init

transformers/tests/models/cohere2_vision/test_image_processing_cohere2_vision.py

@@ -0,0 +1,192 @@
+# Copyright 2025 HuggingFace Inc.
+#
+# 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
+
+import numpy as np
+
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
+
+from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    from PIL import Image
+
+    if is_torchvision_available():
+        from transformers import Cohere2VisionImageProcessorFast
+
+
+class Cohere2VisionImageProcessingTester(unittest.TestCase):
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        image_size=18,
+        min_resolution=30,
+        max_resolution=400,
+        do_resize=True,
+        size=None,
+        do_normalize=True,
+        image_mean=[0.48145466, 0.4578275, 0.40821073],
+        image_std=[0.26862954, 0.26130258, 0.27577711],
+        do_convert_rgb=True,
+    ):
+        super().__init__()
+        size = size if size is not None else {"height": 30, "width": 30}
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.do_resize = do_resize
+        self.size = size
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.do_convert_rgb = do_convert_rgb
+
+    def prepare_image_processor_dict(self):
+        return {
+            "do_resize": self.do_resize,
+            "size": self.size,
+            "do_normalize": self.do_normalize,
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "do_convert_rgb": self.do_convert_rgb,
+        }
+
+    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
+        return prepare_image_inputs(
+            batch_size=self.batch_size,
+            num_channels=self.num_channels,
+            min_resolution=self.min_resolution,
+            max_resolution=self.max_resolution,
+            equal_resolution=equal_resolution,
+            numpify=numpify,
+            torchify=torchify,
+        )
+
+
+@require_torch
+@require_vision
+class Cohere2VisionProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    fast_image_processing_class = Cohere2VisionImageProcessorFast if is_torchvision_available() else None
+    test_slow_image_processor = False
+
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = Cohere2VisionImageProcessingTester(self)
+
+    @property
+    def image_processor_dict(self):
+        return self.image_processor_tester.prepare_image_processor_dict()
+
+    def test_image_processor_properties(self):
+        for image_processing_class in self.image_processor_list:
+            image_processor = image_processing_class(**self.image_processor_dict)
+            self.assertTrue(hasattr(image_processor, "do_resize"))
+            self.assertTrue(hasattr(image_processor, "size"))
+            self.assertTrue(hasattr(image_processor, "do_normalize"))
+            self.assertTrue(hasattr(image_processor, "image_mean"))
+            self.assertTrue(hasattr(image_processor, "image_std"))
+            self.assertTrue(hasattr(image_processor, "do_convert_rgb"))
+
+    def test_call_pil(self):
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random PIL images
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True)
+            for image in image_inputs:
+                self.assertIsInstance(image, Image.Image)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
+            self.assertEqual(tuple(encoded_images.shape), (10, 3, 30, 30))
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
+            self.assertEqual(tuple(encoded_images.shape), (70, 3, 30, 30))
+
+    def test_call_numpy(self):
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random numpy tensors
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, numpify=True)
+            for image in image_inputs:
+                self.assertIsInstance(image, np.ndarray)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
+            self.assertEqual(tuple(encoded_images.shape), (10, 3, 30, 30))
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
+            self.assertEqual(tuple(encoded_images.shape), (70, 3, 30, 30))
+
+    def test_call_pytorch(self):
+        for image_processing_class in self.image_processor_list:
+            # Initialize image_processing
+            image_processing = image_processing_class(**self.image_processor_dict)
+            # create random PyTorch tensors
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True)
+
+            for image in image_inputs:
+                self.assertIsInstance(image, torch.Tensor)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
+            self.assertEqual(tuple(encoded_images.shape), (10, 3, 30, 30))
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
+            self.assertEqual(tuple(encoded_images.shape), (70, 3, 30, 30))
+
+    def test_call_numpy_4_channels(self):
+        for image_processing_class in self.image_processor_list:
+            # Test that can process images which have an arbitrary number of channels
+            # Initialize image_processing
+            image_processor = image_processing_class(**self.image_processor_dict)
+
+            # create random numpy tensors
+            self.image_processor_tester.num_channels = 4
+            image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, numpify=True)
+
+            # Test not batched input
+            encoded_images = image_processor(
+                image_inputs[0],
+                return_tensors="pt",
+                input_data_format="channels_last",
+                image_mean=0,
+                image_std=1,
+            ).pixel_values
+            self.assertEqual(tuple(encoded_images.shape), (10, 4, 30, 30))
+
+            # Test batched
+            encoded_images = image_processor(
+                image_inputs,
+                return_tensors="pt",
+                input_data_format="channels_last",
+                image_mean=0,
+                image_std=1,
+            ).pixel_values
+            self.assertEqual(tuple(encoded_images.shape), (70, 4, 30, 30))

transformers/tests/models/cohere2_vision/test_modeling_cohere2_vision.py

@@ -0,0 +1,450 @@
+# Copyright 2025 The HuggingFace Inc. 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.
