code: 
  
  
  
  
  
  '''simple docstring'''
  
  import unittest
  
  from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
  from transformers.utils import cached_property
  
  
  
  
  
  
  class      UpperCAmelCase			( unittest.TestCase       ):
   @cached_property
   def   UpperCAmelCase_							(       self   :Optional[Any]							)->							Union[str, Any]:
       return BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"							)
   @cached_property
   def   UpperCAmelCase_							(       self   :Dict							)->							List[Any]:
       return BlenderbotTokenizerFast.from_pretrained("facebook/blenderbot-3B"							)
   def   UpperCAmelCase_							(       self   :str							)->							Tuple:
       A__       =    self.tokenizer_ab
       A__       =    " I am a small frog."
       A__       =    tok([src_text]		, padding=__lowerCamelCase		, truncation=__lowerCamelCase							)["input_ids"]
       A__       =    tok.batch_decode(__lowerCamelCase		, skip_special_tokens=__lowerCamelCase		, clean_up_tokenization_spaces=__lowerCamelCase							)[0]
       assert src_text == decoded
   def   UpperCAmelCase_							(       self   :Dict							)->							Tuple:
       A__       =    self.rust_tokenizer_ab
       A__       =    " I am a small frog."
       A__       =    tok([src_text]		, padding=__lowerCamelCase		, truncation=__lowerCamelCase							)["input_ids"]
       A__       =    tok.batch_decode(__lowerCamelCase		, skip_special_tokens=__lowerCamelCase		, clean_up_tokenization_spaces=__lowerCamelCase							)[0]
       assert src_text == decoded
   def   UpperCAmelCase_							(       self   :Tuple							)->							str:
       assert self.tokenizer_ab.add_prefix_space
       assert self.tokenizer_ab([" Sam", "Sam"]							).input_ids == [[55_02, 2], [55_02, 2]]
  
  
  
   def   UpperCAmelCase_							(       self   :int							)->							Optional[Any]:
       assert self.rust_tokenizer_ab.add_prefix_space
       assert self.rust_tokenizer_ab([" Sam", "Sam"]							).input_ids == [[55_02, 2], [55_02, 2]]
  
  
  
  
  
  
  
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 )
  
  
  
  