


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

class        a	(nn.Module   ):




							"""simple docstring"""


							def __init__(							self :						int       )			->					Any:
														super().__init__()
														__snake_case	:    Optional[int]											=     nn.Linear(3					,			4       )
														__snake_case	:    Dict											=     nn.BatchNormad(4       )
														__snake_case	:    Optional[Any]											=     nn.Linear(4					,			5       )






							def 					__snake_case      (							self :						Any					,			lowerCamelCase :						Union[str, Any]       )			->					Tuple:
														return self.lineara(self.batchnorm(self.lineara(lowerCamelCase       )       )       )

class        a	(unittest.TestCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						Union[str, Any]       )			->					Union[str, Any]:
														__snake_case	:    Optional[Any]											=     []

														@find_executable_batch_size(starting_batch_size=128       )
														def mock_training_loop_function(lowerCamelCase :						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 					__snake_case      (							self :						Dict       )			->					Any:
														__snake_case	:    Dict											=     []

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

														__snake_case				,    __snake_case	:    Optional[int]											=     mock_training_loop_function("hello"       )
														self.assertListEqual(lowerCamelCase					,			[128, 64, 32, 16, 8]       )
														self.assertListEqual([bs, arga]					,			[8, "hello"]       )

							def 					__snake_case      (							self :						Dict       )			->					str:
														@find_executable_batch_size(starting_batch_size=0       )
														def mock_training_loop_function(lowerCamelCase :						str       ):
																					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 					__snake_case      (							self :						Dict       )			->					Tuple:
														@find_executable_batch_size(starting_batch_size=16       )
														def mock_training_loop_function(lowerCamelCase :						int       ):
																					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 					__snake_case      (							self :						Optional[int]       )			->					Tuple:
														@find_executable_batch_size(starting_batch_size=128       )
														def mock_training_loop_function(lowerCamelCase :						Optional[int]					,			lowerCamelCase :						Any					,			lowerCamelCase :						List[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 					__snake_case      (							self :						Union[str, Any]       )			->					Dict:
														@find_executable_batch_size(starting_batch_size=16       )
														def mock_training_loop_function(lowerCamelCase :						Optional[int]       ):
																					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 					__snake_case      (							self :						Dict       )			->					Dict:
														__snake_case	:    List[str]											=     torch.cuda.memory_allocated()
														__snake_case	:    Union[str, Any]											=     ModelForTest()
														model.cuda()
														self.assertGreater(torch.cuda.memory_allocated()					,			lowerCamelCase       )
														__snake_case	:    List[Any]											=     release_memory(lowerCamelCase       )
														self.assertEqual(torch.cuda.memory_allocated()					,			lowerCamelCase       )





from maths.prime_check import is_prime







def        lowerCAmelCase_  (      __lowerCamelCase			):
							if not isinstance(__lowerCamelCase							,			__lowerCamelCase			):
														__snake_case	:    List[str]											=     F'Input value of [number={number}] must be an integer'
														raise TypeError(__lowerCamelCase			)
							if is_prime(__lowerCamelCase			) and is_prime(number + 2			):
														return number + 2
							else:
														return -1


if __name__ == "__main__":
			import doctest

			doctest.testmod()

