code: 
  
  
  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,
      convert_to_rgb,
      get_resize_output_image_size,
      normalize,
      rescale,
      resize,
      to_channel_dimension_format,
  )
  from ...image_utils import (
      OPENAI_CLIP_MEAN,
      OPENAI_CLIP_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, is_vision_available, logging
  
  
  lowercase_								=    logging.get_logger(__name__)
  
  
  if is_vision_available():
  		import PIL
  
  
  
  
  class 					_UpperCamelCase (      lowerCamelCase__	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  							_A  				=  ["pixel_values"]
  
  
  
  
  
  
  
  							def __init__(		self  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				bool = True		,				SCREAMING_SNAKE_CASE_  :				Dict[str, int] = None		,				SCREAMING_SNAKE_CASE_  :				PILImageResampling = PILImageResampling.BICUBIC		,				SCREAMING_SNAKE_CASE_  :				bool = True		,				SCREAMING_SNAKE_CASE_  :				Dict[str, int] = None		,				SCREAMING_SNAKE_CASE_  :				bool = True		,				SCREAMING_SNAKE_CASE_  :				Union[int, float] = 1 / 2_5_5		,				SCREAMING_SNAKE_CASE_  :				bool = True		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[float, List[float]]] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[float, List[float]]] = None		,				SCREAMING_SNAKE_CASE_  :				bool = True		,				**SCREAMING_SNAKE_CASE_  :				Optional[Any]		,				):
  													super().__init__(**SCREAMING_SNAKE_CASE_		)
  													_a     =			size if size is not None else {'shortest_edge': 2_2_4}
  													_a     =			get_size_dict(SCREAMING_SNAKE_CASE_		,				default_to_square=SCREAMING_SNAKE_CASE_		)
  													_a     =			crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
  													_a     =			get_size_dict(SCREAMING_SNAKE_CASE_		)
  
  													_a     =			do_resize
  													_a     =			size
  													_a     =			resample
  													_a     =			do_center_crop
  													_a     =			crop_size
  													_a     =			do_rescale
  													_a     =			rescale_factor
  													_a     =			do_normalize
  													_a     =			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
  													_a     =			image_std if image_std is not None else OPENAI_CLIP_STD
  													_a     =			do_convert_rgb
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				str		,				SCREAMING_SNAKE_CASE_  :				np.ndarray		,				SCREAMING_SNAKE_CASE_  :				Dict[str, int]		,				SCREAMING_SNAKE_CASE_  :				PILImageResampling = PILImageResampling.BICUBIC		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[str, ChannelDimension]] = None		,				**SCREAMING_SNAKE_CASE_  :				int		,				):
  													_a     =			get_size_dict(SCREAMING_SNAKE_CASE_		,				default_to_square=SCREAMING_SNAKE_CASE_		)
  													_a     =			get_resize_output_image_size(
  													    SCREAMING_SNAKE_CASE_		,				size=(size['height'], size['width'])		,				default_to_square=SCREAMING_SNAKE_CASE_		)
  													return resize(SCREAMING_SNAKE_CASE_		,				size=SCREAMING_SNAKE_CASE_		,				resample=SCREAMING_SNAKE_CASE_		,				data_format=SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Any		,				SCREAMING_SNAKE_CASE_  :				np.ndarray		,				SCREAMING_SNAKE_CASE_  :				Dict[str, int]		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[str, ChannelDimension]] = None		,				**SCREAMING_SNAKE_CASE_  :				Dict		,				):
  													_a     =			get_size_dict(SCREAMING_SNAKE_CASE_		)
  													return center_crop(SCREAMING_SNAKE_CASE_		,				size=(size['height'], size['width'])		,				data_format=SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				str		,				SCREAMING_SNAKE_CASE_  :				np.ndarray		,				SCREAMING_SNAKE_CASE_  :				Union[int, float]		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[str, ChannelDimension]] = None		,				**SCREAMING_SNAKE_CASE_  :				Dict		,				):
  													return rescale(SCREAMING_SNAKE_CASE_		,				scale=SCREAMING_SNAKE_CASE_		,				data_format=SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				np.ndarray		,				SCREAMING_SNAKE_CASE_  :				Union[float, List[float]]		,				SCREAMING_SNAKE_CASE_  :				Union[float, List[float]]		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[str, ChannelDimension]] = None		,				**SCREAMING_SNAKE_CASE_  :				Union[str, Any]		,				):
  													return normalize(SCREAMING_SNAKE_CASE_		,				mean=SCREAMING_SNAKE_CASE_		,				std=SCREAMING_SNAKE_CASE_		,				data_format=SCREAMING_SNAKE_CASE_		,				**SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				ImageInput		,				SCREAMING_SNAKE_CASE_  :				bool = None		,				SCREAMING_SNAKE_CASE_  :				Dict[str, int] = None		,				SCREAMING_SNAKE_CASE_  :				PILImageResampling = None		,				SCREAMING_SNAKE_CASE_  :				bool = None		,				SCREAMING_SNAKE_CASE_  :				int = None		,				SCREAMING_SNAKE_CASE_  :				bool = None		,				SCREAMING_SNAKE_CASE_  :				float = None		,				SCREAMING_SNAKE_CASE_  :				bool = None		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[float, List[float]]] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[float, List[float]]] = None		,				SCREAMING_SNAKE_CASE_  :				bool = None		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[str, TensorType]] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[ChannelDimension] = ChannelDimension.FIRST		,				**SCREAMING_SNAKE_CASE_  :				Any		,				):
  													_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(SCREAMING_SNAKE_CASE_		,				default_to_square=SCREAMING_SNAKE_CASE_		)
  													_a     =			resample if resample is not None else self.resample
  													_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(SCREAMING_SNAKE_CASE_		)
  													_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     =			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
  
