processing_sarashina2_vision.py

# coding=utf-8
# Copyright 2026 the SB Intuitions.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Processor class for Sarashina2Vision.
"""

import math
from typing import Dict, List, Optional, Union

import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
    AutoImageProcessor,
    AutoVideoProcessor,
    BaseImageProcessor,
    BaseVideoProcessor,
)
from transformers.feature_extraction_utils import BatchFeature
from transformers.image_transforms import (
    convert_to_rgb,
    to_channel_dimension_format,
)
from transformers.image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    get_image_size,
    infer_channel_dimension_format,
    is_scaled_image,
    make_flat_list_of_images,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from transformers.models.qwen2_vl.image_processing_qwen2_vl import smart_resize
from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
from transformers.utils import TensorType, logging
from transformers.video_utils import VideoInput, VideoMetadata, load_video

logger = logging.get_logger(__name__)


class Sarashina2VisionImageProcessor(BaseImageProcessor):
    r"""
    Constructs a Sarashina2Vision image processor that dynamically resizes images based on the original images.

    Args:
        do_resize (`bool`, *optional*, defaults to `True`):
            Whether to resize the image's (height, width) dimensions.
        do_rescale (`bool`, *optional*, defaults to `True`):
            Whether to rescale the image by the specified scale `rescale_factor`.
        rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
            Scale factor to use if rescaling the image.
        do_normalize (`bool`, *optional*, defaults to `True`):
            Whether to normalize the image.
        image_mean (`float` or `List[float]`, *optional*, defaults to `[0.48145466, 0.4578275, 0.40821073]`):
            Mean to use if normalizing the image. This is a float or list of floats for each channel in the image.
        image_std (`float` or `List[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):
            Standard deviation to use if normalizing the image. This is a float or list of floats for each channel in the image.
        do_convert_rgb (`bool`, *optional*, defaults to `True`):
            Whether to convert the image to RGB.
        min_pixels (`int`, *optional*, defaults to `56 * 56`):
            The min pixels of the image to resize the image.
        max_pixels (`int`, *optional*, defaults to `28 * 28 * 1280`):
            The max pixels of the image to resize the image.
        patch_size (`int`, *optional*, defaults to 14):
            The spacial patch size of the vision encoder.
        temporal_patch_size (`int`, *optional*, defaults to 2):
            The temporal patch size of the vision encoder.
        merge_size (`int`, *optional*, defaults to 2):
            The merge size of the vision encoder to llm encoder.
    """

    model_input_names = ["pixel_values", "image_grid_thw"]

    def __init__(
        self,
        do_resize: bool = True,
        do_rescale: bool = True,
        rescale_factor: Union[int, float] = 1 / 255,
        do_normalize: bool = True,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        do_convert_rgb: bool = True,
        min_pixels: int = 56 * 56,
        max_pixels: int = 28 * 28 * 1280,
        patch_size: int = 14,
        temporal_patch_size: int = 2,
        merge_size: int = 2,
        **kwargs,
    ) -> None:
        super().__init__(**kwargs)
        self.do_resize = do_resize
        self.do_rescale = do_rescale
        self.rescale_factor = rescale_factor
        self.do_normalize = do_normalize
        self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        self.image_std = image_std if image_std is not None else OPENAI_CLIP_STD
        self.min_pixels = min_pixels
        self.max_pixels = max_pixels
        self.patch_size = patch_size
        self.temporal_patch_size = temporal_patch_size
        self.merge_size = merge_size
        self.size = {"shortest_edge": min_pixels, "longest_edge": max_pixels}
        self.do_convert_rgb = do_convert_rgb

    def _preprocess(
        self,
        images: ImageInput,
        do_resize: bool = None,
        do_rescale: bool = None,
        rescale_factor: float = None,
        do_normalize: bool = None,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        do_convert_rgb: bool = None,
        data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
    ):
        """
        Preprocess an image or batch of images. Copy of the `preprocess` method from `Sarashina2Vision`.

