"""评分器测试（T13，OV4）。

OV4 裁定：成功标准无量度（无黄金集/评分器）→ 建立黄金集 + 评分器。
本文件测试确定性可机器验证维度（可追溯性/占位符残留/章节完整性），
LLM 语义维度预留钩子；并测试黄金集加载。
"""

from __future__ import annotations

import pytest

from genesis.data_models import (
    CellValue,
    ExcelTable,
    ParsedTemplate,
    Provenance,
    SheetType,
    StructuredSource,
)
from genesis.eval.scorer import (
    ChapterArtifact,
    ChapterScorer,
    DimensionScore,
    EvalReport,
)
from genesis.eval.golden_set import GoldenCase, GoldenSet


# ---------- 小源（供可追溯性维度定位） ----------

def _source() -> StructuredSource:
    cell = CellValue(
        value="登録",
        provenance=Provenance(file_name="f.xlsx", sheet_name="機能一覧", row=3, column="C", column_header="x"),
    )
    table = ExcelTable(
        name="機能一覧", detected_type=SheetType.FUNCTION,
        extraction_method="structured", headers=["v"], rows=[{"v": cell}],
    )
    return StructuredSource(
        tables=[table],
        template=ParsedTemplate(file_name="t.docx", sections=[], placeholders={}, styles={}),
        rule_docs=[], image_analyses=[], existing_system=None, comments=[],
    )


# ---------- 可追溯性维度 ----------

def test_traceability_full_when_all_uris_resolvable():
    source = _source()
    artifact = ChapterArtifact(
        chapter_id="ch3",
        text="機能一覧（出典: f.xlsx#機能一覧!C3）",
        source_uris=["f.xlsx#機能一覧!C3"],
        template_sections_expected=["ch3"],
    )
    report = ChapterScorer().score([artifact], source)
    trace = _dim(report, "traceability")
    assert trace.score == 1.0
    assert trace.passed is True


def test_traceability_zero_when_uris_fake():
    source = _source()
    artifact = ChapterArtifact(
        chapter_id="ch3",
        text="機能（出典: fake.xlsx#X!Z9）",
        source_uris=["fake.xlsx#X!Z9"],
        template_sections_expected=["ch3"],
    )
    report = ChapterScorer().score([artifact], source)
    trace = _dim(report, "traceability")
    assert trace.score == 0.0
    assert trace.passed is False


# ---------- 占位符残留维度 ----------

def test_placeholder_residue_fails():
    source = _source()
    artifact = ChapterArtifact(
        chapter_id="ch3",
        text="未替换占位符 {{section:db_tables}}",
        source_uris=["f.xlsx#機能一覧!C3"],
        template_sections_expected=["ch3"],
    )
    report = ChapterScorer().score([artifact], source)
    dim = _dim(report, "placeholder_residue")
    assert dim.score == 0.0
    assert dim.passed is False


def test_placeholder_residue_ok_when_clean():
    source = _source()
    artifact = ChapterArtifact(
        chapter_id="ch3", text="正常生成内容", source_uris=[], template_sections_expected=["ch3"],
    )
    report = ChapterScorer().score([artifact], source)
    assert _dim(report, "placeholder_residue").score == 1.0


# ---------- 章节完整性维度 ----------

def test_completeness_fails_when_section_missing():
    source = _source()
    # 期望 ch3/ch4 两章，但只生成 ch3
    artifacts = [ChapterArtifact(
        chapter_id="ch3", text="a", source_uris=[], template_sections_expected=["ch3", "ch4"],
    )]
    report = ChapterScorer().score(artifacts, source)
    dim = _dim(report, "chapter_completeness")
    assert dim.score == 0.5
    assert dim.passed is False


# ---------- LLM 维度钩子 ----------

def test_llm_dimension_hook_invoked():
    source = _source()
    called = {}

    def fake_llm(chapter: ChapterArtifact) -> DimensionScore:
        called["hit"] = True
        return DimensionScore(name="llm_accuracy", score=0.8, passed=True, detail="stub")

    artifact = ChapterArtifact(
        chapter_id="ch3", text="x", source_uris=[], template_sections_expected=["ch3"],
    )
    scorer = ChapterScorer(llm_evaluators={"llm_accuracy": fake_llm})
    report = scorer.score([artifact], source)
    assert called.get("hit") is True
    assert _dim(report, "llm_accuracy").score == 0.8


# ---------- 总分聚合 + 通过判定 ----------

def test_total_score_aggregation():
    source = _source()
    artifact = ChapterArtifact(
        chapter_id="ch3", text="正常（出典: f.xlsx#機能一覧!C3）",
        source_uris=["f.xlsx#機能一覧!C3"], template_sections_expected=["ch3"],
    )
    report = ChapterScorer().score([artifact], source)
    assert isinstance(report, EvalReport)
    assert 0.0 <= report.total_score <= 1.0
    # 全部确定性维度满分 → 总分接近 1.0（仅 llm 维度默认中性 0.5）
    assert report.total_score >= 0.8


def test_empty_chapters_does_not_crash():
    """空章节输入：无引用/无约束 → 确定性维度中性满分，不应抛错。"""
    source = _source()
    report = ChapterScorer().score([], source)
    assert report.total_score == 1.0
    assert report.passed is True


# ---------- 黄金集加载 ----------

def test_golden_set_load(tmp_path):
    yaml_text = """
cases:
  - id: g1
    input_ref: sample/要件定義.xlsx
    expected_min_score: 0.7
    note: 脱敏真实样本回归基线
"""
    p = tmp_path / "golden_set.yaml"
    p.write_text(yaml_text, encoding="utf-8")
    gs = GoldenSet.load(p)
    assert len(gs.cases) == 1
    assert gs.cases[0].id == "g1"
    assert gs.cases[0].expected_min_score == 0.7


def _dim(report: EvalReport, name: str) -> DimensionScore:
    for d in report.dimensions:
        if d.name == name:
            return d
    raise AssertionError(f"维度未找到: {name}")