"""simple docstring"""



from __future__ import annotations

SCREAMING_SNAKE_CASE__:int			=	list[tuple[int, int]]

SCREAMING_SNAKE_CASE__:Tuple			=	[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

SCREAMING_SNAKE_CASE__:Dict			=	([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right






class 				snake_case__     :


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						lowerCamelCase  ,						):
        __a     				=  pos_x
        __a     				=  pos_y
        __a     				=  (pos_y, pos_x)
        __a     				=  goal_x
        __a     				=  goal_y
        __a     				=  g_cost
        __a     				=  parent
        __a     				=  self.calculate_heuristic()


 def 		a__ (				self		):
        __a     				=  abs(self.pos_x - self.goal_x		)
        __a     				=  abs(self.pos_y - self.goal_y		)
        return dx + dy






 def __lt__(				self  ,						lowerCamelCase		):
        return self.f_cost < other.f_cost






class 				snake_case__     :


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        __a     				=  Node(start[1]  ,						start[0]  ,						goal[1]  ,						goal[0]  ,						0  ,						lowerCamelCase		)
        __a     				=  Node(goal[1]  ,						goal[0]  ,						goal[1]  ,						goal[0]  ,						99999  ,						lowerCamelCase		)

        __a     				=  [self.start]
        __a     				=  []

        __a     				=  False


 def 		a__ (				self		):
        while self.open_nodes:
               # Open Nodes are sorted using __lt__
               self.open_nodes.sort()
               __a     				=  self.open_nodes.pop(0		)

               if current_node.pos == self.target.pos:
                      __a     				=  True
                      return self.retrace_path(lowerCamelCase		)

               self.closed_nodes.append(lowerCamelCase		)
               __a     				=  self.get_successors(lowerCamelCase		)

               for child_node in successors:
                      if child_node in self.closed_nodes:
                             continue

                      if child_node not in self.open_nodes:
                             self.open_nodes.append(lowerCamelCase		)
                      else:
                             # retrieve the best current path
                             __a     				=  self.open_nodes.pop(self.open_nodes.index(lowerCamelCase		)		)

                             if child_node.g_cost < better_node.g_cost:
                                    self.open_nodes.append(lowerCamelCase		)
                             else:
                                    self.open_nodes.append(lowerCamelCase		)

        if not self.reached:
               return [self.start.pos]
        return None


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  []
        for action in delta:
               __a     				=  parent.pos_x + action[1]
               __a     				=  parent.pos_y + action[0]

               if not (0 <= pos_x <= len(grid[0]		) - 1 and 0 <= pos_y <= len(lowerCamelCase		) - 1):
                      continue

               if grid[pos_y][pos_x] != 0:
                      continue

               successors.append(
                   Node(
                       lowerCamelCase  ,						lowerCamelCase  ,						self.target.pos_y  ,						self.target.pos_x  ,						parent.g_cost + 1  ,						lowerCamelCase  ,						)		)
        return successors






 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  node
        __a     				=  []
        while current_node is not None:
               path.append((current_node.pos_y, current_node.pos_x)		)
               __a     				=  current_node.parent
        path.reverse()
        return path


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Optional[Any]			=	(0, 0)
   SCREAMING_SNAKE_CASE__:Dict			=	(len(grid) - 1, len(grid[0]) - 1)
   for elem in grid:
      print(elem)

   print("""------""")

   SCREAMING_SNAKE_CASE__:Optional[Any]			=	GreedyBestFirst(init, goal)
   SCREAMING_SNAKE_CASE__:Optional[int]			=	greedy_bf.search()
   if path:
      for pos_x, pos_y in path:
         SCREAMING_SNAKE_CASE__:Dict			=	2

      for elem in grid:
         print(elem)

"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE__:List[str]			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:int			=	{
    """microsoft/focalnet-tiny""": """https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json""",
}






class 				snake_case__     (   snake_case_,		snake_case_   ):
 _snake_case   :			List[Any]           =		"""focalnet"""


 def __init__(				self  ,						lowerCamelCase=224  ,						lowerCamelCase=4  ,						lowerCamelCase=3  ,						lowerCamelCase=96  ,						lowerCamelCase=False  ,						lowerCamelCase=[192, 384, 768, 768]  ,						lowerCamelCase=[2, 2, 6, 2]  ,						lowerCamelCase=[2, 2, 2, 2]  ,						lowerCamelCase=[3, 3, 3, 3]  ,						lowerCamelCase="gelu"  ,						lowerCamelCase=4.0  ,						lowerCamelCase=0.0  ,						lowerCamelCase=0.1  ,						lowerCamelCase=False  ,						lowerCamelCase=1E-4  ,						lowerCamelCase=False  ,						lowerCamelCase=False  ,						lowerCamelCase=False  ,						lowerCamelCase=0.02  ,						lowerCamelCase=1E-5  ,						lowerCamelCase=32  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						**lowerCamelCase  ,						):
        super().__init__(**lowerCamelCase		)

        __a     				=  image_size
        __a     				=  patch_size
        __a     				=  num_channels
        __a     				=  embed_dim
        __a     				=  use_conv_embed
        __a     				=  hidden_sizes
        __a     				=  depths
        __a     				=  focal_levels
        __a     				=  focal_windows
        __a     				=  hidden_act
        __a     				=  mlp_ratio
        __a     				=  hidden_dropout_prob
        __a     				=  drop_path_rate
        __a     				=  use_layerscale
        __a     				=  layerscale_value
        __a     				=  use_post_layernorm
        __a     				=  use_post_layernorm_in_modulation
        __a     				=  normalize_modulator
        __a     				=  initializer_range
        __a     				=  layer_norm_eps
        __a     				=  encoder_stride
        __a     				=  ["stem"] + [F"stage{idx}" for idx in range(1  ,						len(self.depths		) + 1		)]
        __a				, __a     				=  get_aligned_output_features_output_indices(
            out_features=lowerCamelCase  ,						out_indices=lowerCamelCase  ,						stage_names=self.stage_names		)
