init

transformers/tests/models/deepseek_vl_hybrid/test_image_processing_deepseek_vl_hybrid.py

@@ -0,0 +1,274 @@
+# coding=utf-8
+# 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.image_utils import load_image
+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
+from ...test_processing_common import url_to_local_path
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import DeepseekVLHybridImageProcessor
+
+    if is_torchvision_available():
+        from transformers import DeepseekVLHybridImageProcessorFast
+
+
+class DeepseekVLHybridImageProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        image_size=18,
+        min_resolution=30,
+        max_resolution=400,
+        do_resize=True,
+        size=None,
+        high_res_size=None,
+        do_normalize=True,
+        image_mean=[0.5, 0.5, 0.5],
+        image_std=[0.5, 0.5, 0.5],
+        high_res_image_mean=[0.5, 0.5, 0.5],
+        high_res_image_std=[0.5, 0.5, 0.5],
+    ):
+        size = size if size is not None else {"height": 18, "width": 18}
+        high_res_size = high_res_size if high_res_size is not None else {"height": 36, "width": 36}
+        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.high_res_size = high_res_size
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.high_res_image_mean = high_res_image_mean
+        self.high_res_image_std = high_res_image_std
+
+    def prepare_image_processor_dict(self):
+        return {
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "high_res_image_mean": self.high_res_image_mean,
+            "high_res_image_std": self.high_res_image_std,
+            "do_normalize": self.do_normalize,
+            "do_resize": self.do_resize,
+            "size": self.size,
+            "high_res_size": self.high_res_size,
+        }
+
+    def expected_output_image_shape(self, images):
+        max_size = max(self.size["height"], self.size["width"])
+        return self.num_channels, max_size, max_size
+
+    def expected_output_high_res_image_shape(self, images):
+        max_size = max(self.high_res_size["height"], self.high_res_size["width"])
+        return self.num_channels, max_size, max_size
+
+    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 DeepseekVLHybridImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    image_processing_class = DeepseekVLHybridImageProcessor if is_vision_available() else None
+    fast_image_processing_class = DeepseekVLHybridImageProcessorFast if is_torchvision_available() else None
+
+    # Copied from tests.models.vit.test_image_processing_vit.ViTImageProcessingTester.setUp with ViT->DeepseekVLHybrid
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = DeepseekVLHybridImageProcessingTester(self)
+
+    @property
+    # Copied from tests.models.vit.test_image_processing_vit.ViTImageProcessingTester.image_processor_dict with ViT->DeepseekVLHybrid
+    def image_processor_dict(self):
+        return self.image_processor_tester.prepare_image_processor_dict()
+
+    # Copied from tests.models.vit.test_image_processing_vit.ViTImageProcessingTester.test_image_processor_from_dict_with_kwargs
+    def test_image_processor_from_dict_with_kwargs(self):
+        for image_processing_class in self.image_processor_list:
+            image_processor = image_processing_class.from_dict(self.image_processor_dict)
+            self.assertEqual(image_processor.size, {"height": 18, "width": 18})
+
+            image_processor = image_processing_class.from_dict(self.image_processor_dict, size=42)
+            self.assertEqual(image_processor.size, {"height": 42, "width": 42})
+
+    def test_image_processor_properties(self):
+        for image_processing_class in self.image_processor_list:
+            image_processing = image_processing_class(**self.image_processor_dict)
+            self.assertTrue(hasattr(image_processing, "image_mean"))
+            self.assertTrue(hasattr(image_processing, "image_std"))
+            self.assertTrue(hasattr(image_processing, "high_res_image_mean"))
+            self.assertTrue(hasattr(image_processing, "high_res_image_std"))
+            self.assertTrue(hasattr(image_processing, "do_normalize"))
+            self.assertTrue(hasattr(image_processing, "do_resize"))
+            self.assertTrue(hasattr(image_processing, "size"))
+            self.assertTrue(hasattr(image_processing, "high_res_size"))
+
+    def test_call_pil_high_res(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=False)
+            for image in image_inputs:
+                self.assertIsInstance(image, Image.Image)
+
+            # Test not batched input
+            encoded_images = image_processing(image_inputs[0], return_tensors="pt").high_res_pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_high_res_image_shape(
+                [image_inputs[0]]
+            )
+            self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_tensors="pt").high_res_pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_high_res_image_shape(
