






"""simple docstring"""



from __future__ import annotations


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Union[str, Any]				)->		List[str]:






     '''simple docstring'''



     for i in range(1			,					len(matrix[0]				)				):
          matrix[0][i] += matrix[0][i - 1]

     # preprocessing the first column
     for i in range(1			,					len(snake_case__				)				):
          matrix[i][0] += matrix[i - 1][0]

     # updating the path cost for current position
     for i in range(1			,					len(snake_case__				)				):
          for j in range(1			,					len(matrix[0]				)				):
               matrix[i][j] += min(matrix[i - 1][j]			,					matrix[i][j - 1]				)

     return matrix[-1][-1]


if __name__ == "__main__":
 import doctest

 doctest.testmod()





"""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  lowerCamelCase    (unittest.TestCase      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     str:
						SCREAMING_SNAKE_CASE__										=   torch.nn.Linear(1_0     ,					1_0							)
						SCREAMING_SNAKE_CASE__										=   torch.optim.SGD(model.parameters()     ,					0.1							)
						SCREAMING_SNAKE_CASE__										=   Accelerator()
						SCREAMING_SNAKE_CASE__										=   accelerator.prepare(__UpperCAmelCase							)
						try:
									pickle.loads(pickle.dumps(__UpperCAmelCase							)							)
						except Exception as e:
									self.fail(F"""Accelerated optimizer pickling failed with {e}"""							)
						AcceleratorState._reset_state()


