



'''simple docstring'''


from math import sqrt







def 				__lowerCamelCase				(   A__    )       ->	int:






   """simple docstring"""
   UpperCamelCase					=  0
   for i in range(1    ,						int(sqrt(A__    ) + 1    )    ):
      if n % i == 0 and i != sqrt(A__    ):
         total += i + n // i
      elif i == sqrt(A__    ):
         total += i
   return total - n







def 				__lowerCamelCase				(   A__ = 10_000    )       ->	int:






   """simple docstring"""
   UpperCamelCase					=  sum(
       i
       for i in range(1    ,						A__    )
       if sum_of_divisors(sum_of_divisors(A__    )    ) == i and sum_of_divisors(A__    ) != i    )
   return total


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






'''simple docstring'''


import inspect
import unittest

from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
   from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor

class  SCREAMING_SNAKE_CASE :






    """simple docstring"""





    def __init__(  self      :					Optional[int]  ,						UpperCamelCase__      :					List[str]  ,						UpperCamelCase__      :					Any=1_3  ,						UpperCamelCase__      :					Optional[int]=3_2  ,						UpperCamelCase__      :					Any=3  ,						UpperCamelCase__      :					Tuple=4  ,						UpperCamelCase__      :					str=[1_0, 2_0, 3_0, 4_0]  ,						UpperCamelCase__      :					str=[2, 2, 3, 2]  ,						UpperCamelCase__      :					Dict=True  ,						UpperCamelCase__      :					List[str]=True  ,						UpperCamelCase__      :					str=3_7  ,						UpperCamelCase__      :					Union[str, Any]="gelu"  ,						UpperCamelCase__      :					Dict=1_0  ,						UpperCamelCase__      :					Union[str, Any]=0.0_2  ,						UpperCamelCase__      :					int=["stage2", "stage3", "stage4"]  ,						UpperCamelCase__      :					List[str]=[2, 3, 4]  ,						UpperCamelCase__      :					Any=None  ,						):


       """simple docstring"""







       UpperCamelCase					=  parent
       UpperCamelCase					=  batch_size
       UpperCamelCase					=  image_size
       UpperCamelCase					=  num_channels
       UpperCamelCase					=  num_stages
       UpperCamelCase					=  hidden_sizes
       UpperCamelCase					=  depths
       UpperCamelCase					=  is_training
       UpperCamelCase					=  use_labels
       UpperCamelCase					=  intermediate_size
       UpperCamelCase					=  hidden_act
       UpperCamelCase					=  num_labels
       UpperCamelCase					=  initializer_range
       UpperCamelCase					=  out_features
       UpperCamelCase					=  out_indices
       UpperCamelCase					=  scope






    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       UpperCamelCase					=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

       UpperCamelCase					=  None
       if self.use_labels:
          UpperCamelCase					=  ids_tensor([self.batch_size]  ,						self.num_labels )

       UpperCamelCase					=  self.get_config()
       return config, pixel_values, labels






    def  A       (  self      :					List[str] ):


       """simple docstring"""







       return ConvNextConfig(
           num_channels=self.num_channels  ,						hidden_sizes=self.hidden_sizes  ,						depths=self.depths  ,						num_stages=self.num_stages  ,						hidden_act=self.hidden_act  ,						is_decoder=UpperCamelCase__  ,						initializer_range=self.initializer_range  ,						out_features=self.out_features  ,						out_indices=self.out_indices  ,						num_labels=self.num_labels  ,						)






    def  A       (  self      :					Union[str, Any]  ,						UpperCamelCase__      :					List[Any]  ,						UpperCamelCase__      :					Any  ,						UpperCamelCase__      :					str ):


       """simple docstring"""







       UpperCamelCase					=  ConvNextModel(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__ )
       # expected last hidden states: B, C, H // 32, W // 32
       self.parent.assertEqual(
           result.last_hidden_state.shape  ,						(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)  ,						)






    def  A       (  self      :					List[str]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Optional[Any]  ,						UpperCamelCase__      :					int ):


       """simple docstring"""







       UpperCamelCase					=  ConvNextForImageClassification(UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__  ,						labels=UpperCamelCase__ )
       self.parent.assertEqual(result.logits.shape  ,						(self.batch_size, self.num_labels) )






    def  A       (  self      :					Tuple  ,						UpperCamelCase__      :					Optional[int]  ,						UpperCamelCase__      :					Tuple  ,						UpperCamelCase__      :					str ):


       """simple docstring"""







       UpperCamelCase					=  ConvNextBackbone(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__ )

       # verify hidden states
       self.parent.assertEqual(len(result.feature_maps )  ,						len(config.out_features ) )
       self.parent.assertListEqual(list(result.feature_maps[0].shape )  ,						[self.batch_size, self.hidden_sizes[1], 4, 4] )

       # verify channels
       self.parent.assertEqual(len(model.channels )  ,						len(config.out_features ) )
       self.parent.assertListEqual(model.channels  ,						config.hidden_sizes[1:] )

       # verify backbone works with out_features=None
       UpperCamelCase					=  None
       UpperCamelCase					=  ConvNextBackbone(config=UpperCamelCase__ )
       model.to(UpperCamelCase__ )
       model.eval()
       UpperCamelCase					=  model(UpperCamelCase__ )

       # verify feature maps
       self.parent.assertEqual(len(result.feature_maps )  ,						1 )
       self.parent.assertListEqual(list(result.feature_maps[0].shape )  ,						[self.batch_size, self.hidden_sizes[-1], 1, 1] )

