



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__	(					):





				'''simple docstring'''

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



class  lowerCamelCase__    (							nn.Module):




		'''simple docstring'''



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

						"""simple docstring"""






						super().__init__()
						lowerCAmelCase__  :       Dict				=  nn.Linear(3	,4				)
						lowerCAmelCase__  :       Dict				=  nn.BatchNormad(4				)
						lowerCAmelCase__  :       Optional[int]				=  nn.Linear(4	,5				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Tuple:

						"""simple docstring"""






						return self.lineara(self.batchnorm(self.lineara(__lowerCamelCase				)				)				)



class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		def 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  []

						@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 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Dict				=  []

						@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__  :       Union[str, Any]				=  mock_training_loop_function('''hello'''				)
						self.assertListEqual(__lowerCamelCase	,[1_28, 64, 32, 16, 8]				)
						self.assertListEqual([bs, arga]	,[8, '''hello''']				)



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

						"""simple docstring"""






						@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 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						@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 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						@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 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						@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 	lowerCAmelCase__   (self				)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Any				=  torch.cuda.memory_allocated()
						lowerCAmelCase__  :       List[Any]				=  ModelForTest()
						model.cuda()
						self.assertGreater(torch.cuda.memory_allocated()	,__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[Any]				=  release_memory(__lowerCamelCase				)
						self.assertEqual(torch.cuda.memory_allocated()	,__lowerCamelCase				)






from __future__ import annotations






def 			lowerCAmelCase__	(					lowerCamelCase_	:	int):





				'''simple docstring'''

				lowerCAmelCase__  :       Optional[int]				=  str(lowerCamelCase_)
				return len(lowerCamelCase_) == 9 and set(lowerCamelCase_) == set('''123456789''')






def 			lowerCAmelCase__	(					):





				'''simple docstring'''

				for base_num in range(9999 ,4999 ,-1):
								lowerCAmelCase__  :       Union[str, Any]				=  100002 * base_num
								if is_9_pandigital(lowerCamelCase_):
												return candidate

				for base_num in range(333 ,99 ,-1):
								lowerCAmelCase__  :       Any				=  1002003 * base_num
								if is_9_pandigital(lowerCamelCase_):
												return candidate

				return None


if __name__ == "__main__":
							print(f"""{solution() = }""")

