
import argparse
import json
import subprocess




def 				a						(							snake_case__:					Union[str, Any]						,   snake_case__:					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(snake_case__						,   shell=snake_case__						,   stdout=subprocess.PIPE					)
				lowercase_       =					output.stdout.decode('''utf-8'''					)
				lowercase_       =					json.loads(snake_case__					)

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

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

				if len(snake_case__					) > 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 				a						(							snake_case__:					Optional[Any]					):


										'''simple docstring'''
										return values.split(''','''					)

						__a  			=					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  			=					parser.parse_args()


						get_runner_status(args.target_runners, args.token)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__a  			=					{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['RemBertTokenizer']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['RemBertTokenizerFast']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'RemBertForCausalLM',
						    'RemBertForMaskedLM',
						    'RemBertForMultipleChoice',
						    'RemBertForQuestionAnswering',
						    'RemBertForSequenceClassification',
						    'RemBertForTokenClassification',
						    'RemBertLayer',
						    'RemBertModel',
						    'RemBertPreTrainedModel',
						    'load_tf_weights_in_rembert',
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFRemBertForCausalLM',
						    'TFRemBertForMaskedLM',
						    'TFRemBertForMultipleChoice',
						    'TFRemBertForQuestionAnswering',
						    'TFRemBertForSequenceClassification',
						    'TFRemBertForTokenClassification',
						    'TFRemBertLayer',
						    'TFRemBertModel',
						    'TFRemBertPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_rembert import RemBertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_rembert_fast import RemBertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rembert import (
												    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RemBertForCausalLM,
												    RemBertForMaskedLM,
												    RemBertForMultipleChoice,
												    RemBertForQuestionAnswering,
												    RemBertForSequenceClassification,
												    RemBertForTokenClassification,
												    RemBertLayer,
												    RemBertModel,
												    RemBertPreTrainedModel,
												    load_tf_weights_in_rembert,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_rembert import (
												    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRemBertForCausalLM,
												    TFRemBertForMaskedLM,
												    TFRemBertForMultipleChoice,
												    TFRemBertForQuestionAnswering,
												    TFRemBertForSequenceClassification,
												    TFRemBertForTokenClassification,
												    TFRemBertLayer,
												    TFRemBertModel,
												    TFRemBertPreTrainedModel,
												)


else:
						import sys

						__a  			=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




