"""The ONE canonical ``analyze(text)`` (plan 02-05, PITFALLS § 10) and its token record.

Every later system - furigana (02-07), the lookup popover (02-08), Phase 3's level guard,
Phase 5's vocabulary tracking - consumes the record these tests pin and nothing else. Each
CONTEXT decision is a named test: D-10 (unlisted -> "N1+"), D-11 (particles / auxiliaries /
punctuation are not words), D-12 (proper nouns -> "name"), D-13 (word axis and kanji axis are
separate), plus the committed reading override (人気 -> ひとけ before の) and the known-ambiguous
readings PITFALLS names (今日, 行った x2, 人気 x2).

Facts recorded by the wave-2 summaries and pinned knowingly here (02-03-SUMMARY.md § Known
Edges): なる resolves to the verb 1375610, which is on the N3 list (n5.csv's なる row is the
archaic copula 2138260 - an upstream yomitan-jlpt-vocab quirk); いい resolves to its own JMdict
entry 2820690 (N5), not 良い 1605820.

All tests use the session-scoped ``analyzer`` fixture (tests/conftest.py): one dictionary open
and one compact-JMdict load per session.
"""

from __future__ import annotations

import concurrent.futures
import functools
import importlib.metadata
import json
import time
from pathlib import Path

from japanese_avatar.nlp.analyzer import TOKEN_KEYS

FIXTURES = Path(__file__).parent / "fixtures"
GOLDEN = FIXTURES / "sentences.json"

LONG_36_MORA = "今日はいい天気ですから、公園を散歩してから、買い物に行きました。"

REGENERATE = (
    "regenerate with .venv/Scripts/python.exe tests/fixtures/make_sentences.py "
    "and REVIEW the diff - a difference means SudachiDict, JMdict, the JLPT lists "
    "or the joining rule changed what learners read"
)


@functools.cache
def _golden() -> dict:
    """The fixed sample sentence set (SC-1): ``{"analyzer": pins, "sentences": [...]}``."""
    return json.loads(GOLDEN.read_text(encoding="utf-8"))


def _golden_texts() -> list[str]:
    return [s["text"] for s in _golden()["sentences"]]


def _by_surface(units: list[dict], surface: str) -> dict:
    matches = [u for u in units if u["surface"] == surface]
    assert len(matches) == 1, f"expected one unit {surface!r}, got {[u['surface'] for u in units]}"
    return matches[0]


def test_record_keys_exact(analyzer):
    """Every unit carries exactly TOKEN_KEYS, in order - the contract Phases 3 and 5 read."""
    units = analyzer("日本語を勉強しています。")
    assert units, "a sentence yields units"
    for u in units:
        assert tuple(u.keys()) == TOKEN_KEYS, u
    assert TOKEN_KEYS == (
        "surface",
        "reading",
        "lemma",
        "level_key",
        "pos",
        "tappable",
        "is_name",
        "jlpt",
        "kanji_levels",
        "ruby",
        "jmdict_id",
        "gloss",
        "start",
        "end",
    )


def test_reading_disambiguation(analyzer):
    """The known-ambiguous readings PITFALLS § 10 names: 今日 and the two 行った."""
    kyou = _by_surface(analyzer("今日はいい天気ですね。"), "今日")
    assert kyou["reading"] == "きょう"

    itta = _by_surface(analyzer("昨日、駅に行った。"), "行った")
    assert itta["reading"] == "いった"
    assert itta["lemma"] == "行く"

    okonatta = _by_surface(analyzer("会議を行った。"), "行った")
    assert okonatta["reading"] == "おこなった"
    assert okonatta["lemma"] == "行う"


def test_hitoke_override(analyzer):
    """人気 is ひとけ before の (the committed override) and にんき N3 elsewhere."""
    hitoke = _by_surface(analyzer("人気のない通りを歩いた。"), "人気")
    assert hitoke["reading"] == "ひとけ"
    assert hitoke["ruby"] == [["人気", "ひとけ"]]

    ninki = _by_surface(analyzer("この店は人気がある。"), "人気")
    assert ninki["reading"] == "にんき"
    assert ninki["jlpt"] == "N3"


