"""큐레이터용 후보 가공 — 여러 엔진 결과를 하나로 병합, 중복 제거, 피처 계산."""
from typing import Dict, List, Any
from . import db
from .recommender import recommend_numbers, recommend_with_heatmap
from .analyzer import get_statistical_report


LOW_HIGH_CUT = 22  # 1~22 저구간, 23~45 고구간


def compute_features(numbers: List[int], hot: set, cold: set) -> Dict[str, Any]:
    nums = sorted(numbers)
    odd = sum(1 for n in nums if n % 2 == 1)
    low = sum(1 for n in nums if n <= LOW_HIGH_CUT)
    buckets = [0, 0, 0, 0, 0]  # 1-10, 11-20, 21-30, 31-40, 41-45
    for n in nums:
        if n <= 10: buckets[0] += 1
        elif n <= 20: buckets[1] += 1
        elif n <= 30: buckets[2] += 1
        elif n <= 40: buckets[3] += 1
        else: buckets[4] += 1
    consecutive = any(nums[i+1] - nums[i] == 1 for i in range(len(nums) - 1))
    return {
        "odd_count": odd,
        "even_count": 6 - odd,
        "low_count": low,
        "high_count": 6 - low,
        "range_distribution": buckets,
        "has_consecutive": consecutive,
        "hot_number_count": len(set(nums) & hot),
        "cold_number_count": len(set(nums) & cold),
        "sum": sum(nums),
    }


def _key(numbers: List[int]) -> str:
    return ",".join(str(n) for n in sorted(numbers))


def collect_candidates(n: int, hot: set, cold: set) -> List[Dict[str, Any]]:
    """여러 엔진에서 후보를 모으고 중복을 제거. 최대 n세트 반환.

    우선순위: simulation best_picks → meta → heatmap → statistics
    """
    seen = {}
    sources_order = []

    # 1. simulation best_picks
    for row in db.get_best_picks(limit=n):
        numbers = row.get("numbers") or []
        if not numbers:
            continue
        k = _key(numbers)
        if k not in seen:
            seen[k] = {"numbers": sorted(numbers), "source": "simulation"}
            sources_order.append(k)

    # 2. meta-strategy (smart)
    try:
        from .generator import generate_smart_recommendation
        meta = generate_smart_recommendation(sets=n)
        for s in meta.get("sets", []):
            numbers = s.get("numbers") or []
            k = _key(numbers)
            if k not in seen and numbers:
                seen[k] = {"numbers": sorted(numbers), "source": "meta"}
                sources_order.append(k)
    except Exception:
        pass

    # 3. heatmap
    try:
        hm = recommend_with_heatmap(count=n)
        for numbers in hm:
            k = _key(numbers)
            if k not in seen and numbers:
                seen[k] = {"numbers": sorted(numbers), "source": "heatmap"}
                sources_order.append(k)
    except Exception:
        pass

    # 4. statistics
    try:
        st = recommend_numbers(count=n)
        for numbers in st:
            k = _key(numbers)
            if k not in seen and numbers:
                seen[k] = {"numbers": sorted(numbers), "source": "statistics"}
                sources_order.append(k)
    except Exception:
        pass

    out = []
    for k in sources_order[:n]:
        item = seen[k]
        item["features"] = compute_features(item["numbers"], hot, cold)
        out.append(item)
    return out


def build_context(hot_limit: int = 3, cold_limit: int = 3) -> Dict[str, Any]:
    """주간 맥락 패키지."""
    report = get_statistical_report()
    latest = db.get_latest_draw()
    freq = report.get("frequency", {})
    sorted_freq = sorted(freq.items(), key=lambda x: -x[1])
    hot = [int(k) for k, _ in sorted_freq[:hot_limit]]

    sorted_cold = sorted(freq.items(), key=lambda x: x[1])
    cold = [int(k) for k, _ in sorted_cold[:cold_limit]]

    last_summary = ""
    if latest:
        nums = [latest.get(f"drwtNo{i}") for i in range(1, 7)]
        odd = sum(1 for n in nums if n and n % 2 == 1)
        low = sum(1 for n in nums if n and n <= LOW_HIGH_CUT)
        last_summary = f"{latest['drwNo']}회: {', '.join(str(n) for n in nums)} (홀{odd}짝{6-odd}, 저{low}고{6-low})"

    my_perf = []
    try:
        from .purchase_manager import get_recent_performance
        my_perf = get_recent_performance(limit=3)
    except Exception:
        my_perf = []

    return {
        "hot_numbers": hot,
        "cold_numbers": cold,
        "last_draw_summary": last_summary,
        "my_recent_performance": my_perf,
    }