Datasets:

zzhowe1207
/

tet-rl

import os
import json
import base64
from io import BytesIO
from openai import OpenAI
from typing import List, Dict
from datasets import load_from_disk
from PIL import Image
import random
from tqdm import tqdm
from concurrent.futures import ThreadPoolExecutor, as_completed
import threading
from filelock import FileLock
import time

# API配置
api_key = "sk-lGBrn5aWRZBPIEtNiUpIblgqBRdLfvephIJ71LBZWQEIp3kc"
base_url = "https://openai.app.msh.team/v1"

client = OpenAI(
    api_key=api_key, 
    base_url=base_url
)

def encode_image_to_base64(image: Image.Image) -> str:
    """
    将PIL Image编码为base64字符串
    
    Args:
        image: PIL Image对象
    
    Returns:
        base64编码的图片字符串
    """
    buffer = BytesIO()
    # 转换为RGB模式（如果需要）
    if image.mode != 'RGB':
        image = image.convert('RGB')
    image.save(buffer, format='JPEG', quality=95)
    image_bytes = buffer.getvalue()
    return base64.b64encode(image_bytes).decode('utf-8')

def save_image_file(image: Image.Image, sample_index: int, output_dir: str = "images/coldstart") -> str:
    """
    保存图片到文件
    
    Args:
        image: PIL Image对象
        sample_index: 样本索引
        output_dir: 输出目录
    
    Returns:
        保存的文件路径
    """
    try:
        # 创建输出目录
        os.makedirs(output_dir, exist_ok=True)
        
        # 生成文件名
        filename = f"sample_{sample_index}.jpg"
        filepath = os.path.join(output_dir, filename)
        
        # 转换为RGB模式并保存
        if image.mode != 'RGB':
            image = image.convert('RGB')
        image.save(filepath, format='JPEG', quality=95)
        
        return filepath
        
    except Exception as e:
        print(f"保存图片时出错: {e}")
        return ""

def get_gemini_response(image: Image.Image, question: str, sample_index: int) -> Dict[str, str]:
    """
    使用Gemini模型对OCR-VQA问题进行回答
    
    Args:
        image: PIL Image对象
        question: 问题文本
        sample_index: 样本索引（用于保存图片）
    
    Returns:
        包含对话格式数据的字典
    """
    try:
        # 保存图片文件
        image_path = save_image_file(image, sample_index)
        
        # 编码图片用于API调用
        base64_image = encode_image_to_base64(image)
        
        # 纯英文prompt，让模型自然推理
        prompt_text = f"Please carefully observe the image and answer the question: {question}. Put your final answer in \\boxed{{}}."

        # 构建消息
        messages = [
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": prompt_text
                    },
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:image/jpeg;base64,{base64_image}"
                        }
                    }
                ]
            }
        ]
        
        # 调用Gemini模型
        response = client.chat.completions.create(
            model="gemini-2.5-pro-preview-05-06",
            messages=messages,
            max_completion_tokens=300
        )
        
        # 提取内容和推理
        choice = response.choices[0]
        message = choice.message
        content = message.content.strip() if message.content else ""
        reasoning = getattr(message, 'reasoning', '') or ""
        
        # 合并思考过程和回答为指定格式
        full_response = ""
        if reasoning:
            full_response += f"<think>{reasoning}</think>"
        full_response += content
        
        return {
            "full_response": full_response,
            "image_path": image_path
        }
        
    except Exception as e:
        error_msg = f"ERROR: 调用Gemini模型时出错: {e}"
        print(error_msg)
        return {
            "full_response": error_msg,
            "image_path": save_image_file(image, sample_index) if image else ""
        }

def load_ocr_vqa_dataset(dataset_path: str, split: str = "validation", num_samples: int = 100):
    """
    加载OCR-VQA数据集
    
    Args:
        dataset_path: 数据集路径
        split: 数据集分割（train/validation/test）
        num_samples: 要处理的样本数量
    
    Returns:
        选定的数据样本列表
    """
    print(f"加载OCR-VQA数据集: {dataset_path}")
    
    try:
        # 加载数据集
        dataset = load_from_disk(dataset_path)
        split_data = dataset[split]
        
        print(f"数据集加载成功！")
        print(f"- {split} 集总样本数: {len(split_data):,}")
        print(f"- 将随机选择 {num_samples} 个样本进行处理")
        
