





import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler





@dataclass
class A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = 0
   lowerCAmelCase__        = False
   lowerCAmelCase__        = 3.0





class A_					(			unittest.TestCase   ):




   def _lowerCAmelCase      (self	:Optional[Any] )->					Optional[int]:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         self.assertDictEqual(MockClass().to_kwargs()    ,   {} )
         self.assertDictEqual(MockClass(a=2 ).to_kwargs()    ,   {'''a''': 2} )
         self.assertDictEqual(MockClass(a=2    ,   b=_UpperCamelCase ).to_kwargs()    ,   {'''a''': 2, '''b''': True} )
         self.assertDictEqual(MockClass(a=2    ,   c=2.2_5 ).to_kwargs()    ,   {'''a''': 2, '''c''': 2.2_5} )




   @require_cuda
   def _lowerCAmelCase      (self	:Optional[Any] )->					Optional[Any]:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         __A          =				GradScalerKwargs(init_scale=1024    ,   growth_factor=2 )
         AcceleratorState._reset_state()
         __A          =				Accelerator(mixed_precision='''fp16'''    ,   kwargs_handlers=[scaler_handler] )
         print(accelerator.use_fpaa )
         __A          =				accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale    ,   1_0_2_4.0 )
         self.assertEqual(scaler._growth_factor    ,   2.0 )

         # Check the other values are at the default
         self.assertEqual(scaler._backoff_factor    ,   0.5 )
         self.assertEqual(scaler._growth_interval    ,   2000 )
         self.assertEqual(scaler._enabled    ,   _UpperCamelCase )






   @require_multi_gpu
   def _lowerCAmelCase      (self	:Tuple )->					Optional[Any]:
         __A          =				['''torchrun''', f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__ )]
         execute_subprocess_async(_UpperCamelCase    ,   env=os.environ.copy() )


if __name__ == "__main__":
       snake_case__					:							Optional[int]													=     DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
       snake_case__					:							Optional[int]													=     Accelerator(kwargs_handlers=[ddp_scaler])
       snake_case__					:							Dict													=     torch.nn.Linear(100, 200)
       snake_case__					:							int													=     accelerator.prepare(model)

       # Check the values changed in kwargs
       snake_case__					:							Optional[Any]													=     ''
       snake_case__					:							int													=     model.bucket_bytes_cap // (1024 * 1024)
       if observed_bucket_cap_map != 15:
              error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
       if model.find_unused_parameters is not True:
              error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

       # Check the values of the defaults
       if model.dim != 0:
              error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
       if model.broadcast_buffers is not True:
              error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
       if model.gradient_as_bucket_view is not False:
              error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

       # Raise error at the end to make sure we don't stop at the first failure.
       if len(error_msg) > 0:
              raise ValueError(error_msg)












import os
from datetime import datetime as dt

from github import Github


snake_case__					:							Union[str, Any]													=     [
    'good first issue',
    'feature request',
    'wip',
]



def _a				(	)	->						List[Any]:







      '''simple docstring'''






      __A          =				Github(os.environ['''GITHUB_TOKEN'''] )
      __A          =				g.get_repo('''huggingface/accelerate''' )
      __A          =				repo.get_issues(state='''open''' )

      for issue in open_issues:
            __A          =				sorted([comment for comment in issue.get_comments()]					,				key=lambda lowerCamelCase : i.created_at					,				reverse=lowerCamelCase )
            __A          =				comments[0] if len(lowerCamelCase ) > 0 else None
            __A          =				dt.utcnow()
            __A          =				(current_time - issue.updated_at).days
            __A          =				(current_time - issue.created_at).days
            if (
                last_comment is not None
                and last_comment.user.login == "github-actions[bot]"
                and days_since_updated > 7
                and days_since_creation >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
            ):
                  # Close issue since it has been 7 days of inactivity since bot mention.
                  issue.edit(state='''closed''' )
            elif (
                days_since_updated > 23
                and days_since_creation >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
            ):
                  # Add stale comment
                  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/accelerate/blob/main/CONTRIBUTING.md) '''
                      '''are likely to be ignored.''' )


if __name__ == "__main__":
       main()





