wehub-resource-sync
69 lines
2.8 KiB
Python
69 lines
2.8 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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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 unittest
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from paddlenlp.transformers import FNetTokenizer
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from ...testing_utils import get_tests_dir
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from ..test_tokenizer_common import TokenizerTesterMixin
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SAMPLE_VOCAB = get_tests_dir("fixtures/spiece.model")
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class TestTokenizationFNet(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = FNetTokenizer
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from_pretrained_vocab_key = "sentencepiece_model_file"
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test_sentencepiece = True
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test_sentencepiece_ignore_case = True
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def setUp(self):
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super().setUp()
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# We have a SentencePiece fixture for testing
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tokenizer = FNetTokenizer(SAMPLE_VOCAB)
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tokenizer.save_pretrained(self.tmpdirname)
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def get_input_output_texts(self, tokenizer):
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input_text = "this is a test"
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output_text = "this is a test"
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return input_text, output_text
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def test_add_special_tokens(self):
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tokenizers = self.get_tokenizers(do_lower_case=False)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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input_text, ids = self.get_clean_sequence(tokenizer)
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special_token = "[SPECIAL_TOKEN]"
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tokenizer.add_special_tokens({"cls_token": special_token})
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encoded_special_token = tokenizer.encode(
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special_token, return_token_type_ids=None, add_special_tokens=False
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)["input_ids"]
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self.assertEqual(len(encoded_special_token), 1)
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text = tokenizer.decode(ids + encoded_special_token, clean_up_tokenization_spaces=False)
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encoded = tokenizer.encode(text, return_token_type_ids=None, add_special_tokens=False)["input_ids"]
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input_encoded = tokenizer.encode(input_text, return_token_type_ids=None, add_special_tokens=False)[
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"input_ids"
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]
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special_token_id = tokenizer.encode(
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special_token, return_token_type_ids=None, add_special_tokens=False
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)["input_ids"]
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self.assertEqual(encoded, input_encoded + special_token_id)
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decoded = tokenizer.decode(encoded, skip_special_tokens=True)
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self.assertTrue(special_token not in decoded)
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