"""多 agent 架构测试。"""
import pytest
from src.llm.agents.base import AgentReport, load_agent_prompt, _is_no_data
from src.llm.context_builder import MatchHeader


class TestNoDataGate:
    """no_data 门控: 切片无数据 → 跳过 LLM。"""

    def test_is_no_data_all_lines(self):
        assert _is_no_data("── 伤停 ──\n  无数据") is True

    def test_is_no_data_with_content(self):
        assert _is_no_data("── 交锋 ──\n  2026-03: A 2-1 B") is False

    def test_is_no_data_empty(self):
        assert _is_no_data("") is True

    def test_is_no_data_mixed(self):
        # 部分有数据部分无 → 不是 no_data
        assert _is_no_data("── xG ──\n  A: xG 1.5 vs B 0.8\n  B: 无 xG 数据") is False

    def test_stub_no_data_report(self):
        from src.llm.agents.base import _stub_no_data
        r = _stub_no_data("standings")
        assert r.status == "no_data"
        assert r.data_sufficiency == "none"
        assert r.home_edge is None


class TestPromptLoading:
    """agent prompt 模板加载。"""

    @pytest.mark.parametrize("name", ["form", "stats", "home_away", "standings", "h2h", "aggregator"])
    def test_all_prompts_exist(self, name):
        tpl = load_agent_prompt(name, "v1")
        assert "{{context}}" in tpl or "{{agent_reports}}" in tpl

    def test_prompt_not_found(self):
        with pytest.raises(FileNotFoundError):
            load_agent_prompt("nonexistent", "v1")


class TestReportParsing:
    """LLM JSON 输出 → AgentReport 解析(宽容处理)。"""

    def _make_header(self) -> MatchHeader:
        return MatchHeader(
            match_id=1, home_name="A", away_name="B", league_name="PL",
            season="2026-2027", match_date="2026-09-15", match_dt=None,
            stage=None, home_team_id=10, away_team_id=20, league_id=1,
        )

    def test_parse_full_report(self):
        from src.llm.agents.base import _parse_report
        from src.llm.provider import LLMResponse

        parsed = {
            "data_sufficiency": "high",
            "analysis": "主队交锋占优",
            "home_edge": 0.6,
            "subjective_confidence": 0.8,
            "key_evidence": ["近5次交锋主队4胜", "主场交锋3连胜"],
        }
        resp = LLMResponse(content="{}", parsed=parsed, prompt_tokens=100, completion_tokens=50, latency_ms=500)
        r = _parse_report("h2h", parsed, resp, "gpt-4o-mini")
        assert r.status == "ok"
        assert r.home_edge == 0.6
        assert r.subjective_confidence == 0.8
        assert len(r.key_evidence) == 2
        assert r.data_sufficiency == "high"

    def test_parse_xg_report_with_score(self):
        from src.llm.agents.base import _parse_report
        from src.llm.provider import LLMResponse

        parsed = {
            "data_sufficiency": "medium",
            "analysis": "主队火力更强",
            "home_edge": 0.4,
            "subjective_confidence": 0.7,
            "key_evidence": ["场均xG 2.1"],
            "exp_home_goals": 2.1,
            "exp_away_goals": 1.2,
            "probable_score": {"home": 2, "away": 1, "prob": 0.14},
        }
        resp = LLMResponse(content="{}", parsed=parsed)
        r = _parse_report("xg", parsed, resp, "gpt-4o-mini")
        assert r.exp_home_goals == 2.1
        assert r.probable_score == "2-1"

    def test_parse_bad_values_forgiving(self):
        """非法数值/字段宽容降级,不抛异常。"""
        from src.llm.agents.base import _parse_report
        from src.llm.provider import LLMResponse

        parsed = {
            "data_sufficiency": "bogus",   # 非法 → medium
            "home_edge": "very strong",    # 非法 → None (宽容降级)
            "subjective_confidence": None,
            "key_evidence": "单字符串",      # → [str]
        }
        resp = LLMResponse(content="{}", parsed=parsed)
        r = _parse_report("form", parsed, resp, "m")
        # 宽容降级: 不抛异常,非法字段变 None 或默认值
        assert r.status == "ok"
        assert r.data_sufficiency == "medium"
        assert r.home_edge is None
        assert r.key_evidence == ["单字符串"]