def test_tappability(analyzer):
    """D-11: は / です / ね / 。 are plain text with no level and no gloss; words are tappable."""
    units = analyzer("今日はいい天気ですね。")
    assert [u["surface"] for u in units] == ["今日", "は", "いい", "天気", "です", "ね", "。"]
    for surface in ("は", "です", "ね", "。"):
        u = _by_surface(units, surface)
        assert u["tappable"] is False, surface
        assert u["jlpt"] is None, surface
        assert u["gloss"] == [], surface
        assert u["jmdict_id"] is None, surface
        assert u["kanji_levels"] == {}, surface
        assert u["ruby"] == [[surface, None]], surface
    for surface in ("今日", "いい", "天気"):
        u = _by_surface(units, surface)
        assert u["tappable"] is True, surface
        assert u["jlpt"] == "N5", surface


def test_level_derivation(analyzer):
    """D-12 name, level from the level_key entry, D-10 N1+ for a word on no list."""
    units = analyzer("田中さんは東京に住んでいます。")
    tanaka = _by_surface(units, "田中さん")
    assert tanaka["is_name"] is True and tanaka["jlpt"] == "name"
    assert tanaka["tappable"] is True, "D-12: names are tappable (the reading is the useful part)"
    tokyo = _by_surface(units, "東京")
    assert tokyo["is_name"] is True and tokyo["jlpt"] == "name"
    sunde = _by_surface(units, "住んでいます")
    assert sunde["lemma"] == "住む" and sunde["jlpt"] == "N5"

    # 話せる has its own, unlisted JMdict entry; its level comes from level_key 話す (N5).
    hanaseru = _by_surface(analyzer("話せるようになりたい"), "話せる")
    assert hanaseru["level_key"] == "話す"
    assert hanaseru["jlpt"] == "N5"
    assert hanaseru["jmdict_id"] == 1562360
    assert any("able to speak" in g for g in hanaseru["gloss"][0]), hanaseru["gloss"]

    # 鬱 is not one of the 2,211 listed kanji: kanji axis None (D-13 / D-02).
    uttoushii = _by_surface(analyzer("鬱陶しい天気だ。"), "鬱陶しい")
    assert uttoushii["kanji_levels"]["鬱"] is None
    assert uttoushii["tappable"] is True

    # D-10: a word on no list is "N1+" - never a guess. The fixture pins 鬱陶しい's observed
    # label (N1 or N1+, whichever the pinned lists say); the D-10 assertion itself uses a rare
    # word that has a JMdict entry and sits on no list, so "N1+" comes from the rule, not from
    # a lookup miss.
    assert uttoushii["jlpt"] in {"N1", "N1+"}
    beyond = _by_surface(analyzer("彼は瀟洒な服を着ている。"), "瀟洒")
    assert beyond["tappable"] is True
    assert beyond["jmdict_id"] is not None, "瀟洒 has a JMdict entry"
    assert beyond["jlpt"] == "N1+", "on no JLPT list -> N1+ (D-10)"


def test_kanji_axis_separate_from_word_axis(analyzer):
    """D-13: the word's level and each kanji's level are two axes in the same record."""
    nihongo = _by_surface(analyzer("日本語を勉強しています。"), "日本語")
    # 日本語 resolves to JMdict 1464530, which is on NONE of the five pinned lists (Waller's
    # lists carry 日本 at N3 and no 日本語 at all) - so the word axis is the honest D-10 "N1+"
    # while every kanji in it is N5. The two axes really are independent; the plan's assumed
    # "N5" was a guess the pinned data contradicts (02-05-SUMMARY.md).
    assert nihongo["jmdict_id"] == 1464530
    assert nihongo["jlpt"] == "N1+"
    assert nihongo["kanji_levels"] == {"日": "N5", "本": "N5", "語": "N5"}

    benkyou = _by_surface(analyzer("日本語を勉強しています。"), "勉強しています")
    assert benkyou["lemma"] == "勉強する"
    assert set(benkyou["kanji_levels"]) == {"勉", "強"}
    assert all(v in {"N5", "N4", "N3", "N2", "N1"} for v in benkyou["kanji_levels"].values())
    # The word is N5 while its kanji are harder - the axes really are independent.
    assert benkyou["jlpt"] == "N5"
    assert benkyou["kanji_levels"]["勉"] != "N5"


