"""Юнит-тесты скоринга плагиата (итоговый процент, который видит студент)."""

from app.scoring import aggregate_results, is_cited


def _m(title: str, pos: int) -> dict:
    return {"source_title": title, "position_start": pos}


def _mp(title: str, start: int, end: int) -> dict:
    return {"source_title": title, "position_start": start, "position_end": end}


def test_empty_input_is_zero():
    out = aggregate_results([], [], [], total_fragments=10)
    assert out["overall_similarity"] == 0.0
    assert out["flagged_fragments"] == 0
    assert out["matches"] == []
    assert out["by_method"] == {"exact": 0, "fuzzy": 0, "semantic_llm": 0}


def test_zero_fragments_does_not_divide_by_zero():
    out = aggregate_results([_m("A", 0)], [], [], total_fragments=0)
    assert out["overall_similarity"] == 0.0


def test_percentage_is_share_of_flagged_positions():
    # 2 из 4 фрагментов помечены → 50%
    level1 = [_m("A", 0), _m("B", 5)]
    out = aggregate_results(level1, [], [], total_fragments=4)
    assert out["overall_similarity"] == 50.0
    assert out["flagged_fragments"] == 2


def test_same_position_multiple_sources_counts_once():
    # Одна позиция совпала с двумя разными источниками — доля не раздувается
    level1 = [_m("A", 0)]
    semantic = [_m("B", 0)]  # та же позиция 0, другой источник
    out = aggregate_results(level1, [], semantic, total_fragments=2)
    assert out["flagged_fragments"] == 1
    assert out["overall_similarity"] == 50.0
    # оба совпадения сохранены в matches (дедуп только по паре title:pos)
    assert len(out["matches"]) == 2


def test_exact_duplicate_match_is_deduplicated():
    # Один и тот же источник в одной позиции пришёл дважды — остаётся один
    dup = _m("A", 3)
    out = aggregate_results([dict(dup)], [dict(dup)], [], total_fragments=5)
    assert len(out["matches"]) == 1


def test_similarity_capped_at_100():
    # Больше уникальных позиций, чем фрагментов (пограничный случай) → не выше 100%
    matches = [_m(f"S{i}", i) for i in range(5)]
    out = aggregate_results(matches, [], [], total_fragments=3)
    assert out["overall_similarity"] == 100.0


def test_by_method_counts_raw_matches_per_level():
    out = aggregate_results(
        [_m("A", 0), _m("B", 1)],   # exact = 2
        [_m("C", 2)],               # fuzzy = 1
        [_m("D", 3), _m("E", 4), _m("F", 5)],  # semantic_llm = 3
        total_fragments=10,
    )
    assert out["by_method"] == {"exact": 2, "fuzzy": 1, "semantic_llm": 3}


def test_percentage_rounded_to_two_decimals():
    # 1 из 3 → 33.333... → 33.33
    out = aggregate_results([_m("A", 0)], [], [], total_fragments=3)
    assert out["overall_similarity"] == 33.33


# ─── is_cited ───────────────────────────────────────────────────────────────

def test_is_cited_quoted_fragment():
    text = 'Автор пишет: «дословная цитата» и развивает мысль.'
    start = text.index("дословная")
    end = start + len("дословная цитата")
    assert is_cited(text, start, end) is True


def test_is_cited_bracket_citation_with_year():
    text = "Текст фрагмента без кавычек [Иванов, 2023] продолжение."
    end = text.index(" [Иванов")
    assert is_cited(text, 0, end) is True


def test_is_cited_apa_style_citation():
    text = "Some borrowed sentence here (Smith, 2020) continues."
    end = text.index(" (Smith")
    assert is_cited(text, 0, end) is True


def test_is_cited_bare_plagiarism_is_false():
    text = "Просто скопированный текст без всякого оформления далее."
    assert is_cited(text, 0, 10) is False


def test_is_cited_bracket_without_year_is_false():
    text = "Текст фрагмента [см. приложение] продолжение."
    end = text.index(" [см")
    assert is_cited(text, 0, end) is False


def test_is_cited_empty_full_text_is_false():
    assert is_cited("", 0, 5) is False


def test_is_cited_out_of_range_position_is_false():
    assert is_cited("короткий текст", 0, 999) is False


# ─── aggregate_results: cited/uncited split ──────────────────────────────────

def test_cited_match_excluded_from_uncited_similarity():
    text = 'Вот «процитированный фрагмент» и текст дальше.'
    start = text.index("процитированный")
    end = start + len("процитированный фрагмент")
    out = aggregate_results([_mp("A", start, end)], [], [], total_fragments=4, full_text=text)
    assert out["matches"][0]["cited"] is True
    assert out["overall_similarity"] == 25.0     # цитата всё ещё учтена в общем %
    assert out["uncited_similarity"] == 0.0      # но не в "некорректных" заимствованиях
    assert out["cited_fragments"] == 1
    assert out["uncited_fragments"] == 0


def test_uncited_match_counts_in_both_percentages():
    text = "Скопированный без указания источника текст фрагмента дальше."
    out = aggregate_results([_mp("A", 0, 20)], [], [], total_fragments=4, full_text=text)
    assert out["matches"][0]["cited"] is False
    assert out["overall_similarity"] == out["uncited_similarity"] == 25.0
    assert out["uncited_fragments"] == 1


def test_no_full_text_means_nothing_marked_cited():
    out = aggregate_results([_m("A", 0)], [], [], total_fragments=2)  # full_text не передан
    assert out["matches"][0]["cited"] is False
    assert out["overall_similarity"] == out["uncited_similarity"]


# ─── aggregate_results: recommendations ──────────────────────────────────────

def _cand(title: str, similarity: float, url: str | None = None) -> dict:
    return {
        "fragment": "фрагмент",
        "position_start": 0,
        "position_end": 8,
        "similarity": similarity,
        "source_title": title,
        "source_url": url,
        "source_db": "openalex",
    }


def test_no_related_candidates_means_empty_recommendations():
    out = aggregate_results([], [], [], total_fragments=10)
    assert out["recommendations"] == []


def test_related_candidates_become_recommendations_sorted_by_similarity():
    out = aggregate_results(
        [], [], [], total_fragments=10,
        related_candidates=[_cand("Low", 60.0), _cand("High", 90.0)],
    )
    titles = [r["source_title"] for r in out["recommendations"]]
    assert titles == ["High", "Low"]


def test_recommendations_dedup_keeps_best_per_source():
    out = aggregate_results(
        [], [], [], total_fragments=10,
        related_candidates=[_cand("A", 60.0, "url-a"), _cand("A", 85.0, "url-a")],
    )
    assert len(out["recommendations"]) == 1
    assert out["recommendations"][0]["similarity"] == 85.0


def test_recommendations_capped_at_ten():
    candidates = [_cand(f"S{i}", float(i), f"url-{i}") for i in range(15)]
    out = aggregate_results([], [], [], total_fragments=10, related_candidates=candidates)
    assert len(out["recommendations"]) == 10


def test_source_already_flagged_as_match_excluded_from_recommendations():
    semantic = [{**_cand("A", 90.0, "url-a"), "method": "semantic+llm"}]
    out = aggregate_results(
        [], [], semantic, total_fragments=10,
        related_candidates=[_cand("A", 60.0, "url-a"), _cand("B", 70.0, "url-b")],
    )
    titles = [r["source_title"] for r in out["recommendations"]]
    assert titles == ["B"]