wehub-resource-sync
132 lines
5.1 KiB
Python
132 lines
5.1 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 os
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import unittest
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from paddlenlp.transformers.roformer.tokenizer import RoFormerTokenizer
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from ...testing_utils import slow
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from ..test_tokenizer_common import TokenizerTesterMixin, filter_non_english
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class RoFormerTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = RoFormerTokenizer
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space_between_special_tokens = True
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from_pretrained_filter = filter_non_english
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def setUp(self):
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self.from_pretrained_kwargs = {"do_lower_case": False}
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super().setUp()
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vocab_tokens = [
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"[UNK]",
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"[CLS]",
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"[SEP]",
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"[PAD]",
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"[MASK]",
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"want",
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"##want",
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"##ed",
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"wa",
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"un",
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"runn",
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"##ing",
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",",
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"low",
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"lowest",
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]
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self.vocab_file = os.path.join(self.tmpdirname, RoFormerTokenizer.resource_files_names["vocab_file"])
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with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
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vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
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def get_input_output_texts(self, tokenizer):
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input_text = "UNwant\u00E9d,running"
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output_text = "unwanted, running"
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return input_text, output_text
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def test_full_tokenizer(self):
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tokenizer = self.tokenizer_class(self.vocab_file)
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tokens = tokenizer.tokenize("UNwant\u00E9d,running")
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self.assertListEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"])
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self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [9, 6, 7, 12, 10, 11])
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def test_chinese(self):
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tokenizer = RoFormerTokenizer.from_pretrained(
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list(RoFormerTokenizer.pretrained_init_configuration.keys())[0], use_jieba=True
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)
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# test jieba tokenizer in rofromer
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tokens = tokenizer.tokenize("ah\u535A\u63A8zz")
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self.assertListEqual(tokens, ["ah", "博", "推", "z", "##z"])
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self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [5829, 713, 2093, 167, 48585])
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def test_clean_text(self):
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tokenizer = self.get_tokenizer()
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# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
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self.assertListEqual([tokenizer.tokenize(t) for t in ["Test", "\xad", "test"]], [["[UNK]"], [], ["[UNK]"]])
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@slow
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def test_sequence_builders(self):
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tokenizer = self.tokenizer_class.from_pretrained("roformer-chinese-small")
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text = tokenizer.encode("sequence builders", return_token_type_ids=None, add_special_tokens=False)["input_ids"]
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text_2 = tokenizer.encode("multi-sequence build", return_token_type_ids=None, add_special_tokens=False)[
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"input_ids"
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]
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encoded_sentence = tokenizer.build_inputs_with_special_tokens(text)
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encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2)
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assert encoded_sentence == [101] + text + [102]
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assert encoded_pair == [101] + text + [102] + text_2 + [102]
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def test_offsets_with_special_characters(self):
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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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tokenizer = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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# sentence = f"testing with {tokenizer.mask_token} simple sentence"
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sentence = f"a simple {tokenizer.mask_token} allennlp sentence."
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tokens = tokenizer.encode(
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sentence,
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return_attention_mask=False,
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return_token_type_ids=False,
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return_offsets_mapping=True,
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add_special_tokens=True,
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)
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expected_results = [
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((0, 0), tokenizer.cls_token),
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((0, 1), "a"),
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((2, 8), "simple"),
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((9, 15), tokenizer.mask_token),
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((16, 21), "allen"),
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((21, 23), "##nl"),
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((23, 24), "##p"),
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((25, 33), "sentence"),
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((33, 34), "."),
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((0, 0), tokenizer.sep_token),
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]
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self.assertEqual(
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[e[1] for e in expected_results], tokenizer.convert_ids_to_tokens(tokens["input_ids"])
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)
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self.assertEqual([e[0] for e in expected_results], tokens["offset_mapping"])
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