


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


snake_case				:    Tuple       				=     logging.get_logger(__name__)

snake_case				:    Dict       				=     {
    '''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       'transfo-xl'
 SCREAMING_SNAKE_CASE__      =       ['mems']
 SCREAMING_SNAKE_CASE__      =       {
     'n_token': 'vocab_size',
     'hidden_size': 'd_model',
     'num_attention_heads': 'n_head',
     'num_hidden_layers': 'n_layer',
 }




 def __init__(		self					,  _lowerCamelCase=26_7735					,  _lowerCamelCase=[2_0000, 4_0000, 20_0000]					,  _lowerCamelCase=1024					,  _lowerCamelCase=1024					,  _lowerCamelCase=16					,  _lowerCamelCase=64					,  _lowerCamelCase=4096					,  _lowerCamelCase=4					,  _lowerCamelCase=False					,  _lowerCamelCase=18					,  _lowerCamelCase=1600					,  _lowerCamelCase=1000					,  _lowerCamelCase=True					,  _lowerCamelCase=True					,  _lowerCamelCase=0					,  _lowerCamelCase=-1					,  _lowerCamelCase=True					,  _lowerCamelCase=0.1					,  _lowerCamelCase=0.0					,  _lowerCamelCase=True					,  _lowerCamelCase="normal"					,  _lowerCamelCase=0.01					,  _lowerCamelCase=0.01					,  _lowerCamelCase=0.02					,  _lowerCamelCase=1e-5					,  _lowerCamelCase=0					,  **_lowerCamelCase					,  ):
       a						:List[str] 							=       vocab_size
       a						:Union[str, Any] 							=       []
       self.cutoffs.extend(_lowerCamelCase    )
       if proj_share_all_but_first:
             a						:Optional[int] 							=       [False] + [True] * len(self.cutoffs    )
       else:
             a						:Any 							=       [False] + [False] * len(self.cutoffs    )
       a						:Optional[int] 							=       d_model
       a						:Union[str, Any] 							=       d_embed
       a						:str 							=       d_head
       a						:Optional[Any] 							=       d_inner
       a						:Optional[Any] 							=       div_val
       a						:int 							=       pre_lnorm
       a						:Dict 							=       n_layer
       a						:List[Any] 							=       n_head
       a						:Any 							=       mem_len
       a						:Any 							=       same_length
       a						:str 							=       attn_type
       a						:Optional[Any] 							=       clamp_len
       a						:Optional[int] 							=       sample_softmax
       a						:Optional[int] 							=       adaptive
       a						:Optional[int] 							=       dropout
       a						:Tuple 							=       dropatt
       a						:Dict 							=       untie_r
       a						:List[Any] 							=       init
       a						:int 							=       init_range
       a						:Optional[int] 							=       proj_init_std
       a						:Optional[Any] 							=       init_std
       a						:Optional[Any] 							=       layer_norm_epsilon
       super().__init__(eos_token_id=_lowerCamelCase					,  **_lowerCamelCase    )




 @property
 def        SCREAMING_SNAKE_CASE__				(		self    ):
       # Message copied from Transformer-XL documentation
       logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''    )
       return -1




 @max_position_embeddings.setter
 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       # Message copied from Transformer-XL documentation
       raise NotImplementedError(
           F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''    )









def 							__lowercase   ( __lowerCAmelCase							:				str					,    __lowerCAmelCase							:				str   ):

							def get_matched_characters(__lowerCAmelCase							:				str					,    __lowerCAmelCase							:				str   ) -> 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(__lowerCAmelCase   )
																												a__             =     F'{_stra[0:_stra.index(__lowerCAmelCase   )]} {_stra[_stra.index(__lowerCAmelCase   ) + 1:]}'

														return "".join(__lowerCAmelCase   )

							# matching characters
							a__             =     get_matched_characters(__lowerCAmelCase					,    __lowerCAmelCase   )
							a__             =     get_matched_characters(__lowerCAmelCase					,    __lowerCAmelCase   )
							a__             =     len(__lowerCAmelCase   )

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

							if not match_count:
														a__             =     0.0
							else:
														a__             =     (
														    1
														    / 3
														    * (
														        match_count / len(__lowerCAmelCase   )
														        + match_count / len(__lowerCAmelCase   )
														        + (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'''))




