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transformers/tests/models/gemma3/test_processing_gemma3.py

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+# 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
+from typing import Optional
+
+from transformers import Gemma3Processor, GemmaTokenizer
+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():
+    from transformers import Gemma3ImageProcessor
+
+SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
+
+
+@require_vision
+class Gemma3ProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = Gemma3Processor
+
+    @classmethod
+    def setUpClass(cls):
+        cls.tmpdirname = tempfile.mkdtemp()
+        gemma3_image_processor_kwargs = {
+            "do_pan_and_scan": True,
+            "pan_and_scan_min_crop_size": 256,
+            "pan_and_scan_max_num_crops": 4,
+            "pan_and_scan_min_ratio_to_activate": 1.2,
+        }
+        image_processor = Gemma3ImageProcessor.from_pretrained(
+            "google/siglip-so400m-patch14-384", **gemma3_image_processor_kwargs
+        )
+
+        extra_special_tokens = {
+            "image_token": "<image_soft_token>",
+            "boi_token": "<start_of_image>",
+            "eoi_token": "<end_of_image>",
+        }
+        tokenizer = GemmaTokenizer(SAMPLE_VOCAB, keep_accents=True, extra_special_tokens=extra_special_tokens)
+        processor_kwargs = cls.prepare_processor_dict()
+        processor = Gemma3Processor(image_processor=image_processor, tokenizer=tokenizer, **processor_kwargs)
+        processor.save_pretrained(cls.tmpdirname)
+        cls.image_token = processor.boi_token
+
+    # 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)
+
+    # TODO: raushan or arthur: add the real chat template
+    @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                {{ '<start_of_image>' }}\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 Gemma3 needs images to be an explicitly nested batch
+    def prepare_image_inputs(self, batch_size: Optional[int] = 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):
+        image_processor = self.get_component("image_processor")
+        tokenizer = self.get_component("tokenizer")
+
+        processor = self.processor_class(tokenizer=tokenizer, image_processor=image_processor)
+        text_multi_images = f"{processor.boi_token}{processor.boi_token}Dummy text!"
+        text_single_image = f"{processor.boi_token}Dummy text!"
+        text_no_image = "Dummy text!"
+
+        image = self.prepare_image_inputs()
+
+        # If text has no image tokens, image should be `None`
+        with self.assertRaises(ValueError):
+            _ = processor(text=text_no_image, images=image, return_tensors="np")
+
+        # 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_pan_and_scan(self):
+        processor_components = self.prepare_components()
+        processor_kwargs = self.prepare_processor_dict()
+        processor = self.processor_class(**processor_components, **processor_kwargs)
+
+        input_str = self.prepare_text_inputs(modalities="image")
+        image_input = self.prepare_image_inputs()
+        inputs = processor(
+            text=input_str,
+            images=image_input,
+            return_tensors="np",
+            do_pan_and_scan=True,
+            image_seq_length=2,
+            pan_and_scan_min_crop_size=10,
+        )
+
+        # base image + 4 crops
+        self.assertEqual(len(inputs[self.images_input_name]), 5)
+        self.assertEqual(len(inputs[self.text_input_name][0]), 67)
+
+    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,
+            )