        # 随机选择样本
        random.seed(42)  # 设置随机种子确保可重现
        indices = random.sample(range(len(split_data)), min(num_samples, len(split_data)))
        selected_data = split_data.select(indices)
        
        # 转换为列表格式便于处理
        samples = []
        for i, item in enumerate(selected_data):
            # 提取问题（去除<image>前缀）
            problem = item['problem']
            question = problem.replace('<image>', '').strip()
            
            samples.append({
                'index': indices[i],  # 原始索引
                'image': item['images'][0],  # PIL Image
                'question': question,
                'ground_truth': item['answer']
            })
        
        print(f"成功选择了 {len(samples)} 个样本")
        return samples
        
    except Exception as e:
        print(f"加载数据集时出错: {e}")
        return []

def append_to_jsonl_file(data: Dict, filename: str, max_retries: int = 3):
    """
    使用filelock安全地追加数据到JSONL文件（每行一个JSON对象）
    
    Args:
        data: 要追加的数据
        filename: 文件名
        max_retries: 最大重试次数
    """
    lock_file = filename + ".lock"
    
    for attempt in range(max_retries):
        try:
            # 使用FileLock进行文件锁定
            with FileLock(lock_file, timeout=10):
                # 直接追加模式写入，避免读-修改-写的竞态条件
                with open(filename, 'a', encoding='utf-8') as f:
                    json.dump(data, f, ensure_ascii=False, separators=(',', ':'))
                    f.write('\n')  # 每个JSON对象一行
                
                return True
                
        except Exception as e:
            print(f"写入文件时出错 (尝试 {attempt + 1}/{max_retries}): {e}")
            if attempt < max_retries - 1:
                time.sleep(0.1 * (attempt + 1))  # 指数退避
            continue
    
    print(f"写入文件失败，已重试 {max_retries} 次")
    return False

def convert_jsonl_to_json(jsonl_file: str, json_file: str):
    """
    将JSONL文件转换为标准JSON数组格式
    
    Args:
        jsonl_file: JSONL文件路径
        json_file: 输出JSON文件路径
    """
    try:
        data_list = []
        with open(jsonl_file, 'r', encoding='utf-8') as f:
            for line in f:
                line = line.strip()
                if line:
                    try:
                        data_list.append(json.loads(line))
                    except json.JSONDecodeError as e:
                        print(f"解析JSON行时出错: {e}, 行内容: {line[:100]}")
        
        # 写入标准JSON格式
        with open(json_file, 'w', encoding='utf-8') as f:
            json.dump(data_list, f, ensure_ascii=False, indent=2)
        
        print(f"成功转换 {len(data_list)} 条数据从 {jsonl_file} 到 {json_file}")
        return len(data_list)
        
    except Exception as e:
        print(f"转换文件时出错: {e}")
        return 0

def process_single_sample(sample: Dict, thread_id: int, output_file: str) -> Dict:
    """
    处理单个样本并直接写入文件（线程安全）
    
    Args:
        sample: 单个样本数据
        thread_id: 线程ID
        output_file: 输出文件名
    
    Returns:
        处理结果字典
    """
    try:
        # 获取Gemini回答
        gemini_result = get_gemini_response(sample['image'], sample['question'], sample['index'])
        
        # 构建对话格式数据
        conversation_data = {
            "messages": [
                {
                    "content": f"<image>{sample['question']}",
                    "role": "user"
                },
                {
                    "content": gemini_result['full_response'],
                    "role": "assistant"
                }
            ],
            "images": [
                gemini_result['image_path']
            ]
        }
        
        # 使用文件锁写入JSONL格式
        write_success = append_to_jsonl_file(conversation_data, output_file)
        
        return {
            'success': True,
            'write_success': write_success,
            'sample_index': sample['index']
        }
        
    except Exception as e:
        print(f"线程{thread_id}处理样本 {sample['index']} 时出错: {e}")
        
        # 记录错误到文件
        error_data = {
            "messages": [
                {
                    "content": f"<image>{sample['question']}",
                    "role": "user"
                },
                {
                    "content": f"ERROR: {str(e)}",
                    "role": "assistant"
                }
            ],
            "images": [
                save_image_file(sample['image'], sample['index']) if sample.get('image') else ""
            ]
        }
        
        write_success = append_to_jsonl_file(error_data, output_file)
        
        return {
            'success': False,
            'write_success': write_success,
            'sample_index': sample['index'],
            'error': str(e)
        }

def process_samples_with_gemini_multithread(samples: List[Dict], output_file: str, max_workers: int = 8):
    """
    使用多线程和Gemini模型处理样本并直接写入文件
    
