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228 lines
10 KiB
Python
228 lines
10 KiB
Python
# coding=utf-8
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import shutil
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import tempfile
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import unittest
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import pytest
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from transformers import (
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BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
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GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
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AutoTokenizer,
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BertTokenizer,
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BertTokenizerFast,
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CTRLTokenizer,
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GPT2Tokenizer,
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GPT2TokenizerFast,
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PreTrainedTokenizerFast,
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RobertaTokenizer,
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RobertaTokenizerFast,
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)
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from transformers.models.auto.configuration_auto import AutoConfig
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from transformers.models.auto.tokenization_auto import (
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TOKENIZER_MAPPING,
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get_tokenizer_config,
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tokenizer_class_from_name,
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)
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from transformers.models.roberta.configuration_roberta import RobertaConfig
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from transformers.testing_utils import (
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DUMMY_DIFF_TOKENIZER_IDENTIFIER,
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DUMMY_UNKNOWN_IDENTIFIER,
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SMALL_MODEL_IDENTIFIER,
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require_tokenizers,
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slow,
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)
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class AutoTokenizerTest(unittest.TestCase):
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@slow
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def test_tokenizer_from_pretrained(self):
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for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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self.assertIsNotNone(tokenizer)
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self.assertIsInstance(tokenizer, (BertTokenizer, BertTokenizerFast))
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self.assertGreater(len(tokenizer), 0)
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for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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self.assertIsNotNone(tokenizer)
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self.assertIsInstance(tokenizer, (GPT2Tokenizer, GPT2TokenizerFast))
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self.assertGreater(len(tokenizer), 0)
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def test_tokenizer_from_pretrained_identifier(self):
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tokenizer = AutoTokenizer.from_pretrained(SMALL_MODEL_IDENTIFIER)
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self.assertIsInstance(tokenizer, (BertTokenizer, BertTokenizerFast))
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self.assertEqual(tokenizer.vocab_size, 12)
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def test_tokenizer_from_model_type(self):
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tokenizer = AutoTokenizer.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER)
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self.assertIsInstance(tokenizer, (RobertaTokenizer, RobertaTokenizerFast))
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self.assertEqual(tokenizer.vocab_size, 20)
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def test_tokenizer_from_tokenizer_class(self):
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config = AutoConfig.from_pretrained(DUMMY_DIFF_TOKENIZER_IDENTIFIER)
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self.assertIsInstance(config, RobertaConfig)
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# Check that tokenizer_type ≠ model_type
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tokenizer = AutoTokenizer.from_pretrained(DUMMY_DIFF_TOKENIZER_IDENTIFIER, config=config)
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self.assertIsInstance(tokenizer, (BertTokenizer, BertTokenizerFast))
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self.assertEqual(tokenizer.vocab_size, 12)
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def test_tokenizer_from_type(self):
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with tempfile.TemporaryDirectory() as tmp_dir:
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shutil.copy("./tests/fixtures/vocab.txt", os.path.join(tmp_dir, "vocab.txt"))
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tokenizer = AutoTokenizer.from_pretrained(tmp_dir, tokenizer_type="bert", use_fast=False)
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self.assertIsInstance(tokenizer, BertTokenizer)
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with tempfile.TemporaryDirectory() as tmp_dir:
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shutil.copy("./tests/fixtures/vocab.json", os.path.join(tmp_dir, "vocab.json"))
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shutil.copy("./tests/fixtures/merges.txt", os.path.join(tmp_dir, "merges.txt"))
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tokenizer = AutoTokenizer.from_pretrained(tmp_dir, tokenizer_type="gpt2", use_fast=False)
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self.assertIsInstance(tokenizer, GPT2Tokenizer)
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@require_tokenizers
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def test_tokenizer_from_type_fast(self):
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with tempfile.TemporaryDirectory() as tmp_dir:
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shutil.copy("./tests/fixtures/vocab.txt", os.path.join(tmp_dir, "vocab.txt"))
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tokenizer = AutoTokenizer.from_pretrained(tmp_dir, tokenizer_type="bert")
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self.assertIsInstance(tokenizer, BertTokenizerFast)
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with tempfile.TemporaryDirectory() as tmp_dir:
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shutil.copy("./tests/fixtures/vocab.json", os.path.join(tmp_dir, "vocab.json"))
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shutil.copy("./tests/fixtures/merges.txt", os.path.join(tmp_dir, "merges.txt"))
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tokenizer = AutoTokenizer.from_pretrained(tmp_dir, tokenizer_type="gpt2")
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self.assertIsInstance(tokenizer, GPT2TokenizerFast)
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def test_tokenizer_from_type_incorrect_name(self):
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with pytest.raises(ValueError):
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AutoTokenizer.from_pretrained("./", tokenizer_type="xxx")
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@require_tokenizers
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def test_tokenizer_identifier_with_correct_config(self):
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for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
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tokenizer = tokenizer_class.from_pretrained("wietsedv/bert-base-dutch-cased")
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self.assertIsInstance(tokenizer, (BertTokenizer, BertTokenizerFast))
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if isinstance(tokenizer, BertTokenizer):
