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tests/models/cohere2_vision/test_processing_cohere2_vision.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 unittest
+
+from transformers import Cohere2VisionProcessor
+from transformers.testing_utils import require_vision
+from transformers.utils import is_torch_available, is_torchvision_available
+
+from ...test_processing_common import ProcessorTesterMixin, url_to_local_path
+
+
+if is_torch_available():
+    import torch
+
+if is_torchvision_available():
+    pass
+
+
+@require_vision
+@unittest.skip("Model not released yet!")
+class Cohere2VisionProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = Cohere2VisionProcessor
+
+    @classmethod
+    def _setup_tokenizer(cls):
+        tokenizer_class = cls._get_component_class_from_processor("tokenizer")
+        return tokenizer_class.from_pretrained("CohereLabs/command-a-vision-07-2025")
+
+    @classmethod
+    def _setup_image_processor(cls):
+        image_processor_class = cls._get_component_class_from_processor("image_processor")
+        return image_processor_class(
+            size={"height": 20, "width": 20},
+            max_patches=3,
+        )
+
+    def test_process_interleaved_images_videos(self):
+        processor = self.get_processor()
+
+        messages = [
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image",
+                            "url": url_to_local_path(
+                                "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
+                            ),
+                        },
+                        {
+                            "type": "image",
+                            "url": url_to_local_path(
+                                "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg"
+                            ),
+                        },
+                        {"type": "text", "text": "What are the differences between these two images?"},
+                    ],
+                },
+            ],
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image",
+                            "url": url_to_local_path("https://llava-vl.github.io/static/images/view.jpg"),
+                        },
+                        {"type": "text", "text": "Write a haiku for this image"},
+                    ],
+                }
+            ],
+        ]
+
+        inputs_batched = processor.apply_chat_template(
+            messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+            padding=True,
+        )
+
+        # Process non batched inputs to check if the pixel_values and input_ids are reconstructed in the correct order when batched together
+        images_patches_index = 0
+        for i, message in enumerate(messages):
+            inputs = processor.apply_chat_template(
+                message,
+                add_generation_prompt=True,
+                tokenize=True,
+                return_dict=True,
+                return_tensors="pt",
+                padding=True,
+            )
+            # We slice with [-inputs["input_ids"].shape[1] :] as the input_ids are left padded
+            torch.testing.assert_close(
+                inputs["input_ids"][0], inputs_batched["input_ids"][i][-inputs["input_ids"].shape[1] :]
+            )
+            torch.testing.assert_close(
+                inputs["pixel_values"],
+                inputs_batched["pixel_values"][
+                    images_patches_index : images_patches_index + inputs["pixel_values"].shape[0]
+                ],
+            )
+            images_patches_index += inputs["pixel_values"].shape[0]