

import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available



def 				snake_case_(*_UpperCamelCase					)							->     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 				snake_case_(_UpperCamelCase					)							->     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 				snake_case_(_UpperCamelCase = None			,	_UpperCamelCase = 128					)							->     str:



  """simple docstring"""






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

  _snake_case						=							starting_batch_size

  def decorator(*_UpperCamelCase			,	**_UpperCamelCase					):
    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




import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


__A      		=     logging.get_logger(__name__)



class   lowercase_   (  __lowercase					):



      def __init__(      self				:    Optional[Any]      , *A__				:    List[Any]      , **A__				:    int		)   ->					None:
        warnings.warn(
            '''The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
            ''' Please use DeformableDetrImageProcessor instead.'''      , A__      , )
        super().__init__(*A__      , **A__		)

