




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     _SCREAMING_SNAKE_CASE    (							):


						'''simple docstring'''



						raise RuntimeError('CUDA out of memory.'  )







class 						A(		nn.Module     ):



	'''simple docstring'''


	def __init__(       self      :						Union[str, Any]						)     ->			Optional[Any]:

							"""simple docstring"""






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





	def        a__				(       self      :						str		,							A_      :						List[str]						)     ->			str:

							"""simple docstring"""






							return self.lineara(self.batchnorm(self.lineara(UpperCamelCase__						)						)						)







class 						A(		unittest.TestCase     ):



	'''simple docstring'''


	def        a__				(       self      :						List[Any]						)     ->			Tuple:

							"""simple docstring"""






							lowerCamelCase_	  =					[]

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

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

	def        a__				(       self      :						Optional[int]						)     ->			Tuple:

							"""simple docstring"""






							lowerCamelCase_	  =					[]

							@find_executable_batch_size(starting_batch_size=128						)
							def mock_training_loop_function(A_      :						Union[str, Any]		,							A_      :						List[str]						):
													nonlocal batch_sizes
													batch_sizes.append(UpperCamelCase__						)
													if batch_size != 8:
																			raise_fake_out_of_memory()
													return batch_size, arga

							lowerCamelCase_	  =					mock_training_loop_function('hello'						)
							self.assertListEqual(UpperCamelCase__		,							[128, 64, 32, 16, 8]						)
							self.assertListEqual([bs, arga]		,							[8, 'hello']						)

	def        a__				(       self      :						List[Any]						)     ->			Dict:

							"""simple docstring"""






							@find_executable_batch_size(starting_batch_size=0						)
							def mock_training_loop_function(A_      :						str						):
													pass

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

	def        a__				(       self      :						str						)     ->			Optional[Any]:

							"""simple docstring"""






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

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

	def        a__				(       self      :						List[str]						)     ->			Optional[int]:

							"""simple docstring"""






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

							with self.assertRaises(UpperCamelCase__						) 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      :						Union[str, Any]						)     ->			int:

							"""simple docstring"""






							@find_executable_batch_size(starting_batch_size=16						)
							def mock_training_loop_function(A_      :						List[str]						):
													raise ValueError('Oops, we had an error!'						)

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





	@require_cuda
	def        a__				(       self      :						Union[str, Any]						)     ->			List[Any]:

							"""simple docstring"""






							lowerCamelCase_	  =					torch.cuda.memory_allocated()
							lowerCamelCase_	  =					ModelForTest()
							model.cuda()
							self.assertGreater(torch.cuda.memory_allocated()		,							UpperCamelCase__						)
							lowerCamelCase_	  =					release_memory(UpperCamelCase__						)
							self.assertEqual(torch.cuda.memory_allocated()		,							UpperCamelCase__						)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase       :	Dict									=     {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	Optional[int]									=     ["ViTFeatureExtractor"]
 lowerCamelCase       :	Dict									=     ["ViTImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	int									=     [
     "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ViTForImageClassification",
     "ViTForMaskedImageModeling",
     "ViTModel",
     "ViTPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	Tuple									=     [
     "TFViTForImageClassification",
     "TFViTModel",
     "TFViTPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase       :	Dict									=     [
     "FlaxViTForImageClassification",
     "FlaxViTModel",
     "FlaxViTPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_vit import ViTFeatureExtractor
  from .image_processing_vit import ViTImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit import (
      VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTForImageClassification,
      ViTForMaskedImageModeling,
      ViTModel,
      ViTPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
 import sys

 lowerCamelCase       :	Any									=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



