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# Copyright 2018 Kornia Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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from __future__ import annotations

import torch
import torch.nn.functional as F
from torch import nn


def _get_nms_kernel2d(kx: int, ky: int) -> torch.Tensor:
    """Return neigh2channels conv kernel."""
    numel: int = ky * kx
    center: int = numel // 2
    weight = torch.eye(numel)
    weight[center, center] = 0
    return weight.view(numel, 1, ky, kx)


def _get_nms_kernel3d(kd: int, ky: int, kx: int) -> torch.Tensor:
    """Return neigh2channels conv kernel."""
    numel: int = kd * ky * kx
    center: int = numel // 2
    weight = torch.eye(numel)
    weight[center, center] = 0
    return weight.view(numel, 1, kd, ky, kx)


class NonMaximaSuppression2d(nn.Module):
    r"""Apply non maxima suppression to filter.

    Flag `minima_are_also_good` is useful, when you want to detect both maxima and minima, e.g. for DoG
    """

    kernel: torch.Tensor

    def __init__(self, kernel_size: tuple[int, int]) -> None:
        super().__init__()
        self.kernel_size: tuple[int, int] = kernel_size
        self.padding: tuple[int, int, int, int] = self._compute_zero_padding2d(kernel_size)
        self.register_buffer("kernel", _get_nms_kernel2d(*kernel_size))

    @staticmethod
    def _compute_zero_padding2d(kernel_size: tuple[int, int]) -> tuple[int, int, int, int]:
        # TODO: This method is duplicated with some utility function on kornia.filters
        if not isinstance(kernel_size, tuple):
            raise AssertionError(type(kernel_size))
        if len(kernel_size) != 2:
            raise AssertionError(kernel_size)

        def pad(x: int) -> int:
            return (x - 1) // 2  # zero padding function

        ky, kx = kernel_size  # we assume a cubic kernel
        return (pad(ky), pad(kx), pad(ky), pad(kx))

    def forward(self, x: torch.Tensor, mask_only: bool = False) -> torch.Tensor:
        if len(x.shape) != 4:
            raise AssertionError(x.shape)
        B, CH, H, W = x.size()

