




import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available



def      __lowerCAmelCase			(					*UpperCamelCase      )     ->	Any:
  if not isinstance(UpperCamelCase	,      UpperCamelCase      ):
    lowerCAmelCase__				:  Optional[int]		       =     list(UpperCamelCase      )
  for i in range(len(UpperCamelCase      )      ):
    lowerCAmelCase__				:  Any		       =     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      )     ->	bool:
  lowerCAmelCase__				:  List[str]		       =     [
      '''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      __lowerCAmelCase			(					UpperCamelCase = None	,      UpperCamelCase = 128      )     ->	str:
  if function is None:
    return functools.partial(UpperCamelCase	,      starting_batch_size=UpperCamelCase      )

  lowerCAmelCase__				:  List[str]		       =     starting_batch_size

  def decorator(*UpperCamelCase	,      **UpperCamelCase      ):
    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()
    lowerCAmelCase__				:  Tuple		       =     list(inspect.signature(UpperCamelCase      ).parameters.keys()      )
    # Guard against user error
    if len(UpperCamelCase      ) < (len(UpperCamelCase      ) + 1):
      lowerCAmelCase__				:  Optional[int]		       =     ''', '''.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      __lowerCAmelCase			(					UpperCamelCase      )     ->	bool:
  if not isinstance(UpperCamelCase	,      UpperCamelCase      ):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''      )
  if len(UpperCamelCase      ) == 0:
    raise ValueError('''Input list must be a non empty list'''      )
  if len(UpperCamelCase      ) == 1:
    return True
  lowerCAmelCase__				:  Tuple		       =     series[1] - series[0]
  for index in range(len(UpperCamelCase      ) - 1      ):
    if series[index + 1] - series[index] != common_diff:
      return False
  return True



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	float:
  if not isinstance(UpperCamelCase	,      UpperCamelCase      ):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''      )
  if len(UpperCamelCase      ) == 0:
    raise ValueError('''Input list must be a non empty list'''      )
  lowerCAmelCase__				:  int		       =     0
  for val in series:
    answer += val
  return answer / len(UpperCamelCase      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