+                image_inputs
+            )
+            self.assertEqual(
+                tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
+            )
+
+    def test_call_numpy_high_res(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=False, 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").high_res_pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_high_res_image_shape(
+                [image_inputs[0]]
+            )
+            self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
+
+            # Test batched
+            encoded_images = image_processing(image_inputs, return_tensors="pt").high_res_pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_high_res_image_shape(
+                image_inputs
+            )
+            self.assertEqual(
+                tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
+            )
+
+    def test_call_pytorch_high_res(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=False, 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").high_res_pixel_values
+            expected_output_image_shape = self.image_processor_tester.expected_output_high_res_image_shape(
+                [image_inputs[0]]
+            )
+            self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
+
+            # Test batched
+            expected_output_image_shape = self.image_processor_tester.expected_output_high_res_image_shape(
+                image_inputs
+            )
+            encoded_images = image_processing(image_inputs, return_tensors="pt").high_res_pixel_values
+            self.assertEqual(
+                tuple(encoded_images.shape),
+                (self.image_processor_tester.batch_size, *expected_output_image_shape),
+            )
+
+    @require_vision
+    @require_torch
+    def test_slow_fast_equivalence(self):
+        if not self.test_slow_image_processor or not self.test_fast_image_processor:
+            self.skipTest(reason="Skipping slow/fast equivalence test")
+
+        if self.image_processing_class is None or self.fast_image_processing_class is None:
+            self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")
+
+        dummy_image = load_image(url_to_local_path("http://images.cocodataset.org/val2017/000000039769.jpg"))
+        image_processor_slow = self.image_processing_class(**self.image_processor_dict)
+        image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
+
+        encoding_slow = image_processor_slow(dummy_image, return_tensors="pt")
+        encoding_fast = image_processor_fast(dummy_image, return_tensors="pt")
+        self._assert_slow_fast_tensors_equivalence(encoding_slow.pixel_values, encoding_fast.pixel_values)
+        self._assert_slow_fast_tensors_equivalence(
+            encoding_slow.high_res_pixel_values, encoding_fast.high_res_pixel_values
+        )
+
+    @require_vision
+    @require_torch
+    def test_slow_fast_equivalence_batched(self):
+        if not self.test_slow_image_processor or not self.test_fast_image_processor:
+            self.skipTest(reason="Skipping slow/fast equivalence test")
+
+        if self.image_processing_class is None or self.fast_image_processing_class is None:
+            self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")
+
+        if hasattr(self.image_processor_tester, "do_center_crop") and self.image_processor_tester.do_center_crop:
+            self.skipTest(
+                reason="Skipping as do_center_crop is True and center_crop functions are not equivalent for fast and slow processors"
+            )
+
+        dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
+        image_processor_slow = self.image_processing_class(**self.image_processor_dict)
+        image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
+
+        encoding_slow = image_processor_slow(dummy_images, return_tensors=None)
+        encoding_fast = image_processor_fast(dummy_images, return_tensors=None)
+
+        # Overwrite as the outputs are not always all of the same shape (kept for BC)
+        for i in range(len(encoding_slow.pixel_values)):
+            self._assert_slow_fast_tensors_equivalence(
+                torch.from_numpy(encoding_slow.pixel_values[i]), encoding_fast.pixel_values[i]
+            )
+        for i in range(len(encoding_slow.high_res_pixel_values)):
+            self._assert_slow_fast_tensors_equivalence(
+                torch.from_numpy(encoding_slow.high_res_pixel_values[i]), encoding_fast.high_res_pixel_values[i]
+            )
+
+    @unittest.skip(reason="Not supported")
+    def test_call_numpy_4_channels(self):
+        pass

transformers/tests/models/deepseek_vl_hybrid/test_modeling_deepseek_vl_hybrid.py

@@ -0,0 +1,404 @@
+# coding=utf-8
+# 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.
+"""Testing suite for the PyTorch DeepseekVLHybrid model."""