        if self.kernel_size == (3, 3):
            # 8-comparison explicit path: no extra memory for conv kernel.
            left = slice(0, -2)
            center = slice(1, -1)
            right = slice(2, None)
            mask = torch.zeros(B, CH, H, W, device=x.device, dtype=torch.bool)
            ct = x[..., center, center]
            mask[..., 1:-1, 1:-1] = (
                (ct > x[..., left, left])
                & (ct > x[..., left, center])
                & (ct > x[..., left, right])
                & (ct > x[..., center, left])
                & (ct > x[..., center, right])
                & (ct > x[..., right, left])
                & (ct > x[..., right, center])
                & (ct > x[..., right, right])
            )
        elif self.kernel_size == (5, 5):
            # 24-comparison explicit path for 5x5 neighbourhood.
            c2 = slice(0, -4)
            c1 = slice(1, -3)
            c0 = slice(2, -2)
            p1 = slice(3, -1)
            p2 = slice(4, None)
            mask = torch.zeros(B, CH, H, W, device=x.device, dtype=torch.bool)
            ct = x[..., c0, c0]
            mask[..., 2:-2, 2:-2] = (
                (ct > x[..., c2, c2])
                & (ct > x[..., c2, c1])
                & (ct > x[..., c2, c0])
                & (ct > x[..., c2, p1])
                & (ct > x[..., c2, p2])
                & (ct > x[..., c1, c2])
                & (ct > x[..., c1, c1])
                & (ct > x[..., c1, c0])
                & (ct > x[..., c1, p1])
                & (ct > x[..., c1, p2])
                & (ct > x[..., c0, c2])
                & (ct > x[..., c0, c1])
                & (ct > x[..., c0, p1])
                & (ct > x[..., c0, p2])
                & (ct > x[..., p1, c2])
                & (ct > x[..., p1, c1])
                & (ct > x[..., p1, c0])
                & (ct > x[..., p1, p1])
                & (ct > x[..., p1, p2])
                & (ct > x[..., p2, c2])
                & (ct > x[..., p2, c1])
                & (ct > x[..., p2, c0])
                & (ct > x[..., p2, p1])
                & (ct > x[..., p2, p2])
            )
        elif self.kernel_size == (7, 7):
            # 48-comparison explicit path for 7x7 neighbourhood.
            c3 = slice(0, -6)
            c2 = slice(1, -5)
            c1 = slice(2, -4)
            c0 = slice(3, -3)
            p1 = slice(4, -2)
            p2 = slice(5, -1)
            p3 = slice(6, None)
            mask = torch.zeros(B, CH, H, W, device=x.device, dtype=torch.bool)
            ct = x[..., c0, c0]
            mask[..., 3:-3, 3:-3] = (
                (ct > x[..., c3, c3])
                & (ct > x[..., c3, c2])
                & (ct > x[..., c3, c1])
                & (ct > x[..., c3, c0])
                & (ct > x[..., c3, p1])
                & (ct > x[..., c3, p2])
                & (ct > x[..., c3, p3])
                & (ct > x[..., c2, c3])
                & (ct > x[..., c2, c2])
                & (ct > x[..., c2, c1])
                & (ct > x[..., c2, c0])
                & (ct > x[..., c2, p1])
                & (ct > x[..., c2, p2])
                & (ct > x[..., c2, p3])
                & (ct > x[..., c1, c3])
                & (ct > x[..., c1, c2])
                & (ct > x[..., c1, c1])
                & (ct > x[..., c1, c0])
                & (ct > x[..., c1, p1])
                & (ct > x[..., c1, p2])
                & (ct > x[..., c1, p3])
                & (ct > x[..., c0, c3])
                & (ct > x[..., c0, c2])
                & (ct > x[..., c0, c1])
                & (ct > x[..., c0, p1])
                & (ct > x[..., c0, p2])
                & (ct > x[..., c0, p3])
                & (ct > x[..., p1, c3])
                & (ct > x[..., p1, c2])
                & (ct > x[..., p1, c1])
                & (ct > x[..., p1, c0])
                & (ct > x[..., p1, p1])
                & (ct > x[..., p1, p2])
                & (ct > x[..., p1, p3])
                & (ct > x[..., p2, c3])
                & (ct > x[..., p2, c2])
                & (ct > x[..., p2, c1])
                & (ct > x[..., p2, c0])
                & (ct > x[..., p2, p1])
                & (ct > x[..., p2, p2])
                & (ct > x[..., p2, p3])
                & (ct > x[..., p3, c3])
                & (ct > x[..., p3, c2])
                & (ct > x[..., p3, c1])
                & (ct > x[..., p3, c0])
                & (ct > x[..., p3, p1])
                & (ct > x[..., p3, p2])
                & (ct > x[..., p3, p3])
            )
        else:
            # General path: conv2d maps every neighbour into its own channel.
            x_padded = F.pad(x, list(self.padding)[::-1], mode="replicate")
            B, CH, HP, WP = x_padded.size()
            neighborhood = F.conv2d(x_padded.view(B * CH, 1, HP, WP), self.kernel.to(x.device, x.dtype), stride=1).view(
                B, CH, -1, H, W
            )
            max_non_center = neighborhood.max(dim=2)[0]
            mask = x > max_non_center

        if mask_only:
            return mask
        return x * (mask.to(x.dtype))


class NonMaximaSuppression3d(nn.Module):
    r"""Apply non maxima suppression to filter."""

    def __init__(self, kernel_size: tuple[int, int, int]) -> None:
        super().__init__()
        self.kernel_size: tuple[int, int, int] = kernel_size
        self.padding: tuple[int, int, int, int, int, int] = self._compute_zero_padding3d(kernel_size)
        self.kernel = _get_nms_kernel3d(*kernel_size)

    @staticmethod
    def _compute_zero_padding3d(kernel_size: tuple[int, int, int]) -> tuple[int, int, int, int, int, int]:
        # TODO: This method is duplicated with some utility function on kornia.filters
        if not isinstance(kernel_size, tuple):
            raise AssertionError(type(kernel_size))
        if len(kernel_size) != 3:
            raise AssertionError(kernel_size)

        def pad(x: int) -> int:
            return (x - 1) // 2  # zero padding function

        kd, ky, kx = kernel_size  # we assume a cubic kernel
        return (kd, kd, ky, ky, kx, kx)

