



def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int = 100				)  ->     int:
   _A      =       set()

   _A      =       0

   _A      =       n + 1  # maximum limit

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


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









import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()





def        __UpperCAmelCase						(							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     Any  ,							lowerCamelCase_ :     int  ,							lowerCamelCase_ :     List[Any]  )       ->  int:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      FunnelConfig.from_json_file(lowerCamelCase_  )
			print(F'Building PyTorch model from configuration: {config}'  )
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      FunnelBaseModel(lowerCamelCase_  ) if base_model else FunnelModel(lowerCamelCase_  )

			# Load weights from tf checkpoint
			load_tf_weights_in_funnel(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  )

			# Save pytorch-model
			print(F'Save PyTorch model to {pytorch_dump_path}'  )
			torch.save(model.state_dict()  ,							lowerCamelCase_  )


if __name__ == "__main__":
						UpperCamelCase__ :   Optional[Any]						=							argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
						)
						parser.add_argument(
						    '''--config_file''',
						    default=None,
						    type=str,
						    required=True,
						    help='''The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
						)
						parser.add_argument(
						    '''--base_model''', action='''store_true''', help='''Whether you want just the base model (no decoder) or not.'''
						)
						UpperCamelCase__ :   Tuple						=							parser.parse_args()
						convert_tf_checkpoint_to_pytorch(
						    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
						)