+
+import re
+import tempfile
+import unittest
+
+from transformers import (
+    AutoProcessor,
+    DeepseekVLHybridConfig,
+    DeepseekVLHybridForConditionalGeneration,
+    DeepseekVLHybridModel,
+    is_torch_available,
+)
+from transformers.testing_utils import (
+    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, random_attention_mask
+
+
+if is_torch_available():
+    import torch
+
+
+class DeepseekVLHybridModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=2,
+        seq_length=25,
+        num_channels=3,
+        initializer_range=0.02,
+        is_training=True,
+        use_cache=False,
+        text_config={
+            "num_hidden_layers": 2,
+            "vocab_size": 99,
+            "hidden_size": 16,
+            "intermediate_size": 37,
+            "max_position_embeddings": 512,
+            "num_attention_heads": 4,
+            "pad_token_id": 1,
+        },
+        vision_config={
+            "num_hidden_layers": 1,
+            "hidden_size": 16,
+            "intermediate_size": 37,
+            "image_size": 32,
+            "patch_size": 8,
+            "hidden_act": "gelu",
+            "vision_use_head": False,
+            "num_attention_heads": 4,
+        },
+        high_res_vision_config={
+            "num_hidden_layers": 2,
+            "global_attn_indexes": [0],
+            "hidden_size": 16,
+            "intermediate_size": 37,
+            "mlp_dim": 24,
+            "output_channels": 4,
+            "image_size": 128,
+            "patch_size": 32,
+            "num_attention_heads": 4,
+        },
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.num_channels = num_channels
+        self.initializer_range = initializer_range
+        self.is_training = is_training
+        self.use_cache = use_cache
+
+        self.text_config = text_config
+        self.vision_config = vision_config
+        self.high_res_vision_config = high_res_vision_config
+        self.vision_config["num_channels"] = self.num_channels
+        self.high_res_vision_config["num_channels"] = self.num_channels
+
+        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"]
+        self.high_res_image_size = high_res_vision_config["image_size"]
+        self.image_size = vision_config["image_size"]
+        self.num_image_tokens = vision_config["image_size"] // vision_config["patch_size"]
+        self.pad_token_id = text_config["pad_token_id"]
+        self.image_token_id = self.vocab_size - 1
+
+    def get_config(self):
+        return DeepseekVLHybridConfig(
+            text_config=self.text_config,
+            vision_config=self.vision_config,
+            high_res_vision_config=self.high_res_vision_config,
+            image_token_id=self.image_token_id,
+        )
+
+    def prepare_config_and_inputs(self):
+        config = self.get_config()
+
+        # create text and vision inputs
+        input_ids = ids_tensor([self.batch_size, self.seq_length], config.text_config.vocab_size - 2) + 1
+        attention_mask = random_attention_mask([self.batch_size, self.seq_length])
+        pixel_values = floats_tensor(
+            [
+                self.batch_size,
+                self.num_channels,
+                self.image_size,
+                self.image_size,
+            ]
+        )
+        high_res_pixel_values = floats_tensor(
+            [
+                self.batch_size,
+                self.num_channels,
+                self.high_res_image_size,
+                self.high_res_image_size,
+            ]
+        )
+        # fill image_tokens
+        input_ids[:, : self.num_image_tokens] = self.image_token_id
+
+        return config, input_ids, attention_mask, pixel_values, high_res_pixel_values
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, input_ids, attention_mask, pixel_values, high_res_pixel_values = config_and_inputs
+        inputs_dict = {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "pixel_values": pixel_values,
+            "high_res_pixel_values": high_res_pixel_values,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class DeepseekVLHybridModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+    all_model_classes = (
+        (DeepseekVLHybridModel, DeepseekVLHybridForConditionalGeneration) if is_torch_available() else ()
+    )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": DeepseekVLHybridModel,
+            "image-text-to-text": DeepseekVLHybridForConditionalGeneration,
+        }
+        if is_torch_available()
+        else {}
+    )
+    _is_composite = True
+    test_pruning = False
+    test_head_masking = False
+
+    def setUp(self):
+        self.model_tester = DeepseekVLHybridModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=DeepseekVLHybridConfig, has_text_modality=False)
+
+    # overwrite inputs_embeds tests because we need to delete "pixel values" for LVLMs
+    def test_inputs_embeds(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            inputs = self._prepare_for_class(inputs_dict, model_class)
+
+            input_ids = inputs["input_ids"]
+            del inputs["input_ids"]
+            del inputs["pixel_values"]
+            del inputs["high_res_pixel_values"]
+
+            wte = model.get_input_embeddings()
+            inputs["inputs_embeds"] = wte(input_ids)
+
+            with torch.no_grad():
+                model(**inputs)
+
+    # overwrite inputs_embeds tests because we need to delete "pixel values" for VLMs.
