code: 
  
  """simple docstring"""
  
  
  
  
  import json
  import os
  from functools import lru_cache
  from typing import List, Optional, Tuple
  
  import regex as re
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  lowercase__  :					int												=	logging.get_logger(__name__)
  
  lowercase__  :					Optional[Any]												=	{
      'vocab_file': 'vocab.json',
      'merges_file': 'merges.txt',
  }
  
  lowercase__  :					Dict												=	{
      'vocab_file': {
          'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
          'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
          'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
          'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
          'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
          'roberta-large-openai-detector': (
              'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
          ),
      },
      'merges_file': {
          'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
          'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
          'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
          'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
          'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
          'roberta-large-openai-detector': (
              'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
          ),
      },
  }
  
  lowercase__  :					Optional[int]												=	{
      'roberta-base': 5_12,
      'roberta-large': 5_12,
      'roberta-large-mnli': 5_12,
      'distilroberta-base': 5_12,
      'roberta-base-openai-detector': 5_12,
      'roberta-large-openai-detector': 5_12,
  }
  @lru_cache()
  def      __SCREAMING_SNAKE_CASE		(		):
  					"""simple docstring"""
  					A    =      (
  					    list(range(ord("!"   )    ,							ord("~"   ) + 1   )   ) + list(range(ord("¡"   )    ,							ord("¬"   ) + 1   )   ) + list(range(ord("®"   )    ,							ord("ÿ"   ) + 1   )   )
  					)
  					A    =      bs[:]
  					A    =      0
  					for b in range(2**8   ):
  										if b not in bs:
  															bs.append(lowercase__   )
  															cs.append(2**8 + n   )
  															n += 1
  					A    =      [chr(lowercase__   ) for n in cs]
  					return dict(zip(lowercase__    ,							lowercase__   )   )
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  					A    =      set()
  					A    =      word[0]
  					for char in word[1:]:
  										pairs.add((prev_char, char)   )
  										A    =      char
  					return pairs
  
  
  
  
  
  class     __UpperCamelCase				(     lowerCAmelCase__		):
  		SCREAMING_SNAKE_CASE              =					VOCAB_FILES_NAMES
  		SCREAMING_SNAKE_CASE              =					PRETRAINED_VOCAB_FILES_MAP
  		SCREAMING_SNAKE_CASE              =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  		SCREAMING_SNAKE_CASE              =					["input_ids", "attention_mask"]
  
  
  		def __init__(self				:	str  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	Any  ,  __SCREAMING_SNAKE_CASE				:	Tuple="replace"  ,  __SCREAMING_SNAKE_CASE				:	Dict="<s>"  ,  __SCREAMING_SNAKE_CASE				:	str="</s>"  ,  __SCREAMING_SNAKE_CASE				:	List[Any]="</s>"  ,  __SCREAMING_SNAKE_CASE				:	int="<s>"  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]="<unk>"  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]="<pad>"  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]="<mask>"  ,  __SCREAMING_SNAKE_CASE				:	Tuple=False  ,  **__SCREAMING_SNAKE_CASE				:	Optional[Any]  ,  ):
  							A    =      AddedToken(_lowerCamelCase  ,  lstrip=_lowerCamelCase  ,  rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase  ,  _lowerCamelCase) else bos_token
  							A    =      AddedToken(_lowerCamelCase  ,  lstrip=_lowerCamelCase  ,  rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase  ,  _lowerCamelCase) else eos_token
  							A    =      AddedToken(_lowerCamelCase  ,  lstrip=_lowerCamelCase  ,  rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase  ,  _lowerCamelCase) else sep_token
  							A    =      AddedToken(_lowerCamelCase  ,  lstrip=_lowerCamelCase  ,  rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase  ,  _lowerCamelCase) else cls_token
  							A    =      AddedToken(_lowerCamelCase  ,  lstrip=_lowerCamelCase  ,  rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase  ,  _lowerCamelCase) else unk_token
  							A    =      AddedToken(_lowerCamelCase  ,  lstrip=_lowerCamelCase  ,  rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase  ,  _lowerCamelCase) else pad_token
  
  							# Mask token behave like a normal word, i.e. include the space before it
  							A    =      AddedToken(_lowerCamelCase  ,  lstrip=_lowerCamelCase  ,  rstrip=_lowerCamelCase) if isinstance(_lowerCamelCase  ,  _lowerCamelCase) else mask_token
  
  							super().__init__(
  							    errors=_lowerCamelCase  ,  bos_token=_lowerCamelCase  ,  eos_token=_lowerCamelCase  ,  unk_token=_lowerCamelCase  ,  sep_token=_lowerCamelCase  ,  cls_token=_lowerCamelCase  ,  pad_token=_lowerCamelCase  ,  mask_token=_lowerCamelCase  ,  add_prefix_space=_lowerCamelCase  ,  **_lowerCamelCase  ,  )
  
  							with open(_lowerCamelCase  ,  encoding="utf-8") as vocab_handle:
  												A    =      json.load(_lowerCamelCase)
  							A    =      {v: k for k, v in self.encoder.items()}
  							A    =      errors  # how to handle errors in decoding
  							A    =      bytes_to_unicode()
  							A    =      {v: k for k, v in self.byte_encoder.items()}
  							with open(_lowerCamelCase  ,  encoding="utf-8") as merges_handle:
  												A    =      merges_handle.read().split("\n")[1:-1]
  							A    =      [tuple(merge.split()) for merge in bpe_merges]
  							A    =      dict(zip(_lowerCamelCase  ,  range(len(_lowerCamelCase))))
  							A    =      {}
  							A    =      add_prefix_space
  