    Args:
        samples: 样本列表
        output_file: 输出文件名
        max_workers: 最大线程数
    
    Returns:
        处理统计信息
    """
    success_count = 0
    error_count = 0
    write_error_count = 0
    failed_samples = []
    count_lock = threading.Lock()  # 线程锁保护计数器
    
    print(f"开始使用{max_workers}个线程处理 {len(samples)} 个样本...")
    
    # 使用JSONL格式临时文件
    jsonl_file = output_file.replace('.json', '.jsonl')
    print(f"结果将写入JSONL临时文件: {jsonl_file}")
    
    # 清空或创建JSONL文件
    with open(jsonl_file, 'w', encoding='utf-8') as f:
        pass  # 创建空文件
    
    # 使用ThreadPoolExecutor进行多线程处理
    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        # 提交所有任务
        future_to_sample = {
            executor.submit(process_single_sample, sample, i % max_workers, jsonl_file): sample 
            for i, sample in enumerate(samples)
        }
        
        # 使用tqdm显示进度
        with tqdm(total=len(samples), desc="多线程处理中") as pbar:
            # 按完成顺序获取结果
            for future in as_completed(future_to_sample):
                try:
                    result = future.result()
                    
                    # 线程安全地更新计数器
                    with count_lock:
                        if result['success']:
                            success_count += 1
                        else:
                            error_count += 1
                        
                        if not result['write_success']:
                            write_error_count += 1
                            failed_samples.append(result['sample_index'])
                            
                except Exception as e:
                    sample = future_to_sample[future]
                    print(f"获取结果时出错，样本 {sample['index']}: {e}")
                    with count_lock:
                        error_count += 1
                        failed_samples.append(sample['index'])
                finally:
                    pbar.update(1)
    
    # 转换JSONL为标准JSON格式
    print(f"\n转换JSONL为标准JSON格式...")
    actual_count = convert_jsonl_to_json(jsonl_file, output_file)
    
    # 清理临时文件
    try:
        os.remove(jsonl_file)
        print(f"已清理临时文件: {jsonl_file}")
    except Exception as e:
        print(f"清理临时文件时出错: {e}")
    
    return {
        'total_samples': len(samples),
        'success_samples': success_count,
        'error_samples': error_count,
        'write_error_count': write_error_count,
        'actual_saved_count': actual_count,
        'failed_samples': failed_samples,
        'success_rate': success_count / len(samples) if len(samples) > 0 else 0,
        'save_rate': actual_count / len(samples) if len(samples) > 0 else 0
    }

def save_stats(stats: Dict, output_file: str):
    """
    保存统计信息
    
    Args:
        stats: 统计信息字典
        output_file: 输出文件名
    """
    try:
        stats_file = output_file.replace('.json', '_stats.json')
        with open(stats_file, 'w', encoding='utf-8') as f:
            json.dump(stats, f, ensure_ascii=False, indent=2)
        
        print(f"统计信息已保存: {stats_file}")
        print(f"处理统计: {stats['success_samples']}/{stats['total_samples']} 成功，成功率: {stats['success_rate']:.2%}")
        
    except Exception as e:
        print(f"保存统计信息时出错: {e}")

def preview_results(output_file: str, num_examples: int = 3):
    """
    预览结果文件（标准JSON格式）
    
    Args:
        output_file: 结果文件名
        num_examples: 要显示的示例数量
    """
    try:
        if not os.path.exists(output_file):
            print(f"文件不存在: {output_file}")
            return
            
        with open(output_file, 'r', encoding='utf-8') as f:
            data = json.load(f)
        
        print(f"\n=== 结果预览 (前{min(num_examples, len(data))}个示例) ===")
        print(f"总共 {len(data)} 个对话")
        
        for i, conversation in enumerate(data[:num_examples]):
            print(f"\n--- 对话 {i+1} ---")
            print(f"用户问题: {conversation['messages'][0]['content']}")
            print(f"助手回答: {conversation['messages'][1]['content'][:200]}{'...' if len(conversation['messages'][1]['content']) > 200 else ''}")
            print(f"图片路径: {conversation['images'][0]}")
            
    except Exception as e:
        print(f"预览结果时出错: {e}")

def main():
    """主函数"""
    print("=== OCR-VQA + Gemini模型 Cold Start数据生成 ===")
    
