



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


_lowercase     =		logging.get_logger(__name__)

_lowercase     =		{
    "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''xglm'''
     UpperCamelCase__		    =				['''past_key_values''']

     UpperCamelCase__		    =				{
         '''num_attention_heads''': '''attention_heads''',
         '''hidden_size''': '''d_model''',
         '''num_hidden_layers''': '''num_layers''',
     }




     def __init__(				self ,						a__=2_5_6_0_0_8 ,						a__=2_0_4_8 ,						a__=1_0_2_4 ,						a__=4_0_9_6 ,						a__=2_4 ,						a__=1_6 ,						a__="gelu" ,						a__=0.1 ,						a__=0.1 ,						a__=0.0 ,						a__=0.0 ,						a__=0.0_2 ,						a__=True ,						a__=True ,						a__=2 ,						a__=1 ,						a__=0 ,						a__=2 ,						**a__ ,						):
         A__		 =					vocab_size
         A__		 =					max_position_embeddings
         A__		 =					d_model
         A__		 =					ffn_dim
         A__		 =					num_layers
         A__		 =					attention_heads
         A__		 =					activation_function
         A__		 =					dropout
         A__		 =					attention_dropout
         A__		 =					activation_dropout
         A__		 =					layerdrop
         A__		 =					init_std
         A__		 =					scale_embedding  # scale factor will be sqrt(d_model) if True
         A__		 =					use_cache

         super().__init__(
             pad_token_id=a__ ,						bos_token_id=a__ ,						eos_token_id=a__ ,						decoder_start_token_id=a__ ,						**a__ ,						)





def 		lowerCAmelCase__    (					UpperCamelCase_				:       str ,       UpperCamelCase_				:       str	)->  float:

    def get_matched_characters(UpperCamelCase_				:       str ,       UpperCamelCase_				:       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(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"))
