"""Tests for eval/qa_judge.py (T5.3)."""
import json

import pytest

from tsmm.eval.qa_judge import (
    RuleJudge,
    rule_score_describe,
    rule_score_anomaly,
    rule_score_forecast,
)


class TestDescribeRule:
    def test_numerical_within_tolerance(self):
        # predicted mean within 5% of reference → 1.0
        out = rule_score_describe(
            pred='{"mean": 50.0, "std": 2.0}',
            ref={"mean": 50.5, "std": 2.1},
            rtol=0.05,
        )
        assert out["score"] == 1.0
        assert out["parsed"] is True

    def test_outside_tolerance(self):
        out = rule_score_describe(
            pred='{"mean": 80.0}',
            ref={"mean": 50.0},
            rtol=0.05,
        )
        assert out["score"] == 0.0
        assert out["parsed"] is True

    def test_unparseable(self):
        out = rule_score_describe(pred="the mean is around 50", ref={"mean": 50.0})
        assert out["parsed"] is False
        assert out["score"] == 0.0


class TestAnomalyRule:
    def test_exact_match(self):
        pred = '{"segments": [[10, 20]]}'
        ref_segs = [(10, 20)]
        out = rule_score_anomaly(pred, ref_segs, T=50)
        assert out["score"] == 1.0
        assert out["parsed"] is True

    def test_iou_partial(self):
        pred = '{"segments": [[10, 30]]}'
        ref_segs = [(20, 40)]
        out = rule_score_anomaly(pred, ref_segs, T=50)
        assert 0.0 < out["score"] < 1.0

    def test_no_overlap(self):
        pred = '{"segments": [[0, 5]]}'
        ref_segs = [(20, 40)]
        out = rule_score_anomaly(pred, ref_segs, T=50)
        assert out["score"] == 0.0


class TestForecastRule:
    def test_within_tolerance(self):
        pred = '{"forecast": [1.0, 2.0, 3.0]}'
        ref = [1.05, 1.95, 3.1]
        out = rule_score_forecast(pred, ref, rtol=0.1)
        assert out["score"] == 1.0
        assert out["parsed"] is True

    def test_one_off(self):
        pred = '{"forecast": [1.0, 2.0, 9.0]}'
        ref = [1.0, 2.0, 3.0]
        out = rule_score_forecast(pred, ref, rtol=0.1)
        assert out["score"] < 1.0


class TestRuleJudgeDispatch:
    def test_judge_anomaly(self):
        judge = RuleJudge()
        out = judge.judge("anomaly", '{"segments": [[5, 8]]}', ref_segs=[(5, 8)], T=20)
        assert out["score"] == 1.0
        assert out["track"] == "rule"

    def test_judge_unknown_category_skips(self):
        judge = RuleJudge()
        out = judge.judge("root_cause", "some text answer", ref_answer="some text answer")
        assert out["track"] == "skip"
        assert out["score"] is None


class TestLLMJudgeStub:
    def test_stub_returns_deterministic(self):
        from tsmm.eval.qa_judge import LLMJudge
        judge = LLMJudge(backend="stub")
        s1 = judge.score("Q?", "ref", "pred")
        s2 = judge.score("Q?", "ref", "pred")
        assert isinstance(s1, dict)
        assert "score" in s1 and 0 <= s1["score"] <= 5
        assert s1 == s2  # deterministic stub

    def test_stub_rewards_exact_match(self):
        from tsmm.eval.qa_judge import LLMJudge
        judge = LLMJudge(backend="stub")
        same = judge.score("Q?", "正常", "正常")["score"]
        diff = judge.score("Q?", "正常", "完全不同的乱码回答xyz")["score"]
        assert same >= diff