





import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available
def 					_A	(		*SCREAMING_SNAKE_CASE					:       str						):





  """simple docstring"""



  if not isinstance(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						):
    a__     : Optional[Any]  =list(SCREAMING_SNAKE_CASE						)
  for i in range(len(SCREAMING_SNAKE_CASE						)						):
    a__     : 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 					_A	(		SCREAMING_SNAKE_CASE					:       Exception						):





  """simple docstring"""



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





def 					_A	(		SCREAMING_SNAKE_CASE					:       callable = None	,	SCREAMING_SNAKE_CASE					:       int = 128						):





  """simple docstring"""



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

  a__     : Dict  =starting_batch_size

  def decorator(*SCREAMING_SNAKE_CASE					:       Tuple	,	**SCREAMING_SNAKE_CASE					:       Tuple						):
    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()
    a__     : Dict  =list(inspect.signature(SCREAMING_SNAKE_CASE						).parameters.keys()						)
    # Guard against user error
    if len(SCREAMING_SNAKE_CASE						) < (len(SCREAMING_SNAKE_CASE						) + 1):
      a__     : Tuple  =", ".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(SCREAMING_SNAKE_CASE	,	*SCREAMING_SNAKE_CASE	,	**SCREAMING_SNAKE_CASE						)
      except Exception as e:
        if should_reduce_batch_size(SCREAMING_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













def 					_A	(		SCREAMING_SNAKE_CASE					:       float	,	SCREAMING_SNAKE_CASE					:       float						):





  """simple docstring"""



  return price * (1 + tax_rate)


if __name__ == "__main__":
     print(F"""{price_plus_tax(100, 0.2_5) = }""")
     print(F"""{price_plus_tax(1_2_5.5_0, 0.0_5) = }""")






