def test_doc_array_to_from_string_attrs(en_vocab, attrs):
    """Test that both Doc.to_array and Doc.from_array accept string attrs,
    as well as single attrs and sequences of attrs.
    """
    words = ["An", "example", "sentence"]
    doc = Doc(en_vocab, words=words)
    Doc(en_vocab, words=words).from_array(attrs, doc.to_array(attrs))

    def __init__(self, nlp, label='GPE'):
        """Initialise the pipeline component. The shared nlp instance is used
        to initialise the matcher with the shared vocab, get the label ID and
        generate Doc objects as phrase match patterns.
        """
        # Make request once on initialisation and store the data
        r = requests.get('https://restcountries.eu/rest/v2/all')
        r.raise_for_status()  # make sure requests raises an error if it fails
        countries = r.json()

        # Convert API response to dict keyed by country name for easy lookup
        # This could also be extended using the alternative and foreign language
        # names provided by the API
        self.countries = {c['name']: c for c in countries}
        self.label = nlp.vocab.strings[label]  # get entity label ID

        # Set up the PhraseMatcher with Doc patterns for each country name
        patterns = [nlp(c) for c in self.countries.keys()]
        self.matcher = PhraseMatcher(nlp.vocab)
        self.matcher.add('COUNTRIES', None, *patterns)

        # Register attribute on the Token. We'll be overwriting this based on
        # the matches, so we're only setting a default value, not a getter.
        # If no default value is set, it defaults to None.
        Token.set_extension('is_country', default=False)
        Token.set_extension('country_capital')
        Token.set_extension('country_latlng')
        Token.set_extension('country_flag')

        # Register attributes on Doc and Span via a getter that checks if one of
        # the contained tokens is set to is_country == True.
        Doc.set_extension('has_country', getter=self.has_country)
        Span.set_extension('has_country', getter=self.has_country)

def get_doc(vocab, words=[], pos=None, heads=None, deps=None, tags=None, ents=None):
    """Create Doc object from given vocab, words and annotations."""
    pos = pos or [""] * len(words)
    tags = tags or [""] * len(words)
    heads = heads or [0] * len(words)
    deps = deps or [""] * len(words)
    for value in deps + tags + pos:
        vocab.strings.add(value)

    doc = Doc(vocab, words=words)
    attrs = doc.to_array([POS, HEAD, DEP])
    for i, (p, head, dep) in enumerate(zip(pos, heads, deps)):
        attrs[i, 0] = doc.vocab.strings[p]
        attrs[i, 1] = head
        attrs[i, 2] = doc.vocab.strings[dep]
    doc.from_array([POS, HEAD, DEP], attrs)
    if ents:
        doc.ents = [
            Span(doc, start, end, label=doc.vocab.strings[label])
            for start, end, label in ents
        ]
    if tags:
        for token in doc:
            token.tag_ = tags[token.i]
    return doc

def test_doc_array_attr_of_token(en_vocab):
    doc = Doc(en_vocab, words=["An", "example", "sentence"])
    example = doc.vocab["example"]
    assert example.orth != example.shape
    feats_array = doc.to_array((ORTH, SHAPE))
    assert feats_array[0][0] != feats_array[0][1]
    assert feats_array[0][0] != feats_array[0][1]

def test_doc_api_similarity_match():
    doc = Doc(Vocab(), words=["a"])
    assert doc.similarity(doc[0]) == 1.0
    assert doc.similarity(doc.vocab["a"]) == 1.0
    doc2 = Doc(doc.vocab, words=["a", "b", "c"])
    with pytest.warns(ModelsWarning):
        assert doc.similarity(doc2[:1]) == 1.0
        assert doc.similarity(doc2) == 0.0

def test_doc_retokenize_merge_extension_attrs_invalid(en_vocab, underscore_attrs):
    Token.set_extension("a", getter=lambda x: x, force=True)
    Token.set_extension("b", method=lambda x: x, force=True)
    doc = Doc(en_vocab, words=["hello", "world", "!"])
    attrs = {"_": underscore_attrs}
    with pytest.raises(ValueError):
        with doc.retokenize() as retokenizer:
            retokenizer.merge(doc[0:2], attrs=attrs)

