# Copyright 2022 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. """Testing suite for the RemBert tokenizer.""" import tempfile import unittest from tests.test_tokenization_common import AddedToken, TokenizerTesterMixin from transformers import RemBertTokenizer, RemBertTokenizerFast from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers SENTENCEPIECE_UNDERLINE = "▁" SPIECE_UNDERLINE = SENTENCEPIECE_UNDERLINE # Kept for backward compatibility SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model") @require_sentencepiece @require_tokenizers class RemBertTokenizationTest(TokenizerTesterMixin, unittest.TestCase): from_pretrained_id = "google/rembert" tokenizer_class = RemBertTokenizer rust_tokenizer_class = RemBertTokenizerFast space_between_special_tokens = True test_rust_tokenizer = True test_sentencepiece_ignore_case = True pre_trained_model_path = "google/rembert" @classmethod def setUpClass(cls): super().setUpClass() tokenizer = RemBertTokenizer(SAMPLE_VOCAB) tokenizer.save_pretrained(cls.tmpdirname) # Copied from ReformerTokenizationTest.get_input_output_texts 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_get_vocab(self): vocab_keys = list(self.get_tokenizer().get_vocab().keys()) self.assertEqual(vocab_keys[0], "") self.assertEqual(vocab_keys[1], "") self.assertEqual(vocab_keys[5], "▁the") self.assertEqual(vocab_keys[2], "") def test_vocab_size(self): self.assertEqual(self.get_tokenizer().vocab_size, 1_000) def test_full_tokenizer(self): tokenizer = RemBertTokenizer(SAMPLE_VOCAB, keep_accents=True) tokens = tokenizer.tokenize("This is a test") self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"]) self.assertListEqual( tokenizer.convert_tokens_to_ids(tokens), [285, 46, 10, 170, 382], ) tokens = tokenizer.tokenize("I was born in 92000, and this is falsé.") self.assertListEqual( tokens, [SPIECE_UNDERLINE + "I",SPIECE_UNDERLINE + "was",SPIECE_UNDERLINE + "b","or","n",SPIECE_UNDERLINE + "in",SPIECE_UNDERLINE + "","9","2","0","0","0",",",SPIECE_UNDERLINE + "and",SPIECE_UNDERLINE + "this",SPIECE_UNDERLINE + "is",SPIECE_UNDERLINE + "f","al","s","é",".",],) # fmt: skip ids = tokenizer.convert_tokens_to_ids(tokens) self.assertListEqual(ids, [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]) def test_encode_decode_round_trip(self): tokenizer = RemBertTokenizer(SAMPLE_VOCAB, keep_accents=True) text = "清水寺は京都にある。" tokens = tokenizer.tokenize(text) self.assertListEqual(tokens, ["▁", "清水寺は京都にある。"]) encoded_string = tokenizer.encode(text) self.assertListEqual(encoded_string, [1000, 7, 0, 1001]) decode_text = tokenizer.convert_tokens_to_string(tokens) self.assertEqual(decode_text, text) text = "That's awesome! 🤩 #HuggingFace, 🌟 Have a great day! 🌈" tokens = tokenizer.tokenize(text) self.assertListEqual( tokens, ['▁That', "'", 's', '▁a', 'w', 'es', 'ome', '!', '▁', '🤩', '▁', '#', 'H', 'u', 'g', 'g', 'ing', 'F', 'a', 'ce', ',', '▁', '🌟', '▁H', 'a', 've', '▁a', '▁great', '▁day', '!', '▁', '🌈']) # fmt: skip decode_text = tokenizer.convert_tokens_to_string(tokens) self.assertEqual(decode_text, "That's awesome! 🤩 #HuggingFace, 🌟 Have a great day! 🌈") text = "In the sky up above" tokens = tokenizer._tokenize(text) self.assertListEqual(tokens, ["▁In", "▁the", "▁s", "k", "y", "▁up", "▁a", "b", "o", "ve"]) # fmt: skip encoded_string = tokenizer.encode(text) self.assertListEqual(encoded_string, [1000, 388, 5, 47, 45, 30, 118, 10, 65, 20, 123, 1001]) decode_text = tokenizer.convert_tokens_to_string(tokens) self.assertEqual(text, decode_text) text = "The cat. . Sat .In a room" tokens = tokenizer.tokenize(text) self.assertListEqual( tokens, ["▁The", "▁c", "at", ".", "▁", ".", "▁S", "at", "▁", "<", "s", ">", ".", "I", "n", "▁a", "▁room"] ) encoded_string = tokenizer.encode(text) self.assertListEqual( encoded_string, [1000, 68, 69, 76, 4, 7, 4, 166, 76, 7, 0, 6, 0, 4, 100, 24, 10, 136, 1001] ) decode_text = tokenizer.convert_tokens_to_string(tokens) self.assertEqual(text, decode_text) text = "Invoice #12345, dated 2023-12-01, is due on 2024-01-15." tokens = tokenizer.tokenize(text) self.assertListEqual(tokens, ['▁In', 'v', 'o', 'ic', 'e', '▁', '#', '1', '2', '34', '5', ',', '▁da', 'ted', '▁', '2', '0', '2', '3', '-', '1', '2', '-', '0', '1', ',', '▁is', '▁d', 'u', 'e', '▁on', '▁', '2', '0', '2', '4', '-', '0', '1', '-', '1', '5', '.']) # fmt: skip encoded_string = tokenizer.encode(text) self.assertListEqual(encoded_string, [1000, 388, 83, 20, 113, 15, 7, 0, 356, 602, 0, 555, 3, 417, 273, 7, 602, 347, 602, 0, 33, 356, 602, 33, 347, 356, 3, 46, 229, 51, 15, 59, 7, 602, 347, 602, 0, 33, 347, 356, 33, 356, 555, 4, 1001]) # fmt: skip decode_text = tokenizer.convert_tokens_to_string(tokens) self.assertEqual(text, decode_text) text = "Lorem ipsum dolor sit amet, consectetur adipiscing elit..." tokens = tokenizer.tokenize(text) self.assertListEqual(tokens, ['▁', 'L', 'or', 'em', '▁', 'i', 'p', 's', 'um', '▁do', 'l', 'or', '▁sit', '▁am', 'e', 't', ',', '▁con', 'se', 'c', 'te', 't', 'ur', '▁a', 'd', 'i', 'p', 'is', 'c', 'ing', '▁', 'el', 'it', '.', '.', '.']) # fmt: skip encoded_string = tokenizer.encode(text) self.assertListEqual( encoded_string, [1000, 7, 279, 55, 300, 7, 23, 29, 6, 155, 92, 27, 55, 615, 219, 15, 14, 3, 247, 114, 28, 181, 14, 108, 10, 16, 23, 29, 125, 28, 17, 7, 168, 137, 4, 4, 4, 1001] ) # fmt: skip decode_text = tokenizer.convert_tokens_to_string(tokens) self.assertEqual(text, decode_text) # for multiple language in one sentence text = "Bonjour! Hello! こんにちは!" tokens = tokenizer.tokenize(text) self.assertListEqual(tokens, ["▁B", "on", "j", "o", "ur", "!", "▁He", "ll", "o", "!", "▁", "こんにちは", "!"]) encoded_string = tokenizer.encode(text) self.assertListEqual(encoded_string, [1000, 295, 109, 999, 20, 108, 146, 156, 86, 20, 146, 7, 0, 146, 1001]) decode_text = tokenizer.convert_tokens_to_string(tokens) self.assertEqual(text, decode_text) text = "Extra spaces\tand\nline breaks\r\nshould be handled." tokens = tokenizer.tokenize(text) self.assertListEqual(tokens, ['▁E', 'x', 't', 'r', 'a', '▁sp', 'a', 'ce', 's', '▁and', '▁line', '▁b', 're', 'a', 'k', 's', '▁should', '▁be', '▁hand', 'led', '.']) # fmt: skip encoded_string = tokenizer.encode(text) self.assertListEqual( encoded_string, [1000, 454, 297, 14, 35, 18, 277, 18, 133, 6, 12, 485, 84, 56, 18, 45, 6, 173, 36, 363, 338, 4, 1001], ) decode_text = tokenizer.convert_tokens_to_string(tokens) self.assertEqual("Extra spaces and line breaks should be handled.", decode_text) def test_sequence_builders(self): tokenizer = RemBertTokenizer(SAMPLE_VOCAB) 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] + text_2 + [ tokenizer.sep_token_id ] def test_added_tokens_serialization(self): # 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.assertTrue(all(item in tokenizer.added_tokens_decoder.items() for item in expected.items())) return tokenizer new_eos = AddedToken("[NEW_EOS]", rstrip=False, lstrip=True, normalized=False, special=True) new_masked_token = AddedToken("[MASK]", lstrip=True, rstrip=False, 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, mask_token=new_masked_token ) EXPECTED_ADDED_TOKENS_DECODER = tokenizer.added_tokens_decoder with self.subTest("Hub -> Slow: Test loading a slow tokenizer from the hub)"): self.assertEqual(tokenizer._special_tokens_map["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.get_rust_tokenizer(pretrained_name, eos_token=new_eos) self.assertEqual(tokenizer_fast._special_tokens_map["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.assertTrue( all( item in tokenizer.added_tokens_decoder.items() for item in EXPECTED_ADDED_TOKENS_DECODER.items() ) ) 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 )