35401fe50f
* Switch to plain unittest for skipping slow tests.
Add a RUN_SLOW environment variable for running them.
* Switch to plain unittest for PyTorch dependency.
* Switch to plain unittest for TensorFlow dependency.
* Avoid leaking open files in the test suite.
This prevents spurious warnings when running tests.
* Fix unicode warning on Python 2 when running tests.
The warning was:
UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
* Support running PyTorch tests on a GPU.
Reverts 27e015bd.
* Tests no longer require pytest.
* Make tests pass on cuda
51 lines
1.9 KiB
Python
51 lines
1.9 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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from transformers.tokenization_distilbert import (DistilBertTokenizer)
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from .tokenization_tests_commons import CommonTestCases
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from .tokenization_bert_test import BertTokenizationTest
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from .utils import slow
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class DistilBertTokenizationTest(BertTokenizationTest):
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tokenizer_class = DistilBertTokenizer
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def get_tokenizer(self, **kwargs):
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return DistilBertTokenizer.from_pretrained(self.tmpdirname, **kwargs)
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@slow
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def test_sequence_builders(self):
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tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased")
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text = tokenizer.encode("sequence builders", add_special_tokens=False)
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text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False)
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encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
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encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
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assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
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assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + \
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text_2 + [tokenizer.sep_token_id]
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if __name__ == '__main__':
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unittest.main()
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