import pytest
from types import SimpleNamespace

from app.services.adaptive_rag_service.mixin_answer_entry import AdaptiveRAGAnswerEntryMixin
from app.services.adaptive_rag_service.mixin_answer_mainline import AdaptiveRAGAnswerMainlineMixin
from app.services.adaptive_rag_service.mixin_deep_guard_answer import AdaptiveRAGDeepGuardAnswerMixin
from app.services.adaptive_rag_service.mixin_deterministic_query_core import (
    AdaptiveRAGDeterministicQueryCoreMixin,
)
from app.services.adaptive_rag_service.shared_part_02 import DEEP_RETRIEVAL_STEPS, DEEP_VECTOR_TOP_K


class _FakeAdaptiveRAGService(
    AdaptiveRAGAnswerEntryMixin,
    AdaptiveRAGAnswerMainlineMixin,
):
    def __init__(self) -> None:
        self.db = object()
        self.tenant_id = 9
        self.ack_calls = 0
        self.base_calls = 0
        self.base_kwargs: dict[str, object] = {}

    def _normalize_thinking_mode(self, thinking_mode: str) -> str:
        return "deep" if str(thinking_mode or "").strip().lower() == "deep" else "normal"

    async def _prepare_answer_runtime(self, **kwargs):
        clean_q = str(kwargs.get("clean_q") or "").strip()
        action_mode = str(kwargs.get("action_mode") or "").strip().lower()
        return {
            "session_id": 101,
            "turn_index": 2,
            "context_session_id": 101,
            "conversation": [],
            "state_row": None,
            "effective_q": clean_q,
            "rewrite_meta": {"mode": "direct"},
            "query_type": "explain",
            "effective_action_mode": action_mode,
        }

    async def _handle_ack_first_requirement(self, **kwargs):
        self.ack_calls += 1
        _ = kwargs
        return {"answer": "ack-required", "processing_stage": "ack_only"}

    async def _execute_base_answer_mainline(self, **kwargs):
        self.base_calls += 1
        self.base_kwargs = dict(kwargs)
        return {"answer": "base-answer", "processing_stage": "qa"}


@pytest.mark.asyncio
async def test_answer_entry_read_mainline_skips_requirement_ack() -> None:
    service = _FakeAdaptiveRAGService()

    result = await service.answer_question(
        question="现在有哪些阻塞？",
        asked_by=1,
        asked_via="pmflow_manual",
        action_mode="read",
        sync_requirement=True,
    )

    assert result["processing_stage"] == "qa"
    assert service.ack_calls == 0
    assert service.base_calls == 1


@pytest.mark.asyncio
async def test_answer_entry_write_mode_uses_requirement_ack_gate() -> None:
    service = _FakeAdaptiveRAGService()

    result = await service.answer_question(
        question="请同步写入一条风险",
        asked_by=1,
        asked_via="pmflow_manual",
        action_mode="write",
        sync_requirement=True,
    )

    assert result["processing_stage"] == "ack_only"
    assert service.ack_calls == 1
    assert service.base_calls == 0


@pytest.mark.asyncio
async def test_answer_entry_high_accuracy_channel_forces_deep_mode() -> None:
    service = _FakeAdaptiveRAGService()

    result = await service.answer_question(
        question="最近有哪些项目？",
        asked_by=1,
        asked_via="pmflow",
        sync_requirement=True,
    )

    assert result["processing_stage"] == "qa"
    assert service.ack_calls == 0
    assert service.base_calls == 1
    assert service.base_kwargs["active_thinking_mode"] == "deep"
    assert service.base_kwargs["deep_thinking"] is True
    assert int(service.base_kwargs["deep_retrieval_steps"]) == DEEP_RETRIEVAL_STEPS
    assert int(service.base_kwargs["deep_vector_top_k"]) == DEEP_VECTOR_TOP_K


@pytest.mark.asyncio
async def test_answer_entry_mixed_request_skips_requirement_ack_gate() -> None:
    service = _FakeAdaptiveRAGService()

    result = await service.answer_question(
        question="现在进展如何，并且新增任务确认流",
        asked_by=1,
        asked_via="pmflow_manual",
        action_mode="read_write",
        sync_requirement=True,
        message_intent="mixed",
    )

    assert result["processing_stage"] == "qa"
    assert service.ack_calls == 0
    assert service.base_calls == 1
    assert service.base_kwargs["active_thinking_mode"] == "deep"
    assert service.base_kwargs["deep_thinking"] is True


@pytest.mark.asyncio
async def test_answer_entry_feishu_write_mode_still_goes_base_mainline() -> None:
    service = _FakeAdaptiveRAGService()

    result = await service.answer_question(
        question="请在飞书里补一条风险",
        asked_by=1,
        asked_via="feishu",
        action_mode="write",
        sync_requirement=True,
    )

    assert result["processing_stage"] == "qa"
    assert service.ack_calls == 0
    assert service.base_calls == 1
    assert service.base_kwargs["active_thinking_mode"] == "deep"
    assert service.base_kwargs["deep_thinking"] is True
    assert int(service.base_kwargs["deep_retrieval_steps"]) == DEEP_RETRIEVAL_STEPS
    assert int(service.base_kwargs["deep_vector_top_k"]) == DEEP_VECTOR_TOP_K


