"""评审服务：保留真实失败原因，便于 internal eval 页面排障。"""

import pytest

from app.core.config import settings
from app.core.llm_call import LLMCallError
from app.features.evaluation.conversation_compare_summary import (
    build_conversation_compare_summary,
)
from app.features.evaluation.judge_schemas import ConversationJudgeOutput
from app.features.evaluation.judge_service import (
    EvalJudgeService,
    _build_memoir_judge_prompt,
    eval_judge_conversation_transcript_max_chars,
    eval_judge_compare_transcript_each_max_chars,
)


def _conversation_payload() -> dict:
    return {
        "emotion_carry": 10,
        "empathy_depth": 8,
        "emotion_safety": 6,
        "emotion_guidance": 6,
        "fact_mining": 8,
        "info_completeness_guide": 8,
        "info_depth_mining": 9,
        "persona_understanding": 7,
        "persona_consistency_verify": 4,
        "persona_expression_guide": 4,
        "interview_structure": 6,
        "context_memory": 5,
        "rhythm_control": 4,
        "question_quality": 7,
        "follow_up_depth": 5,
        "non_leading": 3,
        "total_score": 100.0,
        "rationale": "整体表现稳定。",
    }


def _conversation_payload_variant(**overrides: float | str) -> dict:
    data = _conversation_payload()
    data.update(overrides)
    return data


@pytest.mark.asyncio
async def test_judge_conversation_result_preserves_validation_error(
    monkeypatch: pytest.MonkeyPatch,
) -> None:
    async def _boom(*args, **kwargs):
        raise LLMCallError(
            "validation",
            "pydantic validation failed: total_score mismatch",
        )

    monkeypatch.setattr("app.features.evaluation.judge_service.allm_json_call", _boom)

    svc = EvalJudgeService(object())
    result = await svc.judge_conversation_result(full_transcript="[Turn 1]\n用户: hi\nAI: hello")

    assert result.output is None
    assert result.error is not None
    assert "结果校验失败" in result.error
    assert "total_score mismatch" in result.error


@pytest.mark.asyncio
async def test_judge_conversation_wrapper_keeps_legacy_shape(
    monkeypatch: pytest.MonkeyPatch,
) -> None:
    expected = ConversationJudgeOutput.model_validate(_conversation_payload())

    async def _ok(*args, **kwargs):
        return expected

    monkeypatch.setattr("app.features.evaluation.judge_service.allm_json_call", _ok)

    svc = EvalJudgeService(object())
    out = await svc.judge_conversation(full_transcript="[Turn 1]\n用户: hi\nAI: hello")

    assert out == expected


def test_eval_judge_transcript_budget_exceeds_legacy_8192(
    monkeypatch: pytest.MonkeyPatch,
) -> None:
    monkeypatch.setattr(settings, "eval_judge_max_transcript_chars", 0)
    monkeypatch.setattr(settings, "eval_judge_context_window_tokens", 200_000)
    n = eval_judge_conversation_transcript_max_chars()
    assert n > 90_000
    each = eval_judge_compare_transcript_each_max_chars()
    assert each > 40_000


def test_eval_judge_transcript_budget_respects_explicit_cap(
    monkeypatch: pytest.MonkeyPatch,
) -> None:
    monkeypatch.setattr(settings, "eval_judge_max_transcript_chars", 12_000)
    assert eval_judge_conversation_transcript_max_chars() == 12_000


def test_build_memoir_prompt_includes_source_and_reference_evidence() -> None:
    prompt = _build_memoir_judge_prompt(
        memoir_markdown="# 当前正文\n他后来去了南方。",
        source_transcript="用户: 我后来去了深圳工作。",
        reference_memoir_markdown="# 导出基线\n他去了深圳。",
        evidence_notes="必须严格核对真实性。",
    )

    assert "【评审说明】" in prompt
    assert "【原始访谈/对话证据】" in prompt
    assert "用户: 我后来去了深圳工作。" in prompt
    assert "【结构化记忆证据】" in prompt
    assert "【参考基线/导出成稿】" in prompt
    assert "【当前回忆录正文】" in prompt


def test_build_memoir_prompt_requires_conservative_scoring_without_evidence() -> None:
    prompt = _build_memoir_judge_prompt(
        memoir_markdown="# 当前正文\n他后来去了南方。"
    )

    assert "无可用局部对话证据" in prompt
    assert "必须保守打分" in prompt
    assert "【结构化记忆证据】" in prompt


def test_compare_summary_surpass_gate_and_truncation_flags() -> None:
    baseline = ConversationJudgeOutput.model_validate(_conversation_payload())
    replay = ConversationJudgeOutput.model_validate(
        _conversation_payload_variant(
            emotion_carry=10,
            empathy_depth=8,
            emotion_safety=6,
            emotion_guidance=6,
            fact_mining=8,
            info_completeness_guide=8,
            info_depth_mining=9,
            persona_understanding=7,
            persona_consistency_verify=4,
            persona_expression_guide=4,
            interview_structure=6,
            context_memory=5,
            rhythm_control=4,
            question_quality=7,
            follow_up_depth=5,
            non_leading=3,
            rationale="更稳定。",
        )
    )
    summary = build_conversation_compare_summary(
        baseline_judge=baseline,
        replay_judge=replay,
        baseline_transcript="A" * 400,
        replay_transcript="B" * 1200,
        conv_cap=1000,
        compare_cap_each=500,
        fixture_filename="golden.md",
    )
    assert summary["mode"] == "ab"
    assert summary["gate"]["status"] in {"parity", "surpass"}
    assert summary["truncation"]["replay_truncated_for_compare"] is True
    assert "group_deltas" in summary


def test_compare_summary_flags_repeat_issue_as_regression() -> None:
    baseline = ConversationJudgeOutput.model_validate(_conversation_payload())
    replay = ConversationJudgeOutput.model_validate(
        _conversation_payload_variant(
            context_memory=3,
            rhythm_control=3,
            total_score=0,
            major_issues=["存在重复盘问，忽略已答信息"],
        )
    )
    summary = build_conversation_compare_summary(
        baseline_judge=baseline,
        replay_judge=replay,
        baseline_transcript="[Turn 1]",
        replay_transcript="[Turn 1]",
        conv_cap=1000,
        compare_cap_each=500,
    )
    assert summary["repeat_issue_detected"] is True
    assert summary["gate"]["status"] == "regressed"