import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from torch.nn import init

from model.cbam.bam import BAM

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"
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, 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):
        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):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=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):
        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):
    def __init__(self, block, layers, network_type, num_classes, zero_init_residual=False):

        self.inplanes = 64
        super(ResNet, self).__init__()
        self.network_type = network_type
        # different model config between ImageNet and CIFAR
        if network_type == "ImageNet":
            self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
            self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
            self.avgpool = nn.AvgPool2d(7)
        else:
            self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)

        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)

        bam1 = BAM(64 * block.expansion)
        bam2 = BAM(128 * block.expansion)
        bam3 = BAM(256 * block.expansion)

        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer1.add_module('bam', bam1)

        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer2.add_module('bam', bam2)

        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer3.add_module('bam', bam3)

        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)

        self.fc = nn.Linear(512 * block.expansion, num_classes)

        init.kaiming_normal_(self.fc.weight)
        for key in self.state_dict():
            if key.split('.')[-1] == "weight":
                if "conv" in key:
                    init.kaiming_normal_(self.state_dict()[key], mode='fan_out')
                if "bn" in key:
                    if "SpatialGate" in key:
                        self.state_dict()[key][...] = 0
                    else:
                        self.state_dict()[key][...] = 1
            elif key.split(".")[-1] == 'bias':
                self.state_dict()[key][...] = 0

        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(
                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):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        if self.network_type == "ImageNet":
            x = self.maxpool(x)

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

        if self.network_type == "ImageNet":
            x = self.avgpool(x)
        else:
            x = F.avg_pool2d(x, 4)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        return x


def ResidualNet(network_type, depth, num_classes, zero_init_residual):
    assert network_type in ["ImageNet", "CIFAR10",
                            "CIFAR100"], "network type should be ImageNet or CIFAR10 / CIFAR100"
    assert depth in [18, 34, 50, 101], 'network depth should be 18, 34, 50 or 101'

    if depth == 18:
        model = ResNet(BasicBlock, [2, 2, 2, 2], network_type, num_classes, zero_init_residual)

    elif depth == 34:
        model = ResNet(BasicBlock, [3, 4, 6, 3], network_type, num_classes, zero_init_residual)

    elif depth == 50:
        model = ResNet(Bottleneck, [3, 4, 6, 3], network_type, num_classes, zero_init_residual)

    elif depth == 101:
        model = ResNet(Bottleneck, [3, 4, 23, 3], network_type, num_classes, zero_init_residual)
    else:
        raise ValueError()

    return model


def resnet50_bam(pretrained=False, **kwargs):
    """Constructs a ResNet-50 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResidualNet('ImageNet', 50, 1000, **kwargs)
    if pretrained:
        pretrained_state_dict = model_zoo.load_url(model_urls['resnet50'])
        now_state_dict = model.state_dict()
        now_state_dict.update(pretrained_state_dict)
        model.load_state_dict(now_state_dict)
    return model


def resnet101_bam(pretrained=False, **kwargs):
    """Constructs a ResNet-50 model.
    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResidualNet('ImageNet', 101, 1000, **kwargs)
    if pretrained:
        pretrained_state_dict = model_zoo.load_url(model_urls['resnet101'])
        now_state_dict = model.state_dict()
        now_state_dict.update(pretrained_state_dict)
        model.load_state_dict(now_state_dict)
    return model