    # 配置参数
    dataset_path = "/mnt/moonfs/kimiv-ksyun/xulin/datasets/OCR-VQA/ocr_vqa_clean_dataset"
    split = "validation"  # 使用验证集
    num_samples = 1000  # 处理1000个样本
    max_workers = 20  # 最大线程数
    output_file = "ocr_vqa_coldstart_data.json"
    
    print(f"配置:")
    print(f"- 数据集路径: {dataset_path}")
    print(f"- 数据集分割: {split}")
    print(f"- 处理样本数: {num_samples}")
    print(f"- 最大线程数: {max_workers}")
    print(f"- 使用模型: gemini-2.5-pro-preview-05-06")
    print(f"- 输出文件: {output_file}")
    print(f"- 图片保存目录: images/coldstart/")
    
    # 1. 加载数据集
    samples = load_ocr_vqa_dataset(dataset_path, split, num_samples)
    if not samples:
        print("❌ 数据集加载失败，程序退出")
        return
    
    # 2. 使用多线程和Gemini模型处理样本，直接写入文件
    stats = process_samples_with_gemini_multithread(samples, output_file, max_workers)
    
    # 3. 保存统计信息
    save_stats(stats, output_file)
    
    # 4. 预览结果
    preview_results(output_file)
    
    print(f"\n🎉 Cold Start数据生成完成！")
    print(f"📁 对话数据文件: {output_file}")
    print(f"📊 统计文件: {output_file.replace('.json', '_stats.json')}")
    print(f"🧠 自动提取推理过程: ✅")
    print(f"🖼️  图片保存到images/coldstart/目录: ✅")
    print(f"⚡ 多线程+filelock+JSONL追加: ✅")
    print(f"💬 对话格式数据: ✅")
    print(f"📈 实际保存: {stats['actual_saved_count']}/{stats['total_samples']} 条数据")
    print(f"\n💡 生成的数据可以直接用于多模态模型的冷启动训练!")

if __name__ == "__main__":
    main()

	import os
	import json
	import base64
	from io import BytesIO
	from openai import OpenAI
	from typing import List, Dict
	from datasets import load_from_disk
	from PIL import Image
	import random
	from tqdm import tqdm
	from concurrent.futures import ThreadPoolExecutor, as_completed
	import threading
	from filelock import FileLock
	import time

	# API配置
	api_key = "sk-lGBrn5aWRZBPIEtNiUpIblgqBRdLfvephIJ71LBZWQEIp3kc"
	base_url = "https://openai.app.msh.team/v1"

	client = OpenAI(
	api_key=api_key,
	base_url=base_url
	)

	def encode_image_to_base64(image: Image.Image) -> str:
	"""
	将PIL Image编码为base64字符串

	Args:
	image: PIL Image对象

	Returns:
	base64编码的图片字符串
	"""
	buffer = BytesIO()
	# 转换为RGB模式（如果需要）
	if image.mode != 'RGB':
	image = image.convert('RGB')
	image.save(buffer, format='JPEG', quality=95)
	image_bytes = buffer.getvalue()
	return base64.b64encode(image_bytes).decode('utf-8')

	def save_image_file(image: Image.Image, sample_index: int, output_dir: str = "images/coldstart") -> str:
	"""
	保存图片到文件

	Args:
	image: PIL Image对象
	sample_index: 样本索引
	output_dir: 输出目录

	Returns:
	保存的文件路径
	"""
	try:
	# 创建输出目录
	os.makedirs(output_dir, exist_ok=True)

	# 生成文件名
	filename = f"sample_{sample_index}.jpg"
	filepath = os.path.join(output_dir, filename)

	# 转换为RGB模式并保存
	if image.mode != 'RGB':
	image = image.convert('RGB')
	image.save(filepath, format='JPEG', quality=95)

	return filepath

	except Exception as e:
	print(f"保存图片时出错: {e}")
	return ""

	def get_gemini_response(image: Image.Image, question: str, sample_index: int) -> Dict[str, str]:
	"""
	使用Gemini模型对OCR-VQA问题进行回答

	Args:
	image: PIL Image对象
	question: 问题文本
	sample_index: 样本索引（用于保存图片）

	Returns:
	包含对话格式数据的字典
	"""
	try:
	# 保存图片文件
	image_path = save_image_file(image, sample_index)

	# 编码图片用于API调用
	base64_image = encode_image_to_base64(image)

	# 纯英文prompt，让模型自然推理
	prompt_text = f"Please carefully observe the image and answer the question: {question}. Put your final answer in \\boxed{{}}."

