






import argparse
import json
import subprocess

def      lowerCAmelCase__	(    _a       :			Optional[int]  ,      _a       :			Union[str, Any]    ):
		snake_case_				:							Any						 =       []

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

		snake_case_				:							int						 =       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:
				snake_case_				:							List[str]						 =       "\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       :			Any    ):
									return values.split(","    )

							lowercase  :							List[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.'''
							)
							lowercase  :							List[Any]			  =    parser.parse_args()

							get_runner_status(args.target_runners, args.token)









from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase  :							Any			  =    logging.get_logger(__name__)

lowercase  :							str			  =    {
    '''naver-clova-ix/donut-base''': '''https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json''',
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Tuple						=					'donut-swin'

						A     :   Union[str, Any]						=					{
						    'num_attention_heads': 'num_heads',
						    'num_hidden_layers': 'num_layers',
						}






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=224		,				_SCREAMING_SNAKE_CASE=4		,				_SCREAMING_SNAKE_CASE=3		,				_SCREAMING_SNAKE_CASE=96		,				_SCREAMING_SNAKE_CASE=[2, 2, 6, 2]		,				_SCREAMING_SNAKE_CASE=[3, 6, 12, 24]		,				_SCREAMING_SNAKE_CASE=7		,				_SCREAMING_SNAKE_CASE=4.0		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=0.0		,				_SCREAMING_SNAKE_CASE=0.0		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE="gelu"		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=0.02		,				_SCREAMING_SNAKE_CASE=1e-5		,				**_SCREAMING_SNAKE_CASE		,				) ->				List[str]:
								super().__init__(**_SCREAMING_SNAKE_CASE				)

								snake_case_				:							Optional[int]						 =       image_size
								snake_case_				:							Any						 =       patch_size
								snake_case_				:							str						 =       num_channels
								snake_case_				:							Dict						 =       embed_dim
								snake_case_				:							Tuple						 =       depths
								snake_case_				:							List[Any]						 =       len(_SCREAMING_SNAKE_CASE				)
								snake_case_				:							Optional[int]						 =       num_heads
								snake_case_				:							Optional[int]						 =       window_size
								snake_case_				:							Any						 =       mlp_ratio
								snake_case_				:							str						 =       qkv_bias
								snake_case_				:							Optional[Any]						 =       hidden_dropout_prob
								snake_case_				:							Union[str, Any]						 =       attention_probs_dropout_prob
								snake_case_				:							str						 =       drop_path_rate
								snake_case_				:							List[str]						 =       hidden_act
								snake_case_				:							Optional[int]						 =       use_absolute_embeddings
								snake_case_				:							Tuple						 =       layer_norm_eps
								snake_case_				:							List[Any]						 =       initializer_range
								# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								snake_case_				:							Any						 =       int(embed_dim * 2 ** (len(_SCREAMING_SNAKE_CASE				) - 1)				)
