[transformers x vLLM] standardize processors (#37915)

* standardize

* fix tests

* batch update some processors, not final yet

* oke, now I tested that everything indeed runs. Still needs prettification

* emu3

* fixup

* gemma3 but it doesn't generate anything

* fuyu

* update

* why?

* Update src/transformers/models/aya_vision/processing_aya_vision.py

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* address comments

* bc

* why do we need to guard import this every time?

* i hate guarded imports

* i am blind

---------

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

src/transformers/models/aria/image_processing_aria.py

@@ -500,5 +500,26 @@ class AriaImageProcessor(BaseImageProcessor):
         ]
         return patches
 
+    def get_number_of_image_patches(self, height: int, width: int, images_kwargs=None):
+        """
+        A utility that returns number of image patches for a given image size.
+
+        Args:
+            height (`int`):
+                Height of the input image.
+            width (`int`):
+                Width of the input image.
+            images_kwargs (`dict`, *optional*)
+                Any kwargs to override defaults of the image processor.
+        Returns:
+            `int`: Number of patches per image.
+        """
+        split_image = images_kwargs.get("split_image", None) or self.split_image
+        max_image_size = images_kwargs.get("max_image_size", None) or self.max_image_size
+
+        resized_height, resized_width = select_best_resolution((height, width), self.split_resolutions)
+        num_patches = 1 if not split_image else resized_height // max_image_size * resized_width // max_image_size
+        return num_patches
+
 
 __all__ = ["AriaImageProcessor"]

src/transformers/models/aria/modular_aria.py

@@ -34,7 +34,7 @@ from ...image_utils import (
 )
 from ...modeling_flash_attention_utils import FlashAttentionKwargs
 from ...modeling_utils import PreTrainedModel
-from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
+from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils import PreTokenizedInput, TextInput
 from ...utils import LossKwargs, TensorType, auto_docstring, can_return_tuple, logging
 from ...utils.import_utils import is_torch_available
@@ -884,11 +884,33 @@ class AriaImageProcessor(BaseImageProcessor):
         ]
         return patches
 
+    def get_number_of_image_patches(self, height: int, width: int, images_kwargs=None):
+        """
+        A utility that returns number of image patches for a given image size.
+
+        Args:
+            height (`int`):
+                Height of the input image.
+            width (`int`):
+                Width of the input image.
+            images_kwargs (`dict`, *optional*)
+                Any kwargs to override defaults of the image processor.
+        Returns:
+            `int`: Number of patches per image.
+        """
+        split_image = images_kwargs.get("split_image", None) or self.split_image
+        max_image_size = images_kwargs.get("max_image_size", None) or self.max_image_size
+
+        resized_height, resized_width = select_best_resolution((height, width), self.split_resolutions)
+        num_patches = 1 if not split_image else resized_height // max_image_size * resized_width // max_image_size
+        return num_patches
+
 
 class AriaProcessorKwargs(ProcessingKwargs, total=False):
     _defaults = {
         "text_kwargs": {
             "padding": False,
+            "return_mm_token_type_ids": False,
         },
         "images_kwargs": {
             "max_image_size": 980,
@@ -978,10 +1000,7 @@ class AriaProcessor(ProcessorMixin):
             raise ValueError("Invalid input text. Please provide a string, or a list of strings")
 
         if images is not None:
-            image_inputs = self.image_processor(
-                images,
-                **output_kwargs["images_kwargs"],
-            )
+            image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
             # expand the image_token according to the num_crops and tokens per image
             tokens_per_image = self.size_conversion[image_inputs.pixel_values.shape[2]]
             prompt_strings = []
@@ -995,11 +1014,44 @@ class AriaProcessor(ProcessorMixin):
             prompt_strings = text
 
         return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"])
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
+        text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"], return_tensors=None)
         self._check_special_mm_tokens(prompt_strings, text_inputs, modalities=["image"])
 
+        if return_mm_token_type_ids:
+            array_ids = np.array(text_inputs["input_ids"])
+            mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
+            mm_token_type_ids[array_ids == self.image_token_id] = 1
+            text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
+
         return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
 
+    def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
+        """
+        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+        Args:
+            image_sizes (`List[List[int]]`, *optional*):
+                The input sizes formatted as (height, width) per each image.
+        Returns:
+            `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
+            input modalities, along with other useful data.
+        """
+
+        vision_data = {}
+        if image_sizes is not None:
+            images_kwargs = AriaProcessorKwargs._defaults.get("images_kwargs", {})
+            images_kwargs.update(kwargs)
+
+            max_size = images_kwargs.get("max_image_size", None) or self.image_processor.max_image_size
+            num_image_patches = [
+                self.image_processor.get_number_of_image_patches(*image_size, images_kwargs)
+                for image_size in image_sizes
+            ]
+            num_image_tokens = [self.size_conversion[max_size] * num_patches for num_patches in num_image_patches]
+            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
+
+        return MultiModalData(**vision_data)
+
     def batch_decode(self, *args, **kwargs):
         """
         This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please

src/transformers/models/aria/processing_aria.py

@@ -20,9 +20,11 @@
 # limitations under the License.
 from typing import Dict, List, Optional, Union
 
+import numpy as np
+
 from ...image_processing_utils import BatchFeature
 from ...image_utils import ImageInput
-from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
+from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils import PreTokenizedInput, TextInput
 from ...utils import TensorType
 from ..auto import AutoTokenizer
@@ -32,6 +34,7 @@ class AriaProcessorKwargs(ProcessingKwargs, total=False):
     _defaults = {
         "text_kwargs": {
             "padding": False,
+            "return_mm_token_type_ids": False,
         },
         "images_kwargs": {
             "max_image_size": 980,
@@ -121,10 +124,7 @@ class AriaProcessor(ProcessorMixin):
             raise ValueError("Invalid input text. Please provide a string, or a list of strings")
 
         if images is not None:
-            image_inputs = self.image_processor(
-                images,
-                **output_kwargs["images_kwargs"],
-            )
+            image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
             # expand the image_token according to the num_crops and tokens per image
             tokens_per_image = self.size_conversion[image_inputs.pixel_values.shape[2]]
             prompt_strings = []
@@ -138,11 +138,44 @@ class AriaProcessor(ProcessorMixin):
             prompt_strings = text
 
         return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"])
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
+        text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"], return_tensors=None)
         self._check_special_mm_tokens(prompt_strings, text_inputs, modalities=["image"])
 
+        if return_mm_token_type_ids:
+            array_ids = np.array(text_inputs["input_ids"])
+            mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
+            mm_token_type_ids[array_ids == self.image_token_id] = 1
+            text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
+
         return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
 
+    def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
+        """
+        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+        Args:
+            image_sizes (`List[List[int]]`, *optional*):
+                The input sizes formatted as (height, width) per each image.
+        Returns:
+            `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
+            input modalities, along with other useful data.
+        """
+
+        vision_data = {}
+        if image_sizes is not None:
+            images_kwargs = AriaProcessorKwargs._defaults.get("images_kwargs", {})
+            images_kwargs.update(kwargs)
+
+            max_size = images_kwargs.get("max_image_size", None) or self.image_processor.max_image_size
+            num_image_patches = [
+                self.image_processor.get_number_of_image_patches(*image_size, images_kwargs)
+                for image_size in image_sizes
+            ]
+            num_image_tokens = [self.size_conversion[max_size] * num_patches for num_patches in num_image_patches]
+            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
+
+        return MultiModalData(**vision_data)
+
     def batch_decode(self, *args, **kwargs):
         """
         This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please

src/transformers/models/aya_vision/processing_aya_vision.py

@@ -13,22 +13,23 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-
 from typing import List, Optional, Union
 
-from transformers.processing_utils import (
-    ImagesKwargs,
-    ProcessingKwargs,
-    ProcessorMixin,
-    Unpack,
-)
-from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
+import numpy as np
 
 from ...image_processing_utils import BatchFeature
 from ...image_utils import (
     ImageInput,
     make_flat_list_of_images,
 )
+from ...processing_utils import (
+    ImagesKwargs,
+    MultiModalData,
+    ProcessingKwargs,
+    ProcessorMixin,
+    Unpack,
+)
+from ...tokenization_utils_base import PreTokenizedInput, TextInput
 
 
 class AyaVisionImagesKwargs(ImagesKwargs, total=False):
@@ -43,6 +44,7 @@ class AyaVisionProcessorKwargs(ProcessingKwargs, total=False):
         "text_kwargs": {
             "padding_side": "left",
             "padding": True,
+            "return_mm_token_type_ids": False,
         },
         "images_kwargs": {
             "crop_to_patches": True,
@@ -121,7 +123,6 @@ class AyaVisionProcessor(ProcessorMixin):
         super().__init__(image_processor, tokenizer, chat_template=chat_template)
 
         self.image_token = image_token
-        self.image_token_id = tokenizer.convert_tokens_to_ids(self.image_token)
         self.patch_size = patch_size * downsample_factor
         self.img_size = img_size
 