+    def test_inputs_embeds_matches_input_ids(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            inputs = self._prepare_for_class(inputs_dict, model_class)
+            input_ids = inputs["input_ids"]
+            del inputs["input_ids"]
+            del inputs["pixel_values"]
+            del inputs["high_res_pixel_values"]
+
+            inputs_embeds = model.get_input_embeddings()(input_ids)
+
+            with torch.no_grad():
+                out_ids = model(input_ids=input_ids, **inputs)[0]
+                out_embeds = model(inputs_embeds=inputs_embeds, **inputs)[0]
+            torch.testing.assert_close(out_embeds, out_ids)
+
+    @unittest.skip(reason="Siglip uses a non-standard initialization scheme")
+    # Copied from tests.models.siglip.test_modeling_siglip.SiglipVisionModelTest.test_initialization
+    def test_initialization(self):
+        pass
+
+    def test_sdpa_can_dispatch_composite_models(self):
+        for model_class in self.all_model_classes:
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+            model = model_class(config)
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                model.save_pretrained(tmpdirname)
+
+                # Load the model with SDPA
+                model_sdpa = model_class.from_pretrained(
+                    tmpdirname,
+                    attn_implementation="sdpa",
+                )
+                model_sdpa = model_sdpa.eval().to(torch_device)
+
+                # Load model with eager attention
+                model_eager = model_class.from_pretrained(
+                    tmpdirname,
+                    attn_implementation="eager",
+                )
+                model_eager = model_eager.eval().to(torch_device)
+
+            self.assertTrue(model_sdpa.config._attn_implementation == "sdpa")
+            self.assertTrue(model_eager.config._attn_implementation == "eager")
+
+            if (
+                hasattr(model_sdpa, "vision_model")
+                and hasattr(model_sdpa, "high_res_vision_model")
+                and hasattr(model_sdpa, "language_model")
+            ):
+                self.assertTrue(model_sdpa.language_model.config._attn_implementation == "sdpa")
+                self.assertTrue(model_sdpa.vision_model.config._attn_implementation == "sdpa")
+                self.assertTrue(model_sdpa.high_res_vision_model.config._attn_implementation == "sdpa")
+                self.assertTrue(model_eager.language_model.config._attn_implementation == "eager")
+                self.assertTrue(model_eager.high_res_vision_model.config._attn_implementation == "eager")
+
+            for name, submodule in model_eager.named_modules():
+                class_name = submodule.__class__.__name__
+                if (
+                    any(re.finditer(r"Attention(?!Pool)", class_name))
+                    and getattr(submodule, "config", None)
+                    and submodule.config._attn_implementation == "sdpa"
+                ):
+                    self.assertTrue(submodule.config._attn_implementation == "eager")
+
+            for name, submodule in model_sdpa.named_modules():
+                class_name = submodule.__class__.__name__
+                if (
+                    any(re.finditer(r"Attention(?!Pool)", class_name))
+                    and getattr(submodule, "config", None)
+                    and submodule.config._attn_implementation == "eager"
+                ):
+                    self.assertTrue(submodule.config._attn_implementation == "sdpa")
+
+
+@require_torch
+@require_torch_accelerator
+@slow
+class DeepseekVLHybridIntegrationTest(unittest.TestCase):
+    def setUp(self):
+        self.model_id = "deepseek-community/deepseek-vl-7b-chat"
+
+    def test_model_text_generation(self):
+        model = DeepseekVLHybridForConditionalGeneration.from_pretrained(
+            self.model_id, dtype="auto", device_map="auto"
+        )
+        model.to(torch_device)
+        model.eval()
+        processor = AutoProcessor.from_pretrained(self.model_id)
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image",
+                        "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg",
+                    },
+                    {"type": "text", "text": "Describe this image."},
+                ],
+            }
+        ]
+        EXPECTED_TEXT = 'You are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.\n\nUser: Describe this image.\n\nAssistant:The image depicts a fluffy, beige-colored animal with a long tail, walking on snow. The'  # fmt: skip
+
+        inputs = processor.apply_chat_template(
+            messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
+        )
+        inputs = inputs.to(model.device, dtype=model.dtype)
+        output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
+        text = processor.decode(output[0], skip_special_tokens=True)
+
+        self.assertEqual(
+            text,
+            EXPECTED_TEXT,
+        )
+
+    def test_model_text_generation_batched(self):
+        model = DeepseekVLHybridForConditionalGeneration.from_pretrained(
+            self.model_id, dtype="auto", device_map="auto"
+        )
+        model.to(torch_device)
+        model.eval()
+        processor = AutoProcessor.from_pretrained(self.model_id)
+
+        messages = [
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image",
+                            "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg",
+                        },
+                        {"type": "text", "text": "Describe this image."},