        Args:
            images (`ImageInput`):
                Image or batch of images to preprocess. Expects pixel values ranging from 0 to 255. If pixel values range from 0 to 1, set `do_rescale=False`.
            vision_info (`List[Dict]`, *optional*):
                Optional list of dictionaries containing additional information about vision inputs.
            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
                Whether to resize the image.
            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
                Whether to rescale the image.
            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
                Scale factor to use if rescaling the image.
            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
                Whether to normalize the image.
            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
                Mean to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image.
            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
                Standard deviation to use if normalizing the image. Can be a float or a list of floats corresponding to the number of channels in the image.
            do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
                Whether to convert the image to RGB.
            data_format (`ChannelDimension`, *optional*, defaults to `ChannelDimension.FIRST`):
                The channel dimension format for the output image. Can be one of:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                - Unset: Use the channel dimension format of the input image.
            input_data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format for the input image. Can be one of:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.   - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
        """
        images = make_list_of_images(images)

        if do_convert_rgb:
            images = [convert_to_rgb(image) for image in images]

        # All transformations expect numpy arrays.
        images = [to_numpy_array(image) for image in images]

        if do_rescale and is_scaled_image(images[0]):
            logger.warning_once(
                "It looks like you are trying to rescale already rescaled images. If the input"
                " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
            )
        if input_data_format is None:
            # We assume that all images have the same channel dimension format.
            input_data_format = infer_channel_dimension_format(images[0])

        height, width = get_image_size(images[0], channel_dim=input_data_format)
        resized_height, resized_width = height, width
        processed_images = []
        for image in images:
            if do_rescale:
                image = self.rescale(
                    image, scale=rescale_factor, input_data_format=input_data_format
                )

            if do_normalize:
                image = self.normalize(
                    image=image,
                    mean=image_mean,
                    std=image_std,
                    input_data_format=input_data_format,
                )

            image = to_channel_dimension_format(
                image, data_format, input_channel_dim=input_data_format
            )

            if do_resize:
                resized_height, resized_width = smart_resize(
                    height,
                    width,
                    factor=self.patch_size * self.merge_size,
                    min_pixels=self.min_pixels,
                    max_pixels=self.max_pixels,
                )
                image = (
                    F.interpolate(
                        torch.from_numpy(image).unsqueeze(0),
                        size=(resized_height, resized_width),
                        mode="bicubic",
                    )
                    .squeeze(0)
                    .numpy()
                )

            processed_images.append(image)

        patches = np.array(processed_images)
        if data_format == ChannelDimension.LAST:
            patches = patches.transpose(0, 3, 1, 2)
        if patches.shape[0] % self.temporal_patch_size != 0:
            repeats = np.repeat(patches[-1][np.newaxis], self.temporal_patch_size - 1, axis=0)
            patches = np.concatenate([patches, repeats], axis=0)
        channel = patches.shape[1]
        grid_t = patches.shape[0] // self.temporal_patch_size
        grid_h, grid_w = resized_height // self.patch_size, resized_width // self.patch_size
        patches = patches.reshape(
            grid_t,
            self.temporal_patch_size,
            channel,
            grid_h // self.merge_size,
            self.merge_size,
            self.patch_size,
            grid_w // self.merge_size,
            self.merge_size,
            self.patch_size,
        )
        patches = patches.transpose(0, 3, 6, 4, 7, 2, 1, 5, 8)
        flatten_patches = patches.reshape(
            grid_t * grid_h * grid_w,
            channel * self.temporal_patch_size * self.patch_size * self.patch_size,
        )

        return flatten_patches, (grid_t, grid_h, grid_w)

    def preprocess(
        self,
        images: ImageInput,
        do_resize: bool = None,
        size: Dict[str, int] = None,
        do_rescale: bool = None,
        rescale_factor: float = None,
        do_normalize: bool = None,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        do_convert_rgb: bool = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        data_format: Optional[ChannelDimension] = ChannelDimension.FIRST,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
    ):
        """
        Args:
            images (`ImageInput`):
                Image to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
                passing in images with pixel values between 0 and 1, set `do_rescale=False`.
            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
                Whether to resize the image.
            size (`Dict[str, int]`, *optional*, defaults to `self.size`):
                Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
                the longest edge resized to keep the input aspect ratio.
            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
                Whether to rescale the image.
            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
                Rescale factor to rescale the image by if `do_rescale` is set to `True`.
            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
                Whether to normalize the image.
            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
                Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
                Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
                `True`.
            do_convert_rgb (`bool`, *optional*, defaults to `self.do_convert_rgb`):
                Whether to convert the image to RGB.
            return_tensors (`str` or `TensorType`, *optional*):
                The type of tensors to return. Can be one of:
                - Unset: Return a list of `np.ndarray`.
                - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
                - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
                - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
                - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
                The channel dimension format for the output image. Can be one of:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                - Unset: Use the channel dimension format of the input image.
            input_data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format for the input image. If unset, the channel dimension format is inferred
                from the input image. Can be one of:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.

