# Copyright (c) 2021-2022, NVIDIA CORPORATION. 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 applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import math
import torch
from torch import nn, Tensor

from typing import List, Optional
from model.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.

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

    __annotations__ = {
        "cell_anchors": 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 = [self.generate_anchors(size, aspect_ratio)
                             for size, aspect_ratio in zip(sizes, aspect_ratios)]

    # 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: List[int], aspect_ratios: List[float], dtype: torch.dtype = torch.float32,
                         device: torch.device = torch.device("cpu")):
        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: torch.dtype, device: torch.device):
        self.cell_anchors = [cell_anchor.to(dtype=dtype, device=device)
                             for cell_anchor in self.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: List[List[int]], strides: List[List[Tensor]]) -> List[Tensor]:
        anchors = []
        cell_anchors = self.cell_anchors
        assert cell_anchors is not None

        if not (len(grid_sizes) == len(strides) == len(cell_anchors)):
            raise ValueError("Anchors should be Tuple[Tuple[int]] because each feature "
                             "map could potentially have different sizes and aspect ratios. "
                             "There needs to be a match between the number of "
                             "feature maps passed and the number of sizes / aspect ratios specified.")

        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 forward(self, image_list: Tensor, feature_maps: List[Tensor]) -> List[Tensor]:
        grid_sizes = [feature_map.shape[-2:] for feature_map in feature_maps]
        image_size = image_list.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.grid_anchors(grid_sizes, strides)
        anchors: List[List[torch.Tensor]] = []
        for _ in range(image_list.size(0)):
            anchors_in_image = [anchors_per_feature_map for anchors_per_feature_map in anchors_over_all_feature_maps]
            anchors.append(anchors_in_image)
        anchors = [torch.cat(anchors_per_image) for anchors_per_image in anchors]
        return anchors

    def forward_opt(self, image_shape: torch.Size = None, grid_sizes: List[torch.Size] = None,
                    device: torch.device = None, dtype=torch.float16):
        assert(device is not None)

        image_size = torch.Size([800, 800]) if image_shape is None else image_shape[-2:]
        grid_sizes = [torch.Size([100, 100]), torch.Size([50, 50]),
                      torch.Size([25, 25]), torch.Size([13, 13]),
                      torch.Size([7, 7])] if grid_sizes is None else grid_sizes

        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.grid_anchors(grid_sizes, strides)
        anchors: List[List[torch.Tensor]] = []
        for _ in range(image_shape[0]):
            anchors_in_image = [anchors_per_feature_map for anchors_per_feature_map in anchors_over_all_feature_maps]
            anchors.append(anchors_in_image)
        anchors = [torch.cat(anchors_per_image) for anchors_per_image in anchors]

        return anchors
