'''simple docstring'''

import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow



@is_pipeline_test
@require_torch
class 			UpperCAmelCase  (						unittest.TestCase					):





       '''simple docstring'''



       @require_torch
       def _lowerCAmelCase(						self )  ->  Any:
        lowercase__				:    Optional[Any]  =					pipeline(
            task='''zero-shot-audio-classification'''	,   model='''hf-internal-testing/tiny-clap-htsat-unfused''' )
        lowercase__				:    Optional[int]  =					load_dataset('''ashraq/esc50''' )
        lowercase__				:    Optional[int]  =					dataset['''train''']['''audio'''][-1]['''array''']
        lowercase__				:    Optional[int]  =					audio_classifier(__lowerCAmelCase	,   candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] )
        self.assertEqual(
            nested_simplify(__lowerCAmelCase )	,   [{'''score''': 0.5_0_1, '''label''': '''Sound of a dog'''}, {'''score''': 0.4_9_9, '''label''': '''Sound of vaccum cleaner'''}]	,   )

       @unittest.skip('''No models are available in TF''' )
       def _lowerCAmelCase(						self )  ->  Optional[int]:
        pass

       @slow
       @require_torch
       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    str  =					pipeline(
            task='''zero-shot-audio-classification'''	,   model='''laion/clap-htsat-unfused'''	,   )
        # This is an audio of a dog
        lowercase__				:    Optional[int]  =					load_dataset('''ashraq/esc50''' )
        lowercase__				:    Dict  =					dataset['''train''']['''audio'''][-1]['''array''']
        lowercase__				:    str  =					audio_classifier(__lowerCAmelCase	,   candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] )

        self.assertEqual(
            nested_simplify(__lowerCAmelCase )	,   [
                {'''score''': 0.9_9_9, '''label''': '''Sound of a dog'''},
                {'''score''': 0.0_0_1, '''label''': '''Sound of vaccum cleaner'''},
            ]	,   )

        lowercase__				:    Optional[Any]  =					audio_classifier([audio] * 5	,   candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner'''] )
        self.assertEqual(
            nested_simplify(__lowerCAmelCase )	,   [
                [
                    {'''score''': 0.9_9_9, '''label''': '''Sound of a dog'''},
                    {'''score''': 0.0_0_1, '''label''': '''Sound of vaccum cleaner'''},
                ],
            ]
            * 5	,   )
        lowercase__				:    Union[str, Any]  =					audio_classifier(
            [audio] * 5	,   candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']	,   batch_size=5 )
        self.assertEqual(
            nested_simplify(__lowerCAmelCase )	,   [
                [
                    {'''score''': 0.9_9_9, '''label''': '''Sound of a dog'''},
                    {'''score''': 0.0_0_1, '''label''': '''Sound of vaccum cleaner'''},
                ],
            ]
            * 5	,   )


       @unittest.skip('''No models are available in TF''' )
       def _lowerCAmelCase(						self )  ->  List[Any]:
        pass


'''simple docstring'''

def 			__UpperCamelCase       (   UpperCAmelCase     ):
 lowercase__				:    Optional[int]  =					1
 for i in range(1		,     num + 1     ):
  fact *= i
 return fact
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 lowercase__				:    Optional[Any]  =					0
 while number > 0:
  lowercase__				:    str  =					number % 10
  sum_of_digits += last_digit
  lowercase__				:    List[str]  =					number // 10  # Removing the last_digit from the given number
 return sum_of_digits
def 			__UpperCamelCase       (   UpperCAmelCase = 100     ):
 lowercase__				:    Optional[int]  =					factorial(UpperCAmelCase     )
 lowercase__				:    Dict  =					split_and_add(UpperCAmelCase     )
 return result


if __name__ == "__main__":
  print(solution(int(input("""Enter the Number: """).strip())))