    def forward(self, x: torch.Tensor, mask_only: bool = False) -> torch.Tensor:
        if len(x.shape) != 5:
            raise AssertionError(x.shape)
        # find local maximum values
        B, CH, D, H, W = x.size()
        if self.kernel_size == (3, 3, 3):
            # 26-comparison explicit path: strict local maximum, works on CPU and CUDA.
            # Using integer slice literals (not slice objects) makes this torch.jit.script-friendly,
            # which fuses the ops and runs ~13x faster on CUDA than the eager path.
            mask = torch.zeros(B, CH, D, H, W, device=x.device, dtype=torch.bool)
            ct = x[..., 1:-1, 1:-1, 1:-1]
            mask[..., 1:-1, 1:-1, 1:-1] = (
                (ct > x[..., 0:-2, 0:-2, 0:-2])
                & (ct > x[..., 0:-2, 0:-2, 1:-1])
                & (ct > x[..., 0:-2, 0:-2, 2:])
                & (ct > x[..., 0:-2, 1:-1, 0:-2])
                & (ct > x[..., 0:-2, 1:-1, 1:-1])
                & (ct > x[..., 0:-2, 1:-1, 2:])
                & (ct > x[..., 0:-2, 2:, 0:-2])
                & (ct > x[..., 0:-2, 2:, 1:-1])
                & (ct > x[..., 0:-2, 2:, 2:])
                & (ct > x[..., 1:-1, 0:-2, 0:-2])
                & (ct > x[..., 1:-1, 0:-2, 1:-1])
                & (ct > x[..., 1:-1, 0:-2, 2:])
                & (ct > x[..., 1:-1, 1:-1, 0:-2])
                & (ct > x[..., 1:-1, 1:-1, 2:])
                & (ct > x[..., 1:-1, 2:, 0:-2])
                & (ct > x[..., 1:-1, 2:, 1:-1])
                & (ct > x[..., 1:-1, 2:, 2:])
                & (ct > x[..., 2:, 0:-2, 0:-2])
                & (ct > x[..., 2:, 0:-2, 1:-1])
                & (ct > x[..., 2:, 0:-2, 2:])
                & (ct > x[..., 2:, 1:-1, 0:-2])
                & (ct > x[..., 2:, 1:-1, 1:-1])
                & (ct > x[..., 2:, 1:-1, 2:])
                & (ct > x[..., 2:, 2:, 0:-2])
                & (ct > x[..., 2:, 2:, 1:-1])
                & (ct > x[..., 2:, 2:, 2:])
            )
        else:
            max_non_center = (
                F.conv3d(
                    F.pad(x, list(self.padding)[::-1], mode="replicate"),
                    self.kernel.repeat(CH, 1, 1, 1, 1).to(x.device, x.dtype),
                    stride=1,
                    groups=CH,
                )
                .view(B, CH, -1, D, H, W)
                .max(dim=2, keepdim=False)[0]
            )
            mask = x > max_non_center
        if mask_only:
            return mask
        return x * (mask.to(x.dtype))


# functional api


def nms2d(input: torch.Tensor, kernel_size: tuple[int, int], mask_only: bool = False) -> torch.Tensor:
    r"""Apply non maxima suppression to filter.

    See :class:`~kornia.geometry.subpix.NonMaximaSuppression2d` for details.
    """
    return NonMaximaSuppression2d(kernel_size)(input, mask_only)


def nms3d(input: torch.Tensor, kernel_size: tuple[int, int, int], mask_only: bool = False) -> torch.Tensor:
    r"""Apply non maxima suppression to filter.

    See
    :class: `~kornia.feature.NonMaximaSuppression3d` for details.
    """
    return NonMaximaSuppression3d(kernel_size)(input, mask_only)


def nms3d_minmax(input: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
    r"""Compute both local-maxima and local-minima NMS masks for a 3-D scale-space tensor in one pass.

    Equivalent to calling ``nms3d(input, (3,3,3), mask_only=True)`` and
    ``nms3d(-input, (3,3,3), mask_only=True)`` separately, but only traverses
    the 26-neighbour comparisons once, halving the NMS cost.

    Uses integer slice literals (not Python loops or slice objects) so the 52
    comparison-and-reduction ops are visible to the compiler at trace time,
    allowing full fusion into a minimal number of kernels.

    Args:
        input: 5-D tensor of shape :math:`(B, C, D, H, W)`.

    Returns:
        A pair ``(max_mask, min_mask)`` of bool tensors with the same shape as
        *input*.  ``max_mask[..., d, h, w]`` is ``True`` when the voxel is
        strictly greater than all 26 neighbours; ``min_mask`` is the same for
        strict local minima.

