






"""simple docstring"""


def    lowerCAmelCase							(       __UpperCamelCase = 1_0_0					):




  """simple docstring"""







  __A		     =					1
  __A		     =					0
  for i in range(1						,			num + 1					):
    fact *= i

  for j in str(__UpperCamelCase					):
    result += int(__UpperCamelCase					)

  return result


if __name__ == "__main__":
       print(solution(int(input('Enter the Number: ').strip())))












"""simple docstring"""


import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A		     =					RoFormerConfig.from_json_file(__UpperCamelCase					)
  print(f'Building PyTorch model from configuration: {config}'					)
  __A		     =					RoFormerForMaskedLM(__UpperCamelCase					)

  # Load weights from tf checkpoint
  load_tf_weights_in_roformer(__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					)

  # Save pytorch-model
  print(f'Save PyTorch model to {pytorch_dump_path}'					)
  torch.save(model.state_dict()						,			__UpperCamelCase						,			_use_new_zipfile_serialization=__UpperCamelCase					)


if __name__ == "__main__":
       lowercase_								=	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(
           '--bert_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained BERT 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.'
       )
       lowercase_								=	parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




