code: 
  
  
  
  """simple docstring"""
  
  import warnings
  from contextlib import contextmanager
  
  from ...processing_utils import ProcessorMixin
  class 			lowerCamelCase__    (  lowerCamelCase_			):
  			a__ :      Union[str, Any]								=     ["""image_processor""", """tokenizer"""]
  			a__ :      Union[str, Any]								=     """AutoImageProcessor"""
  			a__ :      Any								=     """AutoTokenizer"""
  
  
  			def __init__(     self				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=None				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					snake_case		:						Tuple								=				None
  					if "feature_extractor" in kwargs:
  							warnings.warn(
  							    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
  							    " instead."				, SCREAMING_SNAKE_CASE				, )
  							snake_case		:						Optional[int]								=				kwargs.pop("feature_extractor"							)
  
  					snake_case		:						int								=				image_processor if image_processor is not None else feature_extractor
  					if image_processor is None:
  							raise ValueError("You need to specify an `image_processor`."							)
  					if tokenizer is None:
  							raise ValueError("You need to specify a `tokenizer`."							)
  
  					super().__init__(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
  					snake_case		:						Tuple								=				self.image_processor
  					snake_case		:						List[Any]								=				False
  
  
  			def __call__(     self				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					if self._in_target_context_manager:
  							return self.current_processor(*SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  					snake_case		:						List[Any]								=				kwargs.pop("images"				, SCREAMING_SNAKE_CASE							)
  					snake_case		:						List[Any]								=				kwargs.pop("text"				, SCREAMING_SNAKE_CASE							)
  					if len(SCREAMING_SNAKE_CASE							) > 0:
  							snake_case		:						str								=				args[0]
  							snake_case		:						List[Any]								=				args[1:]
  
  					if images is None and text is None:
  							raise ValueError("You need to specify either an `images` or `text` input to process."							)
  
  					if images is not None:
  							snake_case		:						Any								=				self.image_processor(SCREAMING_SNAKE_CASE				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  					if text is not None:
  							snake_case		:						Union[str, Any]								=				self.tokenizer(SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  					if text is None:
  							return inputs
  					elif images is None:
  							return encodings
  					else:
  							snake_case		:						str								=				encodings["input_ids"]
  							return inputs
  
  
  			def 							lowerCamelCase_				(     self				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  
  			def 							lowerCamelCase_				(     self				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					return self.tokenizer.decode(*SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
  
  
  			@contextmanager
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					warnings.warn(
  					    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
  					    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
  					    "your images inputs, or in a separate call."							)
  					snake_case		:						Any								=				True
  					snake_case		:						Dict								=				self.tokenizer
  					yield
  					snake_case		:						int								=				self.image_processor
  					snake_case		:						str								=				False
  
  
  			@property
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					warnings.warn(
  					    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				, SCREAMING_SNAKE_CASE				, )
  					return self.image_processor_class
  
  
  
  			@property
  			def 							lowerCamelCase_				(     self							):
  
  
  
  					"""simple docstring"""
  
  
  
  
  
  
  
  					warnings.warn(
  					    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				, SCREAMING_SNAKE_CASE				, )
  					return self.image_processor
  
  
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))
  
  