spaCy/spacy/tests/doc/test_doc_api.py

import weakref

import numpy
from numpy.testing import assert_array_equal
import pytest
from thinc.api import NumpyOps, get_current_ops

from spacy.attrs import DEP, ENT_IOB, ENT_TYPE, HEAD, IS_ALPHA, MORPH, POS
from spacy.attrs import SENT_START, TAG
from spacy.lang.en import English
from spacy.lang.xx import MultiLanguage
from spacy.language import Language
from spacy.lexeme import Lexeme
from spacy.tokens import Doc, Span, Token
from spacy.vocab import Vocab

from .test_underscore import clean_underscore  # noqa: F401


def test_doc_api_init(en_vocab):
    words = ["a", "b", "c", "d"]
    heads = [0, 0, 2, 2]
    # set sent_start by sent_starts
    doc = Doc(en_vocab, words=words, sent_starts=[True, False, True, False])
    assert [t.is_sent_start for t in doc] == [True, False, True, False]

    # set sent_start by heads
    doc = Doc(en_vocab, words=words, heads=heads, deps=["dep"] * 4)
    assert [t.is_sent_start for t in doc] == [True, False, True, False]
    # heads override sent_starts
    doc = Doc(
        en_vocab, words=words, sent_starts=[True] * 4, heads=heads, deps=["dep"] * 4
    )
    assert [t.is_sent_start for t in doc] == [True, False, True, False]


@pytest.mark.issue(1547)
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]


@pytest.mark.issue(1757)
def test_issue1757():
    """Test comparison against None doesn't cause segfault."""
    doc = Doc(Vocab(), words=["a", "b", "c"])
    assert not doc[0] < None
    assert not doc[0] is None
    assert doc[0] >= None
    assert not doc[:2] < None
    assert not doc[:2] is None
    assert doc[:2] >= None
    assert not doc.vocab["a"] is None
    assert not doc.vocab["a"] < None


@pytest.mark.issue(2396)
def test_issue2396(en_vocab):
    words = ["She", "created", "a", "test", "for", "spacy"]
    heads = [1, 1, 3, 1, 3, 4]
    deps = ["dep"] * len(heads)
    matrix = numpy.array(
        [
            [0, 1, 1, 1, 1, 1],
            [1, 1, 1, 1, 1, 1],
            [1, 1, 2, 3, 3, 3],
            [1, 1, 3, 3, 3, 3],
            [1, 1, 3, 3, 4, 4],
            [1, 1, 3, 3, 4, 5],
        ],
        dtype=numpy.int32,
    )
    doc = Doc(en_vocab, words=words, heads=heads, deps=deps)
    span = doc[:]
    assert (doc.get_lca_matrix() == matrix).all()
    assert (span.get_lca_matrix() == matrix).all()


@pytest.mark.parametrize("text", ["-0.23", "+123,456", "±1"])
@pytest.mark.parametrize("lang_cls", [English, MultiLanguage])
@pytest.mark.issue(2782)
def test_issue2782(text, lang_cls):
    """Check that like_num handles + and - before number."""
    nlp = lang_cls()
    doc = nlp(text)
    assert len(doc) == 1
    assert doc[0].like_num


@pytest.mark.parametrize(
    "sentence",
    [
        "The story was to the effect that a young American student recently called on Professor Christlieb with a letter of introduction.",
        "The next month Barry Siddall joined Stoke City on a free transfer, after Chris Pearce had established himself as the Vale's #1.",
        "The next month Barry Siddall joined Stoke City on a free transfer, after Chris Pearce had established himself as the Vale's number one",
        "Indeed, making the one who remains do all the work has installed him into a position of such insolent tyranny, it will take a month at least to reduce him to his proper proportions.",
        "It was a missed assignment, but it shouldn't have resulted in a turnover ...",
    ],
)
@pytest.mark.issue(3869)
def test_issue3869(sentence):
    """Test that the Doc's count_by function works consistently"""
    nlp = English()
    doc = nlp(sentence)
    count = 0
    for token in doc:
        count += token.is_alpha
    assert count == doc.count_by(IS_ALPHA).get(1, 0)


@pytest.mark.issue(3962)
def test_issue3962(en_vocab):
    """Ensure that as_doc does not result in out-of-bound access of tokens.
    This is achieved by setting the head to itself if it would lie out of the span otherwise."""
    # fmt: off
    words = ["He", "jests", "at", "scars", ",", "that", "never", "felt", "a", "wound", "."]
    heads = [1, 7, 1, 2, 7, 7, 7, 7, 9, 7, 7]
    deps = ["nsubj", "ccomp", "prep", "pobj", "punct", "nsubj", "neg", "ROOT", "det", "dobj", "punct"]
    # fmt: on
    doc = Doc(en_vocab, words=words, heads=heads, deps=deps)
    span2 = doc[1:5]  # "jests at scars ,"
    doc2 = span2.as_doc()
    doc2_json = doc2.to_json()
    assert doc2_json
    # head set to itself, being the new artificial root
    assert doc2[0].head.text == "jests"
    assert doc2[0].dep_ == "dep"
    assert doc2[1].head.text == "jests"
    assert doc2[1].dep_ == "prep"
    assert doc2[2].head.text == "at"
    assert doc2[2].dep_ == "pobj"
    assert doc2[3].head.text == "jests"  # head set to the new artificial root
    assert doc2[3].dep_ == "dep"
    # We should still have 1 sentence
    assert len(list(doc2.sents)) == 1
    span3 = doc[6:9]  # "never felt a"
    doc3 = span3.as_doc()
    doc3_json = doc3.to_json()
    assert doc3_json
    assert doc3[0].head.text == "felt"
    assert doc3[0].dep_ == "neg"
    assert doc3[1].head.text == "felt"
    assert doc3[1].dep_ == "ROOT"
    assert doc3[2].head.text == "felt"  # head set to ancestor
    assert doc3[2].dep_ == "dep"
    # We should still have 1 sentence as "a" can be attached to "felt" instead of "wound"
    assert len(list(doc3.sents)) == 1