@@ -131,6 +132,10 @@ class AyaVisionProcessor(ProcessorMixin):
         self.img_line_break_token = img_line_break_token
         self.tile_token = tile_token
         self.tile_global_token = tile_global_token
+        self.image_token_id = tokenizer.convert_tokens_to_ids(self.img_patch_token)
+        self.image_ids = tokenizer.convert_tokens_to_ids(
+            [img_patch_token, tile_token, tile_global_token, start_of_img_token, end_of_img_token]
+        )
 
     def _prompt_split_image(self, num_patches):
         """
@@ -226,11 +231,49 @@ class AyaVisionProcessor(ProcessorMixin):
             text = processed_text
 
         return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
-        self._check_special_mm_tokens(text, text_inputs, modalities=["image"])
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
+        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"], return_tensors=None)
+
+        if return_mm_token_type_ids:
+            array_ids = np.array(text_inputs["input_ids"])
+            mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
+            mm_token_type_ids[np.isin(array_ids, self.image_ids)] = 1
+            text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
 
         return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
 
+    def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
+        """
+        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+
+        Args:
+            image_sizes (`List[List[int]]`, *optional*):
+                The input sizes formatted as (height, width) per each image.
+
+        Returns:
+            `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
+            input modalities, along with other useful data.
+        """
+
+        vision_data = {}
+        if image_sizes is not None:
+            images_kwargs = AyaVisionProcessorKwargs._defaults.get("images_kwargs", {})
+            images_kwargs.update(kwargs)
+
+            num_image_patches = [
+                self.image_processor.get_number_of_image_patches(*image_size, images_kwargs)
+                for image_size in image_sizes
+            ]
+
+            token_per_patch = (self.img_size // self.patch_size) ** 2
+            num_image_tokens = [
+                token_per_patch + 3 + sum(token_per_patch + 1 for _ in range(1, num_patches))
+                for num_patches in num_image_patches
+            ]  # Add +3 and +1 for BOI/EOI and image tile tokens
+            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
+
+        return MultiModalData(**vision_data)
+
     def batch_decode(self, *args, **kwargs):
         """
         This method forwards all its arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please

src/transformers/models/chameleon/processing_chameleon.py

@@ -18,9 +18,18 @@ Processor class for Chameleon.
 
 from typing import List, Optional, Union
 
+import numpy as np
+
 from ...feature_extraction_utils import BatchFeature
 from ...image_utils import ImageInput
-from ...processing_utils import ProcessingKwargs, ProcessorMixin, TextKwargs, Unpack, _validate_images_text_input_order
+from ...processing_utils import (
+    MultiModalData,
+    ProcessingKwargs,
+    ProcessorMixin,
+    TextKwargs,
+    Unpack,
+    _validate_images_text_input_order,
+)
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
 
 
@@ -34,6 +43,7 @@ class ChameleonProcessorKwargs(ProcessingKwargs, total=False):
         "text_kwargs": {
             "padding": False,
             "return_for_text_completion": False,
+            "return_mm_token_type_ids": False,
         },
         "common_kwargs": {
             "return_tensors": "pt",
@@ -73,6 +83,10 @@ class ChameleonProcessor(ProcessorMixin):
             tokenizer.boi_token if hasattr(tokenizer, "boi_token") else "<racm3:break>"
         )  # fixed tokens for start and end, so can hardcode
         self.image_end_token = tokenizer.eoi_token if hasattr(tokenizer, "eoi_token") else "<eoss>"
+        self.image_token_id = tokenizer.convert_tokens_to_ids(self.image_token)
+        self.image_start_token_id = tokenizer.convert_tokens_to_ids(self.image_start_token)
+        self.image_end_token_id = tokenizer.convert_tokens_to_ids(self.image_end_token)
+        self.image_ids = [self.image_token_id, self.image_start_token_id, self.image_end_token_id]
 
         super().__init__(image_processor, tokenizer)
 
@@ -141,14 +155,45 @@ class ChameleonProcessor(ProcessorMixin):
                 sample += self.tokenizer.sep_token  # special Chameleon treatment to add sep for chat mode
             prompt_strings.append(sample)
 
-        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        data = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"])
-        self._check_special_mm_tokens(prompt_strings, data, modalities=["image"])
-
+        image_inputs = {}
         if images is not None:
-            data["pixel_values"] = self.image_processor(images, **output_kwargs["images_kwargs"])["pixel_values"]
+            image_inputs = self.image_processor(images, **output_kwargs["images_kwargs"])
 
-        return BatchFeature(data=data, tensor_type=return_tensors)
+        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
+        text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"], return_tensors=None)
+        self._check_special_mm_tokens(prompt_strings, text_inputs, modalities=["image"])
+
+        if return_mm_token_type_ids:
+            array_ids = np.array(text_inputs["input_ids"])
+            mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
+            mm_token_type_ids[np.isin(array_ids, self.image_ids)] = 1
+            text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
+
+        return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
+
+    def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
+        """
+        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+
+        Args:
+            image_sizes (`List[List[int]]`, *optional*):
+                The input sizes formatted as (height, width) per each image.
+
+        Returns:
+            `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
+            input modalities, along with other useful data.
+        """
+
+        vision_data = {}
+        if image_sizes is not None:
+            # add 2 for BOI and EOI tokens
+            num_image_tokens = [self.image_seq_length + 2] * len(image_sizes)
+            num_image_patches = [1] * len(image_sizes)
+
+            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
+
+        return MultiModalData(**vision_data)
 
     # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
     def batch_decode(self, *args, **kwargs):

src/transformers/models/colpali/processing_colpali.py

@@ -24,7 +24,7 @@ from typing import ClassVar, List, Optional, Union
 
 from ...feature_extraction_utils import BatchFeature
 from ...image_utils import ImageInput, is_valid_image, make_flat_list_of_images
-from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
+from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import AddedToken, PreTokenizedInput, TextInput
 from ...utils import is_torch_available
 
@@ -256,6 +256,25 @@ class ColPaliProcessor(ProcessorMixin):
 
             return batch_query
 
+    def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
+        """
+        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+
+        Args:
+            image_sizes (List[List[str]], *optional*):
+                The input sizes formatted as (height, width) per each image.
+        Returns:
+            Dict[str, List[int]]: A dictionary mapping each modality ("image", "video", "audio")
+            to a list containing the number of placeholder tokens required. If the model doesn't accept
+            a certain modality or no input sizes are provided, the dict value is set to an empty list.
+        """
+        vision_data = {}
+        if image_sizes is not None:
+            num_image_tokens = [self.image_seq_length] * len(image_sizes)
+            num_image_patches = [1] * len(image_sizes)
+            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
+        return MultiModalData(**vision_data)
+
     def batch_decode(self, *args, **kwargs):
         """
         This method forwards all its arguments to GemmaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please

src/transformers/models/emu3/processing_emu3.py

@@ -16,10 +16,17 @@
 
 from typing import List, Optional, Union
 
+import numpy as np
+
 from ...image_processing_utils import BatchFeature
 from ...image_utils import ImageInput
-from ...processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin, TextKwargs, Unpack
+from ...processing_utils import ImagesKwargs, MultiModalData, ProcessingKwargs, ProcessorMixin, TextKwargs, Unpack
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
+from ...utils import is_vision_available
+
+
+if is_vision_available():
+    from .image_processing_emu3 import smart_resize
 
 
 class Emu3TextKwargs(TextKwargs, total=False):
@@ -37,6 +44,7 @@ class Emu3ProcessorKwargs(ProcessingKwargs, total=False):
     _defaults = {
         "text_kwargs": {
             "return_for_image_generation": False,
+            "return_mm_token_type_ids": False,
         },
         "images_kwargs": {
             "ratio": "1:1",
@@ -166,7 +174,7 @@ class Emu3Processor(ProcessorMixin):
 
                     image_placeholder = f"{image_start_tokens}{height}*{width}{self.fake_token_around_image}{'<placeholder>' * image_seq_length}{image_end_tokens}"
                     sample = sample.replace(self.image_token, image_placeholder, 1)
-                    sample = f"{self.bos_token}{sample}"  # add BOS because PT tokenizer doesn't add it
+                    sample = f"{self.bos_token}{sample}"  # add BOS because GPT tokenizer doesn't add it
                 prompt_strings.append(sample)
             text = [sample.replace("<placeholder>", self.image_token) for sample in prompt_strings]
 
@@ -179,12 +187,51 @@ class Emu3Processor(ProcessorMixin):
 
         # else just generate from text-only input, and we do no special treatment for text
         return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        data = self.tokenizer(text, **output_kwargs["text_kwargs"])
-        self._check_special_mm_tokens(text, data, modalities=["image"])
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
+        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"], return_tensors=None)
+        self._check_special_mm_tokens(text, text_inputs, modalities=["image"])
 
-        data.update(**image_features)
+        if return_mm_token_type_ids:
+            array_ids = np.array(text_inputs["input_ids"])
+            mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
+            mm_token_type_ids[array_ids == self.image_token_id] = 1
+            text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
 
-        return BatchFeature(data=data, tensor_type=return_tensors)
+        return BatchFeature(data={**text_inputs, **image_features}, tensor_type=return_tensors)
+
+    def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
+        """
+        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+
+        Args:
+            image_sizes (`List[List[int]]`, *optional*):
+                The input sizes formatted as (height, width) per each image.
+
+        Returns:
+            `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
+            input modalities, along with other useful data.
+        """
+
+        vision_data = {}
+        if image_sizes is not None:
+            num_image_tokens = []
+            for height, width in image_sizes:
+                height, width = smart_resize(
+                    height,
+                    width,
+                    self.image_processor.spatial_factor,
+                    self.image_processor.min_pixels,
+                    self.image_processor.max_pixels,
+                )
+                height = height // self.downsample_ratio
+                width = width // self.downsample_ratio
+                image_seq_length = height * (width + 1)  # +1 for extra row when converting to BPE in modeling code
+                num_image_tokens.append(image_seq_length)
+
+            num_image_patches = [1] * len(image_sizes)
+            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
+
+        return MultiModalData(**vision_data)
 
     def calculate_generate_size(self, ratio, image_area, spatial_factor):
         width, height = map(int, ratio.split(":"))

src/transformers/models/fuyu/modeling_fuyu.py

@@ -130,7 +130,7 @@ class FuyuModel(FuyuPreTrainedModel):
             )
         return output_embeddings
 
