




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 		lowercase							:

     def __init__(						self	,  _a	,  _a	,  _a	,  _a	,  _a	,  _a	,  )		->							None:
           _A							:  Optional[int]    =		pos_x
           _A							:  str    =		pos_y
           _A							:  List[str]    =		(pos_y, pos_x)
           _A							:  Optional[Any]    =		goal_x
           _A							:  Any    =		goal_y
           _A							:  Optional[Any]    =		g_cost
           _A							:  int    =		parent
           _A							:  List[str]    =		self.calculate_heuristic()
           _A							:  Optional[int]    =		self.g_cost + self.h_cost

     def       a__			(						self       )		->							float:
           _A							:  List[str]    =		self.pos_x - self.goal_x
           _A							:  Tuple    =		self.pos_y - self.goal_y
           if HEURISTIC == 1:
                 return abs(lowerCamelCase_       ) + abs(lowerCamelCase_       )
           else:
                 return sqrt(dy**2 + dx**2       )



     def __lt__(						self	,  _a       )		->							bool:
           return self.f_cost < other.f_cost







class 		lowercase							:

     def __init__(						self	,  _a	,  _a       )		->							Union[str, Any]:
           _A							:  str    =		Node(start[1]	,  start[0]	,  goal[1]	,  goal[0]	,  0	,  lowerCamelCase_       )
           _A							:  Dict    =		Node(goal[1]	,  goal[0]	,  goal[1]	,  goal[0]	,  9_9999	,  lowerCamelCase_       )

           _A							:  Optional[Any]    =		[self.start]
           _A							:  Optional[Any]    =		[]

           _A							:  str    =		False

     def       a__			(						self       )		->							list[TPosition]:
           while self.open_nodes:
                 # Open Nodes are sorted using __lt__
                 self.open_nodes.sort()
                 _A							:  Optional[int]    =		self.open_nodes.pop(0       )

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

                 self.closed_nodes.append(lowerCamelCase_       )
                 _A							:  Union[str, Any]    =		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							:  Dict    =		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_       )

           return [self.start.pos]

     def       a__			(						self	,  _a       )		->							list[Node]:
           _A							:  Union[str, Any]    =		[]
           for action in delta:
                 _A							:  Any    =		parent.pos_x + action[1]
                 _A							:  Dict    =		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       a__			(						self	,  _a       )		->							list[TPosition]:
           _A							:  Any    =		node
           _A							:  Optional[Any]    =		[]
           while current_node is not None:
                 path.append((current_node.pos_y, current_node.pos_x)       )
                 _A							:  Optional[int]    =		current_node.parent
           path.reverse()
           return path







class 		lowercase							:

     def __init__(						self	,  _a	,  _a       )		->							None:
           _A							:  Dict    =		AStar(lowerCamelCase_	,  lowerCamelCase_       )
           _A							:  Tuple    =		AStar(lowerCamelCase_	,  lowerCamelCase_       )
           _A							:  List[Any]    =		False

     def       a__			(						self       )		->							list[TPosition]:
           while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
                 self.fwd_astar.open_nodes.sort()
                 self.bwd_astar.open_nodes.sort()
                 _A							:  Optional[Any]    =		self.fwd_astar.open_nodes.pop(0       )
                 _A							:  List[str]    =		self.bwd_astar.open_nodes.pop(0       )

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

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

                 _A							:  Optional[Any]    =		current_bwd_node
                 _A							:  Optional[int]    =		current_fwd_node

                 _A							:  Optional[int]    =		{
                     self.fwd_astar: self.fwd_astar.get_successors(lowerCamelCase_       ),
                     self.bwd_astar: self.bwd_astar.get_successors(lowerCamelCase_       ),
                 }

                 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(lowerCamelCase_       )
                             else:
                                   # retrieve the best current path
                                   _A							:  Optional[Any]    =		astar.open_nodes.pop(
                                       astar.open_nodes.index(lowerCamelCase_       )       )

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

           return [self.fwd_astar.start.pos]



     def       a__			(						self	,  _a	,  _a       )		->							list[TPosition]:
           _A							:  Optional[int]    =		self.fwd_astar.retrace_path(lowerCamelCase_       )
           _A							:  Dict    =		self.bwd_astar.retrace_path(lowerCamelCase_       )
           bwd_path.pop()
           bwd_path.reverse()
           _A							:  int    =		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""")










from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean







def        lowerCAmelCase_			(  snake_case_  ):

      # A local function to see if a dot lands in the circle.
      def is_in_circle(snake_case_,snake_case_  ) -> bool:
            _A							:  List[str]    =		sqrt((x**2) + (y**2)  )
            # Our circle has a radius of 1, so a distance
            # greater than 1 would land outside the circle.
            return distance_from_centre <= 1

      # The proportion of guesses that landed in the circle
      _A							:  Optional[int]    =		mean(
          int(is_in_circle(uniform(-1.0,1.0  ),uniform(-1.0,1.0  )  )  )
          for _ in range(snake_case_  )  )
      # The ratio of the area for circle to square is pi/4.
      _A							:  List[str]    =		proportion * 4
      print(f'''The estimated value of pi is {pi_estimate}'''  )
      print(f'''The numpy value of pi is {pi}'''  )
      print(f'''The total error is {abs(pi - pi_estimate  )}'''  )







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_ = 0.0,snake_case_ = 1.0,):
      return mean(
          function_to_integrate(uniform(snake_case_,snake_case_  )  ) for _ in range(snake_case_  )  ) * (max_value - min_value)







def        lowerCAmelCase_			(  snake_case_,snake_case_ = 0.0,snake_case_ = 1.0  ):

      def identity_function(snake_case_  ) -> float:
            return x

      _A							:  Any    =		area_under_curve_estimator(
          snake_case_,snake_case_,snake_case_,snake_case_  )
      _A							:  Tuple    =		(max_value * max_value - min_value * min_value) / 2

      print("""******************"""  )
      print(f'''Estimating area under y=x where x varies from {min_value} to {max_value}'''  )
      print(f'''Estimated value is {estimated_value}'''  )
      print(f'''Expected value is {expected_value}'''  )
      print(f'''Total error is {abs(estimated_value - expected_value  )}'''  )
      print("""******************"""  )







def        lowerCAmelCase_			(  snake_case_  ):

      def function_to_integrate(snake_case_  ) -> float:
            return sqrt(4.0 - x * x  )

      _A							:  Optional[int]    =		area_under_curve_estimator(
          snake_case_,snake_case_,0.0,2.0  )

      print("""******************"""  )
      print("""Estimating pi using area_under_curve_estimator"""  )
      print(f'''Estimated value is {estimated_value}'''  )
      print(f'''Expected value is {pi}'''  )
      print(f'''Total error is {abs(estimated_value - pi  )}'''  )
      print("""******************"""  )


if __name__ == "__main__":
  import doctest

  doctest.testmod()

