






from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
	import torch

if is_torch_tpu_available(check_device=False):
	import torch_xla.core.xla_model as xm


a__  	=  logging.get_logger(__name__)


@dataclass
class  _lowerCAmelCase      (		lowercase_   ):



				"""simple docstring"""



				_lowercase					: Optional[Any]	     =	[
				    'no_inference',
				    'no_cuda',
				    'no_tpu',
				    'no_speed',
				    'no_memory',
				    'no_env_print',
				    'no_multi_process',
				]



				def __init__(   self     :     Tuple      ,		**UpperCamelCase__     :     List[Any]):






											'''simple docstring'''







											for deprecated_arg in self.deprecated_args:
																		if deprecated_arg in kwargs:
																									snake_case__							=							deprecated_arg[3:]
																									setattr(self      ,		_SCREAMING_SNAKE_CASE      ,		not kwargs.pop(_SCREAMING_SNAKE_CASE))
																									logger.warning(
																									    F'''{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'''
																									    F''' {positive_arg}={kwargs[positive_arg]}''')

											snake_case__							=							kwargs.pop("""torchscript"""      ,		self.torchscript)
											snake_case__							=							kwargs.pop("""torch_xla_tpu_print_metrics"""      ,		self.torch_xla_tpu_print_metrics)
											snake_case__							=							kwargs.pop("""fp16_opt_level"""      ,		self.fpaa_opt_level)
											super().__init__(**_SCREAMING_SNAKE_CASE)

				_lowercase					: bool	     =	field(default=lowercase_  ,   metadata={'''help''': '''Trace the models using torchscript'''}   )
				_lowercase					: bool	     =	field(default=lowercase_  ,   metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}   )
				_lowercase					: str	     =	field(
				    default='''O1'''  ,   metadata={
				        '''help''': (
				            '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
				            '''See details at https://nvidia.github.io/apex/amp.html'''
				        )
				    }  ,   )



				@cached_property
				def        __magic_name__				(   self     :     Tuple):






											'''simple docstring'''







											requires_backends(self      ,		["""torch"""])
											logger.info("""PyTorch: setting up devices""")
											if not self.cuda:
																		snake_case__							=							torch.device("""cpu""")
																		snake_case__							=							0
											elif is_torch_tpu_available():
																		snake_case__							=							xm.xla_device()
																		snake_case__							=							0
											else:
																		snake_case__							=							torch.device("""cuda""" if torch.cuda.is_available() else """cpu""")
																		snake_case__							=							torch.cuda.device_count()
											return device, n_gpu



				@property
				def        __magic_name__				(   self     :     str):






											'''simple docstring'''







											return is_torch_tpu_available() and self.tpu



				@property
				def        __magic_name__				(   self     :     Dict):






											'''simple docstring'''







											requires_backends(self      ,		["""torch"""])
											# TODO(PVP): currently only single GPU is supported
											return torch.cuda.current_device()



				@property
				def        __magic_name__				(   self     :     int):






											'''simple docstring'''







											requires_backends(self      ,		["""torch"""])
											return self._setup_devices[0]



				@property
				def        __magic_name__				(   self     :     int):






											'''simple docstring'''







											requires_backends(self      ,		["""torch"""])
											return self._setup_devices[1]



				@property
				def        __magic_name__				(   self     :     Optional[Any]):






											'''simple docstring'''







											return self.n_gpu > 0




import argparse
import json
import subprocess

def 			lowerCAmelCase__					(     a__:						List[str]     ,    a__:						Optional[int]			)    ->						Union[str, Any]:


							'''simple docstring'''





							_UpperCAmelCase											=     []

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

							_UpperCAmelCase											=     status['runners']
							for runner in runners:
														if runner["name"] in target_runners:
																					if runner["status"] == "offline":
																												offline_runners.append(a__			)

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

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


if __name__ == "__main__":

		def 			lowerCAmelCase__					(     a__:						int			)    ->						List[Any]:


									'''simple docstring'''





									return values.split(','			)

		lowerCAmelCase__    :Optional[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.'''
		)
		lowerCAmelCase__    :Tuple       =				parser.parse_args()







		get_runner_status(args.target_runners, args.token)
