import torch
import torch.nn as nn
import torchvision.transforms.functional as TF


class DoubleConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(DoubleConv, self).__init__()
        self.conv = nn.Sequential(
            # kernel_size=3
            # stride=1(evry rown a nd column is affected)
            # padding=1(same conv)input height i width isti nakon konv
            # bias=false
            # batchnomr-normalizacija
            # relu aktivacijska funk
            nn.Conv2d(in_channels, out_channels, 3, 1, 1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),

            nn.Conv2d(out_channels, out_channels, 3, 1, 1, bias=False),
            nn.BatchNorm2d(out_channels),
            ##inplace means that it will modify the input directly, without allocating any additional output
            nn.ReLU(inplace=True),

        )

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


class UNET(nn.Module):
    ##moguce da broj out kanala promjenis,ovjde 1 jer je binary image segemntation
    def __init__(self, in_channels=3, out_channels=2, features=[64, 128, 256, 512]):
        super(UNET, self).__init__()
        # jer hocemo evaluirat i sve to imamo tu listu zbog layera,spremamo sve te konvolucije
        self.downs = nn.ModuleList()
        self.ups = nn.ModuleList()
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)

        # Down part
        for feature in features:
            self.downs.append(DoubleConv(in_channels, feature))
            in_channels = feature

        # Up part
        for feature in reversed(features):
            self.ups.append(nn.ConvTranspose2d(feature * 2, feature, kernel_size=2,
                                               stride=2))  ##kernel size is tride ce poduplat ovdje height i width slike
            self.ups.append(DoubleConv(feature * 2, feature))

        self.bottleneck = DoubleConv(features[-1], features[-1] * 2)  # onaj zasebni dio
        self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size=1)

    def forward(self, x):
        skip_connections = []
        for down in self.downs:
            x = down(x)
            skip_connections.append(x)
            x = self.pool(x)

        x = self.bottleneck(x)
        skip_connections = skip_connections[::-1]  # obrnuti redoslijed

        for idx in range(0, len(self.ups), 2):
            x = self.ups[idx](x)
            skip_connection = skip_connections[idx // 2]
            if x.shape != skip_connection.shape:
                x = TF.resize(x, size=skip_connection.shape[2:])  ##ako input nije djeljiv s 2, resize se

            concat_skip = torch.cat((skip_connection, x), dim=1)
            x = self.ups[idx + 1](concat_skip)

        return self.final_conv(x)