-    def get_image_features(self, pixel_values: torch.FloatTensor):
+    def get_image_features(self, pixel_values: torch.FloatTensor, **kwargs):
         """
         Encodes images into continuous embeddings that can be forwarded to the language model.
 

src/transformers/models/fuyu/processing_fuyu.py

@@ -22,7 +22,13 @@ from typing import Dict, List, Optional, Tuple, Union
 import numpy as np
 
 from ...image_utils import ImageInput
-from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack, _validate_images_text_input_order
+from ...processing_utils import (
+    MultiModalData,
+    ProcessingKwargs,
+    ProcessorMixin,
+    Unpack,
+    _validate_images_text_input_order,
+)
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...utils import is_torch_available, logging, requires_backends
 from ...utils.import_utils import requires
@@ -64,6 +70,7 @@ class FuyuProcessorKwargs(ProcessingKwargs, total=False):
             "return_token_type_ids": False,
             "return_length": False,
             "verbose": True,
+            "return_mm_token_type_ids": False,
         },
         "images_kwargs": {},
     }
@@ -355,6 +362,8 @@ class FuyuProcessor(ProcessorMixin):
         self.max_position_embeddings = 16384  # TODO Can't derive this from model files: where to set it?
         self.pad_token_id = 0
         self.dummy_image_index = -1
+        self.image_token_id = tokenizer.encode("|SPEAKER|", add_special_tokens=False)[1]
+        self.image_newline_id = tokenizer.encode("|NEWLINE|", add_special_tokens=False)[1]
 
     def _left_pad_inputs_with_attention_mask(self, model_inputs: List[Dict], return_attention_mask: bool):
         max_length_input_ids = max(entry["input_ids"].shape[1] for entry in model_inputs)
@@ -403,6 +412,11 @@ class FuyuProcessor(ProcessorMixin):
         for key in batched_keys:
             batched_inputs[key] = torch.cat(batched_inputs[key], dim=0)
 
+        # Cast images to tensor as well, if only one image passed and no padding needed
+        # NOTE: vLLM expects all processor outputs to be a tensor
+        if len(batched_inputs["image_patches"]) == 1:
+            batched_inputs["image_patches"] = torch.cat(batched_inputs["image_patches"], dim=0)
+
         return batched_inputs
 
     def get_sample_encoding(
@@ -517,6 +531,7 @@ class FuyuProcessor(ProcessorMixin):
             tokenizer_init_kwargs=self.tokenizer.init_kwargs,
             **kwargs,
         )
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
 
         if not output_kwargs["text_kwargs"].setdefault("return_attention_mask", True):
             raise ValueError("`return_attention_mask=False` is not supported for this model.")
@@ -550,8 +565,6 @@ class FuyuProcessor(ProcessorMixin):
 
         # --- Use self.tokenizer to get the ids of special tokens to insert into image ids ---
 
-        image_placeholder_id = self.tokenizer("|SPEAKER|", add_special_tokens=False)["input_ids"][1]
-        image_newline_id = self.tokenizer("|NEWLINE|", add_special_tokens=False)["input_ids"][1]
         tensor_batch_images = torch.stack([img[0] for img in batch_images]).unsqueeze(1)
 
         # --- Use self.image_processor again to obtain the full token ids and batch inputs ---
@@ -565,16 +578,63 @@ class FuyuProcessor(ProcessorMixin):
                 scale_factors=[scale_factor],
                 image_unpadded_heights=torch.tensor([image_unpadded_height]),
                 image_unpadded_widths=torch.tensor([image_unpadded_width]),
-                image_placeholder_id=image_placeholder_id,
-                image_newline_id=image_newline_id,
+                image_placeholder_id=self.image_token_id,
+                image_newline_id=self.image_newline_id,
                 tensor_batch_images=tensor_batch_image.unsqueeze(0),
             )
             all_encodings.append(sample_encoding)
+
         batch_encoding = self._left_pad_inputs_with_attention_mask(
             model_inputs=all_encodings, return_attention_mask=True
         )
+        if return_mm_token_type_ids:
+            input_ids = batch_encoding["input_ids"]
+            mm_token_type_ids = torch.zeros_like(input_ids)
+            mm_token_type_ids[input_ids == self.image_token_id] = 1
+            mm_token_type_ids[input_ids == self.image_newline_id] = 1
+            batch_encoding["mm_token_type_ids"] = mm_token_type_ids
+
         return FuyuBatchFeature(data=batch_encoding)
 
+    def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
+        """
+        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+
+        Args:
+            image_sizes (`List[List[int]]`, *optional*):
+                The input sizes formatted as (height, width) per each image.
+
+        Returns:
+            `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
+            input modalities, along with other useful data.
+        """
+
+        vision_data = {}
+        if image_sizes is not None:
+            size = kwargs.get("size", None) or self.image_processor.size
+            padded_height, padded_width = size["height"], size["width"]
+
+            num_image_tokens = []
+            num_image_patches = [1] * len(image_sizes)
+            for image_size in image_sizes:
+                height_scale_factor = padded_height / image_size[0]
+                width_scale_factor = padded_width / image_size[1]
+                optimal_scale_factor = min(height_scale_factor, width_scale_factor)
+
+                # We can use torch here because Fuyu processor has hard dependency on torch
+                model_image_input = self.image_processor.preprocess_with_tokenizer_info(
+                    image_input=torch.zeros(1, 1, 3, padded_height, padded_width),
+                    image_present=torch.ones(1, 1, 1),
+                    image_unpadded_h=torch.tensor([[int(image_size[0] * optimal_scale_factor)]]),
+                    image_unpadded_w=torch.tensor([[int(image_size[1] * optimal_scale_factor)]]),
+                    image_placeholder_id=0,  # dummy ids, we can be sure `id=0` is never out-of-range
+                    image_newline_id=0,
+                    variable_sized=True,
+                )
+                num_image_tokens.append(model_image_input["image_input_ids"][0][0].shape[-1])
+            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
+        return MultiModalData(**vision_data)
+
     def post_process_box_coordinates(self, outputs, target_sizes=None):
         """
         Transforms raw coordinates detected by [`FuyuForCausalLM`] to the original images' coordinate space.

src/transformers/models/gemma3/processing_gemma3.py

@@ -20,7 +20,7 @@ import numpy as np
 
 from ...feature_extraction_utils import BatchFeature
 from ...image_utils import ImageInput, make_nested_list_of_images
-from ...processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin, Unpack
+from ...processing_utils import ImagesKwargs, MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...utils import to_py_obj
 
@@ -38,6 +38,7 @@ class Gemma3ProcessorKwargs(ProcessingKwargs, total=False):
     _defaults = {
         "text_kwargs": {
             "padding": False,
+            "return_mm_token_type_ids": True,
         },
         "images_kwargs": {
             "do_pan_and_scan": False,
@@ -137,17 +138,42 @@ class Gemma3Processor(ProcessorMixin):
             text = [prompt.replace(self.boi_token, self.full_image_sequence) for prompt in text]
 
         return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        text_inputs = self.tokenizer(text=text, **output_kwargs["text_kwargs"], return_tensors="np")
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
+        text_inputs = self.tokenizer(text=text, **output_kwargs["text_kwargs"])
         self._check_special_mm_tokens(text, text_inputs, modalities=["image"])
 
         # Add token type ids manually, as tokenizer can't do arbitrary position token types
-        array_ids = text_inputs["input_ids"]
-        mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
-        mm_token_type_ids[array_ids == self.image_token_id] = 1
-        text_inputs = {k: v.tolist() for k, v in text_inputs.items()}  # in case user requested list inputs
-        text_inputs["token_type_ids"] = mm_token_type_ids.tolist()
+        if return_mm_token_type_ids:
+            array_ids = np.array(text_inputs["input_ids"])
+            mm_token_type_ids = np.zeros_like(array_ids)
+            mm_token_type_ids[array_ids == self.image_token_id] = 1
+            text_inputs["token_type_ids"] = mm_token_type_ids.tolist()
+
         return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
 
+    def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
+        """
+        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+
+        Args:
+            image_sizes (`List[List[int]]`, *optional*):
+                The input sizes formatted as (height, width) per each image.
+
+        Returns:
+            `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
+            input modalities, along with other useful data.
+        """
+
+        vision_data = {}
+        if image_sizes is not None:
+            # NOTE: no image cropping supported yet
+            num_image_tokens = [self.image_seq_length] * len(image_sizes)
+            num_image_patches = [1] * len(image_sizes)
+
+            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
+
+        return MultiModalData(**vision_data)
+
     # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Gemma
     def batch_decode(self, *args, **kwargs):
         """

src/transformers/models/got_ocr2/image_processing_got_ocr2.py

@@ -491,5 +491,33 @@ class GotOcr2ImageProcessor(BaseImageProcessor):
 
         return processed_images
 
+    def get_number_of_image_patches(self, height: int, width: int, images_kwargs=None):
+        """
+        A utility that returns number patches for a given image size.
+
+        Args:
+            height (`int`):
+                Height of the input image.
+            width (`int`):
+                Width of the input image.
+            images_kwargs (`dict`, *optional*)
+                Any kwargs to override defaults of the image processor.
+        Returns:
+            `int`: Number of patches per image.
+        """
+        min_patches = images_kwargs.get("min_patches", None) or self.min_patches
+        max_patches = images_kwargs.get("max_patches", None) or self.max_patches
+        patch_size = images_kwargs.get("size", None) or self.size
+        crop_to_patches = images_kwargs.get("crop_to_patches", None) or self.crop_to_patches
+
+        num_patches = 1
+        if crop_to_patches and max_patches > 1:
+            num_columns, num_rows = get_optimal_tiled_canvas(
+                (height, width), (patch_size["height"], patch_size["width"]), min_patches, max_patches
+            )
+            num_patches += num_columns * num_rows
+
+        return num_patches
+
 
