




"""simple docstring"""




import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available







def      __UpperCAmelCase     ( *__UpperCamelCase	):
			if not isinstance(_snake_case	,				_snake_case	):
						__lowercase			:  List[Any]								=       list(_snake_case	)
			for i in range(len(_snake_case	)	):
						__lowercase			:  List[str]								=       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     ( __UpperCamelCase	):
			__lowercase			:  Union[str, Any]								=       [
			    '''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     ( __UpperCamelCase = None	,				__UpperCamelCase = 1_28	):
			if function is None:
						return functools.partial(_snake_case	,				starting_batch_size=_snake_case	)

			__lowercase			:  Tuple								=       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()
						__lowercase			:  List[str]								=       list(inspect.signature(_snake_case	).parameters.keys()	)
						# Guard against user error
						if len(_snake_case	) < (len(_snake_case	) + 1):
									__lowercase			:  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(_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








from graphs.minimum_spanning_tree_kruskal import kruskal

def 	SCREAMING_SNAKE_CASE_							(	)  ->     Tuple:
   _A      =       9
   _A      =       [
       [0, 1, 4],
       [0, 7, 8],
       [1, 2, 8],
       [7, 8, 7],
       [7, 6, 1],
       [2, 8, 2],
       [8, 6, 6],
       [2, 3, 7],
       [2, 5, 4],
       [6, 5, 2],
       [3, 5, 14],
       [3, 4, 9],
       [5, 4, 10],
       [1, 7, 11],
   ]

   _A      =       kruskal(_snake_case	,   _snake_case				)

   _A      =       [
       [7, 6, 1],
       [2, 8, 2],
       [6, 5, 2],
       [0, 1, 4],
       [2, 5, 4],
       [2, 3, 7],
       [0, 7, 8],
       [3, 4, 9],
   ]

   assert sorted(_snake_case				) == sorted(_snake_case				)



