



'''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 				snake_case_  (      )   ->     Optional[int]:
							raise RuntimeError('''CUDA out of memory.'''						)

class        SCREAMING_SNAKE_CASE(							nn.Module    ):







	"""simple docstring"""






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







	def   A			(		self    :  Optional[int]	,    __snake_case    :  Optional[Any]	)    ->	str:
								return self.lineara(self.batchnorm(self.lineara(__lowerCamelCase	)	)	)

class        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	def   A			(		self    :  int	)    ->	Optional[Any]:
								UpperCAmelCase				: Union[str, Any]											=	[]

								@find_executable_batch_size(starting_batch_size=128	)
								def mock_training_loop_function(__snake_case    :  Optional[Any]	):
															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   A			(		self    :  Dict	)    ->	Optional[Any]:
								UpperCAmelCase				: Optional[int]											=	[]

								@find_executable_batch_size(starting_batch_size=128	)
								def mock_training_loop_function(__snake_case    :  Tuple	,    __snake_case    :  int	):
															nonlocal batch_sizes
															batch_sizes.append(__lowerCamelCase	)
															if batch_size != 8:
																						raise_fake_out_of_memory()
															return batch_size, arga

								UpperCAmelCase				: str											=	mock_training_loop_function('''hello'''	)
								self.assertListEqual(__lowerCamelCase	,    [128, 64, 32, 16, 8]	)
								self.assertListEqual([bs, arga]	,    [8, '''hello''']	)







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




'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


UpperCamelCase__:     Dict										=    logging.get_logger(__name__)

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






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





