132 lines
5.8 KiB
Python
132 lines
5.8 KiB
Python
# coding=utf-8
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# Copyright 2018 HuggingFace Inc. team.
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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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import os
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import unittest
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from transformers import CamembertTokenizer, CamembertTokenizerFast
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from transformers.file_utils import is_torch_available
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from transformers.testing_utils import require_sentencepiece, require_tokenizers, slow
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from .test_tokenization_common import TokenizerTesterMixin
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SAMPLE_VOCAB = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/test_sentencepiece.model")
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SAMPLE_BPE_VOCAB = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/test_sentencepiece_bpe.model")
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FRAMEWORK = "pt" if is_torch_available() else "tf"
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@require_sentencepiece
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@require_tokenizers
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class CamembertTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = CamembertTokenizer
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rust_tokenizer_class = CamembertTokenizerFast
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test_rust_tokenizer = True
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test_sentencepiece = True
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def setUp(self):
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super().setUp()
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# We have a SentencePiece fixture for testing
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tokenizer = CamembertTokenizer(SAMPLE_VOCAB)
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tokenizer.save_pretrained(self.tmpdirname)
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def test_convert_token_and_id(self):
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"""Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
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token = "<pad>"
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token_id = 1
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self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
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self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
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def test_get_vocab(self):
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vocab_keys = list(self.get_tokenizer().get_vocab().keys())
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self.assertEqual(vocab_keys[0], "<s>NOTUSED")
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self.assertEqual(vocab_keys[1], "<pad>")
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self.assertEqual(vocab_keys[-1], "<mask>")
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self.assertEqual(len(vocab_keys), 1_004)
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def test_vocab_size(self):
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self.assertEqual(self.get_tokenizer().vocab_size, 1_005)
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def test_rust_and_python_bpe_tokenizers(self):
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tokenizer = CamembertTokenizer(SAMPLE_BPE_VOCAB)
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tokenizer.save_pretrained(self.tmpdirname)
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rust_tokenizer = CamembertTokenizerFast.from_pretrained(self.tmpdirname)
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sequence = "I was born in 92000, and this is falsé."
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ids = tokenizer.encode(sequence)
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rust_ids = rust_tokenizer.encode(sequence)
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self.assertListEqual(ids, rust_ids)
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ids = tokenizer.encode(sequence, add_special_tokens=False)
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rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
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self.assertListEqual(ids, rust_ids)
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# <unk> tokens are not the same for `rust` than for `slow`.
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# Because spm gives back raw token instead of `unk` in EncodeAsPieces
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# tokens = tokenizer.tokenize(sequence)
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tokens = tokenizer.convert_ids_to_tokens(ids)
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rust_tokens = rust_tokenizer.tokenize(sequence)
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self.assertListEqual(tokens, rust_tokens)
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def test_rust_and_python_full_tokenizers(self):
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if not self.test_rust_tokenizer:
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return
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tokenizer = self.get_tokenizer()
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rust_tokenizer = self.get_rust_tokenizer()
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sequence = "I was born in 92000, and this is falsé."
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tokens = tokenizer.tokenize(sequence)
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rust_tokens = rust_tokenizer.tokenize(sequence)
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self.assertListEqual(tokens, rust_tokens)
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ids = tokenizer.encode(sequence, add_special_tokens=False)
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rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False)
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self.assertListEqual(ids, rust_ids)
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rust_tokenizer = self.get_rust_tokenizer()
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ids = tokenizer.encode(sequence)
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rust_ids = rust_tokenizer.encode(sequence)
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self.assertListEqual(ids, rust_ids)
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@slow
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def test_tokenizer_integration(self):
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# fmt: off
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expected_encoding = {'input_ids': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 27575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 22804, 18818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 10326, 24, 2267, 20, 416, 5072, 15612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], '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, 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]]} # noqa: E501
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# fmt: on
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# camembert is a french model. So we also use french texts.
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sequences = [
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"Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
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"utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
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"À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
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"pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
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"telles que la traduction et la synthèse de texte.",
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]
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self.tokenizer_integration_test_util(
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expected_encoding=expected_encoding,
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model_name="camembert-base",
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revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf",
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sequences=sequences,
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)
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