





"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase_								=		logging.get_logger(__name__)

lowerCamelCase_								=		{
    "google/mobilenet_v1_1.0_224": "https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json",
    "google/mobilenet_v1_0.75_192": "https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}






class 					_SCREAMING_SNAKE_CASE(   _SCREAMING_SNAKE_CASE  ):
       SCREAMING_SNAKE_CASE_   :      Any							=  '''mobilenet_v1'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=2_24		,SCREAMING_SNAKE_CASE__=1.0		,SCREAMING_SNAKE_CASE__=8		,SCREAMING_SNAKE_CASE__="relu6"		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=0.9_9_9		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=0.0_0_1		,**SCREAMING_SNAKE_CASE__		,)			->      Tuple:





             """simple docstring"""






             super().__init__(**SCREAMING_SNAKE_CASE__							)

             if depth_multiplier <= 0:
                   raise ValueError('''depth_multiplier must be greater than zero.'''							)

             __SCREAMING_SNAKE_CASE   :Any						=  num_channels
             __SCREAMING_SNAKE_CASE   :str						=  image_size
             __SCREAMING_SNAKE_CASE   :List[str]						=  depth_multiplier
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  min_depth
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  hidden_act
             __SCREAMING_SNAKE_CASE   :Dict						=  tf_padding
             __SCREAMING_SNAKE_CASE   :str						=  classifier_dropout_prob
             __SCREAMING_SNAKE_CASE   :List[Any]						=  initializer_range
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  layer_norm_eps






class 					_SCREAMING_SNAKE_CASE(   _SCREAMING_SNAKE_CASE  ):
       SCREAMING_SNAKE_CASE_   :      Tuple							=  version.parse('''1.11'''  )




       @property
       def     _UpperCamelCase     (						self							)			->      Mapping[str, Mapping[int, str]]:





             """simple docstring"""






             return OrderedDict([('''pixel_values''', {0: '''batch'''})]							)




       @property
       def     _UpperCamelCase     (						self							)			->      Mapping[str, Mapping[int, str]]:





             """simple docstring"""






             if self.task == "image-classification":
                   return OrderedDict([('''logits''', {0: '''batch'''})]							)
             else:
                   return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]							)




       @property
       def     _UpperCamelCase     (						self							)			->      float:





             """simple docstring"""






             return 1E-4



'''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())))



