# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the Apache License, Version 2.0
# found in the LICENSE file in the root directory of this source tree.

# References:
#   https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
#   https://github.com/rwightman/pytorch-image-models/tree/master/timm/layers/patch_embed.py

from typing import Callable, Optional, Tuple, Union

import torch
from torch import Tensor
import torch.nn as nn


def make_2tuple(x):
    if isinstance(x, tuple):
        assert len(x) == 2
        return x

    assert isinstance(x, int)
    return (x, x)


vgg = nn.Sequential(
    nn.Conv2d(3, 3, (1, 1)),
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(3, 64, (3, 3)),
    nn.ReLU(),  # relu1-1
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(64, 64, (3, 3)),
    nn.ReLU(),  # relu1-2
    nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(64, 128, (3, 3)),
    nn.ReLU(),  # relu2-1
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(128, 128, (3, 3)),
    nn.ReLU(),  # relu2-2
    nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(128, 256, (3, 3)),
    nn.ReLU(),  # relu3-1
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(256, 256, (3, 3)),
    nn.ReLU(),  # relu3-2
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(256, 256, (3, 3)),
    nn.ReLU(),  # relu3-3
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(256, 256, (3, 3)),
    nn.ReLU(),  # relu3-4
    nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(256, 512, (3, 3)),
    nn.ReLU(),  # relu4-1, this is the last layer used
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(512, 512, (3, 3)),
    nn.ReLU(),  # relu4-2
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(512, 512, (3, 3)),
    nn.ReLU(),  # relu4-3
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(512, 512, (3, 3)),
    nn.ReLU(),  # relu4-4
    nn.MaxPool2d((2, 2), (2, 2), (0, 0), ceil_mode=True),
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(512, 512, (3, 3)),
    nn.ReLU(),  # relu5-1
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(512, 512, (3, 3)),
    nn.ReLU(),  # relu5-2
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(512, 512, (3, 3)),
    nn.ReLU(),  # relu5-3
    nn.ReflectionPad2d((1, 1, 1, 1)),
    nn.Conv2d(512, 512, (3, 3)),
    nn.ReLU()  # relu5-4
)

class FeatureExtractor(nn.Module):
    def __init__(self, encoder):
        super(FeatureExtractor, self).__init__()
        enc_layers = list(encoder.children())
        self.enc_1 = nn.Sequential(*enc_layers[:4])  # input -> relu1_1
        self.enc_2 = nn.Sequential(*enc_layers[4:11])  # relu1_1 -> relu2_1
        self.enc_3 = nn.Sequential(*enc_layers[11:18])  # relu2_1 -> relu3_1
        self.enc_4 = nn.Sequential(*enc_layers[18:31])  # relu3_1 -> relu4_1
        self.mse_loss = nn.MSELoss()

        # fix the encoder
        for name in ['enc_1', 'enc_2', 'enc_3', 'enc_4']:
            for param in getattr(self, name).parameters():
                param.requires_grad = False

    # extract relu1_1, relu2_1, relu3_1, relu4_1 from input image
    def forward(self, input):
        results = [input]
        for i in range(4):
            func = getattr(self, 'enc_{:d}'.format(i + 1))
            results.append(func(results[-1]))
        # resize the results to the same size
        for i in range(1, len(results)):
            results[i] = nn.functional.interpolate(
                results[i], size=results[0].shape[2:], mode='bilinear', align_corners=False
            )
        results = torch.concat(results, dim=1)  # stack relu1_1, relu2_1, relu3_1, relu4_1
        return results

class VGG19PatchEmbed(nn.Module):
    """
    2D image to patch embedding: (B,C,H,W) -> (B,N,D)

    Args:
        img_size: Image size.
        patch_size: Patch token size.
        in_chans: Number of input image channels.
        embed_dim: Number of linear projection output channels.
        norm_layer: Normalization layer.
    """

    def __init__(
        self,
        img_size: Union[int, Tuple[int, int]] = 224,
        patch_size: Union[int, Tuple[int, int]] = 16,
        in_chans: int = 3,
        embed_dim: int = 768,
        norm_layer: Optional[Callable] = None,
        flatten_embedding: bool = True,
    ) -> None:
        super().__init__()
        vgg.load_state_dict(torch.load("checkpoints/vgg_normalised.pth"))
        self.feature_extractor = FeatureExtractor(vgg)
        self.feature_extractor.eval()  # set to eval mode
        self.feature_extractor.requires_grad_(False)
        image_HW = make_2tuple(img_size)
        patch_HW = make_2tuple(patch_size)
        patch_grid_size = (
            image_HW[0] // patch_HW[0],
            image_HW[1] // patch_HW[1],
        )

        self.img_size = image_HW
        self.patch_size = patch_HW
        self.patches_resolution = patch_grid_size
        self.num_patches = patch_grid_size[0] * patch_grid_size[1]

        self.in_chans = 3 + 64 + 128 + 256 + 512  # relu1-1, relu2-1, relu3-1, relu4-1
        self.embed_dim = embed_dim

        self.flatten_embedding = flatten_embedding

        self.proj = nn.Conv2d(self.in_chans, self.embed_dim, kernel_size=patch_HW, stride=patch_HW)
        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()

    def forward(self, x: Tensor) -> Tensor:
        _, _, H, W = x.shape
        patch_H, patch_W = self.patch_size

        assert H % patch_H == 0, f"Input image height {H} is not a multiple of patch height {patch_H}"
        assert W % patch_W == 0, f"Input image width {W} is not a multiple of patch width: {patch_W}"

        x = self.feature_extractor(x)  # B C H W, where C = 3 + 64 + 128 + 256 + 512
        x = self.proj(x)
        H, W = x.size(2), x.size(3)
        x = x.flatten(2).transpose(1, 2)  # B HW C
        x = self.norm(x)
        if not self.flatten_embedding:
            x = x.reshape(-1, H, W, self.embed_dim)  # B H W C
        return x

    def flops(self) -> float:
        Ho, Wo = self.patches_resolution
        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
        if self.norm is not None:
            flops += Ho * Wo * self.embed_dim
        return flops