

import argparse
import json
import subprocess

def 						UpperCamelCase (lowercase_:			List[str]   ,				lowercase_:			Any )					->				str:
	A__							:   Any					       =		[]

	A__							:   Optional[Any]					       =		(
	    f"""curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: Bearer {token}\""""
	    " https://api.github.com/repos/huggingface/transformers/actions/runners"
	)
	A__							:   str					       =		subprocess.run(__lowerCamelCase   ,				shell=__lowerCamelCase   ,				stdout=subprocess.PIPE )
	A__							:   Any					       =		output.stdout.decode("""utf-8""" )
	A__							:   Dict					       =		json.loads(__lowerCamelCase )

	A__							:   Optional[int]					       =		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:
		A__							:   str					       =		"\n".join([x["""name"""] for x in offline_runners] )
		raise ValueError(f"""The following runners are offline:\n{failed}""" )


if __name__ == "__main__":

				def 						UpperCamelCase (lowercase_:			Any )					->				Tuple:
					return values.split(""",""" )

				A_	:			List[Any]  	=      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.'
				)
				A_	:			Optional[int]  	=      parser.parse_args()


				get_runner_status(args.target_runners, args.token)






import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin



class    _a						(__magic_name__    ):


					'''simple docstring'''






					UpperCAmelCase__:       Optional[Any]  							=	'''MCTCTFeatureExtractor'''
					UpperCAmelCase__:       Optional[int]  							=	'''AutoTokenizer'''






					def __init__(			self   ,	A__   ,	A__      ):
						super().__init__(A__   ,	A__      )
						A__							:   List[str]					       =		self.feature_extractor
						A__							:   Optional[int]					       =		False






					def __call__(			self   ,	*A__   ,	**A__      ):
						# For backward compatibility
						if self._in_target_context_manager:
							return self.current_processor(*A__   ,	**A__      )

						if "raw_speech" in kwargs:
							warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""      )
							A__							:   Dict					       =		kwargs.pop("""raw_speech"""      )
						else:
							A__							:   Tuple					       =		kwargs.pop("""audio"""   ,	A__      )
						A__							:   Union[str, Any]					       =		kwargs.pop("""sampling_rate"""   ,	A__      )
						A__							:   int					       =		kwargs.pop("""text"""   ,	A__      )
						if len(A__      ) > 0:
							A__							:   Optional[int]					       =		args[0]
							A__							:   Dict					       =		args[1:]

						if audio is None and text is None:
							raise ValueError("""You need to specify either an `audio` or `text` input to process."""      )

						if audio is not None:
							A__							:   List[str]					       =		self.feature_extractor(A__   ,	*A__   ,	sampling_rate=A__   ,	**A__      )
						if text is not None:
							A__							:   Optional[Any]					       =		self.tokenizer(A__   ,	**A__      )

						if text is None:
							return inputs
						elif audio is None:
							return encodings
						else:
							A__							:   List[Any]					       =		encodings["""input_ids"""]
							return inputs






					def   __A      (			self   ,	*A__   ,	**A__      ):
						return self.tokenizer.batch_decode(*A__   ,	**A__      )






					def   __A      (			self   ,	*A__   ,	**A__      ):
						# For backward compatibility
						if self._in_target_context_manager:
							return self.current_processor.pad(*A__   ,	**A__      )

						A__							:   Optional[Any]					       =		kwargs.pop("""input_features"""   ,	A__      )
						A__							:   Union[str, Any]					       =		kwargs.pop("""labels"""   ,	A__      )
						if len(A__      ) > 0:
							A__							:   List[Any]					       =		args[0]
							A__							:   Optional[int]					       =		args[1:]

						if input_features is not None:
							A__							:   Union[str, Any]					       =		self.feature_extractor.pad(A__   ,	*A__   ,	**A__      )
						if labels is not None:
							A__							:   List[Any]					       =		self.tokenizer.pad(A__   ,	**A__      )

						if labels is None:
							return input_features
						elif input_features is None:
							return labels
						else:
							A__							:   Dict					       =		labels["""input_ids"""]
							return input_features






					def   __A      (			self   ,	*A__   ,	**A__      ):
						return self.tokenizer.decode(*A__   ,	**A__      )






					@contextmanager
					def   __A      (			self      ):
						warnings.warn(
						    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
						    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
						    """your audio inputs, or in a separate call."""      )
						A__							:   int					       =		True
						A__							:   List[Any]					       =		self.tokenizer
						yield
						A__							:   Tuple					       =		self.feature_extractor
						A__							:   Dict					       =		False



