




def       __magic_name__    (			A		: int,  A		: Optional[int],  A		: Tuple       ):
			if right <= left:
						return
			a   =      a   =      left
			a   =      right
			a   =      sorting[left]
			while i <= b:
						if sorting[i] < pivot:
									a   ,		a   =      sorting[i], sorting[a]
									a += 1
									i += 1
						elif sorting[i] > pivot:
									a   ,		a   =      sorting[i], sorting[b]
									b -= 1
						else:
									i += 1
			quick_sort_apartition(SCREAMING_SNAKE_CASE_,  SCREAMING_SNAKE_CASE_,  a - 1       )
			quick_sort_apartition(SCREAMING_SNAKE_CASE_,  b + 1,  SCREAMING_SNAKE_CASE_       )




def       __magic_name__    (			A		: Union[str, Any],  A		: Optional[int],  A		: str       ):
			if left < right:
						a   =      lomuto_partition(SCREAMING_SNAKE_CASE_,  SCREAMING_SNAKE_CASE_,  SCREAMING_SNAKE_CASE_       )
						quick_sort_lomuto_partition(SCREAMING_SNAKE_CASE_,  SCREAMING_SNAKE_CASE_,  pivot_index - 1       )
						quick_sort_lomuto_partition(SCREAMING_SNAKE_CASE_,  pivot_index + 1,  SCREAMING_SNAKE_CASE_       )




def       __magic_name__    (			A		: List[Any],  A		: Union[str, Any],  A		: Union[str, Any]       ):
			a   =      sorting[right]
			a   =      left
			for i in range(SCREAMING_SNAKE_CASE_,  SCREAMING_SNAKE_CASE_       ):
						if sorting[i] < pivot:
									a   ,		a   =      sorting[i], sorting[store_index]
									store_index += 1
			a   ,		a   =      sorting[store_index], sorting[right]
			return store_index




def       __magic_name__    (			A		: List[Any]       ):
			if len(SCREAMING_SNAKE_CASE_       ) <= 1:
						return sorting
			return (
			    three_way_radix_quicksort([i for i in sorting if i < sorting[0]]       )
			    + [i for i in sorting if i == sorting[0]]
			    + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]       )
			)


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)

		__lowerCAmelCase		:    Optional[int]	      =		input('Enter numbers separated by a comma:\n').strip()
		__lowerCAmelCase		:    Dict	      =		[int(item) for item in user_input.split(',')]
		quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
		print(unsorted)












import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class    snake_case__							(_UpperCamelCase       ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_	:							List[Any]										= ["""image_processor""", """tokenizer"""]
				SCREAMING_SNAKE_CASE_	:							List[Any]										= """AutoImageProcessor"""
				SCREAMING_SNAKE_CASE_	:							Dict										= """AutoTokenizer"""





				def __init__(				self    :					int ,				__lowerCamelCase    :					Dict=None ,				__lowerCamelCase    :					int=None ,				**__lowerCamelCase    :					Optional[Any]							) ->					str:
							a   =      None
							if "feature_extractor" in kwargs:
										warnings.warn(
										    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
										    " instead." ,				__lowerCamelCase ,				)
										a   =      kwargs.pop("feature_extractor"							)

							a   =      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__(__lowerCamelCase ,				__lowerCamelCase							)
							a   =      self.image_processor
							a   =      False





				def __call__(				self    :					Any ,				*__lowerCamelCase    :					int ,				**__lowerCamelCase    :					Optional[int]							) ->					int:
							# For backward compatibility
							if self._in_target_context_manager:
										return self.current_processor(*__lowerCamelCase ,				**__lowerCamelCase							)

							a   =      kwargs.pop("images" ,				__lowerCamelCase							)
							a   =      kwargs.pop("text" ,				__lowerCamelCase							)
							if len(__lowerCamelCase							) > 0:
										a   =      args[0]
										a   =      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:
										a   =      self.image_processor(__lowerCamelCase ,				*__lowerCamelCase ,				**__lowerCamelCase							)
							if text is not None:
										a   =      self.tokenizer(__lowerCamelCase ,				**__lowerCamelCase							)

							if text is None:
										return inputs
							elif images is None:
										return encodings
							else:
										a   =      encodings["input_ids"]
										return inputs





				def 							__UpperCAmelCase							(				self    :					Union[str, Any] ,				*__lowerCamelCase    :					str ,				**__lowerCamelCase    :					str							) ->					Optional[Any]:
							return self.tokenizer.batch_decode(*__lowerCamelCase ,				**__lowerCamelCase							)





				def 							__UpperCAmelCase							(				self    :					Optional[Any] ,				*__lowerCamelCase    :					Union[str, Any] ,				**__lowerCamelCase    :					Any							) ->					str:
							return self.tokenizer.decode(*__lowerCamelCase ,				**__lowerCamelCase							)





				@contextmanager
				def 							__UpperCAmelCase							(				self    :					Any							) ->					Optional[int]:
							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."							)
							a   =      True
							a   =      self.tokenizer
							yield
							a   =      self.image_processor
							a   =      False





				@property
				def 							__UpperCAmelCase							(				self    :					Dict							) ->					Optional[Any]:
							warnings.warn(
							    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." ,				__lowerCamelCase ,				)
							return self.image_processor_class





				@property
				def 							__UpperCAmelCase							(				self    :					int							) ->					Union[str, Any]:
							warnings.warn(
							    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,				__lowerCamelCase ,				)
							return self.image_processor






