



from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments

def   lowerCAmelCase__						(							):
				"""simple docstring"""







				__a       =		HfArgumentParser(UpperCamelCase__    )
				__a       =		parser.parse_args_into_dataclasses()[0]
				__a       =		TensorFlowBenchmark(args=UpperCamelCase__    )
				try:
								__a       =		parser.parse_args_into_dataclasses()[0]
				except ValueError as e:
								__a       =		"""Arg --no_{0} is no longer used, please use --no-{0} instead."""
								__a       =		""" """.join(str(UpperCamelCase__    ).split(""" """    )[:-1]    )
								__a       =		""""""
								__a       =		eval(str(UpperCamelCase__    ).split(""" """    )[-1]    )
								__a       =		[]
								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:
												__a       =		full_error_msg + begin_error_msg + str(UpperCamelCase__    )
								raise ValueError(UpperCamelCase__    )
				benchmark.run()


if __name__ == "__main__":
						main()





def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			list				,     _SCREAMING_SNAKE_CASE       :			list				,     _SCREAMING_SNAKE_CASE       :			int				,     _SCREAMING_SNAKE_CASE       :			int				,     _SCREAMING_SNAKE_CASE       :			int    ):
				"""simple docstring"""







				if index == number_of_items:
								return 0
				__a       =		0
				__a       =		0
				__a       =		knapsack(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     index + 1    )
				if weights[index] <= max_weight:
								__a       =		values[index] + knapsack(
								    _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     max_weight - weights[index]				,     index + 1    )
				return max(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