 __all__ = ["GotOcr2ImageProcessor"]

src/transformers/models/got_ocr2/image_processing_got_ocr2_fast.py

@@ -228,5 +228,33 @@ class GotOcr2ImageProcessorFast(BaseImageProcessorFast):
             data={"pixel_values": processed_images, "num_patches": num_patches}, tensor_type=return_tensors
         )
 
+    def get_number_of_image_tokens(self, height: int, width: int, images_kwargs=None):
+        """
+        A utility that returns number patches for a given image size.
+
+        Args:
+            height (`int`):
+                Height of the input image.
+            width (`int`):
+                Width of the input image.
+            images_kwargs (`dict`, *optional*)
+                Any kwargs to override defaults of the image processor.
+        Returns:
+            `int`: Number of patches per image.
+        """
+        min_patches = images_kwargs.get("min_patches", None) or self.min_patches
+        max_patches = images_kwargs.get("max_patches", None) or self.max_patches
+        patch_size = images_kwargs.get("size", None) or self.size
+        crop_to_patches = images_kwargs.get("crop_to_patches", None) or self.crop_to_patches
+
+        num_patches = 1
+        if crop_to_patches and max_patches > 1:
+            num_columns, num_rows = get_optimal_tiled_canvas(
+                (height, width), (patch_size["height"], patch_size["width"]), min_patches, max_patches
+            )
+            num_patches += num_columns * num_rows
+
+        return num_patches
+
 
 __all__ = ["GotOcr2ImageProcessorFast"]

src/transformers/models/idefics3/image_processing_idefics3.py

@@ -850,5 +850,46 @@ class Idefics3ImageProcessor(BaseImageProcessor):
 
         return encoding
 
+    def get_number_of_image_patches(self, height: int, width: int, images_kwargs=None):
+        """
+        A utility that returns number of image patches for a given image size.
+
+        Args:
+            height (`int`):
+                Height of the input image.
+            width (`int`):
+                Width of the input image.
+            images_kwargs (`dict`, *optional*)
+                Any kwargs to override defaults of the image processor.
+        Returns:
+            `int`: Number of patches per image.
+        """
+        do_image_splitting = images_kwargs.get("do_image_splitting", None) or self.do_image_splitting
+        max_image_size = images_kwargs.get("max_image_size", None) or self.max_image_size
+        size = images_kwargs.get("size", None) or self.size
+
+        if do_image_splitting:
+            height, width = _resize_output_size_rescale_to_max_len(height, width, max_len=size["longest_edge"])
+            height, width = _resize_output_size_scale_below_upper_bound(height, width, max_len=4096)
+            aspect_ratio = width / height
+
+            if width >= height:
+                resized_width = math.ceil(width / max_image_size["longest_edge"]) * max_image_size["longest_edge"]
+                resized_height = int(width / aspect_ratio)
+                resized_height = math.ceil(height / max_image_size["longest_edge"]) * max_image_size["longest_edge"]
+            elif height > width:
+                resized_height = math.ceil(height / max_image_size["longest_edge"]) * max_image_size["longest_edge"]
+                resized_width = int(height * aspect_ratio)
+                resized_width = math.ceil(width / max_image_size["longest_edge"]) * max_image_size["longest_edge"]
+
+            max_height = max_width = max_image_size["longest_edge"]
+            if resized_height > max_height or resized_width > max_width:
+                # Calculate the number of splits
+                num_rows = math.ceil(resized_height / max_height)
+                num_cols = math.ceil(resized_width / max_width)
+                num_patches = num_rows * num_cols + 1
+
+        return num_patches
+
 
 __all__ = ["Idefics3ImageProcessor"]

src/transformers/models/idefics3/processing_idefics3.py

@@ -16,13 +16,16 @@
 Processor class for Idefics3.
 """
 
+import math
 import re
 from itertools import accumulate
 from typing import TYPE_CHECKING, Dict, List, Optional, Union
 
+import numpy as np
+
 from ...feature_extraction_utils import BatchFeature
 from ...image_utils import ImageInput, is_valid_image, load_image
-from ...processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin, Unpack
+from ...processing_utils import ImagesKwargs, MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import AddedToken, BatchEncoding, TextInput
 from ...utils import logging
 
@@ -98,6 +101,7 @@ class Idefics3ProcessorKwargs(ProcessingKwargs, total=False):
             "add_special_tokens": True,
             "padding": False,
             "is_split_into_words": False,
+            "return_mm_token_type_ids": False,
         },
         "images_kwargs": {
             "return_row_col_info": True,
@@ -146,6 +150,12 @@ class Idefics3Processor(ProcessorMixin):
         self.end_of_utterance_token = AddedToken("<end_of_utterance>", normalized=False, special=True).content
         self.global_image_tag = "<global-img>"  # https://github.com/huggingface/transformers/pull/32473/files/8063e5e17362571b693f1db95167f5443a3be1b2#r1734825341
         self.image_seq_len = image_seq_len
+        self.image_token_id = tokenizer.convert_tokens_to_ids(self.image_token)
+        self.fake_image_token_id = tokenizer.convert_tokens_to_ids(self.fake_image_token)
+        self.global_image_token_id = tokenizer.convert_tokens_to_ids(self.global_image_tag)
+        self.row_col_ids = [
+            tokenizer.convert_tokens_to_ids(f"<row_{i + 1}_col_{j + 1}>") for i in range(6) for j in range(6)
+        ]
 
         # This regex matches one or more occurrences of <global-img> tags (optionally surrounded by newline characters)
         # or <row_x_col_y> tags (where x and y are digits, also optionally surrounded by newline characters).
@@ -241,6 +251,7 @@ class Idefics3Processor(ProcessorMixin):
         )
 
         image_seq_len = image_seq_len if image_seq_len is not None else self.image_seq_len
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
         return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
 
         n_images_in_text = []
@@ -302,9 +313,11 @@ class Idefics3Processor(ProcessorMixin):
                 global_img_token = self.global_image_tag
 
                 prompt_strings = []
+                batch_image_seq_lengths = []
                 for sample, sample_rows, sample_cols in zip(text, image_rows, image_cols):
                     # Replace the image token with fake tokens around the expanded image token sequence of length `image_seq_len`
                     image_prompt_strings = []
+                    image_seq_lengths = []
                     for n_rows, n_cols in zip(sample_rows, sample_cols):
                         image_prompt_string = get_image_prompt_string(
                             n_rows,
@@ -314,8 +327,12 @@ class Idefics3Processor(ProcessorMixin):
                             fake_token_around_image=fake_image_token,
                             global_img_token=global_img_token,
                         )
+                        # Add +2 and +3 for special BOI/EOI/fake_image_wrapper tokens
+                        row_length = (self.image_seq_len + 2) * n_cols + 1
+                        image_seq_lengths.append((self.image_seq_len + 3) + row_length * n_rows)
                         image_prompt_strings.append(image_prompt_string)
 
+                    batch_image_seq_lengths.append(image_seq_lengths)
                     split_sample = sample.split(image_token)
                     if len(split_sample) == 0:
                         raise ValueError("The image token should be present in the text.")
@@ -338,7 +355,59 @@ class Idefics3Processor(ProcessorMixin):
             text_inputs = self.tokenizer(text=text, **output_kwargs["text_kwargs"])
             inputs.update(text_inputs)
 
-        return BatchFeature(inputs, tensor_type=return_tensors)
+        if return_mm_token_type_ids:
+            array_ids = np.array(inputs["input_ids"])
+            mm_token_type_ids = np.zeros_like(array_ids)
+            for i, seq_lengths in enumerate(batch_image_seq_lengths):
+                image_start_positions = np.where(array_ids[i] == self.fake_image_token_id)[0]
+                j = 0
+                for seq_len in seq_lengths:
+                    if j >= len(image_start_positions):
+                        break
+                    start = image_start_positions[j]
+                    end = start + seq_len
+                    mm_token_type_ids[i, start:end] = 1
+                    j = np.searchsorted(image_start_positions, end)
+
+            inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
+
+        return BatchFeature(data=inputs, tensor_type=return_tensors)
+
+    def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
+        """
+        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+
+        Args:
+            image_sizes (`List[List[int]]`, *optional*):
+                The input sizes formatted as (height, width) per each image.
+
+        Returns:
+            `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
+            input modalities, along with other useful data.
+        """
+
+        vision_data = {}
+        if image_sizes is not None:
+            images_kwargs = Idefics3ProcessorKwargs._defaults.get("images_kwargs", {})
+            images_kwargs.update(kwargs)
+
+            num_image_patches = [
+                self.image_processor.get_number_of_image_patches(*image_size, images_kwargs)
+                for image_size in image_sizes
+            ]
+
+            base_image_length = self.image_seq_len + 3
+            col_length = self.image_seq_len + 2
+            num_image_tokens = []
+
+            for num_patches in num_image_patches:
+                num_cols = num_rows = int(math.sqrt(num_patches - 1))
+                row_length = col_length * num_cols + 1
+                num_image_tokens.append(base_image_length + (row_length * num_rows))
+
+            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
+
+        return MultiModalData(**vision_data)
 
     def batch_decode(self, *args, **kwargs):
         """

src/transformers/models/internvl/processing_internvl.py

@@ -13,25 +13,24 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-
 from typing import List, Optional, Union
 
 import numpy as np
 
-from transformers.processing_utils import (
-    ImagesKwargs,
-    ProcessingKwargs,
-    ProcessorMixin,
-    Unpack,
-)
-from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
-
 from ...image_processing_utils import BatchFeature
 from ...image_utils import (
     ImageInput,
     concatenate_list,
     make_flat_list_of_images,
 )
+from ...processing_utils import (
+    ImagesKwargs,
+    MultiModalData,
+    ProcessingKwargs,
+    ProcessorMixin,
+    Unpack,
+)
+from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...video_utils import VideoInput, VideoMetadata, load_video, make_batched_videos
 
