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	* Migrate regressions 1-1000 * Move serialize test to correct file * Remove tests that won't work in v3 * Migrate regressions 1000-1500 Removed regression test 1250 because v3 doesn't support the old LEX scheme anymore. * Add missing imports in serializer tests * Migrate tests 1500-2000 * Migrate regressions from 2000-2500 * Migrate regressions from 2501-3000 * Migrate regressions from 3000-3501 * Migrate regressions from 3501-4000 * Migrate regressions from 4001-4500 * Migrate regressions from 4501-5000 * Migrate regressions from 5001-5501 * Migrate regressions from 5501 to 7000 * Migrate regressions from 7001 to 8000 * Migrate remaining regression tests * Fixing missing imports * Update docs with new system [ci skip] * Update CONTRIBUTING.md - Fix formatting - Update wording * Remove lemmatizer tests in el lang * Move a few tests into the general tokenizer * Separate Doc and DocBin tests
		
			
				
	
	
		
			196 lines
		
	
	
		
			6.9 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
			
		
		
	
	
			196 lines
		
	
	
		
			6.9 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
| import pickle
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| 
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| import pytest
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| from thinc.api import get_current_ops
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| 
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| import spacy
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| from spacy.lang.en import English
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| from spacy.strings import StringStore
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| from spacy.tokens import Doc
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| from spacy.util import ensure_path, load_model
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| from spacy.vectors import Vectors
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| from spacy.vocab import Vocab
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| 
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| from ..util import make_tempdir
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| 
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| test_strings = [([], []), (["rats", "are", "cute"], ["i", "like", "rats"])]
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| test_strings_attrs = [(["rats", "are", "cute"], "Hello")]
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| 
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| 
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| @pytest.mark.issue(599)
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| def test_issue599(en_vocab):
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|     doc = Doc(en_vocab)
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|     doc2 = Doc(doc.vocab)
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|     doc2.from_bytes(doc.to_bytes())
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|     assert doc2.has_annotation("DEP")
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| 
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| 
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| @pytest.mark.issue(4054)
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| def test_issue4054(en_vocab):
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|     """Test that a new blank model can be made with a vocab from file,
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|     and that serialization does not drop the language at any point."""
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|     nlp1 = English()
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|     vocab1 = nlp1.vocab
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|     with make_tempdir() as d:
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|         vocab_dir = ensure_path(d / "vocab")
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|         if not vocab_dir.exists():
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|             vocab_dir.mkdir()
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|         vocab1.to_disk(vocab_dir)
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|         vocab2 = Vocab().from_disk(vocab_dir)
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|         nlp2 = spacy.blank("en", vocab=vocab2)
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|         nlp_dir = ensure_path(d / "nlp")
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|         if not nlp_dir.exists():
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|             nlp_dir.mkdir()
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|         nlp2.to_disk(nlp_dir)
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|         nlp3 = load_model(nlp_dir)
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|         assert nlp3.lang == "en"
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| 
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| 
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| @pytest.mark.issue(4133)
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| def test_issue4133(en_vocab):
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|     nlp = English()
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|     vocab_bytes = nlp.vocab.to_bytes()
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|     words = ["Apple", "is", "looking", "at", "buying", "a", "startup"]
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|     pos = ["NOUN", "VERB", "ADP", "VERB", "PROPN", "NOUN", "ADP"]
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|     doc = Doc(en_vocab, words=words)
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|     for i, token in enumerate(doc):
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|         token.pos_ = pos[i]
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|     # usually this is already True when starting from proper models instead of blank English
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|     doc_bytes = doc.to_bytes()
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|     vocab = Vocab()
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|     vocab = vocab.from_bytes(vocab_bytes)
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|     doc = Doc(vocab).from_bytes(doc_bytes)
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|     actual = []
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|     for token in doc:
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|         actual.append(token.pos_)
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|     assert actual == pos
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| 
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| 
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| @pytest.mark.parametrize("text", ["rat"])
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| def test_serialize_vocab(en_vocab, text):
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|     text_hash = en_vocab.strings.add(text)
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|     vocab_bytes = en_vocab.to_bytes(exclude=["lookups"])
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|     new_vocab = Vocab().from_bytes(vocab_bytes)
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|     assert new_vocab.strings[text_hash] == text
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|     assert new_vocab.to_bytes(exclude=["lookups"]) == vocab_bytes
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| 
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| 
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| @pytest.mark.parametrize("strings1,strings2", test_strings)
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| def test_serialize_vocab_roundtrip_bytes(strings1, strings2):
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|     vocab1 = Vocab(strings=strings1)
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|     vocab2 = Vocab(strings=strings2)
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|     vocab1_b = vocab1.to_bytes()
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|     vocab2_b = vocab2.to_bytes()
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|     if strings1 == strings2:
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|         assert vocab1_b == vocab2_b
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|     else:
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|         assert vocab1_b != vocab2_b
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|     vocab1 = vocab1.from_bytes(vocab1_b)
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|     assert vocab1.to_bytes() == vocab1_b
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|     new_vocab1 = Vocab().from_bytes(vocab1_b)
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|     assert new_vocab1.to_bytes() == vocab1_b
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|     assert len(new_vocab1.strings) == len(strings1)
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|     assert sorted([s for s in new_vocab1.strings]) == sorted(strings1)
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| 
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| 
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| @pytest.mark.parametrize("strings1,strings2", test_strings)