@pytest.mark.issue(3962)
def test_issue3962_long(en_vocab):
    """Ensure that as_doc does not result in out-of-bound access of tokens.
    This is achieved by setting the head to itself if it would lie out of the span otherwise."""
    # fmt: off
    words = ["He", "jests", "at", "scars", ".", "They", "never", "felt", "a", "wound", "."]
    heads = [1, 1, 1, 2, 1, 7, 7, 7, 9, 7, 7]
    deps = ["nsubj", "ROOT", "prep", "pobj", "punct", "nsubj", "neg", "ROOT", "det", "dobj", "punct"]
    # fmt: on
    two_sent_doc = Doc(en_vocab, words=words, heads=heads, deps=deps)
    span2 = two_sent_doc[1:7]  # "jests at scars. They never"
    doc2 = span2.as_doc()
    doc2_json = doc2.to_json()
    assert doc2_json
    # head set to itself, being the new artificial root (in sentence 1)
    assert doc2[0].head.text == "jests"
    assert doc2[0].dep_ == "ROOT"
    assert doc2[1].head.text == "jests"
    assert doc2[1].dep_ == "prep"
    assert doc2[2].head.text == "at"
    assert doc2[2].dep_ == "pobj"
    assert doc2[3].head.text == "jests"
    assert doc2[3].dep_ == "punct"
    # head set to itself, being the new artificial root (in sentence 2)
    assert doc2[4].head.text == "They"
    assert doc2[4].dep_ == "dep"
    # head set to the new artificial head (in sentence 2)
    assert doc2[4].head.text == "They"
    assert doc2[4].dep_ == "dep"
    # We should still have 2 sentences
    sents = list(doc2.sents)
    assert len(sents) == 2
    assert sents[0].text == "jests at scars ."
    assert sents[1].text == "They never"


@Language.factory("my_pipe")
class CustomPipe:
    def __init__(self, nlp, name="my_pipe"):
        self.name = name
        Span.set_extension("my_ext", getter=self._get_my_ext)
        Doc.set_extension("my_ext", default=None)

    def __call__(self, doc):
        gathered_ext = []
        for sent in doc.sents:
            sent_ext = self._get_my_ext(sent)
            sent._.set("my_ext", sent_ext)
            gathered_ext.append(sent_ext)

        doc._.set("my_ext", "\n".join(gathered_ext))
        return doc

    @staticmethod
    def _get_my_ext(span):
        return str(span.end)


@pytest.mark.issue(4903)
def test_issue4903():
    """Ensure that this runs correctly and doesn't hang or crash on Windows /
    macOS."""
    nlp = English()
    nlp.add_pipe("sentencizer")
    nlp.add_pipe("my_pipe", after="sentencizer")
    text = ["I like bananas.", "Do you like them?", "No, I prefer wasabi."]
    if isinstance(get_current_ops(), NumpyOps):
        docs = list(nlp.pipe(text, n_process=2))
        assert docs[0].text == "I like bananas."
        assert docs[1].text == "Do you like them?"
        assert docs[2].text == "No, I prefer wasabi."


@pytest.mark.issue(5048)
def test_issue5048(en_vocab):
    words = ["This", "is", "a", "sentence"]
    pos_s = ["DET", "VERB", "DET", "NOUN"]
    spaces = [" ", " ", " ", ""]
    deps_s = ["dep", "adj", "nn", "atm"]
    tags_s = ["DT", "VBZ", "DT", "NN"]
    strings = en_vocab.strings
    for w in words:
        strings.add(w)
    deps = [strings.add(d) for d in deps_s]
    pos = [strings.add(p) for p in pos_s]
    tags = [strings.add(t) for t in tags_s]
    attrs = [POS, DEP, TAG]
    array = numpy.array(list(zip(pos, deps, tags)), dtype="uint64")
    doc = Doc(en_vocab, words=words, spaces=spaces)
    doc.from_array(attrs, array)
    v1 = [(token.text, token.pos_, token.tag_) for token in doc]
    doc2 = Doc(en_vocab, words=words, pos=pos_s, deps=deps_s, tags=tags_s)
    v2 = [(token.text, token.pos_, token.tag_) for token in doc2]
    assert v1 == v2


