"""D2 工程债回归测试: 统一预测结果类型。

背景: predict_match 三条路径返回类型不一 —— single 返回 PredictResult,
multi 返回字段重复定义的 MultiPredictResult dataclass,baseline 返回裸 dict。
后果: (1) 预测路由 PredictOut 映射被迫写 isinstance(result, dict) 双分支;
(2) backtest 对 baseline 模式直接 AttributeError(dict 没有 .prediction_id,
潜伏 bug);(3) 字段清单在两处 dataclass 重复维护,加字段必漏一处。

统一方案: 扩展 PredictResult(可选字段)承载全部模式;
MultiPredictResult 变为其别名(保留 orchestrator 签名标记,兼容 re-export);
baseline 返回 PredictResult;路由单一字段映射。

本测试守护四件事:
  1. predict_baseline 返回 PredictResult(属性访问)
  2. MultiPredictResult 与 PredictResult 兼容(orchestrator 构造调用的
     全字段 kwargs 可直接构造别名)
  3. 预测路由不再有 isinstance(result, dict) 分支(源码守卫,仿 R4 范式)
  4. _persist_baseline 用属性访问构造 upsert values(baseline 落库语义不变)
"""
from __future__ import annotations

from pathlib import Path
from types import SimpleNamespace

import pytest

from src.llm.baseline import predict_baseline
from src.llm.predict import PredictResult
from src.llm.agents import MultiPredictResult

ROUTE_PATH = Path(__file__).resolve().parents[1] / "src" / "api" / "routes" / "predict.py"


# ============================================================
# 1. baseline 返回 PredictResult
# ============================================================


@pytest.mark.asyncio
async def test_predict_baseline_returns_predict_result():
    """基线预测返回 PredictResult 实例,mode=baseline,token/延迟为 0。"""
    from unittest.mock import patch

    async def fake_avg(db, *, team_id, side, league_id, before):
        return 2.4 if side == "home" else 1.6

    class FakeMatch:
        id = 1
        home_team_id = 10
        away_team_id = 20
        league_id = 1
        match_status = "scheduled"

    class FakeSession:
        async def get(self, cls, mid):
            return FakeMatch()

    class FakeCM:
        async def __aenter__(self):
            return FakeSession()

        async def __aexit__(self, *a):
            return None

    # P3-2:baseline 在服务层落库(get_uow + _insert_or_find_by_fingerprint),需 mock 掉。
    class FakeUoW:
        async def __aenter__(self):
            return _make_session()

        async def __aexit__(self, *a):
            return None

    captured = {}

    async def fake_upsert(session, *, values):
        captured.update(values)
        return SimpleNamespace(id=77)

    # baseline.py 内部 from-import get_uow / _insert_or_find_by_fingerprint,需 patch 真实来源模块。
    with patch("src.llm.baseline._avg_goals", fake_avg), \
         patch("src.llm.baseline.AsyncSessionLocal") as SLC, \
         patch("src.db.unit_of_work.get_uow", FakeUoW), \
         patch("src.llm.baseline._insert_or_find_by_fingerprint", fake_upsert):
        SLC.return_value = FakeCM()

        result = await predict_baseline(1)

    # P3-2:验证服务层落库被调用且属性映射正确
    assert captured["match_id"] == 1
    assert captured["provider"] == "baseline"
    assert captured["run_type"] == "live"
    assert captured["pred_home_goals"] == 2.0

    assert isinstance(result, PredictResult)
    assert result.mode == "baseline"
    assert result.provider == "baseline"
    assert result.model == "baseline"
    assert result.prompt_version == "baseline_v1"
    assert result.pred_home_goals == 2.0
    assert result.pred_away_goals == 2.0
    assert result.pred_1x2 == "X"
    assert result.subjective_confidence == 0.5
    assert result.prompt_tokens == 0
    assert result.completion_tokens == 0
    assert result.latency_ms == 0
    assert result.status == "success"
    assert "非投注建议" in (result.reasoning or "")
    # baseline 不构建 LLM 上下文,但字段必须存在且可安全序列化
    assert result.context == ""
    # 原始统计快照保留在 raw 中
    assert result.raw is not None
    assert "home_avg" in result.raw


# ============================================================
# 2. MultiPredictResult 与扩展后的 PredictResult 兼容
# ============================================================


def test_multi_predict_result_is_predict_result_alias():
    """multi 结果不再是重复定义的 dataclass,而是扩展 PredictResult 的别名。"""
    assert MultiPredictResult is PredictResult


def test_multi_result_constructor_kwargs_still_supported():
    """orchestrator 现有构造调用的全部字段 kwargs 必须仍可构造(别名完整性)。"""
    # 与 orchestrator.predict_match_multi 的 return MultiPredictResult(...) 逐一对应
    result = MultiPredictResult(
        prediction_id=1,
        provider="openai",
        model="gpt-x",
        prompt_version="multi_v1",
        mode="multi",
        pred_home_goals=2.0,
        pred_away_goals=1.0,
        alt_pred_home_goals=None,
        alt_pred_away_goals=None,
        pred_1x2="1",
        subjective_confidence=0.7,
        reasoning="r",
        status="success",
        agent_outputs=[{"agent": "form"}],
        agent_weights={"form": 0.2},
        context="ctx",
        latency_ms=100,
        prompt_tokens=10,
        completion_tokens=5,
        raw={"final": True},
    )
    assert result.mode == "multi"
    assert result.agent_outputs == [{"agent": "form"}]
    assert result.agent_weights == {"form": 0.2}
    assert result.prompt_tokens == 10
    assert result.completion_tokens == 5


