spaCy/spacy/tests/test_language.py

import itertools
import logging
from unittest import mock
import pytest
from spacy.language import Language
from spacy.scorer import Scorer
from spacy.tokens import Doc, Span
from spacy.vocab import Vocab
from spacy.training import Example
from spacy.lang.en import English
from spacy.lang.de import German
from spacy.util import registry, ignore_error, raise_error, find_matching_language
from spacy.util import load_model_from_config
import spacy
from thinc.api import Config, CupyOps, NumpyOps, get_array_module, get_current_ops

from .util import add_vecs_to_vocab, assert_docs_equal


try:
    import torch

    # Ensure that we don't deadlock in multiprocessing tests.
    torch.set_num_threads(1)
    torch.set_num_interop_threads(1)
except ImportError:
    pass

TAGGER_CFG_STRING = """
    [nlp]
    lang = "en"
    pipeline = ["tok2vec","tagger"]

    [components]

    [components.tagger]
    factory = "tagger"

    [components.tagger.model]
    @architectures = "spacy.Tagger.v2"
    nO = null

    [components.tagger.model.tok2vec]
    @architectures = "spacy.Tok2VecListener.v1"
    width = ${components.tok2vec.model.encode.width}

    [components.tok2vec]
    factory = "tok2vec"

    [components.tok2vec.model]
    @architectures = "spacy.Tok2Vec.v2"

    [components.tok2vec.model.embed]
    @architectures = "spacy.MultiHashEmbed.v1"
    width = ${components.tok2vec.model.encode.width}
    rows = [2000, 1000, 1000, 1000]
    attrs = ["NORM", "PREFIX", "SUFFIX", "SHAPE"]
    include_static_vectors = false

    [components.tok2vec.model.encode]
    @architectures = "spacy.MaxoutWindowEncoder.v2"
    width = 96
    depth = 4
    window_size = 1
    maxout_pieces = 3
    """


TAGGER_TRAIN_DATA = [
    ("I like green eggs", {"tags": ["N", "V", "J", "N"]}),
    ("Eat blue ham", {"tags": ["V", "J", "N"]}),
]


TAGGER_TRAIN_DATA = [
    ("I like green eggs", {"tags": ["N", "V", "J", "N"]}),
    ("Eat blue ham", {"tags": ["V", "J", "N"]}),
]


def evil_component(doc):
    if "2" in doc.text:
        raise ValueError("no dice")
    return doc


def perhaps_set_sentences(doc):
    if not doc.text.startswith("4"):
        doc[-1].is_sent_start = True
    return doc


def assert_sents_error(doc):
    if not doc.has_annotation("SENT_START"):
        raise ValueError("no sents")
    return doc


def warn_error(proc_name, proc, docs, e):
    logger = logging.getLogger("spacy")
    logger.warning(f"Trouble with component {proc_name}.")


@pytest.fixture
def nlp():
    nlp = Language(Vocab())
    textcat = nlp.add_pipe("textcat")
    for label in ("POSITIVE", "NEGATIVE"):
        textcat.add_label(label)
    nlp.initialize()
    return nlp


def test_language_update(nlp):
    text = "hello world"
    annots = {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}
    wrongkeyannots = {"LABEL": True}
    doc = Doc(nlp.vocab, words=text.split(" "))
    example = Example.from_dict(doc, annots)
    nlp.update([example])

    # Not allowed to call with just one Example
    with pytest.raises(TypeError):
        nlp.update(example)

    # Update with text and dict: not supported anymore since v.3
    with pytest.raises(TypeError):
        nlp.update((text, annots))
    # Update with doc object and dict
    with pytest.raises(TypeError):
        nlp.update((doc, annots))

