"""视频级切分科研旋钮单元测试：诊断指纹 + val_wrong_min 功效护栏 + PoolConfig 新字段。

覆盖：
    - diag_fingerprint 对 (prompt 版本 / 模型 / 代码版本) 三元组确定且敏感；
    - split_by_video_assignment 的 val_wrong_min 门控 fail loud（验证信号不足即报错）；
    - val_wrong_min 默认 0 时行为与 Task 11 现有调用完全一致（不回归）；
    - PoolConfig 能接收视频级切分的五个新旋钮字段（纯 dataclass 装配）。
"""

from __future__ import annotations

import pytest

from app.harness.pools import InsufficientValSignal, split_by_video_assignment
from app.harness.split_selection import diag_fingerprint
from core.types import GeneratedQuestion, PoolConfig


def _q(qid: str, vid: str, tt: str = "Counting Problem") -> GeneratedQuestion:
    """构造最小可用题目（补齐 GeneratedQuestion 的必填 source_nodes/difficulty）。"""
    return GeneratedQuestion(
        question_id=qid,
        video_id=vid,
        task_type=tt,
        question="",
        options=("A", "B", "C", "D"),
        answer="A",
        source_nodes=(),
        difficulty="easy",
    )


def test_diag_fingerprint_deterministic_and_sensitive():
    """诊断指纹对相同三元组稳定，对任一分量变化敏感。"""
    a = diag_fingerprint("p1", "deepseek-v4", "abc123")
    assert a == diag_fingerprint("p1", "deepseek-v4", "abc123")  # 确定性
    assert a != diag_fingerprint("p2", "deepseek-v4", "abc123")  # prompt 变则变
    assert a != diag_fingerprint("p1", "kimi", "abc123")  # model 变则变
    assert a != diag_fingerprint("p1", "deepseek-v4", "def456")  # 代码版本变则变
    assert len(a) == 16  # sha256 截断 16 位十六进制


def test_val_wrong_min_enforced():
    """val 错题数 < val_wrong_min 时 fail loud（InsufficientValSignal），不静默兜底。"""
    qs = [_q("v1-1", "v1"), _q("v1-2", "v1"), _q("v1-3", "v1")]
    correctness = {q.question_id: True for q in qs}  # 全对 → val 无错题
    with pytest.raises(InsufficientValSignal):  # val 错题 < val_wrong_min
        split_by_video_assignment(
            qs,
            {"v1": "trainval"},
            correctness=correctness,
            val_ratio=0.5,
            seed=0,
            val_wrong_min=5,
        )


def test_val_wrong_min_default_zero_no_regression():
    """val_wrong_min 默认 0 时不检查错题数，保持 Task 11 现有调用契约不破。"""
    qs = [_q("v1-1", "v1"), _q("v2-1", "v2")]
    assignment = {"v1": "trainval", "v2": "trainval"}
    correctness = {"v1-1": True, "v2-1": True}  # 全对但默认不触发护栏
    pools = split_by_video_assignment(
        qs, assignment, correctness=correctness, val_ratio=1.0, seed=0
    )
    assert len(pools.validation) == 2  # 未抛异常，正常返回


def test_pool_config_accepts_video_split_knobs():
    """PoolConfig 能接收视频级切分五个新旋钮字段（默认惰性，不破坏现有构造点）。"""
    cfg = PoolConfig(
        task_types=None,
        seed=0,
        baseline_run_id="infer_adhoc",
        diag_size=200,
        diag_correct_ratio=0.5,
        val_size=30,
        val_correct_ratio=0.5,
        test_size=60,
        eval_min_per_class=2,
        train_ratio=0.667,
        test_questions_dir=None,
        n_trainval=100,
        floor_k={"Counting Problem": 3},
        epsilon=0.1,
        report_floor=27,
        val_wrong_min=20,
    )
    assert cfg.n_trainval == 100
    assert cfg.floor_k == {"Counting Problem": 3}
    assert cfg.epsilon == 0.1
    assert cfg.report_floor == 27
    assert cfg.val_wrong_min == 20


def test_pool_config_video_split_knobs_default_inert():
    """未传视频级切分字段时默认惰性（0 / 空 dict），不破坏 GlobalPoolStrategy 现有构造。"""
    cfg = PoolConfig(
        task_types=None,
        seed=0,
        baseline_run_id="run_1",
        diag_size=200,
        diag_correct_ratio=0.5,
        val_size=30,
        val_correct_ratio=0.5,
        test_size=60,
        eval_min_per_class=2,
        train_ratio=0.667,
        test_questions_dir=None,
    )
    assert cfg.n_trainval == 0
    assert cfg.floor_k == {}
    assert cfg.epsilon == 0.0
    assert cfg.report_floor == 0
    assert cfg.val_wrong_min == 0