
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments

def 				SCREAMING_SNAKE_CASE     (      ) ->       Any:

				"""simple docstring"""







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


if __name__ == "__main__":
					main()



from PIL import Image

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Image							,						__UpperCamelCase :						float       ) ->       Image:

				"""simple docstring"""







				def brightness(__UpperCamelCase :						int       ) -> float:
								return 1_28 + level + (c - 1_28)

				if not -2_5_5.0 <= level <= 2_5_5.0:
								raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''       )
				return img.point(__UpperCamelCase       )


if __name__ == "__main__":
					# Load image
					with Image.open('image_data/lena.jpg') as img:
										# Change brightness to 100
										_SCREAMING_SNAKE_CASE    :      Dict          =			change_brightness(img, 1_0_0)
										brigt_img.save('image_data/lena_brightness.png', format='png')