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| # coding=utf-8 | |
| # Copyright 2022 The HuggingFace Team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import json | |
| import os | |
| import unittest | |
| from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer | |
| from transformers.testing_utils import slow | |
| from ...test_tokenization_common import TokenizerTesterMixin | |
| class BioGptTokenizationTest(TokenizerTesterMixin, unittest.TestCase): | |
| tokenizer_class = BioGptTokenizer | |
| test_rust_tokenizer = False | |
| def setUp(self): | |
| super().setUp() | |
| # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt | |
| vocab = [ | |
| "l", | |
| "o", | |
| "w", | |
| "e", | |
| "r", | |
| "s", | |
| "t", | |
| "i", | |
| "d", | |
| "n", | |
| "w</w>", | |
| "r</w>", | |
| "t</w>", | |
| "lo", | |
| "low", | |
| "er</w>", | |
| "low</w>", | |
| "lowest</w>", | |
| "newer</w>", | |
| "wider</w>", | |
| "<unk>", | |
| ] | |
| vocab_tokens = dict(zip(vocab, range(len(vocab)))) | |
| merges = ["l o 123", "lo w 1456", "e r</w> 1789", ""] | |
| self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) | |
| self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"]) | |
| with open(self.vocab_file, "w") as fp: | |
| fp.write(json.dumps(vocab_tokens)) | |
| with open(self.merges_file, "w") as fp: | |
| fp.write("\n".join(merges)) | |
| def get_input_output_texts(self, tokenizer): | |
| input_text = "lower newer" | |
| output_text = "lower newer" | |
| return input_text, output_text | |
| def test_full_tokenizer(self): | |
| """Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt""" | |
| tokenizer = BioGptTokenizer(self.vocab_file, self.merges_file) | |
| text = "lower" | |
| bpe_tokens = ["low", "er</w>"] | |
| tokens = tokenizer.tokenize(text) | |
| self.assertListEqual(tokens, bpe_tokens) | |
| input_tokens = tokens + ["<unk>"] | |
| input_bpe_tokens = [14, 15, 20] | |
| self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens) | |
| def test_sequence_builders(self): | |
| tokenizer = BioGptTokenizer.from_pretrained("microsoft/biogpt") | |
| text = tokenizer.encode("sequence builders", add_special_tokens=False) | |
| text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False) | |
| encoded_sentence = tokenizer.build_inputs_with_special_tokens(text) | |
| encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2) | |
| self.assertTrue(encoded_sentence == [2] + text) | |
| self.assertTrue(encoded_pair == [2] + text + [2] + text_2) | |