





from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
snake_case_           =      0

snake_case_           =      [
    [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],
]

snake_case_           =      [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

snake_case_           =      tuple[int, int]

class 		SCREAMING_SNAKE_CASE__    :


      def __init__(self	:				int    ,      a__	:				int    ,      a__	:				int    ,      a__	:				int    ,      a__	:				int    ,      a__	:				int    ,      a__	:				Node | None    ,      ):





         """simple docstring"""



         __snake_case								=       pos_x
         __snake_case								=       pos_y
         __snake_case								=       (pos_y, pos_x)
         __snake_case								=       goal_x
         __snake_case								=       goal_y
         __snake_case								=       g_cost
         __snake_case								=       parent
         __snake_case								=       self.calculate_heuristic()
         __snake_case								=       self.g_cost + self.h_cost


      def        a			(self	:				Optional[int]				):





         """simple docstring"""



         __snake_case								=       self.pos_x - self.goal_x
         __snake_case								=       self.pos_y - self.goal_y
         if HEURISTIC == 1:
            return abs(a__				) + abs(a__				)
         else:
            return sqrt(dy**2 + dx**2				)


      def __lt__(self	:				Union[str, Any]    ,      a__	:				Node				):





         """simple docstring"""



         return self.f_cost < other.f_cost






class 		SCREAMING_SNAKE_CASE__    :


      def __init__(self	:				Any    ,      a__	:				TPosition    ,      a__	:				TPosition				):





         """simple docstring"""



         __snake_case								=       Node(start[1]    ,      start[0]    ,      goal[1]    ,      goal[0]    ,      0    ,      a__				)
         __snake_case								=       Node(goal[1]    ,      goal[0]    ,      goal[1]    ,      goal[0]    ,      9_9999    ,      a__				)

         __snake_case								=       [self.start]
         __snake_case								=       []

         __snake_case								=       False


      def        a			(self	:				str				):





         """simple docstring"""



         while self.open_nodes:
            # Open Nodes are sorted using __lt__
            self.open_nodes.sort()
            __snake_case								=       self.open_nodes.pop(0				)

            if current_node.pos == self.target.pos:
               return self.retrace_path(a__				)

            self.closed_nodes.append(a__				)
            __snake_case								=       self.get_successors(a__				)

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

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

         return [self.start.pos]


      def        a			(self	:				List[str]    ,      a__	:				Node				):





         """simple docstring"""



         __snake_case								=       []
         for action in delta:
            __snake_case								=       parent.pos_x + action[1]
            __snake_case								=       parent.pos_y + action[0]
            if not (0 <= pos_x <= len(grid[0]				) - 1 and 0 <= pos_y <= len(a__				) - 1):
               continue

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

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


      def        a			(self	:				str    ,      a__	:				Node | None				):





         """simple docstring"""



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






class 		SCREAMING_SNAKE_CASE__    :


      def __init__(self	:				List[Any]    ,      a__	:				TPosition    ,      a__	:				TPosition				):





         """simple docstring"""



         __snake_case								=       AStar(a__    ,      a__				)
         __snake_case								=       AStar(a__    ,      a__				)
         __snake_case								=       False


      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
            self.fwd_astar.open_nodes.sort()
            self.bwd_astar.open_nodes.sort()
            __snake_case								=       self.fwd_astar.open_nodes.pop(0				)
            __snake_case								=       self.bwd_astar.open_nodes.pop(0				)

            if current_bwd_node.pos == current_fwd_node.pos:
               return self.retrace_bidirectional_path(
                   a__    ,      a__				)

            self.fwd_astar.closed_nodes.append(a__				)
            self.bwd_astar.closed_nodes.append(a__				)

            __snake_case								=       current_bwd_node
            __snake_case								=       current_fwd_node

            __snake_case								=       {
                self.fwd_astar: self.fwd_astar.get_successors(a__				),
                self.bwd_astar: self.bwd_astar.get_successors(a__				),
            }

            for astar in [self.fwd_astar, self.bwd_astar]:
               for child_node in successors[astar]:
                  if child_node in astar.closed_nodes:
                     continue

                  if child_node not in astar.open_nodes:
                     astar.open_nodes.append(a__				)
                  else:
                     # retrieve the best current path
                     __snake_case								=       astar.open_nodes.pop(
                         astar.open_nodes.index(a__				)				)

                     if child_node.g_cost < better_node.g_cost:
                        astar.open_nodes.append(a__				)
                     else:
                        astar.open_nodes.append(a__				)

         return [self.fwd_astar.start.pos]


      def        a			(self	:				Optional[Any]    ,      a__	:				Node    ,      a__	:				Node				):





         """simple docstring"""