  													_a     =			make_list_of_images(SCREAMING_SNAKE_CASE_		)
  
  													if not valid_images(SCREAMING_SNAKE_CASE_		):
  																			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.'		)
  
  													# PIL RGBA images are converted to RGB
  													if do_convert_rgb:
  																			_a     =			[convert_to_rgb(SCREAMING_SNAKE_CASE_		) for image in images]
  
  													# All transformations expect numpy arrays.
  													_a     =			[to_numpy_array(SCREAMING_SNAKE_CASE_		) for image in images]
  
  													if do_resize:
  																			_a     =			[self.resize(image=SCREAMING_SNAKE_CASE_		,				size=SCREAMING_SNAKE_CASE_		,				resample=SCREAMING_SNAKE_CASE_		) for image in images]
  
  													if do_center_crop:
  																			_a     =			[self.center_crop(image=SCREAMING_SNAKE_CASE_		,				size=SCREAMING_SNAKE_CASE_		) for image in images]
  
  													if do_rescale:
  																			_a     =			[self.rescale(image=SCREAMING_SNAKE_CASE_		,				scale=SCREAMING_SNAKE_CASE_		) for image in images]
  
  													if do_normalize:
  																			_a     =			[self.normalize(image=SCREAMING_SNAKE_CASE_		,				mean=SCREAMING_SNAKE_CASE_		,				std=SCREAMING_SNAKE_CASE_		) for image in images]
  
  													_a     =			[to_channel_dimension_format(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		) for image in images]
  
  													_a     =			{'pixel_values': images}
  													return BatchFeature(data=SCREAMING_SNAKE_CASE_		,				tensor_type=SCREAMING_SNAKE_CASE_		)
  
  
  
style_context: 
  
  
  import math
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  float:
  						_a     =			math.log(2							,     10				) * number
  						_a     =			round((log_number - int(_UpperCAmelCase				))							,     15				)
  
  						return difference
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase = 678910				)    ->  int:
  						_a     =			90
  						_a     =			0
  
  						_a     =			math.log(1.23							,     10				)
  						_a     =			math.log(1.24							,     10				)
  						_a     =			0
  
  						while position < number:
  												_a     =			log_difference(_UpperCAmelCase				)
  
  												if difference >= upper_limit:
  																		power_iterator += 93
  
  												elif difference < lower_limit:
  																		power_iterator += 196
  
  												else:
  																		_a     =			power_iterator
  																		power_iterator += 196
  																		position += 1
  
  						return previous_power
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  		print(f"""{solution() = }""")
  
  
  