script/data_turns_tokens.py

import json
import pandas as pd
import numpy as np
from transformers import AutoTokenizer
from tqdm import tqdm

DATA_PATH = "/home/DataProcess/data/train0_train1_merged.json"
MODEL_PATH = "/home/DataProcess/model/Qwen3-4B"

def main():
    tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)
    with open(DATA_PATH, 'r', encoding='utf-8') as f:
        data = json.load(f)

    stats_list = []

    for i, item in tqdm(enumerate(data), total=len(data), desc="Processing"):
        history = item.get("messages", [])
        response_list = item.get("chosen", [])
        if not response_list:
            response_list = item.get("rejected", [])
        full_conversation = history + response_list
        
        # num_msgs: 列表里的字典总数 (例如: System + User + Assistant + User + Assistant = 5)
        num_msgs = len(full_conversation)
        # turns: 估算的对话轮数 (通常一问一答算一轮，或者和前一条算一轮)
        turns = (num_msgs + 1) // 2 

        # tokenize=False 获取拼接后的字符串
        text = tokenizer.apply_chat_template(full_conversation, tokenize=False, add_generation_prompt=False)
        # 编码计算 token id 数量
        token_ids = tokenizer.encode(text, add_special_tokens=False)
        num_tokens = len(token_ids)

        stats_list.append({
            "index": i,
            "num_messages": num_msgs, # 消息总条数
            "turns": turns,           # 对话轮数
            "tokens": num_tokens      # Token 数量
        })
    df = pd.DataFrame(stats_list)

    print(f"数据总量: {len(df)} 条")
    
    print("\n--- Tokens (长度) 统计 ---")
    print(f"平均 Tokens: {df['tokens'].mean():.2f}")
    print(f"最大 Tokens: {df['tokens'].max()}")
    print(f"最小 Tokens: {df['tokens'].min()}")
    print(f"Token 中位数: {df['tokens'].median()}")
    print(f"Token 95%分位: {df['tokens'].quantile(0.95):.2f}")

    print("\n--- Turns (消息数) 统计 ---")
    print(f"平均消息数 (Messages): {df['num_messages'].mean():.2f}")
    print(f"最大消息数: {df['num_messages'].max()}")
    print(f"平均轮数 (Turns): {df['turns'].mean():.2f}")

    # Token 分布概览
    print("\n--- Token 长度分布 (区间占比) ---")
    token_bins = [0, 512, 1024, 2048, 4096, 8192, 100000]
    token_labels = ['0-512', '512-1024', '1024-2048', '2048-4096', '4096-8192', '8192+']
    df['token_group'] = pd.cut(df['tokens'], bins=token_bins, labels=token_labels)
    print(df['token_group'].value_counts(normalize=True).sort_index() * 100)

    # Turns 分布概览 (新增部分)
    print("\n--- Turns 对话轮数分布 (区间占比) ---")
    # 细致划分：1轮, 2轮, 3轮, 4轮, 5轮, 6-10轮, 11-20轮, 20轮以上
    turn_bins = [0, 1, 2, 3, 4, 5, 10, 20, 1000]
    turn_labels = ['1轮 (Single)', '2轮', '3轮', '4轮', '5轮', '6-10轮', '11-20轮', '20轮+']
    df['turn_group'] = pd.cut(df['turns'], bins=turn_bins, labels=turn_labels)
    print(df['turn_group'].value_counts(normalize=True).sort_index() * 100)

    # 检查是否有超长数据 (超过2048)
    over_len = df[df['tokens'] > 2048]
    if not over_len.empty:
        print(f"\n 注意: 有 {len(over_len)} 条数据 Token 数超过 2048")
    else:
        print("\n 所有数据 Token 数均在 2048 以内")

if __name__ == "__main__":
    main()