



"""simple docstring"""






def 	snake_case__  (						__lowerCamelCase							:       Union[str, Any]      ):
			"""simple docstring"""
			lowerCamelCase__						:						Dict						      =len(__lowerCamelCase      )
			for i in range(length - 1      ):
						lowerCamelCase__						:						Any						      =False
						for j in range(length - 1 - i      ):
									if collection[j] > collection[j + 1]:
												lowerCamelCase__						:						List[str]						      =True
												lowerCamelCase__      ,  lowerCamelCase__						:						List[str]						      =collection[j + 1], collection[j]
						if not swapped:
									break  # Stop iteration if the collection is sorted.
			return collection


if __name__ == "__main__":
		import doctest
		import time

		doctest.testmod()

		_lowercase					:					str 		=		input("Enter numbers separated by a comma:").strip()
		_lowercase					:					Dict 		=		[int(item) for item in user_input.split(",")]
		_lowercase					:					Optional[Any] 		=		time.process_time()
		print(*bubble_sort(unsorted), sep=",")
		print(f'Processing time: {(time.process_time() - start)%1E9 + 7}')





"""simple docstring"""


from ..utils import DummyObject, requires_backends
class   UpperCAmelCase_	(				metaclass=lowercase__   ):
    snake_case_											=      ["""torch""", """transformers""", """k_diffusion"""]
    def __init__(	self			:       Optional[Any]   ,						*_lowercase			:       Optional[int]   ,						**_lowercase			:       List[str]		)						->    Dict:
       requires_backends(self   ,						["torch", "transformers", "k_diffusion"]		)
    @classmethod
    def 		_lowerCamelCase      (	cls			:       Union[str, Any]   ,						*_lowercase			:       Any   ,						**_lowercase			:       List[Any]		)						->    str:
       requires_backends(cls   ,						["torch", "transformers", "k_diffusion"]		)




    @classmethod
    def 		_lowerCamelCase      (	cls			:       Optional[Any]   ,						*_lowercase			:       List[Any]   ,						**_lowercase			:       Any		)						->    Any:
       requires_backends(cls   ,						["torch", "transformers", "k_diffusion"]		)