


import unittest

import numpy as np

from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torchaudio,
    slow,
)

from .test_pipelines_common import ANY



@is_pipeline_test
class        a					(			unittest.TestCase   ):
     UpperCAmelCase__ :  Any						      =	MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
     UpperCAmelCase__ :  Optional[Any]						      =	TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING





     def  a_       (    self					:				str    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Dict							):
        __lowerCamelCase:				Optional[Any]     =	AudioClassificationPipeline(model=SCREAMING_SNAKE_CASE_    ,	feature_extractor=SCREAMING_SNAKE_CASE_							)

        # test with a raw waveform
        __lowerCamelCase:				List[str]     =	np.zeros((34000,)							)
        __lowerCamelCase:				Optional[int]     =	np.zeros((14000,)							)
        return audio_classifier, [audioa, audio]





     def  a_       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]							):
        __lowerCamelCase:				str     =	examples
        __lowerCamelCase:				Dict     =	audio_classifier(SCREAMING_SNAKE_CASE_							)
        # by default a model is initialized with num_labels=2
        self.assertEqual(
            SCREAMING_SNAKE_CASE_    ,	[
                {"""score""": ANY(SCREAMING_SNAKE_CASE_							), """label""": ANY(SCREAMING_SNAKE_CASE_							)},
                {"""score""": ANY(SCREAMING_SNAKE_CASE_							), """label""": ANY(SCREAMING_SNAKE_CASE_							)},
            ]    ,	)
        __lowerCamelCase:				Union[str, Any]     =	audio_classifier(SCREAMING_SNAKE_CASE_    ,	top_k=1							)
        self.assertEqual(
            SCREAMING_SNAKE_CASE_    ,	[
                {"""score""": ANY(SCREAMING_SNAKE_CASE_							), """label""": ANY(SCREAMING_SNAKE_CASE_							)},
            ]    ,	)

        self.run_torchaudio(SCREAMING_SNAKE_CASE_							)





     @require_torchaudio
     def  a_       (    self					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Any							):
        import datasets

        # test with a local file
        __lowerCamelCase:				Dict     =	datasets.load_dataset("""hf-internal-testing/librispeech_asr_dummy"""    ,	"""clean"""    ,	split="""validation"""							)
        __lowerCamelCase:				Tuple     =	dataset[0]["""audio"""]["""array"""]
        __lowerCamelCase:				Optional[int]     =	audio_classifier(SCREAMING_SNAKE_CASE_							)
        self.assertEqual(
            SCREAMING_SNAKE_CASE_    ,	[
                {"""score""": ANY(SCREAMING_SNAKE_CASE_							), """label""": ANY(SCREAMING_SNAKE_CASE_							)},
                {"""score""": ANY(SCREAMING_SNAKE_CASE_							), """label""": ANY(SCREAMING_SNAKE_CASE_							)},
            ]    ,	)





     @require_torch
     def  a_       (    self					:				Any							):
        __lowerCamelCase:				List[Any]     =	"""anton-l/wav2vec2-random-tiny-classifier"""

        __lowerCamelCase:				Dict     =	pipeline("""audio-classification"""    ,	model=SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Union[str, Any]     =	np.ones((8000,)							)
        __lowerCamelCase:				Union[str, Any]     =	audio_classifier(SCREAMING_SNAKE_CASE_    ,	top_k=4							)

        __lowerCamelCase:				List[str]     =	[
            {"""score""": 0.0842, """label""": """no"""},
            {"""score""": 0.0838, """label""": """up"""},
            {"""score""": 0.0837, """label""": """go"""},
            {"""score""": 0.0834, """label""": """right"""},
        ]
        __lowerCamelCase:				Any     =	[
            {"""score""": 0.0845, """label""": """stop"""},
            {"""score""": 0.0844, """label""": """on"""},
            {"""score""": 0.0841, """label""": """right"""},
            {"""score""": 0.0834, """label""": """left"""},
        ]
        self.assertIn(nested_simplify(SCREAMING_SNAKE_CASE_    ,	decimals=4							)    ,	[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]							)

        __lowerCamelCase:				Union[str, Any]     =	{"""array""": np.ones((8000,)							), """sampling_rate""": audio_classifier.feature_extractor.sampling_rate}
        __lowerCamelCase:				Union[str, Any]     =	audio_classifier(SCREAMING_SNAKE_CASE_    ,	top_k=4							)
        self.assertIn(nested_simplify(SCREAMING_SNAKE_CASE_    ,	decimals=4							)    ,	[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]							)





     @require_torch
     @slow
     def  a_       (    self					:				Dict							):
        import datasets

        __lowerCamelCase:				List[str]     =	"""superb/wav2vec2-base-superb-ks"""

        __lowerCamelCase:				Dict     =	pipeline("""audio-classification"""    ,	model=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				int     =	datasets.load_dataset("""anton-l/superb_dummy"""    ,	"""ks"""    ,	split="""test"""							)

        __lowerCamelCase:				Union[str, Any]     =	np.array(dataset[3]["""speech"""]    ,	dtype=np.floataa							)
        __lowerCamelCase:				List[Any]     =	audio_classifier(SCREAMING_SNAKE_CASE_    ,	top_k=4							)
        self.assertEqual(
            nested_simplify(SCREAMING_SNAKE_CASE_    ,	decimals=3							)    ,	[
                {"""score""": 0.981, """label""": """go"""},
                {"""score""": 0.007, """label""": """up"""},
                {"""score""": 0.006, """label""": """_unknown_"""},
                {"""score""": 0.001, """label""": """down"""},
            ]    ,	)







     @require_tf
     @unittest.skip("""Audio classification is not implemented for TF"""							)
     def  a_       (    self					:				int							):
        pass






def     __lowerCAmelCase						(							snake_case	:    int = 1000000 )       ->							int:
   __lowerCamelCase:				Union[str, Any]     =	set(range(3						,      snake_case						,      2 ) )
   primes.add(2 )
   for p in range(3						,      snake_case						,      2 ):
      if p not in primes:
         continue
      primes.difference_update(set(range(p * p						,      snake_case						,      snake_case ) ) )

   __lowerCamelCase:				Any     =	[float(snake_case ) for n in range(limit + 1 )]

   for p in primes:
      for n in range(snake_case						,      limit + 1						,      snake_case ):
         phi[n] *= 1 - 1 / p

   return int(sum(phi[2:] ) )


if __name__ == "__main__":
   print(F"""{solution() = }""")


