


'''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"""



				__UpperCamelCase			:						Optional[int]		=		MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
				__UpperCamelCase			:						str		=		TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING




				def     _snake_case     (self				,	__lowercase				,	__lowercase				,	__lowercase						):
										__lowerCAmelCase											= AudioClassificationPipeline(model=__lowerCAmelCase				,	feature_extractor=__lowerCAmelCase						)

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




				def     _snake_case     (self				,	__lowercase				,	__lowercase						):
										__lowerCAmelCase				,    __lowerCAmelCase											= examples
										__lowerCAmelCase											= 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						)},
										    ]				,	)
										__lowerCAmelCase											= audio_classifier(__lowerCAmelCase				,	top_k=1						)
										self.assertEqual(
										    __lowerCAmelCase				,	[
										        {'''score''': ANY(__lowerCAmelCase						), '''label''': ANY(__lowerCAmelCase						)},
										    ]				,	)

										self.run_torchaudio(__lowerCAmelCase						)




				@require_torchaudio
				def     _snake_case     (self				,	__lowercase						):
										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(__lowerCAmelCase						)
										self.assertEqual(
										    __lowerCAmelCase				,	[
										        {'''score''': ANY(__lowerCAmelCase						), '''label''': ANY(__lowerCAmelCase						)},
										        {'''score''': ANY(__lowerCAmelCase						), '''label''': ANY(__lowerCAmelCase						)},
										    ]				,	)




				@require_torch
				def     _snake_case     (self						):
										__lowerCAmelCase											= '''anton-l/wav2vec2-random-tiny-classifier'''

										__lowerCAmelCase											= pipeline('''audio-classification'''				,	model=__lowerCAmelCase						)

										__lowerCAmelCase											= np.ones((80_00,)						)
										__lowerCAmelCase											= audio_classifier(__lowerCAmelCase				,	top_k=4						)

										__lowerCAmelCase											= [
										    {'''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'''},
										]
										__lowerCAmelCase											= [
										    {'''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(__lowerCAmelCase				,	decimals=4						)				,	[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]						)

										__lowerCAmelCase											= {'''array''': np.ones((80_00,)						), '''sampling_rate''': audio_classifier.feature_extractor.sampling_rate}
										__lowerCAmelCase											= audio_classifier(__lowerCAmelCase				,	top_k=4						)
										self.assertIn(nested_simplify(__lowerCAmelCase				,	decimals=4						)				,	[EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2]						)




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

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

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

										__lowerCAmelCase											= np.array(dataset[3]['''speech''']				,	dtype=np.floataa						)
										__lowerCAmelCase											= audio_classifier(__lowerCAmelCase				,	top_k=4						)
										self.assertEqual(
										    nested_simplify(__lowerCAmelCase				,	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


"""simple docstring"""






import argparse
import json
import subprocess


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Tuple					,		lowerCAmelCase__     :List[Any]		) ->    Optional[Any]:
      '''simple docstring'''


      lowercase														=	[]

      lowercase														=	(
          f'curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'
          """ https://api.github.com/repos/huggingface/transformers/actions/runners"""
      )
      lowercase														=	subprocess.run(lowerCAmelCase__					,		shell=lowerCAmelCase__					,		stdout=subprocess.PIPE		)
      lowercase														=	output.stdout.decode("""utf-8"""		)
      lowercase														=	json.loads(lowerCAmelCase__		)

      lowercase														=	status["""runners"""]
      for runner in runners:
            if runner["name"] in target_runners:
                  if runner["status"] == "offline":
                        offline_runners.append(lowerCAmelCase__		)

    # save the result so we can report them on Slack
      with open("""offline_runners.txt"""					,		"""w"""		) as fp:
            fp.write(json.dumps(lowerCAmelCase__		)		)

      if len(lowerCAmelCase__		) > 0:
            lowercase														=	"""\n""".join([x["""name"""] for x in offline_runners]		)
            raise ValueError(f'The following runners are offline:\n{failed}'		)


if __name__ == "__main__":


       def 			UpperCAmelCase__       (				lowerCAmelCase__     :Optional[int]		) ->    Tuple:
             '''simple docstring'''


             return values.split(""","""		)

       __lowerCAmelCase    :       str					     =argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--target_runners""",
           default=None,
           type=list_str,
           required=True,
           help="""Comma-separated list of runners to check status.""",
       )

       parser.add_argument(
           """--token""", default=None, type=str, required=True, help="""A token that has actions:read permission."""
       )
       __lowerCAmelCase    :       str					     =parser.parse_args()






       get_runner_status(args.target_runners, args.token)



