import math

import torch.nn as nn
import torch.utils.model_zoo as model_zoo

__all__ = ["ResNet", "resnet18", "resnet34", "resnet50", "resnet101", "resnet152"]


model_urls = {
    "resnet18": "https://download.pytorch.org/models/resnet18-5c106cde.pth",
    "resnet34": "https://download.pytorch.org/models/resnet34-333f7ec4.pth",
    "resnet50": "https://download.pytorch.org/models/resnet50-19c8e357.pth",
    "resnet101": "https://download.pytorch.org/models/resnet101-5d3b4d8f.pth",
    "resnet152": "https://download.pytorch.org/models/resnet152-b121ed2d.pth",
}


def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding.

    Args:
        in_planes (int): Number of input channels
        out_planes (int): Number of output channels
        stride (int, optional): Stride of the convolution. Defaults to 1.

    Returns:
        nn.Conv2d: 3x3 convolution layer
    """
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)


class BasicBlock(nn.Module):
    """Basic residual block for ResNet.

    Attributes:
        expansion (int): Expansion factor for the block
    """

    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        """Initialize basic block.

        Args:
            inplanes (int): Number of input channels
            planes (int): Number of intermediate channels
            stride (int, optional): Stride of first conv. Defaults to 1.
            downsample (nn.Module, optional): Downsampling module. Defaults to None.
        """
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        """Forward pass.

        Args:
            x (torch.Tensor): Input tensor

        Returns:
            torch.Tensor: Output tensor
        """
        residual = x

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

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

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

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

        return out


class Bottleneck(nn.Module):
    """Bottleneck residual block for ResNet.

    Attributes:
        expansion (int): Expansion factor for the block
    """

    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        """Initialize bottleneck block.

        Args:
            inplanes (int): Number of input channels
            planes (int): Number of intermediate channels
            stride (int, optional): Stride of conv2. Defaults to 1.
            downsample (nn.Module, optional): Downsampling module. Defaults to None.
        """
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        """Forward pass.

        Args:
            x (torch.Tensor): Input tensor

        Returns:
            torch.Tensor: Output tensor
        """
        residual = 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:
            residual = self.downsample(x)

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

        return out


class ResNet(nn.Module):
    """ResNet model architecture."""

    def __init__(self, block, layers, num_classes=1000):
        """Initialize ResNet model.

        Args:
            block (nn.Module): Type of residual block to use
            layers (list): Number of blocks in each layer
            num_classes (int, optional): Number of output classes. Defaults to 1000.
        """
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        # self.maxpool = nn.MaxPool2d(kernel_size=3, stride=1, padding=1) # previous stride is 2
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AvgPool2d(14)
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2.0 / n))
            elif isinstance(m, nn.BatchNorm2d):
                # m.weight.data.fill_(1)
                # m.bias.data.zero_()
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def _make_layer(self, block, planes, blocks, stride=1):
        """Create a layer of residual blocks.

        Args:
            block (nn.Module): Type of residual block
            planes (int): Number of output channels
            blocks (int): Number of blocks in the layer
            stride (int, optional): Stride of first block. Defaults to 1.

        Returns:
            nn.Sequential: Sequential container of blocks
        """
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(
                    self.inplanes,
                    planes * block.expansion,
                    kernel_size=1,
                    stride=stride,
                    bias=False,
                ),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        """Forward pass.

        Args:
            x (torch.Tensor): Input tensor

        Returns:
            torch.Tensor: Output tensor
        """
        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 = x.view(x.size(0), -1)
        x = self.fc(x)

        return x


def resnet18(pretrained=False, **kwargs):
    """Construct a ResNet-18 model.

    Args:
        pretrained (bool, optional): If True, returns model pre-trained on ImageNet. Defaults to False.
        **kwargs: Additional arguments passed to ResNet constructor

    Returns:
        ResNet: ResNet-18 model
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls["resnet18"]))
    return model


def resnet34(pretrained=False, **kwargs):
    """Construct a ResNet-34 model.

    Args:
        pretrained (bool, optional): If True, returns model pre-trained on ImageNet. Defaults to False.
        **kwargs: Additional arguments passed to ResNet constructor

    Returns:
        ResNet: ResNet-34 model
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls["resnet34"]))
    return model


def resnet50(pretrained=False, **kwargs):
    """Construct a ResNet-50 model.

    Args:
        pretrained (bool, optional): If True, returns model pre-trained on ImageNet. Defaults to False.
        **kwargs: Additional arguments passed to ResNet constructor

    Returns:
        ResNet: ResNet-50 model
    """
    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls["resnet50"]))
    return model


def resnet101(pretrained=False, **kwargs):
    """Construct a ResNet-101 model.

    Args:
        pretrained (bool, optional): If True, returns model pre-trained on ImageNet. Defaults to False.
        **kwargs: Additional arguments passed to ResNet constructor

    Returns:
        ResNet: ResNet-101 model
    """
    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls["resnet101"]))
    return model


def resnet152(pretrained=False, **kwargs):
    """Construct a ResNet-152 model.

    Args:
        pretrained (bool, optional): If True, returns model pre-trained on ImageNet. Defaults to False.
        **kwargs: Additional arguments passed to ResNet constructor

    Returns:
        ResNet: ResNet-152 model
    """
    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls["resnet152"]))
    return model
