




from __future__ import annotations

__magic_name__  :  Union[str, Any]					=       list[tuple[int, int]]

__magic_name__  :  List[Any]					=       [
    [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],
]

__magic_name__  :  List[str]					=       ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right





class     SCREAMING_SNAKE_CASE__		:
     def __init__(						self	:		str       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		float       ,						__lowerCamelCase	:		Node | None       ,						):



       """simple docstring"""
       lowerCAmelCase__										=      pos_x
       lowerCAmelCase__										=      pos_y
       lowerCAmelCase__										=      (pos_y, pos_x)
       lowerCAmelCase__										=      goal_x
       lowerCAmelCase__										=      goal_y
       lowerCAmelCase__										=      g_cost
       lowerCAmelCase__										=      parent
       lowerCAmelCase__										=      self.calculate_heuristic()
     def        A__      (						self	:		Union[str, Any]						):



       """simple docstring"""
       lowerCAmelCase__										=      abs(self.pos_x - self.goal_x						)
       lowerCAmelCase__										=      abs(self.pos_y - self.goal_y						)
       return dx + dy



     def __lt__(						self	:		List[Any]       ,						__lowerCamelCase	:		Optional[Any]						):



       """simple docstring"""
       return self.f_cost < other.f_cost





class     SCREAMING_SNAKE_CASE__		:
     def __init__(						self	:		Union[str, Any]       ,						__lowerCamelCase	:		tuple[int, int]       ,						__lowerCamelCase	:		tuple[int, int]						):



       """simple docstring"""
       lowerCAmelCase__										=      Node(start[1]       ,						start[0]       ,						goal[1]       ,						goal[0]       ,						0       ,						__lowerCamelCase						)
       lowerCAmelCase__										=      Node(goal[1]       ,						goal[0]       ,						goal[1]       ,						goal[0]       ,						9_99_99       ,						__lowerCamelCase						)

       lowerCAmelCase__										=      [self.start]
       lowerCAmelCase__										=      []

       lowerCAmelCase__										=      False
     def        A__      (						self	:		Any						):



       """simple docstring"""
       while self.open_nodes:
         # Open Nodes are sorted using __lt__
         self.open_nodes.sort()
         lowerCAmelCase__										=      self.open_nodes.pop(0						)

         if current_node.pos == self.target.pos:
           lowerCAmelCase__										=      True
           return self.retrace_path(__lowerCamelCase						)

         self.closed_nodes.append(__lowerCamelCase						)
         lowerCAmelCase__										=      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
             lowerCAmelCase__										=      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	:		List[Any]       ,						__lowerCamelCase	:		Node						):



       """simple docstring"""
       lowerCAmelCase__										=      []
       for action in delta:
         lowerCAmelCase__										=      parent.pos_x + action[1]
         lowerCAmelCase__										=      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	:		List[Any]       ,						__lowerCamelCase	:		Node | None						):



       """simple docstring"""
       lowerCAmelCase__										=      node
       lowerCAmelCase__										=      []
       while current_node is not None:
         path.append((current_node.pos_y, current_node.pos_x)						)
         lowerCAmelCase__										=      current_node.parent
       path.reverse()
       return path


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

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

      __magic_name__  :  List[Any]					=       GreedyBestFirst(init, goal)
      __magic_name__  :  str					=       greedy_bf.search()
      if path:
            for pos_x, pos_y in path:
                  __magic_name__  :  Union[str, Any]					=       2

            for elem in grid:
                  print(elem)







import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


__magic_name__  :  List[str]					=       logging.get_logger(__name__)  # pylint: disable=invalid-name





class     SCREAMING_SNAKE_CASE__		(_a			):
     def __init__(						self	:		List[Any]       ,						__lowerCamelCase	:		Tuple       ,						__lowerCamelCase	:		List[Any]=7_68						):



       """simple docstring"""
       super().__init__(__lowerCamelCase						)
       lowerCAmelCase__										=      proj_size

       lowerCAmelCase__										=      CLIPVisionModel(__lowerCamelCase						)
       lowerCAmelCase__										=      PaintByExampleMapper(__lowerCamelCase						)
       lowerCAmelCase__										=      nn.LayerNorm(config.hidden_size						)
       lowerCAmelCase__										=      nn.Linear(config.hidden_size       ,						self.proj_size						)

       # uncondition for scaling
       lowerCAmelCase__										=      nn.Parameter(torch.randn((1, 1, self.proj_size)						)						)



     def        A__      (						self	:		Tuple       ,						__lowerCamelCase	:		int       ,						__lowerCamelCase	:		str=False						):



       """simple docstring"""
       lowerCAmelCase__										=      self.model(pixel_values=__lowerCamelCase						)
       lowerCAmelCase__										=      clip_output.pooler_output
       lowerCAmelCase__										=      self.mapper(latent_states[:, None]						)
       lowerCAmelCase__										=      self.final_layer_norm(__lowerCamelCase						)
       lowerCAmelCase__										=      self.proj_out(__lowerCamelCase						)
       if return_uncond_vector:
         return latent_states, self.uncond_vector

       return latent_states





class     SCREAMING_SNAKE_CASE__		(nn.Module			):
     def __init__(						self	:		Optional[Any]       ,						__lowerCamelCase	:		Tuple						):



       """simple docstring"""
       super().__init__()
       lowerCAmelCase__										=      (config.num_hidden_layers + 1) // 5
       lowerCAmelCase__										=      config.hidden_size
       lowerCAmelCase__										=      1
       lowerCAmelCase__										=      nn.ModuleList(
           [
               BasicTransformerBlock(__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase       ,						activation_fn='''gelu'''       ,						attention_bias=__lowerCamelCase						)
               for _ in range(__lowerCamelCase						)
           ]						)



     def        A__      (						self	:		str       ,						__lowerCamelCase	:		Optional[Any]						):



       """simple docstring"""
       for block in self.blocks:
         lowerCAmelCase__										=      block(__lowerCamelCase						)

       return hidden_states

