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

from torch.utils import model_zoo

__all__ = ['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',
}


class UnetDecoderBlock(nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.layer = nn.Sequential(
            # nn.Upsample(scale_factor=2),
            nn.Conv2d(in_channels, out_channels, 3, padding=1),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        x = F.interpolate(x, scale_factor=2)
        return self.layer(x)


class ConvBottleneck(nn.Module):
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.seq = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, 3, padding=1),
            nn.ReLU(inplace=True)
        )

    def forward(self, dec, enc):
        x = torch.cat([dec, enc], dim=1)
        return self.seq(x)


class AlbUNet(torch.nn.Module):
    def __init__(self, base_model: torch.nn.Module, num_classes: int, weights_url: str = None):
        super().__init__()
        if not hasattr(self, 'decoder_block'):
            self.decoder_block = UnetDecoderBlock
        if not hasattr(self, 'bottleneck_type'):
            self.bottleneck_type = ConvBottleneck

        if weights_url is not None:
            print("Model weights inited by url")

            pretrained_weights = model_zoo.load_url(weights_url)
            model_state_dict = base_model.state_dict()
            pretrained_weights = {k: v for k, v in pretrained_weights.items() if k in model_state_dict}
            base_model.load_state_dict(pretrained_weights)

        filters = [64, 64, 128, 256, 512]

        self.bottlenecks = nn.ModuleList([self.bottleneck_type(f * 2, f) for f in reversed(filters[:-1])])
        self.decoder_stages = nn.ModuleList([self.get_decoder(filters, idx) for idx in range(1, len(filters))])

        self.encoder_stages = nn.ModuleList([self.get_encoder(base_model, idx) for idx in range(len(filters))])

        self.last_upsample = self.decoder_block(filters[0], filters[0])
        self.final = self.make_final_classifier(filters[0], num_classes)

    def forward(self, x):
        # you better run this in debugger and see what happens.
        # initial ideas are U-Net and LinkNet. see papers or blogposts for additional information
        enc_results = []
        for stage in self.encoder_stages:
            x = stage(x)
            enc_results.append(x.clone())

        for idx, bottleneck in enumerate(self.bottlenecks):
            rev_idx = - (idx + 1)
            x = self.decoder_stages[rev_idx](x)
            x = bottleneck(x, enc_results[rev_idx - 1])

        x = self.last_upsample(x)
        f = self.final(x)
        return f

    @staticmethod
    def make_final_classifier(in_filters, num_classes):
        return nn.Sequential(
            nn.Conv2d(in_filters, num_classes, 3, padding=1)
        )

    @staticmethod
    def get_encoder(encoder, layer):
        """
        encoder layers are different sized features from different net depth
        """
        if layer == 0:
            return nn.Sequential(
                encoder.conv1,
                encoder.bn1,
                encoder.relu)
        elif layer == 1:
            return nn.Sequential(
                encoder.maxpool,
                encoder.layer1)
        elif layer == 2:
            return encoder.layer2
        elif layer == 3:
            return encoder.layer3
        elif layer == 4:
            return encoder.layer4

    def get_decoder(self, filters, layer):
        return self.decoder_block(filters[layer], filters[max(layer - 1, 0)])


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, in_channels=3):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, 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)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)

        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 _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)
        x = self.maxpool(x)

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

        return x


def resnet18(classes_num: int, in_channels: int, pretrained: bool = True):
    """Constructs a ResNet-18 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2], in_channels)
    return AlbUnet(model, classes_num, weights_url=model_urls['resnet18'] if pretrained else None)


def resnet34(classes_num: int, in_channels: int, pretrained: bool = True):
    """Constructs a ResNet-34 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3], in_channels)
    return AlbUnet(model, classes_num, weights_url=model_urls['resnet34'] if pretrained else None)


def resnet50(classes_num: int, in_channels: int, pretrained: bool = True):
    """Constructs a ResNet-50 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 6, 3], in_channels)
    return AlbUnet(model, classes_num, weights_url=model_urls['resnet50'] if pretrained else None)


def resnet101(classes_num: int, in_channels: int, pretrained: bool = True):
    """Constructs a ResNet-101 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 23, 3], in_channels)
    return AlbUnet(model, classes_num, weights_url=model_urls['resnet101'] if pretrained else None)


def resnet152(classes_num: int, in_channels: int, pretrained: bool = True):
    """Constructs a ResNet-152 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 8, 36, 3], in_channels)
    return AlbUnet(model, classes_num, weights_url=model_urls['resnet152'] if pretrained else None)
