code: 
  
  
  
  
  
  
  """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        A	(							__snake_case:   Tuple   ,    __snake_case:   Tuple=2_5_6     )						->		Any:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						min(img.size     )
  
         if s < target_image_size:
                raise ValueError(F"""min dim for image {s} < {target_image_size}"""     )
  
         __magic_name__										=						target_image_size / s
         __magic_name__										=						(round(r * img.size[1]     ), round(r * img.size[0]     ))
         __magic_name__										=						TF.resize(__snake_case   ,    __snake_case   ,    interpolation=PIL.Image.LANCZOS     )
         __magic_name__										=						TF.center_crop(__snake_case   ,    output_size=2 * [target_image_size]     )
         __magic_name__										=						torch.unsqueeze(T.ToTensor()(__snake_case     )   ,    0     )
         return img
  
  
  def        A	(							__snake_case:   int     )						->		Any:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						2.0 * x - 1.0
         return x
  
  
  def        A	(							__snake_case:   Any   ,    __snake_case:   List[str]=True   ,    __snake_case:   Union[str, Any]="RGB"     )						->		int:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						x.detach().cpu()
         if process:
                __magic_name__										=						post_process_tensor(__snake_case     )
         __magic_name__										=						x.numpy()
         if process:
                __magic_name__										=						(2_5_5 * x).astype(np.uinta     )
         __magic_name__										=						Image.fromarray(__snake_case     )
         if not x.mode == mode:
                __magic_name__										=						x.convert(__snake_case     )
         return x
  
  
  def        A	(							__snake_case:   Optional[Any]     )						->		Optional[int]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						torch.clamp(__snake_case   ,    -1.0   ,    1.0     )
         __magic_name__										=						(x + 1.0) / 2.0
         __magic_name__										=						x.permute(1   ,    2   ,    0     )
         return x
  
  
  
  
  
  
  def        A	(							__snake_case:   Union[str, Any]     )						->		Union[str, Any]:
  
         """simple docstring"""
  
  
  
  
  
         __magic_name__										=						post_process_tensor(x.squeeze()     )
         return x.permute(2   ,    0   ,    1     ).unsqueeze(0     )
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  snake_case   :   int    		=		logging.get_logger(__name__)
  
  snake_case   :   List[str]    		=		{
      """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
      # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
  }
  
  
  
  class 					UpperCamelCase__   (							a_):
  
  
  
  
  
  
  
      """simple docstring"""
      __UpperCAmelCase											=   """nystromformer"""
  
  
  
  
  
  
  
      def __init__(		self   :  Dict							,						UpperCamelCase_   :  Optional[Any]=3_0_0_0_0							,						UpperCamelCase_   :  Dict=7_6_8							,						UpperCamelCase_   :  Tuple=1_2							,						UpperCamelCase_   :  Any=1_2							,						UpperCamelCase_   :  List[str]=3_0_7_2							,						UpperCamelCase_   :  Tuple="gelu_new"							,						UpperCamelCase_   :  Optional[Any]=0.1							,						UpperCamelCase_   :  str=0.1							,						UpperCamelCase_   :  Tuple=5_1_0							,						UpperCamelCase_   :  Optional[int]=2							,						UpperCamelCase_   :  Optional[int]=6_4							,						UpperCamelCase_   :  Tuple=6_4							,						UpperCamelCase_   :  Optional[int]=6_5							,						UpperCamelCase_   :  Optional[int]=False							,						UpperCamelCase_   :  List[str]=0.02							,						UpperCamelCase_   :  List[Any]=1e-5							,						UpperCamelCase_   :  Union[str, Any]=1							,						UpperCamelCase_   :  Tuple=0							,						UpperCamelCase_   :  List[Any]=2							,						**UpperCamelCase_   :  str							,						):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						vocab_size
             __magic_name__										=						max_position_embeddings
             __magic_name__										=						hidden_size
             __magic_name__										=						num_hidden_layers
             __magic_name__										=						num_attention_heads
             __magic_name__										=						intermediate_size
             __magic_name__										=						hidden_act
             __magic_name__										=						hidden_dropout_prob
             __magic_name__										=						attention_probs_dropout_prob
             __magic_name__										=						initializer_range
             __magic_name__										=						type_vocab_size
             __magic_name__										=						segment_means_seq_len
             __magic_name__										=						num_landmarks
             __magic_name__										=						conv_kernel_size
             __magic_name__										=						inv_coeff_init_option
             __magic_name__										=						layer_norm_eps
             super().__init__(pad_token_id=UpperCamelCase_							,						bos_token_id=UpperCamelCase_							,						eos_token_id=UpperCamelCase_							,						**UpperCamelCase_   )