




from __future__ import annotations

lowerCamelCase				:		int     =   [
    [-1, 0],  # left
    [0, -1],  # down
    [1, 0],  # right
    [0, 1],  # up
]

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Tuple	,							lowercase	:       Optional[Any]	,							lowercase	:       str	,							lowercase	:       Optional[Any]	,							lowercase	:       Any	,							):


      '''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__":
 lowerCamelCase				:		List[str]     =   [
     [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				:		Tuple     =   [0, 0]
 # all coordinates are given in format [y,x]
 lowerCamelCase				:		Optional[int]     =   [len(grid) - 1, len(grid[0]) - 1]
 lowerCamelCase				:		Tuple     =   1

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

 lowerCamelCase    ,    lowerCamelCase				:		str     =   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 logging

from transformers.configuration_utils import PretrainedConfig


__A									=				logging.getLogger(__name__)



class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			"masked_bert"

 def __init__(self				:   Dict       , UpperCAmelCase_				:   Any=30_522       , UpperCAmelCase_				:   List[Any]=768       , UpperCAmelCase_				:   Optional[Any]=12       , UpperCAmelCase_				:   str=12       , UpperCAmelCase_				:   Tuple=3_072       , UpperCAmelCase_				:   str="gelu"       , UpperCAmelCase_				:   Any=0.1       , UpperCAmelCase_				:   Tuple=0.1       , UpperCAmelCase_				:   Optional[Any]=512       , UpperCAmelCase_				:   Union[str, Any]=2       , UpperCAmelCase_				:   str=0.02       , UpperCAmelCase_				:   str=1E-1_2       , UpperCAmelCase_				:   Union[str, Any]=0       , UpperCAmelCase_				:   str="topK"       , UpperCAmelCase_				:   List[str]="constant"       , UpperCAmelCase_				:   str=0.0       , **UpperCAmelCase_				:   int       , )       ->List[Any]:



   '''simple docstring'''



   super().__init__(pad_token_id=UpperCAmelCase_       , **UpperCAmelCase_)

   lowerCamelCase__:      Optional[int]			  =vocab_size
   lowerCamelCase__:      Dict			  =hidden_size
   lowerCamelCase__:      Optional[int]			  =num_hidden_layers
   lowerCamelCase__:      Any			  =num_attention_heads
   lowerCamelCase__:      List[Any]			  =hidden_act
   lowerCamelCase__:      str			  =intermediate_size
   lowerCamelCase__:      Dict			  =hidden_dropout_prob
   lowerCamelCase__:      str			  =attention_probs_dropout_prob
   lowerCamelCase__:      int			  =max_position_embeddings
   lowerCamelCase__:      Tuple			  =type_vocab_size
   lowerCamelCase__:      str			  =initializer_range
   lowerCamelCase__:      List[Any]			  =layer_norm_eps
   lowerCamelCase__:      str			  =pruning_method
   lowerCamelCase__:      Union[str, Any]			  =mask_init
   lowerCamelCase__:      Optional[Any]			  =mask_scale

