def preprocess(
        self,
        images: ImageInput,
        do_resize: Optional[bool] = None,
        size: Optional[Dict[str, int]] = None,
        resample: PILImageResampling = None,
        do_center_crop: Optional[bool] = None,
        crop_size: Optional[Dict[str, int]] = None,
        do_rescale: Optional[bool] = None,
        rescale_factor: Optional[float] = None,
        do_normalize: Optional[bool] = None,
        image_mean: Optional[Union[float, Iterable[float]]] = None,
        image_std: Optional[Union[float, Iterable[float]]] = None,
        return_tensors: Optional[TensorType] = None,
        data_format: ChannelDimension = ChannelDimension.FIRST,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> BatchFeature:
        """
        Preprocess an image or batch of images to be used as input to a LeViT model.

        Args:
            images (`ImageInput`):
                Image or batch of images 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 output image after resizing. If size is a dict with keys "width" and "height", the image
                will be resized to (height, width). If size is a dict with key "shortest_edge", the shortest edge value
                `c` is rescaled to int(`c` * (256/224)). The smaller edge of the image will be matched to this value
                i.e, if height > width, then image will be rescaled to (size * height / width, size).
            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
                Resampling filter to use when resiizing the image.
            do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`):
                Whether to center crop the image.
            crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`):
                Size of the output image after center cropping. Crops images to (crop_size["height"],
                crop_size["width"]).
            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
                Whether to rescale the image pixel values by `rescaling_factor` - typical to values between 0 and 1.
            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
                Factor to rescale the image pixel values by.
            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
                Whether to normalize the image pixel values by `image_mean` and `image_std`.
            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
                Mean to normalize the image pixel values by.
            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
                Standard deviation to normalize the image pixel values by.
            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 (`str` or `ChannelDimension`, *optional*, defaults to `ChannelDimension.FIRST`):
                The channel dimension format for the output image. If unset, the channel dimension format of the input
                image is used. 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.
            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
        resample = resample if resample is not None else self.resample
        do_center_crop = do_center_crop if do_center_crop is not None else self.do_center_crop
        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

        size = size if size is not None else self.size
        size = get_size_dict(size, default_to_square=False)
        crop_size = crop_size if crop_size is not None else self.crop_size
        crop_size = get_size_dict(crop_size, param_name="crop_size")

        images = make_list_of_images(images)

        if 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 do_resize and size is None:
            raise ValueError("Size must be specified if do_resize is True.")

        if do_center_crop and crop_size is None:
            raise ValueError("Crop size must be specified if do_center_crop is True.")

        if do_rescale and rescale_factor is None:
            raise ValueError("Rescale factor must be specified if do_rescale is True.")

        if do_normalize and (image_mean is None or image_std is None):
            raise ValueError("Image mean and std must be specified if do_normalize is True.")

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

        if is_scaled_image(images[0]) and do_rescale:
            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])

        if do_resize:
            images = [self.resize(image, size, resample, input_data_format=input_data_format) for image in images]

        if do_center_crop:
            images = [self.center_crop(image, crop_size, input_data_format=input_data_format) for image in images]

        if do_rescale:
            images = [self.rescale(image, rescale_factor, input_data_format=input_data_format) for image in images]

        if do_normalize:
            images = [
                self.normalize(image, image_mean, image_std, input_data_format=input_data_format) for image in images
            ]

        images = [
            to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) for image in images
        ]

        data = {"pixel_values": images}
        return BatchFeature(data=data, tensor_type=return_tensors)

        Preprocess an image or batch of images to be used as input to a LeViT model.

        Args:
            images (`ImageInput`):
                Image or batch of images 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 output image after resizing. If size is a dict with keys "width" and "height", the image
                will be resized to (height, width). If size is a dict with key "shortest_edge", the shortest edge value
                `c` is rescaled to int(`c` * (256/224)). The smaller edge of the image will be matched to this value
                i.e, if height > width, then image will be rescaled to (size * height / width, size).
            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
                Resampling filter to use when resiizing the image.
            do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`):
                Whether to center crop the image.
            crop_size (`Dict[str, int]`, *optional*, defaults to `self.crop_size`):
                Size of the output image after center cropping. Crops images to (crop_size["height"],
                crop_size["width"]).
            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
                Whether to rescale the image pixel values by `rescaling_factor` - typical to values between 0 and 1.
            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
                Factor to rescale the image pixel values by.
            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
                Whether to normalize the image pixel values by `image_mean` and `image_std`.
            image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
                Mean to normalize the image pixel values by.
            image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
                Standard deviation to normalize the image pixel values by.
            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 (`str` or `ChannelDimension`, *optional*, defaults to `ChannelDimension.FIRST`):
                The channel dimension format for the output image. If unset, the channel dimension format of the input
                image is used. 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.
            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.
        
dpkd/transformers/src/transformers/models/levit/image_processing_levit.py