


'''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  _snake_case       (					)						->   Union[str, Any]:
							raise RuntimeError('''CUDA out of memory.'''	)




class 		a__    (							nn.Module  ):





							'''simple docstring'''






							def __init__(		self       )	->							List[Any]:
														super().__init__()
														lowerCAmelCase__	       =     nn.Linear(3 ,   4       )
														lowerCAmelCase__	       =     nn.BatchNormad(4       )
														lowerCAmelCase__	       =     nn.Linear(4 ,   5       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							Any:
														return self.lineara(self.batchnorm(self.lineara(lowerCamelCase_       )       )       )




class 		a__    (							unittest.TestCase  ):





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														lowerCAmelCase__	       =     []

														@find_executable_batch_size(starting_batch_size=1_28       )
														def mock_training_loop_function(lowerCamelCase_       ):
																					nonlocal batch_sizes
																					batch_sizes.append(lowerCamelCase_       )
																					if batch_size != 8:
																												raise_fake_out_of_memory()

														mock_training_loop_function()
														self.assertListEqual(lowerCamelCase_ ,   [1_28, 64, 32, 16, 8]       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[Any]:
														lowerCAmelCase__	       =     []

														@find_executable_batch_size(starting_batch_size=1_28       )
														def mock_training_loop_function(lowerCamelCase_ ,   lowerCamelCase_       ):
																					nonlocal batch_sizes
																					batch_sizes.append(lowerCamelCase_       )
																					if batch_size != 8:
																												raise_fake_out_of_memory()
																					return batch_size, arga

														lowerCAmelCase__       , lowerCAmelCase__	       =     mock_training_loop_function('''hello'''       )
														self.assertListEqual(lowerCamelCase_ ,   [1_28, 64, 32, 16, 8]       )
														self.assertListEqual([bs, arga] ,   [8, '''hello''']       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														@find_executable_batch_size(starting_batch_size=0       )
														def mock_training_loop_function(lowerCamelCase_       ):
																					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       )	->							Union[str, Any]:
														@find_executable_batch_size(starting_batch_size=16       )
														def mock_training_loop_function(lowerCamelCase_       ):
																					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       )	->							str:
														@find_executable_batch_size(starting_batch_size=1_28       )
														def mock_training_loop_function(lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       ):
																					if batch_size != 8:
																												raise raise_fake_out_of_memory()

														with self.assertRaises(lowerCamelCase_       ) as cm:
																					mock_training_loop_function(1_28 ,   '''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       )	->							Union[str, Any]:
														@find_executable_batch_size(starting_batch_size=16       )
														def mock_training_loop_function(lowerCamelCase_       ):
																					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       )	->							str:
														lowerCAmelCase__	       =     torch.cuda.memory_allocated()
														lowerCAmelCase__	       =     ModelForTest()
														model.cuda()
														self.assertGreater(torch.cuda.memory_allocated() ,   lowerCamelCase_       )
														lowerCAmelCase__	       =     release_memory(lowerCamelCase_       )
														self.assertEqual(torch.cuda.memory_allocated() ,   lowerCamelCase_       )



'''simple docstring'''






def  _snake_case       (					A = 10	)						->   str:
							if not isinstance(A			,   A	) or n < 0:
														raise ValueError('''Invalid input'''	)
							lowerCAmelCase__	       =     10**n
							lowerCAmelCase__	       =     28433 * (pow(2			,   7830457			,   A	)) + 1
							return str(number % modulus	)


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

		testmod()
		print(f"""{solution(10) = }""")