        # 验证范围约束: confidence > 1 会被截断或拒绝
        parsed2 = {"subjective_confidence": 1.5, "home_edge": 2.0}
        r2 = _parse_report("form", parsed2, resp, "m")
        # Pydantic 会拒绝越界值 → parse_error
        assert r2.status == "parse_error"


class TestRunAgent:
    """run_agent 执行器: 门控 + fail-open。"""

    @pytest.mark.asyncio
    async def test_no_data_skips_llm(self):
        """切片无数据 → 不调 LLM,直接 stub。"""
        from src.llm.agents.base import AgentSpec, run_agent

        async def empty_slice(header, before=None):
            return "── 伤停 ──\n  无数据"

        spec = AgentSpec(name="standings", system_prompt="s", slice_fn=empty_slice)
        header = self._make_header()

        class ExplodingProvider:
            async def chat(self, *a, **kw):
                raise AssertionError("LLM 不应被调用")

        r = await run_agent(spec, header, ExplodingProvider(), version="v1")
        assert r.status == "no_data"

    @pytest.mark.asyncio
    async def test_llm_error_fail_open(self):
        """LLM 调用失败 → status=error,不抛异常。"""
        from src.llm.agents.base import AgentSpec, run_agent

        async def good_slice(header, before=None):
            return "── 交锋 ──\n  2026-03: A 2-1 B"

        spec = AgentSpec(name="h2h", system_prompt="s", slice_fn=good_slice)
        header = self._make_header()

        class FailProvider:
            async def chat(self, *a, **kw):
                from src.llm.provider import LLMResponse
                return LLMResponse(content="", error="timeout")

        r = await run_agent(spec, header, FailProvider(), version="v1")
        assert r.status == "error"
        assert "LLM 调用失败" in r.analysis

    @pytest.mark.asyncio
    async def test_success_path(self):
        """正常路径: 切片 → LLM → 解析。"""
        from src.llm.agents.base import AgentSpec, run_agent
        from src.llm.provider import LLMResponse

        async def good_slice(header, before=None):
            return "── 交锋 ──\n  2026-03: A 2-1 B"

        spec = AgentSpec(name="h2h", system_prompt="s", slice_fn=good_slice)
        header = self._make_header()

        class OkProvider:
            model = "test-model"
            async def chat(self, system, user, **kw):
                assert "{{context}}" not in user  # 模板已渲染
                assert "A 2-1 B" in user
                return LLMResponse(
                    content="{}",
                    parsed={"data_sufficiency": "high", "analysis": "ok", "home_edge": 0.5, "subjective_confidence": 0.9},
                    prompt_tokens=10, completion_tokens=5, latency_ms=100,
                )

        r = await run_agent(spec, header, OkProvider(), version="v1")
        assert r.status == "ok"
        assert r.home_edge == 0.5
        assert r.model == "test-model"

    def _make_header(self) -> MatchHeader:
        return MatchHeader(
            match_id=1, home_name="A", away_name="B", league_name="PL",
            season="2026-2027", match_date="2026-09-15", match_dt=None,
            stage=None, home_team_id=10, away_team_id=20, league_id=1,
        )


class TestOrchestratorAggregation:
    """终裁输入拼装逻辑。"""

    def test_reports_to_json(self):
        from src.llm.agents.orchestrator import _reports_to_json, AGENT_LABELS_ZH
        import json

        reports = [
            AgentReport(agent="h2h", status="ok", home_edge=0.5, subjective_confidence=0.8, analysis="a"),
            AgentReport(agent="standings", status="no_data", data_sufficiency="none"),
        ]
        text = _reports_to_json(reports)
        data = json.loads(text)
        assert len(data) == 2
        # 契约: agent 字段序列化为中文专家全名,引导终裁用统一称呼引用
        # (见 orchestrator.AGENT_LABELS_ZH 与 _reports_to_json 的 docstring)
        assert data[0]["agent"] == AGENT_LABELS_ZH["h2h"] == "历史交锋分析专家"
        assert data[1]["status"] == "no_data"
        # 两个 agent 都应被映射,不留英文原键
        assert data[1]["agent"] == AGENT_LABELS_ZH["standings"]

