# coding=utf-8 # 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 os import shutil import tempfile import unittest import pytest from transformers import ( BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, AutoTokenizer, BertTokenizer, BertTokenizerFast, CTRLTokenizer, GPT2Tokenizer, GPT2TokenizerFast, PreTrainedTokenizerFast, RobertaTokenizer, RobertaTokenizerFast, ) from transformers.models.auto.configuration_auto import AutoConfig from transformers.models.auto.tokenization_auto import ( TOKENIZER_MAPPING, get_tokenizer_config, tokenizer_class_from_name, ) from transformers.models.roberta.configuration_roberta import RobertaConfig from transformers.testing_utils import ( DUMMY_DIFF_TOKENIZER_IDENTIFIER, DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, require_tokenizers, slow, ) class AutoTokenizerTest(unittest.TestCase): @slow def test_tokenizer_from_pretrained(self): for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x): tokenizer = AutoTokenizer.from_pretrained(model_name) self.assertIsNotNone(tokenizer) self.assertIsInstance(tokenizer, (BertTokenizer, BertTokenizerFast)) self.assertGreater(len(tokenizer), 0) for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys(): tokenizer = AutoTokenizer.from_pretrained(model_name) self.assertIsNotNone(tokenizer) self.assertIsInstance(tokenizer, (GPT2Tokenizer, GPT2TokenizerFast)) self.assertGreater(len(tokenizer), 0) def test_tokenizer_from_pretrained_identifier(self): tokenizer = AutoTokenizer.from_pretrained(SMALL_MODEL_IDENTIFIER) self.assertIsInstance(tokenizer, (BertTokenizer, BertTokenizerFast)) self.assertEqual(tokenizer.vocab_size, 12) def test_tokenizer_from_model_type(self): tokenizer = AutoTokenizer.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER) self.assertIsInstance(tokenizer, (RobertaTokenizer, RobertaTokenizerFast)) self.assertEqual(tokenizer.vocab_size, 20) def test_tokenizer_from_tokenizer_class(self): config = AutoConfig.from_pretrained(DUMMY_DIFF_TOKENIZER_IDENTIFIER) self.assertIsInstance(config, RobertaConfig) # Check that tokenizer_type ≠ model_type tokenizer = AutoTokenizer.from_pretrained(DUMMY_DIFF_TOKENIZER_IDENTIFIER, config=config) self.assertIsInstance(tokenizer, (BertTokenizer, BertTokenizerFast)) self.assertEqual(tokenizer.vocab_size, 12) def test_tokenizer_from_type(self): with tempfile.TemporaryDirectory() as tmp_dir: shutil.copy("./tests/fixtures/vocab.txt", os.path.join(tmp_dir, "vocab.txt")) tokenizer = AutoTokenizer.from_pretrained(tmp_dir, tokenizer_type="bert", use_fast=False) self.assertIsInstance(tokenizer, BertTokenizer) with tempfile.TemporaryDirectory() as tmp_dir: shutil.copy("./tests/fixtures/vocab.json", os.path.join(tmp_dir, "vocab.json")) shutil.copy("./tests/fixtures/merges.txt", os.path.join(tmp_dir, "merges.txt")) tokenizer = AutoTokenizer.from_pretrained(tmp_dir, tokenizer_type="gpt2", use_fast=False) self.assertIsInstance(tokenizer, GPT2Tokenizer) @require_tokenizers def test_tokenizer_from_type_fast(self): with tempfile.TemporaryDirectory() as tmp_dir: shutil.copy("./tests/fixtures/vocab.txt", os.path.join(tmp_dir, "vocab.txt")) tokenizer = AutoTokenizer.from_pretrained(tmp_dir, tokenizer_type="bert") self.assertIsInstance(tokenizer, BertTokenizerFast) with tempfile.TemporaryDirectory() as tmp_dir: shutil.copy("./tests/fixtures/vocab.json", os.path.join(tmp_dir, "vocab.json")) shutil.copy("./tests/fixtures/merges.txt", os.path.join(tmp_dir, "merges.txt")) tokenizer = AutoTokenizer.from_pretrained(tmp_dir, tokenizer_type="gpt2") self.assertIsInstance(tokenizer, GPT2TokenizerFast) def test_tokenizer_from_type_incorrect_name(self): with pytest.raises(ValueError): AutoTokenizer.from_pretrained("./", tokenizer_type="xxx") @require_tokenizers def test_tokenizer_identifier_with_correct_config(self): for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]: tokenizer = tokenizer_class.from_pretrained("wietsedv/bert-base-dutch-cased") self.assertIsInstance(tokenizer, (BertTokenizer, BertTokenizerFast)) if isinstance(tokenizer, BertTokenizer): self.assertEqual(tokenizer.basic_tokenizer.do_lower_case, False) else: self.assertEqual(tokenizer.do_lower_case, False) self.assertEqual(tokenizer.model_max_length, 