



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		str ,				SCREAMING_SNAKE_CASE__				:		str )     ->   float:

 def get_matched_characters(SCREAMING_SNAKE_CASE__				:		str ,				SCREAMING_SNAKE_CASE__				:		str ) -> str:
  _snake_case			:     Union[str, Any] 		=    []
  _snake_case			:     List[Any] 		=    min(len(_stra ) ,				len(_stra ) ) // 2
  for i, l in enumerate(_stra ):
   _snake_case			:     List[str] 		=    int(max(0 ,				i - limit ) )
   _snake_case			:     List[Any] 		=    int(min(i + limit + 1 ,				len(_stra ) ) )
   if l in _stra[left:right]:
    matched.append(__snake_case )
    _snake_case			:     int 		=    F'''{_stra[0:_stra.index(__snake_case )]} {_stra[_stra.index(__snake_case ) + 1:]}'''

  return "".join(__snake_case )

 # matching characters
 _snake_case			:     Union[str, Any] 		=    get_matched_characters(__snake_case ,				__snake_case )
 _snake_case			:     str 		=    get_matched_characters(__snake_case ,				__snake_case )
 _snake_case			:     Tuple 		=    len(__snake_case )

 # transposition
 _snake_case			:     Optional[int] 		=    (
     len([(ca, ca) for ca, ca in zip(__snake_case ,				__snake_case ) if ca != ca] ) // 2
 )

 if not match_count:
  _snake_case			:     List[Any] 		=    0.0
 else:
  _snake_case			:     int 		=    (
      1
      / 3
      * (
          match_count / len(__snake_case )
          + match_count / len(__snake_case )
          + (match_count - transpositions) / match_count
      )
  )

 # common prefix up to 4 characters
 _snake_case			:     Any 		=    0
 for ca, ca in zip(stra[:4] ,				stra[:4] ):
  if ca == ca:
   prefix_len += 1
  else:
   break

 return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(jaro_winkler("""hello""", """world"""))







'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__    :      int						=    logging.get_logger(__name__)

lowercase__    :      int						=    {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}


class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       '''roberta-prelayernorm'''


     def __init__(						self						,	_UpperCAmelCase=5_0265						,	_UpperCAmelCase=768						,	_UpperCAmelCase=12						,	_UpperCAmelCase=12						,	_UpperCAmelCase=3072						,	_UpperCAmelCase="gelu"						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=512						,	_UpperCAmelCase=2						,	_UpperCAmelCase=0.02						,	_UpperCAmelCase=1e-1_2						,	_UpperCAmelCase=1						,	_UpperCAmelCase=0						,	_UpperCAmelCase=2						,	_UpperCAmelCase="absolute"						,	_UpperCAmelCase=True						,	_UpperCAmelCase=None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          super().__init__(pad_token_id=_UpperCAmelCase						,	bos_token_id=_UpperCAmelCase						,	eos_token_id=_UpperCAmelCase						,	**_UpperCAmelCase)

          __A						: Optional[int]      =       vocab_size
          __A						: List[Any]      =       hidden_size
          __A						: Optional[int]      =       num_hidden_layers
          __A						: Optional[Any]      =       num_attention_heads
          __A						: List[str]      =       hidden_act
          __A						: Dict      =       intermediate_size
          __A						: Optional[int]      =       hidden_dropout_prob
          __A						: Optional[Any]      =       attention_probs_dropout_prob
          __A						: Tuple      =       max_position_embeddings
          __A						: Union[str, Any]      =       type_vocab_size
          __A						: Any      =       initializer_range
          __A						: str      =       layer_norm_eps
          __A						: int      =       position_embedding_type
          __A						: Optional[Any]      =       use_cache
          __A						: Any      =       classifier_dropout




class  SCREAMING_SNAKE_CASE    (a__							):


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          if self.task == "multiple-choice":
               __A						: Dict      =       {0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
               __A						: Union[str, Any]      =       {0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ])