





import os
from datetime import datetime as dt

from github import Github


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




def     A_      (	)      ->    Dict:
       _snake_case   :				str  					=    Github(os.environ['''GITHUB_TOKEN''']	)
       _snake_case   :				Union[str, Any]  					=    g.get_repo('''huggingface/diffusers'''	)
       _snake_case   :				Union[str, Any]  					=    repo.get_issues(state='''open'''	)

       for issue in open_issues:
              _snake_case   :				int  					=    sorted(issue.get_comments()   ,		key=lambda lowercase_	: i.created_at   ,		reverse=_A	)
              _snake_case   :				int  					=    comments[0] if len(_A	) > 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 os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment







def __UpperCamelCase( _A       :					Any	,      _A       :					List[str]=()	,      _A       :					List[str]=None	,      _A       :					Dict="no"	,      _A       :					List[str]="29500"     ):
   '''simple docstring'''
   UpperCAmelCase__				:					int 					=	False
   UpperCAmelCase__				:					List[str] 					=	False
   if any(key.startswith('''KAGGLE'''     ) for key in os.environ.keys()     ):
      UpperCAmelCase__				:					List[str] 					=	True
   elif "IPython" in sys.modules:
      UpperCAmelCase__				:					List[Any] 					=	'''google.colab''' in str(sys.modules['''IPython'''].get_ipython()     )

   try:
      UpperCAmelCase__				:					Tuple 					=	PrecisionType(mixed_precision.lower()     )
   except ValueError:
      raise ValueError(
          F'''Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'''     )

   if (in_colab or in_kaggle) and (os.environ.get('''TPU_NAME'''	,      _A     ) is not None):
      # TPU launch
      import torch_xla.distributed.xla_multiprocessing as xmp

      if len(AcceleratorState._shared_state     ) > 0:
         raise ValueError(
             '''To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '''
             '''your training function. Restart your notebook and make sure no cells initializes an '''
             '''`Accelerator`.'''     )
      if num_processes is None:
         UpperCAmelCase__				:					Tuple 					=	8

      UpperCAmelCase__				:					Optional[Any] 					=	PrepareForLaunch(_A	,      distributed_type='''TPU'''     )
      print(F'''Launching a training on {num_processes} TPU cores.'''     )
      xmp.spawn(_A	,      args=_A	,      nprocs=_A	,      start_method='''fork'''     )
   elif in_colab:
      # No need for a distributed launch otherwise as it's either CPU or one GPU.
      if torch.cuda.is_available():
         print('''Launching training on one GPU.'''     )
      else:
         print('''Launching training on one CPU.'''     )
      function(*_A     )
   else:
      if num_processes is None:
         raise ValueError(
             '''You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.'''     )

      if num_processes > 1:
         # Multi-GPU launch
         from torch.multiprocessing import start_processes
         from torch.multiprocessing.spawn import ProcessRaisedException

         if len(AcceleratorState._shared_state     ) > 0:
            raise ValueError(
                '''To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '''
                '''inside your training function. Restart your notebook and make sure no cells initializes an '''
                '''`Accelerator`.'''     )

         if torch.cuda.is_initialized():
            raise ValueError(
                '''To launch a multi-GPU training from your notebook, you need to avoid running any instruction '''
                '''using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '''
                '''function.'''     )

         # torch.distributed will expect a few environment variable to be here. We set the ones common to each
         # process here (the other ones will be set be the launcher).
         with patch_environment(
             world_size=_A	,      master_addr='''127.0.01'''	,      master_port=_A	,      mixed_precision=_A     ):
            UpperCAmelCase__				:					str 					=	PrepareForLaunch(_A	,      distributed_type='''MULTI_GPU'''     )
            print(F'''Launching training on {num_processes} GPUs.'''     )
            try:
               start_processes(_A	,      args=_A	,      nprocs=_A	,      start_method='''fork'''     )
            except ProcessRaisedException as e:
               if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
                  raise RuntimeError(
                      '''CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '''
                      '''This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '''
                      '''Please review your imports and test them when running the `notebook_launcher()` to identify '''
                      '''which one is problematic.'''     ) from e

      else:
         # No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
         if is_mps_available():
            UpperCAmelCase__				:					Union[str, Any] 					=	'''1'''
            print('''Launching training on MPS.'''     )
         elif torch.cuda.is_available():
            print('''Launching training on one GPU.'''     )
         else:
            print('''Launching training on CPU.'''     )
         function(*_A     )







def __UpperCamelCase( _A       :					List[str]	,      _A       :					Optional[Any]=()	,      _A       :					str=2     ):
   '''simple docstring'''
   from torch.multiprocessing import start_processes

   with tempfile.NamedTemporaryFile() as tmp_file:
      # torch.distributed will expect a few environment variable to be here. We set the ones common to each
      # process here (the other ones will be set be the launcher).
      with patch_environment(
          world_size=_A	,      master_addr='''127.0.01'''	,      master_port='''29500'''	,      accelerate_mixed_precision='''no'''	,      accelerate_debug_rdv_file=tmp_file.name	,      accelerate_use_cpu='''yes'''	,      ):
         UpperCAmelCase__				:					Optional[int] 					=	PrepareForLaunch(_A	,      debug=_A     )
         start_processes(_A	,      args=_A	,      nprocs=_A	,      start_method='''fork'''     )




