"""tools/generate_questions.py 单元测试。

覆盖断点续跑、exemplar 选取、embedding 池重建、JSON 追加写入等纯函数。
"""

from __future__ import annotations

import json
import random
import sys
from pathlib import Path

import numpy as np

# 确保项目根目录在 sys.path 中
PROJECT_ROOT = Path(__file__).resolve().parent.parent.parent
if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))

from core.types import GeneratedQuestion
from tools.generate_questions import (
    _append_to_json,
    _calibrate_exit_code,
    _judge_task_type,
    _load_or_init_progress,
    _rebuild_embedding_pool,
    _save_progress,
    _select_exemplars,
)

# ---------------------------------------------------------------------------
# 辅助工厂
# ---------------------------------------------------------------------------


def _make_question(
    qid: str = "q1",
    vid: str = "v1",
    task_type: str = "Object Recognition",
    question: str = "What is this?",
    answer: str = "A",
) -> GeneratedQuestion:
    """构造测试用 GeneratedQuestion。"""
    return GeneratedQuestion(
        question_id=qid,
        video_id=vid,
        task_type=task_type,
        question=question,
        options=("A. X", "B. Y", "C. Z", "D. W"),
        answer=answer,
        source_nodes=(),
        difficulty="medium",
    )


# ---------------------------------------------------------------------------
# TestLoadOrInitProgress
# ---------------------------------------------------------------------------


class TestLoadOrInitProgress:
    """_load_or_init_progress 测试。"""

    def test_init_fresh(self, tmp_path: Path) -> None:
        """目录为空时返回初始结构。"""
        progress = _load_or_init_progress(tmp_path)
        assert progress["completed"] == {}
        assert progress["output_dir"] == str(tmp_path)

    def test_load_existing(self, tmp_path: Path) -> None:
        """已有 progress.json 时正确加载。"""
        data = {
            "completed": {"Object Recognition": ["gen-x-001"]},
            "output_dir": str(tmp_path),
        }
        (tmp_path / "progress.json").write_text(json.dumps(data))
        progress = _load_or_init_progress(tmp_path)
        assert "gen-x-001" in progress["completed"]["Object Recognition"]

    def test_corrupted_json_reinits(self, tmp_path: Path) -> None:
        """损坏的 JSON 文件导致重新初始化。"""
        (tmp_path / "progress.json").write_text("{invalid json")
        progress = _load_or_init_progress(tmp_path)
        assert progress["completed"] == {}

    def test_invalid_completed_type_reinits(self, tmp_path: Path) -> None:
        """completed 字段类型不正确时重新初始化。"""
        (tmp_path / "progress.json").write_text(
            json.dumps({"completed": "not-a-dict", "output_dir": str(tmp_path)})
        )
        progress = _load_or_init_progress(tmp_path)
        assert progress["completed"] == {}


# ---------------------------------------------------------------------------
# TestSaveProgress
# ---------------------------------------------------------------------------


class TestSaveProgress:
    """_save_progress 测试。"""

    def test_atomic_write(self, tmp_path: Path) -> None:
        """原子写入 progress.json。"""
        progress = {
            "completed": {"Action Reasoning": ["gen-v1-001"]},
            "output_dir": str(tmp_path),
        }
        _save_progress(tmp_path, progress)

        written = json.loads((tmp_path / "progress.json").read_text())
        assert written["completed"]["Action Reasoning"] == ["gen-v1-001"]
        # 临时文件不应残留
        assert not (tmp_path / "progress.json.tmp").exists()


# ---------------------------------------------------------------------------
# TestSelectExemplars
# ---------------------------------------------------------------------------


class TestSelectExemplars:
    """_select_exemplars 测试。"""

    def test_selects_correct_type(self) -> None:
        """只选取匹配题型的示例。"""
        qs = [
            _make_question("q1", "v1", "Object Recognition", "Q1?"),
            _make_question("q2", "v2", "Object Recognition", "Q2?"),
            _make_question("q3", "v1", "Action Reasoning", "Q3?"),
        ]
        result = _select_exemplars(qs, "Object Recognition", 3, random.Random(42))
        assert all(q.task_type == "Object Recognition" for q in result)
        assert len(result) == 2  # 只有 2 个可用

    def test_cross_video_diversity(self) -> None:
        """优先从不同 video_id 选取示例。"""
        qs = [_make_question(f"q{i}", f"v{i}", "Object Recognition", f"Q{i}?") for i in range(10)]
        result = _select_exemplars(qs, "Object Recognition", 3, random.Random(42))
        video_ids = {q.video_id for q in result}
        assert len(video_ids) == 3  # 全部来自不同视频

