


"""simple docstring"""


from __future__ import annotations

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



def   snake_case						(				_a:     list[list[int]]		,   _a:     list[int]		,   _a:     list[int]		,   _a:     int		,   _a:     list[list[int]]		,   )->		tuple[list[list[int]], list[list[int]]]:
      '''simple docstring'''


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

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

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

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

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

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


if __name__ == "__main__":
   _snake_case          =							[
       [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],
   ]

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

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

   _snake_case      ,					_snake_case          =							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"""


import argparse
import json

from tqdm import tqdm



def   snake_case						(				)->		List[Any]:
      '''simple docstring'''


      lowerCamelCase__           =		argparse.ArgumentParser()

      # Required parameters
      parser.add_argument(
          '--src_path'		,   type=_a		,   default='biencoder-nq-dev.json'		,   help='Path to raw DPR training data'		,   )
      parser.add_argument(
          '--evaluation_set'		,   type=_a		,   help='where to store parsed evaluation_set file'		,   )
      parser.add_argument(
          '--gold_data_path'		,   type=_a		,   help='where to store parsed gold_data_path file'		,   )
      lowerCamelCase__           =		parser.parse_args()

      with open(args.src_path		,   'r'							) as src_file, open(args.evaluation_set		,   'w'							) as eval_file, open(
          args.gold_data_path		,   'w'							) as gold_file:
            lowerCamelCase__           =		json.load(_a							)
            for dpr_record in tqdm(_a							):
                  lowerCamelCase__           =		dpr_record['question']
                  lowerCamelCase__           =		[context['title'] for context in dpr_record['positive_ctxs']]
                  eval_file.write(question + '\n'							)
                  gold_file.write('\t'.join(_a							) + '\n'							)


if __name__ == "__main__":
   main()