+"""Testing suite for the PyTorch GotOcr2 model."""
+
+import unittest
+
+from transformers import (
+    AutoProcessor,
+    Cohere2VisionConfig,
+    is_torch_available,
+)
+from transformers.testing_utils import (
+    Expectations,
+    cleanup,
+    get_device_properties,
+    require_deterministic_for_xpu,
+    require_read_token,
+    require_torch,
+    require_torch_accelerator,
+    slow,
+    torch_device,
+)
+
+from ...generation.test_utils import GenerationTesterMixin
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        Cohere2VisionForConditionalGeneration,
+        Cohere2VisionModel,
+    )
+
+
+class Cohere2VisionText2TextModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=3,
+        seq_length=7,
+        downsample_factor=2,
+        alignment_intermediate_size=32,
+        ignore_index=-100,
+        image_token_id=2,
+        num_channels=3,
+        image_size=64,
+        is_training=True,
+        text_config={
+            "model_type": "cohere2",
+            "vocab_size": 99,
+            "hidden_size": 128,
+            "intermediate_size": 37,
+            "num_hidden_layers": 2,
+            "num_attention_heads": 4,
+            "output_channels": 64,
+            "hidden_act": "silu",
+            "max_position_embeddings": 512,
+            "tie_word_embeddings": True,
+            "bos_token_id": 0,
+            "eos_token_id": 0,
+            "pad_token_id": 0,
+        },
+        vision_config={
+            "model_type": "siglip_vision_model",
+            "hidden_size": 32,
+            "num_hidden_layers": 2,
+            "num_attention_heads": 4,
+            "intermediate_size": 128,
+            "image_size": 64,
+            "patch_size": 8,
+            "vision_use_head": False,
+        },
+    ):
+        self.parent = parent
+        self.ignore_index = ignore_index
+        self.bos_token_id = text_config["bos_token_id"]
+        self.eos_token_id = text_config["eos_token_id"]
+        self.pad_token_id = text_config["pad_token_id"]
+        self.image_token_id = image_token_id
+        self.text_config = text_config
+        self.vision_config = vision_config
+        self.batch_size = batch_size
+        self.downsample_factor = downsample_factor
+        self.alignment_intermediate_size = alignment_intermediate_size
+        self.is_training = is_training
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.image_seq_length = 16
+        self.seq_length = seq_length + self.image_seq_length
+
+        self.num_hidden_layers = text_config["num_hidden_layers"]
+        self.vocab_size = text_config["vocab_size"]
+        self.hidden_size = text_config["hidden_size"]
+        self.num_attention_heads = text_config["num_attention_heads"]
+
+    def get_config(self):
+        return Cohere2VisionConfig(
+            text_config=self.text_config,
+            vision_config=self.vision_config,
+            image_token_id=self.image_token_id,
+            downsample_factor=self.downsample_factor,
+            alignment_intermediate_size=self.alignment_intermediate_size,
+        )
+
+    def prepare_config_and_inputs(self):
+        config = self.get_config()
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+
+        return config, pixel_values
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values = config_and_inputs
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+        attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
+        input_ids[input_ids == self.image_token_id] = self.pad_token_id
+        input_ids[:, : self.image_seq_length] = self.image_token_id
+
+        inputs_dict = {
+            "pixel_values": pixel_values,
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class Cohere2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (
+        (
+            Cohere2VisionModel,
+            Cohere2VisionForConditionalGeneration,
+        )
+        if is_torch_available()
+        else ()
+    )
+    all_generative_model_classes = (Cohere2VisionForConditionalGeneration,) if is_torch_available() else ()
+    pipeline_model_mapping = (
+        {
+            "image-text-to-text": Cohere2VisionForConditionalGeneration,
+        }
+        if is_torch_available()
+        else {}
+    )
+    fx_compatible = False
+    test_pruning = False
+    test_torchscript = False
+    test_head_masking = False
+    _is_composite = True
+
+    def setUp(self):
+        self.model_tester = Cohere2VisionText2TextModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=Cohere2VisionConfig, has_text_modality=False)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip(reason="Siglip backbone uses a non-standard initialization scheme")
+    def test_initialization(self):
+        pass
+
+
+@require_read_token
+@require_torch
+class Cohere2IntegrationTest(unittest.TestCase):
+    def setUp(self):
+        self.model_checkpoint = "CohereLabs/command-a-vision-07-2025"
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    def get_model(self, dummy=True):
+        device_type, major, _ = get_device_properties()
+        dtype = torch.float16
+
+        # too large to fit into A10
+        config = Cohere2VisionConfig.from_pretrained(self.model_checkpoint)
+        if dummy:
+            config.text_config.num_hidden_layers = 4
+            config.text_config.layer_types = config.text_config.layer_types[:4]
