
'''simple docstring'''



import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available


def A				(*__lowerCamelCase				:Tuple		):
						if not isinstance(__lowerCamelCase			,     __lowerCamelCase		):
												_lowerCAmelCase  						=			list(__lowerCamelCase		)
						for i in range(len(__lowerCamelCase		)		):
												_lowerCAmelCase  						=			None
						gc.collect()
						if is_xpu_available():
												torch.xpu.empty_cache()
						elif is_npu_available():
												torch.npu.empty_cache()
						else:
												torch.cuda.empty_cache()
						return objects


def A				(__lowerCamelCase				:Exception		):
						_lowerCAmelCase  						=			[
						    """CUDA out of memory.""",  # CUDA OOM
						    """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
						    """DefaultCPUAllocator: can't allocate memory""",  # CPU OOM
						]
						if isinstance(__lowerCamelCase			,     __lowerCamelCase		) and len(exception.args		) == 1:
												return any(err in exception.args[0] for err in _statements		)
						return False





def A				(__lowerCamelCase				:callable = None			,     __lowerCamelCase				:int = 128		):
						if function is None:
												return functools.partial(__lowerCamelCase			,     starting_batch_size=__lowerCamelCase		)

						_lowerCAmelCase  						=			starting_batch_size

						def decorator(*__lowerCamelCase				:List[str]			,     **__lowerCamelCase				:List[str]		):
												nonlocal batch_size
												gc.collect()
												if is_xpu_available():
																		torch.xpu.empty_cache()
												elif is_npu_available():
																		torch.npu.empty_cache()
												else:
																		torch.cuda.empty_cache()
												_lowerCAmelCase  						=			list(inspect.signature(__lowerCamelCase		).parameters.keys()		)
												# Guard against user error
												if len(__lowerCamelCase		) < (len(__lowerCamelCase		) + 1):
																		_lowerCAmelCase  						=			""", """.join([f'{arg}={value}' for arg, value in zip(params[1:]			,     args[1:]		)]		)
																		raise TypeError(
																		    f'Batch size was passed into `{function.__name__}` as the first argument when called.'
																		    f'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'		)
												while True:
																		if batch_size == 0:
																								raise RuntimeError("""No executable batch size found, reached zero."""		)
																		try:
																								return function(__lowerCamelCase			,     *__lowerCamelCase			,     **__lowerCamelCase		)
																		except Exception as e:
																								if should_reduce_batch_size(__lowerCamelCase		):
																														gc.collect()
																														if is_xpu_available():
																																				torch.xpu.empty_cache()
																														elif is_npu_available():
																																				torch.npu.empty_cache()
																														else:
																																				torch.cuda.empty_cache()
																														batch_size //= 2
																								else:
																														raise

						return decorator


'''simple docstring'''



from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase    =		{"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase    =		[
						    """VAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """VanForImageClassification""",
						    """VanModel""",
						    """VanPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_van import (
												    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    VanForImageClassification,
												    VanModel,
												    VanPreTrainedModel,
												)

else:
						import sys

						_lowercase    =		_LazyModule(__name__, globals()["""__file__"""], _import_structure)
