import os
import pandas as pd
import argparse
from tqdm import tqdm

def extract_uttid_from_row(row):
    """
    从CSV的行数据中提取uttid（通过start_frame和end_frame组合）
    """
    uttid = f"{row['id']}_{row['start_frame']}_{row['end_frame']}"
    return uttid

def create_filtered_csv(csv_file, output_latent_folder, output_csv_file):
    """
    创建一个过滤后的CSV文件，只包含需要处理的样本
    只使用uttid匹配，不依赖其他元数据
    """
    # 读取原始CSV
    df = pd.read_csv(csv_file)
    print(f"Original dataset size: {len(df)}")
    
    # 获取已经存在的latent文件
    existing_files = set()
    if os.path.exists(output_latent_folder):
        for filename in os.listdir(output_latent_folder):
            if filename.endswith('.pt'):
                parts = filename[:-3].split('_')
                if len(parts) >= 4:  # 至少要有uttid + 3个元数据
                    uttid_parts = parts[:-3]  # 提取uttid部分
                    uttid = '_'.join(uttid_parts)
                    existing_files.add(uttid)
    
    print(f"Found {len(existing_files)} existing latent files")
    
    # 使用新的方法从行数据中提取uttid
    df_uttids = df.apply(extract_uttid_from_row, axis=1)
    
    # 筛选出未处理的样本
    mask = ~df_uttids.isin(existing_files)
    filtered_df = df[mask]

    # 保存到新的CSV文件
    os.makedirs(os.path.dirname(output_csv_file), exist_ok=True)
    filtered_df.to_csv(output_csv_file, index=False)
    
    print(f"Filtered dataset size: {len(filtered_df)}")
    print(f"Filtered CSV saved to: {output_csv_file}")
    
    return len(filtered_df)

def create_all_filtered_csvs():
    """
    为所有数据集创建过滤后的CSV文件
    """
    base_csv_path = "/mnt/bn/yufan-dev-my/ysh/Ckpts/SpatialVID/SpatialVID-HQ-Final/"
    base_output_latent_path = "/mnt/bn/yufan-dev-my/ysh/Ckpts/SpatialVID/SpatialVID-HQ-Final"

    csv_paths = [
        "data/SpatialVID_HQ_step2.csv", 

    ]
    output_latent_paths = [
        "latents_stride1", 
    ]

    for csv_path, output_latent_path in zip(csv_paths, output_latent_paths):
        original_csv = os.path.join(base_csv_path, csv_path)
        output_latent_folder = os.path.join(base_output_latent_path, output_latent_path)
        
        # 创建过滤后的CSV文件名
        filtered_csv_name = csv_path.replace('.csv', '_filtered.csv')
        filtered_csv_path = os.path.join(base_csv_path, filtered_csv_name)
        
        print(f"\nProcessing: {csv_path}")
        
        filtered_count = create_filtered_csv(
            csv_file=original_csv,
            output_latent_folder=output_latent_folder,
            output_csv_file=filtered_csv_path
        )
        
        print(f"Created filtered CSV: {filtered_csv_path} with {filtered_count} samples")

def main():
    parser = argparse.ArgumentParser(description="Create filtered CSV for processing")
    # parser.add_argument("--csv_file", type=str, help="Original CSV file path")
    # parser.add_argument("--output_latent_folder", type=str, help="Output latent folder path")
    # parser.add_argument("--output_csv_file", type=str, help="Output filtered CSV file path")
    parser.add_argument("--batch", action="store_true", help="Process all datasets in batch")
    
    args = parser.parse_args()
    create_all_filtered_csvs()

    # if args.batch:
    #     # 批量处理所有数据集
    #     create_all_filtered_csvs()
    # else:
    #     # 单个处理
    #     if not all([args.csv_file, args.output_latent_folder, args.output_csv_file]):
    #         print("Error: For single processing, --csv_file, --output_latent_folder, and --output_csv_file are required")
    #         return
            
    #     filtered_count = create_filtered_csv(
    #         csv_file=args.csv_file,
    #         output_latent_folder=args.output_latent_folder,
    #         output_csv_file=args.output_csv_file
    #     )
        
    #     if filtered_count == 0:
    #         print("No samples need processing!")
    #     else:
    #         print(f"Ready to process {filtered_count} samples")

if __name__ == "__main__":
    main()