512) @require_tokenizers def test_tokenizer_identifier_non_existent(self): for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]: with self.assertRaises(EnvironmentError): _ = tokenizer_class.from_pretrained("julien-c/herlolip-not-exists") def test_parents_and_children_in_mappings(self): # Test that the children are placed before the parents in the mappings, as the `instanceof` will be triggered # by the parents and will return the wrong configuration type when using auto models mappings = (TOKENIZER_MAPPING,) for mapping in mappings: mapping = tuple(mapping.items()) for index, (child_config, _) in enumerate(mapping[1:]): for parent_config, _ in mapping[: index + 1]: with self.subTest(msg=f"Testing if {child_config.__name__} is child of {parent_config.__name__}"): self.assertFalse(issubclass(child_config, parent_config)) def test_model_name_edge_cases_in_mappings(self): # tests: https://github.com/huggingface/transformers/pull/13251 # 1. models with `-`, e.g. xlm-roberta -> xlm_roberta # 2. models that don't remap 1-1 from model-name to model file, e.g., openai-gpt -> openai tokenizers = TOKENIZER_MAPPING.values() tokenizer_names = [] for slow_tok, fast_tok in tokenizers: if slow_tok is not None: tokenizer_names.append(slow_tok.__name__) if fast_tok is not None: tokenizer_names.append(fast_tok.__name__) for tokenizer_name in tokenizer_names: # must find the right class tokenizer_class_from_name(tokenizer_name) @require_tokenizers def test_from_pretrained_use_fast_toggle(self): self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased", use_fast=False), BertTokenizer) self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased"), BertTokenizerFast) @require_tokenizers def test_do_lower_case(self): tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased", do_lower_case=False) sample = "Hello, world. How are you?" tokens = tokenizer.tokenize(sample) self.assertEqual("[UNK]", tokens[0]) tokenizer = AutoTokenizer.from_pretrained("microsoft/mpnet-base", do_lower_case=False) tokens = tokenizer.tokenize(sample) self.assertEqual("[UNK]", tokens[0]) @require_tokenizers def test_PreTrainedTokenizerFast_from_pretrained(self): tokenizer = AutoTokenizer.from_pretrained("robot-test/dummy-tokenizer-fast-with-model-config") self.assertEqual(type(tokenizer), PreTrainedTokenizerFast) self.assertEqual(tokenizer.model_max_length, 512) self.assertEqual(tokenizer.vocab_size, 30000) self.assertEqual(tokenizer.unk_token, "[UNK]") self.assertEqual(tokenizer.padding_side, "right") def test_auto_tokenizer_from_local_folder(self): tokenizer = AutoTokenizer.from_pretrained(SMALL_MODEL_IDENTIFIER) self.assertIsInstance(tokenizer, (BertTokenizer, BertTokenizerFast)) with tempfile.TemporaryDirectory() as tmp_dir: tokenizer.save_pretrained(tmp_dir) tokenizer2 = AutoTokenizer.from_pretrained(tmp_dir) self.assertIsInstance(tokenizer2, tokenizer.__class__) self.assertEqual(tokenizer2.vocab_size, 12) def test_auto_tokenizer_fast_no_slow(self): tokenizer = AutoTokenizer.from_pretrained("ctrl") # There is no fast CTRL so this always gives us a slow tokenizer. self.assertIsInstance(tokenizer, CTRLTokenizer) def test_get_tokenizer_config(self): # Check we can load the tokenizer config of an online model. config = get_tokenizer_config("bert-base-cased") # If we ever update bert-base-cased tokenizer config, this dict here will need to be updated. self.assertEqual(config, {"do_lower_case": False}) # This model does not have a tokenizer_config so we get back an empty dict. config = get_tokenizer_config(SMALL_MODEL_IDENTIFIER) self.assertDictEqual(config, {}) # A tokenizer saved with `save_pretrained` always creates a tokenizer config. tokenizer = AutoTokenizer.from_pretrained(SMALL_MODEL_IDENTIFIER) with tempfile.TemporaryDirectory() as tmp_dir: tokenizer.save_pretrained(tmp_dir) config = get_tokenizer_config(tmp_dir) # Check the class of the tokenizer was properly saved (note that it always saves the slow class). self.assertEqual(config["tokenizer_class"], "BertTokenizer") # Check other keys just to make sure the config was properly saved /reloaded. self.assertEqual(config["name_or_path"], SMALL_MODEL_IDENTIFIER)