"""Audyt potencjalnych false-merge'y scen (data quality).

Kontekst: przed 2026-05-12 scoring auto-mergeował sceny w aggregator mode bez
wymaganego strong-signal (fp/duration/date). Hardening z 2026-05-12 (scoring.py:
`aggregator_weak_signal_cap` = 0.85 + duration bump zacieśniony do ≤3s, bug
ef090842) zatrzymuje NOWE false-merge'y, ale starsze zostały w danych.

Candidate-log (`merge_candidates`, status=auto_merged) NIE zapisuje który
external_ref dopięto, więc precyzyjne cofnięcie z samego logu jest niemożliwe.
Zamiast tego wykrywamy false-merge po SKUTKU: scena, do której dopięto źródło
innego wideo, ma `playback_sources` o **niespójnych długościach**. To sygnał
wykrywalny z bieżących danych i daje konkretny target (outlier source).

Read-only — NIC nie mutuje. Remediacja (detach/mark-dead outliera) to osobna
decyzja: krótka długość bywa legit (preview/trailer) albo błąd metadanych, więc
n=2 jest niejednoznaczne (nie wiadomo które źródło jest „prawdziwe”). Do oceny
ludzkiej / osobnego skryptu z jawnym progiem.

Uruchomienie:
  python -m scripts.audit_false_merges                  # podsumowanie + buckety
  python -m scripts.audit_false_merges --list 50        # 50 najgorszych z rozpiską
  python -m scripts.audit_false_merges --min-ratio 3    # tylko ratio >= 3x
  python -m scripts.audit_false_merges --json out.json  # eksport suspektów
"""
from __future__ import annotations

import argparse
import json

from sqlalchemy import text

from app.db import engine

# Suspekt: scena z ≥2 żywymi źródłami mającymi duration, gdzie najdłuższe vs
# najkrótsze różni się o > MIN_ABS_GAP sekund ORAZ ratio >= MIN_RATIO.
MIN_ABS_GAP = 90  # sekund — odsiewa drobne różnice encodingu/trim
DEFAULT_MIN_RATIO = 1.25
MIN_DUR = 30  # ignoruj śmieciowe <30s wpisy w samym progu liczenia

_SUSPECTS_SQL = """
WITH d AS (
    SELECT scene_id, duration_sec
    FROM playback_sources
    WHERE dead_at IS NULL AND duration_sec IS NOT NULL AND duration_sec > :min_dur
),
agg AS (
    SELECT scene_id, count(*) AS n, min(duration_sec) AS mn, max(duration_sec) AS mx
    FROM d GROUP BY scene_id HAVING count(*) >= 2
)
SELECT a.scene_id, a.n, a.mn, a.mx, (a.mx::float / NULLIF(a.mn, 0)) AS ratio
FROM agg a
WHERE (a.mx - a.mn) > :gap AND (a.mx::float / NULLIF(a.mn, 0)) >= :ratio
ORDER BY ratio DESC
"""


def _summary(conn) -> None:
    cov = conn.execute(text("""
        SELECT count(*) FILTER (WHERE duration_sec IS NOT NULL AND duration_sec > 0), count(*)
        FROM playback_sources WHERE dead_at IS NULL
    """)).fetchone()
    print(f"playback_sources with duration: {cov[0]}/{cov[1]} "
          f"({100*cov[0]/max(cov[1],1):.0f}%)")

    total = conn.execute(text(f"SELECT count(*) FROM ({_SUSPECTS_SQL}) q"),
                         {"min_dur": MIN_DUR, "gap": MIN_ABS_GAP, "ratio": DEFAULT_MIN_RATIO}).scalar()
    print(f"\nduration-inconsistent scenes (gap>{MIN_ABS_GAP}s, ratio>={DEFAULT_MIN_RATIO}x): {total}")
    print("severity buckets (by max/min ratio):")
    for lo, hi, lbl in [(1.25, 1.5, "1.25-1.5x"), (1.5, 2.0, "1.5-2x"),
                        (2.0, 3.0, "2-3x"), (3.0, 1e9, ">3x")]:
        n = conn.execute(text(f"SELECT count(*) FROM ({_SUSPECTS_SQL}) q WHERE ratio >= :lo AND ratio < :hi"),
                         {"min_dur": MIN_DUR, "gap": MIN_ABS_GAP, "ratio": DEFAULT_MIN_RATIO,
                          "lo": lo, "hi": hi}).scalar()
        print(f"    {lbl:>10}: {n}")
    print("\nNOTE: read-only audit. >3x ratio = strong false-merge signal "
          "(short clip attached to full scene). n>=3 disambiguates the outlier; "
          "n=2 needs human review. Remediation is a separate, explicit decision.")


def _rows(conn, min_ratio: float):
    return conn.execute(text(_SUSPECTS_SQL),
                        {"min_dur": MIN_DUR, "gap": MIN_ABS_GAP, "ratio": min_ratio}).fetchall()


def _source_breakdown(conn, scene_id):
    return conn.execute(text("""
        SELECT origin, duration_sec, dead_at IS NOT NULL AS dead, page_url
        FROM playback_sources WHERE scene_id = :sid
        ORDER BY duration_sec NULLS LAST
    """), {"sid": scene_id}).fetchall()


def main() -> None:
    ap = argparse.ArgumentParser(description=__doc__)
    ap.add_argument("--list", type=int, default=0, metavar="N",
                    help="wypisz N najgorszych scen z rozpiską źródeł")
    ap.add_argument("--min-ratio", type=float, default=DEFAULT_MIN_RATIO,
                    help=f"minimalny max/min ratio (default {DEFAULT_MIN_RATIO})")
    ap.add_argument("--json", metavar="PATH", help="eksportuj wszystkich suspektów do JSON")
    args = ap.parse_args()

    with engine.connect() as conn:
        _summary(conn)

        if args.list:
            rows = _rows(conn, args.min_ratio)[: args.list]
            print(f"\n=== top {len(rows)} suspects (ratio>={args.min_ratio}x) ===")
            for r in rows:
                title = conn.execute(text("SELECT title FROM scenes WHERE id=:i"),
                                     {"i": r[0]}).scalar()
                print(f"\n{r[0]}  n={r[1]} {r[2]}s..{r[3]}s ratio={r[4]:.1f}x  {(title or '')[:60]}")
                for b in _source_breakdown(conn, r[0]):
                    flag = " DEAD" if b[2] else ""
                    print(f"    {b[0]:<28} {str(b[1]) + 's':>8}{flag}  {(b[3] or '')[:50]}")

        if args.json:
            rows = _rows(conn, args.min_ratio)
            out = [{"scene_id": str(r[0]), "n_sources": r[1], "min_dur": r[2],
                    "max_dur": r[3], "ratio": round(r[4], 3)} for r in rows]
            with open(args.json, "w", encoding="utf-8") as f:
                json.dump(out, f, indent=2)
            print(f"\nwrote {len(out)} suspects -> {args.json}")


if __name__ == "__main__":
    main()