as a list of\n trigger channel indexes.\n slope : '+' | '-'\n How to interpret values on KIT trigger channels when synthesizing a\n Neuromag-style stim channel. With '+', a positive slope (low-to-high)\n is interpreted as an event. With '-', a negative slope (high-to-low)\n is interpreted as an event.\n stimthresh : float\n The threshold level for accepting voltage changes in KIT trigger\n channels as a trigger event.\n %(preload)s\n stim_code : 'binary' | 'channel'\n How to decode trigger values from stim channels. 'binary' read stim\n channel events as binary code, 'channel' encodes channel number.\n allow_unknown_format : bool\n Force reading old data that is not officially supported. Alternatively,\n read and re-save the data with the KIT MEG Laboratory application.\n %(standardize_names)s\n %(verbose)s\n\n Returns\n -------\n raw : instance of RawKIT\n A Raw object containing KIT data.\n\n See Also\n --------\n mne.io.Raw : Documentation of attribute and methods.\n\n Notes\n -----\n If mrk, hsp or elp are array_like inputs, then the numbers in xyz\n coordinates should be in units of meters.\n \"\"\"\n return RawKIT(input_fname=input_fname, mrk=mrk, elp=elp, hsp=hsp,\n stim=stim, slope=slope, stimthresh=stimthresh,\n preload=preload, stim_code=stim_code,\n allow_unknown_format=allow_unknown_format,\n standardize_names=standardize_names, verbose=verbose)\n\n\n@fill_doc\ndef read_epochs_kit(input_fname, events, event_id=None, mrk=None, elp=None,\n hsp=None, allow_unknown_format=False,\n standardize_names=None, verbose=None):\n \"\"\"Reader function for Ricoh/KIT epochs files.\n\n Parameters\n ----------\n input_fname : str\n Path to the sqd file.\n events : array, shape (n_events, 3)\n The events typically returned by the read_events function.\n If some events don't match the events of interest as specified\n by event_id, they will be marked as 'IGNORED' in the drop log.\n event_id : int | list of int | dict | None\n The id of the event to consider. If dict,\n the keys can later be used to access associated events. Example:\n dict(auditory=1, visual=3). If int, a dict will be created with\n the id as string. If a list, all events with the IDs specified\n in the list are used. If None, all events will be used with\n and a dict is created with string integer names corresponding\n to the event id integers.\n mrk : None | str | array_like, shape (5, 3) | list of str or array_like\n Marker points representing the location of the marker coils with\n respect to the MEG Sensors, or path to a marker file.\n If list, all of the markers will be averaged together.\n elp : None | str | array_like, shape (8, 3)\n Digitizer points representing the location of the fiducials and the\n marker coils with respect to the digitized head shape, or path to a\n file containing these points.\n hsp : None | str | array, shape (n_points, 3)\n Digitizer head shape points, or path to head shape file. If more than\n 10,000 points are in the head shape, they are automatically decimated.\n allow_unknown_format : bool\n Force reading old data that is not officially supported. Alternatively,\n read and re-save the data with the KIT MEG Laboratory application.\n %(standardize_names)s\n %(verbose)s\n\n Returns\n -------\n epochs : instance of Epochs\n The epochs.\n\n Notes\n -----\n .. versionadded:: 0.9.0\n \"\"\"\n epochs = EpochsKIT(input_fname=input_fname, events=events,\n event_id=event_id, mrk=mrk, elp=elp, hsp=hsp,\n allow_unknown_format=allow_unknown_format,\n standardize_names=standardize_names,\n verbose=verbose)\n return epochs\n"}}},{"rowIdx":95,"cells":{"function_name":{"kind":"string","value":"get_home_envvars"},"docstring":{"kind":"string","value":"Return dict with env variables to be adjusted for a new HOME\n\nOnly variables found in current os.environ are adjusted.\n\nParameters\n----------\nnew_home: str or Path\n New home path, in native to OS \"schema\""},"masked_code":{"kind":"string","value":"# emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-\n# ex: set sts=4 ts=4 sw=4 et:\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##\n#\n# See COPYING file distributed along with the datalad package for the\n# copyright and license terms.\n#\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##\n\nimport collections\nfrom collections.abc import Callable\nimport re\nimport builtins\nimport time\n\nimport logging\nimport shutil\nimport os\nimport sys\nimport tempfile\nfrom tempfile import NamedTemporaryFile\nimport platform\nimport gc\nimport glob\nimport gzip\nimport stat\nimport string\nimport warnings\n\nimport os.path as op\n\nfrom copy import copy as shallow_copy\nfrom contextlib import contextmanager\nfrom functools import (\n lru_cache,\n wraps,\n)\nfrom time import sleep\nimport inspect\nfrom itertools import tee\n# this import is required because other modules import opj from here.\nfrom os.path import join as opj\nfrom os.path import (\n abspath,\n basename,\n commonprefix,\n curdir,\n dirname,\n exists,\n expanduser,\n expandvars,\n isabs,\n isdir,\n islink,\n lexists,\n normpath,\n pardir,\n relpath,\n sep,\n split,\n splitdrive\n)\n\nimport posixpath\n\nfrom shlex import (\n quote as shlex_quote,\n split as shlex_split,\n)\n\n# from datalad.dochelpers import get_docstring_split\nfrom datalad.consts import TIMESTAMP_FMT\nfrom datalad.support.exceptions import CapturedException\n\n\nunicode_srctypes = str, bytes\n\n\nlgr = logging.getLogger(\"datalad.utils\")\n\nlgr.log(5, \"Importing datalad.utils\")\n#\n# Some useful variables\n#\nplatform_system = platform.system().lower()\non_windows = platform_system == 'windows'\non_osx = platform_system == 'darwin'\non_linux = platform_system == 'linux'\non_msys_tainted_paths = on_windows \\\n and 'MSYS_NO_PATHCONV' not in os.environ \\\n and os.environ.get('MSYSTEM', '')[:4] in ('MSYS', 'MING')\n\n\n# Takes ~200msec, so should not be called at import time\n@lru_cache() # output should not change through life time of datalad process\ndef get_linux_distribution():\n \"\"\"Compatibility wrapper for {platform,distro}.linux_distribution().\n \"\"\"\n if hasattr(platform, \"linux_distribution\"):\n # Use deprecated (but faster) method if it's available.\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n result = platform.linux_distribution()\n else:\n import distro # We require this for Python 3.8 and above.\n result = distro.linux_distribution(full_distribution_name=False)\n return result\n\n\n# Those weren't used for any critical decision making, thus we just set them to None\n# Use get_linux_distribution() directly where needed\nlinux_distribution_name = linux_distribution_release = None\n\n# Maximal length of cmdline string\n# Query the system and use hardcoded \"knowledge\" if None\n# probably getconf ARG_MAX might not be available\n# The last one would be the most conservative/Windows\nCMD_MAX_ARG_HARDCODED = 2097152 if on_linux else 262144 if on_osx else 32767\ntry:\n CMD_MAX_ARG = os.sysconf('SC_ARG_MAX')\n assert CMD_MAX_ARG > 0\n if CMD_MAX_ARG > CMD_MAX_ARG_HARDCODED * 1e6:\n # workaround for some kind of a bug which comes up with python 3.4\n # see https://github.com/datalad/datalad/issues/3150\n # or on older CentOS with conda and python as new as 3.9\n # see https://github.com/datalad/datalad/issues/5943\n # TODO: let Yarik know that the world is a paradise now whenever 1e6\n # is not large enough\n CMD_MAX_ARG = min(CMD_MAX_ARG, CMD_MAX_ARG_HARDCODED)\nexcept Exception as exc:\n # ATM (20181005) SC_ARG_MAX available only on POSIX systems\n # so exception would be thrown e.g. on Windows, or\n # somehow during Debian build for nd14.04 it is coming up with -1:\n # https://github.com/datalad/datalad/issues/3015\n CMD_MAX_ARG = CMD_MAX_ARG_HARDCODED\n lgr.debug(\n \"Failed to query or got useless SC_ARG_MAX sysconf, \"\n \"will use hardcoded value: %s\", exc)\n# Even with all careful computations we do, due to necessity to account for\n# environment and what not, we still could not figure out \"exact\" way to\n# estimate it, but it was shown that 300k safety margin on linux was sufficient.\n# https://github.com/datalad/datalad/pull/2977#issuecomment-436264710\n# 300k is ~15%, so to be safe, and for paranoid us we will just use up to 50%\n# of the length for \"safety margin\". We might probably still blow due to\n# env vars, unicode, etc... so any hard limit imho is not a proper solution\nCMD_MAX_ARG = int(0.5 * CMD_MAX_ARG)\nlgr.debug(\n \"Maximal length of cmdline string (adjusted for safety margin): %d\",\n CMD_MAX_ARG)\n\n#\n# Little helpers\n#\n\n# `getargspec` has been deprecated in Python 3.\nArgSpecFake = collections.namedtuple(\n \"ArgSpecFake\", [\"args\", \"varargs\", \"keywords\", \"defaults\"])\n\n\ndef getargspec(func, *, include_kwonlyargs=False):\n \"\"\"Compat shim for getargspec deprecated in python 3.\n\n The main difference from inspect.getargspec (and inspect.getfullargspec\n for that matter) is that by using inspect.signature we are providing\n correct args/defaults for functools.wraps'ed functions.\n\n `include_kwonlyargs` option was added to centralize getting all args,\n even the ones which are kwonly (follow the ``*,``).\n\n For internal use and not advised for use in 3rd party code.\n Please use inspect.signature directly.\n \"\"\"\n # We use signature, and not getfullargspec, because only signature properly\n # \"passes\" args from a functools.wraps decorated function.\n # Note: getfullargspec works Ok on wrapt-decorated functions\n f_sign = inspect.signature(func)\n # Loop through parameters and compose argspec\n args4 = [[], None, None, {}]\n # Collect all kwonlyargs into a dedicated dict - name: default\n kwonlyargs = {}\n # shortcuts\n args, defaults = args4[0], args4[3]\n P = inspect.Parameter\n\n for p_name, p in f_sign.parameters.items():\n if p.kind in (P.POSITIONAL_ONLY, P.POSITIONAL_OR_KEYWORD):\n assert not kwonlyargs # yoh: must not come after kwonlyarg\n args.append(p_name)\n if p.default is not P.empty:\n defaults[p_name] = p.default\n elif p.kind == P.VAR_POSITIONAL:\n args4[1] = p_name\n elif p.kind == P.VAR_KEYWORD:\n args4[2] = p_name\n elif p.kind == P.KEYWORD_ONLY:\n assert p.default is not P.empty\n kwonlyargs[p_name] = p.default\n\n if kwonlyargs:\n if not include_kwonlyargs:\n raise ValueError(\n 'Function has keyword-only parameters or annotations, either use '\n 'inspect.signature() API which can support them, or provide include_kwonlyargs=True '\n 'to this function'\n )\n else:\n args.extend(list(kwonlyargs))\n defaults.update(kwonlyargs)\n\n # harmonize defaults to how original getargspec returned them -- just a tuple\n args4[3] = None if not defaults else tuple(defaults.values())\n return ArgSpecFake(*args4)\n\n\ndef any_re_search(regexes, value):\n \"\"\"Return if any of regexes (list or str) searches successfully for value\"\"\"\n for regex in ensure_tuple_or_list(regexes):\n if re.search(regex, value):\n return True\n return False\n\n\ndef not_supported_on_windows(msg=None):\n \"\"\"A little helper to be invoked to consistently fail whenever functionality is\n not supported (yet) on Windows\n \"\"\"\n if on_windows:\n raise NotImplementedError(\"This functionality is not yet implemented for Windows OS\"\n + (\": %s\" % msg if msg else \"\"))\n\n\n# MASKED: get_home_envvars function (lines 232-251)\n\n\ndef shortened_repr(value, l=30):\n try:\n if hasattr(value, '__repr__') and (value.__repr__ is not object.__repr__):\n value_repr = repr(value)\n if not value_repr.startswith('<') and len(value_repr) > l:\n value_repr = \"<<%s++%d chars++%s>>\" % (\n value_repr[:l - 16],\n len(value_repr) - (l - 16 + 4),\n value_repr[-4:]\n )\n elif value_repr.startswith('<') and value_repr.endswith('>') and ' object at 0x':\n raise ValueError(\"I hate those useless long reprs\")\n else:\n raise ValueError(\"gimme class\")\n except Exception as e:\n value_repr = \"<%s>\" % value.__class__.__name__.split('.')[-1]\n return value_repr\n\n\ndef __auto_repr__(obj):\n attr_names = tuple()\n if hasattr(obj, '__dict__'):\n attr_names += tuple(obj.__dict__.keys())\n if hasattr(obj, '__slots__'):\n attr_names += tuple(obj.__slots__)\n\n items = []\n for attr in sorted(set(attr_names)):\n if attr.startswith('_'):\n continue\n value = getattr(obj, attr)\n # TODO: should we add this feature to minimize some talktative reprs\n # such as of URL?\n #if value is None:\n # continue\n items.append(\"%s=%s\" % (attr, shortened_repr(value)))\n\n return \"%s(%s)\" % (obj.__class__.__name__, ', '.join(items))\n\n\ndef auto_repr(cls):\n \"\"\"Decorator for a class to assign it an automagic quick and dirty __repr__\n\n It uses public class attributes to prepare repr of a class\n\n Original idea: http://stackoverflow.com/a/27799004/1265472\n \"\"\"\n\n cls.__repr__ = __auto_repr__\n return cls\n\n\ndef _is_stream_tty(stream):\n try:\n # TODO: check on windows if hasattr check would work correctly and\n # add value:\n return stream.isatty()\n except ValueError as exc:\n # Who knows why it is a ValueError, but let's try to be specific\n # If there is a problem with I/O - non-interactive, otherwise reraise\n if \"I/O\" in str(exc):\n return False\n raise\n\n\ndef is_interactive():\n \"\"\"Return True if all in/outs are open and tty.\n\n Note that in a somewhat abnormal case where e.g. stdin is explicitly\n closed, and any operation on it would raise a\n `ValueError(\"I/O operation on closed file\")` exception, this function\n would just return False, since the session cannot be used interactively.\n \"\"\"\n return all(_is_stream_tty(s) for s in (sys.stdin, sys.stdout, sys.stderr))\n\n\ndef get_ipython_shell():\n \"\"\"Detect if running within IPython and returns its `ip` (shell) object\n\n Returns None if not under ipython (no `get_ipython` function)\n \"\"\"\n try:\n return get_ipython()\n except NameError:\n return None\n\n\ndef md5sum(filename):\n \"\"\"Compute an MD5 sum for the given file\n \"\"\"\n from datalad.support.digests import Digester\n return Digester(digests=['md5'])(filename)['md5']\n\n\n# unused in -core\ndef sorted_files(path):\n \"\"\"Return a (sorted) list of files under path\n \"\"\"\n return sorted(sum([[op.join(r, f)[len(path) + 1:] for f in files]\n for r, d, files in os.walk(path)\n if not '.git' in r], []))\n\n_encoded_dirsep = r'\\\\' if on_windows else r'/'\n_VCS_REGEX = r'%s\\.(?:git|gitattributes|svn|bzr|hg)(?:%s|$)' % (\n _encoded_dirsep, _encoded_dirsep)\n_DATALAD_REGEX = r'%s\\.(?:datalad)(?:%s|$)' % (\n _encoded_dirsep, _encoded_dirsep)\n\n\ndef find_files(regex, topdir=curdir, exclude=None, exclude_vcs=True, exclude_datalad=False, dirs=False):\n \"\"\"Generator to find files matching regex\n\n Parameters\n ----------\n regex: basestring\n exclude: basestring, optional\n Matches to exclude\n exclude_vcs:\n If True, excludes commonly known VCS subdirectories. If string, used\n as regex to exclude those files (regex: `%r`)\n exclude_datalad:\n If True, excludes files known to be datalad meta-data files (e.g. under\n .datalad/ subdirectory) (regex: `%r`)\n topdir: basestring, optional\n Directory where to search\n dirs: bool, optional\n Whether to match directories as well as files\n \"\"\"\n for dirpath, dirnames, filenames in os.walk(topdir):\n names = (dirnames + filenames) if dirs else filenames\n # TODO: might want to uniformize on windows to use '/'\n paths = (op.join(dirpath, name) for name in names)\n for path in filter(re.compile(regex).search, paths):\n path = path.rstrip(sep)\n if exclude and re.search(exclude, path):\n continue\n if exclude_vcs and re.search(_VCS_REGEX, path):\n continue\n if exclude_datalad and re.search(_DATALAD_REGEX, path):\n continue\n yield path\nfind_files.__doc__ %= (_VCS_REGEX, _DATALAD_REGEX)\n\n\ndef expandpath(path, force_absolute=True):\n \"\"\"Expand all variables and user handles in a path.\n\n By default return an absolute path\n \"\"\"\n path = expandvars(expanduser(path))\n if force_absolute:\n path = abspath(path)\n return path\n\n\ndef posix_relpath(path, start=None):\n \"\"\"Behave like os.path.relpath, but always return POSIX paths...\n\n on any platform.\"\"\"\n # join POSIX style\n return posixpath.join(\n # split and relpath native style\n # python2.7 ntpath implementation of relpath cannot handle start=None\n *split(\n relpath(path, start=start if start is not None else '')))\n\n\ndef is_explicit_path(path):\n \"\"\"Return whether a path explicitly points to a location\n\n Any absolute path, or relative path starting with either '../' or\n './' is assumed to indicate a location on the filesystem. Any other\n path format is not considered explicit.\"\"\"\n path = expandpath(path, force_absolute=False)\n return isabs(path) \\\n or path.startswith(os.curdir + os.sep) \\\n or path.startswith(os.pardir + os.sep)\n\n\n# handle this dance once, and import pathlib from here\n# in all other places\n\nfrom pathlib import (\n Path,\n PurePath,\n PurePosixPath,\n)\n\ndef rotree(path, ro=True, chmod_files=True):\n \"\"\"To make tree read-only or writable\n\n Parameters\n ----------\n path : string\n Path to the tree/directory to chmod\n ro : bool, optional\n Whether to make it R/O (default) or RW\n chmod_files : bool, optional\n Whether to operate also on files (not just directories)\n \"\"\"\n if ro:\n chmod = lambda f: os.chmod(f, os.stat(f).st_mode & ~stat.S_IWRITE)\n else:\n chmod = lambda f: os.chmod(f, os.stat(f).st_mode | stat.S_IWRITE | stat.S_IREAD)\n\n for root, dirs, files in os.walk(path, followlinks=False):\n if chmod_files:\n for f in files:\n fullf = op.join(root, f)\n # might be the \"broken\" symlink which would fail to stat etc\n if exists(fullf):\n chmod(fullf)\n chmod(root)\n\n\ndef rmtree(path, chmod_files='auto', children_only=False, *args, **kwargs):\n \"\"\"To remove git-annex .git it is needed to make all files and directories writable again first\n\n Parameters\n ----------\n path: Path or str\n Path to remove\n chmod_files : string or bool, optional\n Whether to make files writable also before removal. Usually it is just\n a matter of directories to have write permissions.\n If 'auto' it would chmod files on windows by default\n children_only : bool, optional\n If set, all files and subdirectories would be removed while the path\n itself (must be a directory) would be preserved\n `*args` :\n `**kwargs` :\n Passed into shutil.rmtree call\n \"\"\"\n # Give W permissions back only to directories, no need to bother with files\n if chmod_files == 'auto':\n chmod_files = on_windows\n # TODO: yoh thinks that if we could quickly check our Flyweight for\n # repos if any of them is under the path, and could call .precommit\n # on those to possibly stop batched processes etc, we did not have\n # to do it on case by case\n # Check for open files\n assert_no_open_files(path)\n\n # TODO the whole thing should be reimplemented with pathlib, but for now\n # at least accept Path\n path = str(path)\n\n if children_only:\n if not isdir(path):\n raise ValueError(\"Can remove children only of directories\")\n for p in os.listdir(path):\n rmtree(op.join(path, p))\n return\n if not (islink(path) or not isdir(path)):\n rotree(path, ro=False, chmod_files=chmod_files)\n if on_windows:\n # shutil fails to remove paths that exceed 260 characters on Windows machines\n # that did not enable long path support. A workaround to remove long paths\n # anyway is to preprend \\\\?\\ to the path.\n # https://docs.microsoft.com/en-us/windows/win32/fileio/naming-a-file?redirectedfrom=MSDN#win32-file-namespaces\n path = r'\\\\?\\ '.strip() + path\n _rmtree(path, *args, **kwargs)\n else:\n # just remove the symlink\n unlink(path)\n\n\ndef rmdir(path, *args, **kwargs):\n \"\"\"os.rmdir with our optional checking for open files\"\"\"\n assert_no_open_files(path)\n os.rmdir(path)\n\n\ndef get_open_files(path, log_open=False):\n \"\"\"Get open files under a path\n\n Note: This function is very slow on Windows.\n\n Parameters\n ----------\n path : str\n File or directory to check for open files under\n log_open : bool or int\n If set - logger level to use\n\n Returns\n -------\n dict\n path : pid\n\n \"\"\"\n # Original idea: https://stackoverflow.com/a/11115521/1265472\n import psutil\n files = {}\n # since the ones returned by psutil would not be aware of symlinks in the\n # path we should also get realpath for path\n # do absolute() in addition to always get an absolute path\n # even with non-existing paths on windows\n path = str(Path(path).resolve().absolute())\n for proc in psutil.process_iter():\n try:\n open_paths = [p.path for p in proc.open_files()] + [proc.cwd()]\n for p in open_paths:\n # note: could be done more efficiently so we do not\n # renormalize path over and over again etc\n if path_startswith(p, path):\n files[p] = proc\n # Catch a race condition where a process ends\n # before we can examine its files\n except psutil.NoSuchProcess:\n pass\n except psutil.AccessDenied:\n pass\n\n if files and log_open:\n lgr.log(log_open, \"Open files under %s: %s\", path, files)\n return files\n\n\n_assert_no_open_files_cfg = os.environ.get('DATALAD_ASSERT_NO_OPEN_FILES')\nif _assert_no_open_files_cfg:\n def assert_no_open_files(path):\n files = get_open_files(path, log_open=40)\n if _assert_no_open_files_cfg == 'assert':\n assert not files, \"Got following files still open: %s\" % ','.join(files)\n elif files:\n if _assert_no_open_files_cfg == 'pdb':\n import pdb\n pdb.set_trace()\n elif _assert_no_open_files_cfg == 'epdb':\n import epdb\n epdb.serve()\n pass\n # otherwise we would just issue that error message in the log\nelse:\n def assert_no_open_files(*args, **kwargs):\n pass\n\n\ndef rmtemp(f, *args, **kwargs):\n \"\"\"Wrapper to centralize removing of temp files so we could keep them around\n\n It will not remove the temporary file/directory if DATALAD_TESTS_TEMP_KEEP\n environment variable is defined\n \"\"\"\n if not os.environ.get('DATALAD_TESTS_TEMP_KEEP'):\n if not os.path.lexists(f):\n lgr.debug(\"Path %s does not exist, so can't be removed\", f)\n return\n lgr.log(5, \"Removing temp file: %s\", f)\n # Can also be a directory\n if isdir(f):\n rmtree(f, *args, **kwargs)\n else:\n unlink(f)\n else:\n lgr.info(\"Keeping temp file: %s\", f)\n\n\ndef file_basename(name, return_ext=False):\n \"\"\"\n Strips up to 2 extensions of length up to 4 characters and starting with alpha\n not a digit, so we could get rid of .tar.gz etc\n \"\"\"\n bname = basename(name)\n fbname = re.sub(r'(\\.[a-zA-Z_]\\S{1,4}){0,2}$', '', bname)\n if return_ext:\n return fbname, bname[len(fbname) + 1:]\n else:\n return fbname\n\n\n# unused in -core\ndef escape_filename(filename):\n \"\"\"Surround filename in \"\" and escape \" in the filename\n \"\"\"\n filename = filename.replace('\"', r'\\\"').replace('`', r'\\`')\n filename = '\"%s\"' % filename\n return filename\n\n\n# unused in -core\ndef encode_filename(filename):\n \"\"\"Encode unicode filename\n \"\"\"\n if isinstance(filename, str):\n return filename.encode(sys.getfilesystemencoding())\n else:\n return filename\n\n\n# unused in -core\ndef decode_input(s):\n \"\"\"Given input string/bytes, decode according to stdin codepage (or UTF-8)\n if not defined\n\n If fails -- issue warning and decode allowing for errors\n being replaced\n \"\"\"\n if isinstance(s, str):\n return s\n else:\n encoding = sys.stdin.encoding or 'UTF-8'\n try:\n return s.decode(encoding)\n except UnicodeDecodeError as exc:\n lgr.warning(\n \"Failed to decode input string using %s encoding. \"\n \"Decoding allowing for errors\", encoding)\n return s.decode(encoding, errors='replace')\n\n\n# unused in -core\nif on_windows:\n def lmtime(filepath, mtime):\n \"\"\"Set mtime for files. On Windows a merely adapter to os.utime\n \"\"\"\n os.utime(filepath, (time.time(), mtime))\nelse:\n def lmtime(filepath, mtime):\n \"\"\"Set mtime for files, while not de-referencing symlinks.\n\n To overcome absence of os.lutime\n\n Works only on linux and OSX ATM\n \"\"\"\n from .cmd import WitlessRunner\n # convert mtime to format touch understands [[CC]YY]MMDDhhmm[.SS]\n smtime = time.strftime(\"%Y%m%d%H%M.%S\", time.localtime(mtime))\n lgr.log(3, \"Setting mtime for %s to %s == %s\", filepath, mtime, smtime)\n WitlessRunner().run(['touch', '-h', '-t', '%s' % smtime, filepath])\n filepath = Path(filepath)\n rfilepath = filepath.resolve()\n if filepath.is_symlink() and rfilepath.exists():\n # trust no one - adjust also of the target file\n # since it seemed like downloading under OSX (was it using curl?)\n # didn't bother with timestamps\n lgr.log(3, \"File is a symlink to %s Setting mtime for it to %s\",\n rfilepath, mtime)\n os.utime(str(rfilepath), (time.time(), mtime))\n # doesn't work on OSX\n # Runner().run(['touch', '-h', '-d', '@%s' % mtime, filepath])\n\n\ndef ensure_tuple_or_list(obj):\n \"\"\"Given an object, wrap into a tuple if not list or tuple\n \"\"\"\n if isinstance(obj, (list, tuple)):\n return obj\n return (obj,)\n\n\ndef ensure_iter(s, cls, copy=False, iterate=True):\n \"\"\"Given not a list, would place it into a list. If None - empty list is returned\n\n Parameters\n ----------\n s: list or anything\n cls: class\n Which iterable class to ensure\n copy: bool, optional\n If correct iterable is passed, it would generate its shallow copy\n iterate: bool, optional\n If it is not a list, but something iterable (but not a str)\n iterate over it.\n \"\"\"\n\n if isinstance(s, cls):\n return s if not copy else shallow_copy(s)\n elif isinstance(s, str):\n return cls((s,))\n elif iterate and hasattr(s, '__iter__'):\n return cls(s)\n elif s is None:\n return cls()\n else:\n return cls((s,))\n\n\ndef ensure_list(s, copy=False, iterate=True):\n \"\"\"Given not a list, would place it into a list. If None - empty list is returned\n\n Parameters\n ----------\n s: list or anything\n copy: bool, optional\n If list is passed, it would generate a shallow copy of the list\n iterate: bool, optional\n If it is not a list, but something iterable (but not a str)\n iterate over it.\n \"\"\"\n return ensure_iter(s, list, copy=copy, iterate=iterate)\n\n\ndef ensure_list_from_str(s, sep='\\n'):\n \"\"\"Given a multiline string convert it to a list of return None if empty\n\n Parameters\n ----------\n s: str or list\n \"\"\"\n\n if not s:\n return None\n\n if isinstance(s, list):\n return s\n return s.split(sep)\n\n\ndef ensure_dict_from_str(s, **kwargs):\n \"\"\"Given a multiline string with key=value items convert it to a dictionary\n\n Parameters\n ----------\n s: str or dict\n\n Returns None if input s is empty\n \"\"\"\n\n if not s:\n return None\n\n if isinstance(s, dict):\n return s\n\n out = {}\n for value_str in ensure_list_from_str(s, **kwargs):\n if '=' not in value_str:\n raise ValueError(\"{} is not in key=value format\".format(repr(value_str)))\n k, v = value_str.split('=', 1)\n if k in out:\n err = \"key {} was already defined in {}, but new value {} was provided\".format(k, out, v)\n raise ValueError(err)\n out[k] = v\n return out\n\n\ndef ensure_bytes(s, encoding='utf-8'):\n \"\"\"Convert/encode unicode string to bytes.\n\n If `s` isn't a string, return it as is.\n\n Parameters\n ----------\n encoding: str, optional\n Encoding to use. \"utf-8\" is the default\n \"\"\"\n if not isinstance(s, str):\n return s\n return s.encode(encoding)\n\n\ndef ensure_unicode(s, encoding=None, confidence=None):\n \"\"\"Convert/decode bytestring to unicode.\n\n If `s` isn't a bytestring, return it as is.\n\n Parameters\n ----------\n encoding: str, optional\n Encoding to use. If None, \"utf-8\" is tried, and then if not a valid\n UTF-8, encoding will be guessed\n confidence: float, optional\n A value between 0 and 1, so if guessing of encoding is of lower than\n specified confidence, ValueError is raised\n \"\"\"\n if not isinstance(s, bytes):\n return s\n if encoding is None:\n # Figure out encoding, defaulting to 'utf-8' which is our common\n # target in contemporary digital society\n try:\n return s.decode('utf-8')\n except UnicodeDecodeError as exc:\n lgr.debug(\"Failed to decode a string as utf-8: %s\",\n CapturedException(exc))\n # And now we could try to guess\n from chardet import detect\n enc = detect(s)\n denc = enc.get('encoding', None)\n if denc:\n denc_confidence = enc.get('confidence', 0)\n if confidence is not None and denc_confidence < confidence:\n raise ValueError(\n \"Failed to auto-detect encoding with high enough \"\n \"confidence. Highest confidence was %s for %s\"\n % (denc_confidence, denc)\n )\n lgr.log(5, \"Auto-detected encoding to be %s\", denc)\n return s.decode(denc)\n else:\n raise ValueError(\n \"Could not decode value as utf-8, or to guess its encoding: %s\"\n % repr(s)\n )\n else:\n return s.decode(encoding)\n\n\ndef ensure_bool(s):\n \"\"\"Convert value into boolean following convention for strings\n\n to recognize on,True,yes as True, off,False,no as False\n \"\"\"\n if isinstance(s, str):\n if s.isdigit():\n return bool(int(s))\n sl = s.lower()\n if sl in {'y', 'yes', 'true', 'on'}:\n return True\n elif sl in {'n', 'no', 'false', 'off'}:\n return False\n else:\n raise ValueError(\"Do not know how to treat %r as a boolean\" % s)\n return bool(s)\n\n\ndef as_unicode(val, cast_types=object):\n \"\"\"Given an arbitrary value, would try to obtain unicode value of it\n \n For unicode it would return original value, for python2 str or python3\n bytes it would use ensure_unicode, for None - an empty (unicode) string,\n and for any other type (see `cast_types`) - would apply the unicode \n constructor. If value is not an instance of `cast_types`, TypeError\n is thrown\n \n Parameters\n ----------\n cast_types: type\n Which types to cast to unicode by providing to constructor\n \"\"\"\n if val is None:\n return u''\n elif isinstance(val, str):\n return val\n elif isinstance(val, unicode_srctypes):\n return ensure_unicode(val)\n elif isinstance(val, cast_types):\n return str(val)\n else:\n raise TypeError(\n \"Value %r is not of any of known or provided %s types\"\n % (val, cast_types))\n\n\ndef unique(seq, key=None, reverse=False):\n \"\"\"Given a sequence return a list only with unique elements while maintaining order\n\n This is the fastest solution. See\n https://www.peterbe.com/plog/uniqifiers-benchmark\n and\n http://stackoverflow.com/a/480227/1265472\n for more information.\n Enhancement -- added ability to compare for uniqueness using a key function\n\n Parameters\n ----------\n seq:\n Sequence to analyze\n key: callable, optional\n Function to call on each element so we could decide not on a full\n element, but on its member etc\n reverse: bool, optional\n If True, uniqueness checked in the reverse order, so that the later ones\n will take the order\n \"\"\"\n seen = set()\n seen_add = seen.add\n\n trans = reversed if reverse else lambda x: x\n\n if not key:\n out = [x for x in trans(seq) if not (x in seen or seen_add(x))]\n else:\n # OPT: could be optimized, since key is called twice, but for our cases\n # should be just as fine\n out = [x for x in trans(seq) if not (key(x) in seen or seen_add(key(x)))]\n\n return out[::-1] if reverse else out\n\n\ndef all_same(items):\n \"\"\"Quick check if all items are the same.\n\n Identical to a check like len(set(items)) == 1 but\n should be more efficient while working on generators, since would\n return False as soon as any difference detected thus possibly avoiding\n unnecessary evaluations\n \"\"\"\n first = True\n first_item = None\n for item in items:\n if first:\n first = False\n first_item = item\n else:\n if item != first_item:\n return False\n # So we return False if was empty\n return not first\n\n\ndef map_items(func, v):\n \"\"\"A helper to apply `func` to all elements (keys and values) within dict\n\n No type checking of values passed to func is done, so `func`\n should be resilient to values which it should not handle\n\n Initial usecase - apply_recursive(url_fragment, ensure_unicode)\n \"\"\"\n # map all elements within item\n return v.__class__(\n item.__class__(map(func, item))\n for item in v.items()\n )\n\n\ndef partition(items, predicate=bool):\n \"\"\"Partition `items` by `predicate`.\n\n Parameters\n ----------\n items : iterable\n predicate : callable\n A function that will be mapped over each element in `items`. The\n elements will partitioned based on whether the return value is false or\n true.\n\n Returns\n -------\n A tuple with two generators, the first for 'false' items and the second for\n 'true' ones.\n\n Notes\n -----\n Taken from Peter Otten's snippet posted at\n https://nedbatchelder.com/blog/201306/filter_a_list_into_two_parts.html\n \"\"\"\n a, b = tee((predicate(item), item) for item in items)\n return ((item for pred, item in a if not pred),\n (item for pred, item in b if pred))\n\n\ndef generate_chunks(container, size):\n \"\"\"Given a container, generate chunks from it with size up to `size`\n \"\"\"\n # There could be a \"smarter\" solution but I think this would suffice\n assert size > 0, \"Size should be non-0 positive\"\n while container:\n yield container[:size]\n container = container[size:]\n\n\ndef generate_file_chunks(files, cmd=None):\n \"\"\"Given a list of files, generate chunks of them to avoid exceeding cmdline length\n\n Parameters\n ----------\n files: list of str\n cmd: str or list of str, optional\n Command to account for as well\n \"\"\"\n files = ensure_list(files)\n cmd = ensure_list(cmd)\n\n maxl = max(map(len, files)) if files else 0\n chunk_size = max(\n 1, # should at least be 1. If blows then - not our fault\n (CMD_MAX_ARG\n - sum((len(x) + 3) for x in cmd)\n - 4 # for '--' below\n ) // (maxl + 3) # +3 for possible quotes and a space\n )\n # TODO: additional treatment for \"too many arguments\"? although\n # as https://github.com/datalad/datalad/issues/1883#issuecomment\n # -436272758\n # shows there seems to be no hardcoded limit on # of arguments,\n # but may be we decide to go for smth like follow to be on safe side\n # chunk_size = min(10240 - len(cmd), chunk_size)\n file_chunks = generate_chunks(files, chunk_size)\n return file_chunks\n\n\n#\n# Generators helpers\n#\n\ndef saved_generator(gen):\n \"\"\"Given a generator returns two generators, where 2nd one just replays\n\n So the first one would be going through the generated items and 2nd one\n would be yielding saved items\n \"\"\"\n saved = []\n\n def gen1():\n for x in gen: # iterating over original generator\n saved.append(x)\n yield x\n\n def gen2():\n for x in saved: # yielding saved entries\n yield x\n\n return gen1(), gen2()\n\n\n#\n# Decorators\n#\n\n# Originally better_wraps was created to provide `wrapt`-based, instead of\n# `functools.wraps` implementation to preserve the correct signature of the\n# decorated function. By using inspect.signature in our getargspec, which\n# works fine on `functools.wraps`ed functions, we mediated this necessity.\nbetter_wraps = wraps\n\n\n# Borrowed from pandas\n# Copyright: 2011-2014, Lambda Foundry, Inc. and PyData Development Team\n# License: BSD-3\ndef optional_args(decorator):\n \"\"\"allows a decorator to take optional positional and keyword arguments.\n Assumes that taking a single, callable, positional argument means that\n it is decorating a function, i.e. something like this::\n\n @my_decorator\n def function(): pass\n\n Calls decorator with decorator(f, `*args`, `**kwargs`)\"\"\"\n\n @better_wraps(decorator)\n def wrapper(*args, **kwargs):\n def dec(f):\n return decorator(f, *args, **kwargs)\n\n is_decorating = not kwargs and len(args) == 1 and isinstance(args[0], Callable)\n if is_decorating:\n f = args[0]\n args = []\n return dec(f)\n else:\n return dec\n\n return wrapper\n\n\n# TODO: just provide decorators for tempfile.mk* functions. This is ugly!\ndef get_tempfile_kwargs(tkwargs=None, prefix=\"\", wrapped=None):\n \"\"\"Updates kwargs to be passed to tempfile. calls depending on env vars\n \"\"\"\n if tkwargs is None:\n tkwargs_ = {}\n else:\n # operate on a copy of tkwargs to avoid any side-effects\n tkwargs_ = tkwargs.copy()\n\n # TODO: don't remember why I had this one originally\n # if len(targs)<2 and \\\n if 'prefix' not in tkwargs_:\n tkwargs_['prefix'] = '_'.join(\n ['datalad_temp'] +\n ([prefix] if prefix else []) +\n ([''] if (on_windows or not wrapped) else [wrapped.__name__]))\n\n directory = os.environ.get('TMPDIR')\n if directory and 'dir' not in tkwargs_:\n tkwargs_['dir'] = directory\n\n return tkwargs_\n\n\n@optional_args\ndef line_profile(func):\n \"\"\"Q&D helper to line profile the function and spit out stats\n \"\"\"\n import line_profiler\n prof = line_profiler.LineProfiler()\n\n @wraps(func)\n def _wrap_line_profile(*args, **kwargs):\n try:\n pfunc = prof(func)\n return pfunc(*args, **kwargs)\n finally:\n prof.print_stats()\n return _wrap_line_profile\n\n\n# unused in -core\n@optional_args\ndef collect_method_callstats(func):\n \"\"\"Figure out methods which call the method repeatedly on the same instance\n\n Use case(s):\n - .repo is expensive since does all kinds of checks.\n - .config is expensive transitively since it calls .repo each time\n\n TODO:\n - fancy one could look through the stack for the same id(self) to see if\n that location is already in memo. That would hint to the cases where object\n is not passed into underlying functions, causing them to redo the same work\n over and over again\n - ATM might flood with all \"1 lines\" calls which are not that informative.\n The underlying possibly suboptimal use might be coming from their callers.\n It might or not relate to the previous TODO\n \"\"\"\n from collections import defaultdict\n import traceback\n from time import time\n memo = defaultdict(lambda: defaultdict(int)) # it will be a dict of lineno: count\n # gross timing\n times = []\n toppath = dirname(__file__) + sep\n\n @wraps(func)\n def _wrap_collect_method_callstats(*args, **kwargs):\n try:\n self = args[0]\n stack = traceback.extract_stack()\n caller = stack[-2]\n stack_sig = \\\n \"{relpath}:{s.name}\".format(\n s=caller, relpath=relpath(caller.filename, toppath))\n sig = (id(self), stack_sig)\n # we will count based on id(self) + wherefrom\n memo[sig][caller.lineno] += 1\n t0 = time()\n return func(*args, **kwargs)\n finally:\n times.append(time() - t0)\n pass\n\n def print_stats():\n print(\"The cost of property {}:\".format(func.__name__))\n if not memo:\n print(\"None since no calls\")\n return\n # total count\n counts = {k: sum(v.values()) for k,v in memo.items()}\n total = sum(counts.values())\n ids = {self_id for (self_id, _) in memo}\n print(\" Total: {} calls from {} objects with {} contexts taking {:.2f} sec\"\n .format(total, len(ids), len(memo), sum(times)))\n # now we need to sort by value\n for (self_id, caller), count in sorted(counts.items(), key=lambda x: x[1], reverse=True):\n print(\" {} {}: {} from {} lines\"\n .format(self_id, caller, count, len(memo[(self_id, caller)])))\n\n # Upon total exit we print the stats\n import atexit\n atexit.register(print_stats)\n\n return _wrap_collect_method_callstats\n\n\n# Borrowed from duecredit to wrap duecredit-handling to guarantee failsafe\ndef never_fail(f):\n \"\"\"Assure that function never fails -- all exceptions are caught\n\n Returns `None` if function fails internally.\n \"\"\"\n @wraps(f)\n def wrapped_func(*args, **kwargs):\n try:\n return f(*args, **kwargs)\n except Exception as e:\n lgr.warning(\n \"DataLad internal failure while running %s: %r. \"\n \"Please report at https://github.com/datalad/datalad/issues\"\n % (f, e)\n )\n\n if os.environ.get('DATALAD_ALLOW_FAIL', False):\n return f\n else:\n return wrapped_func\n\n\n#\n# Context Managers\n#\n\n\n# unused in -core\n@contextmanager\ndef nothing_cm():\n \"\"\"Just a dummy cm to programmically switch context managers\"\"\"\n yield\n\n\n@contextmanager\ndef swallow_outputs():\n \"\"\"Context manager to help consuming both stdout and stderr, and print()\n\n stdout is available as cm.out and stderr as cm.err whenever cm is the\n yielded context manager.\n Internally uses temporary files to guarantee absent side-effects of swallowing\n into StringIO which lacks .fileno.\n\n print mocking is necessary for some uses where sys.stdout was already bound\n to original sys.stdout, thus mocking it later had no effect. Overriding\n print function had desired effect\n \"\"\"\n\n class StringIOAdapter(object):\n \"\"\"Little adapter to help getting out/err values\n \"\"\"\n def __init__(self):\n kw = get_tempfile_kwargs({}, prefix=\"outputs\")\n\n self._out = NamedTemporaryFile(delete=False, mode='w', **kw)\n self._err = NamedTemporaryFile(delete=False, mode='w', **kw)\n\n def _read(self, h):\n with open(h.name) as f:\n return f.read()\n\n @property\n def out(self):\n if not self._out.closed:\n self._out.flush()\n return self._read(self._out)\n\n @property\n def err(self):\n if not self._err.closed:\n self._err.flush()\n return self._read(self._err)\n\n @property\n def handles(self):\n return self._out, self._err\n\n def cleanup(self):\n self._out.close()\n self._err.close()\n out_name = self._out.name\n err_name = self._err.name\n from datalad import cfg\n if cfg.getbool('datalad.log', 'outputs', default=False) \\\n and lgr.getEffectiveLevel() <= logging.DEBUG:\n for s, sname in ((self.out, 'stdout'),\n (self.err, 'stderr')):\n if s:\n pref = os.linesep + \"| \"\n lgr.debug(\"Swallowed %s:%s%s\", sname, pref, s.replace(os.linesep, pref))\n else:\n lgr.debug(\"Nothing was swallowed for %s\", sname)\n del self._out\n del self._err\n gc.collect()\n rmtemp(out_name)\n rmtemp(err_name)\n\n def fake_print(*args, **kwargs):\n sep = kwargs.pop('sep', ' ')\n end = kwargs.pop('end', '\\n')\n file = kwargs.pop('file', sys.stdout)\n\n if file in (oldout, olderr, sys.stdout, sys.stderr):\n # we mock\n try:\n sys.stdout.write(sep.join(args) + end)\n except UnicodeEncodeError as exc:\n lgr.error(\n \"Failed to write to mocked stdout, got %s, continue as it \"\n \"didn't happen\", exc)\n else:\n # must be some other file one -- leave it alone\n oldprint(*args, sep=sep, end=end, file=file)\n\n from .ui import ui\n # preserve -- they could have been mocked already\n oldprint = getattr(builtins, 'print')\n oldout, olderr = sys.stdout, sys.stderr\n olduiout = ui.out\n adapter = StringIOAdapter()\n\n try:\n sys.stdout, sys.stderr = adapter.handles\n ui.out = adapter.handles[0]\n setattr(builtins, 'print', fake_print)\n\n yield adapter\n finally:\n sys.stdout, sys.stderr, ui.out = oldout, olderr, olduiout\n setattr(builtins, 'print', oldprint)\n adapter.cleanup()\n\n\n@contextmanager\ndef swallow_logs(new_level=None, file_=None, name='datalad'):\n \"\"\"Context manager to consume all logs.\n\n \"\"\"\n lgr = logging.getLogger(name)\n\n # Keep old settings\n old_level = lgr.level\n old_handlers = lgr.handlers\n\n # Let's log everything into a string\n # TODO: generalize with the one for swallow_outputs\n class StringIOAdapter(object):\n \"\"\"Little adapter to help getting out values\n\n And to stay consistent with how swallow_outputs behaves\n \"\"\"\n def __init__(self):\n if file_ is None:\n kw = get_tempfile_kwargs({}, prefix=\"logs\")\n self._out = NamedTemporaryFile(mode='a', delete=False, **kw)\n else:\n out_file = file_\n # PY3 requires clearly one or another. race condition possible\n self._out = open(out_file, 'a')\n self._final_out = None\n\n def _read(self, h):\n with open(h.name) as f:\n return f.read()\n\n @property\n def out(self):\n if self._final_out is not None:\n # we closed and cleaned up already\n return self._final_out\n else:\n self._out.flush()\n return self._read(self._out)\n\n @property\n def lines(self):\n return self.out.split('\\n')\n\n @property\n def handle(self):\n return self._out\n\n def cleanup(self):\n # store for access while object exists\n self._final_out = self.out\n self._out.close()\n out_name = self._out.name\n del self._out\n gc.collect()\n if not file_:\n rmtemp(out_name)\n\n def assert_logged(self, msg=None, level=None, regex=True, **kwargs):\n \"\"\"Provide assertion on whether a msg was logged at a given level\n\n If neither `msg` nor `level` provided, checks if anything was logged\n at all.\n\n Parameters\n ----------\n msg: str, optional\n Message (as a regular expression, if `regex`) to be searched.\n If no msg provided, checks if anything was logged at a given level.\n level: str, optional\n String representing the level to be logged\n regex: bool, optional\n If False, regular `assert_in` is used\n **kwargs: str, optional\n Passed to `assert_re_in` or `assert_in`\n \"\"\"\n from datalad.tests.utils import assert_re_in\n from datalad.tests.utils import assert_in\n\n if regex:\n match = r'\\[%s\\] ' % level if level else r\"\\[\\S+\\] \"\n else:\n match = '[%s] ' % level if level else ''\n\n if msg:\n match += msg\n\n if match:\n (assert_re_in if regex else assert_in)(match, self.out, **kwargs)\n else:\n assert not kwargs, \"no kwargs to be passed anywhere\"\n assert self.out, \"Nothing was logged!?\"\n\n adapter = StringIOAdapter()\n # TODO: it does store messages but without any formatting, i.e. even without\n # date/time prefix etc. IMHO it should preserve formatting in case if file_ is\n # set\n swallow_handler = logging.StreamHandler(adapter.handle)\n # we want to log levelname so we could test against it\n swallow_handler.setFormatter(\n logging.Formatter('[%(levelname)s] %(message)s'))\n swallow_handler.filters = sum([h.filters for h in old_handlers],\n [])\n lgr.handlers = [swallow_handler]\n if old_level < logging.DEBUG: # so if HEAVYDEBUG etc -- show them!\n lgr.handlers += old_handlers\n\n if isinstance(new_level, str):\n new_level = getattr(logging, new_level)\n\n if new_level is not None:\n lgr.setLevel(new_level)\n\n try:\n yield adapter\n # TODO: if file_ and there was an exception -- most probably worth logging it?\n # although ideally it should be the next log outside added to that file_ ... oh well\n finally:\n lgr.handlers = old_handlers\n lgr.setLevel(old_level)\n adapter.cleanup()\n\n\n# TODO: May be melt in with swallow_logs at some point:\n@contextmanager\ndef disable_logger(logger=None):\n \"\"\"context manager to temporarily disable logging\n\n This is to provide one of swallow_logs' purposes without unnecessarily\n creating temp files (see gh-1865)\n\n Parameters\n ----------\n logger: Logger\n Logger whose handlers will be ordered to not log anything.\n Default: datalad's topmost Logger ('datalad')\n \"\"\"\n\n class NullFilter(logging.Filter):\n \"\"\"Filter class to reject all records\n \"\"\"\n def filter(self, record):\n return 0\n\n if logger is None:\n # default: all of datalad's logging:\n logger = logging.getLogger('datalad')\n\n filter_ = NullFilter(logger.name)\n [h.addFilter(filter_) for h in logger.handlers]\n\n try:\n yield logger\n finally:\n [h.removeFilter(filter_) for h in logger.handlers]\n\n\n#\n# Additional handlers\n#\n_sys_excepthook = sys.excepthook # Just in case we ever need original one\n\n\ndef setup_exceptionhook(ipython=False):\n \"\"\"Overloads default sys.excepthook with our exceptionhook handler.\n\n If interactive, our exceptionhook handler will invoke\n pdb.post_mortem; if not interactive, then invokes default handler.\n \"\"\"\n\n def _datalad_pdb_excepthook(type, value, tb):\n import traceback\n traceback.print_exception(type, value, tb)\n print()\n if is_interactive():\n import pdb\n pdb.post_mortem(tb)\n\n if ipython:\n from IPython.core import ultratb\n sys.excepthook = ultratb.FormattedTB(mode='Verbose',\n # color_scheme='Linux',\n call_pdb=is_interactive())\n else:\n sys.excepthook = _datalad_pdb_excepthook\n\n\ndef ensure_dir(*args):\n \"\"\"Make sure directory exists.\n\n Joins the list of arguments to an os-specific path to the desired\n directory and creates it, if it not exists yet.\n \"\"\"\n dirname = op.join(*args)\n if not exists(dirname):\n os.makedirs(dirname)\n return dirname\n\n\ndef updated(d, update):\n \"\"\"Return a copy of the input with the 'update'\n\n Primarily for updating dictionaries\n \"\"\"\n d = d.copy()\n d.update(update)\n return d\n\n\n_pwd_mode = None\n\n\ndef _switch_to_getcwd(msg, *args):\n global _pwd_mode\n _pwd_mode = 'cwd'\n lgr.debug(\n msg + \". From now on will be returning os.getcwd(). Directory\"\n \" symlinks in the paths will be resolved\",\n *args\n )\n # TODO: we might want to mitigate by going through all flywheighted\n # repos and tuning up their .paths to be resolved?\n\n\ndef getpwd():\n \"\"\"Try to return a CWD without dereferencing possible symlinks\n\n This function will try to use PWD environment variable to provide a current\n working directory, possibly with some directories along the path being\n symlinks to other directories. Unfortunately, PWD is used/set only by the\n shell and such functions as `os.chdir` and `os.getcwd` nohow use or modify\n it, thus `os.getcwd()` returns path with links dereferenced.\n\n While returning current working directory based on PWD env variable we\n verify that the directory is the same as `os.getcwd()` after resolving all\n symlinks. If that verification fails, we fall back to always use\n `os.getcwd()`.\n\n Initial decision to either use PWD env variable or os.getcwd() is done upon\n the first call of this function.\n \"\"\"\n global _pwd_mode\n if _pwd_mode is None:\n # we need to decide!\n try:\n pwd = os.environ['PWD']\n if on_windows and pwd and pwd.startswith('/'):\n # It should be a path from MSYS.\n # - it might start with a drive letter or not\n # - it seems to be \"illegal\" to have a single letter directories\n # under / path, i.e. if created - they aren't found\n # - 'ln -s' does not fail to create a \"symlink\" but it just\n # copies!\n # so we are not likely to need original PWD purpose on\n # those systems\n # Verdict:\n _pwd_mode = 'cwd'\n else:\n _pwd_mode = 'PWD'\n except KeyError:\n _pwd_mode = 'cwd'\n\n if _pwd_mode == 'cwd':\n return os.getcwd()\n elif _pwd_mode == 'PWD':\n try:\n cwd = os.getcwd()\n except OSError as exc:\n if \"o such file\" in str(exc):\n # directory was removed but we promised to be robust and\n # still report the path we might know since we are still in PWD\n # mode\n cwd = None\n else:\n raise\n try:\n pwd = os.environ['PWD']\n # do absolute() in addition to always get an absolute path\n # even with non-existing paths on windows\n pwd_real = str(Path(pwd).resolve().absolute())\n # This logic would fail to catch the case where chdir did happen\n # to the directory where current PWD is pointing to, e.g.\n # $> ls -ld $PWD\n # lrwxrwxrwx 1 yoh yoh 5 Oct 11 13:27 /home/yoh/.tmp/tmp -> /tmp//\n # hopa:~/.tmp/tmp\n # $> python -c 'import os; os.chdir(\"/tmp\"); from datalad.utils import getpwd; print(getpwd(), os.getcwd())'\n # ('/home/yoh/.tmp/tmp', '/tmp')\n # but I guess that should not be too harmful\n if cwd is not None and pwd_real != cwd:\n _switch_to_getcwd(\n \"realpath of PWD=%s is %s whenever os.getcwd()=%s\",\n pwd, pwd_real, cwd\n )\n return cwd\n return pwd\n except KeyError:\n _switch_to_getcwd(\"PWD env variable is no longer available\")\n return cwd # Must not happen, but may be someone\n # evil purges PWD from environ?\n else:\n raise RuntimeError(\n \"Must have not got here. \"\n \"pwd_mode must be either cwd or PWD. And it is now %r\" % (_pwd_mode,)\n )\n\n\nclass chpwd(object):\n \"\"\"Wrapper around os.chdir which also adjusts environ['PWD']\n\n The reason is that otherwise PWD is simply inherited from the shell\n and we have no ability to assess directory path without dereferencing\n symlinks.\n\n If used as a context manager it allows to temporarily change directory\n to the given path\n \"\"\"\n def __init__(self, path, mkdir=False, logsuffix=''):\n\n if path:\n pwd = getpwd()\n self._prev_pwd = pwd\n else:\n self._prev_pwd = None\n return\n\n if not isabs(path):\n path = normpath(op.join(pwd, path))\n if not os.path.exists(path) and mkdir:\n self._mkdir = True\n os.mkdir(path)\n else:\n self._mkdir = False\n lgr.debug(\"chdir %r -> %r %s\", self._prev_pwd, path, logsuffix)\n os.chdir(path) # for grep people -- ok, to chdir here!\n os.environ['PWD'] = str(path)\n\n def __enter__(self):\n # nothing more to do really, chdir was in the constructor\n pass\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n if self._prev_pwd:\n # Need to use self.__class__ so this instance, if the entire\n # thing mocked during the test, still would use correct chpwd\n self.__class__(self._prev_pwd, logsuffix=\"(coming back)\")\n\n\ndef dlabspath(path, norm=False):\n \"\"\"Symlinks-in-the-cwd aware abspath\n\n os.path.abspath relies on os.getcwd() which would not know about symlinks\n in the path\n\n TODO: we might want to norm=True by default to match behavior of\n os .path.abspath?\n \"\"\"\n if not isabs(path):\n # if not absolute -- relative to pwd\n path = op.join(getpwd(), path)\n return normpath(path) if norm else path\n\n\ndef with_pathsep(path):\n \"\"\"Little helper to guarantee that path ends with /\"\"\"\n return path + sep if not path.endswith(sep) else path\n\n\ndef get_path_prefix(path, pwd=None):\n \"\"\"Get path prefix (for current directory)\n\n Returns relative path to the topdir, if we are under topdir, and if not\n absolute path to topdir. If `pwd` is not specified - current directory\n assumed\n \"\"\"\n pwd = pwd or getpwd()\n path = dlabspath(path)\n path_ = with_pathsep(path)\n pwd_ = with_pathsep(pwd)\n common = commonprefix((path_, pwd_))\n if common.endswith(sep) and common in {path_, pwd_}:\n # we are in subdir or above the path = use relative path\n location_prefix = relpath(path, pwd)\n # if benign \"here\" - cut off\n if location_prefix in (curdir, curdir + sep):\n location_prefix = ''\n return location_prefix\n else:\n # just return absolute path\n return path\n\n\ndef _get_normalized_paths(path, prefix):\n if isabs(path) != isabs(prefix):\n raise ValueError(\"Both paths must either be absolute or relative. \"\n \"Got %r and %r\" % (path, prefix))\n path = with_pathsep(path)\n prefix = with_pathsep(prefix)\n return path, prefix\n\n\ndef path_startswith(path, prefix):\n \"\"\"Return True if path starts with prefix path\n\n Parameters\n ----------\n path: str\n prefix: str\n \"\"\"\n path, prefix = _get_normalized_paths(path, prefix)\n return path.startswith(prefix)\n\n\ndef path_is_subpath(path, prefix):\n \"\"\"Return True if path is a subpath of prefix\n\n It will return False if path == prefix.\n\n Parameters\n ----------\n path: str\n prefix: str\n \"\"\"\n path, prefix = _get_normalized_paths(path, prefix)\n return (len(prefix) < len(path)) and path.startswith(prefix)\n\n\ndef knows_annex(path):\n \"\"\"Returns whether at a given path there is information about an annex\n\n It is just a thin wrapper around GitRepo.is_with_annex() classmethod\n which also checks for `path` to exist first.\n\n This includes actually present annexes, but also uninitialized ones, or\n even the presence of a remote annex branch.\n \"\"\"\n from os.path import exists\n if not exists(path):\n lgr.debug(\"No annex: test path {0} doesn't exist\".format(path))\n return False\n from datalad.support.gitrepo import GitRepo\n return GitRepo(path, init=False, create=False).is_with_annex()\n\n\n@contextmanager\ndef make_tempfile(content=None, wrapped=None, **tkwargs):\n \"\"\"Helper class to provide a temporary file name and remove it at the end (context manager)\n\n Parameters\n ----------\n mkdir : bool, optional (default: False)\n If True, temporary directory created using tempfile.mkdtemp()\n content : str or bytes, optional\n Content to be stored in the file created\n wrapped : function, optional\n If set, function name used to prefix temporary file name\n `**tkwargs`:\n All other arguments are passed into the call to tempfile.mk{,d}temp(),\n and resultant temporary filename is passed as the first argument into\n the function t. If no 'prefix' argument is provided, it will be\n constructed using module and function names ('.' replaced with\n '_').\n\n To change the used directory without providing keyword argument 'dir' set\n DATALAD_TESTS_TEMP_DIR.\n\n Examples\n --------\n >>> from os.path import exists\n >>> from datalad.utils import make_tempfile\n >>> with make_tempfile() as fname:\n ... k = open(fname, 'w').write('silly test')\n >>> assert not exists(fname) # was removed\n\n >>> with make_tempfile(content=\"blah\") as fname:\n ... assert open(fname).read() == \"blah\"\n \"\"\"\n\n if tkwargs.get('mkdir', None) and content is not None:\n raise ValueError(\"mkdir=True while providing content makes no sense\")\n\n tkwargs_ = get_tempfile_kwargs(tkwargs, wrapped=wrapped)\n\n # if DATALAD_TESTS_TEMP_DIR is set, use that as directory,\n # let mktemp handle it otherwise. However, an explicitly provided\n # dir=... will override this.\n mkdir = tkwargs_.pop('mkdir', False)\n\n filename = {False: tempfile.mktemp,\n True: tempfile.mkdtemp}[mkdir](**tkwargs_)\n # MIH: not clear to me why we need to perform this (possibly expensive)\n # resolve. It was already part of the original implementation\n # 008d9ab8cc3e0170c0a9b8479e80dee9ffe6eb7f\n filename = Path(filename).resolve()\n\n if content:\n (filename.write_bytes\n if isinstance(content, bytes)\n else filename.write_text)(content)\n\n # TODO globbing below can also be done with pathlib\n filename = str(filename)\n\n if __debug__:\n lgr.debug(\n 'Created temporary %s named %s',\n 'directory' if mkdir else 'file',\n filename)\n try:\n yield filename\n finally:\n # glob here for all files with the same name (-suffix)\n # would be useful whenever we requested .img filename,\n # and function creates .hdr as well\n # MIH: this is undocumented behavior, and undesired in the general\n # case. it should be made conditional and explicit\n lsuffix = len(tkwargs_.get('suffix', ''))\n filename_ = lsuffix and filename[:-lsuffix] or filename\n filenames = glob.glob(filename_ + '*')\n if len(filename_) < 3 or len(filenames) > 5:\n # For paranoid yoh who stepped into this already ones ;-)\n lgr.warning(\"It is unlikely that it was intended to remove all\"\n \" files matching %r. Skipping\" % filename_)\n return\n for f in filenames:\n try:\n rmtemp(f)\n except OSError: # pragma: no cover\n pass\n\n\ndef _path_(*p):\n \"\"\"Given a path in POSIX\" notation, regenerate one in native to the env one\"\"\"\n if on_windows:\n return op.join(*map(lambda x: op.join(*x.split('/')), p))\n else:\n # Assume that all others as POSIX compliant so nothing to be done\n return op.join(*p)\n\n\ndef get_timestamp_suffix(time_=None, prefix='-'):\n \"\"\"Return a time stamp (full date and time up to second)\n\n primarily to be used for generation of log files names\n \"\"\"\n args = []\n if time_ is not None:\n if isinstance(time_, int):\n time_ = time.gmtime(time_)\n args.append(time_)\n return time.strftime(prefix + TIMESTAMP_FMT, *args)\n\n\n# unused in -core\ndef get_logfilename(dspath, cmd='datalad'):\n \"\"\"Return a filename to use for logging under a dataset/repository\n\n directory would be created if doesn't exist, but dspath must exist\n and be a directory\n \"\"\"\n assert(exists(dspath))\n assert(isdir(dspath))\n ds_logdir = ensure_dir(dspath, '.git', 'datalad', 'logs') # TODO: use WEB_META_LOG whenever #789 merged\n return op.join(ds_logdir, 'crawl-%s.log' % get_timestamp_suffix())\n\n\ndef get_trace(edges, start, end, trace=None):\n \"\"\"Return the trace/path to reach a node in a tree.\n\n Parameters\n ----------\n edges : sequence(2-tuple)\n The tree given by a sequence of edges (parent, child) tuples. The\n nodes can be identified by any value and data type that supports\n the '==' operation.\n start :\n Identifier of the start node. Must be present as a value in the parent\n location of an edge tuple in order to be found.\n end :\n Identifier of the target/end node. Must be present as a value in the child\n location of an edge tuple in order to be found.\n trace : list\n Mostly useful for recursive calls, and used internally.\n\n Returns\n -------\n None or list\n Returns a list with the trace to the target (the starts and the target\n are not included in the trace, hence if start and end are directly connected\n an empty list is returned), or None when no trace to the target can be found,\n or start and end are identical.\n \"\"\"\n # the term trace is used to avoid confusion with a path in the sense\n # of a filesystem path, but the analogy fits and nodes can be paths\n if trace is None:\n trace = []\n if not edges:\n raise ValueError(\"no edges given\")\n for cand in edges:\n cand_super, cand_sub = cand\n if cand_sub in trace:\n # only DAGs, skip any cyclic traces\n continue\n if trace and cand_super != trace[-1]:\n # only consider edges that lead off the end of the trace\n continue\n if not trace and cand_super != start:\n # we got nothing yet, and this edges is not matching the start\n continue\n if cand_sub == end:\n return trace\n # dive into potential subnodes\n cand_trace = get_trace(\n edges,\n start,\n end,\n trace + [cand_sub])\n if cand_trace:\n return cand_trace\n return None\n\n\ndef get_dataset_root(path):\n \"\"\"Return the root of an existent dataset containing a given path\n\n The root path is returned in the same absolute or relative form\n as the input argument. If no associated dataset exists, or the\n input path doesn't exist, None is returned.\n\n If `path` is a symlink or something other than a directory, its\n the root dataset containing its parent directory will be reported.\n If none can be found, at a symlink at `path` is pointing to a\n dataset, `path` itself will be reported as the root.\n\n Parameters\n ----------\n path : Path-like\n\n Returns\n -------\n str or None\n \"\"\"\n path = str(path)\n suffix = '.git'\n altered = None\n if islink(path) or not isdir(path):\n altered = path\n path = dirname(path)\n apath = abspath(path)\n # while we can still go up\n while split(apath)[1]:\n if exists(op.join(path, suffix)):\n return path\n # new test path in the format we got it\n path = normpath(op.join(path, os.pardir))\n # no luck, next round\n apath = abspath(path)\n # if we applied dirname() at the top, we give it another go with\n # the actual path, if it was itself a symlink, it could be the\n # top-level dataset itself\n if altered and exists(op.join(altered, suffix)):\n return altered\n\n return None\n\n\n# ATM used in datalad_crawler extension, so do not remove yet\ndef try_multiple(ntrials, exception, base, f, *args, **kwargs):\n \"\"\"Call f multiple times making exponentially growing delay between the calls\"\"\"\n for trial in range(1, ntrials+1):\n try:\n return f(*args, **kwargs)\n except exception as exc:\n if trial == ntrials:\n raise # just reraise on the last trial\n t = base ** trial\n lgr.warning(\"Caught %s on trial #%d. Sleeping %f and retrying\",\n CapturedException(exc), trial, t)\n sleep(t)\n\n\n@optional_args\ndef try_multiple_dec(\n f, ntrials=None, duration=0.1, exceptions=None, increment_type=None,\n exceptions_filter=None,\n logger=None,\n):\n \"\"\"Decorator to try function multiple times.\n\n Main purpose is to decorate functions dealing with removal of files/directories\n and which might need a few seconds to work correctly on Windows which takes\n its time to release files/directories.\n\n Parameters\n ----------\n ntrials: int, optional\n duration: float, optional\n Seconds to sleep before retrying.\n increment_type: {None, 'exponential'}\n Note that if it is exponential, duration should typically be > 1.0\n so it grows with higher power\n exceptions: Exception or tuple of Exceptions, optional\n Exception or a tuple of multiple exceptions, on which to retry\n exceptions_filter: callable, optional\n If provided, this function will be called with a caught exception\n instance. If function returns True - we will re-try, if False - exception\n will be re-raised without retrying.\n logger: callable, optional\n Logger to log upon failure. If not provided, will use stock logger\n at the level of 5 (heavy debug).\n \"\"\"\n if not exceptions:\n exceptions = (OSError, WindowsError, PermissionError) \\\n if on_windows else OSError\n if not ntrials:\n # Life goes fast on proper systems, no need to delay it much\n ntrials = 100 if on_windows else 10\n if logger is None:\n def logger(*args, **kwargs):\n return lgr.log(5, *args, **kwargs)\n assert increment_type in {None, 'exponential'}\n\n @wraps(f)\n def _wrap_try_multiple_dec(*args, **kwargs):\n t = duration\n for trial in range(ntrials):\n try:\n return f(*args, **kwargs)\n except exceptions as exc:\n if exceptions_filter and not exceptions_filter(exc):\n raise\n if trial < ntrials - 1:\n if increment_type == 'exponential':\n t = duration ** (trial + 1)\n logger(\n \"Caught %s on trial #%d. Sleeping %f and retrying\",\n CapturedException(exc), trial, t)\n sleep(t)\n else:\n raise\n\n return _wrap_try_multiple_dec\n\n\n@try_multiple_dec\ndef unlink(f):\n \"\"\"'Robust' unlink. Would try multiple times\n\n On windows boxes there is evidence for a latency of more than a second\n until a file is considered no longer \"in-use\".\n WindowsError is not known on Linux, and if IOError or any other\n exception\n is thrown then if except statement has WindowsError in it -- NameError\n also see gh-2533\n \"\"\"\n # Check for open files\n assert_no_open_files(f)\n return os.unlink(f)\n\n\n@try_multiple_dec\ndef _rmtree(*args, **kwargs):\n \"\"\"Just a helper to decorate shutil.rmtree.\n\n rmtree defined above does more and ideally should not itself be decorated\n since a recursive definition and does checks for open files inside etc -\n might be too runtime expensive\n \"\"\"\n return shutil.rmtree(*args, **kwargs)\n\n\ndef slash_join(base, extension):\n \"\"\"Join two strings with a '/', avoiding duplicate slashes\n\n If any of the strings is None the other is returned as is.\n \"\"\"\n if extension is None:\n return base\n if base is None:\n return extension\n return '/'.join(\n (base.rstrip('/'),\n extension.lstrip('/')))\n\n\n#\n# IO Helpers\n#\n\n# unused in -core\ndef open_r_encdetect(fname, readahead=1000):\n \"\"\"Return a file object in read mode with auto-detected encoding\n\n This is helpful when dealing with files of unknown encoding.\n\n Parameters\n ----------\n readahead: int, optional\n How many bytes to read for guessing the encoding type. If\n negative - full file will be read\n \"\"\"\n from chardet import detect\n import io\n # read some bytes from the file\n with open(fname, 'rb') as f:\n head = f.read(readahead)\n enc = detect(head)\n denc = enc.get('encoding', None)\n lgr.debug(\"Auto-detected encoding %s for file %s (confidence: %s)\",\n denc,\n fname,\n enc.get('confidence', 'unknown'))\n return io.open(fname, encoding=denc)\n\n\ndef read_file(fname, decode=True):\n \"\"\"A helper to read file passing content via ensure_unicode\n\n Parameters\n ----------\n decode: bool, optional\n if False, no ensure_unicode and file content returned as bytes\n \"\"\"\n with open(fname, 'rb') as f:\n content = f.read()\n return ensure_unicode(content) if decode else content\n\n\ndef read_csv_lines(fname, dialect=None, readahead=16384, **kwargs):\n \"\"\"A generator of dict records from a CSV/TSV\n\n Automatically guesses the encoding for each record to convert to UTF-8\n\n Parameters\n ----------\n fname: str\n Filename\n dialect: str, optional\n Dialect to specify to csv.reader. If not specified -- guessed from\n the file, if fails to guess, \"excel-tab\" is assumed\n readahead: int, optional\n How many bytes to read from the file to guess the type\n **kwargs\n Passed to `csv.reader`\n \"\"\"\n import csv\n if dialect is None:\n with open(fname) as tsvfile:\n # add robustness, use a sniffer\n try:\n dialect = csv.Sniffer().sniff(tsvfile.read(readahead))\n except Exception as exc:\n lgr.warning(\n 'Could not determine file-format, assuming TSV: %s',\n CapturedException(exc)\n )\n dialect = 'excel-tab'\n\n kw = dict(encoding='utf-8')\n with open(fname, 'r', **kw) as tsvfile:\n # csv.py doesn't do Unicode; encode temporarily as UTF-8:\n csv_reader = csv.reader(\n tsvfile,\n dialect=dialect,\n **kwargs\n )\n header = None\n for row in csv_reader:\n # decode UTF-8 back to Unicode, cell by cell:\n row_unicode = map(ensure_unicode, row)\n if header is None:\n header = list(row_unicode)\n else:\n yield dict(zip(header, row_unicode))\n\n\ndef import_modules(modnames, pkg, msg=\"Failed to import {module}\", log=lgr.debug):\n \"\"\"Helper to import a list of modules without failing if N/A\n\n Parameters\n ----------\n modnames: list of str\n List of module names to import\n pkg: str\n Package under which to import\n msg: str, optional\n Message template for .format() to log at DEBUG level if import fails.\n Keys {module} and {package} will be provided and ': {exception}' appended\n log: callable, optional\n Logger call to use for logging messages\n \"\"\"\n from importlib import import_module\n _globals = globals()\n mods_loaded = []\n if pkg and not pkg in sys.modules:\n # with python 3.5.1 (ok with 3.5.5) somehow kept running into\n # Failed to import dlsub1: Parent module 'dltestm1' not loaded\n # while running the test. Preloading pkg resolved the issue\n import_module(pkg)\n for modname in modnames:\n try:\n _globals[modname] = mod = import_module(\n '.{}'.format(modname),\n pkg)\n mods_loaded.append(mod)\n except Exception as exc:\n from datalad.support.exceptions import CapturedException\n ce = CapturedException(exc)\n log((msg + ': {exception}').format(\n module=modname, package=pkg, exception=ce.message))\n return mods_loaded\n\n\ndef import_module_from_file(modpath, pkg=None, log=lgr.debug):\n \"\"\"Import provided module given a path\n\n TODO:\n - RF/make use of it in pipeline.py which has similar logic\n - join with import_modules above?\n\n Parameters\n ----------\n pkg: module, optional\n If provided, and modpath is under pkg.__path__, relative import will be\n used\n \"\"\"\n assert(modpath.endswith('.py')) # for now just for .py files\n\n log(\"Importing %s\" % modpath)\n\n modname = basename(modpath)[:-3]\n relmodpath = None\n if pkg:\n for pkgpath in pkg.__path__:\n if path_is_subpath(modpath, pkgpath):\n # for now relying on having .py extension -- assertion above\n relmodpath = '.' + relpath(modpath[:-3], pkgpath).replace(sep, '.')\n break\n\n try:\n if relmodpath:\n from importlib import import_module\n mod = import_module(relmodpath, pkg.__name__)\n else:\n dirname_ = dirname(modpath)\n try:\n sys.path.insert(0, dirname_)\n mod = __import__(modname, level=0)\n finally:\n if dirname_ in sys.path:\n sys.path.pop(sys.path.index(dirname_))\n else:\n log(\"Expected path %s to be within sys.path, but it was gone!\" % dirname_)\n except Exception as e:\n raise RuntimeError(\n \"Failed to import module from %s\" % modpath) from e\n\n return mod\n\n\ndef get_encoding_info():\n \"\"\"Return a dictionary with various encoding/locale information\"\"\"\n import sys, locale\n from collections import OrderedDict\n return OrderedDict([\n ('default', sys.getdefaultencoding()),\n ('filesystem', sys.getfilesystemencoding()),\n ('locale.prefered', locale.getpreferredencoding()),\n ])\n\n\ndef get_envvars_info():\n from collections import OrderedDict\n envs = []\n for var, val in os.environ.items():\n if (\n var.startswith('PYTHON') or\n var.startswith('LC_') or\n var.startswith('GIT_') or\n var in ('LANG', 'LANGUAGE', 'PATH')\n ):\n envs.append((var, val))\n return OrderedDict(envs)\n\n\n# This class is modified from Snakemake (v5.1.4)\nclass SequenceFormatter(string.Formatter):\n \"\"\"string.Formatter subclass with special behavior for sequences.\n\n This class delegates formatting of individual elements to another\n formatter object. Non-list objects are formatted by calling the\n delegate formatter's \"format_field\" method. List-like objects\n (list, tuple, set, frozenset) are formatted by formatting each\n element of the list according to the specified format spec using\n the delegate formatter and then joining the resulting strings with\n a separator (space by default).\n \"\"\"\n\n def __init__(self, separator=\" \", element_formatter=string.Formatter(),\n *args, **kwargs):\n self.separator = separator\n self.element_formatter = element_formatter\n\n def format_element(self, elem, format_spec):\n \"\"\"Format a single element\n\n For sequences, this is called once for each element in a\n sequence. For anything else, it is called on the entire\n object. It is intended to be overridden in subclases.\n \"\"\"\n return self.element_formatter.format_field(elem, format_spec)\n\n def format_field(self, value, format_spec):\n if isinstance(value, (list, tuple, set, frozenset)):\n return self.separator.join(self.format_element(v, format_spec)\n for v in value)\n else:\n return self.format_element(value, format_spec)\n\n\n# TODO: eventually we might want to make use of attr module\nclass File(object):\n \"\"\"Helper for a file entry in the create_tree/@with_tree\n\n It allows to define additional settings for entries\n \"\"\"\n def __init__(self, name, executable=False):\n \"\"\"\n\n Parameters\n ----------\n name : str\n Name of the file\n executable: bool, optional\n Make it executable\n \"\"\"\n self.name = name\n self.executable = executable\n\n def __str__(self):\n return self.name\n\n\ndef create_tree_archive(path, name, load, overwrite=False, archives_leading_dir=True):\n \"\"\"Given an archive `name`, create under `path` with specified `load` tree\n \"\"\"\n from datalad.support.archives import compress_files\n dirname = file_basename(name)\n full_dirname = op.join(path, dirname)\n os.makedirs(full_dirname)\n create_tree(full_dirname, load, archives_leading_dir=archives_leading_dir)\n # create archive\n if archives_leading_dir:\n compress_files([dirname], name, path=path, overwrite=overwrite)\n else:\n compress_files(list(map(basename, glob.glob(op.join(full_dirname, '*')))),\n op.join(pardir, name),\n path=op.join(path, dirname),\n overwrite=overwrite)\n # remove original tree\n rmtree(full_dirname)\n\n\ndef create_tree(path, tree, archives_leading_dir=True, remove_existing=False):\n \"\"\"Given a list of tuples (name, load) create such a tree\n\n if load is a tuple itself -- that would create either a subtree or an archive\n with that content and place it into the tree if name ends with .tar.gz\n \"\"\"\n lgr.log(5, \"Creating a tree under %s\", path)\n if not exists(path):\n os.makedirs(path)\n\n if isinstance(tree, dict):\n tree = tree.items()\n\n for file_, load in tree:\n if isinstance(file_, File):\n executable = file_.executable\n name = file_.name\n else:\n executable = False\n name = file_\n full_name = op.join(path, name)\n if remove_existing and lexists(full_name):\n rmtree(full_name, chmod_files=True)\n if isinstance(load, (tuple, list, dict)):\n if name.endswith('.tar.gz') or name.endswith('.tar') or name.endswith('.zip'):\n create_tree_archive(\n path, name, load,\n archives_leading_dir=archives_leading_dir)\n else:\n create_tree(\n full_name, load,\n archives_leading_dir=archives_leading_dir,\n remove_existing=remove_existing)\n else:\n open_func = open\n if full_name.endswith('.gz'):\n open_func = gzip.open\n elif full_name.split('.')[-1] in ('xz', 'lzma'):\n import lzma\n open_func = lzma.open\n with open_func(full_name, \"wb\") as f:\n f.write(ensure_bytes(load, 'utf-8'))\n if executable:\n os.chmod(full_name, os.stat(full_name).st_mode | stat.S_IEXEC)\n\n\ndef get_suggestions_msg(values, known, sep=\"\\n \"):\n \"\"\"Return a formatted string with suggestions for values given the known ones\n \"\"\"\n import difflib\n suggestions = []\n for value in ensure_list(values): # might not want to do it if we change presentation below\n suggestions += difflib.get_close_matches(value, known)\n suggestions = unique(suggestions)\n msg = \"Did you mean any of these?\"\n if suggestions:\n if '\\n' in sep:\n # if separator includes new line - we add entire separator right away\n msg += sep\n else:\n msg += ' '\n return msg + \"%s\\n\" % sep.join(suggestions)\n return ''\n\n\ndef bytes2human(n, format='%(value).1f %(symbol)sB'):\n \"\"\"\n Convert n bytes into a human readable string based on format.\n symbols can be either \"customary\", \"customary_ext\", \"iec\" or \"iec_ext\",\n see: http://goo.gl/kTQMs\n\n >>> from datalad.utils import bytes2human\n >>> bytes2human(1)\n '1.0 B'\n >>> bytes2human(1024)\n '1.0 KB'\n >>> bytes2human(1048576)\n '1.0 MB'\n >>> bytes2human(1099511627776127398123789121)\n '909.5 YB'\n\n >>> bytes2human(10000, \"%(value).1f %(symbol)s/sec\")\n '9.8 K/sec'\n\n >>> # precision can be adjusted by playing with %f operator\n >>> bytes2human(10000, format=\"%(value).5f %(symbol)s\")\n '9.76562 K'\n\n Taken from: http://goo.gl/kTQMs and subsequently simplified\n Original Author: Giampaolo Rodola' <g.rodola [AT] gmail [DOT] com>\n License: MIT\n \"\"\"\n n = int(n)\n if n < 0:\n raise ValueError(\"n < 0\")\n symbols = ('', 'K', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y')\n prefix = {}\n for i, s in enumerate(symbols[1:]):\n prefix[s] = 1 << (i + 1) * 10\n for symbol in reversed(symbols[1:]):\n if n >= prefix[symbol]:\n value = float(n) / prefix[symbol]\n return format % locals()\n return format % dict(symbol=symbols[0], value=n)\n\n\ndef quote_cmdlinearg(arg):\n \"\"\"Perform platform-appropriate argument quoting\"\"\"\n # https://stackoverflow.com/a/15262019\n return '\"{}\"'.format(\n arg.replace('\"', '\"\"')\n ) if on_windows else shlex_quote(arg)\n\n\ndef guard_for_format(arg):\n \"\"\"Replace { and } with {{ and }}\n\n To be used in cases if arg is not expected to have provided\n by user .format() placeholders, but 'arg' might become a part\n of a composite passed to .format(), e.g. via 'Run'\n \"\"\"\n return arg.replace('{', '{{').replace('}', '}}')\n\n\ndef join_cmdline(args):\n \"\"\"Join command line args into a string using quote_cmdlinearg\n \"\"\"\n return ' '.join(map(quote_cmdlinearg, args))\n\n\ndef split_cmdline(s):\n \"\"\"Perform platform-appropriate command line splitting.\n\n Identical to `shlex.split()` on non-windows platforms.\n\n Modified from https://stackoverflow.com/a/35900070\n \"\"\"\n if not on_windows:\n return shlex_split(s)\n\n # the rest is for windows\n RE_CMD_LEX = r'''\"((?:\"\"|\\\\[\"\\\\]|[^\"])*)\"?()|(\\\\\\\\(?=\\\\*\")|\\\\\")|(&&?|\\|\\|?|\\d?>|[<])|([^\\s\"&|<>]+)|(\\s+)|(.)'''\n\n args = []\n accu = None # collects pieces of one arg\n for qs, qss, esc, pipe, word, white, fail in re.findall(RE_CMD_LEX, s):\n if word:\n pass # most frequent\n elif esc:\n word = esc[1]\n elif white or pipe:\n if accu is not None:\n args.append(accu)\n if pipe:\n args.append(pipe)\n accu = None\n continue\n elif fail:\n raise ValueError(\"invalid or incomplete shell string\")\n elif qs:\n word = qs.replace('\\\\\"', '\"').replace('\\\\\\\\', '\\\\')\n if platform == 0:\n word = word.replace('\"\"', '\"')\n else:\n word = qss # may be even empty; must be last\n\n accu = (accu or '') + word\n\n if accu is not None:\n args.append(accu)\n\n return args\n\n\ndef get_wrapped_class(wrapped):\n \"\"\"Determine the command class a wrapped __call__ belongs to\"\"\"\n mod = sys.modules[wrapped.__module__]\n command_class_name = wrapped.__qualname__.split('.')[-2]\n _func_class = mod.__dict__[command_class_name]\n lgr.debug(\"Determined class of decorated function: %s\", _func_class)\n return _func_class\n\n\ndef _make_assure_kludge(fn):\n old_name = fn.__name__.replace(\"ensure\", \"assure\")\n\n @wraps(fn)\n def compat_fn(*args, **kwargs):\n warnings.warn(\n \"{} is deprecated and will be removed in a future release. \"\n \"Use {} instead.\"\n .format(old_name, fn.__name__),\n DeprecationWarning)\n return fn(*args, **kwargs)\n\n compat_fn.__doc__ = (\"Note: This function is deprecated. Use {} instead.\"\n .format(fn.__name__))\n return compat_fn\n\n\nassure_tuple_or_list = _make_assure_kludge(ensure_tuple_or_list)\nassure_iter = _make_assure_kludge(ensure_iter)\nassure_list = _make_assure_kludge(ensure_list)\nassure_list_from_str = _make_assure_kludge(ensure_list_from_str)\nassure_dict_from_str = _make_assure_kludge(ensure_dict_from_str)\nassure_bytes = _make_assure_kludge(ensure_bytes)\nassure_unicode = _make_assure_kludge(ensure_unicode)\nassure_bool = _make_assure_kludge(ensure_bool)\nassure_dir = _make_assure_kludge(ensure_dir)\n\n\nlgr.log(5, \"Done importing datalad.utils\")\n\n\ndef check_symlink_capability(path, target):\n \"\"\"helper similar to datalad.tests.utils.has_symlink_capability\n\n However, for use in a datalad command context, we shouldn't\n assume to be able to write to tmpfile and also not import a whole lot from\n datalad's test machinery. Finally, we want to know, whether we can create a\n symlink at a specific location, not just somewhere. Therefore use\n arbitrary path to test-build a symlink and delete afterwards. Suitable\n location can therefore be determined by high lever code.\n\n Parameters\n ----------\n path: Path\n target: Path\n\n Returns\n -------\n bool\n \"\"\"\n\n try:\n target.touch()\n path.symlink_to(target)\n return True\n except Exception:\n return False\n finally:\n if path.exists():\n path.unlink()\n if target.exists():\n target.unlink()"},"implementation":{"kind":"string","value":"def get_home_envvars(new_home):\n \"\"\"Return dict with env variables to be adjusted for a new HOME\n\n Only variables found in current os.environ are adjusted.\n\n Parameters\n ----------\n new_home: str or Path\n New home path, in native to OS \"schema\"\n \"\"\"\n new_home = str(new_home)\n out = {'HOME': new_home}\n if on_windows:\n # requires special handling, since it has a number of relevant variables\n # and also Python changed its behavior and started to respect USERPROFILE only\n # since python 3.8: https://bugs.python.org/issue36264\n out['USERPROFILE'] = new_home\n out['HOMEDRIVE'], out['HOMEPATH'] = splitdrive(new_home)\n\n return {v: val for v, val in out.items() if v in os.environ}"},"start_line":{"kind":"number","value":232,"string":"232"},"end_line":{"kind":"number","value":251,"string":"251"},"file_content":{"kind":"string","value":"# emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-\n# ex: set sts=4 ts=4 sw=4 et:\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##\n#\n# See COPYING file distributed along with the datalad package for the\n# copyright and license terms.\n#\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##\n\nimport collections\nfrom collections.abc import Callable\nimport re\nimport builtins\nimport time\n\nimport logging\nimport shutil\nimport os\nimport sys\nimport tempfile\nfrom tempfile import NamedTemporaryFile\nimport platform\nimport gc\nimport glob\nimport gzip\nimport stat\nimport string\nimport warnings\n\nimport os.path as op\n\nfrom copy import copy as shallow_copy\nfrom contextlib import contextmanager\nfrom functools import (\n lru_cache,\n wraps,\n)\nfrom time import sleep\nimport inspect\nfrom itertools import tee\n# this import is required because other modules import opj from here.\nfrom os.path import join as opj\nfrom os.path import (\n abspath,\n basename,\n commonprefix,\n curdir,\n dirname,\n exists,\n expanduser,\n expandvars,\n isabs,\n isdir,\n islink,\n lexists,\n normpath,\n pardir,\n relpath,\n sep,\n split,\n splitdrive\n)\n\nimport posixpath\n\nfrom shlex import (\n quote as shlex_quote,\n split as shlex_split,\n)\n\n# from datalad.dochelpers import get_docstring_split\nfrom datalad.consts import TIMESTAMP_FMT\nfrom datalad.support.exceptions import CapturedException\n\n\nunicode_srctypes = str, bytes\n\n\nlgr = logging.getLogger(\"datalad.utils\")\n\nlgr.log(5, \"Importing datalad.utils\")\n#\n# Some useful variables\n#\nplatform_system = platform.system().lower()\non_windows = platform_system == 'windows'\non_osx = platform_system == 'darwin'\non_linux = platform_system == 'linux'\non_msys_tainted_paths = on_windows \\\n and 'MSYS_NO_PATHCONV' not in os.environ \\\n and os.environ.get('MSYSTEM', '')[:4] in ('MSYS', 'MING')\n\n\n# Takes ~200msec, so should not be called at import time\n@lru_cache() # output should not change through life time of datalad process\ndef get_linux_distribution():\n \"\"\"Compatibility wrapper for {platform,distro}.linux_distribution().\n \"\"\"\n if hasattr(platform, \"linux_distribution\"):\n # Use deprecated (but faster) method if it's available.\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n result = platform.linux_distribution()\n else:\n import distro # We require this for Python 3.8 and above.\n result = distro.linux_distribution(full_distribution_name=False)\n return result\n\n\n# Those weren't used for any critical decision making, thus we just set them to None\n# Use get_linux_distribution() directly where needed\nlinux_distribution_name = linux_distribution_release = None\n\n# Maximal length of cmdline string\n# Query the system and use hardcoded \"knowledge\" if None\n# probably getconf ARG_MAX might not be available\n# The last one would be the most conservative/Windows\nCMD_MAX_ARG_HARDCODED = 2097152 if on_linux else 262144 if on_osx else 32767\ntry:\n CMD_MAX_ARG = os.sysconf('SC_ARG_MAX')\n assert CMD_MAX_ARG > 0\n if CMD_MAX_ARG > CMD_MAX_ARG_HARDCODED * 1e6:\n # workaround for some kind of a bug which comes up with python 3.4\n # see https://github.com/datalad/datalad/issues/3150\n # or on older CentOS with conda and python as new as 3.9\n # see https://github.com/datalad/datalad/issues/5943\n # TODO: let Yarik know that the world is a paradise now whenever 1e6\n # is not large enough\n CMD_MAX_ARG = min(CMD_MAX_ARG, CMD_MAX_ARG_HARDCODED)\nexcept Exception as exc:\n # ATM (20181005) SC_ARG_MAX available only on POSIX systems\n # so exception would be thrown e.g. on Windows, or\n # somehow during Debian build for nd14.04 it is coming up with -1:\n # https://github.com/datalad/datalad/issues/3015\n CMD_MAX_ARG = CMD_MAX_ARG_HARDCODED\n lgr.debug(\n \"Failed to query or got useless SC_ARG_MAX sysconf, \"\n \"will use hardcoded value: %s\", exc)\n# Even with all careful computations we do, due to necessity to account for\n# environment and what not, we still could not figure out \"exact\" way to\n# estimate it, but it was shown that 300k safety margin on linux was sufficient.\n# https://github.com/datalad/datalad/pull/2977#issuecomment-436264710\n# 300k is ~15%, so to be safe, and for paranoid us we will just use up to 50%\n# of the length for \"safety margin\". We might probably still blow due to\n# env vars, unicode, etc... so any hard limit imho is not a proper solution\nCMD_MAX_ARG = int(0.5 * CMD_MAX_ARG)\nlgr.debug(\n \"Maximal length of cmdline string (adjusted for safety margin): %d\",\n CMD_MAX_ARG)\n\n#\n# Little helpers\n#\n\n# `getargspec` has been deprecated in Python 3.\nArgSpecFake = collections.namedtuple(\n \"ArgSpecFake\", [\"args\", \"varargs\", \"keywords\", \"defaults\"])\n\n\ndef getargspec(func, *, include_kwonlyargs=False):\n \"\"\"Compat shim for getargspec deprecated in python 3.\n\n The main difference from inspect.getargspec (and inspect.getfullargspec\n for that matter) is that by using inspect.signature we are providing\n correct args/defaults for functools.wraps'ed functions.\n\n `include_kwonlyargs` option was added to centralize getting all args,\n even the ones which are kwonly (follow the ``*,``).\n\n For internal use and not advised for use in 3rd party code.\n Please use inspect.signature directly.\n \"\"\"\n # We use signature, and not getfullargspec, because only signature properly\n # \"passes\" args from a functools.wraps decorated function.\n # Note: getfullargspec works Ok on wrapt-decorated functions\n f_sign = inspect.signature(func)\n # Loop through parameters and compose argspec\n args4 = [[], None, None, {}]\n # Collect all kwonlyargs into a dedicated dict - name: default\n kwonlyargs = {}\n # shortcuts\n args, defaults = args4[0], args4[3]\n P = inspect.Parameter\n\n for p_name, p in f_sign.parameters.items():\n if p.kind in (P.POSITIONAL_ONLY, P.POSITIONAL_OR_KEYWORD):\n assert not kwonlyargs # yoh: must not come after kwonlyarg\n args.append(p_name)\n if p.default is not P.empty:\n defaults[p_name] = p.default\n elif p.kind == P.VAR_POSITIONAL:\n args4[1] = p_name\n elif p.kind == P.VAR_KEYWORD:\n args4[2] = p_name\n elif p.kind == P.KEYWORD_ONLY:\n assert p.default is not P.empty\n kwonlyargs[p_name] = p.default\n\n if kwonlyargs:\n if not include_kwonlyargs:\n raise ValueError(\n 'Function has keyword-only parameters or annotations, either use '\n 'inspect.signature() API which can support them, or provide include_kwonlyargs=True '\n 'to this function'\n )\n else:\n args.extend(list(kwonlyargs))\n defaults.update(kwonlyargs)\n\n # harmonize defaults to how original getargspec returned them -- just a tuple\n args4[3] = None if not defaults else tuple(defaults.values())\n return ArgSpecFake(*args4)\n\n\ndef any_re_search(regexes, value):\n \"\"\"Return if any of regexes (list or str) searches successfully for value\"\"\"\n for regex in ensure_tuple_or_list(regexes):\n if re.search(regex, value):\n return True\n return False\n\n\ndef not_supported_on_windows(msg=None):\n \"\"\"A little helper to be invoked to consistently fail whenever functionality is\n not supported (yet) on Windows\n \"\"\"\n if on_windows:\n raise NotImplementedError(\"This functionality is not yet implemented for Windows OS\"\n + (\": %s\" % msg if msg else \"\"))\n\n\ndef get_home_envvars(new_home):\n \"\"\"Return dict with env variables to be adjusted for a new HOME\n\n Only variables found in current os.environ are adjusted.\n\n Parameters\n ----------\n new_home: str or Path\n New home path, in native to OS \"schema\"\n \"\"\"\n new_home = str(new_home)\n out = {'HOME': new_home}\n if on_windows:\n # requires special handling, since it has a number of relevant variables\n # and also Python changed its behavior and started to respect USERPROFILE only\n # since python 3.8: https://bugs.python.org/issue36264\n out['USERPROFILE'] = new_home\n out['HOMEDRIVE'], out['HOMEPATH'] = splitdrive(new_home)\n\n return {v: val for v, val in out.items() if v in os.environ}\n\n\ndef shortened_repr(value, l=30):\n try:\n if hasattr(value, '__repr__') and (value.__repr__ is not object.__repr__):\n value_repr = repr(value)\n if not value_repr.startswith('<') and len(value_repr) > l:\n value_repr = \"<<%s++%d chars++%s>>\" % (\n value_repr[:l - 16],\n len(value_repr) - (l - 16 + 4),\n value_repr[-4:]\n )\n elif value_repr.startswith('<') and value_repr.endswith('>') and ' object at 0x':\n raise ValueError(\"I hate those useless long reprs\")\n else:\n raise ValueError(\"gimme class\")\n except Exception as e:\n value_repr = \"<%s>\" % value.__class__.__name__.split('.')[-1]\n return value_repr\n\n\ndef __auto_repr__(obj):\n attr_names = tuple()\n if hasattr(obj, '__dict__'):\n attr_names += tuple(obj.__dict__.keys())\n if hasattr(obj, '__slots__'):\n attr_names += tuple(obj.__slots__)\n\n items = []\n for attr in sorted(set(attr_names)):\n if attr.startswith('_'):\n continue\n value = getattr(obj, attr)\n # TODO: should we add this feature to minimize some talktative reprs\n # such as of URL?\n #if value is None:\n # continue\n items.append(\"%s=%s\" % (attr, shortened_repr(value)))\n\n return \"%s(%s)\" % (obj.__class__.__name__, ', '.join(items))\n\n\ndef auto_repr(cls):\n \"\"\"Decorator for a class to assign it an automagic quick and dirty __repr__\n\n It uses public class attributes to prepare repr of a class\n\n Original idea: http://stackoverflow.com/a/27799004/1265472\n \"\"\"\n\n cls.__repr__ = __auto_repr__\n return cls\n\n\ndef _is_stream_tty(stream):\n try:\n # TODO: check on windows if hasattr check would work correctly and\n # add value:\n return stream.isatty()\n except ValueError as exc:\n # Who knows why it is a ValueError, but let's try to be specific\n # If there is a problem with I/O - non-interactive, otherwise reraise\n if \"I/O\" in str(exc):\n return False\n raise\n\n\ndef is_interactive():\n \"\"\"Return True if all in/outs are open and tty.\n\n Note that in a somewhat abnormal case where e.g. stdin is explicitly\n closed, and any operation on it would raise a\n `ValueError(\"I/O operation on closed file\")` exception, this function\n would just return False, since the session cannot be used interactively.\n \"\"\"\n return all(_is_stream_tty(s) for s in (sys.stdin, sys.stdout, sys.stderr))\n\n\ndef get_ipython_shell():\n \"\"\"Detect if running within IPython and returns its `ip` (shell) object\n\n Returns None if not under ipython (no `get_ipython` function)\n \"\"\"\n try:\n return get_ipython()\n except NameError:\n return None\n\n\ndef md5sum(filename):\n \"\"\"Compute an MD5 sum for the given file\n \"\"\"\n from datalad.support.digests import Digester\n return Digester(digests=['md5'])(filename)['md5']\n\n\n# unused in -core\ndef sorted_files(path):\n \"\"\"Return a (sorted) list of files under path\n \"\"\"\n return sorted(sum([[op.join(r, f)[len(path) + 1:] for f in files]\n for r, d, files in os.walk(path)\n if not '.git' in r], []))\n\n_encoded_dirsep = r'\\\\' if on_windows else r'/'\n_VCS_REGEX = r'%s\\.(?:git|gitattributes|svn|bzr|hg)(?:%s|$)' % (\n _encoded_dirsep, _encoded_dirsep)\n_DATALAD_REGEX = r'%s\\.(?:datalad)(?:%s|$)' % (\n _encoded_dirsep, _encoded_dirsep)\n\n\ndef find_files(regex, topdir=curdir, exclude=None, exclude_vcs=True, exclude_datalad=False, dirs=False):\n \"\"\"Generator to find files matching regex\n\n Parameters\n ----------\n regex: basestring\n exclude: basestring, optional\n Matches to exclude\n exclude_vcs:\n If True, excludes commonly known VCS subdirectories. If string, used\n as regex to exclude those files (regex: `%r`)\n exclude_datalad:\n If True, excludes files known to be datalad meta-data files (e.g. under\n .datalad/ subdirectory) (regex: `%r`)\n topdir: basestring, optional\n Directory where to search\n dirs: bool, optional\n Whether to match directories as well as files\n \"\"\"\n for dirpath, dirnames, filenames in os.walk(topdir):\n names = (dirnames + filenames) if dirs else filenames\n # TODO: might want to uniformize on windows to use '/'\n paths = (op.join(dirpath, name) for name in names)\n for path in filter(re.compile(regex).search, paths):\n path = path.rstrip(sep)\n if exclude and re.search(exclude, path):\n continue\n if exclude_vcs and re.search(_VCS_REGEX, path):\n continue\n if exclude_datalad and re.search(_DATALAD_REGEX, path):\n continue\n yield path\nfind_files.__doc__ %= (_VCS_REGEX, _DATALAD_REGEX)\n\n\ndef expandpath(path, force_absolute=True):\n \"\"\"Expand all variables and user handles in a path.\n\n By default return an absolute path\n \"\"\"\n path = expandvars(expanduser(path))\n if force_absolute:\n path = abspath(path)\n return path\n\n\ndef posix_relpath(path, start=None):\n \"\"\"Behave like os.path.relpath, but always return POSIX paths...\n\n on any platform.\"\"\"\n # join POSIX style\n return posixpath.join(\n # split and relpath native style\n # python2.7 ntpath implementation of relpath cannot handle start=None\n *split(\n relpath(path, start=start if start is not None else '')))\n\n\ndef is_explicit_path(path):\n \"\"\"Return whether a path explicitly points to a location\n\n Any absolute path, or relative path starting with either '../' or\n './' is assumed to indicate a location on the filesystem. Any other\n path format is not considered explicit.\"\"\"\n path = expandpath(path, force_absolute=False)\n return isabs(path) \\\n or path.startswith(os.curdir + os.sep) \\\n or path.startswith(os.pardir + os.sep)\n\n\n# handle this dance once, and import pathlib from here\n# in all other places\n\nfrom pathlib import (\n Path,\n PurePath,\n PurePosixPath,\n)\n\ndef rotree(path, ro=True, chmod_files=True):\n \"\"\"To make tree read-only or writable\n\n Parameters\n ----------\n path : string\n Path to the tree/directory to chmod\n ro : bool, optional\n Whether to make it R/O (default) or RW\n chmod_files : bool, optional\n Whether to operate also on files (not just directories)\n \"\"\"\n if ro:\n chmod = lambda f: os.chmod(f, os.stat(f).st_mode & ~stat.S_IWRITE)\n else:\n chmod = lambda f: os.chmod(f, os.stat(f).st_mode | stat.S_IWRITE | stat.S_IREAD)\n\n for root, dirs, files in os.walk(path, followlinks=False):\n if chmod_files:\n for f in files:\n fullf = op.join(root, f)\n # might be the \"broken\" symlink which would fail to stat etc\n if exists(fullf):\n chmod(fullf)\n chmod(root)\n\n\ndef rmtree(path, chmod_files='auto', children_only=False, *args, **kwargs):\n \"\"\"To remove git-annex .git it is needed to make all files and directories writable again first\n\n Parameters\n ----------\n path: Path or str\n Path to remove\n chmod_files : string or bool, optional\n Whether to make files writable also before removal. Usually it is just\n a matter of directories to have write permissions.\n If 'auto' it would chmod files on windows by default\n children_only : bool, optional\n If set, all files and subdirectories would be removed while the path\n itself (must be a directory) would be preserved\n `*args` :\n `**kwargs` :\n Passed into shutil.rmtree call\n \"\"\"\n # Give W permissions back only to directories, no need to bother with files\n if chmod_files == 'auto':\n chmod_files = on_windows\n # TODO: yoh thinks that if we could quickly check our Flyweight for\n # repos if any of them is under the path, and could call .precommit\n # on those to possibly stop batched processes etc, we did not have\n # to do it on case by case\n # Check for open files\n assert_no_open_files(path)\n\n # TODO the whole thing should be reimplemented with pathlib, but for now\n # at least accept Path\n path = str(path)\n\n if children_only:\n if not isdir(path):\n raise ValueError(\"Can remove children only of directories\")\n for p in os.listdir(path):\n rmtree(op.join(path, p))\n return\n if not (islink(path) or not isdir(path)):\n rotree(path, ro=False, chmod_files=chmod_files)\n if on_windows:\n # shutil fails to remove paths that exceed 260 characters on Windows machines\n # that did not enable long path support. A workaround to remove long paths\n # anyway is to preprend \\\\?\\ to the path.\n # https://docs.microsoft.com/en-us/windows/win32/fileio/naming-a-file?redirectedfrom=MSDN#win32-file-namespaces\n path = r'\\\\?\\ '.strip() + path\n _rmtree(path, *args, **kwargs)\n else:\n # just remove the symlink\n unlink(path)\n\n\ndef rmdir(path, *args, **kwargs):\n \"\"\"os.rmdir with our optional checking for open files\"\"\"\n assert_no_open_files(path)\n os.rmdir(path)\n\n\ndef get_open_files(path, log_open=False):\n \"\"\"Get open files under a path\n\n Note: This function is very slow on Windows.\n\n Parameters\n ----------\n path : str\n File or directory to check for open files under\n log_open : bool or int\n If set - logger level to use\n\n Returns\n -------\n dict\n path : pid\n\n \"\"\"\n # Original idea: https://stackoverflow.com/a/11115521/1265472\n import psutil\n files = {}\n # since the ones returned by psutil would not be aware of symlinks in the\n # path we should also get realpath for path\n # do absolute() in addition to always get an absolute path\n # even with non-existing paths on windows\n path = str(Path(path).resolve().absolute())\n for proc in psutil.process_iter():\n try:\n open_paths = [p.path for p in proc.open_files()] + [proc.cwd()]\n for p in open_paths:\n # note: could be done more efficiently so we do not\n # renormalize path over and over again etc\n if path_startswith(p, path):\n files[p] = proc\n # Catch a race condition where a process ends\n # before we can examine its files\n except psutil.NoSuchProcess:\n pass\n except psutil.AccessDenied:\n pass\n\n if files and log_open:\n lgr.log(log_open, \"Open files under %s: %s\", path, files)\n return files\n\n\n_assert_no_open_files_cfg = os.environ.get('DATALAD_ASSERT_NO_OPEN_FILES')\nif _assert_no_open_files_cfg:\n def assert_no_open_files(path):\n files = get_open_files(path, log_open=40)\n if _assert_no_open_files_cfg == 'assert':\n assert not files, \"Got following files still open: %s\" % ','.join(files)\n elif files:\n if _assert_no_open_files_cfg == 'pdb':\n import pdb\n pdb.set_trace()\n elif _assert_no_open_files_cfg == 'epdb':\n import epdb\n epdb.serve()\n pass\n # otherwise we would just issue that error message in the log\nelse:\n def assert_no_open_files(*args, **kwargs):\n pass\n\n\ndef rmtemp(f, *args, **kwargs):\n \"\"\"Wrapper to centralize removing of temp files so we could keep them around\n\n It will not remove the temporary file/directory if DATALAD_TESTS_TEMP_KEEP\n environment variable is defined\n \"\"\"\n if not os.environ.get('DATALAD_TESTS_TEMP_KEEP'):\n if not os.path.lexists(f):\n lgr.debug(\"Path %s does not exist, so can't be removed\", f)\n return\n lgr.log(5, \"Removing temp file: %s\", f)\n # Can also be a directory\n if isdir(f):\n rmtree(f, *args, **kwargs)\n else:\n unlink(f)\n else:\n lgr.info(\"Keeping temp file: %s\", f)\n\n\ndef file_basename(name, return_ext=False):\n \"\"\"\n Strips up to 2 extensions of length up to 4 characters and starting with alpha\n not a digit, so we could get rid of .tar.gz etc\n \"\"\"\n bname = basename(name)\n fbname = re.sub(r'(\\.[a-zA-Z_]\\S{1,4}){0,2}$', '', bname)\n if return_ext:\n return fbname, bname[len(fbname) + 1:]\n else:\n return fbname\n\n\n# unused in -core\ndef escape_filename(filename):\n \"\"\"Surround filename in \"\" and escape \" in the filename\n \"\"\"\n filename = filename.replace('\"', r'\\\"').replace('`', r'\\`')\n filename = '\"%s\"' % filename\n return filename\n\n\n# unused in -core\ndef encode_filename(filename):\n \"\"\"Encode unicode filename\n \"\"\"\n if isinstance(filename, str):\n return filename.encode(sys.getfilesystemencoding())\n else:\n return filename\n\n\n# unused in -core\ndef decode_input(s):\n \"\"\"Given input string/bytes, decode according to stdin codepage (or UTF-8)\n if not defined\n\n If fails -- issue warning and decode allowing for errors\n being replaced\n \"\"\"\n if isinstance(s, str):\n return s\n else:\n encoding = sys.stdin.encoding or 'UTF-8'\n try:\n return s.decode(encoding)\n except UnicodeDecodeError as exc:\n lgr.warning(\n \"Failed to decode input string using %s encoding. \"\n \"Decoding allowing for errors\", encoding)\n return s.decode(encoding, errors='replace')\n\n\n# unused in -core\nif on_windows:\n def lmtime(filepath, mtime):\n \"\"\"Set mtime for files. On Windows a merely adapter to os.utime\n \"\"\"\n os.utime(filepath, (time.time(), mtime))\nelse:\n def lmtime(filepath, mtime):\n \"\"\"Set mtime for files, while not de-referencing symlinks.\n\n To overcome absence of os.lutime\n\n Works only on linux and OSX ATM\n \"\"\"\n from .cmd import WitlessRunner\n # convert mtime to format touch understands [[CC]YY]MMDDhhmm[.SS]\n smtime = time.strftime(\"%Y%m%d%H%M.%S\", time.localtime(mtime))\n lgr.log(3, \"Setting mtime for %s to %s == %s\", filepath, mtime, smtime)\n WitlessRunner().run(['touch', '-h', '-t', '%s' % smtime, filepath])\n filepath = Path(filepath)\n rfilepath = filepath.resolve()\n if filepath.is_symlink() and rfilepath.exists():\n # trust no one - adjust also of the target file\n # since it seemed like downloading under OSX (was it using curl?)\n # didn't bother with timestamps\n lgr.log(3, \"File is a symlink to %s Setting mtime for it to %s\",\n rfilepath, mtime)\n os.utime(str(rfilepath), (time.time(), mtime))\n # doesn't work on OSX\n # Runner().run(['touch', '-h', '-d', '@%s' % mtime, filepath])\n\n\ndef ensure_tuple_or_list(obj):\n \"\"\"Given an object, wrap into a tuple if not list or tuple\n \"\"\"\n if isinstance(obj, (list, tuple)):\n return obj\n return (obj,)\n\n\ndef ensure_iter(s, cls, copy=False, iterate=True):\n \"\"\"Given not a list, would place it into a list. If None - empty list is returned\n\n Parameters\n ----------\n s: list or anything\n cls: class\n Which iterable class to ensure\n copy: bool, optional\n If correct iterable is passed, it would generate its shallow copy\n iterate: bool, optional\n If it is not a list, but something iterable (but not a str)\n iterate over it.\n \"\"\"\n\n if isinstance(s, cls):\n return s if not copy else shallow_copy(s)\n elif isinstance(s, str):\n return cls((s,))\n elif iterate and hasattr(s, '__iter__'):\n return cls(s)\n elif s is None:\n return cls()\n else:\n return cls((s,))\n\n\ndef ensure_list(s, copy=False, iterate=True):\n \"\"\"Given not a list, would place it into a list. If None - empty list is returned\n\n Parameters\n ----------\n s: list or anything\n copy: bool, optional\n If list is passed, it would generate a shallow copy of the list\n iterate: bool, optional\n If it is not a list, but something iterable (but not a str)\n iterate over it.\n \"\"\"\n return ensure_iter(s, list, copy=copy, iterate=iterate)\n\n\ndef ensure_list_from_str(s, sep='\\n'):\n \"\"\"Given a multiline string convert it to a list of return None if empty\n\n Parameters\n ----------\n s: str or list\n \"\"\"\n\n if not s:\n return None\n\n if isinstance(s, list):\n return s\n return s.split(sep)\n\n\ndef ensure_dict_from_str(s, **kwargs):\n \"\"\"Given a multiline string with key=value items convert it to a dictionary\n\n Parameters\n ----------\n s: str or dict\n\n Returns None if input s is empty\n \"\"\"\n\n if not s:\n return None\n\n if isinstance(s, dict):\n return s\n\n out = {}\n for value_str in ensure_list_from_str(s, **kwargs):\n if '=' not in value_str:\n raise ValueError(\"{} is not in key=value format\".format(repr(value_str)))\n k, v = value_str.split('=', 1)\n if k in out:\n err = \"key {} was already defined in {}, but new value {} was provided\".format(k, out, v)\n raise ValueError(err)\n out[k] = v\n return out\n\n\ndef ensure_bytes(s, encoding='utf-8'):\n \"\"\"Convert/encode unicode string to bytes.\n\n If `s` isn't a string, return it as is.\n\n Parameters\n ----------\n encoding: str, optional\n Encoding to use. \"utf-8\" is the default\n \"\"\"\n if not isinstance(s, str):\n return s\n return s.encode(encoding)\n\n\ndef ensure_unicode(s, encoding=None, confidence=None):\n \"\"\"Convert/decode bytestring to unicode.\n\n If `s` isn't a bytestring, return it as is.\n\n Parameters\n ----------\n encoding: str, optional\n Encoding to use. If None, \"utf-8\" is tried, and then if not a valid\n UTF-8, encoding will be guessed\n confidence: float, optional\n A value between 0 and 1, so if guessing of encoding is of lower than\n specified confidence, ValueError is raised\n \"\"\"\n if not isinstance(s, bytes):\n return s\n if encoding is None:\n # Figure out encoding, defaulting to 'utf-8' which is our common\n # target in contemporary digital society\n try:\n return s.decode('utf-8')\n except UnicodeDecodeError as exc:\n lgr.debug(\"Failed to decode a string as utf-8: %s\",\n CapturedException(exc))\n # And now we could try to guess\n from chardet import detect\n enc = detect(s)\n denc = enc.get('encoding', None)\n if denc:\n denc_confidence = enc.get('confidence', 0)\n if confidence is not None and denc_confidence < confidence:\n raise ValueError(\n \"Failed to auto-detect encoding with high enough \"\n \"confidence. Highest confidence was %s for %s\"\n % (denc_confidence, denc)\n )\n lgr.log(5, \"Auto-detected encoding to be %s\", denc)\n return s.decode(denc)\n else:\n raise ValueError(\n \"Could not decode value as utf-8, or to guess its encoding: %s\"\n % repr(s)\n )\n else:\n return s.decode(encoding)\n\n\ndef ensure_bool(s):\n \"\"\"Convert value into boolean following convention for strings\n\n to recognize on,True,yes as True, off,False,no as False\n \"\"\"\n if isinstance(s, str):\n if s.isdigit():\n return bool(int(s))\n sl = s.lower()\n if sl in {'y', 'yes', 'true', 'on'}:\n return True\n elif sl in {'n', 'no', 'false', 'off'}:\n return False\n else:\n raise ValueError(\"Do not know how to treat %r as a boolean\" % s)\n return bool(s)\n\n\ndef as_unicode(val, cast_types=object):\n \"\"\"Given an arbitrary value, would try to obtain unicode value of it\n \n For unicode it would return original value, for python2 str or python3\n bytes it would use ensure_unicode, for None - an empty (unicode) string,\n and for any other type (see `cast_types`) - would apply the unicode \n constructor. If value is not an instance of `cast_types`, TypeError\n is thrown\n \n Parameters\n ----------\n cast_types: type\n Which types to cast to unicode by providing to constructor\n \"\"\"\n if val is None:\n return u''\n elif isinstance(val, str):\n return val\n elif isinstance(val, unicode_srctypes):\n return ensure_unicode(val)\n elif isinstance(val, cast_types):\n return str(val)\n else:\n raise TypeError(\n \"Value %r is not of any of known or provided %s types\"\n % (val, cast_types))\n\n\ndef unique(seq, key=None, reverse=False):\n \"\"\"Given a sequence return a list only with unique elements while maintaining order\n\n This is the fastest solution. See\n https://www.peterbe.com/plog/uniqifiers-benchmark\n and\n http://stackoverflow.com/a/480227/1265472\n for more information.\n Enhancement -- added ability to compare for uniqueness using a key function\n\n Parameters\n ----------\n seq:\n Sequence to analyze\n key: callable, optional\n Function to call on each element so we could decide not on a full\n element, but on its member etc\n reverse: bool, optional\n If True, uniqueness checked in the reverse order, so that the later ones\n will take the order\n \"\"\"\n seen = set()\n seen_add = seen.add\n\n trans = reversed if reverse else lambda x: x\n\n if not key:\n out = [x for x in trans(seq) if not (x in seen or seen_add(x))]\n else:\n # OPT: could be optimized, since key is called twice, but for our cases\n # should be just as fine\n out = [x for x in trans(seq) if not (key(x) in seen or seen_add(key(x)))]\n\n return out[::-1] if reverse else out\n\n\ndef all_same(items):\n \"\"\"Quick check if all items are the same.\n\n Identical to a check like len(set(items)) == 1 but\n should be more efficient while working on generators, since would\n return False as soon as any difference detected thus possibly avoiding\n unnecessary evaluations\n \"\"\"\n first = True\n first_item = None\n for item in items:\n if first:\n first = False\n first_item = item\n else:\n if item != first_item:\n return False\n # So we return False if was empty\n return not first\n\n\ndef map_items(func, v):\n \"\"\"A helper to apply `func` to all elements (keys and values) within dict\n\n No type checking of values passed to func is done, so `func`\n should be resilient to values which it should not handle\n\n Initial usecase - apply_recursive(url_fragment, ensure_unicode)\n \"\"\"\n # map all elements within item\n return v.__class__(\n item.__class__(map(func, item))\n for item in v.items()\n )\n\n\ndef partition(items, predicate=bool):\n \"\"\"Partition `items` by `predicate`.\n\n Parameters\n ----------\n items : iterable\n predicate : callable\n A function that will be mapped over each element in `items`. The\n elements will partitioned based on whether the return value is false or\n true.\n\n Returns\n -------\n A tuple with two generators, the first for 'false' items and the second for\n 'true' ones.\n\n Notes\n -----\n Taken from Peter Otten's snippet posted at\n https://nedbatchelder.com/blog/201306/filter_a_list_into_two_parts.html\n \"\"\"\n a, b = tee((predicate(item), item) for item in items)\n return ((item for pred, item in a if not pred),\n (item for pred, item in b if pred))\n\n\ndef generate_chunks(container, size):\n \"\"\"Given a container, generate chunks from it with size up to `size`\n \"\"\"\n # There could be a \"smarter\" solution but I think this would suffice\n assert size > 0, \"Size should be non-0 positive\"\n while container:\n yield container[:size]\n container = container[size:]\n\n\ndef generate_file_chunks(files, cmd=None):\n \"\"\"Given a list of files, generate chunks of them to avoid exceeding cmdline length\n\n Parameters\n ----------\n files: list of str\n cmd: str or list of str, optional\n Command to account for as well\n \"\"\"\n files = ensure_list(files)\n cmd = ensure_list(cmd)\n\n maxl = max(map(len, files)) if files else 0\n chunk_size = max(\n 1, # should at least be 1. If blows then - not our fault\n (CMD_MAX_ARG\n - sum((len(x) + 3) for x in cmd)\n - 4 # for '--' below\n ) // (maxl + 3) # +3 for possible quotes and a space\n )\n # TODO: additional treatment for \"too many arguments\"? although\n # as https://github.com/datalad/datalad/issues/1883#issuecomment\n # -436272758\n # shows there seems to be no hardcoded limit on # of arguments,\n # but may be we decide to go for smth like follow to be on safe side\n # chunk_size = min(10240 - len(cmd), chunk_size)\n file_chunks = generate_chunks(files, chunk_size)\n return file_chunks\n\n\n#\n# Generators helpers\n#\n\ndef saved_generator(gen):\n \"\"\"Given a generator returns two generators, where 2nd one just replays\n\n So the first one would be going through the generated items and 2nd one\n would be yielding saved items\n \"\"\"\n saved = []\n\n def gen1():\n for x in gen: # iterating over original generator\n saved.append(x)\n yield x\n\n def gen2():\n for x in saved: # yielding saved entries\n yield x\n\n return gen1(), gen2()\n\n\n#\n# Decorators\n#\n\n# Originally better_wraps was created to provide `wrapt`-based, instead of\n# `functools.wraps` implementation to preserve the correct signature of the\n# decorated function. By using inspect.signature in our getargspec, which\n# works fine on `functools.wraps`ed functions, we mediated this necessity.