






'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import PoolFormerImageProcessor






class        a_		(    unittest.TestCase       ):

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=7    ,	_SCREAMING_SNAKE_CASE=3    ,	_SCREAMING_SNAKE_CASE=30    ,	_SCREAMING_SNAKE_CASE=400    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=0.9    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]    ,	_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]    ,	)						->   int:



        """simple docstring"""






        UpperCamelCase       						=      size if size is not None else {"""shortest_edge""": 30}
        UpperCamelCase       						=      crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
        UpperCamelCase       						=      parent
        UpperCamelCase       						=      batch_size
        UpperCamelCase       						=      num_channels
        UpperCamelCase       						=      min_resolution
        UpperCamelCase       						=      max_resolution
        UpperCamelCase       						=      do_resize_and_center_crop
        UpperCamelCase       						=      size
        UpperCamelCase       						=      crop_pct
        UpperCamelCase       						=      crop_size
        UpperCamelCase       						=      do_normalize
        UpperCamelCase       						=      image_mean
        UpperCamelCase       						=      image_std


  def 						A__		(   self   )						->   List[str]:



        """simple docstring"""






        return {
            "size": self.size,
            "do_resize_and_center_crop": self.do_resize_and_center_crop,
            "crop_pct": self.crop_pct,
            "crop_size": self.crop_size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
        }






@require_torch
@require_vision
class        a_		(    lowerCamelCase			, unittest.TestCase       ):
  lowercase  	=   PoolFormerImageProcessor if is_vision_available() else None

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      PoolFormerImageProcessingTester(self   )

  @property
  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        return self.image_processor_tester.prepare_image_processor_dict()

  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        UpperCamelCase       						=      self.image_processing_class(**self.image_processor_dict   )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,	"""do_resize_and_center_crop"""   )   )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,	"""size"""   )   )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,	"""crop_pct"""   )   )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,	"""do_normalize"""   )   )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,	"""image_mean"""   )   )
        self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,	"""image_std"""   )   )

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



        """simple docstring"""






        UpperCamelCase       						=      self.image_processing_class.from_dict(self.image_processor_dict   )
        self.assertEqual(image_processor.size    ,	{"""shortest_edge""": 30}   )
        self.assertEqual(image_processor.crop_size    ,	{"""height""": 30, """width""": 30}   )

        UpperCamelCase       						=      self.image_processing_class.from_dict(self.image_processor_dict    ,	size=42    ,	crop_size=84   )
        self.assertEqual(image_processor.size    ,	{"""shortest_edge""": 42}   )
        self.assertEqual(image_processor.crop_size    ,	{"""height""": 84, """width""": 84}   )

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



        """simple docstring"""






        pass

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



        """simple docstring"""






        UpperCamelCase       						=      self.image_processing_class(**self.image_processor_dict   )
        # create random PIL images
        UpperCamelCase       						=      prepare_image_inputs(self.image_processor_tester    ,	equal_resolution=_SCREAMING_SNAKE_CASE   )
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,	Image.Image   )

        # Test not batched input
        UpperCamelCase       						=      image_processing(image_inputs[0]    ,	return_tensors="""pt"""   ).pixel_values

        self.assertEqual(
            encoded_images.shape    ,	(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )    ,	)

        # Test batched
        UpperCamelCase       						=      image_processing(_SCREAMING_SNAKE_CASE    ,	return_tensors="""pt"""   ).pixel_values
        self.assertEqual(
            encoded_images.shape    ,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )    ,	)

  def 						A__		(   self   )						->   str:



        """simple docstring"""






        UpperCamelCase       						=      self.image_processing_class(**self.image_processor_dict   )
        # create random numpy tensors
        UpperCamelCase       						=      prepare_image_inputs(self.image_processor_tester    ,	equal_resolution=_SCREAMING_SNAKE_CASE    ,	numpify=_SCREAMING_SNAKE_CASE   )
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,	np.ndarray   )

        # Test not batched input
        UpperCamelCase       						=      image_processing(image_inputs[0]    ,	return_tensors="""pt"""   ).pixel_values
        self.assertEqual(
            encoded_images.shape    ,	(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )    ,	)

        # Test batched
        UpperCamelCase       						=      image_processing(_SCREAMING_SNAKE_CASE    ,	return_tensors="""pt"""   ).pixel_values
        self.assertEqual(
            encoded_images.shape    ,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )    ,	)


  def 						A__		(   self   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      self.image_processing_class(**self.image_processor_dict   )
        # create random PyTorch tensors
        UpperCamelCase       						=      prepare_image_inputs(self.image_processor_tester    ,	equal_resolution=_SCREAMING_SNAKE_CASE    ,	torchify=_SCREAMING_SNAKE_CASE   )
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,	torch.Tensor   )

        # Test not batched input
        UpperCamelCase       						=      image_processing(image_inputs[0]    ,	return_tensors="""pt"""   ).pixel_values
        self.assertEqual(
            encoded_images.shape    ,	(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )    ,	)

        # Test batched
        UpperCamelCase       						=      image_processing(_SCREAMING_SNAKE_CASE    ,	return_tensors="""pt"""   ).pixel_values
        self.assertEqual(
            encoded_images.shape    ,	(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )    ,	)








'''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())))