         __snake_case								=       self.fwd_astar.retrace_path(a__				)
         __snake_case								=       self.bwd_astar.retrace_path(a__				)
         bwd_path.pop()
         bwd_path.reverse()
         __snake_case								=       fwd_path + bwd_path
         return path


if __name__ == "__main__":
    # all coordinates are given in format [y,x]
    snake_case_           =      (0, 0)
    snake_case_           =      (len(grid) - 1, len(grid[0]) - 1)
    for elem in grid:
        print(elem)

    snake_case_           =      time.time()
    snake_case_           =      AStar(init, goal)
    snake_case_           =      a_star.search()
    snake_case_           =      time.time() - start_time
    print(F'AStar execution time = {end_time:f} seconds')

    snake_case_           =      time.time()
    snake_case_           =      BidirectionalAStar(init, goal)
    snake_case_           =      time.time() - bd_start_time
    print(F'BidirectionalAStar execution time = {bd_end_time:f} seconds')








# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
    import torch

class 		SCREAMING_SNAKE_CASE__    (	TensorFormatter[Mapping, 'torch.Tensor', Mapping]  ):


      def __init__(self	:				Optional[int]    ,      a__	:				Tuple=None    ,      **a__	:				Optional[int]				):





         """simple docstring"""



         super().__init__(features=a__				)
         __snake_case								=       torch_tensor_kwargs
         import torch  # noqa import torch at initialization


      def        a			(self	:				Union[str, Any]    ,      a__	:				Union[str, Any]				):





         """simple docstring"""



         import torch

         if isinstance(a__    ,      a__				) and column:
            if all(
                isinstance(a__    ,      torch.Tensor				) and x.shape == column[0].shape and x.dtype == column[0].dtype
                for x in column				):
               return torch.stack(a__				)
         return column


      def        a			(self	:				Optional[Any]    ,      a__	:				str				):





         """simple docstring"""



         import torch

         if isinstance(a__    ,      (str, bytes, type(a__				))				):
            return value
         elif isinstance(a__    ,      (np.character, np.ndarray)				) and np.issubdtype(value.dtype    ,      np.character				):
            return value.tolist()

         __snake_case								=       {}

         if isinstance(a__    ,      (np.number, np.ndarray)				) and np.issubdtype(value.dtype    ,      np.integer				):
            __snake_case								=       {'''dtype''': torch.intaa}
         elif isinstance(a__    ,      (np.number, np.ndarray)				) and np.issubdtype(value.dtype    ,      np.floating				):
            __snake_case								=       {'''dtype''': torch.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
            import PIL.Image

            if isinstance(a__    ,      PIL.Image.Image				):
               __snake_case								=       np.asarray(a__				)
         return torch.tensor(a__    ,      **{**default_dtype, **self.torch_tensor_kwargs}				)


      def        a			(self	:				Optional[int]    ,      a__	:				str				):





         """simple docstring"""



         import torch

         # support for torch, tf, jax etc.
         if hasattr(a__    ,      '''__array__'''				) and not isinstance(a__    ,      torch.Tensor				):
            __snake_case								=       data_struct.__array__()
         # support for nested types like struct of list of struct
         if isinstance(a__    ,      np.ndarray				):
            if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
               return self._consolidate([self.recursive_tensorize(a__				) for substruct in data_struct]				)
         elif isinstance(a__    ,      (list, tuple)				):
            return self._consolidate([self.recursive_tensorize(a__				) for substruct in data_struct]				)
         return self._tensorize(a__				)


      def        a			(self	:				Optional[Any]    ,      a__	:				dict				):





         """simple docstring"""



         return map_nested(self._recursive_tensorize    ,      a__    ,      map_list=a__				)


      def        a			(self	:				Optional[int]    ,      a__	:				pa.Table				):





         """simple docstring"""



         __snake_case								=       self.numpy_arrow_extractor().extract_row(a__				)
         __snake_case								=       self.python_features_decoder.decode_row(a__				)
         return self.recursive_tensorize(a__				)


      def        a			(self	:				List[Any]    ,      a__	:				pa.Table				):





         """simple docstring"""



         __snake_case								=       self.numpy_arrow_extractor().extract_column(a__				)
         __snake_case								=       self.python_features_decoder.decode_column(a__    ,      pa_table.column_names[0]				)
         __snake_case								=       self.recursive_tensorize(a__				)
         __snake_case								=       self._consolidate(a__				)
         return column


      def        a			(self	:				Optional[int]    ,      a__	:				pa.Table				):





         """simple docstring"""



         __snake_case								=       self.numpy_arrow_extractor().extract_batch(a__				)
         __snake_case								=       self.python_features_decoder.decode_batch(a__				)
         __snake_case								=       self.recursive_tensorize(a__				)
         for column_name in batch:
            __snake_case								=       self._consolidate(batch[column_name]				)
         return batch

