class_docstr: Constructs a OneFormer image processor. The image processor can be used to prepare image(s), task input(s) and
  optional text inputs and targets for the model.
  
  This image processor inherits from [`BaseImageProcessor`] which contains most of the main methods. Users should
  refer to this superclass for more information regarding those methods.
  
  Args:
      do_resize (`bool`, *optional*, defaults to `True`):
          Whether to resize the input to a certain `size`.
      size (`int`, *optional*, defaults to 800):
          Resize the input to the given size. Only has an effect if `do_resize` is set to `True`. If size is a
          sequence like `(width, height)`, output size will be matched to this. If size is an int, smaller edge of
          the image will be matched to this number. i.e, if `height > width`, then image will be rescaled to `(size *
          height / width, size)`.
      resample (`int`, *optional*, defaults to `Resampling.BILINEAR`):
          An optional resampling filter. This can be one of `PIL.Image.Resampling.NEAREST`,
          `PIL.Image.Resampling.BOX`, `PIL.Image.Resampling.BILINEAR`, `PIL.Image.Resampling.HAMMING`,
          `PIL.Image.Resampling.BICUBIC` or `PIL.Image.Resampling.LANCZOS`. Only has an effect if `do_resize` is set
          to `True`.
      do_rescale (`bool`, *optional*, defaults to `True`):
          Whether to rescale the input to a certain `scale`.
      rescale_factor (`float`, *optional*, defaults to `1/ 255`):
          Rescale the input by the given factor. Only has an effect if `do_rescale` is set to `True`.
      do_normalize (`bool`, *optional*, defaults to `True`):
          Whether or not to normalize the input with mean and standard deviation.
      image_mean (`int`, *optional*, defaults to `[0.485, 0.456, 0.406]`):
          The sequence of means for each channel, to be used when normalizing images. Defaults to the ImageNet mean.
      image_std (`int`, *optional*, defaults to `[0.229, 0.224, 0.225]`):
          The sequence of standard deviations for each channel, to be used when normalizing images. Defaults to the
          ImageNet std.
      ignore_index (`int`, *optional*):
          Label to be assigned to background pixels in segmentation maps. If provided, segmentation map pixels
          denoted with 0 (background) will be replaced with `ignore_index`.
      do_reduce_labels (`bool`, *optional*, defaults to `False`):
          Whether or not to decrement all label values of segmentation maps by 1. Usually used for datasets where 0
          is used for background, and background itself is not included in all classes of a dataset (e.g. ADE20k).
          The background label will be replaced by `ignore_index`.
      repo_path (`str`, *optional*, defaults to `"shi-labs/oneformer_demo"`):
          Path to hub repo or local directory containing the JSON file with class information for the dataset.
          If unset, will look for `class_info_file` in the current working directory.
      class_info_file (`str`, *optional*):
          JSON file containing class information for the dataset. See `shi-labs/oneformer_demo/cityscapes_panoptic.json` for an example.
      num_text (`int`, *optional*):
          Number of text entries in the text input list.
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docstr: Preprocesses a single image.
code: def _preprocess_image(
      self,
      image: ImageInput,
      do_resize: bool = None,
      size: Dict[str, int] = None,
      resample: PILImageResampling = 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,
      data_format: Optional[Union[str, ChannelDimension]] = None,
      input_data_format: Optional[Union[str, ChannelDimension]] = None,
  ) -> np.ndarray:
      """Preprocesses a single image."""
      # All transformations expect numpy arrays.
      image = to_numpy_array(image)
      if is_scaled_image(image) 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:
          input_data_format = infer_channel_dimension_format(image)
      image = self._preprocess(
          image=image,
          do_resize=do_resize,
          size=size,
          resample=resample,
          do_rescale=do_rescale,
          rescale_factor=rescale_factor,
          do_normalize=do_normalize,
          image_mean=image_mean,
          image_std=image_std,
          input_data_format=input_data_format,
      )
      if data_format is not None:
          image = to_channel_dimension_format(
              image, data_format, input_channel_dim=input_data_format
          )
      return image
  
imports: import json
  import os
  import warnings
  from typing import Any, Dict, Iterable, List, Optional, Set, Tuple, Union
  import numpy
  from huggingface_hub import hf_hub_download
  from huggingface_hub.utils import RepositoryNotFoundError
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import PaddingMode, get_resize_output_image_size, pad, rescale, resize, to_channel_dimension_format
  from ...image_utils import ChannelDimension, ImageInput, PILImageResampling, get_image_size, infer_channel_dimension_format, is_scaled_image, make_list_of_images, to_numpy_array, valid_images
  from ...utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, TensorType, is_torch_available, is_torch_tensor, logging
label_desc: Use image node_id 7 for calling the OneFormerImageProcessor obj's underlying member method code with example usage: obj._preprocess_image(image, do_resize, size, resample, do_rescale, rescale_factor, do_normalize, image_mean, image_std, data_format, input_data_format) and returns: image