"""Task 9：final 全量重写 + 补生成迭代循环 + 难度报告。

覆盖：
- write_final_bank 仅含当前 hash+config 过两门的题、stale-config 旧行被排除、原子重写。
- run_adversarial_rounds 缺额驱动：deficit≤0 或 round≥max → 停止（fake backfill 计数）。
- _report_difficulty：agent 正确率 > 阈值 → WARNING。
"""

import json

import pytest
from loguru import logger

from app.question_gen.adversarial_config import AdversarialFilterConfig
from app.question_gen.adversarial_filter import (
    _report_difficulty,
    question_hash,
    run_adversarial_rounds,
    write_final_bank,
)
from app.question_gen.run_store import QuestionGenStore
from core.types import GeneratedQuestion


def _q(qid, sub=None):
    """构造一条 AR 题；sub=None → 不支持 flip，翻转门直接放行（无需 VLM/树）。"""
    return GeneratedQuestion(
        question_id=qid,
        video_id="v1",
        task_type="Action Recognition",
        question=f"{qid} 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,
    )


class _FakeAgent:
    """完整 agent 试答桩：对每题返回固定预测（AgentRunner Protocol 要求 model/skill_mode）。"""

    def __init__(self, pred="B", model="m1", skill_mode="auto"):
        self._pred = pred
        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._pred for q in questions}


class _CountingBackfill:
    """补生成回调桩：记录调用次数，按工厂返回新题。"""

    def __init__(self, factory):
        self.calls = 0
        self._factory = factory

    async def __call__(self, deficit, round_no, existing):
        self.calls += 1
        return self._factory(deficit, round_no)


def test_write_final_bank_only_passed(tmp_path):
    store = QuestionGenStore(str(tmp_path / "q.db"))
    q1, q2 = _q("q1"), _q("q2")
    store.record_verdict(
        question_id="q1", question_hash=question_hash(q1), stage="cheat",
        round=0, agent_prediction="B", agent_correct=False,
        verdict="passed", pair_id=None, agent_config="c",
    )
    store.record_verdict(
        question_id="q2", question_hash=question_hash(q2), stage="cheat",
        round=0, agent_prediction="A", agent_correct=True,
        verdict="filtered_too_easy", pair_id=None, agent_config="c",
    )
    out = tmp_path / "accepted_questions_final.json"
    write_final_bank(out, store, {"q1": q1, "q2": q2}, "c")
    data = json.loads(out.read_text(encoding="utf-8"))
    assert [d["question_id"] for d in data] == ["q1"]
    store.close()


def test_write_final_bank_excludes_stale_config(tmp_path):
    store = QuestionGenStore(str(tmp_path / "q.db"))
    q1 = _q("q1")
    store.record_verdict(
        question_id="q1", question_hash=question_hash(q1), stage="cheat",
        round=0, agent_prediction="B", agent_correct=False,
        verdict="passed", pair_id=None, agent_config="OLD",
    )
    out = tmp_path / "accepted_questions_final.json"
    write_final_bank(out, store, {"q1": q1}, "NEW")
    assert json.loads(out.read_text(encoding="utf-8")) == []
    store.close()


def test_difficulty_warns_above_threshold(tmp_path):
    store = QuestionGenStore(str(tmp_path / "q.db"))
    for i in range(4):  # 4 题全对 = 正确率 1.0 > 阈值 0.85 → 必触发告警
        store.record_verdict(
            question_id=f"q{i}", question_hash=str(i), stage="cheat",
            round=0, agent_prediction="A", agent_correct=True,
            verdict="filtered_too_easy", pair_id=None, agent_config="c",
        )
    # loguru 不走标准 logging，用项目既定 sink 捕获模式（见 test_pool_strategy）。
    captured: list[str] = []
    sink_id = logger.add(lambda msg: captured.append(str(msg)), level="WARNING")
    try:
        _report_difficulty(store, round_no=0, threshold=0.85)
    finally:
        logger.remove(sink_id)
    assert any("太简单" in m or "简单" in m for m in captured), f"未捕获告警: {captured}"
    store.close()


@pytest.mark.asyncio
async def test_rounds_stop_when_deficit_met(tmp_path):
    """首轮即达标（passed≥target）→ 不调 backfill，迭代立即停止。"""
    store = QuestionGenStore(str(tmp_path / "q.db"))
    agent = _FakeAgent(pred="B")  # 答错 → 过作弊门；sub=None → 过翻转门
    backfill = _CountingBackfill(lambda d, r: [])
    final_path = tmp_path / "accepted_questions_final.json"
    await run_adversarial_rounds(
        [_q("q0")],
        agent=agent,
        vlm=object(),
        store=store,
        trees={},
        config=AdversarialFilterConfig(adversarial_max_rounds=5),
        final_path=final_path,
        target=1,
        backfill=backfill,
        session_id="s",
    )
    assert backfill.calls == 0
    assert [d["question_id"] for d in json.loads(final_path.read_text(encoding="utf-8"))] == ["q0"]
    store.close()


@pytest.mark.asyncio
async def test_rounds_backfill_then_stop_at_max(tmp_path):
    """缺额 > 0 → 调 backfill 补生成；达轮次上限即停（不无限循环）。"""
    store = QuestionGenStore(str(tmp_path / "q.db"))
    agent = _FakeAgent(pred="B")
    backfill = _CountingBackfill(lambda d, r: [_q(f"bf{r}_{i}") for i in range(d)])
    final_path = tmp_path / "accepted_questions_final.json"
    await run_adversarial_rounds(
        [_q("q0")],
        agent=agent,
        vlm=object(),
        store=store,
        trees={},
        config=AdversarialFilterConfig(adversarial_max_rounds=2),
        final_path=final_path,
        target=99,  # 永远达不到 → 靠 max_rounds 终止
        backfill=backfill,
        session_id="s",
    )
    assert backfill.calls == 1  # round0 补生成一次；round1 达上限 break，不再补
    ids = {d["question_id"] for d in json.loads(final_path.read_text(encoding="utf-8"))}
    assert "q0" in ids and any(x.startswith("bf0_") for x in ids)
    store.close()