






'''simple docstring'''

import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE__											=     logging.getLogger(__name__)






@dataclass
class        a_		:
  lowercase  	=   42
  lowercase  	=   42
  lowercase  	=   42






@dataclass
class        a_		:
  lowercase  	=   42
  lowercase  	=   42
  lowercase  	=   None
  lowercase  	=   None






class        a_		(    lowerCamelCase       ):
  lowercase  	=   """train"""
  lowercase  	=   """dev"""
  lowercase  	=   """test"""






class        a_		:

  @staticmethod
  def 						A__		(   _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   List[InputExample]:



        """simple docstring"""






        raise NotImplementedError

  @staticmethod
  def 						A__		(   _SCREAMING_SNAKE_CASE   )						->   List[str]:



        """simple docstring"""






        raise NotImplementedError


  @staticmethod
  def 						A__		(   _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=False    ,	_SCREAMING_SNAKE_CASE="[CLS]"    ,	_SCREAMING_SNAKE_CASE=1    ,	_SCREAMING_SNAKE_CASE="[SEP]"    ,	_SCREAMING_SNAKE_CASE=False    ,	_SCREAMING_SNAKE_CASE=False    ,	_SCREAMING_SNAKE_CASE=0    ,	_SCREAMING_SNAKE_CASE=0    ,	_SCREAMING_SNAKE_CASE=-100    ,	_SCREAMING_SNAKE_CASE=0    ,	_SCREAMING_SNAKE_CASE=True    ,	)						->   List[InputFeatures]:



        """simple docstring"""






        UpperCamelCase       						=      {label: i for i, label in enumerate(_SCREAMING_SNAKE_CASE   )}

        UpperCamelCase       						=      []
        for ex_index, example in enumerate(_SCREAMING_SNAKE_CASE   ):
              if ex_index % 10000 == 0:
                    logger.info("""Writing example %d of %d"""    ,	_SCREAMING_SNAKE_CASE    ,	len(_SCREAMING_SNAKE_CASE   )   )

              UpperCamelCase       						=      []
              UpperCamelCase       						=      []
              for word, label in zip(example.words    ,	example.labels   ):
                    UpperCamelCase       						=      tokenizer.tokenize(_SCREAMING_SNAKE_CASE   )

                    # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                    if len(_SCREAMING_SNAKE_CASE   ) > 0:
                          tokens.extend(_SCREAMING_SNAKE_CASE   )
                          # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                          label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(_SCREAMING_SNAKE_CASE   ) - 1)   )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
              UpperCamelCase       						=      tokenizer.num_special_tokens_to_add()
              if len(_SCREAMING_SNAKE_CASE   ) > max_seq_length - special_tokens_count:
                    UpperCamelCase       						=      tokens[: (max_seq_length - special_tokens_count)]
                    UpperCamelCase       						=      label_ids[: (max_seq_length - special_tokens_count)]

              # The convention in BERT is:
              # (a) For sequence pairs:
              #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
              #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
              # (b) For single sequences:
              #  tokens:   [CLS] the dog is hairy . [SEP]
              #  type_ids:   0   0   0   0  0     0   0
              #
              # Where "type_ids" are used to indicate whether this is the first
              # sequence or the second sequence. The embedding vectors for `type=0` and
              # `type=1` were learned during pre-training and are added to the wordpiece
              # embedding vector (and position vector). This is not *strictly* necessary
              # since the [SEP] token unambiguously separates the sequences, but it makes
              # it easier for the model to learn the concept of sequences.
              #
              # For classification tasks, the first vector (corresponding to [CLS]) is
              # used as the "sentence vector". Note that this only makes sense because
              # the entire model is fine-tuned.
              tokens += [sep_token]
              label_ids += [pad_token_label_id]
              if sep_token_extra:
                    # roberta uses an extra separator b/w pairs of sentences
                    tokens += [sep_token]
                    label_ids += [pad_token_label_id]
              UpperCamelCase       						=      [sequence_a_segment_id] * len(_SCREAMING_SNAKE_CASE   )

              if cls_token_at_end:
                    tokens += [cls_token]
                    label_ids += [pad_token_label_id]
                    segment_ids += [cls_token_segment_id]
              else:
                    UpperCamelCase       						=      [cls_token] + tokens
                    UpperCamelCase       						=      [pad_token_label_id] + label_ids
                    UpperCamelCase       						=      [cls_token_segment_id] + segment_ids