    # Create examples badly
    with pytest.raises(ValueError):
        example = Example.from_dict(doc, None)
    with pytest.raises(KeyError):
        example = Example.from_dict(doc, wrongkeyannots)


def test_language_update_updates():
    config = Config().from_str(TAGGER_CFG_STRING)
    nlp = load_model_from_config(config, auto_fill=True, validate=True)

    train_examples = []
    for t in TAGGER_TRAIN_DATA:
        train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))

    optimizer = nlp.initialize(get_examples=lambda: train_examples)

    docs_before_update = list(nlp.pipe([eg.predicted.copy() for eg in train_examples]))
    nlp.update(train_examples, sgd=optimizer)
    docs_after_update = list(nlp.pipe([eg.predicted.copy() for eg in train_examples]))

    xp = get_array_module(docs_after_update[0].tensor)
    assert xp.any(
        xp.not_equal(docs_before_update[0].tensor, docs_after_update[0].tensor)
    )


def test_language_evaluate(nlp):
    text = "hello world"
    annots = {"doc_annotation": {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}}
    doc = Doc(nlp.vocab, words=text.split(" "))
    example = Example.from_dict(doc, annots)
    scores = nlp.evaluate([example])
    assert scores["speed"] > 0

    # test with generator
    scores = nlp.evaluate(eg for eg in [example])
    assert scores["speed"] > 0

    # Not allowed to call with just one Example
    with pytest.raises(TypeError):
        nlp.evaluate(example)

    # Evaluate with text and dict: not supported anymore since v.3
    with pytest.raises(TypeError):
        nlp.evaluate([(text, annots)])
    # Evaluate with doc object and dict
    with pytest.raises(TypeError):
        nlp.evaluate([(doc, annots)])
    with pytest.raises(TypeError):
        nlp.evaluate([text, annots])


def test_evaluate_no_pipe(nlp):
    """Test that docs are processed correctly within Language.pipe if the
    component doesn't expose a .pipe method."""

    @Language.component("test_evaluate_no_pipe")
    def pipe(doc):
        return doc

    text = "hello world"
    annots = {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}
    nlp = Language(Vocab())
    doc = nlp(text)
    nlp.add_pipe("test_evaluate_no_pipe")
    nlp.evaluate([Example.from_dict(doc, annots)])


def test_evaluate_textcat_multilabel(en_vocab):
    """Test that evaluate works with a multilabel textcat pipe."""
    nlp = Language(en_vocab)
    textcat_multilabel = nlp.add_pipe("textcat_multilabel")
    for label in ("FEATURE", "REQUEST", "BUG", "QUESTION"):
        textcat_multilabel.add_label(label)
    nlp.initialize()

    annots = {"cats": {"FEATURE": 1.0, "QUESTION": 1.0}}
    doc = nlp.make_doc("hello world")
    example = Example.from_dict(doc, annots)
    scores = nlp.evaluate([example])
    labels = nlp.get_pipe("textcat_multilabel").labels
    for label in labels:
        assert scores["cats_f_per_type"].get(label) is not None
    for key in example.reference.cats.keys():
        if key not in labels:
            assert scores["cats_f_per_type"].get(key) is None


def test_evaluate_multiple_textcat_final(en_vocab):
    """Test that evaluate evaluates the final textcat component in a pipeline
    with more than one textcat or textcat_multilabel."""
    nlp = Language(en_vocab)
    textcat = nlp.add_pipe("textcat")
    for label in ("POSITIVE", "NEGATIVE"):
        textcat.add_label(label)
    textcat_multilabel = nlp.add_pipe("textcat_multilabel")
    for label in ("FEATURE", "REQUEST", "BUG", "QUESTION"):
        textcat_multilabel.add_label(label)
    nlp.initialize()

    annots = {
        "cats": {
            "POSITIVE": 1.0,
            "NEGATIVE": 0.0,
            "FEATURE": 1.0,
            "QUESTION": 1.0,
            "POSITIVE": 1.0,
            "NEGATIVE": 0.0,
        }
    }
    doc = nlp.make_doc("hello world")
    example = Example.from_dict(doc, annots)
    scores = nlp.evaluate([example])
    # get the labels from the final pipe
    labels = nlp.get_pipe(nlp.pipe_names[-1]).labels
    for label in labels:
        assert scores["cats_f_per_type"].get(label) is not None
    for key in example.reference.cats.keys():
        if key not in labels:
            assert scores["cats_f_per_type"].get(key) is None


def test_evaluate_multiple_textcat_separate(en_vocab):
    """Test that evaluate can evaluate multiple textcat components separately
    with custom scorers."""