+                    ],
+                }
+            ],
+            [
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image",
+                            "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg",
+                        },
+                        {"type": "text", "text": "What animal do you see in the image?"},
+                    ],
+                }
+            ],
+        ]
+        EXPECTED_TEXT = [
+            "You are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.\n\nUser: Describe this image.\n\nAssistant:The image depicts a fluffy, beige-colored animal with a long tail, walking on snow. The",  # fmt: skip
+            "You are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.\n\nUser: What animal do you see in the image?\n\nAssistant:I see a large, furry animal that appears to be a type of bear.The ",  # fmt: skip
+        ]
+
+        inputs = processor.apply_chat_template(
+            messages, add_generation_prompt=True, tokenize=True, padding=True, return_dict=True, return_tensors="pt"
+        )
+        inputs = inputs.to(model.device, dtype=model.dtype)
+        output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
+        text = processor.batch_decode(output, skip_special_tokens=True)
+
+        self.assertEqual(EXPECTED_TEXT, text)
+
+    def test_model_text_generation_with_multi_image(self):
+        model = DeepseekVLHybridForConditionalGeneration.from_pretrained(
+            self.model_id, dtype="auto", device_map="auto"
+        )
+        model.to(torch_device)
+        model.eval()
+        processor = AutoProcessor.from_pretrained(self.model_id)
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "text": "What's the difference between"},
+                    {"type": "image", "url": "http://images.cocodataset.org/val2017/000000039769.jpg"},
+                    {"type": "text", "text": " and "},
+                    {
+                        "type": "image",
+                        "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg",
+                    },
+                ],
+            }
+        ]
+        EXPECTED_TEXT = "You are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.\n\nUser: What's the difference between and \n\nAssistant:The image shows a street scene with a prominent red stop sign in the foreground. The sign has the"  # fmt: skip
+
+        inputs = processor.apply_chat_template(
+            messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt"
+        )
+        inputs = inputs.to(model.device, dtype=model.dtype)
+        output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
+        text = processor.decode(output[0], skip_special_tokens=True)
+
+        self.assertEqual(
+            text,
+            EXPECTED_TEXT,
+        )

transformers/tests/models/deepseek_vl_hybrid/test_processing_deepseek_vl_hybrid.py

@@ -0,0 +1,58 @@
+# coding=utf-8
+# 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 tempfile
+import unittest
+
+from transformers import DeepseekVLHybridProcessor, LlamaTokenizer
+from transformers.testing_utils import get_tests_dir
+from transformers.utils import is_vision_available
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+if is_vision_available():
+    from transformers import DeepseekVLHybridImageProcessor
+
+
+SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece.model")
+
+
+class DeepseekVLHybridProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = DeepseekVLHybridProcessor
+
+    def setUp(self):
+        self.tmpdirname = tempfile.mkdtemp()
+        image_processor = DeepseekVLHybridImageProcessor()
+        tokenizer = LlamaTokenizer(
+            vocab_file=SAMPLE_VOCAB,
+            extra_special_tokens={
+                "pad_token": "<｜end▁of▁sentence｜>",
+                "image_token": "<image_placeholder>",
+            },
+        )
+        processor_kwargs = self.prepare_processor_dict()
+        processor = self.processor_class(
+            image_processor=image_processor,
+            tokenizer=tokenizer,
+            **processor_kwargs,
+        )
+        processor.save_pretrained(self.tmpdirname)
+
+    @staticmethod
+    def prepare_processor_dict():
+        return {
+            "chat_template": "{% set seps = ['\n\n', '<\uff5cend\u2581of\u2581sentence\uff5c>'] %}{% set i = 0 %}You are a helpful language and vision assistant. You are able to understand the visual content that the user provides, and assist the user with a variety of tasks using natural language.\n\n{% for message in messages %}{% if message['role']|lower == 'user' %}User: {% elif message['role']|lower == 'assistant' %}Assistant:{% if not (loop.last and not add_generation_prompt and message['content'][0]['type']=='text' and message['content'][0]['text']=='') %} {% endif %}{% else %}{{ message['role'].capitalize() }}: {% endif %}{% for content in message['content'] %}{% if content['type'] == 'image' %}<image_placeholder>{% elif content['type'] == 'text' %}{% set text = content['text'] %}{% if loop.first %}{% set text = text.lstrip() %}{% endif %}{% if loop.last %}{% set text = text.rstrip() %}{% endif %}{% if not loop.first and message['content'][loop.index0-1]['type'] == 'text' %}{{ ' ' + text }}{% else %}{{ text }}{% endif %}{% endif %}{% endfor %}{% if not loop.last or add_generation_prompt %}{% if message['role']|lower == 'user' %}{{ seps[0] }}{% else %}{{ seps[1] }}{% endif %}{% endif %}{% endfor %}{% if add_generation_prompt %}Assistant:{% endif %}",
+            "num_image_tokens": 576,
+        }  # fmt: skip