



'''simple docstring'''

import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Any    ,	_SCREAMING_SNAKE_CASE      :Optional[int]    ,	_SCREAMING_SNAKE_CASE      :Dict    ,	_SCREAMING_SNAKE_CASE      :Union[str, Any]       ):
				# Initialise PyTorch model
				SCREAMING_SNAKE_CASE  :						List[Any]      					= FunnelConfig.from_json_file(lowerCAmelCase__       )
				print(F'''Building PyTorch model from configuration: {config}'''       )
				SCREAMING_SNAKE_CASE  :						int      					= FunnelBaseModel(lowerCAmelCase__       ) if base_model else FunnelModel(lowerCAmelCase__       )

				# Load weights from tf checkpoint
				load_tf_weights_in_funnel(lowerCAmelCase__    ,	lowerCAmelCase__    ,	lowerCAmelCase__       )

				# Save pytorch-model
				print(F'''Save PyTorch model to {pytorch_dump_path}'''       )
				torch.save(model.state_dict()    ,	lowerCAmelCase__       )


if __name__ == "__main__":
	snake_case_			=     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(
	    """--config_file""",
	    default=None,
	    type=str,
	    required=True,
	    help="""The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.""",
	)
	parser.add_argument(
	    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
	)
	parser.add_argument(
	    """--base_model""", action="""store_true""", help="""Whether you want just the base model (no decoder) or not."""
	)
	snake_case_			=     parser.parse_args()
	convert_tf_checkpoint_to_pytorch(
	    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
	)






'''simple docstring'''

from __future__ import annotations






def       __lowercase  (_SCREAMING_SNAKE_CASE      :int | str       ):
				SCREAMING_SNAKE_CASE  :						int      					= str(_SCREAMING_SNAKE_CASE       )
				return n == n[::-1]






def       __lowercase  (_SCREAMING_SNAKE_CASE      :int = 1_00_00_00       ):
				SCREAMING_SNAKE_CASE  :						int      					= 0

				for i in range(1    ,	_SCREAMING_SNAKE_CASE       ):
								if is_palindrome(_SCREAMING_SNAKE_CASE       ) and is_palindrome(bin(_SCREAMING_SNAKE_CASE       ).split('''b'''       )[1]       ):
												total += i
				return total


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