    def custom_textcat_score(examples, **kwargs):
        scores = Scorer.score_cats(
            examples,
            "cats",
            multi_label=False,
            **kwargs,
        )
        return {f"custom_{k}": v for k, v in scores.items()}

    @spacy.registry.scorers("test_custom_textcat_scorer")
    def make_custom_textcat_scorer():
        return custom_textcat_score

    nlp = Language(en_vocab)
    textcat = nlp.add_pipe(
        "textcat",
        config={"scorer": {"@scorers": "test_custom_textcat_scorer"}},
    )
    for label in ("POSITIVE", "NEGATIVE"):
        textcat.add_label(label)
    textcat_multilabel = nlp.add_pipe("textcat_multilabel")
    for label in ("FEATURE", "REQUEST", "BUG", "QUESTION"):
        textcat_multilabel.add_label(label)
    nlp.initialize()

    annots = {
        "cats": {
            "POSITIVE": 1.0,
            "NEGATIVE": 0.0,
            "FEATURE": 1.0,
            "QUESTION": 1.0,
            "POSITIVE": 1.0,
            "NEGATIVE": 0.0,
        }
    }
    doc = nlp.make_doc("hello world")
    example = Example.from_dict(doc, annots)
    scores = nlp.evaluate([example])
    # check custom scores for the textcat pipe
    assert "custom_cats_f_per_type" in scores
    labels = nlp.get_pipe("textcat").labels
    assert set(scores["custom_cats_f_per_type"].keys()) == set(labels)
    # check default scores for the textcat_multilabel pipe
    assert "cats_f_per_type" in scores
    labels = nlp.get_pipe("textcat_multilabel").labels
    assert set(scores["cats_f_per_type"].keys()) == set(labels)


def vector_modification_pipe(doc):
    doc.vector += 1
    return doc


def userdata_pipe(doc):
    doc.user_data["foo"] = "bar"
    return doc


def ner_pipe(doc):
    span = Span(doc, 0, 1, label="FIRST")
    doc.ents += (span,)
    return doc


@pytest.fixture
def sample_vectors():
    return [
        ("spacy", [-0.1, -0.2, -0.3]),
        ("world", [-0.2, -0.3, -0.4]),
        ("pipe", [0.7, 0.8, 0.9]),
    ]


@pytest.fixture
def nlp2(nlp, sample_vectors):
    Language.component(
        "test_language_vector_modification_pipe", func=vector_modification_pipe
    )
    Language.component("test_language_userdata_pipe", func=userdata_pipe)
    Language.component("test_language_ner_pipe", func=ner_pipe)
    add_vecs_to_vocab(nlp.vocab, sample_vectors)
    nlp.add_pipe("test_language_vector_modification_pipe")
    nlp.add_pipe("test_language_ner_pipe")
    nlp.add_pipe("test_language_userdata_pipe")
    return nlp


@pytest.fixture
def texts():
    data = [
        "Hello world.",
        "This is spacy.",
        "You can use multiprocessing with pipe method.",
        "Please try!",
    ]
    return data


@pytest.mark.parametrize("n_process", [1, 2])
def test_language_pipe(nlp2, n_process, texts):
    ops = get_current_ops()
    if isinstance(ops, NumpyOps) or n_process < 2:
        texts = texts * 10
        expecteds = [nlp2(text) for text in texts]
        docs = nlp2.pipe(texts, n_process=n_process, batch_size=2)

        for doc, expected_doc in zip(docs, expecteds):
            assert_docs_equal(doc, expected_doc)


@pytest.mark.parametrize("n_process", [1, 2])
def test_language_pipe_stream(nlp2, n_process, texts):
    ops = get_current_ops()
    if isinstance(ops, NumpyOps) or n_process < 2:
        # check if nlp.pipe can handle infinite length iterator properly.
        stream_texts = itertools.cycle(texts)
        texts0, texts1 = itertools.tee(stream_texts)
        expecteds = (nlp2(text) for text in texts0)
        docs = nlp2.pipe(texts1, n_process=n_process, batch_size=2)

        n_fetch = 20
        for doc, expected_doc in itertools.islice(zip(docs, expecteds), n_fetch):
            assert_docs_equal(doc, expected_doc)


