



from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments






def 			lowerCAmelCase__	(					):





				'''simple docstring'''

				lowerCAmelCase__  :       Union[str, Any]				=  HfArgumentParser(lowerCamelCase_)
				lowerCAmelCase__  :       List[str]				=  parser.parse_args_into_dataclasses()[0]
				lowerCAmelCase__  :       Union[str, Any]				=  TensorFlowBenchmark(args=lowerCamelCase_)
				try:
								lowerCAmelCase__  :       Any				=  parser.parse_args_into_dataclasses()[0]
				except ValueError as e:
								lowerCAmelCase__  :       Optional[int]				=  '''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
								lowerCAmelCase__  :       List[Any]				=  ''' '''.join(str(lowerCamelCase_).split(''' ''')[:-1])
								lowerCAmelCase__  :       Optional[Any]				=  ''''''
								lowerCAmelCase__  :       List[Any]				=  eval(str(lowerCamelCase_).split(''' ''')[-1])
								lowerCAmelCase__  :       List[Any]				=  []
								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(lowerCamelCase_)
								if len(lowerCamelCase_) > 0:
												lowerCAmelCase__  :       Any				=  full_error_msg + begin_error_msg + str(lowerCamelCase_)
								raise ValueError(lowerCamelCase_)
				benchmark.run()


if __name__ == "__main__":
							main()






def 			lowerCAmelCase__	(					lowerCamelCase_	:	int):





				'''simple docstring'''

				if length <= 0 or not isinstance(lowerCamelCase_ ,lowerCamelCase_):
								raise ValueError('''Length must be a positive integer.''')
				return [n * (2 * n - 1) for n in range(lowerCamelCase_)]


if __name__ == "__main__":
							print(hexagonal_numbers(length=5))
							print(hexagonal_numbers(length=1_0))

