# 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 json import os import shutil import sys import tempfile import unittest from pathlib import Path import pytest import transformers from transformers import ( AutoTokenizer, BertConfig, BertTokenizer, BertTokenizerFast, CTRLTokenizer, GPT2Tokenizer, GPT2TokenizerFast, PreTrainedTokenizerFast, RobertaTokenizer, RobertaTokenizerFast, is_tokenizers_available, ) from transformers.models.auto.configuration_auto import CONFIG_MAPPING, 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, RequestCounter, is_flaky, require_tokenizers, slow, ) sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils")) from test_module.custom_configuration import CustomConfig # noqa E402 from test_module.custom_tokenization import CustomTokenizer # noqa E402 if is_tokenizers_available(): from test_module.custom_tokenization_fast import CustomTokenizerFast class AutoTokenizerTest(unittest.TestCase): def setUp(self): transformers.dynamic_module_utils.TIME_OUT_REMOTE_CODE = 0 @slow def test_tokenizer_from_pretrained(self): for model_name in {"google-bert/bert-base-uncased", "google-bert/bert-base-cased"}: tokenizer = AutoTokenizer.from_pretrained(model_name) self.assertIsNotNone(tokenizer) self.assertIsInstance(tokenizer, (BertTokenizer, BertTokenizerFast)) self.assertGreater(len(tokenizer), 0) for model_name in ["openai-community/gpt2", "openai-community/gpt2-medium"]: 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 @is_flaky() # This one is flaky even with the new retry logic because it raises an unusual error def test_tokenizer_identifier_non_existent(self): for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]: with self.assertRaisesRegex( EnvironmentError, "julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier", ): _ = tokenizer_class.from_pretrained("julien-c/herlolip-not-exists") 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("google-bert/bert-base-cased", use_fast=False), BertTokenizer ) self.assertIsInstance(AutoTokenizer.from_pretrained("google-bert/bert-base-cased"), BertTokenizerFast) @require_tokenizers def test_do_lower_case(self): tokenizer = AutoTokenizer.from_pretrained("distilbert/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") self.assertEqual(tokenizer.truncation_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("Salesforce/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("google-bert/bert-base-cased") _ = config.pop("_commit_hash", None) # If we ever update google-bert/bert-base-cased tokenizer config, this dict here will need to be updated. self.assertEqual(config, {"do_lower_case": False, "model_max_length": 512}) # 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") def test_new_tokenizer_registration(self): try: AutoConfig.register("custom", CustomConfig) AutoTokenizer.register(CustomConfig, slow_tokenizer_class=CustomTokenizer) # Trying to register something existing in the Transformers library will raise an error with self.assertRaises(ValueError): AutoTokenizer.register(BertConfig, slow_tokenizer_class=BertTokenizer) tokenizer = CustomTokenizer.from_pretrained(SMALL_MODEL_IDENTIFIER) with tempfile.TemporaryDirectory() as tmp_dir: tokenizer.save_pretrained(tmp_dir) new_tokenizer = AutoTokenizer.from_pretrained(tmp_dir) self.assertIsInstance(new_tokenizer, CustomTokenizer) finally: if "custom" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["custom"] if CustomConfig in TOKENIZER_MAPPING._extra_content: del TOKENIZER_MAPPING._extra_content[CustomConfig] @require_tokenizers def test_new_tokenizer_fast_registration(self): try: AutoConfig.register("custom", CustomConfig) # Can register in two steps AutoTokenizer.register(CustomConfig, slow_tokenizer_class=CustomTokenizer) self.assertEqual(TOKENIZER_MAPPING[CustomConfig], (CustomTokenizer, None)) AutoTokenizer.register(CustomConfig, fast_tokenizer_class=CustomTokenizerFast) self.assertEqual(TOKENIZER_MAPPING[CustomConfig], (CustomTokenizer, CustomTokenizerFast)) del TOKENIZER_MAPPING._extra_content[CustomConfig] # Can register in one step AutoTokenizer.register( CustomConfig, slow_tokenizer_class=CustomTokenizer, fast_tokenizer_class=CustomTokenizerFast ) self.assertEqual(TOKENIZER_MAPPING[CustomConfig], (CustomTokenizer, CustomTokenizerFast)) # Trying to register something existing in the Transformers library will raise an error with self.assertRaises(ValueError): AutoTokenizer.register(BertConfig, fast_tokenizer_class=BertTokenizerFast) # We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer # and that model does not