 
@@ -46,6 +45,7 @@ class InternVLProcessorKwargs(ProcessingKwargs, total=False):
     _defaults = {
         "text_kwargs": {
             "padding_side": "left",
+            "return_mm_token_type_ids": False,
         },
         "images_kwargs": {
             "crop_to_patches": True,
@@ -94,9 +94,12 @@ class InternVLProcessor(ProcessorMixin):
         self.image_seq_length = image_seq_length
         self.start_image_token = tokenizer.start_image_token
         self.end_image_token = tokenizer.end_image_token
+        self.start_image_token_id = tokenizer.start_image_token_id
+        self.end_image_token_id = tokenizer.end_image_token_id
         self.image_token = tokenizer.context_image_token
         self.video_token = tokenizer.video_token
         self.image_token_id = tokenizer.context_image_token_id
+        self.image_ids = [self.image_token_id, self.start_image_token_id, self.end_image_token_id]
 
         super().__init__(image_processor, tokenizer, video_processor, chat_template=chat_template, **kwargs)
 
@@ -261,11 +264,46 @@ class InternVLProcessor(ProcessorMixin):
             image_videos_inputs = {"pixel_values": concatenate_list(image_video_patches)}
 
         return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None)
         text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
         self._check_special_mm_tokens(text, text_inputs, modalities=["image"])
 
+        if return_mm_token_type_ids:
+            array_ids = np.array(text_inputs["input_ids"])
+            mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
+            mm_token_type_ids[np.isin(array_ids, self.image_ids)] = 1
+            text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
+
         return BatchFeature(data={**text_inputs, **image_videos_inputs}, tensor_type=return_tensors)
 
+    def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
+        """
+        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+
+        Args:
+            image_sizes (`List[List[int]]`, *optional*):
+                The input sizes formatted as (height, width) per each image.
+
+        Returns:
+            `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
+            input modalities, along with other useful data.
+        """
+
+        vision_data = {}
+        if image_sizes is not None:
+            images_kwargs = InternVLProcessorKwargs._defaults.get("images_kwargs", {})
+            images_kwargs.update(kwargs)
+
+            num_image_patches = [
+                self.image_processor.get_number_of_image_tokens(*image_size, images_kwargs)
+                for image_size in image_sizes
+            ]
+            # Add 2 for BOI and EOI tokens
+            num_image_tokens = [2 + (self.image_seq_length * num_patches) for num_patches in num_image_patches]
+            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
+
+        return MultiModalData(**vision_data)
+
     def sample_indices_fn(
         self, metadata: VideoMetadata, num_frames: Optional[int] = None, initial_shift: Union[bool, float, int] = True
     ):

src/transformers/models/llava/processing_llava.py

@@ -18,9 +18,17 @@ Processor class for Llava.
 
 from typing import List, Union
 
+import numpy as np
+
 from ...feature_extraction_utils import BatchFeature
 from ...image_utils import ImageInput, get_image_size, to_numpy_array
-from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack, _validate_images_text_input_order
+from ...processing_utils import (
+    MultiModalData,
+    ProcessingKwargs,
+    ProcessorMixin,
+    Unpack,
+    _validate_images_text_input_order,
+)
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...utils import logging
 
@@ -30,9 +38,7 @@ logger = logging.get_logger(__name__)
 
 class LlavaProcessorKwargs(ProcessingKwargs, total=False):
     _defaults = {
-        "text_kwargs": {
-            "padding": False,
-        },
+        "text_kwargs": {"padding": False, "return_mm_token_type_ids": False},
         "images_kwargs": {},
     }
 
@@ -89,11 +95,7 @@ class LlavaProcessor(ProcessorMixin):
         self.num_additional_image_tokens = num_additional_image_tokens
         self.vision_feature_select_strategy = vision_feature_select_strategy
         self.image_token = tokenizer.image_token if hasattr(tokenizer, "image_token") else image_token
-        self.image_token_id = (
-            tokenizer.image_token_id
-            if getattr(tokenizer, "image_token_id", None)
-            else tokenizer.convert_tokens_to_ids(self.image_token)
-        )
+        self.image_token_id = tokenizer.encode(self.image_token, add_special_tokens=False)[0]
         super().__init__(image_processor, tokenizer, chat_template=chat_template)
 
     def __call__(
@@ -174,10 +176,49 @@ class LlavaProcessor(ProcessorMixin):
                 prompt_strings.append(sample)
 
         return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"])
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
+        text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"], return_tensors=None)
         self._check_special_mm_tokens(prompt_strings, text_inputs, modalities=["image"])
+
+        if return_mm_token_type_ids:
+            array_ids = np.array(text_inputs["input_ids"])
+            mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
+            mm_token_type_ids[array_ids == self.image_token_id] = 1
+            text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
+
         return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
 
+    def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
+        """
+        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+
+        Args:
+            image_sizes (`List[List[int]]`, *optional*):
+                The input sizes formatted as (height, width) per each image.
+
+        Returns:
+            `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
+            input modalities, along with other useful data.
+        """
+
+        vision_data = {}
+        if image_sizes is not None:
+            images_kwargs = LlavaProcessorKwargs._defaults.get("images_kwargs", {})
+            images_kwargs.update(kwargs)
+            crop_size = images_kwargs.get("crop_size", None) or self.image_processor.crop_size
+            resized_height, resized_width = crop_size["height"], crop_size["width"]
+
+            num_image_tokens = (resized_height // self.patch_size) * (resized_width // self.patch_size)
+            num_image_tokens += self.num_additional_image_tokens
+            if self.vision_feature_select_strategy == "default":
+                num_image_tokens -= 1
+
+            num_image_tokens = [num_image_tokens] * len(image_sizes)
+            num_image_patches = [1] * len(image_sizes)
+            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
+
+        return MultiModalData(**vision_data)
+
     # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
     def batch_decode(self, *args, **kwargs):
         """

src/transformers/models/llava_next/processing_llava_next.py

@@ -18,10 +18,18 @@ Processor class for LLaVa-NeXT.
 
 from typing import List, Union
 
+import numpy as np
+
 from ...feature_extraction_utils import BatchFeature
 from ...image_processing_utils import select_best_resolution
 from ...image_utils import ImageInput, get_image_size, to_numpy_array
-from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack, _validate_images_text_input_order
+from ...processing_utils import (
+    MultiModalData,
+    ProcessingKwargs,
+    ProcessorMixin,
+    Unpack,
+    _validate_images_text_input_order,
+)
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...utils import logging
 
@@ -33,6 +41,7 @@ class LlavaNextProcessorKwargs(ProcessingKwargs, total=False):
     _defaults = {
         "text_kwargs": {
             "padding": False,
+            "return_mm_token_type_ids": False,
         },
         "images_kwargs": {
             "do_pad": True,
@@ -172,9 +181,16 @@ class LlavaNextProcessor(ProcessorMixin):
             prompt_strings = [sample.replace("<placeholder>", self.image_token) for sample in prompt_strings]
 
         return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None)
         text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"])
         self._check_special_mm_tokens(prompt_strings, text_inputs, modalities=["image"])
 
+        if return_mm_token_type_ids:
+            array_ids = np.array(text_inputs["input_ids"])
+            mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
+            mm_token_type_ids[array_ids == self.image_token_id] = 1
+            text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
+
         return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
 
     def _get_number_of_features(self, orig_height: int, orig_width: int, height: int, width: int) -> int:
@@ -219,6 +235,48 @@ class LlavaNextProcessor(ProcessorMixin):
         newline_features = current_height
         return (unpadded_features, newline_features)
 
