"""AI 탐지기 학습 데이터 분리 검증 (#F).

핵심 불변식 두 가지:
  1) fit=train / threshold=val / report=test 로 역할이 섞이지 않는다.
  2) 어떤 source_group 도 두 split 에 동시에 나타나지 않는다(누출 금지).
"""

from __future__ import annotations

import json
import subprocess
import sys
from pathlib import Path

import pytest

ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))

from scripts.build_ai_training_dataset import Record, assign_splits, summarize  # noqa: E402

sklearn = pytest.importorskip("sklearn", reason="scikit-learn 미설치")
pytest.importorskip("joblib", reason="joblib 미설치")


# ---------------------------------------------------------------------------
# 분할 자체의 불변식 (sklearn 불필요 부분)
# ---------------------------------------------------------------------------

def _records(n_groups_per_label: int = 6, per_group: int = 6) -> list[Record]:
    out: list[Record] = []
    for label in (0, 1):
        origin = "human" if label == 0 else "ai"
        for g in range(n_groups_per_label):
            group = f"{origin}:group-{g}"
            for i in range(per_group):
                out.append(Record(
                    text=f"{origin} 문단 {g}-{i}", label=label, origin=origin,
                    source_group=group, book=group,
                ))
    return out


def test_assign_splits_never_shares_a_group():
    records = _records()
    assignment = assign_splits(records, (0.7, 0.15, 0.15), seed=7)
    for rec in records:
        rec.split = assignment[rec.source_group]

    by_split: dict[str, set[str]] = {}
    for rec in records:
        by_split.setdefault(rec.split, set()).add(rec.source_group)
    splits = list(by_split)
    for i, a in enumerate(splits):
        for b in splits[i + 1:]:
            assert not (by_split[a] & by_split[b]), f"{a}/{b} 그룹 중복 = 누출"

    assert summarize(records)["group_overlap_between_splits"] == []


def test_assign_splits_is_deterministic_for_same_seed():
    a = assign_splits(_records(), (0.7, 0.15, 0.15), seed=11)
    b = assign_splits(_records(), (0.7, 0.15, 0.15), seed=11)
    assert a == b


def test_assign_splits_covers_all_three_splits():
    assignment = assign_splits(_records(), (0.7, 0.15, 0.15), seed=3)
    assert set(assignment.values()) == {"train", "val", "test"}


# ---------------------------------------------------------------------------
# 학습 CLI end-to-end
# ---------------------------------------------------------------------------

def _write_dataset(path: Path) -> None:
    human = "비가 왔다. 나는 그날 학교에 가지 않았고 대신 뒷산에 올라가 온종일 앉아 있었다. 춥지는 않았다. 형이 왔다."
    ai = "그날의 기억은 오래도록 남아, 지금까지도 선명하게 떠오르는 장면이 되었다. 아침의 공기는 서늘했고, 발걸음은 조용히 이어졌다."
    rows = []
    for label, body, origin in ((0, human, "human"), (1, ai, "ai")):
        for g in range(6):
            split = "train" if g < 4 else ("val" if g == 4 else "test")
            for i in range(8):
                rows.append({
                    "text": (body + f" 변형 {g}-{i}. ") * 3,
                    "label": label, "origin": origin,
                    "source_group": f"{origin}:g{g}", "book": f"{origin}-{g}",
                    "split": split,
                })
    path.write_text(
        "\n".join(json.dumps(r, ensure_ascii=False) for r in rows), encoding="utf-8"
    )


def _run_trainer(data: Path, out: Path, *extra: str) -> subprocess.CompletedProcess:
    return subprocess.run(
        [sys.executable, "scripts/train_ai_detector.py", "--data", str(data),
         "--out", str(out), *extra],
        cwd=ROOT, capture_output=True, text=True,
    )


def test_trainer_uses_train_val_test_roles(tmp_path):
    data = tmp_path / "ds.jsonl"
    out = tmp_path / "model.joblib"
    _write_dataset(data)

    proc = _run_trainer(data, out)
    assert proc.returncode == 0, proc.stderr[-2000:]

    metrics = json.loads((tmp_path / "model.metrics.json").read_text(encoding="utf-8"))
    # 세 역할이 모두 기록되어야 한다
    assert metrics["n_train"] > 0 and metrics["n_val"] > 0 and metrics["n_test"] > 0
    assert {"train", "validation", "test"} <= set(metrics)
    # 임계값은 val 에서 뽑혔음이 지표에 남아야 한다
    assert "validation_low_point" in metrics["cuts"]
    assert "validation_high_point" in metrics["cuts"]
    # 학습에 쓰인 표본 수와 보고 표본 수가 서로 다른 집합이어야 한다
    assert metrics["n_train"] != metrics["n_test"] or metrics["n_val"] != metrics["n_test"]


def test_trainer_rejects_group_overlap_between_splits(tmp_path):
    data = tmp_path / "leaky.jsonl"
    out = tmp_path / "model.joblib"
    _write_dataset(data)

    rows = [json.loads(line) for line in data.read_text(encoding="utf-8").splitlines()]
    for row in rows:
        # train 그룹 하나를 test 에도 등장시켜 누출을 주입
        if row["source_group"] == "human:g0" and row["split"] == "train":
            row["split"] = "test"
            break
    data.write_text(
        "\n".join(json.dumps(r, ensure_ascii=False) for r in rows), encoding="utf-8"
    )

    proc = _run_trainer(data, out)
    assert proc.returncode == 2, "그룹 누출은 학습을 중단시켜야 한다"
    assert "누출" in proc.stderr or "중복" in proc.stderr


def test_trainer_fails_on_single_class(tmp_path):
    data = tmp_path / "one.jsonl"
    _write_dataset(data)
    rows = [json.loads(line) for line in data.read_text(encoding="utf-8").splitlines()]
    kept = [r for r in rows if r["label"] == 0]
    data.write_text(
        "\n".join(json.dumps(r, ensure_ascii=False) for r in kept), encoding="utf-8"
    )
    proc = _run_trainer(data, tmp_path / "m.joblib")
    assert proc.returncode == 2
    assert "단일 클래스" in proc.stderr


def test_trainer_fails_when_a_split_is_empty(tmp_path):
    data = tmp_path / "noval.jsonl"
    _write_dataset(data)
    rows = [json.loads(line) for line in data.read_text(encoding="utf-8").splitlines()]
    for row in rows:
        if row["split"] == "val":
            row["split"] = "train"
    data.write_text(
        "\n".join(json.dumps(r, ensure_ascii=False) for r in rows), encoding="utf-8"
    )
    proc = _run_trainer(data, tmp_path / "m.joblib")
    assert proc.returncode == 2
    assert "빈 split" in proc.stderr