@pytest.mark.parametrize("text", [["one", "two", "three"]])
def test_doc_api_compare_by_string_position(en_vocab, text):
    doc = Doc(en_vocab, words=text)
    # Get the tokens in this order, so their ID ordering doesn't match the idx
    token3 = doc[-1]
    token2 = doc[-2]
    token1 = doc[-1]
    token1, token2, token3 = doc
    assert token1 < token2 < token3
    assert not token1 > token2
    assert token2 > token1
    assert token2 <= token3
    assert token3 >= token1


def test_doc_api_getitem(en_tokenizer):
    text = "Give it back! He pleaded."
    tokens = en_tokenizer(text)
    assert tokens[0].text == "Give"
    assert tokens[-1].text == "."
    with pytest.raises(IndexError):
        tokens[len(tokens)]

    def to_str(span):
        return "/".join(token.text for token in span)

    span = tokens[1:1]
    assert not to_str(span)
    span = tokens[1:4]
    assert to_str(span) == "it/back/!"
    span = tokens[1:4:1]
    assert to_str(span) == "it/back/!"
    with pytest.raises(ValueError):
        tokens[1:4:2]
    with pytest.raises(ValueError):
        tokens[1:4:-1]

    span = tokens[-3:6]
    assert to_str(span) == "He/pleaded"
    span = tokens[4:-1]
    assert to_str(span) == "He/pleaded"
    span = tokens[-5:-3]
    assert to_str(span) == "back/!"
    span = tokens[5:4]
    assert span.start == span.end == 5 and not to_str(span)
    span = tokens[4:-3]
    assert span.start == span.end == 4 and not to_str(span)

    span = tokens[:]
    assert to_str(span) == "Give/it/back/!/He/pleaded/."
    span = tokens[4:]
    assert to_str(span) == "He/pleaded/."
    span = tokens[:4]
    assert to_str(span) == "Give/it/back/!"
    span = tokens[:-3]
    assert to_str(span) == "Give/it/back/!"
    span = tokens[-3:]
    assert to_str(span) == "He/pleaded/."

    span = tokens[4:50]
    assert to_str(span) == "He/pleaded/."
    span = tokens[-50:4]
    assert to_str(span) == "Give/it/back/!"
    span = tokens[-50:-40]
    assert span.start == span.end == 0 and not to_str(span)
    span = tokens[40:50]
    assert span.start == span.end == 7 and not to_str(span)

    span = tokens[1:4]
    assert span[0].orth_ == "it"
    subspan = span[:]
    assert to_str(subspan) == "it/back/!"
    subspan = span[:2]
    assert to_str(subspan) == "it/back"
    subspan = span[1:]
    assert to_str(subspan) == "back/!"
    subspan = span[:-1]
    assert to_str(subspan) == "it/back"
    subspan = span[-2:]
    assert to_str(subspan) == "back/!"
    subspan = span[1:2]
    assert to_str(subspan) == "back"
    subspan = span[-2:-1]
    assert to_str(subspan) == "back"
    subspan = span[-50:50]
    assert to_str(subspan) == "it/back/!"
    subspan = span[50:-50]
    assert subspan.start == subspan.end == 4 and not to_str(subspan)


@pytest.mark.parametrize(
    "text", ["Give it back! He pleaded.", " Give it back! He pleaded. "]
)
def test_doc_api_serialize(en_tokenizer, text):
    tokens = en_tokenizer(text)
    tokens[0].lemma_ = "lemma"
    tokens[0].norm_ = "norm"
    tokens.ents = [(tokens.vocab.strings["PRODUCT"], 0, 1)]
    tokens[0].ent_kb_id_ = "ent_kb_id"
    tokens[0].ent_id_ = "ent_id"
    new_tokens = Doc(tokens.vocab).from_bytes(tokens.to_bytes())
    assert tokens.text == new_tokens.text
    assert [t.text for t in tokens] == [t.text for t in new_tokens]
    assert [t.orth for t in tokens] == [t.orth for t in new_tokens]
    assert new_tokens[0].lemma_ == "lemma"
    assert new_tokens[0].norm_ == "norm"
    assert new_tokens[0].ent_kb_id_ == "ent_kb_id"
    assert new_tokens[0].ent_id_ == "ent_id"

    new_tokens = Doc(tokens.vocab).from_bytes(
        tokens.to_bytes(exclude=["tensor"]), exclude=["tensor"]
    )
    assert tokens.text == new_tokens.text
    assert [t.text for t in tokens] == [t.text for t in new_tokens]
    assert [t.orth for t in tokens] == [t.orth for t in new_tokens]

    new_tokens = Doc(tokens.vocab).from_bytes(
        tokens.to_bytes(exclude=["sentiment"]), exclude=["sentiment"]
    )
    assert tokens.text == new_tokens.text
    assert [t.text for t in tokens] == [t.text for t in new_tokens]
    assert [t.orth for t in tokens] == [t.orth for t in new_tokens]

    def inner_func(d1, d2):
        return "hello!"

