"""Judge sampling review for exploration sessions (v0.9 票据 04).

会话关闭后，平台抽样对话（≤3 段）经 judge 岗位模型独立复核，质量维度结论
结构化落入会话的 judge_review。复核是异步后台执行：失败落错误不阻塞会话
状态；无模型配置时静默跳过。LLM 调用经可注入 ChatClient 接缝（沿 v0.7
分析 seam），测试用假客户端覆盖（先例：test_campaign_analysis.py）。
"""

import json

import pytest
from agenteval.exploration.judge import (
    MAX_JUDGE_SAMPLES,
    execute_judge_review,
    normalize_judge_review,
    sample_round_indexes,
)
from agenteval.exploration.models import (
    ExplorationMessage,
    ExplorationSession,
    ExplorationSessionStatus,
)
from agenteval.models import Campaign, CampaignPlanEntry
from agenteval.storage.db import ModelConfigDB
from agenteval.storage.model_config_repository import ModelConfigRepository
from agenteval.storage.exploration_repository import ExplorationMessageRepository, ExplorationSessionRepository
from agenteval.storage.repository import (
    CampaignRepository,
)


class FakeChatClient:
    """Queued-response fake for the judge LLM seam."""

    def __init__(self, *responses):
        self._responses = list(responses)
        self.calls: list[list[dict]] = []

    async def __call__(self, messages: list[dict]) -> str:
        self.calls.append(messages)
        if not self._responses:
            raise AssertionError("unexpected extra LLM call")
        item = self._responses.pop(0)
        if isinstance(item, Exception):
            raise item
        return item


REVIEW_JSON = json.dumps(
    {
        "dimensions": [
            {"dimension": "attitude", "rating": "good", "comment": "态度友好"},
            {"dimension": "professionalism", "rating": "acceptable", "comment": "流程基本正确"},
            {"dimension": "hallucination", "rating": "poor", "comment": "编造了不存在的政策"},
        ],
        "summary": "服务态度好但存在幻觉",
    },
    ensure_ascii=False,
)


def _campaign() -> Campaign:
    return Campaign(
        id="camp-1",
        name="24h 正式线",
        target_id="t-1",
        window_seconds=86400,
        time_scale=1.0,
        plan=[CampaignPlanEntry(scenario_id="s-1", offset_seconds=0, count=1)],
    )


def _seed_config(db_session, config_id: str = "mc-default") -> None:
    ModelConfigRepository(db_session).create(
        ModelConfigDB(
            id=config_id,
            name=f"cfg-{config_id}",
            provider="openai_compatible",
            capability="chat",
            endpoint_url="https://models.example.com/v1/chat/completions",
            model_name="m",
            enabled=True,
            is_analysis_default=True,
        )
    )


def _seed_session_with_messages(db_session, rounds: int = 5) -> str:
    CampaignRepository(db_session).create(_campaign())
    repo = ExplorationSessionRepository(db_session)
    session_obj = repo.create(
        ExplorationSession(
            campaign_id="camp-1",
            target_id="t-1",
            persona={"name": "急性子用户"},
            goal="查询账单并缴费",
        )
    )
    session_obj.status = ExplorationSessionStatus.COMPLETED
    repo.update(session_obj)

    message_repo = ExplorationMessageRepository(db_session)
    for i in range(1, rounds + 1):
        message_repo.save_message(
            ExplorationMessage(session_id=session_obj.id, round_index=i, role="user", content=f"用户消息-{i}")
        )
        message_repo.save_message(
            ExplorationMessage(session_id=session_obj.id, round_index=i, role="assistant", content=f"回复内容-{i}")
        )
    return session_obj.id


@pytest.fixture()
def judge_env(db_session, monkeypatch):
    from agenteval.exploration import judge as judge_module

    monkeypatch.setattr(judge_module, "get_session", lambda: db_session)
    return db_session


# ── 抽样 ──────────────────────────────────────────────────────────────


def test_sample_round_indexes_returns_all_within_cap():
    assert sample_round_indexes([1, 2]) == [1, 2]
    assert sample_round_indexes([1, 2, 3]) == [1, 2, 3]


def test_sample_round_indexes_spreads_evenly_over_cap():
    assert sample_round_indexes([1, 2, 3, 4, 5]) == [1, 3, 5]
    assert len(sample_round_indexes(list(range(1, 11)))) == MAX_JUDGE_SAMPLES


