code: 
  
  
  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     )
  
  
style_context: 
  
  
  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     )     )
  
  