
import numpy as np
class 					lowercase__:



	"""simple docstring"""




	def __init__(			self    :     List[str]			)			->	str:
					lowercase_	    =   (0, 0)
					lowercase_	    =   None
					lowercase_	    =   0
					lowercase_	    =   0
					lowercase_	    =   0
	def __eq__(			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     int			)			->	int:
					return self.position == cell.position




	def 		_lowercase    (			self    :     Union[str, Any]			)			->	str:
					print(self.position			)

class 					lowercase__:



	"""simple docstring"""




	def __init__(			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     Dict=(5, 5)			)			->	str:
					lowercase_	    =   np.zeros(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   world_size[0]
					lowercase_	    =   world_size[1]
	def 		_lowercase    (			self    :     Optional[Any]			)			->	Optional[Any]:
					print(self.w			)




	def 		_lowercase    (			self    :     List[str]			,				SCREAMING_SNAKE_CASE_    :     Any			)			->	List[str]:
					lowercase_	    =   [
					    (-1, -1),
					    (-1, 0),
					    (-1, 1),
					    (0, -1),
					    (0, 1),
					    (1, -1),
					    (1, 0),
					    (1, 1),
					]
					lowercase_	    =   cell.position[0]
					lowercase_	    =   cell.position[1]
					lowercase_	    =   []
					for n in neughbour_cord:
									lowercase_	    =   current_x + n[0]
									lowercase_	    =   current_y + n[1]
									if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
													lowercase_	    =   Cell()
													lowercase_	    =   (x, y)
													lowercase_	    =   cell
													neighbours.append(SCREAMING_SNAKE_CASE_			)
					return neighbours




def 							a		(				snake_case__:   str		,  snake_case__:   List[Any]		,  snake_case__:   Union[str, Any]   ):


				'''simple docstring'''
				lowercase_	    =   []
				lowercase_	    =   []
				_open.append(snake_case__   )

				while _open:
								lowercase_	    =   np.argmin([n.f for n in _open]   )
								lowercase_	    =   _open[min_f]
								_closed.append(_open.pop(snake_case__   )   )
								if current == goal:
												break
								for n in world.get_neigbours(snake_case__   ):
												for c in _closed:
																if c == n:
																				continue
												lowercase_	    =   current.g + 1
												lowercase_    ,      lowercase_	    =   n.position
												lowercase_    ,      lowercase_	    =   goal.position
												lowercase_	    =   (ya - ya) ** 2 + (xa - xa) ** 2
												lowercase_	    =   n.h + n.g

												for c in _open:
																if c == n and c.f < n.f:
																				continue
												_open.append(snake_case__   )
				lowercase_	    =   []
				while current.parent is not None:
								path.append(current.position   )
								lowercase_	    =   current.parent
				path.append(current.position   )
				return path[::-1]


if __name__ == "__main__":
						__a         =   Gridworld()
						# Start position and goal
						__a         =   Cell()
						__a         =   (0, 0)
						__a         =   Cell()
						__a         =   (4, 4)
						print(f"path from {start.position} to {goal.position}")
						__a         =   astar(world, start, goal)
						#   Just for visual reasons.
						for i in s:
												__a         =   1
						print(world.w)









from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

_lowerCamelCase    :		Dict		      =    typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
_lowerCamelCase    :		Any		      =    typing.Union[np.floataa, int, float]  # noqa: UP007

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     VectorOut:
					return np.sqrt(np.sum((np.asarray(__lowerCAmelCase   ) - np.asarray(__lowerCAmelCase   )) ** 2   )   )

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     VectorOut:
					return sum((va - va) ** 2 for va, va in zip(__lowerCAmelCase      ,	__lowerCAmelCase   )   ) ** (1 / 2)


if __name__ == "__main__":

			def        __a			(   )     ->     None:
								from timeit import timeit

								print('Without Numpy'   )
								print(
								    timeit(
								        'euclidean_distance_no_np([1, 2, 3], [4, 5, 6])'      ,	number=1_0000      ,	globals=globals()      ,	)   )
								print('With Numpy'   )
								print(
								    timeit(
								        'euclidean_distance([1, 2, 3], [4, 5, 6])'      ,	number=1_0000      ,	globals=globals()      ,	)   )



			benchmark()