'''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 _SCREAMING_SNAKE_CASE(							unittest.TestCase					):
						A_			:							Dict     		= MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
						A_			:							List[Any]     		= TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING




						def      __lowerCamelCase       (    self :       int							,							UpperCamelCase_ :       Optional[Any]							,							UpperCamelCase_ :       List[Any]							,							UpperCamelCase_ :       Tuple      )     ->				str:
													SCREAMING_SNAKE_CASE__     :List[Any]						    = AudioClassificationPipeline(model=UpperCamelCase_							,							feature_extractor=UpperCamelCase_      )

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




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

													self.run_torchaudio(UpperCamelCase_      )




						@require_torchaudio
						def      __lowerCamelCase       (    self :       str							,							UpperCamelCase_ :       Dict      )     ->				int:
													import datasets

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




						@require_torch
						def      __lowerCamelCase       (    self :       Tuple      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Dict						    = 'anton-l/wav2vec2-random-tiny-classifier'

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = pipeline('audio-classification'							,							model=UpperCamelCase_      )

													SCREAMING_SNAKE_CASE__     :Dict						    = np.ones((80_00,)      )
													SCREAMING_SNAKE_CASE__     :List[str]						    = audio_classifier(UpperCamelCase_							,							top_k=4      )

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

													SCREAMING_SNAKE_CASE__     :Optional[int]						    = {'array': np.ones((80_00,)      ), 'sampling_rate': audio_classifier.feature_extractor.sampling_rate}
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = audio_classifier(UpperCamelCase_							,							top_k=4      )
													self.assertIn(nested_simplify(UpperCamelCase_							,							decimals=4      )							,							[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]      )




						@require_torch
						@slow
						def      __lowerCamelCase       (    self :       Dict      )     ->				int:
													import datasets

													SCREAMING_SNAKE_CASE__     :Optional[Any]						    = 'superb/wav2vec2-base-superb-ks'

													SCREAMING_SNAKE_CASE__     :str						    = pipeline('audio-classification'							,							model=UpperCamelCase_      )
													SCREAMING_SNAKE_CASE__     :Any						    = datasets.load_dataset('anton-l/superb_dummy'							,							'ks'							,							split='test'      )

													SCREAMING_SNAKE_CASE__     :str						    = np.array(dataset[3]['speech']							,							dtype=np.floataa      )
													SCREAMING_SNAKE_CASE__     :Any						    = audio_classifier(UpperCamelCase_							,							top_k=4      )
													self.assertEqual(
													    nested_simplify(UpperCamelCase_							,							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      __lowerCamelCase       (    self :       List[str]      )     ->				List[str]:
													pass







'''simple docstring'''







def    lowerCamelCase       (			UpperCAmelCase__				:  int		)			->	int:


							'''simple docstring'''






							assert (
							    isinstance(UpperCAmelCase__				,						UpperCAmelCase__		) and number_of_steps > 0
							), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
							if number_of_steps == 1:
														return 1
							SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Dict						    = 1, 1
							for _ in range(number_of_steps - 1		):
														SCREAMING_SNAKE_CASE__    ,      SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = current + previous, current
							return current


if __name__ == "__main__":
		import doctest

		doctest.testmod()






