#    _____           ______  _____ 
#  / ____/    /\    |  ____ |  __ \
# | |        /  \   | |__   | |__) | Caer - Modern Computer Vision
# | |       / /\ \  |  __|  |  _  /  Languages: Python, C, C++, Cuda
# | |___   / ____ \ | |____ | | \ \  http://github.com/jasmcaus/caer
#  \_____\/_/    \_ \______ |_|  \_\

# Licensed under the MIT License <http://opensource.org/licenses/MIT>
# SPDX-License-Identifier: MIT
# Copyright (c) 2020-2021 The Caer Authors <http://github.com/jasmcaus>


import numbers
import numpy as np
from numpy.lib.stride_tricks import as_strided

from .._sklearn_utils import check_array, check_random_state, _deprecate_positional_args


def _compute_n_patches(i_h, i_w, p_h, p_w, max_patches=None):
    """Compute the number of patches that will be extracted in an image.

    Read more in the :ref:`User Guide <image_feature_extraction>`.

    Parameters
    ----------
    i_h : int
        The image height
    i_w : int
        The image with
    p_h : int
        The height of a patch
    p_w : int
        The width of a patch
    max_patches : int or float, default=None
        The maximum number of patches to extract. If max_patches is a float
        between 0 and 1, it is taken to be a proportion of the total number
        of patches.
    """
    n_h = i_h - p_h + 1
    n_w = i_w - p_w + 1
    all_patches = n_h * n_w

    if max_patches:
        if (isinstance(max_patches, (numbers.Integral))
                and max_patches < all_patches):
            return max_patches
        elif (isinstance(max_patches, (numbers.Integral))
              and max_patches >= all_patches):
            return all_patches
        elif (isinstance(max_patches, (numbers.Real))
                and 0 < max_patches < 1):
            return int(max_patches * all_patches)
        else:
            raise ValueError("Invalid value for max_patches: %r" % max_patches)
    else:
        return all_patches


def _extract_patches(arr, patch_shape=8, extraction_step=1):
    """Extracts patches of any n-dimensional array in place using strides.

    Given an n-dimensional array it will return a 2n-dimensional array with
    the first n dimensions indexing patch position and the last n indexing
    the patch content. This operation is immediate (O(1)). A reshape
    performed on the first n dimensions will cause numpy to copy data, leading
    to a list of extracted patches.

    Read more in the :ref:`User Guide <image_feature_extraction>`.

    Parameters
    ----------
    arr : Tensor
        n-dimensional array of which patches are to be extracted

    patch_shape : int or tuple of length arr.ndim.default=8
        Indicates the shape of the patches to be extracted. If an
        integer is given, the shape will be a hypercube of
        sidelength given by its value.

    extraction_step : int or tuple of length arr.ndim, default=1
        Indicates step size at which extraction shall be performed.
        If integer is given, then the step is uniform in all dimensions.


    Returns
    -------
    patches : strided Tensor
        2n-dimensional array indexing patches on first n dimensions and
        containing patches on the last n dimensions. These dimensions
        are fake, but this way no data is copied. A simple reshape invokes
        a copying operation to obtain a list of patches:
        result.reshape([-1] + list(patch_shape))
    """

    arr_ndim = arr.ndim

    if isinstance(patch_shape, numbers.Number):
        patch_shape = tuple([patch_shape] * arr_ndim)
    if isinstance(extraction_step, numbers.Number):
        extraction_step = tuple([extraction_step] * arr_ndim)

    patch_strides = arr.strides

    slices = tuple(slice(None, None, st) for st in extraction_step)
    indexing_strides = arr[slices].strides

    patch_indices_shape = ((np.array(arr.shape) - np.array(patch_shape)) //
                           np.array(extraction_step)) + 1

    shape = tuple(list(patch_indices_shape) + list(patch_shape))
    strides = tuple(list(indexing_strides) + list(patch_strides))

    patches = as_strided(arr, shape=shape, strides=strides)
    return patches


@_deprecate_positional_args
def extract_patches_2d(image, patch_size, *, max_patches=None,
                       random_state=None):
    """Reshape a 2D image into a collection of patches

    The resulting patches are allocated in a dedicated array.

    Read more in the :ref:`User Guide <image_feature_extraction>`.

    Parameters
    ----------
    image : Tensor of shape (image_height, image_width) or \
        (image_height, image_width, n_channels)
        The original image data. For color images, the last dimension specifies
        the channel: a RGB image would have `n_channels=3`.

    patch_size : tuple of int (patch_height, patch_width)
        The dimensions of one patch.

    max_patches : int or float, default=None
        The maximum number of patches to extract. If `max_patches` is a float
        between 0 and 1, it is taken to be a proportion of the total number
        of patches.

    random_state : int, RandomState instance, default=None
        Determines the random number generator used for random sampling when
        `max_patches` is not None. Use an int to make the randomness
        deterministic.
        See :term:`Glossary <random_state>`.

    Returns
    -------
    patches : array of shape (n_patches, patch_height, patch_width) or \
        (n_patches, patch_height, patch_width, n_channels)
        The collection of patches extracted from the image, where `n_patches`
        is either `max_patches` or the total number of patches that can be
        extracted.

    """
    i_h, i_w = image.shape[:2]
    p_h, p_w = patch_size

    if p_h > i_h:
        raise ValueError("Height of the patch should be less than the height"
                         " of the image.")

    if p_w > i_w:
        raise ValueError("Width of the patch should be less than the width"
                         " of the image.")

    image = check_array(image, allow_nd=True)
    image = image.reshape((i_h, i_w, -1))
    n_colors = image.shape[-1]

    extracted_patches = _extract_patches(image,
                                         patch_shape=(p_h, p_w, n_colors),
                                         extraction_step=1)

    n_patches = _compute_n_patches(i_h, i_w, p_h, p_w, max_patches)
    if max_patches:
        rng = check_random_state(random_state)
        i_s = rng.randint(i_h - p_h + 1, size=n_patches)
        j_s = rng.randint(i_w - p_w + 1, size=n_patches)
        patches = extracted_patches[i_s, j_s, 0]
    else:
        patches = extracted_patches

    patches = patches.reshape(-1, p_h, p_w, n_colors)
    # remove the color dimension if useless
    if patches.shape[-1] == 1:
        return patches.reshape((n_patches, p_h, p_w))
    else:
        return patches
