import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np


class AttentionGate(nn.Module):
    def __init__(self, F_g, F_l, F_int):
        super(AttentionGate, self).__init__()
        self.W_g = nn.Sequential(
            nn.Conv2d(F_g, F_int, kernel_size=1, stride=1, padding=0, bias=True),
            nn.BatchNorm2d(F_int),
        )

        self.W_x = nn.Sequential(
            nn.Conv2d(F_l, F_int, kernel_size=1, stride=1, padding=0, bias=True),
            nn.BatchNorm2d(F_int),
        )

        self.psi = nn.Sequential(
            nn.Conv2d(F_int, 1, kernel_size=1, stride=1, padding=0, bias=True),
            nn.BatchNorm2d(1),
            nn.Sigmoid(),
        )

        self.relu = nn.ReLU(inplace=True)

    def forward(self, g, x):
        g1 = self.W_g(g)
        x1 = self.W_x(x)
        psi = self.relu(g1 + x1)
        psi = self.psi(psi)

        return x * psi


class DoubleConv(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(DoubleConv, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
        )

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


class AttentionUNet(nn.Module):
    def __init__(self, in_channels=3, out_channels=1, features=[64, 128, 256, 512]):
        super(AttentionUNet, self).__init__()
        self.ups = nn.ModuleList()
        self.downs = nn.ModuleList()
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.attention_gates = nn.ModuleList()

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

        # Up part of U-Net
        for feature in reversed(features):
            self.ups.append(
                nn.ConvTranspose2d(
                    feature * 2,
                    feature,
                    kernel_size=2,
                    stride=2,
                )
            )
            # Attention Gate
            self.attention_gates.append(
                AttentionGate(F_g=feature, F_l=feature, F_int=feature // 2)
            )

            self.ups.append(DoubleConv(feature * 2, feature))

        # Bottleneck
        self.bottleneck = DoubleConv(features[-1], features[-1] * 2)

        # Final Conv
        self.final_conv = nn.Conv2d(features[0], out_channels, kernel_size=1)

    def forward(self, x):
        skip_connections = []

        # Encoder path
        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]  # Reverse to use from back

        # Decoder path
        for idx in range(0, len(self.ups), 2):
            x = self.ups[idx](x)
            skip_connection = skip_connections[idx // 2]

            # If sizes don't match
            if x.shape != skip_connection.shape:
                x = F.interpolate(x, size=skip_connection.shape[2:])

            # Apply attention gate
            skip_connection = self.attention_gates[idx // 2](g=x, x=skip_connection)

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

        # Final conv
        return self.final_conv(x)


def load_model(model_path):
    """
    Load the trained model

    Args:
        model_path: Path to the model weights

    Returns:
        Loaded model
    """
    # Define device
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    # Initialize model
    model = AttentionUNet(in_channels=3, out_channels=1)

    # Load model weights
    model.load_state_dict(torch.load(model_path, map_location=device))

    # Set model to evaluation mode
    model.eval()

    return model.to(device)