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| def test_serialize_vocab_roundtrip_disk(strings1, strings2):
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|     vocab1 = Vocab(strings=strings1)
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|     vocab2 = Vocab(strings=strings2)
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|     with make_tempdir() as d:
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|         file_path1 = d / "vocab1"
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|         file_path2 = d / "vocab2"
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|         vocab1.to_disk(file_path1)
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|         vocab2.to_disk(file_path2)
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|         vocab1_d = Vocab().from_disk(file_path1)
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|         vocab2_d = Vocab().from_disk(file_path2)
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|         # check strings rather than lexemes, which are only reloaded on demand
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|         assert set(strings1) == set([s for s in vocab1_d.strings])
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|         assert set(strings2) == set([s for s in vocab2_d.strings])
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|         if set(strings1) == set(strings2):
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|             assert [s for s in vocab1_d.strings] == [s for s in vocab2_d.strings]
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|         else:
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|             assert [s for s in vocab1_d.strings] != [s for s in vocab2_d.strings]
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| 
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| 
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| @pytest.mark.parametrize("strings,lex_attr", test_strings_attrs)
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| def test_serialize_vocab_lex_attrs_bytes(strings, lex_attr):
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|     vocab1 = Vocab(strings=strings)
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|     vocab2 = Vocab()
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|     vocab1[strings[0]].norm_ = lex_attr
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|     assert vocab1[strings[0]].norm_ == lex_attr
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|     assert vocab2[strings[0]].norm_ != lex_attr
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|     vocab2 = vocab2.from_bytes(vocab1.to_bytes())
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|     assert vocab2[strings[0]].norm_ == lex_attr
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| 
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| 
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| @pytest.mark.parametrize("strings,lex_attr", test_strings_attrs)
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| def test_deserialize_vocab_seen_entries(strings, lex_attr):
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|     # Reported in #2153
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|     vocab = Vocab(strings=strings)
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|     vocab.from_bytes(vocab.to_bytes())
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|     assert len(vocab.strings) == len(strings)
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| 
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| 
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| @pytest.mark.parametrize("strings,lex_attr", test_strings_attrs)
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| def test_serialize_vocab_lex_attrs_disk(strings, lex_attr):
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|     vocab1 = Vocab(strings=strings)
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|     vocab2 = Vocab()
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|     vocab1[strings[0]].norm_ = lex_attr
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|     assert vocab1[strings[0]].norm_ == lex_attr
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|     assert vocab2[strings[0]].norm_ != lex_attr
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|     with make_tempdir() as d:
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|         file_path = d / "vocab"
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|         vocab1.to_disk(file_path)
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|         vocab2 = vocab2.from_disk(file_path)
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|     assert vocab2[strings[0]].norm_ == lex_attr
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| 
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| 
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| @pytest.mark.parametrize("strings1,strings2", test_strings)
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| def test_serialize_stringstore_roundtrip_bytes(strings1, strings2):
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|     sstore1 = StringStore(strings=strings1)
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|     sstore2 = StringStore(strings=strings2)
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|     sstore1_b = sstore1.to_bytes()
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|     sstore2_b = sstore2.to_bytes()
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|     if set(strings1) == set(strings2):
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|         assert sstore1_b == sstore2_b
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|     else:
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|         assert sstore1_b != sstore2_b
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|     sstore1 = sstore1.from_bytes(sstore1_b)
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|     assert sstore1.to_bytes() == sstore1_b
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|     new_sstore1 = StringStore().from_bytes(sstore1_b)
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|     assert new_sstore1.to_bytes() == sstore1_b
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|     assert set(new_sstore1) == set(strings1)
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| 
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| 
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| @pytest.mark.parametrize("strings1,strings2", test_strings)
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| def test_serialize_stringstore_roundtrip_disk(strings1, strings2):
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|     sstore1 = StringStore(strings=strings1)
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|     sstore2 = StringStore(strings=strings2)
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|     with make_tempdir() as d:
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|         file_path1 = d / "strings1"
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|         file_path2 = d / "strings2"
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|         sstore1.to_disk(file_path1)
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|         sstore2.to_disk(file_path2)
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|         sstore1_d = StringStore().from_disk(file_path1)
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|         sstore2_d = StringStore().from_disk(file_path2)
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|         assert set(sstore1_d) == set(sstore1)
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|         assert set(sstore2_d) == set(sstore2)
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|         if set(strings1) == set(strings2):
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|             assert set(sstore1_d) == set(sstore2_d)
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|         else:
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|             assert set(sstore1_d) != set(sstore2_d)
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| 
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| 
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| @pytest.mark.parametrize("strings,lex_attr", test_strings_attrs)
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| def test_pickle_vocab(strings, lex_attr):
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|     vocab = Vocab(strings=strings)
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|     ops = get_current_ops()
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|     vectors = Vectors(data=ops.xp.zeros((10, 10)), mode="floret", hash_count=1)
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|     vocab.vectors = vectors
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|     vocab[strings[0]].norm_ = lex_attr
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|     vocab_pickled = pickle.dumps(vocab)
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|     vocab_unpickled = pickle.loads(vocab_pickled)
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|     assert vocab.to_bytes() == vocab_unpickled.to_bytes()
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|     assert vocab_unpickled.vectors.mode == "floret"
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