



'''simple docstring'''



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 			_SCREAMING_SNAKE_CASE				(lowercase__      ):
			A__								=  ['image_processor', 'tokenizer']
			A__								=  'AutoImageProcessor'
			A__								=  'AutoTokenizer'



			def __init__(     self		:						int    ,			__UpperCamelCase		:						int=None    ,			__UpperCamelCase		:						Any=None    ,			**__UpperCamelCase		:						str			)      ->      Optional[int]:
							"""simple docstring"""





							snake_case__      :   Optional[Any]      =    None
							if "feature_extractor" in kwargs:
											warnings.warn(
											    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
											    ''' instead.'''    ,			__UpperCamelCase    ,			)
											snake_case__      :   List[str]      =    kwargs.pop('''feature_extractor'''			)

							snake_case__      :   Any      =    image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
											raise ValueError('''You have to specify an image_processor.'''			)
							if tokenizer is None:
											raise ValueError('''You have to specify a tokenizer.'''			)

							super().__init__(__UpperCamelCase    ,			__UpperCamelCase			)
							snake_case__      :   int      =    self.image_processor



			def __call__(     self		:						Tuple    ,			__UpperCamelCase		:						Optional[Any]=None    ,			__UpperCamelCase		:						Optional[int]=None    ,			__UpperCamelCase		:						Any=None    ,			**__UpperCamelCase		:						Dict			)      ->      Union[str, Any]:
							"""simple docstring"""





							if text is None and images is None:
											raise ValueError('''You have to specify either text or images. Both cannot be none.'''			)

							if text is not None:
											snake_case__      :   int      =    self.tokenizer(__UpperCamelCase    ,			return_tensors=__UpperCamelCase    ,			**__UpperCamelCase			)

							if images is not None:
											snake_case__      :   List[Any]      =    self.image_processor(__UpperCamelCase    ,			return_tensors=__UpperCamelCase    ,			**__UpperCamelCase			)

							if text is not None and images is not None:
											snake_case__      :   Union[str, Any]      =    image_features.pixel_values
											return encoding
							elif text is not None:
											return encoding
							else:
											return BatchEncoding(data=dict(**__UpperCamelCase			)    ,			tensor_type=__UpperCamelCase			)



			def 				lowerCAmelCase (     self		:						Tuple    ,			*__UpperCamelCase		:						int    ,			**__UpperCamelCase		:						List[Any]			)      ->      Dict:
							"""simple docstring"""





							return self.tokenizer.batch_decode(*__UpperCamelCase    ,			**__UpperCamelCase			)



			def 				lowerCAmelCase (     self		:						Union[str, Any]    ,			*__UpperCamelCase		:						Optional[Any]    ,			**__UpperCamelCase		:						Dict			)      ->      int:
							"""simple docstring"""





							return self.tokenizer.decode(*__UpperCamelCase    ,			**__UpperCamelCase			)



			@property
			def 				lowerCAmelCase (     self		:						Tuple			)      ->      Tuple:
							"""simple docstring"""





							snake_case__      :   List[Any]      =    self.tokenizer.model_input_names
							snake_case__      :   str      =    self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)



			@property
			def 				lowerCAmelCase (     self		:						str			)      ->      str:
							"""simple docstring"""





							warnings.warn(
							    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''    ,			__UpperCamelCase    ,			)
							return self.image_processor_class







			@property
			def 				lowerCAmelCase (     self		:						Dict			)      ->      str:
							"""simple docstring"""





							warnings.warn(
							    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''    ,			__UpperCamelCase    ,			)
							return self.image_processor









'''simple docstring'''



def 							__UpperCAmelCase				(				UpperCamelCase__				:int			)		->						Any:
				snake_case__      :   Dict      =    len(UpperCamelCase__			)
				for _ in range(
				    0   ,				int(((no_of_elements - 1) / 2) + 1			)			):  # we don't need to traverse to end of list as
								for j in range(0   ,				no_of_elements - 1			):
												if (
												    lst[j + 1] < lst[j]
												):  # applying bubble sort algorithm from left to right (or forwards)
																snake_case__      :   int      =    lst[j + 1]
																snake_case__      :   Tuple      =    lst[j]
																snake_case__      :   Tuple      =    temp
												if (
												    lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
												):  # applying bubble sort algorithm from right to left (or backwards)
																snake_case__      :   Optional[Any]      =    lst[no_of_elements - 1 - j]
																snake_case__      :   Tuple      =    lst[no_of_elements - 2 - j]
																snake_case__      :   Any      =    temp
				return lst


if __name__ == "__main__":
				print("enter the list to be sorted")
				_lowercase       :						Any						=[int(x) for x in input().split()]  # inputing elements of the list in one line
				_lowercase       :						Tuple						=double_sort(lst)
				print("the sorted list is")
				print(sorted_lst)