    _ = tokens.to_bytes()  # noqa: F841
    with pytest.warns(UserWarning):
        tokens.user_hooks["similarity"] = inner_func
        _ = tokens.to_bytes()  # noqa: F841


def test_doc_api_set_ents(en_tokenizer):
    text = "I use goggle chrone to surf the web"
    tokens = en_tokenizer(text)
    assert len(tokens.ents) == 0
    tokens.ents = [(tokens.vocab.strings["PRODUCT"], 2, 4)]
    assert len(list(tokens.ents)) == 1
    assert [t.ent_iob for t in tokens] == [2, 2, 3, 1, 2, 2, 2, 2]
    assert tokens.ents[0].label_ == "PRODUCT"
    assert tokens.ents[0].start == 2
    assert tokens.ents[0].end == 4


def test_doc_api_sents_empty_string(en_tokenizer):
    doc = en_tokenizer("")
    sents = list(doc.sents)
    assert len(sents) == 0


def test_doc_api_runtime_error(en_tokenizer):
    # Example that caused run-time error while parsing Reddit
    # fmt: off
    text = "67% of black households are single parent \n\n72% of all black babies born out of wedlock \n\n50% of all black kids don\u2019t finish high school"
    deps = ["nummod", "nsubj", "prep", "amod", "pobj", "ROOT", "amod", "attr", "", "nummod", "appos", "prep", "det",
            "amod", "pobj", "acl", "prep", "prep", "pobj",
            "", "nummod", "nsubj", "prep", "det", "amod", "pobj", "aux", "neg", "ccomp", "amod", "dobj"]
    # fmt: on
    tokens = en_tokenizer(text)
    doc = Doc(tokens.vocab, words=[t.text for t in tokens], deps=deps)
    nps = []
    for np in doc.noun_chunks:
        while len(np) > 1 and np[0].dep_ not in ("advmod", "amod", "compound"):
            np = np[1:]
        if len(np) > 1:
            nps.append(np)
    with doc.retokenize() as retokenizer:
        for np in nps:
            attrs = {
                "tag": np.root.tag_,
                "lemma": np.text,
                "ent_type": np.root.ent_type_,
            }
            retokenizer.merge(np, attrs=attrs)


def test_doc_api_right_edge(en_vocab):
    """Test for bug occurring from Unshift action, causing incorrect right edge"""
    # fmt: off
    words = [
        "I", "have", "proposed", "to", "myself", ",", "for", "the", "sake",
        "of", "such", "as", "live", "under", "the", "government", "of", "the",
        "Romans", ",", "to", "translate", "those", "books", "into", "the",
        "Greek", "tongue", "."
    ]
    heads = [2, 2, 2, 2, 3, 2, 21, 8, 6, 8, 11, 8, 11, 12, 15, 13, 15, 18, 16, 12, 21, 2, 23, 21, 21, 27, 27, 24, 2]
    deps = ["dep"] * len(heads)
    # fmt: on
    doc = Doc(en_vocab, words=words, heads=heads, deps=deps)
    assert doc[6].text == "for"
    subtree = [w.text for w in doc[6].subtree]
    # fmt: off
    assert subtree == ["for", "the", "sake", "of", "such", "as", "live", "under", "the", "government", "of", "the", "Romans", ","]
    # fmt: on
    assert doc[6].right_edge.text == ","


def test_doc_api_has_vector():
    vocab = Vocab()
    vocab.reset_vectors(width=2)
    vocab.set_vector("kitten", vector=numpy.asarray([0.0, 2.0], dtype="f"))
    doc = Doc(vocab, words=["kitten"])
    assert doc.has_vector


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(UserWarning):
        assert doc.similarity(doc2[:1]) == 1.0
        assert doc.similarity(doc2) == 0.0


@pytest.mark.parametrize(
    "words,heads,lca_matrix",
    [
        (
            ["the", "lazy", "dog", "slept"],
            [2, 2, 3, 3],
            numpy.array([[0, 2, 2, 3], [2, 1, 2, 3], [2, 2, 2, 3], [3, 3, 3, 3]]),
        ),
        (
            ["The", "lazy", "dog", "slept", ".", "The", "quick", "fox", "jumped"],
            [2, 2, 3, 3, 3, 7, 7, 8, 8],
            numpy.array(
                [
                    [0, 2, 2, 3, 3, -1, -1, -1, -1],
                    [2, 1, 2, 3, 3, -1, -1, -1, -1],
                    [2, 2, 2, 3, 3, -1, -1, -1, -1],
                    [3, 3, 3, 3, 3, -1, -1, -1, -1],
                    [3, 3, 3, 3, 4, -1, -1, -1, -1],
                    [-1, -1, -1, -1, -1, 5, 7, 7, 8],
                    [-1, -1, -1, -1, -1, 7, 6, 7, 8],
                    [-1, -1, -1, -1, -1, 7, 7, 7, 8],
                    [-1, -1, -1, -1, -1, 8, 8, 8, 8],
                ]
            ),
        ),
    ],
)
def test_lowest_common_ancestor(en_vocab, words, heads, lca_matrix):
    doc = Doc(en_vocab, words, heads=heads, deps=["dep"] * len(heads))
    lca = doc.get_lca_matrix()
    assert (lca == lca_matrix).all()
    assert lca[1, 1] == 1
    assert lca[0, 1] == 2
    assert lca[1, 2] == 2


