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tests/models/gemma4_unified/test_processing_gemma4_unified.py

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+# Copyright 2026 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 unittest
+
+import numpy as np
+
+from transformers import Gemma4UnifiedProcessor
+from transformers.testing_utils import get_tests_dir, require_vision
+from transformers.utils import is_vision_available
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+if is_vision_available():
+    pass
+
+SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
+
+
+@require_vision
+class Gemma4UnifiedProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = Gemma4UnifiedProcessor
+    video_unstructured_max_length = 570
+    video_text_kwargs_max_length = 570
+    video_text_kwargs_override_max_length = 570
+
+    @classmethod
+    def _setup_test_attributes(cls, processor):
+        cls.image_token = processor.image_token
+        cls.video_token = processor.video_token
+
+    @classmethod
+    def _setup_video_processor(cls):
+        video_processor_class = cls._get_component_class_from_processor("video_processor")
+        gemma4_video_processor_kwargs = {
+            "patch_size": 28,
+            "max_soft_tokens": 70,
+            "pooling_kernel_size": 3,
+            "num_frames": 2,
+        }
+        return video_processor_class(**gemma4_video_processor_kwargs)
+
+    @classmethod
+    def _setup_feature_extractor(cls):
+        feature_extractor_class = cls._get_component_class_from_processor("feature_extractor")
+        gemma4_feature_extractor_kwargs = {}
+        return feature_extractor_class(**gemma4_feature_extractor_kwargs)
+
+    @classmethod
+    def _setup_image_processor(cls):
+        image_processor_class = cls._get_component_class_from_processor("image_processor")
+        gemma4_image_processor_kwargs = {
+            "patch_size": 28,
+            "max_soft_tokens": 70,
+            "pooling_kernel_size": 3,
+        }
+        return image_processor_class(**gemma4_image_processor_kwargs)
+
+    @classmethod
+    def _setup_tokenizer(cls):
+        tokenizer_class = cls._get_component_class_from_processor("tokenizer")
+        extra_special_tokens = {
+            "image_token": "<image_soft_token>",
+            "boi_token": "<start_of_image>",
+            "eoi_token": "<end_of_image>",
+            "audio_token": "<audio_soft_token>",
+            "boa_token": "<start_of_audio>",
+            "eoa_token": "<end_of_audio>",
+        }
+        tokenizer = tokenizer_class.from_pretrained(
+            SAMPLE_VOCAB, keep_accents=True, extra_special_tokens=extra_special_tokens
+        )
+        tokenizer.pad_token_id = tokenizer.eos_token_id
+        return tokenizer
+
+    # Copied from tests.models.llava.test_processing_llava.LlavaProcessorTest.test_get_num_vision_tokens
+    def test_get_num_vision_tokens(self):
+        "Tests general functionality of the helper used internally in vLLM"
+
+        processor = self.get_processor()
+
+        output = processor._get_num_multimodal_tokens(image_sizes=[(100, 100), (300, 100), (500, 30)])
+        self.assertTrue("num_image_tokens" in output)
+        self.assertEqual(len(output["num_image_tokens"]), 3)
+
+        self.assertTrue("num_image_patches" in output)
+        self.assertEqual(len(output["num_image_patches"]), 3)
+
+    @classmethod
+    def tearDownClass(cls):
+        shutil.rmtree(cls.tmpdirname, ignore_errors=True)
+
+    @staticmethod
+    def prepare_processor_dict():
+        return {
+            "chat_template": "{{ bos_token }}\n{%- if messages[0]['role'] == 'system' -%}\n    {%- set first_user_prefix = messages[0]['content'][0]['text'] + '\n\n' -%}\n    {%- set loop_messages = messages[1:] -%}\n{%- else -%}\n    {%- set first_user_prefix = \"\" -%}\n    {%- set loop_messages = messages -%}\n{%- endif -%}\n{%- for message in loop_messages -%}\n    {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}\n        {{ raise_exception(\"Conversation roles must alternate user/assistant/user/assistant/...\") }}\n    {%- endif -%}\n    {%- if (message['role'] == 'assistant') -%}\n        {%- set role = \"model\" -%}\n    {%- else -%}\n        {%- set role = message['role'] -%}\n    {%- endif -%}\n    {{ '<start_of_turn>' + role + '\n' + (first_user_prefix if loop.first else \"\") }}\n    {%- if message['content'] is string -%}\n        {{ message['content'] | trim }}\n    {%- elif message['content'] is iterable -%}\n        {%- for item in message['content'] -%}\n            {%- if item['type'] == 'image' -%}\n                {{ '<image_soft_token>' }}\n       {%- elif item['type'] == 'video' -%}\n{{ '<video_soft_token>' }}\n      {%- elif item['type'] == 'text' -%}\n                {{ item['text'] | trim }}\n            {%- endif -%}\n        {%- endfor -%}\n    {%- else -%}\n        {{ raise_exception(\"Invalid content type\") }}\n    {%- endif -%}\n    {{ '<end_of_turn>\n' }}\n{%- endfor -%}\n{%- if add_generation_prompt -%}\n    {{'<start_of_turn>model\n'}}\n{%- endif -%}\n",            "image_seq_length": 3,
