






from __future__ import annotations

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







def       lowercase__	(					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"""






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

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

       __UpperCAmelCase     				=False  # flag that is set when search is complete
       __UpperCAmelCase     				=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()
                     __UpperCAmelCase     				=cell.pop()
                     __UpperCAmelCase     				=next_cell[2]
                     __UpperCAmelCase     				=next_cell[3]
                     __UpperCAmelCase     				=next_cell[1]

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

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


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

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

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

   __A				,     __A					=	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'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A   :    str											= logging.get_logger(__name__)

__A   :    int											= {
    "weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}






class   __snake_case			(  _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase  					=   'roc_bert'

    def __init__(  self       :    str       ,		lowerCamelCase       :    Optional[Any]=3_05_22       ,		lowerCamelCase       :    List[Any]=7_68       ,		lowerCamelCase       :    int=12       ,		lowerCamelCase       :    str=12       ,		lowerCamelCase       :    Any=30_72       ,		lowerCamelCase       :    List[Any]="gelu"       ,		lowerCamelCase       :    Optional[int]=0.1       ,		lowerCamelCase       :    List[str]=0.1       ,		lowerCamelCase       :    int=5_12       ,		lowerCamelCase       :    List[Any]=2       ,		lowerCamelCase       :    int=0.02       ,		lowerCamelCase       :    str=1E-12       ,		lowerCamelCase       :    int=True       ,		lowerCamelCase       :    Optional[Any]=0       ,		lowerCamelCase       :    List[Any]="absolute"       ,		lowerCamelCase       :    str=None       ,		lowerCamelCase       :    str=True       ,		lowerCamelCase       :    Tuple=True       ,		lowerCamelCase       :    str=7_68       ,		lowerCamelCase       :    str=9_10       ,		lowerCamelCase       :    Tuple=5_12       ,		lowerCamelCase       :    List[str]=2_48_58       ,		lowerCamelCase       :    Dict=True       ,		**lowerCamelCase       :    Tuple       ,		)  ->    Optional[int]:
      lowerCAmelCase_     : Optional[Any]     =  vocab_size
      lowerCAmelCase_     : Any     =  max_position_embeddings
      lowerCAmelCase_     : Any     =  hidden_size
      lowerCAmelCase_     : int     =  num_hidden_layers
      lowerCAmelCase_     : Tuple     =  num_attention_heads
      lowerCAmelCase_     : List[Any]     =  intermediate_size
      lowerCAmelCase_     : Tuple     =  hidden_act
      lowerCAmelCase_     : List[Any]     =  hidden_dropout_prob
      lowerCAmelCase_     : Dict     =  attention_probs_dropout_prob
      lowerCAmelCase_     : Any     =  initializer_range
      lowerCAmelCase_     : Any     =  type_vocab_size
      lowerCAmelCase_     : Optional[int]     =  layer_norm_eps
      lowerCAmelCase_     : Tuple     =  use_cache
      lowerCAmelCase_     : List[Any]     =  enable_pronunciation
      lowerCAmelCase_     : List[Any]     =  enable_shape
      lowerCAmelCase_     : List[str]     =  pronunciation_embed_dim
      lowerCAmelCase_     : List[str]     =  pronunciation_vocab_size
      lowerCAmelCase_     : Dict     =  shape_embed_dim
      lowerCAmelCase_     : Optional[Any]     =  shape_vocab_size
      lowerCAmelCase_     : List[Any]     =  concat_input
      lowerCAmelCase_     : Any     =  position_embedding_type
      lowerCAmelCase_     : List[str]     =  classifier_dropout
      super().__init__(pad_token_id=lowerCamelCase       ,		**lowerCamelCase						)