\nbetter_wraps = wraps\n\n\n# Borrowed from pandas\n# Copyright: 2011-2014, Lambda Foundry, Inc. and PyData Development Team\n# License: BSD-3\ndef optional_args(decorator):\n \"\"\"allows a decorator to take optional positional and keyword arguments.\n Assumes that taking a single, callable, positional argument means that\n it is decorating a function, i.e. something like this::\n\n @my_decorator\n def function(): pass\n\n Calls decorator with decorator(f, `*args`, `**kwargs`)\"\"\"\n\n @better_wraps(decorator)\n def wrapper(*args, **kwargs):\n def dec(f):\n return decorator(f, *args, **kwargs)\n\n is_decorating = not kwargs and len(args) == 1 and isinstance(args[0], Callable)\n if is_decorating:\n f = args[0]\n args = []\n return dec(f)\n else:\n return dec\n\n return wrapper\n\n\n# TODO: just provide decorators for tempfile.mk* functions. This is ugly!\ndef get_tempfile_kwargs(tkwargs=None, prefix=\"\", wrapped=None):\n \"\"\"Updates kwargs to be passed to tempfile. calls depending on env vars\n \"\"\"\n if tkwargs is None:\n tkwargs_ = {}\n else:\n # operate on a copy of tkwargs to avoid any side-effects\n tkwargs_ = tkwargs.copy()\n\n # TODO: don't remember why I had this one originally\n # if len(targs)<2 and \\\n if 'prefix' not in tkwargs_:\n tkwargs_['prefix'] = '_'.join(\n ['datalad_temp'] +\n ([prefix] if prefix else []) +\n ([''] if (on_windows or not wrapped) else [wrapped.__name__]))\n\n directory = os.environ.get('TMPDIR')\n if directory and 'dir' not in tkwargs_:\n tkwargs_['dir'] = directory\n\n return tkwargs_\n\n\n@optional_args\ndef line_profile(func):\n \"\"\"Q&D helper to line profile the function and spit out stats\n \"\"\"\n import line_profiler\n prof = line_profiler.LineProfiler()\n\n @wraps(func)\n def _wrap_line_profile(*args, **kwargs):\n try:\n pfunc = prof(func)\n return pfunc(*args, **kwargs)\n finally:\n prof.print_stats()\n return _wrap_line_profile\n\n\n# unused in -core\n@optional_args\ndef collect_method_callstats(func):\n \"\"\"Figure out methods which call the method repeatedly on the same instance\n\n Use case(s):\n - .repo is expensive since does all kinds of checks.\n - .config is expensive transitively since it calls .repo each time\n\n TODO:\n - fancy one could look through the stack for the same id(self) to see if\n that location is already in memo. That would hint to the cases where object\n is not passed into underlying functions, causing them to redo the same work\n over and over again\n - ATM might flood with all \"1 lines\" calls which are not that informative.\n The underlying possibly suboptimal use might be coming from their callers.\n It might or not relate to the previous TODO\n \"\"\"\n from collections import defaultdict\n import traceback\n from time import time\n memo = defaultdict(lambda: defaultdict(int)) # it will be a dict of lineno: count\n # gross timing\n times = []\n toppath = dirname(__file__) + sep\n\n @wraps(func)\n def _wrap_collect_method_callstats(*args, **kwargs):\n try:\n self = args[0]\n stack = traceback.extract_stack()\n caller = stack[-2]\n stack_sig = \\\n \"{relpath}:{s.name}\".format(\n s=caller, relpath=relpath(caller.filename, toppath))\n sig = (id(self), stack_sig)\n # we will count based on id(self) + wherefrom\n memo[sig][caller.lineno] += 1\n t0 = time()\n return func(*args, **kwargs)\n finally:\n times.append(time() - t0)\n pass\n\n def print_stats():\n print(\"The cost of property {}:\".format(func.__name__))\n if not memo:\n print(\"None since no calls\")\n return\n # total count\n counts = {k: sum(v.values()) for k,v in memo.items()}\n total = sum(counts.values())\n ids = {self_id for (self_id, _) in memo}\n print(\" Total: {} calls from {} objects with {} contexts taking {:.2f} sec\"\n .format(total, len(ids), len(memo), sum(times)))\n # now we need to sort by value\n for (self_id, caller), count in sorted(counts.items(), key=lambda x: x[1], reverse=True):\n print(\" {} {}: {} from {} lines\"\n .format(self_id, caller, count, len(memo[(self_id, caller)])))\n\n # Upon total exit we print the stats\n import atexit\n atexit.register(print_stats)\n\n return _wrap_collect_method_callstats\n\n\n# Borrowed from duecredit to wrap duecredit-handling to guarantee failsafe\ndef never_fail(f):\n \"\"\"Assure that function never fails -- all exceptions are caught\n\n Returns `None` if function fails internally.\n \"\"\"\n @wraps(f)\n def wrapped_func(*args, **kwargs):\n try:\n return f(*args, **kwargs)\n except Exception as e:\n lgr.warning(\n \"DataLad internal failure while running %s: %r. \"\n \"Please report at https://github.com/datalad/datalad/issues\"\n % (f, e)\n )\n\n if os.environ.get('DATALAD_ALLOW_FAIL', False):\n return f\n else:\n return wrapped_func\n\n\n#\n# Context Managers\n#\n\n\n# unused in -core\n@contextmanager\ndef nothing_cm():\n \"\"\"Just a dummy cm to programmically switch context managers\"\"\"\n yield\n\n\n@contextmanager\ndef swallow_outputs():\n \"\"\"Context manager to help consuming both stdout and stderr, and print()\n\n stdout is available as cm.out and stderr as cm.err whenever cm is the\n yielded context manager.\n Internally uses temporary files to guarantee absent side-effects of swallowing\n into StringIO which lacks .fileno.\n\n print mocking is necessary for some uses where sys.stdout was already bound\n to original sys.stdout, thus mocking it later had no effect. Overriding\n print function had desired effect\n \"\"\"\n\n class StringIOAdapter(object):\n \"\"\"Little adapter to help getting out/err values\n \"\"\"\n def __init__(self):\n kw = get_tempfile_kwargs({}, prefix=\"outputs\")\n\n self._out = NamedTemporaryFile(delete=False, mode='w', **kw)\n self._err = NamedTemporaryFile(delete=False, mode='w', **kw)\n\n def _read(self, h):\n with open(h.name) as f:\n return f.read()\n\n @property\n def out(self):\n if not self._out.closed:\n self._out.flush()\n return self._read(self._out)\n\n @property\n def err(self):\n if not self._err.closed:\n self._err.flush()\n return self._read(self._err)\n\n @property\n def handles(self):\n return self._out, self._err\n\n def cleanup(self):\n self._out.close()\n self._err.close()\n out_name = self._out.name\n err_name = self._err.name\n from datalad import cfg\n if cfg.getbool('datalad.log', 'outputs', default=False) \\\n and lgr.getEffectiveLevel() <= logging.DEBUG:\n for s, sname in ((self.out, 'stdout'),\n (self.err, 'stderr')):\n if s:\n pref = os.linesep + \"| \"\n lgr.debug(\"Swallowed %s:%s%s\", sname, pref, s.replace(os.linesep, pref))\n else:\n lgr.debug(\"Nothing was swallowed for %s\", sname)\n del self._out\n del self._err\n gc.collect()\n rmtemp(out_name)\n rmtemp(err_name)\n\n def fake_print(*args, **kwargs):\n sep = kwargs.pop('sep', ' ')\n end = kwargs.pop('end', '\\n')\n file = kwargs.pop('file', sys.stdout)\n\n if file in (oldout, olderr, sys.stdout, sys.stderr):\n # we mock\n try:\n sys.stdout.write(sep.join(args) + end)\n except UnicodeEncodeError as exc:\n lgr.error(\n \"Failed to write to mocked stdout, got %s, continue as it \"\n \"didn't happen\", exc)\n else:\n # must be some other file one -- leave it alone\n oldprint(*args, sep=sep, end=end, file=file)\n\n from .ui import ui\n # preserve -- they could have been mocked already\n oldprint = getattr(builtins, 'print')\n oldout, olderr = sys.stdout, sys.stderr\n olduiout = ui.out\n adapter = StringIOAdapter()\n\n try:\n sys.stdout, sys.stderr = adapter.handles\n ui.out = adapter.handles[0]\n setattr(builtins, 'print', fake_print)\n\n yield adapter\n finally:\n sys.stdout, sys.stderr, ui.out = oldout, olderr, olduiout\n setattr(builtins, 'print', oldprint)\n adapter.cleanup()\n\n\n@contextmanager\ndef swallow_logs(new_level=None, file_=None, name='datalad'):\n \"\"\"Context manager to consume all logs.\n\n \"\"\"\n lgr = logging.getLogger(name)\n\n # Keep old settings\n old_level = lgr.level\n old_handlers = lgr.handlers\n\n # Let's log everything into a string\n # TODO: generalize with the one for swallow_outputs\n class StringIOAdapter(object):\n \"\"\"Little adapter to help getting out values\n\n And to stay consistent with how swallow_outputs behaves\n \"\"\"\n def __init__(self):\n if file_ is None:\n kw = get_tempfile_kwargs({}, prefix=\"logs\")\n self._out = NamedTemporaryFile(mode='a', delete=False, **kw)\n else:\n out_file = file_\n # PY3 requires clearly one or another. race condition possible\n self._out = open(out_file, 'a')\n self._final_out = None\n\n def _read(self, h):\n with open(h.name) as f:\n return f.read()\n\n @property\n def out(self):\n if self._final_out is not None:\n # we closed and cleaned up already\n return self._final_out\n else:\n self._out.flush()\n return self._read(self._out)\n\n @property\n def lines(self):\n return self.out.split('\\n')\n\n @property\n def handle(self):\n return self._out\n\n def cleanup(self):\n # store for access while object exists\n self._final_out = self.out\n self._out.close()\n out_name = self._out.name\n del self._out\n gc.collect()\n if not file_:\n rmtemp(out_name)\n\n def assert_logged(self, msg=None, level=None, regex=True, **kwargs):\n \"\"\"Provide assertion on whether a msg was logged at a given level\n\n If neither `msg` nor `level` provided, checks if anything was logged\n at all.\n\n Parameters\n ----------\n msg: str, optional\n Message (as a regular expression, if `regex`) to be searched.\n If no msg provided, checks if anything was logged at a given level.\n level: str, optional\n String representing the level to be logged\n regex: bool, optional\n If False, regular `assert_in` is used\n **kwargs: str, optional\n Passed to `assert_re_in` or `assert_in`\n \"\"\"\n from datalad.tests.utils import assert_re_in\n from datalad.tests.utils import assert_in\n\n if regex:\n match = r'\\[%s\\] ' % level if level else r\"\\[\\S+\\] \"\n else:\n match = '[%s] ' % level if level else ''\n\n if msg:\n match += msg\n\n if match:\n (assert_re_in if regex else assert_in)(match, self.out, **kwargs)\n else:\n assert not kwargs, \"no kwargs to be passed anywhere\"\n assert self.out, \"Nothing was logged!?\"\n\n adapter = StringIOAdapter()\n # TODO: it does store messages but without any formatting, i.e. even without\n # date/time prefix etc. IMHO it should preserve formatting in case if file_ is\n # set\n swallow_handler = logging.StreamHandler(adapter.handle)\n # we want to log levelname so we could test against it\n swallow_handler.setFormatter(\n logging.Formatter('[%(levelname)s] %(message)s'))\n swallow_handler.filters = sum([h.filters for h in old_handlers],\n [])\n lgr.handlers = [swallow_handler]\n if old_level < logging.DEBUG: # so if HEAVYDEBUG etc -- show them!\n lgr.handlers += old_handlers\n\n if isinstance(new_level, str):\n new_level = getattr(logging, new_level)\n\n if new_level is not None:\n lgr.setLevel(new_level)\n\n try:\n yield adapter\n # TODO: if file_ and there was an exception -- most probably worth logging it?\n # although ideally it should be the next log outside added to that file_ ... oh well\n finally:\n lgr.handlers = old_handlers\n lgr.setLevel(old_level)\n adapter.cleanup()\n\n\n# TODO: May be melt in with swallow_logs at some point:\n@contextmanager\ndef disable_logger(logger=None):\n \"\"\"context manager to temporarily disable logging\n\n This is to provide one of swallow_logs' purposes without unnecessarily\n creating temp files (see gh-1865)\n\n Parameters\n ----------\n logger: Logger\n Logger whose handlers will be ordered to not log anything.\n Default: datalad's topmost Logger ('datalad')\n \"\"\"\n\n class NullFilter(logging.Filter):\n \"\"\"Filter class to reject all records\n \"\"\"\n def filter(self, record):\n return 0\n\n if logger is None:\n # default: all of datalad's logging:\n logger = logging.getLogger('datalad')\n\n filter_ = NullFilter(logger.name)\n [h.addFilter(filter_) for h in logger.handlers]\n\n try:\n yield logger\n finally:\n [h.removeFilter(filter_) for h in logger.handlers]\n\n\n#\n# Additional handlers\n#\n_sys_excepthook = sys.excepthook # Just in case we ever need original one\n\n\ndef setup_exceptionhook(ipython=False):\n \"\"\"Overloads default sys.excepthook with our exceptionhook handler.\n\n If interactive, our exceptionhook handler will invoke\n pdb.post_mortem; if not interactive, then invokes default handler.\n \"\"\"\n\n def _datalad_pdb_excepthook(type, value, tb):\n import traceback\n traceback.print_exception(type, value, tb)\n print()\n if is_interactive():\n import pdb\n pdb.post_mortem(tb)\n\n if ipython:\n from IPython.core import ultratb\n sys.excepthook = ultratb.FormattedTB(mode='Verbose',\n # color_scheme='Linux',\n call_pdb=is_interactive())\n else:\n sys.excepthook = _datalad_pdb_excepthook\n\n\ndef ensure_dir(*args):\n \"\"\"Make sure directory exists.\n\n Joins the list of arguments to an os-specific path to the desired\n directory and creates it, if it not exists yet.\n \"\"\"\n dirname = op.join(*args)\n if not exists(dirname):\n os.makedirs(dirname)\n return dirname\n\n\ndef updated(d, update):\n \"\"\"Return a copy of the input with the 'update'\n\n Primarily for updating dictionaries\n \"\"\"\n d = d.copy()\n d.update(update)\n return d\n\n\n_pwd_mode = None\n\n\ndef _switch_to_getcwd(msg, *args):\n global _pwd_mode\n _pwd_mode = 'cwd'\n lgr.debug(\n msg + \". From now on will be returning os.getcwd(). Directory\"\n \" symlinks in the paths will be resolved\",\n *args\n )\n # TODO: we might want to mitigate by going through all flywheighted\n # repos and tuning up their .paths to be resolved?\n\n\ndef getpwd():\n \"\"\"Try to return a CWD without dereferencing possible symlinks\n\n This function will try to use PWD environment variable to provide a current\n working directory, possibly with some directories along the path being\n symlinks to other directories. Unfortunately, PWD is used/set only by the\n shell and such functions as `os.chdir` and `os.getcwd` nohow use or modify\n it, thus `os.getcwd()` returns path with links dereferenced.\n\n While returning current working directory based on PWD env variable we\n verify that the directory is the same as `os.getcwd()` after resolving all\n symlinks. If that verification fails, we fall back to always use\n `os.getcwd()`.\n\n Initial decision to either use PWD env variable or os.getcwd() is done upon\n the first call of this function.\n \"\"\"\n global _pwd_mode\n if _pwd_mode is None:\n # we need to decide!\n try:\n pwd = os.environ['PWD']\n if on_windows and pwd and pwd.startswith('/'):\n # It should be a path from MSYS.\n # - it might start with a drive letter or not\n # - it seems to be \"illegal\" to have a single letter directories\n # under / path, i.e. if created - they aren't found\n # - 'ln -s' does not fail to create a \"symlink\" but it just\n # copies!\n # so we are not likely to need original PWD purpose on\n # those systems\n # Verdict:\n _pwd_mode = 'cwd'\n else:\n _pwd_mode = 'PWD'\n except KeyError:\n _pwd_mode = 'cwd'\n\n if _pwd_mode == 'cwd':\n return os.getcwd()\n elif _pwd_mode == 'PWD':\n try:\n cwd = os.getcwd()\n except OSError as exc:\n if \"o such file\" in str(exc):\n # directory was removed but we promised to be robust and\n # still report the path we might know since we are still in PWD\n # mode\n cwd = None\n else:\n raise\n try:\n pwd = os.environ['PWD']\n # do absolute() in addition to always get an absolute path\n # even with non-existing paths on windows\n pwd_real = str(Path(pwd).resolve().absolute())\n # This logic would fail to catch the case where chdir did happen\n # to the directory where current PWD is pointing to, e.g.\n # $> ls -ld $PWD\n # lrwxrwxrwx 1 yoh yoh 5 Oct 11 13:27 /home/yoh/.tmp/tmp -> /tmp//\n # hopa:~/.tmp/tmp\n # $> python -c 'import os; os.chdir(\"/tmp\"); from datalad.utils import getpwd; print(getpwd(), os.getcwd())'\n # ('/home/yoh/.tmp/tmp', '/tmp')\n # but I guess that should not be too harmful\n if cwd is not None and pwd_real != cwd:\n _switch_to_getcwd(\n \"realpath of PWD=%s is %s whenever os.getcwd()=%s\",\n pwd, pwd_real, cwd\n )\n return cwd\n return pwd\n except KeyError:\n _switch_to_getcwd(\"PWD env variable is no longer available\")\n return cwd # Must not happen, but may be someone\n # evil purges PWD from environ?\n else:\n raise RuntimeError(\n \"Must have not got here. \"\n \"pwd_mode must be either cwd or PWD. And it is now %r\" % (_pwd_mode,)\n )\n\n\nclass chpwd(object):\n \"\"\"Wrapper around os.chdir which also adjusts environ['PWD']\n\n The reason is that otherwise PWD is simply inherited from the shell\n and we have no ability to assess directory path without dereferencing\n symlinks.\n\n If used as a context manager it allows to temporarily change directory\n to the given path\n \"\"\"\n def __init__(self, path, mkdir=False, logsuffix=''):\n\n if path:\n pwd = getpwd()\n self._prev_pwd = pwd\n else:\n self._prev_pwd = None\n return\n\n if not isabs(path):\n path = normpath(op.join(pwd, path))\n if not os.path.exists(path) and mkdir:\n self._mkdir = True\n os.mkdir(path)\n else:\n self._mkdir = False\n lgr.debug(\"chdir %r -> %r %s\", self._prev_pwd, path, logsuffix)\n os.chdir(path) # for grep people -- ok, to chdir here!\n os.environ['PWD'] = str(path)\n\n def __enter__(self):\n # nothing more to do really, chdir was in the constructor\n pass\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n if self._prev_pwd:\n # Need to use self.__class__ so this instance, if the entire\n # thing mocked during the test, still would use correct chpwd\n self.__class__(self._prev_pwd, logsuffix=\"(coming back)\")\n\n\ndef dlabspath(path, norm=False):\n \"\"\"Symlinks-in-the-cwd aware abspath\n\n os.path.abspath relies on os.getcwd() which would not know about symlinks\n in the path\n\n TODO: we might want to norm=True by default to match behavior of\n os .path.abspath?\n \"\"\"\n if not isabs(path):\n # if not absolute -- relative to pwd\n path = op.join(getpwd(), path)\n return normpath(path) if norm else path\n\n\ndef with_pathsep(path):\n \"\"\"Little helper to guarantee that path ends with /\"\"\"\n return path + sep if not path.endswith(sep) else path\n\n\ndef get_path_prefix(path, pwd=None):\n \"\"\"Get path prefix (for current directory)\n\n Returns relative path to the topdir, if we are under topdir, and if not\n absolute path to topdir. If `pwd` is not specified - current directory\n assumed\n \"\"\"\n pwd = pwd or getpwd()\n path = dlabspath(path)\n path_ = with_pathsep(path)\n pwd_ = with_pathsep(pwd)\n common = commonprefix((path_, pwd_))\n if common.endswith(sep) and common in {path_, pwd_}:\n # we are in subdir or above the path = use relative path\n location_prefix = relpath(path, pwd)\n # if benign \"here\" - cut off\n if location_prefix in (curdir, curdir + sep):\n location_prefix = ''\n return location_prefix\n else:\n # just return absolute path\n return path\n\n\ndef _get_normalized_paths(path, prefix):\n if isabs(path) != isabs(prefix):\n raise ValueError(\"Both paths must either be absolute or relative. \"\n \"Got %r and %r\" % (path, prefix))\n path = with_pathsep(path)\n prefix = with_pathsep(prefix)\n return path, prefix\n\n\ndef path_startswith(path, prefix):\n \"\"\"Return True if path starts with prefix path\n\n Parameters\n ----------\n path: str\n prefix: str\n \"\"\"\n path, prefix = _get_normalized_paths(path, prefix)\n return path.startswith(prefix)\n\n\ndef path_is_subpath(path, prefix):\n \"\"\"Return True if path is a subpath of prefix\n\n It will return False if path == prefix.\n\n Parameters\n ----------\n path: str\n prefix: str\n \"\"\"\n path, prefix = _get_normalized_paths(path, prefix)\n return (len(prefix) < len(path)) and path.startswith(prefix)\n\n\ndef knows_annex(path):\n \"\"\"Returns whether at a given path there is information about an annex\n\n It is just a thin wrapper around GitRepo.is_with_annex() classmethod\n which also checks for `path` to exist first.\n\n This includes actually present annexes, but also uninitialized ones, or\n even the presence of a remote annex branch.\n \"\"\"\n from os.path import exists\n if not exists(path):\n lgr.debug(\"No annex: test path {0} doesn't exist\".format(path))\n return False\n from datalad.support.gitrepo import GitRepo\n return GitRepo(path, init=False, create=False).is_with_annex()\n\n\n@contextmanager\ndef make_tempfile(content=None, wrapped=None, **tkwargs):\n \"\"\"Helper class to provide a temporary file name and remove it at the end (context manager)\n\n Parameters\n ----------\n mkdir : bool, optional (default: False)\n If True, temporary directory created using tempfile.mkdtemp()\n content : str or bytes, optional\n Content to be stored in the file created\n wrapped : function, optional\n If set, function name used to prefix temporary file name\n `**tkwargs`:\n All other arguments are passed into the call to tempfile.mk{,d}temp(),\n and resultant temporary filename is passed as the first argument into\n the function t. If no 'prefix' argument is provided, it will be\n constructed using module and function names ('.' replaced with\n '_').\n\n To change the used directory without providing keyword argument 'dir' set\n DATALAD_TESTS_TEMP_DIR.\n\n Examples\n --------\n >>> from os.path import exists\n >>> from datalad.utils import make_tempfile\n >>> with make_tempfile() as fname:\n ... k = open(fname, 'w').write('silly test')\n >>> assert not exists(fname) # was removed\n\n >>> with make_tempfile(content=\"blah\") as fname:\n ... assert open(fname).read() == \"blah\"\n \"\"\"\n\n if tkwargs.get('mkdir', None) and content is not None:\n raise ValueError(\"mkdir=True while providing content makes no sense\")\n\n tkwargs_ = get_tempfile_kwargs(tkwargs, wrapped=wrapped)\n\n # if DATALAD_TESTS_TEMP_DIR is set, use that as directory,\n # let mktemp handle it otherwise. However, an explicitly provided\n # dir=... will override this.\n mkdir = tkwargs_.pop('mkdir', False)\n\n filename = {False: tempfile.mktemp,\n True: tempfile.mkdtemp}[mkdir](**tkwargs_)\n # MIH: not clear to me why we need to perform this (possibly expensive)\n # resolve. It was already part of the original implementation\n # 008d9ab8cc3e0170c0a9b8479e80dee9ffe6eb7f\n filename = Path(filename).resolve()\n\n if content:\n (filename.write_bytes\n if isinstance(content, bytes)\n else filename.write_text)(content)\n\n # TODO globbing below can also be done with pathlib\n filename = str(filename)\n\n if __debug__:\n lgr.debug(\n 'Created temporary %s named %s',\n 'directory' if mkdir else 'file',\n filename)\n try:\n yield filename\n finally:\n # glob here for all files with the same name (-suffix)\n # would be useful whenever we requested .img filename,\n # and function creates .hdr as well\n # MIH: this is undocumented behavior, and undesired in the general\n # case. it should be made conditional and explicit\n lsuffix = len(tkwargs_.get('suffix', ''))\n filename_ = lsuffix and filename[:-lsuffix] or filename\n filenames = glob.glob(filename_ + '*')\n if len(filename_) < 3 or len(filenames) > 5:\n # For paranoid yoh who stepped into this already ones ;-)\n lgr.warning(\"It is unlikely that it was intended to remove all\"\n \" files matching %r. Skipping\" % filename_)\n return\n for f in filenames:\n try:\n rmtemp(f)\n except OSError: # pragma: no cover\n pass\n\n\ndef _path_(*p):\n \"\"\"Given a path in POSIX\" notation, regenerate one in native to the env one\"\"\"\n if on_windows:\n return op.join(*map(lambda x: op.join(*x.split('/')), p))\n else:\n # Assume that all others as POSIX compliant so nothing to be done\n return op.join(*p)\n\n\ndef get_timestamp_suffix(time_=None, prefix='-'):\n \"\"\"Return a time stamp (full date and time up to second)\n\n primarily to be used for generation of log files names\n \"\"\"\n args = []\n if time_ is not None:\n if isinstance(time_, int):\n time_ = time.gmtime(time_)\n args.append(time_)\n return time.strftime(prefix + TIMESTAMP_FMT, *args)\n\n\n# unused in -core\ndef get_logfilename(dspath, cmd='datalad'):\n \"\"\"Return a filename to use for logging under a dataset/repository\n\n directory would be created if doesn't exist, but dspath must exist\n and be a directory\n \"\"\"\n assert(exists(dspath))\n assert(isdir(dspath))\n ds_logdir = ensure_dir(dspath, '.git', 'datalad', 'logs') # TODO: use WEB_META_LOG whenever #789 merged\n return op.join(ds_logdir, 'crawl-%s.log' % get_timestamp_suffix())\n\n\ndef get_trace(edges, start, end, trace=None):\n \"\"\"Return the trace/path to reach a node in a tree.\n\n Parameters\n ----------\n edges : sequence(2-tuple)\n The tree given by a sequence of edges (parent, child) tuples. The\n nodes can be identified by any value and data type that supports\n the '==' operation.\n start :\n Identifier of the start node. Must be present as a value in the parent\n location of an edge tuple in order to be found.\n end :\n Identifier of the target/end node. Must be present as a value in the child\n location of an edge tuple in order to be found.\n trace : list\n Mostly useful for recursive calls, and used internally.\n\n Returns\n -------\n None or list\n Returns a list with the trace to the target (the starts and the target\n are not included in the trace, hence if start and end are directly connected\n an empty list is returned), or None when no trace to the target can be found,\n or start and end are identical.\n \"\"\"\n # the term trace is used to avoid confusion with a path in the sense\n # of a filesystem path, but the analogy fits and nodes can be paths\n if trace is None:\n trace = []\n if not edges:\n raise ValueError(\"no edges given\")\n for cand in edges:\n cand_super, cand_sub = cand\n if cand_sub in trace:\n # only DAGs, skip any cyclic traces\n continue\n if trace and cand_super != trace[-1]:\n # only consider edges that lead off the end of the trace\n continue\n if not trace and cand_super != start:\n # we got nothing yet, and this edges is not matching the start\n continue\n if cand_sub == end:\n return trace\n # dive into potential subnodes\n cand_trace = get_trace(\n edges,\n start,\n end,\n trace + [cand_sub])\n if cand_trace:\n return cand_trace\n return None\n\n\ndef get_dataset_root(path):\n \"\"\"Return the root of an existent dataset containing a given path\n\n The root path is returned in the same absolute or relative form\n as the input argument. If no associated dataset exists, or the\n input path doesn't exist, None is returned.\n\n If `path` is a symlink or something other than a directory, its\n the root dataset containing its parent directory will be reported.\n If none can be found, at a symlink at `path` is pointing to a\n dataset, `path` itself will be reported as the root.\n\n Parameters\n ----------\n path : Path-like\n\n Returns\n -------\n str or None\n \"\"\"\n path = str(path)\n suffix = '.git'\n altered = None\n if islink(path) or not isdir(path):\n altered = path\n path = dirname(path)\n apath = abspath(path)\n # while we can still go up\n while split(apath)[1]:\n if exists(op.join(path, suffix)):\n return path\n # new test path in the format we got it\n path = normpath(op.join(path, os.pardir))\n # no luck, next round\n apath = abspath(path)\n # if we applied dirname() at the top, we give it another go with\n # the actual path, if it was itself a symlink, it could be the\n # top-level dataset itself\n if altered and exists(op.join(altered, suffix)):\n return altered\n\n return None\n\n\n# ATM used in datalad_crawler extension, so do not remove yet\ndef try_multiple(ntrials, exception, base, f, *args, **kwargs):\n \"\"\"Call f multiple times making exponentially growing delay between the calls\"\"\"\n for trial in range(1, ntrials+1):\n try:\n return f(*args, **kwargs)\n except exception as exc:\n if trial == ntrials:\n raise # just reraise on the last trial\n t = base ** trial\n lgr.warning(\"Caught %s on trial #%d. Sleeping %f and retrying\",\n CapturedException(exc), trial, t)\n sleep(t)\n\n\n@optional_args\ndef try_multiple_dec(\n f, ntrials=None, duration=0.1, exceptions=None, increment_type=None,\n exceptions_filter=None,\n logger=None,\n):\n \"\"\"Decorator to try function multiple times.\n\n Main purpose is to decorate functions dealing with removal of files/directories\n and which might need a few seconds to work correctly on Windows which takes\n its time to release files/directories.\n\n Parameters\n ----------\n ntrials: int, optional\n duration: float, optional\n Seconds to sleep before retrying.\n increment_type: {None, 'exponential'}\n Note that if it is exponential, duration should typically be > 1.0\n so it grows with higher power\n exceptions: Exception or tuple of Exceptions, optional\n Exception or a tuple of multiple exceptions, on which to retry\n exceptions_filter: callable, optional\n If provided, this function will be called with a caught exception\n instance. If function returns True - we will re-try, if False - exception\n will be re-raised without retrying.\n logger: callable, optional\n Logger to log upon failure. If not provided, will use stock logger\n at the level of 5 (heavy debug).\n \"\"\"\n if not exceptions:\n exceptions = (OSError, WindowsError, PermissionError) \\\n if on_windows else OSError\n if not ntrials:\n # Life goes fast on proper systems, no need to delay it much\n ntrials = 100 if on_windows else 10\n if logger is None:\n def logger(*args, **kwargs):\n return lgr.log(5, *args, **kwargs)\n assert increment_type in {None, 'exponential'}\n\n @wraps(f)\n def _wrap_try_multiple_dec(*args, **kwargs):\n t = duration\n for trial in range(ntrials):\n try:\n return f(*args, **kwargs)\n except exceptions as exc:\n if exceptions_filter and not exceptions_filter(exc):\n raise\n if trial < ntrials - 1:\n if increment_type == 'exponential':\n t = duration ** (trial + 1)\n logger(\n \"Caught %s on trial #%d. Sleeping %f and retrying\",\n CapturedException(exc), trial, t)\n sleep(t)\n else:\n raise\n\n return _wrap_try_multiple_dec\n\n\n@try_multiple_dec\ndef unlink(f):\n \"\"\"'Robust' unlink. Would try multiple times\n\n On windows boxes there is evidence for a latency of more than a second\n until a file is considered no longer \"in-use\".\n WindowsError is not known on Linux, and if IOError or any other\n exception\n is thrown then if except statement has WindowsError in it -- NameError\n also see gh-2533\n \"\"\"\n # Check for open files\n assert_no_open_files(f)\n return os.unlink(f)\n\n\n@try_multiple_dec\ndef _rmtree(*args, **kwargs):\n \"\"\"Just a helper to decorate shutil.rmtree.\n\n rmtree defined above does more and ideally should not itself be decorated\n since a recursive definition and does checks for open files inside etc -\n might be too runtime expensive\n \"\"\"\n return shutil.rmtree(*args, **kwargs)\n\n\ndef slash_join(base, extension):\n \"\"\"Join two strings with a '/', avoiding duplicate slashes\n\n If any of the strings is None the other is returned as is.\n \"\"\"\n if extension is None:\n return base\n if base is None:\n return extension\n return '/'.join(\n (base.rstrip('/'),\n extension.lstrip('/')))\n\n\n#\n# IO Helpers\n#\n\n# unused in -core\ndef open_r_encdetect(fname, readahead=1000):\n \"\"\"Return a file object in read mode with auto-detected encoding\n\n This is helpful when dealing with files of unknown encoding.\n\n Parameters\n ----------\n readahead: int, optional\n How many bytes to read for guessing the encoding type. If\n negative - full file will be read\n \"\"\"\n from chardet import detect\n import io\n # read some bytes from the file\n with open(fname, 'rb') as f:\n head = f.read(readahead)\n enc = detect(head)\n denc = enc.get('encoding', None)\n lgr.debug(\"Auto-detected encoding %s for file %s (confidence: %s)\",\n denc,\n fname,\n enc.get('confidence', 'unknown'))\n return io.open(fname, encoding=denc)\n\n\ndef read_file(fname, decode=True):\n \"\"\"A helper to read file passing content via ensure_unicode\n\n Parameters\n ----------\n decode: bool, optional\n if False, no ensure_unicode and file content returned as bytes\n \"\"\"\n with open(fname, 'rb') as f:\n content = f.read()\n return ensure_unicode(content) if decode else content\n\n\ndef read_csv_lines(fname, dialect=None, readahead=16384, **kwargs):\n \"\"\"A generator of dict records from a CSV/TSV\n\n Automatically guesses the encoding for each record to convert to UTF-8\n\n Parameters\n ----------\n fname: str\n Filename\n dialect: str, optional\n Dialect to specify to csv.reader. If not specified -- guessed from\n the file, if fails to guess, \"excel-tab\" is assumed\n readahead: int, optional\n How many bytes to read from the file to guess the type\n **kwargs\n Passed to `csv.reader`\n \"\"\"\n import csv\n if dialect is None:\n with open(fname) as tsvfile:\n # add robustness, use a sniffer\n try:\n dialect = csv.Sniffer().sniff(tsvfile.read(readahead))\n except Exception as exc:\n lgr.warning(\n 'Could not determine file-format, assuming TSV: %s',\n CapturedException(exc)\n )\n dialect = 'excel-tab'\n\n kw = dict(encoding='utf-8')\n with open(fname, 'r', **kw) as tsvfile:\n # csv.py doesn't do Unicode; encode temporarily as UTF-8:\n csv_reader = csv.reader(\n tsvfile,\n dialect=dialect,\n **kwargs\n )\n header = None\n for row in csv_reader:\n # decode UTF-8 back to Unicode, cell by cell:\n row_unicode = map(ensure_unicode, row)\n if header is None:\n header = list(row_unicode)\n else:\n yield dict(zip(header, row_unicode))\n\n\ndef import_modules(modnames, pkg, msg=\"Failed to import {module}\", log=lgr.debug):\n \"\"\"Helper to import a list of modules without failing if N/A\n\n Parameters\n ----------\n modnames: list of str\n List of module names to import\n pkg: str\n Package under which to import\n msg: str, optional\n Message template for .format() to log at DEBUG level if import fails.\n Keys {module} and {package} will be provided and ': {exception}' appended\n log: callable, optional\n Logger call to use for logging messages\n \"\"\"\n from importlib import import_module\n _globals = globals()\n mods_loaded = []\n if pkg and not pkg in sys.modules:\n # with python 3.5.1 (ok with 3.5.5) somehow kept running into\n # Failed to import dlsub1: Parent module 'dltestm1' not loaded\n # while running the test. Preloading pkg resolved the issue\n import_module(pkg)\n for modname in modnames:\n try:\n _globals[modname] = mod = import_module(\n '.{}'.format(modname),\n pkg)\n mods_loaded.append(mod)\n except Exception as exc:\n from datalad.support.exceptions import CapturedException\n ce = CapturedException(exc)\n log((msg + ': {exception}').format(\n module=modname, package=pkg, exception=ce.message))\n return mods_loaded\n\n\ndef import_module_from_file(modpath, pkg=None, log=lgr.debug):\n \"\"\"Import provided module given a path\n\n TODO:\n - RF/make use of it in pipeline.py which has similar logic\n - join with import_modules above?\n\n Parameters\n ----------\n pkg: module, optional\n If provided, and modpath is under pkg.__path__, relative import will be\n used\n \"\"\"\n assert(modpath.endswith('.py')) # for now just for .py files\n\n log(\"Importing %s\" % modpath)\n\n modname = basename(modpath)[:-3]\n relmodpath = None\n if pkg:\n for pkgpath in pkg.__path__:\n if path_is_subpath(modpath, pkgpath):\n # for now relying on having .py extension -- assertion above\n relmodpath = '.' + relpath(modpath[:-3], pkgpath).replace(sep, '.')\n break\n\n try:\n if relmodpath:\n from importlib import import_module\n mod = import_module(relmodpath, pkg.__name__)\n else:\n dirname_ = dirname(modpath)\n try:\n sys.path.insert(0, dirname_)\n mod = __import__(modname, level=0)\n finally:\n if dirname_ in sys.path:\n sys.path.pop(sys.path.index(dirname_))\n else:\n log(\"Expected path %s to be within sys.path, but it was gone!\" % dirname_)\n except Exception as e:\n raise RuntimeError(\n \"Failed to import module from %s\" % modpath) from e\n\n return mod\n\n\ndef get_encoding_info():\n \"\"\"Return a dictionary with various encoding/locale information\"\"\"\n import sys, locale\n from collections import OrderedDict\n return OrderedDict([\n ('default', sys.getdefaultencoding()),\n ('filesystem', sys.getfilesystemencoding()),\n ('locale.prefered', locale.getpreferredencoding()),\n ])\n\n\ndef get_envvars_info():\n from collections import OrderedDict\n envs = []\n for var, val in os.environ.items():\n if (\n var.startswith('PYTHON') or\n var.startswith('LC_') or\n var.startswith('GIT_') or\n var in ('LANG', 'LANGUAGE', 'PATH')\n ):\n envs.append((var, val))\n return OrderedDict(envs)\n\n\n# This class is modified from Snakemake (v5.1.4)\nclass SequenceFormatter(string.Formatter):\n \"\"\"string.Formatter subclass with special behavior for sequences.\n\n This class delegates formatting of individual elements to another\n formatter object. Non-list objects are formatted by calling the\n delegate formatter's \"format_field\" method. List-like objects\n (list, tuple, set, frozenset) are formatted by formatting each\n element of the list according to the specified format spec using\n the delegate formatter and then joining the resulting strings with\n a separator (space by default).\n \"\"\"\n\n def __init__(self, separator=\" \", element_formatter=string.Formatter(),\n *args, **kwargs):\n self.separator = separator\n self.element_formatter = element_formatter\n\n def format_element(self, elem, format_spec):\n \"\"\"Format a single element\n\n For sequences, this is called once for each element in a\n sequence. For anything else, it is called on the entire\n object. It is intended to be overridden in subclases.\n \"\"\"\n return self.element_formatter.format_field(elem, format_spec)\n\n def format_field(self, value, format_spec):\n if isinstance(value, (list, tuple, set, frozenset)):\n return self.separator.join(self.format_element(v, format_spec)\n for v in value)\n else:\n return self.format_element(value, format_spec)\n\n\n# TODO: eventually we might want to make use of attr module\nclass File(object):\n \"\"\"Helper for a file entry in the create_tree/@with_tree\n\n It allows to define additional settings for entries\n \"\"\"\n def __init__(self, name, executable=False):\n \"\"\"\n\n Parameters\n ----------\n name : str\n Name of the file\n executable: bool, optional\n Make it executable\n \"\"\"\n self.name = name\n self.executable = executable\n\n def __str__(self):\n return self.name\n\n\ndef create_tree_archive(path, name, load, overwrite=False, archives_leading_dir=True):\n \"\"\"Given an archive `name`, create under `path` with specified `load` tree\n \"\"\"\n from datalad.support.archives import compress_files\n dirname = file_basename(name)\n full_dirname = op.join(path, dirname)\n os.makedirs(full_dirname)\n create_tree(full_dirname, load, archives_leading_dir=archives_leading_dir)\n # create archive\n if archives_leading_dir:\n compress_files([dirname], name, path=path, overwrite=overwrite)\n else:\n compress_files(list(map(basename, glob.glob(op.join(full_dirname, '*')))),\n op.join(pardir, name),\n path=op.join(path, dirname),\n overwrite=overwrite)\n # remove original tree\n rmtree(full_dirname)\n\n\ndef create_tree(path, tree, archives_leading_dir=True, remove_existing=False):\n \"\"\"Given a list of tuples (name, load) create such a tree\n\n if load is a tuple itself -- that would create either a subtree or an archive\n with that content and place it into the tree if name ends with .tar.gz\n \"\"\"\n lgr.log(5, \"Creating a tree under %s\", path)\n if not exists(path):\n os.makedirs(path)\n\n if isinstance(tree, dict):\n tree = tree.items()\n\n for file_, load in tree:\n if isinstance(file_, File):\n executable = file_.executable\n name = file_.name\n else:\n executable = False\n name = file_\n full_name = op.join(path, name)\n if remove_existing and lexists(full_name):\n rmtree(full_name, chmod_files=True)\n if isinstance(load, (tuple, list, dict)):\n if name.endswith('.tar.gz') or name.endswith('.tar') or name.endswith('.zip'):\n create_tree_archive(\n path, name, load,\n archives_leading_dir=archives_leading_dir)\n else:\n create_tree(\n full_name, load,\n archives_leading_dir=archives_leading_dir,\n remove_existing=remove_existing)\n else:\n open_func = open\n if full_name.endswith('.gz'):\n open_func = gzip.open\n elif full_name.split('.')[-1] in ('xz', 'lzma'):\n import lzma\n open_func = lzma.open\n with open_func(full_name, \"wb\") as f:\n f.write(ensure_bytes(load, 'utf-8'))\n if executable:\n os.chmod(full_name, os.stat(full_name).st_mode | stat.S_IEXEC)\n\n\ndef get_suggestions_msg(values, known, sep=\"\\n \"):\n \"\"\"Return a formatted string with suggestions for values given the known ones\n \"\"\"\n import difflib\n suggestions = []\n for value in ensure_list(values): # might not want to do it if we change presentation below\n suggestions += difflib.get_close_matches(value, known)\n suggestions = unique(suggestions)\n msg = \"Did you mean any of these?\"\n if suggestions:\n if '\\n' in sep:\n # if separator includes new line - we add entire separator right away\n msg += sep\n else:\n msg += ' '\n return msg + \"%s\\n\" % sep.join(suggestions)\n return ''\n\n\ndef bytes2human(n, format='%(value).1f %(symbol)sB'):\n \"\"\"\n Convert n bytes into a human readable string based on format.\n symbols can be either \"customary\", \"customary_ext\", \"iec\" or \"iec_ext\",\n see: http://goo.gl/kTQMs\n\n >>> from datalad.utils import bytes2human\n >>> bytes2human(1)\n '1.0 B'\n >>> bytes2human(1024)\n '1.0 KB'\n >>> bytes2human(1048576)\n '1.0 MB'\n >>> bytes2human(1099511627776127398123789121)\n '909.5 YB'\n\n >>> bytes2human(10000, \"%(value).1f %(symbol)s/sec\")\n '9.8 K/sec'\n\n >>> # precision can be adjusted by playing with %f operator\n >>> bytes2human(10000, format=\"%(value).5f %(symbol)s\")\n '9.76562 K'\n\n Taken from: http://goo.gl/kTQMs and subsequently simplified\n Original Author: Giampaolo Rodola' <g.rodola [AT] gmail [DOT] com>\n License: MIT\n \"\"\"\n n = int(n)\n if n < 0:\n raise ValueError(\"n < 0\")\n symbols = ('', 'K', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y')\n prefix = {}\n for i, s in enumerate(symbols[1:]):\n prefix[s] = 1 << (i + 1) * 10\n for symbol in reversed(symbols[1:]):\n if n >= prefix[symbol]:\n value = float(n) / prefix[symbol]\n return format % locals()\n return format % dict(symbol=symbols[0], value=n)\n\n\ndef quote_cmdlinearg(arg):\n \"\"\"Perform platform-appropriate argument quoting\"\"\"\n # https://stackoverflow.com/a/15262019\n return '\"{}\"'.format(\n arg.replace('\"', '\"\"')\n ) if on_windows else shlex_quote(arg)\n\n\ndef guard_for_format(arg):\n \"\"\"Replace { and } with {{ and }}\n\n To be used in cases if arg is not expected to have provided\n by user .format() placeholders, but 'arg' might become a part\n of a composite passed to .format(), e.g. via 'Run'\n \"\"\"\n return arg.replace('{', '{{').replace('}', '}}')\n\n\ndef join_cmdline(args):\n \"\"\"Join command line args into a string using quote_cmdlinearg\n \"\"\"\n return ' '.join(map(quote_cmdlinearg, args))\n\n\ndef split_cmdline(s):\n \"\"\"Perform platform-appropriate command line splitting.\n\n Identical to `shlex.split()` on non-windows platforms.\n\n Modified from https://stackoverflow.com/a/35900070\n \"\"\"\n if not on_windows:\n return shlex_split(s)\n\n # the rest is for windows\n RE_CMD_LEX = r'''\"((?:\"\"|\\\\[\"\\\\]|[^\"])*)\"?()|(\\\\\\\\(?=\\\\*\")|\\\\\")|(&&?|\\|\\|?|\\d?>|[<])|([^\\s\"&|<>]+)|(\\s+)|(.)'''\n\n args = []\n accu = None # collects pieces of one arg\n for qs, qss, esc, pipe, word, white, fail in re.findall(RE_CMD_LEX, s):\n if word:\n pass # most frequent\n elif esc:\n word = esc[1]\n elif white or pipe:\n if accu is not None:\n args.append(accu)\n if pipe:\n args.append(pipe)\n accu = None\n continue\n elif fail:\n raise ValueError(\"invalid or incomplete shell string\")\n elif qs:\n word = qs.replace('\\\\\"', '\"').replace('\\\\\\\\', '\\\\')\n if platform == 0:\n word = word.replace('\"\"', '\"')\n else:\n word = qss # may be even empty; must be last\n\n accu = (accu or '') + word\n\n if accu is not None:\n args.append(accu)\n\n return args\n\n\ndef get_wrapped_class(wrapped):\n \"\"\"Determine the command class a wrapped __call__ belongs to\"\"\"\n mod = sys.modules[wrapped.__module__]\n command_class_name = wrapped.__qualname__.split('.')[-2]\n _func_class = mod.__dict__[command_class_name]\n lgr.debug(\"Determined class of decorated function: %s\", _func_class)\n return _func_class\n\n\ndef _make_assure_kludge(fn):\n old_name = fn.__name__.replace(\"ensure\", \"assure\")\n\n @wraps(fn)\n def compat_fn(*args, **kwargs):\n warnings.warn(\n \"{} is deprecated and will be removed in a future release. \"\n \"Use {} instead.\"\n .format(old_name, fn.__name__),\n DeprecationWarning)\n return fn(*args, **kwargs)\n\n compat_fn.__doc__ = (\"Note: This function is deprecated. Use {} instead.\"\n .format(fn.__name__))\n return compat_fn\n\n\nassure_tuple_or_list = _make_assure_kludge(ensure_tuple_or_list)\nassure_iter = _make_assure_kludge(ensure_iter)\nassure_list = _make_assure_kludge(ensure_list)\nassure_list_from_str = _make_assure_kludge(ensure_list_from_str)\nassure_dict_from_str = _make_assure_kludge(ensure_dict_from_str)\nassure_bytes = _make_assure_kludge(ensure_bytes)\nassure_unicode = _make_assure_kludge(ensure_unicode)\nassure_bool = _make_assure_kludge(ensure_bool)\nassure_dir = _make_assure_kludge(ensure_dir)\n\n\nlgr.log(5, \"Done importing datalad.utils\")\n\n\ndef check_symlink_capability(path, target):\n \"\"\"helper similar to datalad.tests.utils.has_symlink_capability\n\n However, for use in a datalad command context, we shouldn't\n assume to be able to write to tmpfile and also not import a whole lot from\n datalad's test machinery. Finally, we want to know, whether we can create a\n symlink at a specific location, not just somewhere. Therefore use\n arbitrary path to test-build a symlink and delete afterwards. Suitable\n location can therefore be determined by high lever code.\n\n Parameters\n ----------\n path: Path\n target: Path\n\n Returns\n -------\n bool\n \"\"\"\n\n try:\n target.touch()\n path.symlink_to(target)\n return True\n except Exception:\n return False\n finally:\n if path.exists():\n path.unlink()\n if target.exists():\n target.unlink()\n"}}},{"rowIdx":96,"cells":{"function_name":{"kind":"string","value":"rmtree"},"docstring":{"kind":"string","value":"To remove git-annex .git it is needed to make all files and directories writable again first\n\nParameters\n----------\npath: Path or str\n Path to remove\nchmod_files : string or bool, optional\n Whether to make files writable also before removal. Usually it is just\n a matter of directories to have write permissions.\n If 'auto' it would chmod files on windows by default\nchildren_only : bool, optional\n If set, all files and subdirectories would be removed while the path\n itself (must be a directory) would be preserved\n`*args` :\n`**kwargs` :\n Passed into shutil.rmtree call"},"masked_code":{"kind":"string","value":"# emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-\n# ex: set sts=4 ts=4 sw=4 et:\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##\n#\n# See COPYING file distributed along with the datalad package for the\n# copyright and license terms.\n#\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##\n\nimport collections\nfrom collections.abc import Callable\nimport re\nimport builtins\nimport time\n\nimport logging\nimport shutil\nimport os\nimport sys\nimport tempfile\nfrom tempfile import NamedTemporaryFile\nimport platform\nimport gc\nimport glob\nimport gzip\nimport stat\nimport string\nimport warnings\n\nimport os.path as op\n\nfrom copy import copy as shallow_copy\nfrom contextlib import contextmanager\nfrom functools import (\n lru_cache,\n wraps,\n)\nfrom time import sleep\nimport inspect\nfrom itertools import tee\n# this import is required because other modules import opj from here.\nfrom os.path import join as opj\nfrom os.path import (\n abspath,\n basename,\n commonprefix,\n curdir,\n dirname,\n exists,\n expanduser,\n expandvars,\n isabs,\n isdir,\n islink,\n lexists,\n normpath,\n pardir,\n relpath,\n sep,\n split,\n splitdrive\n)\n\nimport posixpath\n\nfrom shlex import (\n quote as shlex_quote,\n split as shlex_split,\n)\n\n# from datalad.dochelpers import get_docstring_split\nfrom datalad.consts import TIMESTAMP_FMT\nfrom datalad.support.exceptions import CapturedException\n\n\nunicode_srctypes = str, bytes\n\n\nlgr = logging.getLogger(\"datalad.utils\")\n\nlgr.log(5, \"Importing datalad.utils\")\n#\n# Some useful variables\n#\nplatform_system = platform.system().lower()\non_windows = platform_system == 'windows'\non_osx = platform_system == 'darwin'\non_linux = platform_system == 'linux'\non_msys_tainted_paths = on_windows \\\n and 'MSYS_NO_PATHCONV' not in os.environ \\\n and os.environ.get('MSYSTEM', '')[:4] in ('MSYS', 'MING')\n\n\n# Takes ~200msec, so should not be called at import time\n@lru_cache() # output should not change through life time of datalad process\ndef get_linux_distribution():\n \"\"\"Compatibility wrapper for {platform,distro}.linux_distribution().\n \"\"\"\n if hasattr(platform, \"linux_distribution\"):\n # Use deprecated (but faster) method if it's available.\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n result = platform.linux_distribution()\n else:\n import distro # We require this for Python 3.8 and above.\n result = distro.linux_distribution(full_distribution_name=False)\n return result\n\n\n# Those weren't used for any critical decision making, thus we just set them to None\n# Use get_linux_distribution() directly where needed\nlinux_distribution_name = linux_distribution_release = None\n\n# Maximal length of cmdline string\n# Query the system and use hardcoded \"knowledge\" if None\n# probably getconf ARG_MAX might not be available\n# The last one would be the most conservative/Windows\nCMD_MAX_ARG_HARDCODED = 2097152 if on_linux else 262144 if on_osx else 32767\ntry:\n CMD_MAX_ARG = os.sysconf('SC_ARG_MAX')\n assert CMD_MAX_ARG > 0\n if CMD_MAX_ARG > CMD_MAX_ARG_HARDCODED * 1e6:\n # workaround for some kind of a bug which comes up with python 3.4\n # see https://github.com/datalad/datalad/issues/3150\n # or on older CentOS with conda and python as new as 3.9\n # see https://github.com/datalad/datalad/issues/5943\n # TODO: let Yarik know that the world is a paradise now whenever 1e6\n # is not large enough\n CMD_MAX_ARG = min(CMD_MAX_ARG, CMD_MAX_ARG_HARDCODED)\nexcept Exception as exc:\n # ATM (20181005) SC_ARG_MAX available only on POSIX systems\n # so exception would be thrown e.g. on Windows, or\n # somehow during Debian build for nd14.04 it is coming up with -1:\n # https://github.com/datalad/datalad/issues/3015\n CMD_MAX_ARG = CMD_MAX_ARG_HARDCODED\n lgr.debug(\n \"Failed to query or got useless SC_ARG_MAX sysconf, \"\n \"will use hardcoded value: %s\", exc)\n# Even with all careful computations we do, due to necessity to account for\n# environment and what not, we still could not figure out \"exact\" way to\n# estimate it, but it was shown that 300k safety margin on linux was sufficient.\n# https://github.com/datalad/datalad/pull/2977#issuecomment-436264710\n# 300k is ~15%, so to be safe, and for paranoid us we will just use up to 50%\n# of the length for \"safety margin\". We might probably still blow due to\n# env vars, unicode, etc... so any hard limit imho is not a proper solution\nCMD_MAX_ARG = int(0.5 * CMD_MAX_ARG)\nlgr.debug(\n \"Maximal length of cmdline string (adjusted for safety margin): %d\",\n CMD_MAX_ARG)\n\n#\n# Little helpers\n#\n\n# `getargspec` has been deprecated in Python 3.\nArgSpecFake = collections.namedtuple(\n \"ArgSpecFake\", [\"args\", \"varargs\", \"keywords\", \"defaults\"])\n\n\ndef getargspec(func, *, include_kwonlyargs=False):\n \"\"\"Compat shim for getargspec deprecated in python 3.\n\n The main difference from inspect.getargspec (and inspect.getfullargspec\n for that matter) is that by using inspect.signature we are providing\n correct args/defaults for functools.wraps'ed functions.\n\n `include_kwonlyargs` option was added to centralize getting all args,\n even the ones which are kwonly (follow the ``*,``).\n\n For internal use and not advised for use in 3rd party code.\n Please use inspect.signature directly.\n \"\"\"\n # We use signature, and not getfullargspec, because only signature properly\n # \"passes\" args from a functools.wraps decorated function.\n # Note: getfullargspec works Ok on wrapt-decorated functions\n f_sign = inspect.signature(func)\n # Loop through parameters and compose argspec\n args4 = [[], None, None, {}]\n # Collect all kwonlyargs into a dedicated dict - name: default\n kwonlyargs = {}\n # shortcuts\n args, defaults = args4[0], args4[3]\n P = inspect.Parameter\n\n for p_name, p in f_sign.parameters.items():\n if p.kind in (P.POSITIONAL_ONLY, P.POSITIONAL_OR_KEYWORD):\n assert not kwonlyargs # yoh: must not come after kwonlyarg\n args.append(p_name)\n if p.default is not P.empty:\n defaults[p_name] = p.default\n elif p.kind == P.VAR_POSITIONAL:\n args4[1] = p_name\n elif p.kind == P.VAR_KEYWORD:\n args4[2] = p_name\n elif p.kind == P.KEYWORD_ONLY:\n assert p.default is not P.empty\n kwonlyargs[p_name] = p.default\n\n if kwonlyargs:\n if not include_kwonlyargs:\n raise ValueError(\n 'Function has keyword-only parameters or annotations, either use '\n 'inspect.signature() API which can support them, or provide include_kwonlyargs=True '\n 'to this function'\n )\n else:\n args.extend(list(kwonlyargs))\n defaults.update(kwonlyargs)\n\n # harmonize defaults to how original getargspec returned them -- just a tuple\n args4[3] = None if not defaults else tuple(defaults.values())\n return ArgSpecFake(*args4)\n\n\ndef any_re_search(regexes, value):\n \"\"\"Return if any of regexes (list or str) searches successfully for value\"\"\"\n for regex in ensure_tuple_or_list(regexes):\n if re.search(regex, value):\n return True\n return False\n\n\ndef not_supported_on_windows(msg=None):\n \"\"\"A little helper to be invoked to consistently fail whenever functionality is\n not supported (yet) on Windows\n \"\"\"\n if on_windows:\n raise NotImplementedError(\"This functionality is not yet implemented for Windows OS\"\n + (\": %s\" % msg if msg else \"\"))\n\n\ndef get_home_envvars(new_home):\n \"\"\"Return dict with env variables to be adjusted for a new HOME\n\n Only variables found in current os.environ are adjusted.\n\n Parameters\n ----------\n new_home: str or Path\n New home path, in native to OS \"schema\"\n \"\"\"\n new_home = str(new_home)\n out = {'HOME': new_home}\n if on_windows:\n # requires special handling, since it has a number of relevant variables\n # and also Python changed its behavior and started to respect USERPROFILE only\n # since python 3.8: https://bugs.python.org/issue36264\n out['USERPROFILE'] = new_home\n out['HOMEDRIVE'], out['HOMEPATH'] = splitdrive(new_home)\n\n return {v: val for v, val in out.items() if v in os.environ}\n\n\ndef shortened_repr(value, l=30):\n try:\n if hasattr(value, '__repr__') and (value.__repr__ is not object.__repr__):\n value_repr = repr(value)\n if not value_repr.startswith('<') and len(value_repr) > l:\n value_repr = \"<<%s++%d chars++%s>>\" % (\n value_repr[:l - 16],\n len(value_repr) - (l - 16 + 4),\n value_repr[-4:]\n )\n elif value_repr.startswith('<') and value_repr.endswith('>') and ' object at 0x':\n raise ValueError(\"I hate those useless long reprs\")\n else:\n raise ValueError(\"gimme class\")\n except Exception as e:\n value_repr = \"<%s>\" % value.__class__.__name__.split('.')[-1]\n return value_repr\n\n\ndef __auto_repr__(obj):\n attr_names = tuple()\n if hasattr(obj, '__dict__'):\n attr_names += tuple(obj.__dict__.keys())\n if hasattr(obj, '__slots__'):\n attr_names += tuple(obj.__slots__)\n\n items = []\n for attr in sorted(set(attr_names)):\n if attr.startswith('_'):\n continue\n value = getattr(obj, attr)\n # TODO: should we add this feature to minimize some talktative reprs\n # such as of URL?\n #if value is None:\n # continue\n items.append(\"%s=%s\" % (attr, shortened_repr(value)))\n\n return \"%s(%s)\" % (obj.__class__.__name__, ', '.join(items))\n\n\ndef auto_repr(cls):\n \"\"\"Decorator for a class to assign it an automagic quick and dirty __repr__\n\n It uses public class attributes to prepare repr of a class\n\n Original idea: http://stackoverflow.com/a/27799004/1265472\n \"\"\"\n\n cls.__repr__ = __auto_repr__\n return cls\n\n\ndef _is_stream_tty(stream):\n try:\n # TODO: check on windows if hasattr check would work correctly and\n # add value:\n return stream.isatty()\n except ValueError as exc:\n # Who knows why it is a ValueError, but let's try to be specific\n # If there is a problem with I/O - non-interactive, otherwise reraise\n if \"I/O\" in str(exc):\n return False\n raise\n\n\ndef is_interactive():\n \"\"\"Return True if all in/outs are open and tty.\n\n Note that in a somewhat abnormal case where e.g. stdin is explicitly\n closed, and any operation on it would raise a\n `ValueError(\"I/O operation on closed file\")` exception, this function\n would just return False, since the session cannot be used interactively.\n \"\"\"\n return all(_is_stream_tty(s) for s in (sys.stdin, sys.stdout, sys.stderr))\n\n\ndef get_ipython_shell():\n \"\"\"Detect if running within IPython and returns its `ip` (shell) object\n\n Returns None if not under ipython (no `get_ipython` function)\n \"\"\"\n try:\n return get_ipython()\n except NameError:\n return None\n\n\ndef md5sum(filename):\n \"\"\"Compute an MD5 sum for the given file\n \"\"\"\n from datalad.support.digests import Digester\n return Digester(digests=['md5'])(filename)['md5']\n\n\n# unused in -core\ndef sorted_files(path):\n \"\"\"Return a (sorted) list of files under path\n \"\"\"\n return sorted(sum([[op.join(r, f)[len(path) + 1:] for f in files]\n for r, d, files in os.walk(path)\n if not '.git' in r], []))\n\n_encoded_dirsep = r'\\\\' if on_windows else r'/'\n_VCS_REGEX = r'%s\\.(?:git|gitattributes|svn|bzr|hg)(?:%s|$)' % (\n _encoded_dirsep, _encoded_dirsep)\n_DATALAD_REGEX = r'%s\\.(?:datalad)(?:%s|$)' % (\n _encoded_dirsep, _encoded_dirsep)\n\n\ndef find_files(regex, topdir=curdir, exclude=None, exclude_vcs=True, exclude_datalad=False, dirs=False):\n \"\"\"Generator to find files matching regex\n\n Parameters\n ----------\n regex: basestring\n exclude: basestring, optional\n Matches to exclude\n exclude_vcs:\n If True, excludes commonly known VCS subdirectories. If string, used\n as regex to exclude those files (regex: `%r`)\n exclude_datalad:\n If True, excludes files known to be datalad meta-data files (e.g. under\n .datalad/ subdirectory) (regex: `%r`)\n topdir: basestring, optional\n Directory where to search\n dirs: bool, optional\n Whether to match directories as well as files\n \"\"\"\n for dirpath, dirnames, filenames in os.walk(topdir):\n names = (dirnames + filenames) if dirs else filenames\n # TODO: might want to uniformize on windows to use '/'\n paths = (op.join(dirpath, name) for name in names)\n for path in filter(re.compile(regex).search, paths):\n path = path.rstrip(sep)\n if exclude and re.search(exclude, path):\n continue\n if exclude_vcs and re.search(_VCS_REGEX, path):\n continue\n if exclude_datalad and re.search(_DATALAD_REGEX, path):\n continue\n yield path\nfind_files.__doc__ %= (_VCS_REGEX, _DATALAD_REGEX)\n\n\ndef expandpath(path, force_absolute=True):\n \"\"\"Expand all variables and user handles in a path.\n\n By default return an absolute path\n \"\"\"\n path = expandvars(expanduser(path))\n if force_absolute:\n path = abspath(path)\n return path\n\n\ndef posix_relpath(path, start=None):\n \"\"\"Behave like os.path.relpath, but always return POSIX paths...\n\n on any platform.\"\"\"\n # join POSIX style\n return posixpath.join(\n # split and relpath native style\n # python2.7 ntpath implementation of relpath cannot handle start=None\n *split(\n relpath(path, start=start if start is not None else '')))\n\n\ndef is_explicit_path(path):\n \"\"\"Return whether a path explicitly points to a location\n\n Any absolute path, or relative path starting with either '../' or\n './' is assumed to indicate a location on the filesystem. Any other\n path format is not considered explicit.\"\"\"\n path = expandpath(path, force_absolute=False)\n return isabs(path) \\\n or path.startswith(os.curdir + os.sep) \\\n or path.startswith(os.pardir + os.sep)\n\n\n# handle this dance once, and import pathlib from here\n# in all other places\n\nfrom pathlib import (\n Path,\n PurePath,\n PurePosixPath,\n)\n\ndef rotree(path, ro=True, chmod_files=True):\n \"\"\"To make tree read-only or writable\n\n Parameters\n ----------\n path : string\n Path to the tree/directory to chmod\n ro : bool, optional\n Whether to make it R/O (default) or RW\n chmod_files : bool, optional\n Whether to operate also on files (not just directories)\n \"\"\"\n if ro:\n chmod = lambda f: os.chmod(f, os.stat(f).st_mode & ~stat.S_IWRITE)\n else:\n chmod = lambda f: os.chmod(f, os.stat(f).st_mode | stat.S_IWRITE | stat.S_IREAD)\n\n for root, dirs, files in os.walk(path, followlinks=False):\n if chmod_files:\n for f in files:\n fullf = op.join(root, f)\n # might be the \"broken\" symlink which would fail to stat etc\n if exists(fullf):\n chmod(fullf)\n chmod(root)\n\n\n# MASKED: rmtree function (lines 469-518)\n\n\ndef rmdir(path, *args, **kwargs):\n \"\"\"os.rmdir with our optional checking for open files\"\"\"\n assert_no_open_files(path)\n os.rmdir(path)\n\n\ndef get_open_files(path, log_open=False):\n \"\"\"Get open files under a path\n\n Note: This function is very slow on Windows.\n\n Parameters\n ----------\n path : str\n File or directory to check for open files under\n log_open : bool or int\n If set - logger level to use\n\n Returns\n -------\n dict\n path : pid\n\n \"\"\"\n # Original idea: https://stackoverflow.com/a/11115521/1265472\n import psutil\n files = {}\n # since the ones returned by psutil would not be aware of symlinks in the\n # path we should also get realpath for path\n # do absolute() in addition to always get an absolute path\n # even with non-existing paths on windows\n path = str(Path(path).resolve().absolute())\n for proc in psutil.process_iter():\n try:\n open_paths = [p.path for p in proc.open_files()] + [proc.cwd()]\n for p in open_paths:\n # note: could be done more efficiently so we do not\n # renormalize path over and over again etc\n if path_startswith(p, path):\n files[p] = proc\n # Catch a race condition where a process ends\n # before we can examine its files\n except psutil.NoSuchProcess:\n pass\n except psutil.AccessDenied:\n pass\n\n if files and log_open:\n lgr.log(log_open, \"Open files under %s: %s\", path, files)\n return files\n\n\n_assert_no_open_files_cfg = os.environ.get('DATALAD_ASSERT_NO_OPEN_FILES')\nif _assert_no_open_files_cfg:\n def assert_no_open_files(path):\n files = get_open_files(path, log_open=40)\n if _assert_no_open_files_cfg == 'assert':\n assert not files, \"Got following files still open: %s\" % ','.join(files)\n elif files:\n if _assert_no_open_files_cfg == 'pdb':\n import pdb\n pdb.set_trace()\n elif _assert_no_open_files_cfg == 'epdb':\n import epdb\n epdb.serve()\n pass\n # otherwise we would just issue that error message in the log\nelse:\n def assert_no_open_files(*args, **kwargs):\n pass\n\n\ndef rmtemp(f, *args, **kwargs):\n \"\"\"Wrapper to centralize removing of temp files so we could keep them around\n\n It will not remove the temporary file/directory if DATALAD_TESTS_TEMP_KEEP\n environment variable is defined\n \"\"\"\n if not os.environ.get('DATALAD_TESTS_TEMP_KEEP'):\n if not os.path.lexists(f):\n lgr.debug(\"Path %s does not exist, so can't be removed\", f)\n return\n lgr.log(5, \"Removing temp file: %s\", f)\n # Can also be a directory\n if isdir(f):\n rmtree(f, *args, **kwargs)\n else:\n unlink(f)\n else:\n lgr.info(\"Keeping temp file: %s\", f)\n\n\ndef file_basename(name, return_ext=False):\n \"\"\"\n Strips up to 2 extensions of length up to 4 characters and starting with alpha\n not a digit, so we could get rid of .tar.gz etc\n \"\"\"\n bname = basename(name)\n fbname = re.sub(r'(\\.[a-zA-Z_]\\S{1,4}){0,2}$', '', bname)\n if return_ext:\n return fbname, bname[len(fbname) + 1:]\n else:\n return fbname\n\n\n# unused in -core\ndef escape_filename(filename):\n \"\"\"Surround filename in \"\" and escape \" in the filename\n \"\"\"\n filename = filename.replace('\"', r'\\\"').replace('`', r'\\`')\n filename = '\"%s\"' % filename\n return filename\n\n\n# unused in -core\ndef encode_filename(filename):\n \"\"\"Encode unicode filename\n \"\"\"\n if isinstance(filename, str):\n return filename.encode(sys.getfilesystemencoding())\n else:\n return filename\n\n\n# unused in -core\ndef decode_input(s):\n \"\"\"Given input string/bytes, decode according to stdin codepage (or UTF-8)\n if not defined\n\n If fails -- issue warning and decode allowing for errors\n being replaced\n \"\"\"\n if isinstance(s, str):\n return s\n else:\n encoding = sys.stdin.encoding or 'UTF-8'\n try:\n return s.decode(encoding)\n except UnicodeDecodeError as exc:\n lgr.warning(\n \"Failed to decode input string using %s encoding. \"\n \"Decoding allowing for errors\", encoding)\n return s.decode(encoding, errors='replace')\n\n\n# unused in -core\nif on_windows:\n def lmtime(filepath, mtime):\n \"\"\"Set mtime for files. On Windows a merely adapter to os.utime\n \"\"\"\n os.utime(filepath, (time.time(), mtime))\nelse:\n def lmtime(filepath, mtime):\n \"\"\"Set mtime for files, while not de-referencing symlinks.\n\n To overcome absence of os.lutime\n\n Works only on linux and OSX ATM\n \"\"\"\n from .cmd import WitlessRunner\n # convert mtime to format touch understands [[CC]YY]MMDDhhmm[.SS]\n smtime = time.strftime(\"%Y%m%d%H%M.%S\", time.localtime(mtime))\n lgr.log(3, \"Setting mtime for %s to %s == %s\", filepath, mtime, smtime)\n WitlessRunner().run(['touch', '-h', '-t', '%s' % smtime, filepath])\n filepath = Path(filepath)\n rfilepath = filepath.resolve()\n if filepath.is_symlink() and rfilepath.exists():\n # trust no one - adjust also of the target file\n # since it seemed like downloading under OSX (was it using curl?)\n # didn't bother with timestamps\n lgr.log(3, \"File is a symlink to %s Setting mtime for it to %s\",\n rfilepath, mtime)\n os.utime(str(rfilepath), (time.time(), mtime))\n # doesn't work on OSX\n # Runner().run(['touch', '-h', '-d', '@%s' % mtime, filepath])\n\n\ndef ensure_tuple_or_list(obj):\n \"\"\"Given an object, wrap into a tuple if not list or tuple\n \"\"\"\n if isinstance(obj, (list, tuple)):\n return obj\n return (obj,)\n\n\ndef ensure_iter(s, cls, copy=False, iterate=True):\n \"\"\"Given not a list, would place it into a list. If None - empty list is returned\n\n Parameters\n ----------\n s: list or anything\n cls: class\n Which iterable class to ensure\n copy: bool, optional\n If correct iterable is passed, it would generate its shallow copy\n iterate: bool, optional\n If it is not a list, but something iterable (but not a str)\n iterate over it.\n \"\"\"\n\n if isinstance(s, cls):\n return s if not copy else shallow_copy(s)\n elif isinstance(s, str):\n return cls((s,))\n elif iterate and hasattr(s, '__iter__'):\n return cls(s)\n elif s is None:\n return cls()\n else:\n return cls((s,))\n\n\ndef ensure_list(s, copy=False, iterate=True):\n \"\"\"Given not a list, would place it into a list. If None - empty list is returned\n\n Parameters\n ----------\n s: list or anything\n copy: bool, optional\n If list is passed, it would generate a shallow copy of the list\n iterate: bool, optional\n If it is not a list, but something iterable (but not a str)\n iterate over it.\n \"\"\"\n return ensure_iter(s, list, copy=copy, iterate=iterate)\n\n\ndef ensure_list_from_str(s, sep='\\n'):\n \"\"\"Given a multiline string convert it to a list of return None if empty\n\n Parameters\n ----------\n s: str or list\n \"\"\"\n\n if not s:\n return None\n\n if isinstance(s, list):\n return s\n return s.split(sep)\n\n\ndef ensure_dict_from_str(s, **kwargs):\n \"\"\"Given a multiline string with key=value items convert it to a dictionary\n\n Parameters\n ----------\n s: str or dict\n\n Returns None if input s is empty\n \"\"\"\n\n if not s:\n return None\n\n if isinstance(s, dict):\n return s\n\n out = {}\n for value_str in ensure_list_from_str(s, **kwargs):\n if '=' not in value_str:\n raise ValueError(\"{} is not in key=value format\".format(repr(value_str)))\n k, v = value_str.split('=', 1)\n if k in out:\n err = \"key {} was already defined in {}, but new value {} was provided\".format(k, out, v)\n raise ValueError(err)\n out[k] = v\n return out\n\n\ndef ensure_bytes(s, encoding='utf-8'):\n \"\"\"Convert/encode unicode string to bytes.\n\n If `s` isn't a string, return it as is.\n\n Parameters\n ----------\n encoding: str, optional\n Encoding to use. \"utf-8\" is the default\n \"\"\"\n if not isinstance(s, str):\n return s\n return s.encode(encoding)\n\n\ndef ensure_unicode(s, encoding=None, confidence=None):\n \"\"\"Convert/decode bytestring to unicode.\n\n If `s` isn't a bytestring, return it as is.\n\n Parameters\n ----------\n encoding: str, optional\n Encoding to use. If None, \"utf-8\" is tried, and then if not a valid\n UTF-8, encoding will be guessed\n confidence: float, optional\n A value between 0 and 1, so if guessing of encoding is of lower than\n specified confidence, ValueError is raised\n \"\"\"\n if not isinstance(s, bytes):\n return s\n if encoding is None:\n # Figure out encoding, defaulting to 'utf-8' which is our common\n # target in contemporary digital society\n try:\n return s.decode('utf-8')\n except UnicodeDecodeError as exc:\n lgr.debug(\"Failed to decode a string as utf-8: %s\",\n CapturedException(exc))\n # And now we could try to guess\n from chardet import detect\n enc = detect(s)\n denc = enc.get('encoding', None)\n if denc:\n denc_confidence = enc.get('confidence', 0)\n if confidence is not None and denc_confidence < confidence:\n raise ValueError(\n \"Failed to auto-detect encoding with high enough \"\n \"confidence. Highest confidence was %s for %s\"\n % (denc_confidence, denc)\n )\n lgr.log(5, \"Auto-detected encoding to be %s\", denc)\n return s.decode(denc)\n else:\n raise ValueError(\n \"Could not decode value as utf-8, or to guess its encoding: %s\"\n % repr(s)\n )\n else:\n return s.decode(encoding)\n\n\ndef ensure_bool(s):\n \"\"\"Convert value into boolean following convention for strings\n\n to recognize on,True,yes as True, off,False,no as False\n \"\"\"\n if isinstance(s, str):\n if s.isdigit():\n return bool(int(s))\n sl = s.lower()\n if sl in {'y', 'yes', 'true', 'on'}:\n return True\n elif sl in {'n', 'no', 'false', 'off'}:\n return False\n else:\n raise ValueError(\"Do not know how to treat %r as a boolean\" % s)\n return bool(s)\n\n\ndef as_unicode(val, cast_types=object):\n \"\"\"Given an arbitrary value, would try to obtain unicode value of it\n \n For unicode it would return original value, for python2 str or python3\n bytes it would use ensure_unicode, for None - an empty (unicode) string,\n and for any other type (see `cast_types`) - would apply the unicode \n constructor. If value is not an instance of `cast_types`, TypeError\n is thrown\n \n Parameters\n ----------\n cast_types: type\n Which types to cast to unicode by providing to constructor\n \"\"\"\n if val is None:\n return u''\n elif isinstance(val, str):\n return val\n elif isinstance(val, unicode_srctypes):\n return ensure_unicode(val)\n elif isinstance(val, cast_types):\n return str(val)\n else:\n raise TypeError(\n \"Value %r is not of any of known or provided %s types\"\n % (val, cast_types))\n\n\ndef unique(seq, key=None, reverse=False):\n \"\"\"Given a sequence return a list only with unique elements while maintaining order\n\n This is the fastest solution. See\n https://www.peterbe.com/plog/uniqifiers-benchmark\n and\n http://stackoverflow.com/a/480227/1265472\n for more information.\n Enhancement -- added ability to compare for uniqueness using a key function\n\n Parameters\n ----------\n seq:\n Sequence to analyze\n key: callable, optional\n Function to call on each element so we could decide not on a full\n element, but on its member etc\n reverse: bool, optional\n If True, uniqueness checked in the reverse order, so that the later ones\n will take the order\n \"\"\"\n seen = set()\n seen_add = seen.add\n\n trans = reversed if reverse else lambda x: x\n\n if not key:\n out = [x for x in trans(seq) if not (x in seen or seen_add(x))]\n else:\n # OPT: could be optimized, since key is called twice, but for our cases\n # should be just as fine\n out = [x for x in trans(seq) if not (key(x) in seen or seen_add(key(x)))]\n\n return out[::-1] if reverse else out\n\n\ndef all_same(items):\n \"\"\"Quick check if all items are the same.\n\n Identical to a check like len(set(items)) == 1 but\n should be more efficient while working on generators, since would\n return False as soon as any difference detected thus possibly avoiding\n unnecessary evaluations\n \"\"\"\n first = True\n first_item = None\n for item in items:\n if first:\n first = False\n first_item = item\n else:\n if item != first_item:\n return False\n # So we return False if was empty\n return not first\n\n\ndef map_items(func, v):\n \"\"\"A helper to apply `func` to all elements (keys and values) within dict\n\n No type checking of values passed to func is done, so `func`\n should be resilient to values which it should not handle\n\n Initial usecase - apply_recursive(url_fragment, ensure_unicode)\n \"\"\"\n # map all elements within item\n return v.__class__(\n item.__class__(map(func, item))\n for item in v.items()\n )\n\n\ndef partition(items, predicate=bool):\n \"\"\"Partition `items` by `predicate`.\n\n Parameters\n ----------\n items : iterable\n predicate : callable\n A function that will be mapped over each element in `items`. The\n elements will partitioned based on whether the return value is false or\n true.\n\n Returns\n -------\n A tuple with two generators, the first for 'false' items and the second for\n 'true' ones.\n\n Notes\n -----\n Taken from Peter Otten's snippet posted at\n https://nedbatchelder.com/blog/201306/filter_a_list_into_two_parts.html\n \"\"\"\n a, b = tee((predicate(item), item) for item in items)\n return ((item for pred, item in a if not pred),\n (item for pred, item in b if pred))\n\n\ndef generate_chunks(container, size):\n \"\"\"Given a container, generate chunks from it with size up to `size`\n \"\"\"\n # There could be a \"smarter\" solution but I think this would suffice\n assert size > 0, \"Size should be non-0 positive\"\n while container:\n yield container[:size]\n container = container[size:]\n\n\ndef generate_file_chunks(files, cmd=None):\n \"\"\"Given a list of files, generate chunks of them to avoid exceeding cmdline length\n\n Parameters\n ----------\n files: list of str\n cmd: str or list of str, optional\n Command to account for as well\n \"\"\"\n files = ensure_list(files)\n cmd = ensure_list(cmd)\n\n maxl = max(map(len, files)) if files else 0\n chunk_size = max(\n 1, # should at least be 1. If blows then - not our fault\n (CMD_MAX_ARG\n - sum((len(x) + 3) for x in cmd)\n - 4 # for '--' below\n ) // (maxl + 3) # +3 for possible quotes and a space\n )\n # TODO: additional treatment for \"too many arguments\"? although\n # as https://github.com/datalad/datalad/issues/1883#issuecomment\n # -436272758\n # shows there seems to be no hardcoded limit on # of arguments,\n # but may be we decide to go for smth like follow to be on safe side\n # chunk_size = min(10240 - len(cmd), chunk_size)\n file_chunks = generate_chunks(files, chunk_size)\n return file_chunks\n\n\n#\n# Generators helpers\n#\n\ndef saved_generator(gen):\n \"\"\"Given a generator returns two generators, where 2nd one just replays\n\n So the first one would be going through the generated items and 2nd one\n would be yielding saved items\n \"\"\"\n saved = []\n\n def gen1():\n for x in gen: # iterating over original generator\n saved.append(x)\n yield x\n\n def gen2():\n for x in saved: # yielding saved entries\n yield x\n\n return gen1(), gen2()\n\n\n#\n# Decorators\n#\n\n# Originally better_wraps was created to provide `wrapt`-based, instead of\n# `functools.wraps` implementation to preserve the correct signature of the\n# decorated function. By using inspect.signature in our getargspec, which\n# works fine on `functools.wraps`ed functions, we mediated this necessity.\nbetter_wraps = wraps\n\n\n# Borrowed from pandas\n# Copyright: 2011-2014, Lambda Foundry, Inc. and PyData Development Team\n# License: BSD-3\ndef optional_args(decorator):\n \"\"\"allows a decorator to take optional positional and keyword arguments.\n Assumes that taking a single, callable, positional argument means that\n it is decorating a function, i.e. something like this::\n\n @my_decorator\n def function(): pass\n\n Calls decorator with decorator(f, `*args`, `**kwargs`)\"\"\"\n\n @better_wraps(decorator)\n def wrapper(*args, **kwargs):\n def dec(f):\n return decorator(f, *args, **kwargs)\n\n is_decorating = not kwargs and len(args) == 1 and isinstance(args[0], Callable)\n if is_decorating:\n f = args[0]\n args = []\n return dec(f)\n else:\n return dec\n\n return wrapper\n\n\n# TODO: just provide decorators for tempfile.mk* functions. This is ugly!\ndef get_tempfile_kwargs(tkwargs=None, prefix=\"\", wrapped=None):\n \"\"\"Updates kwargs to be passed to tempfile. calls depending on env vars\n \"\"\"\n if tkwargs is None:\n tkwargs_ = {}\n else:\n # operate on a copy of tkwargs to avoid any side-effects\n tkwargs_ = tkwargs.copy()\n\n # TODO: don't remember why I had this one originally\n # if len(targs)<2 and \\\n if 'prefix' not in tkwargs_:\n tkwargs_['prefix'] = '_'.join(\n ['datalad_temp'] +\n ([prefix] if prefix else []) +\n ([''] if (on_windows or not wrapped) else [wrapped.__name__]))\n\n directory = os.environ.get('TMPDIR')\n if directory and 'dir' not in tkwargs_:\n tkwargs_['dir'] = directory\n\n return tkwargs_\n\n\n@optional_args\ndef line_profile(func):\n \"\"\"Q&D helper to line profile the function and spit out stats\n \"\"\"\n import line_profiler\n prof = line_profiler.LineProfiler()\n\n @wraps(func)\n def _wrap_line_profile(*args, **kwargs):\n try:\n pfunc = prof(func)\n return pfunc(*args, **kwargs)\n finally:\n prof.print_stats()\n return _wrap_line_profile\n\n\n# unused in -core\n@optional_args\ndef collect_method_callstats(func):\n \"\"\"Figure out methods which call the method repeatedly on the same instance\n\n Use case(s):\n - .repo is expensive since does all kinds of checks.\n - .config is expensive transitively since it calls .repo each time\n\n TODO:\n - fancy one could look through the stack for the same id(self) to see if\n that location is already in memo. That would hint to the cases where object\n is not passed into underlying functions, causing them to redo the same work\n over and over again\n - ATM might flood with all \"1 lines\" calls which are not that informative.\n The underlying possibly suboptimal use might be coming from their callers.\n It might or not relate to the previous TODO\n \"\"\"\n from collections import defaultdict\n import traceback\n from time import time\n memo = defaultdict(lambda: defaultdict(int)) # it will be a dict of lineno: count\n # gross timing\n times = []\n toppath = dirname(__file__) + sep\n\n @wraps(func)\n def _wrap_collect_method_callstats(*args, **kwargs):\n try:\n self = args[0]\n stack = traceback.extract_stack()\n caller = stack[-2]\n stack_sig = \\\n \"{relpath}:{s.name}\".format(\n s=caller, relpath=relpath(caller.filename, toppath))\n sig = (id(self), stack_sig)\n # we will count based on id(self) + wherefrom\n memo[sig][caller.lineno] += 1\n t0 = time()\n return func(*args, **kwargs)\n finally:\n times.append(time() - t0)\n pass\n\n def print_stats():\n print(\"The cost of property {}:\".format(func.__name__))\n if not memo:\n print(\"None since no calls\")\n return\n # total count\n counts = {k: sum(v.values()) for k,v in memo.items()}\n total = sum(counts.values())\n ids = {self_id for (self_id, _) in memo}\n print(\" Total: {} calls from {} objects with {} contexts taking {:.2f} sec\"\n .format(total, len(ids), len(memo), sum(times)))\n # now we need to sort by value\n for (self_id, caller), count in sorted(counts.items(), key=lambda x: x[1], reverse=True):\n print(\" {} {}: {} from {} lines\"\n .format(self_id, caller, count, len(memo[(self_id, caller)])))\n\n # Upon total exit we print the stats\n import atexit\n atexit.register(print_stats)\n\n return _wrap_collect_method_callstats\n\n\n# Borrowed from duecredit to wrap duecredit-handling to guarantee failsafe\ndef never_fail(f):\n \"\"\"Assure that function never fails -- all exceptions are caught\n\n Returns `None` if function fails internally.\n \"\"\"\n @wraps(f)\n def wrapped_func(*args, **kwargs):\n try:\n return f(*args, **kwargs)\n except Exception as e:\n lgr.warning(\n \"DataLad internal failure while running %s: %r. \"\n \"Please report at https://github.com/datalad/datalad/issues\"\n % (f, e)\n )\n\n if os.environ.get('DATALAD_ALLOW_FAIL', False):\n return f\n else:\n return wrapped_func\n\n\n#\n# Context Managers\n#\n\n\n# unused in -core\n@contextmanager\ndef nothing_cm():\n \"\"\"Just a dummy cm to programmically switch context managers\"\"\"\n yield\n\n\n@contextmanager\ndef swallow_outputs():\n \"\"\"Context manager to help consuming both stdout and stderr, and print()\n\n stdout is available as cm.out and stderr as cm.err whenever cm is the\n yielded context manager.\n Internally uses temporary files to guarantee absent side-effects of swallowing\n into StringIO which lacks .fileno.\n\n print mocking is necessary for some uses where sys.stdout was already bound\n to original sys.stdout, thus mocking it later had no effect. Overriding\n print function had desired effect\n \"\"\"\n\n class StringIOAdapter(object):\n \"\"\"Little adapter to help getting out/err values\n \"\"\"\n def __init__(self):\n kw = get_tempfile_kwargs({}, prefix=\"outputs\")\n\n self._out = NamedTemporaryFile(delete=False, mode='w', **kw)\n self._err = NamedTemporaryFile(delete=False, mode='w', **kw)\n\n def _read(self, h):\n with open(h.name) as f:\n return f.read()\n\n @property\n def out(self):\n if not self._out.closed:\n self._out.flush()\n return self._read(self._out)\n\n @property\n def err(self):\n if not self._err.closed:\n self._err.flush()\n return self._read(self._err)\n\n @property\n def handles(self):\n return self._out, self._err\n\n def cleanup(self):\n self._out.close()\n self._err.close()\n out_name = self._out.name\n err_name = self._err.name\n from datalad import cfg\n if cfg.getbool('datalad.log', 'outputs', default=False) \\\n and lgr.getEffectiveLevel() <= logging.DEBUG:\n for s, sname in ((self.out, 'stdout'),\n (self.err, 'stderr')):\n if s:\n pref = os.linesep + \"| \"\n lgr.debug(\"Swallowed %s:%s%s\", sname, pref, s.replace(os.linesep, pref))\n else:\n lgr.debug(\"Nothing was swallowed for %s\", sname)\n del self._out\n del self._err\n gc.collect()\n rmtemp(out_name)\n rmtemp(err_name)\n\n def fake_print(*args, **kwargs):\n sep = kwargs.pop('sep', ' ')\n end = kwargs.pop('end', '\\n')\n file = kwargs.pop('file', sys.stdout)\n\n if file in (oldout, olderr, sys.stdout, sys.stderr):\n # we mock\n try:\n sys.stdout.write(sep.join(args) + end)\n except UnicodeEncodeError as exc:\n lgr.error(\n \"Failed to write to mocked stdout, got %s, continue as it \"\n \"didn't happen\", exc)\n else:\n # must be some other file one -- leave it alone\n oldprint(*args, sep=sep, end=end, file=file)\n\n from .ui import ui\n # preserve -- they could have been mocked already\n oldprint = getattr(builtins, 'print')\n oldout, olderr = sys.stdout, sys.stderr\n olduiout = ui.out\n adapter = StringIOAdapter()\n\n try:\n sys.stdout, sys.stderr = adapter.handles\n ui.out = adapter.handles[0]\n setattr(builtins, 'print', fake_print)\n\n yield adapter\n finally:\n sys.stdout, sys.stderr, ui.out = oldout, olderr, olduiout\n setattr(builtins, 'print', oldprint)\n adapter.cleanup()\n\n\n@contextmanager\ndef swallow_logs(new_level=None, file_=None, name='datalad'):\n \"\"\"Context manager to consume all logs.\n\n \"\"\"\n lgr = logging.getLogger(name)\n\n # Keep old settings\n old_level = lgr.level\n old_handlers = lgr.handlers\n\n # Let's log everything into a string\n # TODO: generalize with the one for swallow_outputs\n class StringIOAdapter(object):\n \"\"\"Little adapter to help getting out values\n\n And to stay consistent with how swallow_outputs behaves\n \"\"\"\n def __init__(self):\n if file_ is None:\n kw = get_tempfile_kwargs({}, prefix=\"logs\")\n self._out = NamedTemporaryFile(mode='a', delete=False, **kw)\n else:\n out_file = file_\n # PY3 requires clearly one or another. race condition possible\n self._out = open(out_file, 'a')\n self._final_out = None\n\n def _read(self, h):\n with open(h.name) as f:\n return f.read()\n\n @property\n def out(self):\n if self._final_out is not None:\n # we closed and cleaned up already\n return self._final_out\n else:\n self._out.flush()\n return self._read(self._out)\n\n @property\n def lines(self):\n return self.out.split('\\n')\n\n @property\n def handle(self):\n return self._out\n\n def cleanup(self):\n # store for access while object exists\n self._final_out = self.out\n self._out.close()\n out_name = self._out.name\n del self._out\n gc.collect()\n if not file_:\n rmtemp(out_name)\n\n def assert_logged(self, msg=None, level=None, regex=True, **kwargs):\n \"\"\"Provide assertion on whether a msg was logged at a given level\n\n If neither `msg` nor `level` provided, checks if anything was logged\n at all.\n\n Parameters\n ----------\n msg: str, optional\n Message (as a regular expression, if `regex`) to be searched.\n If no msg provided, checks if anything was logged at a given level.\n level: str, optional\n String representing the level to be logged\n regex: bool, optional\n If False, regular `assert_in` is used\n **kwargs: str, optional\n Passed to `assert_re_in` or `assert_in`\n \"\"\"\n from datalad.tests.utils import assert_re_in\n from datalad.tests.utils import assert_in\n\n if regex:\n match = r'\\[%s\\] ' % level if level else r\"\\[\\S+\\] \"\n else:\n match = '[%s] ' % level if level else ''\n\n if msg:\n match += msg\n\n if match:\n (assert_re_in if regex else assert_in)(match, self.out, **kwargs)\n else:\n assert not kwargs, \"no kwargs to be passed anywhere\"\n assert self.out, \"Nothing was logged!?\"\n\n adapter = StringIOAdapter()\n # TODO: it does store messages but without any formatting, i.e. even without\n # date/time prefix etc. IMHO it should preserve formatting in case if file_ is\n # set\n swallow_handler = logging.StreamHandler(adapter.handle)\n # we want to log levelname so we could test against it\n swallow_handler.setFormatter(\n logging.Formatter('[%(levelname)s] %(message)s'))\n swallow_handler.filters = sum([h.filters for h in old_handlers],\n [])\n lgr.handlers = [swallow_handler]\n if old_level < logging.DEBUG: # so if HEAVYDEBUG etc -- show them!\n lgr.handlers += old_handlers\n\n if isinstance(new_level, str):\n new_level = getattr(logging, new_level)\n\n if new_level is not None:\n lgr.setLevel(new_level)\n\n try:\n yield adapter\n # TODO: if file_ and there was an exception -- most probably worth logging it?\n # although ideally it should be the next log outside added to that file_ ... oh well\n finally:\n lgr.handlers = old_handlers\n lgr.setLevel(old_level)\n adapter.cleanup()\n\n\n# TODO: May be melt in with swallow_logs at some point:\n@contextmanager\ndef disable_logger(logger=None):\n \"\"\"context manager to temporarily disable logging\n\n This is to provide one of swallow_logs' purposes without unnecessarily\n creating temp files (see gh-1865)\n\n Parameters\n ----------\n logger: Logger\n Logger whose handlers will be ordered to not log anything.\n Default: datalad's topmost Logger ('datalad')\n \"\"\"\n\n class NullFilter(logging.Filter):\n \"\"\"Filter class to reject all records\n \"\"\"\n def filter(self, record):\n return 0\n\n if logger is None:\n # default: all of datalad's logging:\n logger = logging.getLogger('datalad')\n\n filter_ = NullFilter(logger.name)\n [h.addFilter(filter_) for h in logger.handlers]\n\n try:\n yield logger\n finally:\n [h.removeFilter(filter_) for h in logger.handlers]\n\n\n#\n# Additional handlers\n#\n_sys_excepthook = sys.excepthook # Just in case we ever need original one\n\n\ndef setup_exceptionhook(ipython=False):\n \"\"\"Overloads default sys.excepthook with our exceptionhook handler.\n\n If interactive, our exceptionhook handler will invoke\n pdb.post_mortem; if not interactive, then invokes default handler.\n \"\"\"\n\n def _datalad_pdb_excepthook(type, value, tb):\n import traceback\n traceback.print_exception(type, value, tb)\n print()\n if is_interactive():\n import pdb\n pdb.post_mortem(tb)\n\n if ipython:\n from IPython.core import ultratb\n sys.excepthook = ultratb.FormattedTB(mode='Verbose',\n # color_scheme='Linux',\n call_pdb=is_interactive())\n else:\n sys.excepthook = _datalad_pdb_excepthook\n\n\ndef ensure_dir(*args):\n \"\"\"Make sure directory exists.\n\n Joins the list of arguments to an os-specific path to the desired\n directory and creates it, if it not exists yet.\n \"\"\"\n dirname = op.join(*args)\n if not exists(dirname):\n os.makedirs(dirname)\n return dirname\n\n\ndef updated(d, update):\n \"\"\"Return a copy of the input with the 'update'\n\n Primarily for updating dictionaries\n \"\"\"\n d = d.copy()\n d.update(update)\n return d\n\n\n_pwd_mode = None\n\n\ndef _switch_to_getcwd(msg, *args):\n global _pwd_mode\n _pwd_mode = 'cwd'\n lgr.debug(\n msg + \". From now on will be returning os.getcwd(). Directory\"\n \" symlinks in the paths will be resolved\",\n *args\n )\n # TODO: we might want to mitigate by going through all flywheighted\n # repos and tuning up their .paths to be resolved?\n\n\ndef getpwd():\n \"\"\"Try to return a CWD without dereferencing possible symlinks\n\n This function will try to use PWD environment variable to provide a current\n working directory, possibly with some directories along the path being\n symlinks to other directories. Unfortunately, PWD is used/set only by the\n shell and such functions as `os.chdir` and `os.getcwd` nohow use or modify\n it, thus `os.getcwd()` returns path with links dereferenced.\n\n While returning current working directory based on PWD env variable we\n verify that the directory is the same as `os.getcwd()` after resolving all\n symlinks. If that verification fails, we fall back to always use\n `os.getcwd()`.\n\n Initial decision to either use PWD env variable or os.getcwd() is done upon\n the first call of this function.\n \"\"\"\n global _pwd_mode\n if _pwd_mode is None:\n # we need to decide!\n try:\n pwd = os.environ['PWD']\n if on_windows and pwd and pwd.startswith('/'):\n # It should be a path from MSYS.\n # - it might start with a drive letter or not\n # - it seems to be \"illegal\" to have a single letter directories\n # under / path, i.e. if created - they aren't found\n # - 'ln -s' does not fail to create a \"symlink\" but it just\n # copies!\n # so we are not likely to need original PWD purpose on\n # those systems\n # Verdict:\n _pwd_mode = 'cwd'\n else:\n _pwd_mode = 'PWD'\n except KeyError:\n _pwd_mode = 'cwd'\n\n if _pwd_mode == 'cwd':\n return os.getcwd()\n elif _pwd_mode == 'PWD':\n try:\n cwd = os.getcwd()\n except OSError as exc:\n if \"o such file\" in str(exc):\n # directory was removed but we promised to be robust and\n # still report the path we might know since we are still in PWD\n # mode\n cwd = None\n else:\n raise\n try:\n pwd = os.environ['PWD']\n # do absolute() in addition to always get an absolute path\n # even with non-existing paths on windows\n pwd_real = str(Path(pwd).resolve().absolute())\n # This logic would fail to catch the case where chdir did happen\n # to the directory where current PWD is pointing to, e.g.\n # $> ls -ld $PWD\n # lrwxrwxrwx 1 yoh yoh 5 Oct 11 13:27 /home/yoh/.tmp/tmp -> /tmp//\n # hopa:~/.tmp/tmp\n # $> python -c 'import os; os.chdir(\"/tmp\"); from datalad.utils import getpwd; print(getpwd(), os.getcwd())'\n # ('/home/yoh/.tmp/tmp', '/tmp')\n # but I guess that should not be too harmful\n if cwd is not None and pwd_real != cwd:\n _switch_to_getcwd(\n \"realpath of PWD=%s is %s whenever os.getcwd()=%s\",\n pwd, pwd_real, cwd\n )\n return cwd\n return pwd\n except KeyError:\n _switch_to_getcwd(\"PWD env variable is no longer available\")\n return cwd # Must not happen, but may be someone\n # evil purges PWD from environ?\n else:\n raise RuntimeError(\n \"Must have not got here. \"\n \"pwd_mode must be either cwd or PWD. And it is now %r\" % (_pwd_mode,)\n )\n\n\nclass chpwd(object):\n \"\"\"Wrapper around os.chdir which also adjusts environ['PWD']\n\n The reason is that otherwise PWD is simply inherited from the shell\n and we have no ability to assess directory path without dereferencing\n symlinks.\n\n If used as a context manager it allows to temporarily change directory\n to the given path\n \"\"\"\n def __init__(self, path, mkdir=False, logsuffix=''):\n\n if path:\n pwd = getpwd()\n self._prev_pwd = pwd\n else:\n self._prev_pwd = None\n return\n\n if not isabs(path):\n path = normpath(op.join(pwd, path))\n if not os.path.exists(path) and mkdir:\n self._mkdir = True\n os.mkdir(path)\n else:\n self._mkdir = False\n lgr.debug(\"chdir %r -> %r %s\", self._prev_pwd, path, logsuffix)\n os.chdir(path) # for grep people -- ok, to chdir here!\n os.environ['PWD'] = str(path)\n\n def __enter__(self):\n # nothing more to do really, chdir was in the constructor\n pass\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n if self._prev_pwd:\n # Need to use self.__class__ so this instance, if the entire\n # thing mocked during the test, still would use correct chpwd\n self.__class__(self._prev_pwd, logsuffix=\"(coming back)\")\n\n\ndef dlabspath(path, norm=False):\n \"\"\"Symlinks-in-the-cwd aware abspath\n\n os.path.abspath relies on os.getcwd() which would not know about symlinks\n in the path\n\n TODO: we might want to norm=True by default to match behavior of\n os .path.abspath?\n \"\"\"\n if not isabs(path):\n # if not absolute -- relative to pwd\n path = op.join(getpwd(), path)\n return normpath(path) if norm else path\n\n\ndef with_pathsep(path):\n \"\"\"Little helper to guarantee that path ends with /\"\"\"\n return path + sep if not path.endswith(sep) else path\n\n\ndef get_path_prefix(path, pwd=None):\n \"\"\"Get path prefix (for current directory)\n\n Returns relative path to the topdir, if we are under topdir, and if not\n absolute path to topdir. If `pwd` is not specified - current directory\n assumed\n \"\"\"\n pwd = pwd or getpwd()\n path = dlabspath(path)\n path_ = with_pathsep(path)\n pwd_ = with_pathsep(pwd)\n common = commonprefix((path_, pwd_))\n if common.endswith(sep) and common in {path_, pwd_}:\n # we are in subdir or above the path = use relative path\n location_prefix = relpath(path, pwd)\n # if benign \"here\" - cut off\n if location_prefix in (curdir, curdir + sep):\n location_prefix = ''\n return location_prefix\n else:\n # just return absolute path\n return path\n\n\ndef _get_normalized_paths(path, prefix):\n if isabs(path) != isabs(prefix):\n raise ValueError(\"Both paths must either be absolute or relative. \"\n \"Got %r and %r\" % (path, prefix))\n path = with_pathsep(path)\n prefix = with_pathsep(prefix)\n return path, prefix\n\n\ndef path_startswith(path, prefix):\n \"\"\"Return True if path starts with prefix path\n\n Parameters\n ----------\n path: str\n prefix: str\n \"\"\"\n path, prefix = _get_normalized_paths(path, prefix)\n return path.startswith(prefix)\n\n\ndef path_is_subpath(path, prefix):\n \"\"\"Return True if path is a subpath of prefix\n\n It will return False if path == prefix.\n\n Parameters\n ----------\n path: str\n prefix: str\n \"\"\"\n path, prefix = _get_normalized_paths(path, prefix)\n return (len(prefix) < len(path)) and path.startswith(prefix)\n\n\ndef knows_annex(path):\n \"\"\"Returns whether at a given path there is information about an annex\n\n It is just a thin wrapper around GitRepo.is_with_annex() classmethod\n which also checks for `path` to exist first.\n\n This includes actually present annexes, but also uninitialized ones, or\n even the presence of a remote annex branch.\n \"\"\"\n from os.path import exists\n if not exists(path):\n lgr.debug(\"No annex: test path {0} doesn't exist\".format(path))\n return False\n from datalad.support.gitrepo import GitRepo\n return GitRepo(path, init=False, create=False).is_with_annex()\n\n\n@contextmanager\ndef make_tempfile(content=None, wrapped=None, **tkwargs):\n \"\"\"Helper class to provide a temporary file name and remove it at the end (context manager)\n\n Parameters\n ----------\n mkdir : bool, optional (default: False)\n If True, temporary directory created using tempfile.mkdtemp()\n content : str or bytes, optional\n Content to be stored in the file created\n wrapped : function, optional\n If set, function name used to prefix temporary file name\n `**tkwargs`:\n All other arguments are passed into the call to tempfile.mk{,d}temp(),\n and resultant temporary filename is passed as the first argument into\n the function t. If no 'prefix' argument is provided, it will be\n constructed using module and function names ('.' replaced with\n '_').\n\n To change the used directory without providing keyword argument 'dir' set\n DATALAD_TESTS_TEMP_DIR.\n\n Examples\n --------\n >>> from os.path import exists\n >>> from datalad.utils import make_tempfile\n >>> with make_tempfile() as fname:\n ... k = open(fname, 'w').write('silly test')\n >>> assert not exists(fname) # was removed\n\n >>> with make_tempfile(content=\"blah\") as fname:\n ... assert open(fname).read() == \"blah\"\n \"\"\"\n\n if tkwargs.get('mkdir', None) and content is not None:\n raise ValueError(\"mkdir=True while providing content makes no sense\")\n\n tkwargs_ = get_tempfile_kwargs(tkwargs, wrapped=wrapped)\n\n # if DATALAD_TESTS_TEMP_DIR is set, use that as directory,\n # let mktemp handle it otherwise. However, an explicitly provided\n # dir=... will override this.\n mkdir = tkwargs_.pop('mkdir', False)\n\n filename = {False: tempfile.mktemp,\n True: tempfile.mkdtemp}[mkdir](**tkwargs_)\n # MIH: not clear to me why we need to perform this (possibly expensive)\n # resolve. It was already part of the original implementation\n # 008d9ab8cc3e0170c0a9b8479e80dee9ffe6eb7f\n filename = Path(filename).resolve()\n\n if content:\n (filename.write_bytes\n if isinstance(content, bytes)\n else filename.write_text)(content)\n\n # TODO globbing below can also be done with pathlib\n filename = str(filename)\n\n if __debug__:\n lgr.debug(\n 'Created temporary %s named %s',\n 'directory' if mkdir else 'file',\n filename)\n try:\n yield filename\n finally:\n # glob here for all files with the same name (-suffix)\n # would be useful whenever we requested .img filename,\n # and function creates .hdr as well\n # MIH: this is undocumented behavior, and undesired in the general\n # case. it should be made conditional and explicit\n lsuffix = len(tkwargs_.get('suffix', ''))\n filename_ = lsuffix and filename[:-lsuffix] or filename\n filenames = glob.glob(filename_ + '*')\n if len(filename_) < 3 or len(filenames) > 5:\n # For paranoid yoh who stepped into this already ones ;-)\n lgr.warning(\"It is unlikely that it was intended to remove all\"\n \" files matching %r. Skipping\" % filename_)\n return\n for f in filenames:\n try:\n rmtemp(f)\n except OSError: # pragma: no cover\n pass\n\n\ndef _path_(*p):\n \"\"\"Given a path in POSIX\" notation, regenerate one in native to the env one\"\"\"\n if on_windows:\n return op.join(*map(lambda x: op.join(*x.split('/')), p))\n else:\n # Assume that all others as POSIX compliant so nothing to be done\n return op.join(*p)\n\n\ndef get_timestamp_suffix(time_=None, prefix='-'):\n \"\"\"Return a time stamp (full date and time up to second)\n\n primarily to be used for generation of log files names\n \"\"\"\n args = []\n if time_ is not None:\n if isinstance(time_, int):\n time_ = time.gmtime(time_)\n args.append(time_)\n return time.strftime(prefix + TIMESTAMP_FMT, *args)\n\n\n# unused in -core\ndef get_logfilename(dspath, cmd='datalad'):\n \"\"\"Return a filename to use for logging under a dataset/repository\n\n directory would be created if doesn't exist, but dspath must exist\n and be a directory\n \"\"\"\n assert(exists(dspath))\n assert(isdir(dspath))\n ds_logdir = ensure_dir(dspath, '.git', 'datalad', 'logs') # TODO: use WEB_META_LOG whenever #789 merged\n return op.join(ds_logdir, 'crawl-%s.log' % get_timestamp_suffix())\n\n\ndef get_trace(edges, start, end, trace=None):\n \"\"\"Return the trace/path to reach a node in a tree.\n\n Parameters\n ----------\n edges : sequence(2-tuple)\n The tree given by a sequence of edges (parent, child) tuples. The\n nodes can be identified by any value and data type that supports\n the '==' operation.\n start :\n Identifier of the start node. Must be present as a value in the parent\n location of an edge tuple in order to be found.\n end :\n Identifier of the target/end node. Must be present as a value in the child\n location of an edge tuple in order to be found.\n trace : list\n Mostly useful for recursive calls, and used internally.\n\n Returns\n -------\n None or list\n Returns a list with the trace to the target (the starts and the target\n are not included in the trace, hence if start and end are directly connected\n an empty list is returned), or None when no trace to the target can be found,\n or start and end are identical.\n \"\"\"\n # the term trace is used to avoid confusion with a path in the sense\n # of a filesystem path, but the analogy fits and nodes can be paths\n if trace is None:\n trace = []\n if not edges:\n raise ValueError(\"no edges given\")\n for cand in edges:\n cand_super, cand_sub = cand\n if cand_sub in trace:\n # only DAGs, skip any cyclic traces\n continue\n if trace and cand_super != trace[-1]:\n # only consider edges that lead off the end of the trace\n continue\n if not trace and cand_super != start:\n # we got nothing yet, and this edges is not matching the start\n continue\n if cand_sub == end:\n return trace\n # dive into potential subnodes\n cand_trace = get_trace(\n edges,\n start,\n end,\n trace + [cand_sub])\n if cand_trace:\n return cand_trace\n return None\n\n\ndef get_dataset_root(path):\n \"\"\"Return the root of an existent dataset containing a given path\n\n The root path is returned in the same absolute or relative form\n as the input argument. If no associated dataset exists, or the\n input path doesn't exist, None is returned.\n\n If `path` is a symlink or something other than a directory, its\n the root dataset containing its parent directory will be reported.\n If none can be found, at a symlink at `path` is pointing to a\n dataset, `path` itself will be reported as the root.\n\n Parameters\n ----------\n path : Path-like\n\n Returns\n -------\n str or None\n \"\"\"\n path = str(path)\n suffix = '.git'\n altered = None\n if islink(path) or not isdir(path):\n altered = path\n path = dirname(path)\n apath = abspath(path)\n # while we can still go up\n while split(apath)[1]:\n if exists(op.join(path, suffix)):\n return path\n # new test path in the format we got it\n path = normpath(op.join(path, os.pardir))\n # no luck, next round\n apath = abspath(path)\n # if we applied dirname() at the top, we give it another go with\n # the actual path, if it was itself a symlink, it could be the\n # top-level dataset itself\n if altered and exists(op.join(altered, suffix)):\n return altered\n\n return None\n\n\n# ATM used in datalad_crawler extension, so do not remove yet\ndef try_multiple(ntrials, exception, base, f, *args, **kwargs):\n \"\"\"Call f multiple times making exponentially growing delay between the calls\"\"\"\n for trial in range(1, ntrials+1):\n try:\n return f(*args, **kwargs)\n except exception as exc:\n if trial == ntrials:\n raise # just reraise on the last trial\n t = base ** trial\n lgr.warning(\"Caught %s on trial #%d. Sleeping %f and retrying\",\n CapturedException(exc), trial, t)\n sleep(t)\n\n\n@optional_args\ndef try_multiple_dec(\n f, ntrials=None, duration=0.1, exceptions=None, increment_type=None,\n exceptions_filter=None,\n logger=None,\n):\n \"\"\"Decorator to try function multiple times.\n\n Main purpose is to decorate functions dealing with removal of files/directories\n and which might need a few seconds to work correctly on Windows which takes\n its time to release files/directories.\n\n Parameters\n ----------\n ntrials: int, optional\n duration: float, optional\n Seconds to sleep before retrying.\n increment_type: {None, 'exponential'}\n Note that if it is exponential, duration should typically be > 1.0\n so it grows with higher power\n exceptions: Exception or tuple of Exceptions, optional\n Exception or a tuple of multiple exceptions, on which to retry\n exceptions_filter: callable, optional\n If provided, this function will be called with a caught exception\n instance. If function returns True - we will re-try, if False - exception\n will be re-raised without retrying.\n logger: callable, optional\n Logger to log upon failure. If not provided, will use stock logger\n at the level of 5 (heavy debug).\n \"\"\"\n if not exceptions:\n exceptions = (OSError, WindowsError, PermissionError) \\\n if on_windows else OSError\n if not ntrials:\n # Life goes fast on proper systems, no need to delay it much\n ntrials = 100 if on_windows else 10\n if logger is None:\n def logger(*args, **kwargs):\n return lgr.log(5, *args, **kwargs)\n assert increment_type in {None, 'exponential'}\n\n @wraps(f)\n def _wrap_try_multiple_dec(*args, **kwargs):\n t = duration\n for trial in range(ntrials):\n try:\n return f(*args, **kwargs)\n except exceptions as exc:\n if exceptions_filter and not exceptions_filter(exc):\n raise\n if trial < ntrials - 1:\n if increment_type == 'exponential':\n t = duration ** (trial + 1)\n logger(\n \"Caught %s on trial #%d. Sleeping %f and retrying\",\n CapturedException(exc), trial, t)\n sleep(t)\n else:\n raise\n\n return _wrap_try_multiple_dec\n\n\n@try_multiple_dec\ndef unlink(f):\n \"\"\"'Robust' unlink. Would try multiple times\n\n On windows boxes there is evidence for a latency of more than a second\n until a file is considered no longer \"in-use\".\n WindowsError is not known on Linux, and if IOError or any other\n exception\n is thrown then if except statement has WindowsError in it -- NameError\n also see gh-2533\n \"\"\"\n # Check for open files\n assert_no_open_files(f)\n return os.unlink(f)\n\n\n@try_multiple_dec\ndef _rmtree(*args, **kwargs):\n \"\"\"Just a helper to decorate shutil.rmtree.\n\n rmtree defined above does more and ideally should not itself be decorated\n since a recursive definition and does checks for open files inside etc -\n might be too runtime expensive\n \"\"\"\n return shutil.rmtree(*args, **kwargs)\n\n\ndef slash_join(base, extension):\n \"\"\"Join two strings with a '/', avoiding duplicate slashes\n\n If any of the strings is None the other is returned as is.\n \"\"\"\n if extension is None:\n return base\n if base is None:\n return extension\n return '/'.join(\n (base.rstrip('/'),\n extension.lstrip('/')))\n\n\n#\n# IO Helpers\n#\n\n# unused in -core\ndef open_r_encdetect(fname, readahead=1000):\n \"\"\"Return a file object in read mode with auto-detected encoding\n\n This is helpful when dealing with files of unknown encoding.\n\n Parameters\n ----------\n readahead: int, optional\n How many bytes to read for guessing the encoding type. If\n negative - full file will be read\n \"\"\"\n from chardet import detect\n import io\n # read some bytes from the file\n with open(fname, 'rb') as f:\n head = f.read(readahead)\n enc = detect(head)\n denc = enc.get('encoding', None)\n lgr.debug(\"Auto-detected encoding %s for file %s (confidence: %s)\",\n denc,\n fname,\n enc.get('confidence', 'unknown'))\n return io.open(fname, encoding=denc)\n\n\ndef read_file(fname, decode=True):\n \"\"\"A helper to read file passing content via ensure_unicode\n\n Parameters\n ----------\n decode: bool, optional\n if False, no ensure_unicode and file content returned as bytes\n \"\"\"\n with open(fname, 'rb') as f:\n content = f.read()\n return ensure_unicode(content) if decode else content\n\n\ndef read_csv_lines(fname, dialect=None, readahead=16384, **kwargs):\n \"\"\"A generator of dict records from a CSV/TSV\n\n Automatically guesses the encoding for each record to convert to UTF-8\n\n Parameters\n ----------\n fname: str\n Filename\n dialect: str, optional\n Dialect to specify to csv.reader. If not specified -- guessed from\n the file, if fails to guess, \"excel-tab\" is assumed\n readahead: int, optional\n How many bytes to read from the file to guess the type\n **kwargs\n Passed to `csv.reader`\n \"\"\"\n import csv\n if dialect is None:\n with open(fname) as tsvfile:\n # add robustness, use a sniffer\n try:\n dialect = csv.Sniffer().sniff(tsvfile.read(readahead))\n except Exception as exc:\n lgr.warning(\n 'Could not determine file-format, assuming TSV: %s',\n CapturedException(exc)\n )\n dialect = 'excel-tab'\n\n kw = dict(encoding='utf-8')\n with open(fname, 'r', **kw) as tsvfile:\n # csv.py doesn't do Unicode; encode temporarily as UTF-8:\n csv_reader = csv.reader(\n tsvfile,\n dialect=dialect,\n **kwargs\n )\n header = None\n for row in csv_reader:\n # decode UTF-8 back to Unicode, cell by cell:\n row_unicode = map(ensure_unicode, row)\n if header is None:\n header = list(row_unicode)\n else:\n yield dict(zip(header, row_unicode))\n\n\ndef import_modules(modnames, pkg, msg=\"Failed to import {module}\", log=lgr.debug):\n \"\"\"Helper to import a list of modules without failing if N/A\n\n Parameters\n ----------\n modnames: list of str\n List of module names to import\n pkg: str\n Package under which to import\n msg: str, optional\n Message template for .format() to log at DEBUG level if import fails.\n Keys {module} and {package} will be provided and ': {exception}' appended\n log: callable, optional\n Logger call to use for logging messages\n \"\"\"\n from importlib import import_module\n _globals = globals()\n mods_loaded = []\n if pkg and not pkg in sys.modules:\n # with python 3.5.1 (ok with 3.5.5) somehow kept running into\n # Failed to import dlsub1: Parent module 'dltestm1' not loaded\n # while running the test. Preloading pkg resolved the issue\n import_module(pkg)\n for modname in modnames:\n try:\n _globals[modname] = mod = import_module(\n '.