'''simple docstring'''

import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available







def      UpperCamelCase_						(					*snake_case_       :       Dict      )					->     Any:






 '''simple docstring'''

 if not isinstance(snake_case_						,		snake_case_      ):
  __lowerCAmelCase											=						list(snake_case_      )
 for i in range(len(snake_case_      )      ):
  __lowerCAmelCase											=						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      UpperCamelCase_						(					snake_case_       :       Exception      )					->     bool:






 '''simple docstring'''

 __lowerCAmelCase											=						[
     """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      UpperCamelCase_						(					snake_case_       :       callable = None						,		snake_case_       :       int = 1_28      )					->     List[Any]:






 '''simple docstring'''

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

 __lowerCAmelCase											=						starting_batch_size

 def decorator(*snake_case_       :       List[str]						,		**snake_case_       :       Union[str, 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()
  __lowerCAmelCase											=						list(inspect.signature(snake_case_      ).parameters.keys()      )
  # Guard against user error
  if len(snake_case_      ) < (len(snake_case_      ) + 1):
   __lowerCAmelCase											=						""", """.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'''

def      UpperCamelCase_						(					snake_case_       :       int						,		snake_case_       :       int      )					->     str:






 '''simple docstring'''

 if a < 0 or b < 0:
  raise ValueError("""the value of both inputs must be positive"""      )

 __lowerCAmelCase											=						str(bin(snake_case_      )      )[2:]  # remove the leading "0b"
 __lowerCAmelCase											=						str(bin(snake_case_      )      )[2:]  # remove the leading "0b"

 __lowerCAmelCase											=						max(len(snake_case_      )						,		len(snake_case_      )      )

 return "0b" + "".join(
     str(int(char_a == """1""" and char_b == """1"""      )      )
     for char_a, char_b in zip(a_binary.zfill(snake_case_      )						,		b_binary.zfill(snake_case_      )      )      )


if __name__ == "__main__":
 import doctest

 doctest.testmod()