	# 构建消息
	messages = [
	{
	"role": "user",
	"content": [
	{
	"type": "text",
	"text": prompt_text
	},
	{
	"type": "image_url",
	"image_url": {
	"url": f"data:image/jpeg;base64,{base64_image}"
	}
	}
	]
	}
	]

	# 调用Gemini模型
	response = client.chat.completions.create(
	model="gemini-2.5-pro-preview-05-06",
	messages=messages,
	max_completion_tokens=300
	)

	# 提取内容和推理
	choice = response.choices[0]
	message = choice.message
	content = message.content.strip() if message.content else ""
	reasoning = getattr(message, 'reasoning', '') or ""

	# 合并思考过程和回答为指定格式
	full_response = ""
	if reasoning:
	full_response += f"<think>{reasoning}</think>"
	full_response += content

	return {
	"full_response": full_response,
	"image_path": image_path
	}

	except Exception as e:
	error_msg = f"ERROR: 调用Gemini模型时出错: {e}"
	print(error_msg)
	return {
	"full_response": error_msg,
	"image_path": save_image_file(image, sample_index) if image else ""
	}

	def load_ocr_vqa_dataset(dataset_path: str, split: str = "validation", num_samples: int = 100):
	"""
	加载OCR-VQA数据集

	Args:
	dataset_path: 数据集路径
	split: 数据集分割（train/validation/test）
	num_samples: 要处理的样本数量

	Returns:
	选定的数据样本列表
	"""
	print(f"加载OCR-VQA数据集: {dataset_path}")

	try:
	# 加载数据集
	dataset = load_from_disk(dataset_path)
	split_data = dataset[split]

	print(f"数据集加载成功！")
	print(f"- {split} 集总样本数: {len(split_data):,}")
	print(f"- 将随机选择 {num_samples} 个样本进行处理")

	# 随机选择样本
	random.seed(42) # 设置随机种子确保可重现
	indices = random.sample(range(len(split_data)), min(num_samples, len(split_data)))
	selected_data = split_data.select(indices)

	# 转换为列表格式便于处理
	samples = []
	for i, item in enumerate(selected_data):
	# 提取问题（去除<image>前缀）
	problem = item['problem']
	question = problem.replace('<image>', '').strip()

	samples.append({
	'index': indices[i], # 原始索引
	'image': item['images'][0], # PIL Image
	'question': question,
	'ground_truth': item['answer']
	})

	print(f"成功选择了 {len(samples)} 个样本")
	return samples

	except Exception as e:
	print(f"加载数据集时出错: {e}")
	return []

	def append_to_jsonl_file(data: Dict, filename: str, max_retries: int = 3):
	"""
	使用filelock安全地追加数据到JSONL文件（每行一个JSON对象）

	Args:
	data: 要追加的数据
	filename: 文件名
	max_retries: 最大重试次数
	"""
	lock_file = filename + ".lock"

	for attempt in range(max_retries):
	try:
	# 使用FileLock进行文件锁定
	with FileLock(lock_file, timeout=10):
	# 直接追加模式写入，避免读-修改-写的竞态条件
	with open(filename, 'a', encoding='utf-8') as f:
	json.dump(data, f, ensure_ascii=False, separators=(',', ':'))
	f.write('\n') # 每个JSON对象一行

	return True

	except Exception as e:
	print(f"写入文件时出错 (尝试 {attempt + 1}/{max_retries}): {e}")
	if attempt < max_retries - 1:
	time.sleep(0.1 * (attempt + 1)) # 指数退避
	continue

	print(f"写入文件失败，已重试 {max_retries} 次")
	return False

	def convert_jsonl_to_json(jsonl_file: str, json_file: str):
	"""
	将JSONL文件转换为标准JSON数组格式