{}'.format(modname),\n pkg)\n mods_loaded.append(mod)\n except Exception as exc:\n from datalad.support.exceptions import CapturedException\n ce = CapturedException(exc)\n log((msg + ': {exception}').format(\n module=modname, package=pkg, exception=ce.message))\n return mods_loaded\n\n\ndef import_module_from_file(modpath, pkg=None, log=lgr.debug):\n \"\"\"Import provided module given a path\n\n TODO:\n - RF/make use of it in pipeline.py which has similar logic\n - join with import_modules above?\n\n Parameters\n ----------\n pkg: module, optional\n If provided, and modpath is under pkg.__path__, relative import will be\n used\n \"\"\"\n assert(modpath.endswith('.py')) # for now just for .py files\n\n log(\"Importing %s\" % modpath)\n\n modname = basename(modpath)[:-3]\n relmodpath = None\n if pkg:\n for pkgpath in pkg.__path__:\n if path_is_subpath(modpath, pkgpath):\n # for now relying on having .py extension -- assertion above\n relmodpath = '.' + relpath(modpath[:-3], pkgpath).replace(sep, '.')\n break\n\n try:\n if relmodpath:\n from importlib import import_module\n mod = import_module(relmodpath, pkg.__name__)\n else:\n dirname_ = dirname(modpath)\n try:\n sys.path.insert(0, dirname_)\n mod = __import__(modname, level=0)\n finally:\n if dirname_ in sys.path:\n sys.path.pop(sys.path.index(dirname_))\n else:\n log(\"Expected path %s to be within sys.path, but it was gone!\" % dirname_)\n except Exception as e:\n raise RuntimeError(\n \"Failed to import module from %s\" % modpath) from e\n\n return mod\n\n\ndef get_encoding_info():\n \"\"\"Return a dictionary with various encoding/locale information\"\"\"\n import sys, locale\n from collections import OrderedDict\n return OrderedDict([\n ('default', sys.getdefaultencoding()),\n ('filesystem', sys.getfilesystemencoding()),\n ('locale.prefered', locale.getpreferredencoding()),\n ])\n\n\ndef get_envvars_info():\n from collections import OrderedDict\n envs = []\n for var, val in os.environ.items():\n if (\n var.startswith('PYTHON') or\n var.startswith('LC_') or\n var.startswith('GIT_') or\n var in ('LANG', 'LANGUAGE', 'PATH')\n ):\n envs.append((var, val))\n return OrderedDict(envs)\n\n\n# This class is modified from Snakemake (v5.1.4)\nclass SequenceFormatter(string.Formatter):\n \"\"\"string.Formatter subclass with special behavior for sequences.\n\n This class delegates formatting of individual elements to another\n formatter object. Non-list objects are formatted by calling the\n delegate formatter's \"format_field\" method. List-like objects\n (list, tuple, set, frozenset) are formatted by formatting each\n element of the list according to the specified format spec using\n the delegate formatter and then joining the resulting strings with\n a separator (space by default).\n \"\"\"\n\n def __init__(self, separator=\" \", element_formatter=string.Formatter(),\n *args, **kwargs):\n self.separator = separator\n self.element_formatter = element_formatter\n\n def format_element(self, elem, format_spec):\n \"\"\"Format a single element\n\n For sequences, this is called once for each element in a\n sequence. For anything else, it is called on the entire\n object. It is intended to be overridden in subclases.\n \"\"\"\n return self.element_formatter.format_field(elem, format_spec)\n\n def format_field(self, value, format_spec):\n if isinstance(value, (list, tuple, set, frozenset)):\n return self.separator.join(self.format_element(v, format_spec)\n for v in value)\n else:\n return self.format_element(value, format_spec)\n\n\n# TODO: eventually we might want to make use of attr module\nclass File(object):\n \"\"\"Helper for a file entry in the create_tree/@with_tree\n\n It allows to define additional settings for entries\n \"\"\"\n def __init__(self, name, executable=False):\n \"\"\"\n\n Parameters\n ----------\n name : str\n Name of the file\n executable: bool, optional\n Make it executable\n \"\"\"\n self.name = name\n self.executable = executable\n\n def __str__(self):\n return self.name\n\n\ndef create_tree_archive(path, name, load, overwrite=False, archives_leading_dir=True):\n \"\"\"Given an archive `name`, create under `path` with specified `load` tree\n \"\"\"\n from datalad.support.archives import compress_files\n dirname = file_basename(name)\n full_dirname = op.join(path, dirname)\n os.makedirs(full_dirname)\n create_tree(full_dirname, load, archives_leading_dir=archives_leading_dir)\n # create archive\n if archives_leading_dir:\n compress_files([dirname], name, path=path, overwrite=overwrite)\n else:\n compress_files(list(map(basename, glob.glob(op.join(full_dirname, '*')))),\n op.join(pardir, name),\n path=op.join(path, dirname),\n overwrite=overwrite)\n # remove original tree\n rmtree(full_dirname)\n\n\ndef create_tree(path, tree, archives_leading_dir=True, remove_existing=False):\n \"\"\"Given a list of tuples (name, load) create such a tree\n\n if load is a tuple itself -- that would create either a subtree or an archive\n with that content and place it into the tree if name ends with .tar.gz\n \"\"\"\n lgr.log(5, \"Creating a tree under %s\", path)\n if not exists(path):\n os.makedirs(path)\n\n if isinstance(tree, dict):\n tree = tree.items()\n\n for file_, load in tree:\n if isinstance(file_, File):\n executable = file_.executable\n name = file_.name\n else:\n executable = False\n name = file_\n full_name = op.join(path, name)\n if remove_existing and lexists(full_name):\n rmtree(full_name, chmod_files=True)\n if isinstance(load, (tuple, list, dict)):\n if name.endswith('.tar.gz') or name.endswith('.tar') or name.endswith('.zip'):\n create_tree_archive(\n path, name, load,\n archives_leading_dir=archives_leading_dir)\n else:\n create_tree(\n full_name, load,\n archives_leading_dir=archives_leading_dir,\n remove_existing=remove_existing)\n else:\n open_func = open\n if full_name.endswith('.gz'):\n open_func = gzip.open\n elif full_name.split('.')[-1] in ('xz', 'lzma'):\n import lzma\n open_func = lzma.open\n with open_func(full_name, \"wb\") as f:\n f.write(ensure_bytes(load, 'utf-8'))\n if executable:\n os.chmod(full_name, os.stat(full_name).st_mode | stat.S_IEXEC)\n\n\ndef get_suggestions_msg(values, known, sep=\"\\n \"):\n \"\"\"Return a formatted string with suggestions for values given the known ones\n \"\"\"\n import difflib\n suggestions = []\n for value in ensure_list(values): # might not want to do it if we change presentation below\n suggestions += difflib.get_close_matches(value, known)\n suggestions = unique(suggestions)\n msg = \"Did you mean any of these?\"\n if suggestions:\n if '\\n' in sep:\n # if separator includes new line - we add entire separator right away\n msg += sep\n else:\n msg += ' '\n return msg + \"%s\\n\" % sep.join(suggestions)\n return ''\n\n\ndef bytes2human(n, format='%(value).1f %(symbol)sB'):\n \"\"\"\n Convert n bytes into a human readable string based on format.\n symbols can be either \"customary\", \"customary_ext\", \"iec\" or \"iec_ext\",\n see: http://goo.gl/kTQMs\n\n >>> from datalad.utils import bytes2human\n >>> bytes2human(1)\n '1.0 B'\n >>> bytes2human(1024)\n '1.0 KB'\n >>> bytes2human(1048576)\n '1.0 MB'\n >>> bytes2human(1099511627776127398123789121)\n '909.5 YB'\n\n >>> bytes2human(10000, \"%(value).1f %(symbol)s/sec\")\n '9.8 K/sec'\n\n >>> # precision can be adjusted by playing with %f operator\n >>> bytes2human(10000, format=\"%(value).5f %(symbol)s\")\n '9.76562 K'\n\n Taken from: http://goo.gl/kTQMs and subsequently simplified\n Original Author: Giampaolo Rodola' <g.rodola [AT] gmail [DOT] com>\n License: MIT\n \"\"\"\n n = int(n)\n if n < 0:\n raise ValueError(\"n < 0\")\n symbols = ('', 'K', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y')\n prefix = {}\n for i, s in enumerate(symbols[1:]):\n prefix[s] = 1 << (i + 1) * 10\n for symbol in reversed(symbols[1:]):\n if n >= prefix[symbol]:\n value = float(n) / prefix[symbol]\n return format % locals()\n return format % dict(symbol=symbols[0], value=n)\n\n\ndef quote_cmdlinearg(arg):\n \"\"\"Perform platform-appropriate argument quoting\"\"\"\n # https://stackoverflow.com/a/15262019\n return '\"{}\"'.format(\n arg.replace('\"', '\"\"')\n ) if on_windows else shlex_quote(arg)\n\n\ndef guard_for_format(arg):\n \"\"\"Replace { and } with {{ and }}\n\n To be used in cases if arg is not expected to have provided\n by user .format() placeholders, but 'arg' might become a part\n of a composite passed to .format(), e.g. via 'Run'\n \"\"\"\n return arg.replace('{', '{{').replace('}', '}}')\n\n\ndef join_cmdline(args):\n \"\"\"Join command line args into a string using quote_cmdlinearg\n \"\"\"\n return ' '.join(map(quote_cmdlinearg, args))\n\n\ndef split_cmdline(s):\n \"\"\"Perform platform-appropriate command line splitting.\n\n Identical to `shlex.split()` on non-windows platforms.\n\n Modified from https://stackoverflow.com/a/35900070\n \"\"\"\n if not on_windows:\n return shlex_split(s)\n\n # the rest is for windows\n RE_CMD_LEX = r'''\"((?:\"\"|\\\\[\"\\\\]|[^\"])*)\"?()|(\\\\\\\\(?=\\\\*\")|\\\\\")|(&&?|\\|\\|?|\\d?>|[<])|([^\\s\"&|<>]+)|(\\s+)|(.)'''\n\n args = []\n accu = None # collects pieces of one arg\n for qs, qss, esc, pipe, word, white, fail in re.findall(RE_CMD_LEX, s):\n if word:\n pass # most frequent\n elif esc:\n word = esc[1]\n elif white or pipe:\n if accu is not None:\n args.append(accu)\n if pipe:\n args.append(pipe)\n accu = None\n continue\n elif fail:\n raise ValueError(\"invalid or incomplete shell string\")\n elif qs:\n word = qs.replace('\\\\\"', '\"').replace('\\\\\\\\', '\\\\')\n if platform == 0:\n word = word.replace('\"\"', '\"')\n else:\n word = qss # may be even empty; must be last\n\n accu = (accu or '') + word\n\n if accu is not None:\n args.append(accu)\n\n return args\n\n\ndef get_wrapped_class(wrapped):\n \"\"\"Determine the command class a wrapped __call__ belongs to\"\"\"\n mod = sys.modules[wrapped.__module__]\n command_class_name = wrapped.__qualname__.split('.')[-2]\n _func_class = mod.__dict__[command_class_name]\n lgr.debug(\"Determined class of decorated function: %s\", _func_class)\n return _func_class\n\n\ndef _make_assure_kludge(fn):\n old_name = fn.__name__.replace(\"ensure\", \"assure\")\n\n @wraps(fn)\n def compat_fn(*args, **kwargs):\n warnings.warn(\n \"{} is deprecated and will be removed in a future release. \"\n \"Use {} instead.\"\n .format(old_name, fn.__name__),\n DeprecationWarning)\n return fn(*args, **kwargs)\n\n compat_fn.__doc__ = (\"Note: This function is deprecated. Use {} instead.\"\n .format(fn.__name__))\n return compat_fn\n\n\nassure_tuple_or_list = _make_assure_kludge(ensure_tuple_or_list)\nassure_iter = _make_assure_kludge(ensure_iter)\nassure_list = _make_assure_kludge(ensure_list)\nassure_list_from_str = _make_assure_kludge(ensure_list_from_str)\nassure_dict_from_str = _make_assure_kludge(ensure_dict_from_str)\nassure_bytes = _make_assure_kludge(ensure_bytes)\nassure_unicode = _make_assure_kludge(ensure_unicode)\nassure_bool = _make_assure_kludge(ensure_bool)\nassure_dir = _make_assure_kludge(ensure_dir)\n\n\nlgr.log(5, \"Done importing datalad.utils\")\n\n\ndef check_symlink_capability(path, target):\n \"\"\"helper similar to datalad.tests.utils.has_symlink_capability\n\n However, for use in a datalad command context, we shouldn't\n assume to be able to write to tmpfile and also not import a whole lot from\n datalad's test machinery. Finally, we want to know, whether we can create a\n symlink at a specific location, not just somewhere. Therefore use\n arbitrary path to test-build a symlink and delete afterwards. Suitable\n location can therefore be determined by high lever code.\n\n Parameters\n ----------\n path: Path\n target: Path\n\n Returns\n -------\n bool\n \"\"\"\n\n try:\n target.touch()\n path.symlink_to(target)\n return True\n except Exception:\n return False\n finally:\n if path.exists():\n path.unlink()\n if target.exists():\n target.unlink()"},"implementation":{"kind":"string","value":"def rmtree(path, chmod_files='auto', children_only=False, *args, **kwargs):\n \"\"\"To remove git-annex .git it is needed to make all files and directories writable again first\n\n Parameters\n ----------\n path: Path or str\n Path to remove\n chmod_files : string or bool, optional\n Whether to make files writable also before removal. Usually it is just\n a matter of directories to have write permissions.\n If 'auto' it would chmod files on windows by default\n children_only : bool, optional\n If set, all files and subdirectories would be removed while the path\n itself (must be a directory) would be preserved\n `*args` :\n `**kwargs` :\n Passed into shutil.rmtree call\n \"\"\"\n # Give W permissions back only to directories, no need to bother with files\n if chmod_files == 'auto':\n chmod_files = on_windows\n # TODO: yoh thinks that if we could quickly check our Flyweight for\n # repos if any of them is under the path, and could call .precommit\n # on those to possibly stop batched processes etc, we did not have\n # to do it on case by case\n # Check for open files\n assert_no_open_files(path)\n\n # TODO the whole thing should be reimplemented with pathlib, but for now\n # at least accept Path\n path = str(path)\n\n if children_only:\n if not isdir(path):\n raise ValueError(\"Can remove children only of directories\")\n for p in os.listdir(path):\n rmtree(op.join(path, p))\n return\n if not (islink(path) or not isdir(path)):\n rotree(path, ro=False, chmod_files=chmod_files)\n if on_windows:\n # shutil fails to remove paths that exceed 260 characters on Windows machines\n # that did not enable long path support. A workaround to remove long paths\n # anyway is to preprend \\\\?\\ to the path.\n # https://docs.microsoft.com/en-us/windows/win32/fileio/naming-a-file?redirectedfrom=MSDN#win32-file-namespaces\n path = r'\\\\?\\ '.strip() + path\n _rmtree(path, *args, **kwargs)\n else:\n # just remove the symlink\n unlink(path)"},"start_line":{"kind":"number","value":469,"string":"469"},"end_line":{"kind":"number","value":518,"string":"518"},"file_content":{"kind":"string","value":"# emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-\n# ex: set sts=4 ts=4 sw=4 et:\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##\n#\n# See COPYING file distributed along with the datalad package for the\n# copyright and license terms.\n#\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##\n\nimport collections\nfrom collections.abc import Callable\nimport re\nimport builtins\nimport time\n\nimport logging\nimport shutil\nimport os\nimport sys\nimport tempfile\nfrom tempfile import NamedTemporaryFile\nimport platform\nimport gc\nimport glob\nimport gzip\nimport stat\nimport string\nimport warnings\n\nimport os.path as op\n\nfrom copy import copy as shallow_copy\nfrom contextlib import contextmanager\nfrom functools import (\n lru_cache,\n wraps,\n)\nfrom time import sleep\nimport inspect\nfrom itertools import tee\n# this import is required because other modules import opj from here.\nfrom os.path import join as opj\nfrom os.path import (\n abspath,\n basename,\n commonprefix,\n curdir,\n dirname,\n exists,\n expanduser,\n expandvars,\n isabs,\n isdir,\n islink,\n lexists,\n normpath,\n pardir,\n relpath,\n sep,\n split,\n splitdrive\n)\n\nimport posixpath\n\nfrom shlex import (\n quote as shlex_quote,\n split as shlex_split,\n)\n\n# from datalad.dochelpers import get_docstring_split\nfrom datalad.consts import TIMESTAMP_FMT\nfrom datalad.support.exceptions import CapturedException\n\n\nunicode_srctypes = str, bytes\n\n\nlgr = logging.getLogger(\"datalad.utils\")\n\nlgr.log(5, \"Importing datalad.utils\")\n#\n# Some useful variables\n#\nplatform_system = platform.system().lower()\non_windows = platform_system == 'windows'\non_osx = platform_system == 'darwin'\non_linux = platform_system == 'linux'\non_msys_tainted_paths = on_windows \\\n and 'MSYS_NO_PATHCONV' not in os.environ \\\n and os.environ.get('MSYSTEM', '')[:4] in ('MSYS', 'MING')\n\n\n# Takes ~200msec, so should not be called at import time\n@lru_cache() # output should not change through life time of datalad process\ndef get_linux_distribution():\n \"\"\"Compatibility wrapper for {platform,distro}.linux_distribution().\n \"\"\"\n if hasattr(platform, \"linux_distribution\"):\n # Use deprecated (but faster) method if it's available.\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n result = platform.linux_distribution()\n else:\n import distro # We require this for Python 3.8 and above.\n result = distro.linux_distribution(full_distribution_name=False)\n return result\n\n\n# Those weren't used for any critical decision making, thus we just set them to None\n# Use get_linux_distribution() directly where needed\nlinux_distribution_name = linux_distribution_release = None\n\n# Maximal length of cmdline string\n# Query the system and use hardcoded \"knowledge\" if None\n# probably getconf ARG_MAX might not be available\n# The last one would be the most conservative/Windows\nCMD_MAX_ARG_HARDCODED = 2097152 if on_linux else 262144 if on_osx else 32767\ntry:\n CMD_MAX_ARG = os.sysconf('SC_ARG_MAX')\n assert CMD_MAX_ARG > 0\n if CMD_MAX_ARG > CMD_MAX_ARG_HARDCODED * 1e6:\n # workaround for some kind of a bug which comes up with python 3.4\n # see https://github.com/datalad/datalad/issues/3150\n # or on older CentOS with conda and python as new as 3.9\n # see https://github.com/datalad/datalad/issues/5943\n # TODO: let Yarik know that the world is a paradise now whenever 1e6\n # is not large enough\n CMD_MAX_ARG = min(CMD_MAX_ARG, CMD_MAX_ARG_HARDCODED)\nexcept Exception as exc:\n # ATM (20181005) SC_ARG_MAX available only on POSIX systems\n # so exception would be thrown e.g. on Windows, or\n # somehow during Debian build for nd14.04 it is coming up with -1:\n # https://github.com/datalad/datalad/issues/3015\n CMD_MAX_ARG = CMD_MAX_ARG_HARDCODED\n lgr.debug(\n \"Failed to query or got useless SC_ARG_MAX sysconf, \"\n \"will use hardcoded value: %s\", exc)\n# Even with all careful computations we do, due to necessity to account for\n# environment and what not, we still could not figure out \"exact\" way to\n# estimate it, but it was shown that 300k safety margin on linux was sufficient.\n# https://github.com/datalad/datalad/pull/2977#issuecomment-436264710\n# 300k is ~15%, so to be safe, and for paranoid us we will just use up to 50%\n# of the length for \"safety margin\". We might probably still blow due to\n# env vars, unicode, etc... so any hard limit imho is not a proper solution\nCMD_MAX_ARG = int(0.5 * CMD_MAX_ARG)\nlgr.debug(\n \"Maximal length of cmdline string (adjusted for safety margin): %d\",\n CMD_MAX_ARG)\n\n#\n# Little helpers\n#\n\n# `getargspec` has been deprecated in Python 3.\nArgSpecFake = collections.namedtuple(\n \"ArgSpecFake\", [\"args\", \"varargs\", \"keywords\", \"defaults\"])\n\n\ndef getargspec(func, *, include_kwonlyargs=False):\n \"\"\"Compat shim for getargspec deprecated in python 3.\n\n The main difference from inspect.getargspec (and inspect.getfullargspec\n for that matter) is that by using inspect.signature we are providing\n correct args/defaults for functools.wraps'ed functions.\n\n `include_kwonlyargs` option was added to centralize getting all args,\n even the ones which are kwonly (follow the ``*,``).\n\n For internal use and not advised for use in 3rd party code.\n Please use inspect.signature directly.\n \"\"\"\n # We use signature, and not getfullargspec, because only signature properly\n # \"passes\" args from a functools.wraps decorated function.\n # Note: getfullargspec works Ok on wrapt-decorated functions\n f_sign = inspect.signature(func)\n # Loop through parameters and compose argspec\n args4 = [[], None, None, {}]\n # Collect all kwonlyargs into a dedicated dict - name: default\n kwonlyargs = {}\n # shortcuts\n args, defaults = args4[0], args4[3]\n P = inspect.Parameter\n\n for p_name, p in f_sign.parameters.items():\n if p.kind in (P.POSITIONAL_ONLY, P.POSITIONAL_OR_KEYWORD):\n assert not kwonlyargs # yoh: must not come after kwonlyarg\n args.append(p_name)\n if p.default is not P.empty:\n defaults[p_name] = p.default\n elif p.kind == P.VAR_POSITIONAL:\n args4[1] = p_name\n elif p.kind == P.VAR_KEYWORD:\n args4[2] = p_name\n elif p.kind == P.KEYWORD_ONLY:\n assert p.default is not P.empty\n kwonlyargs[p_name] = p.default\n\n if kwonlyargs:\n if not include_kwonlyargs:\n raise ValueError(\n 'Function has keyword-only parameters or annotations, either use '\n 'inspect.signature() API which can support them, or provide include_kwonlyargs=True '\n 'to this function'\n )\n else:\n args.extend(list(kwonlyargs))\n defaults.update(kwonlyargs)\n\n # harmonize defaults to how original getargspec returned them -- just a tuple\n args4[3] = None if not defaults else tuple(defaults.values())\n return ArgSpecFake(*args4)\n\n\ndef any_re_search(regexes, value):\n \"\"\"Return if any of regexes (list or str) searches successfully for value\"\"\"\n for regex in ensure_tuple_or_list(regexes):\n if re.search(regex, value):\n return True\n return False\n\n\ndef not_supported_on_windows(msg=None):\n \"\"\"A little helper to be invoked to consistently fail whenever functionality is\n not supported (yet) on Windows\n \"\"\"\n if on_windows:\n raise NotImplementedError(\"This functionality is not yet implemented for Windows OS\"\n + (\": %s\" % msg if msg else \"\"))\n\n\ndef get_home_envvars(new_home):\n \"\"\"Return dict with env variables to be adjusted for a new HOME\n\n Only variables found in current os.environ are adjusted.\n\n Parameters\n ----------\n new_home: str or Path\n New home path, in native to OS \"schema\"\n \"\"\"\n new_home = str(new_home)\n out = {'HOME': new_home}\n if on_windows:\n # requires special handling, since it has a number of relevant variables\n # and also Python changed its behavior and started to respect USERPROFILE only\n # since python 3.8: https://bugs.python.org/issue36264\n out['USERPROFILE'] = new_home\n out['HOMEDRIVE'], out['HOMEPATH'] = splitdrive(new_home)\n\n return {v: val for v, val in out.items() if v in os.environ}\n\n\ndef shortened_repr(value, l=30):\n try:\n if hasattr(value, '__repr__') and (value.__repr__ is not object.__repr__):\n value_repr = repr(value)\n if not value_repr.startswith('<') and len(value_repr) > l:\n value_repr = \"<<%s++%d chars++%s>>\" % (\n value_repr[:l - 16],\n len(value_repr) - (l - 16 + 4),\n value_repr[-4:]\n )\n elif value_repr.startswith('<') and value_repr.endswith('>') and ' object at 0x':\n raise ValueError(\"I hate those useless long reprs\")\n else:\n raise ValueError(\"gimme class\")\n except Exception as e:\n value_repr = \"<%s>\" % value.__class__.__name__.split('.')[-1]\n return value_repr\n\n\ndef __auto_repr__(obj):\n attr_names = tuple()\n if hasattr(obj, '__dict__'):\n attr_names += tuple(obj.__dict__.keys())\n if hasattr(obj, '__slots__'):\n attr_names += tuple(obj.__slots__)\n\n items = []\n for attr in sorted(set(attr_names)):\n if attr.startswith('_'):\n continue\n value = getattr(obj, attr)\n # TODO: should we add this feature to minimize some talktative reprs\n # such as of URL?\n #if value is None:\n # continue\n items.append(\"%s=%s\" % (attr, shortened_repr(value)))\n\n return \"%s(%s)\" % (obj.__class__.__name__, ', '.join(items))\n\n\ndef auto_repr(cls):\n \"\"\"Decorator for a class to assign it an automagic quick and dirty __repr__\n\n It uses public class attributes to prepare repr of a class\n\n Original idea: http://stackoverflow.com/a/27799004/1265472\n \"\"\"\n\n cls.__repr__ = __auto_repr__\n return cls\n\n\ndef _is_stream_tty(stream):\n try:\n # TODO: check on windows if hasattr check would work correctly and\n # add value:\n return stream.isatty()\n except ValueError as exc:\n # Who knows why it is a ValueError, but let's try to be specific\n # If there is a problem with I/O - non-interactive, otherwise reraise\n if \"I/O\" in str(exc):\n return False\n raise\n\n\ndef is_interactive():\n \"\"\"Return True if all in/outs are open and tty.\n\n Note that in a somewhat abnormal case where e.g. stdin is explicitly\n closed, and any operation on it would raise a\n `ValueError(\"I/O operation on closed file\")` exception, this function\n would just return False, since the session cannot be used interactively.\n \"\"\"\n return all(_is_stream_tty(s) for s in (sys.stdin, sys.stdout, sys.stderr))\n\n\ndef get_ipython_shell():\n \"\"\"Detect if running within IPython and returns its `ip` (shell) object\n\n Returns None if not under ipython (no `get_ipython` function)\n \"\"\"\n try:\n return get_ipython()\n except NameError:\n return None\n\n\ndef md5sum(filename):\n \"\"\"Compute an MD5 sum for the given file\n \"\"\"\n from datalad.support.digests import Digester\n return Digester(digests=['md5'])(filename)['md5']\n\n\n# unused in -core\ndef sorted_files(path):\n \"\"\"Return a (sorted) list of files under path\n \"\"\"\n return sorted(sum([[op.join(r, f)[len(path) + 1:] for f in files]\n for r, d, files in os.walk(path)\n if not '.git' in r], []))\n\n_encoded_dirsep = r'\\\\' if on_windows else r'/'\n_VCS_REGEX = r'%s\\.(?:git|gitattributes|svn|bzr|hg)(?:%s|$)' % (\n _encoded_dirsep, _encoded_dirsep)\n_DATALAD_REGEX = r'%s\\.(?:datalad)(?:%s|$)' % (\n _encoded_dirsep, _encoded_dirsep)\n\n\ndef find_files(regex, topdir=curdir, exclude=None, exclude_vcs=True, exclude_datalad=False, dirs=False):\n \"\"\"Generator to find files matching regex\n\n Parameters\n ----------\n regex: basestring\n exclude: basestring, optional\n Matches to exclude\n exclude_vcs:\n If True, excludes commonly known VCS subdirectories. If string, used\n as regex to exclude those files (regex: `%r`)\n exclude_datalad:\n If True, excludes files known to be datalad meta-data files (e.g. under\n .datalad/ subdirectory) (regex: `%r`)\n topdir: basestring, optional\n Directory where to search\n dirs: bool, optional\n Whether to match directories as well as files\n \"\"\"\n for dirpath, dirnames, filenames in os.walk(topdir):\n names = (dirnames + filenames) if dirs else filenames\n # TODO: might want to uniformize on windows to use '/'\n paths = (op.join(dirpath, name) for name in names)\n for path in filter(re.compile(regex).search, paths):\n path = path.rstrip(sep)\n if exclude and re.search(exclude, path):\n continue\n if exclude_vcs and re.search(_VCS_REGEX, path):\n continue\n if exclude_datalad and re.search(_DATALAD_REGEX, path):\n continue\n yield path\nfind_files.__doc__ %= (_VCS_REGEX, _DATALAD_REGEX)\n\n\ndef expandpath(path, force_absolute=True):\n \"\"\"Expand all variables and user handles in a path.\n\n By default return an absolute path\n \"\"\"\n path = expandvars(expanduser(path))\n if force_absolute:\n path = abspath(path)\n return path\n\n\ndef posix_relpath(path, start=None):\n \"\"\"Behave like os.path.relpath, but always return POSIX paths...\n\n on any platform.\"\"\"\n # join POSIX style\n return posixpath.join(\n # split and relpath native style\n # python2.7 ntpath implementation of relpath cannot handle start=None\n *split(\n relpath(path, start=start if start is not None else '')))\n\n\ndef is_explicit_path(path):\n \"\"\"Return whether a path explicitly points to a location\n\n Any absolute path, or relative path starting with either '../' or\n './' is assumed to indicate a location on the filesystem. Any other\n path format is not considered explicit.\"\"\"\n path = expandpath(path, force_absolute=False)\n return isabs(path) \\\n or path.startswith(os.curdir + os.sep) \\\n or path.startswith(os.pardir + os.sep)\n\n\n# handle this dance once, and import pathlib from here\n# in all other places\n\nfrom pathlib import (\n Path,\n PurePath,\n PurePosixPath,\n)\n\ndef rotree(path, ro=True, chmod_files=True):\n \"\"\"To make tree read-only or writable\n\n Parameters\n ----------\n path : string\n Path to the tree/directory to chmod\n ro : bool, optional\n Whether to make it R/O (default) or RW\n chmod_files : bool, optional\n Whether to operate also on files (not just directories)\n \"\"\"\n if ro:\n chmod = lambda f: os.chmod(f, os.stat(f).st_mode & ~stat.S_IWRITE)\n else:\n chmod = lambda f: os.chmod(f, os.stat(f).st_mode | stat.S_IWRITE | stat.S_IREAD)\n\n for root, dirs, files in os.walk(path, followlinks=False):\n if chmod_files:\n for f in files:\n fullf = op.join(root, f)\n # might be the \"broken\" symlink which would fail to stat etc\n if exists(fullf):\n chmod(fullf)\n chmod(root)\n\n\ndef rmtree(path, chmod_files='auto', children_only=False, *args, **kwargs):\n \"\"\"To remove git-annex .git it is needed to make all files and directories writable again first\n\n Parameters\n ----------\n path: Path or str\n Path to remove\n chmod_files : string or bool, optional\n Whether to make files writable also before removal. Usually it is just\n a matter of directories to have write permissions.\n If 'auto' it would chmod files on windows by default\n children_only : bool, optional\n If set, all files and subdirectories would be removed while the path\n itself (must be a directory) would be preserved\n `*args` :\n `**kwargs` :\n Passed into shutil.rmtree call\n \"\"\"\n # Give W permissions back only to directories, no need to bother with files\n if chmod_files == 'auto':\n chmod_files = on_windows\n # TODO: yoh thinks that if we could quickly check our Flyweight for\n # repos if any of them is under the path, and could call .precommit\n # on those to possibly stop batched processes etc, we did not have\n # to do it on case by case\n # Check for open files\n assert_no_open_files(path)\n\n # TODO the whole thing should be reimplemented with pathlib, but for now\n # at least accept Path\n path = str(path)\n\n if children_only:\n if not isdir(path):\n raise ValueError(\"Can remove children only of directories\")\n for p in os.listdir(path):\n rmtree(op.join(path, p))\n return\n if not (islink(path) or not isdir(path)):\n rotree(path, ro=False, chmod_files=chmod_files)\n if on_windows:\n # shutil fails to remove paths that exceed 260 characters on Windows machines\n # that did not enable long path support. A workaround to remove long paths\n # anyway is to preprend \\\\?\\ to the path.\n # https://docs.microsoft.com/en-us/windows/win32/fileio/naming-a-file?redirectedfrom=MSDN#win32-file-namespaces\n path = r'\\\\?\\ '.strip() + path\n _rmtree(path, *args, **kwargs)\n else:\n # just remove the symlink\n unlink(path)\n\n\ndef rmdir(path, *args, **kwargs):\n \"\"\"os.rmdir with our optional checking for open files\"\"\"\n assert_no_open_files(path)\n os.rmdir(path)\n\n\ndef get_open_files(path, log_open=False):\n \"\"\"Get open files under a path\n\n Note: This function is very slow on Windows.\n\n Parameters\n ----------\n path : str\n File or directory to check for open files under\n log_open : bool or int\n If set - logger level to use\n\n Returns\n -------\n dict\n path : pid\n\n \"\"\"\n # Original idea: https://stackoverflow.com/a/11115521/1265472\n import psutil\n files = {}\n # since the ones returned by psutil would not be aware of symlinks in the\n # path we should also get realpath for path\n # do absolute() in addition to always get an absolute path\n # even with non-existing paths on windows\n path = str(Path(path).resolve().absolute())\n for proc in psutil.process_iter():\n try:\n open_paths = [p.path for p in proc.open_files()] + [proc.cwd()]\n for p in open_paths:\n # note: could be done more efficiently so we do not\n # renormalize path over and over again etc\n if path_startswith(p, path):\n files[p] = proc\n # Catch a race condition where a process ends\n # before we can examine its files\n except psutil.NoSuchProcess:\n pass\n except psutil.AccessDenied:\n pass\n\n if files and log_open:\n lgr.log(log_open, \"Open files under %s: %s\", path, files)\n return files\n\n\n_assert_no_open_files_cfg = os.environ.get('DATALAD_ASSERT_NO_OPEN_FILES')\nif _assert_no_open_files_cfg:\n def assert_no_open_files(path):\n files = get_open_files(path, log_open=40)\n if _assert_no_open_files_cfg == 'assert':\n assert not files, \"Got following files still open: %s\" % ','.join(files)\n elif files:\n if _assert_no_open_files_cfg == 'pdb':\n import pdb\n pdb.set_trace()\n elif _assert_no_open_files_cfg == 'epdb':\n import epdb\n epdb.serve()\n pass\n # otherwise we would just issue that error message in the log\nelse:\n def assert_no_open_files(*args, **kwargs):\n pass\n\n\ndef rmtemp(f, *args, **kwargs):\n \"\"\"Wrapper to centralize removing of temp files so we could keep them around\n\n It will not remove the temporary file/directory if DATALAD_TESTS_TEMP_KEEP\n environment variable is defined\n \"\"\"\n if not os.environ.get('DATALAD_TESTS_TEMP_KEEP'):\n if not os.path.lexists(f):\n lgr.debug(\"Path %s does not exist, so can't be removed\", f)\n return\n lgr.log(5, \"Removing temp file: %s\", f)\n # Can also be a directory\n if isdir(f):\n rmtree(f, *args, **kwargs)\n else:\n unlink(f)\n else:\n lgr.info(\"Keeping temp file: %s\", f)\n\n\ndef file_basename(name, return_ext=False):\n \"\"\"\n Strips up to 2 extensions of length up to 4 characters and starting with alpha\n not a digit, so we could get rid of .tar.gz etc\n \"\"\"\n bname = basename(name)\n fbname = re.sub(r'(\\.[a-zA-Z_]\\S{1,4}){0,2}$', '', bname)\n if return_ext:\n return fbname, bname[len(fbname) + 1:]\n else:\n return fbname\n\n\n# unused in -core\ndef escape_filename(filename):\n \"\"\"Surround filename in \"\" and escape \" in the filename\n \"\"\"\n filename = filename.replace('\"', r'\\\"').replace('`', r'\\`')\n filename = '\"%s\"' % filename\n return filename\n\n\n# unused in -core\ndef encode_filename(filename):\n \"\"\"Encode unicode filename\n \"\"\"\n if isinstance(filename, str):\n return filename.encode(sys.getfilesystemencoding())\n else:\n return filename\n\n\n# unused in -core\ndef decode_input(s):\n \"\"\"Given input string/bytes, decode according to stdin codepage (or UTF-8)\n if not defined\n\n If fails -- issue warning and decode allowing for errors\n being replaced\n \"\"\"\n if isinstance(s, str):\n return s\n else:\n encoding = sys.stdin.encoding or 'UTF-8'\n try:\n return s.decode(encoding)\n except UnicodeDecodeError as exc:\n lgr.warning(\n \"Failed to decode input string using %s encoding. \"\n \"Decoding allowing for errors\", encoding)\n return s.decode(encoding, errors='replace')\n\n\n# unused in -core\nif on_windows:\n def lmtime(filepath, mtime):\n \"\"\"Set mtime for files. On Windows a merely adapter to os.utime\n \"\"\"\n os.utime(filepath, (time.time(), mtime))\nelse:\n def lmtime(filepath, mtime):\n \"\"\"Set mtime for files, while not de-referencing symlinks.\n\n To overcome absence of os.lutime\n\n Works only on linux and OSX ATM\n \"\"\"\n from .cmd import WitlessRunner\n # convert mtime to format touch understands [[CC]YY]MMDDhhmm[.SS]\n smtime = time.strftime(\"%Y%m%d%H%M.%S\", time.localtime(mtime))\n lgr.log(3, \"Setting mtime for %s to %s == %s\", filepath, mtime, smtime)\n WitlessRunner().run(['touch', '-h', '-t', '%s' % smtime, filepath])\n filepath = Path(filepath)\n rfilepath = filepath.resolve()\n if filepath.is_symlink() and rfilepath.exists():\n # trust no one - adjust also of the target file\n # since it seemed like downloading under OSX (was it using curl?)\n # didn't bother with timestamps\n lgr.log(3, \"File is a symlink to %s Setting mtime for it to %s\",\n rfilepath, mtime)\n os.utime(str(rfilepath), (time.time(), mtime))\n # doesn't work on OSX\n # Runner().run(['touch', '-h', '-d', '@%s' % mtime, filepath])\n\n\ndef ensure_tuple_or_list(obj):\n \"\"\"Given an object, wrap into a tuple if not list or tuple\n \"\"\"\n if isinstance(obj, (list, tuple)):\n return obj\n return (obj,)\n\n\ndef ensure_iter(s, cls, copy=False, iterate=True):\n \"\"\"Given not a list, would place it into a list. If None - empty list is returned\n\n Parameters\n ----------\n s: list or anything\n cls: class\n Which iterable class to ensure\n copy: bool, optional\n If correct iterable is passed, it would generate its shallow copy\n iterate: bool, optional\n If it is not a list, but something iterable (but not a str)\n iterate over it.\n \"\"\"\n\n if isinstance(s, cls):\n return s if not copy else shallow_copy(s)\n elif isinstance(s, str):\n return cls((s,))\n elif iterate and hasattr(s, '__iter__'):\n return cls(s)\n elif s is None:\n return cls()\n else:\n return cls((s,))\n\n\ndef ensure_list(s, copy=False, iterate=True):\n \"\"\"Given not a list, would place it into a list. If None - empty list is returned\n\n Parameters\n ----------\n s: list or anything\n copy: bool, optional\n If list is passed, it would generate a shallow copy of the list\n iterate: bool, optional\n If it is not a list, but something iterable (but not a str)\n iterate over it.\n \"\"\"\n return ensure_iter(s, list, copy=copy, iterate=iterate)\n\n\ndef ensure_list_from_str(s, sep='\\n'):\n \"\"\"Given a multiline string convert it to a list of return None if empty\n\n Parameters\n ----------\n s: str or list\n \"\"\"\n\n if not s:\n return None\n\n if isinstance(s, list):\n return s\n return s.split(sep)\n\n\ndef ensure_dict_from_str(s, **kwargs):\n \"\"\"Given a multiline string with key=value items convert it to a dictionary\n\n Parameters\n ----------\n s: str or dict\n\n Returns None if input s is empty\n \"\"\"\n\n if not s:\n return None\n\n if isinstance(s, dict):\n return s\n\n out = {}\n for value_str in ensure_list_from_str(s, **kwargs):\n if '=' not in value_str:\n raise ValueError(\"{} is not in key=value format\".format(repr(value_str)))\n k, v = value_str.split('=', 1)\n if k in out:\n err = \"key {} was already defined in {}, but new value {} was provided\".format(k, out, v)\n raise ValueError(err)\n out[k] = v\n return out\n\n\ndef ensure_bytes(s, encoding='utf-8'):\n \"\"\"Convert/encode unicode string to bytes.\n\n If `s` isn't a string, return it as is.\n\n Parameters\n ----------\n encoding: str, optional\n Encoding to use. \"utf-8\" is the default\n \"\"\"\n if not isinstance(s, str):\n return s\n return s.encode(encoding)\n\n\ndef ensure_unicode(s, encoding=None, confidence=None):\n \"\"\"Convert/decode bytestring to unicode.\n\n If `s` isn't a bytestring, return it as is.\n\n Parameters\n ----------\n encoding: str, optional\n Encoding to use. If None, \"utf-8\" is tried, and then if not a valid\n UTF-8, encoding will be guessed\n confidence: float, optional\n A value between 0 and 1, so if guessing of encoding is of lower than\n specified confidence, ValueError is raised\n \"\"\"\n if not isinstance(s, bytes):\n return s\n if encoding is None:\n # Figure out encoding, defaulting to 'utf-8' which is our common\n # target in contemporary digital society\n try:\n return s.decode('utf-8')\n except UnicodeDecodeError as exc:\n lgr.debug(\"Failed to decode a string as utf-8: %s\",\n CapturedException(exc))\n # And now we could try to guess\n from chardet import detect\n enc = detect(s)\n denc = enc.get('encoding', None)\n if denc:\n denc_confidence = enc.get('confidence', 0)\n if confidence is not None and denc_confidence < confidence:\n raise ValueError(\n \"Failed to auto-detect encoding with high enough \"\n \"confidence. Highest confidence was %s for %s\"\n % (denc_confidence, denc)\n )\n lgr.log(5, \"Auto-detected encoding to be %s\", denc)\n return s.decode(denc)\n else:\n raise ValueError(\n \"Could not decode value as utf-8, or to guess its encoding: %s\"\n % repr(s)\n )\n else:\n return s.decode(encoding)\n\n\ndef ensure_bool(s):\n \"\"\"Convert value into boolean following convention for strings\n\n to recognize on,True,yes as True, off,False,no as False\n \"\"\"\n if isinstance(s, str):\n if s.isdigit():\n return bool(int(s))\n sl = s.lower()\n if sl in {'y', 'yes', 'true', 'on'}:\n return True\n elif sl in {'n', 'no', 'false', 'off'}:\n return False\n else:\n raise ValueError(\"Do not know how to treat %r as a boolean\" % s)\n return bool(s)\n\n\ndef as_unicode(val, cast_types=object):\n \"\"\"Given an arbitrary value, would try to obtain unicode value of it\n \n For unicode it would return original value, for python2 str or python3\n bytes it would use ensure_unicode, for None - an empty (unicode) string,\n and for any other type (see `cast_types`) - would apply the unicode \n constructor. If value is not an instance of `cast_types`, TypeError\n is thrown\n \n Parameters\n ----------\n cast_types: type\n Which types to cast to unicode by providing to constructor\n \"\"\"\n if val is None:\n return u''\n elif isinstance(val, str):\n return val\n elif isinstance(val, unicode_srctypes):\n return ensure_unicode(val)\n elif isinstance(val, cast_types):\n return str(val)\n else:\n raise TypeError(\n \"Value %r is not of any of known or provided %s types\"\n % (val, cast_types))\n\n\ndef unique(seq, key=None, reverse=False):\n \"\"\"Given a sequence return a list only with unique elements while maintaining order\n\n This is the fastest solution. See\n https://www.peterbe.com/plog/uniqifiers-benchmark\n and\n http://stackoverflow.com/a/480227/1265472\n for more information.\n Enhancement -- added ability to compare for uniqueness using a key function\n\n Parameters\n ----------\n seq:\n Sequence to analyze\n key: callable, optional\n Function to call on each element so we could decide not on a full\n element, but on its member etc\n reverse: bool, optional\n If True, uniqueness checked in the reverse order, so that the later ones\n will take the order\n \"\"\"\n seen = set()\n seen_add = seen.add\n\n trans = reversed if reverse else lambda x: x\n\n if not key:\n out = [x for x in trans(seq) if not (x in seen or seen_add(x))]\n else:\n # OPT: could be optimized, since key is called twice, but for our cases\n # should be just as fine\n out = [x for x in trans(seq) if not (key(x) in seen or seen_add(key(x)))]\n\n return out[::-1] if reverse else out\n\n\ndef all_same(items):\n \"\"\"Quick check if all items are the same.\n\n Identical to a check like len(set(items)) == 1 but\n should be more efficient while working on generators, since would\n return False as soon as any difference detected thus possibly avoiding\n unnecessary evaluations\n \"\"\"\n first = True\n first_item = None\n for item in items:\n if first:\n first = False\n first_item = item\n else:\n if item != first_item:\n return False\n # So we return False if was empty\n return not first\n\n\ndef map_items(func, v):\n \"\"\"A helper to apply `func` to all elements (keys and values) within dict\n\n No type checking of values passed to func is done, so `func`\n should be resilient to values which it should not handle\n\n Initial usecase - apply_recursive(url_fragment, ensure_unicode)\n \"\"\"\n # map all elements within item\n return v.__class__(\n item.__class__(map(func, item))\n for item in v.items()\n )\n\n\ndef partition(items, predicate=bool):\n \"\"\"Partition `items` by `predicate`.\n\n Parameters\n ----------\n items : iterable\n predicate : callable\n A function that will be mapped over each element in `items`. The\n elements will partitioned based on whether the return value is false or\n true.\n\n Returns\n -------\n A tuple with two generators, the first for 'false' items and the second for\n 'true' ones.\n\n Notes\n -----\n Taken from Peter Otten's snippet posted at\n https://nedbatchelder.com/blog/201306/filter_a_list_into_two_parts.html\n \"\"\"\n a, b = tee((predicate(item), item) for item in items)\n return ((item for pred, item in a if not pred),\n (item for pred, item in b if pred))\n\n\ndef generate_chunks(container, size):\n \"\"\"Given a container, generate chunks from it with size up to `size`\n \"\"\"\n # There could be a \"smarter\" solution but I think this would suffice\n assert size > 0, \"Size should be non-0 positive\"\n while container:\n yield container[:size]\n container = container[size:]\n\n\ndef generate_file_chunks(files, cmd=None):\n \"\"\"Given a list of files, generate chunks of them to avoid exceeding cmdline length\n\n Parameters\n ----------\n files: list of str\n cmd: str or list of str, optional\n Command to account for as well\n \"\"\"\n files = ensure_list(files)\n cmd = ensure_list(cmd)\n\n maxl = max(map(len, files)) if files else 0\n chunk_size = max(\n 1, # should at least be 1. If blows then - not our fault\n (CMD_MAX_ARG\n - sum((len(x) + 3) for x in cmd)\n - 4 # for '--' below\n ) // (maxl + 3) # +3 for possible quotes and a space\n )\n # TODO: additional treatment for \"too many arguments\"? although\n # as https://github.com/datalad/datalad/issues/1883#issuecomment\n # -436272758\n # shows there seems to be no hardcoded limit on # of arguments,\n # but may be we decide to go for smth like follow to be on safe side\n # chunk_size = min(10240 - len(cmd), chunk_size)\n file_chunks = generate_chunks(files, chunk_size)\n return file_chunks\n\n\n#\n# Generators helpers\n#\n\ndef saved_generator(gen):\n \"\"\"Given a generator returns two generators, where 2nd one just replays\n\n So the first one would be going through the generated items and 2nd one\n would be yielding saved items\n \"\"\"\n saved = []\n\n def gen1():\n for x in gen: # iterating over original generator\n saved.append(x)\n yield x\n\n def gen2():\n for x in saved: # yielding saved entries\n yield x\n\n return gen1(), gen2()\n\n\n#\n# Decorators\n#\n\n# Originally better_wraps was created to provide `wrapt`-based, instead of\n# `functools.wraps` implementation to preserve the correct signature of the\n# decorated function. By using inspect.signature in our getargspec, which\n# works fine on `functools.wraps`ed functions, we mediated this necessity.\nbetter_wraps = wraps\n\n\n# Borrowed from pandas\n# Copyright: 2011-2014, Lambda Foundry, Inc. and PyData Development Team\n# License: BSD-3\ndef optional_args(decorator):\n \"\"\"allows a decorator to take optional positional and keyword arguments.\n Assumes that taking a single, callable, positional argument means that\n it is decorating a function, i.e. something like this::\n\n @my_decorator\n def function(): pass\n\n Calls decorator with decorator(f, `*args`, `**kwargs`)\"\"\"\n\n @better_wraps(decorator)\n def wrapper(*args, **kwargs):\n def dec(f):\n return decorator(f, *args, **kwargs)\n\n is_decorating = not kwargs and len(args) == 1 and isinstance(args[0], Callable)\n if is_decorating:\n f = args[0]\n args = []\n return dec(f)\n else:\n return dec\n\n return wrapper\n\n\n# TODO: just provide decorators for tempfile.mk* functions. This is ugly!\ndef get_tempfile_kwargs(tkwargs=None, prefix=\"\", wrapped=None):\n \"\"\"Updates kwargs to be passed to tempfile. calls depending on env vars\n \"\"\"\n if tkwargs is None:\n tkwargs_ = {}\n else:\n # operate on a copy of tkwargs to avoid any side-effects\n tkwargs_ = tkwargs.copy()\n\n # TODO: don't remember why I had this one originally\n # if len(targs)<2 and \\\n if 'prefix' not in tkwargs_:\n tkwargs_['prefix'] = '_'.join(\n ['datalad_temp'] +\n ([prefix] if prefix else []) +\n ([''] if (on_windows or not wrapped) else [wrapped.__name__]))\n\n directory = os.environ.get('TMPDIR')\n if directory and 'dir' not in tkwargs_:\n tkwargs_['dir'] = directory\n\n return tkwargs_\n\n\n@optional_args\ndef line_profile(func):\n \"\"\"Q&D helper to line profile the function and spit out stats\n \"\"\"\n import line_profiler\n prof = line_profiler.LineProfiler()\n\n @wraps(func)\n def _wrap_line_profile(*args, **kwargs):\n try:\n pfunc = prof(func)\n return pfunc(*args, **kwargs)\n finally:\n prof.print_stats()\n return _wrap_line_profile\n\n\n# unused in -core\n@optional_args\ndef collect_method_callstats(func):\n \"\"\"Figure out methods which call the method repeatedly on the same instance\n\n Use case(s):\n - .repo is expensive since does all kinds of checks.\n - .config is expensive transitively since it calls .repo each time\n\n TODO:\n - fancy one could look through the stack for the same id(self) to see if\n that location is already in memo. That would hint to the cases where object\n is not passed into underlying functions, causing them to redo the same work\n over and over again\n - ATM might flood with all \"1 lines\" calls which are not that informative.\n The underlying possibly suboptimal use might be coming from their callers.\n It might or not relate to the previous TODO\n \"\"\"\n from collections import defaultdict\n import traceback\n from time import time\n memo = defaultdict(lambda: defaultdict(int)) # it will be a dict of lineno: count\n # gross timing\n times = []\n toppath = dirname(__file__) + sep\n\n @wraps(func)\n def _wrap_collect_method_callstats(*args, **kwargs):\n try:\n self = args[0]\n stack = traceback.extract_stack()\n caller = stack[-2]\n stack_sig = \\\n \"{relpath}:{s.name}\".format(\n s=caller, relpath=relpath(caller.filename, toppath))\n sig = (id(self), stack_sig)\n # we will count based on id(self) + wherefrom\n memo[sig][caller.lineno] += 1\n t0 = time()\n return func(*args, **kwargs)\n finally:\n times.append(time() - t0)\n pass\n\n def print_stats():\n print(\"The cost of property {}:\".format(func.__name__))\n if not memo:\n print(\"None since no calls\")\n return\n # total count\n counts = {k: sum(v.values()) for k,v in memo.items()}\n total = sum(counts.values())\n ids = {self_id for (self_id, _) in memo}\n print(\" Total: {} calls from {} objects with {} contexts taking {:.2f} sec\"\n .format(total, len(ids), len(memo), sum(times)))\n # now we need to sort by value\n for (self_id, caller), count in sorted(counts.items(), key=lambda x: x[1], reverse=True):\n print(\" {} {}: {} from {} lines\"\n .format(self_id, caller, count, len(memo[(self_id, caller)])))\n\n # Upon total exit we print the stats\n import atexit\n atexit.register(print_stats)\n\n return _wrap_collect_method_callstats\n\n\n# Borrowed from duecredit to wrap duecredit-handling to guarantee failsafe\ndef never_fail(f):\n \"\"\"Assure that function never fails -- all exceptions are caught\n\n Returns `None` if function fails internally.\n \"\"\"\n @wraps(f)\n def wrapped_func(*args, **kwargs):\n try:\n return f(*args, **kwargs)\n except Exception as e:\n lgr.warning(\n \"DataLad internal failure while running %s: %r. \"\n \"Please report at https://github.com/datalad/datalad/issues\"\n % (f, e)\n )\n\n if os.environ.get('DATALAD_ALLOW_FAIL', False):\n return f\n else:\n return wrapped_func\n\n\n#\n# Context Managers\n#\n\n\n# unused in -core\n@contextmanager\ndef nothing_cm():\n \"\"\"Just a dummy cm to programmically switch context managers\"\"\"\n yield\n\n\n@contextmanager\ndef swallow_outputs():\n \"\"\"Context manager to help consuming both stdout and stderr, and print()\n\n stdout is available as cm.out and stderr as cm.err whenever cm is the\n yielded context manager.\n Internally uses temporary files to guarantee absent side-effects of swallowing\n into StringIO which lacks .fileno.\n\n print mocking is necessary for some uses where sys.stdout was already bound\n to original sys.stdout, thus mocking it later had no effect. Overriding\n print function had desired effect\n \"\"\"\n\n class StringIOAdapter(object):\n \"\"\"Little adapter to help getting out/err values\n \"\"\"\n def __init__(self):\n kw = get_tempfile_kwargs({}, prefix=\"outputs\")\n\n self._out = NamedTemporaryFile(delete=False, mode='w', **kw)\n self._err = NamedTemporaryFile(delete=False, mode='w', **kw)\n\n def _read(self, h):\n with open(h.name) as f:\n return f.read()\n\n @property\n def out(self):\n if not self._out.closed:\n self._out.flush()\n return self._read(self._out)\n\n @property\n def err(self):\n if not self._err.closed:\n self._err.flush()\n return self._read(self._err)\n\n @property\n def handles(self):\n return self._out, self._err\n\n def cleanup(self):\n self._out.close()\n self._err.close()\n out_name = self._out.name\n err_name = self._err.name\n from datalad import cfg\n if cfg.getbool('datalad.log', 'outputs', default=False) \\\n and lgr.getEffectiveLevel() <= logging.DEBUG:\n for s, sname in ((self.out, 'stdout'),\n (self.err, 'stderr')):\n if s:\n pref = os.linesep + \"| \"\n lgr.debug(\"Swallowed %s:%s%s\", sname, pref, s.replace(os.linesep, pref))\n else:\n lgr.debug(\"Nothing was swallowed for %s\", sname)\n del self._out\n del self._err\n gc.collect()\n rmtemp(out_name)\n rmtemp(err_name)\n\n def fake_print(*args, **kwargs):\n sep = kwargs.pop('sep', ' ')\n end = kwargs.pop('end', '\\n')\n file = kwargs.pop('file', sys.stdout)\n\n if file in (oldout, olderr, sys.stdout, sys.stderr):\n # we mock\n try:\n sys.stdout.write(sep.join(args) + end)\n except UnicodeEncodeError as exc:\n lgr.error(\n \"Failed to write to mocked stdout, got %s, continue as it \"\n \"didn't happen\", exc)\n else:\n # must be some other file one -- leave it alone\n oldprint(*args, sep=sep, end=end, file=file)\n\n from .ui import ui\n # preserve -- they could have been mocked already\n oldprint = getattr(builtins, 'print')\n oldout, olderr = sys.stdout, sys.stderr\n olduiout = ui.out\n adapter = StringIOAdapter()\n\n try:\n sys.stdout, sys.stderr = adapter.handles\n ui.out = adapter.handles[0]\n setattr(builtins, 'print', fake_print)\n\n yield adapter\n finally:\n sys.stdout, sys.stderr, ui.out = oldout, olderr, olduiout\n setattr(builtins, 'print', oldprint)\n adapter.cleanup()\n\n\n@contextmanager\ndef swallow_logs(new_level=None, file_=None, name='datalad'):\n \"\"\"Context manager to consume all logs.\n\n \"\"\"\n lgr = logging.getLogger(name)\n\n # Keep old settings\n old_level = lgr.level\n old_handlers = lgr.handlers\n\n # Let's log everything into a string\n # TODO: generalize with the one for swallow_outputs\n class StringIOAdapter(object):\n \"\"\"Little adapter to help getting out values\n\n And to stay consistent with how swallow_outputs behaves\n \"\"\"\n def __init__(self):\n if file_ is None:\n kw = get_tempfile_kwargs({}, prefix=\"logs\")\n self._out = NamedTemporaryFile(mode='a', delete=False, **kw)\n else:\n out_file = file_\n # PY3 requires clearly one or another. race condition possible\n self._out = open(out_file, 'a')\n self._final_out = None\n\n def _read(self, h):\n with open(h.name) as f:\n return f.read()\n\n @property\n def out(self):\n if self._final_out is not None:\n # we closed and cleaned up already\n return self._final_out\n else:\n self._out.flush()\n return self._read(self._out)\n\n @property\n def lines(self):\n return self.out.split('\\n')\n\n @property\n def handle(self):\n return self._out\n\n def cleanup(self):\n # store for access while object exists\n self._final_out = self.out\n self._out.close()\n out_name = self._out.name\n del self._out\n gc.collect()\n if not file_:\n rmtemp(out_name)\n\n def assert_logged(self, msg=None, level=None, regex=True, **kwargs):\n \"\"\"Provide assertion on whether a msg was logged at a given level\n\n If neither `msg` nor `level` provided, checks if anything was logged\n at all.\n\n Parameters\n ----------\n msg: str, optional\n Message (as a regular expression, if `regex`) to be searched.\n If no msg provided, checks if anything was logged at a given level.\n level: str, optional\n String representing the level to be logged\n regex: bool, optional\n If False, regular `assert_in` is used\n **kwargs: str, optional\n Passed to `assert_re_in` or `assert_in`\n \"\"\"\n from datalad.tests.utils import assert_re_in\n from datalad.tests.utils import assert_in\n\n if regex:\n match = r'\\[%s\\] ' % level if level else r\"\\[\\S+\\] \"\n else:\n match = '[%s] ' % level if level else ''\n\n if msg:\n match += msg\n\n if match:\n (assert_re_in if regex else assert_in)(match, self.out, **kwargs)\n else:\n assert not kwargs, \"no kwargs to be passed anywhere\"\n assert self.out, \"Nothing was logged!?\"\n\n adapter = StringIOAdapter()\n # TODO: it does store messages but without any formatting, i.e. even without\n # date/time prefix etc. IMHO it should preserve formatting in case if file_ is\n # set\n swallow_handler = logging.StreamHandler(adapter.handle)\n # we want to log levelname so we could test against it\n swallow_handler.setFormatter(\n logging.Formatter('[%(levelname)s] %(message)s'))\n swallow_handler.filters = sum([h.filters for h in old_handlers],\n [])\n lgr.handlers = [swallow_handler]\n if old_level < logging.DEBUG: # so if HEAVYDEBUG etc -- show them!\n lgr.handlers += old_handlers\n\n if isinstance(new_level, str):\n new_level = getattr(logging, new_level)\n\n if new_level is not None:\n lgr.setLevel(new_level)\n\n try:\n yield adapter\n # TODO: if file_ and there was an exception -- most probably worth logging it?\n # although ideally it should be the next log outside added to that file_ ... oh well\n finally:\n lgr.handlers = old_handlers\n lgr.setLevel(old_level)\n adapter.cleanup()\n\n\n# TODO: May be melt in with swallow_logs at some point:\n@contextmanager\ndef disable_logger(logger=None):\n \"\"\"context manager to temporarily disable logging\n\n This is to provide one of swallow_logs' purposes without unnecessarily\n creating temp files (see gh-1865)\n\n Parameters\n ----------\n logger: Logger\n Logger whose handlers will be ordered to not log anything.\n Default: datalad's topmost Logger ('datalad')\n \"\"\"\n\n class NullFilter(logging.Filter):\n \"\"\"Filter class to reject all records\n \"\"\"\n def filter(self, record):\n return 0\n\n if logger is None:\n # default: all of datalad's logging:\n logger = logging.getLogger('datalad')\n\n filter_ = NullFilter(logger.name)\n [h.addFilter(filter_) for h in logger.handlers]\n\n try:\n yield logger\n finally:\n [h.removeFilter(filter_) for h in logger.handlers]\n\n\n#\n# Additional handlers\n#\n_sys_excepthook = sys.excepthook # Just in case we ever need original one\n\n\ndef setup_exceptionhook(ipython=False):\n \"\"\"Overloads default sys.excepthook with our exceptionhook handler.\n\n If interactive, our exceptionhook handler will invoke\n pdb.post_mortem; if not interactive, then invokes default handler.\n \"\"\"\n\n def _datalad_pdb_excepthook(type, value, tb):\n import traceback\n traceback.print_exception(type, value, tb)\n print()\n if is_interactive():\n import pdb\n pdb.post_mortem(tb)\n\n if ipython:\n from IPython.core import ultratb\n sys.excepthook = ultratb.FormattedTB(mode='Verbose',\n # color_scheme='Linux',\n call_pdb=is_interactive())\n else:\n sys.excepthook = _datalad_pdb_excepthook\n\n\ndef ensure_dir(*args):\n \"\"\"Make sure directory exists.\n\n Joins the list of arguments to an os-specific path to the desired\n directory and creates it, if it not exists yet.\n \"\"\"\n dirname = op.join(*args)\n if not exists(dirname):\n os.makedirs(dirname)\n return dirname\n\n\ndef updated(d, update):\n \"\"\"Return a copy of the input with the 'update'\n\n Primarily for updating dictionaries\n \"\"\"\n d = d.copy()\n d.update(update)\n return d\n\n\n_pwd_mode = None\n\n\ndef _switch_to_getcwd(msg, *args):\n global _pwd_mode\n _pwd_mode = 'cwd'\n lgr.debug(\n msg + \". From now on will be returning os.getcwd(). Directory\"\n \" symlinks in the paths will be resolved\",\n *args\n )\n # TODO: we might want to mitigate by going through all flywheighted\n # repos and tuning up their .paths to be resolved?\n\n\ndef getpwd():\n \"\"\"Try to return a CWD without dereferencing possible symlinks\n\n This function will try to use PWD environment variable to provide a current\n working directory, possibly with some directories along the path being\n symlinks to other directories. Unfortunately, PWD is used/set only by the\n shell and such functions as `os.chdir` and `os.getcwd` nohow use or modify\n it, thus `os.getcwd()` returns path with links dereferenced.\n\n While returning current working directory based on PWD env variable we\n verify that the directory is the same as `os.getcwd()` after resolving all\n symlinks. If that verification fails, we fall back to always use\n `os.getcwd()`.\n\n Initial decision to either use PWD env variable or os.getcwd() is done upon\n the first call of this function.\n \"\"\"\n global _pwd_mode\n if _pwd_mode is None:\n # we need to decide!\n try:\n pwd = os.environ['PWD']\n if on_windows and pwd and pwd.startswith('/'):\n # It should be a path from MSYS.\n # - it might start with a drive letter or not\n # - it seems to be \"illegal\" to have a single letter directories\n # under / path, i.e. if created - they aren't found\n # - 'ln -s' does not fail to create a \"symlink\" but it just\n # copies!\n # so we are not likely to need original PWD purpose on\n # those systems\n # Verdict:\n _pwd_mode = 'cwd'\n else:\n _pwd_mode = 'PWD'\n except KeyError:\n _pwd_mode = 'cwd'\n\n if _pwd_mode == 'cwd':\n return os.getcwd()\n elif _pwd_mode == 'PWD':\n try:\n cwd = os.getcwd()\n except OSError as exc:\n if \"o such file\" in str(exc):\n # directory was removed but we promised to be robust and\n # still report the path we might know since we are still in PWD\n # mode\n cwd = None\n else:\n raise\n try:\n pwd = os.environ['PWD']\n # do absolute() in addition to always get an absolute path\n # even with non-existing paths on windows\n pwd_real = str(Path(pwd).resolve().absolute())\n # This logic would fail to catch the case where chdir did happen\n # to the directory where current PWD is pointing to, e.g.\n # $> ls -ld $PWD\n # lrwxrwxrwx 1 yoh yoh 5 Oct 11 13:27 /home/yoh/.tmp/tmp -> /tmp//\n # hopa:~/.tmp/tmp\n # $> python -c 'import os; os.chdir(\"/tmp\"); from datalad.utils import getpwd; print(getpwd(), os.getcwd())'\n # ('/home/yoh/.tmp/tmp', '/tmp')\n # but I guess that should not be too harmful\n if cwd is not None and pwd_real != cwd:\n _switch_to_getcwd(\n \"realpath of PWD=%s is %s whenever os.getcwd()=%s\",\n pwd, pwd_real, cwd\n )\n return cwd\n return pwd\n except KeyError:\n _switch_to_getcwd(\"PWD env variable is no longer available\")\n return cwd # Must not happen, but may be someone\n # evil purges PWD from environ?\n else:\n raise RuntimeError(\n \"Must have not got here. \"\n \"pwd_mode must be either cwd or PWD. And it is now %r\" % (_pwd_mode,)\n )\n\n\nclass chpwd(object):\n \"\"\"Wrapper around os.chdir which also adjusts environ['PWD']\n\n The reason is that otherwise PWD is simply inherited from the shell\n and we have no ability to assess directory path without dereferencing\n symlinks.\n\n If used as a context manager it allows to temporarily change directory\n to the given path\n \"\"\"\n def __init__(self, path, mkdir=False, logsuffix=''):\n\n if path:\n pwd = getpwd()\n self._prev_pwd = pwd\n else:\n self._prev_pwd = None\n return\n\n if not isabs(path):\n path = normpath(op.join(pwd, path))\n if not os.path.exists(path) and mkdir:\n self._mkdir = True\n os.mkdir(path)\n else:\n self._mkdir = False\n lgr.debug(\"chdir %r -> %r %s\", self._prev_pwd, path, logsuffix)\n os.chdir(path) # for grep people -- ok, to chdir here!\n os.environ['PWD'] = str(path)\n\n def __enter__(self):\n # nothing more to do really, chdir was in the constructor\n pass\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n if self._prev_pwd:\n # Need to use self.__class__ so this instance, if the entire\n # thing mocked during the test, still would use correct chpwd\n self.__class__(self._prev_pwd, logsuffix=\"(coming back)\")\n\n\ndef dlabspath(path, norm=False):\n \"\"\"Symlinks-in-the-cwd aware abspath\n\n os.path.abspath relies on os.getcwd() which would not know about symlinks\n in the path\n\n TODO: we might want to norm=True by default to match behavior of\n os .path.abspath?\n \"\"\"\n if not isabs(path):\n # if not absolute -- relative to pwd\n path = op.join(getpwd(), path)\n return normpath(path) if norm else path\n\n\ndef with_pathsep(path):\n \"\"\"Little helper to guarantee that path ends with /\"\"\"\n return path + sep if not path.endswith(sep) else path\n\n\ndef get_path_prefix(path, pwd=None):\n \"\"\"Get path prefix (for current directory)\n\n Returns relative path to the topdir, if we are under topdir, and if not\n absolute path to topdir. If `pwd` is not specified - current directory\n assumed\n \"\"\"\n pwd = pwd or getpwd()\n path = dlabspath(path)\n path_ = with_pathsep(path)\n pwd_ = with_pathsep(pwd)\n common = commonprefix((path_, pwd_))\n if common.endswith(sep) and common in {path_, pwd_}:\n # we are in subdir or above the path = use relative path\n location_prefix = relpath(path, pwd)\n # if benign \"here\" - cut off\n if location_prefix in (curdir, curdir + sep):\n location_prefix = ''\n return location_prefix\n else:\n # just return absolute path\n return path\n\n\ndef _get_normalized_paths(path, prefix):\n if isabs(path) != isabs(prefix):\n raise ValueError(\"Both paths must either be absolute or relative. \"\n \"Got %r and %r\" % (path, prefix))\n path = with_pathsep(path)\n prefix = with_pathsep(prefix)\n return path, prefix\n\n\ndef path_startswith(path, prefix):\n \"\"\"Return True if path starts with prefix path\n\n Parameters\n ----------\n path: str\n prefix: str\n \"\"\"\n path, prefix = _get_normalized_paths(path, prefix)\n return path.startswith(prefix)\n\n\ndef path_is_subpath(path, prefix):\n \"\"\"Return True if path is a subpath of prefix\n\n It will return False if path == prefix.\n\n Parameters\n ----------\n path: str\n prefix: str\n \"\"\"\n path, prefix = _get_normalized_paths(path, prefix)\n return (len(prefix) < len(path)) and path.startswith(prefix)\n\n\ndef knows_annex(path):\n \"\"\"Returns whether at a given path there is information about an annex\n\n It is just a thin wrapper around GitRepo.is_with_annex() classmethod\n which also checks for `path` to exist first.\n\n This includes actually present annexes, but also uninitialized ones, or\n even the presence of a remote annex branch.\n \"\"\"\n from os.path import exists\n if not exists(path):\n lgr.debug(\"No annex: test path {0} doesn't exist\".format(path))\n return False\n from datalad.support.gitrepo import GitRepo\n return GitRepo(path, init=False, create=False).is_with_annex()\n\n\n@contextmanager\ndef make_tempfile(content=None, wrapped=None, **tkwargs):\n \"\"\"Helper class to provide a temporary file name and remove it at the end (context manager)\n\n Parameters\n ----------\n mkdir : bool, optional (default: False)\n If True, temporary directory created using tempfile.mkdtemp()\n content : str or bytes, optional\n Content to be stored in the file created\n wrapped : function, optional\n If set, function name used to prefix temporary file name\n `**tkwargs`:\n All other arguments are passed into the call to tempfile.mk{,d}temp(),\n and resultant temporary filename is passed as the first argument into\n the function t. If no 'prefix' argument is provided, it will be\n constructed using module and function names ('.' replaced with\n '_').\n\n To change the used directory without providing keyword argument 'dir' set\n DATALAD_TESTS_TEMP_DIR.\n\n Examples\n --------\n >>> from os.path import exists\n >>> from datalad.utils import make_tempfile\n >>> with make_tempfile() as fname:\n ... k = open(fname, 'w').write('silly test')\n >>> assert not exists(fname) # was removed\n\n >>> with make_tempfile(content=\"blah\") as fname:\n ... assert open(fname).read() == \"blah\"\n \"\"\"\n\n if tkwargs.get('mkdir', None) and content is not None:\n raise ValueError(\"mkdir=True while providing content makes no sense\")\n\n tkwargs_ = get_tempfile_kwargs(tkwargs, wrapped=wrapped)\n\n # if DATALAD_TESTS_TEMP_DIR is set, use that as directory,\n # let mktemp handle it otherwise. However, an explicitly provided\n # dir=... will override this.\n mkdir = tkwargs_.pop('mkdir', False)\n\n filename = {False: tempfile.mktemp,\n True: tempfile.mkdtemp}[mkdir](**tkwargs_)\n # MIH: not clear to me why we need to perform this (possibly expensive)\n # resolve. It was already part of the original implementation\n # 008d9ab8cc3e0170c0a9b8479e80dee9ffe6eb7f\n filename = Path(filename).resolve()\n\n if content:\n (filename.write_bytes\n if isinstance(content, bytes)\n else filename.write_text)(content)\n\n # TODO globbing below can also be done with pathlib\n filename = str(filename)\n\n if __debug__:\n lgr.debug(\n 'Created temporary %s named %s',\n 'directory' if mkdir else 'file',\n filename)\n try:\n yield filename\n finally:\n # glob here for all files with the same name (-suffix)\n # would be useful whenever we requested .img filename,\n # and function creates .hdr as well\n # MIH: this is undocumented behavior, and undesired in the general\n # case. it should be made conditional and explicit\n lsuffix = len(tkwargs_.get('suffix', ''))\n filename_ = lsuffix and filename[:-lsuffix] or filename\n filenames = glob.glob(filename_ + '*')\n if len(filename_) < 3 or len(filenames) > 5:\n # For paranoid yoh who stepped into this already ones ;-)\n lgr.warning(\"It is unlikely that it was intended to remove all\"\n \" files matching %r. Skipping\" % filename_)\n return\n for f in filenames:\n try:\n rmtemp(f)\n except OSError: # pragma: no cover\n pass\n\n\ndef _path_(*p):\n \"\"\"Given a path in POSIX\" notation, regenerate one in native to the env one\"\"\"\n if on_windows:\n return op.join(*map(lambda x: op.join(*x.split('/')), p))\n else:\n # Assume that all others as POSIX compliant so nothing to be done\n return op.join(*p)\n\n\ndef get_timestamp_suffix(time_=None, prefix='-'):\n \"\"\"Return a time stamp (full date and time up to second)\n\n primarily to be used for generation of log files names\n \"\"\"\n args = []\n if time_ is not None:\n if isinstance(time_, int):\n time_ = time.gmtime(time_)\n args.append(time_)\n return time.strftime(prefix + TIMESTAMP_FMT, *args)\n\n\n# unused in -core\ndef get_logfilename(dspath, cmd='datalad'):\n \"\"\"Return a filename to use for logging under a dataset/repository\n\n directory would be created if doesn't exist, but dspath must exist\n and be a directory\n \"\"\"\n assert(exists(dspath))\n assert(isdir(dspath))\n ds_logdir = ensure_dir(dspath, '.git', 'datalad', 'logs') # TODO: use WEB_META_LOG whenever #789 merged\n return op.join(ds_logdir, 'crawl-%s.log' % get_timestamp_suffix())\n\n\ndef get_trace(edges, start, end, trace=None):\n \"\"\"Return the trace/path to reach a node in a tree.\n\n Parameters\n ----------\n edges : sequence(2-tuple)\n The tree given by a sequence of edges (parent, child) tuples. The\n nodes can be identified by any value and data type that supports\n the '==' operation.\n start :\n Identifier of the start node. Must be present as a value in the parent\n location of an edge tuple in order to be found.\n end :\n Identifier of the target/end node. Must be present as a value in the child\n location of an edge tuple in order to be found.\n trace : list\n Mostly useful for recursive calls, and used internally.\n\n Returns\n -------\n None or list\n Returns a list with the trace to the target (the starts and the target\n are not included in the trace, hence if start and end are directly connected\n an empty list is returned), or None when no trace to the target can be found,\n or start and end are identical.\n \"\"\"\n # the term trace is used to avoid confusion with a path in the sense\n # of a filesystem path, but the analogy fits and nodes can be paths\n if trace is None:\n trace = []\n if not edges:\n raise ValueError(\"no edges given\")\n for cand in edges:\n cand_super, cand_sub = cand\n if cand_sub in trace:\n # only DAGs, skip any cyclic traces\n continue\n if trace and cand_super != trace[-1]:\n # only consider edges that lead off the end of the trace\n continue\n if not trace and cand_super != start:\n # we got nothing yet, and this edges is not matching the start\n continue\n if cand_sub == end:\n return trace\n # dive into potential subnodes\n cand_trace = get_trace(\n edges,\n start,\n end,\n trace + [cand_sub])\n if cand_trace:\n return cand_trace\n return None\n\n\ndef get_dataset_root(path):\n \"\"\"Return the root of an existent dataset containing a given path\n\n The root path is returned in the same absolute or relative form\n as the input argument. If no associated dataset exists, or the\n input path doesn't exist, None is returned.\n\n If `path` is a symlink or something other than a directory, its\n the root dataset containing its parent directory will be reported.\n If none can be found, at a symlink at `path` is pointing to a\n dataset, `path` itself will be reported as the root.\n\n Parameters\n ----------\n path : Path-like\n\n Returns\n -------\n str or None\n \"\"\"\n path = str(path)\n suffix = '.git'\n altered = None\n if islink(path) or not isdir(path):\n altered = path\n path = dirname(path)\n apath = abspath(path)\n # while we can still go up\n while split(apath)[1]:\n if exists(op.join(path, suffix)):\n return path\n # new test path in the format we got it\n path = normpath(op.join(path, os.pardir))\n # no luck, next round\n apath = abspath(path)\n # if we applied dirname() at the top, we give it another go with\n # the actual path, if it was itself a symlink, it could be the\n # top-level dataset itself\n if altered and exists(op.join(altered, suffix)):\n return altered\n\n return None\n\n\n# ATM used in datalad_crawler extension, so do not remove yet\ndef try_multiple(ntrials, exception, base, f, *args, **kwargs):\n \"\"\"Call f multiple times making exponentially growing delay between the calls\"\"\"\n for trial in range(1, ntrials+1):\n try:\n return f(*args, **kwargs)\n except exception as exc:\n if trial == ntrials:\n raise # just reraise on the last trial\n t = base ** trial\n lgr.warning(\"Caught %s on trial #%d. Sleeping %f and retrying\",\n CapturedException(exc), trial, t)\n sleep(t)\n\n\n@optional_args\ndef try_multiple_dec(\n f, ntrials=None, duration=0.1, exceptions=None, increment_type=None,\n exceptions_filter=None,\n logger=None,\n):\n \"\"\"Decorator to try function multiple times.\n\n Main purpose is to decorate functions dealing with removal of files/directories\n and which might need a few seconds to work correctly on Windows which takes\n its time to release files/directories.\n\n Parameters\n ----------\n ntrials: int, optional\n duration: float, optional\n Seconds to sleep before retrying.\n increment_type: {None, 'exponential'}\n Note that if it is exponential, duration should typically be > 1.0\n so it grows with higher power\n exceptions: Exception or tuple of Exceptions, optional\n Exception or a tuple of multiple exceptions, on which to retry\n exceptions_filter: callable, optional\n If provided, this function will be called with a caught exception\n instance. If function returns True - we will re-try, if False - exception\n will be re-raised without retrying.\n logger: callable, optional\n Logger to log upon failure. If not provided, will use stock logger\n at the level of 5 (heavy debug).\n \"\"\"\n if not exceptions:\n exceptions = (OSError, WindowsError, PermissionError) \\\n if on_windows else OSError\n if not ntrials:\n # Life goes fast on proper systems, no need to delay it much\n ntrials = 100 if on_windows else 10\n if logger is None:\n def logger(*args, **kwargs):\n return lgr.log(5, *args, **kwargs)\n assert increment_type in {None, 'exponential'}\n\n @wraps(f)\n def _wrap_try_multiple_dec(*args, **kwargs):\n t = duration\n for trial in range(ntrials):\n try:\n return f(*args, **kwargs)\n except exceptions as exc:\n if exceptions_filter and not exceptions_filter(exc):\n raise\n if trial < ntrials - 1:\n if increment_type == 'exponential':\n t = duration ** (trial + 1)\n logger(\n \"Caught %s on trial #%d. Sleeping %f and retrying\",\n CapturedException(exc), trial, t)\n sleep(t)\n else:\n raise\n\n return _wrap_try_multiple_dec\n\n\n@try_multiple_dec\ndef unlink(f):\n \"\"\"'Robust' unlink. Would try multiple times\n\n On windows boxes there is evidence for a latency of more than a second\n until a file is considered no longer \"in-use\".\n WindowsError is not known on Linux, and if IOError or any other\n exception\n is thrown then if except statement has WindowsError in it -- NameError\n also see gh-2533\n \"\"\"\n # Check for open files\n assert_no_open_files(f)\n return os.unlink(f)\n\n\n@try_multiple_dec\ndef _rmtree(*args, **kwargs):\n \"\"\"Just a helper to decorate shutil.rmtree.\n\n rmtree defined above does more and ideally should not itself be decorated\n since a recursive definition and does checks for open files inside etc -\n might be too runtime expensive\n \"\"\"\n return shutil.rmtree(*args, **kwargs)\n\n\ndef slash_join(base, extension):\n \"\"\"Join two strings with a '/', avoiding duplicate slashes\n\n If any of the strings is None the other is returned as is.\n \"\"\"\n if extension is None:\n return base\n if base is None:\n return extension\n return '/'.join(\n (base.rstrip('/'),\n extension.lstrip('/')))\n\n\n#\n# IO Helpers\n#\n\n# unused in -core\ndef open_r_encdetect(fname, readahead=1000):\n \"\"\"Return a file object in read mode with auto-detected encoding\n\n This is helpful when dealing with files of unknown encoding.\n\n Parameters\n ----------\n readahead: int, optional\n How many bytes to read for guessing the encoding type. If\n negative - full file will be read\n \"\"\"\n from chardet import detect\n import io\n # read some bytes from the file\n with open(fname, 'rb') as f:\n head = f.read(readahead)\n enc = detect(head)\n denc = enc.get('encoding', None)\n lgr.debug(\"Auto-detected encoding %s for file %s (confidence: %s)\",\n denc,\n fname,\n enc.get('confidence', 'unknown'))\n return io.open(fname, encoding=denc)\n\n\ndef read_file(fname, decode=True):\n \"\"\"A helper to read file passing content via ensure_unicode\n\n Parameters\n ----------\n decode: bool, optional\n if False, no ensure_unicode and file content returned as bytes\n \"\"\"\n with open(fname, 'rb') as f:\n content = f.read()\n return ensure_unicode(content) if decode else content\n\n\ndef read_csv_lines(fname, dialect=None, readahead=16384, **kwargs):\n \"\"\"A generator of dict records from a CSV/TSV\n\n Automatically guesses the encoding for each record to convert to UTF-8\n\n Parameters\n ----------\n fname: str\n Filename\n dialect: str, optional\n Dialect to specify to csv.reader. If not specified -- guessed from\n the file, if fails to guess, \"excel-tab\" is assumed\n readahead: int, optional\n How many bytes to read from the file to guess the type\n **kwargs\n Passed to `csv.reader`\n \"\"\"\n import csv\n if dialect is None:\n with open(fname) as tsvfile:\n # add robustness, use a sniffer\n try:\n dialect = csv.Sniffer().sniff(tsvfile.read(readahead))\n except Exception as exc:\n lgr.warning(\n 'Could not determine file-format, assuming TSV: %s',\n CapturedException(exc)\n )\n dialect = 'excel-tab'\n\n kw = dict(encoding='utf-8')\n with open(fname, 'r', **kw) as tsvfile:\n # csv.py doesn't do Unicode; encode temporarily as UTF-8:\n csv_reader = csv.reader(\n tsvfile,\n dialect=dialect,\n **kwargs\n )\n header = None\n for row in csv_reader:\n # decode UTF-8 back to Unicode, cell by cell:\n row_unicode = map(ensure_unicode, row)\n if header is None:\n header = list(row_unicode)\n else:\n yield dict(zip(header, row_unicode))\n\n\ndef import_modules(modnames, pkg, msg=\"Failed to import {module}\", log=lgr.debug):\n \"\"\"Helper to import a list of modules without failing if N/A\n\n Parameters\n ----------\n modnames: list of str\n List of module names to import\n pkg: str\n Package under which to import\n msg: str, optional\n Message template for .format() to log at DEBUG level if import fails.\n Keys {module} and {package} will be provided and ': {exception}' appended\n log: callable, optional\n Logger call to use for logging messages\n \"\"\"\n from importlib import import_module\n _globals = globals()\n mods_loaded = []\n if pkg and not pkg in sys.modules:\n # with python 3.5.1 (ok with 3.5.5) somehow kept running into\n # Failed to import dlsub1: Parent module 'dltestm1' not loaded\n # while running the test. Preloading pkg resolved the issue\n import_module(pkg)\n for modname in modnames:\n try:\n _globals[modname] = mod = import_module(\n '.{}'.format(modname),\n pkg)\n mods_loaded.append(mod)\n except Exception as exc:\n from datalad.support.exceptions import CapturedException\n ce = CapturedException(exc)\n log((msg + ': {exception}').format(\n module=modname, package=pkg, exception=ce.message))\n return mods_loaded\n\n\ndef import_module_from_file(modpath, pkg=None, log=lgr.debug):\n \"\"\"Import provided module given a path\n\n TODO:\n - RF/make use of it in pipeline.py which has similar logic\n - join with import_modules above?\n\n Parameters\n ----------\n pkg: module, optional\n If provided, and modpath is under pkg.__path__, relative import will be\n used\n \"\"\"\n assert(modpath.endswith('.py')) # for now just for .py files\n\n log(\"Importing %s\" % modpath)\n\n modname = basename(modpath)[:-3]\n relmodpath = None\n if pkg:\n for pkgpath in pkg.__path__:\n if path_is_subpath(modpath, pkgpath):\n # for now relying on having .py extension -- assertion above\n relmodpath = '.' + relpath(modpath[:-3], pkgpath).replace(sep, '.')\n break\n\n try:\n if relmodpath:\n from importlib import import_module\n mod = import_module(relmodpath, pkg.__name__)\n else:\n dirname_ = dirname(modpath)\n try:\n sys.path.insert(0, dirname_)\n mod = __import__(modname, level=0)\n finally:\n if dirname_ in sys.path:\n sys.path.pop(sys.path.index(dirname_))\n else:\n log(\"Expected path %s to be within sys.path, but it was gone!\" % dirname_)\n except Exception as e:\n raise RuntimeError(\n \"Failed to import module from %s\" % modpath) from e\n\n return mod\n\n\ndef get_encoding_info():\n \"\"\"Return a dictionary with various encoding/locale information\"\"\"\n import sys, locale\n from collections import OrderedDict\n return OrderedDict([\n ('default', sys.getdefaultencoding()),\n ('filesystem', sys.getfilesystemencoding()),\n ('locale.prefered', locale.getpreferredencoding()),\n ])\n\n\ndef get_envvars_info():\n from collections import OrderedDict\n envs = []\n for var, val in os.environ.items():\n if (\n var.startswith('PYTHON') or\n var.startswith('LC_') or\n var.startswith('GIT_') or\n var in ('LANG', 'LANGUAGE', 'PATH')\n ):\n envs.append((var, val))\n return OrderedDict(envs)\n\n\n# This class is modified from Snakemake (v5.1.4)\nclass SequenceFormatter(string.Formatter):\n \"\"\"string.Formatter subclass with special behavior for sequences.\n\n This class delegates formatting of individual elements to another\n formatter object. Non-list objects are formatted by calling the\n delegate formatter's \"format_field\" method. List-like objects\n (list, tuple, set, frozenset) are formatted by formatting each\n element of the list according to the specified format spec using\n the delegate formatter and then joining the resulting strings with\n a separator (space by default).\n \"\"\"\n\n def __init__(self, separator=\" \", element_formatter=string.Formatter(),\n *args, **kwargs):\n self.separator = separator\n self.element_formatter = element_formatter\n\n def format_element(self, elem, format_spec):\n \"\"\"Format a single element\n\n For sequences, this is called once for each element in a\n sequence. For anything else, it is called on the entire\n object. It is intended to be overridden in subclases.\n \"\"\"\n return self.element_formatter.format_field(elem, format_spec)\n\n def format_field(self, value, format_spec):\n if isinstance(value, (list, tuple, set, frozenset)):\n return self.separator.join(self.format_element(v, format_spec)\n for v in value)\n else:\n return self.format_element(value, format_spec)\n\n\n# TODO: eventually we might want to make use of attr module\nclass File(object):\n \"\"\"Helper for a file entry in the create_tree/@with_tree\n\n It allows to define additional settings for entries\n \"\"\"\n def __init__(self, name, executable=False):\n \"\"\"\n\n Parameters\n ----------\n name : str\n Name of the file\n executable: bool, optional\n Make it executable\n \"\"\"\n self.name = name\n self.executable = executable\n\n def __str__(self):\n return self.name\n\n\ndef create_tree_archive(path, name, load, overwrite=False, archives_leading_dir=True):\n \"\"\"Given an archive `name`, create under `path` with specified `load` tree\n \"\"\"\n from datalad.support.archives import compress_files\n dirname = file_basename(name)\n full_dirname = op.join(path, dirname)\n os.makedirs(full_dirname)\n create_tree(full_dirname, load, archives_leading_dir=archives_leading_dir)\n # create archive\n if archives_leading_dir:\n compress_files([dirname], name, path=path, overwrite=overwrite)\n else:\n compress_files(list(map(basename, glob.glob(op.join(full_dirname, '*')))),\n op.join(pardir, name),\n path=op.join(path, dirname),\n overwrite=overwrite)\n # remove original tree\n rmtree(full_dirname)\n\n\ndef create_tree(path, tree, archives_leading_dir=True, remove_existing=False):\n \"\"\"Given a list of tuples (name, load) create such a tree\n\n if load is a tuple itself -- that would create either a subtree or an archive\n with that content and place it into the tree if name ends with .tar.gz\n \"\"\"\n lgr.log(5, \"Creating a tree under %s\", path)\n if not exists(path):\n os.makedirs(path)\n\n if isinstance(tree, dict):\n tree = tree.items()\n\n for file_, load in tree:\n if isinstance(file_, File):\n executable = file_.executable\n name = file_.name\n else:\n executable = False\n name = file_\n full_name = op.join(path, name)\n if remove_existing and lexists(full_name):\n rmtree(full_name, chmod_files=True)\n if isinstance(load, (tuple, list, dict)):\n if name.endswith('.tar.gz') or name.endswith('.tar') or name.endswith('.zip'):\n create_tree_archive(\n path, name, load,\n archives_leading_dir=archives_leading_dir)\n else:\n create_tree(\n full_name, load,\n archives_leading_dir=archives_leading_dir,\n remove_existing=remove_existing)\n else:\n open_func = open\n if full_name.endswith('.gz'):\n open_func = gzip.open\n elif full_name.split('.')[-1] in ('xz', 'lzma'):\n import lzma\n open_func = lzma.open\n with open_func(full_name, \"wb\") as f:\n f.write(ensure_bytes(load, 'utf-8'))\n if executable:\n os.chmod(full_name, os.stat(full_name).st_mode | stat.S_IEXEC)\n\n\ndef get_suggestions_msg(values, known, sep=\"\\n \"):\n \"\"\"Return a formatted string with suggestions for values given the known ones\n \"\"\"\n import difflib\n suggestions = []\n for value in ensure_list(values): # might not want to do it if we change presentation below\n suggestions += difflib.get_close_matches(value, known)\n suggestions = unique(suggestions)\n msg = \"Did you mean any of these?\"\n if suggestions:\n if '\\n' in sep:\n # if separator includes new line - we add entire separator right away\n msg += sep\n else:\n msg += ' '\n return msg + \"%s\\n\" % sep.join(suggestions)\n return ''\n\n\ndef bytes2human(n, format='%(value).1f %(symbol)sB'):\n \"\"\"\n Convert n bytes into a human readable string based on format.\n symbols can be either \"customary\", \"customary_ext\", \"iec\" or \"iec_ext\",\n see: http://goo.gl/kTQMs\n\n >>> from datalad.utils import bytes2human\n >>> bytes2human(1)\n '1.0 B'\n >>> bytes2human(1024)\n '1.0 KB'\n >>> bytes2human(1048576)\n '1.0 MB'\n >>> bytes2human(1099511627776127398123789121)\n '909.5 YB'\n\n >>> bytes2human(10000, \"%(value).1f %(symbol)s/sec\")\n '9.8 K/sec'\n\n >>> # precision can be adjusted by playing with %f operator\n >>> bytes2human(10000, format=\"%(value).5f %(symbol)s\")\n '9.76562 K'\n\n Taken from: http://goo.gl/kTQMs and subsequently simplified\n Original Author: Giampaolo Rodola' <g.rodola [AT] gmail [DOT] com>\n License: MIT\n \"\"\"\n n = int(n)\n if n < 0:\n raise ValueError(\"n < 0\")\n symbols = ('', 'K', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y')\n prefix = {}\n for i, s in enumerate(symbols[1:]):\n prefix[s] = 1 << (i + 1) * 10\n for symbol in reversed(symbols[1:]):\n if n >= prefix[symbol]:\n value = float(n) / prefix[symbol]\n return format % locals()\n return format % dict(symbol=symbols[0], value=n)\n\n\ndef quote_cmdlinearg(arg):\n \"\"\"Perform platform-appropriate argument quoting\"\"\"\n # https://stackoverflow.com/a/15262019\n return '\"{}\"'.format(\n arg.replace('\"', '\"\"')\n ) if on_windows else shlex_quote(arg)\n\n\ndef guard_for_format(arg):\n \"\"\"Replace { and } with {{ and }}\n\n To be used in cases if arg is not expected to have provided\n by user .format() placeholders, but 'arg' might become a part\n of a composite passed to .format(), e.g. via 'Run'\n \"\"\"\n return arg.replace('{', '{{').replace('}', '}}')\n\n\ndef join_cmdline(args):\n \"\"\"Join command line args into a string using quote_cmdlinearg\n \"\"\"\n return ' '.join(map(quote_cmdlinearg, args))\n\n\ndef split_cmdline(s):\n \"\"\"Perform platform-appropriate command line splitting.\n\n Identical to `shlex.split()` on non-windows platforms.\n\n Modified from https://stackoverflow.com/a/35900070\n \"\"\"\n if not on_windows:\n return shlex_split(s)\n\n # the rest is for windows\n RE_CMD_LEX = r'''\"((?:\"\"|\\\\[\"\\\\]|[^\"])*)\"?()|(\\\\\\\\(?=\\\\*\")|\\\\\")|(&&?|\\|\\|?|\\d?>|[<])|([^\\s\"&|<>]+)|(\\s+)|(.)'''\n\n args = []\n accu = None # collects pieces of one arg\n for qs, qss, esc, pipe, word, white, fail in re.findall(RE_CMD_LEX, s):\n if word:\n pass # most frequent\n elif esc:\n word = esc[1]\n elif white or pipe:\n if accu is not None:\n args.append(accu)\n if pipe:\n args.append(pipe)\n accu = None\n continue\n elif fail:\n raise ValueError(\"invalid or incomplete shell string\")\n elif qs:\n word = qs.replace('\\\\\"', '\"').replace('\\\\\\\\', '\\\\')\n if platform == 0:\n word = word.replace('\"\"', '\"')\n else:\n word = qss # may be even empty; must be last\n\n accu = (accu or '') + word\n\n if accu is not None:\n args.append(accu)\n\n return args\n\n\ndef get_wrapped_class(wrapped):\n \"\"\"Determine the command class a wrapped __call__ belongs to\"\"\"\n mod = sys.modules[wrapped.__module__]\n command_class_name = wrapped.__qualname__.split('.')[-2]\n _func_class = mod.__dict__[command_class_name]\n lgr.debug(\"Determined class of decorated function: %s\", _func_class)\n return _func_class\n\n\ndef _make_assure_kludge(fn):\n old_name = fn.__name__.replace(\"ensure\", \"assure\")\n\n @wraps(fn)\n def compat_fn(*args, **kwargs):\n warnings.warn(\n \"{} is deprecated and will be removed in a future release. \"\n \"Use {} instead.\"\n .format(old_name, fn.__name__),\n DeprecationWarning)\n return fn(*args, **kwargs)\n\n compat_fn.__doc__ = (\"Note: This function is deprecated. Use {} instead.\"\n .format(fn.__name__))\n return compat_fn\n\n\nassure_tuple_or_list = _make_assure_kludge(ensure_tuple_or_list)\nassure_iter = _make_assure_kludge(ensure_iter)\nassure_list = _make_assure_kludge(ensure_list)\nassure_list_from_str = _make_assure_kludge(ensure_list_from_str)\nassure_dict_from_str = _make_assure_kludge(ensure_dict_from_str)\nassure_bytes = _make_assure_kludge(ensure_bytes)\nassure_unicode = _make_assure_kludge(ensure_unicode)\nassure_bool = _make_assure_kludge(ensure_bool)\nassure_dir = _make_assure_kludge(ensure_dir)\n\n\nlgr.log(5, \"Done importing datalad.utils\")\n\n\ndef check_symlink_capability(path, target):\n \"\"\"helper similar to datalad.tests.utils.has_symlink_capability\n\n However, for use in a datalad command context, we shouldn't\n assume to be able to write to tmpfile and also not import a whole lot from\n datalad's test machinery. Finally, we want to know, whether we can create a\n symlink at a specific location, not just somewhere. Therefore use\n arbitrary path to test-build a symlink and delete afterwards. Suitable\n location can therefore be determined by high lever code.\n\n Parameters\n ----------\n path: Path\n target: Path\n\n Returns\n -------\n bool\n \"\"\"\n\n try:\n target.touch()\n path.symlink_to(target)\n return True\n except Exception:\n return False\n finally:\n if path.exists():\n path.unlink()\n if target.exists():\n target.unlink()\n"}}},{"rowIdx":97,"cells":{"function_name":{"kind":"string","value":"ensure_bytes"},"docstring":{"kind":"string","value":"Convert/encode unicode string to bytes.\n\nIf `s` isn't a string, return it as is.\n\nParameters\n----------\nencoding: str, optional\n Encoding to use. \"utf-8\" is the default"},"masked_code":{"kind":"string","value":"# emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-\n# ex: set sts=4 ts=4 sw=4 et:\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##\n#\n# See COPYING file distributed along with the datalad package for the\n# copyright and license terms.\n#\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##\n\nimport collections\nfrom collections.abc import Callable\nimport re\nimport builtins\nimport time\n\nimport logging\nimport shutil\nimport os\nimport sys\nimport tempfile\nfrom tempfile import NamedTemporaryFile\nimport platform\nimport gc\nimport glob\nimport gzip\nimport stat\nimport string\nimport warnings\n\nimport os.path as op\n\nfrom copy import copy as shallow_copy\nfrom contextlib import contextmanager\nfrom functools import (\n lru_cache,\n wraps,\n)\nfrom time import sleep\nimport inspect\nfrom itertools import tee\n# this import is required because other modules import opj from here.\nfrom os.path import join as opj\nfrom os.path import (\n abspath,\n basename,\n commonprefix,\n curdir,\n dirname,\n exists,\n expanduser,\n expandvars,\n isabs,\n isdir,\n islink,\n lexists,\n normpath,\n pardir,\n relpath,\n sep,\n split,\n splitdrive\n)\n\nimport posixpath\n\nfrom shlex import (\n quote as shlex_quote,\n split as shlex_split,\n)\n\n# from datalad.dochelpers import get_docstring_split\nfrom datalad.consts import TIMESTAMP_FMT\nfrom datalad.support.exceptions import CapturedException\n\n\nunicode_srctypes = str, bytes\n\n\nlgr = logging.getLogger(\"datalad.utils\")\n\nlgr.log(5, \"Importing datalad.utils\")\n#\n# Some useful variables\n#\nplatform_system = platform.system().lower()\non_windows = platform_system == 'windows'\non_osx = platform_system == 'darwin'\non_linux = platform_system == 'linux'\non_msys_tainted_paths = on_windows \\\n and 'MSYS_NO_PATHCONV' not in os.environ \\\n and os.environ.get('MSYSTEM', '')[:4] in ('MSYS', 'MING')\n\n\n# Takes ~200msec, so should not be called at import time\n@lru_cache() # output should not change through life time of datalad process\ndef get_linux_distribution():\n \"\"\"Compatibility wrapper for {platform,distro}.linux_distribution().\n \"\"\"\n if hasattr(platform, \"linux_distribution\"):\n # Use deprecated (but faster) method if it's available.\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n result = platform.linux_distribution()\n else:\n import distro # We require this for Python 3.8 and above.\n result = distro.linux_distribution(full_distribution_name=False)\n return result\n\n\n# Those weren't used for any critical decision making, thus we just set them to None\n# Use get_linux_distribution() directly where needed\nlinux_distribution_name = linux_distribution_release = None\n\n# Maximal length of cmdline string\n# Query the system and use hardcoded \"knowledge\" if None\n# probably getconf ARG_MAX might not be available\n# The last one would be the most conservative/Windows\nCMD_MAX_ARG_HARDCODED = 2097152 if on_linux else 262144 if on_osx else 32767\ntry:\n CMD_MAX_ARG = os.sysconf('SC_ARG_MAX')\n assert CMD_MAX_ARG > 0\n if CMD_MAX_ARG > CMD_MAX_ARG_HARDCODED * 1e6:\n # workaround for some kind of a bug which comes up with python 3.4\n # see https://github.com/datalad/datalad/issues/3150\n # or on older CentOS with conda and python as new as 3.9\n # see https://github.com/datalad/datalad/issues/5943\n # TODO: let Yarik know that the world is a paradise now whenever 1e6\n # is not large enough\n CMD_MAX_ARG = min(CMD_MAX_ARG, CMD_MAX_ARG_HARDCODED)\nexcept Exception as exc:\n # ATM (20181005) SC_ARG_MAX available only on POSIX systems\n # so exception would be thrown e.g. on Windows, or\n # somehow during Debian build for nd14.04 it is coming up with -1:\n # https://github.com/datalad/datalad/issues/3015\n CMD_MAX_ARG = CMD_MAX_ARG_HARDCODED\n lgr.debug(\n \"Failed to query or got useless SC_ARG_MAX sysconf, \"\n \"will use hardcoded value: %s\", exc)\n# Even with all careful computations we do, due to necessity to account for\n# environment and what not, we still could not figure out \"exact\" way to\n# estimate it, but it was shown that 300k safety margin on linux was sufficient.\n# https://github.com/datalad/datalad/pull/2977#issuecomment-436264710\n# 300k is ~15%, so to be safe, and for paranoid us we will just use up to 50%\n# of the length for \"safety margin\". We might probably still blow due to\n# env vars, unicode, etc... so any hard limit imho is not a proper solution\nCMD_MAX_ARG = int(0.5 * CMD_MAX_ARG)\nlgr.debug(\n \"Maximal length of cmdline string (adjusted for safety margin): %d\",\n CMD_MAX_ARG)\n\n#\n# Little helpers\n#\n\n# `getargspec` has been deprecated in Python 3.\nArgSpecFake = collections.namedtuple(\n \"ArgSpecFake\", [\"args\", \"varargs\", \"keywords\", \"defaults\"])\n\n\ndef getargspec(func, *, include_kwonlyargs=False):\n \"\"\"Compat shim for getargspec deprecated in python 3.\n\n The main difference from inspect.getargspec (and inspect.getfullargspec\n for that matter) is that by using inspect.signature we are providing\n correct args/defaults for functools.wraps'ed functions.\n\n `include_kwonlyargs` option was added to centralize getting all args,\n even the ones which are kwonly (follow the ``*,``).\n\n For internal use and not advised for use in 3rd party code.\n Please use inspect.signature directly.\n \"\"\"\n # We use signature, and not getfullargspec, because only signature properly\n # \"passes\" args from a functools.wraps decorated function.\n # Note: getfullargspec works Ok on wrapt-decorated functions\n f_sign = inspect.signature(func)\n # Loop through parameters and compose argspec\n args4 = [[], None, None, {}]\n # Collect all kwonlyargs into a dedicated dict - name: default\n kwonlyargs = {}\n # shortcuts\n args, defaults = args4[0], args4[3]\n P = inspect.Parameter\n\n for p_name, p in f_sign.parameters.items():\n if p.kind in (P.POSITIONAL_ONLY, P.POSITIONAL_OR_KEYWORD):\n assert not kwonlyargs # yoh: must not come after kwonlyarg\n args.append(p_name)\n if p.default is not P.empty:\n defaults[p_name] = p.default\n elif p.kind == P.VAR_POSITIONAL:\n args4[1] = p_name\n elif p.kind == P.VAR_KEYWORD:\n args4[2] = p_name\n elif p.kind == P.KEYWORD_ONLY:\n assert p.default is not P.empty\n kwonlyargs[p_name] = p.default\n\n if kwonlyargs:\n if not include_kwonlyargs:\n raise ValueError(\n 'Function has keyword-only parameters or annotations, either use '\n 'inspect.signature() API which can support them, or provide include_kwonlyargs=True '\n 'to this function'\n )\n else:\n args.extend(list(kwonlyargs))\n defaults.update(kwonlyargs)\n\n # harmonize defaults to how original getargspec returned them -- just a tuple\n args4[3] = None if not defaults else tuple(defaults.values())\n return ArgSpecFake(*args4)\n\n\ndef any_re_search(regexes, value):\n \"\"\"Return if any of regexes (list or str) searches successfully for value\"\"\"\n for regex in ensure_tuple_or_list(regexes):\n if re.search(regex, value):\n return True\n return False\n\n\ndef not_supported_on_windows(msg=None):\n \"\"\"A little helper to be invoked to consistently fail whenever functionality is\n not supported (yet) on Windows\n \"\"\"\n if on_windows:\n raise NotImplementedError(\"This functionality is not yet implemented for Windows OS\"\n + (\": %s\" % msg if msg else \"\"))\n\n\ndef get_home_envvars(new_home):\n \"\"\"Return dict with env variables to be adjusted for a new HOME\n\n Only variables found in current os.environ are adjusted.\n\n Parameters\n ----------\n new_home: str or Path\n New home path, in native to OS \"schema\"\n \"\"\"\n new_home = str(new_home)\n out = {'HOME': new_home}\n if on_windows:\n # requires special handling, since it has a number of relevant variables\n # and also Python changed its behavior and started to respect USERPROFILE only\n # since python 3.8: https://bugs.python.org/issue36264\n out['USERPROFILE'] = new_home\n out['HOMEDRIVE'], out['HOMEPATH'] = splitdrive(new_home)\n\n return {v: val for v, val in out.items() if v in os.environ}\n\n\ndef shortened_repr(value, l=30):\n try:\n if hasattr(value, '__repr__') and (value.__repr__ is not object.__repr__):\n value_repr = repr(value)\n if not value_repr.startswith('<') and len(value_repr) > l:\n value_repr = \"<<%s++%d chars++%s>>\" % (\n value_repr[:l - 16],\n len(value_repr) - (l - 16 + 4),\n value_repr[-4:]\n )\n elif value_repr.startswith('<') and value_repr.endswith('>') and ' object at 0x':\n raise ValueError(\"I hate those useless long reprs\")\n else:\n raise ValueError(\"gimme class\")\n except Exception as e:\n value_repr = \"<%s>\" % value.__class__.__name__.split('.')[-1]\n return value_repr\n\n\ndef __auto_repr__(obj):\n attr_names = tuple()\n if hasattr(obj, '__dict__'):\n attr_names += tuple(obj.__dict__.keys())\n if hasattr(obj, '__slots__'):\n attr_names += tuple(obj.__slots__)\n\n items = []\n for attr in sorted(set(attr_names)):\n if attr.startswith('_'):\n continue\n value = getattr(obj, attr)\n # TODO: should we add this feature to minimize some talktative reprs\n # such as of URL?\n #if value is None:\n # continue\n items.append(\"%s=%s\" % (attr, shortened_repr(value)))\n\n return \"%s(%s)\" % (obj.__class__.__name__, ', '.join(items))\n\n\ndef auto_repr(cls):\n \"\"\"Decorator for a class to assign it an automagic quick and dirty __repr__\n\n It uses public class attributes to prepare repr of a class\n\n Original idea: http://stackoverflow.com/a/27799004/1265472\n \"\"\"\n\n cls.__repr__ = __auto_repr__\n return cls\n\n\ndef _is_stream_tty(stream):\n try:\n # TODO: check on windows if hasattr check would work correctly and\n # add value:\n return stream.isatty()\n except ValueError as exc:\n # Who knows why it is a ValueError, but let's try to be specific\n # If there is a problem with I/O - non-interactive, otherwise reraise\n if \"I/O\" in str(exc):\n return False\n raise\n\n\ndef is_interactive():\n \"\"\"Return True if all in/outs are open and tty.\n\n Note that in a somewhat abnormal case where e.g. stdin is explicitly\n closed, and any operation on it would raise a\n `ValueError(\"I/O operation on closed file\")` exception, this function\n would just return False, since the session cannot be used interactively.\n \"\"\"\n return all(_is_stream_tty(s) for s in (sys.stdin, sys.stdout, sys.stderr))\n\n\ndef get_ipython_shell():\n \"\"\"Detect if running within IPython and returns its `ip` (shell) object\n\n Returns None if not under ipython (no `get_ipython` function)\n \"\"\"\n try:\n return get_ipython()\n except NameError:\n return None\n\n\ndef md5sum(filename):\n \"\"\"Compute an MD5 sum for the given file\n \"\"\"\n from datalad.support.digests import Digester\n return Digester(digests=['md5'])(filename)['md5']\n\n\n# unused in -core\ndef sorted_files(path):\n \"\"\"Return a (sorted) list of files under path\n \"\"\"\n return sorted(sum([[op.join(r, f)[len(path) + 1:] for f in files]\n for r, d, files in os.walk(path)\n if not '.git' in r], []))\n\n_encoded_dirsep = r'\\\\' if on_windows else r'/'\n_VCS_REGEX = r'%s\\.(?:git|gitattributes|svn|bzr|hg)(?:%s|$)' % (\n _encoded_dirsep, _encoded_dirsep)\n_DATALAD_REGEX = r'%s\\.(?:datalad)(?:%s|$)' % (\n _encoded_dirsep, _encoded_dirsep)\n\n\ndef find_files(regex, topdir=curdir, exclude=None, exclude_vcs=True, exclude_datalad=False, dirs=False):\n \"\"\"Generator to find files matching regex\n\n Parameters\n ----------\n regex: basestring\n exclude: basestring, optional\n Matches to exclude\n exclude_vcs:\n If True, excludes commonly known VCS subdirectories. If string, used\n as regex to exclude those files (regex: `%r`)\n exclude_datalad:\n If True, excludes files known to be datalad meta-data files (e.g. under\n .datalad/ subdirectory) (regex: `%r`)\n topdir: basestring, optional\n Directory where to search\n dirs: bool, optional\n Whether to match directories as well as files\n \"\"\"\n for dirpath, dirnames, filenames in os.walk(topdir):\n names = (dirnames + filenames) if dirs else filenames\n # TODO: might want to uniformize on windows to use '/'\n paths = (op.join(dirpath, name) for name in names)\n for path in filter(re.compile(regex).search, paths):\n path = path.rstrip(sep)\n if exclude and re.search(exclude, path):\n continue\n if exclude_vcs and re.search(_VCS_REGEX, path):\n continue\n if exclude_datalad and re.search(_DATALAD_REGEX, path):\n continue\n yield path\nfind_files.__doc__ %= (_VCS_REGEX, _DATALAD_REGEX)\n\n\ndef expandpath(path, force_absolute=True):\n \"\"\"Expand all variables and user handles in a path.\n\n By default return an absolute path\n \"\"\"\n path = expandvars(expanduser(path))\n if force_absolute:\n path = abspath(path)\n return path\n\n\ndef posix_relpath(path, start=None):\n \"\"\"Behave like os.path.relpath, but always return POSIX paths...\n\n on any platform.\"\"\"\n # join POSIX style\n return posixpath.join(\n # split and relpath native style\n # python2.7 ntpath implementation of relpath cannot handle start=None\n *split(\n relpath(path, start=start if start is not None else '')))\n\n\ndef is_explicit_path(path):\n \"\"\"Return whether a path explicitly points to a location\n\n Any absolute path, or relative path starting with either '../' or\n './' is assumed to indicate a location on the filesystem. Any other\n path format is not considered explicit.\"\"\"\n path = expandpath(path, force_absolute=False)\n return isabs(path) \\\n or path.startswith(os.curdir + os.sep) \\\n or path.startswith(os.pardir + os.sep)\n\n\n# handle this dance once, and import pathlib from here\n# in all other places\n\nfrom pathlib import (\n Path,\n PurePath,\n PurePosixPath,\n)\n\ndef rotree(path, ro=True, chmod_files=True):\n \"\"\"To make tree read-only or writable\n\n Parameters\n ----------\n path : string\n Path to the tree/directory to chmod\n ro : bool, optional\n Whether to make it R/O (default) or RW\n chmod_files : bool, optional\n Whether to operate also on files (not just directories)\n \"\"\"\n if ro:\n chmod = lambda f: os.chmod(f, os.stat(f).st_mode & ~stat.S_IWRITE)\n else:\n chmod = lambda f: os.chmod(f, os.stat(f).st_mode | stat.S_IWRITE | stat.S_IREAD)\n\n for root, dirs, files in os.walk(path, followlinks=False):\n if chmod_files:\n for f in files:\n fullf = op.join(root, f)\n # might be the \"broken\" symlink which would fail to stat etc\n if exists(fullf):\n chmod(fullf)\n chmod(root)\n\n\ndef rmtree(path, chmod_files='auto', children_only=False, *args, **kwargs):\n \"\"\"To remove git-annex .git it is needed to make all files and directories writable again first\n\n Parameters\n ----------\n path: Path or str\n Path to remove\n chmod_files : string or bool, optional\n Whether to make files writable also before removal. Usually it is just\n a matter of directories to have write permissions.\n If 'auto' it would chmod files on windows by default\n children_only : bool, optional\n If set, all files and subdirectories would be removed while the path\n itself (must be a directory) would be preserved\n `*args` :\n `**kwargs` :\n Passed into shutil.rmtree call\n \"\"\"\n # Give W permissions back only to directories, no need to bother with files\n if chmod_files == 'auto':\n chmod_files = on_windows\n # TODO: yoh thinks that if we could quickly check our Flyweight for\n # repos if any of them is under the path, and could call .precommit\n # on those to possibly stop batched processes etc, we did not have\n # to do it on case by case\n # Check for open files\n assert_no_open_files(path)\n\n # TODO the whole thing should be reimplemented with pathlib, but for now\n # at least accept Path\n path = str(path)\n\n if children_only:\n if not isdir(path):\n raise ValueError(\"Can remove children only of directories\")\n for p in os.listdir(path):\n rmtree(op.join(path, p))\n return\n if not (islink(path) or not isdir(path)):\n rotree(path, ro=False, chmod_files=chmod_files)\n if on_windows:\n # shutil fails to remove paths that exceed 260 characters on Windows machines\n # that did not enable long path support. A workaround to remove long paths\n # anyway is to preprend \\\\?\\ to the path.\n # https://docs.microsoft.com/en-us/windows/win32/fileio/naming-a-file?redirectedfrom=MSDN#win32-file-namespaces\n path = r'\\\\?\\ '.strip() + path\n _rmtree(path, *args, **kwargs)\n else:\n # just remove the symlink\n unlink(path)\n\n\ndef rmdir(path, *args, **kwargs):\n \"\"\"os.rmdir with our optional checking for open files\"\"\"\n assert_no_open_files(path)\n os.rmdir(path)\n\n\ndef get_open_files(path, log_open=False):\n \"\"\"Get open files under a path\n\n Note: This function is very slow on Windows.\n\n Parameters\n ----------\n path : str\n File or directory to check for open files under\n log_open : bool or int\n If set - logger level to use\n\n Returns\n -------\n dict\n path : pid\n\n \"\"\"\n # Original idea: https://stackoverflow.com/a/11115521/1265472\n import psutil\n files = {}\n # since the ones returned by psutil would not be aware of symlinks in the\n # path we should also get realpath for path\n # do absolute() in addition to always get an absolute path\n # even with non-existing paths on windows\n path = str(Path(path).resolve().absolute())\n for proc in psutil.process_iter():\n try:\n open_paths = [p.path for p in proc.open_files()] + [proc.cwd()]\n for p in open_paths:\n # note: could be done more efficiently so we do not\n # renormalize path over and over again etc\n if path_startswith(p, path):\n files[p] = proc\n # Catch a race condition where a process ends\n # before we can examine its files\n except psutil.NoSuchProcess:\n pass\n except psutil.AccessDenied:\n pass\n\n if files and log_open:\n lgr.log(log_open, \"Open files under %s: %s\", path, files)\n return files\n\n\n_assert_no_open_files_cfg = os.environ.get('DATALAD_ASSERT_NO_OPEN_FILES')\nif _assert_no_open_files_cfg:\n def assert_no_open_files(path):\n files = get_open_files(path, log_open=40)\n if _assert_no_open_files_cfg == 'assert':\n assert not files, \"Got following files still open: %s\" % ','.join(files)\n elif files:\n if _assert_no_open_files_cfg == 'pdb':\n import pdb\n pdb.set_trace()\n elif _assert_no_open_files_cfg == 'epdb':\n import epdb\n epdb.serve()\n pass\n # otherwise we would just issue that error message in the log\nelse:\n def assert_no_open_files(*args, **kwargs):\n pass\n\n\ndef rmtemp(f, *args, **kwargs):\n \"\"\"Wrapper to centralize removing of temp files so we could keep them around\n\n It will not remove the temporary file/directory if DATALAD_TESTS_TEMP_KEEP\n environment variable is defined\n \"\"\"\n if not os.environ.get('DATALAD_TESTS_TEMP_KEEP'):\n if not os.path.lexists(f):\n lgr.debug(\"Path %s does not exist, so can't be removed\", f)\n return\n lgr.log(5, \"Removing temp file: %s\", f)\n # Can also be a directory\n if isdir(f):\n rmtree(f, *args, **kwargs)\n else:\n unlink(f)\n else:\n lgr.info(\"Keeping temp file: %s\", f)\n\n\ndef file_basename(name, return_ext=False):\n \"\"\"\n Strips up to 2 extensions of length up to 4 characters and starting with alpha\n not a digit, so we could get rid of .tar.gz etc\n \"\"\"\n bname = basename(name)\n fbname = re.sub(r'(\\.[a-zA-Z_]\\S{1,4}){0,2}$', '', bname)\n if return_ext:\n return fbname, bname[len(fbname) + 1:]\n else:\n return fbname\n\n\n# unused in -core\ndef escape_filename(filename):\n \"\"\"Surround filename in \"\" and escape \" in the filename\n \"\"\"\n filename = filename.replace('\"', r'\\\"').replace('`', r'\\`')\n filename = '\"%s\"' % filename\n return filename\n\n\n# unused in -core\ndef encode_filename(filename):\n \"\"\"Encode unicode filename\n \"\"\"\n if isinstance(filename, str):\n return filename.encode(sys.getfilesystemencoding())\n else:\n return filename\n\n\n# unused in -core\ndef decode_input(s):\n \"\"\"Given input string/bytes, decode according to stdin codepage (or UTF-8)\n if not defined\n\n If fails -- issue warning and decode allowing for errors\n being replaced\n \"\"\"\n if isinstance(s, str):\n return s\n else:\n encoding = sys.stdin.encoding or 'UTF-8'\n try:\n return s.decode(encoding)\n except UnicodeDecodeError as exc:\n lgr.warning(\n \"Failed to decode input string using %s encoding. \"\n \"Decoding allowing for errors\", encoding)\n return s.decode(encoding, errors='replace')\n\n\n# unused in -core\nif on_windows:\n def lmtime(filepath, mtime):\n \"\"\"Set mtime for files. On Windows a merely adapter to os.utime\n \"\"\"\n os.utime(filepath, (time.time(), mtime))\nelse:\n def lmtime(filepath, mtime):\n \"\"\"Set mtime for files, while not de-referencing symlinks.