


import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey






class    _UpperCamelCase     :

				"""simple docstring"""







				def __init__(    self					, a__					, a__					, a__ )						->					int:
					if dst_width < 0 or dst_height < 0:
						raise ValueError("""Destination width/height should be > 0""" )

					A             =					img
					A             =					img.shape[1]
					A             =					img.shape[0]
					A             =					dst_width
					A             =					dst_height

					A             =					self.src_w / self.dst_w
					A             =					self.src_h / self.dst_h

					A             =					A             =					(
					    np.ones((self.dst_h, self.dst_w, 3)					, np.uinta ) * 255
					)

				def   _UpperCAmelCase       (    self )						->					Optional[Any]:
					for i in range(self.dst_h ):
						for j in range(self.dst_w ):
							A             =					self.img[self.get_y(a__ )][self.get_x(a__ )]

				def   _UpperCAmelCase       (    self					, a__ )						->					int:
					return int(self.ratio_x * x )

				def   _UpperCAmelCase       (    self					, a__ )						->					int:
					return int(self.ratio_y * y )


if __name__ == "__main__":
				_lowercase						,				_lowercase							:   List[Any]						=						800, 600
				_lowercase							:   Tuple						=						imread("image_data/lena.jpg", 1)
				_lowercase							:   Optional[int]						=						NearestNeighbour(im, dst_w, dst_h)
				n.process()

				imshow(
				    f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
				)
				waitKey(0)
				destroyAllWindows()







from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments





def 	_lowerCAmelCase					( )			-> str:





	"""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()



