






import os

def      _lowerCamelCase	(						)      ->					Dict:
    with open(os.path.dirname(__A ) + '''/grid.txt''' ) as f:
        _UpperCAmelCase				:      Optional[int]			  =				[]  # noqa: E741
        for _ in range(20 ):
            l.append([int(__A ) for x in f.readline().split()] )

        _UpperCAmelCase				:      str			  =				0

        # right
        for i in range(20 ):
            for j in range(17 ):
                _UpperCAmelCase				:      Tuple			  =				l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
                if temp > maximum:
                    _UpperCAmelCase				:      str			  =				temp

        # down
        for i in range(17 ):
            for j in range(20 ):
                _UpperCAmelCase				:      List[Any]			  =				l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
                if temp > maximum:
                    _UpperCAmelCase				:      Dict			  =				temp

        # diagonal 1
        for i in range(17 ):
            for j in range(17 ):
                _UpperCAmelCase				:      str			  =				l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
                if temp > maximum:
                    _UpperCAmelCase				:      Any			  =				temp

        # diagonal 2
        for i in range(17 ):
            for j in range(3		,		20 ):
                _UpperCAmelCase				:      Dict			  =				l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
                if temp > maximum:
                    _UpperCAmelCase				:      int			  =				temp
        return maximum


if __name__ == "__main__":
  print(solution())












import heapq

def      _lowerCamelCase	(						__A    :							dict )      ->					set[int]:
    _UpperCAmelCase				:      list[list]			  =				[]

    # for each node and his adjacency list add them and the rank of the node to queue
    # using heapq module the queue will be filled like a Priority Queue
    # heapq works with a min priority queue, so I used -1*len(v) to build it
    for key, value in graph.items():
        # O(log(n))
        heapq.heappush(__A		,		[-1 * len(__A ), (key, value)] )

    # chosen_vertices = set of chosen vertices
    _UpperCAmelCase				:      Union[str, Any]			  =				set()

    # while queue isn't empty and there are still edges
    #   (queue[0][0] is the rank of the node with max rank)
    while queue and queue[0][0] != 0:
        # extract vertex with max rank from queue and add it to chosen_vertices
        _UpperCAmelCase				:      Tuple			  =				heapq.heappop(__A )[1][0]
        chosen_vertices.add(__A )

        # Remove all arcs adjacent to argmax
        for elem in queue:
            # if v haven't adjacent node, skip
            if elem[0] == 0:
                continue
            # if argmax is reachable from elem
            # remove argmax from elem's adjacent list and update his rank
            if argmax in elem[1][1]:
                _UpperCAmelCase				:      Dict			  =				elem[1][1].index(__A )
                del elem[1][1][index]
                elem[0] += 1
        # re-order the queue
        heapq.heapify(__A )
    return chosen_vertices


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  SCREAMING_SNAKE_CASE 							=						{0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
  print(F'Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}')




