"""video_split_cli 两阶段编排 CLI 单元测试。

覆盖：
    - 配置解析正确 + 缺关键项 fail loud（SystemExit）；
    - 指纹计算在 main 中被调用（diag_fingerprint 收到 prompt/model/git-sha 三分量）；
    - run_pipeline 两阶段按序触发（Phase 1 诊断 → Phase 2 build_split）；
    - McNemar 功效护栏 fail loud；
    - --dry-run 用假 deps 跑通装配、不真调 LLM（Phase 1 空错题早返回）。

不真跑全量诊断：诊断与 build_split 均以 monkeypatch / 假 deps 替换。
"""

from __future__ import annotations

import asyncio
from types import SimpleNamespace

import pytest

from app.harness import video_split_cli as cli

_RAW_OK = {
    "video_split": {
        "baseline_run_id": "infer_adhoc",
        "n_trainval": 100,
        "epsilon": 0.1,
        "report_floor": 27,
        "val_wrong_min": 20,
        "val_ratio": 0.3,
        "seed": 7,
        "floor_k": {"Counting Problem": 3},
    },
    "diag": {"prompt_version": "diagnose_v1", "model": "deepseek-v4-pro"},
}


def _config(**overrides) -> cli.VideoSplitConfig:
    """构造一个可用 VideoSplitConfig，overrides 覆盖单字段。"""
    base = {
        "baseline_run_id": "infer_adhoc",
        "n_trainval": 100,
        "epsilon": 0.1,
        "report_floor": 27,
        "val_wrong_min": 0,
        "val_ratio": 0.3,
        "seed": 7,
        "floor_k": {"Counting Problem": 3},
        "prompt_version": "diagnose_v1",
        "model": "deepseek-v4-pro",
    }
    base.update(overrides)
    return cli.VideoSplitConfig(**base)


def test_parse_config_ok():
    """完整 yaml 解析为 VideoSplitConfig，各旋钮逐一还原。"""
    cfg = cli.parse_config(_RAW_OK)
    assert cfg.baseline_run_id == "infer_adhoc"
    assert cfg.n_trainval == 100
    assert cfg.epsilon == 0.1
    assert cfg.report_floor == 27
    assert cfg.val_wrong_min == 20
    assert cfg.val_ratio == 0.3
    assert cfg.seed == 7
    assert cfg.floor_k == {"Counting Problem": 3}
    assert cfg.prompt_version == "diagnose_v1"
    assert cfg.model == "deepseek-v4-pro"


def test_parse_config_missing_video_split_key_fails_loud():
    """video_split 段缺关键项 → SystemExit（P5 fail loud）。"""
    raw = {"video_split": dict(_RAW_OK["video_split"]), "diag": dict(_RAW_OK["diag"])}
    del raw["video_split"]["n_trainval"]
    with pytest.raises(SystemExit):
        cli.parse_config(raw)


def test_parse_config_missing_diag_section_fails_loud():
    """缺 diag 段 → SystemExit。"""
    with pytest.raises(SystemExit):
        cli.parse_config({"video_split": dict(_RAW_OK["video_split"])})


def test_load_config_missing_file_fails_loud(tmp_path):
    """config 文件不存在 → SystemExit。"""
    with pytest.raises(SystemExit):
        cli.load_config(tmp_path / "nope.yaml")


def test_check_mcnemar_power_below_threshold_fails_loud():
    """val 错题数 < 阈 → SystemExit（功效不足）。"""
    q = SimpleNamespace(question_id="q1")
    pools = SimpleNamespace(validation=[q], correctness={"q1": False})
    with pytest.raises(SystemExit):
        cli.check_mcnemar_power(pools, val_wrong_min=5)


def test_check_mcnemar_power_zero_threshold_skips():
    """val_wrong_min=0 → 不检查，返回实际错题数。"""
    q = SimpleNamespace(question_id="q1")
    pools = SimpleNamespace(validation=[q], correctness={"q1": True})
    assert cli.check_mcnemar_power(pools, val_wrong_min=0) == 0


def test_run_pipeline_orders_two_phases(monkeypatch, tmp_path):
    """run_pipeline 先跑 Phase 1 诊断、后跑 Phase 2 build_split（按序）。"""
    calls: list[str] = []

    async def fake_diag(**kwargs):
        calls.append("diagnosis")
        assert kwargs["diag_fingerprint"] == "fp"
        assert kwargs["wrong_ids"] == ["q1"]

    def fake_build_split(**kwargs):
        calls.append("build_split")
        assert kwargs["diag_fingerprint"] == "fp"
        pools = SimpleNamespace(validation=[], correctness={})
        return SimpleNamespace(pools=pools, manifest={}, assignment={})

    monkeypatch.setattr(cli, "run_baseline_diagnosis", fake_diag)
    monkeypatch.setattr(cli, "build_split", fake_build_split)

    result = asyncio.run(
        cli.run_pipeline(
            config=_config(),
            fingerprint="fp",
            diagnosis_deps=object(),
            signal_store=object(),
            wrong_ids=["q1"],
            questions={},
            harness_db=tmp_path / "h.db",
            questions_dir=tmp_path,
            out_dir=tmp_path / "out",
            generated_at="2026-07-15T00:00:00Z",
        )
    )
    assert calls == ["diagnosis", "build_split"]
    assert result.pools.validation == []


def test_dry_run_computes_fingerprint_without_llm(monkeypatch, tmp_path, capsys):
    """--dry-run：diag_fingerprint 被调用、Phase 1 走空错题早返回、不真调 LLM。"""
    fp_calls: list[tuple[str, str, str]] = []

    def spy_fingerprint(prompt_version, model, code_version):
        fp_calls.append((prompt_version, model, code_version))
        return "dryfp"

    monkeypatch.setattr(cli, "diag_fingerprint", spy_fingerprint)
    monkeypatch.setattr(cli, "git_short_sha", lambda: "abc123")

    # 用真实 config/video_split.yaml，out-dir 指向 tmp（dry-run 会建临时信号库再清理）。
    cli.main(["--dry-run", "--config", "config/video_split.yaml", "--out-dir", str(tmp_path)])

    assert len(fp_calls) == 1
    prompt_version, model, sha = fp_calls[0]
    assert sha == "abc123"
    assert prompt_version == "diagnose_v1"
    assert model == "deepseek-v4-pro"
    # dry-run 不留下临时信号库
    assert not (tmp_path / "_dry_run_signals.db").exists()


def test_git_short_sha_nonempty():
    """仓库内 git_short_sha 返回非空短 SHA。"""
    sha = cli.git_short_sha()
    assert sha
    assert len(sha) >= 4