





import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_					     =	logging.get_logger(__name__)




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Dict:
  lowercase__			    =						RobertaPreLayerNormConfig.from_pretrained(
      _SCREAMING_SNAKE_CASE	,		architectures=['RobertaPreLayerNormForMaskedLM']	)

  # convert state_dict
  lowercase__			    =						torch.load(hf_hub_download(repo_id=_SCREAMING_SNAKE_CASE	,		filename='pytorch_model.bin'	)	)
  lowercase__			    =						{}
  for tensor_key, tensor_value in original_state_dict.items():
    # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
    if tensor_key.startswith('roberta.'	):
      lowercase__			    =						'roberta_prelayernorm.' + tensor_key[len('roberta.'	) :]

    # The original implementation contains weights which are not used, remove them from the state_dict
    if tensor_key.endswith('.self.LayerNorm.weight'	) or tensor_key.endswith('.self.LayerNorm.bias'	):
      continue

    lowercase__			    =						tensor_value

  lowercase__			    =						RobertaPreLayerNormForMaskedLM.from_pretrained(
      pretrained_model_name_or_path=_SCREAMING_SNAKE_CASE	,		config=_SCREAMING_SNAKE_CASE	,		state_dict=_SCREAMING_SNAKE_CASE	)
  model.save_pretrained(_SCREAMING_SNAKE_CASE	)

  # convert tokenizer
  lowercase__			    =						AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE	)
  tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
      lowercase_					     =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--checkpoint-repo""",
          default=None,
          type=str,
          required=True,
          help="""Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      lowercase_					     =	parser.parse_args()
      convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)











import argparse
import json
import subprocess




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Optional[int]:
  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(_SCREAMING_SNAKE_CASE	,		shell=_SCREAMING_SNAKE_CASE	,		stdout=subprocess.PIPE	)
  lowercase__			    =						output.stdout.decode('utf-8'	)
  lowercase__			    =						json.loads(_SCREAMING_SNAKE_CASE	)

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

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

  if len(_SCREAMING_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     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     str:
        return values.split(','	)

      lowercase_					     =	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_					     =	parser.parse_args()


      get_runner_status(args.target_runners, args.token)


