



'''simple docstring'''

import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class 	a__     (				_lowercase	):
	__magic_name__		:    int	    =     ["image_processor", "tokenizer"]
	__magic_name__		:    int	    =     "AutoImageProcessor"
	__magic_name__		:    List[str]	    =     "AutoTokenizer"


	def __init__(self : str,					__UpperCAmelCase : List[str]=None,					__UpperCAmelCase : Any=None,					**__UpperCAmelCase : int      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= None
					if "feature_extractor" in kwargs:
									warnings.warn(
									    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
									    ''' instead.''',					__UpperCAmelCase,					)
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= kwargs.pop('''feature_extractor'''      )

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
									raise ValueError('''You need to specify an `image_processor`.'''      )
					if tokenizer is None:
									raise ValueError('''You need to specify a `tokenizer`.'''      )

					super().__init__(__UpperCAmelCase,					__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.image_processor
					SCREAMING_SNAKE_CASE  :						int      					= False


	def __call__(self : Union[str, Any],					*__UpperCAmelCase : str,					**__UpperCAmelCase : Any      )     ->			Dict:

					"""simple docstring"""

					if self._in_target_context_manager:
									return self.current_processor(*__UpperCAmelCase,					**__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= kwargs.pop('''images''',					__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= kwargs.pop('''text''',					__UpperCAmelCase      )
					if len(__UpperCAmelCase      ) > 0:
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= args[0]
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= args[1:]

					if images is None and text is None:
									raise ValueError('''You need to specify either an `images` or `text` input to process.'''      )

					if images is not None:
									SCREAMING_SNAKE_CASE  :						int      					= self.image_processor(__UpperCAmelCase,					*__UpperCAmelCase,					**__UpperCAmelCase      )
					if text is not None:
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.tokenizer(__UpperCAmelCase,					**__UpperCAmelCase      )

					if text is None:
									return inputs
					elif images is None:
									return encodings
					else:
									SCREAMING_SNAKE_CASE  :						List[str]      					= encodings['''input_ids''']
									return inputs


	def 				lowercase__  (self : Any,					*__UpperCAmelCase : Optional[int],					**__UpperCAmelCase : Optional[int]      )     ->			List[str]:

					"""simple docstring"""

					return self.tokenizer.batch_decode(*__UpperCAmelCase,					**__UpperCAmelCase      )


	def 				lowercase__  (self : Dict,					*__UpperCAmelCase : str,					**__UpperCAmelCase : int      )     ->			List[str]:

					"""simple docstring"""

					return self.tokenizer.decode(*__UpperCAmelCase,					**__UpperCAmelCase      )


	@contextmanager
	def 				lowercase__  (self : Tuple      )     ->			Union[str, Any]:

					"""simple docstring"""

					warnings.warn(
					    '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
					    '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
					    '''your images inputs, or in a separate call.'''      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= True
					SCREAMING_SNAKE_CASE  :						Any      					= self.tokenizer
					yield
					SCREAMING_SNAKE_CASE  :						List[str]      					= self.image_processor
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= False


	def 				lowercase__  (self : Dict,					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Tuple=False,					__UpperCAmelCase : Union[str, Any]=None      )     ->			Optional[int]:

					"""simple docstring"""

					if added_vocab is None:
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.tokenizer.get_added_vocab()

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= {}

					while tokens:
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= re.search(R'''<s_(.*?)>''',					__UpperCAmelCase,					re.IGNORECASE      )
									if start_token is None:
													break
									SCREAMING_SNAKE_CASE  :						Dict      					= start_token.group(1      )
									SCREAMING_SNAKE_CASE  :						Tuple      					= re.search(RF'''</s_{key}>''',					__UpperCAmelCase,					re.IGNORECASE      )
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= start_token.group()
									if end_token is None:
													SCREAMING_SNAKE_CASE  :						Tuple      					= tokens.replace(__UpperCAmelCase,					''''''      )
									else:
													SCREAMING_SNAKE_CASE  :						List[Any]      					= end_token.group()
													SCREAMING_SNAKE_CASE  :						Any      					= re.escape(__UpperCAmelCase      )
													SCREAMING_SNAKE_CASE  :						List[str]      					= re.escape(__UpperCAmelCase      )
													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= re.search(F'''{start_token_escaped}(.*?){end_token_escaped}''',					__UpperCAmelCase,					re.IGNORECASE      )
													if content is not None:
																	SCREAMING_SNAKE_CASE  :						Tuple      					= content.group(1      ).strip()
																	if r"<s_" in content and r"</s_" in content:  # non-leaf node
																					SCREAMING_SNAKE_CASE  :						Any      					= self.tokenajson(__UpperCAmelCase,					is_inner_value=__UpperCAmelCase,					added_vocab=__UpperCAmelCase      )
																					if value:
																									if len(__UpperCAmelCase      ) == 1:
																													SCREAMING_SNAKE_CASE  :						List[Any]      					= value[0]
																									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= value
																	else:  # leaf nodes
																					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= []
																					for leaf in content.split(R'''<sep/>'''      ):
																									SCREAMING_SNAKE_CASE  :						str      					= leaf.strip()
																									if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																													SCREAMING_SNAKE_CASE  :						List[str]      					= leaf[1:-2]  # for categorical special tokens
																									output[key].append(__UpperCAmelCase      )
																					if len(output[key]      ) == 1:
																									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= output[key][0]

													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= tokens[tokens.find(__UpperCAmelCase      ) + len(__UpperCAmelCase      ) :].strip()
													if tokens[:6] == r"<sep/>":  # non-leaf nodes
																	return [output] + self.tokenajson(tokens[6:],					is_inner_value=__UpperCAmelCase,					added_vocab=__UpperCAmelCase      )

					if len(__UpperCAmelCase      ):
									return [output] if is_inner_value else output
					else:
									return [] if is_inner_value else {"text_sequence": tokens}


	@property
	def 				lowercase__  (self : Union[str, Any]      )     ->			int:

					"""simple docstring"""

					warnings.warn(
					    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''',					__UpperCAmelCase,					)
					return self.image_processor_class






	@property
	def 				lowercase__  (self : Any      )     ->			Union[str, Any]:

					"""simple docstring"""

					warnings.warn(
					    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''',					__UpperCAmelCase,					)
					return self.image_processor






'''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()))))




