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tests/models/granite4_vision/test_processing_granite4_vision.py

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+# Copyright 2026 IBM. 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 json
+import unittest
+
+import torch
+
+from transformers import Granite4VisionProcessor
+from transformers.testing_utils import require_vision
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+@require_vision
+class Granite4VisionProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = Granite4VisionProcessor
+    # Image token expansion with downsample_rate="1/2" produces more tokens than the defaults
+    image_text_kwargs_max_length = 300
+    image_text_kwargs_override_max_length = 280
+    image_unstructured_max_length = 260
+
+    @classmethod
+    def _setup_tokenizer(cls):
+        tokenizer_class = cls._get_component_class_from_processor("tokenizer")
+        tokenizer = tokenizer_class.from_pretrained("huggyllama/llama-7b")
+        tokenizer.add_special_tokens({"additional_special_tokens": ["<image>"]})
+        if not tokenizer.pad_token:
+            tokenizer.pad_token = "[PAD]"
+            if tokenizer.pad_token_id is None:
+                tokenizer.pad_token_id = 0
+        return tokenizer
+
+    @classmethod
+    def _setup_test_attributes(cls, processor):
+        cls.image_token = processor.image_token
+
+    @staticmethod
+    def prepare_processor_dict():
+        return {
+            "chat_template": "{% for message in messages %}{% if message['role'] != 'system' %}{{ message['role'].upper() + ': '}}{% endif %}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '<image>\n' }}{% endfor %}{# Render all text next #}{% if message['role'] != 'assistant' %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ content['text'] + ' '}}{% endfor %}{% else %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ content['text'] + ' '}}{% endgeneration %}{% endfor %}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'ASSISTANT:' }}{% endif %}",
+            "patch_size": 14,
+            "vision_feature_select_strategy": "default",
+            "downsample_rate": "1/2",
+        }  # fmt: skip
+
+    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)
+
+    def test_chat_template_is_saved(self):
+        processor_loaded = self.processor_class.from_pretrained(self.tmpdirname)
+        processor_dict_loaded = json.loads(processor_loaded.to_json_string())
+        # chat templates aren't serialized to json in processors
+        self.assertFalse("chat_template" in processor_dict_loaded)
+
+        # they have to be saved as separate file and loaded back from that file
+        # so we check if the same template is loaded
+        processor_dict = self.prepare_processor_dict()
+        self.assertTrue(processor_loaded.chat_template == processor_dict.get("chat_template", None))
+
+    def test_image_token_filling(self):
+        processor = self.processor_class.from_pretrained(self.tmpdirname)
+        processor.patch_size = 14
+        processor.vision_feature_select_strategy = "default"
+        processor.downsample_rate = "1/2"
+        processor.image_processor.crop_size = {"height": 336, "width": 336}
+        processor.image_processor.size = {"shortest_edge": 336}
+        processor.image_processor.image_grid_pinpoints = [[672, 336]]
+        # Important to check with non square image
+        image = torch.randint(0, 2, (3, 503, 316))
+        image_token_index = processor.image_token_id
+
+        # With downsample_rate="1/2" and patch_size=14:
+        #   patches = 336/14 = 24, after ds: 24*1/2 = 12
+        #   best resolution for (503, 316): [672, 336]
+        #   scale_height=2, scale_width=1
+        #   current = 12*2=24 h, 12*1=12 w
+        #   aspect: 316/503 = 0.628, 12/24 = 0.5 -> orig > current -> new_height = round(503*(12/316)) = 19
+        #   padding = (24-19)//2 = 2, current_height = 24 - 4 = 20
+        #   unpadded = 20*12 = 240, newline = 20
+        #   base = 12*12 + 0 = 144
+        #   total = 240 + 20 + 144 = 404
+        #   with "default" strategy: 404 - 1 = 403
+        expected_image_tokens = 403
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image"},
+                    {"type": "text", "text": "What is shown in this image?"},
+                ],
+            },
+        ]
+        inputs = processor(
+            text=[processor.apply_chat_template(messages)],
+            images=[image],
+            return_tensors="pt",
+        )
+        image_tokens = (inputs["input_ids"] == image_token_index).sum().item()
+        self.assertEqual(expected_image_tokens, image_tokens)