
def 	A_ (							snake_case							:   list ,      snake_case							:   list )      ->    Optional[Any]:



						'''simple docstring'''

						_validate_point(UpperCamelCase__ )
						_validate_point(UpperCamelCase__ )
						if len(UpperCamelCase__ ) != len(UpperCamelCase__ ):
												raise ValueError('''Both points must be in the same n-dimensional space''' )

						return float(sum(abs(a - b ) for a, b in zip(UpperCamelCase__ ,      UpperCamelCase__ ) ) )




def 	A_ (							snake_case							:   list[float] )      ->    Optional[int]:



						'''simple docstring'''

						if point:
												if isinstance(UpperCamelCase__ ,      UpperCamelCase__ ):
																		for item in point:
																								if not isinstance(UpperCamelCase__ ,      (int, float) ):
																														__UpperCamelCase       =			(
																														    '''Expected a list of numbers as input, found '''
																														    f"{type(UpperCamelCase__ ).__name__}"
																														)
																														raise TypeError(UpperCamelCase__ )
												else:
																		__UpperCamelCase       =			f"Expected a list of numbers as input, found {type(UpperCamelCase__ ).__name__}"
																		raise TypeError(UpperCamelCase__ )
						else:
												raise ValueError('''Missing an input''' )




def 	A_ (							snake_case							:   list ,      snake_case							:   list )      ->    Optional[Any]:



						'''simple docstring'''

						_validate_point(UpperCamelCase__ )
						_validate_point(UpperCamelCase__ )
						if len(UpperCamelCase__ ) != len(UpperCamelCase__ ):
												raise ValueError('''Both points must be in the same n-dimensional space''' )

						return float(sum(abs(x - y ) for x, y in zip(UpperCamelCase__ ,      UpperCamelCase__ ) ) )


if __name__ == "__main__":
				import doctest

				doctest.testmod()






import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer




def 	A_ (							snake_case							:   str ,      snake_case							:   str ,      **snake_case							:   List[str] )      ->    Dict:



						'''simple docstring'''

						__UpperCamelCase       =			AutoConfig.from_pretrained(snake_case ,      **snake_case )
						__UpperCamelCase       =			AutoModelForSeqaSeqLM.from_config(snake_case )
						model.save_pretrained(snake_case )
						AutoTokenizer.from_pretrained(snake_case ).save_pretrained(snake_case )
						return model


if __name__ == "__main__":
				fire.Fire(save_randomly_initialized_version)




