

import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available

def       __snake_case						( *__UpperCamelCase	: List[str]    ):



      """simple docstring"""

      if not isinstance(__UpperCamelCase				,__UpperCamelCase    ):
            A_						  =				list(__UpperCamelCase    )
      for i in range(len(__UpperCamelCase    )    ):
            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       __snake_case						( __UpperCamelCase	: Exception    ):



      """simple docstring"""

      A_						  =				[
          "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       __snake_case						( __UpperCamelCase	: callable = None				,__UpperCamelCase	: int = 128    ):



      """simple docstring"""

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

      A_						  =				starting_batch_size

      def decorator(*__UpperCamelCase	: Union[str, Any]				,**__UpperCamelCase	: str    ):
            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(__UpperCamelCase    ).parameters.keys()    )
            # Guard against user error
            if len(__UpperCamelCase    ) < (len(__UpperCamelCase    ) + 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(__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






_lowercase		:  Any			       =[0, 2, 4, 6, 8]
_lowercase		:  List[Any]			       =[1, 3, 5, 7, 9]


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__							,lowerCAmelCase__						):
		if remaining_length == 0:
				if digits[0] == 0 or digits[-1] == 0:
						return 0

				for i in range(length // 2 - 1							,-1							,-1						):
						remainder += digits[i] + digits[length - i - 1]

						if remainder % 2 == 0:
								return 0

						remainder //= 10

				return 1

		if remaining_length == 1:
				if remainder % 2 == 0:
						return 0

				lowerCamelCase_	:     Dict 						=      0
				for digit in range(10						):
						lowerCamelCase_	:     List[Any] 						=      digit
						result += reversible_numbers(
						    0							,(remainder + 2 * digit) // 10							,lowerCAmelCase__							,lowerCAmelCase__						)
				return result

		lowerCamelCase_	:     List[str] 						=      0
		for digita in range(10						):
				lowerCamelCase_	:     Tuple 						=      digita

				if (remainder + digita) % 2 == 0:
						lowerCamelCase_	:     Dict 						=      ODD_DIGITS
				else:
						lowerCamelCase_	:     str 						=      EVEN_DIGITS

				for digita in other_parity_digits:
						lowerCamelCase_	:     Optional[int] 						=      digita
						result += reversible_numbers(
						    remaining_length - 2							,(remainder + digita + digita) // 10							,lowerCAmelCase__							,lowerCAmelCase__							,)
		return result


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__ = 9						):
		lowerCamelCase_	:     int 						=      0
		for length in range(1							,max_power + 1						):
				result += reversible_numbers(lowerCAmelCase__							,0							,[0] * length							,lowerCAmelCase__						)
		return result


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