

"""simple docstring"""





import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available




def   _A  (  *UpperCamelCase_     :       List[Any])	->						List[str]:
   '''simple docstring'''


   if not isinstance(UpperCamelCase_,			UpperCamelCase_):
      __lowercase								=						list(UpperCamelCase_)
   for i in range(len(UpperCamelCase_)):
      __lowercase								=						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  (  UpperCamelCase_     :       Exception)	->						bool:
   '''simple docstring'''


   __lowercase								=						[
       "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   _A  (  UpperCamelCase_     :       callable = None,			UpperCamelCase_     :       int = 128)	->						int:
   '''simple docstring'''


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

   __lowercase								=						starting_batch_size

   def decorator(*UpperCamelCase_     :       List[str],			**UpperCamelCase_     :       Optional[Any]):
      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()
      __lowercase								=						list(inspect.signature(UpperCamelCase_).parameters.keys())
      # Guard against user error
      if len(UpperCamelCase_) < (len(UpperCamelCase_) + 1):
         __lowercase								=						", ".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










def    A						(__A   :    list   ,				__A   :    int   ,				__A   :    int = 0   ,				__A   :    int = 0       )   ->      int:







     """simple docstring"""


     UpperCAmelCase_          =							right or len(__A       ) - 1
     if left > right:
          return -1
     elif list_data[left] == key:
          return left
     elif list_data[right] == key:
          return right
     else:
          return search(__A   ,				__A   ,				left + 1   ,				right - 1       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()






