





"""simple docstring"""




import os
from datetime import datetime as dt

from github import Github


UpperCamelCase_									=[
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """enhancement""",
    """new pipeline/model""",
    """new scheduler""",
    """wip""",
]





def 	a_    (							):
						_UpperCamelCase      : str         =   Github(os.environ['''GITHUB_TOKEN''']       )
						_UpperCamelCase      : List[Any]         =   g.get_repo('''huggingface/diffusers'''       )
						_UpperCamelCase      : Any         =   repo.get_issues(state='''open'''       )

						for issue in open_issues:
												_UpperCamelCase      : Optional[int]         =   sorted(issue.get_comments()	,						key=lambda _lowercase       : i.created_at	,						reverse=_lowercase       )
												_UpperCamelCase      : Optional[Any]         =   comments[0] if len(_lowercase       ) > 0 else None
												if (
												    last_comment is not None
												    and last_comment.user.login == "github-actions[bot]"
												    and (dt.utcnow() - issue.updated_at).days > 7
												    and (dt.utcnow() - issue.created_at).days >= 30
												    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()       )
												):
																		# Closes the issue after 7 days of inactivity since the Stalebot notification.
																		issue.edit(state='''closed'''       )
												elif (
												    "stale" in issue.get_labels()
												    and last_comment is not None
												    and last_comment.user.login != "github-actions[bot]"
												):
																		# Opens the issue if someone other than Stalebot commented.
																		issue.edit(state='''open'''       )
																		issue.remove_from_labels('''stale'''       )
												elif (
												    (dt.utcnow() - issue.updated_at).days > 23
												    and (dt.utcnow() - issue.created_at).days >= 30
												    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()       )
												):
																		# Post a Stalebot notification after 23 days of inactivity.
																		issue.create_comment(
																		    '''This issue has been automatically marked as stale because it has not had '''
																		    '''recent activity. If you think this still needs to be addressed '''
																		    '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
																		    '''[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '''
																		    '''are likely to be ignored.'''       )
																		issue.add_to_labels('''stale'''       )


if __name__ == "__main__":
						main()










"""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 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						# Initialise PyTorch model
						_UpperCamelCase      : List[Any]         =   MobileBertConfig.from_json_file(_lowercase       )
						print(F"""Building PyTorch model from configuration: {config}"""       )
						_UpperCamelCase      : List[str]         =   MobileBertForPreTraining(_lowercase       )
						# Load weights from tf checkpoint
						_UpperCamelCase      : Union[str, Any]         =   load_tf_weights_in_mobilebert(_lowercase	,						_lowercase	,						_lowercase       )
						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}"""       )
						torch.save(model.state_dict()	,						_lowercase       )


if __name__ == "__main__":
						UpperCamelCase_									=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."""
						)
						UpperCamelCase_									=parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



