
import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available




def 				a						(							*snake_case__:					Optional[Any]					):


				'''simple docstring'''
				if not isinstance(snake_case__						,   snake_case__					):
								lowercase_       =					list(snake_case__					)
				for i in range(len(snake_case__					)					):
								lowercase_       =					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						(							snake_case__:					Exception					):


				'''simple docstring'''
				lowercase_       =					[
				    '''CUDA out of memory.''',  # CUDA OOM
				    '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
				    '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
				]
				if isinstance(snake_case__						,   snake_case__					) and len(exception.args					) == 1:
								return any(err in exception.args[0] for err in _statements					)
				return False




def 				a						(							snake_case__:					callable = None						,   snake_case__:					int = 128					):


				'''simple docstring'''
				if function is None:
								return functools.partial(snake_case__						,   starting_batch_size=snake_case__					)

				lowercase_       =					starting_batch_size

				def decorator(*snake_case__:					List[str]						,   **snake_case__:					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()
								lowercase_       =					list(inspect.signature(snake_case__					).parameters.keys()					)
								# Guard against user error
								if len(snake_case__					) < (len(snake_case__					) + 1):
												lowercase_       =					''', '''.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(snake_case__						,   *snake_case__						,   **snake_case__					)
												except Exception as e:
																if should_reduce_batch_size(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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__a  			=					{'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					['DeiTFeatureExtractor']
						__a  			=					['DeiTImageProcessor']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'DeiTForImageClassification',
						    'DeiTForImageClassificationWithTeacher',
						    'DeiTForMaskedImageModeling',
						    'DeiTModel',
						    'DeiTPreTrainedModel',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a  			=					[
						    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFDeiTForImageClassification',
						    'TFDeiTForImageClassificationWithTeacher',
						    'TFDeiTForMaskedImageModeling',
						    'TFDeiTModel',
						    'TFDeiTPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_deit import DeiTFeatureExtractor
												from .image_processing_deit import DeiTImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_deit import (
												    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DeiTForImageClassification,
												    DeiTForImageClassificationWithTeacher,
												    DeiTForMaskedImageModeling,
												    DeiTModel,
												    DeiTPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_deit import (
												    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDeiTForImageClassification,
												    TFDeiTForImageClassificationWithTeacher,
												    TFDeiTForMaskedImageModeling,
												    TFDeiTModel,
												    TFDeiTPreTrainedModel,
												)


else:
						import sys

						__a  			=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




