# coding=utf-8 # Copyright 2018 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 tempfile import unittest from transformers import AddedToken, CamembertTokenizer, CamembertTokenizerFast from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow from transformers.utils import is_torch_available from ...test_tokenization_common import TokenizerTesterMixin SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model") SAMPLE_BPE_VOCAB = get_tests_dir("fixtures/test_sentencepiece_bpe.model") FRAMEWORK = "pt" if is_torch_available() else "tf" @require_sentencepiece @require_tokenizers class CamembertTokenizationTest(TokenizerTesterMixin, unittest.TestCase): tokenizer_class = CamembertTokenizer rust_tokenizer_class = CamembertTokenizerFast test_rust_tokenizer = True test_sentencepiece = True def setUp(self): super().setUp() # We have a SentencePiece fixture for testing tokenizer = CamembertTokenizer(SAMPLE_VOCAB) tokenizer.save_pretrained(self.tmpdirname) @unittest.skip( "Token maps are not equal because someone set the probability of ('NOTUSED', -100), so it's never encoded for fast" ) def test_special_tokens_map_equal(self): return def test_convert_token_and_id(self): """Test ``_convert_token_to_id`` and ``_convert_id_to_token``.""" token = "" token_id = 1 # 1 is the offset id, but in the spm vocab it's 3 self.assertEqual(self.get_tokenizer().convert_tokens_to_ids(token), token_id) self.assertEqual(self.get_tokenizer().convert_ids_to_tokens(token_id), token) def test_get_vocab(self): vocab_keys = list(self.get_tokenizer().get_vocab().keys()) self.assertEqual(vocab_keys[0], "NOTUSED") self.assertEqual(vocab_keys[1], "") self.assertEqual(vocab_keys[-1], "") self.assertEqual(len(vocab_keys), 1_005) def test_vocab_size(self): self.assertEqual(self.get_tokenizer().vocab_size, 1_000) def test_rust_and_python_bpe_tokenizers(self): tokenizer = CamembertTokenizer(SAMPLE_BPE_VOCAB) tokenizer.save_pretrained(self.tmpdirname) rust_tokenizer = CamembertTokenizerFast.from_pretrained(self.tmpdirname) sequence = "I was born in 92000, and this is falsé." ids = tokenizer.encode(sequence) rust_ids = rust_tokenizer.encode(sequence) self.assertListEqual(ids, rust_ids) ids = tokenizer.encode(sequence, add_special_tokens=False) rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False) self.assertListEqual(ids, rust_ids) # tokens are not the same for `rust` than for `slow`. # Because spm gives back raw token instead of `unk` in EncodeAsPieces # tokens = tokenizer.tokenize(sequence) tokens = tokenizer.convert_ids_to_tokens(ids) rust_tokens = rust_tokenizer.tokenize(sequence) self.assertListEqual(tokens, rust_tokens) 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) @slow def test_tokenizer_integration(self): 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]]} # fmt: skip # camembert 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="camembert-base", revision="3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf", sequences=sequences, ) # Overwritten because we have to use from slow (online pretrained is wrong, the tokenizer.json has a whole) def test_added_tokens_serialization(self): self.maxDiff = None # Utility to test the added vocab def _test_added_vocab_and_eos(expected, tokenizer_class, expected_eos, temp_dir): tokenizer = tokenizer_class.from_pretrained(temp_dir) self.assertTrue(str(expected_eos) not in tokenizer.additional_special_tokens) self.assertIn(new_eos, tokenizer.added_tokens_decoder.values()) self.assertEqual(tokenizer.added_tokens_decoder[tokenizer.eos_token_id], new_eos) self.assertDictEqual(expected, tokenizer.added_tokens_decoder) return tokenizer new_eos = AddedToken("[NEW_EOS]", rstrip=False, lstrip=True, normalized=False) for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): # Load a slow tokenizer from the hub, init with the new token for fast to also include it tokenizer = self.tokenizer_class.from_pretrained(pretrained_name, eos_token=new_eos) EXPECTED_ADDED_TOKENS_DECODER = tokenizer.added_tokens_decoder with self.subTest("Hub -> Slow: Test loading a slow tokenizer from the hub)"): self.assertEqual(tokenizer._eos_token, new_eos) self.assertIn(new_eos, list(tokenizer.added_tokens_decoder.values())) with tempfile.TemporaryDirectory() as tmp_dir_2: tokenizer.save_pretrained(tmp_dir_2) with self.subTest( "Hub -> Slow -> Slow: Test saving this slow tokenizer and reloading it in the fast class" ): _test_added_vocab_and_eos( EXPECTED_ADDED_TOKENS_DECODER, self.tokenizer_class, new_eos, tmp_dir_2 ) if self.rust_tokenizer_class is not None: with self.subTest( "Hub -> Slow -> Fast: Test saving this slow tokenizer and reloading it in the fast class" ): tokenizer_fast = _test_added_vocab_and_eos( EXPECTED_ADDED_TOKENS_DECODER, self.rust_tokenizer_class, new_eos, tmp_dir_2 ) with tempfile.TemporaryDirectory() as tmp_dir_3: tokenizer_fast.save_pretrained(tmp_dir_3) with self.subTest( "Hub -> Slow -> Fast -> Fast: Test saving this fast tokenizer and reloading it in the fast class" ): _test_added_vocab_and_eos( EXPECTED_ADDED_TOKENS_DECODER, self.rust_tokenizer_class, new_eos, tmp_dir_3 ) with self.subTest( "Hub -> Slow -> Fast -> Slow: Test saving this slow tokenizer and reloading it in the slow class" ): _test_added_vocab_and_eos( EXPECTED_ADDED_TOKENS_DECODER, self.rust_tokenizer_class, new_eos, tmp_dir_3 ) with self.subTest("Hub -> Fast: Test loading a fast tokenizer from the hub)"): if self.rust_tokenizer_class is not None: tokenizer_fast = self.rust_tokenizer_class.from_pretrained( pretrained_name, eos_token=new_eos, from_slow=True ) self.assertEqual(tokenizer_fast._eos_token, new_eos) self.assertIn(new_eos, list(tokenizer_fast.added_tokens_decoder.values())) # We can't test the following because for BC we kept the default rstrip lstrip in slow not fast. Will comment once normalization is alright with self.subTest("Hub -> Fast == Hub -> Slow: make sure slow and fast tokenizer match"): self.assertDictEqual(EXPECTED_ADDED_TOKENS_DECODER, tokenizer_fast.added_tokens_decoder) EXPECTED_ADDED_TOKENS_DECODER = tokenizer_fast.added_tokens_decoder with tempfile.TemporaryDirectory() as tmp_dir_4: tokenizer_fast.save_pretrained(tmp_dir_4) with self.subTest("Hub -> Fast -> Fast: saving Fast1 locally and loading"): _test_added_vocab_and_eos( EXPECTED_ADDED_TOKENS_DECODER, self.rust_tokenizer_class, new_eos, tmp_dir_4 ) with self.subTest("Hub -> Fast -> Slow: saving Fast1 locally and loading"): _test_added_vocab_and_eos( EXPECTED_ADDED_TOKENS_DECODER, self.tokenizer_class, new_eos, tmp_dir_4 )