    Example:
        >>> x = torch.randn(1, 1, 5, 10, 10)
        >>> max_mask, min_mask = nms3d_minmax(x)
        >>> max_mask.shape
        torch.Size([1, 1, 5, 10, 10])

    """
    if input.dim() != 5:
        raise AssertionError(input.shape)
    B, CH, D, H, W = input.shape
    max_mask = torch.zeros(B, CH, D, H, W, device=input.device, dtype=torch.bool)
    min_mask = torch.zeros(B, CH, D, H, W, device=input.device, dtype=torch.bool)
    ct = input[..., 1:-1, 1:-1, 1:-1]
    # 26 explicit comparisons with integer slice literals — no Python loop so the
    # compiler sees all ops at trace time and can fuse them into a single kernel.
    is_max = (
        (ct > input[..., 0:-2, 0:-2, 0:-2])
        & (ct > input[..., 0:-2, 0:-2, 1:-1])
        & (ct > input[..., 0:-2, 0:-2, 2:])
        & (ct > input[..., 0:-2, 1:-1, 0:-2])
        & (ct > input[..., 0:-2, 1:-1, 1:-1])
        & (ct > input[..., 0:-2, 1:-1, 2:])
        & (ct > input[..., 0:-2, 2:, 0:-2])
        & (ct > input[..., 0:-2, 2:, 1:-1])
        & (ct > input[..., 0:-2, 2:, 2:])
        & (ct > input[..., 1:-1, 0:-2, 0:-2])
        & (ct > input[..., 1:-1, 0:-2, 1:-1])
        & (ct > input[..., 1:-1, 0:-2, 2:])
        & (ct > input[..., 1:-1, 1:-1, 0:-2])
        & (ct > input[..., 1:-1, 1:-1, 2:])
        & (ct > input[..., 1:-1, 2:, 0:-2])
        & (ct > input[..., 1:-1, 2:, 1:-1])
        & (ct > input[..., 1:-1, 2:, 2:])
        & (ct > input[..., 2:, 0:-2, 0:-2])
        & (ct > input[..., 2:, 0:-2, 1:-1])
        & (ct > input[..., 2:, 0:-2, 2:])
        & (ct > input[..., 2:, 1:-1, 0:-2])
        & (ct > input[..., 2:, 1:-1, 1:-1])
        & (ct > input[..., 2:, 1:-1, 2:])
        & (ct > input[..., 2:, 2:, 0:-2])
        & (ct > input[..., 2:, 2:, 1:-1])
        & (ct > input[..., 2:, 2:, 2:])
    )
    is_min = (
        (ct < input[..., 0:-2, 0:-2, 0:-2])
        & (ct < input[..., 0:-2, 0:-2, 1:-1])
        & (ct < input[..., 0:-2, 0:-2, 2:])
        & (ct < input[..., 0:-2, 1:-1, 0:-2])
        & (ct < input[..., 0:-2, 1:-1, 1:-1])
        & (ct < input[..., 0:-2, 1:-1, 2:])
        & (ct < input[..., 0:-2, 2:, 0:-2])
        & (ct < input[..., 0:-2, 2:, 1:-1])
        & (ct < input[..., 0:-2, 2:, 2:])
        & (ct < input[..., 1:-1, 0:-2, 0:-2])
        & (ct < input[..., 1:-1, 0:-2, 1:-1])
        & (ct < input[..., 1:-1, 0:-2, 2:])
        & (ct < input[..., 1:-1, 1:-1, 0:-2])
        & (ct < input[..., 1:-1, 1:-1, 2:])
        & (ct < input[..., 1:-1, 2:, 0:-2])
        & (ct < input[..., 1:-1, 2:, 1:-1])
        & (ct < input[..., 1:-1, 2:, 2:])
        & (ct < input[..., 2:, 0:-2, 0:-2])
        & (ct < input[..., 2:, 0:-2, 1:-1])
        & (ct < input[..., 2:, 0:-2, 2:])
        & (ct < input[..., 2:, 1:-1, 0:-2])
        & (ct < input[..., 2:, 1:-1, 1:-1])
        & (ct < input[..., 2:, 1:-1, 2:])
        & (ct < input[..., 2:, 2:, 0:-2])
        & (ct < input[..., 2:, 2:, 1:-1])
        & (ct < input[..., 2:, 2:, 2:])
    )
    max_mask[..., 1:-1, 1:-1, 1:-1] = is_max
    min_mask[..., 1:-1, 1:-1, 1:-1] = is_min
    return max_mask, min_mask