'''simple docstring'''

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 			UpperCAmelCase  (						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
       SCREAMING_SNAKE_CASE							=    TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Tuple:
        lowercase__				:    List[Any]  =					AudioClassificationPipeline(model=__lowerCAmelCase	,   feature_extractor=__lowerCAmelCase )

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

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Optional[Any]:
        lowercase__ ,							lowercase__				:    List[str]  =					examples
        lowercase__				:    str  =					audio_classifier(__lowerCAmelCase )
        # by default a model is initialized with num_labels=2
        self.assertEqual(
            __lowerCAmelCase	,   [
                {'''score''': ANY(__lowerCAmelCase ), '''label''': ANY(__lowerCAmelCase )},
                {'''score''': ANY(__lowerCAmelCase ), '''label''': ANY(__lowerCAmelCase )},
            ]	,   )
        lowercase__				:    Tuple  =					audio_classifier(__lowerCAmelCase	,   top_k=1 )
        self.assertEqual(
            __lowerCAmelCase	,   [
                {'''score''': ANY(__lowerCAmelCase ), '''label''': ANY(__lowerCAmelCase )},
            ]	,   )

        self.run_torchaudio(__lowerCAmelCase )

       @require_torchaudio
       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  str:
        import datasets

        # test with a local file
        lowercase__				:    str  =					datasets.load_dataset('''hf-internal-testing/librispeech_asr_dummy'''	,   '''clean'''	,   split='''validation''' )
        lowercase__				:    int  =					dataset[0]['''audio''']['''array''']
        lowercase__				:    int  =					audio_classifier(__lowerCAmelCase )
        self.assertEqual(
            __lowerCAmelCase	,   [
                {'''score''': ANY(__lowerCAmelCase ), '''label''': ANY(__lowerCAmelCase )},
                {'''score''': ANY(__lowerCAmelCase ), '''label''': ANY(__lowerCAmelCase )},
            ]	,   )

       @require_torch
       def _lowerCAmelCase(						self )  ->  Union[str, Any]:
        lowercase__				:    List[str]  =					'''anton-l/wav2vec2-random-tiny-classifier'''

        lowercase__				:    Union[str, Any]  =					pipeline('''audio-classification'''	,   model=__lowerCAmelCase )

        lowercase__				:    List[Any]  =					np.ones((8000,) )
        lowercase__				:    Any  =					audio_classifier(__lowerCAmelCase	,   top_k=4 )

        lowercase__				:    Tuple  =					[
            {'''score''': 0.0_8_4_2, '''label''': '''no'''},
            {'''score''': 0.0_8_3_8, '''label''': '''up'''},
            {'''score''': 0.0_8_3_7, '''label''': '''go'''},
            {'''score''': 0.0_8_3_4, '''label''': '''right'''},
        ]
        lowercase__				:    List[str]  =					[
            {'''score''': 0.0_8_4_5, '''label''': '''stop'''},
            {'''score''': 0.0_8_4_4, '''label''': '''on'''},
            {'''score''': 0.0_8_4_1, '''label''': '''right'''},
            {'''score''': 0.0_8_3_4, '''label''': '''left'''},
        ]
        self.assertIn(nested_simplify(__lowerCAmelCase	,   decimals=4 )	,   [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2] )

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

       @require_torch
       @slow
       def _lowerCAmelCase(						self )  ->  Tuple:
        import datasets

        lowercase__				:    List[str]  =					'''superb/wav2vec2-base-superb-ks'''

        lowercase__				:    List[str]  =					pipeline('''audio-classification'''	,   model=__lowerCAmelCase )
        lowercase__				:    Union[str, Any]  =					datasets.load_dataset('''anton-l/superb_dummy'''	,   '''ks'''	,   split='''test''' )

        lowercase__				:    Optional[int]  =					np.array(dataset[3]['''speech''']	,   dtype=np.floataa )
        lowercase__				:    int  =					audio_classifier(__lowerCAmelCase	,   top_k=4 )
        self.assertEqual(
            nested_simplify(__lowerCAmelCase	,   decimals=3 )	,   [
                {'''score''': 0.9_8_1, '''label''': '''go'''},
                {'''score''': 0.0_0_7, '''label''': '''up'''},
                {'''score''': 0.0_0_6, '''label''': '''_unknown_'''},
                {'''score''': 0.0_0_1, '''label''': '''down'''},
            ]	,   )


       @require_tf
       @unittest.skip('''Audio classification is not implemented for TF''' )
       def _lowerCAmelCase(						self )  ->  Union[str, Any]:
        pass


'''simple docstring'''

def 			__UpperCamelCase       (   UpperCAmelCase = 1		,     UpperCAmelCase = 1000     ):
 lowercase__				:    Dict  =					1
 lowercase__				:    Dict  =					0

 for divide_by_number in range(UpperCAmelCase		,     digit + 1     ):
  lowercase__				:    list[int]  =					[]
  lowercase__				:    Union[str, Any]  =					numerator
  for _ in range(1		,     digit + 1     ):
   if now_divide in has_been_divided:
    if longest_list_length < len(UpperCAmelCase     ):
     lowercase__				:    Dict  =					len(UpperCAmelCase     )
     lowercase__				:    Optional[Any]  =					divide_by_number
   else:
    has_been_divided.append(UpperCAmelCase     )
    lowercase__				:    int  =					now_divide * 10 % divide_by_number

 return the_digit


# Tests
if __name__ == "__main__":
  import doctest

  doctest.testmod()

