




"""simple docstring"""




from __future__ import annotations

_lowerCamelCase     :		Union[str, Any]								=       [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_			,    lowercase_			,    lowercase_			,    )       ->	tuple[list[list[int]], list[list[int]]]:



       """simple docstring"""

       A__							=   [
           [0 for col in range(len(grid[0]						)						)] for row in range(len(lowercase_						)						)
       ]  # the reference grid
       A__							=   1
       A__							=   [
           [0 for col in range(len(grid[0]						)						)] for row in range(len(lowercase_						)						)
       ]  # the action grid

       A__							=   init[0]
       A__							=   init[1]
       A__							=   0
       A__							=   g + heuristic[x][y]  # cost from starting cell to destination cell
       A__							=   [[f, g, x, y]]

       A__							=   False  # flag that is set when search is complete
       A__							=   False  # flag set if we can't find expand

       while not found and not resign:
              if len(lowercase_						) == 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()
                     A__							=   cell.pop()
                     A__							=   next_cell[2]
                     A__							=   next_cell[3]
                     A__							=   next_cell[1]

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

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


if __name__ == "__main__":
  _lowerCamelCase     :		Union[str, Any]								=       [
      [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],
  ]

  _lowerCamelCase     :		Any								=       [0, 0]
  # all coordinates are given in format [y,x]
  _lowerCamelCase     :		Dict								=       [len(grid) - 1, len(grid[0]) - 1]
  _lowerCamelCase     :		Optional[int]								=       1

  # the cost map which pushes the path closer to the goal
  _lowerCamelCase     :		Union[str, Any]								=       [[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])):
      _lowerCamelCase     :		List[Any]								=       abs(i - goal[0]) + abs(j - goal[1])
      if grid[i][j] == 1:
        # added extra penalty in the heuristic map
        _lowerCamelCase     :		List[str]								=       99

  _lowerCamelCase     :		Optional[int]								=       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])








from __future__ import annotations

from collections import Counter
from random import random


class     UpperCamelCase_   :

    '''simple docstring'''






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





           '''simple docstring'''

           A__							=   {}


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int] ,			UpperCAmelCase__				:     str)						->None:





           '''simple docstring'''

           A__							=   {}


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     float)						->None:





           '''simple docstring'''

           if nodea not in self.connections:
                  self.add_node(UpperCAmelCase__)
           if nodea not in self.connections:
                  self.add_node(UpperCAmelCase__)
           A__							=   probability


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->list[str]:





           '''simple docstring'''

           return list(self.connections)





    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     str)						->str:





           '''simple docstring'''

           A__							=   0
           A__							=   random()

           for dest in self.connections[node]:
                  current_probability += self.connections[node][dest]
                  if current_probability > random_value:
                         return dest
           return ""







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_			,    lowercase_						)       ->	dict[str, int]:



       """simple docstring"""

       A__							=   MarkovChainGraphUndirectedUnweighted()

       for nodea, nodea, probability in transitions:
              graph.add_transition_probability(lowercase_			,    lowercase_			,    lowercase_						)

       A__							=   Counter(graph.get_nodes()						)
       A__							=   start

       for _ in range(lowercase_						):
              A__							=   graph.transition(lowercase_						)
              visited[node] += 1

       return visited


if __name__ == "__main__":
  import doctest

  doctest.testmod()


