


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


lowercase_	   =     logging.get_logger(__name__)

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


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """lilt"""




      def __init__(    self:	Tuple						,				a:	str=3_0522						,				a:	Tuple=768						,				a:	List[str]=12						,				a:	Optional[Any]=12						,				a:	List[Any]=3072						,				a:	List[Any]="gelu"						,				a:	int=0.1						,				a:	str=0.1						,				a:	List[Any]=512						,				a:	Union[str, Any]=2						,				a:	List[str]=0.0_2						,				a:	Any=1e-12						,				a:	List[Any]=0						,				a:	int="absolute"						,				a:	Optional[Any]=None						,				a:	Union[str, Any]=4						,				a:	Union[str, Any]=1024						,				**a:	Optional[Any]						,				):
       super().__init__(pad_token_id=a						,				**a    )

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





from math import pow
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	):
 if current_sum == needed_sum:
  # If the sum of the powers is equal to needed_sum, then we have a solution.
  solutions_count += 1
  return current_sum, solutions_count

 __lowerCamelCase		:							Optional[Any]		 =						int(pow(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)						)
 if current_sum + i_to_n <= needed_sum:
  # If the sum of the powers is less than needed_sum, then continue adding powers.
  current_sum += i_to_n
  __lowerCamelCase ,					__lowerCamelCase		:							Optional[Any]		 =						backtrack(
      SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	current_number + 1      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)
  current_sum -= i_to_n
 if i_to_n < needed_sum:
  # If the power of i is less than needed_sum, then try with the next power.
  __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						backtrack(
      SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	current_number + 1      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)
 return current_sum, solutions_count
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 if not (1 <= needed_sum <= 1_000 and 2 <= power <= 10):
  raise ValueError(
      'Invalid input\n'
      'needed_sum must be between 1 and 1000, power between 2 and 10.'						)

 return backtrack(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	1      ,	0      ,	0						)[1]  # Return the solutions_count


if __name__ == "__main__":
       import doctest

       doctest.testmod()