def test_issue1547():
    """Test that entity labels still match after merging tokens."""
    words = ["\n", "worda", ".", "\n", "wordb", "-", "Biosphere", "2", "-", " \n"]
    doc = Doc(Vocab(), words=words)
    doc.ents = [Span(doc, 6, 8, label=doc.vocab.strings["PRODUCT"])]
    with doc.retokenize() as retokenizer:
        retokenizer.merge(doc[5:7])
    assert [ent.text for ent in doc.ents]

def test_serialize_empty_doc(en_vocab):
    doc = Doc(en_vocab)
    data = doc.to_bytes()
    doc2 = Doc(en_vocab)
    doc2.from_bytes(data)
    assert len(doc) == len(doc2)
    for token1, token2 in zip(doc, doc2):
        assert token1.text == token2.text

def test_underscore_dir(en_vocab):
    """Test that dir() correctly returns extension attributes. This enables
    things like tab-completion for the attributes in doc._."""
    Doc.set_extension("test_dir", default=None)
    doc = Doc(en_vocab, words=["hello", "world"])
    assert "_" in dir(doc)
    assert "test_dir" in dir(doc._)
    assert "test_dir" not in dir(doc[0]._)
    assert "test_dir" not in dir(doc[0:2]._)

def test_doc_to_json_underscore(doc):
    Doc.set_extension("json_test1", default=False)
    Doc.set_extension("json_test2", default=False)
    doc._.json_test1 = "hello world"
    doc._.json_test2 = [1, 2, 3]
    json_doc = doc.to_json(underscore=["json_test1", "json_test2"])
    assert "_" in json_doc
    assert json_doc["_"]["json_test1"] == "hello world"
    assert json_doc["_"]["json_test2"] == [1, 2, 3]

def test_doc_retokenize_split_extension_attrs_invalid(en_vocab, underscore_attrs):
    Token.set_extension("x", default=False, force=True)
    Token.set_extension("a", getter=lambda x: x, force=True)
    Token.set_extension("b", method=lambda x: x, force=True)
    doc = Doc(en_vocab, words=["LosAngeles", "start"])
    attrs = {"_": underscore_attrs}
    with pytest.raises(ValueError):
        with doc.retokenize() as retokenizer:
            heads = [(doc[0], 1), doc[1]]
            retokenizer.split(doc[0], ["Los", "Angeles"], heads, attrs=attrs)

def test_doc_retokenize_split_heads_error(en_vocab):
    doc = Doc(en_vocab, words=["LosAngeles", "start", "."])
    # Not enough heads
    with pytest.raises(ValueError):
        with doc.retokenize() as retokenizer:
            retokenizer.split(doc[0], ["Los", "Angeles"], [doc[1]])

    # Too many heads
    with pytest.raises(ValueError):
        with doc.retokenize() as retokenizer:
            retokenizer.split(doc[0], ["Los", "Angeles"], [doc[1], doc[1], doc[1]])

def test_doc_add_entities_set_ents_iob(en_vocab):
    doc = Doc(en_vocab, words=["This", "is", "a", "lion"])
    ner = EntityRecognizer(en_vocab)
    ner.begin_training([])
    ner(doc)
    assert len(list(doc.ents)) == 0
    assert [w.ent_iob_ for w in doc] == (["O"] * len(doc))
    doc.ents = [(doc.vocab.strings["ANIMAL"], 3, 4)]
    assert [w.ent_iob_ for w in doc] == ["", "", "", "B"]
    doc.ents = [(doc.vocab.strings["WORD"], 0, 2)]
    assert [w.ent_iob_ for w in doc] == ["B", "I", "", ""]

def test_doc_retokenize_split_orths_mismatch(en_vocab):
    """Test that the regular retokenizer.split raises an error if the orths
    don't match the original token text. There might still be a method that
    allows this, but for the default use cases, merging and splitting should
    always conform with spaCy's non-destructive tokenization policy. Otherwise,
    it can lead to very confusing and unexpected results.
    """
    doc = Doc(en_vocab, words=["LosAngeles", "start", "."])
    with pytest.raises(ValueError):
        with doc.retokenize() as retokenizer:
            retokenizer.split(doc[0], ["L", "A"], [(doc[0], 0), (doc[0], 0)])

