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89 lines
4.0 KiB
Python
89 lines
4.0 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import, division, print_function, unicode_literals
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import os
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import unittest
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from io import open
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import shutil
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import pytest
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from pytorch_pretrained_bert.tokenization_xlnet import (XLNetTokenizer, PRETRAINED_VOCAB_ARCHIVE_MAP)
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SAMPLE_VOCAB = os.path.join(os.path.dirname(
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os.path.dirname(os.path.abspath(__file__))),
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'samples/test_sentencepiece.model')
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class XLNetTokenizationTest(unittest.TestCase):
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def test_full_tokenizer(self):
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tokenizer = XLNetTokenizer(SAMPLE_VOCAB)
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tokens = tokenizer.tokenize('This is a test')
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self.assertListEqual(tokens, ['▁This', '▁is', '▁a', '▁t', 'est'])
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self.assertListEqual(
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tokenizer.convert_tokens_to_ids(tokens), [285, 46, 10, 170, 382])
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vocab_path = "/tmp/"
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vocab_file, special_tokens_file = tokenizer.save_vocabulary(vocab_path)
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tokenizer = tokenizer.from_pretrained(vocab_path,
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keep_accents=True)
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os.remove(vocab_file)
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os.remove(special_tokens_file)
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tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.")
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self.assertListEqual(tokens, ['▁I', '▁was', '▁b', 'or', 'n', '▁in', '▁',
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'9', '2', '0', '0', '0', ',', '▁and', '▁this',
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'▁is', '▁f', 'al', 's', 'é', '.'])
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ids = tokenizer.convert_tokens_to_ids(tokens)
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self.assertListEqual(
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ids, [8, 21, 84, 55, 24, 19, 7, 0,
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602, 347, 347, 347, 3, 12, 66,
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46, 72, 80, 6, 0, 4])
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back_tokens = tokenizer.convert_ids_to_tokens(ids)
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self.assertListEqual(back_tokens, ['▁I', '▁was', '▁b', 'or', 'n', '▁in',
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'▁', '<unk>', '2', '0', '0', '0', ',',
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'▁and', '▁this', '▁is', '▁f', 'al', 's',
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'<unk>', '.'])
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@pytest.mark.slow
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def test_tokenizer_from_pretrained(self):
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cache_dir = "/tmp/pytorch_pretrained_bert_test/"
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for model_name in list(PRETRAINED_VOCAB_ARCHIVE_MAP.keys())[:1]:
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tokenizer = XLNetTokenizer.from_pretrained(model_name, cache_dir=cache_dir)
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shutil.rmtree(cache_dir)
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self.assertIsNotNone(tokenizer)
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def test_tokenizer_lower(self):
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tokenizer = XLNetTokenizer(SAMPLE_VOCAB, do_lower_case=True)
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tokens = tokenizer.tokenize(u"I was born in 92000, and this is falsé.")
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self.assertListEqual(tokens, ['▁', 'i', '▁was', '▁b', 'or', 'n', '▁in', '▁',
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'9', '2', '0', '0', '0', ',', '▁and', '▁this',
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'▁is', '▁f', 'al', 'se', '.'])
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self.assertListEqual(tokenizer.tokenize(u"H\u00E9llo"), ["▁he", "ll", "o"])
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def test_tokenizer_no_lower(self):
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tokenizer = XLNetTokenizer(SAMPLE_VOCAB, do_lower_case=False)
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tokens = tokenizer.tokenize(u"I was born in 92000, and this is falsé.")
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self.assertListEqual(tokens, ['▁I', '▁was', '▁b', 'or', 'n', '▁in', '▁',
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'9', '2', '0', '0', '0', ',', '▁and', '▁this',
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'▁is', '▁f', 'al', 'se', '.'])
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if __name__ == '__main__':
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unittest.main()
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