# ============================================================
# 3. 路由去 dict 分支(源码守卫)
# ============================================================


def test_predict_route_has_no_dict_branch():
    """PredictOut 映射必须统一走属性访问,禁止 isinstance(result, dict) 回潮。"""
    src = ROUTE_PATH.read_text(encoding="utf-8")
    assert "isinstance(result, dict)" not in src
    assert ".get(\"pred_home_goals\")" not in src


# ============================================================
# 4. P3-2:baseline 服务层落库属性映射(落库已从路由移到 baseline.py)
# ============================================================


class _FakeResult:
    """支持 .scalar_one_or_none() 的最小假结果集。"""

    def __init__(self, items):
        self._items = list(items)

    def scalars(self):
        return self

    def all(self):
        return self._items

    def scalar_one_or_none(self):
        return self._items[0] if self._items else None


class _FakeUoW:
    """替代 get_uow 的最小上下文管理器(session.execute 是 async 的)。"""

    def __init__(self):
        self.session = _make_session()

    async def __aenter__(self):
        return self.session

    async def __aexit__(self, *a):
        return None

    def __call__(self):
        return self


def _make_session(existing=None):
    """构造带 async execute / add / flush 的假 session。"""
    sess = SimpleNamespace()

    async def execute(*a, **k):
        return _FakeResult(existing or [])

    sess.execute = execute
    sess.add = lambda *a, **k: None

    async def flush(*a, **k):
        return None

    sess.flush = flush
    return sess


@pytest.mark.asyncio
async def test_baseline_service_persists_with_correct_attributes(monkeypatch):
    """P3-2:baseline 在服务层(predict_baseline)落库,属性映射与路由旧版一致。"""
    captured = {}

    async def fake_upsert(session, *, values):
        captured.update(values)
        return SimpleNamespace(id=77)

    class FakeMatch:
        id = 1
        home_team_id = 10
        away_team_id = 20
        league_id = 1
        match_status = "scheduled"

    class FakeSession:
        async def get(self, cls, mid):
            return FakeMatch()

    class FakeSLC:
        async def __aenter__(self):
            return FakeSession()

        async def __aexit__(self, *a):
            return None

    async def fake_avg(db, *, team_id, side, league_id, before):
        return 2.0 if side == "home" else 1.0

    monkeypatch.setattr("src.llm.baseline._avg_goals", fake_avg)
    monkeypatch.setattr("src.llm.baseline.AsyncSessionLocal", FakeSLC)
    monkeypatch.setattr("src.db.unit_of_work.get_uow", lambda: _FakeUoW())
    # baseline.py 模块级 import _insert_or_find_by_fingerprint(第 15 行),需 patch baseline 模块属性
    monkeypatch.setattr("src.llm.baseline._insert_or_find_by_fingerprint", fake_upsert)

    result = await predict_baseline(1)

    # 落库被调用且属性映射正确
    assert captured, f"predict_baseline 应调用 _insert_or_find_by_fingerprint 落库,但 captured 为空(result.prediction_id={result.prediction_id!r})"
    assert captured["match_id"] == 1
    assert captured["provider"] == "baseline"
    assert captured["model"] == "baseline"
    assert captured["mode"] == "baseline"
    assert captured["run_type"] == "live"
    assert captured["prompt_version"] == "baseline_v1"
    assert captured["pred_home_goals"] == 2.0
    assert captured["pred_away_goals"] == 1.0
    assert captured["pred_1x2"] == "1"
    assert captured["subjective_confidence"] == 0.5
    assert captured["prompt_tokens"] == 0
    assert captured["completion_tokens"] == 0
    assert captured["latency_ms"] == 0
    assert captured["raw_response"] == {"home_avg": 2.0, "away_avg": 1.0}
    assert captured["status"] == "success"

    # 回填真实 prediction_id(服务层落库后取得)
    assert result.prediction_id == 77
    assert result.pred_1x2 == "1"
    assert captured["match_id"] == 1
    assert captured["provider"] == "baseline"
    assert captured["model"] == "baseline"
    assert captured["mode"] == "baseline"
    assert captured["run_type"] == "live"
    assert captured["prompt_version"] == "baseline_v1"
    assert captured["pred_home_goals"] == 2.0
    assert captured["pred_away_goals"] == 1.0
    assert captured["pred_1x2"] == "1"
    assert captured["subjective_confidence"] == 0.5
    assert captured["prompt_tokens"] == 0
    assert captured["completion_tokens"] == 0
    assert captured["latency_ms"] == 0
    assert captured["raw_response"] == {"home_avg": 2.0, "away_avg": 1.0}
    assert captured["status"] == "success"

    # 回填真实 prediction_id(服务层落库后取得)
    assert result.prediction_id == 77