





"""simple docstring"""





import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available


def       a__					(					*_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		if not isinstance(lowerCamelCase__  ,	lowerCamelCase__    ):
				UpperCamelCase   =	list(lowerCamelCase__    )
		for i in range(len(lowerCamelCase__    )    ):
				UpperCamelCase   =	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__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	[
		    "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__					(					_SCREAMING_SNAKE_CASE = None  ,	_SCREAMING_SNAKE_CASE = 128    ):




		"""simple docstring"""


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

		UpperCamelCase   =	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()
				UpperCamelCase   =	list(inspect.signature(lowerCamelCase__    ).parameters.keys()    )
				# Guard against user error
				if len(lowerCamelCase__    ) < (len(lowerCamelCase__    ) + 1):
						UpperCamelCase   =	", ".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"""





lowerCAmelCase__     			=			'''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCAmelCase__     			=			[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCAmelCase__     			=			{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}






