"""翻转门四路径：passed / filtered_no_flip / flip_skipped / 镜像不入库。"""

import json

import pytest

from app.question_gen.adversarial_config import AdversarialFilterConfig
from app.question_gen.adversarial_filter import (
    agent_config_fingerprint,
    question_hash,
    run_flip_gate,
    write_final_bank,
)
from app.question_gen.run_store import QuestionGenStore
from core.types import GeneratedQuestion, LLMResponse


class _FakeAgent:
    """复用 Task 6 语义；带 skill_mode 属性（AgentRunner Protocol 要求）。"""

    def __init__(self, preds, model="m1", skill_mode="auto"):
        self._preds = preds
        self.model = model
        self.skill_mode = skill_mode
        self.calls: list[str] = []

    async def predict(self, questions, *, max_steps, run_id):
        self.calls.extend(q.question_id for q in questions)
        return {q.question_id: self._preds.get(q.question_id) for q in questions}


class _FakeVLM:
    def __init__(self, content):
        self._content = content

    async def chat_with_images(self, messages, images, *, session_id=None, parent_call_id=None):
        return LLMResponse(
            content=self._content,
            thinking="",
            model="fake",
            provider="fake",
            prompt_tokens=0,
            completion_tokens=0,
            latency_ms=0,
            ttft_ms=None,
            max_inter_token_ms=None,
            cache_hit=False,
            call_id="c",
        )


class _FakeMaterial:
    subtitle_sentences = ["先炒后蒸"]
    frame_paths = ["/f1.jpg"]


def _q(qid, sub="temporal_reasoning_failure"):
    return GeneratedQuestion(
        question_id=qid,
        video_id="v1",
        task_type="Action Recognition",
        question="X 之前做了什么？",
        options=("A. 蒸", "B. 炒", "C. 煮", "D. 炸"),
        answer="A",
        source_nodes=("n1",),
        difficulty="hard",
        sub_pattern=sub,
    )


def _fp():
    return agent_config_fingerprint(skill_mode="auto", max_steps=40, model="m1")


@pytest.fixture(autouse=True)
def _stub_material(monkeypatch):
    """隔离建树素材重建，直接给镜像生成喂假素材。"""
    monkeypatch.setattr(
        "app.question_gen.adversarial_filter._rebuild_material",
        lambda tree, source_nodes: _FakeMaterial(),
    )


def _preset_cheat(store, q, pred="A"):
    """预置作弊门 P 预测行（翻转门须复用它，不重跑 agent）。"""
    store.record_verdict(
        question_id=q.question_id,
        question_hash=question_hash(q),
        stage="cheat",
        round=0,
        agent_prediction=pred,
        agent_correct=False,
        verdict="passed",
        pair_id=None,
        agent_config=_fp(),
    )


@pytest.mark.asyncio
async def test_flip_gate_answer_flips_passed(tmp_path):
    store = QuestionGenStore(str(tmp_path / "q.db"))
    q = _q("hard")
    _preset_cheat(store, q, pred="A")  # P canonical="蒸"
    agent = _FakeAgent({"hard_mirror": "A"})  # 镜像洗牌后 A=炒 → canonical≠蒸
    vlm = _FakeVLM(
        json.dumps(
            {
                "mirror": {
                    "question": "X 之后？",
                    "options": ["A. 炒", "B. 蒸", "C. 煮", "D. 炸"],
                    "answer": "A",
                }
            },
            ensure_ascii=False,
        )
    )
    kept = await run_flip_gate(
        [q],
        agent=agent,
        vlm=vlm,
        store=store,
        trees={"v1": object()},
        config=AdversarialFilterConfig(),
        round_no=0,
        run_id="r0",
        session_id="s",
    )
    assert {x.question_id for x in kept} == {"hard"}
    assert agent.calls == ["hard_mirror"]  # C2: 原题 P 未被重跑，只跑镜像
    cheat = store._conn.execute(
        "SELECT verdict FROM adversarial_verdicts WHERE question_id='hard' AND stage='cheat'"
    ).fetchone()[0]
    assert cheat == "passed"
    # flip_original + flip_mirror 两条独立 stage 行，共用同一 pair_id（非空），verdict=passed
    by_stage = {
        r[0]: (r[1], r[2])
        for r in store._conn.execute(
            "SELECT stage, verdict, pair_id FROM adversarial_verdicts "
            "WHERE question_id='hard' AND stage IN ('flip_original', 'flip_mirror')"
        )
    }
    assert set(by_stage) == {"flip_original", "flip_mirror"}
    assert by_stage["flip_original"][0] == "passed"
    assert by_stage["flip_mirror"][0] == "passed"
    assert by_stage["flip_original"][1]  # pair_id 非空
    assert by_stage["flip_original"][1] == by_stage["flip_mirror"][1]  # 共用同一 pair_id
    assert "hard" in store.final_passed_question_ids({"hard": question_hash(q)}, _fp())
    store.close()