@pytest.mark.parametrize("n_process", [1, 2])
def test_language_pipe_error_handler(n_process):
    """Test that the error handling of nlp.pipe works well"""
    ops = get_current_ops()
    if isinstance(ops, NumpyOps) or n_process < 2:
        nlp = English()
        nlp.add_pipe("merge_subtokens")
        nlp.initialize()
        texts = ["Curious to see what will happen to this text.", "And this one."]
        # the pipeline fails because there's no parser
        with pytest.raises(ValueError):
            nlp(texts[0])
        with pytest.raises(ValueError):
            list(nlp.pipe(texts, n_process=n_process))
        nlp.set_error_handler(raise_error)
        with pytest.raises(ValueError):
            list(nlp.pipe(texts, n_process=n_process))
        # set explicitely to ignoring
        nlp.set_error_handler(ignore_error)
        docs = list(nlp.pipe(texts, n_process=n_process))
        assert len(docs) == 0
        nlp(texts[0])


@pytest.mark.parametrize("n_process", [1, 2])
def test_language_pipe_error_handler_custom(en_vocab, n_process):
    """Test the error handling of a custom component that has no pipe method"""
    Language.component("my_evil_component", func=evil_component)
    ops = get_current_ops()
    if isinstance(ops, NumpyOps) or n_process < 2:
        nlp = English()
        nlp.add_pipe("my_evil_component")
        texts = ["TEXT 111", "TEXT 222", "TEXT 333", "TEXT 342", "TEXT 666"]
        with pytest.raises(ValueError):
            # the evil custom component throws an error
            list(nlp.pipe(texts))

        nlp.set_error_handler(warn_error)
        logger = logging.getLogger("spacy")
        with mock.patch.object(logger, "warning") as mock_warning:
            # the errors by the evil custom component raise a warning for each
            # bad doc
            docs = list(nlp.pipe(texts, n_process=n_process))
            # HACK/TODO? the warnings in child processes don't seem to be
            # detected by the mock logger
            if n_process == 1:
                mock_warning.assert_called()
                assert mock_warning.call_count == 2
                assert len(docs) + mock_warning.call_count == len(texts)
            assert [doc.text for doc in docs] == ["TEXT 111", "TEXT 333", "TEXT 666"]


@pytest.mark.parametrize("n_process", [1, 2])
def test_language_pipe_error_handler_input_as_tuples(en_vocab, n_process):
    """Test the error handling of nlp.pipe with input as tuples"""
    Language.component("my_evil_component", func=evil_component)
    ops = get_current_ops()
    if isinstance(ops, NumpyOps) or n_process < 2:
        nlp = English()
        nlp.add_pipe("my_evil_component")
        texts = [
            ("TEXT 111", 111),
            ("TEXT 222", 222),
            ("TEXT 333", 333),
            ("TEXT 342", 342),
            ("TEXT 666", 666),
        ]
        with pytest.raises(ValueError):
            list(nlp.pipe(texts, as_tuples=True))
        nlp.set_error_handler(warn_error)
        logger = logging.getLogger("spacy")
        with mock.patch.object(logger, "warning") as mock_warning:
            tuples = list(nlp.pipe(texts, as_tuples=True, n_process=n_process))
            # HACK/TODO? the warnings in child processes don't seem to be
            # detected by the mock logger
            if n_process == 1:
                mock_warning.assert_called()
                assert mock_warning.call_count == 2
                assert len(tuples) + mock_warning.call_count == len(texts)
            assert (tuples[0][0].text, tuples[0][1]) == ("TEXT 111", 111)
            assert (tuples[1][0].text, tuples[1][1]) == ("TEXT 333", 333)
            assert (tuples[2][0].text, tuples[2][1]) == ("TEXT 666", 666)


