import statistics
def _deals(vals, key="deposit", dt="전세"):
    return [{key: v, "deal_type": dt} for v in vals]

def test_compute_safety_tiers():
    from app.listing_matcher import compute_safety
    deals = _deals([40000, 42000, 44000, 43000])  # median ~42500
    assert compute_safety(28000, "전세", deals)["tier"] == "안전"    # 0.66
    assert compute_safety(32000, "전세", deals)["tier"] == "주의"    # 0.75
    assert compute_safety(38000, "전세", deals)["tier"] == "위험"    # 0.89
    assert compute_safety(28000, "전세", _deals([40000, 41000]))["tier"] == "보류"  # 표본<3

def test_compute_valuation_tiers():
    from app.listing_matcher import compute_valuation
    deals = _deals([80000, 88000, 90000, 87000], key="sale_amount", dt="매매")  # median ~87500
    assert compute_valuation(83000, deals)["tier"] == "저평가"    # 0.95
    assert compute_valuation(89000, deals)["tier"] == "시세"      # 1.02
    assert compute_valuation(95000, deals)["tier"] == "고가"      # 1.09

def test_match_listing_rent_deposit_cap():
    from app.listing_matcher import match_listing
    crit = {"dongs": ["신대방동"], "deal_types": ["전세"], "max_deposit": 32000,
            "min_area": 40.0, "house_types": ["아파트"]}
    ok = match_listing(crit, {"deal_type": "전세", "deposit": 29000, "area_exclusive": 42.0,
                              "dong": "신대방동"}, budget={"max_deposit": 45000})
    assert ok["passed"] is True
    over = match_listing(crit, {"deal_type": "전세", "deposit": 40000, "area_exclusive": 42.0,
                                "dong": "신대방동"}, budget={"max_deposit": 45000})
    assert over["passed"] is False


# ── Task 2: run_listing_matching 단일 conn 통합 테스트 ───────────────────────

def test_run_listing_matching_end_to_end_safe_rent_and_overpriced_sale():
    """listings+market_deals+criteria를 시드하고 run_listing_matching()을 돌려
    listing_matches가 실제로 생성되고 tier/passed가 기대대로인지 확인한다
    (단일 connection 리팩터 후에도 판정 로직이 보존됐는지 검증하는 회귀 테스트)."""
    from app import db
    from app.listing_matcher import run_listing_matching

    # criteria: 신대방동 전세/매매, 보증금 상한 32000, 최소면적 40
    db.update_listing_criteria({
        "dongs": ["신대방동"], "deal_types": ["전세", "매매"],
        "max_deposit": 32000, "min_area": 40.0,
        "house_types": ["아파트"], "equity": 20000, "annual_income": 6000,
    })

    # 전세 실거래 시세(median ~42000) — 안전 매물(28000, ratio 0.67)
    for i, v in enumerate((40000, 42000, 44000)):
        db.upsert_market_deal({"house_type": "아파트", "dong_code": "11590", "dong": "신대방동",
                               "complex_name": "OO", "area": 42.0, "deal_type": "전세",
                               "deposit": v, "sale_amount": None,
                               "deal_ym": f"2026{i+1:02d}", "floor": "5", "source": "molit"})
    l_rent, _ = db.upsert_listing({"article_no": "R1", "source": "naver", "deal_type": "전세",
                                   "deposit": 28000, "area_exclusive": 42.0,
                                   "complex_name": "OO", "dong": "신대방동", "dong_code": "11590"})

    # 매매 실거래 시세(median ~87500) — 고가 매물(95000, ratio 1.09)
    for i, v in enumerate((80000, 88000, 90000, 87000)):
        db.upsert_market_deal({"house_type": "아파트", "dong_code": "11590", "dong": "신대방동",
                               "complex_name": "PP", "area": 59.0, "deal_type": "매매",
                               "deposit": None, "sale_amount": v,
                               "deal_ym": f"2026{i+1:02d}", "floor": "10", "source": "molit"})
    l_sale, _ = db.upsert_listing({"article_no": "R2", "source": "naver", "deal_type": "매매",
                                   "sale_price": 95000, "area_exclusive": 59.0,
                                   "complex_name": "PP", "dong": "신대방동", "dong_code": "11590"})

    run_listing_matching()

    matches = {m["listing_id"]: m for m in db.get_listing_matches()}
    assert len(matches) == 2
    rent_m = matches[l_rent["id"]]
    assert rent_m["passed"] == 1
    assert rent_m["safety_tier"] == "안전"
    assert rent_m["sample_size"] == 3

    sale_m = matches[l_sale["id"]]
    assert sale_m["valuation_tier"] == "고가"
    assert sale_m["sample_size"] == 4


# ── 근본원인 fix: 기존 저장된 listing.dong_code=None(cortarNo 부재) 행도 dong명에서
#    lawd_code를 유도해 즉시 판정돼야 한다(보류 고착 방지) ──

def test_run_listing_matching_derives_dong_code_when_none():
    from app import db
    from app.listing_matcher import run_listing_matching

    db.update_listing_criteria({
        "dongs": ["신길동"], "deal_types": ["매매"],
        "min_area": 40.0, "house_types": ["아파트"],
        "equity": 20000, "annual_income": 6000,
    })

    # 신길동(영등포구=11560) 매매 실거래 시세 3건
    for i, v in enumerate((80000, 88000, 90000)):
        db.upsert_market_deal({"house_type": "아파트", "dong_code": "11560", "dong": "신길동",
                               "complex_name": "QQ", "area": 59.0, "deal_type": "매매",
                               "deposit": None, "sale_amount": v,
                               "deal_ym": f"2026{i+1:02d}", "floor": "8", "source": "molit"})
    # 매물은 dong_code=None으로 저장(네이버 cortarNo 부재 시나리오)
    l_sale, _ = db.upsert_listing({"article_no": "R3", "source": "naver", "deal_type": "매매",
                                   "sale_price": 84000, "area_exclusive": 59.0,
                                   "complex_name": "QQ", "dong": "신길동", "dong_code": None})

    run_listing_matching()

    matches = {m["listing_id"]: m for m in db.get_listing_matches()}
    m = matches[l_sale["id"]]
    assert m["sample_size"] == 3
    assert m["valuation_tier"] != "보류"