import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
import math


class F_mynet3(nn.Module):
    def __init__(self, backbone='resnet18',in_c=3, f_c=64, output_stride=8):
        self.in_c = in_c
        super(F_mynet3, self).__init__()
        self.module = mynet3(backbone=backbone, output_stride=output_stride, f_c=f_c, in_c=self.in_c)
    def forward(self, input):
        return self.module(input)


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

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 ResNet34(output_stride, BatchNorm=nn.BatchNorm2d, pretrained=True, in_c=3):
    """
    output, low_level_feat:
    512, 64
    """
    print(in_c)
    model = ResNet(BasicBlock, [3, 4, 6, 3], output_stride, BatchNorm, in_c=in_c)
    if in_c != 3:
        pretrained = False
    if pretrained:
        model._load_pretrained_model(model_urls['resnet34'])
    return model

def ResNet18(output_stride, BatchNorm=nn.BatchNorm2d, pretrained=True, in_c=3):
    """
    output, low_level_feat:
    512, 256, 128, 64, 64
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2], output_stride, BatchNorm, in_c=in_c)
    if in_c !=3:
        pretrained=False
    if pretrained:
        model._load_pretrained_model(model_urls['resnet18'])
    return model

def ResNet50(output_stride, BatchNorm=nn.BatchNorm2d, pretrained=True, in_c=3):
    """
    output, low_level_feat:
    2048, 256
    """
    model = ResNet(Bottleneck, [3, 4, 6, 3], output_stride, BatchNorm, in_c=in_c)
    if in_c !=3:
        pretrained=False
    if pretrained:
        model._load_pretrained_model(model_urls['resnet50'])
    return model


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, BatchNorm=None):
        super(BasicBlock, self).__init__()

        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride,
                               dilation=dilation, padding=dilation, bias=False)
        self.bn1 = BatchNorm(planes)

        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = BatchNorm(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, dilation=1, downsample=None, BatchNorm=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = BatchNorm(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               dilation=dilation, padding=dilation, bias=False)
        self.bn2 = BatchNorm(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = BatchNorm(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride
        self.dilation = dilation

    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, output_stride, BatchNorm, pretrained=True, in_c=3):

        self.inplanes = 64
        self.in_c = in_c
        print('in_c: ',self.in_c)
        super(ResNet, self).__init__()
        blocks = [1, 2, 4]
        if output_stride == 32:
            strides = [1, 2, 2, 2]
            dilations = [1, 1, 1, 1]
        elif output_stride == 16:
            strides = [1, 2, 2, 1]
            dilations = [1, 1, 1, 2]
        elif output_stride == 8:
            strides = [1, 2, 1, 1]
            dilations = [1, 1, 2, 4]
        elif output_stride == 4:
            strides = [1, 1, 1, 1]
            dilations = [1, 2, 4, 8]
        else:
            raise NotImplementedError

        # Modules
        self.conv1 = nn.Conv2d(self.in_c, 64, kernel_size=7, stride=2, padding=3,
                                bias=False)
        self.bn1 = BatchNorm(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], stride=strides[0], dilation=dilations[0], BatchNorm=BatchNorm)
        self.layer2 = self._make_layer(block, 128, layers[1], stride=strides[1], dilation=dilations[1], BatchNorm=BatchNorm)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=strides[2], dilation=dilations[2], BatchNorm=BatchNorm)
        self.layer4 = self._make_MG_unit(block, 512, blocks=blocks, stride=strides[3], dilation=dilations[3], BatchNorm=BatchNorm)
        # self.layer4 = self._make_layer(block, 512, layers[3], stride=strides[3], dilation=dilations[3], BatchNorm=BatchNorm)
        self._init_weight()


    def _make_layer(self, block, planes, blocks, stride=1, dilation=1, BatchNorm=None):
        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),
                BatchNorm(planes * block.expansion),
            )