\n\n To overcome absence of os.lutime\n\n Works only on linux and OSX ATM\n \"\"\"\n from .cmd import WitlessRunner\n # convert mtime to format touch understands [[CC]YY]MMDDhhmm[.SS]\n smtime = time.strftime(\"%Y%m%d%H%M.%S\", time.localtime(mtime))\n lgr.log(3, \"Setting mtime for %s to %s == %s\", filepath, mtime, smtime)\n WitlessRunner().run(['touch', '-h', '-t', '%s' % smtime, filepath])\n filepath = Path(filepath)\n rfilepath = filepath.resolve()\n if filepath.is_symlink() and rfilepath.exists():\n # trust no one - adjust also of the target file\n # since it seemed like downloading under OSX (was it using curl?)\n # didn't bother with timestamps\n lgr.log(3, \"File is a symlink to %s Setting mtime for it to %s\",\n rfilepath, mtime)\n os.utime(str(rfilepath), (time.time(), mtime))\n # doesn't work on OSX\n # Runner().run(['touch', '-h', '-d', '@%s' % mtime, filepath])\n\n\ndef ensure_tuple_or_list(obj):\n \"\"\"Given an object, wrap into a tuple if not list or tuple\n \"\"\"\n if isinstance(obj, (list, tuple)):\n return obj\n return (obj,)\n\n\ndef ensure_iter(s, cls, copy=False, iterate=True):\n \"\"\"Given not a list, would place it into a list. If None - empty list is returned\n\n Parameters\n ----------\n s: list or anything\n cls: class\n Which iterable class to ensure\n copy: bool, optional\n If correct iterable is passed, it would generate its shallow copy\n iterate: bool, optional\n If it is not a list, but something iterable (but not a str)\n iterate over it.\n \"\"\"\n\n if isinstance(s, cls):\n return s if not copy else shallow_copy(s)\n elif isinstance(s, str):\n return cls((s,))\n elif iterate and hasattr(s, '__iter__'):\n return cls(s)\n elif s is None:\n return cls()\n else:\n return cls((s,))\n\n\ndef ensure_list(s, copy=False, iterate=True):\n \"\"\"Given not a list, would place it into a list. If None - empty list is returned\n\n Parameters\n ----------\n s: list or anything\n copy: bool, optional\n If list is passed, it would generate a shallow copy of the list\n iterate: bool, optional\n If it is not a list, but something iterable (but not a str)\n iterate over it.\n \"\"\"\n return ensure_iter(s, list, copy=copy, iterate=iterate)\n\n\ndef ensure_list_from_str(s, sep='\\n'):\n \"\"\"Given a multiline string convert it to a list of return None if empty\n\n Parameters\n ----------\n s: str or list\n \"\"\"\n\n if not s:\n return None\n\n if isinstance(s, list):\n return s\n return s.split(sep)\n\n\ndef ensure_dict_from_str(s, **kwargs):\n \"\"\"Given a multiline string with key=value items convert it to a dictionary\n\n Parameters\n ----------\n s: str or dict\n\n Returns None if input s is empty\n \"\"\"\n\n if not s:\n return None\n\n if isinstance(s, dict):\n return s\n\n out = {}\n for value_str in ensure_list_from_str(s, **kwargs):\n if '=' not in value_str:\n raise ValueError(\"{} is not in key=value format\".format(repr(value_str)))\n k, v = value_str.split('=', 1)\n if k in out:\n err = \"key {} was already defined in {}, but new value {} was provided\".format(k, out, v)\n raise ValueError(err)\n out[k] = v\n return out\n\n\n# MASKED: ensure_bytes function (lines 792-804)\n\n\ndef ensure_unicode(s, encoding=None, confidence=None):\n \"\"\"Convert/decode bytestring to unicode.\n\n If `s` isn't a bytestring, return it as is.\n\n Parameters\n ----------\n encoding: str, optional\n Encoding to use. If None, \"utf-8\" is tried, and then if not a valid\n UTF-8, encoding will be guessed\n confidence: float, optional\n A value between 0 and 1, so if guessing of encoding is of lower than\n specified confidence, ValueError is raised\n \"\"\"\n if not isinstance(s, bytes):\n return s\n if encoding is None:\n # Figure out encoding, defaulting to 'utf-8' which is our common\n # target in contemporary digital society\n try:\n return s.decode('utf-8')\n except UnicodeDecodeError as exc:\n lgr.debug(\"Failed to decode a string as utf-8: %s\",\n CapturedException(exc))\n # And now we could try to guess\n from chardet import detect\n enc = detect(s)\n denc = enc.get('encoding', None)\n if denc:\n denc_confidence = enc.get('confidence', 0)\n if confidence is not None and denc_confidence < confidence:\n raise ValueError(\n \"Failed to auto-detect encoding with high enough \"\n \"confidence. Highest confidence was %s for %s\"\n % (denc_confidence, denc)\n )\n lgr.log(5, \"Auto-detected encoding to be %s\", denc)\n return s.decode(denc)\n else:\n raise ValueError(\n \"Could not decode value as utf-8, or to guess its encoding: %s\"\n % repr(s)\n )\n else:\n return s.decode(encoding)\n\n\ndef ensure_bool(s):\n \"\"\"Convert value into boolean following convention for strings\n\n to recognize on,True,yes as True, off,False,no as False\n \"\"\"\n if isinstance(s, str):\n if s.isdigit():\n return bool(int(s))\n sl = s.lower()\n if sl in {'y', 'yes', 'true', 'on'}:\n return True\n elif sl in {'n', 'no', 'false', 'off'}:\n return False\n else:\n raise ValueError(\"Do not know how to treat %r as a boolean\" % s)\n return bool(s)\n\n\ndef as_unicode(val, cast_types=object):\n \"\"\"Given an arbitrary value, would try to obtain unicode value of it\n \n For unicode it would return original value, for python2 str or python3\n bytes it would use ensure_unicode, for None - an empty (unicode) string,\n and for any other type (see `cast_types`) - would apply the unicode \n constructor. If value is not an instance of `cast_types`, TypeError\n is thrown\n \n Parameters\n ----------\n cast_types: type\n Which types to cast to unicode by providing to constructor\n \"\"\"\n if val is None:\n return u''\n elif isinstance(val, str):\n return val\n elif isinstance(val, unicode_srctypes):\n return ensure_unicode(val)\n elif isinstance(val, cast_types):\n return str(val)\n else:\n raise TypeError(\n \"Value %r is not of any of known or provided %s types\"\n % (val, cast_types))\n\n\ndef unique(seq, key=None, reverse=False):\n \"\"\"Given a sequence return a list only with unique elements while maintaining order\n\n This is the fastest solution. See\n https://www.peterbe.com/plog/uniqifiers-benchmark\n and\n http://stackoverflow.com/a/480227/1265472\n for more information.\n Enhancement -- added ability to compare for uniqueness using a key function\n\n Parameters\n ----------\n seq:\n Sequence to analyze\n key: callable, optional\n Function to call on each element so we could decide not on a full\n element, but on its member etc\n reverse: bool, optional\n If True, uniqueness checked in the reverse order, so that the later ones\n will take the order\n \"\"\"\n seen = set()\n seen_add = seen.add\n\n trans = reversed if reverse else lambda x: x\n\n if not key:\n out = [x for x in trans(seq) if not (x in seen or seen_add(x))]\n else:\n # OPT: could be optimized, since key is called twice, but for our cases\n # should be just as fine\n out = [x for x in trans(seq) if not (key(x) in seen or seen_add(key(x)))]\n\n return out[::-1] if reverse else out\n\n\ndef all_same(items):\n \"\"\"Quick check if all items are the same.\n\n Identical to a check like len(set(items)) == 1 but\n should be more efficient while working on generators, since would\n return False as soon as any difference detected thus possibly avoiding\n unnecessary evaluations\n \"\"\"\n first = True\n first_item = None\n for item in items:\n if first:\n first = False\n first_item = item\n else:\n if item != first_item:\n return False\n # So we return False if was empty\n return not first\n\n\ndef map_items(func, v):\n \"\"\"A helper to apply `func` to all elements (keys and values) within dict\n\n No type checking of values passed to func is done, so `func`\n should be resilient to values which it should not handle\n\n Initial usecase - apply_recursive(url_fragment, ensure_unicode)\n \"\"\"\n # map all elements within item\n return v.__class__(\n item.__class__(map(func, item))\n for item in v.items()\n )\n\n\ndef partition(items, predicate=bool):\n \"\"\"Partition `items` by `predicate`.\n\n Parameters\n ----------\n items : iterable\n predicate : callable\n A function that will be mapped over each element in `items`. The\n elements will partitioned based on whether the return value is false or\n true.\n\n Returns\n -------\n A tuple with two generators, the first for 'false' items and the second for\n 'true' ones.\n\n Notes\n -----\n Taken from Peter Otten's snippet posted at\n https://nedbatchelder.com/blog/201306/filter_a_list_into_two_parts.html\n \"\"\"\n a, b = tee((predicate(item), item) for item in items)\n return ((item for pred, item in a if not pred),\n (item for pred, item in b if pred))\n\n\ndef generate_chunks(container, size):\n \"\"\"Given a container, generate chunks from it with size up to `size`\n \"\"\"\n # There could be a \"smarter\" solution but I think this would suffice\n assert size > 0, \"Size should be non-0 positive\"\n while container:\n yield container[:size]\n container = container[size:]\n\n\ndef generate_file_chunks(files, cmd=None):\n \"\"\"Given a list of files, generate chunks of them to avoid exceeding cmdline length\n\n Parameters\n ----------\n files: list of str\n cmd: str or list of str, optional\n Command to account for as well\n \"\"\"\n files = ensure_list(files)\n cmd = ensure_list(cmd)\n\n maxl = max(map(len, files)) if files else 0\n chunk_size = max(\n 1, # should at least be 1. If blows then - not our fault\n (CMD_MAX_ARG\n - sum((len(x) + 3) for x in cmd)\n - 4 # for '--' below\n ) // (maxl + 3) # +3 for possible quotes and a space\n )\n # TODO: additional treatment for \"too many arguments\"? although\n # as https://github.com/datalad/datalad/issues/1883#issuecomment\n # -436272758\n # shows there seems to be no hardcoded limit on # of arguments,\n # but may be we decide to go for smth like follow to be on safe side\n # chunk_size = min(10240 - len(cmd), chunk_size)\n file_chunks = generate_chunks(files, chunk_size)\n return file_chunks\n\n\n#\n# Generators helpers\n#\n\ndef saved_generator(gen):\n \"\"\"Given a generator returns two generators, where 2nd one just replays\n\n So the first one would be going through the generated items and 2nd one\n would be yielding saved items\n \"\"\"\n saved = []\n\n def gen1():\n for x in gen: # iterating over original generator\n saved.append(x)\n yield x\n\n def gen2():\n for x in saved: # yielding saved entries\n yield x\n\n return gen1(), gen2()\n\n\n#\n# Decorators\n#\n\n# Originally better_wraps was created to provide `wrapt`-based, instead of\n# `functools.wraps` implementation to preserve the correct signature of the\n# decorated function. By using inspect.signature in our getargspec, which\n# works fine on `functools.wraps`ed functions, we mediated this necessity.\nbetter_wraps = wraps\n\n\n# Borrowed from pandas\n# Copyright: 2011-2014, Lambda Foundry, Inc. and PyData Development Team\n# License: BSD-3\ndef optional_args(decorator):\n \"\"\"allows a decorator to take optional positional and keyword arguments.\n Assumes that taking a single, callable, positional argument means that\n it is decorating a function, i.e. something like this::\n\n @my_decorator\n def function(): pass\n\n Calls decorator with decorator(f, `*args`, `**kwargs`)\"\"\"\n\n @better_wraps(decorator)\n def wrapper(*args, **kwargs):\n def dec(f):\n return decorator(f, *args, **kwargs)\n\n is_decorating = not kwargs and len(args) == 1 and isinstance(args[0], Callable)\n if is_decorating:\n f = args[0]\n args = []\n return dec(f)\n else:\n return dec\n\n return wrapper\n\n\n# TODO: just provide decorators for tempfile.mk* functions. This is ugly!\ndef get_tempfile_kwargs(tkwargs=None, prefix=\"\", wrapped=None):\n \"\"\"Updates kwargs to be passed to tempfile. calls depending on env vars\n \"\"\"\n if tkwargs is None:\n tkwargs_ = {}\n else:\n # operate on a copy of tkwargs to avoid any side-effects\n tkwargs_ = tkwargs.copy()\n\n # TODO: don't remember why I had this one originally\n # if len(targs)<2 and \\\n if 'prefix' not in tkwargs_:\n tkwargs_['prefix'] = '_'.join(\n ['datalad_temp'] +\n ([prefix] if prefix else []) +\n ([''] if (on_windows or not wrapped) else [wrapped.__name__]))\n\n directory = os.environ.get('TMPDIR')\n if directory and 'dir' not in tkwargs_:\n tkwargs_['dir'] = directory\n\n return tkwargs_\n\n\n@optional_args\ndef line_profile(func):\n \"\"\"Q&D helper to line profile the function and spit out stats\n \"\"\"\n import line_profiler\n prof = line_profiler.LineProfiler()\n\n @wraps(func)\n def _wrap_line_profile(*args, **kwargs):\n try:\n pfunc = prof(func)\n return pfunc(*args, **kwargs)\n finally:\n prof.print_stats()\n return _wrap_line_profile\n\n\n# unused in -core\n@optional_args\ndef collect_method_callstats(func):\n \"\"\"Figure out methods which call the method repeatedly on the same instance\n\n Use case(s):\n - .repo is expensive since does all kinds of checks.\n - .config is expensive transitively since it calls .repo each time\n\n TODO:\n - fancy one could look through the stack for the same id(self) to see if\n that location is already in memo. That would hint to the cases where object\n is not passed into underlying functions, causing them to redo the same work\n over and over again\n - ATM might flood with all \"1 lines\" calls which are not that informative.\n The underlying possibly suboptimal use might be coming from their callers.\n It might or not relate to the previous TODO\n \"\"\"\n from collections import defaultdict\n import traceback\n from time import time\n memo = defaultdict(lambda: defaultdict(int)) # it will be a dict of lineno: count\n # gross timing\n times = []\n toppath = dirname(__file__) + sep\n\n @wraps(func)\n def _wrap_collect_method_callstats(*args, **kwargs):\n try:\n self = args[0]\n stack = traceback.extract_stack()\n caller = stack[-2]\n stack_sig = \\\n \"{relpath}:{s.name}\".format(\n s=caller, relpath=relpath(caller.filename, toppath))\n sig = (id(self), stack_sig)\n # we will count based on id(self) + wherefrom\n memo[sig][caller.lineno] += 1\n t0 = time()\n return func(*args, **kwargs)\n finally:\n times.append(time() - t0)\n pass\n\n def print_stats():\n print(\"The cost of property {}:\".format(func.__name__))\n if not memo:\n print(\"None since no calls\")\n return\n # total count\n counts = {k: sum(v.values()) for k,v in memo.items()}\n total = sum(counts.values())\n ids = {self_id for (self_id, _) in memo}\n print(\" Total: {} calls from {} objects with {} contexts taking {:.2f} sec\"\n .format(total, len(ids), len(memo), sum(times)))\n # now we need to sort by value\n for (self_id, caller), count in sorted(counts.items(), key=lambda x: x[1], reverse=True):\n print(\" {} {}: {} from {} lines\"\n .format(self_id, caller, count, len(memo[(self_id, caller)])))\n\n # Upon total exit we print the stats\n import atexit\n atexit.register(print_stats)\n\n return _wrap_collect_method_callstats\n\n\n# Borrowed from duecredit to wrap duecredit-handling to guarantee failsafe\ndef never_fail(f):\n \"\"\"Assure that function never fails -- all exceptions are caught\n\n Returns `None` if function fails internally.\n \"\"\"\n @wraps(f)\n def wrapped_func(*args, **kwargs):\n try:\n return f(*args, **kwargs)\n except Exception as e:\n lgr.warning(\n \"DataLad internal failure while running %s: %r. \"\n \"Please report at https://github.com/datalad/datalad/issues\"\n % (f, e)\n )\n\n if os.environ.get('DATALAD_ALLOW_FAIL', False):\n return f\n else:\n return wrapped_func\n\n\n#\n# Context Managers\n#\n\n\n# unused in -core\n@contextmanager\ndef nothing_cm():\n \"\"\"Just a dummy cm to programmically switch context managers\"\"\"\n yield\n\n\n@contextmanager\ndef swallow_outputs():\n \"\"\"Context manager to help consuming both stdout and stderr, and print()\n\n stdout is available as cm.out and stderr as cm.err whenever cm is the\n yielded context manager.\n Internally uses temporary files to guarantee absent side-effects of swallowing\n into StringIO which lacks .fileno.\n\n print mocking is necessary for some uses where sys.stdout was already bound\n to original sys.stdout, thus mocking it later had no effect. Overriding\n print function had desired effect\n \"\"\"\n\n class StringIOAdapter(object):\n \"\"\"Little adapter to help getting out/err values\n \"\"\"\n def __init__(self):\n kw = get_tempfile_kwargs({}, prefix=\"outputs\")\n\n self._out = NamedTemporaryFile(delete=False, mode='w', **kw)\n self._err = NamedTemporaryFile(delete=False, mode='w', **kw)\n\n def _read(self, h):\n with open(h.name) as f:\n return f.read()\n\n @property\n def out(self):\n if not self._out.closed:\n self._out.flush()\n return self._read(self._out)\n\n @property\n def err(self):\n if not self._err.closed:\n self._err.flush()\n return self._read(self._err)\n\n @property\n def handles(self):\n return self._out, self._err\n\n def cleanup(self):\n self._out.close()\n self._err.close()\n out_name = self._out.name\n err_name = self._err.name\n from datalad import cfg\n if cfg.getbool('datalad.log', 'outputs', default=False) \\\n and lgr.getEffectiveLevel() <= logging.DEBUG:\n for s, sname in ((self.out, 'stdout'),\n (self.err, 'stderr')):\n if s:\n pref = os.linesep + \"| \"\n lgr.debug(\"Swallowed %s:%s%s\", sname, pref, s.replace(os.linesep, pref))\n else:\n lgr.debug(\"Nothing was swallowed for %s\", sname)\n del self._out\n del self._err\n gc.collect()\n rmtemp(out_name)\n rmtemp(err_name)\n\n def fake_print(*args, **kwargs):\n sep = kwargs.pop('sep', ' ')\n end = kwargs.pop('end', '\\n')\n file = kwargs.pop('file', sys.stdout)\n\n if file in (oldout, olderr, sys.stdout, sys.stderr):\n # we mock\n try:\n sys.stdout.write(sep.join(args) + end)\n except UnicodeEncodeError as exc:\n lgr.error(\n \"Failed to write to mocked stdout, got %s, continue as it \"\n \"didn't happen\", exc)\n else:\n # must be some other file one -- leave it alone\n oldprint(*args, sep=sep, end=end, file=file)\n\n from .ui import ui\n # preserve -- they could have been mocked already\n oldprint = getattr(builtins, 'print')\n oldout, olderr = sys.stdout, sys.stderr\n olduiout = ui.out\n adapter = StringIOAdapter()\n\n try:\n sys.stdout, sys.stderr = adapter.handles\n ui.out = adapter.handles[0]\n setattr(builtins, 'print', fake_print)\n\n yield adapter\n finally:\n sys.stdout, sys.stderr, ui.out = oldout, olderr, olduiout\n setattr(builtins, 'print', oldprint)\n adapter.cleanup()\n\n\n@contextmanager\ndef swallow_logs(new_level=None, file_=None, name='datalad'):\n \"\"\"Context manager to consume all logs.\n\n \"\"\"\n lgr = logging.getLogger(name)\n\n # Keep old settings\n old_level = lgr.level\n old_handlers = lgr.handlers\n\n # Let's log everything into a string\n # TODO: generalize with the one for swallow_outputs\n class StringIOAdapter(object):\n \"\"\"Little adapter to help getting out values\n\n And to stay consistent with how swallow_outputs behaves\n \"\"\"\n def __init__(self):\n if file_ is None:\n kw = get_tempfile_kwargs({}, prefix=\"logs\")\n self._out = NamedTemporaryFile(mode='a', delete=False, **kw)\n else:\n out_file = file_\n # PY3 requires clearly one or another. race condition possible\n self._out = open(out_file, 'a')\n self._final_out = None\n\n def _read(self, h):\n with open(h.name) as f:\n return f.read()\n\n @property\n def out(self):\n if self._final_out is not None:\n # we closed and cleaned up already\n return self._final_out\n else:\n self._out.flush()\n return self._read(self._out)\n\n @property\n def lines(self):\n return self.out.split('\\n')\n\n @property\n def handle(self):\n return self._out\n\n def cleanup(self):\n # store for access while object exists\n self._final_out = self.out\n self._out.close()\n out_name = self._out.name\n del self._out\n gc.collect()\n if not file_:\n rmtemp(out_name)\n\n def assert_logged(self, msg=None, level=None, regex=True, **kwargs):\n \"\"\"Provide assertion on whether a msg was logged at a given level\n\n If neither `msg` nor `level` provided, checks if anything was logged\n at all.\n\n Parameters\n ----------\n msg: str, optional\n Message (as a regular expression, if `regex`) to be searched.\n If no msg provided, checks if anything was logged at a given level.\n level: str, optional\n String representing the level to be logged\n regex: bool, optional\n If False, regular `assert_in` is used\n **kwargs: str, optional\n Passed to `assert_re_in` or `assert_in`\n \"\"\"\n from datalad.tests.utils import assert_re_in\n from datalad.tests.utils import assert_in\n\n if regex:\n match = r'\\[%s\\] ' % level if level else r\"\\[\\S+\\] \"\n else:\n match = '[%s] ' % level if level else ''\n\n if msg:\n match += msg\n\n if match:\n (assert_re_in if regex else assert_in)(match, self.out, **kwargs)\n else:\n assert not kwargs, \"no kwargs to be passed anywhere\"\n assert self.out, \"Nothing was logged!?\"\n\n adapter = StringIOAdapter()\n # TODO: it does store messages but without any formatting, i.e. even without\n # date/time prefix etc. IMHO it should preserve formatting in case if file_ is\n # set\n swallow_handler = logging.StreamHandler(adapter.handle)\n # we want to log levelname so we could test against it\n swallow_handler.setFormatter(\n logging.Formatter('[%(levelname)s] %(message)s'))\n swallow_handler.filters = sum([h.filters for h in old_handlers],\n [])\n lgr.handlers = [swallow_handler]\n if old_level < logging.DEBUG: # so if HEAVYDEBUG etc -- show them!\n lgr.handlers += old_handlers\n\n if isinstance(new_level, str):\n new_level = getattr(logging, new_level)\n\n if new_level is not None:\n lgr.setLevel(new_level)\n\n try:\n yield adapter\n # TODO: if file_ and there was an exception -- most probably worth logging it?\n # although ideally it should be the next log outside added to that file_ ... oh well\n finally:\n lgr.handlers = old_handlers\n lgr.setLevel(old_level)\n adapter.cleanup()\n\n\n# TODO: May be melt in with swallow_logs at some point:\n@contextmanager\ndef disable_logger(logger=None):\n \"\"\"context manager to temporarily disable logging\n\n This is to provide one of swallow_logs' purposes without unnecessarily\n creating temp files (see gh-1865)\n\n Parameters\n ----------\n logger: Logger\n Logger whose handlers will be ordered to not log anything.\n Default: datalad's topmost Logger ('datalad')\n \"\"\"\n\n class NullFilter(logging.Filter):\n \"\"\"Filter class to reject all records\n \"\"\"\n def filter(self, record):\n return 0\n\n if logger is None:\n # default: all of datalad's logging:\n logger = logging.getLogger('datalad')\n\n filter_ = NullFilter(logger.name)\n [h.addFilter(filter_) for h in logger.handlers]\n\n try:\n yield logger\n finally:\n [h.removeFilter(filter_) for h in logger.handlers]\n\n\n#\n# Additional handlers\n#\n_sys_excepthook = sys.excepthook # Just in case we ever need original one\n\n\ndef setup_exceptionhook(ipython=False):\n \"\"\"Overloads default sys.excepthook with our exceptionhook handler.\n\n If interactive, our exceptionhook handler will invoke\n pdb.post_mortem; if not interactive, then invokes default handler.\n \"\"\"\n\n def _datalad_pdb_excepthook(type, value, tb):\n import traceback\n traceback.print_exception(type, value, tb)\n print()\n if is_interactive():\n import pdb\n pdb.post_mortem(tb)\n\n if ipython:\n from IPython.core import ultratb\n sys.excepthook = ultratb.FormattedTB(mode='Verbose',\n # color_scheme='Linux',\n call_pdb=is_interactive())\n else:\n sys.excepthook = _datalad_pdb_excepthook\n\n\ndef ensure_dir(*args):\n \"\"\"Make sure directory exists.\n\n Joins the list of arguments to an os-specific path to the desired\n directory and creates it, if it not exists yet.\n \"\"\"\n dirname = op.join(*args)\n if not exists(dirname):\n os.makedirs(dirname)\n return dirname\n\n\ndef updated(d, update):\n \"\"\"Return a copy of the input with the 'update'\n\n Primarily for updating dictionaries\n \"\"\"\n d = d.copy()\n d.update(update)\n return d\n\n\n_pwd_mode = None\n\n\ndef _switch_to_getcwd(msg, *args):\n global _pwd_mode\n _pwd_mode = 'cwd'\n lgr.debug(\n msg + \". From now on will be returning os.getcwd(). Directory\"\n \" symlinks in the paths will be resolved\",\n *args\n )\n # TODO: we might want to mitigate by going through all flywheighted\n # repos and tuning up their .paths to be resolved?\n\n\ndef getpwd():\n \"\"\"Try to return a CWD without dereferencing possible symlinks\n\n This function will try to use PWD environment variable to provide a current\n working directory, possibly with some directories along the path being\n symlinks to other directories. Unfortunately, PWD is used/set only by the\n shell and such functions as `os.chdir` and `os.getcwd` nohow use or modify\n it, thus `os.getcwd()` returns path with links dereferenced.\n\n While returning current working directory based on PWD env variable we\n verify that the directory is the same as `os.getcwd()` after resolving all\n symlinks. If that verification fails, we fall back to always use\n `os.getcwd()`.\n\n Initial decision to either use PWD env variable or os.getcwd() is done upon\n the first call of this function.\n \"\"\"\n global _pwd_mode\n if _pwd_mode is None:\n # we need to decide!\n try:\n pwd = os.environ['PWD']\n if on_windows and pwd and pwd.startswith('/'):\n # It should be a path from MSYS.\n # - it might start with a drive letter or not\n # - it seems to be \"illegal\" to have a single letter directories\n # under / path, i.e. if created - they aren't found\n # - 'ln -s' does not fail to create a \"symlink\" but it just\n # copies!\n # so we are not likely to need original PWD purpose on\n # those systems\n # Verdict:\n _pwd_mode = 'cwd'\n else:\n _pwd_mode = 'PWD'\n except KeyError:\n _pwd_mode = 'cwd'\n\n if _pwd_mode == 'cwd':\n return os.getcwd()\n elif _pwd_mode == 'PWD':\n try:\n cwd = os.getcwd()\n except OSError as exc:\n if \"o such file\" in str(exc):\n # directory was removed but we promised to be robust and\n # still report the path we might know since we are still in PWD\n # mode\n cwd = None\n else:\n raise\n try:\n pwd = os.environ['PWD']\n # do absolute() in addition to always get an absolute path\n # even with non-existing paths on windows\n pwd_real = str(Path(pwd).resolve().absolute())\n # This logic would fail to catch the case where chdir did happen\n # to the directory where current PWD is pointing to, e.g.\n # $> ls -ld $PWD\n # lrwxrwxrwx 1 yoh yoh 5 Oct 11 13:27 /home/yoh/.tmp/tmp -> /tmp//\n # hopa:~/.tmp/tmp\n # $> python -c 'import os; os.chdir(\"/tmp\"); from datalad.utils import getpwd; print(getpwd(), os.getcwd())'\n # ('/home/yoh/.tmp/tmp', '/tmp')\n # but I guess that should not be too harmful\n if cwd is not None and pwd_real != cwd:\n _switch_to_getcwd(\n \"realpath of PWD=%s is %s whenever os.getcwd()=%s\",\n pwd, pwd_real, cwd\n )\n return cwd\n return pwd\n except KeyError:\n _switch_to_getcwd(\"PWD env variable is no longer available\")\n return cwd # Must not happen, but may be someone\n # evil purges PWD from environ?\n else:\n raise RuntimeError(\n \"Must have not got here. \"\n \"pwd_mode must be either cwd or PWD. And it is now %r\" % (_pwd_mode,)\n )\n\n\nclass chpwd(object):\n \"\"\"Wrapper around os.chdir which also adjusts environ['PWD']\n\n The reason is that otherwise PWD is simply inherited from the shell\n and we have no ability to assess directory path without dereferencing\n symlinks.\n\n If used as a context manager it allows to temporarily change directory\n to the given path\n \"\"\"\n def __init__(self, path, mkdir=False, logsuffix=''):\n\n if path:\n pwd = getpwd()\n self._prev_pwd = pwd\n else:\n self._prev_pwd = None\n return\n\n if not isabs(path):\n path = normpath(op.join(pwd, path))\n if not os.path.exists(path) and mkdir:\n self._mkdir = True\n os.mkdir(path)\n else:\n self._mkdir = False\n lgr.debug(\"chdir %r -> %r %s\", self._prev_pwd, path, logsuffix)\n os.chdir(path) # for grep people -- ok, to chdir here!\n os.environ['PWD'] = str(path)\n\n def __enter__(self):\n # nothing more to do really, chdir was in the constructor\n pass\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n if self._prev_pwd:\n # Need to use self.__class__ so this instance, if the entire\n # thing mocked during the test, still would use correct chpwd\n self.__class__(self._prev_pwd, logsuffix=\"(coming back)\")\n\n\ndef dlabspath(path, norm=False):\n \"\"\"Symlinks-in-the-cwd aware abspath\n\n os.path.abspath relies on os.getcwd() which would not know about symlinks\n in the path\n\n TODO: we might want to norm=True by default to match behavior of\n os .path.abspath?\n \"\"\"\n if not isabs(path):\n # if not absolute -- relative to pwd\n path = op.join(getpwd(), path)\n return normpath(path) if norm else path\n\n\ndef with_pathsep(path):\n \"\"\"Little helper to guarantee that path ends with /\"\"\"\n return path + sep if not path.endswith(sep) else path\n\n\ndef get_path_prefix(path, pwd=None):\n \"\"\"Get path prefix (for current directory)\n\n Returns relative path to the topdir, if we are under topdir, and if not\n absolute path to topdir. If `pwd` is not specified - current directory\n assumed\n \"\"\"\n pwd = pwd or getpwd()\n path = dlabspath(path)\n path_ = with_pathsep(path)\n pwd_ = with_pathsep(pwd)\n common = commonprefix((path_, pwd_))\n if common.endswith(sep) and common in {path_, pwd_}:\n # we are in subdir or above the path = use relative path\n location_prefix = relpath(path, pwd)\n # if benign \"here\" - cut off\n if location_prefix in (curdir, curdir + sep):\n location_prefix = ''\n return location_prefix\n else:\n # just return absolute path\n return path\n\n\ndef _get_normalized_paths(path, prefix):\n if isabs(path) != isabs(prefix):\n raise ValueError(\"Both paths must either be absolute or relative. \"\n \"Got %r and %r\" % (path, prefix))\n path = with_pathsep(path)\n prefix = with_pathsep(prefix)\n return path, prefix\n\n\ndef path_startswith(path, prefix):\n \"\"\"Return True if path starts with prefix path\n\n Parameters\n ----------\n path: str\n prefix: str\n \"\"\"\n path, prefix = _get_normalized_paths(path, prefix)\n return path.startswith(prefix)\n\n\ndef path_is_subpath(path, prefix):\n \"\"\"Return True if path is a subpath of prefix\n\n It will return False if path == prefix.\n\n Parameters\n ----------\n path: str\n prefix: str\n \"\"\"\n path, prefix = _get_normalized_paths(path, prefix)\n return (len(prefix) < len(path)) and path.startswith(prefix)\n\n\ndef knows_annex(path):\n \"\"\"Returns whether at a given path there is information about an annex\n\n It is just a thin wrapper around GitRepo.is_with_annex() classmethod\n which also checks for `path` to exist first.\n\n This includes actually present annexes, but also uninitialized ones, or\n even the presence of a remote annex branch.\n \"\"\"\n from os.path import exists\n if not exists(path):\n lgr.debug(\"No annex: test path {0} doesn't exist\".format(path))\n return False\n from datalad.support.gitrepo import GitRepo\n return GitRepo(path, init=False, create=False).is_with_annex()\n\n\n@contextmanager\ndef make_tempfile(content=None, wrapped=None, **tkwargs):\n \"\"\"Helper class to provide a temporary file name and remove it at the end (context manager)\n\n Parameters\n ----------\n mkdir : bool, optional (default: False)\n If True, temporary directory created using tempfile.mkdtemp()\n content : str or bytes, optional\n Content to be stored in the file created\n wrapped : function, optional\n If set, function name used to prefix temporary file name\n `**tkwargs`:\n All other arguments are passed into the call to tempfile.mk{,d}temp(),\n and resultant temporary filename is passed as the first argument into\n the function t. If no 'prefix' argument is provided, it will be\n constructed using module and function names ('.' replaced with\n '_').\n\n To change the used directory without providing keyword argument 'dir' set\n DATALAD_TESTS_TEMP_DIR.\n\n Examples\n --------\n >>> from os.path import exists\n >>> from datalad.utils import make_tempfile\n >>> with make_tempfile() as fname:\n ... k = open(fname, 'w').write('silly test')\n >>> assert not exists(fname) # was removed\n\n >>> with make_tempfile(content=\"blah\") as fname:\n ... assert open(fname).read() == \"blah\"\n \"\"\"\n\n if tkwargs.get('mkdir', None) and content is not None:\n raise ValueError(\"mkdir=True while providing content makes no sense\")\n\n tkwargs_ = get_tempfile_kwargs(tkwargs, wrapped=wrapped)\n\n # if DATALAD_TESTS_TEMP_DIR is set, use that as directory,\n # let mktemp handle it otherwise. However, an explicitly provided\n # dir=... will override this.\n mkdir = tkwargs_.pop('mkdir', False)\n\n filename = {False: tempfile.mktemp,\n True: tempfile.mkdtemp}[mkdir](**tkwargs_)\n # MIH: not clear to me why we need to perform this (possibly expensive)\n # resolve. It was already part of the original implementation\n # 008d9ab8cc3e0170c0a9b8479e80dee9ffe6eb7f\n filename = Path(filename).resolve()\n\n if content:\n (filename.write_bytes\n if isinstance(content, bytes)\n else filename.write_text)(content)\n\n # TODO globbing below can also be done with pathlib\n filename = str(filename)\n\n if __debug__:\n lgr.debug(\n 'Created temporary %s named %s',\n 'directory' if mkdir else 'file',\n filename)\n try:\n yield filename\n finally:\n # glob here for all files with the same name (-suffix)\n # would be useful whenever we requested .img filename,\n # and function creates .hdr as well\n # MIH: this is undocumented behavior, and undesired in the general\n # case. it should be made conditional and explicit\n lsuffix = len(tkwargs_.get('suffix', ''))\n filename_ = lsuffix and filename[:-lsuffix] or filename\n filenames = glob.glob(filename_ + '*')\n if len(filename_) < 3 or len(filenames) > 5:\n # For paranoid yoh who stepped into this already ones ;-)\n lgr.warning(\"It is unlikely that it was intended to remove all\"\n \" files matching %r. Skipping\" % filename_)\n return\n for f in filenames:\n try:\n rmtemp(f)\n except OSError: # pragma: no cover\n pass\n\n\ndef _path_(*p):\n \"\"\"Given a path in POSIX\" notation, regenerate one in native to the env one\"\"\"\n if on_windows:\n return op.join(*map(lambda x: op.join(*x.split('/')), p))\n else:\n # Assume that all others as POSIX compliant so nothing to be done\n return op.join(*p)\n\n\ndef get_timestamp_suffix(time_=None, prefix='-'):\n \"\"\"Return a time stamp (full date and time up to second)\n\n primarily to be used for generation of log files names\n \"\"\"\n args = []\n if time_ is not None:\n if isinstance(time_, int):\n time_ = time.gmtime(time_)\n args.append(time_)\n return time.strftime(prefix + TIMESTAMP_FMT, *args)\n\n\n# unused in -core\ndef get_logfilename(dspath, cmd='datalad'):\n \"\"\"Return a filename to use for logging under a dataset/repository\n\n directory would be created if doesn't exist, but dspath must exist\n and be a directory\n \"\"\"\n assert(exists(dspath))\n assert(isdir(dspath))\n ds_logdir = ensure_dir(dspath, '.git', 'datalad', 'logs') # TODO: use WEB_META_LOG whenever #789 merged\n return op.join(ds_logdir, 'crawl-%s.log' % get_timestamp_suffix())\n\n\ndef get_trace(edges, start, end, trace=None):\n \"\"\"Return the trace/path to reach a node in a tree.\n\n Parameters\n ----------\n edges : sequence(2-tuple)\n The tree given by a sequence of edges (parent, child) tuples. The\n nodes can be identified by any value and data type that supports\n the '==' operation.\n start :\n Identifier of the start node. Must be present as a value in the parent\n location of an edge tuple in order to be found.\n end :\n Identifier of the target/end node. Must be present as a value in the child\n location of an edge tuple in order to be found.\n trace : list\n Mostly useful for recursive calls, and used internally.\n\n Returns\n -------\n None or list\n Returns a list with the trace to the target (the starts and the target\n are not included in the trace, hence if start and end are directly connected\n an empty list is returned), or None when no trace to the target can be found,\n or start and end are identical.\n \"\"\"\n # the term trace is used to avoid confusion with a path in the sense\n # of a filesystem path, but the analogy fits and nodes can be paths\n if trace is None:\n trace = []\n if not edges:\n raise ValueError(\"no edges given\")\n for cand in edges:\n cand_super, cand_sub = cand\n if cand_sub in trace:\n # only DAGs, skip any cyclic traces\n continue\n if trace and cand_super != trace[-1]:\n # only consider edges that lead off the end of the trace\n continue\n if not trace and cand_super != start:\n # we got nothing yet, and this edges is not matching the start\n continue\n if cand_sub == end:\n return trace\n # dive into potential subnodes\n cand_trace = get_trace(\n edges,\n start,\n end,\n trace + [cand_sub])\n if cand_trace:\n return cand_trace\n return None\n\n\ndef get_dataset_root(path):\n \"\"\"Return the root of an existent dataset containing a given path\n\n The root path is returned in the same absolute or relative form\n as the input argument. If no associated dataset exists, or the\n input path doesn't exist, None is returned.\n\n If `path` is a symlink or something other than a directory, its\n the root dataset containing its parent directory will be reported.\n If none can be found, at a symlink at `path` is pointing to a\n dataset, `path` itself will be reported as the root.\n\n Parameters\n ----------\n path : Path-like\n\n Returns\n -------\n str or None\n \"\"\"\n path = str(path)\n suffix = '.git'\n altered = None\n if islink(path) or not isdir(path):\n altered = path\n path = dirname(path)\n apath = abspath(path)\n # while we can still go up\n while split(apath)[1]:\n if exists(op.join(path, suffix)):\n return path\n # new test path in the format we got it\n path = normpath(op.join(path, os.pardir))\n # no luck, next round\n apath = abspath(path)\n # if we applied dirname() at the top, we give it another go with\n # the actual path, if it was itself a symlink, it could be the\n # top-level dataset itself\n if altered and exists(op.join(altered, suffix)):\n return altered\n\n return None\n\n\n# ATM used in datalad_crawler extension, so do not remove yet\ndef try_multiple(ntrials, exception, base, f, *args, **kwargs):\n \"\"\"Call f multiple times making exponentially growing delay between the calls\"\"\"\n for trial in range(1, ntrials+1):\n try:\n return f(*args, **kwargs)\n except exception as exc:\n if trial == ntrials:\n raise # just reraise on the last trial\n t = base ** trial\n lgr.warning(\"Caught %s on trial #%d. Sleeping %f and retrying\",\n CapturedException(exc), trial, t)\n sleep(t)\n\n\n@optional_args\ndef try_multiple_dec(\n f, ntrials=None, duration=0.1, exceptions=None, increment_type=None,\n exceptions_filter=None,\n logger=None,\n):\n \"\"\"Decorator to try function multiple times.\n\n Main purpose is to decorate functions dealing with removal of files/directories\n and which might need a few seconds to work correctly on Windows which takes\n its time to release files/directories.\n\n Parameters\n ----------\n ntrials: int, optional\n duration: float, optional\n Seconds to sleep before retrying.\n increment_type: {None, 'exponential'}\n Note that if it is exponential, duration should typically be > 1.0\n so it grows with higher power\n exceptions: Exception or tuple of Exceptions, optional\n Exception or a tuple of multiple exceptions, on which to retry\n exceptions_filter: callable, optional\n If provided, this function will be called with a caught exception\n instance. If function returns True - we will re-try, if False - exception\n will be re-raised without retrying.\n logger: callable, optional\n Logger to log upon failure. If not provided, will use stock logger\n at the level of 5 (heavy debug).\n \"\"\"\n if not exceptions:\n exceptions = (OSError, WindowsError, PermissionError) \\\n if on_windows else OSError\n if not ntrials:\n # Life goes fast on proper systems, no need to delay it much\n ntrials = 100 if on_windows else 10\n if logger is None:\n def logger(*args, **kwargs):\n return lgr.log(5, *args, **kwargs)\n assert increment_type in {None, 'exponential'}\n\n @wraps(f)\n def _wrap_try_multiple_dec(*args, **kwargs):\n t = duration\n for trial in range(ntrials):\n try:\n return f(*args, **kwargs)\n except exceptions as exc:\n if exceptions_filter and not exceptions_filter(exc):\n raise\n if trial < ntrials - 1:\n if increment_type == 'exponential':\n t = duration ** (trial + 1)\n logger(\n \"Caught %s on trial #%d. Sleeping %f and retrying\",\n CapturedException(exc), trial, t)\n sleep(t)\n else:\n raise\n\n return _wrap_try_multiple_dec\n\n\n@try_multiple_dec\ndef unlink(f):\n \"\"\"'Robust' unlink. Would try multiple times\n\n On windows boxes there is evidence for a latency of more than a second\n until a file is considered no longer \"in-use\".\n WindowsError is not known on Linux, and if IOError or any other\n exception\n is thrown then if except statement has WindowsError in it -- NameError\n also see gh-2533\n \"\"\"\n # Check for open files\n assert_no_open_files(f)\n return os.unlink(f)\n\n\n@try_multiple_dec\ndef _rmtree(*args, **kwargs):\n \"\"\"Just a helper to decorate shutil.rmtree.\n\n rmtree defined above does more and ideally should not itself be decorated\n since a recursive definition and does checks for open files inside etc -\n might be too runtime expensive\n \"\"\"\n return shutil.rmtree(*args, **kwargs)\n\n\ndef slash_join(base, extension):\n \"\"\"Join two strings with a '/', avoiding duplicate slashes\n\n If any of the strings is None the other is returned as is.\n \"\"\"\n if extension is None:\n return base\n if base is None:\n return extension\n return '/'.join(\n (base.rstrip('/'),\n extension.lstrip('/')))\n\n\n#\n# IO Helpers\n#\n\n# unused in -core\ndef open_r_encdetect(fname, readahead=1000):\n \"\"\"Return a file object in read mode with auto-detected encoding\n\n This is helpful when dealing with files of unknown encoding.\n\n Parameters\n ----------\n readahead: int, optional\n How many bytes to read for guessing the encoding type. If\n negative - full file will be read\n \"\"\"\n from chardet import detect\n import io\n # read some bytes from the file\n with open(fname, 'rb') as f:\n head = f.read(readahead)\n enc = detect(head)\n denc = enc.get('encoding', None)\n lgr.debug(\"Auto-detected encoding %s for file %s (confidence: %s)\",\n denc,\n fname,\n enc.get('confidence', 'unknown'))\n return io.open(fname, encoding=denc)\n\n\ndef read_file(fname, decode=True):\n \"\"\"A helper to read file passing content via ensure_unicode\n\n Parameters\n ----------\n decode: bool, optional\n if False, no ensure_unicode and file content returned as bytes\n \"\"\"\n with open(fname, 'rb') as f:\n content = f.read()\n return ensure_unicode(content) if decode else content\n\n\ndef read_csv_lines(fname, dialect=None, readahead=16384, **kwargs):\n \"\"\"A generator of dict records from a CSV/TSV\n\n Automatically guesses the encoding for each record to convert to UTF-8\n\n Parameters\n ----------\n fname: str\n Filename\n dialect: str, optional\n Dialect to specify to csv.reader. If not specified -- guessed from\n the file, if fails to guess, \"excel-tab\" is assumed\n readahead: int, optional\n How many bytes to read from the file to guess the type\n **kwargs\n Passed to `csv.reader`\n \"\"\"\n import csv\n if dialect is None:\n with open(fname) as tsvfile:\n # add robustness, use a sniffer\n try:\n dialect = csv.Sniffer().sniff(tsvfile.read(readahead))\n except Exception as exc:\n lgr.warning(\n 'Could not determine file-format, assuming TSV: %s',\n CapturedException(exc)\n )\n dialect = 'excel-tab'\n\n kw = dict(encoding='utf-8')\n with open(fname, 'r', **kw) as tsvfile:\n # csv.py doesn't do Unicode; encode temporarily as UTF-8:\n csv_reader = csv.reader(\n tsvfile,\n dialect=dialect,\n **kwargs\n )\n header = None\n for row in csv_reader:\n # decode UTF-8 back to Unicode, cell by cell:\n row_unicode = map(ensure_unicode, row)\n if header is None:\n header = list(row_unicode)\n else:\n yield dict(zip(header, row_unicode))\n\n\ndef import_modules(modnames, pkg, msg=\"Failed to import {module}\", log=lgr.debug):\n \"\"\"Helper to import a list of modules without failing if N/A\n\n Parameters\n ----------\n modnames: list of str\n List of module names to import\n pkg: str\n Package under which to import\n msg: str, optional\n Message template for .format() to log at DEBUG level if import fails.\n Keys {module} and {package} will be provided and ': {exception}' appended\n log: callable, optional\n Logger call to use for logging messages\n \"\"\"\n from importlib import import_module\n _globals = globals()\n mods_loaded = []\n if pkg and not pkg in sys.modules:\n # with python 3.5.1 (ok with 3.5.5) somehow kept running into\n # Failed to import dlsub1: Parent module 'dltestm1' not loaded\n # while running the test. Preloading pkg resolved the issue\n import_module(pkg)\n for modname in modnames:\n try:\n _globals[modname] = mod = import_module(\n '.{}'.format(modname),\n pkg)\n mods_loaded.append(mod)\n except Exception as exc:\n from datalad.support.exceptions import CapturedException\n ce = CapturedException(exc)\n log((msg + ': {exception}').format(\n module=modname, package=pkg, exception=ce.message))\n return mods_loaded\n\n\ndef import_module_from_file(modpath, pkg=None, log=lgr.debug):\n \"\"\"Import provided module given a path\n\n TODO:\n - RF/make use of it in pipeline.py which has similar logic\n - join with import_modules above?\n\n Parameters\n ----------\n pkg: module, optional\n If provided, and modpath is under pkg.__path__, relative import will be\n used\n \"\"\"\n assert(modpath.endswith('.py')) # for now just for .py files\n\n log(\"Importing %s\" % modpath)\n\n modname = basename(modpath)[:-3]\n relmodpath = None\n if pkg:\n for pkgpath in pkg.__path__:\n if path_is_subpath(modpath, pkgpath):\n # for now relying on having .py extension -- assertion above\n relmodpath = '.' + relpath(modpath[:-3], pkgpath).replace(sep, '.')\n break\n\n try:\n if relmodpath:\n from importlib import import_module\n mod = import_module(relmodpath, pkg.__name__)\n else:\n dirname_ = dirname(modpath)\n try:\n sys.path.insert(0, dirname_)\n mod = __import__(modname, level=0)\n finally:\n if dirname_ in sys.path:\n sys.path.pop(sys.path.index(dirname_))\n else:\n log(\"Expected path %s to be within sys.path, but it was gone!\" % dirname_)\n except Exception as e:\n raise RuntimeError(\n \"Failed to import module from %s\" % modpath) from e\n\n return mod\n\n\ndef get_encoding_info():\n \"\"\"Return a dictionary with various encoding/locale information\"\"\"\n import sys, locale\n from collections import OrderedDict\n return OrderedDict([\n ('default', sys.getdefaultencoding()),\n ('filesystem', sys.getfilesystemencoding()),\n ('locale.prefered', locale.getpreferredencoding()),\n ])\n\n\ndef get_envvars_info():\n from collections import OrderedDict\n envs = []\n for var, val in os.environ.items():\n if (\n var.startswith('PYTHON') or\n var.startswith('LC_') or\n var.startswith('GIT_') or\n var in ('LANG', 'LANGUAGE', 'PATH')\n ):\n envs.append((var, val))\n return OrderedDict(envs)\n\n\n# This class is modified from Snakemake (v5.1.4)\nclass SequenceFormatter(string.Formatter):\n \"\"\"string.Formatter subclass with special behavior for sequences.\n\n This class delegates formatting of individual elements to another\n formatter object. Non-list objects are formatted by calling the\n delegate formatter's \"format_field\" method. List-like objects\n (list, tuple, set, frozenset) are formatted by formatting each\n element of the list according to the specified format spec using\n the delegate formatter and then joining the resulting strings with\n a separator (space by default).\n \"\"\"\n\n def __init__(self, separator=\" \", element_formatter=string.Formatter(),\n *args, **kwargs):\n self.separator = separator\n self.element_formatter = element_formatter\n\n def format_element(self, elem, format_spec):\n \"\"\"Format a single element\n\n For sequences, this is called once for each element in a\n sequence. For anything else, it is called on the entire\n object. It is intended to be overridden in subclases.\n \"\"\"\n return self.element_formatter.format_field(elem, format_spec)\n\n def format_field(self, value, format_spec):\n if isinstance(value, (list, tuple, set, frozenset)):\n return self.separator.join(self.format_element(v, format_spec)\n for v in value)\n else:\n return self.format_element(value, format_spec)\n\n\n# TODO: eventually we might want to make use of attr module\nclass File(object):\n \"\"\"Helper for a file entry in the create_tree/@with_tree\n\n It allows to define additional settings for entries\n \"\"\"\n def __init__(self, name, executable=False):\n \"\"\"\n\n Parameters\n ----------\n name : str\n Name of the file\n executable: bool, optional\n Make it executable\n \"\"\"\n self.name = name\n self.executable = executable\n\n def __str__(self):\n return self.name\n\n\ndef create_tree_archive(path, name, load, overwrite=False, archives_leading_dir=True):\n \"\"\"Given an archive `name`, create under `path` with specified `load` tree\n \"\"\"\n from datalad.support.archives import compress_files\n dirname = file_basename(name)\n full_dirname = op.join(path, dirname)\n os.makedirs(full_dirname)\n create_tree(full_dirname, load, archives_leading_dir=archives_leading_dir)\n # create archive\n if archives_leading_dir:\n compress_files([dirname], name, path=path, overwrite=overwrite)\n else:\n compress_files(list(map(basename, glob.glob(op.join(full_dirname, '*')))),\n op.join(pardir, name),\n path=op.join(path, dirname),\n overwrite=overwrite)\n # remove original tree\n rmtree(full_dirname)\n\n\ndef create_tree(path, tree, archives_leading_dir=True, remove_existing=False):\n \"\"\"Given a list of tuples (name, load) create such a tree\n\n if load is a tuple itself -- that would create either a subtree or an archive\n with that content and place it into the tree if name ends with .tar.gz\n \"\"\"\n lgr.log(5, \"Creating a tree under %s\", path)\n if not exists(path):\n os.makedirs(path)\n\n if isinstance(tree, dict):\n tree = tree.items()\n\n for file_, load in tree:\n if isinstance(file_, File):\n executable = file_.executable\n name = file_.name\n else:\n executable = False\n name = file_\n full_name = op.join(path, name)\n if remove_existing and lexists(full_name):\n rmtree(full_name, chmod_files=True)\n if isinstance(load, (tuple, list, dict)):\n if name.endswith('.tar.gz') or name.endswith('.tar') or name.endswith('.zip'):\n create_tree_archive(\n path, name, load,\n archives_leading_dir=archives_leading_dir)\n else:\n create_tree(\n full_name, load,\n archives_leading_dir=archives_leading_dir,\n remove_existing=remove_existing)\n else:\n open_func = open\n if full_name.endswith('.gz'):\n open_func = gzip.open\n elif full_name.split('.')[-1] in ('xz', 'lzma'):\n import lzma\n open_func = lzma.open\n with open_func(full_name, \"wb\") as f:\n f.write(ensure_bytes(load, 'utf-8'))\n if executable:\n os.chmod(full_name, os.stat(full_name).st_mode | stat.S_IEXEC)\n\n\ndef get_suggestions_msg(values, known, sep=\"\\n \"):\n \"\"\"Return a formatted string with suggestions for values given the known ones\n \"\"\"\n import difflib\n suggestions = []\n for value in ensure_list(values): # might not want to do it if we change presentation below\n suggestions += difflib.get_close_matches(value, known)\n suggestions = unique(suggestions)\n msg = \"Did you mean any of these?\"\n if suggestions:\n if '\\n' in sep:\n # if separator includes new line - we add entire separator right away\n msg += sep\n else:\n msg += ' '\n return msg + \"%s\\n\" % sep.join(suggestions)\n return ''\n\n\ndef bytes2human(n, format='%(value).1f %(symbol)sB'):\n \"\"\"\n Convert n bytes into a human readable string based on format.\n symbols can be either \"customary\", \"customary_ext\", \"iec\" or \"iec_ext\",\n see: http://goo.gl/kTQMs\n\n >>> from datalad.utils import bytes2human\n >>> bytes2human(1)\n '1.0 B'\n >>> bytes2human(1024)\n '1.0 KB'\n >>> bytes2human(1048576)\n '1.0 MB'\n >>> bytes2human(1099511627776127398123789121)\n '909.5 YB'\n\n >>> bytes2human(10000, \"%(value).1f %(symbol)s/sec\")\n '9.8 K/sec'\n\n >>> # precision can be adjusted by playing with %f operator\n >>> bytes2human(10000, format=\"%(value).5f %(symbol)s\")\n '9.76562 K'\n\n Taken from: http://goo.gl/kTQMs and subsequently simplified\n Original Author: Giampaolo Rodola' <g.rodola [AT] gmail [DOT] com>\n License: MIT\n \"\"\"\n n = int(n)\n if n < 0:\n raise ValueError(\"n < 0\")\n symbols = ('', 'K', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y')\n prefix = {}\n for i, s in enumerate(symbols[1:]):\n prefix[s] = 1 << (i + 1) * 10\n for symbol in reversed(symbols[1:]):\n if n >= prefix[symbol]:\n value = float(n) / prefix[symbol]\n return format % locals()\n return format % dict(symbol=symbols[0], value=n)\n\n\ndef quote_cmdlinearg(arg):\n \"\"\"Perform platform-appropriate argument quoting\"\"\"\n # https://stackoverflow.com/a/15262019\n return '\"{}\"'.format(\n arg.replace('\"', '\"\"')\n ) if on_windows else shlex_quote(arg)\n\n\ndef guard_for_format(arg):\n \"\"\"Replace { and } with {{ and }}\n\n To be used in cases if arg is not expected to have provided\n by user .format() placeholders, but 'arg' might become a part\n of a composite passed to .format(), e.g. via 'Run'\n \"\"\"\n return arg.replace('{', '{{').replace('}', '}}')\n\n\ndef join_cmdline(args):\n \"\"\"Join command line args into a string using quote_cmdlinearg\n \"\"\"\n return ' '.join(map(quote_cmdlinearg, args))\n\n\ndef split_cmdline(s):\n \"\"\"Perform platform-appropriate command line splitting.\n\n Identical to `shlex.split()` on non-windows platforms.\n\n Modified from https://stackoverflow.com/a/35900070\n \"\"\"\n if not on_windows:\n return shlex_split(s)\n\n # the rest is for windows\n RE_CMD_LEX = r'''\"((?:\"\"|\\\\[\"\\\\]|[^\"])*)\"?()|(\\\\\\\\(?=\\\\*\")|\\\\\")|(&&?|\\|\\|?|\\d?>|[<])|([^\\s\"&|<>]+)|(\\s+)|(.)'''\n\n args = []\n accu = None # collects pieces of one arg\n for qs, qss, esc, pipe, word, white, fail in re.findall(RE_CMD_LEX, s):\n if word:\n pass # most frequent\n elif esc:\n word = esc[1]\n elif white or pipe:\n if accu is not None:\n args.append(accu)\n if pipe:\n args.append(pipe)\n accu = None\n continue\n elif fail:\n raise ValueError(\"invalid or incomplete shell string\")\n elif qs:\n word = qs.replace('\\\\\"', '\"').replace('\\\\\\\\', '\\\\')\n if platform == 0:\n word = word.replace('\"\"', '\"')\n else:\n word = qss # may be even empty; must be last\n\n accu = (accu or '') + word\n\n if accu is not None:\n args.append(accu)\n\n return args\n\n\ndef get_wrapped_class(wrapped):\n \"\"\"Determine the command class a wrapped __call__ belongs to\"\"\"\n mod = sys.modules[wrapped.__module__]\n command_class_name = wrapped.__qualname__.split('.')[-2]\n _func_class = mod.__dict__[command_class_name]\n lgr.debug(\"Determined class of decorated function: %s\", _func_class)\n return _func_class\n\n\ndef _make_assure_kludge(fn):\n old_name = fn.__name__.replace(\"ensure\", \"assure\")\n\n @wraps(fn)\n def compat_fn(*args, **kwargs):\n warnings.warn(\n \"{} is deprecated and will be removed in a future release. \"\n \"Use {} instead.\"\n .format(old_name, fn.__name__),\n DeprecationWarning)\n return fn(*args, **kwargs)\n\n compat_fn.__doc__ = (\"Note: This function is deprecated. Use {} instead.\"\n .format(fn.__name__))\n return compat_fn\n\n\nassure_tuple_or_list = _make_assure_kludge(ensure_tuple_or_list)\nassure_iter = _make_assure_kludge(ensure_iter)\nassure_list = _make_assure_kludge(ensure_list)\nassure_list_from_str = _make_assure_kludge(ensure_list_from_str)\nassure_dict_from_str = _make_assure_kludge(ensure_dict_from_str)\nassure_bytes = _make_assure_kludge(ensure_bytes)\nassure_unicode = _make_assure_kludge(ensure_unicode)\nassure_bool = _make_assure_kludge(ensure_bool)\nassure_dir = _make_assure_kludge(ensure_dir)\n\n\nlgr.log(5, \"Done importing datalad.utils\")\n\n\ndef check_symlink_capability(path, target):\n \"\"\"helper similar to datalad.tests.utils.has_symlink_capability\n\n However, for use in a datalad command context, we shouldn't\n assume to be able to write to tmpfile and also not import a whole lot from\n datalad's test machinery. Finally, we want to know, whether we can create a\n symlink at a specific location, not just somewhere. Therefore use\n arbitrary path to test-build a symlink and delete afterwards. Suitable\n location can therefore be determined by high lever code.\n\n Parameters\n ----------\n path: Path\n target: Path\n\n Returns\n -------\n bool\n \"\"\"\n\n try:\n target.touch()\n path.symlink_to(target)\n return True\n except Exception:\n return False\n finally:\n if path.exists():\n path.unlink()\n if target.exists():\n target.unlink()"},"implementation":{"kind":"string","value":"def ensure_bytes(s, encoding='utf-8'):\n \"\"\"Convert/encode unicode string to bytes.\n\n If `s` isn't a string, return it as is.\n\n Parameters\n ----------\n encoding: str, optional\n Encoding to use. \"utf-8\" is the default\n \"\"\"\n if not isinstance(s, str):\n return s\n return s.encode(encoding)"},"start_line":{"kind":"number","value":792,"string":"792"},"end_line":{"kind":"number","value":804,"string":"804"},"file_content":{"kind":"string","value":"# emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-\n# ex: set sts=4 ts=4 sw=4 et:\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##\n#\n# See COPYING file distributed along with the datalad package for the\n# copyright and license terms.\n#\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##\n\nimport collections\nfrom collections.abc import Callable\nimport re\nimport builtins\nimport time\n\nimport logging\nimport shutil\nimport os\nimport sys\nimport tempfile\nfrom tempfile import NamedTemporaryFile\nimport platform\nimport gc\nimport glob\nimport gzip\nimport stat\nimport string\nimport warnings\n\nimport os.path as op\n\nfrom copy import copy as shallow_copy\nfrom contextlib import contextmanager\nfrom functools import (\n lru_cache,\n wraps,\n)\nfrom time import sleep\nimport inspect\nfrom itertools import tee\n# this import is required because other modules import opj from here.\nfrom os.path import join as opj\nfrom os.path import (\n abspath,\n basename,\n commonprefix,\n curdir,\n dirname,\n exists,\n expanduser,\n expandvars,\n isabs,\n isdir,\n islink,\n lexists,\n normpath,\n pardir,\n relpath,\n sep,\n split,\n splitdrive\n)\n\nimport posixpath\n\nfrom shlex import (\n quote as shlex_quote,\n split as shlex_split,\n)\n\n# from datalad.dochelpers import get_docstring_split\nfrom datalad.consts import TIMESTAMP_FMT\nfrom datalad.support.exceptions import CapturedException\n\n\nunicode_srctypes = str, bytes\n\n\nlgr = logging.getLogger(\"datalad.utils\")\n\nlgr.log(5, \"Importing datalad.utils\")\n#\n# Some useful variables\n#\nplatform_system = platform.system().lower()\non_windows = platform_system == 'windows'\non_osx = platform_system == 'darwin'\non_linux = platform_system == 'linux'\non_msys_tainted_paths = on_windows \\\n and 'MSYS_NO_PATHCONV' not in os.environ \\\n and os.environ.get('MSYSTEM', '')[:4] in ('MSYS', 'MING')\n\n\n# Takes ~200msec, so should not be called at import time\n@lru_cache() # output should not change through life time of datalad process\ndef get_linux_distribution():\n \"\"\"Compatibility wrapper for {platform,distro}.linux_distribution().\n \"\"\"\n if hasattr(platform, \"linux_distribution\"):\n # Use deprecated (but faster) method if it's available.\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n result = platform.linux_distribution()\n else:\n import distro # We require this for Python 3.8 and above.\n result = distro.linux_distribution(full_distribution_name=False)\n return result\n\n\n# Those weren't used for any critical decision making, thus we just set them to None\n# Use get_linux_distribution() directly where needed\nlinux_distribution_name = linux_distribution_release = None\n\n# Maximal length of cmdline string\n# Query the system and use hardcoded \"knowledge\" if None\n# probably getconf ARG_MAX might not be available\n# The last one would be the most conservative/Windows\nCMD_MAX_ARG_HARDCODED = 2097152 if on_linux else 262144 if on_osx else 32767\ntry:\n CMD_MAX_ARG = os.sysconf('SC_ARG_MAX')\n assert CMD_MAX_ARG > 0\n if CMD_MAX_ARG > CMD_MAX_ARG_HARDCODED * 1e6:\n # workaround for some kind of a bug which comes up with python 3.4\n # see https://github.com/datalad/datalad/issues/3150\n # or on older CentOS with conda and python as new as 3.9\n # see https://github.com/datalad/datalad/issues/5943\n # TODO: let Yarik know that the world is a paradise now whenever 1e6\n # is not large enough\n CMD_MAX_ARG = min(CMD_MAX_ARG, CMD_MAX_ARG_HARDCODED)\nexcept Exception as exc:\n # ATM (20181005) SC_ARG_MAX available only on POSIX systems\n # so exception would be thrown e.g. on Windows, or\n # somehow during Debian build for nd14.04 it is coming up with -1:\n # https://github.com/datalad/datalad/issues/3015\n CMD_MAX_ARG = CMD_MAX_ARG_HARDCODED\n lgr.debug(\n \"Failed to query or got useless SC_ARG_MAX sysconf, \"\n \"will use hardcoded value: %s\", exc)\n# Even with all careful computations we do, due to necessity to account for\n# environment and what not, we still could not figure out \"exact\" way to\n# estimate it, but it was shown that 300k safety margin on linux was sufficient.\n# https://github.com/datalad/datalad/pull/2977#issuecomment-436264710\n# 300k is ~15%, so to be safe, and for paranoid us we will just use up to 50%\n# of the length for \"safety margin\". We might probably still blow due to\n# env vars, unicode, etc... so any hard limit imho is not a proper solution\nCMD_MAX_ARG = int(0.5 * CMD_MAX_ARG)\nlgr.debug(\n \"Maximal length of cmdline string (adjusted for safety margin): %d\",\n CMD_MAX_ARG)\n\n#\n# Little helpers\n#\n\n# `getargspec` has been deprecated in Python 3.\nArgSpecFake = collections.namedtuple(\n \"ArgSpecFake\", [\"args\", \"varargs\", \"keywords\", \"defaults\"])\n\n\ndef getargspec(func, *, include_kwonlyargs=False):\n \"\"\"Compat shim for getargspec deprecated in python 3.\n\n The main difference from inspect.getargspec (and inspect.getfullargspec\n for that matter) is that by using inspect.signature we are providing\n correct args/defaults for functools.wraps'ed functions.\n\n `include_kwonlyargs` option was added to centralize getting all args,\n even the ones which are kwonly (follow the ``*,``).\n\n For internal use and not advised for use in 3rd party code.\n Please use inspect.signature directly.\n \"\"\"\n # We use signature, and not getfullargspec, because only signature properly\n # \"passes\" args from a functools.wraps decorated function.\n # Note: getfullargspec works Ok on wrapt-decorated functions\n f_sign = inspect.signature(func)\n # Loop through parameters and compose argspec\n args4 = [[], None, None, {}]\n # Collect all kwonlyargs into a dedicated dict - name: default\n kwonlyargs = {}\n # shortcuts\n args, defaults = args4[0], args4[3]\n P = inspect.Parameter\n\n for p_name, p in f_sign.parameters.items():\n if p.kind in (P.POSITIONAL_ONLY, P.POSITIONAL_OR_KEYWORD):\n assert not kwonlyargs # yoh: must not come after kwonlyarg\n args.append(p_name)\n if p.default is not P.empty:\n defaults[p_name] = p.default\n elif p.kind == P.VAR_POSITIONAL:\n args4[1] = p_name\n elif p.kind == P.VAR_KEYWORD:\n args4[2] = p_name\n elif p.kind == P.KEYWORD_ONLY:\n assert p.default is not P.empty\n kwonlyargs[p_name] = p.default\n\n if kwonlyargs:\n if not include_kwonlyargs:\n raise ValueError(\n 'Function has keyword-only parameters or annotations, either use '\n 'inspect.signature() API which can support them, or provide include_kwonlyargs=True '\n 'to this function'\n )\n else:\n args.extend(list(kwonlyargs))\n defaults.update(kwonlyargs)\n\n # harmonize defaults to how original getargspec returned them -- just a tuple\n args4[3] = None if not defaults else tuple(defaults.values())\n return ArgSpecFake(*args4)\n\n\ndef any_re_search(regexes, value):\n \"\"\"Return if any of regexes (list or str) searches successfully for value\"\"\"\n for regex in ensure_tuple_or_list(regexes):\n if re.search(regex, value):\n return True\n return False\n\n\ndef not_supported_on_windows(msg=None):\n \"\"\"A little helper to be invoked to consistently fail whenever functionality is\n not supported (yet) on Windows\n \"\"\"\n if on_windows:\n raise NotImplementedError(\"This functionality is not yet implemented for Windows OS\"\n + (\": %s\" % msg if msg else \"\"))\n\n\ndef get_home_envvars(new_home):\n \"\"\"Return dict with env variables to be adjusted for a new HOME\n\n Only variables found in current os.environ are adjusted.\n\n Parameters\n ----------\n new_home: str or Path\n New home path, in native to OS \"schema\"\n \"\"\"\n new_home = str(new_home)\n out = {'HOME': new_home}\n if on_windows:\n # requires special handling, since it has a number of relevant variables\n # and also Python changed its behavior and started to respect USERPROFILE only\n # since python 3.8: https://bugs.python.org/issue36264\n out['USERPROFILE'] = new_home\n out['HOMEDRIVE'], out['HOMEPATH'] = splitdrive(new_home)\n\n return {v: val for v, val in out.items() if v in os.environ}\n\n\ndef shortened_repr(value, l=30):\n try:\n if hasattr(value, '__repr__') and (value.__repr__ is not object.__repr__):\n value_repr = repr(value)\n if not value_repr.startswith('<') and len(value_repr) > l:\n value_repr = \"<<%s++%d chars++%s>>\" % (\n value_repr[:l - 16],\n len(value_repr) - (l - 16 + 4),\n value_repr[-4:]\n )\n elif value_repr.startswith('<') and value_repr.endswith('>') and ' object at 0x':\n raise ValueError(\"I hate those useless long reprs\")\n else:\n raise ValueError(\"gimme class\")\n except Exception as e:\n value_repr = \"<%s>\" % value.__class__.__name__.split('.')[-1]\n return value_repr\n\n\ndef __auto_repr__(obj):\n attr_names = tuple()\n if hasattr(obj, '__dict__'):\n attr_names += tuple(obj.__dict__.keys())\n if hasattr(obj, '__slots__'):\n attr_names += tuple(obj.__slots__)\n\n items = []\n for attr in sorted(set(attr_names)):\n if attr.startswith('_'):\n continue\n value = getattr(obj, attr)\n # TODO: should we add this feature to minimize some talktative reprs\n # such as of URL?\n #if value is None:\n # continue\n items.append(\"%s=%s\" % (attr, shortened_repr(value)))\n\n return \"%s(%s)\" % (obj.__class__.__name__, ', '.join(items))\n\n\ndef auto_repr(cls):\n \"\"\"Decorator for a class to assign it an automagic quick and dirty __repr__\n\n It uses public class attributes to prepare repr of a class\n\n Original idea: http://stackoverflow.com/a/27799004/1265472\n \"\"\"\n\n cls.__repr__ = __auto_repr__\n return cls\n\n\ndef _is_stream_tty(stream):\n try:\n # TODO: check on windows if hasattr check would work correctly and\n # add value:\n return stream.isatty()\n except ValueError as exc:\n # Who knows why it is a ValueError, but let's try to be specific\n # If there is a problem with I/O - non-interactive, otherwise reraise\n if \"I/O\" in str(exc):\n return False\n raise\n\n\ndef is_interactive():\n \"\"\"Return True if all in/outs are open and tty.\n\n Note that in a somewhat abnormal case where e.g. stdin is explicitly\n closed, and any operation on it would raise a\n `ValueError(\"I/O operation on closed file\")` exception, this function\n would just return False, since the session cannot be used interactively.\n \"\"\"\n return all(_is_stream_tty(s) for s in (sys.stdin, sys.stdout, sys.stderr))\n\n\ndef get_ipython_shell():\n \"\"\"Detect if running within IPython and returns its `ip` (shell) object\n\n Returns None if not under ipython (no `get_ipython` function)\n \"\"\"\n try:\n return get_ipython()\n except NameError:\n return None\n\n\ndef md5sum(filename):\n \"\"\"Compute an MD5 sum for the given file\n \"\"\"\n from datalad.support.digests import Digester\n return Digester(digests=['md5'])(filename)['md5']\n\n\n# unused in -core\ndef sorted_files(path):\n \"\"\"Return a (sorted) list of files under path\n \"\"\"\n return sorted(sum([[op.join(r, f)[len(path) + 1:] for f in files]\n for r, d, files in os.walk(path)\n if not '.git' in r], []))\n\n_encoded_dirsep = r'\\\\' if on_windows else r'/'\n_VCS_REGEX = r'%s\\.(?:git|gitattributes|svn|bzr|hg)(?:%s|$)' % (\n _encoded_dirsep, _encoded_dirsep)\n_DATALAD_REGEX = r'%s\\.(?:datalad)(?:%s|$)' % (\n _encoded_dirsep, _encoded_dirsep)\n\n\ndef find_files(regex, topdir=curdir, exclude=None, exclude_vcs=True, exclude_datalad=False, dirs=False):\n \"\"\"Generator to find files matching regex\n\n Parameters\n ----------\n regex: basestring\n exclude: basestring, optional\n Matches to exclude\n exclude_vcs:\n If True, excludes commonly known VCS subdirectories. If string, used\n as regex to exclude those files (regex: `%r`)\n exclude_datalad:\n If True, excludes files known to be datalad meta-data files (e.g. under\n .datalad/ subdirectory) (regex: `%r`)\n topdir: basestring, optional\n Directory where to search\n dirs: bool, optional\n Whether to match directories as well as files\n \"\"\"\n for dirpath, dirnames, filenames in os.walk(topdir):\n names = (dirnames + filenames) if dirs else filenames\n # TODO: might want to uniformize on windows to use '/'\n paths = (op.join(dirpath, name) for name in names)\n for path in filter(re.compile(regex).search, paths):\n path = path.rstrip(sep)\n if exclude and re.search(exclude, path):\n continue\n if exclude_vcs and re.search(_VCS_REGEX, path):\n continue\n if exclude_datalad and re.search(_DATALAD_REGEX, path):\n continue\n yield path\nfind_files.__doc__ %= (_VCS_REGEX, _DATALAD_REGEX)\n\n\ndef expandpath(path, force_absolute=True):\n \"\"\"Expand all variables and user handles in a path.\n\n By default return an absolute path\n \"\"\"\n path = expandvars(expanduser(path))\n if force_absolute:\n path = abspath(path)\n return path\n\n\ndef posix_relpath(path, start=None):\n \"\"\"Behave like os.path.relpath, but always return POSIX paths...\n\n on any platform.\"\"\"\n # join POSIX style\n return posixpath.join(\n # split and relpath native style\n # python2.7 ntpath implementation of relpath cannot handle start=None\n *split(\n relpath(path, start=start if start is not None else '')))\n\n\ndef is_explicit_path(path):\n \"\"\"Return whether a path explicitly points to a location\n\n Any absolute path, or relative path starting with either '../' or\n './' is assumed to indicate a location on the filesystem. Any other\n path format is not considered explicit.\"\"\"\n path = expandpath(path, force_absolute=False)\n return isabs(path) \\\n or path.startswith(os.curdir + os.sep) \\\n or path.startswith(os.pardir + os.sep)\n\n\n# handle this dance once, and import pathlib from here\n# in all other places\n\nfrom pathlib import (\n Path,\n PurePath,\n PurePosixPath,\n)\n\ndef rotree(path, ro=True, chmod_files=True):\n \"\"\"To make tree read-only or writable\n\n Parameters\n ----------\n path : string\n Path to the tree/directory to chmod\n ro : bool, optional\n Whether to make it R/O (default) or RW\n chmod_files : bool, optional\n Whether to operate also on files (not just directories)\n \"\"\"\n if ro:\n chmod = lambda f: os.chmod(f, os.stat(f).st_mode & ~stat.S_IWRITE)\n else:\n chmod = lambda f: os.chmod(f, os.stat(f).st_mode | stat.S_IWRITE | stat.S_IREAD)\n\n for root, dirs, files in os.walk(path, followlinks=False):\n if chmod_files:\n for f in files:\n fullf = op.join(root, f)\n # might be the \"broken\" symlink which would fail to stat etc\n if exists(fullf):\n chmod(fullf)\n chmod(root)\n\n\ndef rmtree(path, chmod_files='auto', children_only=False, *args, **kwargs):\n \"\"\"To remove git-annex .git it is needed to make all files and directories writable again first\n\n Parameters\n ----------\n path: Path or str\n Path to remove\n chmod_files : string or bool, optional\n Whether to make files writable also before removal. Usually it is just\n a matter of directories to have write permissions.\n If 'auto' it would chmod files on windows by default\n children_only : bool, optional\n If set, all files and subdirectories would be removed while the path\n itself (must be a directory) would be preserved\n `*args` :\n `**kwargs` :\n Passed into shutil.rmtree call\n \"\"\"\n # Give W permissions back only to directories, no need to bother with files\n if chmod_files == 'auto':\n chmod_files = on_windows\n # TODO: yoh thinks that if we could quickly check our Flyweight for\n # repos if any of them is under the path, and could call .precommit\n # on those to possibly stop batched processes etc, we did not have\n # to do it on case by case\n # Check for open files\n assert_no_open_files(path)\n\n # TODO the whole thing should be reimplemented with pathlib, but for now\n # at least accept Path\n path = str(path)\n\n if children_only:\n if not isdir(path):\n raise ValueError(\"Can remove children only of directories\")\n for p in os.listdir(path):\n rmtree(op.join(path, p))\n return\n if not (islink(path) or not isdir(path)):\n rotree(path, ro=False, chmod_files=chmod_files)\n if on_windows:\n # shutil fails to remove paths that exceed 260 characters on Windows machines\n # that did not enable long path support. A workaround to remove long paths\n # anyway is to preprend \\\\?\\ to the path.\n # https://docs.microsoft.com/en-us/windows/win32/fileio/naming-a-file?redirectedfrom=MSDN#win32-file-namespaces\n path = r'\\\\?\\ '.strip() + path\n _rmtree(path, *args, **kwargs)\n else:\n # just remove the symlink\n unlink(path)\n\n\ndef rmdir(path, *args, **kwargs):\n \"\"\"os.rmdir with our optional checking for open files\"\"\"\n assert_no_open_files(path)\n os.rmdir(path)\n\n\ndef get_open_files(path, log_open=False):\n \"\"\"Get open files under a path\n\n Note: This function is very slow on Windows.\n\n Parameters\n ----------\n path : str\n File or directory to check for open files under\n log_open : bool or int\n If set - logger level to use\n\n Returns\n -------\n dict\n path : pid\n\n \"\"\"\n # Original idea: https://stackoverflow.com/a/11115521/1265472\n import psutil\n files = {}\n # since the ones returned by psutil would not be aware of symlinks in the\n # path we should also get realpath for path\n # do absolute() in addition to always get an absolute path\n # even with non-existing paths on windows\n path = str(Path(path).resolve().absolute())\n for proc in psutil.process_iter():\n try:\n open_paths = [p.path for p in proc.open_files()] + [proc.cwd()]\n for p in open_paths:\n # note: could be done more efficiently so we do not\n # renormalize path over and over again etc\n if path_startswith(p, path):\n files[p] = proc\n # Catch a race condition where a process ends\n # before we can examine its files\n except psutil.NoSuchProcess:\n pass\n except psutil.AccessDenied:\n pass\n\n if files and log_open:\n lgr.log(log_open, \"Open files under %s: %s\", path, files)\n return files\n\n\n_assert_no_open_files_cfg = os.environ.get('DATALAD_ASSERT_NO_OPEN_FILES')\nif _assert_no_open_files_cfg:\n def assert_no_open_files(path):\n files = get_open_files(path, log_open=40)\n if _assert_no_open_files_cfg == 'assert':\n assert not files, \"Got following files still open: %s\" % ','.join(files)\n elif files:\n if _assert_no_open_files_cfg == 'pdb':\n import pdb\n pdb.set_trace()\n elif _assert_no_open_files_cfg == 'epdb':\n import epdb\n epdb.serve()\n pass\n # otherwise we would just issue that error message in the log\nelse:\n def assert_no_open_files(*args, **kwargs):\n pass\n\n\ndef rmtemp(f, *args, **kwargs):\n \"\"\"Wrapper to centralize removing of temp files so we could keep them around\n\n It will not remove the temporary file/directory if DATALAD_TESTS_TEMP_KEEP\n environment variable is defined\n \"\"\"\n if not os.environ.get('DATALAD_TESTS_TEMP_KEEP'):\n if not os.path.lexists(f):\n lgr.debug(\"Path %s does not exist, so can't be removed\", f)\n return\n lgr.log(5, \"Removing temp file: %s\", f)\n # Can also be a directory\n if isdir(f):\n rmtree(f, *args, **kwargs)\n else:\n unlink(f)\n else:\n lgr.info(\"Keeping temp file: %s\", f)\n\n\ndef file_basename(name, return_ext=False):\n \"\"\"\n Strips up to 2 extensions of length up to 4 characters and starting with alpha\n not a digit, so we could get rid of .tar.gz etc\n \"\"\"\n bname = basename(name)\n fbname = re.sub(r'(\\.[a-zA-Z_]\\S{1,4}){0,2}$', '', bname)\n if return_ext:\n return fbname, bname[len(fbname) + 1:]\n else:\n return fbname\n\n\n# unused in -core\ndef escape_filename(filename):\n \"\"\"Surround filename in \"\" and escape \" in the filename\n \"\"\"\n filename = filename.replace('\"', r'\\\"').replace('`', r'\\`')\n filename = '\"%s\"' % filename\n return filename\n\n\n# unused in -core\ndef encode_filename(filename):\n \"\"\"Encode unicode filename\n \"\"\"\n if isinstance(filename, str):\n return filename.encode(sys.getfilesystemencoding())\n else:\n return filename\n\n\n# unused in -core\ndef decode_input(s):\n \"\"\"Given input string/bytes, decode according to stdin codepage (or UTF-8)\n if not defined\n\n If fails -- issue warning and decode allowing for errors\n being replaced\n \"\"\"\n if isinstance(s, str):\n return s\n else:\n encoding = sys.stdin.encoding or 'UTF-8'\n try:\n return s.decode(encoding)\n except UnicodeDecodeError as exc:\n lgr.warning(\n \"Failed to decode input string using %s encoding. \"\n \"Decoding allowing for errors\", encoding)\n return s.decode(encoding, errors='replace')\n\n\n# unused in -core\nif on_windows:\n def lmtime(filepath, mtime):\n \"\"\"Set mtime for files. On Windows a merely adapter to os.utime\n \"\"\"\n os.utime(filepath, (time.time(), mtime))\nelse:\n def lmtime(filepath, mtime):\n \"\"\"Set mtime for files, while not de-referencing symlinks.\n\n To overcome absence of os.lutime\n\n Works only on linux and OSX ATM\n \"\"\"\n from .cmd import WitlessRunner\n # convert mtime to format touch understands [[CC]YY]MMDDhhmm[.SS]\n smtime = time.strftime(\"%Y%m%d%H%M.%S\", time.localtime(mtime))\n lgr.log(3, \"Setting mtime for %s to %s == %s\", filepath, mtime, smtime)\n WitlessRunner().run(['touch', '-h', '-t', '%s' % smtime, filepath])\n filepath = Path(filepath)\n rfilepath = filepath.resolve()\n if filepath.is_symlink() and rfilepath.exists():\n # trust no one - adjust also of the target file\n # since it seemed like downloading under OSX (was it using curl?)\n # didn't bother with timestamps\n lgr.log(3, \"File is a symlink to %s Setting mtime for it to %s\",\n rfilepath, mtime)\n os.utime(str(rfilepath), (time.time(), mtime))\n # doesn't work on OSX\n # Runner().run(['touch', '-h', '-d', '@%s' % mtime, filepath])\n\n\ndef ensure_tuple_or_list(obj):\n \"\"\"Given an object, wrap into a tuple if not list or tuple\n \"\"\"\n if isinstance(obj, (list, tuple)):\n return obj\n return (obj,)\n\n\ndef ensure_iter(s, cls, copy=False, iterate=True):\n \"\"\"Given not a list, would place it into a list. If None - empty list is returned\n\n Parameters\n ----------\n s: list or anything\n cls: class\n Which iterable class to ensure\n copy: bool, optional\n If correct iterable is passed, it would generate its shallow copy\n iterate: bool, optional\n If it is not a list, but something iterable (but not a str)\n iterate over it.\n \"\"\"\n\n if isinstance(s, cls):\n return s if not copy else shallow_copy(s)\n elif isinstance(s, str):\n return cls((s,))\n elif iterate and hasattr(s, '__iter__'):\n return cls(s)\n elif s is None:\n return cls()\n else:\n return cls((s,))\n\n\ndef ensure_list(s, copy=False, iterate=True):\n \"\"\"Given not a list, would place it into a list. If None - empty list is returned\n\n Parameters\n ----------\n s: list or anything\n copy: bool, optional\n If list is passed, it would generate a shallow copy of the list\n iterate: bool, optional\n If it is not a list, but something iterable (but not a str)\n iterate over it.\n \"\"\"\n return ensure_iter(s, list, copy=copy, iterate=iterate)\n\n\ndef ensure_list_from_str(s, sep='\\n'):\n \"\"\"Given a multiline string convert it to a list of return None if empty\n\n Parameters\n ----------\n s: str or list\n \"\"\"\n\n if not s:\n return None\n\n if isinstance(s, list):\n return s\n return s.split(sep)\n\n\ndef ensure_dict_from_str(s, **kwargs):\n \"\"\"Given a multiline string with key=value items convert it to a dictionary\n\n Parameters\n ----------\n s: str or dict\n\n Returns None if input s is empty\n \"\"\"\n\n if not s:\n return None\n\n if isinstance(s, dict):\n return s\n\n out = {}\n for value_str in ensure_list_from_str(s, **kwargs):\n if '=' not in value_str:\n raise ValueError(\"{} is not in key=value format\".format(repr(value_str)))\n k, v = value_str.split('=', 1)\n if k in out:\n err = \"key {} was already defined in {}, but new value {} was provided\".format(k, out, v)\n raise ValueError(err)\n out[k] = v\n return out\n\n\ndef ensure_bytes(s, encoding='utf-8'):\n \"\"\"Convert/encode unicode string to bytes.\n\n If `s` isn't a string, return it as is.\n\n Parameters\n ----------\n encoding: str, optional\n Encoding to use. \"utf-8\" is the default\n \"\"\"\n if not isinstance(s, str):\n return s\n return s.encode(encoding)\n\n\ndef ensure_unicode(s, encoding=None, confidence=None):\n \"\"\"Convert/decode bytestring to unicode.\n\n If `s` isn't a bytestring, return it as is.\n\n Parameters\n ----------\n encoding: str, optional\n Encoding to use. If None, \"utf-8\" is tried, and then if not a valid\n UTF-8, encoding will be guessed\n confidence: float, optional\n A value between 0 and 1, so if guessing of encoding is of lower than\n specified confidence, ValueError is raised\n \"\"\"\n if not isinstance(s, bytes):\n return s\n if encoding is None:\n # Figure out encoding, defaulting to 'utf-8' which is our common\n # target in contemporary digital society\n try:\n return s.decode('utf-8')\n except UnicodeDecodeError as exc:\n lgr.debug(\"Failed to decode a string as utf-8: %s\",\n CapturedException(exc))\n # And now we could try to guess\n from chardet import detect\n enc = detect(s)\n denc = enc.get('encoding', None)\n if denc:\n denc_confidence = enc.get('confidence', 0)\n if confidence is not None and denc_confidence < confidence:\n raise ValueError(\n \"Failed to auto-detect encoding with high enough \"\n \"confidence. Highest confidence was %s for %s\"\n % (denc_confidence, denc)\n )\n lgr.log(5, \"Auto-detected encoding to be %s\", denc)\n return s.decode(denc)\n else:\n raise ValueError(\n \"Could not decode value as utf-8, or to guess its encoding: %s\"\n % repr(s)\n )\n else:\n return s.decode(encoding)\n\n\ndef ensure_bool(s):\n \"\"\"Convert value into boolean following convention for strings\n\n to recognize on,True,yes as True, off,False,no as False\n \"\"\"\n if isinstance(s, str):\n if s.isdigit():\n return bool(int(s))\n sl = s.lower()\n if sl in {'y', 'yes', 'true', 'on'}:\n return True\n elif sl in {'n', 'no', 'false', 'off'}:\n return False\n else:\n raise ValueError(\"Do not know how to treat %r as a boolean\" % s)\n return bool(s)\n\n\ndef as_unicode(val, cast_types=object):\n \"\"\"Given an arbitrary value, would try to obtain unicode value of it\n \n For unicode it would return original value, for python2 str or python3\n bytes it would use ensure_unicode, for None - an empty (unicode) string,\n and for any other type (see `cast_types`) - would apply the unicode \n constructor. If value is not an instance of `cast_types`, TypeError\n is thrown\n \n Parameters\n ----------\n cast_types: type\n Which types to cast to unicode by providing to constructor\n \"\"\"\n if val is None:\n return u''\n elif isinstance(val, str):\n return val\n elif isinstance(val, unicode_srctypes):\n return ensure_unicode(val)\n elif isinstance(val, cast_types):\n return str(val)\n else:\n raise TypeError(\n \"Value %r is not of any of known or provided %s types\"\n % (val, cast_types))\n\n\ndef unique(seq, key=None, reverse=False):\n \"\"\"Given a sequence return a list only with unique elements while maintaining order\n\n This is the fastest solution. See\n https://www.peterbe.com/plog/uniqifiers-benchmark\n and\n http://stackoverflow.com/a/480227/1265472\n for more information.\n Enhancement -- added ability to compare for uniqueness using a key function\n\n Parameters\n ----------\n seq:\n Sequence to analyze\n key: callable, optional\n Function to call on each element so we could decide not on a full\n element, but on its member etc\n reverse: bool, optional\n If True, uniqueness checked in the reverse order, so that the later ones\n will take the order\n \"\"\"\n seen = set()\n seen_add = seen.add\n\n trans = reversed if reverse else lambda x: x\n\n if not key:\n out = [x for x in trans(seq) if not (x in seen or seen_add(x))]\n else:\n # OPT: could be optimized, since key is called twice, but for our cases\n # should be just as fine\n out = [x for x in trans(seq) if not (key(x) in seen or seen_add(key(x)))]\n\n return out[::-1] if reverse else out\n\n\ndef all_same(items):\n \"\"\"Quick check if all items are the same.\n\n Identical to a check like len(set(items)) == 1 but\n should be more efficient while working on generators, since would\n return False as soon as any difference detected thus possibly avoiding\n unnecessary evaluations\n \"\"\"\n first = True\n first_item = None\n for item in items:\n if first:\n first = False\n first_item = item\n else:\n if item != first_item:\n return False\n # So we return False if was empty\n return not first\n\n\ndef map_items(func, v):\n \"\"\"A helper to apply `func` to all elements (keys and values) within dict\n\n No type checking of values passed to func is done, so `func`\n should be resilient to values which it should not handle\n\n Initial usecase - apply_recursive(url_fragment, ensure_unicode)\n \"\"\"\n # map all elements within item\n return v.__class__(\n item.__class__(map(func, item))\n for item in v.items()\n )\n\n\ndef partition(items, predicate=bool):\n \"\"\"Partition `items` by `predicate`.\n\n Parameters\n ----------\n items : iterable\n predicate : callable\n A function that will be mapped over each element in `items`. The\n elements will partitioned based on whether the return value is false or\n true.\n\n Returns\n -------\n A tuple with two generators, the first for 'false' items and the second for\n 'true' ones.\n\n Notes\n -----\n Taken from Peter Otten's snippet posted at\n https://nedbatchelder.com/blog/201306/filter_a_list_into_two_parts.html\n \"\"\"\n a, b = tee((predicate(item), item) for item in items)\n return ((item for pred, item in a if not pred),\n (item for pred, item in b if pred))\n\n\ndef generate_chunks(container, size):\n \"\"\"Given a container, generate chunks from it with size up to `size`\n \"\"\"\n # There could be a \"smarter\" solution but I think this would suffice\n assert size > 0, \"Size should be non-0 positive\"\n while container:\n yield container[:size]\n container = container[size:]\n\n\ndef generate_file_chunks(files, cmd=None):\n \"\"\"Given a list of files, generate chunks of them to avoid exceeding cmdline length\n\n Parameters\n ----------\n files: list of str\n cmd: str or list of str, optional\n Command to account for as well\n \"\"\"\n files = ensure_list(files)\n cmd = ensure_list(cmd)\n\n maxl = max(map(len, files)) if files else 0\n chunk_size = max(\n 1, # should at least be 1. If blows then - not our fault\n (CMD_MAX_ARG\n - sum((len(x) + 3) for x in cmd)\n - 4 # for '--' below\n ) // (maxl + 3) # +3 for possible quotes and a space\n )\n # TODO: additional treatment for \"too many arguments\"? although\n # as https://github.com/datalad/datalad/issues/1883#issuecomment\n # -436272758\n # shows there seems to be no hardcoded limit on # of arguments,\n # but may be we decide to go for smth like follow to be on safe side\n # chunk_size = min(10240 - len(cmd), chunk_size)\n file_chunks = generate_chunks(files, chunk_size)\n return file_chunks\n\n\n#\n# Generators helpers\n#\n\ndef saved_generator(gen):\n \"\"\"Given a generator returns two generators, where 2nd one just replays\n\n So the first one would be going through the generated items and 2nd one\n would be yielding saved items\n \"\"\"\n saved = []\n\n def gen1():\n for x in gen: # iterating over original generator\n saved.append(x)\n yield x\n\n def gen2():\n for x in saved: # yielding saved entries\n yield x\n\n return gen1(), gen2()\n\n\n#\n# Decorators\n#\n\n# Originally better_wraps was created to provide `wrapt`-based, instead of\n# `functools.wraps` implementation to preserve the correct signature of the\n# decorated function. By using inspect.signature in our getargspec, which\n# works fine on `functools.wraps`ed functions, we mediated this necessity.\nbetter_wraps = wraps\n\n\n# Borrowed from pandas\n# Copyright: 2011-2014, Lambda Foundry, Inc. and PyData Development Team\n# License: BSD-3\ndef optional_args(decorator):\n \"\"\"allows a decorator to take optional positional and keyword arguments.\n Assumes that taking a single, callable, positional argument means that\n it is decorating a function, i.e. something like this::\n\n @my_decorator\n def function(): pass\n\n Calls decorator with decorator(f, `*args`, `**kwargs`)\"\"\"\n\n @better_wraps(decorator)\n def wrapper(*args, **kwargs):\n def dec(f):\n return decorator(f, *args, **kwargs)\n\n is_decorating = not kwargs and len(args) == 1 and isinstance(args[0], Callable)\n if is_decorating:\n f = args[0]\n args = []\n return dec(f)\n else:\n return dec\n\n return wrapper\n\n\n# TODO: just provide decorators for tempfile.mk* functions. This is ugly!\ndef get_tempfile_kwargs(tkwargs=None, prefix=\"\", wrapped=None):\n \"\"\"Updates kwargs to be passed to tempfile. calls depending on env vars\n \"\"\"\n if tkwargs is None:\n tkwargs_ = {}\n else:\n # operate on a copy of tkwargs to avoid any side-effects\n tkwargs_ = tkwargs.copy()\n\n # TODO: don't remember why I had this one originally\n # if len(targs)<2 and \\\n if 'prefix' not in tkwargs_:\n tkwargs_['prefix'] = '_'.join(\n ['datalad_temp'] +\n ([prefix] if prefix else []) +\n ([''] if (on_windows or not wrapped) else [wrapped.__name__]))\n\n directory = os.environ.get('TMPDIR')\n if directory and 'dir' not in tkwargs_:\n tkwargs_['dir'] = directory\n\n return tkwargs_\n\n\n@optional_args\ndef line_profile(func):\n \"\"\"Q&D helper to line profile the function and spit out stats\n \"\"\"\n import line_profiler\n prof = line_profiler.LineProfiler()\n\n @wraps(func)\n def _wrap_line_profile(*args, **kwargs):\n try:\n pfunc = prof(func)\n return pfunc(*args, **kwargs)\n finally:\n prof.print_stats()\n return _wrap_line_profile\n\n\n# unused in -core\n@optional_args\ndef collect_method_callstats(func):\n \"\"\"Figure out methods which call the method repeatedly on the same instance\n\n Use case(s):\n - .repo is expensive since does all kinds of checks.\n - .config is expensive transitively since it calls .repo each time\n\n TODO:\n - fancy one could look through the stack for the same id(self) to see if\n that location is already in memo. That would hint to the cases where object\n is not passed into underlying functions, causing them to redo the same work\n over and over again\n - ATM might flood with all \"1 lines\" calls which are not that informative.\n The underlying possibly suboptimal use might be coming from their callers.\n It might or not relate to the previous TODO\n \"\"\"\n from collections import defaultdict\n import traceback\n from time import time\n memo = defaultdict(lambda: defaultdict(int)) # it will be a dict of lineno: count\n # gross timing\n times = []\n toppath = dirname(__file__) + sep\n\n @wraps(func)\n def _wrap_collect_method_callstats(*args, **kwargs):\n try:\n self = args[0]\n stack = traceback.extract_stack()\n caller = stack[-2]\n stack_sig = \\\n \"{relpath}:{s.name}\".format(\n s=caller, relpath=relpath(caller.filename, toppath))\n sig = (id(self), stack_sig)\n # we will count based on id(self) + wherefrom\n memo[sig][caller.lineno] += 1\n t0 = time()\n return func(*args, **kwargs)\n finally:\n times.append(time() - t0)\n pass\n\n def print_stats():\n print(\"The cost of property {}:\".format(func.__name__))\n if not memo:\n print(\"None since no calls\")\n return\n # total count\n counts = {k: sum(v.values()) for k,v in memo.items()}\n total = sum(counts.values())\n ids = {self_id for (self_id, _) in memo}\n print(\" Total: {} calls from {} objects with {} contexts taking {:.2f} sec\"\n .format(total, len(ids), len(memo), sum(times)))\n # now we need to sort by value\n for (self_id, caller), count in sorted(counts.items(), key=lambda x: x[1], reverse=True):\n print(\" {} {}: {} from {} lines\"\n .format(self_id, caller, count, len(memo[(self_id, caller)])))\n\n # Upon total exit we print the stats\n import atexit\n atexit.register(print_stats)\n\n return _wrap_collect_method_callstats\n\n\n# Borrowed from duecredit to wrap duecredit-handling to guarantee failsafe\ndef never_fail(f):\n \"\"\"Assure that function never fails -- all exceptions are caught\n\n Returns `None` if function fails internally.\n \"\"\"\n @wraps(f)\n def wrapped_func(*args, **kwargs):\n try:\n return f(*args, **kwargs)\n except Exception as e:\n lgr.warning(\n \"DataLad internal failure while running %s: %r. \"\n \"Please report at https://github.com/datalad/datalad/issues\"\n % (f, e)\n )\n\n if os.environ.get('DATALAD_ALLOW_FAIL', False):\n return f\n else:\n return wrapped_func\n\n\n#\n# Context Managers\n#\n\n\n# unused in -core\n@contextmanager\ndef nothing_cm():\n \"\"\"Just a dummy cm to programmically switch context managers\"\"\"\n yield\n\n\n@contextmanager\ndef swallow_outputs():\n \"\"\"Context manager to help consuming both stdout and stderr, and print()\n\n stdout is available as cm.out and stderr as cm.err whenever cm is the\n yielded context manager.\n Internally uses temporary files to guarantee absent side-effects of swallowing\n into StringIO which lacks .fileno.\n\n print mocking is necessary for some uses where sys.stdout was already bound\n to original sys.stdout, thus mocking it later had no effect. Overriding\n print function had desired effect\n \"\"\"\n\n class StringIOAdapter(object):\n \"\"\"Little adapter to help getting out/err values\n \"\"\"\n def __init__(self):\n kw = get_tempfile_kwargs({}, prefix=\"outputs\")\n\n self._out = NamedTemporaryFile(delete=False, mode='w', **kw)\n self._err = NamedTemporaryFile(delete=False, mode='w', **kw)\n\n def _read(self, h):\n with open(h.name) as f:\n return f.read()\n\n @property\n def out(self):\n if not self._out.closed:\n self._out.flush()\n return self._read(self._out)\n\n @property\n def err(self):\n if not self._err.closed:\n self._err.flush()\n return self._read(self._err)\n\n @property\n def handles(self):\n return self._out, self._err\n\n def cleanup(self):\n self._out.close()\n self._err.close()\n out_name = self._out.name\n err_name = self._err.name\n from datalad import cfg\n if cfg.getbool('datalad.log', 'outputs', default=False) \\\n and lgr.getEffectiveLevel() <= logging.DEBUG:\n for s, sname in ((self.out, 'stdout'),\n (self.err, 'stderr')):\n if s:\n pref = os.linesep + \"| \"\n lgr.debug(\"Swallowed %s:%s%s\", sname, pref, s.replace(os.linesep, pref))\n else:\n lgr.debug(\"Nothing was swallowed for %s\", sname)\n del self._out\n del self._err\n gc.collect()\n rmtemp(out_name)\n rmtemp(err_name)\n\n def fake_print(*args, **kwargs):\n sep = kwargs.pop('sep', ' ')\n end = kwargs.pop('end', '\\n')\n file = kwargs.pop('file', sys.stdout)\n\n if file in (oldout, olderr, sys.stdout, sys.stderr):\n # we mock\n try:\n sys.stdout.write(sep.join(args) + end)\n except UnicodeEncodeError as exc:\n lgr.error(\n \"Failed to write to mocked stdout, got %s, continue as it \"\n \"didn't happen\", exc)\n else:\n # must be some other file one -- leave it alone\n oldprint(*args, sep=sep, end=end, file=file)\n\n from .ui import ui\n # preserve -- they could have been mocked already\n oldprint = getattr(builtins, 'print')\n oldout, olderr = sys.stdout, sys.stderr\n olduiout = ui.out\n adapter = StringIOAdapter()\n\n try:\n sys.stdout, sys.stderr = adapter.handles\n ui.out = adapter.handles[0]\n setattr(builtins, 'print', fake_print)\n\n yield adapter\n finally:\n sys.stdout, sys.stderr, ui.out = oldout, olderr, olduiout\n setattr(builtins, 'print', oldprint)\n adapter.cleanup()\n\n\n@contextmanager\ndef swallow_logs(new_level=None, file_=None, name='datalad'):\n \"\"\"Context manager to consume all logs.\n\n \"\"\"\n lgr = logging.getLogger(name)\n\n # Keep old settings\n old_level = lgr.level\n old_handlers = lgr.handlers\n\n # Let's log everything into a string\n # TODO: generalize with the one for swallow_outputs\n class StringIOAdapter(object):\n \"\"\"Little adapter to help getting out values\n\n And to stay consistent with how swallow_outputs behaves\n \"\"\"\n def __init__(self):\n if file_ is None:\n kw = get_tempfile_kwargs({}, prefix=\"logs\")\n self._out = NamedTemporaryFile(mode='a', delete=False, **kw)\n else:\n out_file = file_\n # PY3 requires clearly one or another. race condition possible\n self._out = open(out_file, 'a')\n self._final_out = None\n\n def _read(self, h):\n with open(h.name) as f:\n return f.read()\n\n @property\n def out(self):\n if self._final_out is not None:\n # we closed and cleaned up already\n return self._final_out\n else:\n self._out.flush()\n return self._read(self._out)\n\n @property\n def lines(self):\n return self.out.split('\\n')\n\n @property\n def handle(self):\n return self._out\n\n def cleanup(self):\n # store for access while object exists\n self._final_out = self.out\n self._out.close()\n out_name = self._out.name\n del self._out\n gc.collect()\n if not file_:\n rmtemp(out_name)\n\n def assert_logged(self, msg=None, level=None, regex=True, **kwargs):\n \"\"\"Provide assertion on whether a msg was logged at a given level\n\n If neither `msg` nor `level` provided, checks if anything was logged\n at all.\n\n Parameters\n ----------\n msg: str, optional\n Message (as a regular expression, if `regex`) to be searched.\n If no msg provided, checks if anything was logged at a given level.\n level: str, optional\n String representing the level to be logged\n regex: bool, optional\n If False, regular `assert_in` is used\n **kwargs: str, optional\n Passed to `assert_re_in` or `assert_in`\n \"\"\"\n from datalad.tests.utils import assert_re_in\n from datalad.tests.utils import assert_in\n\n if regex:\n match = r'\\[%s\\] ' % level if level else r\"\\[\\S+\\] \"\n else:\n match = '[%s] ' % level if level else ''\n\n if msg:\n match += msg\n\n if match:\n (assert_re_in if regex else assert_in)(match, self.out, **kwargs)\n else:\n assert not kwargs, \"no kwargs to be passed anywhere\"\n assert self.out, \"Nothing was logged!?\"\n\n adapter = StringIOAdapter()\n # TODO: it does store messages but without any formatting, i.e. even without\n # date/time prefix etc. IMHO it should preserve formatting in case if file_ is\n # set\n swallow_handler = logging.StreamHandler(adapter.handle)\n # we want to log levelname so we could test against it\n swallow_handler.setFormatter(\n logging.Formatter('[%(levelname)s] %(message)s'))\n swallow_handler.filters = sum([h.filters for h in old_handlers],\n [])\n lgr.handlers = [swallow_handler]\n if old_level < logging.DEBUG: # so if HEAVYDEBUG etc -- show them!\n lgr.handlers += old_handlers\n\n if isinstance(new_level, str):\n new_level = getattr(logging, new_level)\n\n if new_level is not None:\n lgr.setLevel(new_level)\n\n try:\n yield adapter\n # TODO: if file_ and there was an exception -- most probably worth logging it?\n # although ideally it should be the next log outside added to that file_ ... oh well\n finally:\n lgr.handlers = old_handlers\n lgr.setLevel(old_level)\n adapter.cleanup()\n\n\n# TODO: May be melt in with swallow_logs at some point:\n@contextmanager\ndef disable_logger(logger=None):\n \"\"\"context manager to temporarily disable logging\n\n This is to provide one of swallow_logs' purposes without unnecessarily\n creating temp files (see gh-1865)\n\n Parameters\n ----------\n logger: Logger\n Logger whose handlers will be ordered to not log anything.\n Default: datalad's topmost Logger ('datalad')\n \"\"\"\n\n class NullFilter(logging.Filter):\n \"\"\"Filter class to reject all records\n \"\"\"\n def filter(self, record):\n return 0\n\n if logger is None:\n # default: all of datalad's logging:\n logger = logging.getLogger('datalad')\n\n filter_ = NullFilter(logger.name)\n [h.addFilter(filter_) for h in logger.handlers]\n\n try:\n yield logger\n finally:\n [h.removeFilter(filter_) for h in logger.handlers]\n\n\n#\n# Additional handlers\n#\n_sys_excepthook = sys.excepthook # Just in case we ever need original one\n\n\ndef setup_exceptionhook(ipython=False):\n \"\"\"Overloads default sys.excepthook with our exceptionhook handler.\n\n If interactive, our exceptionhook handler will invoke\n pdb.post_mortem; if not interactive, then invokes default handler.\n \"\"\"\n\n def _datalad_pdb_excepthook(type, value, tb):\n import traceback\n traceback.print_exception(type, value, tb)\n print()\n if is_interactive():\n import pdb\n pdb.post_mortem(tb)\n\n if ipython:\n from IPython.core import ultratb\n sys.excepthook = ultratb.FormattedTB(mode='Verbose',\n # color_scheme='Linux',\n call_pdb=is_interactive())\n else:\n sys.excepthook = _datalad_pdb_excepthook\n\n\ndef ensure_dir(*args):\n \"\"\"Make sure directory exists.\n\n Joins the list of arguments to an os-specific path to the desired\n directory and creates it, if it not exists yet.\n \"\"\"\n dirname = op.join(*args)\n if not exists(dirname):\n os.makedirs(dirname)\n return dirname\n\n\ndef updated(d, update):\n \"\"\"Return a copy of the input with the 'update'\n\n Primarily for updating dictionaries\n \"\"\"\n d = d.copy()\n d.update(update)\n return d\n\n\n_pwd_mode = None\n\n\ndef _switch_to_getcwd(msg, *args):\n global _pwd_mode\n _pwd_mode = 'cwd'\n lgr.debug(\n msg + \". From now on will be returning os.getcwd(). Directory\"\n \" symlinks in the paths will be resolved\",\n *args\n )\n # TODO: we might want to mitigate by going through all flywheighted\n # repos and tuning up their .paths to be resolved?\n\n\ndef getpwd():\n \"\"\"Try to return a CWD without dereferencing possible symlinks\n\n This function will try to use PWD environment variable to provide a current\n working directory, possibly with some directories along the path being\n symlinks to other directories. Unfortunately, PWD is used/set only by the\n shell and such functions as `os.chdir` and `os.getcwd` nohow use or modify\n it, thus `os.getcwd()` returns path with links dereferenced.\n\n While returning current working directory based on PWD env variable we\n verify that the directory is the same as `os.getcwd()` after resolving all\n symlinks. If that verification fails, we fall back to always use\n `os.getcwd()`.\n\n Initial decision to either use PWD env variable or os.getcwd() is done upon\n the first call of this function.\n \"\"\"\n global _pwd_mode\n if _pwd_mode is None:\n # we need to decide!\n try:\n pwd = os.environ['PWD']\n if on_windows and pwd and pwd.startswith('/'):\n # It should be a path from MSYS.\n # - it might start with a drive letter or not\n # - it seems to be \"illegal\" to have a single letter directories\n # under / path, i.e. if created - they aren't found\n # - 'ln -s' does not fail to create a \"symlink\" but it just\n # copies!\n # so we are not likely to need original PWD purpose on\n # those systems\n # Verdict:\n _pwd_mode = 'cwd'\n else:\n _pwd_mode = 'PWD'\n except KeyError:\n _pwd_mode = 'cwd'\n\n if _pwd_mode == 'cwd':\n return os.getcwd()\n elif _pwd_mode == 'PWD':\n try:\n cwd = os.getcwd()\n except OSError as exc:\n if \"o such file\" in str(exc):\n # directory was removed but we promised to be robust and\n # still report the path we might know since we are still in PWD\n # mode\n cwd = None\n else:\n raise\n try:\n pwd = os.environ['PWD']\n # do absolute() in addition to always get an absolute path\n # even with non-existing paths on windows\n pwd_real = str(Path(pwd).resolve().absolute())\n # This logic would fail to catch the case where chdir did happen\n # to the directory where current PWD is pointing to, e.g.\n # $> ls -ld $PWD\n # lrwxrwxrwx 1 yoh yoh 5 Oct 11 13:27 /home/yoh/.tmp/tmp -> /tmp//\n # hopa:~/.tmp/tmp\n # $> python -c 'import os; os.chdir(\"/tmp\"); from datalad.utils import getpwd; print(getpwd(), os.getcwd())'\n # ('/home/yoh/.tmp/tmp', '/tmp')\n # but I guess that should not be too harmful\n if cwd is not None and pwd_real != cwd:\n _switch_to_getcwd(\n \"realpath of PWD=%s is %s whenever os.getcwd()=%s\",\n pwd, pwd_real, cwd\n )\n return cwd\n return pwd\n except KeyError:\n _switch_to_getcwd(\"PWD env variable is no longer available\")\n return cwd # Must not happen, but may be someone\n # evil purges PWD from environ?\n else:\n raise RuntimeError(\n \"Must have not got here. \"\n \"pwd_mode must be either cwd or PWD. And it is now %r\" % (_pwd_mode,)\n )\n\n\nclass chpwd(object):\n \"\"\"Wrapper around os.chdir which also adjusts environ['PWD']\n\n The reason is that otherwise PWD is simply inherited from the shell\n and we have no ability to assess directory path without dereferencing\n symlinks.\n\n If used as a context manager it allows to temporarily change directory\n to the given path\n \"\"\"\n def __init__(self, path, mkdir=False, logsuffix=''):\n\n if path:\n pwd = getpwd()\n self._prev_pwd = pwd\n else:\n self._prev_pwd = None\n return\n\n if not isabs(path):\n path = normpath(op.join(pwd, path))\n if not os.path.exists(path) and mkdir:\n self._mkdir = True\n os.mkdir(path)\n else:\n self._mkdir = False\n lgr.debug(\"chdir %r -> %r %s\", self._prev_pwd, path, logsuffix)\n os.chdir(path) # for grep people -- ok, to chdir here!\n os.environ['PWD'] = str(path)\n\n def __enter__(self):\n # nothing more to do really, chdir was in the constructor\n pass\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n if self._prev_pwd:\n # Need to use self.__class__ so this instance, if the entire\n # thing mocked during the test, still would use correct chpwd\n self.__class__(self._prev_pwd, logsuffix=\"(coming back)\")\n\n\ndef dlabspath(path, norm=False):\n \"\"\"Symlinks-in-the-cwd aware abspath\n\n os.path.abspath relies on os.getcwd() which would not know about symlinks\n in the path\n\n TODO: we might want to norm=True by default to match behavior of\n os .path.abspath?\n \"\"\"\n if not isabs(path):\n # if not absolute -- relative to pwd\n path = op.join(getpwd(), path)\n return normpath(path) if norm else path\n\n\ndef with_pathsep(path):\n \"\"\"Little helper to guarantee that path ends with /\"\"\"\n return path + sep if not path.endswith(sep) else path\n\n\ndef get_path_prefix(path, pwd=None):\n \"\"\"Get path prefix (for current directory)\n\n Returns relative path to the topdir, if we are under topdir, and if not\n absolute path to topdir. If `pwd` is not specified - current directory\n assumed\n \"\"\"\n pwd = pwd or getpwd()\n path = dlabspath(path)\n path_ = with_pathsep(path)\n pwd_ = with_pathsep(pwd)\n common = commonprefix((path_, pwd_))\n if common.endswith(sep) and common in {path_, pwd_}:\n # we are in subdir or above the path = use relative path\n location_prefix = relpath(path, pwd)\n # if benign \"here\" - cut off\n if location_prefix in (curdir, curdir + sep):\n location_prefix = ''\n return location_prefix\n else:\n # just return absolute path\n return path\n\n\ndef _get_normalized_paths(path, prefix):\n if isabs(path) != isabs(prefix):\n raise ValueError(\"Both paths must either be absolute or relative. \"\n \"Got %r and %r\" % (path, prefix))\n path = with_pathsep(path)\n prefix = with_pathsep(prefix)\n return path, prefix\n\n\ndef path_startswith(path, prefix):\n \"\"\"Return True if path starts with prefix path\n\n Parameters\n ----------\n path: str\n prefix: str\n \"\"\"\n path, prefix = _get_normalized_paths(path, prefix)\n return path.startswith(prefix)\n\n\ndef path_is_subpath(path, prefix):\n \"\"\"Return True if path is a subpath of prefix\n\n It will return False if path == prefix.\n\n Parameters\n ----------\n path: str\n prefix: str\n \"\"\"\n path, prefix = _get_normalized_paths(path, prefix)\n return (len(prefix) < len(path)) and path.startswith(prefix)\n\n\ndef knows_annex(path):\n \"\"\"Returns whether at a given path there is information about an annex\n\n It is just a thin wrapper around GitRepo.is_with_annex() classmethod\n which also checks for `path` to exist first.\n\n This includes actually present annexes, but also uninitialized ones, or\n even the presence of a remote annex branch.\n \"\"\"\n from os.path import exists\n if not exists(path):\n lgr.debug(\"No annex: test path {0} doesn't exist\".format(path))\n return False\n from datalad.support.gitrepo import GitRepo\n return GitRepo(path, init=False, create=False).is_with_annex()\n\n\n@contextmanager\ndef make_tempfile(content=None, wrapped=None, **tkwargs):\n \"\"\"Helper class to provide a temporary file name and remove it at the end (context manager)\n\n Parameters\n ----------\n mkdir : bool, optional (default: False)\n If True, temporary directory created using tempfile.mkdtemp()\n content : str or bytes, optional\n Content to be stored in the file created\n wrapped : function, optional\n If set, function name used to prefix temporary file name\n `**tkwargs`:\n All other arguments are passed into the call to tempfile.mk{,d}temp(),\n and resultant temporary filename is passed as the first argument into\n the function t. If no 'prefix' argument is provided, it will be\n constructed using module and function names ('.' replaced with\n '_').\n\n To change the used directory without providing keyword argument 'dir' set\n DATALAD_TESTS_TEMP_DIR.\n\n Examples\n --------\n >>> from os.path import exists\n >>> from datalad.utils import make_tempfile\n >>> with make_tempfile() as fname:\n ... k = open(fname, 'w').write('silly test')\n >>> assert not exists(fname) # was removed\n\n >>> with make_tempfile(content=\"blah\") as fname:\n ... assert open(fname).read() == \"blah\"\n \"\"\"\n\n if tkwargs.get('mkdir', None) and content is not None:\n raise ValueError(\"mkdir=True while providing content makes no sense\")\n\n tkwargs_ = get_tempfile_kwargs(tkwargs, wrapped=wrapped)\n\n # if DATALAD_TESTS_TEMP_DIR is set, use that as directory,\n # let mktemp handle it otherwise. However, an explicitly provided\n # dir=... will override this.