have a tokenizer.json with tempfile.TemporaryDirectory() as tmp_dir: bert_tokenizer = BertTokenizerFast.from_pretrained(SMALL_MODEL_IDENTIFIER) bert_tokenizer.save_pretrained(tmp_dir) tokenizer = CustomTokenizerFast.from_pretrained(tmp_dir) with tempfile.TemporaryDirectory() as tmp_dir: tokenizer.save_pretrained(tmp_dir) new_tokenizer = AutoTokenizer.from_pretrained(tmp_dir) self.assertIsInstance(new_tokenizer, CustomTokenizerFast) new_tokenizer = AutoTokenizer.from_pretrained(tmp_dir, use_fast=False) self.assertIsInstance(new_tokenizer, CustomTokenizer) finally: if "custom" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["custom"] if CustomConfig in TOKENIZER_MAPPING._extra_content: del TOKENIZER_MAPPING._extra_content[CustomConfig] def test_from_pretrained_dynamic_tokenizer(self): # If remote code is not set, we will time out when asking whether to load the model. with self.assertRaises(ValueError): tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer") # If remote code is disabled, we can't load this config. with self.assertRaises(ValueError): tokenizer = AutoTokenizer.from_pretrained( "hf-internal-testing/test_dynamic_tokenizer", trust_remote_code=False ) tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer", trust_remote_code=True) self.assertTrue(tokenizer.special_attribute_present) # Test the dynamic module is loaded only once. reloaded_tokenizer = AutoTokenizer.from_pretrained( "hf-internal-testing/test_dynamic_tokenizer", trust_remote_code=True ) self.assertIs(tokenizer.__class__, reloaded_tokenizer.__class__) # Test tokenizer can be reloaded. with tempfile.TemporaryDirectory() as tmp_dir: tokenizer.save_pretrained(tmp_dir) reloaded_tokenizer = AutoTokenizer.from_pretrained(tmp_dir, trust_remote_code=True) self.assertTrue(reloaded_tokenizer.special_attribute_present) if is_tokenizers_available(): self.assertEqual(tokenizer.__class__.__name__, "NewTokenizerFast") self.assertEqual(reloaded_tokenizer.__class__.__name__, "NewTokenizerFast") # Test we can also load the slow version tokenizer = AutoTokenizer.from_pretrained( "hf-internal-testing/test_dynamic_tokenizer", trust_remote_code=True, use_fast=False ) self.assertTrue(tokenizer.special_attribute_present) self.assertEqual(tokenizer.__class__.__name__, "NewTokenizer") # Test tokenizer can be reloaded. with tempfile.TemporaryDirectory() as tmp_dir: tokenizer.save_pretrained(tmp_dir) reloaded_tokenizer = AutoTokenizer.from_pretrained(tmp_dir, trust_remote_code=True, use_fast=False) self.assertEqual(reloaded_tokenizer.__class__.__name__, "NewTokenizer") self.assertTrue(reloaded_tokenizer.special_attribute_present) else: self.assertEqual(tokenizer.__class__.__name__, "NewTokenizer") self.assertEqual(reloaded_tokenizer.__class__.__name__, "NewTokenizer") # The tokenizer file is cached in the snapshot directory. So the module file is not changed after dumping # to a temp dir. Because the revision of the module file is not changed. # Test the dynamic module is loaded only once if the module file is not changed. self.assertIs(tokenizer.__class__, reloaded_tokenizer.__class__) # Test the dynamic module is reloaded if we force it. reloaded_tokenizer = AutoTokenizer.from_pretrained( "hf-internal-testing/test_dynamic_tokenizer", trust_remote_code=True, force_download=True ) self.assertIsNot(tokenizer.__class__, reloaded_tokenizer.__class__) self.assertTrue(reloaded_tokenizer.special_attribute_present) @require_tokenizers def test_from_pretrained_dynamic_tokenizer_conflict(self): class NewTokenizer(BertTokenizer): special_attribute_present = False class NewTokenizerFast(BertTokenizerFast): slow_tokenizer_class = NewTokenizer special_attribute_present = False try: AutoConfig.register("custom", CustomConfig) AutoTokenizer.register(CustomConfig, slow_tokenizer_class=NewTokenizer) AutoTokenizer.register(CustomConfig, fast_tokenizer_class=NewTokenizerFast) # If remote code is not set, the default is to use local tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer") self.assertEqual(tokenizer.__class__.__name__, "NewTokenizerFast") self.assertFalse(tokenizer.special_attribute_present) tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer", use_fast=False) self.assertEqual(tokenizer.__class__.__name__, "NewTokenizer") self.assertFalse(tokenizer.special_attribute_present) # If remote code is disabled, we load the local one. tokenizer = AutoTokenizer.from_pretrained( "hf-internal-testing/test_dynamic_tokenizer", trust_remote_code=False ) self.assertEqual(tokenizer.__class__.