"""simple docstring"""




import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Tuple    ,_lowerCamelCase		: Optional[int]    ,_lowerCamelCase		: List[str]			) ->   Union[str, Any]:
  # Initialise PyTorch model
  _lowerCAmelCase		: Dict						    =  MobileBertConfig.from_json_file(_lowerCamelCase			)
  print(f"Building PyTorch model from configuration: {config}"			)
  _lowerCAmelCase		: Any						    =  MobileBertForPreTraining(_lowerCamelCase			)
  # Load weights from tf checkpoint
  _lowerCAmelCase		: Optional[Any]						    =  load_tf_weights_in_mobilebert(_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase			)
  # Save pytorch-model
  print(f"Save PyTorch model to {pytorch_dump_path}"			)
  torch.save(model.state_dict()    ,_lowerCamelCase			)


if __name__ == "__main__":
     _a   :	Optional[Any] 					=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
     )
     parser.add_argument(
         '--mobilebert_config_file',
         default=None,
         type=str,
         required=True,
         help=(
             'The config json file corresponding to the pre-trained MobileBERT model. \n'
             'This specifies the model architecture.'
         ),
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     _a   :	str 					=     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)





"""simple docstring"""




import argparse
import json
import subprocess





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: Any			) ->   List[Any]:
  _lowerCAmelCase		: Tuple						    =  []

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

  _lowerCAmelCase		: 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:
    _lowerCAmelCase		: int						    =  """\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		: Union[str, Any]			) ->   Optional[int]:
       return values.split(""","""			)

     _a   :	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   :	Tuple 					=     parser.parse_args()






     get_runner_status(args.target_runners, args.token)