    def test_aggregator_prompt_renders(self):
        """终裁 prompt 模板两占位符都能渲染。"""
        tpl = load_agent_prompt("aggregator", "v1")
        rendered = (
            tpl
            .replace("{{match_header}}", "对阵: A vs B")
            .replace("{{agent_reports}}", '[{"agent": "h2h"}]')
        )
        assert "{{match_header}}" not in rendered
        assert "{{agent_reports}}" not in rendered


class TestSliceResultGate:
    """P2-1: 结构化 has_data 门控(替代脆弱的文案子串匹配)。"""

    def test_sliceresult_empty_is_no_data(self):
        from src.llm.agents.base import _slice_has_data
        from src.llm.context_builder import SliceResult

        text, has = _slice_has_data(SliceResult(text="── x ──\n  无数据", has_data=False))
        assert has is False

    def test_sliceresult_with_data_beats_text(self):
        """即使文案里出现「无数据」字样,结构化 has_data=True 也应胜出。

        这正是旧实现的漏洞:文案匹配会把「主队: 无伤停数据 / 客队: 2人伤停」
        这类混合输出……这里显式验证结构化声明优先。
        """
        from src.llm.agents.base import _slice_has_data
        from src.llm.context_builder import SliceResult

        tricky = SliceResult(
            text="── 阵容完整性 ──\n  主队: 无伤停数据\n  客队伤停(1人):\n    - X: 拉伤",
            has_data=True,
        )
        _, has = _slice_has_data(tricky)
        assert has is True

    def test_str_fallback_still_works(self):
        """旧式 str 切片(测试 mock / 自定义切片)仍走文案回退,保持兼容。"""
        from src.llm.agents.base import _slice_has_data

        assert _slice_has_data("── 伤停 ──\n  无数据")[1] is False
        assert _slice_has_data("── 交锋 ──\n  A 2-1 B")[1] is True

    def test_sliceresult_str_compat(self):
        """SliceResult 可当 str 用(老调用点无需改)。"""
        from src.llm.context_builder import SliceResult

        s = SliceResult(text="hello", has_data=True)
        assert str(s) == "hello"


class TestAgentWeightsValidation:
    """P2-2: agent_weights 必须过校验才能落库。"""

    def test_unknown_agent_dropped(self):
        from src.llm.validation import validate_agent_weights

        w = validate_agent_weights({"form": 0.4, "h2h": 0.4, "bogus": 0.2, "stats": 0.4})
        assert "bogus" not in w
        assert set(w) <= {"form", "stats", "home_away", "standings", "h2h"}

    def test_out_of_range_clamped(self):
        from src.llm.validation import validate_agent_weights

        w = validate_agent_weights({"form": 5.0, "h2h": -1.0})
        assert w["form"] == 1.0
        assert w["h2h"] == 0.0

    def test_sum_normalized(self):
        from src.llm.validation import validate_agent_weights

        w = validate_agent_weights({"form": 2.0, "stats": 2.0})
        assert abs(sum(w.values()) - 1.0) < 1e-9

    def test_no_weights_returns_empty(self):
        from src.llm.validation import validate_agent_weights

        assert validate_agent_weights(None) == {}
        assert validate_agent_weights("not a dict") == {}


class TestPredictionConsistencyWarn:
    """P2-3: 比分与 1x2 不一致 → 以比分修正(且告警)。"""

    def test_mismatch_is_corrected_to_score(self):
        from src.llm.validation import validate_prediction_output

        v = validate_prediction_output({
            "pred_home_goals": 2.0,
            "pred_away_goals": 1.0,
            "pred_1x2": "X",   # 与 2-1 矛盾
            "subjective_confidence": 0.7,
        })
        assert v.pred_1x2 == "1"   # 按比分修正

    def test_consistent_passes_through(self):
        from src.llm.validation import validate_prediction_output

        v = validate_prediction_output({
            "pred_home_goals": 0.0,
            "pred_away_goals": 0.0,
            "pred_1x2": "X",
            "subjective_confidence": 0.5,
        })
        assert v.pred_1x2 == "X"