              UpperCamelCase       						=      tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE   )

              # The mask has 1 for real tokens and 0 for padding tokens. Only real
              # tokens are attended to.
              UpperCamelCase       						=      [1 if mask_padding_with_zero else 0] * len(_SCREAMING_SNAKE_CASE   )

              # Zero-pad up to the sequence length.
              UpperCamelCase       						=      max_seq_length - len(_SCREAMING_SNAKE_CASE   )
              if pad_on_left:
                    UpperCamelCase       						=      ([pad_token] * padding_length) + input_ids
                    UpperCamelCase       						=      ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                    UpperCamelCase       						=      ([pad_token_segment_id] * padding_length) + segment_ids
                    UpperCamelCase       						=      ([pad_token_label_id] * padding_length) + label_ids
              else:
                    input_ids += [pad_token] * padding_length
                    input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                    segment_ids += [pad_token_segment_id] * padding_length
                    label_ids += [pad_token_label_id] * padding_length

              assert len(_SCREAMING_SNAKE_CASE   ) == max_seq_length
              assert len(_SCREAMING_SNAKE_CASE   ) == max_seq_length
              assert len(_SCREAMING_SNAKE_CASE   ) == max_seq_length
              assert len(_SCREAMING_SNAKE_CASE   ) == max_seq_length

              if ex_index < 5:
                    logger.info("""*** Example ***"""   )
                    logger.info("""guid: %s"""    ,	example.guid   )
                    logger.info("""tokens: %s"""    ,	""" """.join([str(_SCREAMING_SNAKE_CASE   ) for x in tokens]   )   )
                    logger.info("""input_ids: %s"""    ,	""" """.join([str(_SCREAMING_SNAKE_CASE   ) for x in input_ids]   )   )
                    logger.info("""input_mask: %s"""    ,	""" """.join([str(_SCREAMING_SNAKE_CASE   ) for x in input_mask]   )   )
                    logger.info("""segment_ids: %s"""    ,	""" """.join([str(_SCREAMING_SNAKE_CASE   ) for x in segment_ids]   )   )
                    logger.info("""label_ids: %s"""    ,	""" """.join([str(_SCREAMING_SNAKE_CASE   ) for x in label_ids]   )   )

              if "token_type_ids" not in tokenizer.model_input_names:
                    UpperCamelCase       						=      None

              features.append(
                  InputFeatures(
                      input_ids=_SCREAMING_SNAKE_CASE    ,	attention_mask=_SCREAMING_SNAKE_CASE    ,	token_type_ids=_SCREAMING_SNAKE_CASE    ,	label_ids=_SCREAMING_SNAKE_CASE   )   )
        return features


if is_torch_available():
     import torch
     from torch import nn
     from torch.utils.data import Dataset






     class        a_		(    lowerCamelCase       ):
       lowercase  	=   42
       lowercase  	=   nn.CrossEntropyLoss().ignore_index

       def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE=False    ,	_SCREAMING_SNAKE_CASE = Split.train    ,	)						->   Optional[int]:



             """simple docstring"""






             UpperCamelCase       						=      os.path.join(
                 _SCREAMING_SNAKE_CASE    ,	"""cached_{}_{}_{}""".format(mode.value    ,	tokenizer.__class__.__name__    ,	str(_SCREAMING_SNAKE_CASE   )   )    ,	)

             # Make sure only the first process in distributed training processes the dataset,
             # and the others will use the cache.
             UpperCamelCase       						=      cached_features_file + """.lock"""
             with FileLock(_SCREAMING_SNAKE_CASE   ):
                   if os.path.exists(_SCREAMING_SNAKE_CASE   ) and not overwrite_cache:
                         logger.info(F"Loading features from cached file {cached_features_file}"   )
                         UpperCamelCase       						=      torch.load(_SCREAMING_SNAKE_CASE   )
                   else:
                         logger.info(F"Creating features from dataset file at {data_dir}"   )
                         UpperCamelCase       						=      token_classification_task.read_examples_from_file(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
                         # TODO clean up all this to leverage built-in features of tokenizers
                         UpperCamelCase       						=      token_classification_task.convert_examples_to_features(
                             _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	cls_token_at_end=bool(model_type in ["""xlnet"""]   )    ,	cls_token=tokenizer.cls_token    ,	cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0    ,	sep_token=tokenizer.sep_token    ,	sep_token_extra=_SCREAMING_SNAKE_CASE    ,	pad_on_left=bool(tokenizer.padding_side == """left"""   )    ,	pad_token=tokenizer.pad_token_id    ,	pad_token_segment_id=tokenizer.pad_token_type_id    ,	pad_token_label_id=self.pad_token_label_id    ,	)
                         logger.info(F"Saving features into cached file {cached_features_file}"   )
                         torch.save(self.features    ,	_SCREAMING_SNAKE_CASE   )

       def __len__(   self   )						->   List[Any]:



