'''simple docstring'''


import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


__lowerCAmelCase	:       Any						       =	logging.get_logger(__name__)






class 						A   (   lowercase__   ):





							def __init__(      self				:							int     ,		*__a				:							List[Any]     ,		**__a				:							Optional[int]	)							->	None:
								warnings.warn(
								    '''The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
								    ''' Please use PerceiverImageProcessor instead.'''     ,		__a     ,		)
								super().__init__(*__a     ,		**__a	)

'''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       lowerCAmelCase					(		):


	"""simple docstring"""
	raise RuntimeError('''CUDA out of memory.'''       )






class 						A   (   nn.Module   ):





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






							def       snake_case__						(      self				:							List[str]     ,		__a				:							Optional[int]	)							->	Optional[int]:
								return self.lineara(self.batchnorm(self.lineara(__a	)	)	)






class 						A   (   unittest.TestCase   ):





							def       snake_case__						(      self				:							Optional[int]	)							->	Any:
								__UpperCAmelCase							=			[]

								@find_executable_batch_size(starting_batch_size=1_2_8	)
								def mock_training_loop_function(__a				:							Union[str, Any]	):
									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       snake_case__						(      self				:							str	)							->	int:
								__UpperCAmelCase							=			[]

								@find_executable_batch_size(starting_batch_size=1_2_8	)
								def mock_training_loop_function(__a				:							str     ,		__a				:							Optional[int]	):
									nonlocal batch_sizes
									batch_sizes.append(__a	)
									if batch_size != 8:
										raise_fake_out_of_memory()
									return batch_size, arga

								__UpperCAmelCase				,			__UpperCAmelCase							=			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       snake_case__						(      self				:							Any	)							->	int:
								@find_executable_batch_size(starting_batch_size=0	)
								def mock_training_loop_function(__a				:							Optional[int]	):
									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       snake_case__						(      self				:							Any	)							->	List[Any]:
								@find_executable_batch_size(starting_batch_size=1_6	)
								def mock_training_loop_function(__a				:							Dict	):
									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       snake_case__						(      self				:							List[Any]	)							->	List[str]:
								@find_executable_batch_size(starting_batch_size=1_2_8	)
								def mock_training_loop_function(__a				:							str     ,		__a				:							Union[str, Any]     ,		__a				:							int	):
									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       snake_case__						(      self				:							Tuple	)							->	Optional[Any]:
								@find_executable_batch_size(starting_batch_size=1_6	)
								def mock_training_loop_function(__a				:							Tuple	):
									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       snake_case__						(      self				:							Any	)							->	List[Any]:
								__UpperCAmelCase							=			torch.cuda.memory_allocated()
								__UpperCAmelCase							=			ModelForTest()
								model.cuda()
								self.assertGreater(torch.cuda.memory_allocated()     ,		__a	)
								__UpperCAmelCase							=			release_memory(__a	)
								self.assertEqual(torch.cuda.memory_allocated()     ,		__a	)
