






import argparse

import torch

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


logging.set_verbosity_info()


def     _a					(       UpperCamelCase_   :			Tuple      ,				UpperCamelCase_   :			List[str]      ,				UpperCamelCase_   :			List[Any]				)	->				Union[str, Any]:
						"""simple docstring"""







						lowerCAmelCase__	 =  RoFormerConfig.from_json_file(UpperCamelCase_				)
						print(F"Building PyTorch model from configuration: {config}"				)
						lowerCAmelCase__	 =  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__":
	a_			=      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.'''
	)
	a_			=      parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)














def     _a					(       UpperCamelCase_   :			int = 100				)	->				int:
						"""simple docstring"""







						lowerCAmelCase__	 =  1
						lowerCAmelCase__	 =  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())))






