


'''simple docstring'''






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


__A					:  Any											=      logging.get_logger(__name__)

__A					:  Optional[int]											=      {
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					'lilt'

    def __init__(   self    :  Tuple    ,						lowerCamelCase    :  Union[str, Any]=3_05_22    ,						lowerCamelCase    :  Dict=7_68    ,						lowerCamelCase    :  List[str]=12    ,						lowerCamelCase    :  Dict=12    ,						lowerCamelCase    :  Any=30_72    ,						lowerCamelCase    :  Optional[int]="gelu"    ,						lowerCamelCase    :  Optional[int]=0.1    ,						lowerCamelCase    :  List[str]=0.1    ,						lowerCamelCase    :  List[str]=5_12    ,						lowerCamelCase    :  str=2    ,						lowerCamelCase    :  str=0.02    ,						lowerCamelCase    :  Tuple=1E-12    ,						lowerCamelCase    :  List[str]=0    ,						lowerCamelCase    :  List[Any]="absolute"    ,						lowerCamelCase    :  Tuple=None    ,						lowerCamelCase    :  Dict=4    ,						lowerCamelCase    :  str=10_24    ,						**lowerCamelCase    :  int    ,						)   ->  Optional[int]:
      super().__init__(pad_token_id=lowerCamelCase    ,						**lowerCamelCase		)

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







'''simple docstring'''






def 				UpperCamelCase_			(    A__					:   int = 1_00					):
  '''simple docstring'''





  lowerCAmelCase_			:      int			=   set()

  lowerCAmelCase_			:      Tuple			=   0

  lowerCAmelCase_			:      str			=   n + 1  # maximum limit

  for a in range(2							,    A__					):
    for b in range(2							,    A__					):
      lowerCAmelCase_			:      str			=   a**b  # calculates the current power
      collect_powers.add(A__					)  # adds the result to the set
  return len(A__					)


if __name__ == "__main__":
  print("Number of terms ", solution(int(str(input()).strip())))