def test_doc_is_nered(en_vocab):
    words = ["I", "live", "in", "New", "York"]
    doc = Doc(en_vocab, words=words)
    assert not doc.has_annotation("ENT_IOB")
    doc.ents = [Span(doc, 3, 5, label="GPE")]
    assert doc.has_annotation("ENT_IOB")
    # 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.has_annotation("ENT_IOB")
    # Test serialization
    new_doc = Doc(en_vocab).from_bytes(doc.to_bytes())
    assert new_doc.has_annotation("ENT_IOB")


def test_doc_from_array_sent_starts(en_vocab):
    # fmt: off
    words = ["I", "live", "in", "New", "York", ".", "I", "like", "cats", "."]
    heads = [0, 0, 0, 0, 0, 0, 6, 6, 6, 6]
    deps = ["ROOT", "dep", "dep", "dep", "dep", "dep", "ROOT", "dep", "dep", "dep"]
    # fmt: on
    doc = Doc(en_vocab, words=words, heads=heads, deps=deps)
    # HEAD overrides SENT_START without warning
    attrs = [SENT_START, HEAD]
    arr = doc.to_array(attrs)
    new_doc = Doc(en_vocab, words=words)
    new_doc.from_array(attrs, arr)
    # no warning using default attrs
    attrs = doc._get_array_attrs()
    arr = doc.to_array(attrs)
    with pytest.warns(None) as record:
        new_doc.from_array(attrs, arr)
        assert len(record) == 0
    # only SENT_START uses SENT_START
    attrs = [SENT_START]
    arr = doc.to_array(attrs)
    new_doc = Doc(en_vocab, words=words)
    new_doc.from_array(attrs, arr)
    assert [t.is_sent_start for t in doc] == [t.is_sent_start for t in new_doc]
    assert not new_doc.has_annotation("DEP")
    # only HEAD uses HEAD
    attrs = [HEAD, DEP]
    arr = doc.to_array(attrs)
    new_doc = Doc(en_vocab, words=words)
    new_doc.from_array(attrs, arr)
    assert [t.is_sent_start for t in doc] == [t.is_sent_start for t in new_doc]
    assert new_doc.has_annotation("DEP")


def test_doc_from_array_morph(en_vocab):
    # fmt: off
    words = ["I", "live", "in", "New", "York", "."]
    morphs = ["Feat1=A", "Feat1=B", "Feat1=C", "Feat1=A|Feat2=D", "Feat2=E", "Feat3=F"]
    # fmt: on
    doc = Doc(en_vocab, words=words, morphs=morphs)
    attrs = [MORPH]
    arr = doc.to_array(attrs)
    new_doc = Doc(en_vocab, words=words)
    new_doc.from_array(attrs, arr)
    assert [str(t.morph) for t in new_doc] == morphs
    assert [str(t.morph) for t in doc] == [str(t.morph) for t in new_doc]


@pytest.mark.usefixtures("clean_underscore")
def test_doc_api_from_docs(en_tokenizer, de_tokenizer):
    en_texts = [
        "Merging the docs is fun.",
        "",
        "They don't think alike. ",
        "",
        "Another doc.",
    ]
    en_texts_without_empty = [t for t in en_texts if len(t)]
    de_text = "Wie war die Frage?"
    en_docs = [en_tokenizer(text) for text in en_texts]
    en_docs[0].spans["group"] = [en_docs[0][1:4]]
    en_docs[2].spans["group"] = [en_docs[2][1:4]]
    en_docs[4].spans["group"] = [en_docs[4][0:1]]
    span_group_texts = sorted(
        [en_docs[0][1:4].text, en_docs[2][1:4].text, en_docs[4][0:1].text]
    )
    de_doc = de_tokenizer(de_text)
    Token.set_extension("is_ambiguous", default=False)
    en_docs[0][2]._.is_ambiguous = True  # docs
    en_docs[2][3]._.is_ambiguous = True  # think
    assert Doc.from_docs([]) is None
    assert de_doc is not Doc.from_docs([de_doc])
    assert str(de_doc) == str(Doc.from_docs([de_doc]))

    with pytest.raises(ValueError):
        Doc.from_docs(en_docs + [de_doc])

    m_doc = Doc.from_docs(en_docs)
    assert len(en_texts_without_empty) == len(list(m_doc.sents))
    assert len(m_doc.text) > len(en_texts[0]) + len(en_texts[1])
    assert m_doc.text == " ".join([t.strip() for t in en_texts_without_empty])
    p_token = m_doc[len(en_docs[0]) - 1]
    assert p_token.text == "." and bool(p_token.whitespace_)
    en_docs_tokens = [t for doc in en_docs for t in doc]
    assert len(m_doc) == len(en_docs_tokens)
    think_idx = len(en_texts[0]) + 1 + en_texts[2].index("think")
    assert m_doc[2]._.is_ambiguous is True
    assert m_doc[9].idx == think_idx
    assert m_doc[9]._.is_ambiguous is True
    assert not any([t._.is_ambiguous for t in m_doc[3:8]])
    assert "group" in m_doc.spans
    assert span_group_texts == sorted([s.text for s in m_doc.spans["group"]])
    assert bool(m_doc[11].whitespace_)