+
+        model = Cohere2VisionForConditionalGeneration.from_pretrained(
+            self.model_checkpoint,
+            config=config,
+            dtype=dtype,
+            device_map="auto",
+        )
+        return model
+
+    @slow
+    @require_torch_accelerator
+    def test_model_integration_forward(self):
+        processor = AutoProcessor.from_pretrained(self.model_checkpoint)
+        model = self.get_model(dummy=False)
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
+                    {"type": "text", "text": "Please describe the image explicitly."},
+                ],
+            }
+        ]
+
+        inputs = processor.apply_chat_template(
+            messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
+        ).to(torch_device, dtype=torch.float16)
+        # Forward
+        with torch.inference_mode():
+            output = model(**inputs)
+
+        actual_logits = output.logits[0, -1, :5].cpu()
+
+        EXPECTED_LOGITS = Expectations(
+            {
+                ("xpu", 3): [0.4109, 0.1532, 0.8018, 2.1328, 0.5483],
+                # 4-bit
+                ("cuda", 7): [0.1097, 0.3481, 3.8340, 9.7969, 2.0488],
+                ("cuda", 8): [2.4277, 1.6875, 1.8789, 2.1875, 1.9375],
+            }
+        )  # fmt: skip
+        expected_logits = torch.tensor(EXPECTED_LOGITS.get_expectation(), dtype=torch.float16)
+
+        self.assertTrue(
+            torch.allclose(actual_logits, expected_logits, atol=0.1),
+            f"Actual logits: {actual_logits}"
+            f"\nExpected logits: {expected_logits}"
+            f"\nDifference: {torch.abs(actual_logits - expected_logits)}",
+        )
+
+    @slow
+    @require_torch_accelerator
+    @require_deterministic_for_xpu
+    def test_model_integration_generate_text_only(self):
+        processor = AutoProcessor.from_pretrained(self.model_checkpoint)
+        model = self.get_model()
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "Write a haiku"},
+                ],
+            }
+        ]
+
+        inputs = processor.apply_chat_template(
+            messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
+        ).to(torch_device, dtype=torch.float16)
+        with torch.no_grad():
+            generate_ids = model.generate(**inputs, max_new_tokens=10, do_sample=False)
+            decoded_output = processor.decode(
+                generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True
+            )
+
+        expected_outputs = Expectations(
+            {
+                ("cuda", 8): "<|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|>",
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
+
+        self.assertEqual(decoded_output, expected_output)
+
+    @slow
+    @require_torch_accelerator
+    @require_deterministic_for_xpu
+    def test_model_integration_generate_chat_template(self):
+        processor = AutoProcessor.from_pretrained(self.model_checkpoint)
+        model = self.get_model()
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
+                    {"type": "text", "text": "Please describe the image explicitly."},
+                ],
+            }
+        ]
+
+        inputs = processor.apply_chat_template(
+            messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
+        ).to(torch_device, dtype=torch.float16)
+        with torch.no_grad():
+            generate_ids = model.generate(**inputs, max_new_tokens=10, do_sample=False)
+            decoded_output = processor.decode(
+                generate_ids[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True
+            )
+
+        expected_outputs = Expectations(
+            {
+                ("cuda", 8): '<|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|>',
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
+
+        self.assertEqual(decoded_output, expected_output)
+
+    @slow
+    @require_torch_accelerator
+    def test_model_integration_batched_generate(self):
+        processor = AutoProcessor.from_pretrained(self.model_checkpoint)
+        model = self.get_model(dummy=False)
+        # Prepare inputs
+        messages = [
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "image", "url": "https://llava-vl.github.io/static/images/view.jpg"},
+                        {"type": "text", "text": "Write a haiku for this image"},
+                    ],
+                },
+            ],
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image",
+                            "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg",
+                        },
+                        {"type": "text", "text": "Describe this image"},
+                    ],
+                },
+            ],
+        ]
+        inputs = processor.apply_chat_template(
+            messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
+        ).to(model.device, dtype=torch.float16)
+
+        output = model.generate(**inputs, do_sample=False, max_new_tokens=5)
+
+        # Check first output
+        decoded_output = processor.decode(output[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
+        expected_outputs = Expectations(
+            {
+                ("cuda", 8): 'Dock stretches to calm',
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
+
+        self.assertEqual(
