

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 (	)			->     Optional[int]:

					"""simple docstring"""

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

class 				UpperCAmelCase_		(			nn.Module   ):
							"""simple docstring"""







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




							def 				__lowercase				(			self       ,							_a       )    ->    Tuple:
												return self.lineara(self.batchnorm(self.lineara(_a       )       )       )



class 				UpperCAmelCase_		(			unittest.TestCase   ):
							"""simple docstring"""







							def 				__lowercase				(			self       )    ->    Tuple:
												_a	:    Optional[Any]         =		[]

												@find_executable_batch_size(starting_batch_size=1_2_8       )
												def mock_training_loop_function(_a       ):
																	nonlocal batch_sizes
																	batch_sizes.append(_a       )
																	if batch_size != 8:
																						raise_fake_out_of_memory()

												mock_training_loop_function()
												self.assertListEqual(_a       ,							[1_2_8, 6_4, 3_2, 1_6, 8]       )


							def 				__lowercase				(			self       )    ->    Any:
												_a	:    Dict         =		[]

												@find_executable_batch_size(starting_batch_size=1_2_8       )
												def mock_training_loop_function(_a       ,							_a       ):
																	nonlocal batch_sizes
																	batch_sizes.append(_a       )
																	if batch_size != 8:
																						raise_fake_out_of_memory()
																	return batch_size, arga

												_a      ,					_a	:    Optional[Any]         =		mock_training_loop_function('''hello'''       )
												self.assertListEqual(_a       ,							[1_2_8, 6_4, 3_2, 1_6, 8]       )
												self.assertListEqual([bs, arga]       ,							[8, '''hello''']       )


							def 				__lowercase				(			self       )    ->    str:
												@find_executable_batch_size(starting_batch_size=0       )
												def mock_training_loop_function(_a       ):
																	pass

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


							def 				__lowercase				(			self       )    ->    Optional[int]:
												@find_executable_batch_size(starting_batch_size=1_6       )
												def mock_training_loop_function(_a       ):
																	if batch_size > 0:
																						raise_fake_out_of_memory()
																	pass

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


							def 				__lowercase				(			self       )    ->    str:
												@find_executable_batch_size(starting_batch_size=1_2_8       )
												def mock_training_loop_function(_a       ,							_a       ,							_a       ):
																	if batch_size != 8:
																						raise raise_fake_out_of_memory()

												with self.assertRaises(_a       ) as cm:
																	mock_training_loop_function(1_2_8       ,							'''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 				__lowercase				(			self       )    ->    Dict:
												@find_executable_batch_size(starting_batch_size=1_6       )
												def mock_training_loop_function(_a       ):
																	raise ValueError('''Oops, we had an error!'''       )

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




							@require_cuda
							def 				__lowercase				(			self       )    ->    Optional[int]:
												_a	:    Optional[Any]         =		torch.cuda.memory_allocated()
												_a	:    Tuple         =		ModelForTest()
												model.cuda()
												self.assertGreater(torch.cuda.memory_allocated()       ,							_a       )
												_a	:    int         =		release_memory(_a       )
												self.assertEqual(torch.cuda.memory_allocated()       ,							_a       )






from diffusers.utils.testing_utils import require_onnxruntime

@require_onnxruntime
class 				UpperCAmelCase_		:
							"""simple docstring"""







							pass



