



'''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     __UpperCamelCase					(				_lowercase,					_lowercase,					_lowercase       )			->      int:
	# Initialise PyTorch model
	_lowercase       :							Dict  	=		RoFormerConfig.from_json_file(_lowercase       )
	print(f'''Building PyTorch model from configuration: {config}'''       )
	_lowercase       :							List[str]  	=		RoFormerForMaskedLM(_lowercase       )

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

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


if __name__ == "__main__":
		_A					:     List[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(
		    '''--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					:     Any      =parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





'''simple docstring'''

def     __UpperCamelCase					(				_lowercase = 1000       )			->      int:
	_lowercase       :							Optional[int]  	=		[0, 3, 3, 5, 4, 4, 3, 5, 5, 4, 3, 6, 6, 8, 8, 7, 7, 9, 8, 8]
	# number of letters in twenty, thirty, ..., ninety (0 for numbers less than
	# 20 due to inconsistency in teens)
	_lowercase       :							List[Any]  	=		[0, 0, 6, 6, 5, 5, 5, 7, 6, 6]

	_lowercase       :							Any  	=		0

	for i in range(1,					n + 1       ):
		if i < 1000:
			if i >= 100:
				# add number of letters for "n hundred"
				count += ones_counts[i // 100] + 7

				if i % 100 != 0:
					# add number of letters for "and" if number is not multiple
					# of 100
					count += 3

			if 0 < i % 100 < 20:
				# add number of letters for one, two, three, ..., nineteen
				# (could be combined with below if not for inconsistency in
				# teens)
				count += ones_counts[i % 100]
			else:
				# add number of letters for twenty, twenty one, ..., ninety
				# nine
				count += ones_counts[i % 10]
				count += tens_counts[(i % 100 - i % 10) // 10]
		else:
			count += ones_counts[i // 1000] + 8
	return count


if __name__ == "__main__":
		print(solution(int(input().strip())))
