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| # coding=utf-8 | |
| # Copyright 2020 Ecole Polytechnique and HuggingFace Inc. team. | |
| # | |
| # 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 unittest | |
| from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding | |
| from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow | |
| from ...test_tokenization_common import TokenizerTesterMixin | |
| # see https://github.com/huggingface/transformers/issues/11457 | |
| class BarthezTokenizationTest(TokenizerTesterMixin, unittest.TestCase): | |
| tokenizer_class = BarthezTokenizer | |
| rust_tokenizer_class = BarthezTokenizerFast | |
| test_rust_tokenizer = True | |
| test_sentencepiece = True | |
| def setUp(self): | |
| super().setUp() | |
| tokenizer = BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez") | |
| tokenizer.save_pretrained(self.tmpdirname) | |
| tokenizer.save_pretrained(self.tmpdirname, legacy_format=False) | |
| self.tokenizer = tokenizer | |
| def test_convert_token_and_id(self): | |
| """Test ``_convert_token_to_id`` and ``_convert_id_to_token``.""" | |
| token = "<pad>" | |
| token_id = 1 | |
| self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id) | |
| self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token) | |
| def test_get_vocab(self): | |
| vocab_keys = list(self.get_tokenizer().get_vocab().keys()) | |
| self.assertEqual(vocab_keys[0], "<s>") | |
| self.assertEqual(vocab_keys[1], "<pad>") | |
| self.assertEqual(vocab_keys[-1], "<mask>") | |
| self.assertEqual(len(vocab_keys), 101_122) | |
| def test_vocab_size(self): | |
| self.assertEqual(self.get_tokenizer().vocab_size, 101_122) | |
| def test_prepare_batch(self): | |
| src_text = ["A long paragraph for summarization.", "Another paragraph for summarization."] | |
| expected_src_tokens = [0, 57, 3018, 70307, 91, 2] | |
| batch = self.tokenizer( | |
| src_text, max_length=len(expected_src_tokens), padding=True, truncation=True, return_tensors="pt" | |
| ) | |
| self.assertIsInstance(batch, BatchEncoding) | |
| self.assertEqual((2, 6), batch.input_ids.shape) | |
| self.assertEqual((2, 6), batch.attention_mask.shape) | |
| result = batch.input_ids.tolist()[0] | |
| self.assertListEqual(expected_src_tokens, result) | |
| def test_rust_and_python_full_tokenizers(self): | |
| if not self.test_rust_tokenizer: | |
| return | |
| tokenizer = self.get_tokenizer() | |
| rust_tokenizer = self.get_rust_tokenizer() | |
| sequence = "I was born in 92000, and this is falsé." | |
| tokens = tokenizer.tokenize(sequence) | |
| rust_tokens = rust_tokenizer.tokenize(sequence) | |
| self.assertListEqual(tokens, rust_tokens) | |
| ids = tokenizer.encode(sequence, add_special_tokens=False) | |
| rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False) | |
| self.assertListEqual(ids, rust_ids) | |
| rust_tokenizer = self.get_rust_tokenizer() | |
| ids = tokenizer.encode(sequence) | |
| rust_ids = rust_tokenizer.encode(sequence) | |
| self.assertListEqual(ids, rust_ids) | |
| def test_tokenizer_integration(self): | |
| # fmt: off | |
| expected_encoding = {'input_ids': [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501 | |
| # fmt: on | |
| # moussaKam/mbarthez is a french model. So we also use french texts. | |
| sequences = [ | |
| "Le transformeur est un modèle d'apprentissage profond introduit en 2017, " | |
| "utilisé principalement dans le domaine du traitement automatique des langues (TAL).", | |
| "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus " | |
| "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches " | |
| "telles que la traduction et la synthèse de texte.", | |
| ] | |
| self.tokenizer_integration_test_util( | |
| expected_encoding=expected_encoding, | |
| model_name="moussaKam/mbarthez", | |
| revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6", | |
| sequences=sequences, | |
| ) | |