


from __future__ import annotations

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


def 				lowerCamelCase__	(      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"""
   a__ :Any						  =    [
       [0 for col in range(len(grid[0]				)				)] for row in range(len(a_				)				)
   ]  # the reference grid
   a__ :Optional[Any]						  =    1
   a__ :Dict						  =    [
       [0 for col in range(len(grid[0]				)				)] for row in range(len(a_				)				)
   ]  # the action grid

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

   a__ :Optional[Any]						  =    False  # flag that is set when search is complete
   a__ :List[Any]						  =    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()
         a__ :List[str]						  =    cell.pop()
         a__ :Optional[int]						  =    next_cell[2]
         a__ :int						  =    next_cell[3]
         a__ :Union[str, Any]						  =    next_cell[1]

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

   a__ :Optional[Any]						  =    []
   for i in range(len(a_				)				):
      path.append(invpath[len(a_				) - 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])






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


snake_case__			 =						logging.get_logger(__name__)

snake_case__			 =						{
    '''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     'pegasus'
						lowerCamelCase_         =     ['past_key_values']
						lowerCamelCase_         =     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}




						def __init__(	self						:   Any   ,     __A						:   Dict=50265   ,     __A						:   List[Any]=1024   ,     __A						:   int=12   ,     __A						:   Optional[Any]=4096   ,     __A						:   Optional[int]=16   ,     __A						:   Dict=12   ,     __A						:   List[Any]=4096   ,     __A						:   List[str]=16   ,     __A						:   Optional[int]=0.0   ,     __A						:   List[Any]=0.0   ,     __A						:   List[str]=True   ,     __A						:   Optional[int]=True   ,     __A						:   str="gelu"   ,     __A						:   Tuple=1024   ,     __A						:   Any=0.1   ,     __A						:   List[Any]=0.0   ,     __A						:   List[str]=0.0   ,     __A						:   Tuple=0.02   ,     __A						:   Union[str, Any]=0   ,     __A						:   Union[str, Any]=False   ,     __A						:   Optional[Any]=0   ,     __A						:   Tuple=1   ,     __A						:   str=1   ,     **__A						:   Any   ,     )      ->Union[str, Any]:






									"""simple docstring"""






									a__ :Any						  =    vocab_size
									a__ :List[str]						  =    max_position_embeddings
									a__ :int						  =    d_model
									a__ :Union[str, Any]						  =    encoder_ffn_dim
									a__ :List[Any]						  =    encoder_layers
									a__ :Union[str, Any]						  =    encoder_attention_heads
									a__ :Tuple						  =    decoder_ffn_dim
									a__ :List[Any]						  =    decoder_layers
									a__ :Tuple						  =    decoder_attention_heads
									a__ :Optional[int]						  =    dropout
									a__ :str						  =    attention_dropout
									a__ :Optional[int]						  =    activation_dropout
									a__ :str						  =    activation_function
									a__ :Dict						  =    init_std
									a__ :Any						  =    encoder_layerdrop
									a__ :int						  =    decoder_layerdrop
									a__ :Union[str, Any]						  =    use_cache
									a__ :List[Any]						  =    encoder_layers
									a__ :Dict						  =    scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=__A   ,     eos_token_id=__A   ,     is_encoder_decoder=__A   ,     decoder_start_token_id=__A   ,     forced_eos_token_id=__A   ,     **__A   ,     )




						@property
						def  _snake_case					(	self						:   Optional[Any]						)      ->int:






									"""simple docstring"""






									return self.encoder_attention_heads






						@property
						def  _snake_case					(	self						:   Union[str, Any]						)      ->int:






									"""simple docstring"""






									return self.d_model