  							# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
  							A    =      re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+")
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
  							return len(self.encoder)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Tuple):
  							return dict(self.encoder  ,  **self.added_tokens_encoder)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Tuple  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]):
  
  							if token in self.cache:
  												return self.cache[token]
  							A    =      tuple(_lowerCamelCase)
  							A    =      get_pairs(_lowerCamelCase)
  
  							if not pairs:
  												return token
  
  							while True:
  												A    =      min(_lowerCamelCase  ,  key=lambda __SCREAMING_SNAKE_CASE: self.bpe_ranks.get(_lowerCamelCase  ,  float("inf")))
  												if bigram not in self.bpe_ranks:
  																	break
  												A    =      bigram
  												A    =      []
  												A    =      0
  												while i < len(_lowerCamelCase):
  																	try:
  																						A    =      word.index(_lowerCamelCase  ,  _lowerCamelCase)
  																	except ValueError:
  																						new_word.extend(word[i:])
  																						break
  																	else:
  																						new_word.extend(word[i:j])
  																						A    =      j
  
  																	if word[i] == first and i < len(_lowerCamelCase) - 1 and word[i + 1] == second:
  																						new_word.append(first + second)
  																						i += 2
  																	else:
  																						new_word.append(word[i])
  																						i += 1
  												A    =      tuple(_lowerCamelCase)
  												A    =      new_word
  												if len(_lowerCamelCase) == 1:
  																	break
  												else:
  																	A    =      get_pairs(_lowerCamelCase)
  							A    =      """ """.join(_lowerCamelCase)
  							A    =      word
  							return word
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	Any):
  							A    =      []
  							for token in re.findall(self.pat  ,  _lowerCamelCase):
  												A    =      """""".join(
  												    self.byte_encoder[b] for b in token.encode("utf-8"))  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
  												bpe_tokens.extend(bpe_token for bpe_token in self.bpe(_lowerCamelCase).split(" "))
  							return bpe_tokens
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Dict  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
  							return self.encoder.get(_lowerCamelCase  ,  self.encoder.get(self.unk_token))
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	Any):
  							return self.decoder.get(_lowerCamelCase)
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	List[str]):
  							A    =      """""".join(_lowerCamelCase)
  							A    =      bytearray([self.byte_decoder[c] for c in text]).decode("utf-8"  ,  errors=self.errors)
  							return text
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	int = None):
  
  							if not os.path.isdir(_lowerCamelCase):
  												logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
  												return
  							A    =      os.path.join(
  							    _lowerCamelCase  ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
  							A    =      os.path.join(
  							    _lowerCamelCase  ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"])
  
  							with open(_lowerCamelCase  ,  "w"  ,  encoding="utf-8") as f:
  												f.write(json.dumps(self.encoder  ,  indent=2  ,  sort_keys=_lowerCamelCase  ,  ensure_ascii=_lowerCamelCase) + "\n")
  
  							A    =      0
  							with open(_lowerCamelCase  ,  "w"  ,  encoding="utf-8") as writer:
  												writer.write("#version: 0.2\n")
  												for bpe_tokens, token_index in sorted(self.bpe_ranks.items()  ,  key=lambda __SCREAMING_SNAKE_CASE: kv[1]):
  																	if index != token_index:
  																						logger.warning(
  																						    F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
  																						    " Please check that the tokenizer is not corrupted!")
  																						A    =      token_index
  																	writer.write(" ".join(_lowerCamelCase) + "\n")
  																	index += 1
  
  							return vocab_file, merge_file
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any] = None):
  
  							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 + sep + token_ids_a + sep
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Dict  ,  __SCREAMING_SNAKE_CASE				:	Tuple  ,  __SCREAMING_SNAKE_CASE				:	Dict = None  ,  __SCREAMING_SNAKE_CASE				:	Dict = False):
  
  							if already_has_special_tokens:
  												return super().get_special_tokens_mask(
  												    token_ids_a=_lowerCamelCase  ,  token_ids_a=_lowerCamelCase  ,  already_has_special_tokens=_lowerCamelCase)
  
  							if token_ids_a is None:
  												return [1] + ([0] * len(_lowerCamelCase)) + [1]
  							return [1] + ([0] * len(_lowerCamelCase)) + [1, 1] + ([0] * len(_lowerCamelCase)) + [1]
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	int = None):
  							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 + sep + token_ids_a + sep) * [0]
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	Tuple=False  ,  **__SCREAMING_SNAKE_CASE				:	Union[str, Any]):
  							A    =      kwargs.pop("add_prefix_space"  ,  self.add_prefix_space)
  							if (is_split_into_words or add_prefix_space) and (len(_lowerCamelCase) > 0 and not text[0].isspace()):
  												A    =      """ """ + text
  							return (text, kwargs)
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  __A  :					int												=	{
      'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
  }
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	__A  :					Any												=	[
  	    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
  	    'NystromformerForMaskedLM',
  	    'NystromformerForMultipleChoice',
  	    'NystromformerForQuestionAnswering',
  	    'NystromformerForSequenceClassification',
  	    'NystromformerForTokenClassification',
  	    'NystromformerLayer',
  	    'NystromformerModel',
  	    'NystromformerPreTrainedModel',
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_nystromformer import (
  		    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    NystromformerForMaskedLM,
  		    NystromformerForMultipleChoice,
  		    NystromformerForQuestionAnswering,
  		    NystromformerForSequenceClassification,
  		    NystromformerForTokenClassification,
  		    NystromformerLayer,
  		    NystromformerModel,
  		    NystromformerPreTrainedModel,
  		)
  
  
  else:
  	import sys
  
  	__A  :					Optional[Any]												=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
  