@pytest.mark.asyncio
async def test_flip_gate_same_answer_filtered(tmp_path):
    store = QuestionGenStore(str(tmp_path / "q.db"))
    q = _q("stick")
    _preset_cheat(store, q, pred="A")  # P canonical="蒸"
    agent = _FakeAgent({"stick_mirror": "A"})  # 镜像 A=蒸 → canonical 与 P 相同
    vlm = _FakeVLM(
        json.dumps(
            {
                "mirror": {
                    "question": "X 之后？",
                    "options": ["A. 蒸", "B. 炒", "C. 煮", "D. 炸"],
                    "answer": "B",
                }
            },
            ensure_ascii=False,
        )
    )
    kept = await run_flip_gate(
        [q],
        agent=agent,
        vlm=vlm,
        store=store,
        trees={"v1": object()},
        config=AdversarialFilterConfig(),
        round_no=0,
        run_id="r0",
        session_id="s",
    )
    assert kept == []  # 未随问题翻转 → 剔除
    cheat = store._conn.execute(
        "SELECT verdict FROM adversarial_verdicts WHERE question_id='stick' AND stage='cheat'"
    ).fetchone()[0]
    assert cheat == "filtered_no_flip"  # cheat 行被改写 → final 不含它
    # flip_original + flip_mirror 两条独立 stage 行，共用同一 pair_id（非空），verdict 一致
    by_stage = {
        r[0]: (r[1], r[2])
        for r in store._conn.execute(
            "SELECT stage, verdict, pair_id FROM adversarial_verdicts "
            "WHERE question_id='stick' AND stage IN ('flip_original', 'flip_mirror')"
        )
    }
    assert set(by_stage) == {"flip_original", "flip_mirror"}
    assert by_stage["flip_original"][0] == "filtered_no_flip"
    assert by_stage["flip_mirror"][0] == "filtered_no_flip"
    assert by_stage["flip_original"][1]  # pair_id 非空
    assert by_stage["flip_original"][1] == by_stage["flip_mirror"][1]  # 共用同一 pair_id
    # 改写后的 cheat 行不再计入终判 passed（双保险：cheat 改判 + filtered_no_flip 排除）
    assert "stick" not in store.final_passed_question_ids(
        {"stick": question_hash(q)}, _fp()
    )
    store.close()


@pytest.mark.asyncio
async def test_flip_gate_invalid_mirror_skipped_but_kept(tmp_path):
    store = QuestionGenStore(str(tmp_path / "q.db"))
    q = _q("murky")
    _preset_cheat(store, q, pred="A")
    agent = _FakeAgent({})  # 镜像造不出 → agent 不该被调用
    vlm = _FakeVLM('{"mirror": null}')
    kept = await run_flip_gate(
        [q],
        agent=agent,
        vlm=vlm,
        store=store,
        trees={"v1": object()},
        config=AdversarialFilterConfig(),
        round_no=0,
        run_id="r0",
        session_id="s",
    )
    assert {x.question_id for x in kept} == {"murky"}  # 退回只经作弊门，保留不误杀
    assert agent.calls == []  # 镜像 None → 未跑 agent
    cheat = store._conn.execute(
        "SELECT verdict FROM adversarial_verdicts WHERE question_id='murky' AND stage='cheat'"
    ).fetchone()[0]
    assert cheat == "passed"
    store.close()


