
"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	if index == r:
		for j in range(_UpperCamelCase	):
			print(data[j] ,						end=' '	)
		print(' '	)
		return
	#  When no more elements are there to put in data[]
	if i >= n:
		return
	# current is included, put next at next location
	__lowerCAmelCase    :		Optional[Any]		    =   arr[i]
	combination_util(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						index + 1 ,						_UpperCamelCase ,						i + 1	)
	# current is excluded, replace it with
	# next (Note that i+1 is passed, but
	# index is not changed)
	combination_util(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						i + 1	)
	# The main function that prints all combinations
	# of size r in arr[] of size n. This function
	# mainly uses combinationUtil()
def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	# A temporary array to store all combination one by one
	__lowerCAmelCase    :		List[str]		    =   [0] * r
	# Print all combination using temporary array 'data[]'
	combination_util(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						0 ,						_UpperCamelCase ,						0	)


if __name__ == "__main__":
							# Driver code to check the function above
							lowerCamelCase__           =	[10, 20, 30, 40, 50]
							print_combination(arr, len(arr), 3)
							# This code is contributed by Ambuj sahu

"""simple docstring"""







from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		Tuple		    =   HfArgumentParser(_UpperCamelCase	)
	__lowerCAmelCase    :		Optional[Any]		    =   parser.parse_args_into_dataclasses()[0]
	__lowerCAmelCase    :		int		    =   TensorFlowBenchmark(args=_UpperCamelCase	)
	try:
		__lowerCAmelCase    :		Dict		    =   parser.parse_args_into_dataclasses()[0]
	except ValueError as e:
		__lowerCAmelCase    :		List[Any]		    =   'Arg --no_{0} is no longer used, please use --no-{0} instead.'
		__lowerCAmelCase    :		Union[str, Any]		    =   ' '.join(str(_UpperCamelCase	).split(' '	)[:-1]	)
		__lowerCAmelCase    :		List[str]		    =   ''
		__lowerCAmelCase    :		Any		    =   eval(str(_UpperCamelCase	).split(' '	)[-1]	)
		__lowerCAmelCase    :		Dict		    =   []
		for arg in depreciated_args:
			# arg[2:] removes '--'
			if arg[2:] in TensorFlowBenchmark.deprecated_args:
				# arg[5:] removes '--no_'
				full_error_msg += arg_error_msg.format(arg[5:]	)
			else:
				wrong_args.append(_UpperCamelCase	)
		if len(_UpperCamelCase	) > 0:
			__lowerCAmelCase    :		Any		    =   full_error_msg + begin_error_msg + str(_UpperCamelCase	)
		raise ValueError(_UpperCamelCase	)
	benchmark.run()


if __name__ == "__main__":
							main()