




'''simple docstring'''






from __future__ import annotations

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






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      )    ->   tuple[list[list[int]], list[list[int]]]:
      snake_case__       :   Dict	      =    [
          [0 for col in range(len(grid[0]		)		)] for row in range(len(__SCREAMING_SNAKE_CASE		)		)
      ]  # the reference grid
      snake_case__       :   List[str]	      =    1
      snake_case__       :   List[Any]	      =    [
          [0 for col in range(len(grid[0]		)		)] for row in range(len(__SCREAMING_SNAKE_CASE		)		)
      ]  # the action grid

      snake_case__       :   Optional[Any]	      =    init[0]
      snake_case__       :   List[str]	      =    init[1]
      snake_case__       :   Dict	      =    0
      snake_case__       :   Any	      =    g + heuristic[x][y]  # cost from starting cell to destination cell
      snake_case__       :   List[Any]	      =    [[f, g, x, y]]

      snake_case__       :   List[Any]	      =    False  # flag that is set when search is complete
      snake_case__       :   Dict	      =    False  # flag set if we can't find expand

      while not found and not resign:
            if len(__SCREAMING_SNAKE_CASE		) == 0:
                  raise ValueError('Algorithm is unable to find solution'		)
            else:  # to choose the least costliest action so as to move closer to the goal
                  cell.sort()
                  cell.reverse()
                  snake_case__       :   List[str]	      =    cell.pop()
                  snake_case__       :   Dict	      =    next_cell[2]
                  snake_case__       :   str	      =    next_cell[3]
                  snake_case__       :   Tuple	      =    next_cell[1]

                  if x == goal[0] and y == goal[1]:
                        snake_case__       :   Union[str, Any]	      =    True
                  else:
                        for i in range(len(__SCREAMING_SNAKE_CASE		)		):  # to try out different valid actions
                              snake_case__       :   Union[str, Any]	      =    x + DIRECTIONS[i][0]
                              snake_case__       :   str	      =    y + DIRECTIONS[i][1]
                              if xa >= 0 and xa < len(__SCREAMING_SNAKE_CASE		) and ya >= 0 and ya < len(grid[0]		):
                                    if closed[xa][ya] == 0 and grid[xa][ya] == 0:
                                          snake_case__       :   Union[str, Any]	      =    g + cost
                                          snake_case__       :   Tuple	      =    ga + heuristic[xa][ya]
                                          cell.append([fa, ga, xa, ya]		)
                                          snake_case__       :   List[str]	      =    1
                                          snake_case__       :   Optional[Any]	      =    i
      snake_case__       :   Union[str, Any]	      =    []
      snake_case__       :   str	      =    goal[0]
      snake_case__       :   Dict	      =    goal[1]
      invpath.append([x, y]		)  # we get the reverse path from here
      while x != init[0] or y != init[1]:
            snake_case__       :   Optional[int]	      =    x - DIRECTIONS[action[x][y]][0]
            snake_case__       :   List[Any]	      =    y - DIRECTIONS[action[x][y]][1]
            snake_case__       :   str	      =    xa
            snake_case__       :   Union[str, Any]	      =    ya
            invpath.append([x, y]		)

      snake_case__       :   Tuple	      =    []
      for i in range(len(__SCREAMING_SNAKE_CASE		)		):
            path.append(invpath[len(__SCREAMING_SNAKE_CASE		) - 1 - i]		)
      return path, action


if __name__ == "__main__":
      A_			      =		[
          [0, 1, 0, 0, 0, 0],
          [0, 1, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
          [0, 1, 0, 0, 0, 0],
          [0, 1, 0, 0, 1, 0],
          [0, 0, 0, 0, 1, 0],
      ]

      A_			      =		[0, 0]
      # all coordinates are given in format [y,x]
      A_			      =		[len(grid) - 1, len(grid[0]) - 1]
      A_			      =		1

      # the cost map which pushes the path closer to the goal
      A_			      =		[[0 for row in range(len(grid[0]))] for col in range(len(grid))]
      for i in range(len(grid)):
            for j in range(len(grid[0])):
                  A_			      =		abs(i - goal[0]) + abs(j - goal[1])
                  if grid[i][j] == 1:
                        # added extra penalty in the heuristic map
                        A_			      =		99

      A_      ,    A_			      =		search(grid, init, goal, cost, heuristic)

      print("ACTION MAP")
      for i in range(len(action)):
            print(action[i])

      for i in range(len(path)):
            print(path[i])






'''simple docstring'''






# flake8: noqa
# Lint as: python3
A_			      =		[
    "VerificationMode",
    "Version",
    "disable_progress_bar",
    "enable_progress_bar",
    "is_progress_bar_enabled",
    "experimental",
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental
