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 (
      get_resize_output_image_size,
      normalize,
      pad,
      rescale,
      resize,
      to_channel_dimension_format,
  )
  from ...image_utils import (
      IMAGENET_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      get_image_size,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, logging
  from ...utils.import_utils import is_vision_available
  
  
  a__						:			Optional[int]										=			logging.get_logger(__name__)
  
  
  if is_vision_available():
   import PIL
  class        UpperCAmelCase__  (    UpperCAmelCase_):
   __SCREAMING_SNAKE_CASE					      =				['''pixel_values''']
  
  
  
   def __init__(  self		,	lowercase = True		,	lowercase = None		,	lowercase = PILImageResampling.BILINEAR		,	lowercase = True		,	lowercase = False		,	lowercase = True		,	lowercase = True		,	lowercase = 1 / 2_5_5		,	lowercase = True		,	lowercase = None		,	lowercase = None		,	**lowercase		,	)  ->							None:
    super().__init__(**lowercase			)
  
    __UpperCamelCase				      =  size if size is not None else {"""height""": 2_5_6_0, """width""": 1_9_2_0}
    if isinstance(lowercase		,	(tuple, list)			):
     # The previous feature extractor size parameter was in (width, height) format
     __UpperCamelCase				      =  size[::-1]
    __UpperCamelCase				      =  get_size_dict(lowercase			)
  
    __UpperCamelCase				      =  do_resize
    __UpperCamelCase				      =  size
    __UpperCamelCase				      =  resample
    __UpperCamelCase				      =  do_thumbnail
    __UpperCamelCase				      =  do_align_long_axis
    __UpperCamelCase				      =  do_pad
    __UpperCamelCase				      =  do_rescale
    __UpperCamelCase				      =  rescale_factor
    __UpperCamelCase				      =  do_normalize
    __UpperCamelCase				      =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    __UpperCamelCase				      =  image_std if image_std is not None else IMAGENET_STANDARD_STD
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase = None			)  ->							np.ndarray:
    __UpperCamelCase      ,		__UpperCamelCase				      =  get_image_size(lowercase			)
    __UpperCamelCase      ,		__UpperCamelCase				      =  size["""height"""], size["""width"""]
  
    if (output_width < output_height and input_width > input_height) or (
        output_width > output_height and input_width < input_height
    ):
     __UpperCamelCase				      =  np.rotaa(lowercase		,	3			)
  
    if data_format is not None:
     __UpperCamelCase				      =  to_channel_dimension_format(lowercase		,	lowercase			)
  
    return image
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase = False		,	lowercase = None		,	)  ->							np.ndarray:
    __UpperCamelCase      ,		__UpperCamelCase				      =  size["""height"""], size["""width"""]
    __UpperCamelCase      ,		__UpperCamelCase				      =  get_image_size(lowercase			)
  
    __UpperCamelCase				      =  output_width - input_width
    __UpperCamelCase				      =  output_height - input_height
  
    if random_padding:
     __UpperCamelCase				      =  np.random.randint(low=0		,	high=delta_height + 1			)
     __UpperCamelCase				      =  np.random.randint(low=0		,	high=delta_width + 1			)
    else:
     __UpperCamelCase				      =  delta_height // 2
     __UpperCamelCase				      =  delta_width // 2
  
    __UpperCamelCase				      =  delta_height - pad_top
    __UpperCamelCase				      =  delta_width - pad_left
  
    __UpperCamelCase				      =  ((pad_top, pad_bottom), (pad_left, pad_right))
    return pad(lowercase		,	lowercase		,	data_format=lowercase			)
  
  
  
   def    __lowerCamelCase				(  self		,	*lowercase		,	**lowercase			)  ->							str:
    logger.info("""pad is deprecated and will be removed in version 4.27. Please use pad_image instead."""			)
    return self.pad_image(*lowercase		,	**lowercase			)
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase = PILImageResampling.BICUBIC		,	lowercase = None		,	**lowercase		,	)  ->							np.ndarray:
    __UpperCamelCase      ,		__UpperCamelCase				      =  get_image_size(lowercase			)
    __UpperCamelCase      ,		__UpperCamelCase				      =  size["""height"""], size["""width"""]
  
    # We always resize to the smallest of either the input or output size.
    __UpperCamelCase				      =  min(lowercase		,	lowercase			)
    __UpperCamelCase				      =  min(lowercase		,	lowercase			)
  
    if height == input_height and width == input_width:
     return image
  
    if input_height > input_width:
     __UpperCamelCase				      =  int(input_width * height / input_height			)
    elif input_width > input_height:
     __UpperCamelCase				      =  int(input_height * width / input_width			)
  
    return resize(
        lowercase		,	size=(height, width)		,	resample=lowercase		,	reducing_gap=2.0		,	data_format=lowercase		,	**lowercase			)
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase = PILImageResampling.BICUBIC		,	lowercase = None		,	**lowercase		,	)  ->							np.ndarray:
    __UpperCamelCase				      =  get_size_dict(lowercase			)
    __UpperCamelCase				      =  min(size["""height"""]		,	size["""width"""]			)
    __UpperCamelCase				      =  get_resize_output_image_size(lowercase		,	size=lowercase		,	default_to_square=lowercase			)
    __UpperCamelCase				      =  resize(lowercase		,	size=lowercase		,	resample=lowercase		,	data_format=lowercase		,	**lowercase			)
    return resized_image
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase = None		,	**lowercase		,	)  ->							List[Any]:
    return rescale(lowercase		,	scale=lowercase		,	data_format=lowercase		,	**lowercase			)
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase		,	lowercase		,	lowercase = None		,	**lowercase		,	)  ->							np.ndarray:
    return normalize(lowercase		,	mean=lowercase		,	std=lowercase		,	data_format=lowercase		,	**lowercase			)
  
