



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


_lowercase						  =		logging.get_logger(__name__)

_lowercase						  =		{}



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




     def __init__(				self ,						a__=3_2_0_0_0 ,						a__=4_0_9_6 ,						a__=1_1_0_0_8 ,						a__=3_2 ,						a__=3_2 ,						a__=None ,						a__="silu" ,						a__=2_0_4_8 ,						a__=0.0_2 ,						a__=1e-6 ,						a__=True ,						a__=0 ,						a__=1 ,						a__=2 ,						a__=1 ,						a__=False ,						a__=None ,						**a__ ,						):
         A__		 =					vocab_size
         A__		 =					max_position_embeddings
         A__		 =					hidden_size
         A__		 =					intermediate_size
         A__		 =					num_hidden_layers
         A__		 =					num_attention_heads

         # for backward compatibility
         if num_key_value_heads is None:
             A__		 =					num_attention_heads

         A__		 =					num_key_value_heads
         A__		 =					hidden_act
         A__		 =					initializer_range
         A__		 =					rms_norm_eps
         A__		 =					pretraining_tp
         A__		 =					use_cache
         A__		 =					rope_scaling
         self._rope_scaling_validation()

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




     def 		snake_case_		(				self):
         if self.rope_scaling is None:
             return

         if not isinstance(self.rope_scaling ,						a__) or len(self.rope_scaling) != 2:
             raise ValueError(
                 '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
                 F"got {self.rope_scaling}")
         A__		 =					self.rope_scaling.get('''type''' ,						a__)
         A__		 =					self.rope_scaling.get('''factor''' ,						a__)
         if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
             raise ValueError(
                 F"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}")
         if rope_scaling_factor is None or not isinstance(a__ ,						a__) or rope_scaling_factor <= 1.0:
             raise ValueError(F"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")





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"))
