"""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 		__A							(				unittest.TestCase	):






     @require_torch
     def 					__A						(	self						):
       _lowerCAmelCase		: Optional[Any]						    =  pipeline(
           task="""zero-shot-audio-classification"""       ,  model="""hf-internal-testing/tiny-clap-htsat-unfused"""						)
       _lowerCAmelCase		: List[Any]						    =  load_dataset("""ashraq/esc50"""						)
       _lowerCAmelCase		: Any						    =  dataset["""train"""]["""audio"""][-1]["""array"""]
       _lowerCAmelCase		: str						    =  audio_classifier(a__       ,  candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]						)
       self.assertEqual(
           nested_simplify(a__						)       ,  [{"""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 					__A						(	self						):
       pass






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

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

       _lowerCAmelCase		: Any						    =  audio_classifier([audio] * 5       ,  candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]						)
       self.assertEqual(
           nested_simplify(a__						)       ,  [
               [
                   {"""score""": 0.9_9_9, """label""": """Sound of a dog"""},
                   {"""score""": 0.0_0_1, """label""": """Sound of vaccum cleaner"""},
               ],
           ]
           * 5       ,  )
       _lowerCAmelCase		: List[str]						    =  audio_classifier(
           [audio] * 5       ,  candidate_labels=["""Sound of a dog""", """Sound of vaccum cleaner"""]       ,  batch_size=5						)
       self.assertEqual(
           nested_simplify(a__						)       ,  [
               [
                   {"""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 					__A						(	self						):
       pass





"""simple docstring"""




def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: int = 1000000			) ->   int:
  _lowerCAmelCase		: List[str]						    =  [i - 1 for i in range(limit + 1			)]

  for i in range(2    ,limit + 1			):
    if phi[i] == i - 1:
      for j in range(2 * i    ,limit + 1    ,_lowerCamelCase			):
        phi[j] -= phi[j] // i

  return sum(phi[2 : limit + 1]			)


if __name__ == "__main__":
     print(solution())