	Args:
	jsonl_file: JSONL文件路径
	json_file: 输出JSON文件路径
	"""
	try:
	data_list = []
	with open(jsonl_file, 'r', encoding='utf-8') as f:
	for line in f:
	line = line.strip()
	if line:
	try:
	data_list.append(json.loads(line))
	except json.JSONDecodeError as e:
	print(f"解析JSON行时出错: {e}, 行内容: {line[:100]}")

	# 写入标准JSON格式
	with open(json_file, 'w', encoding='utf-8') as f:
	json.dump(data_list, f, ensure_ascii=False, indent=2)

	print(f"成功转换 {len(data_list)} 条数据从 {jsonl_file} 到 {json_file}")
	return len(data_list)

	except Exception as e:
	print(f"转换文件时出错: {e}")
	return 0

	def process_single_sample(sample: Dict, thread_id: int, output_file: str) -> Dict:
	"""
	处理单个样本并直接写入文件（线程安全）

	Args:
	sample: 单个样本数据
	thread_id: 线程ID
	output_file: 输出文件名

	Returns:
	处理结果字典
	"""
	try:
	# 获取Gemini回答
	gemini_result = get_gemini_response(sample['image'], sample['question'], sample['index'])

	# 构建对话格式数据
	conversation_data = {
	"messages": [
	{
	"content": f"<image>{sample['question']}",
	"role": "user"
	},
	{
	"content": gemini_result['full_response'],
	"role": "assistant"
	}
	],
	"images": [
	gemini_result['image_path']
	]
	}

	# 使用文件锁写入JSONL格式
	write_success = append_to_jsonl_file(conversation_data, output_file)

	return {
	'success': True,
	'write_success': write_success,
	'sample_index': sample['index']
	}

	except Exception as e:
	print(f"线程{thread_id}处理样本 {sample['index']} 时出错: {e}")

	# 记录错误到文件
	error_data = {
	"messages": [
	{
	"content": f"<image>{sample['question']}",
	"role": "user"
	},
	{
	"content": f"ERROR: {str(e)}",
	"role": "assistant"
	}
	],
	"images": [
	save_image_file(sample['image'], sample['index']) if sample.get('image') else ""
	]
	}

	write_success = append_to_jsonl_file(error_data, output_file)

	return {
	'success': False,
	'write_success': write_success,
	'sample_index': sample['index'],
	'error': str(e)
	}

	def process_samples_with_gemini_multithread(samples: List[Dict], output_file: str, max_workers: int = 8):
	"""
	使用多线程和Gemini模型处理样本并直接写入文件

	Args:
	samples: 样本列表
	output_file: 输出文件名
	max_workers: 最大线程数

	Returns:
	处理统计信息
	"""
	success_count = 0
	error_count = 0
	write_error_count = 0
	failed_samples = []
	count_lock = threading.Lock() # 线程锁保护计数器

	print(f"开始使用{max_workers}个线程处理 {len(samples)} 个样本...")

	# 使用JSONL格式临时文件
	jsonl_file = output_file.replace('.json', '.jsonl')
	print(f"结果将写入JSONL临时文件: {jsonl_file}")

	# 清空或创建JSONL文件
	with open(jsonl_file, 'w', encoding='utf-8') as f:
	pass # 创建空文件

	# 使用ThreadPoolExecutor进行多线程处理
	with ThreadPoolExecutor(max_workers=max_workers) as executor:
	# 提交所有任务
	future_to_sample = {
	executor.submit(process_single_sample, sample, i % max_workers, jsonl_file): sample
	for i, sample in enumerate(samples)
	}

	# 使用tqdm显示进度
	with tqdm(total=len(samples), desc="多线程处理中") as pbar:
	# 按完成顺序获取结果
	for future in as_completed(future_to_sample):
	try:
	result = future.result()

	# 线程安全地更新计数器
	with count_lock:
	if result['success']:
	success_count += 1
	else:
	error_count += 1

	if not result['write_success']:
	write_error_count += 1
	failed_samples.append(result['sample_index'])

	except Exception as e:
	sample = future_to_sample[future]
	print(f"获取结果时出错，样本 {sample['index']}: {e}")
	with count_lock:
	error_count += 1
	failed_samples.append(sample['index'])
	finally:
	pbar.update(1)

