





import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available





def    __UpperCamelCase      (    *a) ->Union[str, Any]:
					if not isinstance(a,     a):
										lowerCamelCase__       =       list(a)
					for i in range(len(a)):
										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      (    a) ->bool:
					lowerCamelCase__       =       [
					    "CUDA out of memory.",  # CUDA OOM
					    "cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.",  # CUDNN SNAFU
					    "DefaultCPUAllocator: can't allocate memory",  # CPU OOM
					]
					if isinstance(a,     a) and len(exception.args) == 1:
										return any(err in exception.args[0] for err in _statements)
					return False







def    __UpperCamelCase      (    a = None,     a = 128) ->List[str]:
					if function is None:
										return functools.partial(a,     starting_batch_size=a)

					lowerCamelCase__       =       starting_batch_size

					def decorator(*a,     **a):
										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(a).parameters.keys())
										# Guard against user error
										if len(a) < (len(a) + 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(a,     *a,     **a)
															except Exception as e:
																				if should_reduce_batch_size(a):
																									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    __UpperCamelCase      (    a) ->str:

					if isinstance(a,     a):
										raise TypeError("'float' object cannot be interpreted as an integer")
					if isinstance(a,     a):
										raise TypeError("'str' object cannot be interpreted as an integer")

					if num == 0:
										return "0b0"

					lowerCamelCase__       =       False

					if num < 0:
										lowerCamelCase__       =       True
										lowerCamelCase__       =       -num

					lowerCamelCase__       =       []
					while num > 0:
										binary.insert(0,     num % 2)
										num >>= 1

					if negative:
										return "-0b" + "".join(str(a) for e in binary)

					return "0b" + "".join(str(a) for e in binary)


if __name__ == "__main__":
						import doctest

						doctest.testmod()

