


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_				(      ):
					raise RuntimeError("""CUDA out of memory."""  )







class        UpperCamelCase__   (	nn.Module   ):

					def __init__(     self       :   str       ):






										'''simple docstring'''






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







					def   UpperCAmelCase__       (     self       :   Dict							,					UpperCamelCase__       :   Any       ):






										'''simple docstring'''






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







class        UpperCamelCase__   (	unittest.TestCase   ):

					def   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										lowercase_  							=			[]

										@find_executable_batch_size(starting_batch_size=128       )
										def mock_training_loop_function(UpperCamelCase__       :   Optional[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   UpperCAmelCase__       (     self       :   Union[str, Any]       ):






										'''simple docstring'''






										lowercase_  							=			[]

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

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

					def   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										@find_executable_batch_size(starting_batch_size=0       )
										def mock_training_loop_function(UpperCamelCase__       :   List[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   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										@find_executable_batch_size(starting_batch_size=16       )
										def mock_training_loop_function(UpperCamelCase__       :   List[Any]       ):
															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   UpperCAmelCase__       (     self       :   int       ):






										'''simple docstring'''






										@find_executable_batch_size(starting_batch_size=128       )
										def mock_training_loop_function(UpperCamelCase__       :   Optional[int]							,					UpperCamelCase__       :   Any							,					UpperCamelCase__       :   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   UpperCAmelCase__       (     self       :   Any       ):






										'''simple docstring'''






										@find_executable_batch_size(starting_batch_size=16       )
										def mock_training_loop_function(UpperCamelCase__       :   Any       ):
															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   UpperCAmelCase__       (     self       :   Optional[int]       ):






										'''simple docstring'''






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






from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
							if not (is_transformers_available() and is_torch_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
							from .multicontrolnet import MultiControlNetModel
							from .pipeline_controlnet import StableDiffusionControlNetPipeline
							from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
							from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
							from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline


