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| # coding=utf-8 | |
| # Copyright 2021 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 os | |
| import tempfile | |
| import unittest | |
| from typing import List | |
| from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer | |
| from transformers.testing_utils import require_tokenizers | |
| from transformers.tokenization_utils import PreTrainedTokenizer | |
| from transformers.tokenization_utils_base import PreTrainedTokenizerBase | |
| class ESMTokenizationTest(unittest.TestCase): | |
| tokenizer_class = EsmTokenizer | |
| def setUp(self): | |
| super().setUp() | |
| self.tmpdirname = tempfile.mkdtemp() | |
| # fmt: off | |
| vocab_tokens: List[str] = ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"] # noqa: E501 | |
| # fmt: on | |
| self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) | |
| with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer: | |
| vocab_writer.write("".join([x + "\n" for x in vocab_tokens])) | |
| def get_tokenizers(self, **kwargs) -> List[PreTrainedTokenizerBase]: | |
| return [self.get_tokenizer(**kwargs)] | |
| def get_tokenizer(self, **kwargs) -> PreTrainedTokenizer: | |
| return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs) | |
| def test_tokenizer_single_example(self): | |
| tokenizer = self.tokenizer_class(self.vocab_file) | |
| tokens = tokenizer.tokenize("LAGVS") | |
| self.assertListEqual(tokens, ["L", "A", "G", "V", "S"]) | |
| self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [4, 5, 6, 7, 8]) | |
| def test_tokenizer_encode_single(self): | |
| tokenizer = self.tokenizer_class(self.vocab_file) | |
| seq = "LAGVS" | |
| self.assertListEqual(tokenizer.encode(seq), [0, 4, 5, 6, 7, 8, 2]) | |
| def test_tokenizer_call_no_pad(self): | |
| tokenizer = self.tokenizer_class(self.vocab_file) | |
| seq_batch = ["LAGVS", "WCB"] | |
| tokens_batch = tokenizer(seq_batch, padding=False)["input_ids"] | |
| self.assertListEqual(tokens_batch, [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]) | |
| def test_tokenizer_call_pad(self): | |
| tokenizer = self.tokenizer_class(self.vocab_file) | |
| seq_batch = ["LAGVS", "WCB"] | |
| tokens_batch = tokenizer(seq_batch, padding=True)["input_ids"] | |
| self.assertListEqual(tokens_batch, [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]) | |
| def test_tokenize_special_tokens(self): | |
| """Test `tokenize` with special tokens.""" | |
| tokenizers = self.get_tokenizers(fast=True) | |
| for tokenizer in tokenizers: | |
| with self.subTest(f"{tokenizer.__class__.__name__}"): | |
| SPECIAL_TOKEN_1 = "<unk>" | |
| SPECIAL_TOKEN_2 = "<mask>" | |
| token_1 = tokenizer.tokenize(SPECIAL_TOKEN_1) | |
| token_2 = tokenizer.tokenize(SPECIAL_TOKEN_2) | |
| self.assertEqual(len(token_1), 1) | |
| self.assertEqual(len(token_2), 1) | |
| self.assertEqual(token_1[0], SPECIAL_TOKEN_1) | |
| self.assertEqual(token_2[0], SPECIAL_TOKEN_2) | |