
'''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[Any]   =  MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
	snake_case__   :Dict   =  TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING





	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]   ,				__magic_name__      :							Optional[int]   ,				__magic_name__      :							Optional[Any]   ,				__magic_name__      :							int				):


				"""simple docstring"""

				lowerCAmelCase__										=				AudioClassificationPipeline(model=__magic_name__   ,				feature_extractor=__magic_name__				)

				# test with a raw waveform
				lowerCAmelCase__										=				np.zeros((34000,)				)
				lowerCAmelCase__										=				np.zeros((14000,)				)
				return audio_classifier, [audioa, audio]





	def 				__SCREAMING_SNAKE_CASE	(       self      :							Optional[int]   ,				__magic_name__      :							Optional[int]   ,				__magic_name__      :							Optional[int]				):


				"""simple docstring"""

				lowerCAmelCase__						,lowerCAmelCase__										=				examples
				lowerCAmelCase__										=				audio_classifier(__magic_name__				)
				# by default a model is initialized with num_labels=2
				self.assertEqual(
				    __magic_name__   ,				[
				        {"score": ANY(__magic_name__				), "label": ANY(__magic_name__				)},
				        {"score": ANY(__magic_name__				), "label": ANY(__magic_name__				)},
				    ]   ,				)
				lowerCAmelCase__										=				audio_classifier(__magic_name__   ,				top_k=1				)
				self.assertEqual(
				    __magic_name__   ,				[
				        {"score": ANY(__magic_name__				), "label": ANY(__magic_name__				)},
				    ]   ,				)

				self.run_torchaudio(__magic_name__				)





	@require_torchaudio
	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[str]   ,				__magic_name__      :							List[Any]				):


				"""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(__magic_name__				)
				self.assertEqual(
				    __magic_name__   ,				[
				        {"score": ANY(__magic_name__				), "label": ANY(__magic_name__				)},
				        {"score": ANY(__magic_name__				), "label": ANY(__magic_name__				)},
				    ]   ,				)





	@require_torch
	def 				__SCREAMING_SNAKE_CASE	(       self      :							Dict				):


				"""simple docstring"""

				lowerCAmelCase__										=				"anton-l/wav2vec2-random-tiny-classifier"

				lowerCAmelCase__										=				pipeline("audio-classification"   ,				model=__magic_name__				)

				lowerCAmelCase__										=				np.ones((8000,)				)
				lowerCAmelCase__										=				audio_classifier(__magic_name__   ,				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(__magic_name__   ,				decimals=4				)   ,				[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]				)

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





	@require_torch
	@slow
	def 				__SCREAMING_SNAKE_CASE	(       self      :							List[Any]				):


				"""simple docstring"""

				import datasets

				lowerCAmelCase__										=				"superb/wav2vec2-base-superb-ks"

				lowerCAmelCase__										=				pipeline("audio-classification"   ,				model=__magic_name__				)
				lowerCAmelCase__										=				datasets.load_dataset("anton-l/superb_dummy"   ,				"ks"   ,				split="test"				)

				lowerCAmelCase__										=				np.array(dataset[3]["speech"]   ,				dtype=np.floataa				)
				lowerCAmelCase__										=				audio_classifier(__magic_name__   ,				top_k=4				)
				self.assertEqual(
				    nested_simplify(__magic_name__   ,				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 				__SCREAMING_SNAKE_CASE	(       self      :							int				):


				"""simple docstring"""

				pass













from __future__ import annotations



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      list[int]      ,					__lowercase :      int     )    ->   bool:





    """simple docstring"""

    if len(__lowercase     ) == 0:
        return False
    __A       =						len(__lowercase     ) // 2
    if a_list[midpoint] == item:
        return True
    if item < a_list[midpoint]:
        return binary_search(a_list[:midpoint]      ,					__lowercase     )
    else:
        return binary_search(a_list[midpoint + 1 :]      ,					__lowercase     )


if __name__ == "__main__":
  __a   :							Tuple						= input("Enter numbers separated by comma:\n").strip()
  __a   :							Any						= [int(item.strip()) for item in user_input.split(",")]
  __a   :							List[Any]						= int(input("Enter the number to be found in the list:\n").strip())
  __a   :							Optional[int]						= "" if binary_search(sequence, target) else "not "
  print(f"""{target} was {not_str}found in {sequence}""")




