# Copyright 2019 HuggingFace Inc. # # 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 concurrent.futures import json import os import shutil import tempfile import unittest from tokenizers import Tokenizer, decoders, pre_tokenizers, trainers from tokenizers.models import BPE, WordLevel from transformers import AutoTokenizer, PreTrainedTokenizerFast from transformers.testing_utils import require_tokenizers @require_tokenizers class PreTrainedTokenizationFastTest(unittest.TestCase): rust_tokenizer_class = PreTrainedTokenizerFast from_pretrained_vocab_key = "tokenizer_file" @classmethod def setUpClass(cls): cls.tmpdirname = tempfile.mkdtemp() cls.model_paths = cls._create_test_tokenizers() cls.bytelevel_bpe_model_name = cls.model_paths[1] cls.tokenizers_list = [(cls.rust_tokenizer_class, path, {}) for path in cls.model_paths] @classmethod def tearDownClass(cls): shutil.rmtree(cls.tmpdirname, ignore_errors=True) @classmethod def _create_test_tokenizers(cls): paths = [] wordlevel_dir = os.path.join(cls.tmpdirname, "wordlevel_tokenizer") os.makedirs(wordlevel_dir, exist_ok=True) wl_vocab = {"[UNK]": 0, "[PAD]": 1, "hello": 2, "world": 3, "test": 4} wordlevel_tokenizer = Tokenizer(WordLevel(wl_vocab, unk_token="[UNK]")) wordlevel_tokenizer.pre_tokenizer = pre_tokenizers.Whitespace() fast_wl = PreTrainedTokenizerFast( tokenizer_object=wordlevel_tokenizer, unk_token="[UNK]", pad_token="[PAD]", cls_token="[CLS]", sep_token="[SEP]", mask_token="[MASK]", ) fast_wl.save_pretrained(wordlevel_dir) paths.append(wordlevel_dir) bpe_dir = os.path.join(cls.tmpdirname, "bytelevel_bpe_tokenizer") os.makedirs(bpe_dir, exist_ok=True) bpe_tokenizer = Tokenizer(BPE(unk_token="[UNK]")) trainer = trainers.BpeTrainer( special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"], vocab_size=100, ) corpus = ["Hello world!", "Test the byte level BPE tokenizer.", "Tokenizer fast test."] bpe_tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel() bpe_tokenizer.train_from_iterator(corpus, trainer=trainer) bpe_tokenizer.decoder = decoders.ByteLevel() fast_bpe = PreTrainedTokenizerFast( tokenizer_object=bpe_tokenizer, unk_token="[UNK]", pad_token="[PAD]", cls_token="[CLS]", sep_token="[SEP]", mask_token="[MASK]", ) fast_bpe.save_pretrained(bpe_dir) paths.append(bpe_dir) return paths @unittest.skip( "We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any model" ) def test_encode_decode_with_spaces(self): pass @unittest.skip( "We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any model" ) def test_added_tokens_serialization(self): pass @unittest.skip( "We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any model" ) def test_additional_special_tokens_serialization(self): pass def test_training_new_tokenizer(self): tmpdirname_orig = self.tmpdirname # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel. for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): try: self.tmpdirname = tempfile.mkdtemp() tokenizer = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs) tokenizer.save_pretrained(self.tmpdirname) reloaded = PreTrainedTokenizerFast.from_pretrained(self.tmpdirname) self.assertEqual(reloaded.get_vocab(), tokenizer.get_vocab()) finally: # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer # is restored shutil.rmtree(self.tmpdirname) self.tmpdirname = tmpdirname_orig def test_training_new_tokenizer_with_special_tokens_change(self): tmpdirname_orig = self.tmpdirname # Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel. for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): try: self.tmpdirname = tempfile.mkdtemp() tokenizer = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs) tokenizer.add_special_tokens({"pad_token": ""}) tokenizer.save_pretrained(self.tmpdirname) reloaded = PreTrainedTokenizerFast.from_pretrained(self.tmpdirname) self.assertEqual(reloaded.pad_token, "") finally: # Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer # is restored shutil.rmtree(self.tmpdirname) self.tmpdirname = tmpdirname_orig def test_training_new_tokenizer_with_bytelevel(self): tokenizer = self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name) toy_text_iterator = ("a" for _ in range(1000)) new_tokenizer = tokenizer.train_new_from_iterator(text_iterator=toy_text_iterator, length=1000, vocab_size=50) encoding_ids = new_tokenizer.encode("a🤗") self.assertGreater(len(encoding_ids), 0) self.assertEqual(new_tokenizer.decode(encoding_ids), "a🤗") def test_init_from_tokenizers_model(self): from tokenizers import Tokenizer sentences = ["Hello, y'all!", "How are you 😁 ? There should not be any issue right?"] tokenizer = Tokenizer.from_pretrained("google-t5/t5-base") # Enable padding tokenizer.enable_padding(pad_id=0, pad_token="", length=512, pad_to_multiple_of=8) self.assertEqual( tokenizer.padding, { "length": 512, "pad_to_multiple_of": 8, "pad_id": 0, "pad_token": "", "pad_type_id": 0, "direction": "right", }, ) fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer) tmpdirname = tempfile.mkdtemp() fast_tokenizer.save_pretrained(tmpdirname) fast_from_saved = PreTrainedTokenizerFast.from_pretrained(tmpdirname) for tok in [fast_tokenizer, fast_from_saved]: self.assertEqual(tok.pad_token_id, 0) self.assertEqual(tok.padding_side, "right") self.assertEqual(tok.pad_token, "") self.assertEqual(tok.init_kwargs["max_length"], 