






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 		_snake_case	(	unittest.TestCase       ):
			_lowercase		:     Optional[int]        =				MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
			_lowercase		:     Union[str, Any]        =				TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a)   ->							Union[str, Any]:
						SCREAMING_SNAKE_CASE							=			AudioClassificationPipeline(model=a		,       feature_extractor=a)

						# test with a raw waveform
						SCREAMING_SNAKE_CASE							=			np.zeros((3_4000,))
						SCREAMING_SNAKE_CASE							=			np.zeros((1_4000,))
						return audio_classifier, [audioa, audio]






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a)   ->							Dict:
						SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			examples
						SCREAMING_SNAKE_CASE							=			audio_classifier(a)
						# by default a model is initialized with num_labels=2
						self.assertEqual(
						    a		,       [
						        {'score': ANY(a), 'label': ANY(a)},
						        {'score': ANY(a), 'label': ANY(a)},
						    ]		,       )
						SCREAMING_SNAKE_CASE							=			audio_classifier(a		,       top_k=1)
						self.assertEqual(
						    a		,       [
						        {'score': ANY(a), 'label': ANY(a)},
						    ]		,       )

						self.run_torchaudio(a)






			@require_torchaudio
			def  SCREAMING_SNAKE_CASE__     (							self		,       a)   ->							Union[str, Any]:
						import datasets

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






			@require_torch
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Union[str, Any]:
						SCREAMING_SNAKE_CASE							=			'anton-l/wav2vec2-random-tiny-classifier'

						SCREAMING_SNAKE_CASE							=			pipeline('audio-classification'		,       model=a)

						SCREAMING_SNAKE_CASE							=			np.ones((8000,))
						SCREAMING_SNAKE_CASE							=			audio_classifier(a		,       top_k=4)

						SCREAMING_SNAKE_CASE							=			[
						    {'score': 0.08_42, 'label': 'no'},
						    {'score': 0.08_38, 'label': 'up'},
						    {'score': 0.08_37, 'label': 'go'},
						    {'score': 0.08_34, 'label': 'right'},
						]
						SCREAMING_SNAKE_CASE							=			[
						    {'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(a		,       decimals=4)		,       [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2])

						SCREAMING_SNAKE_CASE							=			{'array': np.ones((8000,)), 'sampling_rate': audio_classifier.feature_extractor.sampling_rate}
						SCREAMING_SNAKE_CASE							=			audio_classifier(a		,       top_k=4)
						self.assertIn(nested_simplify(a		,       decimals=4)		,       [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2])






			@require_torch
			@slow
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[str]:
						import datasets

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

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

						SCREAMING_SNAKE_CASE							=			np.array(dataset[3]['speech']		,       dtype=np.floataa)
						SCREAMING_SNAKE_CASE							=			audio_classifier(a		,       top_k=4)
						self.assertEqual(
						    nested_simplify(a		,       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  SCREAMING_SNAKE_CASE__     (							self)   ->							int:
						pass












from __future__ import annotations







class 		_A						:

  def __init__(    self						:	List[str]   ,				lowerCamelCase__						:	Any=None			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					data
     __UpperCamelCase	:			Union[str, Any]					=					None

  def __repr__(    self						:	int			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					[]
     __UpperCamelCase	:			Optional[int]					=					self
     while temp:
        string_rep.append(f'{temp.data}'			)
        __UpperCamelCase	:			Dict					=					temp.next
     return "->".join(lowerCamelCase__			)







def 				__lowerCamelCase     (							__lowerCAmelCase							:					list							)     ->  Tuple:
   if not elements_list:
      raise Exception("""The Elements List is empty"""							)

   __UpperCamelCase	:			Dict					=					Node(elements_list[0]							)
   for i in range(1					,							len(__lowerCAmelCase							)							):
      __UpperCamelCase	:			List[str]					=					Node(elements_list[i]							)
      __UpperCamelCase	:			Optional[int]					=					current.next
   return head


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Node							)     ->  None:
   if head_node is not None and isinstance(__lowerCAmelCase					,							__lowerCAmelCase							):
      print_reverse(head_node.next							)
      print(head_node.data							)


def 				__lowerCamelCase     (							)     ->  Union[str, Any]:
   from doctest import testmod

   testmod()

   __UpperCamelCase	:			Any					=					make_linked_list([14, 52, 14, 12, 43]							)
   print("""Linked List:"""							)
   print(__lowerCAmelCase							)
   print("""Elements in Reverse:"""							)
   print_reverse(__lowerCAmelCase							)


if __name__ == "__main__":
       main()



