


'''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     _UpperCamelCase				(			):






				"""simple docstring"""



				raise RuntimeError("CUDA out of memory."		)






class 		_snake_case     (					nn.Module		):






						'''simple docstring'''







						def __init__(	self:						int  )				->						int:
										super().__init__()
										__magic_name__					:       Union[str, Any]    							=							nn.Linear(3   ,				4  )
										__magic_name__					:       Optional[int]    							=							nn.BatchNormad(4  )
										__magic_name__					:       Tuple    							=							nn.Linear(4   ,				5  )







						def lowerCAmelCase__			(	self:						List[str]   ,				__UpperCamelCase:						Dict  )				->						Optional[int]:
										return self.lineara(self.batchnorm(self.lineara(lowerCamelCase__  )  )  )






class 		_snake_case     (					unittest.TestCase		):






						'''simple docstring'''







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

										@find_executable_batch_size(starting_batch_size=128  )
										def mock_training_loop_function(__UpperCamelCase:						Tuple  ):
														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 lowerCAmelCase__			(	self:						int  )				->						Union[str, Any]:
										__magic_name__					:       int    							=							[]

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

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







						def lowerCAmelCase__			(	self:						Tuple  )				->						Dict:





										@find_executable_batch_size(starting_batch_size=0  )
										def mock_training_loop_function(__UpperCamelCase:						List[Any]  ):
														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 lowerCAmelCase__			(	self:						Optional[Any]  )				->						str:





										@find_executable_batch_size(starting_batch_size=16  )
										def mock_training_loop_function(__UpperCamelCase:						str  ):
														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 lowerCAmelCase__			(	self:						str  )				->						Union[str, Any]:





										@find_executable_batch_size(starting_batch_size=128  )
										def mock_training_loop_function(__UpperCamelCase:						str   ,				__UpperCamelCase:						Optional[Any]   ,				__UpperCamelCase:						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 lowerCAmelCase__			(	self:						List[Any]  )				->						Optional[int]:





										@find_executable_batch_size(starting_batch_size=16  )
										def mock_training_loop_function(__UpperCamelCase:						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 lowerCAmelCase__			(	self:						Tuple  )				->						Any:
										__magic_name__					:       Union[str, Any]    							=							torch.cuda.memory_allocated()
										__magic_name__					:       Optional[int]    							=							ModelForTest()
										model.cuda()
										self.assertGreater(torch.cuda.memory_allocated()   ,				lowerCamelCase__  )
										__magic_name__					:       str    							=							release_memory(lowerCamelCase__  )
										self.assertEqual(torch.cuda.memory_allocated()   ,				lowerCamelCase__  )


def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE = 1000   ):
      '''simple docstring'''







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


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





