"""PR-B B-1 레거시 문서 tier triage 백필 도구.

사용법 (GPU 서버 fastapi 컨테이너 안에서 실행):
  docker exec hyungi_document_server-fastapi-1 \\
      python /app/scripts/backfill_tier.py --domain safety --limit 50 --dry-run
  docker exec hyungi_document_server-fastapi-1 \\
      python /app/scripts/backfill_tier.py --domain safety --limit 50 --apply

대상 선정 옵션 (--domain):
  safety      : ai_domain LIKE 'Industrial_Safety%%'
  law         : source_channel='law_monitor'
  manual      : source_channel='manual'
  news        : source_channel='news' (대부분 짧아 deep escalate 거의 없음)
  drive_sync  : source_channel='drive_sync'
  memo        : source_channel='memo'

선정 규칙:
  ai_analysis_tier IS NULL   — 아직 tier triage 안 탄 문서
  extracted_text IS NOT NULL — 본문이 있어야 classify 가능
  deleted_at IS NULL
  ORDER BY created_at DESC   — 최근 문서 우선 (사용자 관심도 가정)

실행:
  - --dry-run: SELECT 카운트 + 샘플 5건 출력, 큐 변경 없음
  - --apply:  classify stage 에 pending 으로 INSERT (ON CONFLICT DO NOTHING)
             기존 pending/processing 행이 있으면 skip

MLX 부하 고려:
  legacy classify (primary 26B) + tier triage (4B) + deep_summary (26B escalate 시)
  ~60~120초/건. 50건 ≈ 1시간. 100건+ 백필은 여러 세션 또는 야간 분할 권장.
"""

from __future__ import annotations

import argparse
import asyncio
import os
import sys
from pathlib import Path

sys.path.insert(0, str(Path(__file__).resolve().parent.parent / "app"))

from sqlalchemy import text
from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine


DOMAIN_FILTERS: dict[str, str] = {
    "safety":     "ai_domain LIKE 'Industrial_Safety%'",
    "law":        "source_channel = 'law_monitor'",
    "manual":     "source_channel = 'manual'",
    "news":       "source_channel = 'news'",
    "drive_sync": "source_channel = 'drive_sync'",
    "memo":       "source_channel = 'memo'",
}


COUNT_SQL = """
SELECT COUNT(*)
FROM documents
WHERE deleted_at IS NULL
  AND extracted_text IS NOT NULL
  AND LENGTH(extracted_text) > 0
  AND ai_analysis_tier IS NULL
  AND {filter}
"""

SAMPLE_SQL = """
SELECT id, LEFT(title, 60) AS title, ai_domain, source_channel,
       LENGTH(extracted_text) AS tlen, created_at::date AS created
FROM documents
WHERE deleted_at IS NULL
  AND extracted_text IS NOT NULL
  AND LENGTH(extracted_text) > 0
  AND ai_analysis_tier IS NULL
  AND {filter}
ORDER BY created_at DESC
LIMIT :n
"""

APPLY_SQL = """
INSERT INTO processing_queue (document_id, stage, status, attempts, max_attempts)
SELECT id, 'classify', 'pending', 0, 3
FROM documents
WHERE deleted_at IS NULL
  AND extracted_text IS NOT NULL
  AND LENGTH(extracted_text) > 0
  AND ai_analysis_tier IS NULL
  AND {filter}
ORDER BY created_at DESC
LIMIT :n
ON CONFLICT DO NOTHING
RETURNING document_id
"""


async def run(domain: str, limit: int, apply: bool) -> int:
    if domain not in DOMAIN_FILTERS:
        print(f"[error] --domain 은 {list(DOMAIN_FILTERS)} 중 하나여야 함. got={domain!r}")
        return 1

    filter_clause = DOMAIN_FILTERS[domain]
    database_url = os.getenv(
        "DATABASE_URL",
        "postgresql+asyncpg://pkm:pkm@postgres:5432/pkm",
    )

    engine = create_async_engine(database_url)
    session_factory = async_sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)

    async with session_factory() as session:
        total = (await session.execute(
            text(COUNT_SQL.format(filter=filter_clause))
        )).scalar()
        print(f"=== {domain} 백필 후보 ===")
        print(f"  총 미처리 문서: {total} 건")
        print(f"  이번 실행 대상 limit: {limit} 건")
        print()

        if total == 0:
            print("[done] 백필할 문서 없음.")
            await engine.dispose()
            return 0

        print("=== 샘플 (상위 5건) ===")
        rows = (await session.execute(
            text(SAMPLE_SQL.format(filter=filter_clause)),
            {"n": 5},
        )).all()
        for r in rows:
            print(f"  id={r.id}  ({r.created})  [{r.source_channel} / {r.ai_domain or '-'}]  "
                  f"tlen={r.tlen}  title={r.title}")
        print()

        if not apply:
            print(f"[dry-run] --apply 로 실제 enqueue 수행. 예상 {min(limit, total)} 건.")
            await engine.dispose()
            return 0

        print(f"[apply] classify 큐에 {limit} 건 enqueue...")
        result = await session.execute(
            text(APPLY_SQL.format(filter=filter_clause)),
            {"n": limit},
        )
        enqueued_ids = [r.document_id for r in result.all()]
        await session.commit()
        print(f"  enqueue 성공: {len(enqueued_ids)} 건")
        if enqueued_ids:
            sample = enqueued_ids[:10]
            print(f"  첫 10건 id: {sample}")
        print()
        print("경과 관찰 쿼리:")
        print("  docker exec hyungi_document_server-postgres-1 psql -U pkm -d pkm -c \\")
        print("    \"SELECT stage, status, COUNT(*) FROM processing_queue \\")
        print("      WHERE created_at > NOW() - INTERVAL '3 hours' GROUP BY 1,2 ORDER BY 1,2\"")

    await engine.dispose()
    return 0


def main() -> None:
    parser = argparse.ArgumentParser(description="PR-B tier triage 백필 도구")
    parser.add_argument("--domain", required=True, choices=list(DOMAIN_FILTERS),
                        help="백필 대상 도메인 선택")
    parser.add_argument("--limit", type=int, default=50,
                        help="한 번에 enqueue 할 최대 건수 (기본 50)")
    mode = parser.add_mutually_exclusive_group(required=True)
    mode.add_argument("--dry-run", action="store_true", help="쿼리만 실행, 큐 변경 없음")
    mode.add_argument("--apply", action="store_true", help="classify 큐 실제 enqueue")
    args = parser.parse_args()

    rc = asyncio.run(run(args.domain, args.limit, args.apply))
    sys.exit(rc)


if __name__ == "__main__":
    main()