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase = None		,	lowercase = None		,	lowercase = None		,	lowercase = None		,	lowercase = None		,	lowercase = None		,	lowercase = False		,	lowercase = None		,	lowercase = None		,	lowercase = None		,	lowercase = None		,	lowercase = None		,	lowercase = None		,	lowercase = ChannelDimension.FIRST		,	**lowercase		,	)  ->							PIL.Image.Image:
    __UpperCamelCase				      =  do_resize if do_resize is not None else self.do_resize
    __UpperCamelCase				      =  size if size is not None else self.size
    if isinstance(lowercase		,	(tuple, list)			):
     # Previous feature extractor had size in (width, height) format
     __UpperCamelCase				      =  size[::-1]
    __UpperCamelCase				      =  get_size_dict(lowercase			)
    __UpperCamelCase				      =  resample if resample is not None else self.resample
    __UpperCamelCase				      =  do_thumbnail if do_thumbnail is not None else self.do_thumbnail
    __UpperCamelCase				      =  do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
    __UpperCamelCase				      =  do_pad if do_pad is not None else self.do_pad
    __UpperCamelCase				      =  do_rescale if do_rescale is not None else self.do_rescale
    __UpperCamelCase				      =  rescale_factor if rescale_factor is not None else self.rescale_factor
    __UpperCamelCase				      =  do_normalize if do_normalize is not None else self.do_normalize
    __UpperCamelCase				      =  image_mean if image_mean is not None else self.image_mean
    __UpperCamelCase				      =  image_std if image_std is not None else self.image_std
  
    __UpperCamelCase				      =  make_list_of_images(lowercase			)
  
    if not valid_images(lowercase			):
     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_rescale and rescale_factor is None:
     raise ValueError("""Rescale factor must be specified if do_rescale is True."""			)
  
    if do_pad and size is None:
     raise ValueError("""Size must be specified if do_pad 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.
    __UpperCamelCase				      =  [to_numpy_array(lowercase			) for image in images]
  
    if do_align_long_axis:
     __UpperCamelCase				      =  [self.align_long_axis(lowercase		,	size=lowercase			) for image in images]
  
    if do_resize:
     __UpperCamelCase				      =  [self.resize(image=lowercase		,	size=lowercase		,	resample=lowercase			) for image in images]
  
    if do_thumbnail:
     __UpperCamelCase				      =  [self.thumbnail(image=lowercase		,	size=lowercase			) for image in images]
  
    if do_pad:
     __UpperCamelCase				      =  [self.pad_image(image=lowercase		,	size=lowercase		,	random_padding=lowercase			) for image in images]
  
    if do_rescale:
     __UpperCamelCase				      =  [self.rescale(image=lowercase		,	scale=lowercase			) for image in images]
  
    if do_normalize:
     __UpperCamelCase				      =  [self.normalize(image=lowercase		,	mean=lowercase		,	std=lowercase			) for image in images]
  
    __UpperCamelCase				      =  [to_channel_dimension_format(lowercase		,	lowercase			) for image in images]
  
    __UpperCamelCase				      =  {"""pixel_values""": images}
    return BatchFeature(data=lowercase		,	tensor_type=lowercase			)
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  a__						:			Any										=			{
      '0000': '0',
      '0001': '1',
      '0010': '2',
      '0011': '3',
      '0100': '4',
      '0101': '5',
      '0110': '6',
      '0111': '7',
      '1000': '8',
      '1001': '9',
      '1010': 'a',
      '1011': 'b',
      '1100': 'c',
      '1101': 'd',
      '1110': 'e',
      '1111': 'f',
  }
  
  
  
  
  
  
  
  def _lowercase   (  __A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  str(__A       ).strip()
  
   # Exceptions
   if not binary_str:
    raise ValueError("""Empty string was passed to the function"""       )
   __UpperCamelCase				      =  binary_str[0] == """-"""
   __UpperCamelCase				      =  binary_str[1:] if is_negative else binary_str
   if not all(char in """01""" for char in binary_str       ):
    raise ValueError("""Non-binary value was passed to the function"""       )
  
   __UpperCamelCase				      =  (
       """0""" * (4 * (divmod(len(__A       )							,4       )[0] + 1) - len(__A       )) + binary_str
   )
  
   __UpperCamelCase				      =  []
   for x in range(0							,len(__A       )							,4       ):
    hexadecimal.append(BITS_TO_HEX[binary_str[x : x + 4]]       )
   __UpperCamelCase				      =  """0x""" + """""".join(__A       )
  
   return "-" + hexadecimal_str if is_negative else hexadecimal_str
  
  
  if __name__ == "__main__":
   from doctest import testmod
  
   testmod()
  
  
  
  
  
  