    m_doc = Doc.from_docs(en_docs, ensure_whitespace=False)
    assert len(en_texts_without_empty) == len(list(m_doc.sents))
    assert len(m_doc.text) == sum(len(t) for t in en_texts)
    assert m_doc.text == "".join(en_texts_without_empty)
    p_token = m_doc[len(en_docs[0]) - 1]
    assert p_token.text == "." and not bool(p_token.whitespace_)
    en_docs_tokens = [t for doc in en_docs for t in doc]
    assert len(m_doc) == len(en_docs_tokens)
    think_idx = len(en_texts[0]) + 0 + en_texts[2].index("think")
    assert m_doc[9].idx == think_idx
    assert "group" in m_doc.spans
    assert span_group_texts == sorted([s.text for s in m_doc.spans["group"]])
    assert bool(m_doc[11].whitespace_)

    m_doc = Doc.from_docs(en_docs, attrs=["lemma", "length", "pos"])
    assert len(m_doc.text) > len(en_texts[0]) + len(en_texts[1])
    # space delimiter considered, although spacy attribute was missing
    assert m_doc.text == " ".join([t.strip() for t in en_texts_without_empty])
    p_token = m_doc[len(en_docs[0]) - 1]
    assert p_token.text == "." and bool(p_token.whitespace_)
    en_docs_tokens = [t for doc in en_docs for t in doc]
    assert len(m_doc) == len(en_docs_tokens)
    think_idx = len(en_texts[0]) + 1 + en_texts[2].index("think")
    assert m_doc[9].idx == think_idx
    assert "group" in m_doc.spans
    assert span_group_texts == sorted([s.text for s in m_doc.spans["group"]])

    # can exclude spans
    m_doc = Doc.from_docs(en_docs, exclude=["spans"])
    assert "group" not in m_doc.spans

    # can exclude user_data
    m_doc = Doc.from_docs(en_docs, exclude=["user_data"])
    assert m_doc.user_data == {}

    # can merge empty docs
    doc = Doc.from_docs([en_tokenizer("")] * 10)

    # empty but set spans keys are preserved
    en_docs = [en_tokenizer(text) for text in en_texts]
    m_doc = Doc.from_docs(en_docs)
    assert "group" not in m_doc.spans
    for doc in en_docs:
        doc.spans["group"] = []
    m_doc = Doc.from_docs(en_docs)
    assert "group" in m_doc.spans
    assert len(m_doc.spans["group"]) == 0

    # with tensor
    ops = get_current_ops()
    for doc in en_docs:
        doc.tensor = ops.asarray([[len(t.text), 0.0] for t in doc])
    m_doc = Doc.from_docs(en_docs)
    assert_array_equal(
        ops.to_numpy(m_doc.tensor),
        ops.to_numpy(ops.xp.vstack([doc.tensor for doc in en_docs if len(doc)])),
    )

    # can exclude tensor
    m_doc = Doc.from_docs(en_docs, exclude=["tensor"])
    assert m_doc.tensor.shape == (0,)


def test_doc_api_from_docs_ents(en_tokenizer):
    texts = ["Merging the docs is fun.", "They don't think alike."]
    docs = [en_tokenizer(t) for t in texts]
    docs[0].ents = ()
    docs[1].ents = (Span(docs[1], 0, 1, label="foo"),)
    doc = Doc.from_docs(docs)
    assert len(doc.ents) == 1


def test_doc_lang(en_vocab):
    doc = Doc(en_vocab, words=["Hello", "world"])
    assert doc.lang_ == "en"
    assert doc.lang == en_vocab.strings["en"]
    assert doc[0].lang_ == "en"
    assert doc[0].lang == en_vocab.strings["en"]
    nlp = English()
    doc = nlp("Hello world")
    assert doc.lang_ == "en"
    assert doc.lang == en_vocab.strings["en"]
    assert doc[0].lang_ == "en"
    assert doc[0].lang == en_vocab.strings["en"]


def test_token_lexeme(en_vocab):
    """Test that tokens expose their lexeme."""
    token = Doc(en_vocab, words=["Hello", "world"])[0]
    assert isinstance(token.lex, Lexeme)
    assert token.lex.text == token.text
    assert en_vocab[token.orth] == token.lex


def test_has_annotation(en_vocab):
    doc = Doc(en_vocab, words=["Hello", "world"])
    attrs = ("TAG", "POS", "MORPH", "LEMMA", "DEP", "HEAD", "ENT_IOB", "ENT_TYPE")
    for attr in attrs:
        assert not doc.has_annotation(attr)
        assert not doc.has_annotation(attr, require_complete=True)

    doc[0].tag_ = "A"
    doc[0].pos_ = "X"
    doc[0].set_morph("Feat=Val")
    doc[0].lemma_ = "a"
    doc[0].dep_ = "dep"
    doc[0].head = doc[1]
    doc.set_ents([Span(doc, 0, 1, label="HELLO")], default="missing")

    for attr in attrs:
        assert doc.has_annotation(attr)
        assert not doc.has_annotation(attr, require_complete=True)

    doc[1].tag_ = "A"
    doc[1].pos_ = "X"
    doc[1].set_morph("")
    doc[1].lemma_ = "a"
    doc[1].dep_ = "dep"
    doc.ents = [Span(doc, 0, 2, label="HELLO")]

    for attr in attrs:
        assert doc.has_annotation(attr)
        assert doc.has_annotation(attr, require_complete=True)


def test_has_annotation_sents(en_vocab):
    doc = Doc(en_vocab, words=["Hello", "beautiful", "world"])
    attrs = ("SENT_START", "IS_SENT_START", "IS_SENT_END")
    for attr in attrs:
        assert not doc.has_annotation(attr)
        assert not doc.has_annotation(attr, require_complete=True)

