


'''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 						a__					(					unittest.TestCase   ):





			'''simple docstring'''

			A						:				Tuple					      =			MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
			A						:				int					      =			TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			Tuple			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			Union[str, Any]	)      ->		Union[str, Any]:
						__A=	AudioClassificationPipeline(model=lowerCAmelCase_			,    feature_extractor=lowerCAmelCase_	)

						# test with a raw waveform
						__A=	np.zeros((34_000,)	)
						__A=	np.zeros((14_000,)	)
						return audio_classifier, [audioa, audio]
			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    lowerCAmelCase_							:			Tuple			,    lowerCAmelCase_							:			List[Any]	)      ->		int:
						__A,	__A=	examples
						__A=	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_	)},
						    ]			,    )
						__A=	audio_classifier(lowerCAmelCase_			,    top_k=1	)
						self.assertEqual(
						    lowerCAmelCase_			,    [
						        {'score': ANY(lowerCAmelCase_	), 'label': ANY(lowerCAmelCase_	)},
						    ]			,    )

						self.run_torchaudio(lowerCAmelCase_	)
			@require_torchaudio
			def 					lowerCAmelCase     (  self							:			List[str]			,    lowerCAmelCase_							:			List[Any]	)      ->		Dict:
						import datasets

						# test with a local file
						__A=	datasets.load_dataset('hf-internal-testing/librispeech_asr_dummy'			,    'clean'			,    split='validation'	)
						__A=	dataset[0]['audio']['array']
						__A=	audio_classifier(lowerCAmelCase_	)
						self.assertEqual(
						    lowerCAmelCase_			,    [
						        {'score': ANY(lowerCAmelCase_	), 'label': ANY(lowerCAmelCase_	)},
						        {'score': ANY(lowerCAmelCase_	), 'label': ANY(lowerCAmelCase_	)},
						    ]			,    )
			@require_torch
			def 					lowerCAmelCase     (  self							:			Any	)      ->		List[str]:
						__A=	'anton-l/wav2vec2-random-tiny-classifier'

						__A=	pipeline('audio-classification'			,    model=lowerCAmelCase_	)

						__A=	np.ones((8_000,)	)
						__A=	audio_classifier(lowerCAmelCase_			,    top_k=4	)

						__A=	[
						    {'score': 0.08_42, 'label': 'no'},
						    {'score': 0.08_38, 'label': 'up'},
						    {'score': 0.08_37, 'label': 'go'},
						    {'score': 0.08_34, 'label': 'right'},
						]
						__A=	[
						    {'score': 0.08_45, 'label': 'stop'},
						    {'score': 0.08_44, 'label': 'on'},
						    {'score': 0.08_41, 'label': 'right'},
						    {'score': 0.08_34, 'label': 'left'},
						]
						self.assertIn(nested_simplify(lowerCAmelCase_			,    decimals=4	)			,    [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]	)

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

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

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

						__A=	np.array(dataset[3]['speech']			,    dtype=np.floataa	)
						__A=	audio_classifier(lowerCAmelCase_			,    top_k=4	)
						self.assertEqual(
						    nested_simplify(lowerCAmelCase_			,    decimals=3	)			,    [
						        {'score': 0.9_81, 'label': 'go'},
						        {'score': 0.0_07, 'label': 'up'},
						        {'score': 0.0_06, 'label': '_unknown_'},
						        {'score': 0.0_01, 'label': 'down'},
						    ]			,    )

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







'''simple docstring'''





from __future__ import annotations

import math

UpperCAmelCase__  			=				'''2020.9.26'''
UpperCAmelCase__  			=				'''xcodz-dot, cclaus, dhruvmanila'''






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: float,_SCREAMING_SNAKE_CASE					: float,_SCREAMING_SNAKE_CASE					: float,_SCREAMING_SNAKE_CASE					: float,_SCREAMING_SNAKE_CASE					: float ):






			"""simple docstring"""




			if not all(isinstance(_SCREAMING_SNAKE_CASE,(float, int) ) for val in locals().values() ):
						__A=	f"""Input values must either be float or int: {list(locals().values() )}"""
						raise TypeError(_SCREAMING_SNAKE_CASE )
			__A=	((x * distance) / (z + distance)) * scale
			__A=	((y * distance) / (z + distance)) * scale
			return projected_x, projected_y






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: float,_SCREAMING_SNAKE_CASE					: float,_SCREAMING_SNAKE_CASE					: float,_SCREAMING_SNAKE_CASE					: str,_SCREAMING_SNAKE_CASE					: float ):






			"""simple docstring"""




			if not isinstance(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE ):
						raise TypeError('Axis must be a str' )
			__A=	locals()
			del input_variables["axis"]
			if not all(isinstance(_SCREAMING_SNAKE_CASE,(float, int) ) for val in input_variables.values() ):
						__A=	(
						    'Input values except axis must either be float or int: '
						    f"""{list(input_variables.values() )}"""
						)
						raise TypeError(_SCREAMING_SNAKE_CASE )
			__A=	(angle % 360) / 450 * 180 / math.pi
			if axis == "z":
						__A=	x * math.cos(_SCREAMING_SNAKE_CASE ) - y * math.sin(_SCREAMING_SNAKE_CASE )
						__A=	y * math.cos(_SCREAMING_SNAKE_CASE ) + x * math.sin(_SCREAMING_SNAKE_CASE )
						__A=	z
			elif axis == "x":
						__A=	y * math.cos(_SCREAMING_SNAKE_CASE ) - z * math.sin(_SCREAMING_SNAKE_CASE )
						__A=	z * math.cos(_SCREAMING_SNAKE_CASE ) + y * math.sin(_SCREAMING_SNAKE_CASE )
						__A=	x
			elif axis == "y":
						__A=	x * math.cos(_SCREAMING_SNAKE_CASE ) - z * math.sin(_SCREAMING_SNAKE_CASE )
						__A=	z * math.cos(_SCREAMING_SNAKE_CASE ) + x * math.sin(_SCREAMING_SNAKE_CASE )
						__A=	y
			else:
						raise ValueError('not a valid axis, choose one of \'x\', \'y\', \'z\'' )

			return new_x, new_y, new_z


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(F"""{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }""")
					print(F"""{rotate(1.0, 2.0, 3.0, 'y', 90.0) = }""")



