252 lines
7.3 KiB
Python
252 lines
7.3 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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SRS 解析工具 - 主程序入口
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LLM 增强版 - 默认阿里云千问大模型
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"""
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import argparse
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import os
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import sys
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import logging
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from pathlib import Path
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# 添加当前目录到Python路径
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from src.utils import load_config, setup_logging, validate_file_path, ensure_directory_exists, get_env_or_config
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from src.document_parser import create_parser
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from src.requirement_extractor import RequirementExtractor
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from src.json_generator import JSONGenerator
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logger = logging.getLogger(__name__)
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def create_llm(config: dict):
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"""
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创建LLM实例
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Args:
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config: 配置字典
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Returns:
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LLM实例或None
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"""
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llm_config = config.get('llm', {})
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# 检查是否启用LLM
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if not llm_config.get('enabled', True):
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logger.info("LLM已禁用，使用纯规则提取模式")
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return None
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provider = llm_config.get('provider', 'qwen')
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# 获取API密钥（优先使用环境变量）
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api_key = get_env_or_config('DASHSCOPE_API_KEY', llm_config.get('api_key'))
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if not api_key:
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logger.warning("未配置API密钥，请使用纯规则提取模式")
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logger.warning("请设置环境变量 DASHSCOPE_API_KEY 或在 config.yaml 中配置 llm.api_key")
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return None
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try:
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from src.llm_interface import QwenLLM
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model = llm_config.get('model', 'qwen-plus')
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temperature = llm_config.get('temperature', 0.3)
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max_tokens = llm_config.get('max_tokens', 1024)
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llm = QwenLLM(
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api_key=api_key,
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model=model,
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temperature=temperature,
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max_tokens=max_tokens
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)
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logger.info(f"成功创建LLM实例: {provider} ({model})")
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return llm
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except ImportError as e:
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logger.warning(f"无法导入LLM模块: {e}")
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logger.warning("请运行: pip install dashscope")
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return None
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except Exception as e:
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logger.warning(f"创建LLM实例失败: {e}")
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return None
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def main():
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"""主程序入口"""
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# 解析命令行参数
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parser = argparse.ArgumentParser(
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description='SRS需求文档解析工具',
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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示例用法：
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python main.py --input sample.pdf --output output.json
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python main.py -i requirements.docx -o output.json --verbose
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python main.py -i DC-SRS.pdf -o output.json --no-llm # 禁用LLM
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"""
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)
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parser.add_argument(
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'--input', '-i',
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type=str,
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required=True,
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help='输入的SRS文档路径（支持.docx和.pdf）'
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)
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parser.add_argument(
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'--output', '-o',
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type=str,
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default='output.json',
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help='输出JSON文件路径（默认：output.json）'
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)
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parser.add_argument(
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'--config', '-c',
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type=str,
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default=None,
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help='配置文件路径（默认：./config.yaml）'
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)
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parser.add_argument(
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'--verbose', '-v',
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action='store_true',
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help='输出详细日志'
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)
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parser.add_argument(
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'--no-llm',
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action='store_true',
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help='禁用LLM，使用纯规则提取'
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)
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# 解析命令行参数
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args = parser.parse_args()
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# 加载配置
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config = load_config(args.config)
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# 命令行参数覆盖配置
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if args.no_llm:
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config.setdefault('llm', {})['enabled'] = False
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# 设置日志
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if args.verbose:
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config.setdefault('logging', {})['level'] = 'DEBUG'
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setup_logging(config)
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logger.info("=" * 60)
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logger.info("SRS需求文档解析工具启动（LLM增强版）")
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logger.info("=" * 60)
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try:
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# 验证输入文件
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if not validate_file_path(args.input, ['.pdf', '.docx']):
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logger.error(f"输入文件验证失败: {args.input}")
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return False
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logger.info(f"输入文件: {args.input}")
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# 创建输出目录
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output_dir = os.path.dirname(args.output) or '.'
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if output_dir != '.' and not ensure_directory_exists(output_dir):
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logger.error(f"无法创建输出目录: {output_dir}")
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return False
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logger.info(f"输出文件: {args.output}")
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# 创建LLM实例
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llm = create_llm(config)
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if llm:
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logger.info("LLM增强模式已启用")
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else:
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logger.info("使用纯规则提取模式")
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# 步骤1：解析文档
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logger.info("\n" + "=" * 60)
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logger.info("步骤1：解析文档")
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logger.info("=" * 60)
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doc_parser = create_parser(args.input)
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if llm:
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doc_parser.set_llm(llm)
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sections = doc_parser.parse()
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document_title = doc_parser.get_document_title()
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logger.info(f"成功解析文档，提取{len(sections)}个顶级章节")
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# 打印章节结构
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def print_sections(sections, indent=0):
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for section in sections:
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logger.info(" " * indent + f"- {section.number} {section.title}")
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if section.children:
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print_sections(section.children, indent + 1)
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if args.verbose:
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logger.info("章节结构：")
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print_sections(sections)
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# 步骤2：提取需求
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logger.info("\n" + "=" * 60)
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if llm:
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logger.info("步骤2：提取需求（LLM增强模式）")
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else:
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logger.info("步骤2：提取需求（规则匹配模式）")
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logger.info("=" * 60)
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extractor = RequirementExtractor(config, llm=llm)
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requirements = extractor.extract_from_sections(sections)
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# 统计需求信息
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stats = extractor.get_statistics()
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logger.info(f"\n需求统计：")
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for req_type, count in stats['by_type'].items():
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logger.info(f" {req_type}: {count}项")
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logger.info(f" 总计: {stats['total']}项")
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# 步骤3：生成JSON
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logger.info("\n" + "=" * 60)
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logger.info("步骤3：生成JSON")
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logger.info("=" * 60)
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generator = JSONGenerator(config)
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json_output = generator.generate(
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sections,
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requirements,
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document_title
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)
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logger.info(f"JSON结构生成完成")
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# 步骤4：保存文件
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logger.info("\n" + "=" * 60)
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logger.info("步骤4：保存结果")
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logger.info("=" * 60)
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generator.save_to_file(json_output, args.output)
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logger.info(f"成功保存JSON文件到: {args.output}")
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# 打印输出文件大小
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if os.path.exists(args.output):
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file_size = os.path.getsize(args.output)
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logger.info(f"文件大小: {file_size} 字节")
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logger.info("\n" + "=" * 60)
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logger.info("SRS需求文档解析完成！")
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logger.info("=" * 60)
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return True
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except Exception as e:
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logger.error(f"处理过程中出现错误: {e}", exc_info=True)
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return False
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if __name__ == '__main__':
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success = main()
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sys.exit(0 if success else 1)
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