@pytest.mark.parametrize("n_process", [1, 2])
def test_language_pipe_error_handler_pipe(en_vocab, n_process):
    """Test the error handling of a component's pipe method"""
    Language.component("my_perhaps_sentences", func=perhaps_set_sentences)
    Language.component("assert_sents_error", func=assert_sents_error)
    ops = get_current_ops()
    if isinstance(ops, NumpyOps) or n_process < 2:
        texts = [f"{str(i)} is enough. Done" for i in range(100)]
        nlp = English()
        nlp.add_pipe("my_perhaps_sentences")
        nlp.add_pipe("assert_sents_error")
        nlp.initialize()
        with pytest.raises(ValueError):
            # assert_sents_error requires sentence boundaries, will throw an error otherwise
            docs = list(nlp.pipe(texts, n_process=n_process, batch_size=10))
        nlp.set_error_handler(ignore_error)
        docs = list(nlp.pipe(texts, n_process=n_process, batch_size=10))
        # we lose/ignore the failing 4,40-49 docs
        assert len(docs) == 89


@pytest.mark.parametrize("n_process", [1, 2])
def test_language_pipe_error_handler_make_doc_actual(n_process):
    """Test the error handling for make_doc"""
    # TODO: fix so that the following test is the actual behavior

    ops = get_current_ops()
    if isinstance(ops, NumpyOps) or n_process < 2:
        nlp = English()
        nlp.max_length = 10
        texts = ["12345678901234567890", "12345"] * 10
        with pytest.raises(ValueError):
            list(nlp.pipe(texts, n_process=n_process))
        nlp.default_error_handler = ignore_error
        if n_process == 1:
            with pytest.raises(ValueError):
                list(nlp.pipe(texts, n_process=n_process))
        else:
            docs = list(nlp.pipe(texts, n_process=n_process))
            assert len(docs) == 0


@pytest.mark.xfail
@pytest.mark.parametrize("n_process", [1, 2])
def test_language_pipe_error_handler_make_doc_preferred(n_process):
    """Test the error handling for make_doc"""

    ops = get_current_ops()
    if isinstance(ops, NumpyOps) or n_process < 2:
        nlp = English()
        nlp.max_length = 10
        texts = ["12345678901234567890", "12345"] * 10
        with pytest.raises(ValueError):
            list(nlp.pipe(texts, n_process=n_process))
        nlp.default_error_handler = ignore_error
        docs = list(nlp.pipe(texts, n_process=n_process))
        assert len(docs) == 0


def test_language_from_config_before_after_init():
    name = "test_language_from_config_before_after_init"
    ran_before = False
    ran_after = False
    ran_after_pipeline = False
    ran_before_init = False
    ran_after_init = False

    @registry.callbacks(f"{name}_before")
    def make_before_creation():
        def before_creation(lang_cls):
            nonlocal ran_before
            ran_before = True
            assert lang_cls is English
            lang_cls.Defaults.foo = "bar"
            return lang_cls

        return before_creation

    @registry.callbacks(f"{name}_after")
    def make_after_creation():
        def after_creation(nlp):
            nonlocal ran_after
            ran_after = True
            assert isinstance(nlp, English)
            assert nlp.pipe_names == []
            assert nlp.Defaults.foo == "bar"
            nlp.meta["foo"] = "bar"
            return nlp

        return after_creation

    @registry.callbacks(f"{name}_after_pipeline")
    def make_after_pipeline_creation():
        def after_pipeline_creation(nlp):
            nonlocal ran_after_pipeline
            ran_after_pipeline = True
            assert isinstance(nlp, English)
            assert nlp.pipe_names == ["sentencizer"]
            assert nlp.Defaults.foo == "bar"
            assert nlp.meta["foo"] == "bar"
            nlp.meta["bar"] = "baz"
            return nlp

        return after_pipeline_creation

    @registry.callbacks(f"{name}_before_init")
    def make_before_init():
        def before_init(nlp):
            nonlocal ran_before_init
            ran_before_init = True
            nlp.meta["before_init"] = "before"
            return nlp