# ── 归一化 ────────────────────────────────────────────────────────────


def test_normalize_judge_review_applies_whitelists():
    raw = {
        "dimensions": [
            {"dimension": "attitude", "rating": "good", "comment": "ok"},
            {"dimension": "神秘维度", "rating": "good", "comment": "drop me"},
            {"dimension": "hallucination", "rating": "离谱", "comment": "bad rating"},
        ],
        "summary": "结论",
    }
    review = normalize_judge_review(raw)
    dims = {d["dimension"]: d for d in review["dimensions"]}
    assert set(dims) == {"attitude", "hallucination"}
    assert dims["attitude"]["rating"] == "good"
    assert dims["hallucination"]["rating"] == "acceptable"  # 非法档位归一
    assert review["summary"] == "结论"


def test_normalize_judge_review_rejects_non_list_dimensions():
    review = normalize_judge_review({"dimensions": "不是列表", "summary": 123})
    assert review["dimensions"] == []
    assert review["summary"] == "123"


# ── 后台执行编排 ──────────────────────────────────────────────────────


async def test_execute_persists_structured_review(judge_env):
    session_id = _seed_session_with_messages(judge_env, rounds=5)
    _seed_config(judge_env)

    client = FakeChatClient(REVIEW_JSON)
    await execute_judge_review(session_id, chat_client=client)

    session_obj = ExplorationSessionRepository(judge_env).get(session_id)
    assert session_obj.status == ExplorationSessionStatus.COMPLETED
    review = session_obj.judge_review
    assert review["status"] == "completed"
    assert review["model_config_id"] == "mc-default"
    assert review["sampled_rounds"] == [1, 3, 5]
    assert len(review["dimensions"]) == 3
    assert review["summary"] == "服务态度好但存在幻觉"


async def test_execute_prompt_carries_only_sampled_rounds(judge_env):
    session_id = _seed_session_with_messages(judge_env, rounds=5)
    _seed_config(judge_env)

    client = FakeChatClient(REVIEW_JSON)
    await execute_judge_review(session_id, chat_client=client)

    payload = json.dumps(client.calls[0], ensure_ascii=False)
    assert "用户消息-1" in payload and "用户消息-3" in payload and "用户消息-5" in payload
    assert "用户消息-2" not in payload and "用户消息-4" not in payload


async def test_execute_records_error_on_unparseable_output(judge_env):
    session_id = _seed_session_with_messages(judge_env)
    _seed_config(judge_env)

    await execute_judge_review(session_id, chat_client=FakeChatClient("这不是 JSON"))

    session_obj = ExplorationSessionRepository(judge_env).get(session_id)
    assert session_obj.status == ExplorationSessionStatus.COMPLETED  # 复核失败不阻塞会话
    review = session_obj.judge_review
    assert review["status"] == "failed"
    assert review["error"]
    assert review["model_config_id"] == "mc-default"


async def test_execute_records_error_on_client_exception(judge_env):
    session_id = _seed_session_with_messages(judge_env)
    _seed_config(judge_env)

    await execute_judge_review(session_id, chat_client=FakeChatClient(RuntimeError("模型网关超时")))

    review = ExplorationSessionRepository(judge_env).get(session_id).judge_review
    assert review["status"] == "failed"
    assert "模型网关超时" in review["error"]


async def test_execute_skips_silently_without_model_config(judge_env):
    session_id = _seed_session_with_messages(judge_env)

    client = FakeChatClient(REVIEW_JSON)
    await execute_judge_review(session_id, chat_client=client)

    assert client.calls == []
    assert ExplorationSessionRepository(judge_env).get(session_id).judge_review is None


async def test_execute_skips_session_without_messages(judge_env):
    CampaignRepository(judge_env).create(_campaign())
    session_obj = ExplorationSessionRepository(judge_env).create(
        ExplorationSession(campaign_id="camp-1", target_id="t-1", persona={}, goal="g")
    )
    _seed_config(judge_env)

    client = FakeChatClient(REVIEW_JSON)
    await execute_judge_review(session_obj.id, chat_client=client)

    assert client.calls == []
    assert ExplorationSessionRepository(judge_env).get(session_obj.id).judge_review is None