	# 转换JSONL为标准JSON格式
	print(f"\n转换JSONL为标准JSON格式...")
	actual_count = convert_jsonl_to_json(jsonl_file, output_file)

	# 清理临时文件
	try:
	os.remove(jsonl_file)
	print(f"已清理临时文件: {jsonl_file}")
	except Exception as e:
	print(f"清理临时文件时出错: {e}")

	return {
	'total_samples': len(samples),
	'success_samples': success_count,
	'error_samples': error_count,
	'write_error_count': write_error_count,
	'actual_saved_count': actual_count,
	'failed_samples': failed_samples,
	'success_rate': success_count / len(samples) if len(samples) > 0 else 0,
	'save_rate': actual_count / len(samples) if len(samples) > 0 else 0
	}

	def save_stats(stats: Dict, output_file: str):
	"""
	保存统计信息

	Args:
	stats: 统计信息字典
	output_file: 输出文件名
	"""
	try:
	stats_file = output_file.replace('.json', '_stats.json')
	with open(stats_file, 'w', encoding='utf-8') as f:
	json.dump(stats, f, ensure_ascii=False, indent=2)

	print(f"统计信息已保存: {stats_file}")
	print(f"处理统计: {stats['success_samples']}/{stats['total_samples']} 成功，成功率: {stats['success_rate']:.2%}")

	except Exception as e:
	print(f"保存统计信息时出错: {e}")

	def preview_results(output_file: str, num_examples: int = 3):
	"""
	预览结果文件（标准JSON格式）

	Args:
	output_file: 结果文件名
	num_examples: 要显示的示例数量
	"""
	try:
	if not os.path.exists(output_file):
	print(f"文件不存在: {output_file}")
	return

	with open(output_file, 'r', encoding='utf-8') as f:
	data = json.load(f)

	print(f"\n=== 结果预览 (前{min(num_examples, len(data))}个示例) ===")
	print(f"总共 {len(data)} 个对话")

	for i, conversation in enumerate(data[:num_examples]):
	print(f"\n--- 对话 {i+1} ---")
	print(f"用户问题: {conversation['messages'][0]['content']}")
	print(f"助手回答: {conversation['messages'][1]['content'][:200]}{'...' if len(conversation['messages'][1]['content']) > 200 else ''}")
	print(f"图片路径: {conversation['images'][0]}")

	except Exception as e:
	print(f"预览结果时出错: {e}")

	def main():
	"""主函数"""
	print("=== OCR-VQA + Gemini模型 Cold Start数据生成 ===")

	# 配置参数
	dataset_path = "/mnt/moonfs/kimiv-ksyun/xulin/datasets/OCR-VQA/ocr_vqa_clean_dataset"
	split = "validation" # 使用验证集
	num_samples = 1000 # 处理1000个样本
	max_workers = 20 # 最大线程数
	output_file = "ocr_vqa_coldstart_data.json"

	print(f"配置:")
	print(f"- 数据集路径: {dataset_path}")
	print(f"- 数据集分割: {split}")
	print(f"- 处理样本数: {num_samples}")
	print(f"- 最大线程数: {max_workers}")
	print(f"- 使用模型: gemini-2.5-pro-preview-05-06")
	print(f"- 输出文件: {output_file}")
	print(f"- 图片保存目录: images/coldstart/")

	# 1. 加载数据集
	samples = load_ocr_vqa_dataset(dataset_path, split, num_samples)
	if not samples:
	print("❌ 数据集加载失败，程序退出")
	return

	# 2. 使用多线程和Gemini模型处理样本，直接写入文件
	stats = process_samples_with_gemini_multithread(samples, output_file, max_workers)

	# 3. 保存统计信息
	save_stats(stats, output_file)

	# 4. 预览结果
	preview_results(output_file)

	print(f"\n🎉 Cold Start数据生成完成！")
	print(f"📁 对话数据文件: {output_file}")
	print(f"📊 统计文件: {output_file.replace('.json', '_stats.json')}")
	print(f"🧠 自动提取推理过程: ✅")
	print(f"🖼️ 图片保存到images/coldstart/目录: ✅")
	print(f"⚡ 多线程+filelock+JSONL追加: ✅")
	print(f"💬 对话格式数据: ✅")
	print(f"📈 实际保存: {stats['actual_saved_count']}/{stats['total_samples']} 条数据")
	print(f"\n💡 生成的数据可以直接用于多模态模型的冷启动训练!")

	if __name__ == "__main__":
	main()