import torch
import torch.nn as nn
from torch.hub import load_state_dict_from_url

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, groups=1, dilation=1):
    return nn.Conv2d(
        in_planes,
        out_planes,
        kernel_size=3,
        stride=stride,
        padding=dilation,
        groups=groups,
        bias=False,
        dilation=dilation,
    )


def conv1x1(in_planes, out_planes, stride=1):
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(
        self,
        inplanes,
        planes,
        stride=1,
        downsample=None,
        groups=1,
        base_width=64,
        dilation=1,
        norm_layer=None,
    ):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError("BasicBlock only supports groups=1 and base_width=64")
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = 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:
            identity = self.downsample(x)

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

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(
        self,
        inplanes,
        planes,
        stride=1,
        downsample=None,
        groups=1,
        base_width=64,
        dilation=1,
        norm_layer=None,
    ):
        super(Bottleneck, self).__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):
        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):

    def __init__(
        self,
        block,
        layers,
        num_classes=1000,
        zero_init_residual=False,
        groups=1,
        width_per_group=64,
        replace_stride_with_dilation=None,
        norm_layer=None,
    ):
        super(ResNet, self).__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:
            replace_stride_with_dilation = [False, False, False]

        if len(replace_stride_with_dilation) != 3:
            raise ValueError(
                "replace_stride_with_dilation should be None "
                "or a 3-element tuple, got {}".format(replace_stride_with_dilation)
            )

        self.block = block
        self.groups = groups
        self.base_width = width_per_group

        # 224,224,3 -> 112,112,64
        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)

        # 112,112,64 -> 56,56,64
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        # 56,56,64 -> 56,56,256
        self.layer1 = self._make_layer(block, 64, layers[0])

        # 56,56,256 -> 28,28,512
        self.layer2 = self._make_layer(
            block, 128, layers[1], stride=2, dilate=replace_stride_with_dilation[0]
        )

        # 28,28,512 -> 14,14,1024
        self.layer3 = self._make_layer(
            block, 256, layers[2], stride=2, dilate=replace_stride_with_dilation[1]
        )

        # 14,14,1024 -> 7,7,2048
        self.layer4 = self._make_layer(
            block, 512, layers[3], stride=2, dilate=replace_stride_with_dilation[2]
        )

        # 7,7,2048 -> 2048
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))

        # 2048 -> num_classes
        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)

        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        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 = []
        # Conv_block
        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):
            # identity_block
            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(self, x):
        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 freeze_backbone(self):
        backbone = [
            self.conv1,
            self.bn1,
            self.layer1,
            self.layer2,
            self.layer3,
            self.layer4,
        ]
        for module in backbone:
            for param in module.parameters():
                param.requires_grad = False

    def Unfreeze_backbone(self):
        backbone = [
            self.conv1,
            self.bn1,
            self.layer1,
            self.layer2,
            self.layer3,
            self.layer4,
        ]
        for module in backbone:
            for param in module.parameters():
                param.requires_grad = True


def resnet18(pretrained=False, progress=True, num_classes=1000):
    model = ResNet(BasicBlock, [2, 2, 2, 2])
    if pretrained:
        state_dict = load_state_dict_from_url(
            model_urls["resnet18"], model_dir="./model_data", progress=progress
        )
        model.load_state_dict(state_dict)

    if num_classes != 1000:
        model.fc = nn.Linear(512 * model.block.expansion, num_classes)
    return model


def resnet34(pretrained=False, progress=True, num_classes=1000):
    model = ResNet(BasicBlock, [3, 4, 6, 3])
    if pretrained:
        state_dict = load_state_dict_from_url(
            model_urls["resnet34"], model_dir="./model_data", progress=progress
        )
        model.load_state_dict(state_dict)

    if num_classes != 1000:
        model.fc = nn.Linear(512 * model.block.expansion, num_classes)
    return model


def resnet50(pretrained=False, progress=True, num_classes=1000):
    model = ResNet(Bottleneck, [3, 4, 6, 3])
    if pretrained:
        state_dict = load_state_dict_from_url(
            model_urls["resnet50"], model_dir="./model_data", progress=progress
        )
        model.load_state_dict(state_dict)

    if num_classes != 1000:
        model.fc = nn.Linear(512 * model.block.expansion, num_classes)
    return model


def resnet101(pretrained=False, progress=True, num_classes=1000):
    model = ResNet(Bottleneck, [3, 4, 23, 3])
    if pretrained:
        state_dict = load_state_dict_from_url(
            model_urls["resnet101"], model_dir="./model_data", progress=progress
        )
        model.load_state_dict(state_dict)

    if num_classes != 1000:
        model.fc = nn.Linear(512 * model.block.expansion, num_classes)
    return model


def resnet152(pretrained=False, progress=True, num_classes=1000):
    model = ResNet(Bottleneck, [3, 8, 36, 3])
    if pretrained:
        state_dict = load_state_dict_from_url(
            model_urls["resnet152"], model_dir="./model_data", progress=progress
        )
        model.load_state_dict(state_dict)

    if num_classes != 1000:
        model.fc = nn.Linear(512 * model.block.expansion, num_classes)
    return model
