

from __future__ import annotations

_lowerCAmelCase					:			Any  				=			list[tuple[int, int]]

_lowerCAmelCase					:			List[Any]  				=			[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

_lowerCAmelCase					:			Optional[int]  				=			([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right






class 	__magic_name__    :





			def __init__(		self					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  )   -> Optional[Any]:




						'''simple docstring'''
						__a        =pos_x
						__a        =pos_y
						__a        =(pos_y, pos_x)
						__a        =goal_x
						__a        =goal_y
						__a        =g_cost
						__a        =parent
						__a        =self.calculate_heuristic()





			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						__a        =abs(self.pos_x - self.goal_x					)
						__a        =abs(self.pos_y - self.goal_y					)
						return dx + dy





			def __lt__(		self					,  __snake_case					)   -> List[str]:




						'''simple docstring'''
						return self.f_cost < other.f_cost






class 	__magic_name__    :





			def __init__(		self					,  __snake_case					,  __snake_case					)   -> Dict:




						'''simple docstring'''
						__a        =Node(start[1]					,  start[0]					,  goal[1]					,  goal[0]					,  0					,  _lowerCamelCase					)
						__a        =Node(goal[1]					,  goal[0]					,  goal[1]					,  goal[0]					,  9_9999					,  _lowerCamelCase					)

						__a        =[self.start]
						__a        =[]

						__a        =False





			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						while self.open_nodes:
									# Open Nodes are sorted using __lt__
									self.open_nodes.sort()
									__a        =self.open_nodes.pop(0					)

									if current_node.pos == self.target.pos:
												__a        =True
												return self.retrace_path(_lowerCamelCase					)

									self.closed_nodes.append(_lowerCamelCase					)
									__a        =self.get_successors(_lowerCamelCase					)

									for child_node in successors:
												if child_node in self.closed_nodes:
															continue

												if child_node not in self.open_nodes:
															self.open_nodes.append(_lowerCamelCase					)
												else:
															# retrieve the best current path
															__a        =self.open_nodes.pop(self.open_nodes.index(_lowerCamelCase					)					)

															if child_node.g_cost < better_node.g_cost:
																		self.open_nodes.append(_lowerCamelCase					)
															else:
																		self.open_nodes.append(_lowerCamelCase					)

						if not self.reached:
									return [self.start.pos]
						return None





			def 		__magic_name__    (		self					,  __snake_case					)   -> List[str]:




						'''simple docstring'''
						__a        =[]
						for action in delta:
									__a        =parent.pos_x + action[1]
									__a        =parent.pos_y + action[0]

									if not (0 <= pos_x <= len(grid[0]					) - 1 and 0 <= pos_y <= len(_lowerCamelCase					) - 1):
												continue

									if grid[pos_y][pos_x] != 0:
												continue

									successors.append(
									    Node(
									        _lowerCamelCase					,  _lowerCamelCase					,  self.target.pos_y					,  self.target.pos_x					,  parent.g_cost + 1					,  _lowerCamelCase					,  )					)
						return successors





			def 		__magic_name__    (		self					,  __snake_case					)   -> List[str]:




						'''simple docstring'''
						__a        =node
						__a        =[]
						while current_node is not None:
									path.append((current_node.pos_y, current_node.pos_x)					)
									__a        =current_node.parent
						path.reverse()
						return path


if __name__ == "__main__":
						_lowerCAmelCase					:			Any  				=			(0, 0)
						_lowerCAmelCase					:			int  				=			(len(grid) - 1, len(grid[0]) - 1)
						for elem in grid:
												print(elem)

						print("------")

						_lowerCAmelCase					:			Tuple  				=			GreedyBestFirst(init, goal)
						_lowerCAmelCase					:			Optional[Any]  				=			greedy_bf.search()
						if path:
												for pos_x, pos_y in path:
																		_lowerCAmelCase					:			Optional[int]  				=			2

												for elem in grid:
																		print(elem)














"""simple docstring"""



from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch

@dataclass
class        a     :
    UpperCAmelCase_ :     torch.Tensor  # [batch_size x 3]
    UpperCAmelCase_ :     torch.Tensor  # [batch_size x 3]
    UpperCAmelCase_ :     torch.Tensor  # [batch_size x 3]
    UpperCAmelCase_ :     torch.Tensor  # [batch_size x 3]
    UpperCAmelCase_ :     int
    UpperCAmelCase_ :     int
    UpperCAmelCase_ :     float
    UpperCAmelCase_ :     float
    UpperCAmelCase_ :     Tuple[int]







    def 			UpperCamelCase_      (			self       ):
      assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
      assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
      assert len(self.x.shape       ) == len(self.y.shape       ) == len(self.z.shape       ) == len(self.origin.shape       ) == 2







    def 			UpperCamelCase_      (			self       ):
      return torch.from_numpy(np.array([self.width, self.height]							,		dtype=np.floataa       )       )







    def 			UpperCamelCase_      (			self       ):
      return torch.from_numpy(np.array([self.x_fov, self.y_fov]							,		dtype=np.floataa       )       )







