import logging

import pytest
import torch

from translation.backends.local_seq2seq import MarianMTTranslationBackend, NLLBTranslationBackend
from translation.backends.local_ctranslate2 import NLLBCTranslate2TranslationBackend
from translation.languages import build_nllb_language_catalog, resolve_nllb_language_code
from translation.service import TranslationService
from translation.text_splitter import compute_safe_input_token_limit, split_text_for_translation


class _FakeBatch(dict):
    def to(self, device):
        self["device"] = device
        return self


class _FakeTokenizer:
    def __init__(self):
        self.src_lang = None
        self.pad_token = "</s>"
        self.eos_token = "</s>"
        self.lang_code_to_id = {"eng_Latn": 101, "zho_Hans": 202}
        self.last_call = None

    def __call__(self, texts, **kwargs):
        self.last_call = {"texts": list(texts), **kwargs}
        return _FakeBatch({"input_ids": torch.tensor([[1, 2, 3]])})

    def batch_decode(self, generated, skip_special_tokens=True):
        del generated, skip_special_tokens
        return ["translated" for _ in range(len(self.last_call["texts"]))]

    def convert_tokens_to_ids(self, token):
        return self.lang_code_to_id[token]


class _FakeModel:
    def to(self, device):
        self.device = device
        return self

    def eval(self):
        return self

    def generate(self, **kwargs):
        self.last_generate_kwargs = kwargs
        return [[42]]


class _FakeCT2Tokenizer:
    def __init__(self, src_lang=None):
        self.src_lang = src_lang
        self.pad_token = "</s>"
        self.eos_token = "</s>"
        self.last_call = None

    def __call__(self, texts, **kwargs):
        self.last_call = {"texts": list(texts), **kwargs}
        return {"input_ids": [[1, 2, 3] for _ in texts]}

    def convert_ids_to_tokens(self, ids):
        del ids
        return ["tok_a", "tok_b", "tok_c"]

    def convert_tokens_to_ids(self, tokens):
        if isinstance(tokens, list):
            return [1 for _ in tokens]
        return 1

    def decode(self, token_ids, skip_special_tokens=True):
        del token_ids, skip_special_tokens
        return "translated"


class _FakeCT2Result:
    def __init__(self, tokens):
        self.hypotheses = [tokens]


class _FakeCT2Translator:
    def __init__(self):
        self.last_translate_batch_kwargs = None

    def translate_batch(self, source_tokens, **kwargs):
        self.last_translate_batch_kwargs = {"source_tokens": source_tokens, **kwargs}
        target_prefix = kwargs.get("target_prefix") or []
        return [
            _FakeCT2Result((target_prefix[idx] or []) + ["translated_token"])
            for idx, _ in enumerate(source_tokens)
        ]


def _stub_load_model(self):
    self.tokenizer = _FakeTokenizer()
    self.seq2seq_model = _FakeModel()


def _stub_load_ct2_runtime(self):
    self.tokenizer = _FakeCT2Tokenizer()
    self.translator = _FakeCT2Translator()


def test_marian_language_validation(monkeypatch):
    monkeypatch.setattr(MarianMTTranslationBackend, "_load_model", _stub_load_model)
    backend = MarianMTTranslationBackend(
        name="opus-mt-zh-en",
        model_id="Helsinki-NLP/opus-mt-zh-en",
        model_dir="./models/translation/Helsinki-NLP/opus-mt-zh-en",
        device="cpu",
        torch_dtype="float32",
        batch_size=1,
        max_input_length=16,
        max_new_tokens=16,
        num_beams=1,
        source_langs=["zh"],
        target_langs=["en"],
    )

    result = backend.translate("测试", source_lang="zh", target_lang="en")
    assert result == "translated"

    with pytest.raises(ValueError, match="source languages"):
        backend.translate("test", source_lang="en", target_lang="zh")


def test_nllb_uses_src_lang_and_forced_bos(monkeypatch):
    monkeypatch.setattr(NLLBTranslationBackend, "_load_model", _stub_load_model)
    backend = NLLBTranslationBackend(
        name="nllb-200-distilled-600m",
        model_id="facebook/nllb-200-distilled-600M",
        model_dir="./models/translation/facebook/nllb-200-distilled-600M",
        device="cpu",
        torch_dtype="float32",
        batch_size=1,
        max_input_length=16,
        max_new_tokens=16,
        num_beams=1,
    )

