# scripts/run_cleaning.py

import argparse
import json
import os
import sys
from pathlib import Path

# 添加项目根目录到 path
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))

from src.llm_generation.vllm_client import VLLMClient
from src.llm_generation.cleaning_generator import DataCleaningGenerator

def scan_dataset(root_dir):
    """
    扫描 data/original/${category}/${sequence} 结构
    返回 entries 列表，包含 sequence 的绝对路径
    """
    entries = []
    base_path = Path(root_dir).resolve() # 获取绝对路径
    
    if not base_path.exists():
        print(f"Error: Path {root_dir} does not exist.")
        return []

    print(f"Scanning directory: {base_path} ...")
    
    # 获取所有类别文件夹
    categories = [d for d in base_path.iterdir() if d.is_dir()]
    
    for cat_dir in categories:
        category_name = cat_dir.name
        # 获取该类别下的所有 sequence 文件夹
        sequences = [s for s in cat_dir.iterdir() if s.is_dir()]
        
        for seq_dir in sequences:
            # 直接存绝对路径，避免拼接错误
            entries.append({
                "category": category_name,
                "sequence_name": seq_dir.name,
                "sequence_abs_path": str(seq_dir) # 关键修改：存绝对路径
            })
            
    print(f"Found {len(entries)} sequences across {len(categories)} categories.")
    return entries

def save_jsonl(data, path):
    os.makedirs(os.path.dirname(path), exist_ok=True)
    with open(path, 'w', encoding='utf-8') as f:
        for item in data:
            f.write(json.dumps(item, ensure_ascii=False) + '\n')

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--data_root", type=str, required=True, help="Path to 'data/original'")
    parser.add_argument("--output_file", type=str, default="cleaning_report.jsonl")
    parser.add_argument("--model", type=str, required=True, help="Local model path")
    parser.add_argument("--tp_size", type=int, default=1)
    
    args = parser.parse_args()

    # 1. 扫描数据
    entries = scan_dataset(args.data_root)
    
    if not entries:
        print("No entries found. Exiting.")
        return

    # 2. 初始化 Client
    client = VLLMClient(
        model_path=args.model,
        tensor_parallel_size=args.tp_size,
        gpu_memory_utilization=0.9
    )

    # 3. 初始化 Generator
    # image_root 参数在这里其实已经没用了，因为我们改用了绝对路径，
    # 但为了保持接口兼容或日志打印，可以传个空字符串或 None
    generator = DataCleaningGenerator(
        client=client,
        image_root=None, # 修改：不再依赖 image_root 拼接
        model_name=args.model,
        sample_k=4
    )

    # 4. 运行清洗
    results = generator.process_batch(entries)

    # 5. 保存结果
    save_jsonl(results, args.output_file)
    
    # 6. 打印简报
    # 过滤掉 error_no_images 的，只统计真正跑过的
    valid_results = [r for r in results if r.get('status') != 'error_no_images']
    mismatches = [r for r in valid_results if r.get('is_match') is False]
    
    print(f"\n清洗完成！")
    print(f"总扫描数: {len(results)}")
    print(f"有效处理数: {len(valid_results)}")
    print(f"疑似不匹配: {len(mismatches)}")
    print(f"详细报告已保存至: {args.output_file}")
    
    if mismatches:
        print("\n示例不匹配项:")
        print(json.dumps(mismatches[0], indent=2, ensure_ascii=False))

if __name__ == "__main__":
    main()