
import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available




def 	A_ (							*snake_case							:   Dict )      ->    int:



						'''simple docstring'''

						if not isinstance(snake_case ,      snake_case ):
												__UpperCamelCase       =			list(snake_case )
						for i in range(len(snake_case ) ):
												__UpperCamelCase       =			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 	A_ (							snake_case							:   Exception )      ->    bool:



						'''simple docstring'''

						__UpperCamelCase       =			[
						    '''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 	A_ (							snake_case							:   callable = None ,      snake_case							:   int = 128 )      ->    int:



						'''simple docstring'''

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

						__UpperCamelCase       =			starting_batch_size

						def decorator(*snake_case							:   str ,      **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()
												__UpperCamelCase       =			list(inspect.signature(snake_case ).parameters.keys() )
												# Guard against user error
												if len(snake_case ) < (len(snake_case ) + 1):
																		__UpperCamelCase       =			''', '''.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






def 	A_ (							snake_case							:   float )      ->    float:



						'''simple docstring'''

						if edge <= 0 or not isinstance(snake_case ,      snake_case ):
												raise ValueError('''Length must be a positive.''' )
						return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)




def 	A_ (							snake_case							:   float )      ->    float:



						'''simple docstring'''

						if edge <= 0 or not isinstance(snake_case ,      snake_case ):
												raise ValueError('''Length must be a positive.''' )
						return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
				import doctest

				doctest.testmod()