@pytest.mark.asyncio
async def test_deterministic_query_prefers_project_created_at_answer() -> None:
    calls: list[str] = []

    class _FakeDeterministicService(AdaptiveRAGDeterministicQueryCoreMixin):
        def __init__(self) -> None:
            self.db = object()
            self.tenant_id = 1

        async def _try_fast_project_created_at_answer(self, *, clean_q: str, project_id):
            calls.append("created_at")
            _ = (clean_q, project_id)
            return {"answer": "创建于 2026-03-01 09:30。", "sources": [], "missing_fields": []}

        async def _try_fast_project_progress_answer(self, *, clean_q: str, project_id):
            calls.append("progress")
            _ = (clean_q, project_id)
            return {"answer": "进度回答", "sources": []}

        async def _try_fast_project_existence_answer(self, *, clean_q: str, project_id):
            calls.append("existence")
            _ = (clean_q, project_id)
            return {"answer": "存在性回答", "sources": []}

    service = _FakeDeterministicService()
    result = await service._run_deterministic_query(
        question="他是什么时候创建的？",
        project_id=7,
        query_type="status",
    )

    assert result is not None
    assert "创建于 2026-03-01 09:30" in str(result["answer"])
    assert calls[0] == "created_at"


def test_query_type_classifies_colloquial_list_request_as_list() -> None:
    service = AdaptiveRAGDeterministicQueryCoreMixin()

    assert service._classify_query_type("我感觉很多重复的项目在系统，你能列一下吗？") == "list"


@pytest.mark.asyncio
async def test_deep_guard_answer_raises_on_empty_llm_response() -> None:
    class _FakeLLM:
        async def generate(self, prompt: str, **kwargs):
            _ = (prompt, kwargs)
            return ""

    class _FakeService(AdaptiveRAGDeepGuardAnswerMixin):
        def __init__(self) -> None:
            self.db = object()
            self.tenant_id = 1
            self.llm = _FakeLLM()

        def _normalize_thinking_mode(self, thinking_mode: str) -> str:
            return "normal" if str(thinking_mode or "").strip().lower() != "deep" else "deep"

        async def get_project_manifest(self, project_id):
            _ = project_id
            return {}

        def _select_scene_prompt(self, **kwargs):
            _ = kwargs
            return "external_trend", "请回答问题"

        def _enforce_feature_evidence_in_answer(self, *, question: str, answer_text: str, retrieved, system_snapshot):
            _ = (question, retrieved, system_snapshot)
            return answer_text

    service = _FakeService()

    with pytest.raises(ValueError, match="adaptive_rag_answer_empty_response"):
        await service._generate_answer(
            question="最近AI行业有什么动态",
            retrieved=[],
            conversation=[],
            project_id=None,
            asked_via="feishu",
        )


@pytest.mark.asyncio
async def test_deep_guard_answer_prefers_member_workload_direct_answer() -> None:
    class _FakeResult:
        def __init__(self, rows=None, scalar=None):
            self._rows = rows or []
            self._scalar = scalar

        def all(self):
            return self._rows

        def first(self):
            return self._rows[0] if self._rows else None

        def scalar_one_or_none(self):
            return self._scalar

    class _FakeDB:
        def __init__(self) -> None:
            self.calls = 0

        async def execute(self, stmt, params=None):  # noqa: ANN001
            self.calls += 1
            if self.calls == 1:
                return _FakeResult(rows=[SimpleNamespace(id=24, name="杨宇", username="yangyu")])
            return _FakeResult(rows=[(265.56, 95.6, 60.0)])

    class _FakeLLM:
        async def generate(self, prompt: str, **kwargs):  # pragma: no cover - should not be called
            raise AssertionError("LLM should not be called for direct member workload answer")

    class _FakeService(AdaptiveRAGDeepGuardAnswerMixin):
        def __init__(self) -> None:
            self.db = _FakeDB()
            self.tenant_id = 1
            self.llm = _FakeLLM()

        def _normalize_thinking_mode(self, thinking_mode: str) -> str:
            return "normal"

        async def get_project_manifest(self, project_id):
            _ = project_id
            return {}

        def _select_scene_prompt(self, **kwargs):
            _ = kwargs
            return "member_workload", "member workload prompt"

        def _enforce_feature_evidence_in_answer(self, *, question: str, answer_text: str, retrieved, system_snapshot):
            _ = (question, retrieved, system_snapshot)
            return answer_text

    service = _FakeService()
    result = await service._generate_answer(
        question="杨宇现在负载情况如何？",
        retrieved=[],
        conversation=[],
        project_id=None,
        asked_via="feishu",
    )

    assert "杨宇当前负载265.56%" in result
    assert "严重超载" in result