__name__, "NewTokenizerFast") self.assertFalse(tokenizer.special_attribute_present) tokenizer = AutoTokenizer.from_pretrained( "hf-internal-testing/test_dynamic_tokenizer", trust_remote_code=False, use_fast=False ) self.assertEqual(tokenizer.__class__.__name__, "NewTokenizer") self.assertFalse(tokenizer.special_attribute_present) # If remote is enabled, we load from the Hub tokenizer = AutoTokenizer.from_pretrained( "hf-internal-testing/test_dynamic_tokenizer", trust_remote_code=True ) self.assertEqual(tokenizer.__class__.__name__, "NewTokenizerFast") self.assertTrue(tokenizer.special_attribute_present) tokenizer = AutoTokenizer.from_pretrained( "hf-internal-testing/test_dynamic_tokenizer", trust_remote_code=True, use_fast=False ) self.assertEqual(tokenizer.__class__.__name__, "NewTokenizer") self.assertTrue(tokenizer.special_attribute_present) finally: if "custom" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["custom"] if CustomConfig in TOKENIZER_MAPPING._extra_content: del TOKENIZER_MAPPING._extra_content[CustomConfig] def test_from_pretrained_dynamic_tokenizer_legacy_format(self): tokenizer = AutoTokenizer.from_pretrained( "hf-internal-testing/test_dynamic_tokenizer_legacy", trust_remote_code=True ) self.assertTrue(tokenizer.special_attribute_present) if is_tokenizers_available(): self.assertEqual(tokenizer.__class__.__name__, "NewTokenizerFast") # Test we can also load the slow version tokenizer = AutoTokenizer.from_pretrained( "hf-internal-testing/test_dynamic_tokenizer_legacy", trust_remote_code=True, use_fast=False ) self.assertTrue(tokenizer.special_attribute_present) self.assertEqual(tokenizer.__class__.__name__, "NewTokenizer") else: self.assertEqual(tokenizer.__class__.__name__, "NewTokenizer") def test_repo_not_found(self): with self.assertRaisesRegex( EnvironmentError, "bert-base is not a local folder and is not a valid model identifier" ): _ = AutoTokenizer.from_pretrained("bert-base") def test_revision_not_found(self): with self.assertRaisesRegex( EnvironmentError, r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ): _ = AutoTokenizer.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER, revision="aaaaaa") @unittest.skip("This test is failing on main") # TODO Matt/ydshieh, fix this test! def test_cached_tokenizer_has_minimum_calls_to_head(self): # Make sure we have cached the tokenizer. _ = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert") with RequestCounter() as counter: _ = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert") self.assertEqual(counter["GET"], 0) self.assertEqual(counter["HEAD"], 1) self.assertEqual(counter.total_calls, 1) def test_init_tokenizer_with_trust(self): nop_tokenizer_code = """ import transformers class NopTokenizer(transformers.PreTrainedTokenizer): def get_vocab(self): return {} """ nop_config_code = """ from transformers import PretrainedConfig class NopConfig(PretrainedConfig): model_type = "test_unregistered_dynamic" def __init__(self, **kwargs): super().__init__(**kwargs) """ with tempfile.TemporaryDirectory() as tmp_dir: fake_model_id = "hf-internal-testing/test_unregistered_dynamic" fake_repo = os.path.join(tmp_dir, fake_model_id) os.makedirs(fake_repo) tokenizer_src_file = os.path.join(fake_repo, "tokenizer.py") with open(tokenizer_src_file, "w") as wfp: wfp.write(nop_tokenizer_code) model_config_src_file = os.path.join(fake_repo, "config.py") with open(model_config_src_file, "w") as wfp: wfp.write(nop_config_code) config = { "model_type": "test_unregistered_dynamic", "auto_map": {"AutoConfig": f"{fake_model_id}--config.NopConfig"}, } config_file = os.path.join(fake_repo, "config.json") with open(config_file, "w") as wfp: json.dump(config, wfp, indent=2) tokenizer_config = { "auto_map": { "AutoTokenizer": [ f"{fake_model_id}--tokenizer.NopTokenizer", None, ] } } tokenizer_config_file = os.path.join(fake_repo, "tokenizer_config.json") with open(tokenizer_config_file, "w") as wfp: json.dump(tokenizer_config, wfp, indent=2) prev_dir = os.getcwd() try: # it looks like subdir= is broken in the from_pretrained also, so this is necessary os.chdir(tmp_dir) # this should work because we trust the code _ = AutoTokenizer.from_pretrained(fake_model_id, local_files_only=True, trust_remote_code=True) try: # this should fail because we don't trust and we're not at a terminal for interactive response _ = AutoTokenizer.from_pretrained(fake_model_id, local_files_only=True, trust_remote_code=False) self.fail("AutoTokenizer.from_pretrained with trust_remote_code=False should raise ValueException") except ValueError: pass finally: os.chdir(prev_dir)