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    ):
						lowercase					 =     MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
						lowercase					 =     TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

						def        __a		(	self				:      str							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      Optional[Any]   )							->		Optional[Any]:


									'''simple docstring'''

									lowercase							=		AudioClassificationPipeline(model=__lowerCamelCase							,							feature_extractor=__lowerCamelCase   )

									# test with a raw waveform
									lowercase							=		np.zeros((3_40_00,)   )
									lowercase							=		np.zeros((1_40_00,)   )
									return audio_classifier, [audioa, audio]

						def        __a		(	self				:      Dict							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      Dict   )							->		Dict:


									'''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        __a		(	self				:      Dict							,							__lowerCamelCase				:      Tuple   )							->		Tuple:


									'''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        __a		(	self				:      Union[str, Any]   )							->		int:


									'''simple docstring'''

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

									lowercase							=		pipeline('''audio-classification'''							,							model=__lowerCamelCase   )

									lowercase							=		np.ones((80_00,)   )
									lowercase							=		audio_classifier(__lowerCamelCase							,							top_k=4   )

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

									lowercase							=		{'''array''': np.ones((80_00,)   ), '''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        __a		(	self				:      List[str]   )							->		Union[str, 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''': 0.981, '''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				:      List[str]   )							->		List[Any]:


									'''simple docstring'''

									pass






import cmath
import math






def        __UpperCAmelCase    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase       )->       complex:



			"""simple docstring"""







			lowercase							=		math.radians(UpperCAmelCase       )
			lowercase							=		math.radians(UpperCAmelCase       )

			# Convert voltage and current to rectangular form
			lowercase							=		cmath.rect(UpperCAmelCase,		UpperCAmelCase       )
			lowercase							=		cmath.rect(UpperCAmelCase,		UpperCAmelCase       )

			# Calculate apparent power
			return voltage_rect * current_rect


if __name__ == "__main__":
						import doctest

						doctest.testmod()





