code: 
  
  
  
  
  
  '''simple docstring'''
  
  from ..utils import DummyObject, requires_backends
  
  
  
  
  
  
  class      UpperCAmelCase			( metaclass=UpperCamelCase__       ):
   __lowercase     	=       ["""torch""", """transformers""", """invisible_watermark"""]
   def __init__(       self   :Tuple		, *lowercase_   :Dict		, **lowercase_   :Optional[Any]							)->							List[Any]:
       requires_backends(self		, ["torch", "transformers", "invisible_watermark"]							)
   @classmethod
   def   UpperCAmelCase_							(       cls   :str		, *lowercase_   :int		, **lowercase_   :Dict							)->							Any:
       requires_backends(cls		, ["torch", "transformers", "invisible_watermark"]							)
  
  
  
   @classmethod
   def   UpperCAmelCase_							(       cls   :Optional[int]		, *lowercase_   :Dict		, **lowercase_   :int							)->							str:
       requires_backends(cls		, ["torch", "transformers", "invisible_watermark"]							)
  
  
  
  
  
  
  class      UpperCAmelCase			( metaclass=UpperCamelCase__       ):
   __lowercase     	=       ["""torch""", """transformers""", """invisible_watermark"""]
   def __init__(       self   :Optional[Any]		, *lowercase_   :Union[str, Any]		, **lowercase_   :Optional[Any]							)->							str:
       requires_backends(self		, ["torch", "transformers", "invisible_watermark"]							)
   @classmethod
   def   UpperCAmelCase_							(       cls   :Optional[Any]		, *lowercase_   :Dict		, **lowercase_   :Optional[Any]							)->							List[str]:
       requires_backends(cls		, ["torch", "transformers", "invisible_watermark"]							)
  
  
  
   @classmethod
   def   UpperCAmelCase_							(       cls   :str		, *lowercase_   :List[str]		, **lowercase_   :str							)->							List[str]:
       requires_backends(cls		, ["torch", "transformers", "invisible_watermark"]							)
  
  
  
  
style_context: 
  
  
  
  
  
  '''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 		UpperCamelCase       (							_lowerCamelCase      :      Optional[Any]		, _lowerCamelCase      :      Union[str, Any]=2_56 ):
      A__       =    min(img.size )
  
      if s < target_image_size:
          raise ValueError(F"min dim for image {s} < {target_image_size}" )
  
      A__       =    target_image_size / s
      A__       =    (round(r * img.size[1] ), round(r * img.size[0] ))
      A__       =    TF.resize(_lowerCamelCase		, _lowerCamelCase		, interpolation=PIL.Image.LANCZOS )
      A__       =    TF.center_crop(_lowerCamelCase		, output_size=2 * [target_image_size] )
      A__       =    torch.unsqueeze(T.ToTensor()(_lowerCamelCase )		, 0 )
      return img
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      int ):
      A__       =    2.0 * x - 1.0
      return x
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Optional[Any]		, _lowerCamelCase      :      Tuple=True		, _lowerCamelCase      :      int="RGB" ):
      A__       =    x.detach().cpu()
      if process:
          A__       =    post_process_tensor(_lowerCamelCase )
      A__       =    x.numpy()
      if process:
          A__       =    (2_55 * x).astype(np.uinta )
      A__       =    Image.fromarray(_lowerCamelCase )
      if not x.mode == mode:
          A__       =    x.convert(_lowerCamelCase )
      return x
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Any ):
      A__       =    torch.clamp(_lowerCamelCase		, -1.0		, 1.0 )
      A__       =    (x + 1.0) / 2.0
      A__       =    x.permute(1		, 2		, 0 )
      return x
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      List[Any] ):
      A__       =    post_process_tensor(x.squeeze() )
      return x.permute(2		, 0		, 1 ).unsqueeze(0 )
  
  
  
  