+    def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
+        """
+        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+        Args:
+            image_sizes (List[List[str]], *optional*):
+                The input sizes formatted as (height, width) per each image.
+            video_sizes (List[List[str]], *optional*):
+                The input sizes formatted as (num_frames, height, width) per each video.
+            audio_lengths (List[int], *optional*):
+                The input length formatted as per each audio.
+        Returns:
+            Dict[str, List[int]]: A dictionary mapping each modality ("image", "video", "audio")
+            to a list containing the number of placeholder tokens required. If the model doesn't accept
+            a certain modality or no input sizes are provided, the dict value is set to an empty list.
+        """
+        vision_data = {}
+        if image_sizes is not None:
+            images_kwargs = LlavaNextProcessorKwargs._defaults.get("images_kwargs", {})
+            images_kwargs.update(kwargs)
+
+            size = images_kwargs.get("size", None) or self.image_processor.size
+            size = (
+                (size["shortest_edge"], size["shortest_edge"])
+                if "shortest_edge" in size
+                else (min(size["height"], size["width"]), min(size["height"], size["width"]))
+            )
+            processed_height, processed_width = size
+
+            batch_num_image_tokens = []
+            num_image_patches = [1] * len(image_sizes)  # llava-next doesn't batch pixels as Idefics, thus `1` patch`
+            for image_size in image_sizes:
+                orig_height, orig_width = image_size
+                num_image_tokens = self._get_number_of_features(
+                    orig_height, orig_width, processed_height, processed_width
+                )
+                if self.vision_feature_select_strategy == "default":
+                    num_image_tokens -= 1
+                batch_num_image_tokens.append(num_image_tokens)
+            vision_data.update({"num_image_tokens": batch_num_image_tokens, "num_image_patches": num_image_patches})
+
+        return MultiModalData(**vision_data)
+
     # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
     def batch_decode(self, *args, **kwargs):
         """

src/transformers/models/llava_onevision/processing_llava_onevision.py

@@ -24,7 +24,7 @@ import numpy as np
 from ...feature_extraction_utils import BatchFeature
 from ...image_processing_utils import select_best_resolution
 from ...image_utils import ImageInput, get_image_size, to_numpy_array
-from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
+from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...utils import logging
 from ...video_utils import VideoInput
@@ -38,6 +38,7 @@ class LlavaOnevisionProcessorKwargs(ProcessingKwargs, total=False):
     _defaults = {
         "text_kwargs": {
             "padding": False,
+            "return_mm_token_type_ids": False,
         },
         "image_kwargs": {},
         "videos_kwargs": {},
@@ -196,9 +197,16 @@ class LlavaOnevisionProcessor(ProcessorMixin):
             text = [sample.replace(self.video_token, self.video_token * num_video_tokens) for sample in text]
 
         return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None)
         text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
         self._check_special_mm_tokens(text, text_inputs, modalities=["image"])
 
+        if return_mm_token_type_ids:
+            array_ids = np.array(text_inputs["input_ids"])
+            mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
+            mm_token_type_ids[array_ids == self.image_token_id] = 1
+            text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
+
         return BatchFeature(data={**text_inputs, **image_inputs, **video_inputs}, tensor_type=return_tensors)
 
     def _expand_image_tokens(
@@ -285,6 +293,48 @@ class LlavaOnevisionProcessor(ProcessorMixin):
 
         return (unpadded_features, newline_features)
 
+    def _get_num_multimodal_tokens(self, image_sizes=None, video_sizes=None, **kwargs):
+        """
+        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+        Args:
+            image_sizes (List[List[str]], *optional*):
+                The input sizes formatted as (height, width) per each image.
+            video_sizes (List[List[str]], *optional*):
+                The input sizes formatted as (num_frames, height, width) per each video.
+            audio_lengths (List[int], *optional*):
+                The input length formatted as per each audio.
+        Returns:
+            Dict[str, List[int]]: A dictionary mapping each modality ("image", "video", "audio")
+            to a list containing the number of placeholder tokens required. If the model doesn't accept
+            a certain modality or no input sizes are provided, the dict value is set to an empty list.
+        """
+        vision_data = {}
+        if image_sizes is not None:
+            images_kwargs = LlavaOnevisionProcessorKwargs._defaults.get("images_kwargs", {})
+            images_kwargs.update(kwargs)
+
+            size = images_kwargs.get("size", None) or self.image_processor.size
+            size = (
+                (size["shortest_edge"], size["shortest_edge"])
+                if "shortest_edge" in size
+                else (min(size["height"], size["width"]), min(size["height"], size["width"]))
+            )
+            processed_height, processed_width = size
+
+            batch_num_image_tokens = []
+            num_image_patches = [1] * len(image_sizes)  # llava-ov doesn't batch pixels as Idefics, thus `1` patch`
+            for image_size in image_sizes:
+                orig_height, orig_width = image_size
+                num_image_tokens = self._get_number_of_features(
+                    orig_height, orig_width, processed_height, processed_width
+                )
+                if self.vision_feature_select_strategy == "default":
+                    num_image_tokens -= 1
+                batch_num_image_tokens.append(num_image_tokens)
+            vision_data.update({"num_image_tokens": batch_num_image_tokens, "num_image_patches": num_image_patches})
+
+        return MultiModalData(**vision_data)
+
     # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
     def batch_decode(self, *args, **kwargs):
         """

src/transformers/models/paligemma/processing_paligemma.py

@@ -18,10 +18,13 @@ Processor class for PaliGemma.
 
 from typing import List, Optional, Union
 
+import numpy as np
+
 from ...feature_extraction_utils import BatchFeature
 from ...image_utils import ImageInput, is_valid_image, make_flat_list_of_images
 from ...processing_utils import (
     ImagesKwargs,
+    MultiModalData,
     ProcessingKwargs,
     ProcessorMixin,
     TextKwargs,
@@ -56,6 +59,7 @@ class PaliGemmaProcessorKwargs(ProcessingKwargs, total=False):
     _defaults = {
         "text_kwargs": {
             "padding": False,
+            "return_mm_token_type_ids": False,
         },
         "images_kwargs": {
             "data_format": "channels_first",
@@ -299,6 +303,7 @@ class PaliGemmaProcessor(ProcessorMixin):
         pixel_values = self.image_processor(images, **output_kwargs["images_kwargs"])["pixel_values"]
 
         return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None)
         inputs = self.tokenizer(
             input_strings,
             text_pair=suffix,
@@ -312,8 +317,34 @@ class PaliGemmaProcessor(ProcessorMixin):
         if return_token_type_ids:
             labels = inputs["input_ids"].masked_fill(inputs["token_type_ids"] == 0, -100)
             return_data.update({"labels": labels})
+
+        if return_mm_token_type_ids:
+            array_ids = np.array(return_data["input_ids"])
+            mm_token_type_ids = np.zeros_like(return_data["input_ids"])
+            mm_token_type_ids[array_ids == self.image_token_id] = 1
+            return_data["mm_token_type_ids"] = mm_token_type_ids.tolist()
+
         return BatchFeature(data=return_data, tensor_type=return_tensors)
 
+    def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
+        """
+        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+
+        Args:
+            image_sizes (List[List[str]], *optional*):
+                The input sizes formatted as (height, width) per each image.
+        Returns:
+            Dict[str, List[int]]: A dictionary mapping each modality ("image", "video", "audio")
+            to a list containing the number of placeholder tokens required. If the model doesn't accept
+            a certain modality or no input sizes are provided, the dict value is set to an empty list.
+        """
+        vision_data = {}
+        if image_sizes is not None:
+            num_image_tokens = [self.image_seq_length] * len(image_sizes)
+            num_image_patches = [1] * len(image_sizes)
+            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
+        return MultiModalData(**vision_data)
+
     # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Gemma
     def batch_decode(self, *args, **kwargs):
         """

src/transformers/models/pixtral/processing_pixtral.py

@@ -18,11 +18,23 @@ Processor class for Pixtral.
 
 from typing import List, Union
 
+import numpy as np
+
 from ...feature_extraction_utils import BatchFeature
 from ...image_utils import ImageInput, is_valid_image, load_image
-from ...processing_utils import ProcessingKwargs, ProcessorMixin, Unpack, _validate_images_text_input_order
+from ...processing_utils import (
+    MultiModalData,
+    ProcessingKwargs,
+    ProcessorMixin,
+    Unpack,
+    _validate_images_text_input_order,
+)
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
-from ...utils import logging
+from ...utils import is_vision_available, logging
+
+
+if is_vision_available():
+    from .image_processing_pixtral import get_resize_output_image_size
 
 
 logger = logging.get_logger(__name__)
@@ -32,6 +44,7 @@ class PixtralProcessorKwargs(ProcessingKwargs, total=False):
     _defaults = {
         "text_kwargs": {
             "padding": False,
+            "return_mm_token_type_ids": False,
         },
         "images_kwargs": {},
         "common_kwargs": {
@@ -106,6 +119,10 @@ class PixtralProcessor(ProcessorMixin):
         self.image_token_id = tokenizer.convert_tokens_to_ids(self.image_token)
         self.image_break_token = image_break_token
         self.image_end_token = image_end_token
+        self.image_token_id = tokenizer.convert_tokens_to_ids(self.image_token)
+        self.image_break_token_id = tokenizer.convert_tokens_to_ids(self.image_break_token)
+        self.image_end_token_id = tokenizer.convert_tokens_to_ids(self.image_end_token)
+        self.image_ids = [self.image_token_id, self.image_break_token_id, self.image_end_token_id]
         super().__init__(image_processor, tokenizer, chat_template=chat_template)
 
     def __call__(
@@ -213,10 +230,54 @@ class PixtralProcessor(ProcessorMixin):
                 prompt_strings.append(sample)
 
         return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"])
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
+        text_inputs = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"], return_tensors=None)
         self._check_special_mm_tokens(prompt_strings, text_inputs, modalities=["image"])
+
+        if return_mm_token_type_ids:
+            array_ids = np.array(text_inputs["input_ids"])
+            mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
+            mm_token_type_ids[np.isin(array_ids, self.image_ids)] = 1
+            text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
+
         return BatchFeature(data={**text_inputs, **image_inputs}, tensor_type=return_tensors)
 
