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| # Copyright 2020 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 unittest | |
| from transformers import PegasusTokenizer, PegasusTokenizerFast | |
| from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow | |
| from transformers.utils import cached_property | |
| from ...test_tokenization_common import TokenizerTesterMixin | |
| SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece_no_bos.model") | |
| class PegasusTokenizationTest(TokenizerTesterMixin, unittest.TestCase): | |
| tokenizer_class = PegasusTokenizer | |
| rust_tokenizer_class = PegasusTokenizerFast | |
| test_rust_tokenizer = True | |
| test_sentencepiece = True | |
| def setUp(self): | |
| super().setUp() | |
| # We have a SentencePiece fixture for testing | |
| tokenizer = PegasusTokenizer(SAMPLE_VOCAB) | |
| tokenizer.save_pretrained(self.tmpdirname) | |
| def _large_tokenizer(self): | |
| return PegasusTokenizer.from_pretrained("google/pegasus-large") | |
| def get_tokenizer(self, **kwargs) -> PegasusTokenizer: | |
| return PegasusTokenizer.from_pretrained(self.tmpdirname, **kwargs) | |
| def get_input_output_texts(self, tokenizer): | |
| return ("This is a test", "This is a test") | |
| def test_convert_token_and_id(self): | |
| """Test ``_convert_token_to_id`` and ``_convert_id_to_token``.""" | |
| token = "</s>" | |
| 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], "<pad>") | |
| self.assertEqual(vocab_keys[1], "</s>") | |
| self.assertEqual(vocab_keys[-1], "v") | |
| self.assertEqual(len(vocab_keys), 1_103) | |
| def test_vocab_size(self): | |
| self.assertEqual(self.get_tokenizer().vocab_size, 1_103) | |
| def test_mask_tokens_rust_pegasus(self): | |
| rust_tokenizer = self.rust_tokenizer_class.from_pretrained(self.tmpdirname) | |
| py_tokenizer = self.tokenizer_class.from_pretrained(self.tmpdirname) | |
| raw_input_str = ( | |
| "Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important" | |
| " </s> <pad> <pad> <pad>" | |
| ) | |
| rust_ids = rust_tokenizer([raw_input_str], return_tensors=None, add_special_tokens=False).input_ids[0] | |
| py_ids = py_tokenizer([raw_input_str], return_tensors=None, add_special_tokens=False).input_ids[0] | |
| self.assertListEqual(py_ids, rust_ids) | |
| def test_large_mask_tokens(self): | |
| tokenizer = self._large_tokenizer | |
| # <mask_1> masks whole sentence while <mask_2> masks single word | |
| raw_input_str = "<mask_1> To ensure a <mask_2> flow of bank resolutions." | |
| desired_result = [2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1] | |
| ids = tokenizer([raw_input_str], return_tensors=None).input_ids[0] | |
| self.assertListEqual(desired_result, ids) | |
| def test_large_tokenizer_settings(self): | |
| tokenizer = self._large_tokenizer | |
| # The tracebacks for the following asserts are **better** without messages or self.assertEqual | |
| assert tokenizer.vocab_size == 96103 | |
| assert tokenizer.pad_token_id == 0 | |
| assert tokenizer.eos_token_id == 1 | |
| assert tokenizer.offset == 103 | |
| assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105 | |
| assert tokenizer.unk_token == "<unk>" | |
| assert tokenizer.model_max_length == 1024 | |
| raw_input_str = "To ensure a smooth flow of bank resolutions." | |
| desired_result = [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1] | |
| ids = tokenizer([raw_input_str], return_tensors=None).input_ids[0] | |
| self.assertListEqual(desired_result, ids) | |
| assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"] | |
| def test_large_seq2seq_truncation(self): | |
| src_texts = ["This is going to be way too long." * 150, "short example"] | |
| tgt_texts = ["not super long but more than 5 tokens", "tiny"] | |
| batch = self._large_tokenizer(src_texts, padding=True, truncation=True, return_tensors="pt") | |
| targets = self._large_tokenizer( | |
