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56 lines
2.1 KiB
Python
56 lines
2.1 KiB
Python
# Copyright 2025 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.models.gemma3n import Gemma3nProcessor
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from transformers.testing_utils import (
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require_sentencepiece,
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require_torch,
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require_torchaudio,
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require_vision,
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)
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from ...test_processing_common import ProcessorTesterMixin
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from .test_feature_extraction_gemma3n import floats_list
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# TODO: omni-modal processor can't run tests from `ProcessorTesterMixin`
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@require_torch
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@require_torchaudio
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@require_vision
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@require_sentencepiece
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class Gemma3nProcessorTest(ProcessorTesterMixin, unittest.TestCase):
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processor_class = Gemma3nProcessor
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# Tiny processor created with make_tiny_processor.py from "hf-internal-testing/namespace-google-repo_name-gemma-3n-E4B-it"
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tiny_model_id = "hf-internal-testing/tiny-processor-gemma3n"
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def prepare_image_inputs(self, batch_size: int | None = None, nested: bool = False):
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return super().prepare_image_inputs(batch_size=batch_size, nested=True)
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@classmethod
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def _setup_test_attributes(cls, processor):
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cls.image_token = processor.boi_token
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def test_audio_feature_extractor(self):
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processor = self.get_processor()
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feature_extractor = self.get_component("feature_extractor")
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raw_speech = floats_list((3, 1000))
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input_feat_extract = feature_extractor(raw_speech, return_tensors="pt")
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input_processor = processor(text="Transcribe:", audio=raw_speech, return_tensors="pt")
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for key in input_feat_extract:
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self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
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