        return before_init

    @registry.callbacks(f"{name}_after_init")
    def make_after_init():
        def after_init(nlp):
            nonlocal ran_after_init
            ran_after_init = True
            nlp.meta["after_init"] = "after"
            return nlp

        return after_init

    config = {
        "nlp": {
            "pipeline": ["sentencizer"],
            "before_creation": {"@callbacks": f"{name}_before"},
            "after_creation": {"@callbacks": f"{name}_after"},
            "after_pipeline_creation": {"@callbacks": f"{name}_after_pipeline"},
        },
        "components": {"sentencizer": {"factory": "sentencizer"}},
        "initialize": {
            "before_init": {"@callbacks": f"{name}_before_init"},
            "after_init": {"@callbacks": f"{name}_after_init"},
        },
    }
    nlp = English.from_config(config)
    assert nlp.Defaults.foo == "bar"
    assert nlp.meta["foo"] == "bar"
    assert nlp.meta["bar"] == "baz"
    assert "before_init" not in nlp.meta
    assert "after_init" not in nlp.meta
    assert nlp.pipe_names == ["sentencizer"]
    assert nlp("text")
    nlp.initialize()
    assert nlp.meta["before_init"] == "before"
    assert nlp.meta["after_init"] == "after"
    assert all(
        [ran_before, ran_after, ran_after_pipeline, ran_before_init, ran_after_init]
    )


def test_language_from_config_before_after_init_invalid():
    """Check that an error is raised if function doesn't return nlp."""
    name = "test_language_from_config_before_after_init_invalid"
    registry.callbacks(f"{name}_before1", func=lambda: lambda nlp: None)
    registry.callbacks(f"{name}_before2", func=lambda: lambda nlp: nlp())
    registry.callbacks(f"{name}_after1", func=lambda: lambda nlp: None)
    registry.callbacks(f"{name}_after1", func=lambda: lambda nlp: English)

    for callback_name in [f"{name}_before1", f"{name}_before2"]:
        config = {"nlp": {"before_creation": {"@callbacks": callback_name}}}
        with pytest.raises(ValueError):
            English.from_config(config)
    for callback_name in [f"{name}_after1", f"{name}_after2"]:
        config = {"nlp": {"after_creation": {"@callbacks": callback_name}}}
        with pytest.raises(ValueError):
            English.from_config(config)
    for callback_name in [f"{name}_after1", f"{name}_after2"]:
        config = {"nlp": {"after_pipeline_creation": {"@callbacks": callback_name}}}
        with pytest.raises(ValueError):
            English.from_config(config)


def test_language_whitespace_tokenizer():
    """Test the custom whitespace tokenizer from the docs."""

    class WhitespaceTokenizer:
        def __init__(self, vocab):
            self.vocab = vocab

        def __call__(self, text):
            words = text.split(" ")
            spaces = [True] * len(words)
            # Avoid zero-length tokens
            for i, word in enumerate(words):
                if word == "":
                    words[i] = " "
                    spaces[i] = False
            # Remove the final trailing space
            if words[-1] == " ":
                words = words[0:-1]
                spaces = spaces[0:-1]
            else:
                spaces[-1] = False

            return Doc(self.vocab, words=words, spaces=spaces)

    nlp = spacy.blank("en")
    nlp.tokenizer = WhitespaceTokenizer(nlp.vocab)
    text = "   What's happened to    me? he thought. It wasn't a dream.    "
    doc = nlp(text)
    assert doc.text == text


def test_language_custom_tokenizer():
    """Test that a fully custom tokenizer can be plugged in via the registry."""
    name = "test_language_custom_tokenizer"

    class CustomTokenizer:
        """Dummy "tokenizer" that splits on spaces and adds prefix to each word."""

        def __init__(self, nlp, prefix):
            self.vocab = nlp.vocab
            self.prefix = prefix

        def __call__(self, text):
            words = [f"{self.prefix}{word}" for word in text.split(" ")]
            return Doc(self.vocab, words=words)

