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93 lines
4.1 KiB
Python
93 lines
4.1 KiB
Python
# coding=utf-8
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# Copyright 2019 HuggingFace Inc.
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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 shutil
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import tempfile
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import unittest
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from transformers import PreTrainedTokenizerFast
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from transformers.testing_utils import require_tokenizers
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from .test_tokenization_common import TokenizerTesterMixin
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@require_tokenizers
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class PreTrainedTokenizationFastTest(TokenizerTesterMixin, unittest.TestCase):
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rust_tokenizer_class = PreTrainedTokenizerFast
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test_slow_tokenizer = False
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test_rust_tokenizer = True
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from_pretrained_vocab_key = "tokenizer_file"
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def setUp(self):
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self.test_rust_tokenizer = False # because we don't have pretrained_vocab_files_map
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super().setUp()
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self.test_rust_tokenizer = True
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model_paths = ["robot-test/dummy-tokenizer-fast", "robot-test/dummy-tokenizer-wordlevel"]
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# Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
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self.tokenizers_list = [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]
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tokenizer = PreTrainedTokenizerFast.from_pretrained(model_paths[0])
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tokenizer.save_pretrained(self.tmpdirname)
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def test_pretrained_model_lists(self):
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# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
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# model
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pass
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def test_prepare_for_model(self):
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# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
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# model
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pass
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def test_rust_tokenizer_signature(self):
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# PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
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pass
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def test_training_new_tokenizer(self):
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tmpdirname_orig = self.tmpdirname
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# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
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for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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try:
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self.tmpdirname = tempfile.mkdtemp()
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tokenizer = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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tokenizer.save_pretrained(self.tmpdirname)
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super().test_training_new_tokenizer()
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finally:
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# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
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# is restored
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shutil.rmtree(self.tmpdirname)
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self.tmpdirname = tmpdirname_orig
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def test_training_new_tokenizer_with_special_tokens_change(self):
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tmpdirname_orig = self.tmpdirname
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# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
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for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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try:
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self.tmpdirname = tempfile.mkdtemp()
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tokenizer = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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tokenizer.save_pretrained(self.tmpdirname)
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super().test_training_new_tokenizer_with_special_tokens_change()
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finally:
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# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
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# is restored
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shutil.rmtree(self.tmpdirname)
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self.tmpdirname = tmpdirname_orig
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