





import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase=2_56 )  ->     int:
				lowerCamelCase		       =min(img.size )

				if s < target_image_size:
								raise ValueError(F"""min dim for image {s} < {target_image_size}""" )

				lowerCamelCase		       =target_image_size / s
				lowerCamelCase		       =(round(r * img.size[1] ), round(r * img.size[0] ))
				lowerCamelCase		       =TF.resize(a_	,					a_	,					interpolation=PIL.Image.LANCZOS )
				lowerCamelCase		       =TF.center_crop(a_	,					output_size=2 * [target_image_size] )
				lowerCamelCase		       =torch.unsqueeze(T.ToTensor()(a_ )	,					0 )
				return img





def    _lowercase    (  _UpperCAmelCase )  ->     Tuple:
				lowerCamelCase		       =2.0 * x - 1.0
				return x





def    _lowercase    (  _UpperCAmelCase	,					_UpperCAmelCase=True	,					_UpperCAmelCase="RGB" )  ->     List[Any]:
				lowerCamelCase		       =x.detach().cpu()
				if process:
								lowerCamelCase		       =post_process_tensor(a_ )
				lowerCamelCase		       =x.numpy()
				if process:
								lowerCamelCase		       =(2_55 * x).astype(np.uinta )
				lowerCamelCase		       =Image.fromarray(a_ )
				if not x.mode == mode:
								lowerCamelCase		       =x.convert(a_ )
				return x





def    _lowercase    (  _UpperCAmelCase )  ->     Optional[int]:
				lowerCamelCase		       =torch.clamp(a_	,					-1.0	,					1.0 )
				lowerCamelCase		       =(x + 1.0) / 2.0
				lowerCamelCase		       =x.permute(1	,					2	,					0 )
				return x





def    _lowercase    (  _UpperCAmelCase )  ->     List[str]:
				lowerCamelCase		       =post_process_tensor(x.squeeze() )
				return x.permute(2	,					0	,					1 ).unsqueeze(0 )













from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments
def      lowerCamelCase					(						)	->			Union[str, Any]:
							lowerCAmelCase_										=     HfArgumentParser(a_	)
							try:
														lowerCAmelCase_										=     parser.parse_args_into_dataclasses()[0]
							except ValueError as e:
														lowerCAmelCase_										=     'Arg --no_{0} is no longer used, please use --no-{0} instead.'
														lowerCAmelCase_										=     ' '.join(str(a_	).split(' '	)[:-1]	)
														lowerCAmelCase_										=     ''
														lowerCAmelCase_										=     eval(str(a_	).split(' '	)[-1]	)
														lowerCAmelCase_										=     []
														for arg in depreciated_args:
																					# arg[2:] removes '--'
																					if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
																												# arg[5:] removes '--no_'
																												full_error_msg += arg_error_msg.format(arg[5:]	)
																					else:
																												wrong_args.append(a_	)
														if len(a_	) > 0:
																					lowerCAmelCase_										=     full_error_msg + begin_error_msg + str(a_	)
														raise ValueError(a_	)

							lowerCAmelCase_										=     PyTorchBenchmark(args=a_	)
							benchmark.run()


if __name__ == "__main__":
		main()



