
"""simple docstring"""






def 					UpperCamelCase		( UpperCAmelCase      ,					UpperCAmelCase			)		->int:

   """simple docstring"""



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

      return "".join(UpperCAmelCase			)

   # matching characters
   a_							=			get_matched_characters(UpperCAmelCase      ,					UpperCAmelCase			)
   a_							=			get_matched_characters(UpperCAmelCase      ,					UpperCAmelCase			)
   a_							=			len(UpperCAmelCase			)

   # transposition
   a_							=			(
       len([(ca, ca) for ca, ca in zip(UpperCAmelCase      ,					UpperCAmelCase			) if ca != ca]			) // 2
   )

   if not match_count:
      a_							=			0.0
   else:
      a_							=			(
          1
          / 3
          * (
              match_count / len(UpperCAmelCase			)
              + match_count / len(UpperCAmelCase			)
              + (match_count - transpositions) / match_count
          )
      )

   # common prefix up to 4 characters
   a_							=			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'))





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase		:				Optional[Any]       			=		logging.get_logger(__name__)

__lowerCAmelCase		:				str       			=		{'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Dict							 =					"""openai-gpt"""
				SCREAMING_SNAKE_CASE_       :							Tuple							 =					{
				    """max_position_embeddings""": """n_positions""",
				    """hidden_size""": """n_embd""",
				    """num_attention_heads""": """n_head""",
				    """num_hidden_layers""": """n_layer""",
				}





				def __init__(      self				:     str					,      __lowerCamelCase				:     List[str]=4_04_78					,      __lowerCamelCase				:     List[Any]=5_12					,      __lowerCamelCase				:     List[str]=7_68					,      __lowerCamelCase				:     List[str]=12					,      __lowerCamelCase				:     Optional[Any]=12					,      __lowerCamelCase				:     str="gelu"					,      __lowerCamelCase				:     Optional[int]=0.1					,      __lowerCamelCase				:     Any=0.1					,      __lowerCamelCase				:     Dict=0.1					,      __lowerCamelCase				:     Any=1e-5					,      __lowerCamelCase				:     Optional[int]=0.02					,      __lowerCamelCase				:     Optional[int]="cls_index"					,      __lowerCamelCase				:     List[Any]=True					,      __lowerCamelCase				:     Optional[int]=None					,      __lowerCamelCase				:     Optional[Any]=True					,      __lowerCamelCase				:     Tuple=0.1					,      **__lowerCamelCase				:     Union[str, Any]					,      )					->     List[str]:
							a    =			vocab_size
							a    =			n_positions
							a    =			n_embd
							a    =			n_layer
							a    =			n_head
							a    =			afn
							a    =			resid_pdrop
							a    =			embd_pdrop
							a    =			attn_pdrop
							a    =			layer_norm_epsilon
							a    =			initializer_range
							a    =			summary_type
							a    =			summary_use_proj
							a    =			summary_activation
							a    =			summary_first_dropout
							a    =			summary_proj_to_labels
							super().__init__(**__lowerCamelCase							)






