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1fcaad6df9
* fix * fix * fix * fix --------- Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
97 lines
4.0 KiB
Python
97 lines
4.0 KiB
Python
# coding=utf-8
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# Copyright 2021 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 tempfile
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import unittest
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from functools import lru_cache
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from transformers import RoFormerTokenizer, RoFormerTokenizerFast
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from transformers.testing_utils import require_rjieba, require_tokenizers
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from ...test_tokenization_common import TokenizerTesterMixin, use_cache_if_possible
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@require_rjieba
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@require_tokenizers
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class RoFormerTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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from_pretrained_id = "junnyu/roformer_chinese_small"
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tokenizer_class = RoFormerTokenizer
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rust_tokenizer_class = RoFormerTokenizerFast
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space_between_special_tokens = True
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test_rust_tokenizer = True
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@classmethod
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def setUpClass(cls):
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super().setUpClass()
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tokenizer = cls.tokenizer_class.from_pretrained("junnyu/roformer_chinese_base")
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tokenizer.save_pretrained(cls.tmpdirname)
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@classmethod
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@use_cache_if_possible
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@lru_cache(maxsize=64)
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def get_tokenizer(cls, pretrained_name=None, **kwargs):
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pretrained_name = pretrained_name or cls.tmpdirname
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return cls.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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@classmethod
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@use_cache_if_possible
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@lru_cache(maxsize=64)
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def get_rust_tokenizer(cls, pretrained_name=None, **kwargs):
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pretrained_name = pretrained_name or cls.tmpdirname
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return cls.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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def get_chinese_input_output_texts(self):
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input_text = "永和服装饰品有限公司,今天天气非常好"
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output_text = "永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"
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return input_text, output_text
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def test_tokenizer(self):
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tokenizer = self.get_tokenizer()
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input_text, output_text = self.get_chinese_input_output_texts()
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tokens = tokenizer.tokenize(input_text)
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self.assertListEqual(tokens, output_text.split())
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input_tokens = tokens + [tokenizer.unk_token]
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exp_tokens = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
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self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), exp_tokens)
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def test_rust_tokenizer(self): # noqa: F811
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tokenizer = self.get_rust_tokenizer()
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input_text, output_text = self.get_chinese_input_output_texts()
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tokens = tokenizer.tokenize(input_text)
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self.assertListEqual(tokens, output_text.split())
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input_tokens = tokens + [tokenizer.unk_token]
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exp_tokens = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
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self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), exp_tokens)
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@unittest.skip(reason="Cannot train new tokenizer via Tokenizers lib")
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def test_training_new_tokenizer(self):
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pass
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@unittest.skip(reason="Cannot train new tokenizer via Tokenizers lib")
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def test_training_new_tokenizer_with_special_tokens_change(self):
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pass
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def test_save_slow_from_fast_and_reload_fast(self):
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for cls in [RoFormerTokenizer, RoFormerTokenizerFast]:
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original = cls.from_pretrained("alchemab/antiberta2")
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self.assertEqual(original.encode("生活的真谛是"), [1, 4, 4, 4, 4, 4, 4, 2])
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with tempfile.TemporaryDirectory() as tmp_dir:
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original.save_pretrained(tmp_dir)
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new = cls.from_pretrained(tmp_dir)
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self.assertEqual(new.encode("生活的真谛是"), [1, 4, 4, 4, 4, 4, 4, 2])
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