        """
        do_resize = do_resize if do_resize is not None else self.do_resize
        size = size if size is not None else self.size
        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
        rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
        image_mean = image_mean if image_mean is not None else self.image_mean
        image_std = image_std if image_std is not None else self.image_std
        do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        if images is not None:
            images = make_flat_list_of_images(images)

        if images is not None and not valid_images(images):
            raise ValueError(
                "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                "torch.Tensor, tf.Tensor or jax.ndarray."
            )

        if images is not None:
            pixel_values, vision_grid_thws = [], []
            for image in images:
                patches, image_grid_thw = self._preprocess(
                    image,
                    do_resize=do_resize,
                    do_rescale=do_rescale,
                    rescale_factor=rescale_factor,
                    do_normalize=do_normalize,
                    image_mean=image_mean,
                    image_std=image_std,
                    data_format=data_format,
                    do_convert_rgb=do_convert_rgb,
                    input_data_format=input_data_format,
                )
                pixel_values.extend(patches)
                vision_grid_thws.append(image_grid_thw)
            pixel_values = np.array(pixel_values)
            vision_grid_thws = np.array(vision_grid_thws)
            data = {"pixel_values": pixel_values, "image_grid_thw": vision_grid_thws}

        return BatchFeature(data=data, tensor_type=return_tensors)


class Sarashina2VisionVideoProcessor(BaseVideoProcessor):
    def __init__(
        self,
        do_rescale: bool = True,
        rescale_factor: Union[int, float] = 1 / 255,
        do_normalize: bool = True,
        image_mean: Optional[Union[float, List[float]]] = None,
        image_std: Optional[Union[float, List[float]]] = None,
        max_pixels: int = 28 * 28 * 1280,
        patch_size: int = 14,
        temporal_patch_size: int = 2,
        merge_size: int = 2,
        fps: int = 2,
        fps_min_frames: int = 2,
        fps_max_frames: int = 64,
        video_min_token_num: int = 128,
        video_max_token_num: int = 768,
        total_pixels: int = 3072 * 28 * 28,
        do_sample_frames: bool = True,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.do_rescale = do_rescale
        self.rescale_factor = rescale_factor
        self.do_normalize = do_normalize
        self.image_mean = image_mean
        self.image_std = image_std
        self.max_pixels = max_pixels

        self.patch_size = patch_size
        self.merge_size = merge_size
        self.image_factor = self.patch_size * self.merge_size

        self.fps = fps
        self.fps_min_frames = fps_min_frames
        self.fps_max_frames = fps_max_frames
        self.do_sample_frames = do_sample_frames
        self.video_min_token_num = video_min_token_num
        self.video_max_token_num = video_max_token_num
        self.temporal_patch_size = temporal_patch_size
        self.total_pixels = max(total_pixels, max_pixels)

    def sample_frames(
        self,
        metadata: VideoMetadata,
        **kwargs,
    ):
        total_num_frames = metadata.total_num_frames
        min_frames = (
            math.ceil(self.fps_min_frames / self.temporal_patch_size) * self.temporal_patch_size
        )
        max_frames = min(self.fps_max_frames, total_num_frames)
        nframes = total_num_frames / metadata.fps * self.fps
        if nframes > total_num_frames:
            logger.warning(
                f"smart_nframes: nframes[{nframes}] > total_num_frames[{total_num_frames}]"
            )
        nframes = min(min(max(nframes, min_frames), max_frames), total_num_frames)
        nframes = math.floor(nframes / self.temporal_patch_size) * self.temporal_patch_size
        if not (self.temporal_patch_size <= nframes and nframes <= total_num_frames):
            raise ValueError(
                f"nframes should in interval [{self.temporal_patch_size}, {total_num_frames}], but got {nframes}."
            )
        indices = torch.linspace(0, total_num_frames - 1, nframes).round().long().tolist()
        return indices