+        }  # fmt: skip
+
+    # Override as Gemma4Unified needs images to be an explicitly nested batch
+    def prepare_image_inputs(self, batch_size: int | None = None):
+        """This function prepares a list of PIL images for testing"""
+        images = super().prepare_image_inputs(batch_size)
+        if isinstance(images, (list, tuple)):
+            images = [[image] for image in images]
+        return images
+
+    def test_text_with_image_tokens(self):
+        feature_extractor = self.get_component("feature_extractor")
+        image_processor = self.get_component("image_processor")
+        video_processor = self.get_component("video_processor")
+        tokenizer = self.get_component("tokenizer")
+
+        processor = self.processor_class(
+            feature_extractor=feature_extractor,
+            tokenizer=tokenizer,
+            image_processor=image_processor,
+            video_processor=video_processor,
+        )
+        text_multi_images = f"{processor.image_token}{processor.image_token}Dummy text!"
+        text_single_image = f"{processor.image_token}Dummy text!"
+
+        image = self.prepare_image_inputs()
+
+        # We can't be sure what is users intention: if user wants one image per text OR two images for first text and no image for second text
+        with self.assertRaises(ValueError):
+            _ = processor(text=[text_single_image, text_single_image], images=[image, image], return_tensors="np")
+
+        # The users is expected to be explicit about which image belong to which text by nesting the images list
+        out_multiimages = processor(text=text_multi_images, images=[image, image], return_tensors="np")
+        out_batch_oneimage = processor(
+            text=[text_single_image, text_single_image], images=[[image], [image]], return_tensors="np"
+        )
+        self.assertListEqual(
+            out_batch_oneimage[self.images_input_name].tolist(), out_multiimages[self.images_input_name].tolist()
+        )
+
+    def test_special_mm_token_truncation(self):
+        """Tests that special vision tokens do not get truncated when `truncation=True` is set."""
+
+        processor = self.get_processor()
+
+        input_str = self.prepare_text_inputs(batch_size=2, modalities="image")
+        image_input = self.prepare_image_inputs(batch_size=2)
+        _ = processor(
+            text=input_str,
+            images=image_input,
+            return_tensors="pt",
+            truncation=None,
+            padding=True,
+        )
+
+        with self.assertRaises(ValueError):
+            _ = processor(
+                text=input_str,
+                images=image_input,
+                return_tensors="pt",
+                truncation=True,
+                padding=True,
+                max_length=5,
+            )
+
+    def test_get_num_multimodal_tokens_matches_processor_call(self):
+        "Tests that the helper used internally in vLLM works correctly"
+
+        processor = self.get_processor()
+        if processor.tokenizer.pad_token_id is None:
+            processor.tokenizer.pad_token_id = processor.tokenizer.eos_token_id
+
+        if not hasattr(processor, "_get_num_multimodal_tokens"):
+            self.skipTest("Processor doesn't support `_get_num_multimodal_tokens` yet")
+
+        image_sizes = [(100, 100), (300, 100), (500, 30), (213, 167)]
+
+        # Overwritten because Gemma3 needs nested image inputs
+        image_inputs = []
+        for h, w in image_sizes:
+            image_inputs.append([np.random.randint(255, size=(h, w, 3), dtype=np.uint8)])
+
+        text = [f"This is an image {getattr(self, 'image_token', '')}"] * len(image_inputs)
+        inputs = processor(
+            text=text, images=image_inputs, padding=True, return_mm_token_type_ids=True, return_tensors="pt"
+        )
+
+        if "mm_token_type_ids" not in inputs:
+            self.skipTest("Processor doesn't support `mm_token_type_ids`")
+
+        num_image_tokens_from_call = inputs.mm_token_type_ids.sum(-1).tolist()
+        num_image_tokens_from_helper = processor._get_num_multimodal_tokens(image_sizes=image_sizes)
+        self.assertListEqual(num_image_tokens_from_call, num_image_tokens_from_helper["num_image_tokens"])
+
+    @unittest.skip("This test seems to be loading a different video, check for all models and fix")
+    def test_apply_chat_template_video_frame_sampling(self):
+        pass