






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      UpperCamelCase (				unittest.TestCase ):







		"""simple docstring"""



		snake_case    =   MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
		snake_case    =   TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
		def       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       Any						,_SCREAMING_SNAKE_CASE		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       Tuple		)    ->   Dict:


						'''simple docstring'''
						A	 =      AudioClassificationPipeline(model=_SCREAMING_SNAKE_CASE						,feature_extractor=_SCREAMING_SNAKE_CASE		)

						# test with a raw waveform
						A	 =      np.zeros((3_4_0_0_0,)		)
						A	 =      np.zeros((1_4_0_0_0,)		)
						return audio_classifier, [audioa, audio]
		def       A(  self		:       int						,_SCREAMING_SNAKE_CASE		:       Tuple						,_SCREAMING_SNAKE_CASE		:       List[Any]		)    ->   Union[str, Any]:


						'''simple docstring'''
						A ,		A	 =      examples
						A	 =      audio_classifier(_SCREAMING_SNAKE_CASE		)
						# by default a model is initialized with num_labels=2
						self.assertEqual(
						    _SCREAMING_SNAKE_CASE						,[
						        {'score': ANY(_SCREAMING_SNAKE_CASE		), 'label': ANY(_SCREAMING_SNAKE_CASE		)},
						        {'score': ANY(_SCREAMING_SNAKE_CASE		), 'label': ANY(_SCREAMING_SNAKE_CASE		)},
						    ]						,)
						A	 =      audio_classifier(_SCREAMING_SNAKE_CASE						,top_k=1		)
						self.assertEqual(
						    _SCREAMING_SNAKE_CASE						,[
						        {'score': ANY(_SCREAMING_SNAKE_CASE		), 'label': ANY(_SCREAMING_SNAKE_CASE		)},
						    ]						,)

						self.run_torchaudio(_SCREAMING_SNAKE_CASE		)
		@require_torchaudio
		def       A(  self		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   Optional[Any]:


						'''simple docstring'''
						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(_SCREAMING_SNAKE_CASE		)
						self.assertEqual(
						    _SCREAMING_SNAKE_CASE						,[
						        {'score': ANY(_SCREAMING_SNAKE_CASE		), 'label': ANY(_SCREAMING_SNAKE_CASE		)},
						        {'score': ANY(_SCREAMING_SNAKE_CASE		), 'label': ANY(_SCREAMING_SNAKE_CASE		)},
						    ]						,)
		@require_torch
		def       A(  self		:       int		)    ->   str:


						'''simple docstring'''
						A	 =      'anton-l/wav2vec2-random-tiny-classifier'

						A	 =      pipeline('audio-classification'						,model=_SCREAMING_SNAKE_CASE		)

						A	 =      np.ones((8_0_0_0,)		)
						A	 =      audio_classifier(_SCREAMING_SNAKE_CASE						,top_k=4		)

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

						A	 =      {'array': np.ones((8_0_0_0,)		), 'sampling_rate': audio_classifier.feature_extractor.sampling_rate}
						A	 =      audio_classifier(_SCREAMING_SNAKE_CASE						,top_k=4		)
						self.assertIn(nested_simplify(_SCREAMING_SNAKE_CASE						,decimals=4		)						,[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]		)
		@require_torch
		@slow
		def       A(  self		:       Optional[int]		)    ->   Union[str, Any]:


						'''simple docstring'''
						import datasets

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

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

						A	 =      np.array(dataset[3]['speech']						,dtype=np.floataa		)
						A	 =      audio_classifier(_SCREAMING_SNAKE_CASE						,top_k=4		)
						self.assertEqual(
						    nested_simplify(_SCREAMING_SNAKE_CASE						,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		:       Any		)    ->   int:


						'''simple docstring'''
						pass













from math import factorial
class      UpperCamelCase :







		"""simple docstring"""



		def __init__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       List[str]		)    ->   List[str]:


						'''simple docstring'''
						A	 =      real
						if isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      [1] * rank
						else:
										A	 =      rank
		def __repr__(  self		:       Any		)    ->   Optional[Any]:


						'''simple docstring'''
						return (
						    f'{self.real}+'
						    f'{"+".join(str(_SCREAMING_SNAKE_CASE		)+"E"+str(n+1		)for n,dual in enumerate(self.duals		)		)}'
						)
		def       A(  self		:       Optional[Any]		)    ->   Tuple:


						'''simple docstring'''
						A	 =      self.duals.copy()
						while cur[-1] == 0:
										cur.pop(-1		)
						return Dual(self.real						,_SCREAMING_SNAKE_CASE		)
		def __add__(  self		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   Any:


						'''simple docstring'''
						if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										return Dual(self.real + other						,self.duals		)
						A	 =      self.duals.copy()
						A	 =      other.duals.copy()
						if len(_SCREAMING_SNAKE_CASE		) > len(_SCREAMING_SNAKE_CASE		):
										o_dual.extend([1] * (len(_SCREAMING_SNAKE_CASE		) - len(_SCREAMING_SNAKE_CASE		))		)
						elif len(_SCREAMING_SNAKE_CASE		) < len(_SCREAMING_SNAKE_CASE		):
										s_dual.extend([1] * (len(_SCREAMING_SNAKE_CASE		) - len(_SCREAMING_SNAKE_CASE		))		)
						A	 =      []
						for i in range(len(_SCREAMING_SNAKE_CASE		)		):
										new_duals.append(s_dual[i] + o_dual[i]		)
						return Dual(self.real + other.real						,_SCREAMING_SNAKE_CASE		)

		snake_case    =   __add__
		def __sub__(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       Dict		)    ->   Optional[Any]:


						'''simple docstring'''
						return self + other * -1
		def __mul__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]		)    ->   Union[str, Any]:


						'''simple docstring'''
						if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      []
										for i in self.duals:
														new_duals.append(i * other		)
										return Dual(self.real * other						,_SCREAMING_SNAKE_CASE		)
						A	 =      [0] * (len(self.duals		) + len(other.duals		) + 1)
						for i, item in enumerate(self.duals		):
										for j, jtem in enumerate(other.duals		):
														new_duals[i + j + 1] += item * jtem
						for k in range(len(self.duals		)		):
										new_duals[k] += self.duals[k] * other.real
						for index in range(len(other.duals		)		):
										new_duals[index] += other.duals[index] * self.real
						return Dual(self.real * other.real						,_SCREAMING_SNAKE_CASE		)

		snake_case    =   __mul__
		def __truediv__(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       int		)    ->   List[str]:


						'''simple docstring'''
						if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      []
										for i in self.duals:
														new_duals.append(i / other		)
										return Dual(self.real / other						,_SCREAMING_SNAKE_CASE		)
						raise ValueError
		def __floordiv__(  self		:       Any						,_SCREAMING_SNAKE_CASE		:       Any		)    ->   Dict:


						'''simple docstring'''
						if not isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										A	 =      []
										for i in self.duals:
														new_duals.append(i // other		)
										return Dual(self.real // other						,_SCREAMING_SNAKE_CASE		)
						raise ValueError







		def __pow__(  self		:       int						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]		)    ->   str:


						'''simple docstring'''
						if n < 0 or isinstance(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		):
										raise ValueError('power must be a positive integer'		)
						if n == 0:
										return 1
						if n == 1:
										return self
						A	 =      self
						for _ in range(n - 1		):
										x *= self
						return x







def      snake_case		(							UpperCAmelCase	:       Optional[int],						UpperCAmelCase	:       List[Any],						UpperCAmelCase	:       int   ):
				if not callable(UpperCAmelCase   ):
								raise ValueError('differentiate() requires a function as input for func'   )
				if not isinstance(UpperCAmelCase,						(float, int)   ):
								raise ValueError('differentiate() requires a float as input for position'   )
				if not isinstance(UpperCAmelCase,						UpperCAmelCase   ):
								raise ValueError('differentiate() requires an int as input for order'   )
				A	 =      Dual(UpperCAmelCase,						1   )
				A	 =      func(UpperCAmelCase   )
				if order == 0:
								return result.real
				return result.duals[order - 1] * factorial(UpperCAmelCase   )


if __name__ == "__main__":
					import doctest

					doctest.testmod()







					def      snake_case		(							UpperCAmelCase	:       int   ):
									return y**2 * y**4



					print(differentiate(f, 9, 2))





