


'''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 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ):






					def  a_					(			self		)       ->    Any:
										_a						=		torch.nn.Linear(10     ,							10		)
										_a						=		torch.optim.SGD(model.parameters()     ,							0.1		)
										_a						=		Accelerator()
										_a						=		accelerator.prepare(SCREAMING_SNAKE_CASE_		)
										try:
															pickle.loads(pickle.dumps(SCREAMING_SNAKE_CASE_		)		)
										except Exception as e:
															self.fail(f"Accelerated optimizer pickling failed with {e}"		)
										AcceleratorState._reset_state()





'''simple docstring'''

from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst






def __UpperCamelCase   (			)      ->     Tuple:







     '''simple docstring'''






     _a			,			_a						=		9, 14  # noqa: F841
     _a						=		[
         [0, 1, 4],
         [0, 7, 8],
         [1, 2, 8],
         [7, 8, 7],
         [7, 6, 1],
         [2, 8, 2],
         [8, 6, 6],
         [2, 3, 7],
         [2, 5, 4],
         [6, 5, 2],
         [3, 5, 14],
         [3, 4, 9],
         [5, 4, 10],
         [1, 7, 11],
     ]

     _a						=		defaultdict(__lowerCamelCase				)
     for nodea, nodea, cost in edges:
          adjancency[nodea].append([nodea, cost]				)
          adjancency[nodea].append([nodea, cost]				)

     _a						=		mst(__lowerCamelCase				)

     _a						=		[
         [7, 6, 1],
         [2, 8, 2],
         [6, 5, 2],
         [0, 1, 4],
         [2, 5, 4],
         [2, 3, 7],
         [0, 7, 8],
         [3, 4, 9],
     ]

     for answer in expected:
          _a						=		tuple(answer[:2]				)
          _a						=		tuple(edge[::-1]				)
          assert edge in result or reverse in result

