





'''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 							__lowercase       (      unittest.TestCase						):







	'''simple docstring'''

	a    :				str											=       MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
	a    :				List[str]											=       TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase     ,_lowerCamelCase )							->							Any:







				'''simple docstring'''





				__lowercase										=							AudioClassificationPipeline(model=_lowerCamelCase     ,feature_extractor=_lowerCamelCase )

				# test with a raw waveform
				__lowercase										=							np.zeros((34000,) )
				__lowercase										=							np.zeros((14000,) )
				return audio_classifier, [audioa, audio]


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase )							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase						,		__lowercase										=							examples
				__lowercase										=							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										=							audio_classifier(_lowerCamelCase     ,top_k=1 )
				self.assertEqual(
				    _lowerCamelCase     ,[
				        {'''score''': ANY(_lowerCamelCase ), '''label''': ANY(_lowerCamelCase )},
				    ]     ,)

				self.run_torchaudio(_lowerCamelCase )


	@require_torchaudio
	def 						_UpperCAmelCase     (self     ,_lowerCamelCase )							->							Optional[Any]:







				'''simple docstring'''





				import datasets

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


	@require_torch
	def 						_UpperCAmelCase     (self )							->							int:







				'''simple docstring'''





				__lowercase										=							'''anton-l/wav2vec2-random-tiny-classifier'''

				__lowercase										=							pipeline('''audio-classification'''     ,model=_lowerCamelCase )

				__lowercase										=							np.ones((8000,) )
				__lowercase										=							audio_classifier(_lowerCamelCase     ,top_k=4 )

				__lowercase										=							[
				    {'''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										=							[
				    {'''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										=							{'''array''': np.ones((8000,) ), '''sampling_rate''': audio_classifier.feature_extractor.sampling_rate}
				__lowercase										=							audio_classifier(_lowerCamelCase     ,top_k=4 )
				self.assertIn(nested_simplify(_lowerCamelCase     ,decimals=4 )     ,[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2] )


	@require_torch
	@slow
	def 						_UpperCAmelCase     (self )							->							Any:







				'''simple docstring'''





				import datasets

				__lowercase										=							'''superb/wav2vec2-base-superb-ks'''

				__lowercase										=							pipeline('''audio-classification'''     ,model=_lowerCamelCase )
				__lowercase										=							datasets.load_dataset('''anton-l/superb_dummy'''     ,'''ks'''     ,split='''test''' )

				__lowercase										=							np.array(dataset[3]['''speech''']     ,dtype=np.floataa )
				__lowercase										=							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 						_UpperCAmelCase     (self )							->							Any:







				'''simple docstring'''





				pass












'''simple docstring'''



import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


_SCREAMING_SNAKE_CASE					=    logging.get_logger(__name__)






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	def __init__(self     ,*_lowerCamelCase     ,**_lowerCamelCase )							->							None:







				'''simple docstring'''





				warnings.warn(
				    '''The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
				    ''' use GLPNImageProcessor instead.'''     ,_lowerCamelCase     ,)
				super().__init__(*_lowerCamelCase     ,**_lowerCamelCase )





