import os
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
from tqdm import tqdm
import subprocess
import gc  # 引入垃圾回收机制

def get_inode_count(path):
    """获取指定路径下的 inode 使用数 (仅限 Linux/macOS)"""
    try:
        count = subprocess.check_output(['find', path, '-printf', 'i'], stderr=subprocess.DEVNULL)
        return len(count)
    except:
        return "无法获取 (可能非类Unix系统)"

def convert_trajectory_dataset(src_root, output_parquet, max_samples=None, chunk_size=500):
    initial_inodes = get_inode_count(src_root)
    print(f"[*] 初始路径: {src_root}")
    print(f"[*] 原始目录估算占用 Inodes: {initial_inodes}")

    # 获取所有轨迹文件夹
    traj_folders = [f for f in os.listdir(src_root) if os.path.isdir(os.path.join(src_root, f))]
    
    # 对文件夹进行排序
    traj_folders.sort(key=lambda x: int(x) if x.isdigit() else x)
    
    # 切片逻辑
    if max_samples is not None:
        traj_folders = traj_folders[:max_samples]
        print(f"[*] ⚠️ 测试模式开启: 仅处理前 {max_samples} 个轨迹文件夹")
    else:
        print(f"[*] 🚀 全量模式开启: 准备处理全部 {len(traj_folders)} 个轨迹...")

    # ================= 修改核心区域 =================
    writer = None
    chunk_data = []

    print(f"[*] 采用流式写入，每 {chunk_size} 个轨迹刷新一次内存...")
    
    for i, traj_id in enumerate(tqdm(traj_folders)):
        traj_path = os.path.join(src_root, traj_id)
        img_files = sorted([f for f in os.listdir(traj_path) if f.endswith('.png')])
        
        images_binary = []
        instructions = []
        
        for img_name in img_files:
            img_path = os.path.join(traj_path, img_name)
            with open(img_path, 'rb') as f:
                images_binary.append(f.read())
            
            instruction = img_name.replace('step_', '').replace('.png', '')
            instructions.append(instruction)
        
        chunk_data.append({
            'trajectory_id': traj_id,
            'steps': instructions,
            'images': images_binary
        })

        # 当达到 chunk_size 或者遍历到最后一个文件夹时，触发写入
        is_last_item = (i + 1) == len(traj_folders)
        if (i + 1) % chunk_size == 0 or is_last_item:
            # 1. 转换为 DataFrame 和 PyArrow Table
            df_chunk = pd.DataFrame(chunk_data)
            table = pa.Table.from_pandas(df_chunk)

            # 2. 如果是第一批数据，初始化 ParquetWriter (需要依赖第一批数据的 schema)
            if writer is None:
                writer = pq.ParquetWriter(output_parquet, table.schema, compression='snappy')

            # 3. 将这一块数据追加写入 Parquet 文件
            writer.write_table(table)

            # 4. 彻底清空本轮数据，释放内存 (核心操作)
            del chunk_data
            del df_chunk
            del table
            chunk_data = []
            gc.collect()  # 强制进行垃圾回收
            
    # 循环结束后，关闭文件写入器
    if writer is not None:
        writer.close()
    # ================================================
    
    print(f"\n[+] 转换完成! 文件已保存至: {output_parquet}")
    print(f"[*] 当前 Parquet 文件占用 Inode: 1")
    
    # 估算节省的 Inode
    if isinstance(initial_inodes, int) and max_samples is None:
        print(f"[!] 全量转换理论节省 Inode 数量: {initial_inodes - 1}")
    elif max_samples is not None:
        estimated_saved = sum([len(os.listdir(os.path.join(src_root, d))) for d in traj_folders]) + len(traj_folders)
        print(f"[!] 本次测试批次理论节省 Inode 数量: {estimated_saved} (包括文件夹与文件)")

if __name__ == "__main__":
    source_directory = "/home/catlab/Project/JanusVLN-main/data/trajectory_data/R2R-CE-640x480/train" 
    
    # 将输出文件放到和 train 平级的目录
    output_dir = os.path.dirname(source_directory)
    
    # ---------------------------------------------------------
    # 【配置区】
    # ---------------------------------------------------------
    TEST_BATCH_SIZE = None   # None 表示跑全量。你可以先设为 200 测试一下内存占用
    CHUNK_SIZE = 500         # 【新增】每次读入内存的轨迹数量。64GB 内存设为 500 甚至 1000 都毫无压力
    
    if TEST_BATCH_SIZE is not None:
        output_filename = f"r2r_train_test_{TEST_BATCH_SIZE}.parquet"
    else:
        output_filename = "r2r_train_full.parquet"
        
    output_filepath = os.path.join(output_dir, output_filename)
        
    if os.path.exists(source_directory):
        print(f"[*] 计划将文件输出至: {output_filepath}")
        convert_trajectory_dataset(
            source_directory, 
            output_filepath, 
            max_samples=TEST_BATCH_SIZE, 
            chunk_size=CHUNK_SIZE
        )
    else:
        print(f"错误: 找不到源路径 {source_directory}，请检查当前工作目录。")