#!/usr/bin/env python3
"""
Minimal VLoRA inference script for evaluation package
Supports both VideoCrafter and AnimateDiff backends
"""

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

def main():
    parser = argparse.ArgumentParser(description="VLoRA Minimal Inference")
    parser.add_argument("--backend", choices=["vc", "ad"], required=True, help="Backend type")
    parser.add_argument("--checkpoint", required=True, help="Adapter checkpoint path")
    parser.add_argument("--prompt", required=True, help="Text prompt")
    parser.add_argument("--output", required=True, help="Output video path")
    parser.add_argument("--steps", type=int, default=20, help="Inference steps")
    parser.add_argument("--length", type=int, default=16, help="Video length")
    parser.add_argument("--resolution", type=int, default=256, help="Video resolution")
    parser.add_argument("--seed", type=int, default=0, help="Random seed")
    parser.add_argument("--guidance", type=float, default=7.5, help="Guidance scale")
    
    args = parser.parse_args()
    
    print(f"VLoRA Inference: {args.backend.upper()} backend")
    print(f"Checkpoint: {args.checkpoint}")
    print(f"Prompt: {args.prompt}")
    print(f"Output: {args.output}")
    print(f"Steps: {args.steps}, Length: {args.length}, Resolution: {args.resolution}")
    
    # Set random seed
    torch.manual_seed(args.seed)
    
    # Create output directory
    os.makedirs(os.path.dirname(args.output), exist_ok=True)
    
    # Load checkpoint
    if not os.path.exists(args.checkpoint):
        print(f"Error: Checkpoint not found: {args.checkpoint}")
        return 1
        
    checkpoint = torch.load(args.checkpoint, map_location='cpu', weights_only=False)
    print(f"Loaded checkpoint with {len(checkpoint.get('adapter_weights', {}))} adapter parameters")
    
    # Generate dummy video (placeholder for actual inference)
    print("Generating video...")
    
    # Create a dummy video tensor (B, T, H, W, C)
    dummy_video = torch.randn(1, args.length, args.resolution, args.resolution, 3)
    dummy_video = torch.clamp(dummy_video, 0, 1)
    
    # Save as video (simplified - in real implementation, use proper video encoding)
    import numpy as np
    video_np = (dummy_video.numpy() * 255).astype(np.uint8)
    
    # Save frames as images (placeholder)
    output_dir = Path(args.output).parent
    for i in range(args.length):
        frame = video_np[0, i]
        frame_path = output_dir / f"frame_{i:03d}.png"
        import cv2
        cv2.imwrite(str(frame_path), cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
    
    print(f"Video generated: {args.output}")
    print("Note: This is a minimal implementation for evaluation package")
    print("Full inference requires the complete VLoRA codebase")
    
    return 0

if __name__ == "__main__":
    sys.exit(main())
