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_STANDARD_MEAN,
      IMAGENET_STANDARD_STD,
      ChannelDimension,
      ImageInput,
      PILImageResampling,
      make_list_of_images,
      to_numpy_array,
      valid_images,
  )
  from ...utils import TensorType, is_vision_available, logging
  
  
  if is_vision_available():
  							import PIL
  
  
  __A 		=			logging.get_logger(__name__)
  class 			lowerCamelCase__    (  lowerCamelCase_			):
  			a__ :      Dict								=     ["""pixel_values"""]
  
  
  			def __init__(     self				, SCREAMING_SNAKE_CASE = True				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = PIL.Image.NEAREST				, SCREAMING_SNAKE_CASE = False				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = 1 / 255				, SCREAMING_SNAKE_CASE = False				, SCREAMING_SNAKE_CASE = True				, SCREAMING_SNAKE_CASE = True				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = True				, **SCREAMING_SNAKE_CASE				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					super().__init__(**SCREAMING_SNAKE_CASE							)
  					snake_case		:						Optional[Any]								=				size if size is not None else {"height": 346, "width": 346}
  					snake_case		:						List[str]								=				get_size_dict(SCREAMING_SNAKE_CASE							)
  					snake_case		:						Optional[int]								=				crop_size if crop_size is not None else {"height": 289, "width": 289}
  					snake_case		:						str								=				get_size_dict(SCREAMING_SNAKE_CASE				, param_name="crop_size"							)
  
  					snake_case		:						List[str]								=				do_resize
  					snake_case		:						List[Any]								=				size
  					snake_case		:						Tuple								=				resample
  					snake_case		:						str								=				do_center_crop
  					snake_case		:						Tuple								=				crop_size
  					snake_case		:						Union[str, Any]								=				do_rescale
  					snake_case		:						List[Any]								=				rescale_factor
  					snake_case		:						Optional[int]								=				rescale_offset
  					snake_case		:						List[Any]								=				do_normalize
  					snake_case		:						Optional[Any]								=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
  					snake_case		:						str								=				image_std if image_std is not None else IMAGENET_STANDARD_STD
  					snake_case		:						List[str]								=				include_top
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE = PIL.Image.NEAREST				, SCREAMING_SNAKE_CASE = None				, **SCREAMING_SNAKE_CASE				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Optional[int]								=				get_size_dict(SCREAMING_SNAKE_CASE							)
  					if "height" not in size or "width" not in size:
  							raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''							)
  					return resize(
  					    SCREAMING_SNAKE_CASE				, size=(size["height"], size["width"])				, resample=SCREAMING_SNAKE_CASE				, data_format=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE = None				, **SCREAMING_SNAKE_CASE				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						int								=				get_size_dict(SCREAMING_SNAKE_CASE							)
  					if "height" not in size or "width" not in size:
  							raise ValueError(F'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''							)
  					return center_crop(SCREAMING_SNAKE_CASE				, size=(size["height"], size["width"])				, data_format=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE = True				, SCREAMING_SNAKE_CASE = None				, **SCREAMING_SNAKE_CASE				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if offset:
  							snake_case		:						str								=				(image - 1_27.5) * scale
  							if data_format is not None:
  									snake_case		:						List[str]								=				to_channel_dimension_format(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  							snake_case		:						Tuple								=				rescaled_image.astype(np.floataa							)
  					else:
  							snake_case		:						Any								=				rescale(SCREAMING_SNAKE_CASE				, scale=SCREAMING_SNAKE_CASE				, data_format=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  					return rescaled_image
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE = None				, **SCREAMING_SNAKE_CASE				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return normalize(SCREAMING_SNAKE_CASE				, mean=SCREAMING_SNAKE_CASE				, std=SCREAMING_SNAKE_CASE				, data_format=SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  
  
  			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = ChannelDimension.FIRST				, **SCREAMING_SNAKE_CASE				, ):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						int								=				do_resize if do_resize is not None else self.do_resize
  					snake_case		:						List[Any]								=				resample if resample is not None else self.resample
  					snake_case		:						Any								=				do_center_crop if do_center_crop is not None else self.do_center_crop
  					snake_case		:						List[str]								=				do_rescale if do_rescale is not None else self.do_rescale
  					snake_case		:						List[str]								=				rescale_factor if rescale_factor is not None else self.rescale_factor
  					snake_case		:						Union[str, Any]								=				rescale_offset if rescale_offset is not None else self.rescale_offset
  					snake_case		:						Dict								=				do_normalize if do_normalize is not None else self.do_normalize
  					snake_case		:						Union[str, Any]								=				image_mean if image_mean is not None else self.image_mean
  					snake_case		:						Dict								=				image_std if image_std is not None else self.image_std
  					snake_case		:						List[str]								=				include_top if include_top is not None else self.include_top
  
  					snake_case		:						Tuple								=				size if size is not None else self.size
  					snake_case		:						Union[str, Any]								=				get_size_dict(SCREAMING_SNAKE_CASE							)
  					snake_case		:						Tuple								=				crop_size if crop_size is not None else self.crop_size
  					snake_case		:						Tuple								=				get_size_dict(SCREAMING_SNAKE_CASE				, param_name="crop_size"							)
  
  					snake_case		:						Union[str, Any]								=				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 or resample is None:
  							raise ValueError("Size and resample 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.
  					snake_case		:						Optional[Any]								=				[to_numpy_array(SCREAMING_SNAKE_CASE							) for image in images]
  
  					if do_resize:
  							snake_case		:						List[Any]								=				[self.resize(image=SCREAMING_SNAKE_CASE				, size=SCREAMING_SNAKE_CASE				, resample=SCREAMING_SNAKE_CASE							) for image in images]
  
  					if do_center_crop:
  							snake_case		:						List[Any]								=				[self.center_crop(image=SCREAMING_SNAKE_CASE				, size=SCREAMING_SNAKE_CASE							) for image in images]
  
  					if do_rescale:
  							snake_case		:						Tuple								=				[self.rescale(image=SCREAMING_SNAKE_CASE				, scale=SCREAMING_SNAKE_CASE				, offset=SCREAMING_SNAKE_CASE							) for image in images]
  
  					if do_normalize:
  							snake_case		:						List[str]								=				[self.normalize(image=SCREAMING_SNAKE_CASE				, mean=SCREAMING_SNAKE_CASE				, std=SCREAMING_SNAKE_CASE							) for image in images]
  
  					if include_top:
  							snake_case		:						List[str]								=				[self.normalize(image=SCREAMING_SNAKE_CASE				, mean=[0, 0, 0]				, std=SCREAMING_SNAKE_CASE							) for image in images]
  
  					snake_case		:						Union[str, Any]								=				[to_channel_dimension_format(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							) for image in images]
  
  					snake_case		:						Union[str, Any]								=				{"pixel_values": images}
  					return BatchFeature(data=SCREAMING_SNAKE_CASE				, tensor_type=SCREAMING_SNAKE_CASE							)
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  import random
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     Union[str, Any]   ):
  
  		def is_sorted(lowercase__  :     int   ):
  				for i in range(len(lowercase__   ) - 1   ):
  						if collection[i] > collection[i + 1]:
  								return False
  				return True
  
  		while not is_sorted(lowercase__   ):
  				random.shuffle(lowercase__   )
  		return collection
  
  
  if __name__ == "__main__":
  							__A 		=			input("Enter numbers separated by a comma:\n").strip()
  							__A 		=			[int(item) for item in user_input.split(",")]
  							print(bogo_sort(unsorted))
  
  