





import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available


def 		_SCREAMING_SNAKE_CASE     ( *lowerCAmelCase__						):
		if not isinstance(lowerCAmelCase__							,lowerCAmelCase__						):
				lowerCamelCase_	:     List[Any] 						=      list(lowerCAmelCase__						)
		for i in range(len(lowerCAmelCase__						)						):
				lowerCamelCase_	:     str 						=      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     ( lowerCAmelCase__						):
		lowerCamelCase_	:     Dict 						=      [
		    '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 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__ = None							,lowerCAmelCase__ = 1_28						):
		if function is None:
				return functools.partial(lowerCAmelCase__							,starting_batch_size=lowerCAmelCase__						)

		lowerCamelCase_	:     List[str] 						=      starting_batch_size

		def decorator(*lowerCAmelCase__							,**lowerCAmelCase__						):
				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_	:     int 						=      list(inspect.signature(lowerCAmelCase__						).parameters.keys()						)
				# Guard against user error
				if len(lowerCAmelCase__						) < (len(lowerCAmelCase__						) + 1):
						lowerCamelCase_	:     Dict 						=      ', '.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







import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


_lowercase		:  int			       =logging.get_logger(__name__)






class 						UpperCamelCase_  (  snake_case__							):



					def __init__(		self     :     Tuple       ,      *lowerCamelCase     :     List[Any]       ,      **lowerCamelCase     :     Union[str, Any]						):
							warnings.warn(
							    'The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
							    ' use YolosImageProcessor instead.'       ,      lowerCamelCase       ,      )
							super().__init__(*lowerCamelCase       ,      **lowerCamelCase						)