    result = backend.translate("test", source_lang="en", target_lang="zh")

    assert result == "translated"
    assert backend.tokenizer.src_lang == "eng_Latn"
    assert backend.seq2seq_model.last_generate_kwargs["forced_bos_token_id"] == 202


def test_nllb_accepts_finnish_short_code(monkeypatch):
    monkeypatch.setattr(NLLBTranslationBackend, "_load_model", _stub_load_model)
    backend = NLLBTranslationBackend(
        name="nllb-200-distilled-600m",
        model_id="facebook/nllb-200-distilled-600M",
        model_dir="./models/translation/facebook/nllb-200-distilled-600M",
        device="cpu",
        torch_dtype="float32",
        batch_size=1,
        max_input_length=16,
        max_new_tokens=16,
        num_beams=1,
    )

    result = backend.translate("test", source_lang="fi", target_lang="zh")

    assert result == "translated"
    assert backend.tokenizer.src_lang == "fin_Latn"
    assert backend.seq2seq_model.last_generate_kwargs["forced_bos_token_id"] == 202


def test_nllb_ctranslate2_accepts_finnish_short_code(monkeypatch):
    created_tokenizers = []

    def _fake_from_pretrained(source, src_lang=None, **kwargs):
        del source, kwargs
        tokenizer = _FakeCT2Tokenizer(src_lang=src_lang)
        created_tokenizers.append(tokenizer)
        return tokenizer

    monkeypatch.setattr(NLLBCTranslate2TranslationBackend, "_load_runtime", _stub_load_ct2_runtime)
    monkeypatch.setattr(
        "translation.backends.local_ctranslate2.AutoTokenizer.from_pretrained",
        _fake_from_pretrained,
    )
    backend = NLLBCTranslate2TranslationBackend(
        name="nllb-200-distilled-600m",
        model_id="facebook/nllb-200-distilled-600M",
        model_dir="./models/translation/facebook/nllb-200-distilled-600M",
        device="cpu",
        torch_dtype="float32",
        batch_size=1,
        max_input_length=16,
        max_new_tokens=16,
        num_beams=1,
    )

    result = backend.translate("test", source_lang="fi", target_lang="zh")

    assert result == "translated"
    assert len(created_tokenizers) == 1
    assert created_tokenizers[0].src_lang == "fin_Latn"
    assert backend.translator.last_translate_batch_kwargs["target_prefix"] == [["zho_Hans"]]


def test_nllb_ctranslate2_falls_back_to_model_id_when_local_dir_is_wrong_type(tmp_path, monkeypatch):
    wrong_dir = tmp_path / "wrong-nllb"
    wrong_dir.mkdir()
    (wrong_dir / "config.json").write_text('{"model_type":"led"}', encoding="utf-8")

    monkeypatch.setattr(NLLBCTranslate2TranslationBackend, "_load_runtime", _stub_load_ct2_runtime)

    backend = NLLBCTranslate2TranslationBackend(
        name="nllb-200-distilled-600m",
        model_id="facebook/nllb-200-distilled-600M",
        model_dir=str(wrong_dir),
        device="cpu",
        torch_dtype="float32",
        batch_size=1,
        max_input_length=16,
        max_new_tokens=16,
        num_beams=1,
    )

    assert backend._model_source() == "facebook/nllb-200-distilled-600M"
    assert backend._tokenizer_source() == "facebook/nllb-200-distilled-600M"


def test_nllb_ctranslate2_falls_back_to_model_id_when_local_dir_is_incomplete(tmp_path, monkeypatch):
    incomplete_dir = tmp_path / "incomplete-nllb"
    incomplete_dir.mkdir()
    (incomplete_dir / "ctranslate2-float16").mkdir()

    monkeypatch.setattr(NLLBCTranslate2TranslationBackend, "_load_runtime", _stub_load_ct2_runtime)

