Support arbitrary processor (#30875)
* Support arbitrary processor * fix * nit * update * nit * nit * fix and revert * add a small test * better check * fixup * bug so let's just use class for now * oups * .
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@ -41,8 +41,8 @@ class LlavaProcessor(ProcessorMixin):
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"""
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attributes = ["image_processor", "tokenizer"]
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image_processor_class = "CLIPImageProcessor"
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tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")
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image_processor_class = "AutoImageProcessor"
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tokenizer_class = "AutoTokenizer"
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def __init__(self, image_processor=None, tokenizer=None):
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super().__init__(image_processor, tokenizer)
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@ -0,0 +1,30 @@
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# Copyright 2021 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 transformers.testing_utils import require_vision
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from transformers.utils import is_vision_available
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if is_vision_available():
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from transformers import AutoTokenizer, LlavaProcessor
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@require_vision
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class LlavaProcessorTest(unittest.TestCase):
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def test_can_load_various_tokenizers(self):
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for checkpoint in ["Intel/llava-gemma-2b", "llava-hf/llava-1.5-7b-hf"]:
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processor = LlavaProcessor.from_pretrained(checkpoint)
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tokenizer = AutoTokenizer.from_pretrained(checkpoint)
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self.assertEqual(processor.tokenizer.__class__, tokenizer.__class__)
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