





def  lowerCAmelCase_ (							UpperCamelCase_ = 1000  )     ->		int:
       UpperCamelCase_          =  2**power
       UpperCamelCase_          =  str(UpperCamelCase_  )
       UpperCamelCase_          =  list(UpperCamelCase_  )
       UpperCamelCase_          =  0

       for i in list_num:
              sum_of_num += int(UpperCamelCase_  )

       return sum_of_num


if __name__ == "__main__":
     _UpperCAmelCase        =				int(input('Enter the power of 2: ').strip())
     print('2 ^ ', power, ' = ', 2**power)
     _UpperCAmelCase        =				solution(power)
     print('Sum of the digits is: ', result)








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  lowerCAmelCase_ (							)     ->		Dict:
       raise RuntimeError("CUDA out of memory."  )



class 							_UpperCamelCase					(	nn.Module     ):

       def __init__(		self: int		)       -> List[Any]:




              """simple docstring"""


              super().__init__()
              UpperCamelCase_          =  nn.Linear(3					,   4		)
              UpperCamelCase_          =  nn.BatchNormad(4		)
              UpperCamelCase_          =  nn.Linear(4					,   5		)



       def lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: Optional[int]		)       -> Optional[Any]:




              """simple docstring"""


              return self.lineara(self.batchnorm(self.lineara(_SCREAMING_SNAKE_CASE		)		)		)



class 							_UpperCamelCase					(	unittest.TestCase     ):

       def lowercase (		self: List[Any]		)       -> Any:




              """simple docstring"""


              UpperCamelCase_          =  []

              @find_executable_batch_size(starting_batch_size=128		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: str		):
                     nonlocal batch_sizes
                     batch_sizes.append(_SCREAMING_SNAKE_CASE		)
                     if batch_size != 8:
                            raise_fake_out_of_memory()

              mock_training_loop_function()
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   [128, 64, 32, 16, 8]		)

       def lowercase (		self: Optional[int]		)       -> List[Any]:




              """simple docstring"""


              UpperCamelCase_          =  []

              @find_executable_batch_size(starting_batch_size=128		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: List[str]					,   _SCREAMING_SNAKE_CASE: Optional[Any]		):
                     nonlocal batch_sizes
                     batch_sizes.append(_SCREAMING_SNAKE_CASE		)
                     if batch_size != 8:
                            raise_fake_out_of_memory()
                     return batch_size, arga

              UpperCamelCase_       , UpperCamelCase_          =  mock_training_loop_function("hello"		)
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   [128, 64, 32, 16, 8]		)
              self.assertListEqual([bs, arga]					,   [8, "hello"]		)

       def lowercase (		self: Any		)       -> Optional[int]:




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=0		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: Union[str, Any]		):
                     pass

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

       def lowercase (		self: Dict		)       -> Optional[int]:




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=16		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: Optional[int]		):
                     if batch_size > 0:
                            raise_fake_out_of_memory()
                     pass

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

       def lowercase (		self: Optional[int]		)       -> Dict:




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=128		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: int					,   _SCREAMING_SNAKE_CASE: Tuple		):
                     if batch_size != 8:
                            raise raise_fake_out_of_memory()

              with self.assertRaises(_SCREAMING_SNAKE_CASE		) as cm:
                     mock_training_loop_function(128					,   "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 lowercase (		self: List[str]		)       -> Optional[Any]:




              """simple docstring"""


              @find_executable_batch_size(starting_batch_size=16		)
              def mock_training_loop_function(_SCREAMING_SNAKE_CASE: List[Any]		):
                     raise ValueError("Oops, we had an error!"		)

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



       @require_cuda
       def lowercase (		self: Optional[int]		)       -> Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_          =  torch.cuda.memory_allocated()
              UpperCamelCase_          =  ModelForTest()
              model.cuda()
              self.assertGreater(torch.cuda.memory_allocated()					,   _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  release_memory(_SCREAMING_SNAKE_CASE		)
              self.assertEqual(torch.cuda.memory_allocated()					,   _SCREAMING_SNAKE_CASE		)

