import random
from collections import Counter
from typing import Dict, Any, List, Tuple

def recommend_numbers(
    draws: List[Tuple[int, List[int]]],
    *,
    recent_window: int = 200,
    recent_weight: float = 2.0,
    avoid_recent_k: int = 5,
    seed: int | None = None,
) -> Dict[str, Any]:
    """
    가벼운 통계 기반 추천:
    - 전체 빈도 + 최근(recent_window) 빈도에 가중치를 더한 가중 샘플링
    - 최근 avoid_recent_k 회차에 나온 번호는 확률을 낮춤(완전 제외는 아님)
    """
    if seed is not None:
        random.seed(seed)

    # 전체 빈도
    all_nums = [n for _, nums in draws for n in nums]
    freq_all = Counter(all_nums)

    # 최근 빈도
    recent = draws[-recent_window:] if len(draws) >= recent_window else draws
    recent_nums = [n for _, nums in recent for n in nums]
    freq_recent = Counter(recent_nums)

    # 최근 k회차 번호(패널티)
    last_k = draws[-avoid_recent_k:] if len(draws) >= avoid_recent_k else draws
    last_k_nums = set(n for _, nums in last_k for n in nums)

    # 가중치 구성
    weights = {}
    for n in range(1, 46):
        w = freq_all[n] + recent_weight * freq_recent[n]
        if n in last_k_nums:
            w *= 0.6  # 최근에 너무 방금 나온 건 살짝 덜 뽑히게
        weights[n] = max(w, 0.1)

    # 중복 없이 6개 뽑기(가중 샘플링)
    chosen = []
    pool = list(range(1, 46))
    for _ in range(6):
        total = sum(weights[n] for n in pool)
        r = random.random() * total
        acc = 0.0
        for n in pool:
            acc += weights[n]
            if acc >= r:
                chosen.append(n)
                pool.remove(n)
                break

    chosen_sorted = sorted(chosen)

    explain = {
        "recent_window": recent_window,
        "recent_weight": recent_weight,
        "avoid_recent_k": avoid_recent_k,
        "top_all": [n for n, _ in freq_all.most_common(10)],
        "top_recent": [n for n, _ in freq_recent.most_common(10)],
        "last_k_draws": [d for d, _ in last_k],
    }

    return {"numbers": chosen_sorted, "explain": explain}