from __future__ import annotations

from kornia.core import Module, Tensor
from kornia.core.check import KORNIA_CHECK_IS_TENSOR

from .filter import filter2d, filter2d_separable
from .kernels import _unpack_2d_ks, get_box_kernel1d, get_box_kernel2d


def box_blur(
    input: Tensor, kernel_size: tuple[int, int] | int, border_type: str = 'reflect', separable: bool = False
) -> Tensor:
    r"""Blur an image using the box filter.

    .. image:: _static/img/box_blur.png

    The function smooths an image using the kernel:

    .. math::
        K = \frac{1}{\text{kernel_size}_x * \text{kernel_size}_y}
        \begin{bmatrix}
            1 & 1 & 1 & \cdots & 1 & 1 \\
            1 & 1 & 1 & \cdots & 1 & 1 \\
            \vdots & \vdots & \vdots & \ddots & \vdots & \vdots \\
            1 & 1 & 1 & \cdots & 1 & 1 \\
        \end{bmatrix}

    Args:
        image: the image to blur with shape :math:`(B,C,H,W)`.
        kernel_size: the blurring kernel size.
        border_type: the padding mode to be applied before convolving.
          The expected modes are: ``'constant'``, ``'reflect'``, ``'replicate'`` or ``'circular'``.
        separable: run as composition of two 1d-convolutions.

    Returns:
        the blurred tensor with shape :math:`(B,C,H,W)`.

    .. note::
       See a working example `here <https://kornia.github.io/tutorials/nbs/filtering_operators.html>`__.

    Example:
        >>> input = torch.rand(2, 4, 5, 7)
        >>> output = box_blur(input, (3, 3))  # 2x4x5x7
        >>> output.shape
        torch.Size([2, 4, 5, 7])
    """
    KORNIA_CHECK_IS_TENSOR(input)

    if separable:
        ky, kx = _unpack_2d_ks(kernel_size)
        kernel_y = get_box_kernel1d(ky, device=input.device, dtype=input.dtype)
        kernel_x = get_box_kernel1d(kx, device=input.device, dtype=input.dtype)
        out = filter2d_separable(input, kernel_x, kernel_y, border_type)
    else:
        kernel = get_box_kernel2d(kernel_size, device=input.device, dtype=input.dtype)
        out = filter2d(input, kernel, border_type)

    return out


class BoxBlur(Module):
    r"""Blur an image using the box filter.

    The function smooths an image using the kernel:

    .. math::
        K = \frac{1}{\text{kernel_size}_x * \text{kernel_size}_y}
        \begin{bmatrix}
            1 & 1 & 1 & \cdots & 1 & 1 \\
            1 & 1 & 1 & \cdots & 1 & 1 \\
            \vdots & \vdots & \vdots & \ddots & \vdots & \vdots \\
            1 & 1 & 1 & \cdots & 1 & 1 \\
        \end{bmatrix}

    Args:
        kernel_size: the blurring kernel size.
        border_type: the padding mode to be applied before convolving.
          The expected modes are: ``'constant'``, ``'reflect'``,
          ``'replicate'`` or ``'circular'``. Default: ``'reflect'``.
        separable: run as composition of two 1d-convolutions.

    Returns:
        the blurred input tensor.

    Shape:
        - Input: :math:`(B, C, H, W)`
        - Output: :math:`(B, C, H, W)`

    Example:
        >>> input = torch.rand(2, 4, 5, 7)
        >>> blur = BoxBlur((3, 3))
        >>> output = blur(input)  # 2x4x5x7
        >>> output.shape
        torch.Size([2, 4, 5, 7])
    """

    def __init__(
        self, kernel_size: tuple[int, int] | int, border_type: str = 'reflect', separable: bool = False
    ) -> None:
        super().__init__()
        self.kernel_size = kernel_size
        self.border_type = border_type
        self.separable = separable

        if separable:
            ky, kx = _unpack_2d_ks(self.kernel_size)
            self.register_buffer("kernel_y", get_box_kernel1d(ky))
            self.register_buffer("kernel_x", get_box_kernel1d(kx))
            self.kernel_y: Tensor
            self.kernel_x: Tensor
        else:
            self.register_buffer("kernel", get_box_kernel2d(kernel_size))
            self.kernel: Tensor

    def __repr__(self) -> str:
        return (
            f"{self.__class__.__name__}"
            f"(kernel_size={self.kernel_size}, "
            f"border_type={self.border_type}, "
            f"separable={self.separable})"
        )

    def forward(self, input: Tensor) -> Tensor:
        KORNIA_CHECK_IS_TENSOR(input)
        if self.separable:
            return filter2d_separable(input, self.kernel_x, self.kernel_y, self.border_type)
        return filter2d(input, self.kernel, self.border_type)
