ai-content-maker/.venv/Lib/site-packages/spacy/tests/serialize/test_serialize_tokenizer.py

import pickle
import re

import pytest

from spacy.attrs import ENT_IOB, ENT_TYPE
from spacy.lang.en import English
from spacy.tokenizer import Tokenizer
from spacy.tokens import Doc
from spacy.util import (
    compile_infix_regex,
    compile_prefix_regex,
    compile_suffix_regex,
    get_lang_class,
    load_model,
)

from ..util import assert_packed_msg_equal, make_tempdir


def load_tokenizer(b):
    tok = get_lang_class("en")().tokenizer
    tok.from_bytes(b)
    return tok


@pytest.mark.issue(2833)
def test_issue2833(en_vocab):
    """Test that a custom error is raised if a token or span is pickled."""
    doc = Doc(en_vocab, words=["Hello", "world"])
    with pytest.raises(NotImplementedError):
        pickle.dumps(doc[0])
    with pytest.raises(NotImplementedError):
        pickle.dumps(doc[0:2])


@pytest.mark.issue(3012)
def test_issue3012(en_vocab):
    """Test that the is_tagged attribute doesn't get overwritten when we from_array
    without tag information."""
    words = ["This", "is", "10", "%", "."]
    tags = ["DT", "VBZ", "CD", "NN", "."]
    pos = ["DET", "VERB", "NUM", "NOUN", "PUNCT"]
    ents = ["O", "O", "B-PERCENT", "I-PERCENT", "O"]
    doc = Doc(en_vocab, words=words, tags=tags, pos=pos, ents=ents)
    assert doc.has_annotation("TAG")
    expected = ("10", "NUM", "CD", "PERCENT")
    assert (doc[2].text, doc[2].pos_, doc[2].tag_, doc[2].ent_type_) == expected
    header = [ENT_IOB, ENT_TYPE]
    ent_array = doc.to_array(header)
    doc.from_array(header, ent_array)
    assert (doc[2].text, doc[2].pos_, doc[2].tag_, doc[2].ent_type_) == expected
    # Serializing then deserializing
    doc_bytes = doc.to_bytes()
    doc2 = Doc(en_vocab).from_bytes(doc_bytes)
    assert (doc2[2].text, doc2[2].pos_, doc2[2].tag_, doc2[2].ent_type_) == expected


@pytest.mark.issue(4190)
def test_issue4190():
    def customize_tokenizer(nlp):
        prefix_re = compile_prefix_regex(nlp.Defaults.prefixes)
        suffix_re = compile_suffix_regex(nlp.Defaults.suffixes)
        infix_re = compile_infix_regex(nlp.Defaults.infixes)
        # Remove all exceptions where a single letter is followed by a period (e.g. 'h.')
        exceptions = {
            k: v
            for k, v in dict(nlp.Defaults.tokenizer_exceptions).items()
            if not (len(k) == 2 and k[1] == ".")
        }
        new_tokenizer = Tokenizer(
            nlp.vocab,
            exceptions,
            prefix_search=prefix_re.search,
            suffix_search=suffix_re.search,
            infix_finditer=infix_re.finditer,
            token_match=nlp.tokenizer.token_match,
            faster_heuristics=False,
        )
        nlp.tokenizer = new_tokenizer

    test_string = "Test c."
    # Load default language
    nlp_1 = English()
    doc_1a = nlp_1(test_string)
    result_1a = [token.text for token in doc_1a]  # noqa: F841
    # Modify tokenizer
    customize_tokenizer(nlp_1)
    doc_1b = nlp_1(test_string)
    result_1b = [token.text for token in doc_1b]
    # Save and Reload
    with make_tempdir() as model_dir:
        nlp_1.to_disk(model_dir)
        nlp_2 = load_model(model_dir)
    # This should be the modified tokenizer
    doc_2 = nlp_2(test_string)
    result_2 = [token.text for token in doc_2]
    assert result_1b == result_2
    assert nlp_2.tokenizer.faster_heuristics is False


def test_serialize_custom_tokenizer(en_vocab, en_tokenizer):
    """Test that custom tokenizer with not all functions defined or empty
    properties can be serialized and deserialized correctly (see #2494,
    #4991)."""
    tokenizer = Tokenizer(en_vocab, suffix_search=en_tokenizer.suffix_search)
    tokenizer_bytes = tokenizer.to_bytes()
    Tokenizer(en_vocab).from_bytes(tokenizer_bytes)

    # test that empty/unset values are set correctly on deserialization
    tokenizer = get_lang_class("en")().tokenizer
    tokenizer.token_match = re.compile("test").match
    assert tokenizer.rules != {}
    assert tokenizer.token_match is not None
    assert tokenizer.url_match is not None
    assert tokenizer.prefix_search is not None
    assert tokenizer.infix_finditer is not None
    tokenizer.from_bytes(tokenizer_bytes)
    assert tokenizer.rules == {}
    assert tokenizer.token_match is None
    assert tokenizer.url_match is None
    assert tokenizer.prefix_search is None
    assert tokenizer.infix_finditer is None

    tokenizer = Tokenizer(en_vocab, rules={"ABC.": [{"ORTH": "ABC"}, {"ORTH": "."}]})
    tokenizer.rules = {}
    tokenizer_bytes = tokenizer.to_bytes()
    tokenizer_reloaded = Tokenizer(en_vocab).from_bytes(tokenizer_bytes)
    assert tokenizer_reloaded.rules == {}


@pytest.mark.parametrize("text", ["I💜you", "they’re", "“hello”"])
def test_serialize_tokenizer_roundtrip_bytes(en_tokenizer, text):
    tokenizer = en_tokenizer
    new_tokenizer = load_tokenizer(tokenizer.to_bytes())
    assert_packed_msg_equal(new_tokenizer.to_bytes(), tokenizer.to_bytes())
    assert new_tokenizer.to_bytes() == tokenizer.to_bytes()
    doc1 = tokenizer(text)
    doc2 = new_tokenizer(text)
    assert [token.text for token in doc1] == [token.text for token in doc2]


def test_serialize_tokenizer_roundtrip_disk(en_tokenizer):
    tokenizer = en_tokenizer
    with make_tempdir() as d:
        file_path = d / "tokenizer"
        tokenizer.to_disk(file_path)
        tokenizer_d = en_tokenizer.from_disk(file_path)
        assert tokenizer.to_bytes() == tokenizer_d.to_bytes()