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540a10848c
* fix * fix * oups forgot style --------- Co-authored-by: ydshieh <ydshieh@users.noreply.github.com> Co-authored-by: Cyril Vallez <cyril.vallez@gmail.com>
192 lines
8.1 KiB
Python
192 lines
8.1 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 shutil
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import tempfile
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import unittest
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import numpy as np
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from parameterized import parameterized
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from transformers import GemmaTokenizerFast, SiglipImageProcessorFast, is_speech_available
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from transformers.testing_utils import require_sentencepiece, require_torch, require_torchaudio, require_vision
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from .test_feature_extraction_gemma3n import floats_list
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if is_speech_available():
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from transformers.models.gemma3n import Gemma3nAudioFeatureExtractor, Gemma3nProcessor
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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(unittest.TestCase):
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def setUp(self):
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# TODO: update to google?
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self.model_id = "hf-internal-testing/namespace-google-repo_name-gemma-3n-E4B-it"
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self.tmpdirname = tempfile.mkdtemp(suffix="gemma3n")
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self.maxDiff = None
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def get_tokenizer(self, **kwargs):
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return GemmaTokenizerFast.from_pretrained(self.model_id, **kwargs)
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def get_feature_extractor(self, **kwargs):
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return Gemma3nAudioFeatureExtractor.from_pretrained(self.model_id, **kwargs)
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def get_image_processor(self, **kwargs):
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return SiglipImageProcessorFast.from_pretrained(self.model_id, **kwargs)
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def tearDown(self):
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shutil.rmtree(self.tmpdirname)
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def test_save_load_pretrained_default(self):
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# NOTE: feature_extractor and image_processor both use the same filename, preprocessor_config.json, when saved to
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# disk, but the files are overwritten by processor.save_pretrained(). This test does not attempt to address
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# this potential issue, and as such, does not guarantee content accuracy.
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tokenizer = self.get_tokenizer()
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feature_extractor = self.get_feature_extractor()
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image_processor = self.get_image_processor()
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processor = Gemma3nProcessor(
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tokenizer=tokenizer, feature_extractor=feature_extractor, image_processor=image_processor
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)
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processor.save_pretrained(self.tmpdirname)
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processor = Gemma3nProcessor.from_pretrained(self.tmpdirname)
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self.assertIsInstance(processor.tokenizer, GemmaTokenizerFast)
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self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
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# `disable_grouping` is a new attribute that got added on main while gemma3n was being released - so was
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# not part of the saved processor
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del processor.feature_extractor.disable_grouping
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self.assertIsInstance(processor.feature_extractor, Gemma3nAudioFeatureExtractor)
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self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
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def test_save_load_pretrained_additional_features(self):
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tokenizer = self.get_tokenizer()
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feature_extractor = self.get_feature_extractor()
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image_processor = self.get_image_processor()
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processor = Gemma3nProcessor(
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tokenizer=tokenizer, feature_extractor=feature_extractor, image_processor=image_processor
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)
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processor.save_pretrained(self.tmpdirname)
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tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS-BOS)", eos_token="(EOS-EOS)")
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feature_extractor_add_kwargs = self.get_feature_extractor(dither=5.0, padding_value=1.0)
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processor = Gemma3nProcessor.from_pretrained(
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self.tmpdirname, bos_token="(BOS-BOS)", eos_token="(EOS-EOS)", dither=5.0, padding_value=1.0
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)
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self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
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self.assertIsInstance(processor.tokenizer, GemmaTokenizerFast)
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# `disable_grouping` is a new attribute that got added on main while gemma3n was being released - so was
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# not part of the saved processor
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del processor.feature_extractor.disable_grouping
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self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
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self.assertIsInstance(processor.feature_extractor, Gemma3nAudioFeatureExtractor)
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@parameterized.expand([256, 512, 768, 1024])
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def test_image_processor(self, image_size: int):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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image_processor = self.get_image_processor()
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processor = Gemma3nProcessor(
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tokenizer=tokenizer, feature_extractor=feature_extractor, image_processor=image_processor
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)
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raw_image = np.random.randint(0, 256, size=(image_size, image_size, 3), dtype=np.uint8)
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input_image_processor = image_processor(raw_image, return_tensors="pt")
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input_processor = processor(text="Describe:", images=raw_image, return_tensors="pt")
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for key in input_image_processor.keys():
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self.assertAlmostEqual(input_image_processor[key].sum(), input_processor[key].sum(), delta=1e-2)
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if "pixel_values" in key:
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# NOTE: all images should be re-scaled to 768x768
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self.assertEqual(input_image_processor[key].shape, (1, 3, 768, 768))
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self.assertEqual(input_processor[key].shape, (1, 3, 768, 768))
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def test_audio_feature_extractor(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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image_processor = self.get_image_processor()
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processor = Gemma3nProcessor(
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tokenizer=tokenizer, feature_extractor=feature_extractor, image_processor=image_processor
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)
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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.keys():
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self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
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def test_tokenizer(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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image_processor = self.get_image_processor()
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processor = Gemma3nProcessor(
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tokenizer=tokenizer, feature_extractor=feature_extractor, image_processor=image_processor
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)
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input_str = "This is a test string"
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encoded_processor = processor(text=input_str)
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encoded_tok = tokenizer(input_str)
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for key in encoded_tok.keys():
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self.assertListEqual(encoded_tok[key], encoded_processor[key][0])
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def test_tokenizer_decode(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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image_processor = self.get_image_processor()
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processor = Gemma3nProcessor(
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tokenizer=tokenizer, feature_extractor=feature_extractor, image_processor=image_processor
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)
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predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
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decoded_processor = processor.batch_decode(predicted_ids)
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decoded_tok = tokenizer.batch_decode(predicted_ids)
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self.assertListEqual(decoded_tok, decoded_processor)
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def test_model_input_names(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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image_processor = self.get_image_processor()
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processor = Gemma3nProcessor(
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tokenizer=tokenizer, feature_extractor=feature_extractor, image_processor=image_processor
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)
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for key in feature_extractor.model_input_names:
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self.assertIn(
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key,
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processor.model_input_names,
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)
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for key in image_processor.model_input_names:
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self.assertIn(
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key,
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processor.model_input_names,
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)
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