       # verify channels
       self.parent.assertEqual(len(model.channels )  ,						1 )
       self.parent.assertListEqual(model.channels  ,						[config.hidden_sizes[-1]] )






    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase					=  self.prepare_config_and_inputs()
       UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  config_and_inputs
       UpperCamelCase					=  {'pixel_values': pixel_values}
       return config, inputs_dict

@require_torch
class  SCREAMING_SNAKE_CASE (  _a   ,						_a   ,						unittest.TestCase		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  (
        (
            ConvNextModel,
            ConvNextForImageClassification,
            ConvNextBackbone,
        )
        if is_torch_available()
        else ()
    )
    _SCREAMING_SNAKE_CASE	  =  (
        {"""feature-extraction""": ConvNextModel, """image-classification""": ConvNextForImageClassification}
        if is_torch_available()
        else {}
    )

    _SCREAMING_SNAKE_CASE	  =  True
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False
    _SCREAMING_SNAKE_CASE	  =  False






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  ConvNextModelTester(self )
       UpperCamelCase					=  ConfigTester(self  ,						config_class=UpperCamelCase__  ,						has_text_modality=UpperCamelCase__  ,						hidden_size=3_7 )






    def  A       (  self      :					List[str] ):


       """simple docstring"""







       self.create_and_test_config_common_properties()
       self.config_tester.create_and_test_config_to_json_string()
       self.config_tester.create_and_test_config_to_json_file()
       self.config_tester.create_and_test_config_from_and_save_pretrained()
       self.config_tester.create_and_test_config_with_num_labels()
       self.config_tester.check_config_can_be_init_without_params()
       self.config_tester.check_config_arguments_init()






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       return






    @unittest.skip(reason='ConvNext does not use inputs_embeds' )
    def  A       (  self      :					List[str] ):


       """simple docstring"""







       pass






    @unittest.skip(reason='ConvNext does not support input and output embeddings' )
    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       pass






    @unittest.skip(reason='ConvNext does not use feedforward chunking' )
    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       pass






    def  A       (  self      :					Any ):


       """simple docstring"""







       UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          UpperCamelCase					=  model_class(UpperCamelCase__ )
          UpperCamelCase					=  inspect.signature(model.forward )
          # signature.parameters is an OrderedDict => so arg_names order is deterministic
          UpperCamelCase					=  [*signature.parameters.keys()]

          UpperCamelCase					=  ['pixel_values']
          self.assertListEqual(arg_names[:1]  ,						UpperCamelCase__ )






    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCamelCase__ )






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*UpperCamelCase__ )






    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       def check_hidden_states_output(UpperCamelCase__      :					Dict  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Tuple ):
          UpperCamelCase					=  model_class(UpperCamelCase__ )
          model.to(UpperCamelCase__ )
          model.eval()

          with torch.no_grad():
             UpperCamelCase					=  model(**self._prepare_for_class(UpperCamelCase__  ,						UpperCamelCase__ ) )

          UpperCamelCase					=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          UpperCamelCase					=  self.model_tester.num_stages
          self.assertEqual(len(UpperCamelCase__ )  ,						expected_num_stages + 1 )

          # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
          self.assertListEqual(
              list(hidden_states[0].shape[-2:] )  ,						[self.model_tester.image_size // 4, self.model_tester.image_size // 4]  ,						)

       UpperCamelCase				,      UpperCamelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
          UpperCamelCase					=  True
          check_hidden_states_output(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          UpperCamelCase					=  True

          check_hidden_states_output(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )






    def  A       (  self      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__ )






    @slow
    def  A       (  self      :					Dict ):


       """simple docstring"""







       for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          UpperCamelCase					=  ConvNextModel.from_pretrained(UpperCamelCase__ )
          self.assertIsNotNone(UpperCamelCase__ )







def 				__lowerCamelCase				(   )       ->	Any:






   """simple docstring"""
   UpperCamelCase					=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
   return image

@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE (  unittest.TestCase		):






    """simple docstring"""





    @cached_property
    def  A       (  self      :					Optional[Any] ):


       """simple docstring"""







       return AutoImageProcessor.from_pretrained('facebook/convnext-tiny-224' ) if is_vision_available() else None






    @slow
    def  A       (  self      :					List[Any] ):


       """simple docstring"""







       UpperCamelCase					=  ConvNextForImageClassification.from_pretrained('facebook/convnext-tiny-224' ).to(UpperCamelCase__ )

       UpperCamelCase					=  self.default_image_processor
       UpperCamelCase					=  prepare_img()
       UpperCamelCase					=  image_processor(images=UpperCamelCase__  ,						return_tensors='pt' ).to(UpperCamelCase__ )

       # forward pass
       with torch.no_grad():
          UpperCamelCase					=  model(**UpperCamelCase__ )

       # verify the logits
       UpperCamelCase					=  torch.Size((1, 1_0_0_0) )
       self.assertEqual(outputs.logits.shape  ,						UpperCamelCase__ )

       UpperCamelCase					=  torch.tensor([-0.0_2_6_0, -0.4_7_3_9, 0.1_9_1_1] ).to(UpperCamelCase__ )

       self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,						UpperCamelCase__  ,						atol=1E-4 ) )

@require_torch
class  SCREAMING_SNAKE_CASE (  unittest.TestCase   ,						_a		):






    """simple docstring"""





    _SCREAMING_SNAKE_CASE	  =  (ConvNextBackbone,) if is_torch_available() else ()
    _SCREAMING_SNAKE_CASE	  =  ConvNextConfig

    _SCREAMING_SNAKE_CASE	  =  False






    def  A       (  self      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  ConvNextModelTester(self )

