
"""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							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Dict							=					MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
				SCREAMING_SNAKE_CASE_:							int							=					TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING


				def 				__lowerCamelCase	(					self	:			Any				,     UpperCAmelCase_	:			Union[str, Any]				,     UpperCAmelCase_	:			List[str]				,     UpperCAmelCase_	:			Optional[int]  )		->  Dict:







											"""simple docstring"""




											_lowerCAmelCase  	=			AudioClassificationPipeline(model=UpperCAmelCase_				,     feature_extractor=UpperCAmelCase_  )

											# test with a raw waveform
											_lowerCAmelCase  	=			np.zeros((34_000,)  )
											_lowerCAmelCase  	=			np.zeros((14_000,)  )
											return audio_classifier, [audioa, audio]


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







											"""simple docstring"""




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

											self.run_torchaudio(UpperCAmelCase_  )


				@require_torchaudio
				def 				__lowerCamelCase	(					self	:			Optional[Any]				,     UpperCAmelCase_	:			List[str]  )		->  Optional[int]:







											"""simple docstring"""




											import datasets

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


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







											"""simple docstring"""




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

											_lowerCAmelCase  	=			pipeline('audio-classification'				,     model=UpperCAmelCase_  )

											_lowerCAmelCase  	=			np.ones((8_000,)  )
											_lowerCAmelCase  	=			audio_classifier(UpperCAmelCase_				,     top_k=4  )

											_lowerCAmelCase  	=			[
											    {'score': 0.0842, 'label': 'no'},
											    {'score': 0.0838, 'label': 'up'},
											    {'score': 0.0837, 'label': 'go'},
											    {'score': 0.0834, 'label': 'right'},
											]
											_lowerCAmelCase  	=			[
											    {'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]  )

											_lowerCAmelCase  	=			{'array': np.ones((8_000,)  ), 'sampling_rate': audio_classifier.feature_extractor.sampling_rate}
											_lowerCAmelCase  	=			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	:			List[Any]  )		->  Union[str, Any]:







											"""simple docstring"""




											import datasets

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

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

											_lowerCAmelCase  	=			np.array(dataset[3]['speech']				,     dtype=np.floataa  )
											_lowerCAmelCase  	=			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	:			Optional[Any]  )		->  Optional[Any]:







											"""simple docstring"""




											pass








"""simple docstring"""



from pathlib import Path

import numpy as np
from PIL import Image






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							_lowerCAmelCase	,		_lowerCAmelCase	,		_lowerCAmelCase  	=			rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
							return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							return (gray > 127) & (gray <= 255)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					np.ndarray     ,  SCREAMING_SNAKE_CASE:					np.ndarray						):






							"""simple docstring"""



							_lowerCAmelCase  	=			np.zeros_like(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			np.zeros(
							    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)						)

							# Copy image to padded image
							_lowerCAmelCase  	=			image

							# Iterate over image & apply kernel
							for x in range(image.shape[1]						):
														for y in range(image.shape[0]						):
																					_lowerCAmelCase  	=			(
																					    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
																					).sum()
																					_lowerCAmelCase  	=			int(summation > 0						)
							return output


if __name__ == "__main__":
	# read original image
	_snake_case          =						Path(__file__).resolve().parent / '''image_data''' / '''lena.jpg'''
	_snake_case          =						np.array(Image.open(lena_path))
	# kernel to be applied
	_snake_case          =						np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
	_snake_case          =						dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
	# Save the output image
	_snake_case          =						Image.fromarray(output).convert('''RGB''')
	pil_img.save('''result_dilation.png''')






