


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=256     )    ->Union[str, Any]:







						"""simple docstring"""

						lowercase			:   Dict					   =		min(img.size     )

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

						lowercase			:   str					   =		target_image_size / s
						lowercase			:   List[Any]					   =		(round(r * img.size[1]     ), round(r * img.size[0]     ))
						lowercase			:   Tuple					   =		TF.resize(_UpperCamelCase,    _UpperCamelCase,    interpolation=PIL.Image.LANCZOS     )
						lowercase			:   Tuple					   =		TF.center_crop(_UpperCamelCase,    output_size=2 * [target_image_size]     )
						lowercase			:   Dict					   =		torch.unsqueeze(T.ToTensor()(_UpperCamelCase     ),    0     )
						return img




def 			__lowercase	(						_UpperCamelCase     )    ->Optional[Any]:







						"""simple docstring"""

						lowercase			:   Any					   =		2.0 * x - 1.0
						return x




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase=True,    _UpperCamelCase="RGB"     )    ->int:







						"""simple docstring"""

						lowercase			:   List[str]					   =		x.detach().cpu()
						if process:
												lowercase			:   Optional[int]					   =		post_process_tensor(_UpperCamelCase     )
						lowercase			:   str					   =		x.numpy()
						if process:
												lowercase			:   Dict					   =		(255 * x).astype(np.uinta     )
						lowercase			:   List[str]					   =		Image.fromarray(_UpperCamelCase     )
						if not x.mode == mode:
												lowercase			:   Union[str, Any]					   =		x.convert(_UpperCamelCase     )
						return x




def 			__lowercase	(						_UpperCamelCase     )    ->Optional[Any]:







						"""simple docstring"""

						lowercase			:   List[Any]					   =		torch.clamp(_UpperCamelCase,    -1.0,    1.0     )
						lowercase			:   Tuple					   =		(x + 1.0) / 2.0
						lowercase			:   Optional[int]					   =		x.permute(1,    2,    0     )
						return x




def 			__lowercase	(						_UpperCamelCase     )    ->int:







						"""simple docstring"""

						lowercase			:   int					   =		post_process_tensor(x.squeeze()     )
						return x.permute(2,    0,    1     ).unsqueeze(0     )





import numpy as np




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->Dict:







      """simple docstring"""

      lowercase			:   Optional[Any]					   =		np.array(_UpperCamelCase     )
      lowercase			:   Dict					   =		np.array(_UpperCamelCase     )

      lowercase			:   List[Any]					   =		abs(predict - actual     )
      lowercase			:   int					   =		difference.mean()

      return score




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[Any]:







      """simple docstring"""

      lowercase			:   Tuple					   =		np.array(_UpperCamelCase     )
      lowercase			:   str					   =		np.array(_UpperCamelCase     )

      lowercase			:   List[str]					   =		predict - actual
      lowercase			:   Tuple					   =		np.square(_UpperCamelCase     )

      lowercase			:   Union[str, Any]					   =		square_diff.mean()
      return score




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->str:







      """simple docstring"""

      lowercase			:   Union[str, Any]					   =		np.array(_UpperCamelCase     )
      lowercase			:   Optional[Any]					   =		np.array(_UpperCamelCase     )

      lowercase			:   Tuple					   =		predict - actual
      lowercase			:   Dict					   =		np.square(_UpperCamelCase     )
      lowercase			:   Union[str, Any]					   =		square_diff.mean()
      lowercase			:   Union[str, Any]					   =		np.sqrt(_UpperCamelCase     )
      return score




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[Any]:







      """simple docstring"""

      lowercase			:   List[str]					   =		np.array(_UpperCamelCase     )
      lowercase			:   List[Any]					   =		np.array(_UpperCamelCase     )

      lowercase			:   Dict					   =		np.log(predict + 1     )
      lowercase			:   List[str]					   =		np.log(actual + 1     )

      lowercase			:   int					   =		log_predict - log_actual
      lowercase			:   Tuple					   =		np.square(_UpperCamelCase     )
      lowercase			:   List[Any]					   =		square_diff.mean()

      lowercase			:   int					   =		np.sqrt(_UpperCamelCase     )

      return score




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[str]:







      """simple docstring"""

      lowercase			:   List[Any]					   =		np.array(_UpperCamelCase     )
      lowercase			:   Optional[int]					   =		np.array(_UpperCamelCase     )

      lowercase			:   Optional[int]					   =		predict - actual
      lowercase			:   Optional[int]					   =		np.sum(_UpperCamelCase     ) / len(_UpperCamelCase     )
      lowercase			:   Dict					   =		np.sum(_UpperCamelCase     ) / len(_UpperCamelCase     )
      # print(numerator, denumerator)
      lowercase			:   int					   =		float(_UpperCamelCase     ) / denumerator * 100

      return score




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[Any]:







      """simple docstring"""

      return np.mean(np.array(_UpperCamelCase     ) == np.array(_UpperCamelCase     )     )

