






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


class      UpperCamelCase		(nn.Module     ):






						def __init__(		self  :Tuple				)      ->Optional[int]:
										super().__init__()
										lowercase    :      str       = nn.Linear(3			, 4				)
										lowercase    :      Union[str, Any]       = nn.BatchNormad(4				)
										lowercase    :      Any       = nn.Linear(4			, 5				)






						def      __snake_case      (		self  :Optional[int]			, __magic_name__  :str				)      ->Optional[int]:
										return self.lineara(self.batchnorm(self.lineara(__lowerCAmelCase				)				)				)


class      UpperCamelCase		(unittest.TestCase     ):






						def      __snake_case      (		self  :Optional[Any]				)      ->Tuple:
										lowercase    :      List[str]       = []

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

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






						def      __snake_case      (		self  :str				)      ->Dict:
										lowercase    :      Optional[Any]       = []

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

										lowercase    :      str       = mock_training_loop_function("""hello"""				)
										self.assertListEqual(__lowerCAmelCase			, [128, 64, 32, 16, 8]				)
										self.assertListEqual([bs, arga]			, [8, """hello"""]				)






						def      __snake_case      (		self  :Union[str, Any]				)      ->Any:
										@find_executable_batch_size(starting_batch_size=0				)
										def mock_training_loop_function(__magic_name__  :Optional[int]				):
														pass

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






						def      __snake_case      (		self  :List[str]				)      ->List[str]:
										@find_executable_batch_size(starting_batch_size=16				)
										def mock_training_loop_function(__magic_name__  :Optional[Any]				):
														if batch_size > 0:
																		raise_fake_out_of_memory()
														pass

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






						def      __snake_case      (		self  :List[Any]				)      ->Optional[Any]:
										@find_executable_batch_size(starting_batch_size=128				)
										def mock_training_loop_function(__magic_name__  :List[Any]			, __magic_name__  :Optional[int]			, __magic_name__  :Dict				):
														if batch_size != 8:
																		raise raise_fake_out_of_memory()

										with self.assertRaises(__lowerCAmelCase				) 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      __snake_case      (		self  :Any				)      ->int:
										@find_executable_batch_size(starting_batch_size=16				)
										def mock_training_loop_function(__magic_name__  :Union[str, Any]				):
														raise ValueError("""Oops, we had an error!"""				)

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






						@require_cuda
						def      __snake_case      (		self  :List[str]				)      ->Optional[Any]:
										lowercase    :      int       = torch.cuda.memory_allocated()
										lowercase    :      List[str]       = ModelForTest()
										model.cuda()
										self.assertGreater(torch.cuda.memory_allocated()			, __lowerCAmelCase				)
										lowercase    :      List[str]       = release_memory(__lowerCAmelCase				)
										self.assertEqual(torch.cuda.memory_allocated()			, __lowerCAmelCase				)








'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase      					={
    "configuration_clap": [
        "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
        "ClapAudioConfig",
        "ClapConfig",
        "ClapTextConfig",
    ],
    "processing_clap": ["ClapProcessor"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase      					=[
          "CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
          "ClapModel",
          "ClapPreTrainedModel",
          "ClapTextModel",
          "ClapTextModelWithProjection",
          "ClapAudioModel",
          "ClapAudioModelWithProjection",
      ]
      UpperCamelCase      					=["ClapFeatureExtractor"]

if TYPE_CHECKING:
      from .configuration_clap import (
          CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
          ClapAudioConfig,
          ClapConfig,
          ClapTextConfig,
      )
      from .processing_clap import ClapProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_clap import ClapFeatureExtractor
            from .modeling_clap import (
                CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
                ClapAudioModel,
                ClapAudioModelWithProjection,
                ClapModel,
                ClapPreTrainedModel,
                ClapTextModel,
                ClapTextModelWithProjection,
            )


else:
      import sys

      UpperCamelCase      					=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






