
"""simple docstring"""



from __future__ import annotations

UpperCAmelCase__        =					[
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]
def    _UpperCAmelCase (					__lowerCamelCase  :			Tuple					,   __lowerCamelCase  :			Tuple					,   __lowerCamelCase  :			List[str]					,   __lowerCamelCase  :			List[str]					,   __lowerCamelCase  :			Union[str, Any]					,   )	->   int:
  _snake_case   	=			[
      [0 for col in range(len(grid[0]						)						)] for row in range(len(__UpperCAmelCase						)						)
  ]  # the reference grid
  _snake_case   	=			1
  _snake_case   	=			[
      [0 for col in range(len(grid[0]						)						)] for row in range(len(__UpperCAmelCase						)						)
  ]  # 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(__UpperCAmelCase						) == 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(__UpperCAmelCase						)						):  # to try out different valid actions
          _snake_case   	=			x + DIRECTIONS[i][0]
          _snake_case   	=			y + DIRECTIONS[i][1]
          if xa >= 0 and xa < len(__UpperCAmelCase						) 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(__UpperCAmelCase						)						):
    path.append(invpath[len(__UpperCAmelCase						) - 1 - i]						)
  return path, action


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

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

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

 UpperCAmelCase__        =					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])








import requests
from bsa import BeautifulSoup






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= BeautifulSoup(requests.get(__UpperCAmelCase							,  params=__UpperCAmelCase       ).content							,  '''html.parser'''       )
 a__												= soup.find('''div'''							,  attrs={'''class''': '''gs_ri'''}       )
 a__												= div.find('''div'''							,  attrs={'''class''': '''gs_fl'''}       ).find_all('''a'''       )
 return anchors[2].get_text()


if __name__ == "__main__":
     a_      :      Optional[Any]    =     {
         'title': (
             'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
             'capacitance, volumetric capacitance, and energy density'
         ),
         'journal': 'Chem. Mater.',
         'volume': 30,
         'pages': '3979-3990',
         'year': 20_18,
         'hl': 'en',
     }
     print(get_citation('https://scholar.google.com/scholar_lookup', params=params))





