"""回归测试: P0-3 — LLM 解析失败不能产生假成功预测。

验证链路:
  1. provider.py: JSON 解析失败时必须设置 error
  2. predict.py: resp.parsed is None 时必须抛错,不能 fallback 到 {}
  3. validation.py: 必填字段缺失时必须失败,不能静默给默认值
"""
from __future__ import annotations

from unittest.mock import MagicMock

import pytest

from src.llm.provider import LLMProvider, LLMResponse
from src.llm.validation import validate_prediction_output


class TestProviderJsonParseError:
    """P0-3 Part 1: provider.py JSON 解析失败必须设置 error。"""

    @pytest.mark.asyncio
    async def test_invalid_json_sets_error(self, monkeypatch):
        """LLM 返回非 JSON 内容时,error 必须非空。"""
        async def fake_post(*args, **kwargs):
            class FakeResp:
                status_code = 200
                def raise_for_status(self): pass
                def json(self):
                    return {
                        "choices": [{"message": {"content": "我不确定,可能是平局"}}],
                        "usage": {"prompt_tokens": 10, "completion_tokens": 5},
                    }
            return FakeResp()

        import httpx
        monkeypatch.setattr(httpx.AsyncClient, "post", fake_post)

        p = LLMProvider(api_key="test", model="gpt-4o")
        resp = await p.chat("sys", "user", json_mode=True)
        # P0-3: JSON 解析失败必须设置 error
        assert resp.error is not None, "JSON 解析失败应设置 error"
        assert resp.parsed is None

    @pytest.mark.asyncio
    async def test_code_block_json_works(self, monkeypatch):
        """LLM 返回 ```json {...}}``` 时应成功解析。"""
        async def fake_post(*args, **kwargs):
            class FakeResp:
                status_code = 200
                def raise_for_status(self): pass
                def json(self):
                    return {
                        "choices": [{"message": {"content": '```json\n{"pred_home_goals": 1.5, "pred_away_goals": 1.0, "pred_1x2": "1", "subjective_confidence": 0.7}\n```'}}],
                        "usage": {"prompt_tokens": 10, "completion_tokens": 5},
                    }
            return FakeResp()

        import httpx
        monkeypatch.setattr(httpx.AsyncClient, "post", fake_post)

        p = LLMProvider(api_key="test", model="gpt-4o")
        resp = await p.chat("sys", "user", json_mode=True)
        assert resp.error is None
        assert resp.parsed is not None
        assert resp.parsed["pred_1x2"] == "1"


class TestValidationNoSilentDefaults:
    """P0-3 Part 3: validation.py 必填字段缺失时必须失败。"""

    def test_missing_pred_home_goals_raises(self):
        """缺少 pred_home_goals 必须报错,不能默认为 0。"""
        with pytest.raises((ValueError, KeyError)):
            validate_prediction_output({
                "pred_away_goals": 1,
                "pred_1x2": "1",
                "subjective_confidence": 0.7,
            })

    def test_missing_pred_1x2_raises(self):
        """缺少 pred_1x2 必须报错,不能默认为 X。"""
        with pytest.raises(ValueError, match="Missing required field: pred_1x2"):
            validate_prediction_output({
                "pred_home_goals": 1,
                "pred_away_goals": 0,
                "subjective_confidence": 0.7,
            })

    def test_missing_confidence_raises(self):
        """缺少 subjective_confidence 必须报错,不能默认为 0.5。"""
        with pytest.raises(ValueError, match="Missing required field: subjective_confidence"):
            validate_prediction_output({
                "pred_home_goals": 1,
                "pred_away_goals": 0,
                "pred_1x2": "1",
            })

    def test_empty_dict_raises(self):
        """空 dict 必须报错(不能产生 0-0 X 0.5 的假预测)。"""
        with pytest.raises((ValueError, KeyError)):
            validate_prediction_output({})

    def test_valid_input_passes(self):
        """完整的合法输入应通过。"""
        result = validate_prediction_output({
            "pred_home_goals": 1.5,
            "pred_away_goals": 1.0,
            "pred_1x2": "1",
            "subjective_confidence": 0.7,
        })
        assert result.pred_home_goals == 2  # 1.5 → round → 2
        assert result.pred_away_goals == 1
        assert result.pred_1x2 == "1"
        assert result.subjective_confidence == 0.7