import argparse
import torch
from torchvision.transforms import Compose, Resize, ToTensor, Normalize, ToPILImage
from torchvision.utils import save_image
from PIL import Image
import os
import io
from huggingface_hub import hf_hub_download
import sys
import matplotlib.pyplot as plt

# Import the model architecture - assuming it's locally available
# If not, we'll need to define it here
try:
    from modeling_pix2pix import GeneratorUNet
except ImportError:
    print("Couldn't import model architecture, defining it here...")
    # Define the UNet architecture as it appears in the original code
    import torch.nn as nn
    import torch.nn.functional as F

    def weights_init_normal(m):
        classname = m.__class__.__name__
        if classname.find("Conv") != -1:
            torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
        elif classname.find("BatchNorm2d") != -1:
            torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
            torch.nn.init.constant_(m.bias.data, 0.0)

    class UNetDown(nn.Module):
        def __init__(self, in_size, out_size, normalize=True, dropout=0.0):
            super(UNetDown, self).__init__()
            layers = [nn.Conv2d(in_size, out_size, 4, 2, 1, bias=False)]
            if normalize:
                layers.append(nn.InstanceNorm2d(out_size))
            layers.append(nn.LeakyReLU(0.2))
            if dropout:
                layers.append(nn.Dropout(dropout))
            self.model = nn.Sequential(*layers)

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

    class UNetUp(nn.Module):
        def __init__(self, in_size, out_size, dropout=0.0):
            super(UNetUp, self).__init__()
            layers = [
                nn.ConvTranspose2d(in_size, out_size, 4, 2, 1, bias=False),
                nn.InstanceNorm2d(out_size),
                nn.ReLU(inplace=True),
            ]
            if dropout:
                layers.append(nn.Dropout(dropout))
            self.model = nn.Sequential(*layers)

        def forward(self, x, skip_input):
            x = self.model(x)
            x = torch.cat((x, skip_input), 1)
            return x

    class GeneratorUNet(nn.Module):
        def __init__(self, in_channels=3, out_channels=3):
            super(GeneratorUNet, self).__init__()

            self.down1 = UNetDown(in_channels, 64, normalize=False)
            self.down2 = UNetDown(64, 128)
            self.down3 = UNetDown(128, 256)
            self.down4 = UNetDown(256, 512, dropout=0.5)
            self.down5 = UNetDown(512, 512, dropout=0.5)
            self.down6 = UNetDown(512, 512, dropout=0.5)
            self.down7 = UNetDown(512, 512, dropout=0.5)
            self.down8 = UNetDown(512, 512, normalize=False, dropout=0.5)

            self.up1 = UNetUp(512, 512, dropout=0.5)
            self.up2 = UNetUp(1024, 512, dropout=0.5)
            self.up3 = UNetUp(1024, 512, dropout=0.5)
            self.up4 = UNetUp(1024, 512, dropout=0.5)
            self.up5 = UNetUp(1024, 256)
            self.up6 = UNetUp(512, 128)
            self.up7 = UNetUp(256, 64)

            self.final = nn.Sequential(
                nn.ConvTranspose2d(128, out_channels, 4, 2, 1),
                nn.Tanh(),
            )

        def forward(self, x):
            # U-Net generator with skip connections from encoder to decoder
            d1 = self.down1(x)
            d2 = self.down2(d1)
            d3 = self.down3(d2)
            d4 = self.down4(d3)
            d5 = self.down5(d4)
            d6 = self.down6(d5)
            d7 = self.down7(d6)
            d8 = self.down8(d7)
            u1 = self.up1(d8, d7)
            u2 = self.up2(u1, d6)
            u3 = self.up3(u2, d5)
            u4 = self.up4(u3, d4)
            u5 = self.up5(u4, d3)
            u6 = self.up6(u5, d2)
            u7 = self.up7(u6, d1)
            return self.final(u7)


def parse_args():
    parser = argparse.ArgumentParser(description="Generate images using Pix2Pix model from HuggingFace Hub")
    parser.add_argument(
        "--repo_id",
        type=str,
        required=True,
        help="HuggingFace Hub repository ID (e.g., 'username/model_name')"
    )
    parser.add_argument(
        "--model_file",
        type=str,
        default="model.pt",
        help="Name of the model file in the repository"
    )
    parser.add_argument(
        "--input_image",
        type=str,
        required=True,
        help="Path to input image (night image to transform to day)"
    )
    parser.add_argument(
        "--output_image",
        type=str,
        default="output.png",
        help="Path to save the generated image"
    )
    parser.add_argument(
        "--image_size",
        type=int,
        default=256,
        help="Size of the input/output images"
    )
    parser.add_argument(
        "--display",
        action="store_true",
        help="Display input and output images using matplotlib"
    )
    parser.add_argument(
        "--token",
        type=str,
        default=None,
        help="HuggingFace token for accessing private repositories"
    )
    return parser.parse_args()


def main():
    args = parse_args()
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    
    print(f"Using device: {device}")
    
    # Set up image transformations
    transform_input = Compose([
        Resize((args.image_size, args.image_size)),
        ToTensor(),
        Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
    ])
    
    # Initialize model
    print("Initializing model...")
    generator = GeneratorUNet()
    generator.to(device)
    
    # Download model from Hugging Face Hub
    print(f"Downloading model from {args.repo_id}...")
    try:
        model_path = hf_hub_download(
            repo_id=args.repo_id,
            filename=args.model_file,
            token=args.token
        )
        print(f"Model downloaded to {model_path}")
    except Exception as e:
        print(f"Error downloading model: {e}")
        sys.exit(1)
    
    # Load model weights
    try:
        generator.load_state_dict(torch.load(model_path, map_location=device))
        generator.eval()
        print("Model loaded successfully")
    except Exception as e:
        print(f"Error loading model weights: {e}")
        sys.exit(1)
    
    # Load and preprocess input image
    try:
        image = Image.open(args.input_image).convert("RGB")
        original_image = image.copy()
        input_tensor = transform_input(image).unsqueeze(0).to(device)
        print(f"Input image loaded: {args.input_image}")
    except Exception as e:
        print(f"Error loading input image: {e}")
        sys.exit(1)
    
    # Generate output image
    print("Generating image...")
    with torch.no_grad():
        fake_B = generator(input_tensor)
    
    # Save the output image
    try:
        # Denormalize and convert back to image
        output_image = fake_B.cpu()
        save_image(output_image, args.output_image, normalize=True)
        print(f"Output image saved to {args.output_image}")
        
        # Create a PIL image for display if needed
        to_pil = ToPILImage()
        output_pil = to_pil(output_image.squeeze(0) * 0.5 + 0.5)
    except Exception as e:
        print(f"Error saving output image: {e}")
        sys.exit(1)
    
    # Display images if requested
    if args.display:
        try:
            plt.figure(figsize=(10, 5))
            
            plt.subplot(1, 2, 1)
            plt.title("Input Image (Night)")
            plt.imshow(original_image)
            plt.axis("off")
            
            plt.subplot(1, 2, 2)
            plt.title("Generated Image (Day)")
            plt.imshow(output_pil)
            plt.axis("off")
            
            plt.tight_layout()
            plt.show()
        except Exception as e:
            print(f"Error displaying images: {e}")


if __name__ == "__main__":
    main()