+            decoded_output,
+            expected_output,
+            f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
+        )
+
+        # Check second output
+        decoded_output = processor.decode(output[1, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
+
+        expected_outputs = Expectations(
+            {
+                ("cuda", 8): 'The image depicts a',
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
+
+        self.assertEqual(
+            decoded_output,
+            expected_output,
+            f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
+        )
+
+    @slow
+    @require_torch_accelerator
+    @require_deterministic_for_xpu
+    def test_model_integration_batched_generate_multi_image(self):
+        processor = AutoProcessor.from_pretrained(self.model_checkpoint)
+        model = self.get_model()
+        # Prepare inputs
+        messages = [
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "image", "url": "https://llava-vl.github.io/static/images/view.jpg"},
+                        {"type": "text", "text": "Write a haiku for this image"},
+                    ],
+                },
+            ],
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image",
+                            "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg",
+                        },
+                        {
+                            "type": "image",
+                            "url": "https://thumbs.dreamstime.com/b/golden-gate-bridge-san-francisco-purple-flowers-california-echium-candicans-36805947.jpg",
+                        },
+                        {
+                            "type": "text",
+                            "text": "These images depict two different landmarks. Can you identify them?",
+                        },
+                    ],
+                },
+            ],
+        ]
+        inputs = processor.apply_chat_template(
+            messages, padding=True, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
+        ).to(model.device, dtype=torch.float16)
+        output = model.generate(**inputs, do_sample=False, max_new_tokens=10)
+
+        # Check first output
+        decoded_output = processor.decode(output[0, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
+        # Batching seems to alter the output slightly, but it is also the case in the original implementation. This seems to be expected: https://github.com/huggingface/transformers/issues/23017#issuecomment-1649630232
+        expected_outputs = Expectations(
+            {
+                ("cuda", 8): '<|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|>',
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
+
+        self.assertEqual(
+            decoded_output,
+            expected_output,
+            f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
+        )
+
+        # Check second output
+        decoded_output = processor.decode(output[1, inputs["input_ids"].shape[1] :], skip_special_tokens=True)
+        expected_outputs = Expectations(
+            {
+                ("cuda", 8): '<|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|><|CHATBOT_TOKEN|>',
+            }
+        )  # fmt: skip
+        expected_output = expected_outputs.get_expectation()
+
+        self.assertEqual(
+            decoded_output,
+            expected_output,
+            f"Decoded output: {decoded_output}\nExpected output: {expected_output}",
+        )

transformers/tests/models/cohere2_vision/test_processing_cohere2_vision.py

@@ -0,0 +1,143 @@
+# 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 transformers import AutoProcessor, AutoTokenizer, Cohere2VisionProcessor
+from transformers.testing_utils import require_read_token, require_torch, 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():
+    from transformers import Cohere2VisionImageProcessorFast
+
+
+@require_read_token
+@require_vision
+@unittest.skip("Model not released yet!")
+class Cohere2VisionProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = Cohere2VisionProcessor
+
+    @classmethod
+    def setUpClass(cls):
+        cls.tmpdirname = tempfile.mkdtemp()
+        image_processor = Cohere2VisionImageProcessorFast(
+            size={"height": 20, "width": 20},
+            max_patches=3,
+        )
+        tokenizer = AutoTokenizer.from_pretrained("CohereLabs/command-a-vision-07-2025")
+
+        processor_kwargs = cls.prepare_processor_dict()
+        processor = Cohere2VisionProcessor(
+            image_processor=image_processor,
+            tokenizer=tokenizer,
+            **processor_kwargs,
+        )
+        processor.save_pretrained(cls.tmpdirname)
+        cls.image_token = processor.image_token
+
+    @staticmethod
+    def prepare_processor_dict():
+        return {}
+
+    def get_tokenizer(self, **kwargs):
+        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer
+
+    def get_image_processor(self, **kwargs):
+        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor
+
+    def get_processor(self, **kwargs):
+        return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs)
+
+    @classmethod
+    def tearDownClass(cls):
+        shutil.rmtree(cls.tmpdirname, ignore_errors=True)
+
+    @require_torch
+    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]