    backend = NLLBCTranslate2TranslationBackend(
        name="nllb-200-distilled-600m",
        model_id="facebook/nllb-200-distilled-600M",
        model_dir=str(incomplete_dir),
        device="cpu",
        torch_dtype="float32",
        batch_size=1,
        max_input_length=16,
        max_new_tokens=16,
        num_beams=1,
    )

    assert backend._model_source() == "facebook/nllb-200-distilled-600M"


def test_nllb_resolves_flores_short_tags_and_iso_no():
    cat = build_nllb_language_catalog(None)
    assert resolve_nllb_language_code("ca", cat) == "cat_Latn"
    assert resolve_nllb_language_code("da", cat) == "dan_Latn"
    assert resolve_nllb_language_code("eu", cat) == "eus_Latn"
    assert resolve_nllb_language_code("gl", cat) == "glg_Latn"
    assert resolve_nllb_language_code("hu", cat) == "hun_Latn"
    assert resolve_nllb_language_code("id", cat) == "ind_Latn"
    assert resolve_nllb_language_code("nl", cat) == "nld_Latn"
    assert resolve_nllb_language_code("no", cat) == "nob_Latn"
    assert resolve_nllb_language_code("ro", cat) == "ron_Latn"
    assert resolve_nllb_language_code("SV", cat) == "swe_Latn"
    assert resolve_nllb_language_code("tr", cat) == "tur_Latn"
    assert resolve_nllb_language_code("deu_Latn", cat) == "deu_Latn"


def test_translation_service_preloads_enabled_backends(monkeypatch):
    created = []

    def _fake_create_backend(self, *, name, backend_type, cfg):
        del self, cfg
        created.append((name, backend_type))

        class _Backend:
            model = name

            @property
            def supports_batch(self):
                return True

            def translate(self, text, target_lang, source_lang=None, scene=None):
                del target_lang, source_lang, scene
                return text

        return _Backend()

    monkeypatch.setattr(TranslationService, "_create_backend", _fake_create_backend)
    config = {
        "service_url": "http://127.0.0.1:6006",
        "timeout_sec": 10.0,
        "default_model": "opus-mt-en-zh",
        "default_scene": "general",
        "capabilities": {
            "opus-mt-en-zh": {
                "enabled": True,
                "backend": "local_marian",
                "use_cache": True,
                "model_id": "dummy",
                "model_dir": "dummy",
                "device": "cpu",
                "torch_dtype": "float32",
                "batch_size": 1,
                "max_input_length": 8,
                "max_new_tokens": 8,
                "num_beams": 1,
            },
            "nllb-200-distilled-600m": {
                "enabled": True,
                "backend": "local_nllb",
                "use_cache": True,
                "model_id": "dummy",
                "model_dir": "dummy",
                "device": "cpu",
                "torch_dtype": "float32",
                "batch_size": 1,
                "max_input_length": 8,
                "max_new_tokens": 8,
                "num_beams": 1,
            },
        },
        "cache": {
            "ttl_seconds": 60,
            "sliding_expiration": True,
        },
    }

    service = TranslationService(config)

    assert service.available_models == ["opus-mt-en-zh", "nllb-200-distilled-600m"]
    assert service.loaded_models == ["opus-mt-en-zh", "nllb-200-distilled-600m"]
    assert created == [
        ("opus-mt-en-zh", "local_marian"),
        ("nllb-200-distilled-600m", "local_nllb"),
    ]

    backend = service.get_backend("opus-mt-en-zh")
    assert backend.model == "opus-mt-en-zh"


def test_compute_safe_input_token_limit_uses_decode_constraints():
    nllb_limit = compute_safe_input_token_limit(
        max_input_length=256,
        max_new_tokens=64,
        decoding_length_mode="source",
        decoding_length_extra=8,
    )
    opus_limit = compute_safe_input_token_limit(
        max_input_length=256,
        max_new_tokens=256,
    )

    assert nllb_limit == 56
    assert opus_limit == 248


def test_split_text_for_translation_prefers_sentence_boundaries():
    text = (
        "这是一条很长的中文商品描述，包含材质、尺码和适用场景。"
        "适合春夏通勤，也适合日常出街穿搭；"
        "如果长度超了，应该优先按完整语义分句，而不是切成很碎的小片段。"
    )