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self.assertEqual(tokenizer.basic_tokenizer.do_lower_case, False)
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else:
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self.assertEqual(tokenizer.do_lower_case, False)
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self.assertEqual(tokenizer.model_max_length, 512)
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@require_tokenizers
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def test_tokenizer_identifier_non_existent(self):
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for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
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with self.assertRaises(EnvironmentError):
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_ = tokenizer_class.from_pretrained("julien-c/herlolip-not-exists")
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def test_parents_and_children_in_mappings(self):
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# Test that the children are placed before the parents in the mappings, as the `instanceof` will be triggered
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# by the parents and will return the wrong configuration type when using auto models
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mappings = (TOKENIZER_MAPPING,)
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for mapping in mappings:
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mapping = tuple(mapping.items())
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for index, (child_config, _) in enumerate(mapping[1:]):
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for parent_config, _ in mapping[: index + 1]:
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with self.subTest(msg=f"Testing if {child_config.__name__} is child of {parent_config.__name__}"):
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self.assertFalse(issubclass(child_config, parent_config))
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def test_model_name_edge_cases_in_mappings(self):
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# tests: https://github.com/huggingface/transformers/pull/13251
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# 1. models with `-`, e.g. xlm-roberta -> xlm_roberta
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# 2. models that don't remap 1-1 from model-name to model file, e.g., openai-gpt -> openai
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tokenizers = TOKENIZER_MAPPING.values()
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tokenizer_names = []
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for slow_tok, fast_tok in tokenizers:
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if slow_tok is not None:
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tokenizer_names.append(slow_tok.__name__)
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if fast_tok is not None:
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tokenizer_names.append(fast_tok.__name__)
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for tokenizer_name in tokenizer_names:
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# must find the right class
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tokenizer_class_from_name(tokenizer_name)
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@require_tokenizers
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def test_from_pretrained_use_fast_toggle(self):
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self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased", use_fast=False), BertTokenizer)
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self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased"), BertTokenizerFast)
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@require_tokenizers
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def test_do_lower_case(self):
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tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased", do_lower_case=False)
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sample = "Hello, world. How are you?"
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tokens = tokenizer.tokenize(sample)
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self.assertEqual("[UNK]", tokens[0])
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tokenizer = AutoTokenizer.from_pretrained("microsoft/mpnet-base", do_lower_case=False)
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tokens = tokenizer.tokenize(sample)
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self.assertEqual("[UNK]", tokens[0])
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@require_tokenizers
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def test_PreTrainedTokenizerFast_from_pretrained(self):
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tokenizer = AutoTokenizer.from_pretrained("robot-test/dummy-tokenizer-fast-with-model-config")
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self.assertEqual(type(tokenizer), PreTrainedTokenizerFast)
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self.assertEqual(tokenizer.model_max_length, 512)
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self.assertEqual(tokenizer.vocab_size, 30000)
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self.assertEqual(tokenizer.unk_token, "[UNK]")
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self.assertEqual(tokenizer.padding_side, "right")
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def test_auto_tokenizer_from_local_folder(self):
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tokenizer = AutoTokenizer.from_pretrained(SMALL_MODEL_IDENTIFIER)
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self.assertIsInstance(tokenizer, (BertTokenizer, BertTokenizerFast))
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with tempfile.TemporaryDirectory() as tmp_dir:
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tokenizer.save_pretrained(tmp_dir)
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tokenizer2 = AutoTokenizer.from_pretrained(tmp_dir)
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self.assertIsInstance(tokenizer2, tokenizer.__class__)
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self.assertEqual(tokenizer2.vocab_size, 12)
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def test_auto_tokenizer_fast_no_slow(self):
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tokenizer = AutoTokenizer.from_pretrained("ctrl")
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# There is no fast CTRL so this always gives us a slow tokenizer.
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self.assertIsInstance(tokenizer, CTRLTokenizer)
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def test_get_tokenizer_config(self):
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# Check we can load the tokenizer config of an online model.
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config = get_tokenizer_config("bert-base-cased")
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# If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
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self.assertEqual(config, {"do_lower_case": False})
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# This model does not have a tokenizer_config so we get back an empty dict.
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config = get_tokenizer_config(SMALL_MODEL_IDENTIFIER)
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self.assertDictEqual(config, {})
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# A tokenizer saved with `save_pretrained` always creates a tokenizer config.
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tokenizer = AutoTokenizer.from_pretrained(SMALL_MODEL_IDENTIFIER)
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with tempfile.TemporaryDirectory() as tmp_dir:
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tokenizer.save_pretrained(tmp_dir)
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config = get_tokenizer_config(tmp_dir)
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# Check the class of the tokenizer was properly saved (note that it always saves the slow class).
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self.assertEqual(config["tokenizer_class"], "BertTokenizer")
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# Check other keys just to make sure the config was properly saved /reloaded.
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self.assertEqual(config["name_or_path"], SMALL_MODEL_IDENTIFIER)
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