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

        return nn.Sequential(*layers)

    def _make_MG_unit(self, block, planes, blocks, stride=1, dilation=1, BatchNorm=None):
        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),
                BatchNorm(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, dilation=blocks[0]*dilation,
                            downsample=downsample, BatchNorm=BatchNorm))
        self.inplanes = planes * block.expansion
        for i in range(1, len(blocks)):
            layers.append(block(self.inplanes, planes, stride=1,
                                dilation=blocks[i]*dilation, BatchNorm=BatchNorm))

        return nn.Sequential(*layers)

    def forward(self, input):
        x = self.conv1(input)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)   # | 4
        x = self.layer1(x)  # | 4
        low_level_feat2 = x  # | 4
        x = self.layer2(x)  # | 8
        low_level_feat3 = x
        x = self.layer3(x)  # | 16
        low_level_feat4 = x
        x = self.layer4(x)  # | 32
        return x, low_level_feat2, low_level_feat3, low_level_feat4

    def _init_weight(self):
        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. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _load_pretrained_model(self, model_path):
        pretrain_dict = model_zoo.load_url(model_path)
        model_dict = {}
        state_dict = self.state_dict()
        for k, v in pretrain_dict.items():
            if k in state_dict:
                model_dict[k] = v
        state_dict.update(model_dict)
        self.load_state_dict(state_dict)



def build_backbone(backbone, output_stride, BatchNorm, in_c=3):
    if backbone == 'resnet50':
        return ResNet50(output_stride, BatchNorm, in_c=in_c)
    elif backbone == 'resnet34':
        return ResNet34(output_stride, BatchNorm, in_c=in_c)
    elif backbone == 'resnet18':
        return ResNet18(output_stride, BatchNorm, in_c=in_c)
    else:
        raise NotImplementedError



class DR(nn.Module):
    def __init__(self, in_d, out_d):
        super(DR, self).__init__()
        self.in_d = in_d
        self.out_d = out_d
        self.conv1 = nn.Conv2d(self.in_d, self.out_d, 1, bias=False)
        self.bn1 = nn.BatchNorm2d(self.out_d)
        self.relu = nn.ReLU()

    def forward(self, input):
        x = self.conv1(input)
        x = self.bn1(x)
        x = self.relu(x)
        return x


class Decoder(nn.Module):
    def __init__(self, fc, BatchNorm):
        super(Decoder, self).__init__()
        self.fc = fc
        self.dr2 = DR(64, 96)
        self.dr3 = DR(128, 96)
        self.dr4 = DR(256, 96)
        self.dr5 = DR(512, 96)
        self.last_conv = nn.Sequential(nn.Conv2d(384, 256, kernel_size=3, stride=1, padding=1, bias=False),
                                       BatchNorm(256),
                                       nn.ReLU(),
                                       nn.Dropout(0.5),
                                       nn.Conv2d(256, self.fc, kernel_size=1, stride=1, padding=0, bias=False),
                                       BatchNorm(self.fc),
                                       nn.ReLU(),
                                       )

        self._init_weight()


    def forward(self, x,low_level_feat2, low_level_feat3, low_level_feat4):

        # x1 = self.dr1(low_level_feat1)
        x2 = self.dr2(low_level_feat2)
        x3 = self.dr3(low_level_feat3)
        x4 = self.dr4(low_level_feat4)
        x = self.dr5(x)
        x = F.interpolate(x, size=x2.size()[2:], mode='bilinear', align_corners=True)
        # x2 = F.interpolate(x2, size=x3.size()[2:], mode='bilinear', align_corners=True)
        x3 = F.interpolate(x3, size=x2.size()[2:], mode='bilinear', align_corners=True)
        x4 = F.interpolate(x4, size=x2.size()[2:], mode='bilinear', align_corners=True)

        x = torch.cat((x, x2, x3, x4), dim=1)

        x = self.last_conv(x)

        return x

    def _init_weight(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                torch.nn.init.kaiming_normal_(m.weight)
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()


def build_decoder(fc, backbone, BatchNorm):
    return Decoder(fc, BatchNorm)


class mynet3(nn.Module):
    def __init__(self, backbone='resnet18', output_stride=16, f_c=64, freeze_bn=False, in_c=3):
        super(mynet3, self).__init__()
        print('arch: mynet3')
        BatchNorm = nn.BatchNorm2d
        self.backbone = build_backbone(backbone, output_stride, BatchNorm, in_c)
        self.decoder = build_decoder(f_c, backbone, BatchNorm)

        if freeze_bn:
            self.freeze_bn()

    def forward(self, input):
        x, f2, f3, f4 = self.backbone(input)
        x = self.decoder(x, f2, f3, f4)
        return x

    def freeze_bn(self):
        for m in self.modules():
            if isinstance(m, nn.BatchNorm2d):
                m.eval()