\n mkdir = tkwargs_.pop('mkdir', False)\n\n filename = {False: tempfile.mktemp,\n True: tempfile.mkdtemp}[mkdir](**tkwargs_)\n # MIH: not clear to me why we need to perform this (possibly expensive)\n # resolve. It was already part of the original implementation\n # 008d9ab8cc3e0170c0a9b8479e80dee9ffe6eb7f\n filename = Path(filename).resolve()\n\n if content:\n (filename.write_bytes\n if isinstance(content, bytes)\n else filename.write_text)(content)\n\n # TODO globbing below can also be done with pathlib\n filename = str(filename)\n\n if __debug__:\n lgr.debug(\n 'Created temporary %s named %s',\n 'directory' if mkdir else 'file',\n filename)\n try:\n yield filename\n finally:\n # glob here for all files with the same name (-suffix)\n # would be useful whenever we requested .img filename,\n # and function creates .hdr as well\n # MIH: this is undocumented behavior, and undesired in the general\n # case. it should be made conditional and explicit\n lsuffix = len(tkwargs_.get('suffix', ''))\n filename_ = lsuffix and filename[:-lsuffix] or filename\n filenames = glob.glob(filename_ + '*')\n if len(filename_) < 3 or len(filenames) > 5:\n # For paranoid yoh who stepped into this already ones ;-)\n lgr.warning(\"It is unlikely that it was intended to remove all\"\n \" files matching %r. Skipping\" % filename_)\n return\n for f in filenames:\n try:\n rmtemp(f)\n except OSError: # pragma: no cover\n pass\n\n\ndef _path_(*p):\n \"\"\"Given a path in POSIX\" notation, regenerate one in native to the env one\"\"\"\n if on_windows:\n return op.join(*map(lambda x: op.join(*x.split('/')), p))\n else:\n # Assume that all others as POSIX compliant so nothing to be done\n return op.join(*p)\n\n\ndef get_timestamp_suffix(time_=None, prefix='-'):\n \"\"\"Return a time stamp (full date and time up to second)\n\n primarily to be used for generation of log files names\n \"\"\"\n args = []\n if time_ is not None:\n if isinstance(time_, int):\n time_ = time.gmtime(time_)\n args.append(time_)\n return time.strftime(prefix + TIMESTAMP_FMT, *args)\n\n\n# unused in -core\ndef get_logfilename(dspath, cmd='datalad'):\n \"\"\"Return a filename to use for logging under a dataset/repository\n\n directory would be created if doesn't exist, but dspath must exist\n and be a directory\n \"\"\"\n assert(exists(dspath))\n assert(isdir(dspath))\n ds_logdir = ensure_dir(dspath, '.git', 'datalad', 'logs') # TODO: use WEB_META_LOG whenever #789 merged\n return op.join(ds_logdir, 'crawl-%s.log' % get_timestamp_suffix())\n\n\ndef get_trace(edges, start, end, trace=None):\n \"\"\"Return the trace/path to reach a node in a tree.\n\n Parameters\n ----------\n edges : sequence(2-tuple)\n The tree given by a sequence of edges (parent, child) tuples. The\n nodes can be identified by any value and data type that supports\n the '==' operation.\n start :\n Identifier of the start node. Must be present as a value in the parent\n location of an edge tuple in order to be found.\n end :\n Identifier of the target/end node. Must be present as a value in the child\n location of an edge tuple in order to be found.\n trace : list\n Mostly useful for recursive calls, and used internally.\n\n Returns\n -------\n None or list\n Returns a list with the trace to the target (the starts and the target\n are not included in the trace, hence if start and end are directly connected\n an empty list is returned), or None when no trace to the target can be found,\n or start and end are identical.\n \"\"\"\n # the term trace is used to avoid confusion with a path in the sense\n # of a filesystem path, but the analogy fits and nodes can be paths\n if trace is None:\n trace = []\n if not edges:\n raise ValueError(\"no edges given\")\n for cand in edges:\n cand_super, cand_sub = cand\n if cand_sub in trace:\n # only DAGs, skip any cyclic traces\n continue\n if trace and cand_super != trace[-1]:\n # only consider edges that lead off the end of the trace\n continue\n if not trace and cand_super != start:\n # we got nothing yet, and this edges is not matching the start\n continue\n if cand_sub == end:\n return trace\n # dive into potential subnodes\n cand_trace = get_trace(\n edges,\n start,\n end,\n trace + [cand_sub])\n if cand_trace:\n return cand_trace\n return None\n\n\ndef get_dataset_root(path):\n \"\"\"Return the root of an existent dataset containing a given path\n\n The root path is returned in the same absolute or relative form\n as the input argument. If no associated dataset exists, or the\n input path doesn't exist, None is returned.\n\n If `path` is a symlink or something other than a directory, its\n the root dataset containing its parent directory will be reported.\n If none can be found, at a symlink at `path` is pointing to a\n dataset, `path` itself will be reported as the root.\n\n Parameters\n ----------\n path : Path-like\n\n Returns\n -------\n str or None\n \"\"\"\n path = str(path)\n suffix = '.git'\n altered = None\n if islink(path) or not isdir(path):\n altered = path\n path = dirname(path)\n apath = abspath(path)\n # while we can still go up\n while split(apath)[1]:\n if exists(op.join(path, suffix)):\n return path\n # new test path in the format we got it\n path = normpath(op.join(path, os.pardir))\n # no luck, next round\n apath = abspath(path)\n # if we applied dirname() at the top, we give it another go with\n # the actual path, if it was itself a symlink, it could be the\n # top-level dataset itself\n if altered and exists(op.join(altered, suffix)):\n return altered\n\n return None\n\n\n# ATM used in datalad_crawler extension, so do not remove yet\ndef try_multiple(ntrials, exception, base, f, *args, **kwargs):\n \"\"\"Call f multiple times making exponentially growing delay between the calls\"\"\"\n for trial in range(1, ntrials+1):\n try:\n return f(*args, **kwargs)\n except exception as exc:\n if trial == ntrials:\n raise # just reraise on the last trial\n t = base ** trial\n lgr.warning(\"Caught %s on trial #%d. Sleeping %f and retrying\",\n CapturedException(exc), trial, t)\n sleep(t)\n\n\n@optional_args\ndef try_multiple_dec(\n f, ntrials=None, duration=0.1, exceptions=None, increment_type=None,\n exceptions_filter=None,\n logger=None,\n):\n \"\"\"Decorator to try function multiple times.\n\n Main purpose is to decorate functions dealing with removal of files/directories\n and which might need a few seconds to work correctly on Windows which takes\n its time to release files/directories.\n\n Parameters\n ----------\n ntrials: int, optional\n duration: float, optional\n Seconds to sleep before retrying.\n increment_type: {None, 'exponential'}\n Note that if it is exponential, duration should typically be > 1.0\n so it grows with higher power\n exceptions: Exception or tuple of Exceptions, optional\n Exception or a tuple of multiple exceptions, on which to retry\n exceptions_filter: callable, optional\n If provided, this function will be called with a caught exception\n instance. If function returns True - we will re-try, if False - exception\n will be re-raised without retrying.\n logger: callable, optional\n Logger to log upon failure. If not provided, will use stock logger\n at the level of 5 (heavy debug).\n \"\"\"\n if not exceptions:\n exceptions = (OSError, WindowsError, PermissionError) \\\n if on_windows else OSError\n if not ntrials:\n # Life goes fast on proper systems, no need to delay it much\n ntrials = 100 if on_windows else 10\n if logger is None:\n def logger(*args, **kwargs):\n return lgr.log(5, *args, **kwargs)\n assert increment_type in {None, 'exponential'}\n\n @wraps(f)\n def _wrap_try_multiple_dec(*args, **kwargs):\n t = duration\n for trial in range(ntrials):\n try:\n return f(*args, **kwargs)\n except exceptions as exc:\n if exceptions_filter and not exceptions_filter(exc):\n raise\n if trial < ntrials - 1:\n if increment_type == 'exponential':\n t = duration ** (trial + 1)\n logger(\n \"Caught %s on trial #%d. Sleeping %f and retrying\",\n CapturedException(exc), trial, t)\n sleep(t)\n else:\n raise\n\n return _wrap_try_multiple_dec\n\n\n@try_multiple_dec\ndef unlink(f):\n \"\"\"'Robust' unlink. Would try multiple times\n\n On windows boxes there is evidence for a latency of more than a second\n until a file is considered no longer \"in-use\".\n WindowsError is not known on Linux, and if IOError or any other\n exception\n is thrown then if except statement has WindowsError in it -- NameError\n also see gh-2533\n \"\"\"\n # Check for open files\n assert_no_open_files(f)\n return os.unlink(f)\n\n\n@try_multiple_dec\ndef _rmtree(*args, **kwargs):\n \"\"\"Just a helper to decorate shutil.rmtree.\n\n rmtree defined above does more and ideally should not itself be decorated\n since a recursive definition and does checks for open files inside etc -\n might be too runtime expensive\n \"\"\"\n return shutil.rmtree(*args, **kwargs)\n\n\ndef slash_join(base, extension):\n \"\"\"Join two strings with a '/', avoiding duplicate slashes\n\n If any of the strings is None the other is returned as is.\n \"\"\"\n if extension is None:\n return base\n if base is None:\n return extension\n return '/'.join(\n (base.rstrip('/'),\n extension.lstrip('/')))\n\n\n#\n# IO Helpers\n#\n\n# unused in -core\ndef open_r_encdetect(fname, readahead=1000):\n \"\"\"Return a file object in read mode with auto-detected encoding\n\n This is helpful when dealing with files of unknown encoding.\n\n Parameters\n ----------\n readahead: int, optional\n How many bytes to read for guessing the encoding type. If\n negative - full file will be read\n \"\"\"\n from chardet import detect\n import io\n # read some bytes from the file\n with open(fname, 'rb') as f:\n head = f.read(readahead)\n enc = detect(head)\n denc = enc.get('encoding', None)\n lgr.debug(\"Auto-detected encoding %s for file %s (confidence: %s)\",\n denc,\n fname,\n enc.get('confidence', 'unknown'))\n return io.open(fname, encoding=denc)\n\n\ndef read_file(fname, decode=True):\n \"\"\"A helper to read file passing content via ensure_unicode\n\n Parameters\n ----------\n decode: bool, optional\n if False, no ensure_unicode and file content returned as bytes\n \"\"\"\n with open(fname, 'rb') as f:\n content = f.read()\n return ensure_unicode(content) if decode else content\n\n\ndef read_csv_lines(fname, dialect=None, readahead=16384, **kwargs):\n \"\"\"A generator of dict records from a CSV/TSV\n\n Automatically guesses the encoding for each record to convert to UTF-8\n\n Parameters\n ----------\n fname: str\n Filename\n dialect: str, optional\n Dialect to specify to csv.reader. If not specified -- guessed from\n the file, if fails to guess, \"excel-tab\" is assumed\n readahead: int, optional\n How many bytes to read from the file to guess the type\n **kwargs\n Passed to `csv.reader`\n \"\"\"\n import csv\n if dialect is None:\n with open(fname) as tsvfile:\n # add robustness, use a sniffer\n try:\n dialect = csv.Sniffer().sniff(tsvfile.read(readahead))\n except Exception as exc:\n lgr.warning(\n 'Could not determine file-format, assuming TSV: %s',\n CapturedException(exc)\n )\n dialect = 'excel-tab'\n\n kw = dict(encoding='utf-8')\n with open(fname, 'r', **kw) as tsvfile:\n # csv.py doesn't do Unicode; encode temporarily as UTF-8:\n csv_reader = csv.reader(\n tsvfile,\n dialect=dialect,\n **kwargs\n )\n header = None\n for row in csv_reader:\n # decode UTF-8 back to Unicode, cell by cell:\n row_unicode = map(ensure_unicode, row)\n if header is None:\n header = list(row_unicode)\n else:\n yield dict(zip(header, row_unicode))\n\n\ndef import_modules(modnames, pkg, msg=\"Failed to import {module}\", log=lgr.debug):\n \"\"\"Helper to import a list of modules without failing if N/A\n\n Parameters\n ----------\n modnames: list of str\n List of module names to import\n pkg: str\n Package under which to import\n msg: str, optional\n Message template for .format() to log at DEBUG level if import fails.\n Keys {module} and {package} will be provided and ': {exception}' appended\n log: callable, optional\n Logger call to use for logging messages\n \"\"\"\n from importlib import import_module\n _globals = globals()\n mods_loaded = []\n if pkg and not pkg in sys.modules:\n # with python 3.5.1 (ok with 3.5.5) somehow kept running into\n # Failed to import dlsub1: Parent module 'dltestm1' not loaded\n # while running the test. Preloading pkg resolved the issue\n import_module(pkg)\n for modname in modnames:\n try:\n _globals[modname] = mod = import_module(\n '.{}'.format(modname),\n pkg)\n mods_loaded.append(mod)\n except Exception as exc:\n from datalad.support.exceptions import CapturedException\n ce = CapturedException(exc)\n log((msg + ': {exception}').format(\n module=modname, package=pkg, exception=ce.message))\n return mods_loaded\n\n\ndef import_module_from_file(modpath, pkg=None, log=lgr.debug):\n \"\"\"Import provided module given a path\n\n TODO:\n - RF/make use of it in pipeline.py which has similar logic\n - join with import_modules above?\n\n Parameters\n ----------\n pkg: module, optional\n If provided, and modpath is under pkg.__path__, relative import will be\n used\n \"\"\"\n assert(modpath.endswith('.py')) # for now just for .py files\n\n log(\"Importing %s\" % modpath)\n\n modname = basename(modpath)[:-3]\n relmodpath = None\n if pkg:\n for pkgpath in pkg.__path__:\n if path_is_subpath(modpath, pkgpath):\n # for now relying on having .py extension -- assertion above\n relmodpath = '.' + relpath(modpath[:-3], pkgpath).replace(sep, '.')\n break\n\n try:\n if relmodpath:\n from importlib import import_module\n mod = import_module(relmodpath, pkg.__name__)\n else:\n dirname_ = dirname(modpath)\n try:\n sys.path.insert(0, dirname_)\n mod = __import__(modname, level=0)\n finally:\n if dirname_ in sys.path:\n sys.path.pop(sys.path.index(dirname_))\n else:\n log(\"Expected path %s to be within sys.path, but it was gone!\" % dirname_)\n except Exception as e:\n raise RuntimeError(\n \"Failed to import module from %s\" % modpath) from e\n\n return mod\n\n\ndef get_encoding_info():\n \"\"\"Return a dictionary with various encoding/locale information\"\"\"\n import sys, locale\n from collections import OrderedDict\n return OrderedDict([\n ('default', sys.getdefaultencoding()),\n ('filesystem', sys.getfilesystemencoding()),\n ('locale.prefered', locale.getpreferredencoding()),\n ])\n\n\ndef get_envvars_info():\n from collections import OrderedDict\n envs = []\n for var, val in os.environ.items():\n if (\n var.startswith('PYTHON') or\n var.startswith('LC_') or\n var.startswith('GIT_') or\n var in ('LANG', 'LANGUAGE', 'PATH')\n ):\n envs.append((var, val))\n return OrderedDict(envs)\n\n\n# This class is modified from Snakemake (v5.1.4)\nclass SequenceFormatter(string.Formatter):\n \"\"\"string.Formatter subclass with special behavior for sequences.\n\n This class delegates formatting of individual elements to another\n formatter object. Non-list objects are formatted by calling the\n delegate formatter's \"format_field\" method. List-like objects\n (list, tuple, set, frozenset) are formatted by formatting each\n element of the list according to the specified format spec using\n the delegate formatter and then joining the resulting strings with\n a separator (space by default).\n \"\"\"\n\n def __init__(self, separator=\" \", element_formatter=string.Formatter(),\n *args, **kwargs):\n self.separator = separator\n self.element_formatter = element_formatter\n\n def format_element(self, elem, format_spec):\n \"\"\"Format a single element\n\n For sequences, this is called once for each element in a\n sequence. For anything else, it is called on the entire\n object. It is intended to be overridden in subclases.\n \"\"\"\n return self.element_formatter.format_field(elem, format_spec)\n\n def format_field(self, value, format_spec):\n if isinstance(value, (list, tuple, set, frozenset)):\n return self.separator.join(self.format_element(v, format_spec)\n for v in value)\n else:\n return self.format_element(value, format_spec)\n\n\n# TODO: eventually we might want to make use of attr module\nclass File(object):\n \"\"\"Helper for a file entry in the create_tree/@with_tree\n\n It allows to define additional settings for entries\n \"\"\"\n def __init__(self, name, executable=False):\n \"\"\"\n\n Parameters\n ----------\n name : str\n Name of the file\n executable: bool, optional\n Make it executable\n \"\"\"\n self.name = name\n self.executable = executable\n\n def __str__(self):\n return self.name\n\n\ndef create_tree_archive(path, name, load, overwrite=False, archives_leading_dir=True):\n \"\"\"Given an archive `name`, create under `path` with specified `load` tree\n \"\"\"\n from datalad.support.archives import compress_files\n dirname = file_basename(name)\n full_dirname = op.join(path, dirname)\n os.makedirs(full_dirname)\n create_tree(full_dirname, load, archives_leading_dir=archives_leading_dir)\n # create archive\n if archives_leading_dir:\n compress_files([dirname], name, path=path, overwrite=overwrite)\n else:\n compress_files(list(map(basename, glob.glob(op.join(full_dirname, '*')))),\n op.join(pardir, name),\n path=op.join(path, dirname),\n overwrite=overwrite)\n # remove original tree\n rmtree(full_dirname)\n\n\ndef create_tree(path, tree, archives_leading_dir=True, remove_existing=False):\n \"\"\"Given a list of tuples (name, load) create such a tree\n\n if load is a tuple itself -- that would create either a subtree or an archive\n with that content and place it into the tree if name ends with .tar.gz\n \"\"\"\n lgr.log(5, \"Creating a tree under %s\", path)\n if not exists(path):\n os.makedirs(path)\n\n if isinstance(tree, dict):\n tree = tree.items()\n\n for file_, load in tree:\n if isinstance(file_, File):\n executable = file_.executable\n name = file_.name\n else:\n executable = False\n name = file_\n full_name = op.join(path, name)\n if remove_existing and lexists(full_name):\n rmtree(full_name, chmod_files=True)\n if isinstance(load, (tuple, list, dict)):\n if name.endswith('.tar.gz') or name.endswith('.tar') or name.endswith('.zip'):\n create_tree_archive(\n path, name, load,\n archives_leading_dir=archives_leading_dir)\n else:\n create_tree(\n full_name, load,\n archives_leading_dir=archives_leading_dir,\n remove_existing=remove_existing)\n else:\n open_func = open\n if full_name.endswith('.gz'):\n open_func = gzip.open\n elif full_name.split('.')[-1] in ('xz', 'lzma'):\n import lzma\n open_func = lzma.open\n with open_func(full_name, \"wb\") as f:\n f.write(ensure_bytes(load, 'utf-8'))\n if executable:\n os.chmod(full_name, os.stat(full_name).st_mode | stat.S_IEXEC)\n\n\ndef get_suggestions_msg(values, known, sep=\"\\n \"):\n \"\"\"Return a formatted string with suggestions for values given the known ones\n \"\"\"\n import difflib\n suggestions = []\n for value in ensure_list(values): # might not want to do it if we change presentation below\n suggestions += difflib.get_close_matches(value, known)\n suggestions = unique(suggestions)\n msg = \"Did you mean any of these?\"\n if suggestions:\n if '\\n' in sep:\n # if separator includes new line - we add entire separator right away\n msg += sep\n else:\n msg += ' '\n return msg + \"%s\\n\" % sep.join(suggestions)\n return ''\n\n\ndef bytes2human(n, format='%(value).1f %(symbol)sB'):\n \"\"\"\n Convert n bytes into a human readable string based on format.\n symbols can be either \"customary\", \"customary_ext\", \"iec\" or \"iec_ext\",\n see: http://goo.gl/kTQMs\n\n >>> from datalad.utils import bytes2human\n >>> bytes2human(1)\n '1.0 B'\n >>> bytes2human(1024)\n '1.0 KB'\n >>> bytes2human(1048576)\n '1.0 MB'\n >>> bytes2human(1099511627776127398123789121)\n '909.5 YB'\n\n >>> bytes2human(10000, \"%(value).1f %(symbol)s/sec\")\n '9.8 K/sec'\n\n >>> # precision can be adjusted by playing with %f operator\n >>> bytes2human(10000, format=\"%(value).5f %(symbol)s\")\n '9.76562 K'\n\n Taken from: http://goo.gl/kTQMs and subsequently simplified\n Original Author: Giampaolo Rodola' <g.rodola [AT] gmail [DOT] com>\n License: MIT\n \"\"\"\n n = int(n)\n if n < 0:\n raise ValueError(\"n < 0\")\n symbols = ('', 'K', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y')\n prefix = {}\n for i, s in enumerate(symbols[1:]):\n prefix[s] = 1 << (i + 1) * 10\n for symbol in reversed(symbols[1:]):\n if n >= prefix[symbol]:\n value = float(n) / prefix[symbol]\n return format % locals()\n return format % dict(symbol=symbols[0], value=n)\n\n\ndef quote_cmdlinearg(arg):\n \"\"\"Perform platform-appropriate argument quoting\"\"\"\n # https://stackoverflow.com/a/15262019\n return '\"{}\"'.format(\n arg.replace('\"', '\"\"')\n ) if on_windows else shlex_quote(arg)\n\n\ndef guard_for_format(arg):\n \"\"\"Replace { and } with {{ and }}\n\n To be used in cases if arg is not expected to have provided\n by user .format() placeholders, but 'arg' might become a part\n of a composite passed to .format(), e.g. via 'Run'\n \"\"\"\n return arg.replace('{', '{{').replace('}', '}}')\n\n\ndef join_cmdline(args):\n \"\"\"Join command line args into a string using quote_cmdlinearg\n \"\"\"\n return ' '.join(map(quote_cmdlinearg, args))\n\n\ndef split_cmdline(s):\n \"\"\"Perform platform-appropriate command line splitting.\n\n Identical to `shlex.split()` on non-windows platforms.\n\n Modified from https://stackoverflow.com/a/35900070\n \"\"\"\n if not on_windows:\n return shlex_split(s)\n\n # the rest is for windows\n RE_CMD_LEX = r'''\"((?:\"\"|\\\\[\"\\\\]|[^\"])*)\"?()|(\\\\\\\\(?=\\\\*\")|\\\\\")|(&&?|\\|\\|?|\\d?>|[<])|([^\\s\"&|<>]+)|(\\s+)|(.)'''\n\n args = []\n accu = None # collects pieces of one arg\n for qs, qss, esc, pipe, word, white, fail in re.findall(RE_CMD_LEX, s):\n if word:\n pass # most frequent\n elif esc:\n word = esc[1]\n elif white or pipe:\n if accu is not None:\n args.append(accu)\n if pipe:\n args.append(pipe)\n accu = None\n continue\n elif fail:\n raise ValueError(\"invalid or incomplete shell string\")\n elif qs:\n word = qs.replace('\\\\\"', '\"').replace('\\\\\\\\', '\\\\')\n if platform == 0:\n word = word.replace('\"\"', '\"')\n else:\n word = qss # may be even empty; must be last\n\n accu = (accu or '') + word\n\n if accu is not None:\n args.append(accu)\n\n return args\n\n\ndef get_wrapped_class(wrapped):\n \"\"\"Determine the command class a wrapped __call__ belongs to\"\"\"\n mod = sys.modules[wrapped.__module__]\n command_class_name = wrapped.__qualname__.split('.')[-2]\n _func_class = mod.__dict__[command_class_name]\n lgr.debug(\"Determined class of decorated function: %s\", _func_class)\n return _func_class\n\n\ndef _make_assure_kludge(fn):\n old_name = fn.__name__.replace(\"ensure\", \"assure\")\n\n @wraps(fn)\n def compat_fn(*args, **kwargs):\n warnings.warn(\n \"{} is deprecated and will be removed in a future release. \"\n \"Use {} instead.\"\n .format(old_name, fn.__name__),\n DeprecationWarning)\n return fn(*args, **kwargs)\n\n compat_fn.__doc__ = (\"Note: This function is deprecated. Use {} instead.\"\n .format(fn.__name__))\n return compat_fn\n\n\nassure_tuple_or_list = _make_assure_kludge(ensure_tuple_or_list)\nassure_iter = _make_assure_kludge(ensure_iter)\nassure_list = _make_assure_kludge(ensure_list)\nassure_list_from_str = _make_assure_kludge(ensure_list_from_str)\nassure_dict_from_str = _make_assure_kludge(ensure_dict_from_str)\nassure_bytes = _make_assure_kludge(ensure_bytes)\nassure_unicode = _make_assure_kludge(ensure_unicode)\nassure_bool = _make_assure_kludge(ensure_bool)\nassure_dir = _make_assure_kludge(ensure_dir)\n\n\nlgr.log(5, \"Done importing datalad.utils\")\n\n\ndef check_symlink_capability(path, target):\n \"\"\"helper similar to datalad.tests.utils.has_symlink_capability\n\n However, for use in a datalad command context, we shouldn't\n assume to be able to write to tmpfile and also not import a whole lot from\n datalad's test machinery. Finally, we want to know, whether we can create a\n symlink at a specific location, not just somewhere. Therefore use\n arbitrary path to test-build a symlink and delete afterwards. Suitable\n location can therefore be determined by high lever code.\n\n Parameters\n ----------\n path: Path\n target: Path\n\n Returns\n -------\n bool\n \"\"\"\n\n try:\n target.touch()\n path.symlink_to(target)\n return True\n except Exception:\n return False\n finally:\n if path.exists():\n path.unlink()\n if target.exists():\n target.unlink()\n"}}},{"rowIdx":98,"cells":{"function_name":{"kind":"string","value":"ensure_unicode"},"docstring":{"kind":"string","value":"Convert/decode bytestring to unicode.\n\nIf `s` isn't a bytestring, return it as is.\n\nParameters\n----------\nencoding: str, optional\n Encoding to use. If None, \"utf-8\" is tried, and then if not a valid\n UTF-8, encoding will be guessed\nconfidence: float, optional\n A value between 0 and 1, so if guessing of encoding is of lower than\n specified confidence, ValueError is raised"},"masked_code":{"kind":"string","value":"# emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-\n# ex: set sts=4 ts=4 sw=4 et:\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##\n#\n# See COPYING file distributed along with the datalad package for the\n# copyright and license terms.\n#\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##\n\nimport collections\nfrom collections.abc import Callable\nimport re\nimport builtins\nimport time\n\nimport logging\nimport shutil\nimport os\nimport sys\nimport tempfile\nfrom tempfile import NamedTemporaryFile\nimport platform\nimport gc\nimport glob\nimport gzip\nimport stat\nimport string\nimport warnings\n\nimport os.path as op\n\nfrom copy import copy as shallow_copy\nfrom contextlib import contextmanager\nfrom functools import (\n lru_cache,\n wraps,\n)\nfrom time import sleep\nimport inspect\nfrom itertools import tee\n# this import is required because other modules import opj from here.\nfrom os.path import join as opj\nfrom os.path import (\n abspath,\n basename,\n commonprefix,\n curdir,\n dirname,\n exists,\n expanduser,\n expandvars,\n isabs,\n isdir,\n islink,\n lexists,\n normpath,\n pardir,\n relpath,\n sep,\n split,\n splitdrive\n)\n\nimport posixpath\n\nfrom shlex import (\n quote as shlex_quote,\n split as shlex_split,\n)\n\n# from datalad.dochelpers import get_docstring_split\nfrom datalad.consts import TIMESTAMP_FMT\nfrom datalad.support.exceptions import CapturedException\n\n\nunicode_srctypes = str, bytes\n\n\nlgr = logging.getLogger(\"datalad.utils\")\n\nlgr.log(5, \"Importing datalad.utils\")\n#\n# Some useful variables\n#\nplatform_system = platform.system().lower()\non_windows = platform_system == 'windows'\non_osx = platform_system == 'darwin'\non_linux = platform_system == 'linux'\non_msys_tainted_paths = on_windows \\\n and 'MSYS_NO_PATHCONV' not in os.environ \\\n and os.environ.get('MSYSTEM', '')[:4] in ('MSYS', 'MING')\n\n\n# Takes ~200msec, so should not be called at import time\n@lru_cache() # output should not change through life time of datalad process\ndef get_linux_distribution():\n \"\"\"Compatibility wrapper for {platform,distro}.linux_distribution().\n \"\"\"\n if hasattr(platform, \"linux_distribution\"):\n # Use deprecated (but faster) method if it's available.\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n result = platform.linux_distribution()\n else:\n import distro # We require this for Python 3.8 and above.\n result = distro.linux_distribution(full_distribution_name=False)\n return result\n\n\n# Those weren't used for any critical decision making, thus we just set them to None\n# Use get_linux_distribution() directly where needed\nlinux_distribution_name = linux_distribution_release = None\n\n# Maximal length of cmdline string\n# Query the system and use hardcoded \"knowledge\" if None\n# probably getconf ARG_MAX might not be available\n# The last one would be the most conservative/Windows\nCMD_MAX_ARG_HARDCODED = 2097152 if on_linux else 262144 if on_osx else 32767\ntry:\n CMD_MAX_ARG = os.sysconf('SC_ARG_MAX')\n assert CMD_MAX_ARG > 0\n if CMD_MAX_ARG > CMD_MAX_ARG_HARDCODED * 1e6:\n # workaround for some kind of a bug which comes up with python 3.4\n # see https://github.com/datalad/datalad/issues/3150\n # or on older CentOS with conda and python as new as 3.9\n # see https://github.com/datalad/datalad/issues/5943\n # TODO: let Yarik know that the world is a paradise now whenever 1e6\n # is not large enough\n CMD_MAX_ARG = min(CMD_MAX_ARG, CMD_MAX_ARG_HARDCODED)\nexcept Exception as exc:\n # ATM (20181005) SC_ARG_MAX available only on POSIX systems\n # so exception would be thrown e.g. on Windows, or\n # somehow during Debian build for nd14.04 it is coming up with -1:\n # https://github.com/datalad/datalad/issues/3015\n CMD_MAX_ARG = CMD_MAX_ARG_HARDCODED\n lgr.debug(\n \"Failed to query or got useless SC_ARG_MAX sysconf, \"\n \"will use hardcoded value: %s\", exc)\n# Even with all careful computations we do, due to necessity to account for\n# environment and what not, we still could not figure out \"exact\" way to\n# estimate it, but it was shown that 300k safety margin on linux was sufficient.\n# https://github.com/datalad/datalad/pull/2977#issuecomment-436264710\n# 300k is ~15%, so to be safe, and for paranoid us we will just use up to 50%\n# of the length for \"safety margin\". We might probably still blow due to\n# env vars, unicode, etc... so any hard limit imho is not a proper solution\nCMD_MAX_ARG = int(0.5 * CMD_MAX_ARG)\nlgr.debug(\n \"Maximal length of cmdline string (adjusted for safety margin): %d\",\n CMD_MAX_ARG)\n\n#\n# Little helpers\n#\n\n# `getargspec` has been deprecated in Python 3.\nArgSpecFake = collections.namedtuple(\n \"ArgSpecFake\", [\"args\", \"varargs\", \"keywords\", \"defaults\"])\n\n\ndef getargspec(func, *, include_kwonlyargs=False):\n \"\"\"Compat shim for getargspec deprecated in python 3.\n\n The main difference from inspect.getargspec (and inspect.getfullargspec\n for that matter) is that by using inspect.signature we are providing\n correct args/defaults for functools.wraps'ed functions.\n\n `include_kwonlyargs` option was added to centralize getting all args,\n even the ones which are kwonly (follow the ``*,``).\n\n For internal use and not advised for use in 3rd party code.\n Please use inspect.signature directly.\n \"\"\"\n # We use signature, and not getfullargspec, because only signature properly\n # \"passes\" args from a functools.wraps decorated function.\n # Note: getfullargspec works Ok on wrapt-decorated functions\n f_sign = inspect.signature(func)\n # Loop through parameters and compose argspec\n args4 = [[], None, None, {}]\n # Collect all kwonlyargs into a dedicated dict - name: default\n kwonlyargs = {}\n # shortcuts\n args, defaults = args4[0], args4[3]\n P = inspect.Parameter\n\n for p_name, p in f_sign.parameters.items():\n if p.kind in (P.POSITIONAL_ONLY, P.POSITIONAL_OR_KEYWORD):\n assert not kwonlyargs # yoh: must not come after kwonlyarg\n args.append(p_name)\n if p.default is not P.empty:\n defaults[p_name] = p.default\n elif p.kind == P.VAR_POSITIONAL:\n args4[1] = p_name\n elif p.kind == P.VAR_KEYWORD:\n args4[2] = p_name\n elif p.kind == P.KEYWORD_ONLY:\n assert p.default is not P.empty\n kwonlyargs[p_name] = p.default\n\n if kwonlyargs:\n if not include_kwonlyargs:\n raise ValueError(\n 'Function has keyword-only parameters or annotations, either use '\n 'inspect.signature() API which can support them, or provide include_kwonlyargs=True '\n 'to this function'\n )\n else:\n args.extend(list(kwonlyargs))\n defaults.update(kwonlyargs)\n\n # harmonize defaults to how original getargspec returned them -- just a tuple\n args4[3] = None if not defaults else tuple(defaults.values())\n return ArgSpecFake(*args4)\n\n\ndef any_re_search(regexes, value):\n \"\"\"Return if any of regexes (list or str) searches successfully for value\"\"\"\n for regex in ensure_tuple_or_list(regexes):\n if re.search(regex, value):\n return True\n return False\n\n\ndef not_supported_on_windows(msg=None):\n \"\"\"A little helper to be invoked to consistently fail whenever functionality is\n not supported (yet) on Windows\n \"\"\"\n if on_windows:\n raise NotImplementedError(\"This functionality is not yet implemented for Windows OS\"\n + (\": %s\" % msg if msg else \"\"))\n\n\ndef get_home_envvars(new_home):\n \"\"\"Return dict with env variables to be adjusted for a new HOME\n\n Only variables found in current os.environ are adjusted.\n\n Parameters\n ----------\n new_home: str or Path\n New home path, in native to OS \"schema\"\n \"\"\"\n new_home = str(new_home)\n out = {'HOME': new_home}\n if on_windows:\n # requires special handling, since it has a number of relevant variables\n # and also Python changed its behavior and started to respect USERPROFILE only\n # since python 3.8: https://bugs.python.org/issue36264\n out['USERPROFILE'] = new_home\n out['HOMEDRIVE'], out['HOMEPATH'] = splitdrive(new_home)\n\n return {v: val for v, val in out.items() if v in os.environ}\n\n\ndef shortened_repr(value, l=30):\n try:\n if hasattr(value, '__repr__') and (value.__repr__ is not object.__repr__):\n value_repr = repr(value)\n if not value_repr.startswith('<') and len(value_repr) > l:\n value_repr = \"<<%s++%d chars++%s>>\" % (\n value_repr[:l - 16],\n len(value_repr) - (l - 16 + 4),\n value_repr[-4:]\n )\n elif value_repr.startswith('<') and value_repr.endswith('>') and ' object at 0x':\n raise ValueError(\"I hate those useless long reprs\")\n else:\n raise ValueError(\"gimme class\")\n except Exception as e:\n value_repr = \"<%s>\" % value.__class__.__name__.split('.')[-1]\n return value_repr\n\n\ndef __auto_repr__(obj):\n attr_names = tuple()\n if hasattr(obj, '__dict__'):\n attr_names += tuple(obj.__dict__.keys())\n if hasattr(obj, '__slots__'):\n attr_names += tuple(obj.__slots__)\n\n items = []\n for attr in sorted(set(attr_names)):\n if attr.startswith('_'):\n continue\n value = getattr(obj, attr)\n # TODO: should we add this feature to minimize some talktative reprs\n # such as of URL?\n #if value is None:\n # continue\n items.append(\"%s=%s\" % (attr, shortened_repr(value)))\n\n return \"%s(%s)\" % (obj.__class__.__name__, ', '.join(items))\n\n\ndef auto_repr(cls):\n \"\"\"Decorator for a class to assign it an automagic quick and dirty __repr__\n\n It uses public class attributes to prepare repr of a class\n\n Original idea: http://stackoverflow.com/a/27799004/1265472\n \"\"\"\n\n cls.__repr__ = __auto_repr__\n return cls\n\n\ndef _is_stream_tty(stream):\n try:\n # TODO: check on windows if hasattr check would work correctly and\n # add value:\n return stream.isatty()\n except ValueError as exc:\n # Who knows why it is a ValueError, but let's try to be specific\n # If there is a problem with I/O - non-interactive, otherwise reraise\n if \"I/O\" in str(exc):\n return False\n raise\n\n\ndef is_interactive():\n \"\"\"Return True if all in/outs are open and tty.\n\n Note that in a somewhat abnormal case where e.g. stdin is explicitly\n closed, and any operation on it would raise a\n `ValueError(\"I/O operation on closed file\")` exception, this function\n would just return False, since the session cannot be used interactively.\n \"\"\"\n return all(_is_stream_tty(s) for s in (sys.stdin, sys.stdout, sys.stderr))\n\n\ndef get_ipython_shell():\n \"\"\"Detect if running within IPython and returns its `ip` (shell) object\n\n Returns None if not under ipython (no `get_ipython` function)\n \"\"\"\n try:\n return get_ipython()\n except NameError:\n return None\n\n\ndef md5sum(filename):\n \"\"\"Compute an MD5 sum for the given file\n \"\"\"\n from datalad.support.digests import Digester\n return Digester(digests=['md5'])(filename)['md5']\n\n\n# unused in -core\ndef sorted_files(path):\n \"\"\"Return a (sorted) list of files under path\n \"\"\"\n return sorted(sum([[op.join(r, f)[len(path) + 1:] for f in files]\n for r, d, files in os.walk(path)\n if not '.git' in r], []))\n\n_encoded_dirsep = r'\\\\' if on_windows else r'/'\n_VCS_REGEX = r'%s\\.(?:git|gitattributes|svn|bzr|hg)(?:%s|$)' % (\n _encoded_dirsep, _encoded_dirsep)\n_DATALAD_REGEX = r'%s\\.(?:datalad)(?:%s|$)' % (\n _encoded_dirsep, _encoded_dirsep)\n\n\ndef find_files(regex, topdir=curdir, exclude=None, exclude_vcs=True, exclude_datalad=False, dirs=False):\n \"\"\"Generator to find files matching regex\n\n Parameters\n ----------\n regex: basestring\n exclude: basestring, optional\n Matches to exclude\n exclude_vcs:\n If True, excludes commonly known VCS subdirectories. If string, used\n as regex to exclude those files (regex: `%r`)\n exclude_datalad:\n If True, excludes files known to be datalad meta-data files (e.g. under\n .datalad/ subdirectory) (regex: `%r`)\n topdir: basestring, optional\n Directory where to search\n dirs: bool, optional\n Whether to match directories as well as files\n \"\"\"\n for dirpath, dirnames, filenames in os.walk(topdir):\n names = (dirnames + filenames) if dirs else filenames\n # TODO: might want to uniformize on windows to use '/'\n paths = (op.join(dirpath, name) for name in names)\n for path in filter(re.compile(regex).search, paths):\n path = path.rstrip(sep)\n if exclude and re.search(exclude, path):\n continue\n if exclude_vcs and re.search(_VCS_REGEX, path):\n continue\n if exclude_datalad and re.search(_DATALAD_REGEX, path):\n continue\n yield path\nfind_files.__doc__ %= (_VCS_REGEX, _DATALAD_REGEX)\n\n\ndef expandpath(path, force_absolute=True):\n \"\"\"Expand all variables and user handles in a path.\n\n By default return an absolute path\n \"\"\"\n path = expandvars(expanduser(path))\n if force_absolute:\n path = abspath(path)\n return path\n\n\ndef posix_relpath(path, start=None):\n \"\"\"Behave like os.path.relpath, but always return POSIX paths...\n\n on any platform.\"\"\"\n # join POSIX style\n return posixpath.join(\n # split and relpath native style\n # python2.7 ntpath implementation of relpath cannot handle start=None\n *split(\n relpath(path, start=start if start is not None else '')))\n\n\ndef is_explicit_path(path):\n \"\"\"Return whether a path explicitly points to a location\n\n Any absolute path, or relative path starting with either '../' or\n './' is assumed to indicate a location on the filesystem. Any other\n path format is not considered explicit.\"\"\"\n path = expandpath(path, force_absolute=False)\n return isabs(path) \\\n or path.startswith(os.curdir + os.sep) \\\n or path.startswith(os.pardir + os.sep)\n\n\n# handle this dance once, and import pathlib from here\n# in all other places\n\nfrom pathlib import (\n Path,\n PurePath,\n PurePosixPath,\n)\n\ndef rotree(path, ro=True, chmod_files=True):\n \"\"\"To make tree read-only or writable\n\n Parameters\n ----------\n path : string\n Path to the tree/directory to chmod\n ro : bool, optional\n Whether to make it R/O (default) or RW\n chmod_files : bool, optional\n Whether to operate also on files (not just directories)\n \"\"\"\n if ro:\n chmod = lambda f: os.chmod(f, os.stat(f).st_mode & ~stat.S_IWRITE)\n else:\n chmod = lambda f: os.chmod(f, os.stat(f).st_mode | stat.S_IWRITE | stat.S_IREAD)\n\n for root, dirs, files in os.walk(path, followlinks=False):\n if chmod_files:\n for f in files:\n fullf = op.join(root, f)\n # might be the \"broken\" symlink which would fail to stat etc\n if exists(fullf):\n chmod(fullf)\n chmod(root)\n\n\ndef rmtree(path, chmod_files='auto', children_only=False, *args, **kwargs):\n \"\"\"To remove git-annex .git it is needed to make all files and directories writable again first\n\n Parameters\n ----------\n path: Path or str\n Path to remove\n chmod_files : string or bool, optional\n Whether to make files writable also before removal. Usually it is just\n a matter of directories to have write permissions.\n If 'auto' it would chmod files on windows by default\n children_only : bool, optional\n If set, all files and subdirectories would be removed while the path\n itself (must be a directory) would be preserved\n `*args` :\n `**kwargs` :\n Passed into shutil.rmtree call\n \"\"\"\n # Give W permissions back only to directories, no need to bother with files\n if chmod_files == 'auto':\n chmod_files = on_windows\n # TODO: yoh thinks that if we could quickly check our Flyweight for\n # repos if any of them is under the path, and could call .precommit\n # on those to possibly stop batched processes etc, we did not have\n # to do it on case by case\n # Check for open files\n assert_no_open_files(path)\n\n # TODO the whole thing should be reimplemented with pathlib, but for now\n # at least accept Path\n path = str(path)\n\n if children_only:\n if not isdir(path):\n raise ValueError(\"Can remove children only of directories\")\n for p in os.listdir(path):\n rmtree(op.join(path, p))\n return\n if not (islink(path) or not isdir(path)):\n rotree(path, ro=False, chmod_files=chmod_files)\n if on_windows:\n # shutil fails to remove paths that exceed 260 characters on Windows machines\n # that did not enable long path support. A workaround to remove long paths\n # anyway is to preprend \\\\?\\ to the path.\n # https://docs.microsoft.com/en-us/windows/win32/fileio/naming-a-file?redirectedfrom=MSDN#win32-file-namespaces\n path = r'\\\\?\\ '.strip() + path\n _rmtree(path, *args, **kwargs)\n else:\n # just remove the symlink\n unlink(path)\n\n\ndef rmdir(path, *args, **kwargs):\n \"\"\"os.rmdir with our optional checking for open files\"\"\"\n assert_no_open_files(path)\n os.rmdir(path)\n\n\ndef get_open_files(path, log_open=False):\n \"\"\"Get open files under a path\n\n Note: This function is very slow on Windows.\n\n Parameters\n ----------\n path : str\n File or directory to check for open files under\n log_open : bool or int\n If set - logger level to use\n\n Returns\n -------\n dict\n path : pid\n\n \"\"\"\n # Original idea: https://stackoverflow.com/a/11115521/1265472\n import psutil\n files = {}\n # since the ones returned by psutil would not be aware of symlinks in the\n # path we should also get realpath for path\n # do absolute() in addition to always get an absolute path\n # even with non-existing paths on windows\n path = str(Path(path).resolve().absolute())\n for proc in psutil.process_iter():\n try:\n open_paths = [p.path for p in proc.open_files()] + [proc.cwd()]\n for p in open_paths:\n # note: could be done more efficiently so we do not\n # renormalize path over and over again etc\n if path_startswith(p, path):\n files[p] = proc\n # Catch a race condition where a process ends\n # before we can examine its files\n except psutil.NoSuchProcess:\n pass\n except psutil.AccessDenied:\n pass\n\n if files and log_open:\n lgr.log(log_open, \"Open files under %s: %s\", path, files)\n return files\n\n\n_assert_no_open_files_cfg = os.environ.get('DATALAD_ASSERT_NO_OPEN_FILES')\nif _assert_no_open_files_cfg:\n def assert_no_open_files(path):\n files = get_open_files(path, log_open=40)\n if _assert_no_open_files_cfg == 'assert':\n assert not files, \"Got following files still open: %s\" % ','.join(files)\n elif files:\n if _assert_no_open_files_cfg == 'pdb':\n import pdb\n pdb.set_trace()\n elif _assert_no_open_files_cfg == 'epdb':\n import epdb\n epdb.serve()\n pass\n # otherwise we would just issue that error message in the log\nelse:\n def assert_no_open_files(*args, **kwargs):\n pass\n\n\ndef rmtemp(f, *args, **kwargs):\n \"\"\"Wrapper to centralize removing of temp files so we could keep them around\n\n It will not remove the temporary file/directory if DATALAD_TESTS_TEMP_KEEP\n environment variable is defined\n \"\"\"\n if not os.environ.get('DATALAD_TESTS_TEMP_KEEP'):\n if not os.path.lexists(f):\n lgr.debug(\"Path %s does not exist, so can't be removed\", f)\n return\n lgr.log(5, \"Removing temp file: %s\", f)\n # Can also be a directory\n if isdir(f):\n rmtree(f, *args, **kwargs)\n else:\n unlink(f)\n else:\n lgr.info(\"Keeping temp file: %s\", f)\n\n\ndef file_basename(name, return_ext=False):\n \"\"\"\n Strips up to 2 extensions of length up to 4 characters and starting with alpha\n not a digit, so we could get rid of .tar.gz etc\n \"\"\"\n bname = basename(name)\n fbname = re.sub(r'(\\.[a-zA-Z_]\\S{1,4}){0,2}$', '', bname)\n if return_ext:\n return fbname, bname[len(fbname) + 1:]\n else:\n return fbname\n\n\n# unused in -core\ndef escape_filename(filename):\n \"\"\"Surround filename in \"\" and escape \" in the filename\n \"\"\"\n filename = filename.replace('\"', r'\\\"').replace('`', r'\\`')\n filename = '\"%s\"' % filename\n return filename\n\n\n# unused in -core\ndef encode_filename(filename):\n \"\"\"Encode unicode filename\n \"\"\"\n if isinstance(filename, str):\n return filename.encode(sys.getfilesystemencoding())\n else:\n return filename\n\n\n# unused in -core\ndef decode_input(s):\n \"\"\"Given input string/bytes, decode according to stdin codepage (or UTF-8)\n if not defined\n\n If fails -- issue warning and decode allowing for errors\n being replaced\n \"\"\"\n if isinstance(s, str):\n return s\n else:\n encoding = sys.stdin.encoding or 'UTF-8'\n try:\n return s.decode(encoding)\n except UnicodeDecodeError as exc:\n lgr.warning(\n \"Failed to decode input string using %s encoding. \"\n \"Decoding allowing for errors\", encoding)\n return s.decode(encoding, errors='replace')\n\n\n# unused in -core\nif on_windows:\n def lmtime(filepath, mtime):\n \"\"\"Set mtime for files. On Windows a merely adapter to os.utime\n \"\"\"\n os.utime(filepath, (time.time(), mtime))\nelse:\n def lmtime(filepath, mtime):\n \"\"\"Set mtime for files, while not de-referencing symlinks.\n\n To overcome absence of os.lutime\n\n Works only on linux and OSX ATM\n \"\"\"\n from .cmd import WitlessRunner\n # convert mtime to format touch understands [[CC]YY]MMDDhhmm[.SS]\n smtime = time.strftime(\"%Y%m%d%H%M.%S\", time.localtime(mtime))\n lgr.log(3, \"Setting mtime for %s to %s == %s\", filepath, mtime, smtime)\n WitlessRunner().run(['touch', '-h', '-t', '%s' % smtime, filepath])\n filepath = Path(filepath)\n rfilepath = filepath.resolve()\n if filepath.is_symlink() and rfilepath.exists():\n # trust no one - adjust also of the target file\n # since it seemed like downloading under OSX (was it using curl?)\n # didn't bother with timestamps\n lgr.log(3, \"File is a symlink to %s Setting mtime for it to %s\",\n rfilepath, mtime)\n os.utime(str(rfilepath), (time.time(), mtime))\n # doesn't work on OSX\n # Runner().run(['touch', '-h', '-d', '@%s' % mtime, filepath])\n\n\ndef ensure_tuple_or_list(obj):\n \"\"\"Given an object, wrap into a tuple if not list or tuple\n \"\"\"\n if isinstance(obj, (list, tuple)):\n return obj\n return (obj,)\n\n\ndef ensure_iter(s, cls, copy=False, iterate=True):\n \"\"\"Given not a list, would place it into a list. If None - empty list is returned\n\n Parameters\n ----------\n s: list or anything\n cls: class\n Which iterable class to ensure\n copy: bool, optional\n If correct iterable is passed, it would generate its shallow copy\n iterate: bool, optional\n If it is not a list, but something iterable (but not a str)\n iterate over it.\n \"\"\"\n\n if isinstance(s, cls):\n return s if not copy else shallow_copy(s)\n elif isinstance(s, str):\n return cls((s,))\n elif iterate and hasattr(s, '__iter__'):\n return cls(s)\n elif s is None:\n return cls()\n else:\n return cls((s,))\n\n\ndef ensure_list(s, copy=False, iterate=True):\n \"\"\"Given not a list, would place it into a list. If None - empty list is returned\n\n Parameters\n ----------\n s: list or anything\n copy: bool, optional\n If list is passed, it would generate a shallow copy of the list\n iterate: bool, optional\n If it is not a list, but something iterable (but not a str)\n iterate over it.\n \"\"\"\n return ensure_iter(s, list, copy=copy, iterate=iterate)\n\n\ndef ensure_list_from_str(s, sep='\\n'):\n \"\"\"Given a multiline string convert it to a list of return None if empty\n\n Parameters\n ----------\n s: str or list\n \"\"\"\n\n if not s:\n return None\n\n if isinstance(s, list):\n return s\n return s.split(sep)\n\n\ndef ensure_dict_from_str(s, **kwargs):\n \"\"\"Given a multiline string with key=value items convert it to a dictionary\n\n Parameters\n ----------\n s: str or dict\n\n Returns None if input s is empty\n \"\"\"\n\n if not s:\n return None\n\n if isinstance(s, dict):\n return s\n\n out = {}\n for value_str in ensure_list_from_str(s, **kwargs):\n if '=' not in value_str:\n raise ValueError(\"{} is not in key=value format\".format(repr(value_str)))\n k, v = value_str.split('=', 1)\n if k in out:\n err = \"key {} was already defined in {}, but new value {} was provided\".format(k, out, v)\n raise ValueError(err)\n out[k] = v\n return out\n\n\ndef ensure_bytes(s, encoding='utf-8'):\n \"\"\"Convert/encode unicode string to bytes.\n\n If `s` isn't a string, return it as is.\n\n Parameters\n ----------\n encoding: str, optional\n Encoding to use. \"utf-8\" is the default\n \"\"\"\n if not isinstance(s, str):\n return s\n return s.encode(encoding)\n\n\n# MASKED: ensure_unicode function (lines 807-851)\n\n\ndef ensure_bool(s):\n \"\"\"Convert value into boolean following convention for strings\n\n to recognize on,True,yes as True, off,False,no as False\n \"\"\"\n if isinstance(s, str):\n if s.isdigit():\n return bool(int(s))\n sl = s.lower()\n if sl in {'y', 'yes', 'true', 'on'}:\n return True\n elif sl in {'n', 'no', 'false', 'off'}:\n return False\n else:\n raise ValueError(\"Do not know how to treat %r as a boolean\" % s)\n return bool(s)\n\n\ndef as_unicode(val, cast_types=object):\n \"\"\"Given an arbitrary value, would try to obtain unicode value of it\n \n For unicode it would return original value, for python2 str or python3\n bytes it would use ensure_unicode, for None - an empty (unicode) string,\n and for any other type (see `cast_types`) - would apply the unicode \n constructor. If value is not an instance of `cast_types`, TypeError\n is thrown\n \n Parameters\n ----------\n cast_types: type\n Which types to cast to unicode by providing to constructor\n \"\"\"\n if val is None:\n return u''\n elif isinstance(val, str):\n return val\n elif isinstance(val, unicode_srctypes):\n return ensure_unicode(val)\n elif isinstance(val, cast_types):\n return str(val)\n else:\n raise TypeError(\n \"Value %r is not of any of known or provided %s types\"\n % (val, cast_types))\n\n\ndef unique(seq, key=None, reverse=False):\n \"\"\"Given a sequence return a list only with unique elements while maintaining order\n\n This is the fastest solution. See\n https://www.peterbe.com/plog/uniqifiers-benchmark\n and\n http://stackoverflow.com/a/480227/1265472\n for more information.\n Enhancement -- added ability to compare for uniqueness using a key function\n\n Parameters\n ----------\n seq:\n Sequence to analyze\n key: callable, optional\n Function to call on each element so we could decide not on a full\n element, but on its member etc\n reverse: bool, optional\n If True, uniqueness checked in the reverse order, so that the later ones\n will take the order\n \"\"\"\n seen = set()\n seen_add = seen.add\n\n trans = reversed if reverse else lambda x: x\n\n if not key:\n out = [x for x in trans(seq) if not (x in seen or seen_add(x))]\n else:\n # OPT: could be optimized, since key is called twice, but for our cases\n # should be just as fine\n out = [x for x in trans(seq) if not (key(x) in seen or seen_add(key(x)))]\n\n return out[::-1] if reverse else out\n\n\ndef all_same(items):\n \"\"\"Quick check if all items are the same.\n\n Identical to a check like len(set(items)) == 1 but\n should be more efficient while working on generators, since would\n return False as soon as any difference detected thus possibly avoiding\n unnecessary evaluations\n \"\"\"\n first = True\n first_item = None\n for item in items:\n if first:\n first = False\n first_item = item\n else:\n if item != first_item:\n return False\n # So we return False if was empty\n return not first\n\n\ndef map_items(func, v):\n \"\"\"A helper to apply `func` to all elements (keys and values) within dict\n\n No type checking of values passed to func is done, so `func`\n should be resilient to values which it should not handle\n\n Initial usecase - apply_recursive(url_fragment, ensure_unicode)\n \"\"\"\n # map all elements within item\n return v.__class__(\n item.__class__(map(func, item))\n for item in v.items()\n )\n\n\ndef partition(items, predicate=bool):\n \"\"\"Partition `items` by `predicate`.\n\n Parameters\n ----------\n items : iterable\n predicate : callable\n A function that will be mapped over each element in `items`. The\n elements will partitioned based on whether the return value is false or\n true.\n\n Returns\n -------\n A tuple with two generators, the first for 'false' items and the second for\n 'true' ones.\n\n Notes\n -----\n Taken from Peter Otten's snippet posted at\n https://nedbatchelder.com/blog/201306/filter_a_list_into_two_parts.html\n \"\"\"\n a, b = tee((predicate(item), item) for item in items)\n return ((item for pred, item in a if not pred),\n (item for pred, item in b if pred))\n\n\ndef generate_chunks(container, size):\n \"\"\"Given a container, generate chunks from it with size up to `size`\n \"\"\"\n # There could be a \"smarter\" solution but I think this would suffice\n assert size > 0, \"Size should be non-0 positive\"\n while container:\n yield container[:size]\n container = container[size:]\n\n\ndef generate_file_chunks(files, cmd=None):\n \"\"\"Given a list of files, generate chunks of them to avoid exceeding cmdline length\n\n Parameters\n ----------\n files: list of str\n cmd: str or list of str, optional\n Command to account for as well\n \"\"\"\n files = ensure_list(files)\n cmd = ensure_list(cmd)\n\n maxl = max(map(len, files)) if files else 0\n chunk_size = max(\n 1, # should at least be 1. If blows then - not our fault\n (CMD_MAX_ARG\n - sum((len(x) + 3) for x in cmd)\n - 4 # for '--' below\n ) // (maxl + 3) # +3 for possible quotes and a space\n )\n # TODO: additional treatment for \"too many arguments\"? although\n # as https://github.com/datalad/datalad/issues/1883#issuecomment\n # -436272758\n # shows there seems to be no hardcoded limit on # of arguments,\n # but may be we decide to go for smth like follow to be on safe side\n # chunk_size = min(10240 - len(cmd), chunk_size)\n file_chunks = generate_chunks(files, chunk_size)\n return file_chunks\n\n\n#\n# Generators helpers\n#\n\ndef saved_generator(gen):\n \"\"\"Given a generator returns two generators, where 2nd one just replays\n\n So the first one would be going through the generated items and 2nd one\n would be yielding saved items\n \"\"\"\n saved = []\n\n def gen1():\n for x in gen: # iterating over original generator\n saved.append(x)\n yield x\n\n def gen2():\n for x in saved: # yielding saved entries\n yield x\n\n return gen1(), gen2()\n\n\n#\n# Decorators\n#\n\n# Originally better_wraps was created to provide `wrapt`-based, instead of\n# `functools.wraps` implementation to preserve the correct signature of the\n# decorated function. By using inspect.signature in our getargspec, which\n# works fine on `functools.wraps`ed functions, we mediated this necessity.\nbetter_wraps = wraps\n\n\n# Borrowed from pandas\n# Copyright: 2011-2014, Lambda Foundry, Inc. and PyData Development Team\n# License: BSD-3\ndef optional_args(decorator):\n \"\"\"allows a decorator to take optional positional and keyword arguments.\n Assumes that taking a single, callable, positional argument means that\n it is decorating a function, i.e. something like this::\n\n @my_decorator\n def function(): pass\n\n Calls decorator with decorator(f, `*args`, `**kwargs`)\"\"\"\n\n @better_wraps(decorator)\n def wrapper(*args, **kwargs):\n def dec(f):\n return decorator(f, *args, **kwargs)\n\n is_decorating = not kwargs and len(args) == 1 and isinstance(args[0], Callable)\n if is_decorating:\n f = args[0]\n args = []\n return dec(f)\n else:\n return dec\n\n return wrapper\n\n\n# TODO: just provide decorators for tempfile.mk* functions. This is ugly!\ndef get_tempfile_kwargs(tkwargs=None, prefix=\"\", wrapped=None):\n \"\"\"Updates kwargs to be passed to tempfile. calls depending on env vars\n \"\"\"\n if tkwargs is None:\n tkwargs_ = {}\n else:\n # operate on a copy of tkwargs to avoid any side-effects\n tkwargs_ = tkwargs.copy()\n\n # TODO: don't remember why I had this one originally\n # if len(targs)<2 and \\\n if 'prefix' not in tkwargs_:\n tkwargs_['prefix'] = '_'.join(\n ['datalad_temp'] +\n ([prefix] if prefix else []) +\n ([''] if (on_windows or not wrapped) else [wrapped.__name__]))\n\n directory = os.environ.get('TMPDIR')\n if directory and 'dir' not in tkwargs_:\n tkwargs_['dir'] = directory\n\n return tkwargs_\n\n\n@optional_args\ndef line_profile(func):\n \"\"\"Q&D helper to line profile the function and spit out stats\n \"\"\"\n import line_profiler\n prof = line_profiler.LineProfiler()\n\n @wraps(func)\n def _wrap_line_profile(*args, **kwargs):\n try:\n pfunc = prof(func)\n return pfunc(*args, **kwargs)\n finally:\n prof.print_stats()\n return _wrap_line_profile\n\n\n# unused in -core\n@optional_args\ndef collect_method_callstats(func):\n \"\"\"Figure out methods which call the method repeatedly on the same instance\n\n Use case(s):\n - .repo is expensive since does all kinds of checks.\n - .config is expensive transitively since it calls .repo each time\n\n TODO:\n - fancy one could look through the stack for the same id(self) to see if\n that location is already in memo. That would hint to the cases where object\n is not passed into underlying functions, causing them to redo the same work\n over and over again\n - ATM might flood with all \"1 lines\" calls which are not that informative.\n The underlying possibly suboptimal use might be coming from their callers.\n It might or not relate to the previous TODO\n \"\"\"\n from collections import defaultdict\n import traceback\n from time import time\n memo = defaultdict(lambda: defaultdict(int)) # it will be a dict of lineno: count\n # gross timing\n times = []\n toppath = dirname(__file__) + sep\n\n @wraps(func)\n def _wrap_collect_method_callstats(*args, **kwargs):\n try:\n self = args[0]\n stack = traceback.extract_stack()\n caller = stack[-2]\n stack_sig = \\\n \"{relpath}:{s.name}\".format(\n s=caller, relpath=relpath(caller.filename, toppath))\n sig = (id(self), stack_sig)\n # we will count based on id(self) + wherefrom\n memo[sig][caller.lineno] += 1\n t0 = time()\n return func(*args, **kwargs)\n finally:\n times.append(time() - t0)\n pass\n\n def print_stats():\n print(\"The cost of property {}:\".format(func.__name__))\n if not memo:\n print(\"None since no calls\")\n return\n # total count\n counts = {k: sum(v.values()) for k,v in memo.items()}\n total = sum(counts.values())\n ids = {self_id for (self_id, _) in memo}\n print(\" Total: {} calls from {} objects with {} contexts taking {:.2f} sec\"\n .format(total, len(ids), len(memo), sum(times)))\n # now we need to sort by value\n for (self_id, caller), count in sorted(counts.items(), key=lambda x: x[1], reverse=True):\n print(\" {} {}: {} from {} lines\"\n .format(self_id, caller, count, len(memo[(self_id, caller)])))\n\n # Upon total exit we print the stats\n import atexit\n atexit.register(print_stats)\n\n return _wrap_collect_method_callstats\n\n\n# Borrowed from duecredit to wrap duecredit-handling to guarantee failsafe\ndef never_fail(f):\n \"\"\"Assure that function never fails -- all exceptions are caught\n\n Returns `None` if function fails internally.\n \"\"\"\n @wraps(f)\n def wrapped_func(*args, **kwargs):\n try:\n return f(*args, **kwargs)\n except Exception as e:\n lgr.warning(\n \"DataLad internal failure while running %s: %r. \"\n \"Please report at https://github.com/datalad/datalad/issues\"\n % (f, e)\n )\n\n if os.environ.get('DATALAD_ALLOW_FAIL', False):\n return f\n else:\n return wrapped_func\n\n\n#\n# Context Managers\n#\n\n\n# unused in -core\n@contextmanager\ndef nothing_cm():\n \"\"\"Just a dummy cm to programmically switch context managers\"\"\"\n yield\n\n\n@contextmanager\ndef swallow_outputs():\n \"\"\"Context manager to help consuming both stdout and stderr, and print()\n\n stdout is available as cm.out and stderr as cm.err whenever cm is the\n yielded context manager.\n Internally uses temporary files to guarantee absent side-effects of swallowing\n into StringIO which lacks .fileno.\n\n print mocking is necessary for some uses where sys.stdout was already bound\n to original sys.stdout, thus mocking it later had no effect. Overriding\n print function had desired effect\n \"\"\"\n\n class StringIOAdapter(object):\n \"\"\"Little adapter to help getting out/err values\n \"\"\"\n def __init__(self):\n kw = get_tempfile_kwargs({}, prefix=\"outputs\")\n\n self._out = NamedTemporaryFile(delete=False, mode='w', **kw)\n self._err = NamedTemporaryFile(delete=False, mode='w', **kw)\n\n def _read(self, h):\n with open(h.name) as f:\n return f.read()\n\n @property\n def out(self):\n if not self._out.closed:\n self._out.flush()\n return self._read(self._out)\n\n @property\n def err(self):\n if not self._err.closed:\n self._err.flush()\n return self._read(self._err)\n\n @property\n def handles(self):\n return self._out, self._err\n\n def cleanup(self):\n self._out.close()\n self._err.close()\n out_name = self._out.name\n err_name = self._err.name\n from datalad import cfg\n if cfg.getbool('datalad.log', 'outputs', default=False) \\\n and lgr.getEffectiveLevel() <= logging.DEBUG:\n for s, sname in ((self.out, 'stdout'),\n (self.err, 'stderr')):\n if s:\n pref = os.linesep + \"| \"\n lgr.debug(\"Swallowed %s:%s%s\", sname, pref, s.replace(os.linesep, pref))\n else:\n lgr.debug(\"Nothing was swallowed for %s\", sname)\n del self._out\n del self._err\n gc.collect()\n rmtemp(out_name)\n rmtemp(err_name)\n\n def fake_print(*args, **kwargs):\n sep = kwargs.pop('sep', ' ')\n end = kwargs.pop('end', '\\n')\n file = kwargs.pop('file', sys.stdout)\n\n if file in (oldout, olderr, sys.stdout, sys.stderr):\n # we mock\n try:\n sys.stdout.write(sep.join(args) + end)\n except UnicodeEncodeError as exc:\n lgr.error(\n \"Failed to write to mocked stdout, got %s, continue as it \"\n \"didn't happen\", exc)\n else:\n # must be some other file one -- leave it alone\n oldprint(*args, sep=sep, end=end, file=file)\n\n from .ui import ui\n # preserve -- they could have been mocked already\n oldprint = getattr(builtins, 'print')\n oldout, olderr = sys.stdout, sys.stderr\n olduiout = ui.out\n adapter = StringIOAdapter()\n\n try:\n sys.stdout, sys.stderr = adapter.handles\n ui.out = adapter.handles[0]\n setattr(builtins, 'print', fake_print)\n\n yield adapter\n finally:\n sys.stdout, sys.stderr, ui.out = oldout, olderr, olduiout\n setattr(builtins, 'print', oldprint)\n adapter.cleanup()\n\n\n@contextmanager\ndef swallow_logs(new_level=None, file_=None, name='datalad'):\n \"\"\"Context manager to consume all logs.\n\n \"\"\"\n lgr = logging.getLogger(name)\n\n # Keep old settings\n old_level = lgr.level\n old_handlers = lgr.handlers\n\n # Let's log everything into a string\n # TODO: generalize with the one for swallow_outputs\n class StringIOAdapter(object):\n \"\"\"Little adapter to help getting out values\n\n And to stay consistent with how swallow_outputs behaves\n \"\"\"\n def __init__(self):\n if file_ is None:\n kw = get_tempfile_kwargs({}, prefix=\"logs\")\n self._out = NamedTemporaryFile(mode='a', delete=False, **kw)\n else:\n out_file = file_\n # PY3 requires clearly one or another. race condition possible\n self._out = open(out_file, 'a')\n self._final_out = None\n\n def _read(self, h):\n with open(h.name) as f:\n return f.read()\n\n @property\n def out(self):\n if self._final_out is not None:\n # we closed and cleaned up already\n return self._final_out\n else:\n self._out.flush()\n return self._read(self._out)\n\n @property\n def lines(self):\n return self.out.split('\\n')\n\n @property\n def handle(self):\n return self._out\n\n def cleanup(self):\n # store for access while object exists\n self._final_out = self.out\n self._out.close()\n out_name = self._out.name\n del self._out\n gc.collect()\n if not file_:\n rmtemp(out_name)\n\n def assert_logged(self, msg=None, level=None, regex=True, **kwargs):\n \"\"\"Provide assertion on whether a msg was logged at a given level\n\n If neither `msg` nor `level` provided, checks if anything was logged\n at all.\n\n Parameters\n ----------\n msg: str, optional\n Message (as a regular expression, if `regex`) to be searched.\n If no msg provided, checks if anything was logged at a given level.\n level: str, optional\n String representing the level to be logged\n regex: bool, optional\n If False, regular `assert_in` is used\n **kwargs: str, optional\n Passed to `assert_re_in` or `assert_in`\n \"\"\"\n from datalad.tests.utils import assert_re_in\n from datalad.tests.utils import assert_in\n\n if regex:\n match = r'\\[%s\\] ' % level if level else r\"\\[\\S+\\] \"\n else:\n match = '[%s] ' % level if level else ''\n\n if msg:\n match += msg\n\n if match:\n (assert_re_in if regex else assert_in)(match, self.out, **kwargs)\n else:\n assert not kwargs, \"no kwargs to be passed anywhere\"\n assert self.out, \"Nothing was logged!?\"\n\n adapter = StringIOAdapter()\n # TODO: it does store messages but without any formatting, i.e. even without\n # date/time prefix etc. IMHO it should preserve formatting in case if file_ is\n # set\n swallow_handler = logging.StreamHandler(adapter.handle)\n # we want to log levelname so we could test against it\n swallow_handler.setFormatter(\n logging.Formatter('[%(levelname)s] %(message)s'))\n swallow_handler.filters = sum([h.filters for h in old_handlers],\n [])\n lgr.handlers = [swallow_handler]\n if old_level < logging.DEBUG: # so if HEAVYDEBUG etc -- show them!\n lgr.handlers += old_handlers\n\n if isinstance(new_level, str):\n new_level = getattr(logging, new_level)\n\n if new_level is not None:\n lgr.setLevel(new_level)\n\n try:\n yield adapter\n # TODO: if file_ and there was an exception -- most probably worth logging it?\n # although ideally it should be the next log outside added to that file_ ... oh well\n finally:\n lgr.handlers = old_handlers\n lgr.setLevel(old_level)\n adapter.cleanup()\n\n\n# TODO: May be melt in with swallow_logs at some point:\n@contextmanager\ndef disable_logger(logger=None):\n \"\"\"context manager to temporarily disable logging\n\n This is to provide one of swallow_logs' purposes without unnecessarily\n creating temp files (see gh-1865)\n\n Parameters\n ----------\n logger: Logger\n Logger whose handlers will be ordered to not log anything.\n Default: datalad's topmost Logger ('datalad')\n \"\"\"\n\n class NullFilter(logging.Filter):\n \"\"\"Filter class to reject all records\n \"\"\"\n def filter(self, record):\n return 0\n\n if logger is None:\n # default: all of datalad's logging:\n logger = logging.getLogger('datalad')\n\n filter_ = NullFilter(logger.name)\n [h.addFilter(filter_) for h in logger.handlers]\n\n try:\n yield logger\n finally:\n [h.removeFilter(filter_) for h in logger.handlers]\n\n\n#\n# Additional handlers\n#\n_sys_excepthook = sys.excepthook # Just in case we ever need original one\n\n\ndef setup_exceptionhook(ipython=False):\n \"\"\"Overloads default sys.excepthook with our exceptionhook handler.\n\n If interactive, our exceptionhook handler will invoke\n pdb.post_mortem; if not interactive, then invokes default handler.\n \"\"\"\n\n def _datalad_pdb_excepthook(type, value, tb):\n import traceback\n traceback.print_exception(type, value, tb)\n print()\n if is_interactive():\n import pdb\n pdb.post_mortem(tb)\n\n if ipython:\n from IPython.core import ultratb\n sys.excepthook = ultratb.FormattedTB(mode='Verbose',\n # color_scheme='Linux',\n call_pdb=is_interactive())\n else:\n sys.excepthook = _datalad_pdb_excepthook\n\n\ndef ensure_dir(*args):\n \"\"\"Make sure directory exists.\n\n Joins the list of arguments to an os-specific path to the desired\n directory and creates it, if it not exists yet.\n \"\"\"\n dirname = op.join(*args)\n if not exists(dirname):\n os.makedirs(dirname)\n return dirname\n\n\ndef updated(d, update):\n \"\"\"Return a copy of the input with the 'update'\n\n Primarily for updating dictionaries\n \"\"\"\n d = d.copy()\n d.update(update)\n return d\n\n\n_pwd_mode = None\n\n\ndef _switch_to_getcwd(msg, *args):\n global _pwd_mode\n _pwd_mode = 'cwd'\n lgr.debug(\n msg + \". From now on will be returning os.getcwd(). Directory\"\n \" symlinks in the paths will be resolved\",\n *args\n )\n # TODO: we might want to mitigate by going through all flywheighted\n # repos and tuning up their .paths to be resolved?\n\n\ndef getpwd():\n \"\"\"Try to return a CWD without dereferencing possible symlinks\n\n This function will try to use PWD environment variable to provide a current\n working directory, possibly with some directories along the path being\n symlinks to other directories. Unfortunately, PWD is used/set only by the\n shell and such functions as `os.chdir` and `os.getcwd` nohow use or modify\n it, thus `os.getcwd()` returns path with links dereferenced.\n\n While returning current working directory based on PWD env variable we\n verify that the directory is the same as `os.getcwd()` after resolving all\n symlinks. If that verification fails, we fall back to always use\n `os.getcwd()`.\n\n Initial decision to either use PWD env variable or os.getcwd() is done upon\n the first call of this function.\n \"\"\"\n global _pwd_mode\n if _pwd_mode is None:\n # we need to decide!\n try:\n pwd = os.environ['PWD']\n if on_windows and pwd and pwd.startswith('/'):\n # It should be a path from MSYS.\n # - it might start with a drive letter or not\n # - it seems to be \"illegal\" to have a single letter directories\n # under / path, i.e. if created - they aren't found\n # - 'ln -s' does not fail to create a \"symlink\" but it just\n # copies!\n # so we are not likely to need original PWD purpose on\n # those systems\n # Verdict:\n _pwd_mode = 'cwd'\n else:\n _pwd_mode = 'PWD'\n except KeyError:\n _pwd_mode = 'cwd'\n\n if _pwd_mode == 'cwd':\n return os.getcwd()\n elif _pwd_mode == 'PWD':\n try:\n cwd = os.getcwd()\n except OSError as exc:\n if \"o such file\" in str(exc):\n # directory was removed but we promised to be robust and\n # still report the path we might know since we are still in PWD\n # mode\n cwd = None\n else:\n raise\n try:\n pwd = os.environ['PWD']\n # do absolute() in addition to always get an absolute path\n # even with non-existing paths on windows\n pwd_real = str(Path(pwd).resolve().absolute())\n # This logic would fail to catch the case where chdir did happen\n # to the directory where current PWD is pointing to, e.g.\n # $> ls -ld $PWD\n # lrwxrwxrwx 1 yoh yoh 5 Oct 11 13:27 /home/yoh/.tmp/tmp -> /tmp//\n # hopa:~/.tmp/tmp\n # $> python -c 'import os; os.chdir(\"/tmp\"); from datalad.utils import getpwd; print(getpwd(), os.getcwd())'\n # ('/home/yoh/.tmp/tmp', '/tmp')\n # but I guess that should not be too harmful\n if cwd is not None and pwd_real != cwd:\n _switch_to_getcwd(\n \"realpath of PWD=%s is %s whenever os.getcwd()=%s\",\n pwd, pwd_real, cwd\n )\n return cwd\n return pwd\n except KeyError:\n _switch_to_getcwd(\"PWD env variable is no longer available\")\n return cwd # Must not happen, but may be someone\n # evil purges PWD from environ?\n else:\n raise RuntimeError(\n \"Must have not got here. \"\n \"pwd_mode must be either cwd or PWD. And it is now %r\" % (_pwd_mode,)\n )\n\n\nclass chpwd(object):\n \"\"\"Wrapper around os.chdir which also adjusts environ['PWD']\n\n The reason is that otherwise PWD is simply inherited from the shell\n and we have no ability to assess directory path without dereferencing\n symlinks.\n\n If used as a context manager it allows to temporarily change directory\n to the given path\n \"\"\"\n def __init__(self, path, mkdir=False, logsuffix=''):\n\n if path:\n pwd = getpwd()\n self._prev_pwd = pwd\n else:\n self._prev_pwd = None\n return\n\n if not isabs(path):\n path = normpath(op.join(pwd, path))\n if not os.path.exists(path) and mkdir:\n self._mkdir = True\n os.mkdir(path)\n else:\n self._mkdir = False\n lgr.debug(\"chdir %r -> %r %s\", self._prev_pwd, path, logsuffix)\n os.chdir(path) # for grep people -- ok, to chdir here!\n os.environ['PWD'] = str(path)\n\n def __enter__(self):\n # nothing more to do really, chdir was in the constructor\n pass\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n if self._prev_pwd:\n # Need to use self.__class__ so this instance, if the entire\n # thing mocked during the test, still would use correct chpwd\n self.__class__(self._prev_pwd, logsuffix=\"(coming back)\")\n\n\ndef dlabspath(path, norm=False):\n \"\"\"Symlinks-in-the-cwd aware abspath\n\n os.path.abspath relies on os.getcwd() which would not know about symlinks\n in the path\n\n TODO: we might want to norm=True by default to match behavior of\n os .path.abspath?\n \"\"\"\n if not isabs(path):\n # if not absolute -- relative to pwd\n path = op.join(getpwd(), path)\n return normpath(path) if norm else path\n\n\ndef with_pathsep(path):\n \"\"\"Little helper to guarantee that path ends with /\"\"\"\n return path + sep if not path.endswith(sep) else path\n\n\ndef get_path_prefix(path, pwd=None):\n \"\"\"Get path prefix (for current directory)\n\n Returns relative path to the topdir, if we are under topdir, and if not\n absolute path to topdir. If `pwd` is not specified - current directory\n assumed\n \"\"\"\n pwd = pwd or getpwd()\n path = dlabspath(path)\n path_ = with_pathsep(path)\n pwd_ = with_pathsep(pwd)\n common = commonprefix((path_, pwd_))\n if common.endswith(sep) and common in {path_, pwd_}:\n # we are in subdir or above the path = use relative path\n location_prefix = relpath(path, pwd)\n # if benign \"here\" - cut off\n if location_prefix in (curdir, curdir + sep):\n location_prefix = ''\n return location_prefix\n else:\n # just return absolute path\n return path\n\n\ndef _get_normalized_paths(path, prefix):\n if isabs(path) != isabs(prefix):\n raise ValueError(\"Both paths must either be absolute or relative. \"\n \"Got %r and %r\" % (path, prefix))\n path = with_pathsep(path)\n prefix = with_pathsep(prefix)\n return path, prefix\n\n\ndef path_startswith(path, prefix):\n \"\"\"Return True if path starts with prefix path\n\n Parameters\n ----------\n path: str\n prefix: str\n \"\"\"\n path, prefix = _get_normalized_paths(path, prefix)\n return path.startswith(prefix)\n\n\ndef path_is_subpath(path, prefix):\n \"\"\"Return True if path is a subpath of prefix\n\n It will return False if path == prefix.\n\n Parameters\n ----------\n path: str\n prefix: str\n \"\"\"\n path, prefix = _get_normalized_paths(path, prefix)\n return (len(prefix) < len(path)) and path.startswith(prefix)\n\n\ndef knows_annex(path):\n \"\"\"Returns whether at a given path there is information about an annex\n\n It is just a thin wrapper around GitRepo.is_with_annex() classmethod\n which also checks for `path` to exist first.\n\n This includes actually present annexes, but also uninitialized ones, or\n even the presence of a remote annex branch.\n \"\"\"\n from os.path import exists\n if not exists(path):\n lgr.debug(\"No annex: test path {0} doesn't exist\".format(path))\n return False\n from datalad.support.gitrepo import GitRepo\n return GitRepo(path, init=False, create=False).is_with_annex()\n\n\n@contextmanager\ndef make_tempfile(content=None, wrapped=None, **tkwargs):\n \"\"\"Helper class to provide a temporary file name and remove it at the end (context manager)\n\n Parameters\n ----------\n mkdir : bool, optional (default: False)\n If True, temporary directory created using tempfile.mkdtemp()\n content : str or bytes, optional\n Content to be stored in the file created\n wrapped : function, optional\n If set, function name used to prefix temporary file name\n `**tkwargs`:\n All other arguments are passed into the call to tempfile.mk{,d}temp(),\n and resultant temporary filename is passed as the first argument into\n the function t. If no 'prefix' argument is provided, it will be\n constructed using module and function names ('.' replaced with\n '_').\n\n To change the used directory without providing keyword argument 'dir' set\n DATALAD_TESTS_TEMP_DIR.\n\n Examples\n --------\n >>> from os.path import exists\n >>> from datalad.utils import make_tempfile\n >>> with make_tempfile() as fname:\n ... k = open(fname, 'w').write('silly test')\n >>> assert not exists(fname) # was removed\n\n >>> with make_tempfile(content=\"blah\") as fname:\n ... assert open(fname).read() == \"blah\"\n \"\"\"\n\n if tkwargs.get('mkdir', None) and content is not None:\n raise ValueError(\"mkdir=True while providing content makes no sense\")\n\n tkwargs_ = get_tempfile_kwargs(tkwargs, wrapped=wrapped)\n\n # if DATALAD_TESTS_TEMP_DIR is set, use that as directory,\n # let mktemp handle it otherwise. However, an explicitly provided\n # dir=... will override this.\n mkdir = tkwargs_.pop('mkdir', False)\n\n filename = {False: tempfile.mktemp,\n True: tempfile.mkdtemp}[mkdir](**tkwargs_)\n # MIH: not clear to me why we need to perform this (possibly expensive)\n # resolve. It was already part of the original implementation\n # 008d9ab8cc3e0170c0a9b8479e80dee9ffe6eb7f\n filename = Path(filename).resolve()\n\n if content:\n (filename.write_bytes\n if isinstance(content, bytes)\n else filename.write_text)(content)\n\n # TODO globbing below can also be done with pathlib\n filename = str(filename)\n\n if __debug__:\n lgr.debug(\n 'Created temporary %s named %s',\n 'directory' if mkdir else 'file',\n filename)\n try:\n yield filename\n finally:\n # glob here for all files with the same name (-suffix)\n # would be useful whenever we requested .img filename,\n # and function creates .hdr as well\n # MIH: this is undocumented behavior, and undesired in the general\n # case. it should be made conditional and explicit\n lsuffix = len(tkwargs_.get('suffix', ''))\n filename_ = lsuffix and filename[:-lsuffix] or filename\n filenames = glob.glob(filename_ + '*')\n if len(filename_) < 3 or len(filenames) > 5:\n # For paranoid yoh who stepped into this already ones ;-)\n lgr.warning(\"It is unlikely that it was intended to remove all\"\n \" files matching %r. Skipping\" % filename_)\n return\n for f in filenames:\n try:\n rmtemp(f)\n except OSError: # pragma: no cover\n pass\n\n\ndef _path_(*p):\n \"\"\"Given a path in POSIX\" notation, regenerate one in native to the env one\"\"\"\n if on_windows:\n return op.join(*map(lambda x: op.join(*x.split('/')), p))\n else:\n # Assume that all others as POSIX compliant so nothing to be done\n return op.join(*p)\n\n\ndef get_timestamp_suffix(time_=None, prefix='-'):\n \"\"\"Return a time stamp (full date and time up to second)\n\n primarily to be used for generation of log files names\n \"\"\"\n args = []\n if time_ is not None:\n if isinstance(time_, int):\n time_ = time.gmtime(time_)\n args.append(time_)\n return time.strftime(prefix + TIMESTAMP_FMT, *args)\n\n\n# unused in -core\ndef get_logfilename(dspath, cmd='datalad'):\n \"\"\"Return a filename to use for logging under a dataset/repository\n\n directory would be created if doesn't exist, but dspath must exist\n and be a directory\n \"\"\"\n assert(exists(dspath))\n assert(isdir(dspath))\n ds_logdir = ensure_dir(dspath, '.git', 'datalad', 'logs') # TODO: use WEB_META_LOG whenever #789 merged\n return op.join(ds_logdir, 'crawl-%s.log' % get_timestamp_suffix())\n\n\ndef get_trace(edges, start, end, trace=None):\n \"\"\"Return the trace/path to reach a node in a tree.\n\n Parameters\n ----------\n edges : sequence(2-tuple)\n The tree given by a sequence of edges (parent, child) tuples. The\n nodes can be identified by any value and data type that supports\n the '==' operation.\n start :\n Identifier of the start node. Must be present as a value in the parent\n location of an edge tuple in order to be found.\n end :\n Identifier of the target/end node. Must be present as a value in the child\n location of an edge tuple in order to be found.\n trace : list\n Mostly useful for recursive calls, and used internally.\n\n Returns\n -------\n None or list\n Returns a list with the trace to the target (the starts and the target\n are not included in the trace, hence if start and end are directly connected\n an empty list is returned), or None when no trace to the target can be found,\n or start and end are identical.\n \"\"\"\n # the term trace is used to avoid confusion with a path in the sense\n # of a filesystem path, but the analogy fits and nodes can be paths\n if trace is None:\n trace = []\n if not edges:\n raise ValueError(\"no edges given\")\n for cand in edges:\n cand_super, cand_sub = cand\n if cand_sub in trace:\n # only DAGs, skip any cyclic traces\n continue\n if trace and cand_super != trace[-1]:\n # only consider edges that lead off the end of the trace\n continue\n if not trace and cand_super != start:\n # we got nothing yet, and this edges is not matching the start\n continue\n if cand_sub == end:\n return trace\n # dive into potential subnodes\n cand_trace = get_trace(\n edges,\n start,\n end,\n trace + [cand_sub])\n if cand_trace:\n return cand_trace\n return None\n\n\ndef get_dataset_root(path):\n \"\"\"Return the root of an existent dataset containing a given path\n\n The root path is returned in the same absolute or relative form\n as the input argument. If no associated dataset exists, or the\n input path doesn't exist, None is returned.\n\n If `path` is a symlink or something other than a directory, its\n the root dataset containing its parent directory will be reported.\n If none can be found, at a symlink at `path` is pointing to a\n dataset, `path` itself will be reported as the root.\n\n Parameters\n ----------\n path : Path-like\n\n Returns\n -------\n str or None\n \"\"\"\n path = str(path)\n suffix = '.git'\n altered = None\n if islink(path) or not isdir(path):\n altered = path\n path = dirname(path)\n apath = abspath(path)\n # while we can still go up\n while split(apath)[1]:\n if exists(op.join(path, suffix)):\n return path\n # new test path in the format we got it\n path = normpath(op.join(path, os.pardir))\n # no luck, next round\n apath = abspath(path)\n # if we applied dirname() at the top, we give it another go with\n # the actual path, if it was itself a symlink, it could be the\n # top-level dataset itself\n if altered and exists(op.join(altered, suffix)):\n return altered\n\n return None\n\n\n# ATM used in datalad_crawler extension, so do not remove yet\ndef try_multiple(ntrials, exception, base, f, *args, **kwargs):\n \"\"\"Call f multiple times making exponentially growing delay between the calls\"\"\"\n for trial in range(1, ntrials+1):\n try:\n return f(*args, **kwargs)\n except exception as exc:\n if trial == ntrials:\n raise # just reraise on the last trial\n t = base ** trial\n lgr.warning(\"Caught %s on trial #%d. Sleeping %f and retrying\",\n CapturedException(exc), trial, t)\n sleep(t)\n\n\n@optional_args\ndef try_multiple_dec(\n f, ntrials=None, duration=0.1, exceptions=None, increment_type=None,\n exceptions_filter=None,\n logger=None,\n):\n \"\"\"Decorator to try function multiple times.\n\n Main purpose is to decorate functions dealing with removal of files/directories\n and which might need a few seconds to work correctly on Windows which takes\n its time to release files/directories.\n\n Parameters\n ----------\n ntrials: int, optional\n duration: float, optional\n Seconds to sleep before retrying.\n increment_type: {None, 'exponential'}\n Note that if it is exponential, duration should typically be > 1.0\n so it grows with higher power\n exceptions: Exception or tuple of Exceptions, optional\n Exception or a tuple of multiple exceptions, on which to retry\n exceptions_filter: callable, optional\n If provided, this function will be called with a caught exception\n instance. If function returns True - we will re-try, if False - exception\n will be re-raised without retrying.\n logger: callable, optional\n Logger to log upon failure. If not provided, will use stock logger\n at the level of 5 (heavy debug).\n \"\"\"\n if not exceptions:\n exceptions = (OSError, WindowsError, PermissionError) \\\n if on_windows else OSError\n if not ntrials:\n # Life goes fast on proper systems, no need to delay it much\n ntrials = 100 if on_windows else 10\n if logger is None:\n def logger(*args, **kwargs):\n return lgr.log(5, *args, **kwargs)\n assert increment_type in {None, 'exponential'}\n\n @wraps(f)\n def _wrap_try_multiple_dec(*args, **kwargs):\n t = duration\n for trial in range(ntrials):\n try:\n return f(*args, **kwargs)\n except exceptions as exc:\n if exceptions_filter and not exceptions_filter(exc):\n raise\n if trial < ntrials - 1:\n if increment_type == 'exponential':\n t = duration ** (trial + 1)\n logger(\n \"Caught %s on trial #%d. Sleeping %f and retrying\",\n CapturedException(exc), trial, t)\n sleep(t)\n else:\n raise\n\n return _wrap_try_multiple_dec\n\n\n@try_multiple_dec\ndef unlink(f):\n \"\"\"'Robust' unlink. Would try multiple times\n\n On windows boxes there is evidence for a latency of more than a second\n until a file is considered no longer \"in-use\".\n WindowsError is not known on Linux, and if IOError or any other\n exception\n is thrown then if except statement has WindowsError in it -- NameError\n also see gh-2533\n \"\"\"\n # Check for open files\n assert_no_open_files(f)\n return os.unlink(f)\n\n\n@try_multiple_dec\ndef _rmtree(*args, **kwargs):\n \"\"\"Just a helper to decorate shutil.rmtree.\n\n rmtree defined above does more and ideally should not itself be decorated\n since a recursive definition and does checks for open files inside etc -\n might be too runtime expensive\n \"\"\"\n return shutil.rmtree(*args, **kwargs)\n\n\ndef slash_join(base, extension):\n \"\"\"Join two strings with a '/', avoiding duplicate slashes\n\n If any of the strings is None the other is returned as is.\n \"\"\"\n if extension is None:\n return base\n if base is None:\n return extension\n return '/'.join(\n (base.rstrip('/'),\n extension.lstrip('/')))\n\n\n#\n# IO Helpers\n#\n\n# unused in -core\ndef open_r_encdetect(fname, readahead=1000):\n \"\"\"Return a file object in read mode with auto-detected encoding\n\n This is helpful when dealing with files of unknown encoding.\n\n Parameters\n ----------\n readahead: int, optional\n How many bytes to read for guessing the encoding type. If\n negative - full file will be read\n \"\"\"\n from chardet import detect\n import io\n # read some bytes from the file\n with open(fname, 'rb') as f:\n head = f.read(readahead)\n enc = detect(head)\n denc = enc.get('encoding', None)\n lgr.debug(\"Auto-detected encoding %s for file %s (confidence: %s)\",\n denc,\n fname,\n enc.get('confidence', 'unknown'))\n return io.open(fname, encoding=denc)\n\n\ndef read_file(fname, decode=True):\n \"\"\"A helper to read file passing content via ensure_unicode\n\n Parameters\n ----------\n decode: bool, optional\n if False, no ensure_unicode and file content returned as bytes\n \"\"\"\n with open(fname, 'rb') as f:\n content = f.read()\n return ensure_unicode(content) if decode else content\n\n\ndef read_csv_lines(fname, dialect=None, readahead=16384, **kwargs):\n \"\"\"A generator of dict records from a CSV/TSV\n\n Automatically guesses the encoding for each record to convert to UTF-8\n\n Parameters\n ----------\n fname: str\n Filename\n dialect: str, optional\n Dialect to specify to csv.reader. If not specified -- guessed from\n the file, if fails to guess, \"excel-tab\" is assumed\n readahead: int, optional\n How many bytes to read from the file to guess the type\n **kwargs\n Passed to `csv.reader`\n \"\"\"\n import csv\n if dialect is None:\n with open(fname) as tsvfile:\n # add robustness, use a sniffer\n try:\n dialect = csv.Sniffer().sniff(tsvfile.read(readahead))\n except Exception as exc:\n lgr.warning(\n 'Could not determine file-format, assuming TSV: %s',\n CapturedException(exc)\n )\n dialect = 'excel-tab'\n\n kw = dict(encoding='utf-8')\n with open(fname, 'r', **kw) as tsvfile:\n # csv.py doesn't do Unicode; encode temporarily as UTF-8:\n csv_reader = csv.reader(\n tsvfile,\n dialect=dialect,\n **kwargs\n )\n header = None\n for row in csv_reader:\n # decode UTF-8 back to Unicode, cell by cell:\n row_unicode = map(ensure_unicode, row)\n if header is None:\n header = list(row_unicode)\n else:\n yield dict(zip(header, row_unicode))\n\n\ndef import_modules(modnames, pkg, msg=\"Failed to import {module}\", log=lgr.debug):\n \"\"\"Helper to import a list of modules without failing if N/A\n\n Parameters\n ----------\n modnames: list of str\n List of module names to import\n pkg: str\n Package under which to import\n msg: str, optional\n Message template for .format() to log at DEBUG level if import fails.\n Keys {module} and {package} will be provided and ': {exception}' appended\n log: callable, optional\n Logger call to use for logging messages\n \"\"\"\n from importlib import import_module\n _globals = globals()\n mods_loaded = []\n if pkg and not pkg in sys.modules:\n # with python 3.5.1 (ok with 3.5.5) somehow kept running into\n # Failed to import dlsub1: Parent module 'dltestm1' not loaded\n # while running the test. Preloading pkg resolved the issue\n import_module(pkg)\n for modname in modnames:\n try:\n _globals[modname] = mod = import_module(\n '.{}'.format(modname),\n pkg)\n mods_loaded.append(mod)\n except Exception as exc:\n from datalad.support.exceptions import CapturedException\n ce = CapturedException(exc)\n log((msg + ': {exception}').format(\n module=modname, package=pkg, exception=ce.message))\n return mods_loaded\n\n\ndef import_module_from_file(modpath, pkg=None, log=lgr.debug):\n \"\"\"Import provided module given a path\n\n TODO:\n - RF/make use of it in pipeline.py which has similar logic\n - join with import_modules above?\n\n Parameters\n ----------\n pkg: module, optional\n If provided, and modpath is under pkg.__path__, relative import will be\n used\n \"\"\"\n assert(modpath.endswith('.py')) # for now just for .py files\n\n log(\"Importing %s\" % modpath)\n\n modname = basename(modpath)[:-3]\n relmodpath = None\n if pkg:\n for pkgpath in pkg.__path__:\n if path_is_subpath(modpath, pkgpath):\n # for now relying on having .py extension -- assertion above\n relmodpath = '.' + relpath(modpath[:-3], pkgpath).replace(sep, '.')\n break\n\n try:\n if relmodpath:\n from importlib import import_module\n mod = import_module(relmodpath, pkg.__name__)\n else:\n dirname_ = dirname(modpath)\n try:\n sys.path.insert(0, dirname_)\n mod = __import__(modname, level=0)\n finally:\n if dirname_ in sys.path:\n sys.path.pop(sys.path.index(dirname_))\n else:\n log(\"Expected path %s to be within sys.path, but it was gone!\" % dirname_)\n except Exception as e:\n raise RuntimeError(\n \"Failed to import module from %s\" % modpath) from e\n\n return mod\n\n\ndef get_encoding_info():\n \"\"\"Return a dictionary with various encoding/locale information\"\"\"\n import sys, locale\n from collections import OrderedDict\n return OrderedDict([\n ('default', sys.getdefaultencoding()),\n ('filesystem', sys.getfilesystemencoding()),\n ('locale.prefered', locale.getpreferredencoding()),\n ])\n\n\ndef get_envvars_info():\n from collections import OrderedDict\n envs = []\n for var, val in os.environ.items():\n if (\n var.startswith('PYTHON') or\n var.startswith('LC_') or\n var.startswith('GIT_') or\n var in ('LANG', 'LANGUAGE', 'PATH')\n ):\n envs.append((var, val))\n return OrderedDict(envs)\n\n\n# This class is modified from Snakemake (v5.1.4)\nclass SequenceFormatter(string.Formatter):\n \"\"\"string.Formatter subclass with special behavior for sequences.\n\n This class delegates formatting of individual elements to another\n formatter object. Non-list objects are formatted by calling the\n delegate formatter's \"format_field\" method. List-like objects\n (list, tuple, set, frozenset) are formatted by formatting each\n element of the list according to the specified format spec using\n the delegate formatter and then joining the resulting strings with\n a separator (space by default).\n \"\"\"\n\n def __init__(self, separator=\" \", element_formatter=string.Formatter(),\n *args, **kwargs):\n self.separator = separator\n self.element_formatter = element_formatter\n\n def format_element(self, elem, format_spec):\n \"\"\"Format a single element\n\n For sequences, this is called once for each element in a\n sequence. For anything else, it is called on the entire\n object. It is intended to be overridden in subclases.\n \"\"\"\n return self.element_formatter.format_field(elem, format_spec)\n\n def format_field(self, value, format_spec):\n if isinstance(value, (list, tuple, set, frozenset)):\n return self.separator.join(self.format_element(v, format_spec)\n for v in value)\n else:\n return self.format_element(value, format_spec)\n\n\n# TODO: eventually we might want to make use of attr module\nclass File(object):\n \"\"\"Helper for a file entry in the create_tree/@with_tree\n\n It allows to define additional settings for entries\n \"\"\"\n def __init__(self, name, executable=False):\n \"\"\"\n\n Parameters\n ----------\n name : str\n Name of the file\n executable: bool, optional\n Make it executable\n \"\"\"\n self.name = name\n self.executable = executable\n\n def __str__(self):\n return self.name\n\n\ndef create_tree_archive(path, name, load, overwrite=False, archives_leading_dir=True):\n \"\"\"Given an archive `name`, create under `path` with specified `load` tree\n \"\"\"\n from datalad.support.archives import compress_files\n dirname = file_basename(name)\n full_dirname = op.join(path, dirname)\n os.makedirs(full_dirname)\n create_tree(full_dirname, load, archives_leading_dir=archives_leading_dir)\n # create archive\n if archives_leading_dir:\n compress_files([dirname], name, path=path, overwrite=overwrite)\n else:\n compress_files(list(map(basename, glob.glob(op.join(full_dirname, '*')))),\n op.join(pardir, name),\n path=op.join(path, dirname),\n overwrite=overwrite)\n # remove original tree\n rmtree(full_dirname)\n\n\ndef create_tree(path, tree, archives_leading_dir=True, remove_existing=False):\n \"\"\"Given a list of tuples (name, load) create such a tree\n\n if load is a tuple itself -- that would create either a subtree or an archive\n with that content and place it into the tree if name ends with .tar.gz\n \"\"\"\n lgr.log(5, \"Creating a tree under %s\", path)\n if not exists(path):\n os.makedirs(path)\n\n if isinstance(tree, dict):\n tree = tree.items()\n\n for file_, load in tree:\n if isinstance(file_, File):\n executable = file_.executable\n name = file_.name\n else:\n executable = False\n name = file_\n full_name = op.join(path, name)\n if remove_existing and lexists(full_name):\n rmtree(full_name, chmod_files=True)\n if isinstance(load, (tuple, list, dict)):\n if name.endswith('.tar.gz') or name.endswith('.tar') or name.endswith('.zip'):\n create_tree_archive(\n path, name, load,\n archives_leading_dir=archives_leading_dir)\n else:\n create_tree(\n full_name, load,\n archives_leading_dir=archives_leading_dir,\n remove_existing=remove_existing)\n else:\n open_func = open\n if full_name.endswith('.gz'):\n open_func = gzip.open\n elif full_name.split('.')[-1] in ('xz', 'lzma'):\n import lzma\n open_func = lzma.open\n with open_func(full_name, \"wb\") as f:\n f.write(ensure_bytes(load, 'utf-8'))\n if executable:\n os.chmod(full_name, os.stat(full_name).st_mode | stat.S_IEXEC)\n\n\ndef get_suggestions_msg(values, known, sep=\"\\n \"):\n \"\"\"Return a formatted string with suggestions for values given the known ones\n \"\"\"\n import difflib\n suggestions = []\n for value in ensure_list(values): # might not want to do it if we change presentation below\n suggestions += difflib.get_close_matches(value, known)\n suggestions = unique(suggestions)\n msg = \"Did you mean any of these?\"\n if suggestions:\n if '\\n' in sep:\n # if separator includes new line - we add entire separator right away\n msg += sep\n else:\n msg += ' '\n return msg + \"%s\\n\" % sep.join(suggestions)\n return ''\n\n\ndef bytes2human(n, format='%(value).1f %(symbol)sB'):\n \"\"\"\n Convert n bytes into a human readable string based on format.\n symbols can be either \"customary\", \"customary_ext\", \"iec\" or \"iec_ext\",\n see: http://goo.gl/kTQMs\n\n >>> from datalad.utils import bytes2human\n >>> bytes2human(1)\n '1.0 B'\n >>> bytes2human(1024)\n '1.0 KB'\n >>> bytes2human(1048576)\n '1.0 MB'\n >>> bytes2human(1099511627776127398123789121)\n '909.5 YB'\n\n >>> bytes2human(10000, \"%(value).1f %(symbol)s/sec\")\n '9.8 K/sec'\n\n >>> # precision can be adjusted by playing with %f operator\n >>> bytes2human(10000, format=\"%(value).5f %(symbol)s\")\n '9.76562 K'\n\n Taken from: http://goo.gl/kTQMs and subsequently simplified\n Original Author: Giampaolo Rodola' <g.rodola [AT] gmail [DOT] com>\n License: MIT\n \"\"\"\n n = int(n)\n if n < 0:\n raise ValueError(\"n < 0\")\n symbols = ('', 'K', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y')\n prefix = {}\n for i, s in enumerate(symbols[1:]):\n prefix[s] = 1 << (i + 1) * 10\n for symbol in reversed(symbols[1:]):\n if n >= prefix[symbol]:\n value = float(n) / prefix[symbol]\n return format % locals()\n return format % dict(symbol=symbols[0], value=n)\n\n\ndef quote_cmdlinearg(arg):\n \"\"\"Perform platform-appropriate argument quoting\"\"\"\n # https://stackoverflow.com/a/15262019\n return '\"{}\"'.format(\n arg.replace('\"', '\"\"')\n ) if on_windows else shlex_quote(arg)\n\n\ndef guard_for_format(arg):\n \"\"\"Replace { and } with {{ and }}\n\n To be used in cases if arg is not expected to have provided\n by user .format() placeholders, but 'arg' might become a part\n of a composite passed to .format(), e.g. via 'Run'\n \"\"\"\n return arg.replace('{', '{{').replace('}', '}}')\n\n\ndef join_cmdline(args):\n \"\"\"Join command line args into a string using quote_cmdlinearg\n \"\"\"\n return ' '.join(map(quote_cmdlinearg, args))\n\n\ndef split_cmdline(s):\n \"\"\"Perform platform-appropriate command line splitting.\n\n Identical to `shlex.split()` on non-windows platforms.\n\n Modified from https://stackoverflow.com/a/35900070\n \"\"\"\n if not on_windows:\n return shlex_split(s)\n\n # the rest is for windows\n RE_CMD_LEX = r'''\"((?:\"\"|\\\\[\"\\\\]|[^\"])*)\"?()|(\\\\\\\\(?=\\\\*\")|\\\\\")|(&&?|\\|\\|?|\\d?>|[<])|([^\\s\"&|<>]+)|(\\s+)|(.)'''\n\n args = []\n accu = None # collects pieces of one arg\n for qs, qss, esc, pipe, word, white, fail in re.findall(RE_CMD_LEX, s):\n if word:\n pass # most frequent\n elif esc:\n word = esc[1]\n elif white or pipe:\n if accu is not None:\n args.append(accu)\n if pipe:\n args.append(pipe)\n accu = None\n continue\n elif fail:\n raise ValueError(\"invalid or incomplete shell string\")\n elif qs:\n word = qs.replace('\\\\\"', '\"').replace('\\\\\\\\', '\\\\')\n if platform == 0:\n word = word.replace('\"\"', '\"')\n else:\n word = qss # may be even empty; must be last\n\n accu = (accu or '') + word\n\n if accu is not None:\n args.append(accu)\n\n return args\n\n\ndef get_wrapped_class(wrapped):\n \"\"\"Determine the command class a wrapped __call__ belongs to\"\"\"\n mod = sys.modules[wrapped.__module__]\n command_class_name = wrapped.__qualname__.split('.')[-2]\n _func_class = mod.__dict__[command_class_name]\n lgr.debug(\"Determined class of decorated function: %s\", _func_class)\n return _func_class\n\n\ndef _make_assure_kludge(fn):\n old_name = fn.__name__.replace(\"ensure\", \"assure\")\n\n @wraps(fn)\n def compat_fn(*args, **kwargs):\n warnings.warn(\n \"{} is deprecated and will be removed in a future release. \"\n \"Use {} instead.\"\n .format(old_name, fn.__name__),\n DeprecationWarning)\n return fn(*args, **kwargs)\n\n compat_fn.__doc__ = (\"Note: This function is deprecated. Use {} instead.\"\n .format(fn.__name__))\n return compat_fn\n\n\nassure_tuple_or_list = _make_assure_kludge(ensure_tuple_or_list)\nassure_iter = _make_assure_kludge(ensure_iter)\nassure_list = _make_assure_kludge(ensure_list)\nassure_list_from_str = _make_assure_kludge(ensure_list_from_str)\nassure_dict_from_str = _make_assure_kludge(ensure_dict_from_str)\nassure_bytes = _make_assure_kludge(ensure_bytes)\nassure_unicode = _make_assure_kludge(ensure_unicode)\nassure_bool = _make_assure_kludge(ensure_bool)\nassure_dir = _make_assure_kludge(ensure_dir)\n\n\nlgr.log(5, \"Done importing datalad.utils\")\n\n\ndef check_symlink_capability(path, target):\n \"\"\"helper similar to datalad.tests.utils.has_symlink_capability\n\n However, for use in a datalad command context, we shouldn't\n assume to be able to write to tmpfile and also not import a whole lot from\n datalad's test machinery. Finally, we want to know, whether we can create a\n symlink at a specific location, not just somewhere. Therefore use\n arbitrary path to test-build a symlink and delete afterwards. Suitable\n location can therefore be determined by high lever code.\n\n Parameters\n ----------\n path: Path\n target: Path\n\n Returns\n -------\n bool\n \"\"\"\n\n try:\n target.touch()\n path.symlink_to(target)\n return True\n except Exception:\n return False\n finally:\n if path.exists():\n path.unlink()\n if target.exists():\n target.unlink()"},"implementation":{"kind":"string","value":"def ensure_unicode(s, encoding=None, confidence=None):\n \"\"\"Convert/decode bytestring to unicode.\n\n If `s` isn't a bytestring, return it as is.\n\n Parameters\n ----------\n encoding: str, optional\n Encoding to use. If None, \"utf-8\" is tried, and then if not a valid\n UTF-8, encoding will be guessed\n confidence: float, optional\n A value between 0 and 1, so if guessing of encoding is of lower than\n specified confidence, ValueError is raised\n \"\"\"\n if not isinstance(s, bytes):\n return s\n if encoding is None:\n # Figure out encoding, defaulting to 'utf-8' which is our common\n # target in contemporary digital society\n try:\n return s.decode('utf-8')\n except UnicodeDecodeError as exc:\n lgr.debug(\"Failed to decode a string as utf-8: %s\",\n CapturedException(exc))\n # And now we could try to guess\n from chardet import detect\n enc = detect(s)\n denc = enc.get('encoding', None)\n if denc:\n denc_confidence = enc.get('confidence', 0)\n if confidence is not None and denc_confidence < confidence:\n raise ValueError(\n \"Failed to auto-detect encoding with high enough \"\n \"confidence. Highest confidence was %s for %s\"\n % (denc_confidence, denc)\n )\n lgr.log(5, \"Auto-detected encoding to be %s\", denc)\n return s.decode(denc)\n else:\n raise ValueError(\n \"Could not decode value as utf-8, or to guess its encoding: %s\"\n % repr(s)\n )\n else:\n return s.decode(encoding)"},"start_line":{"kind":"number","value":807,"string":"807"},"end_line":{"kind":"number","value":851,"string":"851"},"file_content":{"kind":"string","value":"# emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-\n# ex: set sts=4 ts=4 sw=4 et:\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##\n#\n# See COPYING file distributed along with the datalad package for the\n# copyright and license terms.\n#\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##\n\nimport collections\nfrom collections.abc import Callable\nimport re\nimport builtins\nimport time\n\nimport logging\nimport shutil\nimport os\nimport sys\nimport tempfile\nfrom tempfile import NamedTemporaryFile\nimport platform\nimport gc\nimport glob\nimport gzip\nimport stat\nimport string\nimport warnings\n\nimport os.path as op\n\nfrom copy import copy as shallow_copy\nfrom contextlib import contextmanager\nfrom functools import (\n lru_cache,\n wraps,\n)\nfrom time import sleep\nimport inspect\nfrom itertools import tee\n# this import is required because other modules import opj from here.\nfrom os.path import join as opj\nfrom os.path import (\n abspath,\n basename,\n commonprefix,\n curdir,\n dirname,\n exists,\n expanduser,\n expandvars,\n isabs,\n isdir,\n islink,\n lexists,\n normpath,\n pardir,\n relpath,\n sep,\n split,\n splitdrive\n)\n\nimport posixpath\n\nfrom shlex import (\n quote as shlex_quote,\n split as shlex_split,\n)\n\n# from datalad.dochelpers import get_docstring_split\nfrom datalad.consts import TIMESTAMP_FMT\nfrom datalad.support.exceptions import CapturedException\n\n\nunicode_srctypes = str, bytes\n\n\nlgr = logging.getLogger(\"datalad.utils\")\n\nlgr.log(5, \"Importing datalad.utils\")\n#\n# Some useful variables\n#\nplatform_system = platform.system().lower()\non_windows = platform_system == 'windows'\non_osx = platform_system == 'darwin'\non_linux = platform_system == 'linux'\non_msys_tainted_paths = on_windows \\\n and 'MSYS_NO_PATHCONV' not in os.environ \\\n and os.environ.get('MSYSTEM', '')[:4] in ('MSYS', 'MING')\n\n\n# Takes ~200msec, so should not be called at import time\n@lru_cache() # output should not change through life time of datalad process\ndef get_linux_distribution():\n \"\"\"Compatibility wrapper for {platform,distro}.linux_distribution().\n \"\"\"\n if hasattr(platform, \"linux_distribution\"):\n # Use deprecated (but faster) method if it's available.\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n result = platform.linux_distribution()\n else:\n import distro # We require this for Python 3.8 and above.\n result = distro.linux_distribution(full_distribution_name=False)\n return result\n\n\n# Those weren't used for any critical decision making, thus we just set them to None\n# Use get_linux_distribution() directly where needed\nlinux_distribution_name = linux_distribution_release = None\n\n# Maximal length of cmdline string\n# Query the system and use hardcoded \"knowledge\" if None\n# probably getconf ARG_MAX might not be available\n# The last one would be the most conservative/Windows\nCMD_MAX_ARG_HARDCODED = 2097152 if on_linux else 262144 if on_osx else 32767\ntry:\n CMD_MAX_ARG = os.sysconf('SC_ARG_MAX')\n assert CMD_MAX_ARG > 0\n if CMD_MAX_ARG > CMD_MAX_ARG_HARDCODED * 1e6:\n # workaround for some kind of a bug which comes up with python 3.4\n # see https://github.com/datalad/datalad/issues/3150\n # or on older CentOS with conda and python as new as 3.9\n # see https://github.com/datalad/datalad/issues/5943\n # TODO: let Yarik know that the world is a paradise now whenever 1e6\n # is not large enough\n CMD_MAX_ARG = min(CMD_MAX_ARG, CMD_MAX_ARG_HARDCODED)\nexcept Exception as exc:\n # ATM (20181005) SC_ARG_MAX available only on POSIX systems\n # so exception would be thrown e.g. on Windows, or\n # somehow during Debian build for nd14.04 it is coming up with -1:\n # https://github.com/datalad/datalad/issues/3015\n CMD_MAX_ARG = CMD_MAX_ARG_HARDCODED\n lgr.debug(\n \"Failed to query or got useless SC_ARG_MAX sysconf, \"\n \"will use hardcoded value: %s\", exc)\n# Even with all careful computations we do, due to necessity to account for\n# environment and what not, we still could not figure out \"exact\" way to\n# estimate it, but it was shown that 300k safety margin on linux was sufficient.\n# https://github.com/datalad/datalad/pull/2977#issuecomment-436264710\n# 300k is ~15%, so to be safe, and for paranoid us we will just use up to 50%\n# of the length for \"safety margin\". We might probably still blow due to\n# env vars, unicode, etc... so any hard limit imho is not a proper solution\nCMD_MAX_ARG = int(0.5 * CMD_MAX_ARG)\nlgr.debug(\n \"Maximal length of cmdline string (adjusted for safety margin): %d\",\n CMD_MAX_ARG)\n\n#\n# Little helpers\n#\n\n# `getargspec` has been deprecated in Python 3.\nArgSpecFake = collections.namedtuple(\n \"ArgSpecFake\", [\"args\", \"varargs\", \"keywords\", \"defaults\"])\n\n\ndef getargspec(func, *, include_kwonlyargs=False):\n \"\"\"Compat shim for getargspec deprecated in python 3.\n\n The main difference from inspect.getargspec (and inspect.getfullargspec\n for that matter) is that by using inspect.signature we are providing\n correct args/defaults for functools.wraps'ed functions.\n\n `include_kwonlyargs` option was added to centralize getting all args,\n even the ones which are kwonly (follow the ``*,``).\n\n For internal use and not advised for use in 3rd party code.\n Please use inspect.signature directly.\n \"\"\"\n # We use signature, and not getfullargspec, because only signature properly\n # \"passes\" args from a functools.wraps decorated function.\n # Note: getfullargspec works Ok on wrapt-decorated functions\n f_sign = inspect.signature(func)\n # Loop through parameters and compose argspec\n args4 = [[], None, None, {}]\n # Collect all kwonlyargs into a dedicated dict - name: default\n kwonlyargs = {}\n # shortcuts\n args, defaults = args4[0], args4[3]\n P = inspect.Parameter\n\n for p_name, p in f_sign.parameters.items():\n if p.kind in (P.POSITIONAL_ONLY, P.POSITIONAL_OR_KEYWORD):\n assert not kwonlyargs # yoh: must not come after kwonlyarg\n args.append(p_name)\n if p.default is not P.empty:\n defaults[p_name] = p.default\n elif p.kind == P.VAR_POSITIONAL:\n args4[1] = p_name\n elif p.kind == P.VAR_KEYWORD:\n args4[2] = p_name\n elif p.kind == P.KEYWORD_ONLY:\n assert p.default is not P.empty\n kwonlyargs[p_name] = p.default\n\n if kwonlyargs:\n if not include_kwonlyargs:\n raise ValueError(\n 'Function has keyword-only parameters or annotations, either use '\n 'inspect.signature() API which can support them, or provide include_kwonlyargs=True '\n 'to this function'\n )\n else:\n args.extend(list(kwonlyargs))\n defaults.update(kwonlyargs)\n\n # harmonize defaults to how original getargspec returned them -- just a tuple\n args4[3] = None if not defaults else tuple(defaults.values())\n return ArgSpecFake(*args4)\n\n\ndef any_re_search(regexes, value):\n \"\"\"Return if any of regexes (list or str) searches successfully for value\"\"\"\n for regex in ensure_tuple_or_list(regexes):\n if re.search(regex, value):\n return True\n return False\n\n\ndef not_supported_on_windows(msg=None):\n \"\"\"A little helper to be invoked to consistently fail whenever functionality is\n not supported (yet) on Windows\n \"\"\"\n if on_windows:\n raise NotImplementedError(\"This functionality is not yet implemented for Windows OS\"\n + (\": %s\" % msg if msg else \"\"))\n\n\ndef get_home_envvars(new_home):\n \"\"\"Return dict with env variables to be adjusted for a new HOME\n\n Only variables found in current os.environ are adjusted.\n\n Parameters\n ----------\n new_home: str or Path\n New home path, in native to OS \"schema\"\n \"\"\"\n new_home = str(new_home)\n out = {'HOME': new_home}\n if on_windows:\n # requires special handling, since it has a number of relevant variables\n # and also Python changed its behavior and started to respect USERPROFILE only\n # since python 3.8: https://bugs.python.org/issue36264\n out['USERPROFILE'] = new_home\n out['HOMEDRIVE'], out['HOMEPATH'] = splitdrive(new_home)\n\n return {v: val for v, val in out.items() if v in os.environ}\n\n\ndef shortened_repr(value, l=30):\n try:\n if hasattr(value, '__repr__') and (value.__repr__ is not object.__repr__):\n value_repr = repr(value)\n if not value_repr.startswith('<') and len(value_repr) > l:\n value_repr = \"<<%s++%d chars++%s>>\" % (\n value_repr[:l - 16],\n len(value_repr) - (l - 16 + 4),\n value_repr[-4:]\n )\n elif value_repr.startswith('<') and value_repr.endswith('>') and ' object at 0x':\n raise ValueError(\"I hate those useless long reprs\")\n else:\n raise ValueError(\"gimme class\")\n except Exception as e:\n value_repr = \"<%s>\" % value.__class__.__name__.split('.')[-1]\n return value_repr\n\n\ndef __auto_repr__(obj):\n attr_names = tuple()\n if hasattr(obj, '__dict__'):\n attr_names += tuple(obj.__dict__.keys())\n if hasattr(obj, '__slots__'):\n attr_names += tuple(obj.__slots__)\n\n items = []\n for attr in sorted(set(attr_names)):\n if attr.startswith('_'):\n continue\n value = getattr(obj, attr)\n # TODO: should we add this feature to minimize some talktative reprs\n # such as of URL?\n #if value is None:\n # continue\n items.append(\"%s=%s\" % (attr, shortened_repr(value)))\n\n return \"%s(%s)\" % (obj.__class__.__name__, ', '.join(items))\n\n\ndef auto_repr(cls):\n \"\"\"Decorator for a class to assign it an automagic quick and dirty __repr__\n\n It uses public class attributes to prepare repr of a class\n\n Original idea: http://stackoverflow.com/a/27799004/1265472\n \"\"\"\n\n cls.__repr__ = __auto_repr__\n return cls\n\n\ndef _is_stream_tty(stream):\n try:\n # TODO: check on windows if hasattr check would work correctly and\n # add value:\n return stream.isatty()\n except ValueError as exc:\n # Who knows why it is a ValueError, but let's try to be specific\n # If there is a problem with I/O - non-interactive, otherwise reraise\n if \"I/O\" in str(exc):\n return False\n raise\n\n\ndef is_interactive():\n \"\"\"Return True if all in/outs are open and tty.\n\n Note that in a somewhat abnormal case where e.g. stdin is explicitly\n closed, and any operation on it would raise a\n `ValueError(\"I/O operation on closed file\")` exception, this function\n would just return False, since the session cannot be used interactively.\n \"\"\"\n return all(_is_stream_tty(s) for s in (sys.stdin, sys.stdout, sys.stderr))\n\n\ndef get_ipython_shell():\n \"\"\"Detect if running within IPython and returns its `ip` (shell) object\n\n Returns None if not under ipython (no `get_ipython` function)\n \"\"\"\n try:\n return get_ipython()\n except NameError:\n return None\n\n\ndef md5sum(filename):\n \"\"\"Compute an MD5 sum for the given file\n \"\"\"\n from datalad.support.digests import Digester\n return Digester(digests=['md5'])(filename)['md5']\n\n\n# unused in -core\ndef sorted_files(path):\n \"\"\"Return a (sorted) list of files under path\n \"\"\"\n return sorted(sum([[op.join(r, f)[len(path) + 1:] for f in files]\n for r, d, files in os.walk(path)\n if not '.git' in r], []))\n\n_encoded_dirsep = r'\\\\' if on_windows else r'/'\n_VCS_REGEX = r'%s\\.