

'''simple docstring'''
from math import sqrt







def  _A						(     snake_case					)      ->     int:
   _lowercase      :				str      					=       0
   for i in range(1		,							int(sqrt(snake_case					) + 1					)					):
      if n % i == 0 and i != sqrt(snake_case					):
         total += i + n // i
      elif i == sqrt(snake_case					):
         total += i
   return total - n







def  _A						(     snake_case = 1_00_00					)      ->     int:
   _lowercase      :				List[Any]      					=       sum(
       i
       for i in range(1		,							snake_case					)
       if sum_of_divisors(sum_of_divisors(snake_case					)					) == i and sum_of_divisors(snake_case					) != i					)
   return total


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









'''simple docstring'''
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score

import datasets


_snake_case         =  '\\n    @inproceedings{kakwani2020indicnlpsuite,\n    title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},\n    author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},\n    year={2020},\n    booktitle={Findings of EMNLP},\n}\n'

_snake_case         =  '\\n    IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide\n    variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.\n'

_snake_case         =  '\nCompute IndicGLUE evaluation metric associated to each IndicGLUE dataset.\nArgs:\n    predictions: list of predictions to score (as int64),\n        except for \'cvit-mkb-clsr\' where each prediction is a vector (of float32).\n    references: list of ground truth labels corresponding to the predictions (as int64),\n        except for \'cvit-mkb-clsr\' where each reference is a vector (of float32).\nReturns: depending on the IndicGLUE subset, one or several of:\n    "accuracy": Accuracy\n    "f1": F1 score\n    "precision": Precision@10\nExamples:\n\n    >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wnli\')  # \'wnli\' or any of ["copa", "sna", "csqa", "wstp", "inltkh", "bbca", "iitp-mr", "iitp-pr", "actsa-sc", "md"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'wiki-ner\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> indic_glue_metric = datasets.load_metric(\'indic_glue\', \'cvit-mkb-clsr\')\n    >>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n    >>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]\n    >>> results = indic_glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'precision@10\': 1.0}\n\n'







def  _A		(     snake_case		,							snake_case					)      ->     int:
   return float((preds == labels).mean()					)







def  _A		(     snake_case		,							snake_case					)      ->     Union[str, Any]:
   _lowercase      :				Any      					=       simple_accuracy(snake_case		,							snake_case					)
   _lowercase      :				List[Any]      					=       float(fa_score(y_true=snake_case		,							y_pred=snake_case					)					)
   return {
       "accuracy": acc,
       "f1": fa,
   }







def  _A		(     snake_case		,							snake_case					)      ->     List[str]:
   _lowercase      :				Any      					=       np.array(snake_case					)
   _lowercase      :				Any      					=       np.array(snake_case					)
   _lowercase      :				str      					=       en_sentvecs.shape[0]

   # mean centering
   _lowercase      :				List[Any]      					=       en_sentvecs - np.mean(snake_case		,							axis=0					)
   _lowercase      :				Tuple      					=       in_sentvecs - np.mean(snake_case		,							axis=0					)

   _lowercase      :				Any      					=       cdist(snake_case		,							snake_case		,							"cosine"					)
   _lowercase      :				Any      					=       np.array(range(snake_case					)					)
   _lowercase      :				Dict      					=       sim.argsort(axis=1					)[:, :10]
   _lowercase      :				List[str]      					=       np.any(preds == actual[:, None]		,							axis=1					)
   return float(matches.mean()					)


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,				_KWARGS_DESCRIPTION  )
class        a__  (							datasets.Metric  ):


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      if self.config_name not in [
          "wnli",
          "copa",
          "sna",
          "csqa",
          "wstp",
          "inltkh",
          "bbca",
          "cvit-mkb-clsr",
          "iitp-mr",
          "iitp-pr",
          "actsa-sc",
          "md",
          "wiki-ner",
      ]:
         raise KeyError(
             "You should supply a configuration name selected in "
             "[\"wnli\", \"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", "
             "\"cvit-mkb-clsr\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\", "
             "\"wiki-ner\"]"  )
      return datasets.MetricInfo(
          description=_DESCRIPTION      ,    citation=_CITATION      ,    inputs_description=_KWARGS_DESCRIPTION      ,    features=datasets.Features(
              {
                  "predictions": datasets.Value("int64"  )
                  if self.config_name != "cvit-mkb-clsr"
                  else datasets.Sequence(datasets.Value("float32"  )  ),
                  "references": datasets.Value("int64"  )
                  if self.config_name != "cvit-mkb-clsr"
                  else datasets.Sequence(datasets.Value("float32"  )  ),
              }  )      ,    codebase_urls=[]      ,    reference_urls=[]      ,    format="numpy" if self.config_name != "cvit-mkb-clsr" else None      ,    )






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      if self.config_name == "cvit-mkb-clsr":
         return {"precision@10": precision_at_aa(_UpperCamelCase      ,    _UpperCamelCase  )}
      elif self.config_name in ["wiki-ner"]:
         return acc_and_fa(_UpperCamelCase      ,    _UpperCamelCase  )
      elif self.config_name in [
          "wnli",
          "copa",
          "sna",
          "csqa",
          "wstp",
          "inltkh",
          "bbca",
          "iitp-mr",
          "iitp-pr",
          "actsa-sc",
          "md",
      ]:
         return {"accuracy": simple_accuracy(_UpperCamelCase      ,    _UpperCamelCase  )}
      else:
         raise KeyError(
             "You should supply a configuration name selected in "
             "[\"wnli\", \"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", "
             "\"cvit-mkb-clsr\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\", "
             "\"wiki-ner\"]"  )






