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

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

    def __init__(self, in_channels, out_channels, mid_channels=None):
        super().__init__()
        if not mid_channels:
            mid_channels = out_channels
        self.double_conv = nn.Sequential(
            nn.Conv2d(in_channels, mid_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(mid_channels),
            nn.ReLU(),
            
            nn.Conv2d(mid_channels, out_channels, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(),
        )

    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)
            self.conv = DoubleConv(in_channels, out_channels, in_channels // 2)
        else:
            self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)
            self.conv = DoubleConv(in_channels, out_channels)

    def forward(self, x1, x2):
        x1 = self.up(x1)
        # input is CHW
        diffY = x2.size()[2] - x1.size()[2]
        diffX = x2.size()[3] - x1.size()[3]

        x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
                        diffY // 2, diffY - diffY // 2])
        x = torch.cat([x2, x1], dim=1)
        
        return 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)

class UNet_512(nn.Module):
    def __init__(self, n_channels = 3, bilinear=True):
        super(UNet_512, self).__init__()
        self.n_channels = n_channels
        self.bilinear = bilinear

        self.inc = DoubleConv(n_channels, 32)
        self.down1 = Down(32, 64)
        self.down2 = Down(64, 128)
        self.down3 = Down(128, 256)
        self.down4 = Down(256, 512)
        factor = 2 if bilinear else 1
        self.down5 = Down(512, 1024 // factor)
        
        self.up0 = Up(1024, 512 // factor, bilinear)
        self.up1 = Up(512, 256 // factor, bilinear)
        self.up2 = Up(256, 128 // factor, bilinear)
        self.up3 = Up(128, 64 // factor, bilinear)
        self.up4 = Up(64, 32, bilinear)
        self.outc = OutConv(32,1)
        self.activation = torch.nn.Sigmoid()
                                        
    def forward(self, x):

        x1 = self.inc(x)
        x2 = self.down1(x1)
        x3 = self.down2(x2)
        x4 = self.down3(x3)
        x5 = self.down4(x4)
        x6 = self.down5(x5)
        
        z1 = self.up0(x6, x5)
        z2 = self.up1(z1, x4)
        z3 = self.up2(z2, x3)
        z4 = self.up3(z3, x2)
        z5 = self.up4(z4, x1)
        logits1 = self.outc(z5)
         
        return self.activation(logits1) 


# Input_Image_Channels = 3
# def model() -> UNet_512:
#     model = UNet_512()
#     return model
# DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
# from torchsummary import summary
# model = model()
# model.to(device=DEVICE,dtype=torch.float)
# summary(model, [(Input_Image_Channels, 256,256)])


class UNet_1024(nn.Module):
    def __init__(self, n_channels = 3, bilinear=False):
        super(UNet_1024, self).__init__()
        self.n_channels = n_channels
        self.bilinear = bilinear

        self.inc = DoubleConv(n_channels, 32)
        self.down1 = Down(32, 64)
        self.down2 = Down(64, 128)
        self.down3 = Down(128, 256)
        self.down4 = Down(256, 512)
        factor = 2 if bilinear else 1
        self.down5 = Down(512, 1024 // factor)
        
        self.up0 = Up(1024, 512 // factor, bilinear)
        self.up1 = Up(512, 256 // factor, bilinear)
        self.up2 = Up(256, 128 // factor, bilinear)
        self.up3 = Up(128, 64 // factor, bilinear)
        self.up4 = Up(64, 32, bilinear)
        self.outc = OutConv(32,1)
        self.activation = torch.nn.Sigmoid()
                                        
    def forward(self, x):

        x1 = self.inc(x)
        x2 = self.down1(x1)
        x3 = self.down2(x2)
        x4 = self.down3(x3)
        x5 = self.down4(x4)
        x6 = self.down5(x5)
        
        z1 = self.up0(x6, x5)
        z2 = self.up1(z1, x4)
        z3 = self.up2(z2, x3)
        z4 = self.up3(z3, x2)
        z5 = self.up4(z4, x1)
        logits1 = self.outc(z5)
         
        return self.activation(logits1) 
    
# Input_Image_Channels = 3
# def model() -> UNet_1024:
#     model = UNet_1024()
#     return model
# DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
# from torchsummary import summary
# model = model()
# model.to(device=DEVICE,dtype=torch.float)
# summary(model, [(Input_Image_Channels, 256,256)])