# 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)