




import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available
def 					lowerCamelCase_    (    *SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			if not isinstance(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
						SCREAMING_SNAKE_CASE           =					list(SCREAMING_SNAKE_CASE   )
			for i in range(len(SCREAMING_SNAKE_CASE   )   ):
						SCREAMING_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 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_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 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE = None					,	SCREAMING_SNAKE_CASE = 1_28   ):
			'''simple docstring'''
			if function is None:
						return functools.partial(SCREAMING_SNAKE_CASE					,	starting_batch_size=SCREAMING_SNAKE_CASE   )

			SCREAMING_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()
						SCREAMING_SNAKE_CASE           =					list(inspect.signature(SCREAMING_SNAKE_CASE   ).parameters.keys()   )
						# Guard against user error
						if len(SCREAMING_SNAKE_CASE   ) < (len(SCREAMING_SNAKE_CASE   ) + 1):
									SCREAMING_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












from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

__A						:							Dict							=  typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
__A						:							Tuple							=  typing.Union[np.floataa, int, float]  # noqa: UP007
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return np.sqrt(np.sum((np.asarray(SCREAMING_SNAKE_CASE   ) - np.asarray(SCREAMING_SNAKE_CASE   )) ** 2   )   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			return sum((va - va) ** 2 for va, va in zip(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )   ) ** (1 / 2)


if __name__ == "__main__":
		def 					lowerCamelCase_    (    ):
					'''simple docstring'''
					from timeit import timeit

					print("""Without Numpy"""   )
					print(
					    timeit(
					        """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"""					,	number=1_00_00					,	globals=globals()					,	)   )
					print("""With Numpy"""   )
					print(
					    timeit(
					        """euclidean_distance([1, 2, 3], [4, 5, 6])"""					,	number=1_00_00					,	globals=globals()					,	)   )




		benchmark()






