75 lines
2.5 KiB
Python
75 lines
2.5 KiB
Python
# coding=utf-8
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# Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors.
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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 transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
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from transformers.testing_utils import require_tokenizers
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from transformers.tokenization_layoutlm import VOCAB_FILES_NAMES
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from .test_tokenization_common import TokenizerTesterMixin
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@require_tokenizers
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class LayoutLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = LayoutLMTokenizer
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rust_tokenizer_class = LayoutLMTokenizerFast
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test_rust_tokenizer = True
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space_between_special_tokens = True
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def setUp(self):
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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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"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, VOCAB_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_tokenizer(self, **kwargs):
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return LayoutLMTokenizer.from_pretrained(self.tmpdirname, **kwargs)
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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), [7, 4, 5, 10, 8, 9])
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def test_special_tokens_as_you_expect(self):
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"""If you are training a seq2seq model that expects a decoder_prefix token make sure it is prepended to decoder_input_ids """
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pass
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