

'''simple docstring'''
from __future__ import annotations

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







def  _A		(     snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case		,							)      ->     tuple[list[list[int]], list[list[int]]]:
   _lowercase      :				Tuple      					=       [
       [0 for col in range(len(grid[0]					)					)] for row in range(len(snake_case					)					)
   ]  # the reference grid
   _lowercase      :				List[Any]      					=       1
   _lowercase      :				str      					=       [
       [0 for col in range(len(grid[0]					)					)] for row in range(len(snake_case					)					)
   ]  # the action grid

   _lowercase      :				List[str]      					=       init[0]
   _lowercase      :				Any      					=       init[1]
   _lowercase      :				int      					=       0
   _lowercase      :				int      					=       g + heuristic[x][y]  # cost from starting cell to destination cell
   _lowercase      :				Optional[int]      					=       [[f, g, x, y]]

   _lowercase      :				Tuple      					=       False  # flag that is set when search is complete
   _lowercase      :				List[str]      					=       False  # flag set if we can't find expand

   while not found and not resign:
      if len(snake_case					) == 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()
         _lowercase      :				List[str]      					=       cell.pop()
         _lowercase      :				Any      					=       next_cell[2]
         _lowercase      :				Dict      					=       next_cell[3]
         _lowercase      :				Optional[Any]      					=       next_cell[1]

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

   _lowercase      :				Optional[int]      					=       []
   for i in range(len(snake_case					)					):
      path.append(invpath[len(snake_case					) - 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'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case         =  logging.get_logger(__name__)

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


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					Any     	=							'roc_bert'


   def __init__(			self      ,    _UpperCamelCase=30522      ,    _UpperCamelCase=768      ,    _UpperCamelCase=12      ,    _UpperCamelCase=12      ,    _UpperCamelCase=3072      ,    _UpperCamelCase="gelu"      ,    _UpperCamelCase=0.1      ,    _UpperCamelCase=0.1      ,    _UpperCamelCase=512      ,    _UpperCamelCase=2      ,    _UpperCamelCase=0.0_2      ,    _UpperCamelCase=1E-1_2      ,    _UpperCamelCase=True      ,    _UpperCamelCase=0      ,    _UpperCamelCase="absolute"      ,    _UpperCamelCase=None      ,    _UpperCamelCase=True      ,    _UpperCamelCase=True      ,    _UpperCamelCase=768      ,    _UpperCamelCase=910      ,    _UpperCamelCase=512      ,    _UpperCamelCase=24858      ,    _UpperCamelCase=True      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      _lowercase      :				str      					=       vocab_size
      _lowercase      :				List[str]      					=       max_position_embeddings
      _lowercase      :				List[Any]      					=       hidden_size
      _lowercase      :				Dict      					=       num_hidden_layers
      _lowercase      :				str      					=       num_attention_heads
      _lowercase      :				int      					=       intermediate_size
      _lowercase      :				Optional[Any]      					=       hidden_act
      _lowercase      :				Union[str, Any]      					=       hidden_dropout_prob
      _lowercase      :				Dict      					=       attention_probs_dropout_prob
      _lowercase      :				Dict      					=       initializer_range
      _lowercase      :				List[Any]      					=       type_vocab_size
      _lowercase      :				Tuple      					=       layer_norm_eps
      _lowercase      :				Optional[int]      					=       use_cache
      _lowercase      :				Tuple      					=       enable_pronunciation
      _lowercase      :				Optional[int]      					=       enable_shape
      _lowercase      :				int      					=       pronunciation_embed_dim
      _lowercase      :				List[str]      					=       pronunciation_vocab_size
      _lowercase      :				int      					=       shape_embed_dim
      _lowercase      :				str      					=       shape_vocab_size
      _lowercase      :				str      					=       concat_input
      _lowercase      :				Dict      					=       position_embedding_type
      _lowercase      :				Optional[Any]      					=       classifier_dropout
      super().__init__(pad_token_id=_UpperCamelCase      ,    **_UpperCamelCase  )