def test_gloss_shipped_with_token(analyzer):
    """D-07 / research § Q3: glosses ride inside the token so a tap needs no round trip."""
    units = analyzer("食べました")
    assert len(units) == 1
    u = units[0]
    assert u["lemma"] == "食べる"
    assert isinstance(u["jmdict_id"], int)
    assert u["gloss"], "the token carries its glosses"
    assert any("eat" in g for g in u["gloss"][0]), u["gloss"]
    assert len(u["gloss"]) <= 3 and all(len(sense) <= 3 for sense in u["gloss"])
    assert u["ruby"] == [["食", "た"], ["べました", None]]


def test_offsets_cover_text(analyzer):
    """Units tile every fixture sentence and each offset pair reads back its surface."""
    for text in _golden_texts():
        units = analyzer(text)
        assert "".join(u["surface"] for u in units) == text
        pos = 0
        for u in units:
            assert u["start"] == pos, (text, u)
            assert u["end"] == pos + len(u["surface"]), (text, u)
            assert text[u["start"] : u["end"]] == u["surface"], (text, u)
            pos = u["end"]
        assert pos == len(text)


def test_analyze_is_fast(analyzer):
    """Warm analysis is milliseconds: 100 runs of the 36-mora sentence, mean well under 50 ms."""
    analyzer(LONG_36_MORA)  # warm any lazily built table
    runs = 100
    started = time.perf_counter()
    for _ in range(runs):
        analyzer(LONG_36_MORA)
    mean_ms = (time.perf_counter() - started) * 1000 / runs
    print(f"\nanalyze({len(LONG_36_MORA)} chars): mean {mean_ms:.3f} ms over {runs} runs")
    assert mean_ms < 50, f"{mean_ms:.1f} ms per analysis (budget 50 ms; expected < 5 ms)"


def test_analyze_is_thread_safe(analyzer):
    """Plan 02-07: concurrent analyses all succeed and all agree. The Space serves them.

    ``sudachipy.Tokenizer`` is a PyO3 object that takes a mutable borrow for the duration of
    a ``tokenize`` call, so ONE shared instance answers the first caller and raises
    ``RuntimeError: Already borrowed`` at every thread that arrives while it is busy - a lost
    analysis, not a wrong one. Before ``nlp.tokenizer`` gave each thread its own tokenizer,
    8 threads on this sentence lost 5 of 8 calls; the deployed symptom was a directive
    carrying ``tokens: []`` and an avatar line rendered with no furigana at all.

    It became reachable in 02-07 because the host page now analyses the learner's line while
    the turn's own analyse stage runs, so a single typed turn issues two overlapping
    requests - and a busy Space multiplies that by its visitors.

    A real regression test: reverting tokenizer.get_tokenizer to one shared instance fails
    this, and the failure is the exception, not a flaky count.
    """
    workers = 8
    rounds = 4
    expected = analyzer(LONG_36_MORA)
    with concurrent.futures.ThreadPoolExecutor(max_workers=workers) as pool:
        futures = [pool.submit(analyzer, LONG_36_MORA) for _ in range(workers * rounds)]
        results = [f.result() for f in futures]
    assert len(results) == workers * rounds
    for i, units in enumerate(results):
        assert units == expected, f"thread {i} disagreed with the single-threaded analysis"


def test_empty_text(analyzer):
    """Nothing in, nothing out; whitespace is a non-tappable unit with no level."""
    assert analyzer("") == []
    units = analyzer("   ")
    assert "".join(u["surface"] for u in units) == "   "
    for u in units:
        assert u["surface"].strip() == ""
        assert u["tappable"] is False and u["jlpt"] is None and u["gloss"] == []
        assert tuple(u.keys()) == TOKEN_KEYS


def test_known_list_gaps_are_honest_n1_plus(analyzer):
    """Words the pinned lists simply do not carry badge "N1+" (D-10) - pinned by JMdict id.

