






'''simple docstring'''

import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available

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 torch import nn

     from transformers import ASTForAudioClassification, ASTModel
     from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
         AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
     )


if is_torchaudio_available():
     import torchaudio

     from transformers import ASTFeatureExtractor






class        a_		:

  def __init__(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=13    ,	_SCREAMING_SNAKE_CASE=2    ,	_SCREAMING_SNAKE_CASE=24    ,	_SCREAMING_SNAKE_CASE=16    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=True    ,	_SCREAMING_SNAKE_CASE=32    ,	_SCREAMING_SNAKE_CASE=5    ,	_SCREAMING_SNAKE_CASE=4    ,	_SCREAMING_SNAKE_CASE=37    ,	_SCREAMING_SNAKE_CASE="gelu"    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=0.1    ,	_SCREAMING_SNAKE_CASE=10    ,	_SCREAMING_SNAKE_CASE=0.0_2    ,	_SCREAMING_SNAKE_CASE=None    ,	_SCREAMING_SNAKE_CASE=2    ,	_SCREAMING_SNAKE_CASE=2    ,	)						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      parent
        UpperCamelCase       						=      batch_size
        UpperCamelCase       						=      patch_size
        UpperCamelCase       						=      max_length
        UpperCamelCase       						=      num_mel_bins
        UpperCamelCase       						=      is_training
        UpperCamelCase       						=      use_labels
        UpperCamelCase       						=      hidden_size
        UpperCamelCase       						=      num_hidden_layers
        UpperCamelCase       						=      num_attention_heads
        UpperCamelCase       						=      intermediate_size
        UpperCamelCase       						=      hidden_act
        UpperCamelCase       						=      hidden_dropout_prob
        UpperCamelCase       						=      attention_probs_dropout_prob
        UpperCamelCase       						=      type_sequence_label_size
        UpperCamelCase       						=      initializer_range
        UpperCamelCase       						=      scope
        UpperCamelCase       						=      frequency_stride
        UpperCamelCase       						=      time_stride

        # in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
        UpperCamelCase       						=      (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
        UpperCamelCase       						=      (self.max_length - self.patch_size) // self.time_stride + 1
        UpperCamelCase       						=      frequency_out_dimension * time_out_dimension
        UpperCamelCase       						=      num_patches + 2

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      floats_tensor([self.batch_size, self.max_length, self.num_mel_bins]   )

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

        UpperCamelCase       						=      self.get_config()

        return config, input_values, labels

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        return ASTConfig(
            patch_size=self.patch_size    ,	max_length=self.max_length    ,	num_mel_bins=self.num_mel_bins    ,	hidden_size=self.hidden_size    ,	num_hidden_layers=self.num_hidden_layers    ,	num_attention_heads=self.num_attention_heads    ,	intermediate_size=self.intermediate_size    ,	hidden_act=self.hidden_act    ,	hidden_dropout_prob=self.hidden_dropout_prob    ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,	is_decoder=_SCREAMING_SNAKE_CASE    ,	initializer_range=self.initializer_range    ,	frequency_stride=self.frequency_stride    ,	time_stride=self.time_stride    ,	)

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      ASTModel(config=_SCREAMING_SNAKE_CASE   )
        model.to(_SCREAMING_SNAKE_CASE   )
        model.eval()
        UpperCamelCase       						=      model(_SCREAMING_SNAKE_CASE   )
        self.parent.assertEqual(result.last_hidden_state.shape    ,	(self.batch_size, self.seq_length, self.hidden_size)   )


  def 						A__		(   self   )						->   str:



        """simple docstring"""






        UpperCamelCase       						=      self.prepare_config_and_inputs()
        (
            (
            UpperCamelCase
        )						,(
            UpperCamelCase
        )						,(
            UpperCamelCase
        )						,
        )       						=      config_and_inputs
        UpperCamelCase       						=      {"""input_values""": input_values}
        return config, inputs_dict






@require_torch
class        a_		(    lowerCamelCase			, lowerCamelCase			, unittest.TestCase       ):
  lowercase  	=   (
      (
          ASTModel,
          ASTForAudioClassification,
      )
      if is_torch_available()
      else ()
  )
  lowercase  	=   (
      {"""audio-classification""": ASTForAudioClassification, """feature-extraction""": ASTModel}
      if is_torch_available()
      else {}
  )
  lowercase  	=   False
  lowercase  	=   False
  lowercase  	=   False
  lowercase  	=   False

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   )						->   Tuple:



        """simple docstring"""






        if pipeline_test_casse_name == "AudioClassificationPipelineTests":
              return True

        return False

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



        """simple docstring"""






        UpperCamelCase       						=      ASTModelTester(self   )
        UpperCamelCase       						=      ConfigTester(self    ,	config_class=_SCREAMING_SNAKE_CASE    ,	has_text_modality=_SCREAMING_SNAKE_CASE    ,	hidden_size=37   )

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



        """simple docstring"""






        self.config_tester.run_common_tests()

  @unittest.skip(reason="""AST does not use inputs_embeds"""   )
  def 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        pass

  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              UpperCamelCase       						=      model_class(_SCREAMING_SNAKE_CASE   )
              self.assertIsInstance(model.get_input_embeddings()    ,	(nn.Module)   )
              UpperCamelCase       						=      model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE    ,	nn.Linear   )   )

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



        """simple docstring"""






        UpperCamelCase						,UpperCamelCase       						=      self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCamelCase       						=      ["""input_values"""]
              self.assertListEqual(arg_names[:1]    ,	_SCREAMING_SNAKE_CASE   )

  def 						A__		(   self   )						->   str:



        """simple docstring"""






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


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



        """simple docstring"""






        for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCamelCase       						=      ASTModel.from_pretrained(_SCREAMING_SNAKE_CASE   )
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE   )







def 					lowercase__				(   )->				Union[str, Any]:
      UpperCamelCase       						=      hf_hub_download(
          repo_id="""nielsr/audio-spectogram-transformer-checkpoint"""	,		filename="""sample_audio.flac"""	,		repo_type="""dataset"""					)

      UpperCamelCase						,UpperCamelCase       						=      torchaudio.load(__UpperCamelCase					)

      return audio, sampling_rate






@require_torch
@require_torchaudio
class        a_		(    unittest.TestCase       ):

  @cached_property
  def 						A__		(   self   )						->   int:



        """simple docstring"""






        return (
            ASTFeatureExtractor.from_pretrained("""MIT/ast-finetuned-audioset-10-10-0.4593"""   )
            if is_torchaudio_available()
            else None
        )


  @slow
  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.default_feature_extractor
        UpperCamelCase       						=      ASTForAudioClassification.from_pretrained("""MIT/ast-finetuned-audioset-10-10-0.4593"""   ).to(_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      self.default_feature_extractor
        UpperCamelCase						,UpperCamelCase       						=      prepare_audio()
        UpperCamelCase       						=      audio.squeeze().numpy()
        UpperCamelCase       						=      feature_extractor(_SCREAMING_SNAKE_CASE    ,	sampling_rate=_SCREAMING_SNAKE_CASE    ,	return_tensors="""pt"""   ).to(_SCREAMING_SNAKE_CASE   )

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

        # verify the logits
        UpperCamelCase       						=      torch.Size((1, 527)   )
        self.assertEqual(outputs.logits.shape    ,	_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      torch.tensor([-0.8_7_6_0, -7.0_0_4_2, -8.6_6_0_2]   ).to(_SCREAMING_SNAKE_CASE   )

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








'''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())))
