


'''simple docstring'''






import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()







def 				UpperCamelCase_			(    A__					:   Union[str, Any]							,    A__					:   List[str]							,    A__					:   str					):
  '''simple docstring'''





  lowerCAmelCase_			:      List[str]			=   RemBertConfig.from_json_file(A__					)
  print("""Building PyTorch model from configuration: {}""".format(str(A__					)					)					)
  lowerCAmelCase_			:      Optional[Any]			=   RemBertModel(A__					)

  # Load weights from tf checkpoint
  load_tf_weights_in_rembert(A__							,    A__							,    A__					)

  # Save pytorch-model
  print("""Save PyTorch model to {}""".format(A__					)					)
  torch.save(model.state_dict()							,    A__					)


if __name__ == "__main__":
  __A					:  str											=      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(
      "--rembert_config_file",
      default=None,
      type=str,
      required=True,
      help=(
          "The config json file corresponding to the pre-trained RemBERT model. \n"
          "This specifies the model architecture."
      ),
  )
  parser.add_argument(
      "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  __A					:  List[str]											=      parser.parse_args()
  convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)







'''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())))



