mirror of
https://github.com/huggingface/transformers.git
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4d0de5f73a
* More limited setup -> setupclass conversion * make fixup * Trigger tests * Fixup UDOP * Missed a spot * tearDown -> tearDownClass where appropriate * Couple more class fixes * Fixups for UDOP and VisionTextDualEncoder * Ignore errors when removing the tmpdir, in case it already got cleaned up somewhere * CLIP fixes * More correct classmethods * Wav2Vec2Bert fixes * More methods become static * More class methods * More class methods * Revert changes for integration tests / modeling files * Use a different tempdir for tests that actually write to it * Remove addClassCleanup and just use teardownclass * Remove changes in modeling files * Cleanup get_processor_dict() for got_ocr2 * Fix regression on Wav2Vec2BERT test that was masked by this before * Rework tests that modify the tmpdir * make fix-copies * revert clvp modeling test changes * Fix CLIP processor test * make fix-copies
64 lines
2.2 KiB
Python
64 lines
2.2 KiB
Python
# Copyright 2022 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 tempfile
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import unittest
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from transformers import DonutImageProcessor, DonutProcessor, XLMRobertaTokenizerFast
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from ...test_processing_common import ProcessorTesterMixin
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class DonutProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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from_pretrained_id = "naver-clova-ix/donut-base"
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processor_class = DonutProcessor
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@classmethod
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def setUpClass(cls):
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cls.processor = DonutProcessor.from_pretrained(cls.from_pretrained_id)
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cls.tmpdirname = tempfile.mkdtemp()
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image_processor = DonutImageProcessor()
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tokenizer = XLMRobertaTokenizerFast.from_pretrained(cls.from_pretrained_id)
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processor = DonutProcessor(image_processor, tokenizer)
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processor.save_pretrained(cls.tmpdirname)
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def test_token2json(self):
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expected_json = {
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"name": "John Doe",
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"age": "99",
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"city": "Atlanta",
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"state": "GA",
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"zip": "30301",
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"phone": "123-4567",
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"nicknames": [{"nickname": "Johnny"}, {"nickname": "JD"}],
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"multiline": "text\nwith\nnewlines",
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"empty": "",
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}
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sequence = (
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"<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>"
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"<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>"
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"<s_nicknames><s_nickname>Johnny</s_nickname>"
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"<sep/><s_nickname>JD</s_nickname></s_nicknames>"
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"<s_multiline>text\nwith\nnewlines</s_multiline>"
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"<s_empty></s_empty>"
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)
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actual_json = self.processor.token2json(sequence)
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self.assertDictEqual(actual_json, expected_json)
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