init

transformers/tests/models/gemma3n/test_processing_gemma3n.py

@@ -0,0 +1,166 @@
+# Copyright 2025 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import shutil
+import tempfile
+import unittest
+
+import numpy as np
+from parameterized import parameterized
+
+from transformers import GemmaTokenizerFast, SiglipImageProcessorFast, is_speech_available
+from transformers.testing_utils import require_sentencepiece, require_torch, require_torchaudio, require_vision
+
+from .test_feature_extraction_gemma3n import floats_list
+
+
+if is_speech_available():
+    from transformers.models.gemma3n import Gemma3nAudioFeatureExtractor, Gemma3nProcessor
+
+
+# TODO: omni-modal processor can't run tests from `ProcessorTesterMixin`
+@require_torch
+@require_torchaudio
+@require_vision
+@require_sentencepiece
+class Gemma3nProcessorTest(unittest.TestCase):
+    def setUp(self):
+        # TODO: update to google?
+        self.model_id = "hf-internal-testing/namespace-google-repo_name-gemma-3n-E4B-it"
+        self.tmpdirname = tempfile.mkdtemp(suffix="gemma3n")
+        self.maxDiff = None
+
+    def get_tokenizer(self, **kwargs):
+        return GemmaTokenizerFast.from_pretrained(self.model_id, **kwargs)
+
+    def get_feature_extractor(self, **kwargs):
+        return Gemma3nAudioFeatureExtractor.from_pretrained(self.model_id, **kwargs)
+
+    def get_image_processor(self, **kwargs):
+        return SiglipImageProcessorFast.from_pretrained(self.model_id, **kwargs)
+
+    def tearDown(self):
+        shutil.rmtree(self.tmpdirname)
+
+    def test_save_load_pretrained_default(self):
+        # NOTE: feature_extractor and image_processor both use the same filename, preprocessor_config.json, when saved to
+        # disk, but the files are overwritten by processor.save_pretrained(). This test does not attempt to address
+        # this potential issue, and as such, does not guarantee content accuracy.
+
+        tokenizer = self.get_tokenizer()
+        feature_extractor = self.get_feature_extractor()
+        image_processor = self.get_image_processor()
+
+        processor = Gemma3nProcessor(
+            tokenizer=tokenizer, feature_extractor=feature_extractor, image_processor=image_processor
+        )
+
+        processor.save_pretrained(self.tmpdirname, legacy_serialization=False)
+        processor = Gemma3nProcessor.from_pretrained(self.tmpdirname)
+
+        self.assertIsInstance(processor.tokenizer, GemmaTokenizerFast)
+        self.assertEqual(processor.tokenizer.get_vocab(), tokenizer.get_vocab())
+
+        self.assertIsInstance(processor.feature_extractor, Gemma3nAudioFeatureExtractor)
+        self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor.to_json_string())
+
+    def test_save_load_pretrained_additional_features(self):
+        tokenizer = self.get_tokenizer()
+        feature_extractor = self.get_feature_extractor()
+        image_processor = self.get_image_processor()
+
+        processor = Gemma3nProcessor(
+            tokenizer=tokenizer, feature_extractor=feature_extractor, image_processor=image_processor
+        )
+        processor.save_pretrained(self.tmpdirname, legacy_serialization=False)
+
+        tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS-BOS)", eos_token="(EOS-EOS)")
+        feature_extractor_add_kwargs = self.get_feature_extractor(dither=5.0, padding_value=1.0)
+
+        processor = Gemma3nProcessor.from_pretrained(
+            self.tmpdirname, bos_token="(BOS-BOS)", eos_token="(EOS-EOS)", dither=5.0, padding_value=1.0
+        )
+
+        self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
+        self.assertIsInstance(processor.tokenizer, GemmaTokenizerFast)
+
+        self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
+        self.assertIsInstance(processor.feature_extractor, Gemma3nAudioFeatureExtractor)
+
+    @parameterized.expand([256, 512, 768, 1024])
+    def test_image_processor(self, image_size: int):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+        image_processor = self.get_image_processor()
+        processor = Gemma3nProcessor(
+            tokenizer=tokenizer, feature_extractor=feature_extractor, image_processor=image_processor
+        )
+
+        raw_image = np.random.randint(0, 256, size=(image_size, image_size, 3), dtype=np.uint8)
+        input_image_processor = image_processor(raw_image, return_tensors="pt")
+        input_processor = processor(text="Describe:", images=raw_image, return_tensors="pt")
+
+        for key in input_image_processor:
+            self.assertAlmostEqual(input_image_processor[key].sum(), input_processor[key].sum(), delta=1e-2)
+            if "pixel_values" in key:
+                # NOTE: all images should be re-scaled to 768x768
+                self.assertEqual(input_image_processor[key].shape, (1, 3, 768, 768))
+                self.assertEqual(input_processor[key].shape, (1, 3, 768, 768))
+
+    def test_audio_feature_extractor(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+        image_processor = self.get_image_processor()
+        processor = Gemma3nProcessor(
+            tokenizer=tokenizer, feature_extractor=feature_extractor, image_processor=image_processor
+        )
+
+        raw_speech = floats_list((3, 1000))
+        input_feat_extract = feature_extractor(raw_speech, return_tensors="pt")
+        input_processor = processor(text="Transcribe:", audio=raw_speech, return_tensors="pt")
+
+        for key in input_feat_extract:
+            self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
+
+    def test_tokenizer(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+        image_processor = self.get_image_processor()
+        processor = Gemma3nProcessor(
+            tokenizer=tokenizer, feature_extractor=feature_extractor, image_processor=image_processor
+        )
+
+        input_str = "This is a test string"
+
+        encoded_processor = processor(text=input_str)
+
+        encoded_tok = tokenizer(input_str)
+
+        for key in encoded_tok:
+            self.assertListEqual(encoded_tok[key], encoded_processor[key][0])
+
+    def test_tokenizer_decode(self):
+        feature_extractor = self.get_feature_extractor()
+        tokenizer = self.get_tokenizer()
+        image_processor = self.get_image_processor()
+        processor = Gemma3nProcessor(
+            tokenizer=tokenizer, feature_extractor=feature_extractor, image_processor=image_processor
+        )
+
+        predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
+
+        decoded_processor = processor.batch_decode(predicted_ids)
+        decoded_tok = tokenizer.batch_decode(predicted_ids)
+
+        self.assertListEqual(decoded_tok, decoded_processor)