





'''simple docstring'''




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


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

lowercase__       : List[Any]         =       {
    '''SCUT-DLVCLab/lilt-roberta-en-base''': (
        '''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'''
    ),
}


class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			'''lilt'''


     def __init__(		self		,      _UpperCAmelCase=3_0522		,      _UpperCAmelCase=768		,      _UpperCAmelCase=12		,      _UpperCAmelCase=12		,      _UpperCAmelCase=3072		,      _UpperCAmelCase="gelu"		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=512		,      _UpperCAmelCase=2		,      _UpperCAmelCase=0.02		,      _UpperCAmelCase=1e-1_2		,      _UpperCAmelCase=0		,      _UpperCAmelCase="absolute"		,      _UpperCAmelCase=None		,      _UpperCAmelCase=4		,      _UpperCAmelCase=1024		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






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

          __A		:					str						=			vocab_size
          __A		:					Any						=			hidden_size
          __A		:					Optional[int]						=			num_hidden_layers
          __A		:					List[Any]						=			num_attention_heads
          __A		:					Union[str, Any]						=			hidden_act
          __A		:					Union[str, Any]						=			intermediate_size
          __A		:					str						=			hidden_dropout_prob
          __A		:					str						=			attention_probs_dropout_prob
          __A		:					Any						=			max_position_embeddings
          __A		:					Tuple						=			type_vocab_size
          __A		:					Optional[int]						=			initializer_range
          __A		:					Union[str, Any]						=			layer_norm_eps
          __A		:					str						=			position_embedding_type
          __A		:					int						=			classifier_dropout
          __A		:					int						=			channel_shrink_ratio
          __A		:					int						=			max_ad_position_embeddings






'''simple docstring'''




def 			_lowerCAmelCase     (    __snake_case    :					int					,				__snake_case    :					int					,				__snake_case    :					int     )	->	float:
     __A		:					Dict						=			(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
     # formula for sum of series
     return total






def 			_lowerCAmelCase     (    )	->	Union[str, Any]:
     print(sum_of_series(1					,				1					,				10     )     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()