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


class UNet(nn.Module):
    """
    Architecture based on U-Net: Convolutional Networks for Biomedical Image Segmentation
    Link - https://arxiv.org/abs/1505.04597

    Parameters:
        num_classes (int): Number of output classes required (default 19 for KITTI dataset)
        bilinear (bool): Whether to use bilinear interpolation or transposed
            convolutions for upsampling.
    """

    def __init__(self, num_classes=19, bilinear=False):
        super().__init__()
        self.layer1 = DoubleConv(3, 64)
        self.layer2 = Down(64, 128)
        self.layer3 = Down(128, 256)
        self.layer4 = Down(256, 512)
        self.layer5 = Down(512, 1024)

        self.layer6 = Up(1024, 512, bilinear=bilinear)
        self.layer7 = Up(512, 256, bilinear=bilinear)
        self.layer8 = Up(256, 128, bilinear=bilinear)
        self.layer9 = Up(128, 64, bilinear=bilinear)

        self.layer10 = nn.Conv2d(64, num_classes, kernel_size=1)

    def forward(self, x):
        x1 = self.layer1(x)
        x2 = self.layer2(x1)
        x3 = self.layer3(x2)
        x4 = self.layer4(x3)
        x5 = self.layer5(x4)

        x6 = self.layer6(x5, x4)
        x6 = self.layer7(x6, x3)
        x6 = self.layer8(x6, x2)
        x6 = self.layer9(x6, x1)

        return self.layer10(x6)


class DoubleConv(nn.Module):
    """
    Double Convolution and BN and ReLU
    (3x3 conv -> BN -> ReLU) ** 2
    """

    def __init__(self, in_ch, out_ch):
        super().__init__()
        self.net = nn.Sequential(
            nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_ch),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1),
            nn.BatchNorm2d(out_ch),
            nn.ReLU(inplace=True)
        )

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


class Down(nn.Module):
    """
    Combination of MaxPool2d and DoubleConv in series
    """

    def __init__(self, in_ch, out_ch):
        super().__init__()
        self.net = nn.Sequential(
            nn.MaxPool2d(kernel_size=2, stride=2),
            DoubleConv(in_ch, out_ch)
        )

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


class Up(nn.Module):
    """
    Upsampling (by either bilinear interpolation or transpose convolutions)
    followed by concatenation of feature map from contracting path,
    followed by double 3x3 convolution.
    """

    def __init__(self, in_ch, out_ch, bilinear=False):
        super().__init__()
        self.upsample = None
        if bilinear:
            self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
        else:
            self.upsample = nn.ConvTranspose2d(in_ch, in_ch // 2, kernel_size=2, stride=2)

        self.conv = DoubleConv(in_ch, out_ch)

    def forward(self, x1, x2):
        x1 = self.upsample(x1)

        # Pad x1 to the size of x2
        diff_h = x2.shape[2] - x1.shape[2]
        diff_w = x2.shape[3] - x1.shape[3]

        x1 = F.pad(x1, [diff_w // 2, diff_w - diff_w // 2, diff_h // 2, diff_h - diff_h // 2])

        # Concatenate along the channels axis
        x = torch.cat([x2, x1], dim=1)
        return self.conv(x)