






"""simple docstring"""







import argparse
import json
import subprocess





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  []

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

					a__          =  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__          =  '''\n'''.join([x['''name'''] for x in offline_runners])
										raise ValueError(f'The following runners are offline:\n{failed}')


if __name__ == "__main__":





	def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
						return values.split(''',''')

	__a       :	Optional[int]             = 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[Any]             = parser.parse_args()
	get_runner_status(args.target_runners, args.token)










"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__a       :	Tuple             = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	Tuple             = ['DeiTFeatureExtractor']
	__a       :	Optional[Any]             = ['DeiTImageProcessor']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	Optional[int]             = [
	    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'DeiTForImageClassification',
	    'DeiTForImageClassificationWithTeacher',
	    'DeiTForMaskedImageModeling',
	    'DeiTModel',
	    'DeiTPreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__a       :	Dict             = [
	    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFDeiTForImageClassification',
	    'TFDeiTForImageClassificationWithTeacher',
	    'TFDeiTForMaskedImageModeling',
	    'TFDeiTModel',
	    'TFDeiTPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_deit import DeiTFeatureExtractor
		from .image_processing_deit import DeiTImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_deit import (
		    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DeiTForImageClassification,
		    DeiTForImageClassificationWithTeacher,
		    DeiTForMaskedImageModeling,
		    DeiTModel,
		    DeiTPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_deit import (
		    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFDeiTForImageClassification,
		    TFDeiTForImageClassificationWithTeacher,
		    TFDeiTForMaskedImageModeling,
		    TFDeiTModel,
		    TFDeiTPreTrainedModel,
		)


else:
	import sys

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


