code: 
  
  
  
  
  
  '''simple docstring'''
  
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
    from .tokenization_xglm import XGLMTokenizer
  else:
    __lowerCAmelCase							:			Optional[int]				=None
  
  
  __lowerCAmelCase							:			List[str]				=logging.get_logger(__name__)
  
  __lowerCAmelCase							:			Dict				={"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
  
  __lowerCAmelCase							:			str				={
      "vocab_file": {
          "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
      },
      "tokenizer_file": {
          "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json",
      },
  }
  
  __lowerCAmelCase							:			int				={
      "facebook/xglm-564M": 2048,
  }
  
  
  
  
  
  
  class      UpperCAmelCase			( UpperCamelCase__       ):
   __lowercase     	=       VOCAB_FILES_NAMES
   __lowercase     	=       PRETRAINED_VOCAB_FILES_MAP
   __lowercase     	=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowercase     	=       ["""input_ids""", """attention_mask"""]
   __lowercase     	=       XGLMTokenizer
   def __init__(       self   :int		, lowercase_   :Dict=None		, lowercase_   :List[Any]=None		, lowercase_   :Union[str, Any]="<s>"		, lowercase_   :Tuple="</s>"		, lowercase_   :Any="</s>"		, lowercase_   :int="<s>"		, lowercase_   :Any="<unk>"		, lowercase_   :Any="<pad>"		, **lowercase_   :Dict		, )->							Dict:
       # Compatibility with the original tokenizer
       A__       =    7
       A__       =    [F"<madeupword{i}>" for i in range(self.num_madeup_words							)]
  
       A__       =    kwargs.get("additional_special_tokens"		, []							)
       kwargs["additional_special_tokens"] += [
           word for word in madeup_words if word not in kwargs["additional_special_tokens"]
       ]
  
       super().__init__(
           lowercase_		, tokenizer_file=lowercase_		, bos_token=lowercase_		, eos_token=lowercase_		, sep_token=lowercase_		, cls_token=lowercase_		, unk_token=lowercase_		, pad_token=lowercase_		, **lowercase_		, )
  
       A__       =    vocab_file
       A__       =    False if not self.vocab_file else True
   def   UpperCAmelCase_							(       self   :Dict		, lowercase_   :List[int]		, lowercase_   :Optional[List[int]] = None							)->							List[int]:
  
       if token_ids_a is None:
           return [self.sep_token_id] + token_ids_a
       A__       =    [self.sep_token_id]
       return sep + token_ids_a + sep + sep + token_ids_a
   def   UpperCAmelCase_							(       self   :List[str]		, lowercase_   :List[int]		, lowercase_   :Optional[List[int]] = None							)->							List[int]:
       A__       =    [self.sep_token_id]
  
       if token_ids_a is None:
           return len(sep + token_ids_a							) * [0]
       return len(sep + token_ids_a + sep + sep + token_ids_a							) * [0]
  
  
  
   def   UpperCAmelCase_							(       self   :Optional[int]		, lowercase_   :str		, lowercase_   :Optional[str] = None							)->							Tuple[str]:
       if not self.can_save_slow_tokenizer:
           raise ValueError(
               "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
               "tokenizer."							)
  
       if not os.path.isdir(lowercase_							):
           logger.error(F"Vocabulary path ({save_directory}) should be a directory."							)
           return
       A__       =    os.path.join(
           lowercase_		, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)
  
       if os.path.abspath(self.vocab_file							) != os.path.abspath(lowercase_							):
           copyfile(self.vocab_file		, lowercase_							)
  
       return (out_vocab_file,)
  
  
  
  
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 )
  
  
  
  