





"""simple docstring"""
def 					__lowerCamelCase       (							a_					:		int							)      ->				int:
      __SCREAMING_SNAKE_CASE       :Tuple		    =  [1]

      __SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE       :Optional[int]		    =  0, 0, 0
      __SCREAMING_SNAKE_CASE       :List[str]		    =  ugly_nums[ia] * 2
      __SCREAMING_SNAKE_CASE       :List[Any]		    =  ugly_nums[ia] * 3
      __SCREAMING_SNAKE_CASE       :List[str]		    =  ugly_nums[ia] * 5

      for _ in range(1			,       a_							):
            __SCREAMING_SNAKE_CASE       :List[Any]		    =  min(a_			,       a_			,       a_							)
            ugly_nums.append(a_							)
            if next_num == next_a:
                  ia += 1
                  __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  ugly_nums[ia] * 2
            if next_num == next_a:
                  ia += 1
                  __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  ugly_nums[ia] * 3
            if next_num == next_a:
                  ia += 1
                  __SCREAMING_SNAKE_CASE       :Dict		    =  ugly_nums[ia] * 5
      return ugly_nums[-1]


if __name__ == "__main__":
  from doctest import testmod

  testmod(verbose=True)
  print(f'{ugly_numbers(2_0_0) = }')






"""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  _SCREAMING_SNAKE_CASE(       unittest.TestCase  ):




       @require_torch
       def    _UpperCamelCase					(						self							)      ->				str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Dict		    =  pipeline(
                 task='''zero-shot-audio-classification'''		,model='''hf-internal-testing/tiny-clap-htsat-unfused'''							)
             __SCREAMING_SNAKE_CASE       :Any		    =  load_dataset('''ashraq/esc50'''							)
             __SCREAMING_SNAKE_CASE       :int		    =  dataset['''train''']['''audio'''][-1]['''array''']
             __SCREAMING_SNAKE_CASE       :Dict		    =  audio_classifier(SCREAMING_SNAKE_CASE__		,candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']							)
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__							)		,[{'''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    _UpperCamelCase					(						self							)      ->				Optional[int]:





             """simple docstring"""






             pass




       @slow
       @require_torch
       def    _UpperCamelCase					(						self							)      ->				Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  pipeline(
                 task='''zero-shot-audio-classification'''		,model='''laion/clap-htsat-unfused'''		,)
             # This is an audio of a dog
             __SCREAMING_SNAKE_CASE       :List[Any]		    =  load_dataset('''ashraq/esc50'''							)
             __SCREAMING_SNAKE_CASE       :Tuple		    =  dataset['''train''']['''audio'''][-1]['''array''']
             __SCREAMING_SNAKE_CASE       :str		    =  audio_classifier(SCREAMING_SNAKE_CASE__		,candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']							)

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

             __SCREAMING_SNAKE_CASE       :Dict		    =  audio_classifier([audio] * 5		,candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']							)
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__							)		,[
                     [
                         {'''score''': 0.9_9_9, '''label''': '''Sound of a dog'''},
                         {'''score''': 0.0_0_1, '''label''': '''Sound of vaccum cleaner'''},
                     ],
                 ]
                 * 5		,)
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  audio_classifier(
                 [audio] * 5		,candidate_labels=['''Sound of a dog''', '''Sound of vaccum cleaner''']		,batch_size=5							)
             self.assertEqual(
                 nested_simplify(SCREAMING_SNAKE_CASE__							)		,[
                     [
                         {'''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    _UpperCamelCase					(						self							)      ->				List[str]:





             """simple docstring"""






             pass