"""simple docstring"""




_a   :	Any 					=     {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
_a   :	Any 					=     ['a', 'b', 'c', 'd', 'e']





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Union[str, Any]    ,_lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: Dict			) ->   int:
  _lowerCAmelCase		: List[Any]						    =  start
  # add current to visited
  visited.append(_lowerCamelCase			)
  _lowerCAmelCase		: Union[str, Any]						    =  edges[current]
  for neighbor in neighbors:
    # if neighbor not in visited, visit
    if neighbor not in visited:
      _lowerCAmelCase		: str						    =  topological_sort(_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase			)
    # if all neighbors visited add current to sort
  sort.append(_lowerCamelCase			)
  # if all vertices haven't been visited select a new one to visit
  if len(_lowerCamelCase			) != len(_lowerCamelCase			):
    for vertice in vertices:
      if vertice not in visited:
        _lowerCAmelCase		: List[Any]						    =  topological_sort(_lowerCamelCase    ,_lowerCamelCase    ,_lowerCamelCase			)
    # return sort
  return sort


if __name__ == "__main__":
     _a   :	Tuple 					=     topological_sort('a', [], [])
     print(sort)





"""simple docstring"""




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class 		__A							(				unittest.TestCase	):






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  torch.nn.Linear(10       ,  10						)
       _lowerCAmelCase		: Optional[Any]						    =  torch.optim.SGD(model.parameters()       ,  0.1						)
       _lowerCAmelCase		: Optional[Any]						    =  Accelerator()
       _lowerCAmelCase		: Tuple						    =  accelerator.prepare(a__						)
       try:
         pickle.loads(pickle.dumps(a__						)						)
       except Exception as e:
         self.fail(F"Accelerated optimizer pickling failed with {e}"						)
       AcceleratorState._reset_state()




