
import argparse

import torch

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


logging.set_verbosity_info()





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):
    # Initialise PyTorch model
    snake_case__						=				RemBertConfig.from_json_file(__lowerCAmelCase							)
    print("Building PyTorch model from configuration: {}".format(str(__lowerCAmelCase							)							)							)
    snake_case__						=				RemBertModel(__lowerCAmelCase							)

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

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


if __name__ == "__main__":
     __magic_name__            =  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.'''
     )
     __magic_name__            =  parser.parse_args()
     convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)





from math import factorial





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase = 100							):
    return sum(int(__lowerCAmelCase							) for x in str(factorial(__lowerCAmelCase							)							)							)


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