             """simple docstring"""






             return len(self.features   )


       def __getitem__(   self    ,	_SCREAMING_SNAKE_CASE   )						->   InputFeatures:



             """simple docstring"""






             return self.features[i]


if is_tf_available():
     import tensorflow as tf






     class        a_		:
       lowercase  	=   42
       lowercase  	=   -1_00

       def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE = None    ,	_SCREAMING_SNAKE_CASE=False    ,	_SCREAMING_SNAKE_CASE = Split.train    ,	)						->   str:



             """simple docstring"""






             UpperCamelCase       						=      token_classification_task.read_examples_from_file(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )
             # TODO clean up all this to leverage built-in features of tokenizers
             UpperCamelCase       						=      token_classification_task.convert_examples_to_features(
                 _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	cls_token_at_end=bool(model_type in ["""xlnet"""]   )    ,	cls_token=tokenizer.cls_token    ,	cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0    ,	sep_token=tokenizer.sep_token    ,	sep_token_extra=_SCREAMING_SNAKE_CASE    ,	pad_on_left=bool(tokenizer.padding_side == """left"""   )    ,	pad_token=tokenizer.pad_token_id    ,	pad_token_segment_id=tokenizer.pad_token_type_id    ,	pad_token_label_id=self.pad_token_label_id    ,	)

             def gen():
                   for ex in self.features:
                         if ex.token_type_ids is None:
                               yield (
                                   {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                                   ex.label_ids,
                               )
                         else:
                               yield (
                                   {
                                       "input_ids": ex.input_ids,
                                       "attention_mask": ex.attention_mask,
                                       "token_type_ids": ex.token_type_ids,
                                   },
                                   ex.label_ids,
                               )

             if "token_type_ids" not in tokenizer.model_input_names:
                   UpperCamelCase       						=      tf.data.Dataset.from_generator(
                       _SCREAMING_SNAKE_CASE    ,	({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)    ,	(
                           {"""input_ids""": tf.TensorShape([None]   ), """attention_mask""": tf.TensorShape([None]   )},
                           tf.TensorShape([None]   ),
                       )    ,	)
             else:
                   UpperCamelCase       						=      tf.data.Dataset.from_generator(
                       _SCREAMING_SNAKE_CASE    ,	({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa)    ,	(
                           {
                               """input_ids""": tf.TensorShape([None]   ),
                               """attention_mask""": tf.TensorShape([None]   ),
                               """token_type_ids""": tf.TensorShape([None]   ),
                           },
                           tf.TensorShape([None]   ),
                       )    ,	)

       def 						A__		(   self   )						->   Optional[Any]:



             """simple docstring"""






             UpperCamelCase       						=      self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features   )   )   )

             return self.dataset

       def __len__(   self   )						->   int:



             """simple docstring"""






             return len(self.features   )


       def __getitem__(   self    ,	_SCREAMING_SNAKE_CASE   )						->   InputFeatures:



             """simple docstring"""






             return self.features[i]








'''simple docstring'''

from math import sqrt







def 					lowercase__				(   __UpperCamelCase					)->				int:
      UpperCamelCase       						=      0
      for i in range(1	,		int(sqrt(__UpperCamelCase					) + 1					)					):
            if n % i == 0 and i != sqrt(__UpperCamelCase					):
                  total += i + n // i
            elif i == sqrt(__UpperCamelCase					):
                  total += i
      return total - n







def 					lowercase__				(   __UpperCamelCase = 10000					)->				int:
      UpperCamelCase       						=      sum(
          i
          for i in range(1	,		__UpperCamelCase					)
          if sum_of_divisors(sum_of_divisors(__UpperCamelCase					)					) == i and sum_of_divisors(__UpperCamelCase					) != i					)
      return total


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