    These look wrong to a Japanese teacher and are right by the data: none of the five
    yomitan-jlpt-vocab CSVs lists ない / 無い (1529520) or 日本語 (1464530), and 人気 read ひとけ is
    the separate entry 1367020 (unlisted), not にんき's 1367010 (N3). The lookup resolves each to
    the correct entry; the label follows the lists. If the product later wants a level alias for
    them, that is a new override table and a conscious change to this test, never a weaker lookup.
    """
    nai = _by_surface(analyzer("人気のない通りを歩いた。"), "ない")
    assert nai["tappable"] is True and nai["lemma"] == "ない" and nai["level_key"] == "無い"
    assert nai["jmdict_id"] == 1529520 and nai["jlpt"] == "N1+"
    nakatta = _by_surface(analyzer("きれいじゃなかった"), "なかった")
    assert nakatta["jmdict_id"] == 1529520 and nakatta["jlpt"] == "N1+"
    hitoke = _by_surface(analyzer("人気のない通りを歩いた。"), "人気")
    assert hitoke["jmdict_id"] == 1367020 and hitoke["jlpt"] == "N1+"
    assert any("human presence" in g for g in hitoke["gloss"][0]), hitoke["gloss"]
    ninki = _by_surface(analyzer("この店は人気がある。"), "人気")
    assert ninki["jmdict_id"] == 1367010 and ninki["jlpt"] == "N3"
    # The other side of the same coin: こんにちは IS listed, at N3 (and N1), so it badges N3.
    konnichiwa = analyzer("こんにちは")
    assert len(konnichiwa) == 1
    assert konnichiwa[0]["jmdict_id"] == 1289400 and konnichiwa[0]["jlpt"] == "N3"


# --- the fixed sample sentence set (success criterion 1) ---------------------------------


def test_fixture_sentences(analyzer):
    """The analyzer reproduces every golden unit of every fixture sentence exactly.

    This is JPN-01's quick-loop row (02-VALIDATION.md). A SudachiDict, JMdict or JLPT-list bump,
    or a change to the joining rule, must show up here as a reviewed diff - never as a silent
    change to what learners read.
    """
    golden = _golden()
    assert len(golden["sentences"]) == 28
    for sentence in golden["sentences"]:
        text, expected = sentence["text"], sentence["units"]
        got = analyzer(text)
        assert got == expected, f"{text!r}: analyze() differs from sentences.json - {REGENERATE}"


def test_fixture_set_covers_the_contract():
    """The set holds every case the phase promised to pin (ambiguous readings, joins, edges)."""
    texts = _golden_texts()
    assert len(texts) >= 25
    joined = "\n".join(texts)
    for needle in (
        "今日",
        "食べました",
        "田中さん",
        "勉強し",
        "読んで",
        "待ち合わせ",
        "鬱",
        "美味しくない",
        "きれいじゃなかった",
    ):
        assert needle in joined, needle
    assert sum("行った" in t for t in texts) >= 2, "both readings of 行った"
    assert sum("人気" in t for t in texts) >= 2, "both readings of 人気"
    # The known-ambiguous readings are pinned as READINGS, not just as texts.
    readings = {
        (s["text"], u["surface"]): u["reading"] for s in _golden()["sentences"] for u in s["units"]
    }
    assert readings[("今日はいい天気ですね。", "今日")] == "きょう"
    assert readings[("昨日、駅に行った。", "行った")] == "いった"
    assert readings[("会議を行った。", "行った")] == "おこなった"
    assert readings[("人気のない通りを歩いた。", "人気")] == "ひとけ"
    assert readings[("この店は人気がある。", "人気")] == "にんき"


def test_fixture_pins_match_installed():
    """The golden file was generated by the data this environment has, or it is stale."""
    import sudachipy

    from japanese_avatar.nlp import jmdict

    pins = _golden()["analyzer"]
    assert pins["sudachipy"] == sudachipy.__version__
    assert pins["sudachidict_core"] == importlib.metadata.version("sudachidict_core")
    assert pins["jmdict"] == jmdict.load().meta["version"]
    assert pins["jlpt_vocab"] == "2025.08.01.0"
    assert pins["kanji_data"] == "00fd7079c3890f430759536f91aa5e854ec0ca4f"