+    def _get_num_multimodal_tokens(self, image_sizes=None, **kwargs):
+        """
+        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+
+        Args:
+            image_sizes (`List[List[int]]`, *optional*):
+                The input sizes formatted as (height, width) per each image.
+
+        Returns:
+            `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
+            input modalities, along with other useful data.
+        """
+        vision_data = {}
+        if image_sizes is not None:
+            images_kwargs = PixtralProcessorKwargs._defaults.get("images_kwargs", {})
+            images_kwargs.update(kwargs)
+
+            size = images_kwargs.get("size", None) or self.image_processor.size
+            patch_size = self.patch_size * self.spatial_merge_size
+
+            num_image_tokens = []
+            for height, width in image_sizes:
+                resized_height, resized_width = get_resize_output_image_size(
+                    image=np.zeros((height, width, 3)),
+                    size=(size["longest_edge"], size["longest_edge"]),
+                    patch_size=(patch_size, patch_size),
+                )
+                num_height_tokens = resized_height // patch_size
+                num_width_tokens = resized_width // patch_size
+                num_image_tokens.append((num_width_tokens + 1) * num_height_tokens)
+
+            num_image_patches = [1] * len(image_sizes)
+            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
+
+        return MultiModalData(**vision_data)
+
     # Copied from transformers.models.clip.processing_clip.CLIPProcessor.batch_decode with CLIP->Llama
     def batch_decode(self, *args, **kwargs):
         """

src/transformers/models/qwen2_5_vl/modular_qwen2_5_vl.py

@@ -22,6 +22,7 @@
 from dataclasses import dataclass
 from typing import List, Optional, Tuple, Union
 
+import numpy as np
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -48,7 +49,7 @@ from ...configuration_utils import PretrainedConfig
 from ...feature_extraction_utils import BatchFeature
 from ...image_utils import ImageInput
 from ...modeling_flash_attention_utils import is_flash_attn_available
-from ...processing_utils import ProcessingKwargs, Unpack, VideosKwargs
+from ...processing_utils import MultiModalData, ProcessingKwargs, Unpack, VideosKwargs
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...utils import is_torchdynamo_compiling, logging
 from ...video_utils import VideoInput
@@ -925,6 +926,7 @@ class Qwen2_5_VLProcessorKwargs(ProcessingKwargs, total=False):
     _defaults = {
         "text_kwargs": {
             "padding": False,
+            "return_mm_token_type_ids": False,
         },
         "videos_kwargs": {"fps": 2.0},
     }
@@ -1050,11 +1052,56 @@ class Qwen2_5_VLProcessor(Qwen2VLProcessor):
                 text[i] = text[i].replace("<|placeholder|>", self.video_token)
 
         return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None)
         text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
         self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"])
 
+        if return_mm_token_type_ids:
+            array_ids = np.array(text_inputs["input_ids"])
+            mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
+            mm_token_type_ids[array_ids == self.image_token_id] = 1
+            text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
+
         return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors)
 
+    def _get_num_multimodal_tokens(self, image_sizes=None, video_sizes=None, **kwargs):
+        """
+        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+        Args:
+            image_sizes (`List[List[int]]`, *optional*):
+                The input sizes formatted as (height, width) per each image.
+            video_sizes (`List[List[int]]`, *optional*):
+                The input sizes formatted as (num_frames, height, width) per each video.
+        Returns:
+            `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
+            input modalities, along with other useful data.
+        """
+
+        vision_data = {}
+        if image_sizes is not None:
+            images_kwargs = Qwen2_5_VLProcessorKwargs._defaults.get("images_kwargs", {})
+            images_kwargs.update(kwargs)
+            merge_size = images_kwargs.get("merge_size", None) or self.image_processor.merge_size
+
+            num_image_patches = [
+                self.image_processor.get_number_of_image_patches(*image_size, images_kwargs)
+                for image_size in image_sizes
+            ]
+            num_image_tokens = [(num_patches // merge_size**2) for num_patches in num_image_patches]
+            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
+
+        if video_sizes is not None:
+            videos_kwargs = Qwen2_5_VLProcessorKwargs._defaults.get("videos_kwargs", {})
+            videos_kwargs.update(kwargs)
+            num_video_patches = [
+                self.video_processor.get_number_of_video_patches(*video_size, videos_kwargs)
+                for video_size in video_sizes
+            ]
+            num_video_tokens = [(num_patches // merge_size**2) for num_patches in num_video_patches]
+            vision_data["num_video_tokens"] = num_video_tokens
+
+        return MultiModalData(**vision_data)
+
 
 __all__ = [
     "Qwen2_5_VLConfig",

src/transformers/models/qwen2_5_vl/processing_qwen2_5_vl.py

@@ -25,9 +25,11 @@
 # limitations under the License.
 from typing import List, Optional, Union
 
+import numpy as np
+
 from ...feature_extraction_utils import BatchFeature
 from ...image_utils import ImageInput
-from ...processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin, Unpack, VideosKwargs
+from ...processing_utils import ImagesKwargs, MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack, VideosKwargs
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...video_utils import VideoInput
 
@@ -50,6 +52,7 @@ class Qwen2_5_VLProcessorKwargs(ProcessingKwargs, total=False):
     _defaults = {
         "text_kwargs": {
             "padding": False,
+            "return_mm_token_type_ids": False,
         },
         "videos_kwargs": {"fps": 2.0},
     }
@@ -188,11 +191,56 @@ class Qwen2_5_VLProcessor(ProcessorMixin):
                 text[i] = text[i].replace("<|placeholder|>", self.video_token)
 
         return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None)
         text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
         self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"])
 
+        if return_mm_token_type_ids:
+            array_ids = np.array(text_inputs["input_ids"])
+            mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
+            mm_token_type_ids[array_ids == self.image_token_id] = 1
+            text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
+
         return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors)
 
+    def _get_num_multimodal_tokens(self, image_sizes=None, video_sizes=None, **kwargs):
+        """
+        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+        Args:
+            image_sizes (`List[List[int]]`, *optional*):
+                The input sizes formatted as (height, width) per each image.
+            video_sizes (`List[List[int]]`, *optional*):
+                The input sizes formatted as (num_frames, height, width) per each video.
+        Returns:
+            `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
+            input modalities, along with other useful data.
+        """
+
+        vision_data = {}
+        if image_sizes is not None:
+            images_kwargs = Qwen2_5_VLProcessorKwargs._defaults.get("images_kwargs", {})
+            images_kwargs.update(kwargs)
+            merge_size = images_kwargs.get("merge_size", None) or self.image_processor.merge_size
+
+            num_image_patches = [
+                self.image_processor.get_number_of_image_patches(*image_size, images_kwargs)
+                for image_size in image_sizes
+            ]
+            num_image_tokens = [(num_patches // merge_size**2) for num_patches in num_image_patches]
+            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
+
+        if video_sizes is not None:
+            videos_kwargs = Qwen2_5_VLProcessorKwargs._defaults.get("videos_kwargs", {})
+            videos_kwargs.update(kwargs)
+            num_video_patches = [
+                self.video_processor.get_number_of_video_patches(*video_size, videos_kwargs)
+                for video_size in video_sizes
+            ]
+            num_video_tokens = [(num_patches // merge_size**2) for num_patches in num_video_patches]
+            vision_data["num_video_tokens"] = num_video_tokens
+
+        return MultiModalData(**vision_data)
+
     def batch_decode(self, *args, **kwargs):
         """
         This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please

src/transformers/models/qwen2_vl/image_processing_qwen2_vl.py

@@ -490,5 +490,31 @@ class Qwen2VLImageProcessor(BaseImageProcessor):
 
         return BatchFeature(data=data, tensor_type=return_tensors)
 
+    def get_number_of_image_patches(self, height: int, width: int, images_kwargs=None):
+        """
+        A utility that returns number of image patches for a given image size.
+
+        Args:
+            height (`int`):
+                Height of the input image.
+            width (`int`):
+                Width of the input image.
+            images_kwargs (`dict`, *optional*)
+                Any kwargs to override defaults of the image processor.
+        Returns:
+            `int`: Number of image patches per image.
+        """
+        min_pixels = images_kwargs.get("min_pixels", None) or self.size["shortest_edge"]
+        max_pixels = images_kwargs.get("max_pixels", None) or self.size["longest_edge"]
+        patch_size = images_kwargs.get("patch_size", None) or self.patch_size
+        merge_size = images_kwargs.get("merge_size", None) or self.merge_size
+
+        factor = patch_size * merge_size
+        resized_height, resized_width = smart_resize(
+            height, width, factor, min_pixels=min_pixels, max_pixels=max_pixels
+        )
+        grid_h, grid_w = resized_height // patch_size, resized_width // patch_size
+        return grid_h * grid_w
+
 
 __all__ = ["Qwen2VLImageProcessor"]

src/transformers/models/qwen2_vl/image_processing_qwen2_vl_fast.py

@@ -402,5 +402,31 @@ class Qwen2VLImageProcessorFast(BaseImageProcessorFast):
 
         return BatchFeature(data=data, tensor_type=return_tensors)
 
