code: 
  
  
  '''simple docstring'''
  
  from typing import List
  
  from tokenizers import NormalizedString, PreTokenizedString, normalizers
  class _lowerCamelCase :
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(      self	:		List[str]						,  _A	:		str    )						->				None:
  			__magic_name__ :					Dict  			=  vocab
  			__magic_name__ :					Tuple  			=  normalizers.BertNormalizer(
  			    clean_text=_A						,  handle_chinese_chars=_A						,  strip_accents=_A						,  lowercase=_A						,  )
  			try:
  				import rjieba
  			except ImportError:
  				raise ImportError(
  				    'You need to install rjieba to use RoFormerTokenizer. '
  				    'See https://pypi.org/project/rjieba/ for installation.'    )
  			__magic_name__ :					int  			=  rjieba
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		int						,  _A	:		Any						,  _A	:		Optional[Any]    )						->				List[NormalizedString]:
  			__magic_name__ :					List[Any]  			=  []
  
  			# this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
  			for token, start, end in self.jieba.tokenize(str(_A    )						,  hmm=_A    ):
  				if token in self.vocab:
  					splits.append(normalized_string[start:end]    )
  				else:
  					__magic_name__ :					Any  			=  self.normalizers.normalize_str(_A    ).split()
  					for token in token_list:
  						if token:
  							__magic_name__ :					Optional[Any]  			=  start + len(_A    )
  							splits.append(normalized_string[start:end]    )
  							__magic_name__ :					List[str]  			=  end
  
          # this code test_alignement_methods can't pass but fast (300ms)
          # for token in self.jieba.cut(str(normalized_string), False):
          #     if token in self.vocab:
          #         splits.append(NormalizedString(token))
          #     else:
          #         token_list = self.normalizers.normalize_str(token).split()
          #         for token in token_list:
          #             if token:
          #                 splits.append(NormalizedString(token))
  
  			return splits
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]						,  _A	:		Tuple    )						->				str:
  			pretok.split(self.jieba_split    )
style_context: 
  
  
  '''simple docstring'''
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowerCAmelCase   :List[Any]								=    logging.get_logger(__name__)
  
  lowerCAmelCase   :Dict								=    {
      '''uw-madison/yoso-4096''': '''https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json''',
      # See all YOSO models at https://huggingface.co/models?filter=yoso
  }
  class _lowerCamelCase (		lowercase__							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		A_			:    Optional[Any]   					=       """yoso"""
  
  
  
  
  
  
  
  		def __init__(      self	:		Any						,  _A	:		List[Any]=50265						,  _A	:		Any=768						,  _A	:		List[str]=12						,  _A	:		Dict=12						,  _A	:		Union[str, Any]=3072						,  _A	:		List[Any]="gelu"						,  _A	:		Dict=0.1						,  _A	:		Union[str, Any]=0.1						,  _A	:		Union[str, Any]=4096						,  _A	:		Dict=1						,  _A	:		Tuple=0.02						,  _A	:		Any=1E-12						,  _A	:		Optional[int]="absolute"						,  _A	:		Tuple=True						,  _A	:		Any=9						,  _A	:		Tuple=64						,  _A	:		str=None						,  _A	:		Dict=True						,  _A	:		str=True						,  _A	:		int=1						,  _A	:		Optional[int]=0						,  _A	:		Tuple=2						,  **_A	:		Optional[Any]						,  )						->				Any:
  			super().__init__(pad_token_id=_A						,  bos_token_id=_A						,  eos_token_id=_A						,  **_A    )
  
  			__magic_name__ :					List[Any]  			=  vocab_size
  			__magic_name__ :					int  			=  max_position_embeddings
  			__magic_name__ :					int  			=  hidden_size
  			__magic_name__ :					Any  			=  num_hidden_layers
  			__magic_name__ :					List[str]  			=  num_attention_heads
  			__magic_name__ :					str  			=  intermediate_size
  			__magic_name__ :					Any  			=  hidden_act
  			__magic_name__ :					Union[str, Any]  			=  hidden_dropout_prob
  			__magic_name__ :					str  			=  attention_probs_dropout_prob
  			__magic_name__ :					int  			=  initializer_range
  			__magic_name__ :					List[Any]  			=  type_vocab_size
  			__magic_name__ :					Tuple  			=  layer_norm_eps
  			__magic_name__ :					Tuple  			=  position_embedding_type
  			__magic_name__ :					Optional[Any]  			=  use_expectation
  			__magic_name__ :					Union[str, Any]  			=  hash_code_len
  			__magic_name__ :					int  			=  num_hash
  			__magic_name__ :					List[Any]  			=  conv_window
  			__magic_name__ :					Optional[Any]  			=  use_fast_hash
  			__magic_name__ :					int  			=  lsh_backward