




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		(	)      ->       Tuple:
	raise RuntimeError("""CUDA out of memory."""    )

class 		__lowerCAmelCase	(	nn.Module ):
							def __init__(						self						:Any				):







								'''simple docstring'''







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





							def       lowerCamelCase__							(						self						:List[str]		,      __magic_name__						:List[str]				):







								'''simple docstring'''







								return self.lineara(self.batchnorm(self.lineara(__magic_name__				)				)				)







class 		__lowerCAmelCase	(	unittest.TestCase ):
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







								a										=							[]

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

								mock_training_loop_function()
								self.assertListEqual(__magic_name__		,      [128, 64, 32, 16, 8]				)
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								a										=							[]

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

								a				, a										=							mock_training_loop_function("""hello"""				)
								self.assertListEqual(__magic_name__		,      [128, 64, 32, 16, 8]				)
								self.assertListEqual([bs, arga]		,      [8, """hello"""]				)
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								@find_executable_batch_size(starting_batch_size=0				)
								def mock_training_loop_function(__magic_name__						:Any				):
									pass

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







								'''simple docstring'''







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

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







								'''simple docstring'''







								@find_executable_batch_size(starting_batch_size=128				)
								def mock_training_loop_function(__magic_name__						:str		,      __magic_name__						:List[Any]		,      __magic_name__						:List[Any]				):
									if batch_size != 8:
										raise raise_fake_out_of_memory()

								with self.assertRaises(__magic_name__				) 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       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







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

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





							@require_cuda
							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								a										=							torch.cuda.memory_allocated()
								a										=							ModelForTest()
								model.cuda()
								self.assertGreater(torch.cuda.memory_allocated()		,      __magic_name__				)
								a										=							release_memory(__magic_name__				)
								self.assertEqual(torch.cuda.memory_allocated()		,      __magic_name__				)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase					:       Optional[Any]    					=      {
    "configuration_jukebox": [
        "JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "JukeboxConfig",
        "JukeboxPriorConfig",
        "JukeboxVQVAEConfig",
    ],
    "tokenization_jukebox": ["JukeboxTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase					:       Tuple    					=      [
		    "JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "JukeboxModel",
		    "JukeboxPreTrainedModel",
		    "JukeboxVQVAE",
		    "JukeboxPrior",
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

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

