code: 
  
  
  
  '''simple docstring'''
  
  
  
  
  
  from typing import Dict, List, Optional, Union
  
  import numpy as np
  
  from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
  from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
  from ...image_utils import (
      IMAGENET_DEFAULT_MEAN,
      IMAGENET_DEFAULT_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      get_image_size,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, is_vision_available, logging
  
  
  if is_vision_available():
      import PIL
  
  
  __lowerCamelCase  				=					logging.get_logger(__name__)
  
  
  
  class 		_snake_case      (			snake_case__				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   lowerCamelCase_          =			['pixel_values']
   def __init__(						self	,    a = True	,    a = None	,    a = True	,    a = None	,    a = PILImageResampling.BICUBIC	,    a = True	,    a = 1 / 2_5_5	,    a = True	,    a = None	,    a = None	,    **a	,    )	->							int:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     super().__init__(**a					)
     _A           =    crop_size if crop_size is not None else {'height': 2_5_6, 'width': 2_5_6}
     _A           =    get_size_dict(a	,    param_name='''crop_size'''					)
     _A           =    size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
     _A           =    get_size_dict(a					)
  
     _A           =    do_center_crop
     _A           =    crop_size
     _A           =    do_resize
     _A           =    size
     _A           =    resample
     _A           =    do_rescale
     _A           =    rescale_factor
     _A           =    do_normalize
     _A           =    image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
     _A           =    image_std if image_std is not None else IMAGENET_DEFAULT_STD
   def lowercase_		(						self	,    a	,    a	,    a = None	,    a = None	,    **a	,    )	->							Any:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    self.size if size is None else size
     _A           =    get_size_dict(a					)
     _A           =    get_size_dict(a	,    param_name='''crop_size'''					)
  
     _A           =    get_image_size(a					)
     _A           =    min(a	,    a					)
     _A           =    (size['height'] / crop_size['height']) * min_dim
     _A           =    (size['width'] / crop_size['width']) * min_dim
     return center_crop(a	,    size=(cropped_height, cropped_width)	,    data_format=a	,    **a					)
   def lowercase_		(						self	,    a	,    a	,    a = PIL.Image.BICUBIC	,    a = None	,    **a	,    )	->							Dict:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    get_size_dict(a					)
     if "height" not in size or "width" not in size:
       raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''					)
     return resize(
         a	,    size=(size['''height'''], size['''width'''])	,    resample=a	,    data_format=a	,    **a					)
   def lowercase_		(						self	,    a	,    a	,    a = None	,    **a	,    )	->							Tuple:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return rescale(a	,    scale=a	,    data_format=a	,    **a					)
   def lowercase_		(						self	,    a	,    a	,    a	,    a = None	,    **a	,    )	->							Dict:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return normalize(a	,    mean=a	,    std=a	,    data_format=a	,    **a					)
  
  
  
   def lowercase_		(						self	,    a	,    a = None	,    a = None	,    a = None	,    a = None	,    a = None	,    a = None	,    a = None	,    a = None	,    a = None	,    a = None	,    a = None	,    a = ChannelDimension.FIRST	,    **a	,    )	->							Union[str, Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _A           =    do_center_crop if do_center_crop is not None else self.do_center_crop
     _A           =    crop_size if crop_size is not None else self.crop_size
     _A           =    get_size_dict(a	,    param_name='''crop_size'''					)
     _A           =    do_resize if do_resize is not None else self.do_resize
     _A           =    size if size is not None else self.size
     _A           =    get_size_dict(a					)
     _A           =    resample if resample is not None else self.resample
     _A           =    do_rescale if do_rescale is not None else self.do_rescale
     _A           =    rescale_factor if rescale_factor is not None else self.rescale_factor
     _A           =    do_normalize if do_normalize is not None else self.do_normalize
     _A           =    image_mean if image_mean is not None else self.image_mean
     _A           =    image_std if image_std is not None else self.image_std
  
     _A           =    make_list_of_images(a					)
  
     if not valid_images(a					):
       raise ValueError(
           '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
           '''torch.Tensor, tf.Tensor or jax.ndarray.'''					)
  
     if do_center_crop and crop_size is None:
       raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.'''					)
  
     if do_resize and size is None:
       raise ValueError('''Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True.'''					)
  
     # All transformations expect numpy arrays.
     _A           =    [to_numpy_array(a					) for image in images]
  
     if do_center_crop:
       _A           =    [self.center_crop(a	,    a	,    size=a					) for image in images]
  
     if do_resize:
       _A           =    [self.resize(image=a	,    size=a	,    resample=a					) for image in images]
  
     if do_rescale:
       _A           =    [self.rescale(image=a	,    scale=a					) for image in images]
  
     if do_normalize:
       _A           =    [self.normalize(image=a	,    mean=a	,    std=a					) for image in images]
  
     _A           =    [to_channel_dimension_format(a	,    a					) for image in images]
  
     _A           =    {'pixel_values': images}
     return BatchFeature(data=a	,    tensor_type=a					)
style_context: 
  
  
  
  from ..utils import requires_backends
  
  
  __lowerCamelCase  				=					None
  
  
  
  class 		_snake_case      :
  
  
  
  
  
   """simple docstring"""
  
  
  
   def __init__(						self	,    *a	,    **a					)	->							Tuple:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     requires_backends(self	,    ['''detectron2''']					)
  
  
  
   @classmethod
   def lowercase_		(						cls	,    *a	,    **a					)	->							Optional[int]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     requires_backends(cls	,    ['''detectron2''']					)