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

import torchvision

# VGG = torchvision.models.vgg19
from .base_network import vgg19

class UNet_Pretrained(nn.Module):
    def __init__(self, n_channels, n_classes):
        super(UNet_Pretrained, self).__init__()
        self.n_channels = n_channels
        self.n_classes = n_classes
        bilinear = True

        self.vgg = vgg19(pretrained=True)
        self.up1 = Up(1024, 256, bilinear)
        self.up2 = Up(512, 128, bilinear)
        self.up3 = Up(256, 64, bilinear)
        self.up4 = Up(128, 128, bilinear)
        self.up5 = Up(128, 64, bilinear)
      
        self.final = nn.Conv2d(64, self.n_classes*3, kernel_size=1, padding=0)

    def forward(self, x):
        _, features = self.vgg(x, get_features=True)

        x = self.up1(features[4], features[3])
        x = self.up2(x, features[2])
        x = self.up3(x, features[1])
        x = self.up4(x, features[0])
        x = self.up5(x)

        x = self.final(x)
        
        heatmap = F.sigmoid(x[:,:self.n_classes,:,:])
        regression_x = x[:,self.n_classes:2*self.n_classes,:,:]
        regression_y = x[:,2*self.n_classes:,:,:]

        return heatmap, regression_y, regression_x


class UNet(nn.Module):
    def __init__(self, n_channels, n_classes, bilinear=True):
        super(UNet, self).__init__()
        self.n_channels = n_channels
        self.n_classes = n_classes
        self.bilinear = bilinear

        self.inc = DoubleConv(n_channels, 64)
        self.down0 = Down(64, 64)
        self.down1 = Down(64, 128)
        self.down2 = Down(128, 256)
        self.down3 = Down(256, 512)
        self.down4 = Down(512, 512)
        # self.up1 = Up(1024, 256, bilinear)
        # self.up2 = Up(512, 128, bilinear)
        # self.up3 = Up(256, 64, bilinear)
        # self.up4 = Up(128, 64, bilinear)
        # self.up5 = Up(128, 64, bilinear)
        self.up1 = Up(512, 256, bilinear)
        self.up2 = Up(256, 128, bilinear)
        self.up3 = Up(128, 64, bilinear)
        self.up4 = Up(64, 64, bilinear)
        self.up5 = Up(64, 64, bilinear)        
        self.outc = OutConv_Sigmoid(64, n_classes)
        self.outc_regression_x = OutConv(64, n_classes)
        self.outc_regression_y = OutConv(64, n_classes)

    def forward(self, x):
        x1 = self.inc(x)
        x0 = self.down0(x1)
        x2 = self.down1(x0)
        x3 = self.down2(x2)
        x4 = self.down3(x3)
        x5 = self.down4(x4)
        x = self.up1(x5, x4)
        x = self.up2(x, x3)
        x = self.up3(x, x2)
        x = self.up4(x, x0)
        x = self.up5(x, x1)
        logits = self.outc(x)
        regression_x = self.outc_regression_x(x)
        regression_y = self.outc_regression_y(x)
        return logits, regression_y, regression_x

class DoubleConv(nn.Module):
    """(convolution => [BN] => ReLU) * 2"""

    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.double_conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1),
            nn.InstanceNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1),
            nn.InstanceNorm2d(out_channels),
            nn.ReLU(inplace=True)
        )

    def forward(self, x):
        return self.double_conv(x)


class Down(nn.Module):
    """Downscaling with maxpool then double conv"""

    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.maxpool_conv = nn.Sequential(
            nn.MaxPool2d(2),
            DoubleConv(in_channels, out_channels)
        )

    def forward(self, x):
        return self.maxpool_conv(x)


class Up(nn.Module):
    """Upscaling then double conv"""

    def __init__(self, in_channels, out_channels, bilinear=True):
        super().__init__()

        # if bilinear, use the normal convolutions to reduce the number of channels
        if bilinear:
            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
        else:
            self.up = nn.ConvTranspose2d(in_channels // 2, in_channels // 2, kernel_size=2, stride=2)

        self.conv = DoubleConv(in_channels, out_channels)

    def forward(self, x1, x2=None):
        x1 = self.up(x1)
        # input is CHW
        if x2 is not None:
            diffY = torch.tensor([x2.size()[2] - x1.size()[2]])
            diffX = torch.tensor([x2.size()[3] - x1.size()[3]])

            x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
                            diffY // 2, diffY - diffY // 2])
            # if you have padding issues, see
            # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
            # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
            x1 = torch.cat([x2, x1], dim=1)
        x = x1
        return self.conv(x)


class OutConv_Sigmoid(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(OutConv_Sigmoid, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        return self.sigmoid(self.conv(x))

class OutConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(OutConv, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)

    def forward(self, x):
        return self.conv(x)

if __name__ == '__main__':
    test = UNet_Pretrained(3, 57)
    wtf = torch.zeros([1, 3, 224, 224], dtype=torch.float)
    wtf = test(wtf)
    import ipdb; ipdb.set_trace()

