



import argparse
import json
import subprocess
def  UpperCamelCase						(       _A			:				List[Any]			,							_A			:				str    )->       List[str]:





						"""simple docstring"""





						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(_A			,							shell=_A			,							stdout=subprocess.PIPE    )
						A__							=       output.stdout.decode("utf-8"    )
						A__							=       json.loads(_A    )

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


if __name__ == "__main__":
			def  UpperCamelCase						(       _A			:				Optional[int]    )->       List[Any]:





									"""simple docstring"""





									return values.split(","    )

			UpperCAmelCase_							:      Dict      =  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."
			)
			UpperCAmelCase_							:      int      =  parser.parse_args()
			get_runner_status(args.target_runners, args.token)










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()
UpperCAmelCase_							:      Union[str, Any]      =  logging.get_logger(__name__)
def  UpperCamelCase						(       _A			:				str			,							_A			:				str    )->       Any:





						"""simple docstring"""





						A__							=       RobertaPreLayerNormConfig.from_pretrained(
						    _A			,							architectures=["RobertaPreLayerNormForMaskedLM"]    )

						# convert state_dict
						A__							=       torch.load(hf_hub_download(repo_id=_A			,							filename="pytorch_model.bin"    )    )
						A__							=       {}
						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."    ):
																		A__							=       "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

												A__							=       tensor_value

						A__							=       RobertaPreLayerNormForMaskedLM.from_pretrained(
						    pretrained_model_name_or_path=_A			,							config=_A			,							state_dict=_A    )
						model.save_pretrained(_A    )

						# convert tokenizer
						A__							=       AutoTokenizer.from_pretrained(_A    )
						tokenizer.save_pretrained(_A    )


if __name__ == "__main__":
						UpperCAmelCase_							:      str      =  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."
						)
						UpperCAmelCase_							:      List[Any]      =  parser.parse_args()
						convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)