def test_spans_override_sentiment(en_tokenizer):
    """Test span.sentiment property's default averaging behaviour"""
    text = "good stuff bad stuff"
    tokens = en_tokenizer(text)
    tokens.vocab[tokens[0].text].sentiment = 3.0
    tokens.vocab[tokens[2].text].sentiment = -2.0
    doc = Doc(tokens.vocab, words=[t.text for t in tokens])
    doc.user_span_hooks["sentiment"] = lambda span: 10.0
    assert doc[:2].sentiment == 10.0
    assert doc[-2:].sentiment == 10.0
    assert doc[:-1].sentiment == 10.0

def test_underscore_docstring(en_vocab):
    """Test that docstrings are available for extension methods, even though
    they're partials."""

    def test_method(doc, arg1=1, arg2=2):
        """I am a docstring"""
        return (arg1, arg2)

    Doc.set_extension("test_docstrings", method=test_method)
    doc = Doc(en_vocab, words=["hello", "world"])
    assert test_method.__doc__ == "I am a docstring"
    assert doc._.test_docstrings.__doc__.rsplit(". ")[-1] == "I am a docstring"

def test_doc_retokenize_split_dependencies(en_vocab):
    doc = Doc(en_vocab, words=["LosAngeles", "start", "."])
    dep1 = doc.vocab.strings.add("amod")
    dep2 = doc.vocab.strings.add("subject")
    with doc.retokenize() as retokenizer:
        retokenizer.split(
            doc[0],
            ["Los", "Angeles"],
            [(doc[0], 1), doc[1]],
            attrs={"dep": [dep1, dep2]},
        )
    assert doc[0].dep == dep1
    assert doc[1].dep == dep2

def test_sbd_serialization_projective(EN):
    """
    test that before and after serialization, the sentence boundaries are the same.
    """

    example = EN.tokenizer.tokens_from_list(u"I bought a couch from IKEA. It was n't very comfortable .".split(' '))
    EN.tagger(example)
    apply_transition_sequence(EN, example, ['L-nsubj','S','L-det','R-dobj','D','R-prep','R-pobj','B-ROOT','L-nsubj','R-neg','D','S','L-advmod','R-acomp','D','R-punct'])

    example_serialized = Doc(EN.vocab).from_bytes(example.to_bytes())

    assert example.to_bytes() == example_serialized.to_bytes()
    assert [s.text for s in example.sents] == [s.text for s in example_serialized.sents]

def test_doc_is_nered(en_vocab):
    words = ["I", "live", "in", "New", "York"]
    doc = Doc(en_vocab, words=words)
    assert not doc.is_nered
    doc.ents = [Span(doc, 3, 5, label="GPE")]
    assert doc.is_nered
    # Test creating doc from array with unknown values
    arr = numpy.array([[0, 0], [0, 0], [0, 0], [384, 3], [384, 1]], dtype="uint64")
    doc = Doc(en_vocab, words=words).from_array([ENT_TYPE, ENT_IOB], arr)
    assert doc.is_nered
    # Test serialization
    new_doc = Doc(en_vocab).from_bytes(doc.to_bytes())
    assert new_doc.is_nered

def test_doc_retokenize_spans_entity_split_iob():
    # Test entity IOB stays consistent after merging
    words = ["abc", "d", "e"]
    doc = Doc(Vocab(), words=words)
    doc.ents = [(doc.vocab.strings.add("ent-abcd"), 0, 2)]
    assert doc[0].ent_iob_ == "B"
    assert doc[1].ent_iob_ == "I"
    with doc.retokenize() as retokenizer:
        retokenizer.split(doc[0], ["a", "b", "c"], [(doc[0], 1), (doc[0], 2), doc[1]])
    assert doc[0].ent_iob_ == "B"
    assert doc[1].ent_iob_ == "I"
    assert doc[2].ent_iob_ == "I"
    assert doc[3].ent_iob_ == "I"