




import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available




def 						SCREAMING_SNAKE_CASE__ (   *lowercase					)  ->       Optional[int]:
   if not isinstance(lowercase						,lowercase					):
      snake_case				:    str           =	list(lowercase					)
   for i in range(len(lowercase					)					):
      snake_case				:    Dict           =	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__ (   lowercase					)  ->       bool:
   snake_case				:    str           =	[
       """CUDA out of memory.""",  # CUDA OOM
       """cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.""",  # CUDNN SNAFU
       """DefaultCPUAllocator: can't allocate memory""",  # CPU OOM
   ]
   if isinstance(lowercase						,lowercase					) and len(exception.args					) == 1:
      return any(err in exception.args[0] for err in _statements					)
   return False




def 						SCREAMING_SNAKE_CASE__ (   lowercase = None						,lowercase = 128					)  ->       int:
   if function is None:
      return functools.partial(lowercase						,starting_batch_size=lowercase					)

   snake_case				:    Optional[Any]           =	starting_batch_size

   def decorator(*lowercase						,**lowercase					):
      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[str]           =	list(inspect.signature(lowercase					).parameters.keys()					)
      # Guard against user error
      if len(lowercase					) < (len(lowercase					) + 1):
         snake_case				:    str           =	""", """.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(lowercase						,*lowercase						,**lowercase					)
         except Exception as e:
            if should_reduce_batch_size(lowercase					):
               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 						SCREAMING_SNAKE_CASE__ (   lowercase = 1000					)  ->       int:
   snake_case				:    Optional[int]           =	3
   snake_case				:    List[Any]           =	0
   while a < n:
      if a % 3 == 0 or a % 5 == 0:
         result += a
      elif a % 15 == 0:
         result -= a
      a += 1
   return result


if __name__ == "__main__":
       print(f"""{solution() = }""")





