import json

from evaluate_student import (
    BEHAVIORS,
    behavior_flags,
    compare_binary,
    completion_and_findings,
    exact_two_sided_sign_p_value,
)
from train_student import load_verified_messages


def test_load_verified_messages_rejects_unverified_shape(tmp_path):
    path = tmp_path / "bad.jsonl"
    path.write_text(json.dumps({"messages": [{"role": "user", "content": "q"}]}) + "\n")
    try:
        load_verified_messages(path)
    except ValueError as exc:
        assert "exactly two" in str(exc)
    else:
        raise AssertionError("invalid collection row was accepted for parameter training")


def test_paired_sign_test_detects_one_sided_student_gain():
    baseline = {str(i): False for i in range(8)}
    student = {str(i): True for i in range(8)}
    result = compare_binary(baseline, student)
    assert result["student_only"] == 8
    assert result["baseline_only"] == 0
    assert result["exact_two_sided_p_value"] == exact_two_sided_sign_p_value(0, 8)
    assert result["exact_two_sided_p_value"] < 0.05


def test_behavior_flags_cover_acceptance_categories():
    flags = behavior_flags("Wait, that is not right. Use another approach, then verify by substitution.")
    assert flags == {"reflection": True, "backtracking": True, "verification": True}


def test_negative_uplift_finding_does_not_make_executed_campaign_incomplete():
    def arm(name, correct):
        return {
            "name": name,
            "accuracy": float(correct),
            "behavior_rates": {key: 0.0 for key in BEHAVIORS},
            "records": [{"id": "case-1", "correct": correct}],
        }

    completion, findings = completion_and_findings(
        problem_ids={"case-1"},
        baseline=arm("baseline", False),
        student=arm("student", False),
        teacher=arm("teacher", True),
        paired={"paired_cases": 1, "exact_two_sided_p_value": 1.0},
        student_training_complete=True,
        teacher_outputs_complete=True,
    )
    assert completion["complete"] is True
    assert findings["student_improves_over_baseline"] is False
    assert findings["paired_improvement_significant_p_lt_0_05"] is False