"""리더보드 API — 누적 포인트 1위 (docs/SCORING.md §3 랭킹 규칙).

user_points 테이블(채점 시 누적 갱신) 기준 랭킹.
1차 정렬: 누적 포인트. 동점: 정확스코어 횟수 → 적중률 → 참여수 → 최초도달.
주최측/임직원 배제(P1). 이메일은 마스킹하여 노출.
"""
from __future__ import annotations

from fastapi import APIRouter, Depends, Query
from sqlalchemy import func, select
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy.orm import selectinload

from ..database import get_db
from ..models import Match, UserPoints
from ..scoring import is_excluded, score_prediction
from ..schemas import (
    AILeaderboardOut,
    AIStandingOut,
    LeaderboardOut,
    StandingOut,
)

router = APIRouter(prefix="/api/leaderboard", tags=["leaderboard"])


def _mask(email: str) -> str:
    name, _, domain = email.partition("@")
    head = name[:2] if len(name) >= 2 else name
    return f"{head}{'*' * max(1, len(name) - 2)}@{domain}"


@router.get("", response_model=LeaderboardOut)
async def leaderboard(
    limit: int = Query(50, ge=1, le=200), db: AsyncSession = Depends(get_db)
) -> LeaderboardOut:
    rows = (
        await db.execute(select(UserPoints))
    ).scalars().all()

    ranked = sorted(
        (r for r in rows if not is_excluded(r.email)),
        key=lambda r: (
            -r.total_points,
            -r.exact_count,
            -(r.total_points / r.matches_played if r.matches_played else 0),
            -r.matches_played,
            r.first_scored_at.timestamp() if r.first_scored_at else 0,
        ),
    )

    scored_matches = (
        await db.execute(
            select(func.count())
            .select_from(Match)
            .where(Match.result_outcome.is_not(None))
        )
    ).scalar() or 0

    standings = [
        StandingOut(
            rank=i + 1,
            emailMasked=_mask(r.email),
            totalPoints=r.total_points,
            exactCount=r.exact_count,
            matchesPlayed=r.matches_played,
        )
        for i, r in enumerate(ranked[:limit])
    ]
    return LeaderboardOut(standings=standings, scoredMatches=scored_matches)


@router.get("/ai", response_model=AILeaderboardOut)
async def ai_leaderboard(db: AsyncSession = Depends(get_db)) -> AILeaderboardOut:
    """AI 모델 누적 랭킹 — 종료된 경기의 AI 예측을 유저와 동일한 배점으로 채점·합산.

    별도 누적 테이블 없이 매 조회 시 종료 경기 전수 재계산 → 결과/배점 변동에
    항상 일관. (모델 3개 × 경기 수라 비용 무시 가능.)
    """
    matches = (
        await db.execute(
            select(Match)
            .where(Match.result_outcome.is_not(None))
            .options(selectinload(Match.predictions))
        )
    ).scalars().all()

    # model → [points, exact_count, matches_played]
    agg: dict[str, list[int]] = {}
    for m in matches:
        for p in m.predictions:
            pts = score_prediction(p.score_a, p.score_b, m.result_score_a, m.result_score_b)
            row = agg.setdefault(p.model, [0, 0, 0])
            row[0] += pts
            row[1] += 1 if (p.score_a == m.result_score_a and p.score_b == m.result_score_b) else 0
            row[2] += 1

    order = {"GPT": 0, "Claude": 1, "Gemini": 2}
    ranked = sorted(
        agg.items(),
        key=lambda kv: (-kv[1][0], -kv[1][1], -kv[1][2], order.get(kv[0], 9)),
    )

    standings = [
        AIStandingOut(
            rank=i + 1,
            model=model,  # type: ignore[arg-type]
            totalPoints=pts,
            exactCount=exact,
            matchesPlayed=played,
        )
        for i, (model, (pts, exact, played)) in enumerate(ranked)
    ]
    return AILeaderboardOut(standings=standings, scoredMatches=len(matches))