(?:git|gitattributes|svn|bzr|hg)(?:%s|$)' % (\n _encoded_dirsep, _encoded_dirsep)\n_DATALAD_REGEX = r'%s\\.(?:datalad)(?:%s|$)' % (\n _encoded_dirsep, _encoded_dirsep)\n\n\ndef find_files(regex, topdir=curdir, exclude=None, exclude_vcs=True, exclude_datalad=False, dirs=False):\n \"\"\"Generator to find files matching regex\n\n Parameters\n ----------\n regex: basestring\n exclude: basestring, optional\n Matches to exclude\n exclude_vcs:\n If True, excludes commonly known VCS subdirectories. If string, used\n as regex to exclude those files (regex: `%r`)\n exclude_datalad:\n If True, excludes files known to be datalad meta-data files (e.g. under\n .datalad/ subdirectory) (regex: `%r`)\n topdir: basestring, optional\n Directory where to search\n dirs: bool, optional\n Whether to match directories as well as files\n \"\"\"\n for dirpath, dirnames, filenames in os.walk(topdir):\n names = (dirnames + filenames) if dirs else filenames\n # TODO: might want to uniformize on windows to use '/'\n paths = (op.join(dirpath, name) for name in names)\n for path in filter(re.compile(regex).search, paths):\n path = path.rstrip(sep)\n if exclude and re.search(exclude, path):\n continue\n if exclude_vcs and re.search(_VCS_REGEX, path):\n continue\n if exclude_datalad and re.search(_DATALAD_REGEX, path):\n continue\n yield path\nfind_files.__doc__ %= (_VCS_REGEX, _DATALAD_REGEX)\n\n\ndef expandpath(path, force_absolute=True):\n \"\"\"Expand all variables and user handles in a path.\n\n By default return an absolute path\n \"\"\"\n path = expandvars(expanduser(path))\n if force_absolute:\n path = abspath(path)\n return path\n\n\ndef posix_relpath(path, start=None):\n \"\"\"Behave like os.path.relpath, but always return POSIX paths...\n\n on any platform.\"\"\"\n # join POSIX style\n return posixpath.join(\n # split and relpath native style\n # python2.7 ntpath implementation of relpath cannot handle start=None\n *split(\n relpath(path, start=start if start is not None else '')))\n\n\ndef is_explicit_path(path):\n \"\"\"Return whether a path explicitly points to a location\n\n Any absolute path, or relative path starting with either '../' or\n './' is assumed to indicate a location on the filesystem. Any other\n path format is not considered explicit.\"\"\"\n path = expandpath(path, force_absolute=False)\n return isabs(path) \\\n or path.startswith(os.curdir + os.sep) \\\n or path.startswith(os.pardir + os.sep)\n\n\n# handle this dance once, and import pathlib from here\n# in all other places\n\nfrom pathlib import (\n Path,\n PurePath,\n PurePosixPath,\n)\n\ndef rotree(path, ro=True, chmod_files=True):\n \"\"\"To make tree read-only or writable\n\n Parameters\n ----------\n path : string\n Path to the tree/directory to chmod\n ro : bool, optional\n Whether to make it R/O (default) or RW\n chmod_files : bool, optional\n Whether to operate also on files (not just directories)\n \"\"\"\n if ro:\n chmod = lambda f: os.chmod(f, os.stat(f).st_mode & ~stat.S_IWRITE)\n else:\n chmod = lambda f: os.chmod(f, os.stat(f).st_mode | stat.S_IWRITE | stat.S_IREAD)\n\n for root, dirs, files in os.walk(path, followlinks=False):\n if chmod_files:\n for f in files:\n fullf = op.join(root, f)\n # might be the \"broken\" symlink which would fail to stat etc\n if exists(fullf):\n chmod(fullf)\n chmod(root)\n\n\ndef rmtree(path, chmod_files='auto', children_only=False, *args, **kwargs):\n \"\"\"To remove git-annex .git it is needed to make all files and directories writable again first\n\n Parameters\n ----------\n path: Path or str\n Path to remove\n chmod_files : string or bool, optional\n Whether to make files writable also before removal. Usually it is just\n a matter of directories to have write permissions.\n If 'auto' it would chmod files on windows by default\n children_only : bool, optional\n If set, all files and subdirectories would be removed while the path\n itself (must be a directory) would be preserved\n `*args` :\n `**kwargs` :\n Passed into shutil.rmtree call\n \"\"\"\n # Give W permissions back only to directories, no need to bother with files\n if chmod_files == 'auto':\n chmod_files = on_windows\n # TODO: yoh thinks that if we could quickly check our Flyweight for\n # repos if any of them is under the path, and could call .precommit\n # on those to possibly stop batched processes etc, we did not have\n # to do it on case by case\n # Check for open files\n assert_no_open_files(path)\n\n # TODO the whole thing should be reimplemented with pathlib, but for now\n # at least accept Path\n path = str(path)\n\n if children_only:\n if not isdir(path):\n raise ValueError(\"Can remove children only of directories\")\n for p in os.listdir(path):\n rmtree(op.join(path, p))\n return\n if not (islink(path) or not isdir(path)):\n rotree(path, ro=False, chmod_files=chmod_files)\n if on_windows:\n # shutil fails to remove paths that exceed 260 characters on Windows machines\n # that did not enable long path support. A workaround to remove long paths\n # anyway is to preprend \\\\?\\ to the path.\n # https://docs.microsoft.com/en-us/windows/win32/fileio/naming-a-file?redirectedfrom=MSDN#win32-file-namespaces\n path = r'\\\\?\\ '.strip() + path\n _rmtree(path, *args, **kwargs)\n else:\n # just remove the symlink\n unlink(path)\n\n\ndef rmdir(path, *args, **kwargs):\n \"\"\"os.rmdir with our optional checking for open files\"\"\"\n assert_no_open_files(path)\n os.rmdir(path)\n\n\ndef get_open_files(path, log_open=False):\n \"\"\"Get open files under a path\n\n Note: This function is very slow on Windows.\n\n Parameters\n ----------\n path : str\n File or directory to check for open files under\n log_open : bool or int\n If set - logger level to use\n\n Returns\n -------\n dict\n path : pid\n\n \"\"\"\n # Original idea: https://stackoverflow.com/a/11115521/1265472\n import psutil\n files = {}\n # since the ones returned by psutil would not be aware of symlinks in the\n # path we should also get realpath for path\n # do absolute() in addition to always get an absolute path\n # even with non-existing paths on windows\n path = str(Path(path).resolve().absolute())\n for proc in psutil.process_iter():\n try:\n open_paths = [p.path for p in proc.open_files()] + [proc.cwd()]\n for p in open_paths:\n # note: could be done more efficiently so we do not\n # renormalize path over and over again etc\n if path_startswith(p, path):\n files[p] = proc\n # Catch a race condition where a process ends\n # before we can examine its files\n except psutil.NoSuchProcess:\n pass\n except psutil.AccessDenied:\n pass\n\n if files and log_open:\n lgr.log(log_open, \"Open files under %s: %s\", path, files)\n return files\n\n\n_assert_no_open_files_cfg = os.environ.get('DATALAD_ASSERT_NO_OPEN_FILES')\nif _assert_no_open_files_cfg:\n def assert_no_open_files(path):\n files = get_open_files(path, log_open=40)\n if _assert_no_open_files_cfg == 'assert':\n assert not files, \"Got following files still open: %s\" % ','.join(files)\n elif files:\n if _assert_no_open_files_cfg == 'pdb':\n import pdb\n pdb.set_trace()\n elif _assert_no_open_files_cfg == 'epdb':\n import epdb\n epdb.serve()\n pass\n # otherwise we would just issue that error message in the log\nelse:\n def assert_no_open_files(*args, **kwargs):\n pass\n\n\ndef rmtemp(f, *args, **kwargs):\n \"\"\"Wrapper to centralize removing of temp files so we could keep them around\n\n It will not remove the temporary file/directory if DATALAD_TESTS_TEMP_KEEP\n environment variable is defined\n \"\"\"\n if not os.environ.get('DATALAD_TESTS_TEMP_KEEP'):\n if not os.path.lexists(f):\n lgr.debug(\"Path %s does not exist, so can't be removed\", f)\n return\n lgr.log(5, \"Removing temp file: %s\", f)\n # Can also be a directory\n if isdir(f):\n rmtree(f, *args, **kwargs)\n else:\n unlink(f)\n else:\n lgr.info(\"Keeping temp file: %s\", f)\n\n\ndef file_basename(name, return_ext=False):\n \"\"\"\n Strips up to 2 extensions of length up to 4 characters and starting with alpha\n not a digit, so we could get rid of .tar.gz etc\n \"\"\"\n bname = basename(name)\n fbname = re.sub(r'(\\.[a-zA-Z_]\\S{1,4}){0,2}$', '', bname)\n if return_ext:\n return fbname, bname[len(fbname) + 1:]\n else:\n return fbname\n\n\n# unused in -core\ndef escape_filename(filename):\n \"\"\"Surround filename in \"\" and escape \" in the filename\n \"\"\"\n filename = filename.replace('\"', r'\\\"').replace('`', r'\\`')\n filename = '\"%s\"' % filename\n return filename\n\n\n# unused in -core\ndef encode_filename(filename):\n \"\"\"Encode unicode filename\n \"\"\"\n if isinstance(filename, str):\n return filename.encode(sys.getfilesystemencoding())\n else:\n return filename\n\n\n# unused in -core\ndef decode_input(s):\n \"\"\"Given input string/bytes, decode according to stdin codepage (or UTF-8)\n if not defined\n\n If fails -- issue warning and decode allowing for errors\n being replaced\n \"\"\"\n if isinstance(s, str):\n return s\n else:\n encoding = sys.stdin.encoding or 'UTF-8'\n try:\n return s.decode(encoding)\n except UnicodeDecodeError as exc:\n lgr.warning(\n \"Failed to decode input string using %s encoding. \"\n \"Decoding allowing for errors\", encoding)\n return s.decode(encoding, errors='replace')\n\n\n# unused in -core\nif on_windows:\n def lmtime(filepath, mtime):\n \"\"\"Set mtime for files. On Windows a merely adapter to os.utime\n \"\"\"\n os.utime(filepath, (time.time(), mtime))\nelse:\n def lmtime(filepath, mtime):\n \"\"\"Set mtime for files, while not de-referencing symlinks.\n\n To overcome absence of os.lutime\n\n Works only on linux and OSX ATM\n \"\"\"\n from .cmd import WitlessRunner\n # convert mtime to format touch understands [[CC]YY]MMDDhhmm[.SS]\n smtime = time.strftime(\"%Y%m%d%H%M.%S\", time.localtime(mtime))\n lgr.log(3, \"Setting mtime for %s to %s == %s\", filepath, mtime, smtime)\n WitlessRunner().run(['touch', '-h', '-t', '%s' % smtime, filepath])\n filepath = Path(filepath)\n rfilepath = filepath.resolve()\n if filepath.is_symlink() and rfilepath.exists():\n # trust no one - adjust also of the target file\n # since it seemed like downloading under OSX (was it using curl?)\n # didn't bother with timestamps\n lgr.log(3, \"File is a symlink to %s Setting mtime for it to %s\",\n rfilepath, mtime)\n os.utime(str(rfilepath), (time.time(), mtime))\n # doesn't work on OSX\n # Runner().run(['touch', '-h', '-d', '@%s' % mtime, filepath])\n\n\ndef ensure_tuple_or_list(obj):\n \"\"\"Given an object, wrap into a tuple if not list or tuple\n \"\"\"\n if isinstance(obj, (list, tuple)):\n return obj\n return (obj,)\n\n\ndef ensure_iter(s, cls, copy=False, iterate=True):\n \"\"\"Given not a list, would place it into a list. If None - empty list is returned\n\n Parameters\n ----------\n s: list or anything\n cls: class\n Which iterable class to ensure\n copy: bool, optional\n If correct iterable is passed, it would generate its shallow copy\n iterate: bool, optional\n If it is not a list, but something iterable (but not a str)\n iterate over it.\n \"\"\"\n\n if isinstance(s, cls):\n return s if not copy else shallow_copy(s)\n elif isinstance(s, str):\n return cls((s,))\n elif iterate and hasattr(s, '__iter__'):\n return cls(s)\n elif s is None:\n return cls()\n else:\n return cls((s,))\n\n\ndef ensure_list(s, copy=False, iterate=True):\n \"\"\"Given not a list, would place it into a list. If None - empty list is returned\n\n Parameters\n ----------\n s: list or anything\n copy: bool, optional\n If list is passed, it would generate a shallow copy of the list\n iterate: bool, optional\n If it is not a list, but something iterable (but not a str)\n iterate over it.\n \"\"\"\n return ensure_iter(s, list, copy=copy, iterate=iterate)\n\n\ndef ensure_list_from_str(s, sep='\\n'):\n \"\"\"Given a multiline string convert it to a list of return None if empty\n\n Parameters\n ----------\n s: str or list\n \"\"\"\n\n if not s:\n return None\n\n if isinstance(s, list):\n return s\n return s.split(sep)\n\n\ndef ensure_dict_from_str(s, **kwargs):\n \"\"\"Given a multiline string with key=value items convert it to a dictionary\n\n Parameters\n ----------\n s: str or dict\n\n Returns None if input s is empty\n \"\"\"\n\n if not s:\n return None\n\n if isinstance(s, dict):\n return s\n\n out = {}\n for value_str in ensure_list_from_str(s, **kwargs):\n if '=' not in value_str:\n raise ValueError(\"{} is not in key=value format\".format(repr(value_str)))\n k, v = value_str.split('=', 1)\n if k in out:\n err = \"key {} was already defined in {}, but new value {} was provided\".format(k, out, v)\n raise ValueError(err)\n out[k] = v\n return out\n\n\ndef ensure_bytes(s, encoding='utf-8'):\n \"\"\"Convert/encode unicode string to bytes.\n\n If `s` isn't a string, return it as is.\n\n Parameters\n ----------\n encoding: str, optional\n Encoding to use. \"utf-8\" is the default\n \"\"\"\n if not isinstance(s, str):\n return s\n return s.encode(encoding)\n\n\ndef ensure_unicode(s, encoding=None, confidence=None):\n \"\"\"Convert/decode bytestring to unicode.\n\n If `s` isn't a bytestring, return it as is.\n\n Parameters\n ----------\n encoding: str, optional\n Encoding to use. If None, \"utf-8\" is tried, and then if not a valid\n UTF-8, encoding will be guessed\n confidence: float, optional\n A value between 0 and 1, so if guessing of encoding is of lower than\n specified confidence, ValueError is raised\n \"\"\"\n if not isinstance(s, bytes):\n return s\n if encoding is None:\n # Figure out encoding, defaulting to 'utf-8' which is our common\n # target in contemporary digital society\n try:\n return s.decode('utf-8')\n except UnicodeDecodeError as exc:\n lgr.debug(\"Failed to decode a string as utf-8: %s\",\n CapturedException(exc))\n # And now we could try to guess\n from chardet import detect\n enc = detect(s)\n denc = enc.get('encoding', None)\n if denc:\n denc_confidence = enc.get('confidence', 0)\n if confidence is not None and denc_confidence < confidence:\n raise ValueError(\n \"Failed to auto-detect encoding with high enough \"\n \"confidence. Highest confidence was %s for %s\"\n % (denc_confidence, denc)\n )\n lgr.log(5, \"Auto-detected encoding to be %s\", denc)\n return s.decode(denc)\n else:\n raise ValueError(\n \"Could not decode value as utf-8, or to guess its encoding: %s\"\n % repr(s)\n )\n else:\n return s.decode(encoding)\n\n\ndef ensure_bool(s):\n \"\"\"Convert value into boolean following convention for strings\n\n to recognize on,True,yes as True, off,False,no as False\n \"\"\"\n if isinstance(s, str):\n if s.isdigit():\n return bool(int(s))\n sl = s.lower()\n if sl in {'y', 'yes', 'true', 'on'}:\n return True\n elif sl in {'n', 'no', 'false', 'off'}:\n return False\n else:\n raise ValueError(\"Do not know how to treat %r as a boolean\" % s)\n return bool(s)\n\n\ndef as_unicode(val, cast_types=object):\n \"\"\"Given an arbitrary value, would try to obtain unicode value of it\n \n For unicode it would return original value, for python2 str or python3\n bytes it would use ensure_unicode, for None - an empty (unicode) string,\n and for any other type (see `cast_types`) - would apply the unicode \n constructor. If value is not an instance of `cast_types`, TypeError\n is thrown\n \n Parameters\n ----------\n cast_types: type\n Which types to cast to unicode by providing to constructor\n \"\"\"\n if val is None:\n return u''\n elif isinstance(val, str):\n return val\n elif isinstance(val, unicode_srctypes):\n return ensure_unicode(val)\n elif isinstance(val, cast_types):\n return str(val)\n else:\n raise TypeError(\n \"Value %r is not of any of known or provided %s types\"\n % (val, cast_types))\n\n\ndef unique(seq, key=None, reverse=False):\n \"\"\"Given a sequence return a list only with unique elements while maintaining order\n\n This is the fastest solution. See\n https://www.peterbe.com/plog/uniqifiers-benchmark\n and\n http://stackoverflow.com/a/480227/1265472\n for more information.\n Enhancement -- added ability to compare for uniqueness using a key function\n\n Parameters\n ----------\n seq:\n Sequence to analyze\n key: callable, optional\n Function to call on each element so we could decide not on a full\n element, but on its member etc\n reverse: bool, optional\n If True, uniqueness checked in the reverse order, so that the later ones\n will take the order\n \"\"\"\n seen = set()\n seen_add = seen.add\n\n trans = reversed if reverse else lambda x: x\n\n if not key:\n out = [x for x in trans(seq) if not (x in seen or seen_add(x))]\n else:\n # OPT: could be optimized, since key is called twice, but for our cases\n # should be just as fine\n out = [x for x in trans(seq) if not (key(x) in seen or seen_add(key(x)))]\n\n return out[::-1] if reverse else out\n\n\ndef all_same(items):\n \"\"\"Quick check if all items are the same.\n\n Identical to a check like len(set(items)) == 1 but\n should be more efficient while working on generators, since would\n return False as soon as any difference detected thus possibly avoiding\n unnecessary evaluations\n \"\"\"\n first = True\n first_item = None\n for item in items:\n if first:\n first = False\n first_item = item\n else:\n if item != first_item:\n return False\n # So we return False if was empty\n return not first\n\n\ndef map_items(func, v):\n \"\"\"A helper to apply `func` to all elements (keys and values) within dict\n\n No type checking of values passed to func is done, so `func`\n should be resilient to values which it should not handle\n\n Initial usecase - apply_recursive(url_fragment, ensure_unicode)\n \"\"\"\n # map all elements within item\n return v.__class__(\n item.__class__(map(func, item))\n for item in v.items()\n )\n\n\ndef partition(items, predicate=bool):\n \"\"\"Partition `items` by `predicate`.\n\n Parameters\n ----------\n items : iterable\n predicate : callable\n A function that will be mapped over each element in `items`. The\n elements will partitioned based on whether the return value is false or\n true.\n\n Returns\n -------\n A tuple with two generators, the first for 'false' items and the second for\n 'true' ones.\n\n Notes\n -----\n Taken from Peter Otten's snippet posted at\n https://nedbatchelder.com/blog/201306/filter_a_list_into_two_parts.html\n \"\"\"\n a, b = tee((predicate(item), item) for item in items)\n return ((item for pred, item in a if not pred),\n (item for pred, item in b if pred))\n\n\ndef generate_chunks(container, size):\n \"\"\"Given a container, generate chunks from it with size up to `size`\n \"\"\"\n # There could be a \"smarter\" solution but I think this would suffice\n assert size > 0, \"Size should be non-0 positive\"\n while container:\n yield container[:size]\n container = container[size:]\n\n\ndef generate_file_chunks(files, cmd=None):\n \"\"\"Given a list of files, generate chunks of them to avoid exceeding cmdline length\n\n Parameters\n ----------\n files: list of str\n cmd: str or list of str, optional\n Command to account for as well\n \"\"\"\n files = ensure_list(files)\n cmd = ensure_list(cmd)\n\n maxl = max(map(len, files)) if files else 0\n chunk_size = max(\n 1, # should at least be 1. If blows then - not our fault\n (CMD_MAX_ARG\n - sum((len(x) + 3) for x in cmd)\n - 4 # for '--' below\n ) // (maxl + 3) # +3 for possible quotes and a space\n )\n # TODO: additional treatment for \"too many arguments\"? although\n # as https://github.com/datalad/datalad/issues/1883#issuecomment\n # -436272758\n # shows there seems to be no hardcoded limit on # of arguments,\n # but may be we decide to go for smth like follow to be on safe side\n # chunk_size = min(10240 - len(cmd), chunk_size)\n file_chunks = generate_chunks(files, chunk_size)\n return file_chunks\n\n\n#\n# Generators helpers\n#\n\ndef saved_generator(gen):\n \"\"\"Given a generator returns two generators, where 2nd one just replays\n\n So the first one would be going through the generated items and 2nd one\n would be yielding saved items\n \"\"\"\n saved = []\n\n def gen1():\n for x in gen: # iterating over original generator\n saved.append(x)\n yield x\n\n def gen2():\n for x in saved: # yielding saved entries\n yield x\n\n return gen1(), gen2()\n\n\n#\n# Decorators\n#\n\n# Originally better_wraps was created to provide `wrapt`-based, instead of\n# `functools.wraps` implementation to preserve the correct signature of the\n# decorated function. By using inspect.signature in our getargspec, which\n# works fine on `functools.wraps`ed functions, we mediated this necessity.\nbetter_wraps = wraps\n\n\n# Borrowed from pandas\n# Copyright: 2011-2014, Lambda Foundry, Inc. and PyData Development Team\n# License: BSD-3\ndef optional_args(decorator):\n \"\"\"allows a decorator to take optional positional and keyword arguments.\n Assumes that taking a single, callable, positional argument means that\n it is decorating a function, i.e. something like this::\n\n @my_decorator\n def function(): pass\n\n Calls decorator with decorator(f, `*args`, `**kwargs`)\"\"\"\n\n @better_wraps(decorator)\n def wrapper(*args, **kwargs):\n def dec(f):\n return decorator(f, *args, **kwargs)\n\n is_decorating = not kwargs and len(args) == 1 and isinstance(args[0], Callable)\n if is_decorating:\n f = args[0]\n args = []\n return dec(f)\n else:\n return dec\n\n return wrapper\n\n\n# TODO: just provide decorators for tempfile.mk* functions. This is ugly!\ndef get_tempfile_kwargs(tkwargs=None, prefix=\"\", wrapped=None):\n \"\"\"Updates kwargs to be passed to tempfile. calls depending on env vars\n \"\"\"\n if tkwargs is None:\n tkwargs_ = {}\n else:\n # operate on a copy of tkwargs to avoid any side-effects\n tkwargs_ = tkwargs.copy()\n\n # TODO: don't remember why I had this one originally\n # if len(targs)<2 and \\\n if 'prefix' not in tkwargs_:\n tkwargs_['prefix'] = '_'.join(\n ['datalad_temp'] +\n ([prefix] if prefix else []) +\n ([''] if (on_windows or not wrapped) else [wrapped.__name__]))\n\n directory = os.environ.get('TMPDIR')\n if directory and 'dir' not in tkwargs_:\n tkwargs_['dir'] = directory\n\n return tkwargs_\n\n\n@optional_args\ndef line_profile(func):\n \"\"\"Q&D helper to line profile the function and spit out stats\n \"\"\"\n import line_profiler\n prof = line_profiler.LineProfiler()\n\n @wraps(func)\n def _wrap_line_profile(*args, **kwargs):\n try:\n pfunc = prof(func)\n return pfunc(*args, **kwargs)\n finally:\n prof.print_stats()\n return _wrap_line_profile\n\n\n# unused in -core\n@optional_args\ndef collect_method_callstats(func):\n \"\"\"Figure out methods which call the method repeatedly on the same instance\n\n Use case(s):\n - .repo is expensive since does all kinds of checks.\n - .config is expensive transitively since it calls .repo each time\n\n TODO:\n - fancy one could look through the stack for the same id(self) to see if\n that location is already in memo. That would hint to the cases where object\n is not passed into underlying functions, causing them to redo the same work\n over and over again\n - ATM might flood with all \"1 lines\" calls which are not that informative.\n The underlying possibly suboptimal use might be coming from their callers.\n It might or not relate to the previous TODO\n \"\"\"\n from collections import defaultdict\n import traceback\n from time import time\n memo = defaultdict(lambda: defaultdict(int)) # it will be a dict of lineno: count\n # gross timing\n times = []\n toppath = dirname(__file__) + sep\n\n @wraps(func)\n def _wrap_collect_method_callstats(*args, **kwargs):\n try:\n self = args[0]\n stack = traceback.extract_stack()\n caller = stack[-2]\n stack_sig = \\\n \"{relpath}:{s.name}\".format(\n s=caller, relpath=relpath(caller.filename, toppath))\n sig = (id(self), stack_sig)\n # we will count based on id(self) + wherefrom\n memo[sig][caller.lineno] += 1\n t0 = time()\n return func(*args, **kwargs)\n finally:\n times.append(time() - t0)\n pass\n\n def print_stats():\n print(\"The cost of property {}:\".format(func.__name__))\n if not memo:\n print(\"None since no calls\")\n return\n # total count\n counts = {k: sum(v.values()) for k,v in memo.items()}\n total = sum(counts.values())\n ids = {self_id for (self_id, _) in memo}\n print(\" Total: {} calls from {} objects with {} contexts taking {:.2f} sec\"\n .format(total, len(ids), len(memo), sum(times)))\n # now we need to sort by value\n for (self_id, caller), count in sorted(counts.items(), key=lambda x: x[1], reverse=True):\n print(\" {} {}: {} from {} lines\"\n .format(self_id, caller, count, len(memo[(self_id, caller)])))\n\n # Upon total exit we print the stats\n import atexit\n atexit.register(print_stats)\n\n return _wrap_collect_method_callstats\n\n\n# Borrowed from duecredit to wrap duecredit-handling to guarantee failsafe\ndef never_fail(f):\n \"\"\"Assure that function never fails -- all exceptions are caught\n\n Returns `None` if function fails internally.\n \"\"\"\n @wraps(f)\n def wrapped_func(*args, **kwargs):\n try:\n return f(*args, **kwargs)\n except Exception as e:\n lgr.warning(\n \"DataLad internal failure while running %s: %r. \"\n \"Please report at https://github.com/datalad/datalad/issues\"\n % (f, e)\n )\n\n if os.environ.get('DATALAD_ALLOW_FAIL', False):\n return f\n else:\n return wrapped_func\n\n\n#\n# Context Managers\n#\n\n\n# unused in -core\n@contextmanager\ndef nothing_cm():\n \"\"\"Just a dummy cm to programmically switch context managers\"\"\"\n yield\n\n\n@contextmanager\ndef swallow_outputs():\n \"\"\"Context manager to help consuming both stdout and stderr, and print()\n\n stdout is available as cm.out and stderr as cm.err whenever cm is the\n yielded context manager.\n Internally uses temporary files to guarantee absent side-effects of swallowing\n into StringIO which lacks .fileno.\n\n print mocking is necessary for some uses where sys.stdout was already bound\n to original sys.stdout, thus mocking it later had no effect. Overriding\n print function had desired effect\n \"\"\"\n\n class StringIOAdapter(object):\n \"\"\"Little adapter to help getting out/err values\n \"\"\"\n def __init__(self):\n kw = get_tempfile_kwargs({}, prefix=\"outputs\")\n\n self._out = NamedTemporaryFile(delete=False, mode='w', **kw)\n self._err = NamedTemporaryFile(delete=False, mode='w', **kw)\n\n def _read(self, h):\n with open(h.name) as f:\n return f.read()\n\n @property\n def out(self):\n if not self._out.closed:\n self._out.flush()\n return self._read(self._out)\n\n @property\n def err(self):\n if not self._err.closed:\n self._err.flush()\n return self._read(self._err)\n\n @property\n def handles(self):\n return self._out, self._err\n\n def cleanup(self):\n self._out.close()\n self._err.close()\n out_name = self._out.name\n err_name = self._err.name\n from datalad import cfg\n if cfg.getbool('datalad.log', 'outputs', default=False) \\\n and lgr.getEffectiveLevel() <= logging.DEBUG:\n for s, sname in ((self.out, 'stdout'),\n (self.err, 'stderr')):\n if s:\n pref = os.linesep + \"| \"\n lgr.debug(\"Swallowed %s:%s%s\", sname, pref, s.replace(os.linesep, pref))\n else:\n lgr.debug(\"Nothing was swallowed for %s\", sname)\n del self._out\n del self._err\n gc.collect()\n rmtemp(out_name)\n rmtemp(err_name)\n\n def fake_print(*args, **kwargs):\n sep = kwargs.pop('sep', ' ')\n end = kwargs.pop('end', '\\n')\n file = kwargs.pop('file', sys.stdout)\n\n if file in (oldout, olderr, sys.stdout, sys.stderr):\n # we mock\n try:\n sys.stdout.write(sep.join(args) + end)\n except UnicodeEncodeError as exc:\n lgr.error(\n \"Failed to write to mocked stdout, got %s, continue as it \"\n \"didn't happen\", exc)\n else:\n # must be some other file one -- leave it alone\n oldprint(*args, sep=sep, end=end, file=file)\n\n from .ui import ui\n # preserve -- they could have been mocked already\n oldprint = getattr(builtins, 'print')\n oldout, olderr = sys.stdout, sys.stderr\n olduiout = ui.out\n adapter = StringIOAdapter()\n\n try:\n sys.stdout, sys.stderr = adapter.handles\n ui.out = adapter.handles[0]\n setattr(builtins, 'print', fake_print)\n\n yield adapter\n finally:\n sys.stdout, sys.stderr, ui.out = oldout, olderr, olduiout\n setattr(builtins, 'print', oldprint)\n adapter.cleanup()\n\n\n@contextmanager\ndef swallow_logs(new_level=None, file_=None, name='datalad'):\n \"\"\"Context manager to consume all logs.\n\n \"\"\"\n lgr = logging.getLogger(name)\n\n # Keep old settings\n old_level = lgr.level\n old_handlers = lgr.handlers\n\n # Let's log everything into a string\n # TODO: generalize with the one for swallow_outputs\n class StringIOAdapter(object):\n \"\"\"Little adapter to help getting out values\n\n And to stay consistent with how swallow_outputs behaves\n \"\"\"\n def __init__(self):\n if file_ is None:\n kw = get_tempfile_kwargs({}, prefix=\"logs\")\n self._out = NamedTemporaryFile(mode='a', delete=False, **kw)\n else:\n out_file = file_\n # PY3 requires clearly one or another. race condition possible\n self._out = open(out_file, 'a')\n self._final_out = None\n\n def _read(self, h):\n with open(h.name) as f:\n return f.read()\n\n @property\n def out(self):\n if self._final_out is not None:\n # we closed and cleaned up already\n return self._final_out\n else:\n self._out.flush()\n return self._read(self._out)\n\n @property\n def lines(self):\n return self.out.split('\\n')\n\n @property\n def handle(self):\n return self._out\n\n def cleanup(self):\n # store for access while object exists\n self._final_out = self.out\n self._out.close()\n out_name = self._out.name\n del self._out\n gc.collect()\n if not file_:\n rmtemp(out_name)\n\n def assert_logged(self, msg=None, level=None, regex=True, **kwargs):\n \"\"\"Provide assertion on whether a msg was logged at a given level\n\n If neither `msg` nor `level` provided, checks if anything was logged\n at all.\n\n Parameters\n ----------\n msg: str, optional\n Message (as a regular expression, if `regex`) to be searched.\n If no msg provided, checks if anything was logged at a given level.\n level: str, optional\n String representing the level to be logged\n regex: bool, optional\n If False, regular `assert_in` is used\n **kwargs: str, optional\n Passed to `assert_re_in` or `assert_in`\n \"\"\"\n from datalad.tests.utils import assert_re_in\n from datalad.tests.utils import assert_in\n\n if regex:\n match = r'\\[%s\\] ' % level if level else r\"\\[\\S+\\] \"\n else:\n match = '[%s] ' % level if level else ''\n\n if msg:\n match += msg\n\n if match:\n (assert_re_in if regex else assert_in)(match, self.out, **kwargs)\n else:\n assert not kwargs, \"no kwargs to be passed anywhere\"\n assert self.out, \"Nothing was logged!?\"\n\n adapter = StringIOAdapter()\n # TODO: it does store messages but without any formatting, i.e. even without\n # date/time prefix etc. IMHO it should preserve formatting in case if file_ is\n # set\n swallow_handler = logging.StreamHandler(adapter.handle)\n # we want to log levelname so we could test against it\n swallow_handler.setFormatter(\n logging.Formatter('[%(levelname)s] %(message)s'))\n swallow_handler.filters = sum([h.filters for h in old_handlers],\n [])\n lgr.handlers = [swallow_handler]\n if old_level < logging.DEBUG: # so if HEAVYDEBUG etc -- show them!\n lgr.handlers += old_handlers\n\n if isinstance(new_level, str):\n new_level = getattr(logging, new_level)\n\n if new_level is not None:\n lgr.setLevel(new_level)\n\n try:\n yield adapter\n # TODO: if file_ and there was an exception -- most probably worth logging it?\n # although ideally it should be the next log outside added to that file_ ... oh well\n finally:\n lgr.handlers = old_handlers\n lgr.setLevel(old_level)\n adapter.cleanup()\n\n\n# TODO: May be melt in with swallow_logs at some point:\n@contextmanager\ndef disable_logger(logger=None):\n \"\"\"context manager to temporarily disable logging\n\n This is to provide one of swallow_logs' purposes without unnecessarily\n creating temp files (see gh-1865)\n\n Parameters\n ----------\n logger: Logger\n Logger whose handlers will be ordered to not log anything.\n Default: datalad's topmost Logger ('datalad')\n \"\"\"\n\n class NullFilter(logging.Filter):\n \"\"\"Filter class to reject all records\n \"\"\"\n def filter(self, record):\n return 0\n\n if logger is None:\n # default: all of datalad's logging:\n logger = logging.getLogger('datalad')\n\n filter_ = NullFilter(logger.name)\n [h.addFilter(filter_) for h in logger.handlers]\n\n try:\n yield logger\n finally:\n [h.removeFilter(filter_) for h in logger.handlers]\n\n\n#\n# Additional handlers\n#\n_sys_excepthook = sys.excepthook # Just in case we ever need original one\n\n\ndef setup_exceptionhook(ipython=False):\n \"\"\"Overloads default sys.excepthook with our exceptionhook handler.\n\n If interactive, our exceptionhook handler will invoke\n pdb.post_mortem; if not interactive, then invokes default handler.\n \"\"\"\n\n def _datalad_pdb_excepthook(type, value, tb):\n import traceback\n traceback.print_exception(type, value, tb)\n print()\n if is_interactive():\n import pdb\n pdb.post_mortem(tb)\n\n if ipython:\n from IPython.core import ultratb\n sys.excepthook = ultratb.FormattedTB(mode='Verbose',\n # color_scheme='Linux',\n call_pdb=is_interactive())\n else:\n sys.excepthook = _datalad_pdb_excepthook\n\n\ndef ensure_dir(*args):\n \"\"\"Make sure directory exists.\n\n Joins the list of arguments to an os-specific path to the desired\n directory and creates it, if it not exists yet.\n \"\"\"\n dirname = op.join(*args)\n if not exists(dirname):\n os.makedirs(dirname)\n return dirname\n\n\ndef updated(d, update):\n \"\"\"Return a copy of the input with the 'update'\n\n Primarily for updating dictionaries\n \"\"\"\n d = d.copy()\n d.update(update)\n return d\n\n\n_pwd_mode = None\n\n\ndef _switch_to_getcwd(msg, *args):\n global _pwd_mode\n _pwd_mode = 'cwd'\n lgr.debug(\n msg + \". From now on will be returning os.getcwd(). Directory\"\n \" symlinks in the paths will be resolved\",\n *args\n )\n # TODO: we might want to mitigate by going through all flywheighted\n # repos and tuning up their .paths to be resolved?\n\n\ndef getpwd():\n \"\"\"Try to return a CWD without dereferencing possible symlinks\n\n This function will try to use PWD environment variable to provide a current\n working directory, possibly with some directories along the path being\n symlinks to other directories. Unfortunately, PWD is used/set only by the\n shell and such functions as `os.chdir` and `os.getcwd` nohow use or modify\n it, thus `os.getcwd()` returns path with links dereferenced.\n\n While returning current working directory based on PWD env variable we\n verify that the directory is the same as `os.getcwd()` after resolving all\n symlinks. If that verification fails, we fall back to always use\n `os.getcwd()`.\n\n Initial decision to either use PWD env variable or os.getcwd() is done upon\n the first call of this function.\n \"\"\"\n global _pwd_mode\n if _pwd_mode is None:\n # we need to decide!\n try:\n pwd = os.environ['PWD']\n if on_windows and pwd and pwd.startswith('/'):\n # It should be a path from MSYS.\n # - it might start with a drive letter or not\n # - it seems to be \"illegal\" to have a single letter directories\n # under / path, i.e. if created - they aren't found\n # - 'ln -s' does not fail to create a \"symlink\" but it just\n # copies!\n # so we are not likely to need original PWD purpose on\n # those systems\n # Verdict:\n _pwd_mode = 'cwd'\n else:\n _pwd_mode = 'PWD'\n except KeyError:\n _pwd_mode = 'cwd'\n\n if _pwd_mode == 'cwd':\n return os.getcwd()\n elif _pwd_mode == 'PWD':\n try:\n cwd = os.getcwd()\n except OSError as exc:\n if \"o such file\" in str(exc):\n # directory was removed but we promised to be robust and\n # still report the path we might know since we are still in PWD\n # mode\n cwd = None\n else:\n raise\n try:\n pwd = os.environ['PWD']\n # do absolute() in addition to always get an absolute path\n # even with non-existing paths on windows\n pwd_real = str(Path(pwd).resolve().absolute())\n # This logic would fail to catch the case where chdir did happen\n # to the directory where current PWD is pointing to, e.g.\n # $> ls -ld $PWD\n # lrwxrwxrwx 1 yoh yoh 5 Oct 11 13:27 /home/yoh/.tmp/tmp -> /tmp//\n # hopa:~/.tmp/tmp\n # $> python -c 'import os; os.chdir(\"/tmp\"); from datalad.utils import getpwd; print(getpwd(), os.getcwd())'\n # ('/home/yoh/.tmp/tmp', '/tmp')\n # but I guess that should not be too harmful\n if cwd is not None and pwd_real != cwd:\n _switch_to_getcwd(\n \"realpath of PWD=%s is %s whenever os.getcwd()=%s\",\n pwd, pwd_real, cwd\n )\n return cwd\n return pwd\n except KeyError:\n _switch_to_getcwd(\"PWD env variable is no longer available\")\n return cwd # Must not happen, but may be someone\n # evil purges PWD from environ?\n else:\n raise RuntimeError(\n \"Must have not got here. \"\n \"pwd_mode must be either cwd or PWD. And it is now %r\" % (_pwd_mode,)\n )\n\n\nclass chpwd(object):\n \"\"\"Wrapper around os.chdir which also adjusts environ['PWD']\n\n The reason is that otherwise PWD is simply inherited from the shell\n and we have no ability to assess directory path without dereferencing\n symlinks.\n\n If used as a context manager it allows to temporarily change directory\n to the given path\n \"\"\"\n def __init__(self, path, mkdir=False, logsuffix=''):\n\n if path:\n pwd = getpwd()\n self._prev_pwd = pwd\n else:\n self._prev_pwd = None\n return\n\n if not isabs(path):\n path = normpath(op.join(pwd, path))\n if not os.path.exists(path) and mkdir:\n self._mkdir = True\n os.mkdir(path)\n else:\n self._mkdir = False\n lgr.debug(\"chdir %r -> %r %s\", self._prev_pwd, path, logsuffix)\n os.chdir(path) # for grep people -- ok, to chdir here!\n os.environ['PWD'] = str(path)\n\n def __enter__(self):\n # nothing more to do really, chdir was in the constructor\n pass\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n if self._prev_pwd:\n # Need to use self.__class__ so this instance, if the entire\n # thing mocked during the test, still would use correct chpwd\n self.__class__(self._prev_pwd, logsuffix=\"(coming back)\")\n\n\ndef dlabspath(path, norm=False):\n \"\"\"Symlinks-in-the-cwd aware abspath\n\n os.path.abspath relies on os.getcwd() which would not know about symlinks\n in the path\n\n TODO: we might want to norm=True by default to match behavior of\n os .path.abspath?\n \"\"\"\n if not isabs(path):\n # if not absolute -- relative to pwd\n path = op.join(getpwd(), path)\n return normpath(path) if norm else path\n\n\ndef with_pathsep(path):\n \"\"\"Little helper to guarantee that path ends with /\"\"\"\n return path + sep if not path.endswith(sep) else path\n\n\ndef get_path_prefix(path, pwd=None):\n \"\"\"Get path prefix (for current directory)\n\n Returns relative path to the topdir, if we are under topdir, and if not\n absolute path to topdir. If `pwd` is not specified - current directory\n assumed\n \"\"\"\n pwd = pwd or getpwd()\n path = dlabspath(path)\n path_ = with_pathsep(path)\n pwd_ = with_pathsep(pwd)\n common = commonprefix((path_, pwd_))\n if common.endswith(sep) and common in {path_, pwd_}:\n # we are in subdir or above the path = use relative path\n location_prefix = relpath(path, pwd)\n # if benign \"here\" - cut off\n if location_prefix in (curdir, curdir + sep):\n location_prefix = ''\n return location_prefix\n else:\n # just return absolute path\n return path\n\n\ndef _get_normalized_paths(path, prefix):\n if isabs(path) != isabs(prefix):\n raise ValueError(\"Both paths must either be absolute or relative. \"\n \"Got %r and %r\" % (path, prefix))\n path = with_pathsep(path)\n prefix = with_pathsep(prefix)\n return path, prefix\n\n\ndef path_startswith(path, prefix):\n \"\"\"Return True if path starts with prefix path\n\n Parameters\n ----------\n path: str\n prefix: str\n \"\"\"\n path, prefix = _get_normalized_paths(path, prefix)\n return path.startswith(prefix)\n\n\ndef path_is_subpath(path, prefix):\n \"\"\"Return True if path is a subpath of prefix\n\n It will return False if path == prefix.\n\n Parameters\n ----------\n path: str\n prefix: str\n \"\"\"\n path, prefix = _get_normalized_paths(path, prefix)\n return (len(prefix) < len(path)) and path.startswith(prefix)\n\n\ndef knows_annex(path):\n \"\"\"Returns whether at a given path there is information about an annex\n\n It is just a thin wrapper around GitRepo.is_with_annex() classmethod\n which also checks for `path` to exist first.\n\n This includes actually present annexes, but also uninitialized ones, or\n even the presence of a remote annex branch.\n \"\"\"\n from os.path import exists\n if not exists(path):\n lgr.debug(\"No annex: test path {0} doesn't exist\".format(path))\n return False\n from datalad.support.gitrepo import GitRepo\n return GitRepo(path, init=False, create=False).is_with_annex()\n\n\n@contextmanager\ndef make_tempfile(content=None, wrapped=None, **tkwargs):\n \"\"\"Helper class to provide a temporary file name and remove it at the end (context manager)\n\n Parameters\n ----------\n mkdir : bool, optional (default: False)\n If True, temporary directory created using tempfile.mkdtemp()\n content : str or bytes, optional\n Content to be stored in the file created\n wrapped : function, optional\n If set, function name used to prefix temporary file name\n `**tkwargs`:\n All other arguments are passed into the call to tempfile.mk{,d}temp(),\n and resultant temporary filename is passed as the first argument into\n the function t. If no 'prefix' argument is provided, it will be\n constructed using module and function names ('.' replaced with\n '_').\n\n To change the used directory without providing keyword argument 'dir' set\n DATALAD_TESTS_TEMP_DIR.\n\n Examples\n --------\n >>> from os.path import exists\n >>> from datalad.utils import make_tempfile\n >>> with make_tempfile() as fname:\n ... k = open(fname, 'w').write('silly test')\n >>> assert not exists(fname) # was removed\n\n >>> with make_tempfile(content=\"blah\") as fname:\n ... assert open(fname).read() == \"blah\"\n \"\"\"\n\n if tkwargs.get('mkdir', None) and content is not None:\n raise ValueError(\"mkdir=True while providing content makes no sense\")\n\n tkwargs_ = get_tempfile_kwargs(tkwargs, wrapped=wrapped)\n\n # if DATALAD_TESTS_TEMP_DIR is set, use that as directory,\n # let mktemp handle it otherwise. However, an explicitly provided\n # dir=... will override this.\n mkdir = tkwargs_.pop('mkdir', False)\n\n filename = {False: tempfile.mktemp,\n True: tempfile.mkdtemp}[mkdir](**tkwargs_)\n # MIH: not clear to me why we need to perform this (possibly expensive)\n # resolve. It was already part of the original implementation\n # 008d9ab8cc3e0170c0a9b8479e80dee9ffe6eb7f\n filename = Path(filename).resolve()\n\n if content:\n (filename.write_bytes\n if isinstance(content, bytes)\n else filename.write_text)(content)\n\n # TODO globbing below can also be done with pathlib\n filename = str(filename)\n\n if __debug__:\n lgr.debug(\n 'Created temporary %s named %s',\n 'directory' if mkdir else 'file',\n filename)\n try:\n yield filename\n finally:\n # glob here for all files with the same name (-suffix)\n # would be useful whenever we requested .img filename,\n # and function creates .hdr as well\n # MIH: this is undocumented behavior, and undesired in the general\n # case. it should be made conditional and explicit\n lsuffix = len(tkwargs_.get('suffix', ''))\n filename_ = lsuffix and filename[:-lsuffix] or filename\n filenames = glob.glob(filename_ + '*')\n if len(filename_) < 3 or len(filenames) > 5:\n # For paranoid yoh who stepped into this already ones ;-)\n lgr.warning(\"It is unlikely that it was intended to remove all\"\n \" files matching %r. Skipping\" % filename_)\n return\n for f in filenames:\n try:\n rmtemp(f)\n except OSError: # pragma: no cover\n pass\n\n\ndef _path_(*p):\n \"\"\"Given a path in POSIX\" notation, regenerate one in native to the env one\"\"\"\n if on_windows:\n return op.join(*map(lambda x: op.join(*x.split('/')), p))\n else:\n # Assume that all others as POSIX compliant so nothing to be done\n return op.join(*p)\n\n\ndef get_timestamp_suffix(time_=None, prefix='-'):\n \"\"\"Return a time stamp (full date and time up to second)\n\n primarily to be used for generation of log files names\n \"\"\"\n args = []\n if time_ is not None:\n if isinstance(time_, int):\n time_ = time.gmtime(time_)\n args.append(time_)\n return time.strftime(prefix + TIMESTAMP_FMT, *args)\n\n\n# unused in -core\ndef get_logfilename(dspath, cmd='datalad'):\n \"\"\"Return a filename to use for logging under a dataset/repository\n\n directory would be created if doesn't exist, but dspath must exist\n and be a directory\n \"\"\"\n assert(exists(dspath))\n assert(isdir(dspath))\n ds_logdir = ensure_dir(dspath, '.git', 'datalad', 'logs') # TODO: use WEB_META_LOG whenever #789 merged\n return op.join(ds_logdir, 'crawl-%s.log' % get_timestamp_suffix())\n\n\ndef get_trace(edges, start, end, trace=None):\n \"\"\"Return the trace/path to reach a node in a tree.\n\n Parameters\n ----------\n edges : sequence(2-tuple)\n The tree given by a sequence of edges (parent, child) tuples. The\n nodes can be identified by any value and data type that supports\n the '==' operation.\n start :\n Identifier of the start node. Must be present as a value in the parent\n location of an edge tuple in order to be found.\n end :\n Identifier of the target/end node. Must be present as a value in the child\n location of an edge tuple in order to be found.\n trace : list\n Mostly useful for recursive calls, and used internally.\n\n Returns\n -------\n None or list\n Returns a list with the trace to the target (the starts and the target\n are not included in the trace, hence if start and end are directly connected\n an empty list is returned), or None when no trace to the target can be found,\n or start and end are identical.\n \"\"\"\n # the term trace is used to avoid confusion with a path in the sense\n # of a filesystem path, but the analogy fits and nodes can be paths\n if trace is None:\n trace = []\n if not edges:\n raise ValueError(\"no edges given\")\n for cand in edges:\n cand_super, cand_sub = cand\n if cand_sub in trace:\n # only DAGs, skip any cyclic traces\n continue\n if trace and cand_super != trace[-1]:\n # only consider edges that lead off the end of the trace\n continue\n if not trace and cand_super != start:\n # we got nothing yet, and this edges is not matching the start\n continue\n if cand_sub == end:\n return trace\n # dive into potential subnodes\n cand_trace = get_trace(\n edges,\n start,\n end,\n trace + [cand_sub])\n if cand_trace:\n return cand_trace\n return None\n\n\ndef get_dataset_root(path):\n \"\"\"Return the root of an existent dataset containing a given path\n\n The root path is returned in the same absolute or relative form\n as the input argument. If no associated dataset exists, or the\n input path doesn't exist, None is returned.\n\n If `path` is a symlink or something other than a directory, its\n the root dataset containing its parent directory will be reported.\n If none can be found, at a symlink at `path` is pointing to a\n dataset, `path` itself will be reported as the root.\n\n Parameters\n ----------\n path : Path-like\n\n Returns\n -------\n str or None\n \"\"\"\n path = str(path)\n suffix = '.git'\n altered = None\n if islink(path) or not isdir(path):\n altered = path\n path = dirname(path)\n apath = abspath(path)\n # while we can still go up\n while split(apath)[1]:\n if exists(op.join(path, suffix)):\n return path\n # new test path in the format we got it\n path = normpath(op.join(path, os.pardir))\n # no luck, next round\n apath = abspath(path)\n # if we applied dirname() at the top, we give it another go with\n # the actual path, if it was itself a symlink, it could be the\n # top-level dataset itself\n if altered and exists(op.join(altered, suffix)):\n return altered\n\n return None\n\n\n# ATM used in datalad_crawler extension, so do not remove yet\ndef try_multiple(ntrials, exception, base, f, *args, **kwargs):\n \"\"\"Call f multiple times making exponentially growing delay between the calls\"\"\"\n for trial in range(1, ntrials+1):\n try:\n return f(*args, **kwargs)\n except exception as exc:\n if trial == ntrials:\n raise # just reraise on the last trial\n t = base ** trial\n lgr.warning(\"Caught %s on trial #%d. Sleeping %f and retrying\",\n CapturedException(exc), trial, t)\n sleep(t)\n\n\n@optional_args\ndef try_multiple_dec(\n f, ntrials=None, duration=0.1, exceptions=None, increment_type=None,\n exceptions_filter=None,\n logger=None,\n):\n \"\"\"Decorator to try function multiple times.\n\n Main purpose is to decorate functions dealing with removal of files/directories\n and which might need a few seconds to work correctly on Windows which takes\n its time to release files/directories.\n\n Parameters\n ----------\n ntrials: int, optional\n duration: float, optional\n Seconds to sleep before retrying.\n increment_type: {None, 'exponential'}\n Note that if it is exponential, duration should typically be > 1.0\n so it grows with higher power\n exceptions: Exception or tuple of Exceptions, optional\n Exception or a tuple of multiple exceptions, on which to retry\n exceptions_filter: callable, optional\n If provided, this function will be called with a caught exception\n instance. If function returns True - we will re-try, if False - exception\n will be re-raised without retrying.\n logger: callable, optional\n Logger to log upon failure. If not provided, will use stock logger\n at the level of 5 (heavy debug).\n \"\"\"\n if not exceptions:\n exceptions = (OSError, WindowsError, PermissionError) \\\n if on_windows else OSError\n if not ntrials:\n # Life goes fast on proper systems, no need to delay it much\n ntrials = 100 if on_windows else 10\n if logger is None:\n def logger(*args, **kwargs):\n return lgr.log(5, *args, **kwargs)\n assert increment_type in {None, 'exponential'}\n\n @wraps(f)\n def _wrap_try_multiple_dec(*args, **kwargs):\n t = duration\n for trial in range(ntrials):\n try:\n return f(*args, **kwargs)\n except exceptions as exc:\n if exceptions_filter and not exceptions_filter(exc):\n raise\n if trial < ntrials - 1:\n if increment_type == 'exponential':\n t = duration ** (trial + 1)\n logger(\n \"Caught %s on trial #%d. Sleeping %f and retrying\",\n CapturedException(exc), trial, t)\n sleep(t)\n else:\n raise\n\n return _wrap_try_multiple_dec\n\n\n@try_multiple_dec\ndef unlink(f):\n \"\"\"'Robust' unlink. Would try multiple times\n\n On windows boxes there is evidence for a latency of more than a second\n until a file is considered no longer \"in-use\".\n WindowsError is not known on Linux, and if IOError or any other\n exception\n is thrown then if except statement has WindowsError in it -- NameError\n also see gh-2533\n \"\"\"\n # Check for open files\n assert_no_open_files(f)\n return os.unlink(f)\n\n\n@try_multiple_dec\ndef _rmtree(*args, **kwargs):\n \"\"\"Just a helper to decorate shutil.rmtree.\n\n rmtree defined above does more and ideally should not itself be decorated\n since a recursive definition and does checks for open files inside etc -\n might be too runtime expensive\n \"\"\"\n return shutil.rmtree(*args, **kwargs)\n\n\ndef slash_join(base, extension):\n \"\"\"Join two strings with a '/', avoiding duplicate slashes\n\n If any of the strings is None the other is returned as is.\n \"\"\"\n if extension is None:\n return base\n if base is None:\n return extension\n return '/'.join(\n (base.rstrip('/'),\n extension.lstrip('/')))\n\n\n#\n# IO Helpers\n#\n\n# unused in -core\ndef open_r_encdetect(fname, readahead=1000):\n \"\"\"Return a file object in read mode with auto-detected encoding\n\n This is helpful when dealing with files of unknown encoding.\n\n Parameters\n ----------\n readahead: int, optional\n How many bytes to read for guessing the encoding type. If\n negative - full file will be read\n \"\"\"\n from chardet import detect\n import io\n # read some bytes from the file\n with open(fname, 'rb') as f:\n head = f.read(readahead)\n enc = detect(head)\n denc = enc.get('encoding', None)\n lgr.debug(\"Auto-detected encoding %s for file %s (confidence: %s)\",\n denc,\n fname,\n enc.get('confidence', 'unknown'))\n return io.open(fname, encoding=denc)\n\n\ndef read_file(fname, decode=True):\n \"\"\"A helper to read file passing content via ensure_unicode\n\n Parameters\n ----------\n decode: bool, optional\n if False, no ensure_unicode and file content returned as bytes\n \"\"\"\n with open(fname, 'rb') as f:\n content = f.read()\n return ensure_unicode(content) if decode else content\n\n\ndef read_csv_lines(fname, dialect=None, readahead=16384, **kwargs):\n \"\"\"A generator of dict records from a CSV/TSV\n\n Automatically guesses the encoding for each record to convert to UTF-8\n\n Parameters\n ----------\n fname: str\n Filename\n dialect: str, optional\n Dialect to specify to csv.reader. If not specified -- guessed from\n the file, if fails to guess, \"excel-tab\" is assumed\n readahead: int, optional\n How many bytes to read from the file to guess the type\n **kwargs\n Passed to `csv.reader`\n \"\"\"\n import csv\n if dialect is None:\n with open(fname) as tsvfile:\n # add robustness, use a sniffer\n try:\n dialect = csv.Sniffer().sniff(tsvfile.read(readahead))\n except Exception as exc:\n lgr.warning(\n 'Could not determine file-format, assuming TSV: %s',\n CapturedException(exc)\n )\n dialect = 'excel-tab'\n\n kw = dict(encoding='utf-8')\n with open(fname, 'r', **kw) as tsvfile:\n # csv.py doesn't do Unicode; encode temporarily as UTF-8:\n csv_reader = csv.reader(\n tsvfile,\n dialect=dialect,\n **kwargs\n )\n header = None\n for row in csv_reader:\n # decode UTF-8 back to Unicode, cell by cell:\n row_unicode = map(ensure_unicode, row)\n if header is None:\n header = list(row_unicode)\n else:\n yield dict(zip(header, row_unicode))\n\n\ndef import_modules(modnames, pkg, msg=\"Failed to import {module}\", log=lgr.debug):\n \"\"\"Helper to import a list of modules without failing if N/A\n\n Parameters\n ----------\n modnames: list of str\n List of module names to import\n pkg: str\n Package under which to import\n msg: str, optional\n Message template for .format() to log at DEBUG level if import fails.\n Keys {module} and {package} will be provided and ': {exception}' appended\n log: callable, optional\n Logger call to use for logging messages\n \"\"\"\n from importlib import import_module\n _globals = globals()\n mods_loaded = []\n if pkg and not pkg in sys.modules:\n # with python 3.5.1 (ok with 3.5.5) somehow kept running into\n # Failed to import dlsub1: Parent module 'dltestm1' not loaded\n # while running the test. Preloading pkg resolved the issue\n import_module(pkg)\n for modname in modnames:\n try:\n _globals[modname] = mod = import_module(\n '.{}'.format(modname),\n pkg)\n mods_loaded.append(mod)\n except Exception as exc:\n from datalad.support.exceptions import CapturedException\n ce = CapturedException(exc)\n log((msg + ': {exception}').format(\n module=modname, package=pkg, exception=ce.message))\n return mods_loaded\n\n\ndef import_module_from_file(modpath, pkg=None, log=lgr.debug):\n \"\"\"Import provided module given a path\n\n TODO:\n - RF/make use of it in pipeline.py which has similar logic\n - join with import_modules above?\n\n Parameters\n ----------\n pkg: module, optional\n If provided, and modpath is under pkg.__path__, relative import will be\n used\n \"\"\"\n assert(modpath.endswith('.py')) # for now just for .py files\n\n log(\"Importing %s\" % modpath)\n\n modname = basename(modpath)[:-3]\n relmodpath = None\n if pkg:\n for pkgpath in pkg.__path__:\n if path_is_subpath(modpath, pkgpath):\n # for now relying on having .py extension -- assertion above\n relmodpath = '.' + relpath(modpath[:-3], pkgpath).replace(sep, '.')\n break\n\n try:\n if relmodpath:\n from importlib import import_module\n mod = import_module(relmodpath, pkg.__name__)\n else:\n dirname_ = dirname(modpath)\n try:\n sys.path.insert(0, dirname_)\n mod = __import__(modname, level=0)\n finally:\n if dirname_ in sys.path:\n sys.path.pop(sys.path.index(dirname_))\n else:\n log(\"Expected path %s to be within sys.path, but it was gone!\" % dirname_)\n except Exception as e:\n raise RuntimeError(\n \"Failed to import module from %s\" % modpath) from e\n\n return mod\n\n\ndef get_encoding_info():\n \"\"\"Return a dictionary with various encoding/locale information\"\"\"\n import sys, locale\n from collections import OrderedDict\n return OrderedDict([\n ('default', sys.getdefaultencoding()),\n ('filesystem', sys.getfilesystemencoding()),\n ('locale.prefered', locale.getpreferredencoding()),\n ])\n\n\ndef get_envvars_info():\n from collections import OrderedDict\n envs = []\n for var, val in os.environ.items():\n if (\n var.startswith('PYTHON') or\n var.startswith('LC_') or\n var.startswith('GIT_') or\n var in ('LANG', 'LANGUAGE', 'PATH')\n ):\n envs.append((var, val))\n return OrderedDict(envs)\n\n\n# This class is modified from Snakemake (v5.1.4)\nclass SequenceFormatter(string.Formatter):\n \"\"\"string.Formatter subclass with special behavior for sequences.\n\n This class delegates formatting of individual elements to another\n formatter object. Non-list objects are formatted by calling the\n delegate formatter's \"format_field\" method. List-like objects\n (list, tuple, set, frozenset) are formatted by formatting each\n element of the list according to the specified format spec using\n the delegate formatter and then joining the resulting strings with\n a separator (space by default).\n \"\"\"\n\n def __init__(self, separator=\" \", element_formatter=string.Formatter(),\n *args, **kwargs):\n self.separator = separator\n self.element_formatter = element_formatter\n\n def format_element(self, elem, format_spec):\n \"\"\"Format a single element\n\n For sequences, this is called once for each element in a\n sequence. For anything else, it is called on the entire\n object. It is intended to be overridden in subclases.\n \"\"\"\n return self.element_formatter.format_field(elem, format_spec)\n\n def format_field(self, value, format_spec):\n if isinstance(value, (list, tuple, set, frozenset)):\n return self.separator.join(self.format_element(v, format_spec)\n for v in value)\n else:\n return self.format_element(value, format_spec)\n\n\n# TODO: eventually we might want to make use of attr module\nclass File(object):\n \"\"\"Helper for a file entry in the create_tree/@with_tree\n\n It allows to define additional settings for entries\n \"\"\"\n def __init__(self, name, executable=False):\n \"\"\"\n\n Parameters\n ----------\n name : str\n Name of the file\n executable: bool, optional\n Make it executable\n \"\"\"\n self.name = name\n self.executable = executable\n\n def __str__(self):\n return self.name\n\n\ndef create_tree_archive(path, name, load, overwrite=False, archives_leading_dir=True):\n \"\"\"Given an archive `name`, create under `path` with specified `load` tree\n \"\"\"\n from datalad.support.archives import compress_files\n dirname = file_basename(name)\n full_dirname = op.join(path, dirname)\n os.makedirs(full_dirname)\n create_tree(full_dirname, load, archives_leading_dir=archives_leading_dir)\n # create archive\n if archives_leading_dir:\n compress_files([dirname], name, path=path, overwrite=overwrite)\n else:\n compress_files(list(map(basename, glob.glob(op.join(full_dirname, '*')))),\n op.join(pardir, name),\n path=op.join(path, dirname),\n overwrite=overwrite)\n # remove original tree\n rmtree(full_dirname)\n\n\ndef create_tree(path, tree, archives_leading_dir=True, remove_existing=False):\n \"\"\"Given a list of tuples (name, load) create such a tree\n\n if load is a tuple itself -- that would create either a subtree or an archive\n with that content and place it into the tree if name ends with .tar.gz\n \"\"\"\n lgr.log(5, \"Creating a tree under %s\", path)\n if not exists(path):\n os.makedirs(path)\n\n if isinstance(tree, dict):\n tree = tree.items()\n\n for file_, load in tree:\n if isinstance(file_, File):\n executable = file_.executable\n name = file_.name\n else:\n executable = False\n name = file_\n full_name = op.join(path, name)\n if remove_existing and lexists(full_name):\n rmtree(full_name, chmod_files=True)\n if isinstance(load, (tuple, list, dict)):\n if name.endswith('.tar.gz') or name.endswith('.tar') or name.endswith('.zip'):\n create_tree_archive(\n path, name, load,\n archives_leading_dir=archives_leading_dir)\n else:\n create_tree(\n full_name, load,\n archives_leading_dir=archives_leading_dir,\n remove_existing=remove_existing)\n else:\n open_func = open\n if full_name.endswith('.gz'):\n open_func = gzip.open\n elif full_name.split('.')[-1] in ('xz', 'lzma'):\n import lzma\n open_func = lzma.open\n with open_func(full_name, \"wb\") as f:\n f.write(ensure_bytes(load, 'utf-8'))\n if executable:\n os.chmod(full_name, os.stat(full_name).st_mode | stat.S_IEXEC)\n\n\ndef get_suggestions_msg(values, known, sep=\"\\n \"):\n \"\"\"Return a formatted string with suggestions for values given the known ones\n \"\"\"\n import difflib\n suggestions = []\n for value in ensure_list(values): # might not want to do it if we change presentation below\n suggestions += difflib.get_close_matches(value, known)\n suggestions = unique(suggestions)\n msg = \"Did you mean any of these?\"\n if suggestions:\n if '\\n' in sep:\n # if separator includes new line - we add entire separator right away\n msg += sep\n else:\n msg += ' '\n return msg + \"%s\\n\" % sep.join(suggestions)\n return ''\n\n\ndef bytes2human(n, format='%(value).1f %(symbol)sB'):\n \"\"\"\n Convert n bytes into a human readable string based on format.\n symbols can be either \"customary\", \"customary_ext\", \"iec\" or \"iec_ext\",\n see: http://goo.gl/kTQMs\n\n >>> from datalad.utils import bytes2human\n >>> bytes2human(1)\n '1.0 B'\n >>> bytes2human(1024)\n '1.0 KB'\n >>> bytes2human(1048576)\n '1.0 MB'\n >>> bytes2human(1099511627776127398123789121)\n '909.5 YB'\n\n >>> bytes2human(10000, \"%(value).1f %(symbol)s/sec\")\n '9.8 K/sec'\n\n >>> # precision can be adjusted by playing with %f operator\n >>> bytes2human(10000, format=\"%(value).5f %(symbol)s\")\n '9.76562 K'\n\n Taken from: http://goo.gl/kTQMs and subsequently simplified\n Original Author: Giampaolo Rodola' <g.rodola [AT] gmail [DOT] com>\n License: MIT\n \"\"\"\n n = int(n)\n if n < 0:\n raise ValueError(\"n < 0\")\n symbols = ('', 'K', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y')\n prefix = {}\n for i, s in enumerate(symbols[1:]):\n prefix[s] = 1 << (i + 1) * 10\n for symbol in reversed(symbols[1:]):\n if n >= prefix[symbol]:\n value = float(n) / prefix[symbol]\n return format % locals()\n return format % dict(symbol=symbols[0], value=n)\n\n\ndef quote_cmdlinearg(arg):\n \"\"\"Perform platform-appropriate argument quoting\"\"\"\n # https://stackoverflow.com/a/15262019\n return '\"{}\"'.format(\n arg.replace('\"', '\"\"')\n ) if on_windows else shlex_quote(arg)\n\n\ndef guard_for_format(arg):\n \"\"\"Replace { and } with {{ and }}\n\n To be used in cases if arg is not expected to have provided\n by user .format() placeholders, but 'arg' might become a part\n of a composite passed to .format(), e.g. via 'Run'\n \"\"\"\n return arg.replace('{', '{{').replace('}', '}}')\n\n\ndef join_cmdline(args):\n \"\"\"Join command line args into a string using quote_cmdlinearg\n \"\"\"\n return ' '.join(map(quote_cmdlinearg, args))\n\n\ndef split_cmdline(s):\n \"\"\"Perform platform-appropriate command line splitting.\n\n Identical to `shlex.split()` on non-windows platforms.\n\n Modified from https://stackoverflow.com/a/35900070\n \"\"\"\n if not on_windows:\n return shlex_split(s)\n\n # the rest is for windows\n RE_CMD_LEX = r'''\"((?:\"\"|\\\\[\"\\\\]|[^\"])*)\"?()|(\\\\\\\\(?=\\\\*\")|\\\\\")|(&&?|\\|\\|?|\\d?>|[<])|([^\\s\"&|<>]+)|(\\s+)|(.)'''\n\n args = []\n accu = None # collects pieces of one arg\n for qs, qss, esc, pipe, word, white, fail in re.findall(RE_CMD_LEX, s):\n if word:\n pass # most frequent\n elif esc:\n word = esc[1]\n elif white or pipe:\n if accu is not None:\n args.append(accu)\n if pipe:\n args.append(pipe)\n accu = None\n continue\n elif fail:\n raise ValueError(\"invalid or incomplete shell string\")\n elif qs:\n word = qs.replace('\\\\\"', '\"').replace('\\\\\\\\', '\\\\')\n if platform == 0:\n word = word.replace('\"\"', '\"')\n else:\n word = qss # may be even empty; must be last\n\n accu = (accu or '') + word\n\n if accu is not None:\n args.append(accu)\n\n return args\n\n\ndef get_wrapped_class(wrapped):\n \"\"\"Determine the command class a wrapped __call__ belongs to\"\"\"\n mod = sys.modules[wrapped.__module__]\n command_class_name = wrapped.__qualname__.split('.')[-2]\n _func_class = mod.__dict__[command_class_name]\n lgr.debug(\"Determined class of decorated function: %s\", _func_class)\n return _func_class\n\n\ndef _make_assure_kludge(fn):\n old_name = fn.__name__.replace(\"ensure\", \"assure\")\n\n @wraps(fn)\n def compat_fn(*args, **kwargs):\n warnings.warn(\n \"{} is deprecated and will be removed in a future release. \"\n \"Use {} instead.\"\n .format(old_name, fn.__name__),\n DeprecationWarning)\n return fn(*args, **kwargs)\n\n compat_fn.__doc__ = (\"Note: This function is deprecated. Use {} instead.\"\n .format(fn.__name__))\n return compat_fn\n\n\nassure_tuple_or_list = _make_assure_kludge(ensure_tuple_or_list)\nassure_iter = _make_assure_kludge(ensure_iter)\nassure_list = _make_assure_kludge(ensure_list)\nassure_list_from_str = _make_assure_kludge(ensure_list_from_str)\nassure_dict_from_str = _make_assure_kludge(ensure_dict_from_str)\nassure_bytes = _make_assure_kludge(ensure_bytes)\nassure_unicode = _make_assure_kludge(ensure_unicode)\nassure_bool = _make_assure_kludge(ensure_bool)\nassure_dir = _make_assure_kludge(ensure_dir)\n\n\nlgr.log(5, \"Done importing datalad.utils\")\n\n\ndef check_symlink_capability(path, target):\n \"\"\"helper similar to datalad.tests.utils.has_symlink_capability\n\n However, for use in a datalad command context, we shouldn't\n assume to be able to write to tmpfile and also not import a whole lot from\n datalad's test machinery. Finally, we want to know, whether we can create a\n symlink at a specific location, not just somewhere. Therefore use\n arbitrary path to test-build a symlink and delete afterwards. Suitable\n location can therefore be determined by high lever code.\n\n Parameters\n ----------\n path: Path\n target: Path\n\n Returns\n -------\n bool\n \"\"\"\n\n try:\n target.touch()\n path.symlink_to(target)\n return True\n except Exception:\n return False\n finally:\n if path.exists():\n path.unlink()\n if target.exists():\n target.unlink()\n"}}},{"rowIdx":99,"cells":{"function_name":{"kind":"string","value":"unique"},"docstring":{"kind":"string","value":"Given a sequence return a list only with unique elements while maintaining order\n\nThis is the fastest solution. See\nhttps://www.peterbe.com/plog/uniqifiers-benchmark\nand\nhttp://stackoverflow.com/a/480227/1265472\nfor more information.\nEnhancement -- added ability to compare for uniqueness using a key function\n\nParameters\n----------\nseq:\n Sequence to analyze\nkey: callable, optional\n Function to call on each element so we could decide not on a full\n element, but on its member etc\nreverse: bool, optional\n If True, uniqueness checked in the reverse order, so that the later ones\n will take the order"},"masked_code":{"kind":"string","value":"# emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-\n# ex: set sts=4 ts=4 sw=4 et:\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##\n#\n# See COPYING file distributed along with the datalad package for the\n# copyright and license terms.\n#\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##\n\nimport collections\nfrom collections.abc import Callable\nimport re\nimport builtins\nimport time\n\nimport logging\nimport shutil\nimport os\nimport sys\nimport tempfile\nfrom tempfile import NamedTemporaryFile\nimport platform\nimport gc\nimport glob\nimport gzip\nimport stat\nimport string\nimport warnings\n\nimport os.path as op\n\nfrom copy import copy as shallow_copy\nfrom contextlib import contextmanager\nfrom functools import (\n lru_cache,\n wraps,\n)\nfrom time import sleep\nimport inspect\nfrom itertools import tee\n# this import is required because other modules import opj from here.\nfrom os.path import join as opj\nfrom os.path import (\n abspath,\n basename,\n commonprefix,\n curdir,\n dirname,\n exists,\n expanduser,\n expandvars,\n isabs,\n isdir,\n islink,\n lexists,\n normpath,\n pardir,\n relpath,\n sep,\n split,\n splitdrive\n)\n\nimport posixpath\n\nfrom shlex import (\n quote as shlex_quote,\n split as shlex_split,\n)\n\n# from datalad.dochelpers import get_docstring_split\nfrom datalad.consts import TIMESTAMP_FMT\nfrom datalad.support.exceptions import CapturedException\n\n\nunicode_srctypes = str, bytes\n\n\nlgr = logging.getLogger(\"datalad.utils\")\n\nlgr.log(5, \"Importing datalad.utils\")\n#\n# Some useful variables\n#\nplatform_system = platform.system().lower()\non_windows = platform_system == 'windows'\non_osx = platform_system == 'darwin'\non_linux = platform_system == 'linux'\non_msys_tainted_paths = on_windows \\\n and 'MSYS_NO_PATHCONV' not in os.environ \\\n and os.environ.get('MSYSTEM', '')[:4] in ('MSYS', 'MING')\n\n\n# Takes ~200msec, so should not be called at import time\n@lru_cache() # output should not change through life time of datalad process\ndef get_linux_distribution():\n \"\"\"Compatibility wrapper for {platform,distro}.linux_distribution().\n \"\"\"\n if hasattr(platform, \"linux_distribution\"):\n # Use deprecated (but faster) method if it's available.\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n result = platform.linux_distribution()\n else:\n import distro # We require this for Python 3.8 and above.\n result = distro.linux_distribution(full_distribution_name=False)\n return result\n\n\n# Those weren't used for any critical decision making, thus we just set them to None\n# Use get_linux_distribution() directly where needed\nlinux_distribution_name = linux_distribution_release = None\n\n# Maximal length of cmdline string\n# Query the system and use hardcoded \"knowledge\" if None\n# probably getconf ARG_MAX might not be available\n# The last one would be the most conservative/Windows\nCMD_MAX_ARG_HARDCODED = 2097152 if on_linux else 262144 if on_osx else 32767\ntry:\n CMD_MAX_ARG = os.sysconf('SC_ARG_MAX')\n assert CMD_MAX_ARG > 0\n if CMD_MAX_ARG > CMD_MAX_ARG_HARDCODED * 1e6:\n # workaround for some kind of a bug which comes up with python 3.4\n # see https://github.com/datalad/datalad/issues/3150\n # or on older CentOS with conda and python as new as 3.9\n # see https://github.com/datalad/datalad/issues/5943\n # TODO: let Yarik know that the world is a paradise now whenever 1e6\n # is not large enough\n CMD_MAX_ARG = min(CMD_MAX_ARG, CMD_MAX_ARG_HARDCODED)\nexcept Exception as exc:\n # ATM (20181005) SC_ARG_MAX available only on POSIX systems\n # so exception would be thrown e.g. on Windows, or\n # somehow during Debian build for nd14.04 it is coming up with -1:\n # https://github.com/datalad/datalad/issues/3015\n CMD_MAX_ARG = CMD_MAX_ARG_HARDCODED\n lgr.debug(\n \"Failed to query or got useless SC_ARG_MAX sysconf, \"\n \"will use hardcoded value: %s\", exc)\n# Even with all careful computations we do, due to necessity to account for\n# environment and what not, we still could not figure out \"exact\" way to\n# estimate it, but it was shown that 300k safety margin on linux was sufficient.\n# https://github.com/datalad/datalad/pull/2977#issuecomment-436264710\n# 300k is ~15%, so to be safe, and for paranoid us we will just use up to 50%\n# of the length for \"safety margin\". We might probably still blow due to\n# env vars, unicode, etc... so any hard limit imho is not a proper solution\nCMD_MAX_ARG = int(0.5 * CMD_MAX_ARG)\nlgr.debug(\n \"Maximal length of cmdline string (adjusted for safety margin): %d\",\n CMD_MAX_ARG)\n\n#\n# Little helpers\n#\n\n# `getargspec` has been deprecated in Python 3.\nArgSpecFake = collections.namedtuple(\n \"ArgSpecFake\", [\"args\", \"varargs\", \"keywords\", \"defaults\"])\n\n\ndef getargspec(func, *, include_kwonlyargs=False):\n \"\"\"Compat shim for getargspec deprecated in python 3.\n\n The main difference from inspect.getargspec (and inspect.getfullargspec\n for that matter) is that by using inspect.signature we are providing\n correct args/defaults for functools.wraps'ed functions.\n\n `include_kwonlyargs` option was added to centralize getting all args,\n even the ones which are kwonly (follow the ``*,``).\n\n For internal use and not advised for use in 3rd party code.\n Please use inspect.signature directly.\n \"\"\"\n # We use signature, and not getfullargspec, because only signature properly\n # \"passes\" args from a functools.wraps decorated function.\n # Note: getfullargspec works Ok on wrapt-decorated functions\n f_sign = inspect.signature(func)\n # Loop through parameters and compose argspec\n args4 = [[], None, None, {}]\n # Collect all kwonlyargs into a dedicated dict - name: default\n kwonlyargs = {}\n # shortcuts\n args, defaults = args4[0], args4[3]\n P = inspect.Parameter\n\n for p_name, p in f_sign.parameters.items():\n if p.kind in (P.POSITIONAL_ONLY, P.POSITIONAL_OR_KEYWORD):\n assert not kwonlyargs # yoh: must not come after kwonlyarg\n args.append(p_name)\n if p.default is not P.empty:\n defaults[p_name] = p.default\n elif p.kind == P.VAR_POSITIONAL:\n args4[1] = p_name\n elif p.kind == P.VAR_KEYWORD:\n args4[2] = p_name\n elif p.kind == P.KEYWORD_ONLY:\n assert p.default is not P.empty\n kwonlyargs[p_name] = p.default\n\n if kwonlyargs:\n if not include_kwonlyargs:\n raise ValueError(\n 'Function has keyword-only parameters or annotations, either use '\n 'inspect.signature() API which can support them, or provide include_kwonlyargs=True '\n 'to this function'\n )\n else:\n args.extend(list(kwonlyargs))\n defaults.update(kwonlyargs)\n\n # harmonize defaults to how original getargspec returned them -- just a tuple\n args4[3] = None if not defaults else tuple(defaults.values())\n return ArgSpecFake(*args4)\n\n\ndef any_re_search(regexes, value):\n \"\"\"Return if any of regexes (list or str) searches successfully for value\"\"\"\n for regex in ensure_tuple_or_list(regexes):\n if re.search(regex, value):\n return True\n return False\n\n\ndef not_supported_on_windows(msg=None):\n \"\"\"A little helper to be invoked to consistently fail whenever functionality is\n not supported (yet) on Windows\n \"\"\"\n if on_windows:\n raise NotImplementedError(\"This functionality is not yet implemented for Windows OS\"\n + (\": %s\" % msg if msg else \"\"))\n\n\ndef get_home_envvars(new_home):\n \"\"\"Return dict with env variables to be adjusted for a new HOME\n\n Only variables found in current os.environ are adjusted.\n\n Parameters\n ----------\n new_home: str or Path\n New home path, in native to OS \"schema\"\n \"\"\"\n new_home = str(new_home)\n out = {'HOME': new_home}\n if on_windows:\n # requires special handling, since it has a number of relevant variables\n # and also Python changed its behavior and started to respect USERPROFILE only\n # since python 3.8: https://bugs.python.org/issue36264\n out['USERPROFILE'] = new_home\n out['HOMEDRIVE'], out['HOMEPATH'] = splitdrive(new_home)\n\n return {v: val for v, val in out.items() if v in os.environ}\n\n\ndef shortened_repr(value, l=30):\n try:\n if hasattr(value, '__repr__') and (value.__repr__ is not object.__repr__):\n value_repr = repr(value)\n if not value_repr.startswith('<') and len(value_repr) > l:\n value_repr = \"<<%s++%d chars++%s>>\" % (\n value_repr[:l - 16],\n len(value_repr) - (l - 16 + 4),\n value_repr[-4:]\n )\n elif value_repr.startswith('<') and value_repr.endswith('>') and ' object at 0x':\n raise ValueError(\"I hate those useless long reprs\")\n else:\n raise ValueError(\"gimme class\")\n except Exception as e:\n value_repr = \"<%s>\" % value.__class__.__name__.split('.')[-1]\n return value_repr\n\n\ndef __auto_repr__(obj):\n attr_names = tuple()\n if hasattr(obj, '__dict__'):\n attr_names += tuple(obj.__dict__.keys())\n if hasattr(obj, '__slots__'):\n attr_names += tuple(obj.__slots__)\n\n items = []\n for attr in sorted(set(attr_names)):\n if attr.startswith('_'):\n continue\n value = getattr(obj, attr)\n # TODO: should we add this feature to minimize some talktative reprs\n # such as of URL?\n #if value is None:\n # continue\n items.append(\"%s=%s\" % (attr, shortened_repr(value)))\n\n return \"%s(%s)\" % (obj.__class__.__name__, ', '.join(items))\n\n\ndef auto_repr(cls):\n \"\"\"Decorator for a class to assign it an automagic quick and dirty __repr__\n\n It uses public class attributes to prepare repr of a class\n\n Original idea: http://stackoverflow.com/a/27799004/1265472\n \"\"\"\n\n cls.__repr__ = __auto_repr__\n return cls\n\n\ndef _is_stream_tty(stream):\n try:\n # TODO: check on windows if hasattr check would work correctly and\n # add value:\n return stream.isatty()\n except ValueError as exc:\n # Who knows why it is a ValueError, but let's try to be specific\n # If there is a problem with I/O - non-interactive, otherwise reraise\n if \"I/O\" in str(exc):\n return False\n raise\n\n\ndef is_interactive():\n \"\"\"Return True if all in/outs are open and tty.\n\n Note that in a somewhat abnormal case where e.g. stdin is explicitly\n closed, and any operation on it would raise a\n `ValueError(\"I/O operation on closed file\")` exception, this function\n would just return False, since the session cannot be used interactively.\n \"\"\"\n return all(_is_stream_tty(s) for s in (sys.stdin, sys.stdout, sys.stderr))\n\n\ndef get_ipython_shell():\n \"\"\"Detect if running within IPython and returns its `ip` (shell) object\n\n Returns None if not under ipython (no `get_ipython` function)\n \"\"\"\n try:\n return get_ipython()\n except NameError:\n return None\n\n\ndef md5sum(filename):\n \"\"\"Compute an MD5 sum for the given file\n \"\"\"\n from datalad.support.digests import Digester\n return Digester(digests=['md5'])(filename)['md5']\n\n\n# unused in -core\ndef sorted_files(path):\n \"\"\"Return a (sorted) list of files under path\n \"\"\"\n return sorted(sum([[op.join(r, f)[len(path) + 1:] for f in files]\n for r, d, files in os.walk(path)\n if not '.git' in r], []))\n\n_encoded_dirsep = r'\\\\' if on_windows else r'/'\n_VCS_REGEX = r'%s\\.(?:git|gitattributes|svn|bzr|hg)(?:%s|$)' % (\n _encoded_dirsep, _encoded_dirsep)\n_DATALAD_REGEX = r'%s\\.(?:datalad)(?:%s|$)' % (\n _encoded_dirsep, _encoded_dirsep)\n\n\ndef find_files(regex, topdir=curdir, exclude=None, exclude_vcs=True, exclude_datalad=False, dirs=False):\n \"\"\"Generator to find files matching regex\n\n Parameters\n ----------\n regex: basestring\n exclude: basestring, optional\n Matches to exclude\n exclude_vcs:\n If True, excludes commonly known VCS subdirectories. If string, used\n as regex to exclude those files (regex: `%r`)\n exclude_datalad:\n If True, excludes files known to be datalad meta-data files (e.g. under\n .datalad/ subdirectory) (regex: `%r`)\n topdir: basestring, optional\n Directory where to search\n dirs: bool, optional\n Whether to match directories as well as files\n \"\"\"\n for dirpath, dirnames, filenames in os.walk(topdir):\n names = (dirnames + filenames) if dirs else filenames\n # TODO: might want to uniformize on windows to use '/'\n paths = (op.join(dirpath, name) for name in names)\n for path in filter(re.compile(regex).search, paths):\n path = path.rstrip(sep)\n if exclude and re.search(exclude, path):\n continue\n if exclude_vcs and re.search(_VCS_REGEX, path):\n continue\n if exclude_datalad and re.search(_DATALAD_REGEX, path):\n continue\n yield path\nfind_files.__doc__ %= (_VCS_REGEX, _DATALAD_REGEX)\n\n\ndef expandpath(path, force_absolute=True):\n \"\"\"Expand all variables and user handles in a path.\n\n By default return an absolute path\n \"\"\"\n path = expandvars(expanduser(path))\n if force_absolute:\n path = abspath(path)\n return path\n\n\ndef posix_relpath(path, start=None):\n \"\"\"Behave like os.path.relpath, but always return POSIX paths...\n\n on any platform.\"\"\"\n # join POSIX style\n return posixpath.join(\n # split and relpath native style\n # python2.7 ntpath implementation of relpath cannot handle start=None\n *split(\n relpath(path, start=start if start is not None else '')))\n\n\ndef is_explicit_path(path):\n \"\"\"Return whether a path explicitly points to a location\n\n Any absolute path, or relative path starting with either '../' or\n './' is assumed to indicate a location on the filesystem. Any other\n path format is not considered explicit.\"\"\"\n path = expandpath(path, force_absolute=False)\n return isabs(path) \\\n or path.startswith(os.curdir + os.sep) \\\n or path.startswith(os.pardir + os.sep)\n\n\n# handle this dance once, and import pathlib from here\n# in all other places\n\nfrom pathlib import (\n Path,\n PurePath,\n PurePosixPath,\n)\n\ndef rotree(path, ro=True, chmod_files=True):\n \"\"\"To make tree read-only or writable\n\n Parameters\n ----------\n path : string\n Path to the tree/directory to chmod\n ro : bool, optional\n Whether to make it R/O (default) or RW\n chmod_files : bool, optional\n Whether to operate also on files (not just directories)\n \"\"\"\n if ro:\n chmod = lambda f: os.chmod(f, os.stat(f).st_mode & ~stat.S_IWRITE)\n else:\n chmod = lambda f: os.chmod(f, os.stat(f).st_mode | stat.S_IWRITE | stat.S_IREAD)\n\n for root, dirs, files in os.walk(path, followlinks=False):\n if chmod_files:\n for f in files:\n fullf = op.join(root, f)\n # might be the \"broken\" symlink which would fail to stat etc\n if exists(fullf):\n chmod(fullf)\n chmod(root)\n\n\ndef rmtree(path, chmod_files='auto', children_only=False, *args, **kwargs):\n \"\"\"To remove git-annex .git it is needed to make all files and directories writable again first\n\n Parameters\n ----------\n path: Path or str\n Path to remove\n chmod_files : string or bool, optional\n Whether to make files writable also before removal. Usually it is just\n a matter of directories to have write permissions.\n If 'auto' it would chmod files on windows by default\n children_only : bool, optional\n If set, all files and subdirectories would be removed while the path\n itself (must be a directory) would be preserved\n `*args` :\n `**kwargs` :\n Passed into shutil.rmtree call\n \"\"\"\n # Give W permissions back only to directories, no need to bother with files\n if chmod_files == 'auto':\n chmod_files = on_windows\n # TODO: yoh thinks that if we could quickly check our Flyweight for\n # repos if any of them is under the path, and could call .precommit\n # on those to possibly stop batched processes etc, we did not have\n # to do it on case by case\n # Check for open files\n assert_no_open_files(path)\n\n # TODO the whole thing should be reimplemented with pathlib, but for now\n # at least accept Path\n path = str(path)\n\n if children_only:\n if not isdir(path):\n raise ValueError(\"Can remove children only of directories\")\n for p in os.listdir(path):\n rmtree(op.join(path, p))\n return\n if not (islink(path) or not isdir(path)):\n rotree(path, ro=False, chmod_files=chmod_files)\n if on_windows:\n # shutil fails to remove paths that exceed 260 characters on Windows machines\n # that did not enable long path support. A workaround to remove long paths\n # anyway is to preprend \\\\?\\ to the path.\n # https://docs.microsoft.com/en-us/windows/win32/fileio/naming-a-file?redirectedfrom=MSDN#win32-file-namespaces\n path = r'\\\\?\\ '.strip() + path\n _rmtree(path, *args, **kwargs)\n else:\n # just remove the symlink\n unlink(path)\n\n\ndef rmdir(path, *args, **kwargs):\n \"\"\"os.rmdir with our optional checking for open files\"\"\"\n assert_no_open_files(path)\n os.rmdir(path)\n\n\ndef get_open_files(path, log_open=False):\n \"\"\"Get open files under a path\n\n Note: This function is very slow on Windows.\n\n Parameters\n ----------\n path : str\n File or directory to check for open files under\n log_open : bool or int\n If set - logger level to use\n\n Returns\n -------\n dict\n path : pid\n\n \"\"\"\n # Original idea: https://stackoverflow.com/a/11115521/1265472\n import psutil\n files = {}\n # since the ones returned by psutil would not be aware of symlinks in the\n # path we should also get realpath for path\n # do absolute() in addition to always get an absolute path\n # even with non-existing paths on windows\n path = str(Path(path).resolve().absolute())\n for proc in psutil.process_iter():\n try:\n open_paths = [p.path for p in proc.open_files()] + [proc.cwd()]\n for p in open_paths:\n # note: could be done more efficiently so we do not\n # renormalize path over and over again etc\n if path_startswith(p, path):\n files[p] = proc\n # Catch a race condition where a process ends\n # before we can examine its files\n except psutil.NoSuchProcess:\n pass\n except psutil.AccessDenied:\n pass\n\n if files and log_open:\n lgr.log(log_open, \"Open files under %s: %s\", path, files)\n return files\n\n\n_assert_no_open_files_cfg = os.environ.get('DATALAD_ASSERT_NO_OPEN_FILES')\nif _assert_no_open_files_cfg:\n def assert_no_open_files(path):\n files = get_open_files(path, log_open=40)\n if _assert_no_open_files_cfg == 'assert':\n assert not files, \"Got following files still open: %s\" % ','.join(files)\n elif files:\n if _assert_no_open_files_cfg == 'pdb':\n import pdb\n pdb.set_trace()\n elif _assert_no_open_files_cfg == 'epdb':\n import epdb\n epdb.serve()\n pass\n # otherwise we would just issue that error message in the log\nelse:\n def assert_no_open_files(*args, **kwargs):\n pass\n\n\ndef rmtemp(f, *args, **kwargs):\n \"\"\"Wrapper to centralize removing of temp files so we could keep them around\n\n It will not remove the temporary file/directory if DATALAD_TESTS_TEMP_KEEP\n environment variable is defined\n \"\"\"\n if not os.environ.get('DATALAD_TESTS_TEMP_KEEP'):\n if not os.path.lexists(f):\n lgr.debug(\"Path %s does not exist, so can't be removed\", f)\n return\n lgr.log(5, \"Removing temp file: %s\", f)\n # Can also be a directory\n if isdir(f):\n rmtree(f, *args, **kwargs)\n else:\n unlink(f)\n else:\n lgr.info(\"Keeping temp file: %s\", f)\n\n\ndef file_basename(name, return_ext=False):\n \"\"\"\n Strips up to 2 extensions of length up to 4 characters and starting with alpha\n not a digit, so we could get rid of .tar.gz etc\n \"\"\"\n bname = basename(name)\n fbname = re.sub(r'(\\.[a-zA-Z_]\\S{1,4}){0,2}$', '', bname)\n if return_ext:\n return fbname, bname[len(fbname) + 1:]\n else:\n return fbname\n\n\n# unused in -core\ndef escape_filename(filename):\n \"\"\"Surround filename in \"\" and escape \" in the filename\n \"\"\"\n filename = filename.replace('\"', r'\\\"').replace('`', r'\\`')\n filename = '\"%s\"' % filename\n return filename\n\n\n# unused in -core\ndef encode_filename(filename):\n \"\"\"Encode unicode filename\n \"\"\"\n if isinstance(filename, str):\n return filename.encode(sys.getfilesystemencoding())\n else:\n return filename\n\n\n# unused in -core\ndef decode_input(s):\n \"\"\"Given input string/bytes, decode according to stdin codepage (or UTF-8)\n if not defined\n\n If fails -- issue warning and decode allowing for errors\n being replaced\n \"\"\"\n if isinstance(s, str):\n return s\n else:\n encoding = sys.stdin.encoding or 'UTF-8'\n try:\n return s.decode(encoding)\n except UnicodeDecodeError as exc:\n lgr.warning(\n \"Failed to decode input string using %s encoding. \"\n \"Decoding allowing for errors\", encoding)\n return s.decode(encoding, errors='replace')\n\n\n# unused in -core\nif on_windows:\n def lmtime(filepath, mtime):\n \"\"\"Set mtime for files. On Windows a merely adapter to os.utime\n \"\"\"\n os.utime(filepath, (time.time(), mtime))\nelse:\n def lmtime(filepath, mtime):\n \"\"\"Set mtime for files, while not de-referencing symlinks.\n\n To overcome absence of os.lutime\n\n Works only on linux and OSX ATM\n \"\"\"\n from .cmd import WitlessRunner\n # convert mtime to format touch understands [[CC]YY]MMDDhhmm[.SS]\n smtime = time.strftime(\"%Y%m%d%H%M.%S\", time.localtime(mtime))\n lgr.log(3, \"Setting mtime for %s to %s == %s\", filepath, mtime, smtime)\n WitlessRunner().run(['touch', '-h', '-t', '%s' % smtime, filepath])\n filepath = Path(filepath)\n rfilepath = filepath.resolve()\n if filepath.is_symlink() and rfilepath.exists():\n # trust no one - adjust also of the target file\n # since it seemed like downloading under OSX (was it using curl?)\n # didn't bother with timestamps\n lgr.log(3, \"File is a symlink to %s Setting mtime for it to %s\",\n rfilepath, mtime)\n os.utime(str(rfilepath), (time.time(), mtime))\n # doesn't work on OSX\n # Runner().run(['touch', '-h', '-d', '@%s' % mtime, filepath])\n\n\ndef ensure_tuple_or_list(obj):\n \"\"\"Given an object, wrap into a tuple if not list or tuple\n \"\"\"\n if isinstance(obj, (list, tuple)):\n return obj\n return (obj,)\n\n\ndef ensure_iter(s, cls, copy=False, iterate=True):\n \"\"\"Given not a list, would place it into a list. If None - empty list is returned\n\n Parameters\n ----------\n s: list or anything\n cls: class\n Which iterable class to ensure\n copy: bool, optional\n If correct iterable is passed, it would generate its shallow copy\n iterate: bool, optional\n If it is not a list, but something iterable (but not a str)\n iterate over it.\n \"\"\"\n\n if isinstance(s, cls):\n return s if not copy else shallow_copy(s)\n elif isinstance(s, str):\n return cls((s,))\n elif iterate and hasattr(s, '__iter__'):\n return cls(s)\n elif s is None:\n return cls()\n else:\n return cls((s,))\n\n\ndef ensure_list(s, copy=False, iterate=True):\n \"\"\"Given not a list, would place it into a list. If None - empty list is returned\n\n Parameters\n ----------\n s: list or anything\n copy: bool, optional\n If list is passed, it would generate a shallow copy of the list\n iterate: bool, optional\n If it is not a list, but something iterable (but not a str)\n iterate over it.\n \"\"\"\n return ensure_iter(s, list, copy=copy, iterate=iterate)\n\n\ndef ensure_list_from_str(s, sep='\\n'):\n \"\"\"Given a multiline string convert it to a list of return None if empty\n\n Parameters\n ----------\n s: str or list\n \"\"\"\n\n if not s:\n return None\n\n if isinstance(s, list):\n return s\n return s.split(sep)\n\n\ndef ensure_dict_from_str(s, **kwargs):\n \"\"\"Given a multiline string with key=value items convert it to a dictionary\n\n Parameters\n ----------\n s: str or dict\n\n Returns None if input s is empty\n \"\"\"\n\n if not s:\n return None\n\n if isinstance(s, dict):\n return s\n\n out = {}\n for value_str in ensure_list_from_str(s, **kwargs):\n if '=' not in value_str:\n raise ValueError(\"{} is not in key=value format\".format(repr(value_str)))\n k, v = value_str.split('=', 1)\n if k in out:\n err = \"key {} was already defined in {}, but new value {} was provided\".format(k, out, v)\n raise ValueError(err)\n out[k] = v\n return out\n\n\ndef ensure_bytes(s, encoding='utf-8'):\n \"\"\"Convert/encode unicode string to bytes.\n\n If `s` isn't a string, return it as is.\n\n Parameters\n ----------\n encoding: str, optional\n Encoding to use. \"utf-8\" is the default\n \"\"\"\n if not isinstance(s, str):\n return s\n return s.encode(encoding)\n\n\ndef ensure_unicode(s, encoding=None, confidence=None):\n \"\"\"Convert/decode bytestring to unicode.\n\n If `s` isn't a bytestring, return it as is.\n\n Parameters\n ----------\n encoding: str, optional\n Encoding to use. If None, \"utf-8\" is tried, and then if not a valid\n UTF-8, encoding will be guessed\n confidence: float, optional\n A value between 0 and 1, so if guessing of encoding is of lower than\n specified confidence, ValueError is raised\n \"\"\"\n if not isinstance(s, bytes):\n return s\n if encoding is None:\n # Figure out encoding, defaulting to 'utf-8' which is our common\n # target in contemporary digital society\n try:\n return s.decode('utf-8')\n except UnicodeDecodeError as exc:\n lgr.debug(\"Failed to decode a string as utf-8: %s\",\n CapturedException(exc))\n # And now we could try to guess\n from chardet import detect\n enc = detect(s)\n denc = enc.get('encoding', None)\n if denc:\n denc_confidence = enc.get('confidence', 0)\n if confidence is not None and denc_confidence < confidence:\n raise ValueError(\n \"Failed to auto-detect encoding with high enough \"\n \"confidence. Highest confidence was %s for %s\"\n % (denc_confidence, denc)\n )\n lgr.log(5, \"Auto-detected encoding to be %s\", denc)\n return s.decode(denc)\n else:\n raise ValueError(\n \"Could not decode value as utf-8, or to guess its encoding: %s\"\n % repr(s)\n )\n else:\n return s.decode(encoding)\n\n\ndef ensure_bool(s):\n \"\"\"Convert value into boolean following convention for strings\n\n to recognize on,True,yes as True, off,False,no as False\n \"\"\"\n if isinstance(s, str):\n if s.isdigit():\n return bool(int(s))\n sl = s.lower()\n if sl in {'y', 'yes', 'true', 'on'}:\n return True\n elif sl in {'n', 'no', 'false', 'off'}:\n return False\n else:\n raise ValueError(\"Do not know how to treat %r as a boolean\" % s)\n return bool(s)\n\n\ndef as_unicode(val, cast_types=object):\n \"\"\"Given an arbitrary value, would try to obtain unicode value of it\n \n For unicode it would return original value, for python2 str or python3\n bytes it would use ensure_unicode, for None - an empty (unicode) string,\n and for any other type (see `cast_types`) - would apply the unicode \n constructor. If value is not an instance of `cast_types`, TypeError\n is thrown\n \n Parameters\n ----------\n cast_types: type\n Which types to cast to unicode by providing to constructor\n \"\"\"\n if val is None:\n return u''\n elif isinstance(val, str):\n return val\n elif isinstance(val, unicode_srctypes):\n return ensure_unicode(val)\n elif isinstance(val, cast_types):\n return str(val)\n else:\n raise TypeError(\n \"Value %r is not of any of known or provided %s types\"\n % (val, cast_types))\n\n\n# MASKED: unique function (lines 900-933)\n\n\ndef all_same(items):\n \"\"\"Quick check if all items are the same.\n\n Identical to a check like len(set(items)) == 1 but\n should be more efficient while working on generators, since would\n return False as soon as any difference detected thus possibly avoiding\n unnecessary evaluations\n \"\"\"\n first = True\n first_item = None\n for item in items:\n if first:\n first = False\n first_item = item\n else:\n if item != first_item:\n return False\n # So we return False if was empty\n return not first\n\n\ndef map_items(func, v):\n \"\"\"A helper to apply `func` to all elements (keys and values) within dict\n\n No type checking of values passed to func is done, so `func`\n should be resilient to values which it should not handle\n\n Initial usecase - apply_recursive(url_fragment, ensure_unicode)\n \"\"\"\n # map all elements within item\n return v.__class__(\n item.__class__(map(func, item))\n for item in v.items()\n )\n\n\ndef partition(items, predicate=bool):\n \"\"\"Partition `items` by `predicate`.\n\n Parameters\n ----------\n items : iterable\n predicate : callable\n A function that will be mapped over each element in `items`. The\n elements will partitioned based on whether the return value is false or\n true.\n\n Returns\n -------\n A tuple with two generators, the first for 'false' items and the second for\n 'true' ones.\n\n Notes\n -----\n Taken from Peter Otten's snippet posted at\n https://nedbatchelder.com/blog/201306/filter_a_list_into_two_parts.html\n \"\"\"\n a, b = tee((predicate(item), item) for item in items)\n return ((item for pred, item in a if not pred),\n (item for pred, item in b if pred))\n\n\ndef generate_chunks(container, size):\n \"\"\"Given a container, generate chunks from it with size up to `size`\n \"\"\"\n # There could be a \"smarter\" solution but I think this would suffice\n assert size > 0, \"Size should be non-0 positive\"\n while container:\n yield container[:size]\n container = container[size:]\n\n\ndef generate_file_chunks(files, cmd=None):\n \"\"\"Given a list of files, generate chunks of them to avoid exceeding cmdline length\n\n Parameters\n ----------\n files: list of str\n cmd: str or list of str, optional\n Command to account for as well\n \"\"\"\n files = ensure_list(files)\n cmd = ensure_list(cmd)\n\n maxl = max(map(len, files)) if files else 0\n chunk_size = max(\n 1, # should at least be 1. If blows then - not our fault\n (CMD_MAX_ARG\n - sum((len(x) + 3) for x in cmd)\n - 4 # for '--' below\n ) // (maxl + 3) # +3 for possible quotes and a space\n )\n # TODO: additional treatment for \"too many arguments\"? although\n # as https://github.com/datalad/datalad/issues/1883#issuecomment\n # -436272758\n # shows there seems to be no hardcoded limit on # of arguments,\n # but may be we decide to go for smth like follow to be on safe side\n # chunk_size = min(10240 - len(cmd), chunk_size)\n file_chunks = generate_chunks(files, chunk_size)\n return file_chunks\n\n\n#\n# Generators helpers\n#\n\ndef saved_generator(gen):\n \"\"\"Given a generator returns two generators, where 2nd one just replays\n\n So the first one would be going through the generated items and 2nd one\n would be yielding saved items\n \"\"\"\n saved = []\n\n def gen1():\n for x in gen: # iterating over original generator\n saved.append(x)\n yield x\n\n def gen2():\n for x in saved: # yielding saved entries\n yield x\n\n return gen1(), gen2()\n\n\n#\n# Decorators\n#\n\n# Originally better_wraps was created to provide `wrapt`-based, instead of\n# `functools.wraps` implementation to preserve the correct signature of the\n# decorated function. By using inspect.signature in our getargspec, which\n# works fine on `functools.wraps`ed functions, we mediated this necessity.\nbetter_wraps = wraps\n\n\n# Borrowed from pandas\n# Copyright: 2011-2014, Lambda Foundry, Inc. and PyData Development Team\n# License: BSD-3\ndef optional_args(decorator):\n \"\"\"allows a decorator to take optional positional and keyword arguments.\n Assumes that taking a single, callable, positional argument means that\n it is decorating a function, i.e. something like this::\n\n @my_decorator\n def function(): pass\n\n Calls decorator with decorator(f, `*args`, `**kwargs`)\"\"\"\n\n @better_wraps(decorator)\n def wrapper(*args, **kwargs):\n def dec(f):\n return decorator(f, *args, **kwargs)\n\n is_decorating = not kwargs and len(args) == 1 and isinstance(args[0], Callable)\n if is_decorating:\n f = args[0]\n args = []\n return dec(f)\n else:\n return dec\n\n return wrapper\n\n\n# TODO: just provide decorators for tempfile.mk* functions. This is ugly!\ndef get_tempfile_kwargs(tkwargs=None, prefix=\"\", wrapped=None):\n \"\"\"Updates kwargs to be passed to tempfile. calls depending on env vars\n \"\"\"\n if tkwargs is None:\n tkwargs_ = {}\n else:\n # operate on a copy of tkwargs to avoid any side-effects\n tkwargs_ = tkwargs.copy()\n\n # TODO: don't remember why I had this one originally\n # if len(targs)<2 and \\\n if 'prefix' not in tkwargs_:\n tkwargs_['prefix'] = '_'.join(\n ['datalad_temp'] +\n ([prefix] if prefix else []) +\n ([''] if (on_windows or not wrapped) else [wrapped.__name__]))\n\n directory = os.environ.get('TMPDIR')\n if directory and 'dir' not in tkwargs_:\n tkwargs_['dir'] = directory\n\n return tkwargs_\n\n\n@optional_args\ndef line_profile(func):\n \"\"\"Q&D helper to line profile the function and spit out stats\n \"\"\"\n import line_profiler\n prof = line_profiler.LineProfiler()\n\n @wraps(func)\n def _wrap_line_profile(*args, **kwargs):\n try:\n pfunc = prof(func)\n return pfunc(*args, **kwargs)\n finally:\n prof.print_stats()\n return _wrap_line_profile\n\n\n# unused in -core\n@optional_args\ndef collect_method_callstats(func):\n \"\"\"Figure out methods which call the method repeatedly on the same instance\n\n Use case(s):\n - .repo is expensive since does all kinds of checks.\n - .config is expensive transitively since it calls .repo each time\n\n TODO:\n - fancy one could look through the stack for the same id(self) to see if\n that location is already in memo. That would hint to the cases where object\n is not passed into underlying functions, causing them to redo the same work\n over and over again\n - ATM might flood with all \"1 lines\" calls which are not that informative.\n The underlying possibly suboptimal use might be coming from their callers.\n It might or not relate to the previous TODO\n \"\"\"\n from collections import defaultdict\n import traceback\n from time import time\n memo = defaultdict(lambda: defaultdict(int)) # it will be a dict of lineno: count\n # gross timing\n times = []\n toppath = dirname(__file__) + sep\n\n @wraps(func)\n def _wrap_collect_method_callstats(*args, **kwargs):\n try:\n self = args[0]\n stack = traceback.extract_stack()\n caller = stack[-2]\n stack_sig = \\\n \"{relpath}:{s.name}\".format(\n s=caller, relpath=relpath(caller.filename, toppath))\n sig = (id(self), stack_sig)\n # we will count based on id(self) + wherefrom\n memo[sig][caller.lineno] += 1\n t0 = time()\n return func(*args, **kwargs)\n finally:\n times.append(time() - t0)\n pass\n\n def print_stats():\n print(\"The cost of property {}:\".format(func.__name__))\n if not memo:\n print(\"None since no calls\")\n return\n # total count\n counts = {k: sum(v.values()) for k,v in memo.items()}\n total = sum(counts.values())\n ids = {self_id for (self_id, _) in memo}\n print(\" Total: {} calls from {} objects with {} contexts taking {:.2f} sec\"\n .format(total, len(ids), len(memo), sum(times)))\n # now we need to sort by value\n for (self_id, caller), count in sorted(counts.items(), key=lambda x: x[1], reverse=True):\n print(\" {} {}: {} from {} lines\"\n .format(self_id, caller, count, len(memo[(self_id, caller)])))\n\n # Upon total exit we print the stats\n import atexit\n atexit.register(print_stats)\n\n return _wrap_collect_method_callstats\n\n\n# Borrowed from duecredit to wrap duecredit-handling to guarantee failsafe\ndef never_fail(f):\n \"\"\"Assure that function never fails -- all exceptions are caught\n\n Returns `None` if function fails internally.\n \"\"\"\n @wraps(f)\n def wrapped_func(*args, **kwargs):\n try:\n return f(*args, **kwargs)\n except Exception as e:\n lgr.warning(\n \"DataLad internal failure while running %s: %r. \"\n \"Please report at https://github.com/datalad/datalad/issues\"\n % (f, e)\n )\n\n if os.environ.get('DATALAD_ALLOW_FAIL', False):\n return f\n else:\n return wrapped_func\n\n\n#\n# Context Managers\n#\n\n\n# unused in -core\n@contextmanager\ndef nothing_cm():\n \"\"\"Just a dummy cm to programmically switch context managers\"\"\"\n yield\n\n\n@contextmanager\ndef swallow_outputs():\n \"\"\"Context manager to help consuming both stdout and stderr, and print()\n\n stdout is available as cm.out and stderr as cm.err whenever cm is the\n yielded context manager.\n Internally uses temporary files to guarantee absent side-effects of swallowing\n into StringIO which lacks .fileno.\n\n print mocking is necessary for some uses where sys.stdout was already bound\n to original sys.stdout, thus mocking it later had no effect. Overriding\n print function had desired effect\n \"\"\"\n\n class StringIOAdapter(object):\n \"\"\"Little adapter to help getting out/err values\n \"\"\"\n def __init__(self):\n kw = get_tempfile_kwargs({}, prefix=\"outputs\")\n\n self._out = NamedTemporaryFile(delete=False, mode='w', **kw)\n self._err = NamedTemporaryFile(delete=False, mode='w', **kw)\n\n def _read(self, h):\n with open(h.name) as f:\n return f.read()\n\n @property\n def out(self):\n if not self._out.closed:\n self._out.flush()\n return self._read(self._out)\n\n @property\n def err(self):\n if not self._err.closed:\n self._err.flush()\n return self._read(self._err)\n\n @property\n def handles(self):\n return self._out, self._err\n\n def cleanup(self):\n self._out.close()\n self._err.close()\n out_name = self._out.name\n err_name = self._err.name\n from datalad import cfg\n if cfg.getbool('datalad.log', 'outputs', default=False) \\\n and lgr.getEffectiveLevel() <= logging.DEBUG:\n for s, sname in ((self.out, 'stdout'),\n (self.err, 'stderr')):\n if s:\n pref = os.linesep + \"| \"\n lgr.debug(\"Swallowed %s:%s%s\", sname, pref, s.replace(os.linesep, pref))\n else:\n lgr.debug(\"Nothing was swallowed for %s\", sname)\n del self._out\n del self._err\n gc.collect()\n rmtemp(out_name)\n rmtemp(err_name)\n\n def fake_print(*args, **kwargs):\n sep = kwargs.pop('sep', ' ')\n end = kwargs.pop('end', '\\n')\n file = kwargs.pop('file', sys.stdout)\n\n if file in (oldout, olderr, sys.stdout, sys.stderr):\n # we mock\n try:\n sys.stdout.write(sep.join(args) + end)\n except UnicodeEncodeError as exc:\n lgr.error(\n \"Failed to write to mocked stdout, got %s, continue as it \"\n \"didn't happen\", exc)\n else:\n # must be some other file one -- leave it alone\n oldprint(*args, sep=sep, end=end, file=file)\n\n from .ui import ui\n # preserve -- they could have been mocked already\n oldprint = getattr(builtins, 'print')\n oldout, olderr = sys.stdout, sys.stderr\n olduiout = ui.out\n adapter = StringIOAdapter()\n\n try:\n sys.stdout, sys.stderr = adapter.handles\n ui.out = adapter.handles[0]\n setattr(builtins, 'print', fake_print)\n\n yield adapter\n finally:\n sys.stdout, sys.stderr, ui.out = oldout, olderr, olduiout\n setattr(builtins, 'print', oldprint)\n adapter.cleanup()\n\n\n@contextmanager\ndef swallow_logs(new_level=None, file_=None, name='datalad'):\n \"\"\"Context manager to consume all logs.\n\n \"\"\"\n lgr = logging.getLogger(name)\n\n # Keep old settings\n old_level = lgr.level\n old_handlers = lgr.handlers\n\n # Let's log everything into a string\n # TODO: generalize with the one for swallow_outputs\n class StringIOAdapter(object):\n \"\"\"Little adapter to help getting out values\n\n And to stay consistent with how swallow_outputs behaves\n \"\"\"\n def __init__(self):\n if file_ is None:\n kw = get_tempfile_kwargs({}, prefix=\"logs\")\n self._out = NamedTemporaryFile(mode='a', delete=False, **kw)\n else:\n out_file = file_\n # PY3 requires clearly one or another. race condition possible\n self._out = open(out_file, 'a')\n self._final_out = None\n\n def _read(self, h):\n with open(h.name) as f:\n return f.read()\n\n @property\n def out(self):\n if self._final_out is not None:\n # we closed and cleaned up already\n return self._final_out\n else:\n self._out.flush()\n return self._read(self._out)\n\n @property\n def lines(self):\n return self.out.split('\\n')\n\n @property\n def handle(self):\n return self._out\n\n def cleanup(self):\n # store for access while object exists\n self._final_out = self.out\n self._out.close()\n out_name = self._out.name\n del self._out\n gc.collect()\n if not file_:\n rmtemp(out_name)\n\n def assert_logged(self, msg=None, level=None, regex=True, **kwargs):\n \"\"\"Provide assertion on whether a msg was logged at a given level\n\n If neither `msg` nor `level` provided, checks if anything was logged\n at all.\n\n Parameters\n ----------\n msg: str, optional\n Message (as a regular expression, if `regex`) to be searched.\n If no msg provided, checks if anything was logged at a given level.\n level: str, optional\n String representing the level to be logged\n regex: bool, optional\n If False, regular `assert_in` is used\n **kwargs: str, optional\n Passed to `assert_re_in` or `assert_in`\n \"\"\"\n from datalad.tests.utils import assert_re_in\n from datalad.tests.utils import assert_in\n\n if regex:\n match = r'\\[%s\\] ' % level if level else r\"\\[\\S+\\] \"\n else:\n match = '[%s] ' % level if level else ''\n\n if msg:\n match += msg\n\n if match:\n (assert_re_in if regex else assert_in)(match, self.out, **kwargs)\n else:\n assert not kwargs, \"no kwargs to be passed anywhere\"\n assert self.out, \"Nothing was logged!?\"\n\n adapter = StringIOAdapter()\n # TODO: it does store messages but without any formatting, i.e. even without\n # date/time prefix etc. IMHO it should preserve formatting in case if file_ is\n # set\n swallow_handler = logging.StreamHandler(adapter.handle)\n # we want to log levelname so we could test against it\n swallow_handler.setFormatter(\n logging.Formatter('[%(levelname)s] %(message)s'))\n swallow_handler.filters = sum([h.filters for h in old_handlers],\n [])\n lgr.handlers = [swallow_handler]\n if old_level < logging.DEBUG: # so if HEAVYDEBUG etc -- show them!\n lgr.handlers += old_handlers\n\n if isinstance(new_level, str):\n new_level = getattr(logging, new_level)\n\n if new_level is not None:\n lgr.setLevel(new_level)\n\n try:\n yield adapter\n # TODO: if file_ and there was an exception -- most probably worth logging it?\n # although ideally it should be the next log outside added to that file_ ... oh well\n finally:\n lgr.handlers = old_handlers\n lgr.setLevel(old_level)\n adapter.cleanup()\n\n\n# TODO: May be melt in with swallow_logs at some point:\n@contextmanager\ndef disable_logger(logger=None):\n \"\"\"context manager to temporarily disable logging\n\n This is to provide one of swallow_logs' purposes without unnecessarily\n creating temp files (see gh-1865)\n\n Parameters\n ----------\n logger: Logger\n Logger whose handlers will be ordered to not log anything.\n Default: datalad's topmost Logger ('datalad')\n \"\"\"\n\n class NullFilter(logging.Filter):\n \"\"\"Filter class to reject all records\n \"\"\"\n def filter(self, record):\n return 0\n\n if logger is None:\n # default: all of datalad's logging:\n logger = logging.getLogger('datalad')\n\n filter_ = NullFilter(logger.name)\n [h.addFilter(filter_) for h in logger.handlers]\n\n try:\n yield logger\n finally:\n [h.removeFilter(filter_) for h in logger.handlers]\n\n\n#\n# Additional handlers\n#\n_sys_excepthook = sys.excepthook # Just in case we ever need original one\n\n\ndef setup_exceptionhook(ipython=False):\n \"\"\"Overloads default sys.excepthook with our exceptionhook handler.\n\n If interactive, our exceptionhook handler will invoke\n pdb.post_mortem; if not interactive, then invokes default handler.\n \"\"\"\n\n def _datalad_pdb_excepthook(type, value, tb):\n import traceback\n traceback.print_exception(type, value, tb)\n print()\n if is_interactive():\n import pdb\n pdb.post_mortem(tb)\n\n if ipython:\n from IPython.core import ultratb\n sys.excepthook = ultratb.FormattedTB(mode='Verbose',\n # color_scheme='Linux',\n call_pdb=is_interactive())\n else:\n sys.excepthook = _datalad_pdb_excepthook\n\n\ndef ensure_dir(*args):\n \"\"\"Make sure directory exists.\n\n Joins the list of arguments to an os-specific path to the desired\n directory and creates it, if it not exists yet.\n \"\"\"\n dirname = op.join(*args)\n if not exists(dirname):\n os.makedirs(dirname)\n return dirname\n\n\ndef updated(d, update):\n \"\"\"Return a copy of the input with the 'update'\n\n Primarily for updating dictionaries\n \"\"\"\n d = d.copy()\n d.update(update)\n return d\n\n\n_pwd_mode = None\n\n\ndef _switch_to_getcwd(msg, *args):\n global _pwd_mode\n _pwd_mode = 'cwd'\n lgr.debug(\n msg + \". From now on will be returning os.getcwd(). Directory\"\n \" symlinks in the paths will be resolved\",\n *args\n )\n # TODO: we might want to mitigate by going through all flywheighted\n # repos and tuning up their .paths to be resolved?\n\n\ndef getpwd():\n \"\"\"Try to return a CWD without dereferencing possible symlinks\n\n This function will try to use PWD environment variable to provide a current\n working directory, possibly with some directories along the path being\n symlinks to other directories. Unfortunately, PWD is used/set only by the\n shell and such functions as `os.chdir` and `os.getcwd` nohow use or modify\n it, thus `os.getcwd()` returns path with links dereferenced.