| text_target=tgt_texts, max_length=5, padding=True, truncation=True, return_tensors="pt" | |
| ) | |
| assert batch.input_ids.shape == (2, 1024) | |
| assert batch.attention_mask.shape == (2, 1024) | |
| assert targets["input_ids"].shape == (2, 5) | |
| assert len(batch) == 2 # input_ids, attention_mask. | |
| def test_tokenizer_integration(self): | |
| # fmt: off | |
| expected_encoding = {'input_ids': [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 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, 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], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501 | |
| # fmt: on | |
| self.tokenizer_integration_test_util( | |
| expected_encoding=expected_encoding, | |
| model_name="google/bigbird-pegasus-large-arxiv", | |
| revision="ba85d0851d708441f91440d509690f1ab6353415", | |
| ) | |
| class BigBirdPegasusTokenizationTest(TokenizerTesterMixin, unittest.TestCase): | |
| tokenizer_class = PegasusTokenizer | |
| rust_tokenizer_class = PegasusTokenizerFast | |
| test_rust_tokenizer = True | |
| test_sentencepiece = True | |
| def setUp(self): | |
| super().setUp() | |
| # We have a SentencePiece fixture for testing | |
| tokenizer = PegasusTokenizer(SAMPLE_VOCAB, offset=0, mask_token_sent=None, mask_token="[MASK]") | |
| tokenizer.save_pretrained(self.tmpdirname) | |
| def _large_tokenizer(self): | |
| return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv") | |
| def get_tokenizer(self, **kwargs) -> PegasusTokenizer: | |
| return PegasusTokenizer.from_pretrained(self.tmpdirname, **kwargs) | |
| def get_input_output_texts(self, tokenizer): | |
| return ("This is a test", "This is a test") | |
| def test_mask_tokens_rust_pegasus(self): | |
| rust_tokenizer = self.rust_tokenizer_class.from_pretrained(self.tmpdirname) | |
| py_tokenizer = self.tokenizer_class.from_pretrained(self.tmpdirname) | |
| raw_input_str = ( | |
| "Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>" | |
| " <pad> <pad> <pad>" | |
| ) | |
| rust_ids = rust_tokenizer([raw_input_str], return_tensors=None, add_special_tokens=False).input_ids[0] | |
| py_ids = py_tokenizer([raw_input_str], return_tensors=None, add_special_tokens=False).input_ids[0] | |
| self.assertListEqual(py_ids, rust_ids) | |
| def test_large_seq2seq_truncation(self): | |
| src_texts = ["This is going to be way too long." * 1000, "short example"] | |
| tgt_texts = ["not super long but more than 5 tokens", "tiny"] | |
| batch = self._large_tokenizer(src_texts, padding=True, truncation=True, return_tensors="pt") | |
| targets = self._large_tokenizer( | |
| text_target=tgt_texts, max_length=5, padding=True, truncation=True, return_tensors="pt" | |
| ) | |
| assert batch.input_ids.shape == (2, 4096) | |
| assert batch.attention_mask.shape == (2, 4096) | |
| assert targets["input_ids"].shape == (2, 5) | |
| assert len(batch) == 2 # input_ids, attention_mask. | |
| def test_equivalence_to_orig_tokenizer(self): | |
| """ | |
| To run with original TF tokenizer: | |
| !wget https://github.com/google-research/bigbird/raw/master/bigbird/vocab/pegasus.model | |
| !pip install tensorflow-text | |
| import tensorflow.compat.v2 as tf | |
| import tensorflow_text as tft | |
| VOCAB_FILE = "./pegasus.model" | |
| tf.enable_v2_behavior() | |
| test_str = "This is an example string that is used to test the original TF implementation against the HF implementation" | |
| tokenizer = tft.SentencepieceTokenizer(model=tf.io.gfile.GFile(VOCAB_FILE, "rb").read()) | |
| tokenizer.tokenize(test_str) | |
| """ | |
| test_str = ( | |
| "This is an example string that is used to test the original TF implementation against the HF" | |
| " implementation" | |
| ) | |
| token_ids = self._large_tokenizer(test_str).input_ids | |
| self.assertListEqual( | |
| token_ids, | |
| [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1], | |
| ) | |