    def test_empty_benchmark(self) -> None:
        """benchmark 为空时返回空列表。"""
        result = _select_exemplars([], "Object Recognition", 3, random.Random(42))
        assert result == []

    def test_no_matching_type(self) -> None:
        """无匹配题型时返回空列表。"""
        qs = [_make_question("q1", "v1", "Action Reasoning", "Q1?")]
        result = _select_exemplars(qs, "Object Recognition", 3, random.Random(42))
        assert result == []

    def test_request_more_than_available(self) -> None:
        """请求数超过可用数时返回全部。"""
        qs = [
            _make_question("q1", "v1", "Object Recognition", "Q1?"),
            _make_question("q2", "v2", "Object Recognition", "Q2?"),
        ]
        result = _select_exemplars(qs, "Object Recognition", 10, random.Random(42))
        assert len(result) == 2


# ---------------------------------------------------------------------------
# TestProgressResume
# ---------------------------------------------------------------------------


class TestProgressResume:
    """断点续跑集成测试。"""

    def test_skips_completed_and_rebuilds_pool(self, tmp_path: Path) -> None:
        """已完成的题目从 progress 加载，embedding 池从已生成 JSON 重建。"""
        output_dir = tmp_path / "output"
        output_dir.mkdir()
        (output_dir / "test_vid.json").write_text(
            json.dumps(
                [
                    {
                        "question_id": "gen-test_vid-001",
                        "task_type": "Object Recognition",
                        "question": "Existing question?",
                        "options": ["A. X", "B. Y", "C. Z", "D. W"],
                        "answer": "A",
                        "source_nodes": ["L3_001"],
                        "difficulty": "medium",
                    }
                ]
            )
        )
        progress = {
            "completed": {"Object Recognition": ["gen-test_vid-001"]},
            "output_dir": str(output_dir),
        }
        (output_dir / "progress.json").write_text(json.dumps(progress))

        loaded = _load_or_init_progress(output_dir)
        assert "gen-test_vid-001" in loaded["completed"]["Object Recognition"]


# ---------------------------------------------------------------------------
# TestRebuildEmbeddingPool
# ---------------------------------------------------------------------------


class TestRebuildEmbeddingPool:
    """_rebuild_embedding_pool 测试。"""

    @staticmethod
    def _fake_embed_fn(texts):
        """伪嵌入函数：返回固定维度的随机向量。"""
        if isinstance(texts, str):
            texts = [texts]
        return np.random.RandomState(0).randn(len(texts), 8).astype(np.float32)

    def test_empty_dir(self, tmp_path: Path) -> None:
        """空目录时所有池为空。"""
        pools = _rebuild_embedding_pool(tmp_path, self._fake_embed_fn, [])
        # 所有 12 种题型都应有条目
        assert len(pools) >= 12
        for v in pools.values():
            assert v.shape[0] == 0 or v.ndim == 2

    def test_with_generated_json(self, tmp_path: Path) -> None:
        """从已生成的 JSON 文件重建池。"""
        (tmp_path / "vid1.json").write_text(
            json.dumps(
                [
                    {
                        "question_id": "gen-vid1-001",
                        "task_type": "Object Recognition",
                        "question": "Test question 1?",
                        "options": ["A. X", "B. Y", "C. Z", "D. W"],
                        "answer": "A",
                        "source_nodes": [],
                        "difficulty": "medium",
                    },
                    {
                        "question_id": "gen-vid1-002",
                        "task_type": "Object Recognition",
                        "question": "Test question 2?",
                        "options": ["A. X", "B. Y", "C. Z", "D. W"],
                        "answer": "B",
                        "source_nodes": [],
                        "difficulty": "medium",
                    },
                ]
            )
        )
        pools = _rebuild_embedding_pool(tmp_path, self._fake_embed_fn, [])
        assert pools["Object Recognition"].shape[0] == 2
        assert pools["Object Recognition"].shape[1] == 8

    def test_with_benchmark_questions(self, tmp_path: Path) -> None:
        """benchmark 题目也加入去重池。"""
        benchmark = [
            _make_question("bm1", "v1", "Action Reasoning", "Benchmark Q1?"),
            _make_question("bm2", "v2", "Action Reasoning", "Benchmark Q2?"),
        ]
        pools = _rebuild_embedding_pool(tmp_path, self._fake_embed_fn, benchmark)
        assert pools["Action Reasoning"].shape[0] == 2

    def test_progress_json_excluded(self, tmp_path: Path) -> None:
        """progress.json 不被当作题目文件。"""
        (tmp_path / "progress.json").write_text(
            json.dumps({"completed": {}, "output_dir": str(tmp_path)})
        )
        pools = _rebuild_embedding_pool(tmp_path, self._fake_embed_fn, [])
        for v in pools.values():
            assert v.shape[0] == 0 or v.ndim == 2


