

import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available


def        __lowerCAmelCase    (       *_UpperCamelCase		:  int	)		->							Dict:
 '''simple docstring'''


 if not isinstance(__lowerCAmelCase		,							__lowerCAmelCase	):
  SCREAMING_SNAKE_CASE								=			list(__lowerCAmelCase	)
 for i in range(len(__lowerCAmelCase	)	):
  SCREAMING_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        __lowerCAmelCase    (       _UpperCamelCase		:  Exception	)		->							Dict:
 '''simple docstring'''


 SCREAMING_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(__lowerCAmelCase		,							__lowerCAmelCase	) and len(exception.args	) == 1:
  return any(err in exception.args[0] for err in _statements	)
 return False




def        __lowerCAmelCase    (       _UpperCamelCase		:  callable = None		,							_UpperCamelCase		:  int = 1_28	)		->							Optional[int]:
 '''simple docstring'''


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

 SCREAMING_SNAKE_CASE								=			starting_batch_size

 def decorator(*_UpperCamelCase		:  Optional[Any]		,							**_UpperCamelCase		:  int	):
  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()
  SCREAMING_SNAKE_CASE								=			list(inspect.signature(__lowerCAmelCase	).parameters.keys()	)
  # Guard against user error
  if len(__lowerCAmelCase	) < (len(__lowerCAmelCase	) + 1):
   SCREAMING_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(__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 numpy as np


def        __lowerCAmelCase    (       _UpperCamelCase		:  np.ndarray	)		->							np.ndarray:
 '''simple docstring'''


 return 1 / (1 + np.exp(-vector	))


def        __lowerCAmelCase    (       _UpperCamelCase		:  np.ndarray	)		->							np.ndarray:
 '''simple docstring'''


 return vector * sigmoid(_UpperCamelCase	)


if __name__ == "__main__":
      import doctest

      doctest.testmod()

