"""Composite scoring dla movies — title + year + studio + cast Jaccard.

Movies vs Scenes scoring:
- Movies rzadko mają fingerprinty — tytuł jest najsilniejszym sygnałem (weight 0.5).
- Year (production year) ważny ale czasem nieznany (paradisehill często NULL); wagę
  redystrybuujemy proporcjonalnie gdy brak.
- Studio: binary signal — match=1.0, mismatch=0.0; gdy któraś strona ma NULL, sygnał
  pomijamy (None waga).
- Cast: Jaccard po canonical UUID-ach performerów. Bonus signal — niektóre source'y
  (paradisehill często, dooplay zawsze) mają cast; tube'y bez cast → None.

Threshold (lower niż scenes — title dla movies ma niższy false-positive rate):
  ≥0.85 auto-merge
  0.65-0.85 review
  <0.65 nowy kanon
"""
from __future__ import annotations

import uuid
from collections.abc import Iterable
from dataclasses import dataclass

from rapidfuzz import fuzz
from sqlalchemy.orm import Session

from app.models.movie import Movie, MoviePerformer


@dataclass
class MovieScoreBreakdown:
    title: float | None = None
    year: float | None = None
    studio_match: bool | None = None
    cast: float | None = None
    composite: float = 0.0
    reasons: dict | None = None

    def to_dict(self) -> dict:
        return {
            "title": self.title,
            "year": self.year,
            "studio_match": self.studio_match,
            "cast": self.cast,
            "composite": self.composite,
            "reasons": self.reasons or {},
        }


_BASE_WEIGHTS = {
    "title": 0.50,
    "year": 0.20,
    "studio": 0.20,
    "cast": 0.10,
}

# Movies-specific thresholds (lower niż scenes ze względu na unique titles)
AUTO_THRESHOLD = 0.85
REVIEW_THRESHOLD = 0.65


def score_movie_candidate(
    session: Session,
    *,
    candidate: Movie,
    norm_title_normalized: str,
    norm_release_year: int | None,
    norm_studio_id: uuid.UUID | None,
    norm_performer_ids: Iterable[uuid.UUID],
) -> MovieScoreBreakdown:
    """Liczy score dopasowania incoming → candidate. Wszystko w [0,1]."""
    breakdown = MovieScoreBreakdown(reasons={})

    # Title (always)
    if candidate.title_normalized and norm_title_normalized:
        breakdown.title = fuzz.token_set_ratio(
            candidate.title_normalized, norm_title_normalized
        ) / 100.0

    # Year — exact lub ±1 = wysoka, ±2 = średnia
    if candidate.release_year is not None and norm_release_year is not None:
        delta = abs(candidate.release_year - norm_release_year)
        if delta == 0:
            breakdown.year = 1.0
        elif delta == 1:
            breakdown.year = 0.7
        elif delta == 2:
            breakdown.year = 0.4
        else:
            breakdown.year = 0.0

    # Studio match — binary
    if candidate.studio_id and norm_studio_id:
        breakdown.studio_match = candidate.studio_id == norm_studio_id

    # Cast Jaccard — gdy obie strony mają performerów
    norm_perf_set = {str(pid) for pid in norm_performer_ids if pid is not None}
    if norm_perf_set:
        cand_perf_rows = session.execute(
            MoviePerformer.__table__.select().where(
                MoviePerformer.movie_id == candidate.id
            )
        ).fetchall()
        cand_perf_set = {str(r.performer_id) for r in cand_perf_rows}
        if cand_perf_set:
            inter = norm_perf_set & cand_perf_set
            union = norm_perf_set | cand_perf_set
            breakdown.cast = len(inter) / len(union) if union else 0.0

    # Composite — redystrybuuj wagi po dostępnych sygnałach
    available: dict[str, float] = {}
    if breakdown.title is not None:
        available["title"] = breakdown.title
    if breakdown.year is not None:
        available["year"] = breakdown.year
    if breakdown.studio_match is not None:
        available["studio"] = 1.0 if breakdown.studio_match else 0.0
    if breakdown.cast is not None:
        available["cast"] = breakdown.cast

    if not available:
        breakdown.composite = 0.0
        breakdown.reasons["no_signals"] = True
        return breakdown

    weights = {k: _BASE_WEIGHTS[k] for k in available}
    total_w = sum(weights.values())
    if total_w == 0.0:
        breakdown.composite = 0.0
        return breakdown
    norm_w = {k: w / total_w for k, w in weights.items()}
    score = sum(available[k] * norm_w[k] for k in available)

    # Hard reject: studio mismatch + year mismatch (delta ≥3) → różne filmy z podobnym
    # tytułem. "Tushy Anal Bliss 7" vs "Anal Bliss 7" z innego studia rok różny — to NIE
    # jest mirror, to różne wydania.
    if (
        breakdown.studio_match is False
        and breakdown.year is not None
        and breakdown.year < 0.4
    ):
        breakdown.reasons["studio_year_mismatch_reject"] = True
        score = 0.0

    breakdown.composite = max(0.0, min(1.0, score))
    breakdown.reasons["weights"] = norm_w
    breakdown.reasons["sub_scores"] = available
    return breakdown


def triage_movie(score: float) -> str:
    if score >= AUTO_THRESHOLD:
        return "auto"
    if score >= REVIEW_THRESHOLD:
        return "review"
    return "reject"