


"""simple docstring"""
import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available







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







def   _a			( _SCREAMING_SNAKE_CASE = None			,	_SCREAMING_SNAKE_CASE = 128			)      ->			Any:
							if function is None:
														return functools.partial(_SCREAMING_SNAKE_CASE			,	starting_batch_size=_SCREAMING_SNAKE_CASE			)

							snake_case_          =						starting_batch_size

							def decorator(*_SCREAMING_SNAKE_CASE			,	**_SCREAMING_SNAKE_CASE			):
														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()
														snake_case_          =						list(inspect.signature(_SCREAMING_SNAKE_CASE			).parameters.keys()			)
														# Guard against user error
														if len(_SCREAMING_SNAKE_CASE			) < (len(_SCREAMING_SNAKE_CASE			) + 1):
																					snake_case_          =						""", """.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(_SCREAMING_SNAKE_CASE			,	*_SCREAMING_SNAKE_CASE			,	**_SCREAMING_SNAKE_CASE			)
																					except Exception as e:
																												if should_reduce_batch_size(_SCREAMING_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"""
import math





class        __A       :





							'''simple docstring'''

							def __init__(			self							: List[str]						,    UpperCAmelCase_							: Tuple=0    )   ->Optional[int]:  # a graph with Node 0,1,...,N-1



														"""simple docstring"""



														snake_case_          =						n
														snake_case_          =						[
														    [math.inf for j in range(0						,    UpperCAmelCase_    )] for i in range(0						,    UpperCAmelCase_    )
														]  # adjacency matrix for weight
														snake_case_          =						[
														    [math.inf for j in range(0						,    UpperCAmelCase_    )] for i in range(0						,    UpperCAmelCase_    )
														]  # dp[i][j] stores minimum distance from i to j



							def 						lowerCAmelCase    (			self							: List[str]						,    UpperCAmelCase_							: Any						,    UpperCAmelCase_							: List[str]						,    UpperCAmelCase_							: Optional[Any]    )   ->Optional[int]:



														"""simple docstring"""



														snake_case_          =						w



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->Any:



														"""simple docstring"""



														for k in range(0						,    self.n    ):
																					for i in range(0						,    self.n    ):
																												for j in range(0						,    self.n    ):
																																			snake_case_          =						min(self.dp[i][j]						,    self.dp[i][k] + self.dp[k][j]    )



							def 						lowerCAmelCase    (			self							: Optional[int]						,    UpperCAmelCase_							: Dict						,    UpperCAmelCase_							: Optional[Any]    )   ->Union[str, Any]:



														"""simple docstring"""



														return self.dp[u][v]


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:      Optional[int]						=      Graph(5)
				graph.add_edge(0, 2, 9)
				graph.add_edge(0, 4, 10)
				graph.add_edge(1, 3, 5)
				graph.add_edge(2, 3, 7)
				graph.add_edge(3, 0, 10)
				graph.add_edge(3, 1, 2)
				graph.add_edge(3, 2, 1)
				graph.add_edge(3, 4, 6)
				graph.add_edge(4, 1, 3)
				graph.add_edge(4, 2, 4)
				graph.add_edge(4, 3, 9)
				graph.floyd_warshall()
				graph.show_min(1, 4)
				graph.show_min(0, 3)



