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98 lines
3.9 KiB
Python
98 lines
3.9 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 json
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import unittest
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from io import open
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from pytorch_transformers.tokenization_roberta import RobertaTokenizer, VOCAB_FILES_NAMES
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from .tokenization_tests_commons import CommonTestCases
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class RobertaTokenizationTest(CommonTestCases.CommonTokenizerTester):
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tokenizer_class = RobertaTokenizer
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def setUp(self):
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super(RobertaTokenizationTest, self).setUp()
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# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
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vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n",
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"\u0120", "\u0120l", "\u0120n",
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"\u0120lo", "\u0120low", "er",
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"\u0120lowest", "\u0120newer", "\u0120wider", "<unk>"]
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vocab_tokens = dict(zip(vocab, range(len(vocab))))
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merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
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self.special_tokens_map = {"unk_token": "<unk>"}
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self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['vocab_file'])
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self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES['merges_file'])
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with open(self.vocab_file, "w", encoding="utf-8") as fp:
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fp.write(json.dumps(vocab_tokens) + "\n")
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with open(self.merges_file, "w", encoding="utf-8") as fp:
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fp.write("\n".join(merges))
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def get_tokenizer(self):
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return RobertaTokenizer.from_pretrained(self.tmpdirname, **self.special_tokens_map)
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def get_input_output_texts(self):
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input_text = u"lower newer"
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output_text = u" lower newer"
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return input_text, output_text
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def test_full_tokenizer(self):
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tokenizer = RobertaTokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map)
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text = "lower newer"
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bpe_tokens = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
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tokens = tokenizer.tokenize(text)
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self.assertListEqual(tokens, bpe_tokens)
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input_tokens = tokens + [tokenizer.unk_token]
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input_bpe_tokens = [14, 15, 10, 9, 3, 2, 15, 19]
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self.assertListEqual(
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tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
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def roberta_dict_integration_testing(self):
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tokenizer = self.get_tokenizer()
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self.assertListEqual(
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tokenizer.encode('Hello world!'),
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[0, 31414, 232, 328, 2]
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)
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self.assertListEqual(
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tokenizer.encode('Hello world! cécé herlolip 418'),
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[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]
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)
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def test_sequence_builders(self):
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tokenizer = RobertaTokenizer.from_pretrained("roberta-base")
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text = tokenizer.encode("sequence builders")
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text_2 = tokenizer.encode("multi-sequence build")
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encoded_text_from_decode = tokenizer.encode("sequence builders", add_special_tokens=True)
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encoded_pair_from_decode = tokenizer.encode("sequence builders", "multi-sequence build", add_special_tokens=True)
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encoded_sentence = tokenizer.add_special_tokens_single_sentence(text)
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encoded_pair = tokenizer.add_special_tokens_sentences_pair(text, text_2)
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assert encoded_sentence == encoded_text_from_decode
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assert encoded_pair == encoded_pair_from_decode
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if __name__ == '__main__':
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unittest.main()
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