






'''simple docstring'''
import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image




def 						__SCREAMING_SNAKE_CASE    (     _UpperCamelCase  ,						_UpperCamelCase=256							):


   """simple docstring"""




   lowercase_		:			Union[str, Any]							=   min(img.size							)

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

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




def 						__SCREAMING_SNAKE_CASE    (     _UpperCamelCase							):


   """simple docstring"""




   lowercase_		:			Tuple							=   2.0 * x - 1.0
   return x




def 						__SCREAMING_SNAKE_CASE    (     _UpperCamelCase  ,						_UpperCamelCase=True  ,						_UpperCamelCase="RGB"							):


   """simple docstring"""




   lowercase_		:			str							=   x.detach().cpu()
   if process:
      lowercase_		:			List[str]							=   post_process_tensor(__lowerCAmelCase							)
   lowercase_		:			Optional[Any]							=   x.numpy()
   if process:
      lowercase_		:			Tuple							=   (255 * x).astype(np.uinta							)
   lowercase_		:			List[str]							=   Image.fromarray(__lowerCAmelCase							)
   if not x.mode == mode:
      lowercase_		:			List[Any]							=   x.convert(__lowerCAmelCase							)
   return x




def 						__SCREAMING_SNAKE_CASE    (     _UpperCamelCase							):


   """simple docstring"""




   lowercase_		:			Union[str, Any]							=   torch.clamp(__lowerCAmelCase  ,						-1.0  ,						1.0							)
   lowercase_		:			Optional[int]							=   (x + 1.0) / 2.0
   lowercase_		:			Optional[Any]							=   x.permute(1  ,						2  ,						0							)
   return x





def 						__SCREAMING_SNAKE_CASE    (     _UpperCamelCase							):


   """simple docstring"""




   lowercase_		:			Tuple							=   post_process_tensor(x.squeeze()							)
   return x.permute(2  ,						0  ,						1							).unsqueeze(0							)





"""simple docstring"""


from ..utils import DummyObject, requires_backends






class    __UpperCAmelCase						(	metaclass=__lowerCAmelCase	):
				A__      :   List[Any]  							=       ['''torch''', '''librosa''']

				def __init__(      self	, *_lowerCamelCase	, **_lowerCamelCase      ):
										requires_backends(self	, ["torch", "librosa"]      )

				@classmethod
				def 		_a	(      cls	, *_lowerCamelCase	, **_lowerCamelCase      ):
										requires_backends(cls	, ["torch", "librosa"]      )






				@classmethod
				def 		_a	(      cls	, *_lowerCamelCase	, **_lowerCamelCase      ):
										requires_backends(cls	, ["torch", "librosa"]      )






class    __UpperCAmelCase						(	metaclass=__lowerCAmelCase	):
				A__      :   List[str]  							=       ['''torch''', '''librosa''']

				def __init__(      self	, *_lowerCamelCase	, **_lowerCamelCase      ):
										requires_backends(self	, ["torch", "librosa"]      )

				@classmethod
				def 		_a	(      cls	, *_lowerCamelCase	, **_lowerCamelCase      ):
										requires_backends(cls	, ["torch", "librosa"]      )






				@classmethod
				def 		_a	(      cls	, *_lowerCamelCase	, **_lowerCamelCase      ):
										requires_backends(cls	, ["torch", "librosa"]      )


