
"""simple docstring"""







import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available
def 	__UpperCamelCase (       *SCREAMING_SNAKE_CASE			)	->  Dict:







							"""simple docstring"""


							if not isinstance(__UpperCamelCase				,     __UpperCamelCase			):
														__snake_case        =							list(__UpperCamelCase			)
							for i in range(len(__UpperCamelCase			)			):
														__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 	__UpperCamelCase (       SCREAMING_SNAKE_CASE			)	->  bool:







							"""simple docstring"""


							__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(__UpperCamelCase				,     __UpperCamelCase			) and len(exception.args			) == 1:
														return any(err in exception.args[0] for err in _statements			)
							return False



def 	__UpperCamelCase (       SCREAMING_SNAKE_CASE = None				,     SCREAMING_SNAKE_CASE = 1_28			)	->  List[str]:







							"""simple docstring"""


							if function is None:
														return functools.partial(__UpperCamelCase				,     starting_batch_size=__UpperCamelCase			)

							__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(__UpperCamelCase			).parameters.keys()			)
														# Guard against user error
														if len(__UpperCamelCase			) < (len(__UpperCamelCase			) + 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(__UpperCamelCase				,     *__UpperCamelCase				,     **__UpperCamelCase			)
																					except Exception as e:
																												if should_reduce_batch_size(__UpperCamelCase			):
																																			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





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__   			=   {'''configuration_mra''': ['''MRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MraConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__   			=   [
							    '''MRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''MraForMaskedLM''',
							    '''MraForMultipleChoice''',
							    '''MraForQuestionAnswering''',
							    '''MraForSequenceClassification''',
							    '''MraForTokenClassification''',
							    '''MraLayer''',
							    '''MraModel''',
							    '''MraPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mra import (
														    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MraForMaskedLM,
														    MraForMultipleChoice,
														    MraForQuestionAnswering,
														    MraForSequenceClassification,
														    MraForTokenClassification,
														    MraLayer,
														    MraModel,
														    MraPreTrainedModel,
														)
else:
							import sys

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




