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58 lines
1.5 KiB
Python
58 lines
1.5 KiB
Python
# coding: utf-8
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from __future__ import unicode_literals
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from spacy.language import Language
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from spacy.compat import pickle, unicode_
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def test_pickle_single_doc():
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nlp = Language()
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doc = nlp("pickle roundtrip")
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data = pickle.dumps(doc, 1)
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doc2 = pickle.loads(data)
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assert doc2.text == "pickle roundtrip"
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def test_list_of_docs_pickles_efficiently():
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nlp = Language()
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for i in range(10000):
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_ = nlp.vocab[unicode_(i)] # noqa: F841
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one_pickled = pickle.dumps(nlp("0"), -1)
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docs = list(nlp.pipe(unicode_(i) for i in range(100)))
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many_pickled = pickle.dumps(docs, -1)
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assert len(many_pickled) < (len(one_pickled) * 2)
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many_unpickled = pickle.loads(many_pickled)
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assert many_unpickled[0].text == "0"
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assert many_unpickled[-1].text == "99"
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assert len(many_unpickled) == 100
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def test_user_data_from_disk():
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nlp = Language()
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doc = nlp("Hello")
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doc.user_data[(0, 1)] = False
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b = doc.to_bytes()
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doc2 = doc.__class__(doc.vocab).from_bytes(b)
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assert doc2.user_data[(0, 1)] is False
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def test_user_data_unpickles():
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nlp = Language()
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doc = nlp("Hello")
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doc.user_data[(0, 1)] = False
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b = pickle.dumps(doc)
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doc2 = pickle.loads(b)
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assert doc2.user_data[(0, 1)] is False
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def test_hooks_unpickle():
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def inner_func(d1, d2):
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return "hello!"
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nlp = Language()
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doc = nlp("Hello")
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doc.user_hooks["similarity"] = inner_func
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b = pickle.dumps(doc)
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doc2 = pickle.loads(b)
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assert doc2.similarity(None) == "hello!"
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