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       torch.arange(self.height * self.width       )
      lowercase							    =       torch.stack(
          [
              pixel_indices % self.width,
              torch.div(_lowerCamelCase							,		self.width							,		rounding_mode='trunc'       ),
          ]							,		axis=1							,		)
      return coords







    @property
    def 			UpperCamelCase_      (			self       ):
      lowercase    ,     *lowercase							    =       self.shape
      lowercase							    =       int(np.prod(_lowerCamelCase       )       )

      lowercase							    =       self.get_image_coords()
      lowercase							    =       torch.broadcast_to(coords.unsqueeze(0       )							,		[batch_size * inner_batch_size, *coords.shape]       )
      lowercase							    =       self.get_camera_rays(_lowerCamelCase       )

      lowercase							    =       rays.view(_lowerCamelCase							,		inner_batch_size * self.height * self.width							,		2							,		3       )

      return rays







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase       ):
      lowercase    ,     *lowercase    ,     lowercase							    =       coords.shape
      assert n_coords == 2
      assert batch_size == self.origin.shape[0]

      lowercase							    =       coords.view(_lowerCamelCase							,		-1							,		2       )

      lowercase							    =       self.resolution()
      lowercase							    =       self.fov()

      lowercase							    =       (flat.float() / (res - 1)) * 2 - 1
      lowercase							    =       fracs * torch.tan(fov / 2       )

      lowercase							    =       fracs.view(_lowerCamelCase							,		-1							,		2       )
      lowercase							    =       (
          self.z.view(_lowerCamelCase							,		1							,		3       )
          + self.x.view(_lowerCamelCase							,		1							,		3       ) * fracs[:, :, :1]
          + self.y.view(_lowerCamelCase							,		1							,		3       ) * fracs[:, :, 1:]
      )
      lowercase							    =       directions / directions.norm(dim=-1							,		keepdim=_lowerCamelCase       )
      lowercase							    =       torch.stack(
          [
              torch.broadcast_to(self.origin.view(_lowerCamelCase							,		1							,		3       )							,		[batch_size, directions.shape[1], 3]       ),
              directions,
          ]							,		dim=2							,		)
      return rays.view(_lowerCamelCase							,		*_lowerCamelCase							,		2							,		3       )







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase       ):
      assert width * self.height == height * self.width, "The aspect ratio should not change."
      return DifferentiableProjectiveCamera(
          origin=self.origin							,		x=self.x							,		y=self.y							,		z=self.z							,		width=_lowerCamelCase							,		height=_lowerCamelCase							,		x_fov=self.x_fov							,		y_fov=self.y_fov							,		)


def 						_SCREAMING_SNAKE_CASE (    __snake_case			:  int    ):





  '''simple docstring'''





  lowercase							    =       []
  lowercase							    =       []
  lowercase							    =       []
  lowercase							    =       []
  for theta in np.linspace(0		,					2 * np.pi		,					num=20    ):
    lowercase							    =       np.array([np.sin(__snake_case    ), np.cos(__snake_case    ), -0.5]    )
    z /= np.sqrt(np.sum(z**2    )    )
    lowercase							    =       -z * 4
    lowercase							    =       np.array([np.cos(__snake_case    ), -np.sin(__snake_case    ), 0.0]    )
    lowercase							    =       np.cross(__snake_case		,					__snake_case    )
    origins.append(__snake_case    )
    xs.append(__snake_case    )
    ys.append(__snake_case    )
    zs.append(__snake_case    )
  return DifferentiableProjectiveCamera(
      origin=torch.from_numpy(np.stack(__snake_case		,					axis=0    )    ).float()		,					x=torch.from_numpy(np.stack(__snake_case		,					axis=0    )    ).float()		,					y=torch.from_numpy(np.stack(__snake_case		,					axis=0    )    ).float()		,					z=torch.from_numpy(np.stack(__snake_case		,					axis=0    )    ).float()		,					width=__snake_case		,					height=__snake_case		,					x_fov=0.7		,					y_fov=0.7		,					shape=(1, len(__snake_case    ))		,					)


