221 lines
7.6 KiB
Python
221 lines
7.6 KiB
Python
"""M4 LLM 投资事件抽取批处理。
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输入: data/deduped/{YYYYMMDD}/uniques/*.json (M3 唯一文章产物)
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或者 data/processed/{source}/{YYYYMMDD}/*.json (M2 直出,跳过去重时使用)
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输出: data/events/{YYYYMMDD}/{url_hash}.json (ExtractedEvent)
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data/events/{YYYYMMDD}/index.jsonl (扁平摘要)
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data/events/{YYYYMMDD}/failed.jsonl (失败列表)
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用法:
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uv run python -m scripts.run_event_extraction
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uv run python -m scripts.run_event_extraction --date 20260616
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uv run python -m scripts.run_event_extraction --provider qwen --model qwen-plus
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uv run python -m scripts.run_event_extraction --concurrency 5 --limit 10
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uv run python -m scripts.run_event_extraction --input-root data/processed --no-deduped
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"""
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from __future__ import annotations
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import argparse
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import asyncio
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import json
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import sys
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import time
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from collections import Counter
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from datetime import date
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from pathlib import Path
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from dotenv import load_dotenv
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from loguru import logger
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from pydantic import ValidationError
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from extractor import Article
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from llm import (
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ExtractedEvent,
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LLMCallError,
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PromptTemplate,
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extract_event_async,
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load_llm_config,
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make_async_client,
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)
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def _setup_logger(level: str) -> None:
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logger.remove()
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logger.add(
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sys.stderr,
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level=level,
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format="{time:YYYY-MM-DD HH:mm:ss} | {level} | {name} | {message}",
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)
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log_path = Path("logs") / "llm.log"
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log_path.parent.mkdir(parents=True, exist_ok=True)
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logger.add(log_path, level="DEBUG", rotation="10 MB", retention=5, encoding="utf-8")
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def _collect_inputs(
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input_root: Path,
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day: str,
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use_deduped: bool,
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source_filter: str | None,
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) -> list[Path]:
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"""根据是否走 dedup 选择输入文件清单。"""
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if use_deduped:
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# data/deduped/{day}/uniques/*.json
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d = input_root / day / "uniques"
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if not d.is_dir():
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return []
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return sorted(d.glob("*.json"))
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# data/processed/{source}/{day}/*.json
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files: list[Path] = []
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if source_filter:
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srcs = [source_filter]
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else:
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srcs = sorted(p.name for p in input_root.iterdir() if p.is_dir())
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for src in srcs:
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d = input_root / src / day
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if d.is_dir():
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files.extend(sorted(d.glob("*.json")))
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return files
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def _load_article(p: Path) -> Article | None:
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try:
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return Article.model_validate(json.loads(p.read_text(encoding="utf-8")))
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except (json.JSONDecodeError, ValidationError) as e:
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logger.warning("跳过无法解析的 article 文件 {}: {}", p, e)
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return None
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async def _run(args: argparse.Namespace) -> int:
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load_dotenv() # 读 .env 到 os.environ
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config = load_llm_config(provider=args.provider, model=args.model)
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logger.info(
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"LLM provider={} model={} base_url={}",
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config.provider, config.model, config.base_url,
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)
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input_root = Path(args.input_root)
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use_deduped = not args.no_deduped
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files = _collect_inputs(input_root, args.date, use_deduped, args.source)
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if args.limit:
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files = files[: args.limit]
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if not files:
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logger.error(
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"{} 下未发现 {} 的文章(use_deduped={})",
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input_root, args.date, use_deduped,
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)
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return 2
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logger.info("待处理文章数: {}", len(files))
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out_dir = Path(args.out_root) / args.date
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out_dir.mkdir(parents=True, exist_ok=True)
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index_path = out_dir / "index.jsonl"
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failed_path = out_dir / "failed.jsonl"
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# 重跑时清掉旧的 jsonl,避免重复追加
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for p in (index_path, failed_path):
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if p.exists():
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p.unlink()
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template = PromptTemplate(args.prompt)
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semaphore = asyncio.Semaphore(args.concurrency)
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succ_cnt = 0
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fail_cnt = 0
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sentiment_cnt: Counter = Counter()
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layer_attempts: Counter = Counter()
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started = time.time()
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async with make_async_client(config) as client:
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async def _process(fp: Path) -> tuple[Path, ExtractedEvent | None, str | None]:
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article = _load_article(fp)
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if article is None:
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return fp, None, "无法解析输入"
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try:
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event = await extract_event_async(
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client=client,
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config=config,
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article=article,
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template=template,
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max_attempts=args.max_attempts,
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semaphore=semaphore,
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)
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return fp, event, None
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except LLMCallError as e:
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return fp, None, str(e)
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tasks = [_process(fp) for fp in files]
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for coro in asyncio.as_completed(tasks):
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fp, event, err = await coro
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if event is None:
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fail_cnt += 1
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with failed_path.open("a", encoding="utf-8") as f:
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f.write(
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json.dumps(
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{"file": str(fp), "error": err},
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ensure_ascii=False,
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)
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+ "\n"
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)
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continue
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succ_cnt += 1
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sentiment_cnt[event.event.sentiment.value] += 1
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layer_attempts[event.attempts] += 1
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event_path = out_dir / f"{event.url_hash}.json"
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event_path.write_text(event.model_dump_json(indent=2), encoding="utf-8")
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meta = event.model_dump(mode="json")
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# 扁平 index 不含完整 content,但保留 event 字段
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with index_path.open("a", encoding="utf-8") as f:
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f.write(json.dumps(meta, ensure_ascii=False) + "\n")
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logger.info(event.short_summary())
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elapsed = time.time() - started
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total = succ_cnt + fail_cnt
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rate = succ_cnt / max(total, 1)
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logger.info(
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"完成: 成功 {}/{} 成功率 {:.1%} 用时 {:.1f}s 情绪={} 重试分布={}",
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succ_cnt, total, rate, elapsed, dict(sentiment_cnt), dict(layer_attempts),
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)
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# 验收门槛: ≥ 95% JSON 成功率
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return 0 if rate >= 0.95 or total == 0 else 1
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def main() -> int:
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parser = argparse.ArgumentParser(description="A 股 LLM 投资事件抽取 (M4)")
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parser.add_argument(
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"--input-root", default="data/deduped",
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help="输入根目录(默认 dedup 输出);配合 --no-deduped 时改为 data/processed",
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)
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parser.add_argument("--no-deduped", action="store_true",
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help="跳过 M3,直接读 M2 处理产物")
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parser.add_argument("--source", default=None, help="--no-deduped 时按源过滤")
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parser.add_argument("--out-root", default="data/events")
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parser.add_argument(
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"--date", default=date.today().strftime("%Y%m%d"),
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help="日期 YYYYMMDD,默认今日",
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)
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parser.add_argument("--provider", default=None,
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help="LLM provider: deepseek / qwen,默认读 .env")
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parser.add_argument("--model", default=None, help="模型名,默认 provider 默认值")
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parser.add_argument("--concurrency", type=int, default=3,
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help="LLM 异步并发上限")
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parser.add_argument("--max-attempts", type=int, default=3,
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help="单篇文章最大重试次数")
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parser.add_argument("--limit", type=int, default=0,
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help="最多处理 N 篇,0=不限制(用于联调)")
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parser.add_argument("--prompt", default="prompts/event_extraction.md",
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help="Prompt 模板路径")
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parser.add_argument("--log-level", default="INFO")
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args = parser.parse_args()
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_setup_logger(args.log_level)
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return asyncio.run(_run(args))
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if __name__ == "__main__":
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raise SystemExit(main())
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