\n\n While returning current working directory based on PWD env variable we\n verify that the directory is the same as `os.getcwd()` after resolving all\n symlinks. If that verification fails, we fall back to always use\n `os.getcwd()`.\n\n Initial decision to either use PWD env variable or os.getcwd() is done upon\n the first call of this function.\n \"\"\"\n global _pwd_mode\n if _pwd_mode is None:\n # we need to decide!\n try:\n pwd = os.environ['PWD']\n if on_windows and pwd and pwd.startswith('/'):\n # It should be a path from MSYS.\n # - it might start with a drive letter or not\n # - it seems to be \"illegal\" to have a single letter directories\n # under / path, i.e. if created - they aren't found\n # - 'ln -s' does not fail to create a \"symlink\" but it just\n # copies!\n # so we are not likely to need original PWD purpose on\n # those systems\n # Verdict:\n _pwd_mode = 'cwd'\n else:\n _pwd_mode = 'PWD'\n except KeyError:\n _pwd_mode = 'cwd'\n\n if _pwd_mode == 'cwd':\n return os.getcwd()\n elif _pwd_mode == 'PWD':\n try:\n cwd = os.getcwd()\n except OSError as exc:\n if \"o such file\" in str(exc):\n # directory was removed but we promised to be robust and\n # still report the path we might know since we are still in PWD\n # mode\n cwd = None\n else:\n raise\n try:\n pwd = os.environ['PWD']\n # do absolute() in addition to always get an absolute path\n # even with non-existing paths on windows\n pwd_real = str(Path(pwd).resolve().absolute())\n # This logic would fail to catch the case where chdir did happen\n # to the directory where current PWD is pointing to, e.g.\n # $> ls -ld $PWD\n # lrwxrwxrwx 1 yoh yoh 5 Oct 11 13:27 /home/yoh/.tmp/tmp -> /tmp//\n # hopa:~/.tmp/tmp\n # $> python -c 'import os; os.chdir(\"/tmp\"); from datalad.utils import getpwd; print(getpwd(), os.getcwd())'\n # ('/home/yoh/.tmp/tmp', '/tmp')\n # but I guess that should not be too harmful\n if cwd is not None and pwd_real != cwd:\n _switch_to_getcwd(\n \"realpath of PWD=%s is %s whenever os.getcwd()=%s\",\n pwd, pwd_real, cwd\n )\n return cwd\n return pwd\n except KeyError:\n _switch_to_getcwd(\"PWD env variable is no longer available\")\n return cwd # Must not happen, but may be someone\n # evil purges PWD from environ?\n else:\n raise RuntimeError(\n \"Must have not got here. \"\n \"pwd_mode must be either cwd or PWD. And it is now %r\" % (_pwd_mode,)\n )\n\n\nclass chpwd(object):\n \"\"\"Wrapper around os.chdir which also adjusts environ['PWD']\n\n The reason is that otherwise PWD is simply inherited from the shell\n and we have no ability to assess directory path without dereferencing\n symlinks.\n\n If used as a context manager it allows to temporarily change directory\n to the given path\n \"\"\"\n def __init__(self, path, mkdir=False, logsuffix=''):\n\n if path:\n pwd = getpwd()\n self._prev_pwd = pwd\n else:\n self._prev_pwd = None\n return\n\n if not isabs(path):\n path = normpath(op.join(pwd, path))\n if not os.path.exists(path) and mkdir:\n self._mkdir = True\n os.mkdir(path)\n else:\n self._mkdir = False\n lgr.debug(\"chdir %r -> %r %s\", self._prev_pwd, path, logsuffix)\n os.chdir(path) # for grep people -- ok, to chdir here!\n os.environ['PWD'] = str(path)\n\n def __enter__(self):\n # nothing more to do really, chdir was in the constructor\n pass\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n if self._prev_pwd:\n # Need to use self.__class__ so this instance, if the entire\n # thing mocked during the test, still would use correct chpwd\n self.__class__(self._prev_pwd, logsuffix=\"(coming back)\")\n\n\ndef dlabspath(path, norm=False):\n \"\"\"Symlinks-in-the-cwd aware abspath\n\n os.path.abspath relies on os.getcwd() which would not know about symlinks\n in the path\n\n TODO: we might want to norm=True by default to match behavior of\n os .path.abspath?\n \"\"\"\n if not isabs(path):\n # if not absolute -- relative to pwd\n path = op.join(getpwd(), path)\n return normpath(path) if norm else path\n\n\ndef with_pathsep(path):\n \"\"\"Little helper to guarantee that path ends with /\"\"\"\n return path + sep if not path.endswith(sep) else path\n\n\ndef get_path_prefix(path, pwd=None):\n \"\"\"Get path prefix (for current directory)\n\n Returns relative path to the topdir, if we are under topdir, and if not\n absolute path to topdir. If `pwd` is not specified - current directory\n assumed\n \"\"\"\n pwd = pwd or getpwd()\n path = dlabspath(path)\n path_ = with_pathsep(path)\n pwd_ = with_pathsep(pwd)\n common = commonprefix((path_, pwd_))\n if common.endswith(sep) and common in {path_, pwd_}:\n # we are in subdir or above the path = use relative path\n location_prefix = relpath(path, pwd)\n # if benign \"here\" - cut off\n if location_prefix in (curdir, curdir + sep):\n location_prefix = ''\n return location_prefix\n else:\n # just return absolute path\n return path\n\n\ndef _get_normalized_paths(path, prefix):\n if isabs(path) != isabs(prefix):\n raise ValueError(\"Both paths must either be absolute or relative. \"\n \"Got %r and %r\" % (path, prefix))\n path = with_pathsep(path)\n prefix = with_pathsep(prefix)\n return path, prefix\n\n\ndef path_startswith(path, prefix):\n \"\"\"Return True if path starts with prefix path\n\n Parameters\n ----------\n path: str\n prefix: str\n \"\"\"\n path, prefix = _get_normalized_paths(path, prefix)\n return path.startswith(prefix)\n\n\ndef path_is_subpath(path, prefix):\n \"\"\"Return True if path is a subpath of prefix\n\n It will return False if path == prefix.\n\n Parameters\n ----------\n path: str\n prefix: str\n \"\"\"\n path, prefix = _get_normalized_paths(path, prefix)\n return (len(prefix) < len(path)) and path.startswith(prefix)\n\n\ndef knows_annex(path):\n \"\"\"Returns whether at a given path there is information about an annex\n\n It is just a thin wrapper around GitRepo.is_with_annex() classmethod\n which also checks for `path` to exist first.\n\n This includes actually present annexes, but also uninitialized ones, or\n even the presence of a remote annex branch.\n \"\"\"\n from os.path import exists\n if not exists(path):\n lgr.debug(\"No annex: test path {0} doesn't exist\".format(path))\n return False\n from datalad.support.gitrepo import GitRepo\n return GitRepo(path, init=False, create=False).is_with_annex()\n\n\n@contextmanager\ndef make_tempfile(content=None, wrapped=None, **tkwargs):\n \"\"\"Helper class to provide a temporary file name and remove it at the end (context manager)\n\n Parameters\n ----------\n mkdir : bool, optional (default: False)\n If True, temporary directory created using tempfile.mkdtemp()\n content : str or bytes, optional\n Content to be stored in the file created\n wrapped : function, optional\n If set, function name used to prefix temporary file name\n `**tkwargs`:\n All other arguments are passed into the call to tempfile.mk{,d}temp(),\n and resultant temporary filename is passed as the first argument into\n the function t. If no 'prefix' argument is provided, it will be\n constructed using module and function names ('.' replaced with\n '_').\n\n To change the used directory without providing keyword argument 'dir' set\n DATALAD_TESTS_TEMP_DIR.\n\n Examples\n --------\n >>> from os.path import exists\n >>> from datalad.utils import make_tempfile\n >>> with make_tempfile() as fname:\n ... k = open(fname, 'w').write('silly test')\n >>> assert not exists(fname) # was removed\n\n >>> with make_tempfile(content=\"blah\") as fname:\n ... assert open(fname).read() == \"blah\"\n \"\"\"\n\n if tkwargs.get('mkdir', None) and content is not None:\n raise ValueError(\"mkdir=True while providing content makes no sense\")\n\n tkwargs_ = get_tempfile_kwargs(tkwargs, wrapped=wrapped)\n\n # if DATALAD_TESTS_TEMP_DIR is set, use that as directory,\n # let mktemp handle it otherwise. However, an explicitly provided\n # dir=... will override this.\n mkdir = tkwargs_.pop('mkdir', False)\n\n filename = {False: tempfile.mktemp,\n True: tempfile.mkdtemp}[mkdir](**tkwargs_)\n # MIH: not clear to me why we need to perform this (possibly expensive)\n # resolve. It was already part of the original implementation\n # 008d9ab8cc3e0170c0a9b8479e80dee9ffe6eb7f\n filename = Path(filename).resolve()\n\n if content:\n (filename.write_bytes\n if isinstance(content, bytes)\n else filename.write_text)(content)\n\n # TODO globbing below can also be done with pathlib\n filename = str(filename)\n\n if __debug__:\n lgr.debug(\n 'Created temporary %s named %s',\n 'directory' if mkdir else 'file',\n filename)\n try:\n yield filename\n finally:\n # glob here for all files with the same name (-suffix)\n # would be useful whenever we requested .img filename,\n # and function creates .hdr as well\n # MIH: this is undocumented behavior, and undesired in the general\n # case. it should be made conditional and explicit\n lsuffix = len(tkwargs_.get('suffix', ''))\n filename_ = lsuffix and filename[:-lsuffix] or filename\n filenames = glob.glob(filename_ + '*')\n if len(filename_) < 3 or len(filenames) > 5:\n # For paranoid yoh who stepped into this already ones ;-)\n lgr.warning(\"It is unlikely that it was intended to remove all\"\n \" files matching %r. Skipping\" % filename_)\n return\n for f in filenames:\n try:\n rmtemp(f)\n except OSError: # pragma: no cover\n pass\n\n\ndef _path_(*p):\n \"\"\"Given a path in POSIX\" notation, regenerate one in native to the env one\"\"\"\n if on_windows:\n return op.join(*map(lambda x: op.join(*x.split('/')), p))\n else:\n # Assume that all others as POSIX compliant so nothing to be done\n return op.join(*p)\n\n\ndef get_timestamp_suffix(time_=None, prefix='-'):\n \"\"\"Return a time stamp (full date and time up to second)\n\n primarily to be used for generation of log files names\n \"\"\"\n args = []\n if time_ is not None:\n if isinstance(time_, int):\n time_ = time.gmtime(time_)\n args.append(time_)\n return time.strftime(prefix + TIMESTAMP_FMT, *args)\n\n\n# unused in -core\ndef get_logfilename(dspath, cmd='datalad'):\n \"\"\"Return a filename to use for logging under a dataset/repository\n\n directory would be created if doesn't exist, but dspath must exist\n and be a directory\n \"\"\"\n assert(exists(dspath))\n assert(isdir(dspath))\n ds_logdir = ensure_dir(dspath, '.git', 'datalad', 'logs') # TODO: use WEB_META_LOG whenever #789 merged\n return op.join(ds_logdir, 'crawl-%s.log' % get_timestamp_suffix())\n\n\ndef get_trace(edges, start, end, trace=None):\n \"\"\"Return the trace/path to reach a node in a tree.\n\n Parameters\n ----------\n edges : sequence(2-tuple)\n The tree given by a sequence of edges (parent, child) tuples. The\n nodes can be identified by any value and data type that supports\n the '==' operation.\n start :\n Identifier of the start node. Must be present as a value in the parent\n location of an edge tuple in order to be found.\n end :\n Identifier of the target/end node. Must be present as a value in the child\n location of an edge tuple in order to be found.\n trace : list\n Mostly useful for recursive calls, and used internally.\n\n Returns\n -------\n None or list\n Returns a list with the trace to the target (the starts and the target\n are not included in the trace, hence if start and end are directly connected\n an empty list is returned), or None when no trace to the target can be found,\n or start and end are identical.\n \"\"\"\n # the term trace is used to avoid confusion with a path in the sense\n # of a filesystem path, but the analogy fits and nodes can be paths\n if trace is None:\n trace = []\n if not edges:\n raise ValueError(\"no edges given\")\n for cand in edges:\n cand_super, cand_sub = cand\n if cand_sub in trace:\n # only DAGs, skip any cyclic traces\n continue\n if trace and cand_super != trace[-1]:\n # only consider edges that lead off the end of the trace\n continue\n if not trace and cand_super != start:\n # we got nothing yet, and this edges is not matching the start\n continue\n if cand_sub == end:\n return trace\n # dive into potential subnodes\n cand_trace = get_trace(\n edges,\n start,\n end,\n trace + [cand_sub])\n if cand_trace:\n return cand_trace\n return None\n\n\ndef get_dataset_root(path):\n \"\"\"Return the root of an existent dataset containing a given path\n\n The root path is returned in the same absolute or relative form\n as the input argument. If no associated dataset exists, or the\n input path doesn't exist, None is returned.\n\n If `path` is a symlink or something other than a directory, its\n the root dataset containing its parent directory will be reported.\n If none can be found, at a symlink at `path` is pointing to a\n dataset, `path` itself will be reported as the root.\n\n Parameters\n ----------\n path : Path-like\n\n Returns\n -------\n str or None\n \"\"\"\n path = str(path)\n suffix = '.git'\n altered = None\n if islink(path) or not isdir(path):\n altered = path\n path = dirname(path)\n apath = abspath(path)\n # while we can still go up\n while split(apath)[1]:\n if exists(op.join(path, suffix)):\n return path\n # new test path in the format we got it\n path = normpath(op.join(path, os.pardir))\n # no luck, next round\n apath = abspath(path)\n # if we applied dirname() at the top, we give it another go with\n # the actual path, if it was itself a symlink, it could be the\n # top-level dataset itself\n if altered and exists(op.join(altered, suffix)):\n return altered\n\n return None\n\n\n# ATM used in datalad_crawler extension, so do not remove yet\ndef try_multiple(ntrials, exception, base, f, *args, **kwargs):\n \"\"\"Call f multiple times making exponentially growing delay between the calls\"\"\"\n for trial in range(1, ntrials+1):\n try:\n return f(*args, **kwargs)\n except exception as exc:\n if trial == ntrials:\n raise # just reraise on the last trial\n t = base ** trial\n lgr.warning(\"Caught %s on trial #%d. Sleeping %f and retrying\",\n CapturedException(exc), trial, t)\n sleep(t)\n\n\n@optional_args\ndef try_multiple_dec(\n f, ntrials=None, duration=0.1, exceptions=None, increment_type=None,\n exceptions_filter=None,\n logger=None,\n):\n \"\"\"Decorator to try function multiple times.\n\n Main purpose is to decorate functions dealing with removal of files/directories\n and which might need a few seconds to work correctly on Windows which takes\n its time to release files/directories.\n\n Parameters\n ----------\n ntrials: int, optional\n duration: float, optional\n Seconds to sleep before retrying.\n increment_type: {None, 'exponential'}\n Note that if it is exponential, duration should typically be > 1.0\n so it grows with higher power\n exceptions: Exception or tuple of Exceptions, optional\n Exception or a tuple of multiple exceptions, on which to retry\n exceptions_filter: callable, optional\n If provided, this function will be called with a caught exception\n instance. If function returns True - we will re-try, if False - exception\n will be re-raised without retrying.\n logger: callable, optional\n Logger to log upon failure. If not provided, will use stock logger\n at the level of 5 (heavy debug).\n \"\"\"\n if not exceptions:\n exceptions = (OSError, WindowsError, PermissionError) \\\n if on_windows else OSError\n if not ntrials:\n # Life goes fast on proper systems, no need to delay it much\n ntrials = 100 if on_windows else 10\n if logger is None:\n def logger(*args, **kwargs):\n return lgr.log(5, *args, **kwargs)\n assert increment_type in {None, 'exponential'}\n\n @wraps(f)\n def _wrap_try_multiple_dec(*args, **kwargs):\n t = duration\n for trial in range(ntrials):\n try:\n return f(*args, **kwargs)\n except exceptions as exc:\n if exceptions_filter and not exceptions_filter(exc):\n raise\n if trial < ntrials - 1:\n if increment_type == 'exponential':\n t = duration ** (trial + 1)\n logger(\n \"Caught %s on trial #%d. Sleeping %f and retrying\",\n CapturedException(exc), trial, t)\n sleep(t)\n else:\n raise\n\n return _wrap_try_multiple_dec\n\n\n@try_multiple_dec\ndef unlink(f):\n \"\"\"'Robust' unlink. Would try multiple times\n\n On windows boxes there is evidence for a latency of more than a second\n until a file is considered no longer \"in-use\".\n WindowsError is not known on Linux, and if IOError or any other\n exception\n is thrown then if except statement has WindowsError in it -- NameError\n also see gh-2533\n \"\"\"\n # Check for open files\n assert_no_open_files(f)\n return os.unlink(f)\n\n\n@try_multiple_dec\ndef _rmtree(*args, **kwargs):\n \"\"\"Just a helper to decorate shutil.rmtree.\n\n rmtree defined above does more and ideally should not itself be decorated\n since a recursive definition and does checks for open files inside etc -\n might be too runtime expensive\n \"\"\"\n return shutil.rmtree(*args, **kwargs)\n\n\ndef slash_join(base, extension):\n \"\"\"Join two strings with a '/', avoiding duplicate slashes\n\n If any of the strings is None the other is returned as is.\n \"\"\"\n if extension is None:\n return base\n if base is None:\n return extension\n return '/'.join(\n (base.rstrip('/'),\n extension.lstrip('/')))\n\n\n#\n# IO Helpers\n#\n\n# unused in -core\ndef open_r_encdetect(fname, readahead=1000):\n \"\"\"Return a file object in read mode with auto-detected encoding\n\n This is helpful when dealing with files of unknown encoding.\n\n Parameters\n ----------\n readahead: int, optional\n How many bytes to read for guessing the encoding type. If\n negative - full file will be read\n \"\"\"\n from chardet import detect\n import io\n # read some bytes from the file\n with open(fname, 'rb') as f:\n head = f.read(readahead)\n enc = detect(head)\n denc = enc.get('encoding', None)\n lgr.debug(\"Auto-detected encoding %s for file %s (confidence: %s)\",\n denc,\n fname,\n enc.get('confidence', 'unknown'))\n return io.open(fname, encoding=denc)\n\n\ndef read_file(fname, decode=True):\n \"\"\"A helper to read file passing content via ensure_unicode\n\n Parameters\n ----------\n decode: bool, optional\n if False, no ensure_unicode and file content returned as bytes\n \"\"\"\n with open(fname, 'rb') as f:\n content = f.read()\n return ensure_unicode(content) if decode else content\n\n\ndef read_csv_lines(fname, dialect=None, readahead=16384, **kwargs):\n \"\"\"A generator of dict records from a CSV/TSV\n\n Automatically guesses the encoding for each record to convert to UTF-8\n\n Parameters\n ----------\n fname: str\n Filename\n dialect: str, optional\n Dialect to specify to csv.reader. If not specified -- guessed from\n the file, if fails to guess, \"excel-tab\" is assumed\n readahead: int, optional\n How many bytes to read from the file to guess the type\n **kwargs\n Passed to `csv.reader`\n \"\"\"\n import csv\n if dialect is None:\n with open(fname) as tsvfile:\n # add robustness, use a sniffer\n try:\n dialect = csv.Sniffer().sniff(tsvfile.read(readahead))\n except Exception as exc:\n lgr.warning(\n 'Could not determine file-format, assuming TSV: %s',\n CapturedException(exc)\n )\n dialect = 'excel-tab'\n\n kw = dict(encoding='utf-8')\n with open(fname, 'r', **kw) as tsvfile:\n # csv.py doesn't do Unicode; encode temporarily as UTF-8:\n csv_reader = csv.reader(\n tsvfile,\n dialect=dialect,\n **kwargs\n )\n header = None\n for row in csv_reader:\n # decode UTF-8 back to Unicode, cell by cell:\n row_unicode = map(ensure_unicode, row)\n if header is None:\n header = list(row_unicode)\n else:\n yield dict(zip(header, row_unicode))\n\n\ndef import_modules(modnames, pkg, msg=\"Failed to import {module}\", log=lgr.debug):\n \"\"\"Helper to import a list of modules without failing if N/A\n\n Parameters\n ----------\n modnames: list of str\n List of module names to import\n pkg: str\n Package under which to import\n msg: str, optional\n Message template for .format() to log at DEBUG level if import fails.\n Keys {module} and {package} will be provided and ': {exception}' appended\n log: callable, optional\n Logger call to use for logging messages\n \"\"\"\n from importlib import import_module\n _globals = globals()\n mods_loaded = []\n if pkg and not pkg in sys.modules:\n # with python 3.5.1 (ok with 3.5.5) somehow kept running into\n # Failed to import dlsub1: Parent module 'dltestm1' not loaded\n # while running the test. Preloading pkg resolved the issue\n import_module(pkg)\n for modname in modnames:\n try:\n _globals[modname] = mod = import_module(\n '.{}'.format(modname),\n pkg)\n mods_loaded.append(mod)\n except Exception as exc:\n from datalad.support.exceptions import CapturedException\n ce = CapturedException(exc)\n log((msg + ': {exception}').format(\n module=modname, package=pkg, exception=ce.message))\n return mods_loaded\n\n\ndef import_module_from_file(modpath, pkg=None, log=lgr.debug):\n \"\"\"Import provided module given a path\n\n TODO:\n - RF/make use of it in pipeline.py which has similar logic\n - join with import_modules above?\n\n Parameters\n ----------\n pkg: module, optional\n If provided, and modpath is under pkg.__path__, relative import will be\n used\n \"\"\"\n assert(modpath.endswith('.py')) # for now just for .py files\n\n log(\"Importing %s\" % modpath)\n\n modname = basename(modpath)[:-3]\n relmodpath = None\n if pkg:\n for pkgpath in pkg.__path__:\n if path_is_subpath(modpath, pkgpath):\n # for now relying on having .py extension -- assertion above\n relmodpath = '.' + relpath(modpath[:-3], pkgpath).replace(sep, '.')\n break\n\n try:\n if relmodpath:\n from importlib import import_module\n mod = import_module(relmodpath, pkg.__name__)\n else:\n dirname_ = dirname(modpath)\n try:\n sys.path.insert(0, dirname_)\n mod = __import__(modname, level=0)\n finally:\n if dirname_ in sys.path:\n sys.path.pop(sys.path.index(dirname_))\n else:\n log(\"Expected path %s to be within sys.path, but it was gone!\" % dirname_)\n except Exception as e:\n raise RuntimeError(\n \"Failed to import module from %s\" % modpath) from e\n\n return mod\n\n\ndef get_encoding_info():\n \"\"\"Return a dictionary with various encoding/locale information\"\"\"\n import sys, locale\n from collections import OrderedDict\n return OrderedDict([\n ('default', sys.getdefaultencoding()),\n ('filesystem', sys.getfilesystemencoding()),\n ('locale.prefered', locale.getpreferredencoding()),\n ])\n\n\ndef get_envvars_info():\n from collections import OrderedDict\n envs = []\n for var, val in os.environ.items():\n if (\n var.startswith('PYTHON') or\n var.startswith('LC_') or\n var.startswith('GIT_') or\n var in ('LANG', 'LANGUAGE', 'PATH')\n ):\n envs.append((var, val))\n return OrderedDict(envs)\n\n\n# This class is modified from Snakemake (v5.1.4)\nclass SequenceFormatter(string.Formatter):\n \"\"\"string.Formatter subclass with special behavior for sequences.\n\n This class delegates formatting of individual elements to another\n formatter object. Non-list objects are formatted by calling the\n delegate formatter's \"format_field\" method. List-like objects\n (list, tuple, set, frozenset) are formatted by formatting each\n element of the list according to the specified format spec using\n the delegate formatter and then joining the resulting strings with\n a separator (space by default).\n \"\"\"\n\n def __init__(self, separator=\" \", element_formatter=string.Formatter(),\n *args, **kwargs):\n self.separator = separator\n self.element_formatter = element_formatter\n\n def format_element(self, elem, format_spec):\n \"\"\"Format a single element\n\n For sequences, this is called once for each element in a\n sequence. For anything else, it is called on the entire\n object. It is intended to be overridden in subclases.\n \"\"\"\n return self.element_formatter.format_field(elem, format_spec)\n\n def format_field(self, value, format_spec):\n if isinstance(value, (list, tuple, set, frozenset)):\n return self.separator.join(self.format_element(v, format_spec)\n for v in value)\n else:\n return self.format_element(value, format_spec)\n\n\n# TODO: eventually we might want to make use of attr module\nclass File(object):\n \"\"\"Helper for a file entry in the create_tree/@with_tree\n\n It allows to define additional settings for entries\n \"\"\"\n def __init__(self, name, executable=False):\n \"\"\"\n\n Parameters\n ----------\n name : str\n Name of the file\n executable: bool, optional\n Make it executable\n \"\"\"\n self.name = name\n self.executable = executable\n\n def __str__(self):\n return self.name\n\n\ndef create_tree_archive(path, name, load, overwrite=False, archives_leading_dir=True):\n \"\"\"Given an archive `name`, create under `path` with specified `load` tree\n \"\"\"\n from datalad.support.archives import compress_files\n dirname = file_basename(name)\n full_dirname = op.join(path, dirname)\n os.makedirs(full_dirname)\n create_tree(full_dirname, load, archives_leading_dir=archives_leading_dir)\n # create archive\n if archives_leading_dir:\n compress_files([dirname], name, path=path, overwrite=overwrite)\n else:\n compress_files(list(map(basename, glob.glob(op.join(full_dirname, '*')))),\n op.join(pardir, name),\n path=op.join(path, dirname),\n overwrite=overwrite)\n # remove original tree\n rmtree(full_dirname)\n\n\ndef create_tree(path, tree, archives_leading_dir=True, remove_existing=False):\n \"\"\"Given a list of tuples (name, load) create such a tree\n\n if load is a tuple itself -- that would create either a subtree or an archive\n with that content and place it into the tree if name ends with .tar.gz\n \"\"\"\n lgr.log(5, \"Creating a tree under %s\", path)\n if not exists(path):\n os.makedirs(path)\n\n if isinstance(tree, dict):\n tree = tree.items()\n\n for file_, load in tree:\n if isinstance(file_, File):\n executable = file_.executable\n name = file_.name\n else:\n executable = False\n name = file_\n full_name = op.join(path, name)\n if remove_existing and lexists(full_name):\n rmtree(full_name, chmod_files=True)\n if isinstance(load, (tuple, list, dict)):\n if name.endswith('.tar.gz') or name.endswith('.tar') or name.endswith('.zip'):\n create_tree_archive(\n path, name, load,\n archives_leading_dir=archives_leading_dir)\n else:\n create_tree(\n full_name, load,\n archives_leading_dir=archives_leading_dir,\n remove_existing=remove_existing)\n else:\n open_func = open\n if full_name.endswith('.gz'):\n open_func = gzip.open\n elif full_name.split('.')[-1] in ('xz', 'lzma'):\n import lzma\n open_func = lzma.open\n with open_func(full_name, \"wb\") as f:\n f.write(ensure_bytes(load, 'utf-8'))\n if executable:\n os.chmod(full_name, os.stat(full_name).st_mode | stat.S_IEXEC)\n\n\ndef get_suggestions_msg(values, known, sep=\"\\n \"):\n \"\"\"Return a formatted string with suggestions for values given the known ones\n \"\"\"\n import difflib\n suggestions = []\n for value in ensure_list(values): # might not want to do it if we change presentation below\n suggestions += difflib.get_close_matches(value, known)\n suggestions = unique(suggestions)\n msg = \"Did you mean any of these?\"\n if suggestions:\n if '\\n' in sep:\n # if separator includes new line - we add entire separator right away\n msg += sep\n else:\n msg += ' '\n return msg + \"%s\\n\" % sep.join(suggestions)\n return ''\n\n\ndef bytes2human(n, format='%(value).1f %(symbol)sB'):\n \"\"\"\n Convert n bytes into a human readable string based on format.\n symbols can be either \"customary\", \"customary_ext\", \"iec\" or \"iec_ext\",\n see: http://goo.gl/kTQMs\n\n >>> from datalad.utils import bytes2human\n >>> bytes2human(1)\n '1.0 B'\n >>> bytes2human(1024)\n '1.0 KB'\n >>> bytes2human(1048576)\n '1.0 MB'\n >>> bytes2human(1099511627776127398123789121)\n '909.5 YB'\n\n >>> bytes2human(10000, \"%(value).1f %(symbol)s/sec\")\n '9.8 K/sec'\n\n >>> # precision can be adjusted by playing with %f operator\n >>> bytes2human(10000, format=\"%(value).5f %(symbol)s\")\n '9.76562 K'\n\n Taken from: http://goo.gl/kTQMs and subsequently simplified\n Original Author: Giampaolo Rodola' <g.rodola [AT] gmail [DOT] com>\n License: MIT\n \"\"\"\n n = int(n)\n if n < 0:\n raise ValueError(\"n < 0\")\n symbols = ('', 'K', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y')\n prefix = {}\n for i, s in enumerate(symbols[1:]):\n prefix[s] = 1 << (i + 1) * 10\n for symbol in reversed(symbols[1:]):\n if n >= prefix[symbol]:\n value = float(n) / prefix[symbol]\n return format % locals()\n return format % dict(symbol=symbols[0], value=n)\n\n\ndef quote_cmdlinearg(arg):\n \"\"\"Perform platform-appropriate argument quoting\"\"\"\n # https://stackoverflow.com/a/15262019\n return '\"{}\"'.format(\n arg.replace('\"', '\"\"')\n ) if on_windows else shlex_quote(arg)\n\n\ndef guard_for_format(arg):\n \"\"\"Replace { and } with {{ and }}\n\n To be used in cases if arg is not expected to have provided\n by user .format() placeholders, but 'arg' might become a part\n of a composite passed to .format(), e.g. via 'Run'\n \"\"\"\n return arg.replace('{', '{{').replace('}', '}}')\n\n\ndef join_cmdline(args):\n \"\"\"Join command line args into a string using quote_cmdlinearg\n \"\"\"\n return ' '.join(map(quote_cmdlinearg, args))\n\n\ndef split_cmdline(s):\n \"\"\"Perform platform-appropriate command line splitting.\n\n Identical to `shlex.split()` on non-windows platforms.\n\n Modified from https://stackoverflow.com/a/35900070\n \"\"\"\n if not on_windows:\n return shlex_split(s)\n\n # the rest is for windows\n RE_CMD_LEX = r'''\"((?:\"\"|\\\\[\"\\\\]|[^\"])*)\"?()|(\\\\\\\\(?=\\\\*\")|\\\\\")|(&&?|\\|\\|?|\\d?>|[<])|([^\\s\"&|<>]+)|(\\s+)|(.)'''\n\n args = []\n accu = None # collects pieces of one arg\n for qs, qss, esc, pipe, word, white, fail in re.findall(RE_CMD_LEX, s):\n if word:\n pass # most frequent\n elif esc:\n word = esc[1]\n elif white or pipe:\n if accu is not None:\n args.append(accu)\n if pipe:\n args.append(pipe)\n accu = None\n continue\n elif fail:\n raise ValueError(\"invalid or incomplete shell string\")\n elif qs:\n word = qs.replace('\\\\\"', '\"').replace('\\\\\\\\', '\\\\')\n if platform == 0:\n word = word.replace('\"\"', '\"')\n else:\n word = qss # may be even empty; must be last\n\n accu = (accu or '') + word\n\n if accu is not None:\n args.append(accu)\n\n return args\n\n\ndef get_wrapped_class(wrapped):\n \"\"\"Determine the command class a wrapped __call__ belongs to\"\"\"\n mod = sys.modules[wrapped.__module__]\n command_class_name = wrapped.__qualname__.split('.')[-2]\n _func_class = mod.__dict__[command_class_name]\n lgr.debug(\"Determined class of decorated function: %s\", _func_class)\n return _func_class\n\n\ndef _make_assure_kludge(fn):\n old_name = fn.__name__.replace(\"ensure\", \"assure\")\n\n @wraps(fn)\n def compat_fn(*args, **kwargs):\n warnings.warn(\n \"{} is deprecated and will be removed in a future release. \"\n \"Use {} instead.\"\n .format(old_name, fn.__name__),\n DeprecationWarning)\n return fn(*args, **kwargs)\n\n compat_fn.__doc__ = (\"Note: This function is deprecated. Use {} instead.\"\n .format(fn.__name__))\n return compat_fn\n\n\nassure_tuple_or_list = _make_assure_kludge(ensure_tuple_or_list)\nassure_iter = _make_assure_kludge(ensure_iter)\nassure_list = _make_assure_kludge(ensure_list)\nassure_list_from_str = _make_assure_kludge(ensure_list_from_str)\nassure_dict_from_str = _make_assure_kludge(ensure_dict_from_str)\nassure_bytes = _make_assure_kludge(ensure_bytes)\nassure_unicode = _make_assure_kludge(ensure_unicode)\nassure_bool = _make_assure_kludge(ensure_bool)\nassure_dir = _make_assure_kludge(ensure_dir)\n\n\nlgr.log(5, \"Done importing datalad.utils\")\n\n\ndef check_symlink_capability(path, target):\n \"\"\"helper similar to datalad.tests.utils.has_symlink_capability\n\n However, for use in a datalad command context, we shouldn't\n assume to be able to write to tmpfile and also not import a whole lot from\n datalad's test machinery. Finally, we want to know, whether we can create a\n symlink at a specific location, not just somewhere. Therefore use\n arbitrary path to test-build a symlink and delete afterwards. Suitable\n location can therefore be determined by high lever code.\n\n Parameters\n ----------\n path: Path\n target: Path\n\n Returns\n -------\n bool\n \"\"\"\n\n try:\n target.touch()\n path.symlink_to(target)\n return True\n except Exception:\n return False\n finally:\n if path.exists():\n path.unlink()\n if target.exists():\n target.unlink()"},"implementation":{"kind":"string","value":"def unique(seq, key=None, reverse=False):\n \"\"\"Given a sequence return a list only with unique elements while maintaining order\n\n This is the fastest solution. See\n https://www.peterbe.com/plog/uniqifiers-benchmark\n and\n http://stackoverflow.com/a/480227/1265472\n for more information.\n Enhancement -- added ability to compare for uniqueness using a key function\n\n Parameters\n ----------\n seq:\n Sequence to analyze\n key: callable, optional\n Function to call on each element so we could decide not on a full\n element, but on its member etc\n reverse: bool, optional\n If True, uniqueness checked in the reverse order, so that the later ones\n will take the order\n \"\"\"\n seen = set()\n seen_add = seen.add\n\n trans = reversed if reverse else lambda x: x\n\n if not key:\n out = [x for x in trans(seq) if not (x in seen or seen_add(x))]\n else:\n # OPT: could be optimized, since key is called twice, but for our cases\n # should be just as fine\n out = [x for x in trans(seq) if not (key(x) in seen or seen_add(key(x)))]\n\n return out[::-1] if reverse else out"},"start_line":{"kind":"number","value":900,"string":"900"},"end_line":{"kind":"number","value":933,"string":"933"},"file_content":{"kind":"string","value":"# emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-\n# ex: set sts=4 ts=4 sw=4 et:\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##\n#\n# See COPYING file distributed along with the datalad package for the\n# copyright and license terms.\n#\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##\n\nimport collections\nfrom collections.abc import Callable\nimport re\nimport builtins\nimport time\n\nimport logging\nimport shutil\nimport os\nimport sys\nimport tempfile\nfrom tempfile import NamedTemporaryFile\nimport platform\nimport gc\nimport glob\nimport gzip\nimport stat\nimport string\nimport warnings\n\nimport os.path as op\n\nfrom copy import copy as shallow_copy\nfrom contextlib import contextmanager\nfrom functools import (\n lru_cache,\n wraps,\n)\nfrom time import sleep\nimport inspect\nfrom itertools import tee\n# this import is required because other modules import opj from here.\nfrom os.path import join as opj\nfrom os.path import (\n abspath,\n basename,\n commonprefix,\n curdir,\n dirname,\n exists,\n expanduser,\n expandvars,\n isabs,\n isdir,\n islink,\n lexists,\n normpath,\n pardir,\n relpath,\n sep,\n split,\n splitdrive\n)\n\nimport posixpath\n\nfrom shlex import (\n quote as shlex_quote,\n split as shlex_split,\n)\n\n# from datalad.dochelpers import get_docstring_split\nfrom datalad.consts import TIMESTAMP_FMT\nfrom datalad.support.exceptions import CapturedException\n\n\nunicode_srctypes = str, bytes\n\n\nlgr = logging.getLogger(\"datalad.utils\")\n\nlgr.log(5, \"Importing datalad.utils\")\n#\n# Some useful variables\n#\nplatform_system = platform.system().lower()\non_windows = platform_system == 'windows'\non_osx = platform_system == 'darwin'\non_linux = platform_system == 'linux'\non_msys_tainted_paths = on_windows \\\n and 'MSYS_NO_PATHCONV' not in os.environ \\\n and os.environ.get('MSYSTEM', '')[:4] in ('MSYS', 'MING')\n\n\n# Takes ~200msec, so should not be called at import time\n@lru_cache() # output should not change through life time of datalad process\ndef get_linux_distribution():\n \"\"\"Compatibility wrapper for {platform,distro}.linux_distribution().\n \"\"\"\n if hasattr(platform, \"linux_distribution\"):\n # Use deprecated (but faster) method if it's available.\n with warnings.catch_warnings():\n warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n result = platform.linux_distribution()\n else:\n import distro # We require this for Python 3.8 and above.\n result = distro.linux_distribution(full_distribution_name=False)\n return result\n\n\n# Those weren't used for any critical decision making, thus we just set them to None\n# Use get_linux_distribution() directly where needed\nlinux_distribution_name = linux_distribution_release = None\n\n# Maximal length of cmdline string\n# Query the system and use hardcoded \"knowledge\" if None\n# probably getconf ARG_MAX might not be available\n# The last one would be the most conservative/Windows\nCMD_MAX_ARG_HARDCODED = 2097152 if on_linux else 262144 if on_osx else 32767\ntry:\n CMD_MAX_ARG = os.sysconf('SC_ARG_MAX')\n assert CMD_MAX_ARG > 0\n if CMD_MAX_ARG > CMD_MAX_ARG_HARDCODED * 1e6:\n # workaround for some kind of a bug which comes up with python 3.4\n # see https://github.com/datalad/datalad/issues/3150\n # or on older CentOS with conda and python as new as 3.9\n # see https://github.com/datalad/datalad/issues/5943\n # TODO: let Yarik know that the world is a paradise now whenever 1e6\n # is not large enough\n CMD_MAX_ARG = min(CMD_MAX_ARG, CMD_MAX_ARG_HARDCODED)\nexcept Exception as exc:\n # ATM (20181005) SC_ARG_MAX available only on POSIX systems\n # so exception would be thrown e.g. on Windows, or\n # somehow during Debian build for nd14.04 it is coming up with -1:\n # https://github.com/datalad/datalad/issues/3015\n CMD_MAX_ARG = CMD_MAX_ARG_HARDCODED\n lgr.debug(\n \"Failed to query or got useless SC_ARG_MAX sysconf, \"\n \"will use hardcoded value: %s\", exc)\n# Even with all careful computations we do, due to necessity to account for\n# environment and what not, we still could not figure out \"exact\" way to\n# estimate it, but it was shown that 300k safety margin on linux was sufficient.\n# https://github.com/datalad/datalad/pull/2977#issuecomment-436264710\n# 300k is ~15%, so to be safe, and for paranoid us we will just use up to 50%\n# of the length for \"safety margin\". We might probably still blow due to\n# env vars, unicode, etc... so any hard limit imho is not a proper solution\nCMD_MAX_ARG = int(0.5 * CMD_MAX_ARG)\nlgr.debug(\n \"Maximal length of cmdline string (adjusted for safety margin): %d\",\n CMD_MAX_ARG)\n\n#\n# Little helpers\n#\n\n# `getargspec` has been deprecated in Python 3.\nArgSpecFake = collections.namedtuple(\n \"ArgSpecFake\", [\"args\", \"varargs\", \"keywords\", \"defaults\"])\n\n\ndef getargspec(func, *, include_kwonlyargs=False):\n \"\"\"Compat shim for getargspec deprecated in python 3.\n\n The main difference from inspect.getargspec (and inspect.getfullargspec\n for that matter) is that by using inspect.signature we are providing\n correct args/defaults for functools.wraps'ed functions.\n\n `include_kwonlyargs` option was added to centralize getting all args,\n even the ones which are kwonly (follow the ``*,``).\n\n For internal use and not advised for use in 3rd party code.\n Please use inspect.signature directly.\n \"\"\"\n # We use signature, and not getfullargspec, because only signature properly\n # \"passes\" args from a functools.wraps decorated function.\n # Note: getfullargspec works Ok on wrapt-decorated functions\n f_sign = inspect.signature(func)\n # Loop through parameters and compose argspec\n args4 = [[], None, None, {}]\n # Collect all kwonlyargs into a dedicated dict - name: default\n kwonlyargs = {}\n # shortcuts\n args, defaults = args4[0], args4[3]\n P = inspect.Parameter\n\n for p_name, p in f_sign.parameters.items():\n if p.kind in (P.POSITIONAL_ONLY, P.POSITIONAL_OR_KEYWORD):\n assert not kwonlyargs # yoh: must not come after kwonlyarg\n args.append(p_name)\n if p.default is not P.empty:\n defaults[p_name] = p.default\n elif p.kind == P.VAR_POSITIONAL:\n args4[1] = p_name\n elif p.kind == P.VAR_KEYWORD:\n args4[2] = p_name\n elif p.kind == P.KEYWORD_ONLY:\n assert p.default is not P.empty\n kwonlyargs[p_name] = p.default\n\n if kwonlyargs:\n if not include_kwonlyargs:\n raise ValueError(\n 'Function has keyword-only parameters or annotations, either use '\n 'inspect.signature() API which can support them, or provide include_kwonlyargs=True '\n 'to this function'\n )\n else:\n args.extend(list(kwonlyargs))\n defaults.update(kwonlyargs)\n\n # harmonize defaults to how original getargspec returned them -- just a tuple\n args4[3] = None if not defaults else tuple(defaults.values())\n return ArgSpecFake(*args4)\n\n\ndef any_re_search(regexes, value):\n \"\"\"Return if any of regexes (list or str) searches successfully for value\"\"\"\n for regex in ensure_tuple_or_list(regexes):\n if re.search(regex, value):\n return True\n return False\n\n\ndef not_supported_on_windows(msg=None):\n \"\"\"A little helper to be invoked to consistently fail whenever functionality is\n not supported (yet) on Windows\n \"\"\"\n if on_windows:\n raise NotImplementedError(\"This functionality is not yet implemented for Windows OS\"\n + (\": %s\" % msg if msg else \"\"))\n\n\ndef get_home_envvars(new_home):\n \"\"\"Return dict with env variables to be adjusted for a new HOME\n\n Only variables found in current os.environ are adjusted.\n\n Parameters\n ----------\n new_home: str or Path\n New home path, in native to OS \"schema\"\n \"\"\"\n new_home = str(new_home)\n out = {'HOME': new_home}\n if on_windows:\n # requires special handling, since it has a number of relevant variables\n # and also Python changed its behavior and started to respect USERPROFILE only\n # since python 3.8: https://bugs.python.org/issue36264\n out['USERPROFILE'] = new_home\n out['HOMEDRIVE'], out['HOMEPATH'] = splitdrive(new_home)\n\n return {v: val for v, val in out.items() if v in os.environ}\n\n\ndef shortened_repr(value, l=30):\n try:\n if hasattr(value, '__repr__') and (value.__repr__ is not object.__repr__):\n value_repr = repr(value)\n if not value_repr.startswith('<') and len(value_repr) > l:\n value_repr = \"<<%s++%d chars++%s>>\" % (\n value_repr[:l - 16],\n len(value_repr) - (l - 16 + 4),\n value_repr[-4:]\n )\n elif value_repr.startswith('<') and value_repr.endswith('>') and ' object at 0x':\n raise ValueError(\"I hate those useless long reprs\")\n else:\n raise ValueError(\"gimme class\")\n except Exception as e:\n value_repr = \"<%s>\" % value.__class__.__name__.split('.')[-1]\n return value_repr\n\n\ndef __auto_repr__(obj):\n attr_names = tuple()\n if hasattr(obj, '__dict__'):\n attr_names += tuple(obj.__dict__.keys())\n if hasattr(obj, '__slots__'):\n attr_names += tuple(obj.__slots__)\n\n items = []\n for attr in sorted(set(attr_names)):\n if attr.startswith('_'):\n continue\n value = getattr(obj, attr)\n # TODO: should we add this feature to minimize some talktative reprs\n # such as of URL?\n #if value is None:\n # continue\n items.append(\"%s=%s\" % (attr, shortened_repr(value)))\n\n return \"%s(%s)\" % (obj.__class__.__name__, ', '.join(items))\n\n\ndef auto_repr(cls):\n \"\"\"Decorator for a class to assign it an automagic quick and dirty __repr__\n\n It uses public class attributes to prepare repr of a class\n\n Original idea: http://stackoverflow.com/a/27799004/1265472\n \"\"\"\n\n cls.__repr__ = __auto_repr__\n return cls\n\n\ndef _is_stream_tty(stream):\n try:\n # TODO: check on windows if hasattr check would work correctly and\n # add value:\n return stream.isatty()\n except ValueError as exc:\n # Who knows why it is a ValueError, but let's try to be specific\n # If there is a problem with I/O - non-interactive, otherwise reraise\n if \"I/O\" in str(exc):\n return False\n raise\n\n\ndef is_interactive():\n \"\"\"Return True if all in/outs are open and tty.\n\n Note that in a somewhat abnormal case where e.g. stdin is explicitly\n closed, and any operation on it would raise a\n `ValueError(\"I/O operation on closed file\")` exception, this function\n would just return False, since the session cannot be used interactively.\n \"\"\"\n return all(_is_stream_tty(s) for s in (sys.stdin, sys.stdout, sys.stderr))\n\n\ndef get_ipython_shell():\n \"\"\"Detect if running within IPython and returns its `ip` (shell) object\n\n Returns None if not under ipython (no `get_ipython` function)\n \"\"\"\n try:\n return get_ipython()\n except NameError:\n return None\n\n\ndef md5sum(filename):\n \"\"\"Compute an MD5 sum for the given file\n \"\"\"\n from datalad.support.digests import Digester\n return Digester(digests=['md5'])(filename)['md5']\n\n\n# unused in -core\ndef sorted_files(path):\n \"\"\"Return a (sorted) list of files under path\n \"\"\"\n return sorted(sum([[op.join(r, f)[len(path) + 1:] for f in files]\n for r, d, files in os.walk(path)\n if not '.git' in r], []))\n\n_encoded_dirsep = r'\\\\' if on_windows else r'/'\n_VCS_REGEX = r'%s\\.(?:git|gitattributes|svn|bzr|hg)(?:%s|$)' % (\n _encoded_dirsep, _encoded_dirsep)\n_DATALAD_REGEX = r'%s\\.(?:datalad)(?:%s|$)' % (\n _encoded_dirsep, _encoded_dirsep)\n\n\ndef find_files(regex, topdir=curdir, exclude=None, exclude_vcs=True, exclude_datalad=False, dirs=False):\n \"\"\"Generator to find files matching regex\n\n Parameters\n ----------\n regex: basestring\n exclude: basestring, optional\n Matches to exclude\n exclude_vcs:\n If True, excludes commonly known VCS subdirectories. If string, used\n as regex to exclude those files (regex: `%r`)\n exclude_datalad:\n If True, excludes files known to be datalad meta-data files (e.g. under\n .datalad/ subdirectory) (regex: `%r`)\n topdir: basestring, optional\n Directory where to search\n dirs: bool, optional\n Whether to match directories as well as files\n \"\"\"\n for dirpath, dirnames, filenames in os.walk(topdir):\n names = (dirnames + filenames) if dirs else filenames\n # TODO: might want to uniformize on windows to use '/'\n paths = (op.join(dirpath, name) for name in names)\n for path in filter(re.compile(regex).search, paths):\n path = path.rstrip(sep)\n if exclude and re.search(exclude, path):\n continue\n if exclude_vcs and re.search(_VCS_REGEX, path):\n continue\n if exclude_datalad and re.search(_DATALAD_REGEX, path):\n continue\n yield path\nfind_files.__doc__ %= (_VCS_REGEX, _DATALAD_REGEX)\n\n\ndef expandpath(path, force_absolute=True):\n \"\"\"Expand all variables and user handles in a path.\n\n By default return an absolute path\n \"\"\"\n path = expandvars(expanduser(path))\n if force_absolute:\n path = abspath(path)\n return path\n\n\ndef posix_relpath(path, start=None):\n \"\"\"Behave like os.path.relpath, but always return POSIX paths...\n\n on any platform.\"\"\"\n # join POSIX style\n return posixpath.join(\n # split and relpath native style\n # python2.7 ntpath implementation of relpath cannot handle start=None\n *split(\n relpath(path, start=start if start is not None else '')))\n\n\ndef is_explicit_path(path):\n \"\"\"Return whether a path explicitly points to a location\n\n Any absolute path, or relative path starting with either '../' or\n './' is assumed to indicate a location on the filesystem. Any other\n path format is not considered explicit.\"\"\"\n path = expandpath(path, force_absolute=False)\n return isabs(path) \\\n or path.startswith(os.curdir + os.sep) \\\n or path.startswith(os.pardir + os.sep)\n\n\n# handle this dance once, and import pathlib from here\n# in all other places\n\nfrom pathlib import (\n Path,\n PurePath,\n PurePosixPath,\n)\n\ndef rotree(path, ro=True, chmod_files=True):\n \"\"\"To make tree read-only or writable\n\n Parameters\n ----------\n path : string\n Path to the tree/directory to chmod\n ro : bool, optional\n Whether to make it R/O (default) or RW\n chmod_files : bool, optional\n Whether to operate also on files (not just directories)\n \"\"\"\n if ro:\n chmod = lambda f: os.chmod(f, os.stat(f).st_mode & ~stat.S_IWRITE)\n else:\n chmod = lambda f: os.chmod(f, os.stat(f).st_mode | stat.S_IWRITE | stat.S_IREAD)\n\n for root, dirs, files in os.walk(path, followlinks=False):\n if chmod_files:\n for f in files:\n fullf = op.join(root, f)\n # might be the \"broken\" symlink which would fail to stat etc\n if exists(fullf):\n chmod(fullf)\n chmod(root)\n\n\ndef rmtree(path, chmod_files='auto', children_only=False, *args, **kwargs):\n \"\"\"To remove git-annex .git it is needed to make all files and directories writable again first\n\n Parameters\n ----------\n path: Path or str\n Path to remove\n chmod_files : string or bool, optional\n Whether to make files writable also before removal. Usually it is just\n a matter of directories to have write permissions.\n If 'auto' it would chmod files on windows by default\n children_only : bool, optional\n If set, all files and subdirectories would be removed while the path\n itself (must be a directory) would be preserved\n `*args` :\n `**kwargs` :\n Passed into shutil.rmtree call\n \"\"\"\n # Give W permissions back only to directories, no need to bother with files\n if chmod_files == 'auto':\n chmod_files = on_windows\n # TODO: yoh thinks that if we could quickly check our Flyweight for\n # repos if any of them is under the path, and could call .precommit\n # on those to possibly stop batched processes etc, we did not have\n # to do it on case by case\n # Check for open files\n assert_no_open_files(path)\n\n # TODO the whole thing should be reimplemented with pathlib, but for now\n # at least accept Path\n path = str(path)\n\n if children_only:\n if not isdir(path):\n raise ValueError(\"Can remove children only of directories\")\n for p in os.listdir(path):\n rmtree(op.join(path, p))\n return\n if not (islink(path) or not isdir(path)):\n rotree(path, ro=False, chmod_files=chmod_files)\n if on_windows:\n # shutil fails to remove paths that exceed 260 characters on Windows machines\n # that did not enable long path support. A workaround to remove long paths\n # anyway is to preprend \\\\?\\ to the path.\n # https://docs.microsoft.com/en-us/windows/win32/fileio/naming-a-file?redirectedfrom=MSDN#win32-file-namespaces\n path = r'\\\\?\\ '.strip() + path\n _rmtree(path, *args, **kwargs)\n else:\n # just remove the symlink\n unlink(path)\n\n\ndef rmdir(path, *args, **kwargs):\n \"\"\"os.rmdir with our optional checking for open files\"\"\"\n assert_no_open_files(path)\n os.rmdir(path)\n\n\ndef get_open_files(path, log_open=False):\n \"\"\"Get open files under a path\n\n Note: This function is very slow on Windows.\n\n Parameters\n ----------\n path : str\n File or directory to check for open files under\n log_open : bool or int\n If set - logger level to use\n\n Returns\n -------\n dict\n path : pid\n\n \"\"\"\n # Original idea: https://stackoverflow.com/a/11115521/1265472\n import psutil\n files = {}\n # since the ones returned by psutil would not be aware of symlinks in the\n # path we should also get realpath for path\n # do absolute() in addition to always get an absolute path\n # even with non-existing paths on windows\n path = str(Path(path).resolve().absolute())\n for proc in psutil.process_iter():\n try:\n open_paths = [p.path for p in proc.open_files()] + [proc.cwd()]\n for p in open_paths:\n # note: could be done more efficiently so we do not\n # renormalize path over and over again etc\n if path_startswith(p, path):\n files[p] = proc\n # Catch a race condition where a process ends\n # before we can examine its files\n except psutil.NoSuchProcess:\n pass\n except psutil.AccessDenied:\n pass\n\n if files and log_open:\n lgr.log(log_open, \"Open files under %s: %s\", path, files)\n return files\n\n\n_assert_no_open_files_cfg = os.environ.get('DATALAD_ASSERT_NO_OPEN_FILES')\nif _assert_no_open_files_cfg:\n def assert_no_open_files(path):\n files = get_open_files(path, log_open=40)\n if _assert_no_open_files_cfg == 'assert':\n assert not files, \"Got following files still open: %s\" % ','.join(files)\n elif files:\n if _assert_no_open_files_cfg == 'pdb':\n import pdb\n pdb.set_trace()\n elif _assert_no_open_files_cfg == 'epdb':\n import epdb\n epdb.serve()\n pass\n # otherwise we would just issue that error message in the log\nelse:\n def assert_no_open_files(*args, **kwargs):\n pass\n\n\ndef rmtemp(f, *args, **kwargs):\n \"\"\"Wrapper to centralize removing of temp files so we could keep them around\n\n It will not remove the temporary file/directory if DATALAD_TESTS_TEMP_KEEP\n environment variable is defined\n \"\"\"\n if not os.environ.get('DATALAD_TESTS_TEMP_KEEP'):\n if not os.path.lexists(f):\n lgr.debug(\"Path %s does not exist, so can't be removed\", f)\n return\n lgr.log(5, \"Removing temp file: %s\", f)\n # Can also be a directory\n if isdir(f):\n rmtree(f, *args, **kwargs)\n else:\n unlink(f)\n else:\n lgr.info(\"Keeping temp file: %s\", f)\n\n\ndef file_basename(name, return_ext=False):\n \"\"\"\n Strips up to 2 extensions of length up to 4 characters and starting with alpha\n not a digit, so we could get rid of .tar.gz etc\n \"\"\"\n bname = basename(name)\n fbname = re.sub(r'(\\.[a-zA-Z_]\\S{1,4}){0,2}$', '', bname)\n if return_ext:\n return fbname, bname[len(fbname) + 1:]\n else:\n return fbname\n\n\n# unused in -core\ndef escape_filename(filename):\n \"\"\"Surround filename in \"\" and escape \" in the filename\n \"\"\"\n filename = filename.replace('\"', r'\\\"').replace('`', r'\\`')\n filename = '\"%s\"' % filename\n return filename\n\n\n# unused in -core\ndef encode_filename(filename):\n \"\"\"Encode unicode filename\n \"\"\"\n if isinstance(filename, str):\n return filename.encode(sys.getfilesystemencoding())\n else:\n return filename\n\n\n# unused in -core\ndef decode_input(s):\n \"\"\"Given input string/bytes, decode according to stdin codepage (or UTF-8)\n if not defined\n\n If fails -- issue warning and decode allowing for errors\n being replaced\n \"\"\"\n if isinstance(s, str):\n return s\n else:\n encoding = sys.stdin.encoding or 'UTF-8'\n try:\n return s.decode(encoding)\n except UnicodeDecodeError as exc:\n lgr.warning(\n \"Failed to decode input string using %s encoding. \"\n \"Decoding allowing for errors\", encoding)\n return s.decode(encoding, errors='replace')\n\n\n# unused in -core\nif on_windows:\n def lmtime(filepath, mtime):\n \"\"\"Set mtime for files. On Windows a merely adapter to os.utime\n \"\"\"\n os.utime(filepath, (time.time(), mtime))\nelse:\n def lmtime(filepath, mtime):\n \"\"\"Set mtime for files, while not de-referencing symlinks.\n\n To overcome absence of os.lutime\n\n Works only on linux and OSX ATM\n \"\"\"\n from .cmd import WitlessRunner\n # convert mtime to format touch understands [[CC]YY]MMDDhhmm[.SS]\n smtime = time.strftime(\"%Y%m%d%H%M.%S\", time.localtime(mtime))\n lgr.log(3, \"Setting mtime for %s to %s == %s\", filepath, mtime, smtime)\n WitlessRunner().run(['touch', '-h', '-t', '%s' % smtime, filepath])\n filepath = Path(filepath)\n rfilepath = filepath.resolve()\n if filepath.is_symlink() and rfilepath.exists():\n # trust no one - adjust also of the target file\n # since it seemed like downloading under OSX (was it using curl?)\n # didn't bother with timestamps\n lgr.log(3, \"File is a symlink to %s Setting mtime for it to %s\",\n rfilepath, mtime)\n os.utime(str(rfilepath), (time.time(), mtime))\n # doesn't work on OSX\n # Runner().run(['touch', '-h', '-d', '@%s' % mtime, filepath])\n\n\ndef ensure_tuple_or_list(obj):\n \"\"\"Given an object, wrap into a tuple if not list or tuple\n \"\"\"\n if isinstance(obj, (list, tuple)):\n return obj\n return (obj,)\n\n\ndef ensure_iter(s, cls, copy=False, iterate=True):\n \"\"\"Given not a list, would place it into a list. If None - empty list is returned\n\n Parameters\n ----------\n s: list or anything\n cls: class\n Which iterable class to ensure\n copy: bool, optional\n If correct iterable is passed, it would generate its shallow copy\n iterate: bool, optional\n If it is not a list, but something iterable (but not a str)\n iterate over it.\n \"\"\"\n\n if isinstance(s, cls):\n return s if not copy else shallow_copy(s)\n elif isinstance(s, str):\n return cls((s,))\n elif iterate and hasattr(s, '__iter__'):\n return cls(s)\n elif s is None:\n return cls()\n else:\n return cls((s,))\n\n\ndef ensure_list(s, copy=False, iterate=True):\n \"\"\"Given not a list, would place it into a list. If None - empty list is returned\n\n Parameters\n ----------\n s: list or anything\n copy: bool, optional\n If list is passed, it would generate a shallow copy of the list\n iterate: bool, optional\n If it is not a list, but something iterable (but not a str)\n iterate over it.\n \"\"\"\n return ensure_iter(s, list, copy=copy, iterate=iterate)\n\n\ndef ensure_list_from_str(s, sep='\\n'):\n \"\"\"Given a multiline string convert it to a list of return None if empty\n\n Parameters\n ----------\n s: str or list\n \"\"\"\n\n if not s:\n return None\n\n if isinstance(s, list):\n return s\n return s.split(sep)\n\n\ndef ensure_dict_from_str(s, **kwargs):\n \"\"\"Given a multiline string with key=value items convert it to a dictionary\n\n Parameters\n ----------\n s: str or dict\n\n Returns None if input s is empty\n \"\"\"\n\n if not s:\n return None\n\n if isinstance(s, dict):\n return s\n\n out = {}\n for value_str in ensure_list_from_str(s, **kwargs):\n if '=' not in value_str:\n raise ValueError(\"{} is not in key=value format\".format(repr(value_str)))\n k, v = value_str.split('=', 1)\n if k in out:\n err = \"key {} was already defined in {}, but new value {} was provided\".format(k, out, v)\n raise ValueError(err)\n out[k] = v\n return out\n\n\ndef ensure_bytes(s, encoding='utf-8'):\n \"\"\"Convert/encode unicode string to bytes.\n\n If `s` isn't a string, return it as is.\n\n Parameters\n ----------\n encoding: str, optional\n Encoding to use. \"utf-8\" is the default\n \"\"\"\n if not isinstance(s, str):\n return s\n return s.encode(encoding)\n\n\ndef ensure_unicode(s, encoding=None, confidence=None):\n \"\"\"Convert/decode bytestring to unicode.\n\n If `s` isn't a bytestring, return it as is.\n\n Parameters\n ----------\n encoding: str, optional\n Encoding to use. If None, \"utf-8\" is tried, and then if not a valid\n UTF-8, encoding will be guessed\n confidence: float, optional\n A value between 0 and 1, so if guessing of encoding is of lower than\n specified confidence, ValueError is raised\n \"\"\"\n if not isinstance(s, bytes):\n return s\n if encoding is None:\n # Figure out encoding, defaulting to 'utf-8' which is our common\n # target in contemporary digital society\n try:\n return s.decode('utf-8')\n except UnicodeDecodeError as exc:\n lgr.debug(\"Failed to decode a string as utf-8: %s\",\n CapturedException(exc))\n # And now we could try to guess\n from chardet import detect\n enc = detect(s)\n denc = enc.get('encoding', None)\n if denc:\n denc_confidence = enc.get('confidence', 0)\n if confidence is not None and denc_confidence < confidence:\n raise ValueError(\n \"Failed to auto-detect encoding with high enough \"\n \"confidence. Highest confidence was %s for %s\"\n % (denc_confidence, denc)\n )\n lgr.log(5, \"Auto-detected encoding to be %s\", denc)\n return s.decode(denc)\n else:\n raise ValueError(\n \"Could not decode value as utf-8, or to guess its encoding: %s\"\n % repr(s)\n )\n else:\n return s.decode(encoding)\n\n\ndef ensure_bool(s):\n \"\"\"Convert value into boolean following convention for strings\n\n to recognize on,True,yes as True, off,False,no as False\n \"\"\"\n if isinstance(s, str):\n if s.isdigit():\n return bool(int(s))\n sl = s.lower()\n if sl in {'y', 'yes', 'true', 'on'}:\n return True\n elif sl in {'n', 'no', 'false', 'off'}:\n return False\n else:\n raise ValueError(\"Do not know how to treat %r as a boolean\" % s)\n return bool(s)\n\n\ndef as_unicode(val, cast_types=object):\n \"\"\"Given an arbitrary value, would try to obtain unicode value of it\n \n For unicode it would return original value, for python2 str or python3\n bytes it would use ensure_unicode, for None - an empty (unicode) string,\n and for any other type (see `cast_types`) - would apply the unicode \n constructor. If value is not an instance of `cast_types`, TypeError\n is thrown\n \n Parameters\n ----------\n cast_types: type\n Which types to cast to unicode by providing to constructor\n \"\"\"\n if val is None:\n return u''\n elif isinstance(val, str):\n return val\n elif isinstance(val, unicode_srctypes):\n return ensure_unicode(val)\n elif isinstance(val, cast_types):\n return str(val)\n else:\n raise TypeError(\n \"Value %r is not of any of known or provided %s types\"\n % (val, cast_types))\n\n\ndef unique(seq, key=None, reverse=False):\n \"\"\"Given a sequence return a list only with unique elements while maintaining order\n\n This is the fastest solution. See\n https://www.peterbe.com/plog/uniqifiers-benchmark\n and\n http://stackoverflow.com/a/480227/1265472\n for more information.\n Enhancement -- added ability to compare for uniqueness using a key function\n\n Parameters\n ----------\n seq:\n Sequence to analyze\n key: callable, optional\n Function to call on each element so we could decide not on a full\n element, but on its member etc\n reverse: bool, optional\n If True, uniqueness checked in the reverse order, so that the later ones\n will take the order\n \"\"\"\n seen = set()\n seen_add = seen.add\n\n trans = reversed if reverse else lambda x: x\n\n if not key:\n out = [x for x in trans(seq) if not (x in seen or seen_add(x))]\n else:\n # OPT: could be optimized, since key is called twice, but for our cases\n # should be just as fine\n out = [x for x in trans(seq) if not (key(x) in seen or seen_add(key(x)))]\n\n return out[::-1] if reverse else out\n\n\ndef all_same(items):\n \"\"\"Quick check if all items are the same.\n\n Identical to a check like len(set(items)) == 1 but\n should be more efficient while working on generators, since would\n return False as soon as any difference detected thus possibly avoiding\n unnecessary evaluations\n \"\"\"\n first = True\n first_item = None\n for item in items:\n if first:\n first = False\n first_item = item\n else:\n if item != first_item:\n return False\n # So we return False if was empty\n return not first\n\n\ndef map_items(func, v):\n \"\"\"A helper to apply `func` to all elements (keys and values) within dict\n\n No type checking of values passed to func is done, so `func`\n should be resilient to values which it should not handle\n\n Initial usecase - apply_recursive(url_fragment, ensure_unicode)\n \"\"\"\n # map all elements within item\n return v.__class__(\n item.__class__(map(func, item))\n for item in v.items()\n )\n\n\ndef partition(items, predicate=bool):\n \"\"\"Partition `items` by `predicate`.\n\n Parameters\n ----------\n items : iterable\n predicate : callable\n A function that will be mapped over each element in `items`. The\n elements will partitioned based on whether the return value is false or\n true.\n\n Returns\n -------\n A tuple with two generators, the first for 'false' items and the second for\n 'true' ones.\n\n Notes\n -----\n Taken from Peter Otten's snippet posted at\n https://nedbatchelder.com/blog/201306/filter_a_list_into_two_parts.html\n \"\"\"\n a, b = tee((predicate(item), item) for item in items)\n return ((item for pred, item in a if not pred),\n (item for pred, item in b if pred))\n\n\ndef generate_chunks(container, size):\n \"\"\"Given a container, generate chunks from it with size up to `size`\n \"\"\"\n # There could be a \"smarter\" solution but I think this would suffice\n assert size > 0, \"Size should be non-0 positive\"\n while container:\n yield container[:size]\n container = container[size:]\n\n\ndef generate_file_chunks(files, cmd=None):\n \"\"\"Given a list of files, generate chunks of them to avoid exceeding cmdline length\n\n Parameters\n ----------\n files: list of str\n cmd: str or list of str, optional\n Command to account for as well\n \"\"\"\n files = ensure_list(files)\n cmd = ensure_list(cmd)\n\n maxl = max(map(len, files)) if files else 0\n chunk_size = max(\n 1, # should at least be 1. If blows then - not our fault\n (CMD_MAX_ARG\n - sum((len(x) + 3) for x in cmd)\n - 4 # for '--' below\n ) // (maxl + 3) # +3 for possible quotes and a space\n )\n # TODO: additional treatment for \"too many arguments\"? although\n # as https://github.com/datalad/datalad/issues/1883#issuecomment\n # -436272758\n # shows there seems to be no hardcoded limit on # of arguments,\n # but may be we decide to go for smth like follow to be on safe side\n # chunk_size = min(10240 - len(cmd), chunk_size)\n file_chunks = generate_chunks(files, chunk_size)\n return file_chunks\n\n\n#\n# Generators helpers\n#\n\ndef saved_generator(gen):\n \"\"\"Given a generator returns two generators, where 2nd one just replays\n\n So the first one would be going through the generated items and 2nd one\n would be yielding saved items\n \"\"\"\n saved = []\n\n def gen1():\n for x in gen: # iterating over original generator\n saved.append(x)\n yield x\n\n def gen2():\n for x in saved: # yielding saved entries\n yield x\n\n return gen1(), gen2()\n\n\n#\n# Decorators\n#\n\n# Originally better_wraps was created to provide `wrapt`-based, instead of\n# `functools.wraps` implementation to preserve the correct signature of the\n# decorated function. By using inspect.signature in our getargspec, which\n# works fine on `functools.wraps`ed functions, we mediated this necessity.\nbetter_wraps = wraps\n\n\n# Borrowed from pandas\n# Copyright: 2011-2014, Lambda Foundry, Inc. and PyData Development Team\n# License: BSD-3\ndef optional_args(decorator):\n \"\"\"allows a decorator to take optional positional and keyword arguments.\n Assumes that taking a single, callable, positional argument means that\n it is decorating a function, i.e. something like this::\n\n @my_decorator\n def function(): pass\n\n Calls decorator with decorator(f, `*args`, `**kwargs`)\"\"\"\n\n @better_wraps(decorator)\n def wrapper(*args, **kwargs):\n def dec(f):\n return decorator(f, *args, **kwargs)\n\n is_decorating = not kwargs and len(args) == 1 and isinstance(args[0], Callable)\n if is_decorating:\n f = args[0]\n args = []\n return dec(f)\n else:\n return dec\n\n return wrapper\n\n\n# TODO: just provide decorators for tempfile.mk* functions. This is ugly!\ndef get_tempfile_kwargs(tkwargs=None, prefix=\"\", wrapped=None):\n \"\"\"Updates kwargs to be passed to tempfile. calls depending on env vars\n \"\"\"\n if tkwargs is None:\n tkwargs_ = {}\n else:\n # operate on a copy of tkwargs to avoid any side-effects\n tkwargs_ = tkwargs.copy()\n\n # TODO: don't remember why I had this one originally\n # if len(targs)<2 and \\\n if 'prefix' not in tkwargs_:\n tkwargs_['prefix'] = '_'.join(\n ['datalad_temp'] +\n ([prefix] if prefix else []) +\n ([''] if (on_windows or not wrapped) else [wrapped.__name__]))\n\n directory = os.environ.get('TMPDIR')\n if directory and 'dir' not in tkwargs_:\n tkwargs_['dir'] = directory\n\n return tkwargs_\n\n\n@optional_args\ndef line_profile(func):\n \"\"\"Q&D helper to line profile the function and spit out stats\n \"\"\"\n import line_profiler\n prof = line_profiler.LineProfiler()\n\n @wraps(func)\n def _wrap_line_profile(*args, **kwargs):\n try:\n pfunc = prof(func)\n return pfunc(*args, **kwargs)\n finally:\n prof.print_stats()\n return _wrap_line_profile\n\n\n# unused in -core\n@optional_args\ndef collect_method_callstats(func):\n \"\"\"Figure out methods which call the method repeatedly on the same instance\n\n Use case(s):\n - .repo is expensive since does all kinds of checks.\n - .config is expensive transitively since it calls .repo each time\n\n TODO:\n - fancy one could look through the stack for the same id(self) to see if\n that location is already in memo. That would hint to the cases where object\n is not passed into underlying functions, causing them to redo the same work\n over and over again\n - ATM might flood with all \"1 lines\" calls which are not that informative.\n The underlying possibly suboptimal use might be coming from their callers.\n It might or not relate to the previous TODO\n \"\"\"\n from collections import defaultdict\n import traceback\n from time import time\n memo = defaultdict(lambda: defaultdict(int)) # it will be a dict of lineno: count\n # gross timing\n times = []\n toppath = dirname(__file__) + sep\n\n @wraps(func)\n def _wrap_collect_method_callstats(*args, **kwargs):\n try:\n self = args[0]\n stack = traceback.extract_stack()\n caller = stack[-2]\n stack_sig = \\\n \"{relpath}:{s.name}\".format(\n s=caller, relpath=relpath(caller.filename, toppath))\n sig = (id(self), stack_sig)\n # we will count based on id(self) + wherefrom\n memo[sig][caller.lineno] += 1\n t0 = time()\n return func(*args, **kwargs)\n finally:\n times.append(time() - t0)\n pass\n\n def print_stats():\n print(\"The cost of property {}:\".format(func.__name__))\n if not memo:\n print(\"None since no calls\")\n return\n # total count\n counts = {k: sum(v.values()) for k,v in memo.items()}\n total = sum(counts.values())\n ids = {self_id for (self_id, _) in memo}\n print(\" Total: {} calls from {} objects with {} contexts taking {:.2f} sec\"\n .format(total, len(ids), len(memo), sum(times)))\n # now we need to sort by value\n for (self_id, caller), count in sorted(counts.items(), key=lambda x: x[1], reverse=True):\n print(\" {} {}: {} from {} lines\"\n .format(self_id, caller, count, len(memo[(self_id, caller)])))\n\n # Upon total exit we print the stats\n import atexit\n atexit.register(print_stats)\n\n return _wrap_collect_method_callstats\n\n\n# Borrowed from duecredit to wrap duecredit-handling to guarantee failsafe\ndef never_fail(f):\n \"\"\"Assure that function never fails -- all exceptions are caught\n\n Returns `None` if function fails internally.\n \"\"\"\n @wraps(f)\n def wrapped_func(*args, **kwargs):\n try:\n return f(*args, **kwargs)\n except Exception as e:\n lgr.warning(\n \"DataLad internal failure while running %s: %r. \"\n \"Please report at https://github.com/datalad/datalad/issues\"\n % (f, e)\n )\n\n if os.environ.get('DATALAD_ALLOW_FAIL', False):\n return f\n else:\n return wrapped_func\n\n\n#\n# Context Managers\n#\n\n\n# unused in -core\n@contextmanager\ndef nothing_cm():\n \"\"\"Just a dummy cm to programmically switch context managers\"\"\"\n yield\n\n\n@contextmanager\ndef swallow_outputs():\n \"\"\"Context manager to help consuming both stdout and stderr, and print()\n\n stdout is available as cm.out and stderr as cm.err whenever cm is the\n yielded context manager.\n Internally uses temporary files to guarantee absent side-effects of swallowing\n into StringIO which lacks .fileno.\n\n print mocking is necessary for some uses where sys.stdout was already bound\n to original sys.stdout, thus mocking it later had no effect. Overriding\n print function had desired effect\n \"\"\"\n\n class StringIOAdapter(object):\n \"\"\"Little adapter to help getting out/err values\n \"\"\"\n def __init__(self):\n kw = get_tempfile_kwargs({}, prefix=\"outputs\")\n\n self._out = NamedTemporaryFile(delete=False, mode='w', **kw)\n self._err = NamedTemporaryFile(delete=False, mode='w', **kw)\n\n def _read(self, h):\n with open(h.name) as f:\n return f.read()\n\n @property\n def out(self):\n if not self._out.closed:\n self._out.flush()\n return self._read(self._out)\n\n @property\n def err(self):\n if not self._err.closed:\n self._err.flush()\n return self._read(self._err)\n\n @property\n def handles(self):\n return self._out, self._err\n\n def cleanup(self):\n self._out.close()\n self._err.close()\n out_name = self._out.name\n err_name = self._err.name\n from datalad import cfg\n if cfg.getbool('datalad.log', 'outputs', default=False) \\\n and lgr.getEffectiveLevel() <= logging.DEBUG:\n for s, sname in ((self.out, 'stdout'),\n (self.err, 'stderr')):\n if s:\n pref = os.linesep + \"| \"\n lgr.debug(\"Swallowed %s:%s%s\", sname, pref, s.replace(os.linesep, pref))\n else:\n lgr.debug(\"Nothing was swallowed for %s\", sname)\n del self._out\n del self._err\n gc.collect()\n rmtemp(out_name)\n rmtemp(err_name)\n\n def fake_print(*args, **kwargs):\n sep = kwargs.pop('sep', ' ')\n end = kwargs.pop('end', '\\n')\n file = kwargs.pop('file', sys.stdout)\n\n if file in (oldout, olderr, sys.stdout, sys.stderr):\n # we mock\n try:\n sys.stdout.write(sep.join(args) + end)\n except UnicodeEncodeError as exc:\n lgr.error(\n \"Failed to write to mocked stdout, got %s, continue as it \"\n \"didn't happen\", exc)\n else:\n # must be some other file one -- leave it alone\n oldprint(*args, sep=sep, end=end, file=file)\n\n from .ui import ui\n # preserve -- they could have been mocked already\n oldprint = getattr(builtins, 'print')\n oldout, olderr = sys.stdout, sys.stderr\n olduiout = ui.out\n adapter = StringIOAdapter()\n\n try:\n sys.stdout, sys.stderr = adapter.handles\n ui.out = adapter.handles[0]\n setattr(builtins, 'print', fake_print)\n\n yield adapter\n finally:\n sys.stdout, sys.stderr, ui.out = oldout, olderr, olduiout\n setattr(builtins, 'print', oldprint)\n adapter.cleanup()\n\n\n@contextmanager\ndef swallow_logs(new_level=None, file_=None, name='datalad'):\n \"\"\"Context manager to consume all logs.\n\n \"\"\"\n lgr = logging.getLogger(name)\n\n # Keep old settings\n old_level = lgr.level\n old_handlers = lgr.handlers\n\n # Let's log everything into a string\n # TODO: generalize with the one for swallow_outputs\n class StringIOAdapter(object):\n \"\"\"Little adapter to help getting out values\n\n And to stay consistent with how swallow_outputs behaves\n \"\"\"\n def __init__(self):\n if file_ is None:\n kw = get_tempfile_kwargs({}, prefix=\"logs\")\n self._out = NamedTemporaryFile(mode='a', delete=False, **kw)\n else:\n out_file = file_\n # PY3 requires clearly one or another. race condition possible\n self._out = open(out_file, 'a')\n self._final_out = None\n\n def _read(self, h):\n with open(h.name) as f:\n return f.read()\n\n @property\n def out(self):\n if self._final_out is not None:\n # we closed and cleaned up already\n return self._final_out\n else:\n self._out.flush()\n return self._read(self._out)\n\n @property\n def lines(self):\n return self.out.split('\\n')\n\n @property\n def handle(self):\n return self._out\n\n def cleanup(self):\n # store for access while object exists\n self._final_out = self.out\n self._out.close()\n out_name = self._out.name\n del self._out\n gc.collect()\n if not file_:\n rmtemp(out_name)\n\n def assert_logged(self, msg=None, level=None, regex=True, **kwargs):\n \"\"\"Provide assertion on whether a msg was logged at a given level\n\n If neither `msg` nor `level` provided, checks if anything was logged\n at all.\n\n Parameters\n ----------\n msg: str, optional\n Message (as a regular expression, if `regex`) to be searched.\n If no msg provided, checks if anything was logged at a given level.\n level: str, optional\n String representing the level to be logged\n regex: bool, optional\n If False, regular `assert_in` is used\n **kwargs: str, optional\n Passed to `assert_re_in` or `assert_in`\n \"\"\"\n from datalad.tests.utils import assert_re_in\n from datalad.tests.utils import assert_in\n\n if regex:\n match = r'\\[%s\\] ' % level if level else r\"\\[\\S+\\] \"\n else:\n match = '[%s] ' % level if level else ''\n\n if msg:\n match += msg\n\n if match:\n (assert_re_in if regex else assert_in)(match, self.out, **kwargs)\n else:\n assert not kwargs, \"no kwargs to be passed anywhere\"\n assert self.out, \"Nothing was logged!?\"\n\n adapter = StringIOAdapter()\n # TODO: it does store messages but without any formatting, i.e. even without\n # date/time prefix etc. IMHO it should preserve formatting in case if file_ is\n # set\n swallow_handler = logging.StreamHandler(adapter.handle)\n # we want to log levelname so we could test against it\n swallow_handler.setFormatter(\n logging.Formatter('[%(levelname)s] %(message)s'))\n swallow_handler.filters = sum([h.filters for h in old_handlers],\n [])\n lgr.handlers = [swallow_handler]\n if old_level < logging.DEBUG: # so if HEAVYDEBUG etc -- show them!\n lgr.handlers += old_handlers\n\n if isinstance(new_level, str):\n new_level = getattr(logging, new_level)\n\n if new_level is not None:\n lgr.setLevel(new_level)\n\n try:\n yield adapter\n # TODO: if file_ and there was an exception -- most probably worth logging it?\n # although ideally it should be the next log outside added to that file_ ... oh well\n finally:\n lgr.handlers = old_handlers\n lgr.setLevel(old_level)\n adapter.cleanup()\n\n\n# TODO: May be melt in with swallow_logs at some point:\n@contextmanager\ndef disable_logger(logger=None):\n \"\"\"context manager to temporarily disable logging\n\n This is to provide one of swallow_logs' purposes without unnecessarily\n creating temp files (see gh-1865)\n\n Parameters\n ----------\n logger: Logger\n Logger whose handlers will be ordered to not log anything.\n Default: datalad's topmost Logger ('datalad')\n \"\"\"\n\n class NullFilter(logging.Filter):\n \"\"\"Filter class to reject all records\n \"\"\"\n def filter(self, record):\n return 0\n\n if logger is None:\n # default: all of datalad's logging:\n logger = logging.getLogger('datalad')\n\n filter_ = NullFilter(logger.name)\n [h.addFilter(filter_) for h in logger.handlers]\n\n try:\n yield logger\n finally:\n [h.removeFilter(filter_) for h in logger.handlers]\n\n\n#\n# Additional handlers\n#\n_sys_excepthook = sys.excepthook # Just in case we ever need original one\n\n\ndef setup_exceptionhook(ipython=False):\n \"\"\"Overloads default sys.excepthook with our exceptionhook handler.\n\n If interactive, our exceptionhook handler will invoke\n pdb.post_mortem; if not interactive, then invokes default handler.\n \"\"\"\n\n def _datalad_pdb_excepthook(type, value, tb):\n import traceback\n traceback.print_exception(type, value, tb)\n print()\n if is_interactive():\n import pdb\n pdb.post_mortem(tb)\n\n if ipython:\n from IPython.core import ultratb\n sys.excepthook = ultratb.FormattedTB(mode='Verbose',\n # color_scheme='Linux',\n call_pdb=is_interactive())\n else:\n sys.excepthook = _datalad_pdb_excepthook\n\n\ndef ensure_dir(*args):\n \"\"\"Make sure directory exists.\n\n Joins the list of arguments to an os-specific path to the desired\n directory and creates it, if it not exists yet.\n \"\"\"\n dirname = op.join(*args)\n if not exists(dirname):\n os.makedirs(dirname)\n return dirname\n\n\ndef updated(d, update):\n \"\"\"Return a copy of the input with the 'update'\n\n Primarily for updating dictionaries\n \"\"\"\n d = d.copy()\n d.update(update)\n return d\n\n\n_pwd_mode = None\n\n\ndef _switch_to_getcwd(msg, *args):\n global _pwd_mode\n _pwd_mode = 'cwd'\n lgr.debug(\n msg + \". From now on will be returning os.getcwd(). Directory\"\n \" symlinks in the paths will be resolved\",\n *args\n )\n # TODO: we might want to mitigate by going through all flywheighted\n # repos and tuning up their .paths to be resolved?\n\n\ndef getpwd():\n \"\"\"Try to return a CWD without dereferencing possible symlinks\n\n This function will try to use PWD environment variable to provide a current\n working directory, possibly with some directories along the path being\n symlinks to other directories. Unfortunately, PWD is used/set only by the\n shell and such functions as `os.chdir` and `os.getcwd` nohow use or modify\n it, thus `os.getcwd()` returns path with links dereferenced.\n\n While returning current working directory based on PWD env variable we\n verify that the directory is the same as `os.getcwd()` after resolving all\n symlinks. If that verification fails, we fall back to always use\n `os.getcwd()`.\n\n Initial decision to either use PWD env variable or os.getcwd() is done upon\n the first call of this function.\n \"\"\"\n global _pwd_mode\n if _pwd_mode is None:\n # we need to decide!\n try:\n pwd = os.environ['PWD']\n if on_windows and pwd and pwd.startswith('/'):\n # It should be a path from MSYS.\n # - it might start with a drive letter or not\n # - it seems to be \"illegal\" to have a single letter directories\n # under / path, i.e. if created - they aren't found\n # - 'ln -s' does not fail to create a \"symlink\" but it just\n # copies!\n # so we are not likely to need original PWD purpose on\n # those systems\n # Verdict:\n _pwd_mode = 'cwd'\n else:\n _pwd_mode = 'PWD'\n except KeyError:\n _pwd_mode = 'cwd'\n\n if _pwd_mode == 'cwd':\n return os.getcwd()\n elif _pwd_mode == 'PWD':\n try:\n cwd = os.getcwd()\n except OSError as exc:\n if \"o such file\" in str(exc):\n # directory was removed but we promised to be robust and\n # still report the path we might know since we are still in PWD\n # mode\n cwd = None\n else:\n raise\n try:\n pwd = os.environ['PWD']\n # do absolute() in addition to always get an absolute path\n # even with non-existing paths on windows\n pwd_real = str(Path(pwd).resolve().absolute())\n # This logic would fail to catch the case where chdir did happen\n # to the directory where current PWD is pointing to, e.g.\n # $> ls -ld $PWD\n # lrwxrwxrwx 1 yoh yoh 5 Oct 11 13:27 /home/yoh/.tmp/tmp -> /tmp//\n # hopa:~/.tmp/tmp\n # $> python -c 'import os; os.chdir(\"/tmp\"); from datalad.utils import getpwd; print(getpwd(), os.getcwd())'\n # ('/home/yoh/.tmp/tmp', '/tmp')\n # but I guess that should not be too harmful\n if cwd is not None and pwd_real != cwd:\n _switch_to_getcwd(\n \"realpath of PWD=%s is %s whenever os.getcwd()=%s\",\n pwd, pwd_real, cwd\n )\n return cwd\n return pwd\n except KeyError:\n _switch_to_getcwd(\"PWD env variable is no longer available\")\n return cwd # Must not happen, but may be someone\n # evil purges PWD from environ?\n else:\n raise RuntimeError(\n \"Must have not got here. \"\n \"pwd_mode must be either cwd or PWD. And it is now %r\" % (_pwd_mode,)\n )\n\n\nclass chpwd(object):\n \"\"\"Wrapper around os.chdir which also adjusts environ['PWD']\n\n The reason is that otherwise PWD is simply inherited from the shell\n and we have no ability to assess directory path without dereferencing\n symlinks.\n\n If used as a context manager it allows to temporarily change directory\n to the given path\n \"\"\"\n def __init__(self, path, mkdir=False, logsuffix=''):\n\n if path:\n pwd = getpwd()\n self._prev_pwd = pwd\n else:\n self._prev_pwd = None\n return\n\n if not isabs(path):\n path = normpath(op.join(pwd, path))\n if not os.path.exists(path) and mkdir:\n self._mkdir = True\n os.mkdir(path)\n else:\n self._mkdir = False\n lgr.debug(\"chdir %r -> %r %s\", self._prev_pwd, path, logsuffix)\n os.chdir(path) # for grep people -- ok, to chdir here!\n os.environ['PWD'] = str(path)\n\n def __enter__(self):\n # nothing more to do really, chdir was in the constructor\n pass\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n if self._prev_pwd:\n # Need to use self.__class__ so this instance, if the entire\n # thing mocked during the test, still would use correct chpwd\n self.__class__(self._prev_pwd, logsuffix=\"(coming back)\")\n\n\ndef dlabspath(path, norm=False):\n \"\"\"Symlinks-in-the-cwd aware abspath\n\n os.path.abspath relies on os.getcwd() which would not know about symlinks\n in the path\n\n TODO: we might want to norm=True by default to match behavior of\n os .path.abspath?\n \"\"\"\n if not isabs(path):\n # if not absolute -- relative to pwd\n path = op.join(getpwd(), path)\n return normpath(path) if norm else path\n\n\ndef with_pathsep(path):\n \"\"\"Little helper to guarantee that path ends with /\"\"\"\n return path + sep if not path.endswith(sep) else path\n\n\ndef get_path_prefix(path, pwd=None):\n \"\"\"Get path prefix (for current directory)\n\n Returns relative path to the topdir, if we are under topdir, and if not\n absolute path to topdir. If `pwd` is not specified - current directory\n assumed\n \"\"\"\n pwd = pwd or getpwd()\n path = dlabspath(path)\n path_ = with_pathsep(path)\n pwd_ = with_pathsep(pwd)\n common = commonprefix((path_, pwd_))\n if common.endswith(sep) and common in {path_, pwd_}:\n # we are in subdir or above the path = use relative path\n location_prefix = relpath(path, pwd)\n # if benign \"here\" - cut off\n if location_prefix in (curdir, curdir + sep):\n location_prefix = ''\n return location_prefix\n else:\n # just return absolute path\n return path\n\n\ndef _get_normalized_paths(path, prefix):\n if isabs(path) != isabs(prefix):\n raise ValueError(\"Both paths must either be absolute or relative. \"\n \"Got %r and %r\" % (path, prefix))\n path = with_pathsep(path)\n prefix = with_pathsep(prefix)\n return path, prefix\n\n\ndef path_startswith(path, prefix):\n \"\"\"Return True if path starts with prefix path\n\n Parameters\n ----------\n path: str\n prefix: str\n \"\"\"\n path, prefix = _get_normalized_paths(path, prefix)\n return path.startswith(prefix)\n\n\ndef path_is_subpath(path, prefix):\n \"\"\"Return True if path is a subpath of prefix\n\n It will return False if path == prefix.\n\n Parameters\n ----------\n path: str\n prefix: str\n \"\"\"\n path, prefix = _get_normalized_paths(path, prefix)\n return (len(prefix) < len(path)) and path.startswith(prefix)\n\n\ndef knows_annex(path):\n \"\"\"Returns whether at a given path there is information about an annex\n\n It is just a thin wrapper around GitRepo.is_with_annex() classmethod\n which also checks for `path` to exist first.\n\n This includes actually present annexes, but also uninitialized ones, or\n even the presence of a remote annex branch.\n \"\"\"\n from os.path import exists\n if not exists(path):\n lgr.debug(\"No annex: test path {0} doesn't exist\".format(path))\n return False\n from datalad.support.gitrepo import GitRepo\n return GitRepo(path, init=False, create=False).is_with_annex()\n\n\n@contextmanager\ndef make_tempfile(content=None, wrapped=None, **tkwargs):\n \"\"\"Helper class to provide a temporary file name and remove it at the end (context manager)\n\n Parameters\n ----------\n mkdir : bool, optional (default: False)\n If True, temporary directory created using tempfile.mkdtemp()\n content : str or bytes, optional\n Content to be stored in the file created\n wrapped : function, optional\n If set, function name used to prefix temporary file name\n `**tkwargs`:\n All other arguments are passed into the call to tempfile.mk{,d}temp(),\n and resultant temporary filename is passed as the first argument into\n the function t. If no 'prefix' argument is provided, it will be\n constructed using module and function names ('.' replaced with\n '_').\n\n To change the used directory without providing keyword argument 'dir' set\n DATALAD_TESTS_TEMP_DIR.\n\n Examples\n --------\n >>> from os.path import exists\n >>> from datalad.utils import make_tempfile\n >>> with make_tempfile() as fname:\n ... k = open(fname, 'w').write('silly test')\n >>> assert not exists(fname) # was removed\n\n >>> with make_tempfile(content=\"blah\") as fname:\n ... assert open(fname).read() == \"blah\"\n \"\"\"\n\n if tkwargs.get('mkdir', None) and content is not None:\n raise ValueError(\"mkdir=True while providing content makes no sense\")\n\n tkwargs_ = get_tempfile_kwargs(tkwargs, wrapped=wrapped)\n\n # if DATALAD_TESTS_TEMP_DIR is set, use that as directory,\n # let mktemp handle it otherwise. However, an explicitly provided\n # dir=... will override this.\n mkdir = tkwargs_.pop('mkdir', False)\n\n filename = {False: tempfile.mktemp,\n True: tempfile.mkdtemp}[mkdir](**tkwargs_)\n # MIH: not clear to me why we need to perform this (possibly expensive)\n # resolve. It was already part of the original implementation\n # 008d9ab8cc3e0170c0a9b8479e80dee9ffe6eb7f\n filename = Path(filename).resolve()\n\n if content:\n (filename.write_bytes\n if isinstance(content, bytes)\n else filename.write_text)(content)\n\n # TODO globbing below can also be done with pathlib\n filename = str(filename)\n\n if __debug__:\n lgr.debug(\n 'Created temporary %s named %s',\n 'directory' if mkdir else 'file',\n filename)\n try:\n yield filename\n finally:\n # glob here for all files with the same name (-suffix)\n # would be useful whenever we requested .img filename,\n # and function creates .hdr as well\n # MIH: this is undocumented behavior, and undesired in the general\n # case. it should be made conditional and explicit\n lsuffix = len(tkwargs_.get('suffix', ''))\n filename_ = lsuffix and filename[:-lsuffix] or filename\n filenames = glob.glob(filename_ + '*')\n if len(filename_) < 3 or len(filenames) > 5:\n # For paranoid yoh who stepped into this already ones ;-)\n lgr.warning(\"It is unlikely that it was intended to remove all\"\n \" files matching %r. Skipping\" % filename_)\n return\n for f in filenames:\n try:\n rmtemp(f)\n except OSError: # pragma: no cover\n pass\n\n\ndef _path_(*p):\n \"\"\"Given a path in POSIX\" notation, regenerate one in native to the env one\"\"\"\n if on_windows:\n return op.join(*map(lambda x: op.join(*x.split('/')), p))\n else:\n # Assume that all others as POSIX compliant so nothing to be done\n return op.join(*p)\n\n\ndef get_timestamp_suffix(time_=None, prefix='-'):\n \"\"\"Return a time stamp (full date and time up to second)\n\n primarily to be used for generation of log files names\n \"\"\"\n args = []\n if time_ is not None:\n if isinstance(time_, int):\n time_ = time.gmtime(time_)\n args.append(time_)\n return time.strftime(prefix + TIMESTAMP_FMT, *args)\n\n\n# unused in -core\ndef get_logfilename(dspath, cmd='datalad'):\n \"\"\"Return a filename to use for logging under a dataset/repository\n\n directory would be created if doesn't exist, but dspath must exist\n and be a directory\n \"\"\"\n assert(exists(dspath))\n assert(isdir(dspath))\n ds_logdir = ensure_dir(dspath, '.git', 'datalad', 'logs') # TODO: use WEB_META_LOG whenever #789 merged\n return op.join(ds_logdir, 'crawl-%s.log' % get_timestamp_suffix())\n\n\ndef get_trace(edges, start, end, trace=None):\n \"\"\"Return the trace/path to reach a node in a tree.\n\n Parameters\n ----------\n edges : sequence(2-tuple)\n The tree given by a sequence of edges (parent, child) tuples. The\n nodes can be identified by any value and data type that supports\n the '==' operation.\n start :\n Identifier of the start node. Must be present as a value in the parent\n location of an edge tuple in order to be found.\n end :\n Identifier of the target/end node. Must be present as a value in the child\n location of an edge tuple in order to be found.\n trace : list\n Mostly useful for recursive calls, and used internally.\n\n Returns\n -------\n None or list\n Returns a list with the trace to the target (the starts and the target\n are not included in the trace, hence if start and end are directly connected\n an empty list is returned), or None when no trace to the target can be found,\n or start and end are identical.\n \"\"\"\n # the term trace is used to avoid confusion with a path in the sense\n # of a filesystem path, but the analogy fits and nodes can be paths\n if trace is None:\n trace = []\n if not edges:\n raise ValueError(\"no edges given\")\n for cand in edges:\n cand_super, cand_sub = cand\n if cand_sub in trace:\n # only DAGs, skip any cyclic traces\n continue\n if trace and cand_super != trace[-1]:\n # only consider edges that lead off the end of the trace\n continue\n if not trace and cand_super != start:\n # we got nothing yet, and this edges is not matching the start\n continue\n if cand_sub == end:\n return trace\n # dive into potential subnodes\n cand_trace = get_trace(\n edges,\n start,\n end,\n trace + [cand_sub])\n if cand_trace:\n return cand_trace\n return None\n\n\ndef get_dataset_root(path):\n \"\"\"Return the root of an existent dataset containing a given path\n\n The root path is returned in the same absolute or relative form\n as the input argument. If no associated dataset exists, or the\n input path doesn't exist, None is returned.\n\n If `path` is a symlink or something other than a directory, its\n the root dataset containing its parent directory will be reported.\n If none can be found, at a symlink at `path` is pointing to a\n dataset, `path` itself will be reported as the root.\n\n Parameters\n ----------\n path : Path-like\n\n Returns\n -------\n str or None\n \"\"\"\n path = str(path)\n suffix = '.git'\n altered = None\n if islink(path) or not isdir(path):\n altered = path\n path = dirname(path)\n apath = abspath(path)\n # while we can still go up\n while split(apath)[1]:\n if exists(op.join(path, suffix)):\n return path\n # new test path in the format we got it\n path = normpath(op.join(path, os.pardir))\n # no luck, next round\n apath = abspath(path)\n # if we applied dirname() at the top, we give it another go with\n # the actual path, if it was itself a symlink, it could be the\n # top-level dataset itself\n if altered and exists(op.join(altered, suffix)):\n return altered\n\n return None\n\n\n# ATM used in datalad_crawler extension, so do not remove yet\ndef try_multiple(ntrials, exception, base, f, *args, **kwargs):\n \"\"\"Call f multiple times making exponentially growing delay between the calls\"\"\"\n for trial in range(1, ntrials+1):\n try:\n return f(*args, **kwargs)\n except exception as exc:\n if trial == ntrials:\n raise # just reraise on the last trial\n t = base ** trial\n lgr.warning(\"Caught %s on trial #%d. Sleeping %f and retrying\",\n CapturedException(exc), trial, t)\n sleep(t)\n\n\n@optional_args\ndef try_multiple_dec(\n f, ntrials=None, duration=0.1, exceptions=None, increment_type=None,\n exceptions_filter=None,\n logger=None,\n):\n \"\"\"Decorator to try function multiple times.\n\n Main purpose is to decorate functions dealing with removal of files/directories\n and which might need a few seconds to work correctly on Windows which takes\n its time to release files/directories.\n\n Parameters\n ----------\n ntrials: int, optional\n duration: float, optional\n Seconds to sleep before retrying.\n increment_type: {None, 'exponential'}\n Note that if it is exponential, duration should typically be > 1.0\n so it grows with higher power\n exceptions: Exception or tuple of Exceptions, optional\n Exception or a tuple of multiple exceptions, on which to retry\n exceptions_filter: callable, optional\n If provided, this function will be called with a caught exception\n instance. If function returns True - we will re-try, if False - exception\n will be re-raised without retrying.\n logger: callable, optional\n Logger to log upon failure. If not provided, will use stock logger\n at the level of 5 (heavy debug).\n \"\"\"\n if not exceptions:\n exceptions = (OSError, WindowsError, PermissionError) \\\n if on_windows else OSError\n if not ntrials:\n # Life goes fast on proper systems, no need to delay it much\n ntrials = 100 if on_windows else 10\n if logger is None:\n def logger(*args, **kwargs):\n return lgr.log(5, *args, **kwargs)\n assert increment_type in {None, 'exponential'}\n\n @wraps(f)\n def _wrap_try_multiple_dec(*args, **kwargs):\n t = duration\n for trial in range(ntrials):\n try:\n return f(*args, **kwargs)\n except exceptions as exc:\n if exceptions_filter and not exceptions_filter(exc):\n raise\n if trial < ntrials - 1:\n if increment_type == 'exponential':\n t = duration ** (trial + 1)\n logger(\n \"Caught %s on trial #%d. Sleeping %f and retrying\",\n CapturedException(exc), trial, t)\n sleep(t)\n else:\n raise\n\n return _wrap_try_multiple_dec\n\n\n@try_multiple_dec\ndef unlink(f):\n \"\"\"'Robust' unlink. Would try multiple times\n\n On windows boxes there is evidence for a latency of more than a second\n until a file is considered no longer \"in-use\".\n WindowsError is not known on Linux, and if IOError or any other\n exception\n is thrown then if except statement has WindowsError in it -- NameError\n also see gh-2533\n \"\"\"\n # Check for open files\n assert_no_open_files(f)\n return os.unlink(f)\n\n\n@try_multiple_dec\ndef _rmtree(*args, **kwargs):\n \"\"\"Just a helper to decorate shutil.rmtree.\n\n rmtree defined above does more and ideally should not itself be decorated\n since a recursive definition and does checks for open files inside etc -\n might be too runtime expensive\n \"\"\"\n return shutil.rmtree(*args, **kwargs)\n\n\ndef slash_join(base, extension):\n \"\"\"Join two strings with a '/', avoiding duplicate slashes\n\n If any of the strings is None the other is returned as is.\n \"\"\"\n if extension is None:\n return base\n if base is None:\n return extension\n return '/'.join(\n (base.rstrip('/'),\n extension.lstrip('/')))\n\n\n#\n# IO Helpers\n#\n\n# unused in -core\ndef open_r_encdetect(fname, readahead=1000):\n \"\"\"Return a file object in read mode with auto-detected encoding\n\n This is helpful when dealing with files of unknown encoding.\n\n Parameters\n ----------\n readahead: int, optional\n How many bytes to read for guessing the encoding type. If\n negative - full file will be read\n \"\"\"\n from chardet import detect\n import io\n # read some bytes from the file\n with open(fname, 'rb') as f:\n head = f.read(readahead)\n enc = detect(head)\n denc = enc.get('encoding', None)\n lgr.debug(\"Auto-detected encoding %s for file %s (confidence: %s)\",\n denc,\n fname,\n enc.get('confidence', 'unknown'))\n return io.open(fname, encoding=denc)\n\n\ndef read_file(fname, decode=True):\n \"\"\"A helper to read file passing content via ensure_unicode\n\n Parameters\n ----------\n decode: bool, optional\n if False, no ensure_unicode and file content returned as bytes\n \"\"\"\n with open(fname, 'rb') as f:\n content = f.read()\n return ensure_unicode(content) if decode else content\n\n\ndef read_csv_lines(fname, dialect=None, readahead=16384, **kwargs):\n \"\"\"A generator of dict records from a CSV/TSV\n\n Automatically guesses the encoding for each record to convert to UTF-8\n\n Parameters\n ----------\n fname: str\n Filename\n dialect: str, optional\n Dialect to specify to csv.reader. If not specified -- guessed from\n the file, if fails to guess, \"excel-tab\" is assumed\n readahead: int, optional\n How many bytes to read from the file to guess the type\n **kwargs\n Passed to `csv.reader`\n \"\"\"\n import csv\n if dialect is None:\n with open(fname) as tsvfile:\n # add robustness, use a sniffer\n try:\n dialect = csv.Sniffer().sniff(tsvfile.read(readahead))\n except Exception as exc:\n lgr.warning(\n 'Could not determine file-format, assuming TSV: %s',\n CapturedException(exc)\n )\n dialect = 'excel-tab'\n\n kw = dict(encoding='utf-8')\n with open(fname, 'r', **kw) as tsvfile:\n # csv.py doesn't do Unicode; encode temporarily as UTF-8:\n csv_reader = csv.reader(\n tsvfile,\n dialect=dialect,\n **kwargs\n )\n header = None\n for row in csv_reader:\n # decode UTF-8 back to Unicode, cell by cell:\n row_unicode = map(ensure_unicode, row)\n if header is None:\n header = list(row_unicode)\n else:\n yield dict(zip(header, row_unicode))\n\n\ndef import_modules(modnames, pkg, msg=\"Failed to import {module}\", log=lgr.debug):\n \"\"\"Helper to import a list of modules without failing if N/A\n\n Parameters\n ----------\n modnames: list of str\n List of module names to import\n pkg: str\n Package under which to import\n msg: str, optional\n Message template for .format() to log at DEBUG level if import fails.\n Keys {module} and {package} will be provided and ': {exception}' appended\n log: callable, optional\n Logger call to use for logging messages\n \"\"\"\n from importlib import import_module\n _globals = globals()\n mods_loaded = []\n if pkg and not pkg in sys.modules:\n # with python 3.5.1 (ok with 3.5.5) somehow kept running into\n # Failed to import dlsub1: Parent module 'dltestm1' not loaded\n # while running the test. Preloading pkg resolved the issue\n import_module(pkg)\n for modname in modnames:\n try:\n _globals[modname] = mod = import_module(\n '.{}'.format(modname),\n pkg)\n mods_loaded.append(mod)\n except Exception as exc:\n from datalad.support.exceptions import CapturedException\n ce = CapturedException(exc)\n log((msg + ': {exception}').format(\n module=modname, package=pkg, exception=ce.message))\n return mods_loaded\n\n\ndef import_module_from_file(modpath, pkg=None, log=lgr.debug):\n \"\"\"Import provided module given a path\n\n TODO:\n - RF/make use of it in pipeline.py which has similar logic\n - join with import_modules above?\n\n Parameters\n ----------\n pkg: module, optional\n If provided, and modpath is under pkg.__path__, relative import will be\n used\n \"\"\"\n assert(modpath.endswith('.py')) # for now just for .py files\n\n log(\"Importing %s\" % modpath)\n\n modname = basename(modpath)[:-3]\n relmodpath = None\n if pkg:\n for pkgpath in pkg.__path__:\n if path_is_subpath(modpath, pkgpath):\n # for now relying on having .py extension -- assertion above\n relmodpath = '.' + relpath(modpath[:-3], pkgpath).replace(sep, '.')\n break\n\n try:\n if relmodpath:\n from importlib import import_module\n mod = import_module(relmodpath, pkg.__name__)\n else:\n dirname_ = dirname(modpath)\n try:\n sys.path.insert(0, dirname_)\n mod = __import__(modname, level=0)\n finally:\n if dirname_ in sys.path:\n sys.path.pop(sys.path.index(dirname_))\n else:\n log(\"Expected path %s to be within sys.path, but it was gone!\" % dirname_)\n except Exception as e:\n raise RuntimeError(\n \"Failed to import module from %s\" % modpath) from e\n\n return mod\n\n\ndef get_encoding_info():\n \"\"\"Return a dictionary with various encoding/locale information\"\"\"\n import sys, locale\n from collections import OrderedDict\n return OrderedDict([\n ('default', sys.getdefaultencoding()),\n ('filesystem', sys.getfilesystemencoding()),\n ('locale.prefered', locale.getpreferredencoding()),\n ])\n\n\ndef get_envvars_info():\n from collections import OrderedDict\n envs = []\n for var, val in os.environ.items():\n if (\n var.startswith('PYTHON') or\n var.startswith('LC_') or\n var.startswith('GIT_') or\n var in ('LANG', 'LANGUAGE', 'PATH')\n ):\n envs.append((var, val))\n return OrderedDict(envs)\n\n\n# This class is modified from Snakemake (v5.1.4)\nclass SequenceFormatter(string.Formatter):\n \"\"\"string.Formatter subclass with special behavior for sequences.\n\n This class delegates formatting of individual elements to another\n formatter object. Non-list objects are formatted by calling the\n delegate formatter's \"format_field\" method. List-like objects\n (list, tuple, set, frozenset) are formatted by formatting each\n element of the list according to the specified format spec using\n the delegate formatter and then joining the resulting strings with\n a separator (space by default).\n \"\"\"\n\n def __init__(self, separator=\" \", element_formatter=string.Formatter(),\n *args, **kwargs):\n self.separator = separator\n self.element_formatter = element_formatter\n\n def format_element(self, elem, format_spec):\n \"\"\"Format a single element\n\n For sequences, this is called once for each element in a\n sequence. For anything else, it is called on the entire\n object. It is intended to be overridden in subclases.\n \"\"\"\n return self.element_formatter.format_field(elem, format_spec)\n\n def format_field(self, value, format_spec):\n if isinstance(value, (list, tuple, set, frozenset)):\n return self.separator.join(self.format_element(v, format_spec)\n for v in value)\n else:\n return self.format_element(value, format_spec)\n\n\n# TODO: eventually we might want to make use of attr module\nclass File(object):\n \"\"\"Helper for a file entry in the create_tree/@with_tree\n\n It allows to define additional settings for entries\n \"\"\"\n def __init__(self, name, executable=False):\n \"\"\"\n\n Parameters\n ----------\n name : str\n Name of the file\n executable: bool, optional\n Make it executable\n \"\"\"\n self.name = name\n self.executable = executable\n\n def __str__(self):\n return self.name\n\n\ndef create_tree_archive(path, name, load, overwrite=False, archives_leading_dir=True):\n \"\"\"Given an archive `name`, create under `path` with specified `load` tree\n \"\"\"\n from datalad.support.archives import compress_files\n dirname = file_basename(name)\n full_dirname = op.join(path, dirname)\n os.makedirs(full_dirname)\n create_tree(full_dirname, load, archives_leading_dir=archives_leading_dir)\n # create archive\n if archives_leading_dir:\n compress_files([dirname], name, path=path, overwrite=overwrite)\n else:\n compress_files(list(map(basename, glob.glob(op.join(full_dirname, '*')))),\n op.join(pardir, name),\n path=op.join(path, dirname),\n overwrite=overwrite)\n # remove original tree\n rmtree(full_dirname)\n\n\ndef create_tree(path, tree, archives_leading_dir=True, remove_existing=False):\n \"\"\"Given a list of tuples (name, load) create such a tree\n\n if load is a tuple itself -- that would create either a subtree or an archive\n with that content and place it into the tree if name ends with .tar.gz\n \"\"\"\n lgr.log(5, \"Creating a tree under %s\", path)\n if not exists(path):\n os.makedirs(path)\n\n if isinstance(tree, dict):\n tree = tree.items()\n\n for file_, load in tree:\n if isinstance(file_, File):\n executable = file_.executable\n name = file_.name\n else:\n executable = False\n name = file_\n full_name = op.join(path, name)\n if remove_existing and lexists(full_name):\n rmtree(full_name, chmod_files=True)\n if isinstance(load, (tuple, list, dict)):\n if name.endswith('.tar.gz') or name.endswith('.tar') or name.endswith('.zip'):\n create_tree_archive(\n path, name, load,\n archives_leading_dir=archives_leading_dir)\n else:\n create_tree(\n full_name, load,\n archives_leading_dir=archives_leading_dir,\n remove_existing=remove_existing)\n else:\n open_func = open\n if full_name.endswith('.gz'):\n open_func = gzip.open\n elif full_name.split('.')[-1] in ('xz', 'lzma'):\n import lzma\n open_func = lzma.open\n with open_func(full_name, \"wb\") as f:\n f.write(ensure_bytes(load, 'utf-8'))\n if executable:\n os.chmod(full_name, os.stat(full_name).st_mode | stat.S_IEXEC)\n\n\ndef get_suggestions_msg(values, known, sep=\"\\n \"):\n \"\"\"Return a formatted string with suggestions for values given the known ones\n \"\"\"\n import difflib\n suggestions = []\n for value in ensure_list(values): # might not want to do it if we change presentation below\n suggestions += difflib.get_close_matches(value, known)\n suggestions = unique(suggestions)\n msg = \"Did you mean any of these?\"\n if suggestions:\n if '\\n' in sep:\n # if separator includes new line - we add entire separator right away\n msg += sep\n else:\n msg += ' '\n return msg + \"%s\\n\" % sep.join(suggestions)\n return ''\n\n\ndef bytes2human(n, format='%(value).1f %(symbol)sB'):\n \"\"\"\n Convert n bytes into a human readable string based on format.\n symbols can be either \"customary\", \"customary_ext\", \"iec\" or \"iec_ext\",\n see: http://goo.gl/kTQMs\n\n >>> from datalad.utils import bytes2human\n >>> bytes2human(1)\n '1.0 B'\n >>> bytes2human(1024)\n '1.0 KB'\n >>> bytes2human(1048576)\n '1.0 MB'\n >>> bytes2human(1099511627776127398123789121)\n '909.5 YB'\n\n >>> bytes2human(10000, \"%(value).1f %(symbol)s/sec\")\n '9.8 K/sec'\n\n >>> # precision can be adjusted by playing with %f operator\n >>> bytes2human(10000, format=\"%(value).5f %(symbol)s\")\n '9.76562 K'\n\n Taken from: http://goo.gl/kTQMs and subsequently simplified\n Original Author: Giampaolo Rodola' <g.rodola [AT] gmail [DOT] com>\n License: MIT\n \"\"\"\n n = int(n)\n if n < 0:\n raise ValueError(\"n < 0\")\n symbols = ('', 'K', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y')\n prefix = {}\n for i, s in enumerate(symbols[1:]):\n prefix[s] = 1 << (i + 1) * 10\n for symbol in reversed(symbols[1:]):\n if n >= prefix[symbol]:\n value = float(n) / prefix[symbol]\n return format % locals()\n return format % dict(symbol=symbols[0], value=n)\n\n\ndef quote_cmdlinearg(arg):\n \"\"\"Perform platform-appropriate argument quoting\"\"\"\n # https://stackoverflow.com/a/15262019\n return '\"{}\"'.format(\n arg.replace('\"', '\"\"')\n ) if on_windows else shlex_quote(arg)\n\n\ndef guard_for_format(arg):\n \"\"\"Replace { and } with {{ and }}\n\n To be used in cases if arg is not expected to have provided\n by user .format() placeholders, but 'arg' might become a part\n of a composite passed to .format(), e.g. via 'Run'\n \"\"\"\n return arg.replace('{', '{{').replace('}', '}}')\n\n\ndef join_cmdline(args):\n \"\"\"Join command line args into a string using quote_cmdlinearg\n \"\"\"\n return ' '.join(map(quote_cmdlinearg, args))\n\n\ndef split_cmdline(s):\n \"\"\"Perform platform-appropriate command line splitting.\n\n Identical to `shlex.split()` on non-windows platforms.\n\n Modified from https://stackoverflow.com/a/35900070\n \"\"\"\n if not on_windows:\n return shlex_split(s)\n\n # the rest is for windows\n RE_CMD_LEX = r'''\"((?:\"\"|\\\\[\"\\\\]|[^\"])*)\"?()|(\\\\\\\\(?=\\\\*\")|\\\\\")|(&&?|\\|\\|?|\\d?>|[<])|([^\\s\"&|<>]+)|(\\s+)|(.)'''\n\n args = []\n accu = None # collects pieces of one arg\n for qs, qss, esc, pipe, word, white, fail in re.findall(RE_CMD_LEX, s):\n if word:\n pass # most frequent\n elif esc:\n word = esc[1]\n elif white or pipe:\n if accu is not None:\n args.append(accu)\n if pipe:\n args.append(pipe)\n accu = None\n continue\n elif fail:\n raise ValueError(\"invalid or incomplete shell string\")\n elif qs:\n word = qs.replace('\\\\\"', '\"').replace('\\\\\\\\', '\\\\')\n if platform == 0:\n word = word.replace('\"\"', '\"')\n else:\n word = qss # may be even empty; must be last\n\n accu = (accu or '') + word\n\n if accu is not None:\n args.append(accu)\n\n return args\n\n\ndef get_wrapped_class(wrapped):\n \"\"\"Determine the command class a wrapped __call__ belongs to\"\"\"\n mod = sys.modules[wrapped.__module__]\n command_class_name = wrapped.__qualname__.split('.')[-2]\n _func_class = mod.__dict__[command_class_name]\n lgr.debug(\"Determined class of decorated function: %s\", _func_class)\n return _func_class\n\n\ndef _make_assure_kludge(fn):\n old_name = fn.__name__.replace(\"ensure\", \"assure\")\n\n @wraps(fn)\n def compat_fn(*args, **kwargs):\n warnings.warn(\n \"{} is deprecated and will be removed in a future release. \"\n \"Use {} instead.\"\n .format(old_name, fn.__name__),\n DeprecationWarning)\n return fn(*args, **kwargs)\n\n compat_fn.__doc__ = (\"Note: This function is deprecated. Use {} instead.\"\n .format(fn.__name__))\n return compat_fn\n\n\nassure_tuple_or_list = _make_assure_kludge(ensure_tuple_or_list)\nassure_iter = _make_assure_kludge(ensure_iter)\nassure_list = _make_assure_kludge(ensure_list)\nassure_list_from_str = _make_assure_kludge(ensure_list_from_str)\nassure_dict_from_str = _make_assure_kludge(ensure_dict_from_str)\nassure_bytes = _make_assure_kludge(ensure_bytes)\nassure_unicode = _make_assure_kludge(ensure_unicode)\nassure_bool = _make_assure_kludge(ensure_bool)\nassure_dir = _make_assure_kludge(ensure_dir)\n\n\nlgr.log(5, \"Done importing datalad.utils\")\n\n\ndef check_symlink_capability(path, target):\n \"\"\"helper similar to datalad.tests.utils.has_symlink_capability\n\n However, for use in a datalad command context, we shouldn't\n assume to be able to write to tmpfile and also not import a whole lot from\n datalad's test machinery. Finally, we want to know, whether we can create a\n symlink at a specific location, not just somewhere. Therefore use\n arbitrary path to test-build a symlink and delete afterwards. Suitable\n location can therefore be determined by high lever code.\n\n Parameters\n ----------\n path: Path\n target: Path\n\n Returns\n -------\n bool\n \"\"\"\n\n try:\n target.touch()\n path.symlink_to(target)\n return True\n except Exception:\n return False\n finally:\n if path.exists():\n path.unlink()\n if target.exists():\n target.unlink()\n"}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":0,"numItemsPerPage":100,"numTotalItems":2760,"offset":0,"length":100}},"jwt":"eyJhbGciOiJFZERTQSJ9.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc3NjI3MjkwNywic3ViIjoiL2RhdGFzZXRzL0FTU0VSVC1LVEgvc3RhY2stc21vbC1kb2NzdHJpbmdzIiwiZXhwIjoxNzc2Mjc2NTA3LCJpc3MiOiJodHRwczovL2h1Z2dpbmdmYWNlLmNvIn0.y1HuTo43qfCbRSJa3haKYcjHjl7fhTuEEt558qoOYvLKi7m51oAO1iShYsZwkt3MYyE_eKM5e5toDQiHq4AZDw","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":2760,"numBytesParquet":64655948}]},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
function_name stringlengths 1 63 | docstring stringlengths 50 5.89k | masked_code stringlengths 50 882k | implementation stringlengths 169 12.9k | start_line int32 1 14.6k | end_line int32 16 14.6k | file_content stringlengths 274 882k |
|---|---|---|---|---|---|---|
add_collision_mesh | Add a collision mesh to the planning scene.
