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# Copyright 2018 Kornia Team
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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from typing import Callable, Dict, List, Optional, Type

import torch
from torch import nn

urls: Dict[str, str] = {}
urls["defmo_encoder"] = "http://ptak.felk.cvut.cz/personal/rozumden/defmo_saved_models/encoder_best.pt"
urls["defmo_rendering"] = "http://ptak.felk.cvut.cz/personal/rozumden/defmo_saved_models/rendering_best.pt"


# conv1x1, conv3x3, Bottleneck, ResNet are taken from:
# https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py


def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d:
    """1x1 convolution."""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv2d:
    """3x3 convolution with padding."""
    return nn.Conv2d(
        in_planes,
        out_planes,
        kernel_size=3,
        stride=stride,
        padding=dilation,
        groups=groups,
        bias=False,
        dilation=dilation,
    )


class Bottleneck(nn.Module):
    """Implement the Bottleneck building block for ResNet.

    This block follows the ResNet V1.5 design where the stride is applied to the 3x3 convolution
    instead of the first 1x1 convolution to better preserve spatial information.

    Args:
        inplanes: The number of input channels.
        planes: The number of intermediate channels.
        stride: The stride size for the convolution. Default: 1.
        downsample: An optional module to downsample the input identity. Default: None.
        groups: The number of blocked connections from input channels to output channels. Default: 1.
        base_width: The width of each group. Default: 64.
        dilation: The spacing between kernel elements. Default: 1.
        norm_layer: The normalization layer to use. Default: None.
    """

    # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
    # while original implementation places the stride at the first 1x1 convolution(self.conv1)
    # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
    # This variant is also known as ResNet V1.5 and improves accuracy according to
    # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.

    expansion: int = 4

    def __init__(
        self,
        inplanes: int,
        planes: int,
        stride: int = 1,
        downsample: Optional[nn.Module] = None,
        groups: int = 1,
        base_width: int = 64,
        dilation: int = 1,
        norm_layer: Optional[Callable[..., nn.Module]] = None,
    ) -> None:
        super().__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.0)) * groups
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class ResNet(nn.Module):
    """Implement the ResNet architecture for feature extraction.

    This implementation provides a flexible backbone used as the encoder in the DeFMO framework.

    Args:
        block: The block type to use, typically :class:`Bottleneck`.
        layers: A list containing the number of blocks in each of the four stages.
        num_classes: The number of output classes. Default: 1000.
        zero_init_residual: Whether to initialize the last batch norm in each residual branch to zero. Default: False.
        groups: The number of groups for the convolution. Default: 1.
        width_per_group: The width of each group. Default: 64.
        replace_stride_with_dilation: A list of booleans indicating
            if stride should be replaced by dilation in each stage. Default: None.
        norm_layer: The normalization layer to use. Default: None.
    """

    def __init__(
        self,
        block: Type[Bottleneck],
        layers: List[int],
        num_classes: int = 1000,
        zero_init_residual: bool = False,
        groups: int = 1,
        width_per_group: int = 64,
        replace_stride_with_dilation: Optional[List[bool]] = None,
        norm_layer: Optional[Callable[..., nn.Module]] = None,
    ) -> None:
        super().__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError(
                f"replace_stride_with_dilation should be None or a 3-element tuple, got {replace_stride_with_dilation}"
            )
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2])
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with torch.zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck) and isinstance(m.bn3.weight, torch.Tensor):
                    nn.init.constant_(m.bn3.weight, 0)

    def _make_layer(
        self, block: Type[Bottleneck], planes: int, blocks: int, stride: int = 1, dilate: bool = False
    ) -> nn.Sequential:
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride), norm_layer(planes * block.expansion)
            )

        layers = []
        layers.append(
            block(
                self.inplanes, planes, stride, downsample, self.groups, self.base_width, previous_dilation, norm_layer
            )
        )
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(
                block(
                    self.inplanes,
                    planes,
                    groups=self.groups,
                    base_width=self.base_width,
                    dilation=self.dilation,
                    norm_layer=norm_layer,
                )
            )

        return nn.Sequential(*layers)

    def _forward_impl(self, x: torch.Tensor) -> torch.Tensor:
        # See note [TorchScript super()]
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)

        return x

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self._forward_impl(x)


class EncoderDeFMO(nn.Module):
    """Implement the Encoder module for the Deblurring Fast Moving Objects (DeFMO) model.

    The encoder extracts latent features from the concatenation of the blurred input image and
    the estimated background. It uses a modified ResNet-50 backbone to accept 6-channel inputs.


