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50 lines
1.7 KiB
Python
50 lines
1.7 KiB
Python
# Copyright 2024 The HuggingFace Inc. 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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"""Testing suite for the PyTorch chameleon model."""
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import tempfile
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import unittest
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from transformers import ChameleonProcessor, LlamaTokenizer
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from transformers.testing_utils import get_tests_dir
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from transformers.utils import is_vision_available
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from ...test_processing_common import ProcessorTesterMixin
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if is_vision_available():
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from transformers import ChameleonImageProcessor
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SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
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class ChameleonProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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processor_class = ChameleonProcessor
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@classmethod
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def setUpClass(cls):
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cls.tmpdirname = tempfile.mkdtemp()
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image_processor = ChameleonImageProcessor()
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tokenizer = LlamaTokenizer(vocab_file=SAMPLE_VOCAB)
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tokenizer.pad_token_id = 0
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tokenizer.sep_token_id = 1
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processor = cls.processor_class(image_processor=image_processor, tokenizer=tokenizer, image_seq_length=2)
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processor.save_pretrained(cls.tmpdirname)
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cls.image_token = processor.image_token
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@staticmethod
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def prepare_processor_dict():
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return {"image_seq_length": 2} # fmt: skip
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