"""匹配引擎测试：评分权重、差距清单、可迁移技能、学习路径拓扑序、简历脱敏。"""
from __future__ import annotations

from app.extract.base import load_lexicon
from app.match.engine import MatchEngine, topological_stages
from app.match.resume import mask_text, parse_resume
from app.models import JobDefinition, ResumeProfile

JOB = JobDefinition(
    job_id="job_X", title="X工程师", category="后端开发",
    required_skills=["Java", "Spring Boot", "MySQL", "Redis"],
    preferred_skills=["Docker", "Kubernetes"],
    scenarios=["电商", "金融"], expected_years=3,
)


def _engine(**weights) -> MatchEngine:
    return MatchEngine(lexicon=load_lexicon(), weights=weights or None)


def test_scoring_weights_and_dimensions():
    """四维加权：必备50/加分15/经验20/场景15。"""
    profile = ResumeProfile(
        skills=["Java", "Spring Boot", "Docker"],   # 必备 2/4，加分 1/2
        years=1.5,                                   # 经验 1.5/3 = 0.5
        scenarios=["电商"],                          # 场景 1/2
    )
    r = _engine().match(profile, JOB)
    assert r.dimension_scores == {"required": 50.0, "preferred": 50.0,
                                  "experience": 50.0, "scenario": 50.0}
    # 总分 = 0.5*50 + 0.15*50 + 0.2*50 + 0.15*50 = 50
    assert r.total_score == 50.0
    assert not r.is_match  # < 60 阈值


def test_custom_weights_override():
    profile = ResumeProfile(skills=["Java", "Spring Boot", "MySQL", "Redis"],
                            years=3, scenarios=["电商", "金融"])
    r = _engine(required=1.0, preferred=0.0, experience=0.0, scenario=0.0).match(profile, JOB)
    assert r.total_score == 100.0 and r.is_match


def test_gap_list_and_transferable():
    profile = ResumeProfile(skills=["Java", "Spring", "MySQL"], years=3,
                            scenarios=["电商", "金融"])
    r = _engine().match(profile, JOB)
    assert r.missing_required == ["Spring Boot", "Redis"]
    assert r.matched_required == ["Java", "MySQL"]
    # Spring 与缺失的 Spring Boot 同属“后端框架”→ 可迁移
    assert "Spring" in r.transferable_skills


def test_learning_path_topological_order():
    """缺失技能按前置关系拓扑分层：Spring → Spring Boot → Spring Cloud。"""
    lexicon = load_lexicon()
    stages = topological_stages(["Spring Cloud", "Spring Boot", "Spring"], lexicon)
    assert stages[0]["skills"] == ["Spring"]
    assert stages[1]["skills"] == ["Spring Boot"]
    assert stages[2]["skills"] == ["Spring Cloud"]
    # 全部缺失时拓扑序稳定且不漏项
    stages2 = topological_stages(["Kubernetes", "Docker", "Linux"], lexicon)
    flat = [s for st in stages2 for s in st["skills"]]
    assert flat == ["Linux", "Docker", "Kubernetes"]


def test_learning_path_acceptance():
    """每个阶段附带结构化验收标准：产出物/量化验收项/自测问答，且按技能类别差异化。"""
    lexicon = load_lexicon()
    stages = topological_stages(["Spring Cloud", "Spring Boot", "Spring"], lexicon)
    for st in stages:
        acc = st["acceptance"]
        assert acc["deliverable"] and len(acc["criteria"]) >= 3 and len(acc["self_check"]) >= 2
        assert all("{" not in c for c in acc["criteria"] + acc["self_check"])  # 占位符全部替换
        assert any(s in acc["deliverable"] for s in st["skills"])  # 产出物点名本阶段技能
    # 类别差异化：后端框架类与产品类的验收内容应不同
    be = topological_stages(["Spring"], lexicon)[0]["acceptance"]
    pm = topological_stages(["A/B测试"], lexicon)[0]["acceptance"]
    assert be["category"] != pm["category"] or be["criteria"] != pm["criteria"]


def test_resume_masking():
    text = "姓名：王小明\n手机：13812345678\n邮箱：wxm@example.com\n3年开发经验"
    masked = mask_text(text)
    assert "王小明" not in masked and "王**" in masked
    assert "13812345678" not in masked and "138****78" in masked
    assert "wxm@example.com" not in masked and "w***@example.com" in masked


def test_resume_parse_skills_and_years():
    profile = parse_resume("姓名：李四\n5年后端开发经验，熟悉Java、MySQL与Docker。")
    assert profile.years == 5.0
    assert set(profile.skills) == {"Java", "MySQL", "Docker"}
    assert "138" not in profile.raw_masked_text