    # The first token (index 0) is always assumed to be a sentence start,
    # and ignored by the check in doc.has_annotation

    doc[1].is_sent_start = False
    for attr in attrs:
        assert doc.has_annotation(attr)
        assert not doc.has_annotation(attr, require_complete=True)

    doc[2].is_sent_start = False
    for attr in attrs:
        assert doc.has_annotation(attr)
        assert doc.has_annotation(attr, require_complete=True)


def test_is_flags_deprecated(en_tokenizer):
    doc = en_tokenizer("test")
    with pytest.deprecated_call():
        doc.is_tagged
    with pytest.deprecated_call():
        doc.is_parsed
    with pytest.deprecated_call():
        doc.is_nered
    with pytest.deprecated_call():
        doc.is_sentenced


def test_doc_set_ents(en_tokenizer):
    # set ents
    doc = en_tokenizer("a b c d e")
    doc.set_ents([Span(doc, 0, 1, 10), Span(doc, 1, 3, 11)])
    assert [t.ent_iob for t in doc] == [3, 3, 1, 2, 2]
    assert [t.ent_type for t in doc] == [10, 11, 11, 0, 0]

    # add ents, invalid IOB repaired
    doc = en_tokenizer("a b c d e")
    doc.set_ents([Span(doc, 0, 1, 10), Span(doc, 1, 3, 11)])
    doc.set_ents([Span(doc, 0, 2, 12)], default="unmodified")
    assert [t.ent_iob for t in doc] == [3, 1, 3, 2, 2]
    assert [t.ent_type for t in doc] == [12, 12, 11, 0, 0]

    # missing ents
    doc = en_tokenizer("a b c d e")
    doc.set_ents([Span(doc, 0, 1, 10), Span(doc, 1, 3, 11)], missing=[doc[4:5]])
    assert [t.ent_iob for t in doc] == [3, 3, 1, 2, 0]
    assert [t.ent_type for t in doc] == [10, 11, 11, 0, 0]

    # outside ents
    doc = en_tokenizer("a b c d e")
    doc.set_ents(
        [Span(doc, 0, 1, 10), Span(doc, 1, 3, 11)],
        outside=[doc[4:5]],
        default="missing",
    )
    assert [t.ent_iob for t in doc] == [3, 3, 1, 0, 2]
    assert [t.ent_type for t in doc] == [10, 11, 11, 0, 0]

    # blocked ents
    doc = en_tokenizer("a b c d e")
    doc.set_ents([], blocked=[doc[1:2], doc[3:5]], default="unmodified")
    assert [t.ent_iob for t in doc] == [0, 3, 0, 3, 3]
    assert [t.ent_type for t in doc] == [0, 0, 0, 0, 0]
    assert doc.ents == tuple()

    # invalid IOB repaired after blocked
    doc.ents = [Span(doc, 3, 5, "ENT")]
    assert [t.ent_iob for t in doc] == [2, 2, 2, 3, 1]
    doc.set_ents([], blocked=[doc[3:4]], default="unmodified")
    assert [t.ent_iob for t in doc] == [2, 2, 2, 3, 3]

    # all types
    doc = en_tokenizer("a b c d e")
    doc.set_ents(
        [Span(doc, 0, 1, 10)],
        blocked=[doc[1:2]],
        missing=[doc[2:3]],
        outside=[doc[3:4]],
        default="unmodified",
    )
    assert [t.ent_iob for t in doc] == [3, 3, 0, 2, 0]
    assert [t.ent_type for t in doc] == [10, 0, 0, 0, 0]

    doc = en_tokenizer("a b c d e")
    # single span instead of a list
    with pytest.raises(ValueError):
        doc.set_ents([], missing=doc[1:2])
    # invalid default mode
    with pytest.raises(ValueError):
        doc.set_ents([], missing=[doc[1:2]], default="none")
    # conflicting/overlapping specifications
    with pytest.raises(ValueError):
        doc.set_ents([], missing=[doc[1:2]], outside=[doc[1:2]])


def test_doc_ents_setter():
    """Test that both strings and integers can be used to set entities in
    tuple format via doc.ents."""
    words = ["a", "b", "c", "d", "e"]
    doc = Doc(Vocab(), words=words)
    doc.ents = [("HELLO", 0, 2), (doc.vocab.strings.add("WORLD"), 3, 5)]
    assert [e.label_ for e in doc.ents] == ["HELLO", "WORLD"]
    vocab = Vocab()
    ents = [("HELLO", 0, 2), (vocab.strings.add("WORLD"), 3, 5)]
    ents = ["B-HELLO", "I-HELLO", "O", "B-WORLD", "I-WORLD"]
    doc = Doc(vocab, words=words, ents=ents)
    assert [e.label_ for e in doc.ents] == ["HELLO", "WORLD"]


def test_doc_morph_setter(en_tokenizer, de_tokenizer):
    doc1 = en_tokenizer("a b")
    doc1b = en_tokenizer("c d")
    doc2 = de_tokenizer("a b")

