code: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils import AddedToken
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
      from .tokenization_xlm_roberta import XLMRobertaTokenizer
  else:
      A_			  =    None
  
  
  A_			  =    logging.get_logger(__name__)
  
  A_			  =    {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
  
  A_			  =    {
      "vocab_file": {
          "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
          "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
          "xlm-roberta-large-finetuned-conll02-dutch": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
          ),
          "xlm-roberta-large-finetuned-conll02-spanish": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
          ),
          "xlm-roberta-large-finetuned-conll03-english": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
          ),
          "xlm-roberta-large-finetuned-conll03-german": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
          ),
      },
      "tokenizer_file": {
          "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json",
          "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json",
          "xlm-roberta-large-finetuned-conll02-dutch": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"
          ),
          "xlm-roberta-large-finetuned-conll02-spanish": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"
          ),
          "xlm-roberta-large-finetuned-conll03-english": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"
          ),
          "xlm-roberta-large-finetuned-conll03-german": (
              "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"
          ),
      },
  }
  
  A_			  =    {
      "xlm-roberta-base": 512,
      "xlm-roberta-large": 512,
      "xlm-roberta-large-finetuned-conll02-dutch": 512,
      "xlm-roberta-large-finetuned-conll02-spanish": 512,
      "xlm-roberta-large-finetuned-conll03-english": 512,
      "xlm-roberta-large-finetuned-conll03-german": 512,
  }
  class 						__lowerCAmelCase   (			UpperCAmelCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    __lowerCamelCase       :    Union[str, Any]     	=			VOCAB_FILES_NAMES
    __lowerCamelCase       :    str     	=			PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase       :    Union[str, Any]     	=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase       :    str     	=			["input_ids", "attention_mask"]
    __lowerCamelCase       :    Tuple     	=			XLMRobertaTokenizer
    def __init__(      self:		str				,			UpperCamelCase_:		Dict=None				,			UpperCamelCase_:		int=None				,			UpperCamelCase_:		Tuple="<s>"				,			UpperCamelCase_:		List[Any]="</s>"				,			UpperCamelCase_:		Optional[Any]="</s>"				,			UpperCamelCase_:		List[str]="<s>"				,			UpperCamelCase_:		Any="<unk>"				,			UpperCamelCase_:		Optional[Any]="<pad>"				,			UpperCamelCase_:		Any="<mask>"				,			**UpperCamelCase_:		Tuple				,			):
       # Mask token behave like a normal word, i.e. include the space before it
       UpperCamelCase_     =AddedToken(UpperCamelCase_				,			lstrip=UpperCamelCase_				,			rstrip=UpperCamelCase_					) if isinstance(UpperCamelCase_				,			UpperCamelCase_					) else mask_token
  
       super().__init__(
           UpperCamelCase_				,			tokenizer_file=UpperCamelCase_				,			bos_token=UpperCamelCase_				,			eos_token=UpperCamelCase_				,			sep_token=UpperCamelCase_				,			cls_token=UpperCamelCase_				,			unk_token=UpperCamelCase_				,			pad_token=UpperCamelCase_				,			mask_token=UpperCamelCase_				,			**UpperCamelCase_				,			)
  
       UpperCamelCase_     =vocab_file
       UpperCamelCase_     =False if not self.vocab_file else True
    def 					UpperCamelCase__   (      self:		Tuple				,			UpperCamelCase_:		List[int]				,			UpperCamelCase_:		Optional[List[int]] = None					):
  
       if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       UpperCamelCase_     =[self.cls_token_id]
       UpperCamelCase_     =[self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep
    def 					UpperCamelCase__   (      self:		Tuple				,			UpperCamelCase_:		List[int]				,			UpperCamelCase_:		Optional[List[int]] = None					):
       UpperCamelCase_     =[self.sep_token_id]
       UpperCamelCase_     =[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 + sep + token_ids_a + sep					) * [0]
  
  
  
  
  
    def 					UpperCamelCase__   (      self:		Dict				,			UpperCamelCase_:		str				,			UpperCamelCase_:		Optional[str] = None					):
       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(UpperCamelCase_					):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""					)
          return
       UpperCamelCase_     =os.path.join(
           UpperCamelCase_				,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]					)
  
       if os.path.abspath(self.vocab_file					) != os.path.abspath(UpperCamelCase_					):
          copyfile(self.vocab_file				,			UpperCamelCase_					)
  
       return (out_vocab_file,)
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  from .hash_table import HashTable
  from .number_theory.prime_numbers import is_prime, next_prime
  class 						__lowerCAmelCase   (			UpperCAmelCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def __init__(      self:		List[Any]				,			*UpperCamelCase_:		Dict				,			**UpperCamelCase_:		Optional[int]					):
       super().__init__(*UpperCamelCase_				,			**UpperCamelCase_					)
    def 					UpperCamelCase__   (      self:		Any				,			UpperCamelCase_:		Optional[int]				,			UpperCamelCase_:		Optional[int]					):
       UpperCamelCase_     =(
           next_prime(value % self.size_table					)
           if not is_prime(value % self.size_table					)
           else value % self.size_table
       )  # gt = bigger than
       return next_prime_gt - (data % next_prime_gt)
    def 					UpperCamelCase__   (      self:		str				,			UpperCamelCase_:		List[str]				,			UpperCamelCase_:		str				,			UpperCamelCase_:		Tuple					):
       return (increment * self.__hash_function_a(UpperCamelCase_				,			UpperCamelCase_					)) % self.size_table
  
  
  
  
  
    def 					UpperCamelCase__   (      self:		str				,			UpperCamelCase_:		int				,			UpperCamelCase_:		str=None					):
       UpperCamelCase_     =1
       UpperCamelCase_     =self.hash_function(UpperCamelCase_					)
  
       while self.values[new_key] is not None and self.values[new_key] != key:
          UpperCamelCase_     =(
              self.__hash_double_function(UpperCamelCase_				,			UpperCamelCase_				,			UpperCamelCase_					)
              if self.balanced_factor() >= self.lim_charge
              else None
          )
          if new_key is None:
             break
          else:
             i += 1
  
       return new_key
  