

from __future__ import annotations

lowercase_       		=  list[tuple[int, int]]

lowercase_       		=  [
    [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],
]

lowercase_       		=  ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right


class  A						:







    """simple docstring"""





    def __init__(      self :       Optional[Any],lowercase_ :       int,lowercase_ :       int,lowercase_ :       int,lowercase_ :       int,lowercase_ :       float,lowercase_ :       Node | None,)->    Optional[int]:





        '''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 						snake_case__     (      self :       Dict		)->    float:





        '''simple docstring'''







        A__          =   abs(self.pos_x - self.goal_x		)
        A__          =   abs(self.pos_y - self.goal_y		)
        return dx + dy


    def __lt__(      self :       Tuple,lowercase_ :       Optional[Any]		)->    bool:





        '''simple docstring'''







        return self.f_cost < other.f_cost




class  A						:







    """simple docstring"""





    def __init__(      self :       Tuple,lowercase_ :       tuple[int, int],lowercase_ :       tuple[int, int]		)->    Dict:





        '''simple docstring'''







        A__          =   Node(start[1],start[0],goal[1],goal[0],0,lowercase_		)
        A__          =   Node(goal[1],goal[0],goal[1],goal[0],9_9_9_9_9,lowercase_		)

        A__          =   [self.start]
        A__          =   []

        A__          =   False


    def 						snake_case__     (      self :       Optional[int]		)->    Path | None:





        '''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(lowercase_		)

            self.closed_nodes.append(lowercase_		)
            A__          =   self.get_successors(lowercase_		)

            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(lowercase_		)
                else:
                    # retrieve the best current path
                    A__          =   self.open_nodes.pop(self.open_nodes.index(lowercase_		)		)

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

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


    def 						snake_case__     (      self :       Any,lowercase_ :       Node		)->    list[Node]:





        '''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(lowercase_		) - 1):
                continue

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

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


    def 						snake_case__     (      self :       List[str],lowercase_ :       Node | None		)->    Path:





        '''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__":
     lowercase_       		=  (0, 0)
     lowercase_       		=  (len(grid) - 1, len(grid[0]) - 1)
     for elem in grid:
          print(elem)

     print("------")

     lowercase_       		=  GreedyBestFirst(init, goal)
     lowercase_       		=  greedy_bf.search()
     if path:
          for pos_x, pos_y in path:
               lowercase_       		=  2

          for elem in grid:
               print(elem)




from typing import Union

import fire
import torch
from tqdm import tqdm
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	str = "cpu"    ,			SCREAMING_SNAKE_CASE__			:	Union[str, None] = None      )							->      None:





    '''simple docstring'''






    A__          =   torch.load(SCREAMING_SNAKE_CASE__    ,			map_location=SCREAMING_SNAKE_CASE__      )
    for k, v in tqdm(state_dict.items()      ):
        if not isinstance(SCREAMING_SNAKE_CASE__    ,			torch.Tensor      ):
            raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'      )
        A__          =   v.half()
    if save_path is None:  # overwrite src_path
        A__          =   src_path
    torch.save(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )


if __name__ == "__main__":
     fire.Fire(convert)

