
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       (					)   ->  Dict:
		'''simple docstring'''

		raise RuntimeError('CUDA out of memory.' )


class 				lowercase					(		nn.Module   ):



		def __init__( self       )  ->					List[str]:
				"""simple docstring"""
				super().__init__()
				UpperCamelCase		=     nn.Linear(3				,      4       )
				UpperCamelCase		=     nn.BatchNormad(4       )
				UpperCamelCase		=     nn.Linear(4				,      5       )



		def __UpperCamelCase ( self				,      A_       )  ->					Dict:
				"""simple docstring"""
				return self.lineara(self.batchnorm(self.lineara(A_       )       )       )






class 				lowercase					(		unittest.TestCase   ):



		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     []

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

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



		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				UpperCamelCase		=     []

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

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



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				@find_executable_batch_size(starting_batch_size=0       )
				def mock_training_loop_function(A_       ):
						pass

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



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				@find_executable_batch_size(starting_batch_size=16       )
				def mock_training_loop_function(A_       ):
						if batch_size > 0:
								raise_fake_out_of_memory()
						pass

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



		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				@find_executable_batch_size(starting_batch_size=128       )
				def mock_training_loop_function(A_				,      A_				,      A_       ):
						if batch_size != 8:
								raise raise_fake_out_of_memory()

				with self.assertRaises(A_       ) 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       )  ->					str:
				"""simple docstring"""
				@find_executable_batch_size(starting_batch_size=16       )
				def mock_training_loop_function(A_       ):
						raise ValueError('Oops, we had an error!'       )

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



		@require_cuda
		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     torch.cuda.memory_allocated()
				UpperCamelCase		=     ModelForTest()
				model.cuda()
				self.assertGreater(torch.cuda.memory_allocated()				,      A_       )
				UpperCamelCase		=     release_memory(A_       )
				self.assertEqual(torch.cuda.memory_allocated()				,      A_       )


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase					: Union[str, Any]      =       {
    "configuration_instructblip": [
        "INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "InstructBlipConfig",
        "InstructBlipQFormerConfig",
        "InstructBlipVisionConfig",
    ],
    "processing_instructblip": ["InstructBlipProcessor"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase					: List[Any]      =       [
					    "INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "InstructBlipQFormerModel",
					    "InstructBlipPreTrainedModel",
					    "InstructBlipForConditionalGeneration",
					    "InstructBlipVisionModel",
					]

if TYPE_CHECKING:
					from .configuration_instructblip import (
					    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    InstructBlipConfig,
					    InstructBlipQFormerConfig,
					    InstructBlipVisionConfig,
					)
					from .processing_instructblip import InstructBlipProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_instructblip import (
										    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    InstructBlipForConditionalGeneration,
										    InstructBlipPreTrainedModel,
										    InstructBlipQFormerModel,
										    InstructBlipVisionModel,
										)

else:
					import sys

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