512) self.assertEqual(tok.init_kwargs["pad_to_multiple_of"], 8) self.assertEqual(tok(sentences, padding = True), {'input_ids': [[8774, 6, 3, 63, 31, 1748, 55, 1, 0, 0, 0, 0,0, 0, 0, 0],[ 571, 33, 25, 3, 2, 3, 58, 290, 225, 59, 36, 136, 962, 269, 58, 1]], 'token_type_ids': [[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, 0, 0, 0, 0, 0, 0, 0, 0],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}) # fmt: skip tokenizer.enable_truncation(8, stride=0, strategy="longest_first", direction="right") self.assertEqual( tokenizer.truncation, {"max_length": 8, "stride": 0, "strategy": "longest_first", "direction": "right"} ) fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer) tmpdirname = tempfile.mkdtemp() fast_tokenizer.save_pretrained(tmpdirname) fast_from_saved = PreTrainedTokenizerFast.from_pretrained(tmpdirname) for tok in [fast_tokenizer, fast_from_saved]: self.assertEqual(tok.truncation_side, "right") self.assertEqual(tok.init_kwargs["truncation_strategy"], "longest_first") self.assertEqual(tok.init_kwargs["max_length"], 8) self.assertEqual(tok.init_kwargs["stride"], 0) # NOTE even if the model has a default max_length, it is not used... # thus tok(sentences, truncation = True) does nothing and does not warn either self.assertEqual(tok(sentences, truncation = True, max_length = 8), {'input_ids': [[8774, 6, 3, 63, 31, 1748, 55, 1],[ 571, 33, 25, 3, 2, 3, 58, 1]], 'token_type_ids': [[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]]}) # fmt: skip def test_class_after_save_and_reload(self): model_id = self.model_paths[0] with tempfile.TemporaryDirectory() as temp_dir: tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True) self.assertIsInstance(tokenizer, PreTrainedTokenizerFast) tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False) self.assertIsInstance(tokenizer, PreTrainedTokenizerFast) tokenizer.save_pretrained(temp_dir) tokenizer = AutoTokenizer.from_pretrained(temp_dir, use_fast=False) self.assertIsInstance(tokenizer, PreTrainedTokenizerFast) tokenizer = AutoTokenizer.from_pretrained(temp_dir, use_fast=True) self.assertIsInstance(tokenizer, PreTrainedTokenizerFast) @require_tokenizers class TokenizerVersioningTest(unittest.TestCase): def test_local_versioning(self): tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-cased") json_tokenizer = json.loads(tokenizer._tokenizer.to_str()) json_tokenizer["model"]["vocab"]["huggingface"] = len(tokenizer) with tempfile.TemporaryDirectory() as tmp_dir: # Hack to save this in the tokenizer_config.json tokenizer.init_kwargs["fast_tokenizer_files"] = ["tokenizer.4.0.0.json"] tokenizer.save_pretrained(tmp_dir) json.dump(json_tokenizer, open(os.path.join(tmp_dir, "tokenizer.4.0.0.json"), "w")) # This should pick the new tokenizer file as the version of Transformers is > 4.0.0 new_tokenizer = AutoTokenizer.from_pretrained(tmp_dir) self.assertEqual(len(new_tokenizer), len(tokenizer) + 1) json_tokenizer = json.loads(new_tokenizer._tokenizer.to_str()) self.assertIn("huggingface", json_tokenizer["model"]["vocab"]) # Will need to be adjusted if we reach v42 and this test is still here. # Should pick the old tokenizer file as the version of Transformers is < 4.0.0 shutil.move(os.path.join(tmp_dir, "tokenizer.4.0.0.json"), os.path.join(tmp_dir, "tokenizer.42.0.0.json")) tokenizer.init_kwargs["fast_tokenizer_files"] = ["tokenizer.42.0.0.json"] tokenizer.save_pretrained(tmp_dir) new_tokenizer = AutoTokenizer.from_pretrained(tmp_dir) self.assertEqual(len(new_tokenizer), len(tokenizer)) json_tokenizer = json.loads(new_tokenizer._tokenizer.to_str()) self.assertNotIn("huggingface", json_tokenizer["model"]["vocab"]) def test_repo_versioning(self): # This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower. repo = "hf-internal-testing/test-two-tokenizers" # This should pick the new tokenizer file as the version of Transformers is > 4.0.0 tokenizer = AutoTokenizer.from_pretrained(repo) self.assertEqual(len(tokenizer), 28997) json_tokenizer = json.loads(tokenizer._tokenizer.to_str()) self.assertIn("huggingface", json_tokenizer["model"]["vocab"]) # Testing an older version by monkey-patching the version in the module it's used. import transformers as old_transformers old_transformers.tokenization_utils_base.__version__ = "3.0.0" old_tokenizer = old_transformers.models.auto.AutoTokenizer.from_pretrained(repo) self.assertEqual(len(old_tokenizer), 28996) json_tokenizer = json.loads(old_tokenizer._tokenizer.to_str()) self.assertNotIn("huggingface", json_tokenizer["model"]["vocab"]) @require_tokenizers class ReduceMutableBorrowTests(unittest.TestCase): def test_async_share_tokenizer(self): # See https://github.com/huggingface/transformers/pull/12550 # and https://github.com/huggingface/tokenizers/issues/537 tokenizer = PreTrainedTokenizerFast.from_pretrained("robot-test/dummy-tokenizer-wordlevel") text = "The Matrix is a 1999 science fiction action film." with concurrent.futures.ThreadPoolExecutor() as executor: futures = [executor.submit(self.fetch, tokenizer, text) for i in range(10)] return_value = [future.result() for future in futures] self.assertEqual(return_value, [[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10)]) def fetch(self, tokenizer, text): return tokenizer.encode(text, truncation="longest_first", padding="longest")