

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation


__A	:   int	=					logging.get_logger(__name__)

__A	:   List[str]	=					{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__A	:   List[Any]	=					{
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

__A	:   List[Any]	=					{
    'microsoft/deberta-base': 5_12,
    'microsoft/deberta-large': 5_12,
    'microsoft/deberta-xlarge': 5_12,
    'microsoft/deberta-base-mnli': 5_12,
    'microsoft/deberta-large-mnli': 5_12,
    'microsoft/deberta-xlarge-mnli': 5_12,
}

__A	:   int	=					{
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}


class 			_SCREAMING_SNAKE_CASE      (  __a):
						_UpperCamelCase:Optional[int]	=		VOCAB_FILES_NAMES
						_UpperCamelCase:List[str]	=		PRETRAINED_VOCAB_FILES_MAP
						_UpperCamelCase:Optional[Any]	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_UpperCamelCase:Tuple	=		["input_ids", "attention_mask", "token_type_ids"]
						_UpperCamelCase:int	=		DebertaTokenizer







						def __init__(			self     ,						_SCREAMING_SNAKE_CASE=None     ,						_SCREAMING_SNAKE_CASE=None     ,						_SCREAMING_SNAKE_CASE=None     ,						_SCREAMING_SNAKE_CASE="replace"     ,						_SCREAMING_SNAKE_CASE="[CLS]"     ,						_SCREAMING_SNAKE_CASE="[SEP]"     ,						_SCREAMING_SNAKE_CASE="[SEP]"     ,						_SCREAMING_SNAKE_CASE="[CLS]"     ,						_SCREAMING_SNAKE_CASE="[UNK]"     ,						_SCREAMING_SNAKE_CASE="[PAD]"     ,						_SCREAMING_SNAKE_CASE="[MASK]"     ,						_SCREAMING_SNAKE_CASE=False     ,						**_SCREAMING_SNAKE_CASE     ,						)->							Optional[Any]:
													super().__init__(
													    lowerCAmelCase_     ,						lowerCAmelCase_     ,						tokenizer_file=lowerCAmelCase_     ,						errors=lowerCAmelCase_     ,						bos_token=lowerCAmelCase_     ,						eos_token=lowerCAmelCase_     ,						unk_token=lowerCAmelCase_     ,						sep_token=lowerCAmelCase_     ,						cls_token=lowerCAmelCase_     ,						pad_token=lowerCAmelCase_     ,						mask_token=lowerCAmelCase_     ,						add_prefix_space=lowerCAmelCase_     ,						**lowerCAmelCase_     ,						)
													lowerCamelCase_								=kwargs.pop("""add_bos_token"""     ,						lowerCAmelCase_							)

													lowerCamelCase_								=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
													if pre_tok_state.get("""add_prefix_space"""     ,						lowerCAmelCase_							) != add_prefix_space:
																				lowerCamelCase_								=getattr(lowerCAmelCase_     ,						pre_tok_state.pop("""type"""							)							)
																				lowerCamelCase_								=add_prefix_space
																				lowerCamelCase_								=pre_tok_class(**lowerCAmelCase_							)

													lowerCamelCase_								=add_prefix_space







						@property
						def        _snake_case				(			self							)->							str:







													if self._mask_token is None:
																				if self.verbose:
																											logger.error("""Using mask_token, but it is not set yet."""							)
																				return None
													return str(self._mask_token							)







						@mask_token.setter
						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							Optional[int]:
													lowerCamelCase_								=AddedToken(lowerCAmelCase_     ,						lstrip=lowerCAmelCase_     ,						rstrip=lowerCAmelCase_							) if isinstance(lowerCAmelCase_     ,						lowerCAmelCase_							) else value
													lowerCamelCase_								=value







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE = None							)->							List[int]:







													if token_ids_a is None:
																				return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
													lowerCamelCase_								=[self.cls_token_id]
													lowerCamelCase_								=[self.sep_token_id]
													return cls + token_ids_a + sep + token_ids_a + sep







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE = None							)->							List[int]:
													lowerCamelCase_								=[self.sep_token_id]
													lowerCamelCase_								=[self.cls_token_id]

													if token_ids_a is None:
																				return len(cls + token_ids_a + sep							) * [0]
													return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]







						def        _snake_case				(			self     ,						*_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE							)->							BatchEncoding:
													lowerCamelCase_								=kwargs.get("""is_split_into_words"""     ,						lowerCAmelCase_							)
													assert self.add_prefix_space or not is_split_into_words, (
													    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
													    "to use it with pretokenized inputs."
													)

													return super()._batch_encode_plus(*lowerCAmelCase_     ,						**lowerCAmelCase_							)







						def        _snake_case				(			self     ,						*_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE							)->							BatchEncoding:
													lowerCamelCase_								=kwargs.get("""is_split_into_words"""     ,						lowerCAmelCase_							)

													assert self.add_prefix_space or not is_split_into_words, (
													    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
													    "to use it with pretokenized inputs."
													)

													return super()._encode_plus(*lowerCAmelCase_     ,						**lowerCAmelCase_							)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE = None							)->							Tuple[str]:
													lowerCamelCase_								=self._tokenizer.model.save(lowerCAmelCase_     ,						name=lowerCAmelCase_							)
													return tuple(lowerCAmelCase_							)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							List[int]:
													lowerCamelCase_								=[]
													for is_user, text in conversation.iter_texts():
																				input_ids.extend(self.encode(lowerCAmelCase_     ,						add_special_tokens=lowerCAmelCase_							) + [self.eos_token_id]							)

													if len(lowerCAmelCase_							) > self.model_max_length:
																				lowerCamelCase_								=input_ids[-self.model_max_length :]
													return input_ids





'''simple docstring'''


from math import floor, sqrt






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     a_									=0.0
     a_									=1.0
     a_									=int(sqrt(lowercase__							)							)
     a_									=root
     a_									=0
     while integer_part != 2 * root:
          a_									=denominator * integer_part - numerator
          a_									=(n - numerator**2) / denominator
          a_									=int((root + numerator) / denominator							)
          period += 1
     return period






def 			UpperCAmelCase_							(		lowercase__ = 1_0_0_0_0							):


     '''simple docstring'''







     a_									=0
     for i in range(2							,						n + 1							):
          a_									=sqrt(lowercase__							)
          if sr - floor(lowercase__							) != 0 and continuous_fraction_period(lowercase__							) % 2 == 1:
               count_odd_periods += 1
     return count_odd_periods


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


