'''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 ):
       snake_case							:List[str]					     =       MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
       snake_case							:Dict					     =       TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING






       def       _snake_case    (   self					,  UpperCamelCase_					,  UpperCamelCase_					,  UpperCamelCase_   ):
              __UpperCAmelCase	:					Any												=		AudioClassificationPipeline(model=a_					,  feature_extractor=a_   )

              # test with a raw waveform
              __UpperCAmelCase	:					Dict												=		np.zeros((3_40_00,)   )
              __UpperCAmelCase	:					Any												=		np.zeros((1_40_00,)   )
              return audio_classifier, [audioa, audio]






       def       _snake_case    (   self					,  UpperCamelCase_					,  UpperCamelCase_   ):
              __UpperCAmelCase					,			__UpperCAmelCase	:					List[Any]												=		examples
              __UpperCAmelCase	:					Any												=		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_   )},
                  ]					,  )
              __UpperCAmelCase	:					str												=		audio_classifier(a_					,  top_k=1   )
              self.assertEqual(
                  a_					,  [
                      {"score": ANY(a_   ), "label": ANY(a_   )},
                  ]					,  )

              self.run_torchaudio(a_   )






       @require_torchaudio
       def       _snake_case    (   self					,  UpperCamelCase_   ):
              import datasets

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






       @require_torch
       def       _snake_case    (   self   ):
              __UpperCAmelCase	:					int												=		"anton-l/wav2vec2-random-tiny-classifier"

              __UpperCAmelCase	:					List[Any]												=		pipeline("audio-classification"					,  model=a_   )

              __UpperCAmelCase	:					Dict												=		np.ones((80_00,)   )
              __UpperCAmelCase	:					Optional[int]												=		audio_classifier(a_					,  top_k=4   )

              __UpperCAmelCase	:					Optional[Any]												=		[
                  {"score": 0.0_8_4_2, "label": "no"},
                  {"score": 0.0_8_3_8, "label": "up"},
                  {"score": 0.0_8_3_7, "label": "go"},
                  {"score": 0.0_8_3_4, "label": "right"},
              ]
              __UpperCAmelCase	:					Any												=		[
                  {"score": 0.0_8_4_5, "label": "stop"},
                  {"score": 0.0_8_4_4, "label": "on"},
                  {"score": 0.0_8_4_1, "label": "right"},
                  {"score": 0.0_8_3_4, "label": "left"},
              ]
              self.assertIn(nested_simplify(a_					,  decimals=4   )					,  [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]   )

              __UpperCAmelCase	:					Any												=		{"array": np.ones((80_00,)   ), "sampling_rate": audio_classifier.feature_extractor.sampling_rate}
              __UpperCAmelCase	:					Optional[Any]												=		audio_classifier(a_					,  top_k=4   )
              self.assertIn(nested_simplify(a_					,  decimals=4   )					,  [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]   )






       @require_torch
       @slow
       def       _snake_case    (   self   ):
              import datasets

              __UpperCAmelCase	:					Any												=		"superb/wav2vec2-base-superb-ks"

              __UpperCAmelCase	:					Dict												=		pipeline("audio-classification"					,  model=a_   )
              __UpperCAmelCase	:					Dict												=		datasets.load_dataset("anton-l/superb_dummy"					,  "ks"					,  split="test"   )

              __UpperCAmelCase	:					List[Any]												=		np.array(dataset[3]["speech"]					,  dtype=np.floataa   )
              __UpperCAmelCase	:					Optional[int]												=		audio_classifier(a_					,  top_k=4   )
              self.assertEqual(
                  nested_simplify(a_					,  decimals=3   )					,  [
                      {"score": 0.9_8_1, "label": "go"},
                      {"score": 0.0_0_7, "label": "up"},
                      {"score": 0.0_0_6, "label": "_unknown_"},
                      {"score": 0.0_0_1, "label": "down"},
                  ]					,  )







       @require_tf
       @unittest.skip("Audio classification is not implemented for TF"   )
       def       _snake_case    (   self   ):
              pass











import random
from typing import Any







def     UpperCAmelCase_		(					snake_case__					)	->     list[Any]:



				"""simple docstring"""





				for _ in range(len(snake_case__					)					):
								lowerCAmelCase__		  =  random.randint(0    ,			len(snake_case__					) - 1					)
								lowerCAmelCase__		  =  random.randint(0    ,			len(snake_case__					) - 1					)
								lowerCAmelCase__  ,   lowerCAmelCase__		  =  data[b], data[a]
				return data


if __name__ == "__main__":
	_lowerCAmelCase	:   int    =   [0, 1, 2, 3, 4, 5, 6, 7]
	_lowerCAmelCase	:   Optional[Any]    =   ["python", "says", "hello", "!"]
	print("Fisher-Yates Shuffle:")
	print("List", integers, strings)
	print("FY Shuffle", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