+    def get_number_of_image_patches(self, height: int, width: int, images_kwargs=None):
+        """
+        A utility that returns number of image patches for a given image size.
+
+        Args:
+            height (`int`):
+                Height of the input image.
+            width (`int`):
+                Width of the input image.
+            images_kwargs (`dict`, *optional*)
+                Any kwargs to override defaults of the image processor.
+        Returns:
+            `int`: Number of image patches per image.
+        """
+        min_pixels = images_kwargs.get("min_pixels", None) or self.size["shortest_edge"]
+        max_pixels = images_kwargs.get("max_pixels", None) or self.size["longest_edge"]
+        patch_size = images_kwargs.get("patch_size", None) or self.patch_size
+        merge_size = images_kwargs.get("merge_size", None) or self.merge_size
+
+        factor = patch_size * merge_size
+        resized_height, resized_width = smart_resize(
+            height, width, factor, min_pixels=min_pixels, max_pixels=max_pixels
+        )
+        grid_h, grid_w = resized_height // patch_size, resized_width // patch_size
+        return grid_h * grid_w
+
 
 __all__ = ["Qwen2VLImageProcessorFast"]

src/transformers/models/qwen2_vl/processing_qwen2_vl.py

@@ -23,9 +23,11 @@ Processor class for Qwen2-VL.
 
 from typing import List, Optional, Union
 
+import numpy as np
+
 from ...feature_extraction_utils import BatchFeature
 from ...image_utils import ImageInput
-from ...processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin, Unpack
+from ...processing_utils import ImagesKwargs, MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
 from ...tokenization_utils_base import PreTokenizedInput, TextInput
 from ...utils import logging
 from ...video_utils import VideoInput
@@ -47,6 +49,7 @@ class Qwen2VLProcessorKwargs(ProcessingKwargs, total=False):
     _defaults = {
         "text_kwargs": {
             "padding": False,
+            "return_mm_token_type_ids": False,
         },
     }
 
@@ -172,10 +175,56 @@ class Qwen2VLProcessor(ProcessorMixin):
                 text[i] = text[i].replace("<|placeholder|>", self.video_token)
 
         return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
-        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])
+        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False)
+        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"], return_tensors=None)
         self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"])
+
+        if return_mm_token_type_ids:
+            array_ids = np.array(text_inputs["input_ids"])
+            mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
+            mm_token_type_ids[array_ids == self.image_token_id] = 1
+            text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()
+
         return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors)
 
+    def _get_num_multimodal_tokens(self, image_sizes=None, video_sizes=None, **kwargs):
+        """
+        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
+        Args:
+            image_sizes (`List[List[int]]`, *optional*):
+                The input sizes formatted as (height, width) per each image.
+            video_sizes (`List[List[int]]`, *optional*):
+                The input sizes formatted as (num_frames, height, width) per each video.
+        Returns:
+            `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
+            input modalities, along with other useful data.
+        """
+
+        vision_data = {}
+        if image_sizes is not None:
+            images_kwargs = Qwen2VLProcessorKwargs._defaults.get("images_kwargs", {})
+            images_kwargs.update(kwargs)
+            merge_size = images_kwargs.get("merge_size", None) or self.image_processor.merge_size
+
+            num_image_patches = [
+                self.image_processor.get_number_of_image_patches(*image_size, images_kwargs)
+                for image_size in image_sizes
+            ]
+            num_image_tokens = [(num_patches // merge_size**2) for num_patches in num_image_patches]
+            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})
+
+        if video_sizes is not None:
+            videos_kwargs = Qwen2VLProcessorKwargs._defaults.get("videos_kwargs", {})
+            videos_kwargs.update(kwargs)
+            num_video_patches = [
+                self.video_processor.get_number_of_video_patches(*video_size, videos_kwargs)
+                for video_size in video_sizes
+            ]
+            num_video_tokens = [(num_patches // merge_size**2) for num_patches in num_video_patches]
+            vision_data["num_video_tokens"] = num_video_tokens
+
+        return MultiModalData(**vision_data)
+
     def batch_decode(self, *args, **kwargs):
         """
         This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please

src/transformers/models/qwen2_vl/video_processing_qwen2_vl.py

@@ -204,5 +204,35 @@ class Qwen2VLVideoProcessor(BaseVideoProcessor):
             tensor_type=return_tensors,
         )
 
+    def get_num_of_video_patches(self, num_frames: int, height: int, width: int, videos_kwargs=None):
+        """
+        A utility that returns number of video patches a given video size.
+
+        Args:
+            num_frames (`int`):
+                Number of frames in the input video.
+            height (`int`):
+                Height of the input video.
+            width (`int`):
+                Width of the input video.
+            videos_kwargs (`dict`, *optional*)
+                Any kwargs to override defaults of the video processor.
+        Returns:
+            `Tuple(int, int)`: Number of placeholder tokens required and number of patches per image.
+        """
+        min_pixels = videos_kwargs.get("min_pixels", None) or self.size["shortest_edge"]
+        max_pixels = videos_kwargs.get("max_pixels", None) or self.size["longest_edge"]
+        patch_size = videos_kwargs.get("patch_size", None) or self.patch_size
+        merge_size = videos_kwargs.get("merge_size", None) or self.merge_size
+        temporal_patch_size = videos_kwargs.get("temporal_patch_size", None) or self.temporal_patch_size
+
+        factor = patch_size * merge_size
+        resized_height, resized_width = smart_resize(
+            height, width, factor, min_pixels=min_pixels, max_pixels=max_pixels
+        )
+        grid_h, grid_w = resized_height // patch_size, resized_width // patch_size
+        grid_t = num_frames // temporal_patch_size
+        return grid_t * grid_h * grid_w
+
 
 __all__ = ["Qwen2VLVideoProcessor"]

src/transformers/models/smolvlm/image_processing_smolvlm.py

@@ -847,5 +847,46 @@ class SmolVLMImageProcessor(BaseImageProcessor):
 
         return encoding
 
+    def get_number_of_image_patches(self, height: int, width: int, images_kwargs=None):
+        """
+        A utility that returns number of image patches for a given image size.
+
+        Args:
+            height (`int`):
+                Height of the input image.
+            width (`int`):
+                Width of the input image.
+            images_kwargs (`dict`, *optional*)
+                Any kwargs to override defaults of the image processor.
+        Returns:
+            `int`: Number of patches per image.
+        """
+        do_image_splitting = images_kwargs.get("do_image_splitting", None) or self.do_image_splitting
+        max_image_size = images_kwargs.get("max_image_size", None) or self.max_image_size
+        size = images_kwargs.get("size", None) or self.size
+
+        if do_image_splitting:
+            height, width = _resize_output_size_rescale_to_max_len(height, width, max_len=size["longest_edge"])
+            height, width = _resize_output_size_scale_below_upper_bound(height, width, max_len=4096)
+            aspect_ratio = width / height
+
+            if width >= height:
+                resized_width = math.ceil(width / max_image_size["longest_edge"]) * max_image_size["longest_edge"]
+                resized_height = int(width / aspect_ratio)
+                resized_height = math.ceil(height / max_image_size["longest_edge"]) * max_image_size["longest_edge"]
+            elif height > width:
+                resized_height = math.ceil(height / max_image_size["longest_edge"]) * max_image_size["longest_edge"]
+                resized_width = int(height * aspect_ratio)
+                resized_width = math.ceil(width / max_image_size["longest_edge"]) * max_image_size["longest_edge"]
+
+            max_height = max_width = max_image_size["longest_edge"]
+            if resized_height > max_height or resized_width > max_width:
+                # Calculate the number of splits
+                num_rows = math.ceil(resized_height / max_height)
+                num_cols = math.ceil(resized_width / max_width)
+                num_patches = num_rows * num_cols + 1
+
+        return num_patches
+
 
 __all__ = ["SmolVLMImageProcessor"]

src/transformers/processing_utils.py

@@ -22,6 +22,7 @@ import os
 import sys
 import typing
 import warnings
+from dataclasses import dataclass
 from pathlib import Path
 from typing import Any, Dict, List, Optional, TypedDict, Union
 
@@ -120,6 +121,8 @@ class TextKwargs(TypedDict, total=False):
             Whether or not to print more information and warnings.
         padding_side (`str`, *optional*):
             The side on which padding will be applied.
+        return_mm_token_type_ids (`bool`, *optional*):
+            Whether to return multimodal token type ids indicating mm placeholder token positions.
     """
 
     text_pair: Optional[Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]]]
@@ -140,6 +143,7 @@ class TextKwargs(TypedDict, total=False):
     return_length: Optional[bool]
     verbose: Optional[bool]
     padding_side: Optional[str]
+    return_mm_token_type_ids: Optional[bool]
 
 
 class ImagesKwargs(TypedDict, total=False):
@@ -455,6 +459,32 @@ class AllKwargsForChatTemplate(
     }
 
 
+@dataclass
+class MultiModalData:
+    """
+    Dataclass that holds extra useful data for processing
+    multimodal data. Processors currently cannot return keys,
+    unless it is used in model's forward. Thus we have helper
+    methods that calculate and return useful data from processing
+    input multimodals (images/videos).
+    Note that this dataclass is aimed to be used only in vLLM
+    and we might change its API in the future.
+    """
+
+    num_image_tokens: list[int] = None
+    num_video_tokens: list[int] = None
+    num_audio_tokens: list[int] = None
+    num_image_patches: list[int] = None
+
+    def __contains__(self, key):
+        return hasattr(self, key) and getattr(self, key) is not None
+
+    def __getitem__(self, key):
+        if hasattr(self, key):
+            return getattr(self, key)
+        raise AttributeError(f"{self.__class__.__name__} has no attribute {key}")
+
+
 class ProcessorMixin(PushToHubMixin):
     """
     This is a mixin used to provide saving/loading functionality for all processor classes.