
'''simple docstring'''
import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available






def  __SCREAMING_SNAKE_CASE				(  *_SCREAMING_SNAKE_CASE				):
							if not isinstance(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):
														_snake_case       =  list(_SCREAMING_SNAKE_CASE				)
							for i in range(len(_SCREAMING_SNAKE_CASE				)				):
														_snake_case       =  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  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
							_snake_case       =  [
							    """CUDA out of memory.""",  # CUDA OOM
							    """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
							    """DefaultCPUAllocator: can't allocate memory""",  # CPU OOM
							]
							if isinstance(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				) and len(exception.args				) == 1:
														return any(err in exception.args[0] for err in _statements				)
							return False






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE = None							, _SCREAMING_SNAKE_CASE = 128				):
							if function is None:
														return functools.partial(_SCREAMING_SNAKE_CASE							, starting_batch_size=_SCREAMING_SNAKE_CASE				)

							_snake_case       =  starting_batch_size

							def decorator(*_SCREAMING_SNAKE_CASE							, **_SCREAMING_SNAKE_CASE				):
														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()
														_snake_case       =  list(inspect.signature(_SCREAMING_SNAKE_CASE				).parameters.keys()				)
														# Guard against user error
														if len(_SCREAMING_SNAKE_CASE				) < (len(_SCREAMING_SNAKE_CASE				) + 1):
																					_snake_case       =  """, """.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(_SCREAMING_SNAKE_CASE							, *_SCREAMING_SNAKE_CASE							, **_SCREAMING_SNAKE_CASE				)
																					except Exception as e:
																												if should_reduce_batch_size(_SCREAMING_SNAKE_CASE				):
																																			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_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCAmelCase												=	{
    'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'ResNetForImageClassification',
				    'ResNetModel',
				    'ResNetPreTrainedModel',
				    'ResNetBackbone',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFResNetForImageClassification',
				    'TFResNetModel',
				    'TFResNetPreTrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'FlaxResNetForImageClassification',
				    'FlaxResNetModel',
				    'FlaxResNetPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_resnet import (
								    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ResNetBackbone,
								    ResNetForImageClassification,
								    ResNetModel,
								    ResNetPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_resnet import (
								    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFResNetForImageClassification,
								    TFResNetModel,
								    TFResNetPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
				import sys

				__lowerCAmelCase												=	_LazyModule(__name__, globals()['__file__'], _import_structure)