    @registry.tokenizers(name)
    def custom_create_tokenizer(prefix: str = "_"):
        def create_tokenizer(nlp):
            return CustomTokenizer(nlp, prefix=prefix)

        return create_tokenizer

    config = {"nlp": {"tokenizer": {"@tokenizers": name}}}
    nlp = English.from_config(config)
    doc = nlp("hello world")
    assert [t.text for t in doc] == ["_hello", "_world"]
    doc = list(nlp.pipe(["hello world"]))[0]
    assert [t.text for t in doc] == ["_hello", "_world"]


def test_language_from_config_invalid_lang():
    """Test that calling Language.from_config raises an error and lang defined
    in config needs to match language-specific subclasses."""
    config = {"nlp": {"lang": "en"}}
    with pytest.raises(ValueError):
        Language.from_config(config)
    with pytest.raises(ValueError):
        German.from_config(config)


def test_spacy_blank():
    nlp = spacy.blank("en")
    assert nlp.config["training"]["dropout"] == 0.1
    config = {"training": {"dropout": 0.2}}
    meta = {"name": "my_custom_model"}
    nlp = spacy.blank("en", config=config, meta=meta)
    assert nlp.config["training"]["dropout"] == 0.2
    assert nlp.meta["name"] == "my_custom_model"


@pytest.mark.parametrize(
    "lang,target",
    [
        ("en", "en"),
        ("fra", "fr"),
        ("fre", "fr"),
        ("iw", "he"),
        ("is", "isl"),
        ("mo", "ro"),
        ("mul", "mul"),
        ("no", "nb"),
        ("pt-BR", "pt"),
        ("xx", "mul"),
        ("zh-Hans", "zh"),
        ("zh-Hant", None),
        ("zxx", None),
    ],
)
def test_language_matching(lang, target):
    """
    Test that we can look up languages by equivalent or nearly-equivalent
    language codes.
    """
    assert find_matching_language(lang) == target


@pytest.mark.parametrize(
    "lang,target",
    [
        ("en", "en"),
        ("fra", "fr"),
        ("fre", "fr"),
        ("iw", "he"),
        ("is", "isl"),
        ("mo", "ro"),
        ("xx", "mul"),
        ("no", "nb"),
        ("pt-BR", "pt"),
        ("zh-Hans", "zh"),
    ],
)
def test_blank_languages(lang, target):
    """
    Test that we can get spacy.blank in various languages, including codes
    that are defined to be equivalent or that match by CLDR language matching.
    """
    nlp = spacy.blank(lang)
    assert nlp.lang == target


@pytest.mark.parametrize("value", [False, None, ["x", "y"], Language, Vocab])
def test_language_init_invalid_vocab(value):
    err_fragment = "invalid value"
    with pytest.raises(ValueError) as e:
        Language(value)
    assert err_fragment in str(e.value)


def test_language_source_and_vectors(nlp2):
    nlp = Language(Vocab())
    textcat = nlp.add_pipe("textcat")
    for label in ("POSITIVE", "NEGATIVE"):
        textcat.add_label(label)
    nlp.initialize()
    long_string = "thisisalongstring"
    assert long_string not in nlp.vocab.strings
    assert long_string not in nlp2.vocab.strings
    nlp.vocab.strings.add(long_string)
    assert nlp.vocab.vectors.to_bytes() != nlp2.vocab.vectors.to_bytes()
    vectors_bytes = nlp.vocab.vectors.to_bytes()
    with pytest.warns(UserWarning):
        nlp2.add_pipe("textcat", name="textcat2", source=nlp)
    # strings should be added
    assert long_string in nlp2.vocab.strings
    # vectors should remain unmodified
    assert nlp.vocab.vectors.to_bytes() == vectors_bytes