    segments = split_text_for_translation(
        text,
        max_tokens=36,
        token_length_fn=len,
    )

    assert len(segments) >= 2
    assert "".join(segments) == text
    assert all(len(segment) <= 36 for segment in segments)
    assert segments[0].endswith(("。", "；"))


class _SegmentingMarianBackend(MarianMTTranslationBackend):
    def _load_model(self):
        self.translated_batches = []

    def _token_count(self, text, target_lang, source_lang=None):
        del target_lang, source_lang
        return len(text)

    def _translate_batch(self, texts, target_lang, source_lang=None):
        del source_lang
        self.translated_batches.append(list(texts))
        if target_lang == "zh":
            return [f"<{text.strip()}>" for text in texts]
        return [f"[{text.strip()}]" for text in texts]


def test_local_backend_splits_oversized_text_before_translation():
    backend = _SegmentingMarianBackend(
        name="opus-mt-en-zh",
        model_id="Helsinki-NLP/opus-mt-en-zh",
        model_dir="./models/translation/Helsinki-NLP/opus-mt-en-zh",
        device="cpu",
        torch_dtype="float32",
        batch_size=8,
        max_input_length=24,
        max_new_tokens=24,
        num_beams=1,
        source_langs=["en"],
        target_langs=["zh"],
    )

    text = (
        "This soft cotton dress is breathable and lightweight, "
        "works well for spring travel and everyday wear, "
        "and should be split on natural clause boundaries when it gets too long."
    )

    result = backend.translate(text, source_lang="en", target_lang="zh")

    assert result is not None
    all_segments = [piece for batch in backend.translated_batches for piece in batch]
    assert len(all_segments) >= 2
    assert all(len(batch) <= backend.batch_size for batch in backend.translated_batches)
    assert all(len(piece) <= 16 for piece in all_segments)
    assert result == "".join(f"<{piece.strip()}>" for piece in all_segments)


def test_local_backend_batches_after_segmentation():
    backend = _SegmentingMarianBackend(
        name="opus-mt-en-zh",
        model_id="Helsinki-NLP/opus-mt-en-zh",
        model_dir="./models/translation/Helsinki-NLP/opus-mt-en-zh",
        device="cpu",
        torch_dtype="float32",
        batch_size=4,
        max_input_length=24,
        max_new_tokens=24,
        num_beams=1,
        source_langs=["en"],
        target_langs=["zh"],
    )

    texts = [
        "alpha beta gamma delta, epsilon zeta eta theta, iota kappa lambda mu.",
        "nu xi omicron pi, rho sigma tau upsilon, phi chi psi omega.",
        "dress shirt coat pants, socks shoes belt scarf, hat gloves bag watch.",
    ]

    result = backend.translate(texts, source_lang="en", target_lang="zh")

    assert isinstance(result, list)
    assert len(result) == 3
    assert len(backend.translated_batches) >= 2
    assert all(len(batch) <= backend.batch_size for batch in backend.translated_batches)
    assert sum(len(batch) for batch in backend.translated_batches) > backend.batch_size
    assert all(item is not None for item in result)


def test_local_backend_logs_segmentation_and_inference_batches(caplog):
    backend = _SegmentingMarianBackend(
        name="opus-mt-en-zh",
        model_id="Helsinki-NLP/opus-mt-en-zh",
        model_dir="./models/translation/Helsinki-NLP/opus-mt-en-zh",
        device="cpu",
        torch_dtype="float32",
        batch_size=2,
        max_input_length=24,
        max_new_tokens=24,
        num_beams=1,
        source_langs=["en"],
        target_langs=["zh"],
    )

    texts = [
        "one two three four, five six seven eight, nine ten eleven twelve.",
        "thirteen fourteen fifteen sixteen, seventeen eighteen nineteen twenty.",
    ]

    with caplog.at_level(logging.INFO):
        backend.translate(texts, source_lang="en", target_lang="zh")

    messages = [record.getMessage() for record in caplog.records]

    assert any(message.startswith("Translation segmentation summary |") for message in messages)
    inference_logs = [
        message for message in messages if message.startswith("Translation inference batch |")
    ]
    assert len(inference_logs) >= 2