# ---------------------------------------------------------------------------
# TestAppendToJson
# ---------------------------------------------------------------------------


class TestAppendToJson:
    """_append_to_json 测试。"""

    def test_create_new_file(self, tmp_path: Path) -> None:
        """文件不存在时创建新文件。"""
        q = _make_question("gen-v1-001", "v1", "Object Recognition", "Q?")
        _append_to_json(tmp_path, q)

        written = json.loads((tmp_path / "v1.json").read_text())
        assert len(written) == 1
        assert written[0]["question_id"] == "gen-v1-001"

    def test_append_to_existing(self, tmp_path: Path) -> None:
        """追加到已有文件。"""
        existing = [
            {
                "question_id": "gen-v1-001",
                "task_type": "Object Recognition",
                "question": "Existing?",
                "options": ["A. X", "B. Y", "C. Z", "D. W"],
                "answer": "A",
                "source_nodes": [],
                "difficulty": "medium",
            }
        ]
        (tmp_path / "v1.json").write_text(json.dumps(existing))

        q = _make_question("gen-v1-002", "v1", "Object Recognition", "New?")
        _append_to_json(tmp_path, q)

        written = json.loads((tmp_path / "v1.json").read_text())
        assert len(written) == 2
        assert written[1]["question_id"] == "gen-v1-002"

    def test_different_video_ids(self, tmp_path: Path) -> None:
        """不同 video_id 写入不同文件。"""
        q1 = _make_question("gen-v1-001", "v1", "Object Recognition", "Q1?")
        q2 = _make_question("gen-v2-001", "v2", "Action Reasoning", "Q2?")
        _append_to_json(tmp_path, q1)
        _append_to_json(tmp_path, q2)

        assert (tmp_path / "v1.json").exists()
        assert (tmp_path / "v2.json").exists()
        v1_data = json.loads((tmp_path / "v1.json").read_text())
        v2_data = json.loads((tmp_path / "v2.json").read_text())
        assert len(v1_data) == 1
        assert len(v2_data) == 1


# ---------------------------------------------------------------------------
# TestCalibrateJudgment
# ---------------------------------------------------------------------------


class TestCalibrateJudgment:
    """_judge_task_type 校准判定测试。"""

    def test_pass_when_delta_small(self) -> None:
        """差值在容忍范围内判定为 PASS。"""
        verdict = _judge_task_type(
            bench_correct=60,
            bench_total=100,
            gen_correct=12,
            gen_total=20,
            tolerance=0.10,
            alpha=0.05,
        )
        assert verdict == "PASS"

    def test_fail_when_delta_large_and_significant(self) -> None:
        """差值超阈值且统计显著判定为 FAIL。"""
        verdict = _judge_task_type(
            bench_correct=144,
            bench_total=240,
            gen_correct=6,
            gen_total=20,
            tolerance=0.10,
            alpha=0.05,
        )
        assert verdict == "FAIL"

    def test_warn_when_delta_large_but_not_significant(self) -> None:
        """差值超阈值但不统计显著判定为 WARN。"""
        verdict = _judge_task_type(
            bench_correct=2,
            bench_total=3,
            gen_correct=8,
            gen_total=20,
            tolerance=0.10,
            alpha=0.05,
        )
        assert verdict == "WARN"


# ---------------------------------------------------------------------------
# TestCalibrateIntegration
# ---------------------------------------------------------------------------


class TestCalibrateIntegration:
    """calibrate 辅助函数集成测试。"""

    def test_has_fail_returns_exit_code_1(self) -> None:
        """存在 FAIL 时返回退出码 1。"""
        verdicts = {"Object Recognition": "PASS", "Action Reasoning": "FAIL"}
        assert _calibrate_exit_code(verdicts) == 1

    def test_all_pass_or_warn_returns_exit_code_0(self) -> None:
        """全部 PASS 或 WARN 时返回退出码 0。"""
        verdicts = {"Object Recognition": "PASS", "Spatial Perception": "WARN"}
        assert _calibrate_exit_code(verdicts) == 0

    def test_all_pass_returns_exit_code_0(self) -> None:
        """全部 PASS 时返回退出码 0。"""
        verdicts = {"Object Recognition": "PASS", "Action Reasoning": "PASS"}
        assert _calibrate_exit_code(verdicts) == 0

    def test_empty_verdicts_returns_exit_code_0(self) -> None:
        """空 verdicts 时返回退出码 0。"""
        assert _calibrate_exit_code({}) == 0