






import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image
def      lowerCamelCase					(						a_ ,						a_=256	)	->			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      lowerCamelCase					(						a_	)	->			Tuple:
							lowerCAmelCase_										=     2.0 * x - 1.0
							return x
def      lowerCamelCase					(						a_ ,						a_=True ,						a_="RGB"	)	->			List[Any]:
							lowerCAmelCase_										=     x.detach().cpu()
							if process:
														lowerCAmelCase_										=     post_process_tensor(a_	)
							lowerCAmelCase_										=     x.numpy()
							if process:
														lowerCAmelCase_										=     (255 * x).astype(np.uinta	)
							lowerCAmelCase_										=     Image.fromarray(a_	)
							if not x.mode == mode:
														lowerCAmelCase_										=     x.convert(a_	)
							return x
def      lowerCamelCase					(						a_	)	->			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      lowerCamelCase					(						a_	)	->			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()



