


"""simple docstring"""






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


__snake_case     :   Tuple		=       logging.get_logger(__name__)

__snake_case     :   str		=       {
    '''SCUT-DLVCLab/lilt-roberta-en-base''': (
        '''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'''
    ),
}


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	int    =		'lilt'


     def __init__(      self    ,				__UpperCAmelCase=30522    ,				__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    ,				)       ->							Optional[Any]:
          super().__init__(pad_token_id=__UpperCAmelCase    ,				**__UpperCAmelCase							)

          _a    	=				vocab_size
          _a    	=				hidden_size
          _a    	=				num_hidden_layers
          _a    	=				num_attention_heads
          _a    	=				hidden_act
          _a    	=				intermediate_size
          _a    	=				hidden_dropout_prob
          _a    	=				attention_probs_dropout_prob
          _a    	=				max_position_embeddings
          _a    	=				type_vocab_size
          _a    	=				initializer_range
          _a    	=				layer_norm_eps
          _a    	=				position_embedding_type
          _a    	=				classifier_dropout
          _a    	=				channel_shrink_ratio
          _a    	=				max_ad_position_embeddings



"""simple docstring"""






def       A_   (   _lowerCAmelCase     :					list,     _lowerCAmelCase     :					list,     _lowerCAmelCase     :					int       ):






     """simple docstring"""

     _a    	=				len(_lowerCAmelCase       )
     _a    	=				[[0] * n for i in range(_lowerCAmelCase       )]
     for i in range(_lowerCAmelCase       ):
          _a    	=				y_points[i]

     for i in range(2,     _lowerCAmelCase       ):
          for j in range(_lowerCAmelCase,     _lowerCAmelCase       ):
               _a    	=				(
                   (xa - x_points[j - i + 1]) * q[j][i - 1]
                   - (xa - x_points[j]) * q[j - 1][i - 1]
               ) / (x_points[j] - x_points[j - i + 1])

     return [q[n - 1][n - 1], q]


if __name__ == "__main__":
   import doctest

   doctest.testmod()