    def _preprocess(
        self,
        videos: list["torch.Tensor"],
        do_rescale: bool,
        rescale_factor: float,
        do_normalize: bool,
        image_mean: Optional[Union[float, list[float]]],
        image_std: Optional[Union[float, list[float]]],
        return_tensors: Optional[Union[str, TensorType]] = None,
        **kwargs,
    ) -> BatchFeature:
        pixel_values = []
        vision_grid_thws = []
        for video in videos:
            video = self.convert_to_rgb(video)
            video = self.rescale_and_normalize(
                video,
                do_rescale,
                rescale_factor,
                do_normalize,
                image_mean,
                image_std,
            )
            nframes, _, height, width = video.shape
            min_pixels = self.video_min_token_num * (self.image_factor**2)
            total_pixels = self.total_pixels
            max_pixels = min(
                self.max_pixels,
                max(
                    total_pixels / nframes * self.temporal_patch_size,
                    int(min_pixels * 1.05),
                ),
            )
            resized_height, resized_width = smart_resize(
                height,
                width,
                factor=self.image_factor,
                min_pixels=min_pixels,
                max_pixels=max_pixels,
            )
            video = F.interpolate(
                video,
                size=(resized_height, resized_width),
                mode="bicubic",
            )

            if video.shape[0] % self.temporal_patch_size != 0:
                repeats = video[-1].unsqueeze(0).repeat(self.temporal_patch_size - 1, 1, 1, 1)
                patch = torch.cat([video, repeats], dim=0)
            else:
                patch = video

            grid_t = patch.shape[0] // self.temporal_patch_size
            channel = patch.shape[1]
            grid_h, grid_w = resized_height // self.patch_size, resized_width // self.patch_size

            patch = patch.reshape(
                grid_t,
                self.temporal_patch_size,
                channel,
                grid_h // self.merge_size,
                self.merge_size,
                self.patch_size,
                grid_w // self.merge_size,
                self.merge_size,
                self.patch_size,
            )
            patch = patch.permute(0, 3, 6, 4, 7, 2, 1, 5, 8)
            flatten_patch = patch.reshape(
                grid_t * grid_h * grid_w,
                channel * self.temporal_patch_size * self.patch_size * self.patch_size,
            )

            pixel_values.extend(np.array(flatten_patch))
            vision_grid_thws.append((grid_t, grid_h, grid_w))
        data = {
            "pixel_values_video": np.array(pixel_values),
            "video_grid_thw": np.array(vision_grid_thws),
        }

        return BatchFeature(data=data, tensor_type=return_tensors)

    def fetch_videos(
        self,
        video_url_or_urls: Union[str, list[str], list[list[str]]],
        sample_indices_fn=None,
        backend="torchvision",
    ):
        """
        Convert a single or a list of urls into the corresponding `np.array` objects.

        If a single url is passed, the return value will be a single object. If a list is passed a list of objects is
        returned.
        """

        if isinstance(video_url_or_urls, list):
            return list(
                zip(
                    *[
                        self.fetch_videos(x, sample_indices_fn=sample_indices_fn, backend=backend)
                        for x in video_url_or_urls
                    ]
                )
            )
        else:
            device = self.device if hasattr(self, "device") and self.device is not None else "cpu"
            return load_video(
                video_url_or_urls,
                backend=backend,
                sample_indices_fn=sample_indices_fn,
                device=device,
            )


class Sarashina2VisionProcessorKwargs(ProcessingKwargs, total=False):
    _defaults = {
        "text_kwargs": {
            "padding": False,
        },
    }


class Sarashina2VisionProcessor(ProcessorMixin):
    r"""
    Constructs Sarashina2Vision processor which wraps a Sarashina2Vision image processor and a LLama tokenizer into a single processor.
    [`Sarashina2VisionProcessor`] offers all the functionalities of [`Sarashina2VisionImageProcessor`] and [`LlamaTokenizerFast`]. See the
    [`~Sarashina2VisionProcessor.__call__`] and [`~Sarashina2VisionProcessor.decode`] for more information.
    Args:
        image_processor ([`Sarashina2VisionImageProcessor`], *optional*):
            The image processor is a required input.
        tokenizer ([`LlamaTokenizerFast`], *optional*):
            The tokenizer is a required input.
        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
            in a chat into a tokenizable string.
    """

