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

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",
    'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
}



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


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    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):
        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):
        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):
        super(Bottleneck, self).__init__()
        self.conv1 = conv1x1(inplanes, planes)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = conv3x3(planes, planes, stride)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = conv1x1(planes, planes * self.expansion)
        self.bn3 = nn.BatchNorm2d(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, zero_init_residual=False):
        super(ResNet, self).__init__()
        self.inplanes = 64
        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=2, padding=1)
        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)  # 6
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)  # 3

        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.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 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):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride), nn.BatchNorm2d(planes * block.expansion),
            )

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

        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)

        return x


def wide_resnet50_2(pretrained=False,  **kwargs):

    kwargs['width_per_group'] = 64 * 2
    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)

    if pretrained:
        pretrained_dict = model_zoo.load_url(model_urls["wide_resnet50_2"])

        model_dict = model.state_dict()
        # 1. filter out unnecessary keys
        pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
        # 2. overwrite entries in the existing state dict
        model_dict.update(pretrained_dict)
        # 3. load the new state dict
        model.load_state_dict(model_dict)

    return model

def resnet18(pretrained=False, **kwargs):
    """Constructs a ResNet-18 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
    if pretrained:
        pretrained_dict = model_zoo.load_url(model_urls["resnet18"])

        model_dict = model.state_dict()
        # 1. filter out unnecessary keys
        pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
        # 2. overwrite entries in the existing state dict
        model_dict.update(pretrained_dict)
        # 3. load the new state dict
        model.load_state_dict(model_dict)
    return model


def resnet34(pretrained=False, **kwargs):
    """Constructs a ResNet-34 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
    if pretrained:
        pretrained_dict = model_zoo.load_url(model_urls["resnet34"])

        model_dict = model.state_dict()
        # 1. filter out unnecessary keys
        pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
        # 2. overwrite entries in the existing state dict
        model_dict.update(pretrained_dict)
        # 3. load the new state dict
        model.load_state_dict(model_dict)
    return model


def resnet50(pretrained=False, **kwargs):
    """Constructs a ResNet-50 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)

    if pretrained:
        pretrained_dict = model_zoo.load_url(model_urls["resnet50"])

        model_dict = model.state_dict()
        # 1. filter out unnecessary keys
        pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
        # 2. overwrite entries in the existing state dict
        model_dict.update(pretrained_dict)
        # 3. load the new state dict
        model.load_state_dict(model_dict)

    return model


def resnet101(pretrained=False, **kwargs):
    """Constructs a ResNet-101 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
    if pretrained:
        pretrained_dict = model_zoo.load_url(model_urls["resnet101"])

        model_dict = model.state_dict()
        # 1. filter out unnecessary keys
        pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
        # 2. overwrite entries in the existing state dict
        model_dict.update(pretrained_dict)
        # 3. load the new state dict
        model.load_state_dict(model_dict)
    return model


def resnet152(pretrained=False, **kwargs):
    """Constructs a ResNet-152 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)

    if pretrained:
        pretrained_dict = model_zoo.load_url(model_urls["resnet152"])

        model_dict = model.state_dict()
        # 1. filter out unnecessary keys
        pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
        # 2. overwrite entries in the existing state dict
        model_dict.update(pretrained_dict)
        # 3. load the new state dict
        model.load_state_dict(model_dict)

    return model


def Backbone_ResNet50_in3(pretrained=True):
    if pretrained:
        print("The backbone model loads the pretrained parameters...")
    net = resnet50(pretrained=pretrained)
    div_2 = nn.Sequential(*list(net.children())[:3])
    div_4 = nn.Sequential(*list(net.children())[3:5])
    div_8 = net.layer2
    div_16 = net.layer3
    div_32 = net.layer4

    return div_2, div_4, div_8, div_16, div_32


def Backbone_ResNet101_in1(pretrained=True):
    if pretrained:
        print("The backbone model loads the pretrained parameters...")
    net = resnet101(pretrained=pretrained)
    net.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
    div_2 = nn.Sequential(*list(net.children())[:3])
    div_4 = nn.Sequential(*list(net.children())[3:5])
    div_8 = net.layer2
    div_16 = net.layer3
    div_32 = net.layer4

    return div_2, div_4, div_8, div_16, div_32

def Backbone_ResNet101_in3(pretrained=True):
    if pretrained:
        print("The backbone model loads the pretrained parameters...")
    net = resnet101(pretrained=pretrained)
    div_2 = nn.Sequential(*list(net.children())[:3])
    div_4 = nn.Sequential(*list(net.children())[3:5])
    div_8 = net.layer2
    div_16 = net.layer3
    div_32 = net.layer4

    return div_2, div_4, div_8, div_16, div_32


def Backbone_ResNet50_in1(pretrained=True):
    if pretrained:
        print("The backbone model loads the pretrained parameters...")
    net = resnet50(pretrained=pretrained)
    net.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
    div_2 = nn.Sequential(*list(net.children())[:3])
    div_4 = nn.Sequential(*list(net.children())[3:5])
    div_8 = net.layer2
    div_16 = net.layer3
    div_32 = net.layer4

    return div_2, div_4, div_8, div_16, div_32


def Backbone_WResNet50_in3(pretrained=True):
    if pretrained:
        print("The backbone model loads the pretrained parameters...")
    net = wide_resnet50_2(pretrained=pretrained)
    div_2 = nn.Sequential(*list(net.children())[:3])
    div_4 = nn.Sequential(*list(net.children())[3:5])
    div_8 = net.layer2
    div_16 = net.layer3
    div_32 = net.layer4

    return div_2, div_4, div_8, div_16, div_32


def Backbone_WResNet50_in1(pretrained=True):
    if pretrained:
        print("The backbone model loads the pretrained parameters...")
    net = wide_resnet50_2(pretrained=pretrained)
    net.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
    div_2 = nn.Sequential(*list(net.children())[:3])
    div_4 = nn.Sequential(*list(net.children())[3:5])
    div_8 = net.layer2
    div_16 = net.layer3
    div_32 = net.layer4

    return div_2, div_4, div_8, div_16, div_32

if __name__ == "__main__":
    div_2, div_4, div_8, div_16, div_32 = Backbone_ResNet50_in1()
    indata = torch.rand(4, 1, 320, 320)
    print(div_32)