@pytest.mark.parametrize("n_process", [1, 2])
def test_pass_doc_to_pipeline(nlp, n_process):
    texts = ["cats", "dogs", "guinea pigs"]
    docs = [nlp.make_doc(text) for text in texts]
    assert not any(len(doc.cats) for doc in docs)
    doc = nlp(docs[0])
    assert doc.text == texts[0]
    assert len(doc.cats) > 0
    if isinstance(get_current_ops(), NumpyOps) or n_process < 2:
        docs = nlp.pipe(docs, n_process=n_process)
        assert [doc.text for doc in docs] == texts
        assert all(len(doc.cats) for doc in docs)


def test_invalid_arg_to_pipeline(nlp):
    str_list = ["This is a text.", "This is another."]
    with pytest.raises(ValueError):
        nlp(str_list)  # type: ignore
    assert len(list(nlp.pipe(str_list))) == 2
    int_list = [1, 2, 3]
    with pytest.raises(ValueError):
        list(nlp.pipe(int_list))  # type: ignore
    with pytest.raises(ValueError):
        nlp(int_list)  # type: ignore


@pytest.mark.skipif(
    not isinstance(get_current_ops(), CupyOps), reason="test requires GPU"
)
def test_multiprocessing_gpu_warning(nlp2, texts):
    texts = texts * 10
    docs = nlp2.pipe(texts, n_process=2, batch_size=2)

    with pytest.warns(UserWarning, match="multiprocessing with GPU models"):
        with pytest.raises(ValueError):
            # Trigger multi-processing.
            for _ in docs:
                pass


def test_dot_in_factory_names(nlp):
    Language.component("my_evil_component", func=evil_component)
    nlp.add_pipe("my_evil_component")

    with pytest.raises(ValueError, match="not permitted"):
        Language.component("my.evil.component.v1", func=evil_component)

    with pytest.raises(ValueError, match="not permitted"):
        Language.factory("my.evil.component.v1", func=evil_component)


def test_component_return():
    """Test that an error is raised if components return a type other than a
    doc."""
    nlp = English()

    @Language.component("test_component_good_pipe")
    def good_pipe(doc):
        return doc

    nlp.add_pipe("test_component_good_pipe")
    nlp("text")
    nlp.remove_pipe("test_component_good_pipe")

    @Language.component("test_component_bad_pipe")
    def bad_pipe(doc):
        return doc.text

    nlp.add_pipe("test_component_bad_pipe")
    with pytest.raises(ValueError, match="instead of a Doc"):
        nlp("text")


@pytest.mark.slow
@pytest.mark.parametrize("teacher_tagger_name", ["tagger", "teacher_tagger"])
def test_distill(teacher_tagger_name):
    teacher = English()
    teacher_tagger = teacher.add_pipe("tagger", name=teacher_tagger_name)
    train_examples = []
    for t in TAGGER_TRAIN_DATA:
        train_examples.append(Example.from_dict(teacher.make_doc(t[0]), t[1]))

    optimizer = teacher.initialize(get_examples=lambda: train_examples)

    for i in range(50):
        losses = {}
        teacher.update(train_examples, sgd=optimizer, losses=losses)
    assert losses[teacher_tagger_name] < 0.00001

    student = English()
    student_tagger = student.add_pipe("tagger")
    student_tagger.min_tree_freq = 1
    student_tagger.initialize(
        get_examples=lambda: train_examples, labels=teacher_tagger.label_data
    )

    distill_examples = [
        Example.from_dict(teacher.make_doc(t[0]), {}) for t in TAGGER_TRAIN_DATA
    ]

    student_to_teacher = (
        None
        if teacher_tagger.name == student_tagger.name
        else {student_tagger.name: teacher_tagger.name}
    )

    for i in range(50):
        losses = {}
        student.distill(
            teacher,
            distill_examples,
            sgd=optimizer,
            losses=losses,
            student_to_teacher=student_to_teacher,
        )
    assert losses["tagger"] < 0.00001

    test_text = "I like blue eggs"
    doc = student(test_text)
    assert doc[0].tag_ == "N"
    assert doc[1].tag_ == "V"
    assert doc[2].tag_ == "J"
    assert doc[3].tag_ == "N"

    # Do an extra update to check if annotates works, though we can't really
    # validate the resuls, since the annotations are ephemeral.
    student.distill(
        teacher,
        distill_examples,
        sgd=optimizer,
        losses=losses,
        student_to_teacher=student_to_teacher,
        annotates=["tagger"],
    )