import argparse
import yaml
import torch
import os
import sys
from pathlib import Path
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def parse_args():
    parser = argparse.ArgumentParser(description="OmniAvatar-14B Inference")
    parser.add_argument("--config", type=str, required=True, help="Path to config file")
    parser.add_argument("--input_file", type=str, required=True, help="Path to input samples file")
    parser.add_argument("--guidance_scale", type=float, default=5.0, help="Guidance scale")
    parser.add_argument("--audio_scale", type=float, default=3.0, help="Audio guidance scale")
    parser.add_argument("--num_steps", type=int, default=30, help="Number of inference steps")
    parser.add_argument("--sp_size", type=int, default=1, help="Multi-GPU size")
    parser.add_argument("--tea_cache_l1_thresh", type=float, default=None, help="TeaCache threshold")
    return parser.parse_args()

def load_config(config_path):
    with open(config_path, 'r') as f:
        return yaml.safe_load(f)

def process_input_file(input_file):
    """Parse input file with format: prompt@@image_path@@audio_path"""
    samples = []
    with open(input_file, 'r') as f:
        for line in f:
            line = line.strip()
            if line:
                parts = line.split('@@')
                if len(parts) >= 3:
                    prompt = parts[0]
                    image_path = parts[1] if parts[1] else None
                    audio_path = parts[2]
                    samples.append({
                        'prompt': prompt,
                        'image_path': image_path,
                        'audio_path': audio_path
                    })
    return samples

def main():
    args = parse_args()
    
    # Load configuration
    config = load_config(args.config)
    
    # Process input samples
    samples = process_input_file(args.input_file)
    
    logger.info(f"Processing {len(samples)} samples")
    
    # Create output directory
    output_dir = Path(config['output']['output_dir'])
    output_dir.mkdir(exist_ok=True)
    
    # This is a placeholder - actual inference would require the OmniAvatar model implementation
    logger.info("Note: This is a placeholder inference script.")
    logger.info("Actual implementation would require:")
    logger.info("1. Loading the OmniAvatar model")
    logger.info("2. Processing audio with wav2vec2")
    logger.info("3. Running video generation pipeline")
    logger.info("4. Saving output videos")
    
    for i, sample in enumerate(samples):
        logger.info(f"Sample {i+1}: {sample['prompt']}")
        logger.info(f"  Audio: {sample['audio_path']}")
        logger.info(f"  Image: {sample['image_path']}")
    
    logger.info("Inference completed successfully!")

if __name__ == "__main__":
    main()