






import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available





def 					lowercase_ (    *__snake_case     :							int      )     -> Union[str, Any]:





				'''simple docstring'''


				if not isinstance(__snake_case	,	__snake_case      ):
								snake_case__      :List[str]         =   list(__snake_case      )
				for i in range(len(__snake_case      )      ):
								snake_case__      :int         =   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 					lowercase_ (    __snake_case     :							Exception      )     -> bool:





				'''simple docstring'''


				snake_case__      :List[str]         =   [
				    "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 					lowercase_ (    __snake_case     :							callable = None	,	__snake_case     :							int = 1_28      )     -> str:





				'''simple docstring'''


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

				snake_case__      :Tuple         =   starting_batch_size

				def decorator(*__snake_case     :							List[Any]	,	**__snake_case     :							str      ):
								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[str]         =   list(inspect.signature(__snake_case      ).parameters.keys()      )
								# Guard against user error
								if len(__snake_case      ) < (len(__snake_case      ) + 1):
												snake_case__      :Any         =   ", ".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 __future__ import annotations

import math





def 					lowercase_ (    __snake_case     :							int	,	__snake_case     :							int	,	__snake_case     :							bool	,	__snake_case     :							list[int]	,	__snake_case     :							float      )     -> int:





				'''simple docstring'''


				if depth < 0:
								raise ValueError("Depth cannot be less than 0"      )

				if len(__snake_case      ) == 0:
								raise ValueError("Scores cannot be empty"      )

				if depth == height:
								return scores[node_index]

				if is_max:
								return max(
								    minimax(depth + 1	,	node_index * 2	,	__snake_case	,	__snake_case	,	__snake_case      )	,	minimax(depth + 1	,	node_index * 2 + 1	,	__snake_case	,	__snake_case	,	__snake_case      )	,	)

				return min(
				    minimax(depth + 1	,	node_index * 2	,	__snake_case	,	__snake_case	,	__snake_case      )	,	minimax(depth + 1	,	node_index * 2 + 1	,	__snake_case	,	__snake_case	,	__snake_case      )	,	)





def 					lowercase_ (    )     -> None:





				'''simple docstring'''


				snake_case__      :List[Any]         =   [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
				snake_case__      :int         =   math.log(len(__snake_case      )	,	2      )
				print("Optimal value : "	,	end=""      )
				print(minimax(0	,	0	,	__snake_case	,	__snake_case	,	__snake_case      )      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()