    Shape:
        - Input: (B, 6, H, W) where 6 represents the concatenated blurred image and background.
        - Output: A list of feature maps from different stages of the ResNet backbone.
    """

    def __init__(self) -> None:
        super().__init__()
        model = ResNet(Bottleneck, [3, 4, 6, 3])  # ResNet50
        modelc1 = nn.Sequential(*list(model.children())[:3])
        modelc2 = nn.Sequential(*list(model.children())[4:8])
        modelc1[0] = nn.Conv2d(6, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
        self.net = nn.Sequential(modelc1, modelc2)

    def forward(self, input_data: torch.Tensor) -> torch.Tensor:
        return self.net(input_data)


class RenderingDeFMO(nn.Module):
    """Implement the Rendering module for the Deblurring Fast Moving Objects (DeFMO) model.

    This module acts as a decoder that transforms the latent features from the encoder into
    a temporal sequence of sharp sub-frames, recovering the object's appearance and motion.

    Shape:
        - Input: Latent feature representation from the :class:`EncoderDeFMO`.
        - Output: (B, T, 4, H, W) where T is the number of sub-frames and 4 represents RGBA channels.
    """

    def __init__(self) -> None:
        super().__init__()
        self.tsr_steps: int = 24
        model = nn.Sequential(
            nn.Conv2d(2049, 1024, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),
            nn.ReLU(inplace=True),
            Bottleneck(1024, 256),
            nn.PixelShuffle(2),
            Bottleneck(256, 64),
            nn.PixelShuffle(2),
            Bottleneck(64, 16),
            nn.PixelShuffle(2),
            nn.Conv2d(16, 16, kernel_size=3, stride=1, padding=1, bias=False),
            nn.PixelShuffle(2),
            nn.Conv2d(4, 4, kernel_size=3, stride=1, padding=1, bias=True),
            nn.ReLU(inplace=True),
            nn.Conv2d(4, 4, kernel_size=3, stride=1, padding=1, bias=True),
        )
        self.net = model
        self.times = torch.linspace(0, 1, self.tsr_steps)

    def forward(self, latent: torch.Tensor) -> torch.Tensor:
        times = self.times.to(latent.device).unsqueeze(0).repeat(latent.shape[0], 1)
        renders = []
        for ki in range(times.shape[1]):
            t_tensor = (
                times[list(range(times.shape[0])), ki]
                .unsqueeze(-1)
                .unsqueeze(-1)
                .unsqueeze(-1)
                .repeat(1, 1, latent.shape[2], latent.shape[3])
            )
            latenti = torch.cat((t_tensor, latent), 1)
            result = self.net(latenti)
            renders.append(result)
        renders_stacked = torch.stack(renders, 1).contiguous()
        renders_stacked[:, :, :4] = torch.sigmoid(renders_stacked[:, :, :4])
        return renders_stacked


class DeFMO(nn.Module):
    """nn.Module that disentangle a fast-moving object from the background and performs deblurring.

    This is based on the original code from paper "DeFMO: Deblurring and Shape Recovery
        of Fast Moving Objects". See :cite:`DeFMO2021` for more details.

    Args:
        pretrained: Download and set pretrained weights to the model. Default: false.

    Returns:
        Temporal super-resolution without background.
    Shape:
        - Input: (B, 6, H, W)
        - Output: (B, S, 4, H, W)

    Examples:
        >>> import kornia
        >>> input = torch.rand(2, 6, 240, 320)
        >>> defmo = kornia.feature.DeFMO()
        >>> tsr_nobgr = defmo(input) # 2x24x4x240x320

    """

    def __init__(self, pretrained: bool = False) -> None:
        super().__init__()
        self.encoder = EncoderDeFMO()
        self.rendering = RenderingDeFMO()

        # use torch.hub to load pretrained model
        if pretrained:
            pretrained_dict = torch.hub.load_state_dict_from_url(
                urls["defmo_encoder"], map_location=torch.device("cpu")
            )
            self.encoder.load_state_dict(pretrained_dict, strict=True)
            pretrained_dict_ren = torch.hub.load_state_dict_from_url(
                urls["defmo_rendering"], map_location=torch.device("cpu")
            )
            self.rendering.load_state_dict(pretrained_dict_ren, strict=True)
        self.eval()

    def forward(self, input_data: torch.Tensor) -> torch.Tensor:
        latent = self.encoder(input_data)
        x_out = self.rendering(latent)
        return x_out