"""
Byte Dream - Command Line Inference Tool
Generate images from text prompts using the command line
"""

import argparse
from pathlib import Path
import torch


def main():
    parser = argparse.ArgumentParser(
        description="Byte Dream - AI Image Generation",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog="""
Examples:
  # Basic usage
  python infer.py --prompt "A beautiful sunset over mountains"
  
  # With custom parameters
  python infer.py --prompt "Cyberpunk city" --negative "blurry" --steps 75 --guidance 8.0
  
  # Specify output and size
  python infer.py --prompt "Fantasy landscape" --output fantasy.png --width 768 --height 768
  
  # With seed for reproducibility
  python infer.py --prompt "Dragon" --seed 42 --output dragon.png
        """
    )
    
    parser.add_argument(
        "--prompt", "-p",
        type=str,
        required=True,
        help="Text prompt describing the desired image"
    )
    
    parser.add_argument(
        "--negative", "-n",
        type=str,
        default="",
        help="Negative prompt - things to avoid in the image"
    )
    
    parser.add_argument(
        "--output", "-o",
        type=str,
        default="output.png",
        help="Output image filename (default: output.png)"
    )
    
    parser.add_argument(
        "--width", "-W",
        type=int,
        default=512,
        help="Image width in pixels (default: 512)"
    )
    
    parser.add_argument(
        "--height", "-H",
        type=int,
        default=512,
        help="Image height in pixels (default: 512)"
    )
    
    parser.add_argument(
        "--steps", "-s",
        type=int,
        default=50,
        help="Number of inference steps (default: 50)"
    )
    
    parser.add_argument(
        "--guidance", "-g",
        type=float,
        default=7.5,
        help="Guidance scale - how closely to follow prompt (default: 7.5)"
    )
    
    parser.add_argument(
        "--seed",
        type=int,
        default=None,
        help="Random seed for reproducibility (default: random)"
    )
    
    parser.add_argument(
        "--model", "-m",
        type=str,
        default=None,
        help="Path to model directory or Hugging Face repo ID (default: uses config)"
    )
    
    parser.add_argument(
        "--hf_repo",
        type=str,
        default=None,
        help="Hugging Face repository ID to load model from (e.g., username/repo)"
    )
    
    parser.add_argument(
        "--config", "-c",
        type=str,
        default="config.yaml",
        help="Path to config file (default: config.yaml)"
    )
    
    parser.add_argument(
        "--device",
        type=str,
        default="cpu",
        help="Device to run on: cpu or cuda (default: cpu)"
    )
    
    args = parser.parse_args()
    
    # Import generator
    from bytedream.generator import ByteDreamGenerator
    
    # Initialize generator
    print("="*60)
    print("Byte Dream - AI Image Generator")
    print("="*60)
    
    # Determine if loading from HF or local
    if args.hf_repo:
        print(f"Loading model from Hugging Face: {args.hf_repo}")
        generator = ByteDreamGenerator(
            hf_repo_id=args.hf_repo,
            config_path=args.config,
            device=args.device,
        )
    else:
        generator = ByteDreamGenerator(
            model_path=args.model,
            config_path=args.config,
            device=args.device,
        )
    
    # Print model info
    info = generator.get_model_info()
    print(f"\nModel: {info['name']} v{info['version']}")
    print(f"Device: {info['device']}")
    print(f"Parameters: {info['unet_parameters']}")
    print("="*60)
    
    # Generate image
    image = generator.generate(
        prompt=args.prompt,
        negative_prompt=args.negative if args.negative else None,
        width=args.width,
        height=args.height,
        num_inference_steps=args.steps,
        guidance_scale=args.guidance,
        seed=args.seed,
    )
    
    # Save image
    output_path = Path(args.output)
    image.save(output_path)
    print(f"\n✓ Image saved to: {output_path.absolute()}")
    print("="*60)


if __name__ == "__main__":
    main()