# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn

from torch.jit.annotations import List, Optional, Dict
from .image_list import ImageList


class AnchorGenerator(nn.Module):
    """
    Module that generates anchors for a set of feature maps and
    image sizes.

    The module support computing anchors at multiple sizes and aspect ratios
    per feature map. This module assumes aspect ratio = height / width for
    each anchor.

    sizes and aspect_ratios should have the same number of elements, and it should
    correspond to the number of feature maps.

    sizes[i] and aspect_ratios[i] can have an arbitrary number of elements,
    and AnchorGenerator will output a set of sizes[i] * aspect_ratios[i] anchors
    per spatial location for feature map i.

    Arguments:
        sizes (Tuple[Tuple[int]]):
        aspect_ratios (Tuple[Tuple[float]]):
    """

    __annotations__ = {
        "cell_anchors": Optional[List[torch.Tensor]],
        "_cache": Dict[str, List[torch.Tensor]]
    }

    def __init__(
        self,
        sizes=((128, 256, 512),),
        aspect_ratios=((0.5, 1.0, 2.0),),
    ):
        super(AnchorGenerator, self).__init__()

        if not isinstance(sizes[0], (list, tuple)):
            # TODO change this
            sizes = tuple((s,) for s in sizes)
        if not isinstance(aspect_ratios[0], (list, tuple)):
            aspect_ratios = (aspect_ratios,) * len(sizes)

        assert len(sizes) == len(aspect_ratios)

        self.sizes = sizes
        self.aspect_ratios = aspect_ratios
        self.cell_anchors = None
        self._cache = {}

    # TODO: https://github.com/pytorch/pytorch/issues/26792
    # For every (aspect_ratios, scales) combination, output a zero-centered anchor with those values.
    # (scales, aspect_ratios) are usually an element of zip(self.scales, self.aspect_ratios)
    # This method assumes aspect ratio = height / width for an anchor.
    def generate_anchors(self, scales, aspect_ratios, dtype=torch.float32, device="cpu"):
        # type: (List[int], List[float], int, Device) -> Tensor  # noqa: F821
        scales = torch.as_tensor(scales, dtype=dtype, device=device)
        aspect_ratios = torch.as_tensor(aspect_ratios, dtype=dtype, device=device)
        h_ratios = torch.sqrt(aspect_ratios)
        w_ratios = 1 / h_ratios

        ws = (w_ratios[:, None] * scales[None, :]).view(-1)
        hs = (h_ratios[:, None] * scales[None, :]).view(-1)

        base_anchors = torch.stack([-ws, -hs, ws, hs], dim=1) / 2
        return base_anchors.round()

    def set_cell_anchors(self, dtype, device):
        # type: (int, Device) -> None  # noqa: F821
        if self.cell_anchors is not None:
            cell_anchors = self.cell_anchors
            assert cell_anchors is not None
            # suppose that all anchors have the same device
            # which is a valid assumption in the current state of the codebase
            if cell_anchors[0].device == device:
                return

        cell_anchors = [
            self.generate_anchors(
                sizes,
                aspect_ratios,
                dtype,
                device
            )
            for sizes, aspect_ratios in zip(self.sizes, self.aspect_ratios)
        ]
        self.cell_anchors = cell_anchors

    def num_anchors_per_location(self):
        return [len(s) * len(a) for s, a in zip(self.sizes, self.aspect_ratios)]

    # For every combination of (a, (g, s), i) in (self.cell_anchors, zip(grid_sizes, strides), 0:2),
    # output g[i] anchors that are s[i] distance apart in direction i, with the same dimensions as a.
    def grid_anchors(self, grid_sizes, strides):
        # type: (List[List[int]], List[List[Tensor]]) -> List[Tensor]
        anchors = []
        cell_anchors = self.cell_anchors
        assert cell_anchors is not None
        assert len(grid_sizes) == len(strides) == len(cell_anchors)

        for size, stride, base_anchors in zip(
            grid_sizes, strides, cell_anchors
        ):
            grid_height, grid_width = size
            stride_height, stride_width = stride
            device = base_anchors.device

            # For output anchor, compute [x_center, y_center, x_center, y_center]
            shifts_x = torch.arange(
                0, grid_width, dtype=torch.float32, device=device
            ) * stride_width
            shifts_y = torch.arange(
                0, grid_height, dtype=torch.float32, device=device
            ) * stride_height
            shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
            shift_x = shift_x.reshape(-1)
            shift_y = shift_y.reshape(-1)
            shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)

            # For every (base anchor, output anchor) pair,
            # offset each zero-centered base anchor by the center of the output anchor.
            anchors.append(
                (shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4)
            )

        return anchors

    def cached_grid_anchors(self, grid_sizes, strides):
        # type: (List[List[int]], List[List[Tensor]]) -> List[Tensor]
        key = str(grid_sizes) + str(strides)
        if key in self._cache:
            return self._cache[key]
        anchors = self.grid_anchors(grid_sizes, strides)
        self._cache[key] = anchors
        return anchors

    def forward(self, image_list, feature_maps):
        # type: (ImageList, List[Tensor]) -> List[Tensor]
        grid_sizes = list([feature_map.shape[-2:] for feature_map in feature_maps])
        image_size = image_list.tensors.shape[-2:]
        dtype, device = feature_maps[0].dtype, feature_maps[0].device
        strides = [[torch.tensor(image_size[0] // g[0], dtype=torch.int64, device=device),
                    torch.tensor(image_size[1] // g[1], dtype=torch.int64, device=device)] for g in grid_sizes]
        self.set_cell_anchors(dtype, device)
        anchors_over_all_feature_maps = self.cached_grid_anchors(grid_sizes, strides)
        anchors = torch.jit.annotate(List[List[torch.Tensor]], [])
        for i, (image_height, image_width) in enumerate(image_list.image_sizes):
            anchors_in_image = []
            for anchors_per_feature_map in anchors_over_all_feature_maps:
                anchors_in_image.append(anchors_per_feature_map)
            anchors.append(anchors_in_image)
        anchors = [torch.cat(anchors_per_image) for anchors_per_image in anchors]
        # Clear the cache in case that memory leaks.
        self._cache.clear()
        return anchors