
'''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 A ():
						raise RuntimeError("""CUDA out of memory."""						)




class     UpperCAmelCase_	(						nn.Module					):






						'''simple docstring'''






						def __init__(   self ):

												"""simple docstring"""



												super().__init__()
												_lowerCAmelCase    		=					nn.Linear(3       ,  4 )
												_lowerCAmelCase    		=					nn.BatchNormad(4 )
												_lowerCAmelCase    		=					nn.Linear(4       ,  5 )




						def    _lowercase      (   self       ,  _lowercase ):

												"""simple docstring"""



												return self.lineara(self.batchnorm(self.lineara(_lowercase ) ) )






class     UpperCAmelCase_	(						unittest.TestCase					):






						'''simple docstring'''






						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					[]

												@find_executable_batch_size(starting_batch_size=128 )
												def mock_training_loop_function(_lowercase ):
																		nonlocal batch_sizes
																		batch_sizes.append(_lowercase )
																		if batch_size != 8:
																								raise_fake_out_of_memory()

												mock_training_loop_function()
												self.assertListEqual(_lowercase       ,  [128, 64, 32, 16, 8] )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					[]

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

												_lowerCAmelCase		,       _lowerCAmelCase    		=					mock_training_loop_function("""hello""" )
												self.assertListEqual(_lowercase       ,  [128, 64, 32, 16, 8] )
												self.assertListEqual([bs, arga]       ,  [8, """hello"""] )




						def    _lowercase      (   self ):

												"""simple docstring"""



												@find_executable_batch_size(starting_batch_size=0 )
												def mock_training_loop_function(_lowercase ):
																		pass

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




						def    _lowercase      (   self ):

												"""simple docstring"""



												@find_executable_batch_size(starting_batch_size=16 )
												def mock_training_loop_function(_lowercase ):
																		if batch_size > 0:
																								raise_fake_out_of_memory()
																		pass

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




						def    _lowercase      (   self ):

												"""simple docstring"""



												@find_executable_batch_size(starting_batch_size=128 )
												def mock_training_loop_function(_lowercase       ,  _lowercase       ,  _lowercase ):
																		if batch_size != 8:
																								raise raise_fake_out_of_memory()

												with self.assertRaises(_lowercase ) 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    _lowercase      (   self ):

												"""simple docstring"""



												@find_executable_batch_size(starting_batch_size=16 )
												def mock_training_loop_function(_lowercase ):
																		raise ValueError("""Oops, we had an error!""" )

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




						@require_cuda
						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					torch.cuda.memory_allocated()
												_lowerCAmelCase    		=					ModelForTest()
												model.cuda()
												self.assertGreater(torch.cuda.memory_allocated()       ,  _lowercase )
												_lowerCAmelCase    		=					release_memory(_lowercase )
												self.assertEqual(torch.cuda.memory_allocated()       ,  _lowercase )


'''simple docstring'''



import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_lowercase          =						logging.get_logger(__name__)




class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						def __init__(   self       ,  *_lowercase       ,  **_lowercase ):

												"""simple docstring"""



												warnings.warn(
												    """The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
												    """ use DonutImageProcessor instead."""       ,  _lowercase       ,  )
												super().__init__(*_lowercase       ,  **_lowercase )
