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| # coding=utf-8 | |
| # Copyright 2023 The HuggingFace Inc. and Baidu 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. | |
| """ Testing suite for the PyTorch ErnieM model. """ | |
| import unittest | |
| from transformers import ErnieMTokenizer | |
| from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow | |
| from ...test_tokenization_common import TokenizerTesterMixin | |
| SAMPLE_VOCAB = get_tests_dir("fixtures/spiece.model") | |
| class ErnieMTokenizationTest(TokenizerTesterMixin, unittest.TestCase): | |
| tokenizer_class = ErnieMTokenizer | |
| test_seq2seq = False | |
| test_sentencepiece = True | |
| test_rust_tokenizer = False | |
| test_sentencepiece_ignore_case = False | |
| def setUp(self): | |
| super().setUp() | |
| # We have a SentencePiece fixture for testing | |
| tokenizer = ErnieMTokenizer(SAMPLE_VOCAB, unk_token="<unk>", pad_token="<pad>") | |
| tokenizer.save_pretrained(self.tmpdirname) | |
| def get_input_output_texts(self, tokenizer): | |
| input_text = "this is a test" | |
| output_text = "this is a test" | |
| return input_text, output_text | |
| def test_convert_token_and_id(self): | |
| """Test ``_convert_token_to_id`` and ``_convert_id_to_token``.""" | |
| token = "<pad>" | |
| token_id = 0 | |
| 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], "<pad>") | |
| self.assertEqual(vocab_keys[1], "<unk>") | |
| self.assertEqual(vocab_keys[-1], "▁eloquent") | |
| self.assertEqual(len(vocab_keys), 30_000) | |
| def test_vocab_size(self): | |
| self.assertEqual(self.get_tokenizer().vocab_size, 30_000) | |
| 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_full_tokenizer(self): | |
| tokenizer = ErnieMTokenizer(SAMPLE_VOCAB, do_lower_case=True, unk_token="<unk>", pad_token="<pad>") | |
| tokens = tokenizer.tokenize("This is a test") | |
| self.assertListEqual(tokens, ["▁this", "▁is", "▁a", "▁test"]) | |
| self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [48, 25, 21, 1289]) | |
| tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.") | |
| # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9' | |
| self.assertListEqual( | |
| tokens, ["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."] | |
| ) | |
| ids = tokenizer.convert_tokens_to_ids(tokens) | |
| self.assertListEqual(ids, [31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]) | |
| back_tokens = tokenizer.convert_ids_to_tokens(ids) | |
| self.assertListEqual( | |
| back_tokens, | |
| ["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."], | |
| ) | |
| def test_sequence_builders(self): | |
| tokenizer = ErnieMTokenizer(SAMPLE_VOCAB, unk_token="<unk>", pad_token="<pad>") | |
| text = tokenizer.encode("sequence builders") | |
| text_2 = tokenizer.encode("multi-sequence build") | |
| encoded_sentence = tokenizer.build_inputs_with_special_tokens(text) | |
| encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2) | |
| assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] | |
| assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [ | |
| tokenizer.sep_token_id | |
| ] + text_2 + [tokenizer.sep_token_id] | |
| def test_tokenizer_integration(self): | |
| # fmt: off | |
| expected_encoding = {'input_ids': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 9, 304, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 5, 5, 5, 16, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 122009, 115774, 23, 3559, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 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, 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], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1]]} | |
| # fmt: on | |
| self.tokenizer_integration_test_util( | |
| expected_encoding=expected_encoding, | |
| model_name="susnato/ernie-m-base_pytorch", | |
| sequences=[ | |
| "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides " | |
| "general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural " | |
| "Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained " | |
| "models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.", | |
| "BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly " | |
| "conditioning on both left and right context in all layers.", | |
| "The quick brown fox jumps over the lazy dog.", | |
| ], | |
| ) | |