



import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available

def 	SCREAMING_SNAKE_CASE_							(	*_snake_case	:Optional[int]				)  ->     Optional[int]:
   if not isinstance(_snake_case	,   _snake_case				):
      _A      =       list(_snake_case				)
   for i in range(len(_snake_case				)				):
      _A      =       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_							(	_snake_case	:Exception				)  ->     bool:
   _A      =       [
       '''CUDA out of memory.''',  # CUDA OOM
       '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
       '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
   ]
   if isinstance(_snake_case	,   _snake_case				) and len(exception.args				) == 1:
      return any(err in exception.args[0] for err in _statements				)
   return False


def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:callable = None	,   _snake_case	:int = 128				)  ->     Optional[int]:
   if function is None:
      return functools.partial(_snake_case	,   starting_batch_size=_snake_case				)

   _A      =       starting_batch_size

   def decorator(*_snake_case	:Tuple	,   **_snake_case	:List[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()
      _A      =       list(inspect.signature(_snake_case				).parameters.keys()				)
      # Guard against user error
      if len(_snake_case				) < (len(_snake_case				) + 1):
         _A      =       ''', '''.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(_snake_case	,   *_snake_case	,   **_snake_case				)
         except Exception as e:
            if should_reduce_batch_size(_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





"""simple docstring"""



import random
def 					lowerCamelCase   (     _snake_case ):
    UpperCAmelCase__	:      Tuple     							=					num - 1
    UpperCAmelCase__	:      Dict     							=					0

    while s % 2 == 0:
        UpperCAmelCase__	:      Optional[int]     							=					s // 2
        t += 1

    for _ in range(5 ):
        UpperCAmelCase__	:      Optional[int]     							=					random.randrange(2    ,num - 1 )
        UpperCAmelCase__	:      List[str]     							=					pow(_snake_case    ,_snake_case    ,_snake_case )
        if v != 1:
            UpperCAmelCase__	:      List[Any]     							=					0
            while v != (num - 1):
                if i == t - 1:
                    return False
                else:
                    UpperCAmelCase__	:      Optional[Any]     							=					i + 1
                    UpperCAmelCase__	:      Any     							=					(v**2) % num
    return True
def 					lowerCamelCase   (     _snake_case ):
    if num < 2:
        return False

    UpperCAmelCase__	:      str     							=					[
        2,
        3,
        5,
        7,
        11,
        13,
        17,
        19,
        23,
        29,
        31,
        37,
        41,
        43,
        47,
        53,
        59,
        61,
        67,
        71,
        73,
        79,
        83,
        89,
        97,
        101,
        103,
        107,
        109,
        113,
        127,
        131,
        137,
        139,
        149,
        151,
        157,
        163,
        167,
        173,
        179,
        181,
        191,
        193,
        197,
        199,
        211,
        223,
        227,
        229,
        233,
        239,
        241,
        251,
        257,
        263,
        269,
        271,
        277,
        281,
        283,
        293,
        307,
        311,
        313,
        317,
        331,
        337,
        347,
        349,
        353,
        359,
        367,
        373,
        379,
        383,
        389,
        397,
        401,
        409,
        419,
        421,
        431,
        433,
        439,
        443,
        449,
        457,
        461,
        463,
        467,
        479,
        487,
        491,
        499,
        503,
        509,
        521,
        523,
        541,
        547,
        557,
        563,
        569,
        571,
        577,
        587,
        593,
        599,
        601,
        607,
        613,
        617,
        619,
        631,
        641,
        643,
        647,
        653,
        659,
        661,
        673,
        677,
        683,
        691,
        701,
        709,
        719,
        727,
        733,
        739,
        743,
        751,
        757,
        761,
        769,
        773,
        787,
        797,
        809,
        811,
        821,
        823,
        827,
        829,
        839,
        853,
        857,
        859,
        863,
        877,
        881,
        883,
        887,
        907,
        911,
        919,
        929,
        937,
        941,
        947,
        953,
        967,
        971,
        977,
        983,
        991,
        997,
    ]

    if num in low_primes:
        return True

    for prime in low_primes:
        if (num % prime) == 0:
            return False

    return rabin_miller(_snake_case )
def 					lowerCamelCase   (     _snake_case = 1024 ):
    while True:
        UpperCAmelCase__	:      List[Any]     							=					random.randrange(2 ** (keysize - 1)    ,2 ** (keysize) )
        if is_prime_low_num(_snake_case ):
            return num


if __name__ == "__main__":
    UpperCamelCase__          =	generate_large_prime()
    print(('Prime number:', num))
    print(('is_prime_low_num:', is_prime_low_num(num)))

