code: 
  
  
  
  
  
  '''simple docstring'''
  
  import json
  from typing import TYPE_CHECKING, List, Optional, Tuple
  
  from tokenizers import pre_tokenizers
  
  from ...tokenization_utils_base import AddedToken, BatchEncoding
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_deberta import DebertaTokenizer
  
  
  if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation
  
  
  __lowerCAmelCase							:			List[Any]				=logging.get_logger(__name__)
  
  __lowerCAmelCase							:			Dict				={"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
  
  __lowerCAmelCase							:			List[str]				={
      "vocab_file": {
          "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json",
          "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json",
          "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json",
          "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json",
          "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json",
          "microsoft/deberta-xlarge-mnli": (
              "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"
          ),
      },
      "merges_file": {
          "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt",
          "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt",
          "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt",
          "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt",
          "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt",
          "microsoft/deberta-xlarge-mnli": (
              "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"
          ),
      },
  }
  
  __lowerCAmelCase							:			int				={
      "microsoft/deberta-base": 512,
      "microsoft/deberta-large": 512,
      "microsoft/deberta-xlarge": 512,
      "microsoft/deberta-base-mnli": 512,
      "microsoft/deberta-large-mnli": 512,
      "microsoft/deberta-xlarge-mnli": 512,
  }
  
  __lowerCAmelCase							:			Optional[Any]				={
      "microsoft/deberta-base": {"do_lower_case": False},
      "microsoft/deberta-large": {"do_lower_case": False},
  }
  
  
  
  
  
  
  class      UpperCAmelCase			( SCREAMING_SNAKE_CASE__       ):
   __lowercase     	=       VOCAB_FILES_NAMES
   __lowercase     	=       PRETRAINED_VOCAB_FILES_MAP
   __lowercase     	=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowercase     	=       ["""input_ids""", """attention_mask""", """token_type_ids"""]
   __lowercase     	=       DebertaTokenizer
   def __init__(       self   :Dict		, lowercase_   :Any=None		, lowercase_   :Tuple=None		, lowercase_   :Dict=None		, lowercase_   :int="replace"		, lowercase_   :int="[CLS]"		, lowercase_   :Dict="[SEP]"		, lowercase_   :Union[str, Any]="[SEP]"		, lowercase_   :List[Any]="[CLS]"		, lowercase_   :Union[str, Any]="[UNK]"		, lowercase_   :Any="[PAD]"		, lowercase_   :Dict="[MASK]"		, lowercase_   :Optional[Any]=False		, **lowercase_   :Optional[int]		, )->							List[str]:
       super().__init__(
           lowercase_		, lowercase_		, tokenizer_file=lowercase_		, errors=lowercase_		, bos_token=lowercase_		, eos_token=lowercase_		, unk_token=lowercase_		, sep_token=lowercase_		, cls_token=lowercase_		, pad_token=lowercase_		, mask_token=lowercase_		, add_prefix_space=lowercase_		, **lowercase_		, )
       A__       =    kwargs.pop("add_bos_token"		, lowercase_							)
  
       A__       =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
       if pre_tok_state.get("add_prefix_space"		, lowercase_							) != add_prefix_space:
           A__       =    getattr(lowercase_		, pre_tok_state.pop("type"							)							)
           A__       =    add_prefix_space
           A__       =    pre_tok_class(**lowercase_							)
  
       A__       =    add_prefix_space
   @property
   def   UpperCAmelCase_							(       self   :Optional[Any]							)->							str:
  
  
  
       if self._mask_token is None:
           if self.verbose:
               logger.error("Using mask_token, but it is not set yet."							)
           return None
       return str(self._mask_token							)
   @mask_token.setter
   def   UpperCAmelCase_							(       self   :Union[str, Any]		, lowercase_   :int							)->							Tuple:
       A__       =    AddedToken(lowercase_		, lstrip=lowercase_		, rstrip=lowercase_							) if isinstance(lowercase_		, lowercase_							) else value
       A__       =    value
   def   UpperCAmelCase_							(       self   :Any		, lowercase_   :Any		, lowercase_   :Any = None							)->							List[int]:
  
  
  
       if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       A__       =    [self.cls_token_id]
       A__       =    [self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep
   def   UpperCAmelCase_							(       self   :List[Any]		, lowercase_   :List[str]		, lowercase_   :Any = None							)->							List[int]:
       A__       =    [self.sep_token_id]
       A__       =    [self.cls_token_id]
  
       if token_ids_a is None:
           return len(cls + token_ids_a + sep							) * [0]
       return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]
   def   UpperCAmelCase_							(       self   :Optional[int]		, *lowercase_   :Union[str, Any]		, **lowercase_   :Any							)->							BatchEncoding:
       A__       =    kwargs.get("is_split_into_words"		, lowercase_							)
       assert self.add_prefix_space or not is_split_into_words, (
           F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
           "to use it with pretokenized inputs."
       )
  
       return super()._batch_encode_plus(*lowercase_		, **lowercase_							)
   def   UpperCAmelCase_							(       self   :Tuple		, *lowercase_   :str		, **lowercase_   :Optional[int]							)->							BatchEncoding:
       A__       =    kwargs.get("is_split_into_words"		, lowercase_							)
  
       assert self.add_prefix_space or not is_split_into_words, (
           F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
           "to use it with pretokenized inputs."
       )
  
       return super()._encode_plus(*lowercase_		, **lowercase_							)
   def   UpperCAmelCase_							(       self   :Tuple		, lowercase_   :Tuple		, lowercase_   :List[Any] = None							)->							Tuple[str]:
       A__       =    self._tokenizer.model.save(lowercase_		, name=lowercase_							)
       return tuple(lowercase_							)
  
  
  
   def   UpperCAmelCase_							(       self   :Optional[int]		, lowercase_   :List[str]							)->							List[int]:
       A__       =    []
       for is_user, text in conversation.iter_texts():
           input_ids.extend(self.encode(lowercase_		, add_special_tokens=lowercase_							) + [self.eos_token_id]							)
  
       if len(lowercase_							) > self.model_max_length:
           A__       =    input_ids[-self.model_max_length :]
       return input_ids
  
  
  
  
  
  
  
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 )
  
  
  
  