





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    __lowercase   (      ) ->   Union[str, Any]:


      '''simple docstring'''


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

class 		UpperCamelCase__ (      nn.Module   ):



     '''simple docstring'''

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




           '''simple docstring'''


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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]						,lowerCamelCase__						:    Dict       )     ->		List[str]:




           '''simple docstring'''


           return self.lineara(self.batchnorm(self.lineara(lowerCamelCase__       )       )       )

class 		UpperCamelCase__ (      unittest.TestCase   ):



     '''simple docstring'''

     def 				SCREAMING_SNAKE_CASE__  (							self						:    int       )     ->		str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[]

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

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






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[]

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

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    str       )     ->		List[Any]:




           '''simple docstring'''


           @find_executable_batch_size(starting_batch_size=0       )
           def mock_training_loop_function(lowerCamelCase__						:    Tuple       ):
                 pass

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    int       )     ->		str:




           '''simple docstring'''


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

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






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Dict       )     ->		str:




           '''simple docstring'''


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

           with self.assertRaises(lowerCamelCase__       ) 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 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		Union[str, Any]:




           '''simple docstring'''


           @find_executable_batch_size(starting_batch_size=16       )
           def mock_training_loop_function(lowerCamelCase__						:    Tuple       ):
                 raise ValueError("""Oops, we had an error!"""       )

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






     @require_cuda
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						torch.cuda.memory_allocated()
           SCREAMING_SNAKE_CASE		=						ModelForTest()
           model.cuda()
           self.assertGreater(torch.cuda.memory_allocated()						,lowerCamelCase__       )
           SCREAMING_SNAKE_CASE		=						release_memory(lowerCamelCase__       )
           self.assertEqual(torch.cuda.memory_allocated()						,lowerCamelCase__       )









def    __lowercase   (      _SCREAMING_SNAKE_CASE = 10	) ->   str:


      '''simple docstring'''


      if not isinstance(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE	) or n < 0:
            raise ValueError("""Invalid input"""	)
      SCREAMING_SNAKE_CASE		=						10**n
      SCREAMING_SNAKE_CASE		=						2_84_33 * (pow(2 ,						7_83_04_57 ,						_SCREAMING_SNAKE_CASE	)) + 1
      return str(number % modulus	)


if __name__ == "__main__":
      from doctest import testmod

      testmod()
      print(F'''{solution(1_0) = }''')


