





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


_lowerCAmelCase							:      Any						=	logging.get_logger(__name__)

_lowerCAmelCase							:      Any						=	{
    '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/config.json''',
    '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json''',
    '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/config.json''',
    '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json''',
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}


class 					__magic_name__  (							lowerCamelCase__							):





     """simple docstring"""



     __UpperCamelCase					    =     '''splinter'''



     def __init__(							self       :str			,      snake_case       :Dict=30_522			,      snake_case       :Optional[Any]=768			,      snake_case       :str=12			,      snake_case       :Dict=12			,      snake_case       :Any=3_072			,      snake_case       :List[str]="gelu"			,      snake_case       :List[Any]=0.1			,      snake_case       :List[str]=0.1			,      snake_case       :Dict=512			,      snake_case       :Any=2			,      snake_case       :Optional[Any]=0.02			,      snake_case       :Optional[Any]=1e-12			,      snake_case       :int=True			,      snake_case       :int=0			,      snake_case       :str=104			,      **snake_case       :List[str]			,      ):





        '''simple docstring'''




        super().__init__(pad_token_id=snake_case			,      **snake_case  )

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







def      __snake_case						(					_lowerCAmelCase : float				,				_lowerCAmelCase : float				,				_lowerCAmelCase : int	) ->						float:
   A_					:				List[str]          =			[1 / first_term]
   A_					:				Tuple          =			1 / first_term
   for _ in range(number_of_terms - 1	):
      first_term += common_difference
      arithmetic_progression.append(_lowerCAmelCase	)
   A_					:				List[str]          =			[1 / step for step in arithmetic_progression]
   return sum(_lowerCAmelCase	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(sum_of_harmonic_progression(1 / 2, 2, 2))
