





"""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       lowerCamelCase_       (   )						->List[str]:





  """simple docstring"""


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




class 							snake_case							(			nn.Module ):

      '''simple docstring'''



      def __init__(							self       :      List[Any] ):


        '''simple docstring'''




        super().__init__()
        __UpperCAmelCase    :						Optional[int]       =						nn.Linear(3						,				4 )
        __UpperCAmelCase    :						Union[str, Any]       =						nn.BatchNormad(4 )
        __UpperCAmelCase    :						List[str]       =						nn.Linear(4						,				5 )







      def       A_					(							self       :      List[Any]						,				__lowercase       :      str ):


        '''simple docstring'''




        return self.lineara(self.batchnorm(self.lineara(__lowercase ) ) )




class 							snake_case							(			unittest.TestCase ):

      '''simple docstring'''



      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						[]

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

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

      def       A_					(							self       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						List[str]       =						[]

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

        __UpperCAmelCase       ,     __UpperCAmelCase    :						Tuple       =						mock_training_loop_function('''hello''' )
        self.assertListEqual(__lowercase						,				[128, 64, 32, 16, 8] )
        self.assertListEqual([bs, arga]						,				[8, '''hello'''] )

      def       A_					(							self       :      str ):


        '''simple docstring'''




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

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

      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        @find_executable_batch_size(starting_batch_size=16 )
        def mock_training_loop_function(__lowercase       :      str ):
          if batch_size > 0:
            raise_fake_out_of_memory()
          pass

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

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        @find_executable_batch_size(starting_batch_size=128 )
        def mock_training_loop_function(__lowercase       :      Tuple						,				__lowercase       :      Union[str, Any]						,				__lowercase       :      Union[str, Any] ):
          if batch_size != 8:
            raise raise_fake_out_of_memory()

        with self.assertRaises(__lowercase ) 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       A_					(							self       :      List[Any] ):


        '''simple docstring'''




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

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







      @require_cuda
      def       A_					(							self       :      Any ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[Any]       =						torch.cuda.memory_allocated()
        __UpperCAmelCase    :						Optional[Any]       =						ModelForTest()
        model.cuda()
        self.assertGreater(torch.cuda.memory_allocated()						,				__lowercase )
        __UpperCAmelCase    :						int       =						release_memory(__lowercase )
        self.assertEqual(torch.cuda.memory_allocated()						,				__lowercase )






"""simple docstring"""





def       lowerCamelCase_       (   UpperCAmelCase_ = 10_00  )						->int:





  """simple docstring"""


  return sum(2 * a * ((a - 1) // 2) for a in range(3  ,			n + 1  )  )


if __name__ == "__main__":
  print(solution())