Parameters
----------
collision_mesh : :class:`compas_fab.robots.CollisionMesh`
Object containing the collision mesh to be added.
options : dict, optional
Unused parameter.
Returns
-------
``None`` | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from compas.utilities import await_callback
from compas_fab.backends.interfaces import AddCollisionMesh
from compas_fab.backends.ros.messages import ApplyPlanningSceneRequest
from compas_fab.backends.ros.messa... | def add_collision_mesh(self, collision_mesh, options=None):
"""Add a collision mesh to the planning scene.
Parameters
----------
collision_mesh : :class:`compas_fab.robots.CollisionMesh`
Object containing the collision mesh to be added.
options : dict, optional
... | 31 | 49 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from compas.utilities import await_callback
from compas_fab.backends.interfaces import AddCollisionMesh
from compas_fab.backends.ros.messages import ApplyPlanningSceneRequest
from compas_fab.backends.ros.messa... |
get_init_code | Gets initial latent codes as the start point for optimization.
The input image is assumed to have already been preprocessed, meaning to
have shape [self.G.image_channels, self.G.resolution, self.G.resolution],
channel order `self.G.channel_order`, and pixel range [self.G.min_val,
self.G.max_val]. | # python 3.7
"""Utility functions to invert a given image back to a latent code."""
from tqdm import tqdm
import cv2
import numpy as np
import torch
from models.stylegan_generator import StyleGANGenerator
from models.stylegan_encoder import StyleGANEncoder
from models.perceptual_model import PerceptualModel
__all__... | def get_init_code(self, image):
"""Gets initial latent codes as the start point for optimization.
The input image is assumed to have already been preprocessed, meaning to
have shape [self.G.image_channels, self.G.resolution, self.G.resolution],
channel order `self.G.channel_order`, and pixel range [s... | 131 | 142 | # python 3.7
"""Utility functions to invert a given image back to a latent code."""
from tqdm import tqdm
import cv2
import numpy as np
import torch
from models.stylegan_generator import StyleGANGenerator
from models.stylegan_encoder import StyleGANEncoder
from models.perceptual_model import PerceptualModel
__all__... |
state | A decorator that identifies which methods are states.
The presence of the farc_state attr, not the value of the attr,
determines statehood.
The Spy debugging system uses the farc_state attribute
to determine which methods inside a class are actually states.
Other uses of the attribute may come in the future. | import asyncio
import collections
import math
import signal
import sys
from functools import wraps
class Spy(object):
"""Spy is the debugging system for farc.
farc contains a handful of Spy.on_*() methods
placed at useful locations in the framework.
It is up to a Spy driver (such as the included VcdSp... | def state(func):
"""A decorator that identifies which methods are states.
The presence of the farc_state attr, not the value of the attr,
determines statehood.
The Spy debugging system uses the farc_state attribute
to determine which methods inside a class are actually states... | 168 | 183 | import asyncio
import collections
import math
import signal
import sys
from functools import wraps
class Spy(object):
"""Spy is the debugging system for farc.
farc contains a handful of Spy.on_*() methods
placed at useful locations in the framework.
It is up to a Spy driver (such as the included VcdSp... |
subscribe | Adds the given Ahsm to the subscriber table list
for the given signal. The argument, signame, is a string of the name
of the Signal to which the Ahsm is subscribing. Using a string allows
the Signal to be created in the registry if it is not already. | import asyncio
import collections
import math
import signal
import sys
from functools import wraps
class Spy(object):
"""Spy is the debugging system for farc.
farc contains a handful of Spy.on_*() methods
placed at useful locations in the framework.
It is up to a Spy driver (such as the included VcdSp... | @staticmethod
def subscribe(signame, act):
"""Adds the given Ahsm to the subscriber table list
for the given signal. The argument, signame, is a string of the name
of the Signal to which the Ahsm is subscribing. Using a string allows
the Signal to be created in the registry if ... | 439 | 449 | import asyncio
import collections
import math
import signal
import sys
from functools import wraps
class Spy(object):
"""Spy is the debugging system for farc.
farc contains a handful of Spy.on_*() methods
placed at useful locations in the framework.
It is up to a Spy driver (such as the included VcdSp... |
timeEventCallback | The callback function for all TimeEvents.
Posts the event to the event's target Ahsm.
If the TimeEvent is periodic, re-insort the event
in the list of active time events. | import asyncio
import collections
import math
import signal
import sys
from functools import wraps
class Spy(object):
"""Spy is the debugging system for farc.
farc contains a handful of Spy.on_*() methods
placed at useful locations in the framework.
It is up to a Spy driver (such as the included VcdSp... | @staticmethod
def timeEventCallback(tm_event, expiration):
"""The callback function for all TimeEvents.
Posts the event to the event's target Ahsm.
If the TimeEvent is periodic, re-insort the event
in the list of active time events.
"""
assert expiration in Framew... | 538 | 571 | import asyncio
import collections
import math
import signal
import sys
from functools import wraps
class Spy(object):
"""Spy is the debugging system for farc.
farc contains a handful of Spy.on_*() methods
placed at useful locations in the framework.
It is up to a Spy driver (such as the included VcdSp... |
_apply_relativistic_doppler_shift | Given a `SpectralQuantity` and a velocity, return a new `SpectralQuantity`
that is Doppler shifted by this amount.
Note that the Doppler shift applied is the full relativistic one, so
`SpectralQuantity` currently expressed in velocity and not using the
relativistic convention will temporarily be converted to use the
r... | import warnings
from textwrap import indent
import astropy.units as u
import numpy as np
from astropy.constants import c
from astropy.coordinates import (ICRS,
CartesianDifferential,
CartesianRepresentation, SkyCoord)
from astropy.coordinates.spectral_q... | def _apply_relativistic_doppler_shift(scoord, velocity):
"""
Given a `SpectralQuantity` and a velocity, return a new `SpectralQuantity`
that is Doppler shifted by this amount.
Note that the Doppler shift applied is the full relativistic one, so
`SpectralQuantity` currently expressed in velocity and... | 53 | 85 | import warnings
from textwrap import indent
import astropy.units as u
import numpy as np
from astropy.constants import c
from astropy.coordinates import (ICRS,
CartesianDifferential,
CartesianRepresentation, SkyCoord)
from astropy.coordinates.spectral_q... |
_validate_coordinate | Checks the type of the frame and whether a velocity differential and a
distance has been defined on the frame object.
If no distance is defined, the target is assumed to be "really far
away", and the observer is assumed to be "in the solar system".
Parameters
----------
coord : `~astropy.coordinates.BaseCoordinateFra... | import warnings
from textwrap import indent
import astropy.units as u
import numpy as np
from astropy.constants import c
from astropy.coordinates import (ICRS,
CartesianDifferential,
CartesianRepresentation, SkyCoord)
from astropy.coordinates.spectral_q... | @staticmethod
def _validate_coordinate(coord, label=''):
"""
Checks the type of the frame and whether a velocity differential and a
distance has been defined on the frame object.
If no distance is defined, the target is assumed to be "really far
away", and the observer i... | 247 | 299 | import warnings
from textwrap import indent
import astropy.units as u
import numpy as np
from astropy.constants import c
from astropy.coordinates import (ICRS,
CartesianDifferential,
CartesianRepresentation, SkyCoord)
from astropy.coordinates.spectral_q... |
replicate | Return a replica of the `SpectralCoord`, optionally changing the
values or attributes.
Note that no conversion is carried out by this method - this keeps
all the values and attributes the same, except for the ones explicitly
passed to this method which are changed.
If ``copy`` is set to `True` then a full copy of the... | import warnings
from textwrap import indent
import astropy.units as u
import numpy as np
from astropy.constants import c
from astropy.coordinates import (ICRS,
CartesianDifferential,
CartesianRepresentation, SkyCoord)
from astropy.coordinates.spectral_q... | def replicate(self, value=None, unit=None,
observer=None, target=None,
radial_velocity=None, redshift=None,
doppler_convention=None, doppler_rest=None,
copy=False):
"""
Return a replica of the `SpectralCoord`, optionally changin... | 301 | 374 | import warnings
from textwrap import indent
import astropy.units as u
import numpy as np
from astropy.constants import c
from astropy.coordinates import (ICRS,
CartesianDifferential,
CartesianRepresentation, SkyCoord)
from astropy.coordinates.spectral_q... |
_normalized_position_vector | Calculate the normalized position vector between two frames.
Parameters
----------
observer : `~astropy.coordinates.BaseCoordinateFrame` or `~astropy.coordinates.SkyCoord`
The observation frame or coordinate.
target : `~astropy.coordinates.BaseCoordinateFrame` or `~astropy.coordinates.SkyCoord`
The target fram... | import warnings
from textwrap import indent
import astropy.units as u
import numpy as np
from astropy.constants import c
from astropy.coordinates import (ICRS,
CartesianDifferential,
CartesianRepresentation, SkyCoord)
from astropy.coordinates.spectral_q... | @staticmethod
def _normalized_position_vector(observer, target):
"""
Calculate the normalized position vector between two frames.
Parameters
----------
observer : `~astropy.coordinates.BaseCoordinateFrame` or `~astropy.coordinates.SkyCoord`
The observation fr... | 519 | 546 | import warnings
from textwrap import indent
import astropy.units as u
import numpy as np
from astropy.constants import c
from astropy.coordinates import (ICRS,
CartesianDifferential,
CartesianRepresentation, SkyCoord)
from astropy.coordinates.spectral_q... |
with_radial_velocity_shift | Apply a velocity shift to this spectral coordinate.
The shift can be provided as a redshift (float value) or radial
velocity (`~astropy.units.Quantity` with physical type of 'speed').
Parameters
----------
target_shift : float or `~astropy.units.Quantity`
Shift value to apply to current target.
observer_shift : f... | import warnings
from textwrap import indent
import astropy.units as u
import numpy as np
from astropy.constants import c
from astropy.coordinates import (ICRS,
CartesianDifferential,
CartesianRepresentation, SkyCoord)
from astropy.coordinates.spectral_q... | def with_radial_velocity_shift(self, target_shift=None, observer_shift=None):
"""
Apply a velocity shift to this spectral coordinate.
The shift can be provided as a redshift (float value) or radial
velocity (`~astropy.units.Quantity` with physical type of 'speed').
Paramete... | 635 | 716 | import warnings
from textwrap import indent
import astropy.units as u
import numpy as np
from astropy.constants import c
from astropy.coordinates import (ICRS,
CartesianDifferential,
CartesianRepresentation, SkyCoord)
from astropy.coordinates.spectral_q... |
_compress | Set or check the compression of a :py:class:`pycdf.CDF` or :py:class:`pycdf.Var`
@param obj: object on which to set or check compression
@type obj: :py:class:`pycdf.CDF` or :py:class:`pycdf.Var`
@param comptype: type of compression to change to, see CDF C reference
manual section 4.10. Constants for t... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... | def _compress(obj, comptype=None, param=None):
"""Set or check the compression of a :py:class:`pycdf.CDF` or :py:class:`pycdf.Var`
@param obj: object on which to set or check compression
@type obj: :py:class:`pycdf.CDF` or :py:class:`pycdf.Var`
@param comptype: type of compression to change to, see CDF... | 1,283 | 1,356 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... |
_find_lib | Search for the CDF library
Searches in likely locations for CDF libraries and attempts to load
them. Stops at first successful load and, if fails, reports all
the files that were tried as libraries.
Returns
=======
out : tuple
This is either (path to library, loaded library)
or, in the event of failure, (No... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... | @staticmethod
def _find_lib():
"""
Search for the CDF library
Searches in likely locations for CDF libraries and attempts to load
them. Stops at first successful load and, if fails, reports all
the files that were tried as libraries.
Returns
=======
... | 455 | 478 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... |
check_status | Raise exception or warning based on return status of CDF call
Parameters
==========
status : int
status returned by the C library
Other Parameters
================
ignore : sequence of ctypes.c_long
CDF statuses to ignore. If any of these is returned by CDF library,
any related warnings or exceptions will... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... | def check_status(self, status, ignore=()):
"""
Raise exception or warning based on return status of CDF call
Parameters
==========
status : int
status returned by the C library
Other Parameters
================
ignore : sequence of ctypes... | 535 | 571 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... |
set_backward | Set backward compatibility mode for new CDFs
Unless backward compatible mode is set, CDF files created by
the version 3 library can not be read by V2.
Parameters
==========
backward : boolean
Set backward compatible mode if True; clear it if False.
Raises
======
ValueError : if backward=False and underlying CDF ... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... | def set_backward(self, backward=True):
"""
Set backward compatibility mode for new CDFs
Unless backward compatible mode is set, CDF files created by
the version 3 library can not be read by V2.
Parameters
==========
backward : boolean
Set backwar... | 618 | 641 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... |
epoch_to_epoch16 | Converts a CDF EPOCH to a CDF EPOCH16 value
Parameters
==========
epoch : double
EPOCH to convert. Lists and numpy arrays are acceptable.
Returns
=======
out : (double, double)
EPOCH16 corresponding to epoch | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... | def epoch_to_epoch16(self, epoch):
"""
Converts a CDF EPOCH to a CDF EPOCH16 value
Parameters
==========
epoch : double
EPOCH to convert. Lists and numpy arrays are acceptable.
Returns
=======
out : (double, double)
EPOCH16 co... | 809 | 832 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... |
_datetime_to_tt2000_typepunned | Converts a Python datetime to a CDF TT2000 value
Typepunned version that passes doubles as longlongs, to get around
ARM calling convention oddness.
Parameters
==========
dt : :class:`datetime.datetime`
date and time to convert
Returns
=======
out : int
tt2000 corresponding to dt
See Also
========
v_datetim... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... | def _datetime_to_tt2000_typepunned(self, dt):
"""
Converts a Python datetime to a CDF TT2000 value
Typepunned version that passes doubles as longlongs, to get around
ARM calling convention oddness.
Parameters
==========
dt : :class:`datetime.datetime`
... | 969 | 1,014 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... |
__init__ | Create a CDF Exception
Uses CDF C library to look up an appropriate error message.
Parameters
==========
status : ctypes.c_long
CDF status | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... | def __init__(self, status):
"""
Create a CDF Exception
Uses CDF C library to look up an appropriate error message.
Parameters
==========
status : ctypes.c_long
CDF status
"""
self.status = status
self.string = 'CDF error ' + repr(... | 1,216 | 1,241 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... |
__init__ | Open or create a CDF file.
Parameters
==========
pathname : string
name of the file to open or create
masterpath : string
name of the master CDF file to use in creating
a new file. If not provided, an existing file is
opened; if provided but evaluates to ``False``
(e.g., ``''``), an empty new CDF i... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... | def __init__(self, pathname, masterpath=None, create=None, readonly=None):
"""Open or create a CDF file.
Parameters
==========
pathname : string
name of the file to open or create
masterpath : string
name of the master CDF file to use in creating
... | 1,605 | 1,665 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... |
_open | Opens the CDF file (called on init)
Will open an existing CDF file read/write.
Raises
======
CDFError : if CDF library reports an error
CDFWarning : if CDF library reports a warning and interpreter
is set to error on warnings.
.. note:
Not intended for direct call; pass parameters to
:py:class:`p... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... | def _open(self, readonly=True):
"""Opens the CDF file (called on init)
Will open an existing CDF file read/write.
Raises
======
CDFError : if CDF library reports an error
CDFWarning : if CDF library reports a warning and interpreter
is set to er... | 1,819 | 1,837 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... |
readonly | Sets or check the readonly status of this CDF
If the CDF has been changed since opening, setting readonly mode
will have no effect.
.. note::
Closing a CDF that has been opened readonly, or setting readonly
False, may take a substantial amount of time if there are many
variables in the CDF, as a (potentia... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... | def readonly(self, ro=None):
"""
Sets or check the readonly status of this CDF
If the CDF has been changed since opening, setting readonly mode
will have no effect.
.. note::
Closing a CDF that has been opened readonly, or setting readonly
False, may... | 1,979 | 2,023 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... |
checksum | Set or check the checksum status of this CDF. If checksums
are enabled, the checksum will be verified every time the file
is opened.
Other Parameters
================
new_val : boolean
True to enable checksum, False to disable, or leave out
to simply check.
Returns
=======
out : boolean
True if the checks... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... | def checksum(self, new_val=None):
"""
Set or check the checksum status of this CDF. If checksums
are enabled, the checksum will be verified every time the file
is opened.
Other Parameters
================
new_val : boolean
True to enable checksum,... | 2,025 | 2,050 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... |
__init__ | Create or locate a variable
Parameters
==========
cdf_file : :py:class:`pycdf.CDF`
CDF file containing this variable
var_name : string
name of this variable
Other Parameters
================
args
additional arguments passed to :py:meth:`_create`. If none,
opens an existing variable. If provided, creat... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... | def __init__(self, cdf_file, var_name, *args):
"""Create or locate a variable
Parameters
==========
cdf_file : :py:class:`pycdf.CDF`
CDF file containing this variable
var_name : string
name of this variable
Other Parameters
==========... | 2,766 | 2,804 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... |
_create | Creates a new zVariable
@param var_name: name of this variable
@type var_name: string
@param datatype: CDF data type
@type datatype: ctypes.c_long
@param n_elements: number of elements (should be 1 except for
CDF_CHAR variables).
@type n_elements: long
@param dims: size of each dimension for multi-d... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... | def _create(self, var_name, datatype, n_elements = 1, dims = (),
recVary = const.VARY, dimVarys = None):
"""Creates a new zVariable
@param var_name: name of this variable
@type var_name: string
@param datatype: CDF data type
@type datatype: ctypes.c_long
... | 3,013 | 3,050 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... |
rv | Gets or sets whether this variable has record variance
If the variance is unknown, True is assumed
(this replicates the apparent behavior of the CDF library on
variable creation).
Other Parameters
================
new_rv : boolean
True to change to record variance, False to change to NRV,
unspecified to simpl... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... | def rv(self, new_rv=None):
"""
Gets or sets whether this variable has record variance
If the variance is unknown, True is assumed
(this replicates the apparent behavior of the CDF library on
variable creation).
Other Parameters
================
new_r... | 3,177 | 3,201 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... |
dv | Gets or sets dimension variance of each dimension of variable.
If the variance is unknown, True is assumed
(this replicates the apparent behavior of the
CDF library on variable creation).
Parameters
==========
new_dv : list of boolean
Each element True to change that dimension to dimension
variance, False to ... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... | def dv(self, new_dv=None):
"""
Gets or sets dimension variance of each dimension of variable.
If the variance is unknown, True is assumed
(this replicates the apparent behavior of the
CDF library on variable creation).
Parameters
==========
new_dv : ... | 3,203 | 3,236 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... |
__init__ | Create a Hyperslice
@param zvar: zVariable that this slices
@type zvar: :py:class:`pycdf.Var`
@param key: Python multi-dimensional slice as passed to
__getitem__
@type key: tuple of slice and/or int
@raise IndexError: if slice is out of range, mismatches dimensions, or
otherwise unparsab... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... | def __init__(self, zvar, key):
"""Create a Hyperslice
@param zvar: zVariable that this slices
@type zvar: :py:class:`pycdf.Var`
@param key: Python multi-dimensional slice as passed to
__getitem__
@type key: tuple of slice and/or int
@raise IndexEr... | 3,510 | 3,571 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... |
expand | Expands the record dimension of this slice to hold a set of data
If the length of data (outermost dimension) is larger than the record
count (counts[0]) for this slice, expand the slice to hold all the data.
This requires that the record dimension of the slice not be degenerate,
and also that it not have been complete... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... | def expand(self, data):
"""Expands the record dimension of this slice to hold a set of data
If the length of data (outermost dimension) is larger than the record
count (counts[0]) for this slice, expand the slice to hold all the data.
This requires that the record dimension of the s... | 3,583 | 3,607 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... |
__init__ | Initialize this attribute
@param cdf_file: CDF file containing this attribute
@type cdf_file: :py:class:`pycdf.CDF`
@param attr_name: Name of this attribute
@type attr_name: str
@param create: True to create attribute, False to look up existing.
@type create: bool | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... | def __init__(self, cdf_file, attr_name, create=False):
"""Initialize this attribute
@param cdf_file: CDF file containing this attribute
@type cdf_file: :py:class:`pycdf.CDF`
@param attr_name: Name of this attribute
@type attr_name: str
@param create: True to create a... | 4,040 | 4,085 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... |
__getitem__ | Return a slice of Entries.
Because Attributes may be sparse, a multi-element slice will return
None for those elements which do not have associated Entries.
@param key: index or range of Entry number to return
@type key: slice or int
@return: a list of entries, appropriate type.
@raise IndexError: if L{key} is an int... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... | def __getitem__(self, key):
"""Return a slice of Entries.
Because Attributes may be sparse, a multi-element slice will return
None for those elements which do not have associated Entries.
@param key: index or range of Entry number to return
@type key: slice or int
@... | 4,087 | 4,109 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... |
__len__ | Number of Entries for this Attr. NOT same as max Entry number.
@return: Number of Entries
@rtype: int | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... | def __len__(self):
"""Number of Entries for this Attr. NOT same as max Entry number.
@return: Number of Entries
@rtype: int
"""
count = ctypes.c_long(0)
self._call(const.GET_, self.ATTR_NUMENTRIES_, ctypes.byref(count))
return count.value | 4,287 | 4,295 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... |
type | Find or change the CDF type of a zEntry in this zVar
@param name: name of the zAttr to check or change
@type name: str
@param new_type: type to change it to, see :py:mod:`pycdf.const`
@type new_type: ctypes.c_long
@return: CDF variable type, see :py:mod:`pycdf.const`
@rtype: int
@note: If changing types, old and new m... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... | def type(self, name, new_type=None):
"""Find or change the CDF type of a zEntry in this zVar
@param name: name of the zAttr to check or change
@type name: str
@param new_type: type to change it to, see :py:mod:`pycdf.const`
@type new_type: ctypes.c_long
@return: CDF ... | 5,290 | 5,307 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
das developers note:
This a is modification of the original SpacePy pycdf package. All
refereneces to the greater spacepy package have been removed to create
a small standalone module.
--cwp 2018-10-18
The libcdf.so location code has been changed to f... |
dot | This operator calculates inner product for vectors.
.. note::
Support 1-d and 2-d Tensor. When it is 2d, the first dimension of this matrix
is the batch dimension, which means that the vectors of multiple batches are dotted.
Parameters:
x(Tensor): 1-D or 2-D ``Tensor``. Its dtype should be ``float32``, ``fl... | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | def dot(x, y, name=None):
"""
This operator calculates inner product for vectors.
.. note::
Support 1-d and 2-d Tensor. When it is 2d, the first dimension of this matrix
is the batch dimension, which means that the vectors of multiple batches are dotted.
Parameters:
x(Tensor): 1-... | 871 | 925 | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
t | Transpose <=2-D tensor.
0-D and 1-D tensors are returned as it is and 2-D tensor is equal to
the paddle.transpose function which perm dimensions set 0 and 1.
Args:
input (Tensor): The input Tensor. It is a N-D (N<=2) Tensor of data types float16, float32, float64, int32.
name(str, optional): The default value ... | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | def t(input, name=None):
"""
Transpose <=2-D tensor.
0-D and 1-D tensors are returned as it is and 2-D tensor is equal to
the paddle.transpose function which perm dimensions set 0 and 1.
Args:
input (Tensor): The input Tensor. It is a N-D (N<=2) Tensor of data types float16, float32, float6... | 1,041 | 1,112 | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
bmm | Applies batched matrix multiplication to two tensors.
Both of the two input tensors must be three-dementional and share the same batch size.
if x is a (b, m, k) tensor, y is a (b, k, n) tensor, the output will be a (b, m, n) tensor.
Args:
x (Tensor): The input Tensor.
y (Tensor): The input Tensor.
name(s... | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | def bmm(x, y, name=None):
"""
Applies batched matrix multiplication to two tensors.
Both of the two input tensors must be three-dementional and share the same batch size.
if x is a (b, m, k) tensor, y is a (b, k, n) tensor, the output will be a (b, m, n) tensor.
Args:
x (Tensor): The inpu... | 1,315 | 1,373 | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
histogram | Computes the histogram of a tensor. The elements are sorted into equal width bins between min and max.
If min and max are both zero, the minimum and maximum values of the data are used.
Args:
input (Tensor): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor
s... | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | def histogram(input, bins=100, min=0, max=0, name=None):
"""
Computes the histogram of a tensor. The elements are sorted into equal width bins between min and max.
If min and max are both zero, the minimum and maximum values of the data are used.
Args:
input (Tensor): A Tensor(or LoDTensor) wit... | 1,376 | 1,414 | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
bincount | Computes frequency of each value in the input tensor.
Args:
x (Tensor): A Tensor with non-negative integer. Should be 1-D tensor.
weights (Tensor, optional): Weight for each value in the input tensor. Should have the same shape as input. Default is None.
minlength (int, optional): Minimum number of bins. ... | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | def bincount(x, weights=None, minlength=0, name=None):
"""
Computes frequency of each value in the input tensor.
Args:
x (Tensor): A Tensor with non-negative integer. Should be 1-D tensor.
weights (Tensor, optional): Weight for each value in the input tensor. Should have the same shape as ... | 1,417 | 1,467 | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
eig | This API performs the eigenvalue decomposition of a square matrix or a batch of square matrices.
.. note::
If the matrix is a Hermitian or a real symmetric matrix, please use :ref:`paddle.linalg.eigh` instead, which is much faster.
If only eigenvalues is needed, please use :ref:`paddle.linalg.eigvals` instead.... | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | def eig(x, name=None):
"""
This API performs the eigenvalue decomposition of a square matrix or a batch of square matrices.
.. note::
If the matrix is a Hermitian or a real symmetric matrix, please use :ref:`paddle.linalg.eigh` instead, which is much faster.
If only eigenvalues is needed, p... | 2,039 | 2,102 | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
eigh | Compute the eigenvalues and eigenvectors of a
complex Hermitian (conjugate symmetric) or a real symmetric matrix.
Args:
x (Tensor): A tensor with shape :math:`[*, N, N]` , The data type of the input Tensor x
should be one of float32, float64, complex64, complex128.
UPLO(str, optional): (string, default... | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | def eigh(x, UPLO='L', name=None):
"""
Compute the eigenvalues and eigenvectors of a
complex Hermitian (conjugate symmetric) or a real symmetric matrix.
Args:
x (Tensor): A tensor with shape :math:`[*, N, N]` , The data type of the input Tensor x
should be one of float32, float64, co... | 2,245 | 2,311 | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
eigvalsh | Computes the eigenvalues of a
complex Hermitian (conjugate symmetric) or a real symmetric matrix.
Args:
x (Tensor): A tensor with shape :math:`[_, M, M]` , The data type of the input Tensor x
should be one of float32, float64, complex64, complex128.
UPLO(str, optional): Lower triangular part of a (‘L’... | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | def eigvalsh(x, UPLO='L', name=None):
"""
Computes the eigenvalues of a
complex Hermitian (conjugate symmetric) or a real symmetric matrix.
Args:
x (Tensor): A tensor with shape :math:`[_, M, M]` , The data type of the input Tensor x
should be one of float32, float64, complex64, co... | 2,766 | 2,830 | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
lstsq | Computes a solution to
the least squares problem of a system of linear equations.
Args:
x (Tensor): A tensor with shape ``(*, M, N)`` , the data type of the input Tensor ``x``
should be one of float32, float64.
y (Tensor): A tensor with shape ``(*, M, K)`` , the data type of the input Tensor ``y``
... | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | def lstsq(x, y, rcond=None, driver=None, name=None):
"""
Computes a solution to
the least squares problem of a system of linear equations.
Args:
x (Tensor): A tensor with shape ``(*, M, N)`` , the data type of the input Tensor ``x``
should be one of float32, float64.
y (Tens... | 2,833 | 3,005 | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
frobenius_norm | The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`.
Args:
input (Variable): Tensor, data type float32, float64.
dim (list, optional): None for last two dimensions.
keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False. | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | def frobenius_norm(input, dim=None, keepdim=False, name=None):
"""
The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`.
Args:
input (Variable): Tensor, data type float32, float64.
dim (list, optional): None for last two dim... | 242 | 275 | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
vector_norm | Calculate the p-order vector norm for certain dimension of Tensor `input`.
Args:
input (Variable): Tensor, data type float32, float64.
porder (float, optional): None for porder=2.0.
axis (int, optional): None for last dimension.
keepdim (bool, optional): Whether keep the dimensions as the `input`, Default Fals... | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... | def vector_norm(input,
porder=None,
axis=None,
keepdim=False,
asvector=False,
name=None):
"""
Calculate the p-order vector norm for certain dimension of Tensor `input`.
Args:
input ... | 277 | 318 | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
on_off_command | Send an on or off command to an appliance
Sends the specified command to the homevision through netio interface to control the specified appliance.
Args:
details: {"appliance": string, "state": string} |
import socket
class UserException(Exception):
pass
def user_exception(s): raise UserException(s)
class Macro:
"""Represents a macro to be run"""
def __init__(self, code):
"""code: int - index of macro to run"""
self.code = code
class Command:
"""Represents a macro to be run"""
def __init__(self, ... | def on_off_command(self, details):
"""Send an on or off command to an appliance
Sends the specified command to the homevision through netio interface to control the specified appliance.
Args:
details: {"appliance": string, "state": string}
"""
if "appliance" not in details:
... | 61 | 84 |
import socket
class UserException(Exception):
pass
def user_exception(s): raise UserException(s)
class Macro:
"""Represents a macro to be run"""
def __init__(self, code):
"""code: int - index of macro to run"""
self.code = code
class Command:
"""Represents a macro to be run"""
def __init__(self, ... |
action_command | Send an action command
Sends the specified command to the homevision through netio interface.
Args:
details: {"command": string} |
import socket
class UserException(Exception):
pass
def user_exception(s): raise UserException(s)
class Macro:
"""Represents a macro to be run"""
def __init__(self, code):
"""code: int - index of macro to run"""
self.code = code
class Command:
"""Represents a macro to be run"""
def __init__(self, ... | def action_command(self, details):
"""Send an action command
Sends the specified command to the homevision through netio interface.
Args:
details: {"command": string}
"""
if "command" not in details:
raise Exception("Command not specified")
if details["command"] not in ... | 86 | 100 |
import socket
class UserException(Exception):
pass
def user_exception(s): raise UserException(s)
class Macro:
"""Represents a macro to be run"""
def __init__(self, code):
"""code: int - index of macro to run"""
self.code = code
class Command:
"""Represents a macro to be run"""
def __init__(self, ... |
start_stop_command | Starts or stops a process
Sends the specified command to the homevision through netio interface to control the specified process.
Args:
details: {"action": string, "process": string} |
import socket
class UserException(Exception):
pass
def user_exception(s): raise UserException(s)
class Macro:
"""Represents a macro to be run"""
def __init__(self, code):
"""code: int - index of macro to run"""
self.code = code
class Command:
"""Represents a macro to be run"""
def __init__(self, ... | def start_stop_command(self, details):
"""Starts or stops a process
Sends the specified command to the homevision through netio interface to control the specified process.
Args:
details: {"action": string, "process": string}
"""
if "action" not in details:
raise Exception("a... | 102 | 123 |
import socket
class UserException(Exception):
pass
def user_exception(s): raise UserException(s)
class Macro:
"""Represents a macro to be run"""
def __init__(self, code):
"""code: int - index of macro to run"""
self.code = code
class Command:
"""Represents a macro to be run"""
def __init__(self, ... |
var_query | Returns the answer to a query on variable
Returns the answer to a query on the specified variable using netio
Args:
details: {"query": string} |
import socket
class UserException(Exception):
pass
def user_exception(s): raise UserException(s)
class Macro:
"""Represents a macro to be run"""
def __init__(self, code):
"""code: int - index of macro to run"""
self.code = code
class Command:
"""Represents a macro to be run"""
def __init__(self, ... | def var_query(self, details):
"""Returns the answer to a query on variable
Returns the answer to a query on the specified variable using netio
Args:
details: {"query": string}
"""
if "query" not in details:
raise Exception("query not specified")
if details["query"] ... | 142 | 164 |
import socket
class UserException(Exception):
pass
def user_exception(s): raise UserException(s)
class Macro:
"""Represents a macro to be run"""
def __init__(self, code):
"""code: int - index of macro to run"""
self.code = code
class Command:
"""Represents a macro to be run"""
def __init__(self, ... |
flag_query | Returns the answer to a query on flag
Returns the answer to a query on the specified variable using netio
Args:
details: {"query": string} |
import socket
class UserException(Exception):
pass
def user_exception(s): raise UserException(s)
class Macro:
"""Represents a macro to be run"""
def __init__(self, code):
"""code: int - index of macro to run"""
self.code = code
class Command:
"""Represents a macro to be run"""
def __init__(self, ... | def flag_query(self, details):
"""Returns the answer to a query on flag
Returns the answer to a query on the specified variable using netio
Args:
details: {"query": string}
"""
if "query" not in details:
raise Exception("query not specified")
if details["query"] not... | 166 | 182 |
import socket
class UserException(Exception):
pass
def user_exception(s): raise UserException(s)
class Macro:
"""Represents a macro to be run"""
def __init__(self, code):
"""code: int - index of macro to run"""
self.code = code
class Command:
"""Represents a macro to be run"""
def __init__(self, ... |
_find_files | Find files in directory with optional extensions.
Args:
directory (string)
keywords (list): e.g. ["SelfpacedRota", "ButtonPress] (optional)
extensions (list): e.g. [".json" or "tsv"] (optional)
verbose (bool): verbosity level (optional, default=True) | """Define abstract base classes to construct FileFinder classes."""
import os
import shutil
from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Sequence, Union
import mne_bids
@dataclass
class FileFinder(ABC):
"""Basic representation... | def _find_files(
self,
directory: Union[Path, str],
extensions: Optional[Union[list, str]] = None,
) -> None:
"""Find files in directory with optional extensions.
Args:
directory (string)
keywords (list): e.g. ["SelfpacedRota", "ButtonPress] (opti... | 88 | 108 | """Define abstract base classes to construct FileFinder classes."""
import os
import shutil
from abc import ABC, abstractmethod
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Sequence, Union
import mne_bids
@dataclass
class FileFinder(ABC):
"""Basic representation... |
irfft2 | Compute the 2-dimensional inverse FFT of a real array.
Parameters
----------
a : array_like
The input tensor
s : sequence of ints, optional
Shape of the inverse FFT.
axes : sequence of ints, optional
The axes over which to compute the inverse fft.
Default is the last two axes.
norm : {None, "ortho"}, o... | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2020 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... | def irfft2(a, s=None, axes=(-2, -1), norm=None):
"""
Compute the 2-dimensional inverse FFT of a real array.
Parameters
----------
a : array_like
The input tensor
s : sequence of ints, optional
Shape of the inverse FFT.
axes : sequence of ints, optional
The axes over ... | 31 | 67 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2020 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-... |
_send_notification | Send a notification to a specific node_id
This version of the overriden method includes the necessary crypto
headers for the notification.
:type notification: autopush.utils.WebPushNotification | """WebPush Style Autopush Router
This router handles notifications that should be dispatched to an Autopush
node, or stores each individual message, along with its data, in a Message
table for retrieval by the client.
"""
import json
import time
from StringIO import StringIO
from typing import Any # noqa
from botoc... | def _send_notification(self, uaid, node_id, notification):
"""Send a notification to a specific node_id
This version of the overriden method includes the necessary crypto
headers for the notification.
:type notification: autopush.utils.WebPushNotification
"""
paylo... | 179 | 197 | """WebPush Style Autopush Router
This router handles notifications that should be dispatched to an Autopush
node, or stores each individual message, along with its data, in a Message
table for retrieval by the client.
"""
import json
import time
from StringIO import StringIO
from typing import Any # noqa
from botoc... |
_save_notification | Saves a notification, returns a deferred.
This version of the overridden method saves each individual message
to the message table along with relevant request headers if
available.
:type uaid_data: dict | """WebPush Style Autopush Router
This router handles notifications that should be dispatched to an Autopush
node, or stores each individual message, along with its data, in a Message
table for retrieval by the client.
"""
import json
import time
from StringIO import StringIO
from typing import Any # noqa
from botoc... | def _save_notification(self, uaid_data, notification):
"""Saves a notification, returns a deferred.
This version of the overridden method saves each individual message
to the message table along with relevant request headers if
available.
:type uaid_data: dict
"""
... | 207 | 239 | """WebPush Style Autopush Router
This router handles notifications that should be dispatched to an Autopush
node, or stores each individual message, along with its data, in a Message
table for retrieval by the client.
"""
import json
import time
from StringIO import StringIO
from typing import Any # noqa
from botoc... |
_recursive_apply | This function is "applied" to every child in the block. This function in turn
registers the forward hook to each module. It helps logging the input output tensors
of that module. | # Third Party
import mxnet as mx
from mxnet.ndarray import NDArray
# First Party
from smdebug.core.collection import DEFAULT_MXNET_COLLECTIONS, CollectionKeys
from smdebug.core.hook import CallbackHook
from smdebug.core.json_config import DEFAULT_WORKER_NAME
from smdebug.core.utils import FRAMEWORK, error_handling_age... | def _recursive_apply(self, block):
"""
This function is "applied" to every child in the block. This function in turn
registers the forward hook to each module. It helps logging the input output tensors
of that module.
"""
# Check if the hook is already registered for ... | 185 | 196 | # Third Party
import mxnet as mx
from mxnet.ndarray import NDArray
# First Party
from smdebug.core.collection import DEFAULT_MXNET_COLLECTIONS, CollectionKeys
from smdebug.core.hook import CallbackHook
from smdebug.core.json_config import DEFAULT_WORKER_NAME
from smdebug.core.utils import FRAMEWORK, error_handling_age... |
register_block | This function registers the forward hook. If user wants to register the hook
for every child in the given block, then the function calls "apply" API for
registration of the hook.
The hook is registered recursively, if user has specified the collections that are more than
the default collectors viz. gradients, weight an... | # Third Party
import mxnet as mx
from mxnet.ndarray import NDArray
# First Party
from smdebug.core.collection import DEFAULT_MXNET_COLLECTIONS, CollectionKeys
from smdebug.core.hook import CallbackHook
from smdebug.core.json_config import DEFAULT_WORKER_NAME
from smdebug.core.utils import FRAMEWORK, error_handling_age... | @error_handling_agent.catch_smdebug_errors()
def register_block(self, block):
"""
This function registers the forward hook. If user wants to register the hook
for every child in the given block, then the function calls "apply" API for
registration of the hook.
The hook is... | 224 | 258 | # Third Party
import mxnet as mx
from mxnet.ndarray import NDArray
# First Party
from smdebug.core.collection import DEFAULT_MXNET_COLLECTIONS, CollectionKeys
from smdebug.core.hook import CallbackHook
from smdebug.core.json_config import DEFAULT_WORKER_NAME
from smdebug.core.utils import FRAMEWORK, error_handling_age... |
step | Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss. | """
"""
from __future__ import division
from torch.optim.optimizer import Optimizer, required
import numpy as np
import torch
from typing import NamedTuple, List
from dataclasses import dataclass
from enum import Enum
from typing import Union, Tuple
# from scipy.sparse.linalg import svds
from scipy.optimize import mi... | def step(self, closure: callable = None):
"""Performs a single optimization step.
Arguments:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closur... | 432 | 473 | """
"""
from __future__ import division
from torch.optim.optimizer import Optimizer, required
import numpy as np
import torch
from typing import NamedTuple, List
from dataclasses import dataclass
from enum import Enum
from typing import Union, Tuple
# from scipy.sparse.linalg import svds
from scipy.optimize import mi... |
get_value | Returns the displayed text for the desired column in this row.
The formula or input which generated the displayed value is not accessible
through the list feed, to see the user's input, use the cells feed.
If a column is not present in this spreadsheet, or there is no value
for a column in this row, this method will ... | #!/usr/bin/env python
#
# Copyright (C) 2009 Google Inc.
#
# Licensed under the Apache License 2.0;
# This module is used for version 2 of the Google Data APIs.
"""Provides classes and constants for the XML in the Google Spreadsheets API.
Documentation for the raw XML which these classes represent can be found he... | def get_value(self, column_name):
"""Returns the displayed text for the desired column in this row.
The formula or input which generated the displayed value is not accessible
through the list feed, to see the user's input, use the cells feed.
If a column is not present in this spre... | 244 | 256 | #!/usr/bin/env python
#
# Copyright (C) 2009 Google Inc.
#
# Licensed under the Apache License 2.0;
# This module is used for version 2 of the Google Data APIs.
"""Provides classes and constants for the XML in the Google Spreadsheets API.
Documentation for the raw XML which these classes represent can be found he... |
set_value | Changes the value of cell in this row under the desired column name.
Warning: if the cell contained a formula, it will be wiped out by setting
the value using the list feed since the list feed only works with
displayed values.
No client side checking is performed on the column_name, you need to
ensure that the column... | #!/usr/bin/env python
#
# Copyright (C) 2009 Google Inc.
#
# Licensed under the Apache License 2.0;
# This module is used for version 2 of the Google Data APIs.
"""Provides classes and constants for the XML in the Google Spreadsheets API.
Documentation for the raw XML which these classes represent can be found he... | def set_value(self, column_name, value):
"""Changes the value of cell in this row under the desired column name.
Warning: if the cell contained a formula, it will be wiped out by setting
the value using the list feed since the list feed only works with
displayed values.
No ... | 258 | 279 | #!/usr/bin/env python
#
# Copyright (C) 2009 Google Inc.
#
# Licensed under the Apache License 2.0;
# This module is used for version 2 of the Google Data APIs.
"""Provides classes and constants for the XML in the Google Spreadsheets API.
Documentation for the raw XML which these classes represent can be found he... |
__getattribute__ | Attribute lookup method for custom class attributes.
ReFrame test variables are descriptors injected at the class level.
If a variable descriptor has already been injected into the class,
do not return the descriptor object and return the default value
associated with that variable instead.
.. warning::
.. versio... | # Copyright 2016-2021 Swiss National Supercomputing Centre (CSCS/ETH Zurich)
# ReFrame Project Developers. See the top-level LICENSE file for details.
#
# SPDX-License-Identifier: BSD-3-Clause
#
# Meta-class for creating regression tests.
#
import functools
import types
import reframe.core.namespaces as namespaces
i... | def __getattribute__(cls, name):
'''Attribute lookup method for custom class attributes.
ReFrame test variables are descriptors injected at the class level.
If a variable descriptor has already been injected into the class,
do not return the descriptor object and return the default ... | 446 | 471 | # Copyright 2016-2021 Swiss National Supercomputing Centre (CSCS/ETH Zurich)
# ReFrame Project Developers. See the top-level LICENSE file for details.
#
# SPDX-License-Identifier: BSD-3-Clause
#
# Meta-class for creating regression tests.
#
import functools
import types
import reframe.core.namespaces as namespaces
i... |
__getattr__ | Backup attribute lookup method into custom namespaces.
Some ReFrame built-in types are stored under their own sub-namespaces.
This method will perform an attribute lookup on these sub-namespaces
if a call to the default :func:`__getattribute__` method fails to
retrieve the requested class attribute. | # Copyright 2016-2021 Swiss National Supercomputing Centre (CSCS/ETH Zurich)
# ReFrame Project Developers. See the top-level LICENSE file for details.
#
# SPDX-License-Identifier: BSD-3-Clause
#
# Meta-class for creating regression tests.
#
import functools
import types
import reframe.core.namespaces as namespaces
i... | def __getattr__(cls, name):
'''Backup attribute lookup method into custom namespaces.
Some ReFrame built-in types are stored under their own sub-namespaces.
This method will perform an attribute lookup on these sub-namespaces
if a call to the default :func:`__getattribute__` method ... | 473 | 500 | # Copyright 2016-2021 Swiss National Supercomputing Centre (CSCS/ETH Zurich)
# ReFrame Project Developers. See the top-level LICENSE file for details.
#
# SPDX-License-Identifier: BSD-3-Clause
#
# Meta-class for creating regression tests.
#
import functools
import types
import reframe.core.namespaces as namespaces
i... |
performance_function | Decorate a function to extract a performance variable.
The ``units`` argument indicates the units of the performance
variable to be extracted.
The ``perf_key`` optional arg will be used as the name of the
performance variable. If not provided, the function name will
be used as the performance variable name. | # Copyright 2016-2021 Swiss National Supercomputing Centre (CSCS/ETH Zurich)
# ReFrame Project Developers. See the top-level LICENSE file for details.
#
# SPDX-License-Identifier: BSD-3-Clause
#
# Meta-class for creating regression tests.
#
import functools
import types
import reframe.core.namespaces as namespaces
i... | def performance_function(units, *, perf_key=None):
'''Decorate a function to extract a performance variable.
The ``units`` argument indicates the units of the performance
variable to be extracted.
The ``perf_key`` optional arg will be used as the name of the
... | 293 | 325 | # Copyright 2016-2021 Swiss National Supercomputing Centre (CSCS/ETH Zurich)
# ReFrame Project Developers. See the top-level LICENSE file for details.
#
# SPDX-License-Identifier: BSD-3-Clause
#
# Meta-class for creating regression tests.
#
import functools
import types
import reframe.core.namespaces as namespaces
i... |
create_test_server | Wrapper utility that returns a test server.
This wrapper utility calls the common create test server and
returns a test server. The purpose of this wrapper is to minimize
the impact on the code of the tests already using this
function.
:param validatable: Whether the server will be pingable or sshable.
:param volume_... | # Copyright 2012 OpenStack Foundation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requ... | @classmethod
def create_test_server(cls, validatable=False, volume_backed=False,
validation_resources=None, clients=None, **kwargs):
"""Wrapper utility that returns a test server.
This wrapper utility calls the common create test server and
returns a test serv... | 232 | 284 | # Copyright 2012 OpenStack Foundation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requ... |
recreate_server | Destroy an existing class level server and creates a new one
Some test classes use a test server that can be used by multiple
tests. This is done to optimise runtime and test load.
If something goes wrong with the test server, it can be rebuilt
using this helper.
This helper can also be used for the initial provision... | # Copyright 2012 OpenStack Foundation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requ... | @classmethod
def recreate_server(cls, server_id, validatable=False, **kwargs):
"""Destroy an existing class level server and creates a new one
Some test classes use a test server that can be used by multiple
tests. This is done to optimise runtime and test load.
If something goe... | 402 | 434 | # Copyright 2012 OpenStack Foundation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requ... |
create_volume | Create a volume and wait for it to become 'available'.
:param image_ref: Specify an image id to create a bootable volume.
:param kwargs: other parameters to create volume.
:returns: The available volume. | # Copyright 2012 OpenStack Foundation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requ... | @classmethod
def create_volume(cls, image_ref=None, **kwargs):
"""Create a volume and wait for it to become 'available'.
:param image_ref: Specify an image id to create a bootable volume.
:param kwargs: other parameters to create volume.
:returns: The available volume.
"... | 503 | 529 | # Copyright 2012 OpenStack Foundation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requ... |
attach_volume | Attaches volume to server and waits for 'in-use' volume status.
The volume will be detached when the test tears down.
:param server: The server to which the volume will be attached.
:param volume: The volume to attach.
:param device: Optional mountpoint for the attached volume. Note that
this is not guaranteed fo... | # Copyright 2012 OpenStack Foundation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requ... | def attach_volume(self, server, volume, device=None, tag=None):
"""Attaches volume to server and waits for 'in-use' volume status.
The volume will be detached when the test tears down.
:param server: The server to which the volume will be attached.
:param volume: The volume to atta... | 548 | 585 | # Copyright 2012 OpenStack Foundation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requ... |
list | This method is a generator which yields queue objects.
This is almost the copy of list method of resource.Resource class.
The only difference is the request header now includes `Client-ID`
and `X-PROJECT-ID` fields which are required by Zaqar v2 API. | # Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under t... | @classmethod
def list(cls, session, paginated=False, **params):
"""This method is a generator which yields queue objects.
This is almost the copy of list method of resource.Resource class.
The only difference is the request header now includes `Client-ID`
and `X-PROJECT-ID` fiel... | 67 | 106 | # Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under t... |
ComputeConvOutputShape | Computes output shape for convolution and pooling layers.
If `in_shape` is a dynamic shape, the output will be Tensors, while if
`in_shape` is a list of ints then the output will also be a list of ints.
Args:
in_shape: A length 4 Tensor or list representing the input shape.
t_stride: The stride along the time dim... | # Lint as: python2, python3
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
... | def ComputeConvOutputShape(in_shape,
t_stride,
f_stride,
outc=None,
padding='SAME'):
"""Computes output shape for convolution and pooling layers.
If `in_shape` is a dynamic shape, the output will be Tensors,... | 30 | 67 | # Lint as: python2, python3
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
... |
ComputeConvOutputPadding | Computes paddings for convolution and pooling output.
out_padding[i] == 1 iff any in_padding corresponding to that output is 1.
Args:
paddings: The paddings tensor. It is expected to be of shape [batch, time].
window: The size of the windows.
stride: The time-stride between adjacent windows.
padding_algorithm... | # Lint as: python2, python3
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
... | def ComputeConvOutputPadding(paddings, window, stride,
padding_algorithm='SAME'):
"""Computes paddings for convolution and pooling output.
out_padding[i] == 1 iff any in_padding corresponding to that output is 1.
Args:
paddings: The paddings tensor. It is expected to be of shape... | 70 | 99 | # Lint as: python2, python3
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
... |
list_topic_keys | Namespace/ServiceBus Connection String
API Version: 2017-04-01.
:param str authorization_rule_name: The authorization rule name.
:param str namespace_name: The namespace name
:param str resource_group_name: Name of the Resource group within the Azure subscription.
:param str topic_name: The topic name. | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union
from .. import _utilities, _tables
__al... | def list_topic_keys(authorization_rule_name: Optional[str] = None,
namespace_name: Optional[str] = None,
resource_group_name: Optional[str] = None,
topic_name: Optional[str] = None,
opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableL... | 117 | 150 | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union
from .. import _utilities, _tables
__al... |
__init__ | Instantiate the pager.
Args:
method (Callable): The method that was originally called, and
which instantiated this pager.
request (google.cloud.compute_v1.types.AggregatedListTargetInstancesRequest):
The initial request object.
response (google.cloud.compute_v1.types.TargetInstanceAggregate... | # -*- coding: utf-8 -*-
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | def __init__(
self,
method: Callable[..., compute.TargetInstanceAggregatedList],
request: compute.AggregatedListTargetInstancesRequest,
response: compute.TargetInstanceAggregatedList,
*,
metadata: Sequence[Tuple[str, str]] = ()
):
"""Instantiate the pager.... | 48 | 71 | # -*- coding: utf-8 -*-
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... |
__init__ | Instantiate the pager.
Args:
method (Callable): The method that was originally called, and
which instantiated this pager.
request (google.cloud.compute_v1.types.ListTargetInstancesRequest):
The initial request object.
response (google.cloud.compute_v1.types.TargetInstanceList):
The ... | # -*- coding: utf-8 -*-
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... | def __init__(
self,
method: Callable[..., compute.TargetInstanceList],
request: compute.ListTargetInstancesRequest,
response: compute.TargetInstanceList,
*,
metadata: Sequence[Tuple[str, str]] = ()
):
"""Instantiate the pager.
Args:
me... | 113 | 136 | # -*- coding: utf-8 -*-
# Copyright 2020 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... |
__init__ | Dataset that loads tensors via a csv containing file paths to audio files and transcripts separated by
a comma. Each new line is a different sample. Example below:
/path/to/audio.wav,/path/to/audio.txt
...
:param audio_conf: Dictionary containing the sample rate, window and the window length/stride in seconds
:param ... | import os
import subprocess
from tempfile import NamedTemporaryFile
from torch.distributed import get_rank
from torch.distributed import get_world_size
from torch.utils.data.sampler import Sampler
import librosa
import numpy as np
import scipy.signal
import torch
from scipy.io.wavfile import read
import math
from tor... | def __init__(self, audio_conf, manifest_filepath, labels, normalize=False, speed_volume_perturb=False, spec_augment=False):
"""
Dataset that loads tensors via a csv containing file paths to audio files and transcripts separated by
a comma. Each new line is a different sample. Example below:
... | 172 | 199 | import os
import subprocess
from tempfile import NamedTemporaryFile
from torch.distributed import get_rank
from torch.distributed import get_world_size
from torch.utils.data.sampler import Sampler
import librosa
import numpy as np
import scipy.signal
import torch
from scipy.io.wavfile import read
import math
from tor... |
validate_meta_info | Validate meta information
Adds 'BIDS.NA' if no BIDS info present
Adds 'BIDS.valid' and 'BIDS.error_message'
to communicate to user if values are valid
Currently, validation is only checking if
mandatory properties are non-empty strings
Could add the following checks:
Are the values alpha numeric? | import argparse
import logging
import json
import os
import tempfile
import sys
import re
import flywheel
from .supporting_files import bidsify_flywheel, utils, templates
from .supporting_files.project_tree import get_project_tree
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger('curate-bids')
def... | def validate_meta_info(container, template):
""" Validate meta information
Adds 'BIDS.NA' if no BIDS info present
Adds 'BIDS.valid' and 'BIDS.error_message'
to communicate to user if values are valid
Currently, validation is only checking if
mandatory properties are non-empty strings
... | 27 | 78 | import argparse
import logging
import json
import os
import tempfile
import sys
import re
import flywheel
from .supporting_files import bidsify_flywheel, utils, templates
from .supporting_files.project_tree import get_project_tree
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger('curate-bids')
def... |
__add | :param name:
系列名称，用于 tooltip 的显示，legend 的图例筛选。
:param x_axis:
x 坐标轴数据。
:param y_axis:
y 坐标轴数据。数据中，每一行是一个『数据项』，每一列属于一个『维度』。
数据项具体为 [open, close, lowest, highest] （即：[开盘值, 收盘值,
最低值, 最高值]）。
:param kwargs: | # coding=utf-8
from pyecharts.chart import Chart
def kline_tooltip_formatter(params):
text = (
params[0].seriesName
+ "<br/>"
+ "- open:"
+ params[0].data[1]
+ "<br/>"
+ "- close:"
+ params[0].data[2]
+ "<br/>"
+ "- lowest:"
+ params... | def __add(self, name, x_axis, y_axis, **kwargs):
"""
:param name:
系列名称，用于 tooltip 的显示，legend 的图例筛选。
:param x_axis:
x 坐标轴数据。
:param y_axis:
y 坐标轴数据。数据中，每一行是一个『数据项』，每一列属于一个『维度』。
数据项具体为 [open, close, lowest, highest] （即：[开盘值, 收盘值,
... | 39 | 77 | # coding=utf-8
from pyecharts.chart import Chart
def kline_tooltip_formatter(params):
text = (
params[0].seriesName
+ "<br/>"
+ "- open:"
+ params[0].data[1]
+ "<br/>"
+ "- close:"
+ params[0].data[2]
+ "<br/>"
+ "- lowest:"
... |
PsdArray2Noise_1d | Generates a noise pattern whose Power Spectral density is given by Psd.
Parameters
---------------------
Psd : 1d array
Contains the numeric Psd (treated as evenly spaced array)
Semiaxis :
0 : does nothing
1 : halvens Pds, then replicates the halven part for left frequencies,
... | # -*- coding: utf-8 -*-
"""
Created on Thu Jul 07 14:08:31 2016
@author: Mic
"""
from __future__ import division
from wiselib2.must import *
import numpy as np
import wiselib2.Rayman as rm
Gauss1d = lambda x ,y : None
from scipy import interpolate as interpolate
from matplotlib import pyplot as plt
class PsdFuns:
... | def PsdArray2Noise_1d(PsdArray, N, Semiaxis = True, Real = True):
'''
Generates a noise pattern whose Power Spectral density is given by Psd.
Parameters
---------------------
Psd : 1d array
Contains the numeric Psd (treated as evenly spaced array)
Semiaxis :
0 : does nothing
1 : halvens Pds, then replica... | 136 | 177 | # -*- coding: utf-8 -*-
"""
Created on Thu Jul 07 14:08:31 2016
@author: Mic
"""
from __future__ import division
from wiselib2.must import *
import numpy as np
import wiselib2.Rayman as rm
Gauss1d = lambda x ,y : None
from scipy import interpolate as interpolate
from matplotlib import pyplot as plt
class PsdFuns:
... |
MakeProfile | Evaluates the psd according to .PsdType, .PsdParams and .Options directives
Returns an evenly-spaced array.
If PsdType = NumericArray, linear interpolation is performed.
:PARAM: N: # of samples
:PARAM: dx: grid spacing (spatial frequency)
returns:
1d arr | # -*- coding: utf-8 -*-
"""
Created on Thu Jul 07 14:08:31 2016
@author: Mic
"""
from __future__ import division
from wiselib2.must import *
import numpy as np
import wiselib2.Rayman as rm
Gauss1d = lambda x ,y : None
from scipy import interpolate as interpolate
from matplotlib import pyplot as plt
class PsdFuns:
... | def MakeProfile(self, L,N):
'''
Evaluates the psd according to .PsdType, .PsdParams and .Options directives
Returns an evenly-spaced array.
If PsdType = NumericArray, linear interpolation is performed.
:PARAM: N: # of samples
:PARAM: dx: grid spacing (spatial frequency)
returns:
1d arr
'''
... | 393 | 413 | # -*- coding: utf-8 -*-
"""
Created on Thu Jul 07 14:08:31 2016
@author: Mic
"""
from __future__ import division
from wiselib2.must import *
import numpy as np
import wiselib2.Rayman as rm
Gauss1d = lambda x ,y : None
from scipy import interpolate as interpolate
from matplotlib import pyplot as plt
class PsdFuns:
... |
__getitem__ | Get the procedure params according to the index.
Create the register when it does not exist.
:param index:
:return: ProcedureParamStorage | #!/usr/bin/python3
# -*- coding: utf8 -*-
# Copyright (c) 2020 Baidu, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE... | def __getitem__(self, index):
"""
Get the procedure params according to the index.
Create the register when it does not exist.
:param index:
:return: ProcedureParamStorage
"""
value = self.paramsDict.get(index)
if value is not None:
retu... | 35 | 50 | #!/usr/bin/python3
# -*- coding: utf8 -*-
# Copyright (c) 2020 Baidu, Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE... |
build_model | Build DCE using the initialized attributes
Args:
norm: boolean, wheher to add a normalization layer at the begining
of the autoencoder
act: string, keras activation function name for autoencoder | """
DeepChEmbed （DCE) Models
"""
from dimreducer import DeepAutoEncoder
from cluster import KMeansLayer
from cluster import KMeans
from keras import Model
from keras import optimizers
from keras.utils import normalize
import numpy as np
class DCE():
"""
The class to build a deep chemical embedding model.
... | def build_model(self, norm=True, act='relu'):
"""Build DCE using the initialized attributes
Args:
norm: boolean, wheher to add a normalization layer at the begining
of the autoencoder
act: string, keras activation function name for autoencoder
"""
... | 52 | 68 | """
DeepChEmbed （DCE) Models
"""
from dimreducer import DeepAutoEncoder
from cluster import KMeansLayer
from cluster import KMeans
from keras import Model
from keras import optimizers
from keras.utils import normalize
import numpy as np
class DCE():
"""
The class to build a deep chemical embedding model.
... |
hardening | hardening distribution P and return Q
Args:
q: input distributions.
h_func: input harderning function.
strength: hardening strength.
returns:
p: hardened and normatlized distributions. | """
DeepChEmbed （DCE) Models
"""
from dimreducer import DeepAutoEncoder
from cluster import KMeansLayer
from cluster import KMeans
from keras import Model
from keras import optimizers
from keras.utils import normalize
import numpy as np
class DCE():
"""
The class to build a deep chemical embedding model.
... | @staticmethod
def hardening(q, h_func, stength):
"""hardening distribution P and return Q
Args:
q: input distributions.
h_func: input harderning function.
strength: hardening strength.
returns:
p: hardened and normatlized distributions.
... | 207 | 222 | """
DeepChEmbed （DCE) Models
"""
from dimreducer import DeepAutoEncoder
from cluster import KMeansLayer
from cluster import KMeans
from keras import Model
from keras import optimizers
from keras.utils import normalize
import numpy as np
class DCE():
"""
The class to build a deep chemical embedding model.
... |
authenticate_active | Generate a WLS 'success' response based on interaction with the user
This function creates a WLS response specifying that the principal was
authenticated based on 'fresh' interaction with the user (e.g. input of
a username and password).
Args:
request (AuthRequest): the original WAA request
principal (AuthPri... | import datetime
from . import status
from .errors import InvalidAuthRequest, ProtocolVersionUnsupported, NoMutualAuthType
from .signing import Key
from .response import AuthResponse
class AuthPrincipal:
def __init__(self, userid, auth_methods, ptags=None, session_expiry=None):
self.userid = userid
... | def authenticate_active(self, request, principal, auth, life=None,
sign=True, skip_handling_check=False, *args, **kwargs):
"""Generate a WLS 'success' response based on interaction with the user
This function creates a WLS response specifying that the principal was
... | 68 | 108 | import datetime
from . import status
from .errors import InvalidAuthRequest, ProtocolVersionUnsupported, NoMutualAuthType
from .signing import Key
from .response import AuthResponse
class AuthPrincipal:
def __init__(self, userid, auth_methods, ptags=None, session_expiry=None):
self.userid = userid
... |
authenticate_passive | Generate a WLS 'success' response based on a pre-existing identity
This function creates a WLS response specifying that the principal was
authenticated based on previous successful authentication (e.g. an
existing WLS session cookie).
Args:
request (AuthRequest): the original WAA request
principal (AuthPrinci... | import datetime
from . import status
from .errors import InvalidAuthRequest, ProtocolVersionUnsupported, NoMutualAuthType
from .signing import Key
from .response import AuthResponse
class AuthPrincipal:
def __init__(self, userid, auth_methods, ptags=None, session_expiry=None):
self.userid = userid
... | def authenticate_passive(self, request, principal, sso=[], sign=True,
skip_handling_check=False, *args, **kwargs):
"""Generate a WLS 'success' response based on a pre-existing identity
This function creates a WLS response specifying that the principal was
authen... | 110 | 158 | import datetime
from . import status
from .errors import InvalidAuthRequest, ProtocolVersionUnsupported, NoMutualAuthType
from .signing import Key
from .response import AuthResponse
class AuthPrincipal:
def __init__(self, userid, auth_methods, ptags=None, session_expiry=None):
self.userid = userid
... |
__init__ | :param bool enable_magnetic_store_writes: A flag to enable magnetic store writes.
:param 'TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocationArgs' magnetic_store_rejected_data_location: The location to write error reports for records rejected asynchronously during magnetic store writes. See Magnetic Stor... | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import... | def __init__(__self__, *,
enable_magnetic_store_writes: Optional[bool] = None,
magnetic_store_rejected_data_location: Optional['outputs.TableMagneticStoreWritePropertiesMagneticStoreRejectedDataLocation'] = None):
"""
:param bool enable_magnetic_store_writes: A flag... | 40 | 50 | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import... |
__init__ | :param str bucket_name: Bucket name of the customer S3 bucket.
:param str encryption_option: Encryption option for the customer s3 location. Options are S3 server side encryption with an S3-managed key or KMS managed key. Valid values are `SSE_KMS` and `SSE_S3`.
:param str kms_key_id: KMS key arn for the customer s3 lo... | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import... | def __init__(__self__, *,
bucket_name: Optional[str] = None,
encryption_option: Optional[str] = None,
kms_key_id: Optional[str] = None,
object_key_prefix: Optional[str] = None):
"""
:param str bucket_name: Bucket name of the custome... | 130 | 148 | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import... |
post | Handle the POST request and sign up the user if form validation passes
Returns:
A redirect or a template with the validation errors | from flask import render_template, flash, redirect, url_for, request
from flask.views import MethodView
from app.middleware import auth
from app.models.user import User
from app.validators.register_form import RegisterForm
from app.services import avatar_service
class RegisterController(MethodView):
@auth.optional... | @auth.optional
def post(self):
"""
Handle the POST request and sign up the user if form validation passes
Returns:
A redirect or a template with the validation errors
"""
form = RegisterForm()
if form.validate_on_submit():
form.validate_username(form.username)
avatar = '... | 21 | 45 | from flask import render_template, flash, redirect, url_for, request
from flask.views import MethodView
from app.middleware import auth
from app.models.user import User
from app.validators.register_form import RegisterForm
from app.services import avatar_service
class RegisterController(MethodView):
@auth.optional... |
__init__ | Initializer for linear model.
Args:
num_inputs: the dimension of input data.
num_hidden_layers: the number of hidden layers.
num_inner_features: the number of features in the hidden layers | """Polynomial model class used by agents for building stuff.
"""
from torch import nn, optim
import torch
import torch.nn.functional as F
from stock_trading_backend.agent.model import Model
class NNModel(nn.Module):
"""Torch neural network model.
"""
# MASKED: __init__ function (lines 14-29)
def forwar... | def __init__(self, num_inputs, num_hidden_layers, num_inner_features):
"""Initializer for linear model.
Args:
num_inputs: the dimension of input data.
num_hidden_layers: the number of hidden layers.
num_inner_features: the number of features in the hidden layers
... | 14 | 29 | """Polynomial model class used by agents for building stuff.
"""
from torch import nn, optim
import torch
import torch.nn.functional as F
from stock_trading_backend.agent.model import Model
class NNModel(nn.Module):
"""Torch neural network model.
"""
def __init__(self, num_inputs, num_hidden_layers, num... |
forward | Forward pass on the neural network model.
Args:
input_tensor: the input tensor.
Returns:
Tensor with model results. | """Polynomial model class used by agents for building stuff.
"""
from torch import nn, optim
import torch
import torch.nn.functional as F
from stock_trading_backend.agent.model import Model
class NNModel(nn.Module):
"""Torch neural network model.
"""
def __init__(self, num_inputs, num_hidden_layers, num... | def forward(self, input_tensor):
"""Forward pass on the neural network model.
Args:
input_tensor: the input tensor.
Returns:
Tensor with model results.
"""
output = F.relu(self.input_layer(input_tensor))
output = self.hidden_layers(output)
... | 31 | 43 | """Polynomial model class used by agents for building stuff.
"""
from torch import nn, optim
import torch
import torch.nn.functional as F
from stock_trading_backend.agent.model import Model
class NNModel(nn.Module):
"""Torch neural network model.
"""
def __init__(self, num_inputs, num_hidden_layers, num... |
__init__ | Initializer for model class.
Args:
learning_rate: the learning rate of the model.
num_hidden_layers: number of hidden layers in the network.
num_inner_features: number of features in the hidden layers. | """Polynomial model class used by agents for building stuff.
"""
from torch import nn, optim
import torch
import torch.nn.functional as F
from stock_trading_backend.agent.model import Model
class NNModel(nn.Module):
"""Torch neural network model.
"""
def __init__(self, num_inputs, num_hidden_layers, num... | def __init__(self, learning_rate=1e-3, num_hidden_layers=1, num_inner_features=100):
"""Initializer for model class.
Args:
learning_rate: the learning rate of the model.
num_hidden_layers: number of hidden layers in the network.
num_inner_features: number of feat... | 51 | 67 | """Polynomial model class used by agents for building stuff.
"""
from torch import nn, optim
import torch
import torch.nn.functional as F
from stock_trading_backend.agent.model import Model
class NNModel(nn.Module):
"""Torch neural network model.
"""
def __init__(self, num_inputs, num_hidden_layers, num... |
_predict | Use provided information to make a prediction.
Args:
state_action_tensor: pytorch tensor with state-action values.
Returns:
Predicted values for observation-action tensors. | """Polynomial model class used by agents for building stuff.
"""
from torch import nn, optim
import torch
import torch.nn.functional as F
from stock_trading_backend.agent.model import Model
class NNModel(nn.Module):
"""Torch neural network model.
"""
def __init__(self, num_inputs, num_hidden_layers, num... | def _predict(self, state_action_tensor):
"""Use provided information to make a prediction.
Args:
state_action_tensor: pytorch tensor with state-action values.
Returns:
Predicted values for observation-action tensors.
"""
if self.model is None:
... | 78 | 89 | """Polynomial model class used by agents for building stuff.
"""
from torch import nn, optim
import torch
import torch.nn.functional as F
from stock_trading_backend.agent.model import Model
class NNModel(nn.Module):
"""Torch neural network model.
"""
def __init__(self, num_inputs, num_hidden_layers, num... |
_train | Train the model for 1 epoch.
Args:
state_action_tensor: pytorch tensor with state-action expected_values.
expected_values: pytorch tensor with expected values for each state-action.
Returns:
The loss before trainig. | """Polynomial model class used by agents for building stuff.
"""
from torch import nn, optim
import torch
import torch.nn.functional as F
from stock_trading_backend.agent.model import Model
class NNModel(nn.Module):
"""Torch neural network model.
"""
def __init__(self, num_inputs, num_hidden_layers, num... | def _train(self, state_action_tensor, expected_values_tensor):
"""Train the model for 1 epoch.
Args:
state_action_tensor: pytorch tensor with state-action expected_values.
expected_values: pytorch tensor with expected values for each state-action.
Returns:
... | 91 | 110 | """Polynomial model class used by agents for building stuff.
"""
from torch import nn, optim
import torch
import torch.nn.functional as F
from stock_trading_backend.agent.model import Model
class NNModel(nn.Module):
"""Torch neural network model.
"""
def __init__(self, num_inputs, num_hidden_layers, num... |
getNetworkCellularGatewaySettingsDhcp | **List common DHCP settings of MGs**
https://developer.cisco.com/meraki/api/#!get-network-cellular-gateway-settings-dhcp
- networkId (string) | class MGDHCPSettings(object):
def __init__(self, session):
super(MGDHCPSettings, self).__init__()
self._session = session
# MASKED: getNetworkCellularGatewaySettingsDhcp function (lines 6-20)
def updateNetworkCellularGatewaySettingsDhcp(self, networkId: str, **kwargs):
"""
... | def getNetworkCellularGatewaySettingsDhcp(self, networkId: str):
"""
**List common DHCP settings of MGs**
https://developer.cisco.com/meraki/api/#!get-network-cellular-gateway-settings-dhcp
- networkId (string)
"""
metadata = {
'tags': ['MG DHCP ... | 6 | 20 | class MGDHCPSettings(object):
def __init__(self, session):
super(MGDHCPSettings, self).__init__()
self._session = session
def getNetworkCellularGatewaySettingsDhcp(self, networkId: str):
"""
**List common DHCP settings of MGs**
https://developer.cisco.com/meraki/api/... |
blend | Blend image1 and image2 using 'factor'.
A value of factor 0.0 means only image1 is used.
A value of 1.0 means only image2 is used. A value between 0.0 and
1.0 means we linearly interpolate the pixel values between the two
images. A value greater than 1.0 "extrapolates" the difference
between the two pixel values, an... | # Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | def blend(image1, image2, factor):
"""Blend image1 and image2 using 'factor'.
A value of factor 0.0 means only image1 is used.
A value of 1.0 means only image2 is used. A value between 0.0 and
1.0 means we linearly interpolate the pixel values between the two
images. A value greater than 1.0 "extrapolates"... | 25 | 45 | # Copyright 2019 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
get_book_quote_page | Find the ``<a>`` element pointing to the quote page of a book.
Args:
soup (bs4.element.Tag):
Returns: | """
Scrape quotes, books and authors from ``Good Reads`` website.
"""
import bs4
from .utils import *
def get_author_name(soup):
"""Get the author's name from its main page.
Args:
soup (bs4.element.Tag): connection to the author page.
Returns:
string: name of the author.
Examples::... | def get_book_quote_page(soup):
"""Find the ``<a>`` element pointing to the quote page of a book.
Args:
soup (bs4.element.Tag):
Returns:
"""
quote_div = soup.findAll('div', attrs={'class': ' clearFloats bigBox'})
if quote_div:
return quote_div[-1].find('a')
return None | 380 | 392 | """
Scrape quotes, books and authors from ``Good Reads`` website.
"""
import bs4
from .utils import *
def get_author_name(soup):
"""Get the author's name from its main page.
Args:
soup (bs4.element.Tag): connection to the author page.
Returns:
string: name of the author.
Examples::... |
find_generator | The order of any element in a group can be divided by p-1.
Step 1: Calculate all Divisors.
Step 2: Test for a random element e of G wether e to the power of a Divisor is 1.
if neither is one but e to the power of p-1, a generator is found. | """
This file implements the signature scheme from "Unique Ring Signatures: A Practical
Construction" by Matthew Franklin and Haibin Zhang
"""
import sys
import math
from random import randint
import hashlib
from libsig.AbstractRingSignatureScheme import AbstractRingSignatureScheme
#from AbstractRingSignatureScheme im... | def find_generator(p):
'''
The order of any element in a group can be divided by p-1.
Step 1: Calculate all Divisors.
Step 2: Test for a random element e of G wether e to the power of a Divisor is 1.
if neither is one but e to the power of p-1, a generator is found.
'''
# Init
#... | 34 | 69 | """
This file implements the signature scheme from "Unique Ring Signatures: A Practical
Construction" by Matthew Franklin and Haibin Zhang
"""
import sys
import math
from random import randint
import hashlib
from libsig.AbstractRingSignatureScheme import AbstractRingSignatureScheme
#from AbstractRingSignatureScheme im... |
children | Get childen nodes of this node.
Arguments:
depth: Number of levels of children to traverse. 0 returns only this node.
include_self: Includes this node in the results.
include_parents: Includes nodes that match in the results, when they also have
child nodes that match.
include_children: If True... | #!/usr/bin/python3
import functools
from copy import deepcopy
from .grammar import BASE_NODE_TYPES
class NodeBase:
"""Represents a node within the solidity AST.
Attributes:
depth: Number of nodes between this node and the SourceUnit
offset: Absolute source offsets as a (start, stop) tuple
... | def children(
self,
depth=None,
include_self=False,
include_parents=True,
include_children=True,
required_offset=None,
offset_limits=None,
filters=None,
exclude_filter=None,
):
"""Get childen nodes of this node.
Arguments:
... | 71 | 114 | #!/usr/bin/python3
import functools
from copy import deepcopy
from .grammar import BASE_NODE_TYPES
class NodeBase:
"""Represents a node within the solidity AST.
Attributes:
depth: Number of nodes between this node and the SourceUnit
offset: Absolute source offsets as a (start, stop) tuple
... |
pqcost | Splits the gencost variable into two pieces if costs are given for Qg.
Checks whether C{gencost} has cost information for reactive power
generation (rows C{ng+1} to C{2*ng}). If so, it returns the first C{ng}
rows in C{pcost} and the last C{ng} rows in C{qcost}. Otherwise, leaves
C{qcost} empty. Also does some error c... | # Copyright (c) 1996-2015 PSERC. All rights reserved.
# Use of this source code is governed by a BSD-style
# license that can be found in the LICENSE file.
"""Splits the gencost variable into two pieces if costs are given for Qg.
"""
from sys import stderr
from numpy import array, arange
# MASKED: pqcost function ... | def pqcost(gencost, ng, on=None):
"""Splits the gencost variable into two pieces if costs are given for Qg.
Checks whether C{gencost} has cost information for reactive power
generation (rows C{ng+1} to C{2*ng}). If so, it returns the first C{ng}
rows in C{pcost} and the last C{ng} rows in C{qcost}. Oth... | 13 | 37 | # Copyright (c) 1996-2015 PSERC. All rights reserved.
# Use of this source code is governed by a BSD-style
# license that can be found in the LICENSE file.
"""Splits the gencost variable into two pieces if costs are given for Qg.
"""
from sys import stderr
from numpy import array, arange
def pqcost(gencost, ng, on... |
thumbIncrementCheck | Checks whether your thumb is up or not.
No matter what hand you use.
returns 1 if thumb is up else 0 | """Python 3.9.5"""
import cv2
import HandTrackingModule as htm
# MASKED: thumbIncrementCheck function (lines 7-20)
def textOutput(count, cc) -> str:
"""Returns an appropriate text output depending on
`count` and `cc`."""
text = "NOTHING"
if (count, cc) == (2, 2):
text = "SCISSOR"
elif c... | def thumbIncrementCheck(lmList: list[list[int]]) -> int:
"""Checks whether your thumb is up or not.
No matter what hand you use.
returns 1 if thumb is up else 0"""
count = 0
t_x = lmList[4][1]
p_x = lmList[17][1]
if t_x > p_x: # If true: RIGHT hand
if lmList[4][1] >= lmList[2][1]:
... | 7 | 20 | """Python 3.9.5"""
import cv2
import HandTrackingModule as htm
def thumbIncrementCheck(lmList: list[list[int]]) -> int:
"""Checks whether your thumb is up or not.
No matter what hand you use.
returns 1 if thumb is up else 0"""
count = 0
t_x = lmList[4][1]
p_x = lmList[17][1]
if t_x > p_x:... |
_read_kit_data | Read epochs data.
Returns
-------
data : array, [channels x samples]
the data matrix (channels x samples).
times : array, [samples]
returns the time values corresponding to the samples. | """Conversion tool from SQD to FIF.
RawKIT class is adapted from Denis Engemann et al.'s mne_bti2fiff.py.
"""
# Authors: Teon Brooks <teon.brooks@gmail.com>
# Joan Massich <mailsik@gmail.com>
# Christian Brodbeck <christianbrodbeck@nyu.edu>
#
# License: BSD (3-clause)
from collections import defaul... | def _read_kit_data(self):
"""Read epochs data.
Returns
-------
data : array, [channels x samples]
the data matrix (channels x samples).
times : array, [samples]
returns the time values corresponding to the samples.
"""
info = self._raw_... | 427 | 454 | """Conversion tool from SQD to FIF.
RawKIT class is adapted from Denis Engemann et al.'s mne_bti2fiff.py.
"""
# Authors: Teon Brooks <teon.brooks@gmail.com>
# Joan Massich <mailsik@gmail.com>
# Christian Brodbeck <christianbrodbeck@nyu.edu>
#
# License: BSD (3-clause)
from collections import defaul... |
get_home_envvars | Return dict with env variables to be adjusted for a new HOME
Only variables found in current os.environ are adjusted.
Parameters
----------
new_home: str or Path
New home path, in native to OS "schema" | # emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-
# ex: set sts=4 ts=4 sw=4 et:
# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
#
# See COPYING file distributed along with the datalad package for the
# copyright and license terms.
#
# ## ### ### ... | def get_home_envvars(new_home):
"""Return dict with env variables to be adjusted for a new HOME
Only variables found in current os.environ are adjusted.
Parameters
----------
new_home: str or Path
New home path, in native to OS "schema"
"""
new_home = str(new_home)
out = {'HOME':... | 232 | 251 | # emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-
# ex: set sts=4 ts=4 sw=4 et:
# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
#
# See COPYING file distributed along with the datalad package for the
# copyright and license terms.
#
# ## ### ### ... |
rmtree | To remove git-annex .git it is needed to make all files and directories writable again first
Parameters
----------
path: Path or str
Path to remove
chmod_files : string or bool, optional
Whether to make files writable also before removal. Usually it is just
a matter of directories to have write permissions.
... | # emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-
# ex: set sts=4 ts=4 sw=4 et:
# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
#
# See COPYING file distributed along with the datalad package for the
# copyright and license terms.
#
# ## ### ### ... | def rmtree(path, chmod_files='auto', children_only=False, *args, **kwargs):
"""To remove git-annex .git it is needed to make all files and directories writable again first
Parameters
----------
path: Path or str
Path to remove
chmod_files : string or bool, optional
Whether to make fil... | 469 | 518 | # emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-
# ex: set sts=4 ts=4 sw=4 et:
# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
#
# See COPYING file distributed along with the datalad package for the
# copyright and license terms.
#
# ## ### ### ... |
ensure_bytes | Convert/encode unicode string to bytes.
If `s` isn't a string, return it as is.
Parameters
----------
encoding: str, optional
Encoding to use. "utf-8" is the default | # emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-
# ex: set sts=4 ts=4 sw=4 et:
# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
#
# See COPYING file distributed along with the datalad package for the
# copyright and license terms.
#
# ## ### ### ... | def ensure_bytes(s, encoding='utf-8'):
"""Convert/encode unicode string to bytes.
If `s` isn't a string, return it as is.
Parameters
----------
encoding: str, optional
Encoding to use. "utf-8" is the default
"""
if not isinstance(s, str):
return s
return s.encode(encodin... | 792 | 804 | # emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-
# ex: set sts=4 ts=4 sw=4 et:
# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
#
# See COPYING file distributed along with the datalad package for the
# copyright and license terms.
#
# ## ### ### ... |
ensure_unicode | Convert/decode bytestring to unicode.
If `s` isn't a bytestring, return it as is.
Parameters
----------
encoding: str, optional
Encoding to use. If None, "utf-8" is tried, and then if not a valid
UTF-8, encoding will be guessed
confidence: float, optional
A value between 0 and 1, so if guessing of encoding is ... | # emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-
# ex: set sts=4 ts=4 sw=4 et:
# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
#
# See COPYING file distributed along with the datalad package for the
# copyright and license terms.
#
# ## ### ### ... | def ensure_unicode(s, encoding=None, confidence=None):
"""Convert/decode bytestring to unicode.
If `s` isn't a bytestring, return it as is.
Parameters
----------
encoding: str, optional
Encoding to use. If None, "utf-8" is tried, and then if not a valid
UTF-8, encoding will be guessed... | 807 | 851 | # emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-
# ex: set sts=4 ts=4 sw=4 et:
# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
#
# See COPYING file distributed along with the datalad package for the
# copyright and license terms.
#
# ## ### ### ... |
unique | Given a sequence return a list only with unique elements while maintaining order
This is the fastest solution. See
https://www.peterbe.com/plog/uniqifiers-benchmark
and
http://stackoverflow.com/a/480227/1265472
for more information.
Enhancement -- added ability to compare for uniqueness using a key function
Paramete... | # emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-
# ex: set sts=4 ts=4 sw=4 et:
# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
#
# See COPYING file distributed along with the datalad package for the
# copyright and license terms.
#
# ## ### ### ... | def unique(seq, key=None, reverse=False):
"""Given a sequence return a list only with unique elements while maintaining order
This is the fastest solution. See
https://www.peterbe.com/plog/uniqifiers-benchmark
and
http://stackoverflow.com/a/480227/1265472
for more information.
Enhancement ... | 900 | 933 | # emacs: -*- mode: python; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-
# ex: set sts=4 ts=4 sw=4 et:
# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##
#
# See COPYING file distributed along with the datalad package for the
# copyright and license terms.
#
# ## ### ### ... |
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