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https://github.com/huggingface/transformers.git
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c0c6815dc9
* add check and prepare args for BC to ProcessorMixin, improve ProcessorTesterMixin * change size and crop_size in processor kwargs tests to do_rescale and rescale_factor * remove unnecessary llava processor kwargs test overwrite * nit * change data_arg_name to input_name * Remove unnecessary test override * Remove unnecessary tests Paligemma * Move test_prepare_and_validate_optional_call_args to TesterMixin, add docstring
64 lines
2.3 KiB
Python
64 lines
2.3 KiB
Python
# Copyright 2024 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 shutil
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import tempfile
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import unittest
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from transformers import GemmaTokenizer
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from transformers.testing_utils import get_tests_dir, require_torch, require_vision
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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 (
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PaliGemmaProcessor,
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SiglipImageProcessor,
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is_vision_available,
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)
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SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
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@require_vision
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class PaliGemmaProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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processor_class = PaliGemmaProcessor
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def setUp(self):
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self.tmpdirname = tempfile.mkdtemp()
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image_processor = SiglipImageProcessor.from_pretrained("google/siglip-so400m-patch14-384")
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image_processor.image_seq_length = 0
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tokenizer = GemmaTokenizer(SAMPLE_VOCAB, keep_accents=True)
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processor = PaliGemmaProcessor(image_processor=image_processor, tokenizer=tokenizer)
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processor.save_pretrained(self.tmpdirname)
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def tearDown(self):
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shutil.rmtree(self.tmpdirname)
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@require_torch
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@require_vision
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def test_image_seq_length(self):
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input_str = "lower newer"
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image_input = self.prepare_image_inputs()
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image_processor = self.get_component("image_processor")
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tokenizer = self.get_component("tokenizer", max_length=112, padding="max_length")
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image_processor.image_seq_length = 14
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processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
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inputs = processor(
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text=input_str, images=image_input, return_tensors="pt", max_length=112, padding="max_length"
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)
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self.assertEqual(len(inputs["input_ids"][0]), 112 + 14)
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