    # unset values can be copied
    doc1[0].morph = doc1[1].morph
    assert doc1[0].morph.key == 0
    assert doc1[1].morph.key == 0

    # morph values from the same vocab can be copied
    doc1[0].set_morph("Feat=Val")
    doc1[1].morph = doc1[0].morph
    assert doc1[0].morph == doc1[1].morph

    # ... also across docs
    doc1b[0].morph = doc1[0].morph
    assert doc1[0].morph == doc1b[0].morph

    doc2[0].set_morph("Feat2=Val2")

    # the morph value must come from the same vocab
    with pytest.raises(ValueError):
        doc1[0].morph = doc2[0].morph


def test_doc_init_iob():
    """Test ents validation/normalization in Doc.__init__"""
    words = ["a", "b", "c", "d", "e"]
    ents = ["O"] * len(words)
    doc = Doc(Vocab(), words=words, ents=ents)
    assert doc.ents == ()

    ents = ["B-PERSON", "I-PERSON", "O", "I-PERSON", "I-PERSON"]
    doc = Doc(Vocab(), words=words, ents=ents)
    assert len(doc.ents) == 2

    ents = ["B-PERSON", "I-PERSON", "O", "I-PERSON", "I-GPE"]
    doc = Doc(Vocab(), words=words, ents=ents)
    assert len(doc.ents) == 3

    # None is missing
    ents = ["B-PERSON", "I-PERSON", "O", None, "I-GPE"]
    doc = Doc(Vocab(), words=words, ents=ents)
    assert len(doc.ents) == 2

    # empty tag is missing
    ents = ["", "B-PERSON", "O", "B-PERSON", "I-PERSON"]
    doc = Doc(Vocab(), words=words, ents=ents)
    assert len(doc.ents) == 2

    # invalid IOB
    ents = ["Q-PERSON", "I-PERSON", "O", "I-PERSON", "I-GPE"]
    with pytest.raises(ValueError):
        doc = Doc(Vocab(), words=words, ents=ents)

    # no dash
    ents = ["OPERSON", "I-PERSON", "O", "I-PERSON", "I-GPE"]
    with pytest.raises(ValueError):
        doc = Doc(Vocab(), words=words, ents=ents)

    # no ent type
    ents = ["O", "B-", "O", "I-PERSON", "I-GPE"]
    with pytest.raises(ValueError):
        doc = Doc(Vocab(), words=words, ents=ents)

    # not strings or None
    ents = [0, "B-", "O", "I-PERSON", "I-GPE"]
    with pytest.raises(ValueError):
        doc = Doc(Vocab(), words=words, ents=ents)


def test_doc_set_ents_invalid_spans(en_tokenizer):
    doc = en_tokenizer("Some text about Colombia and the Czech Republic")
    spans = [Span(doc, 3, 4, label="GPE"), Span(doc, 6, 8, label="GPE")]
    with doc.retokenize() as retokenizer:
        for span in spans:
            retokenizer.merge(span)
    with pytest.raises(IndexError):
        doc.ents = spans


def test_doc_noun_chunks_not_implemented():
    """Test that a language without noun_chunk iterator, throws a NotImplementedError"""
    text = "Může data vytvářet a spravovat, ale především je dokáže analyzovat, najít v nich nové vztahy a vše přehledně vizualizovat."
    nlp = MultiLanguage()
    doc = nlp(text)
    with pytest.raises(NotImplementedError):
        _ = list(doc.noun_chunks)  # noqa: F841


def test_span_groups(en_tokenizer):
    doc = en_tokenizer("Some text about Colombia and the Czech Republic")
    doc.spans["hi"] = [Span(doc, 3, 4, label="bye")]
    assert "hi" in doc.spans
    assert "bye" not in doc.spans
    assert len(doc.spans["hi"]) == 1
    assert doc.spans["hi"][0].label_ == "bye"
    doc.spans["hi"].append(doc[0:3])
    assert len(doc.spans["hi"]) == 2
    assert doc.spans["hi"][1].text == "Some text about"
    assert [span.text for span in doc.spans["hi"]] == ["Colombia", "Some text about"]
    assert not doc.spans["hi"].has_overlap
    doc.ents = [Span(doc, 3, 4, label="GPE"), Span(doc, 6, 8, label="GPE")]
    doc.spans["hi"].extend(doc.ents)
    assert len(doc.spans["hi"]) == 4
    assert [span.label_ for span in doc.spans["hi"]] == ["bye", "", "GPE", "GPE"]
    assert doc.spans["hi"].has_overlap
    del doc.spans["hi"]
    assert "hi" not in doc.spans


def test_doc_spans_copy(en_tokenizer):
    doc1 = en_tokenizer("Some text about Colombia and the Czech Republic")
    assert weakref.ref(doc1) == doc1.spans.doc_ref
    doc2 = doc1.copy()
    assert weakref.ref(doc2) == doc2.spans.doc_ref