


from __future__ import annotations

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



def       _lowerCAmelCase    (				lowerCAmelCase_				:list[list[int]]		,    lowerCAmelCase_				:list[int]		,    lowerCAmelCase_				:list[int]		,    lowerCAmelCase_				:int		,    lowerCAmelCase_				:list[list[int]]		,    )->tuple[list[list[int]], list[list[int]]]:
   '''simple docstring'''






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

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

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

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

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

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


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE						:Optional[int]			=  [
       [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],
   ]

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

   # the cost map which pushes the path closer to the goal
   SCREAMING_SNAKE_CASE						: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])):
         SCREAMING_SNAKE_CASE						:Optional[int]			=  abs(i - goal[0]) + abs(j - goal[1])
         if grid[i][j] == 1:
            # added extra penalty in the heuristic map
            SCREAMING_SNAKE_CASE						:Tuple			=  99

   SCREAMING_SNAKE_CASE		,						SCREAMING_SNAKE_CASE						:Union[str, Any]			=  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



def       _lowerCAmelCase    (				lowerCAmelCase_				:int		,    lowerCAmelCase_				:int				)->list[str]:
   '''simple docstring'''






   if partitions <= 0:
      raise ValueError("partitions must be a positive number!"				)
   if partitions > number_of_bytes:
      raise ValueError("partitions can not > number_of_bytes!"				)
   snake_case_            =       number_of_bytes // partitions
   snake_case_            =       []
   for i in range(lowerCAmelCase_				):
      snake_case_            =       i * bytes_per_partition + 1
      snake_case_            =       (
          number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
      )
      allocation_list.append(F'''{start_bytes}-{end_bytes}'''				)
   return allocation_list


if __name__ == "__main__":
   import doctest

   doctest.testmod()