@pytest.mark.asyncio
async def test_flip_gate_mirror_ok_but_agent_refuses_skipped(tmp_path):
    """镜像生成成功但 agent 对镜像 Q 拒答(None) → flip_skipped，survivor 保留不误杀。

    与 invalid_mirror 用例互补：此处镜像题**造得出来**（走到 agent），只因 agent 无
    有效预测→canonical(Q)=None→保守 flip_skipped，绝不据此误杀原题。
    """
    store = QuestionGenStore(str(tmp_path / "q.db"))
    q = _q("refuse")
    _preset_cheat(store, q, pred="A")  # P canonical="蒸"
    agent = _FakeAgent({})  # 镜像题无预测 → predict 返回 None（拒答）
    vlm = _FakeVLM(
        json.dumps(
            {
                "mirror": {
                    "question": "X 之后？",
                    "options": ["A. 炒", "B. 蒸", "C. 煮", "D. 炸"],
                    "answer": "A",
                }
            },
            ensure_ascii=False,
        )
    )
    kept = await run_flip_gate(
        [q],
        agent=agent,
        vlm=vlm,
        store=store,
        trees={"v1": object()},
        config=AdversarialFilterConfig(),
        round_no=0,
        run_id="r0",
        session_id="s",
    )
    assert {x.question_id for x in kept} == {"refuse"}  # 保守保留，不误杀
    assert agent.calls == ["refuse_mirror"]  # 镜像被跑一次，C2：原题 P 未被重跑
    by_stage = {
        r[0]: r[1]
        for r in store._conn.execute(
            "SELECT stage, verdict FROM adversarial_verdicts "
            "WHERE question_id='refuse' AND stage IN ('flip_original', 'flip_mirror')"
        )
    }
    assert by_stage == {"flip_original": "flip_skipped", "flip_mirror": "flip_skipped"}
    cheat = store._conn.execute(
        "SELECT verdict FROM adversarial_verdicts WHERE question_id='refuse' AND stage='cheat'"
    ).fetchone()[0]
    assert cheat == "passed"  # cheat 行保持 passed（保留只经作弊门）
    # flip_skipped 不算 filtered_no_flip → 仍计入终判 passed（保留题最终可入库）
    assert "refuse" in store.final_passed_question_ids(
        {"refuse": question_hash(q)}, _fp()
    )
    store.close()


@pytest.mark.asyncio
async def test_flip_gate_mirror_excluded_and_unsupported_passes(tmp_path):
    store = QuestionGenStore(str(tmp_path / "q.db"))
    q = _q("hard")  # 支持 flip
    npq = _q("plain", sub="premature_evidence_anchoring")  # 不支持 flip
    _preset_cheat(store, q, pred="A")
    _preset_cheat(store, npq, pred="B")
    agent = _FakeAgent({"hard_mirror": "A"})
    vlm = _FakeVLM(
        json.dumps(
            {
                "mirror": {
                    "question": "X 之后？",
                    "options": ["A. 炒", "B. 蒸", "C. 煮", "D. 炸"],
                    "answer": "A",
                }
            },
            ensure_ascii=False,
        )
    )
    kept = await run_flip_gate(
        [q, npq],
        agent=agent,
        vlm=vlm,
        store=store,
        trees={"v1": object()},
        config=AdversarialFilterConfig(),
        round_no=0,
        run_id="r0",
        session_id="s",
    )
    assert {x.question_id for x in kept} == {"hard", "plain"}  # 不支持 flip 直接 passed
    assert agent.calls == ["hard_mirror"]  # 不支持 flip 的题不跑 agent/VLM
    out = tmp_path / "final.json"
    write_final_bank(out, store, {"hard": q, "plain": npq}, _fp())
    ids = [d["question_id"] for d in json.loads(out.read_text(encoding="utf-8"))]
    assert "hard_mirror" not in ids and set(ids) == {"hard", "plain"}  # 镜像不入题库
    store.close()