    attributes = ["image_processor", "video_processor", "tokenizer"]
    valid_kwargs = ["chat_template"]
    image_processor_class = "AutoImageProcessor"
    video_processor_class = "AutoVideoProcessor"
    tokenizer_class = ("LlamaTokenizer", "LlamaTokenizerFast")

    def __init__(
        self,
        image_processor=None,
        video_processor=None,
        tokenizer=None,
        chat_template=None,
        **kwargs,
    ):
        self.image_token = (
            "<|file|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token
        )
        self.video_token = (
            "<|middle|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_token
        )
        super().__init__(image_processor, video_processor, tokenizer, chat_template=chat_template)

    def __call__(
        self,
        images: ImageInput = None,
        videos: VideoInput = None,
        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
        **kwargs: Unpack[Sarashina2VisionProcessorKwargs],
    ) -> BatchFeature:
        """
        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
        and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode
        the text. To prepare the vision inputs, this method forwards the `vision_infos` and `kwrags` arguments to
        Sarashina2VisionImageProcessor's [`~Sarashina2VisionImageProcessor.__call__`] if `vision_infos` is not `None`.

        Args:
            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
                tensor. Both channels-first and channels-last formats are supported.
            text (`str`, `List[str]`, `List[List[str]]`):
                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
            return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors of a particular framework. Acceptable values are:
                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return NumPy `np.ndarray` objects.
                - `'jax'`: Return JAX `jnp.ndarray` objects.

        Returns:
            [`BatchFeature`]: A [`BatchFeature`] with the following fields:

            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
              `None`).
            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
            - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
        """
        output_kwargs = self._merge_kwargs(
            Sarashina2VisionProcessorKwargs,
            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
            **kwargs,
        )
        if images is not None:
            image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"])
            image_grid_thw = image_inputs["image_grid_thw"]
        else:
            image_inputs = {}
            image_grid_thw = None

        if videos is not None:
            video_inputs = self.video_processor(videos=videos, **output_kwargs["images_kwargs"])
            video_grid_thw = video_inputs["video_grid_thw"]
        else:
            video_inputs = {}
            video_grid_thw = None

        if not isinstance(text, list):
            text = [text]

        if image_grid_thw is not None or video_grid_thw is not None:
            merge_length = self.image_processor.merge_size**2
            image_index = 0
            video_index = 0
            for i in range(len(text)):
                if images is not None:
                    while self.image_token in text[i]:
                        text[i] = text[i].replace(
                            self.image_token,
                            "<|placeholder|>"
                            * (image_grid_thw[image_index].prod() // merge_length),
                            1,
                        )
                        image_index += 1
                    text[i] = text[i].replace("<|placeholder|>", self.image_token)
                if videos is not None:
                    while self.video_token in text[i]:
                        text[i] = text[i].replace(
                            self.video_token,
                            "<|placeholder|>"
                            * (video_grid_thw[video_index].prod() // merge_length),
                            1,
                        )
                        video_index += 1
                    text[i] = text[i].replace("<|placeholder|>", self.video_token)

        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])

        return BatchFeature(data={**text_inputs, **image_inputs, **video_inputs})

    def batch_decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.batch_decode`].
        """
        return self.tokenizer.batch_decode(*args, **kwargs)

    def decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`].
        """
        return self.tokenizer.decode(*args, **kwargs)

    def post_process_image_text_to_text(self, generated_outputs):
        """
        Post-process the output of the model to decode the text.

        Args:
            generated_outputs (`torch.Tensor` or `np.ndarray`):
                The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
                or `(sequence_length,)`.

        Returns:
            `List[str]`: The decoded text.
        """
        return self.tokenizer.batch_decode(
            generated_outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False
        )

    @property
    def model_input_names(self):
        tokenizer_input_names = self.tokenizer.model_input_names
        image_processor_input_names = self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))


Sarashina2VisionProcessor.register_for_auto_class("AutoProcessor")
AutoImageProcessor.register("Sarashina2VisionImageProcessor", Sarashina2VisionImageProcessor)
AutoVideoProcessor.register("Sarashina2VisionVideoProcessor", Sarashina2VisionVideoProcessor)