




"""simple docstring"""







import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory





def      __snake_case (				):


    """simple docstring"""







    raise RuntimeError('''CUDA out of memory.'''    )

class 				snake_case_			(					nn.Module			):







   """simple docstring"""




   def __init__(		self)				->   Any:
       super().__init__()
       UpperCamelCase              =	nn.Linear(3       ,					4)
       UpperCamelCase              =	nn.BatchNormad(4)
       UpperCamelCase              =	nn.Linear(4       ,					5)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_)				->   Union[str, Any]:
       return self.lineara(self.batchnorm(self.lineara(lowerCamelCase_)))

class 				snake_case_			(					unittest.TestCase			):







   """simple docstring"""




   def   UpperCAmelCase__			(		self)				->   List[Any]:
       UpperCamelCase              =	[]

       @find_executable_batch_size(starting_batch_size=1_2_8)
       def mock_training_loop_function(lowerCamelCase_):
           nonlocal batch_sizes
           batch_sizes.append(lowerCamelCase_)
           if batch_size != 8:
               raise_fake_out_of_memory()

       mock_training_loop_function()
       self.assertListEqual(lowerCamelCase_       ,					[1_2_8, 6_4, 3_2, 1_6, 8])







   def   UpperCAmelCase__			(		self)				->   Optional[Any]:
       UpperCamelCase              =	[]

       @find_executable_batch_size(starting_batch_size=1_2_8)
       def mock_training_loop_function(lowerCamelCase_       ,					lowerCamelCase_):
           nonlocal batch_sizes
           batch_sizes.append(lowerCamelCase_)
           if batch_size != 8:
               raise_fake_out_of_memory()
           return batch_size, arga

       UpperCamelCase       ,	UpperCamelCase              =	mock_training_loop_function('''hello''')
       self.assertListEqual(lowerCamelCase_       ,					[1_2_8, 6_4, 3_2, 1_6, 8])
       self.assertListEqual([bs, arga]       ,					[8, '''hello'''])







   def   UpperCAmelCase__			(		self)				->   Tuple:
       @find_executable_batch_size(starting_batch_size=0)
       def mock_training_loop_function(lowerCamelCase_):
           pass

       with self.assertRaises(lowerCamelCase_) as cm:
           mock_training_loop_function()
           self.assertIn('''No executable batch size found, reached zero.'''       ,					cm.exception.args[0])







   def   UpperCAmelCase__			(		self)				->   List[Any]:
       @find_executable_batch_size(starting_batch_size=1_6)
       def mock_training_loop_function(lowerCamelCase_):
           if batch_size > 0:
               raise_fake_out_of_memory()
           pass

       with self.assertRaises(lowerCamelCase_) as cm:
           mock_training_loop_function()
           self.assertIn('''No executable batch size found, reached zero.'''       ,					cm.exception.args[0])







   def   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       @find_executable_batch_size(starting_batch_size=1_2_8)
       def mock_training_loop_function(lowerCamelCase_       ,					lowerCamelCase_       ,					lowerCamelCase_):
           if batch_size != 8:
               raise raise_fake_out_of_memory()

       with self.assertRaises(lowerCamelCase_) as cm:
           mock_training_loop_function(1_2_8       ,					'''hello'''       ,					'''world''')
           self.assertIn('''Batch size was passed into `f`'''       ,					cm.exception.args[0])
           self.assertIn('''`f(arg1=\'hello\', arg2=\'world\')'''       ,					cm.exception.args[0])







   def   UpperCAmelCase__			(		self)				->   Dict:
       @find_executable_batch_size(starting_batch_size=1_6)
       def mock_training_loop_function(lowerCamelCase_):
           raise ValueError('''Oops, we had an error!''')

       with self.assertRaises(lowerCamelCase_) as cm:
           mock_training_loop_function()
           self.assertIn('''Oops, we had an error!'''       ,					cm.exception.args[0])







   @require_cuda
   def   UpperCAmelCase__			(		self)				->   Optional[int]:
       UpperCamelCase              =	torch.cuda.memory_allocated()
       UpperCamelCase              =	ModelForTest()
       model.cuda()
       self.assertGreater(torch.cuda.memory_allocated()       ,					lowerCamelCase_)
       UpperCamelCase              =	release_memory(lowerCamelCase_)
       self.assertEqual(torch.cuda.memory_allocated()       ,					lowerCamelCase_)







from datetime import datetime as dt
import os

from github import Github


__magic_name__   :  Dict        =							[
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]
def 		lowercase__  (				) ->			Dict:
      """simple docstring"""

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

      for issue in open_issues:
            UpperCamelCase					       =  sorted([comment for comment in issue.get_comments()]       ,							key=lambda _UpperCamelCase: i.created_at       ,							reverse=_UpperCamelCase)
            UpperCamelCase					       =  comments[0] if len(_UpperCamelCase) > 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())
            ):
                  # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
                  issue.edit(state='closed')
            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())
            ):
                  # print(f"Would add stale comment to {issue.number}")
                  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/transformers/blob/main/CONTRIBUTING.md) '
                      'are likely to be ignored.')


if __name__ == "__main__":
       main()



