
'''simple docstring'''

from __future__ import annotations

snake_case									=    list[tuple[int, int]]

snake_case									=    [
    [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],
]

snake_case									=    ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right




class    lowerCAmelCase       :


 def __init__(    self  : List[Any]			,							a__  : int			,							a__  : int			,							a__  : int			,							a__  : int			,							a__  : float			,							a__  : Node | None			,							):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[int]     		=	pos_x
  lowerCAmelCase__     :   int     		=	pos_y
  lowerCAmelCase__     :   List[str]     		=	(pos_y, pos_x)
  lowerCAmelCase__     :   str     		=	goal_x
  lowerCAmelCase__     :   Any     		=	goal_y
  lowerCAmelCase__     :   Any     		=	g_cost
  lowerCAmelCase__     :   List[str]     		=	parent
  lowerCAmelCase__     :   List[str]     		=	self.calculate_heuristic()


 def    _A	(    self  : Tuple    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	abs(self.pos_x - self.goal_x    )
  lowerCAmelCase__     :   Union[str, Any]     		=	abs(self.pos_y - self.goal_y    )
  return dx + dy


 def __lt__(    self  : int			,							a__  : List[Any]    ):
  '''simple docstring'''

  return self.f_cost < other.f_cost




class    lowerCAmelCase       :


 def __init__(    self  : Optional[Any]			,							a__  : tuple[int, int]			,							a__  : tuple[int, int]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Dict     		=	Node(start[1]			,							start[0]			,							goal[1]			,							goal[0]			,							0			,							a__    )
  lowerCAmelCase__     :   Dict     		=	Node(goal[1]			,							goal[0]			,							goal[1]			,							goal[0]			,							9_9999			,							a__    )

  lowerCAmelCase__     :   List[str]     		=	[self.start]
  lowerCAmelCase__     :   list[Node]     		=	[]

  lowerCAmelCase__     :   Dict     		=	False


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

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

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

   self.closed_nodes.append(a__    )
   lowerCAmelCase__     :   str     		=	self.get_successors(a__    )

   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(a__    )
    else:
     # retrieve the best current path
     lowerCAmelCase__     :   Tuple     		=	self.open_nodes.pop(self.open_nodes.index(a__    )    )

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

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


 def    _A	(    self  : Tuple			,							a__  : Node    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[Any]     		=	[]
  for action in delta:
   lowerCAmelCase__     :   Any     		=	parent.pos_x + action[1]
   lowerCAmelCase__     :   Optional[Any]     		=	parent.pos_y + action[0]

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

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

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


 def    _A	(    self  : int			,							a__  : Node | None    ):
  '''simple docstring'''

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


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

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

     snake_case									=    GreedyBestFirst(init, goal)
     snake_case									=    greedy_bf.search()
     if path:
          for pos_x, pos_y in path:
               snake_case									=    2

          for elem in grid:
               print(elem)







'''simple docstring'''

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

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


snake_case									=    logging.get_logger(__name__)  # pylint: disable=invalid-name




class    lowerCAmelCase       (     UpperCamelCase_  ):


 def __init__(    self  : Union[str, Any]			,							a__  : int			,							a__  : List[Any]=768    ):
  '''simple docstring'''

  super().__init__(a__    )
  lowerCAmelCase__     :   int     		=	proj_size

  lowerCAmelCase__     :   str     		=	CLIPVisionModel(a__    )
  lowerCAmelCase__     :   int     		=	PaintByExampleMapper(a__    )
  lowerCAmelCase__     :   Optional[int]     		=	nn.LayerNorm(config.hidden_size    )
  lowerCAmelCase__     :   Optional[Any]     		=	nn.Linear(config.hidden_size			,							self.proj_size    )

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


 def    _A	(    self  : Tuple			,							a__  : int			,							a__  : Optional[Any]=False    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[Any]     		=	self.model(pixel_values=a__    )
  lowerCAmelCase__     :   Any     		=	clip_output.pooler_output
  lowerCAmelCase__     :   Union[str, Any]     		=	self.mapper(latent_states[:, None]    )
  lowerCAmelCase__     :   List[Any]     		=	self.final_layer_norm(a__    )
  lowerCAmelCase__     :   Optional[int]     		=	self.proj_out(a__    )
  if return_uncond_vector:
   return latent_states, self.uncond_vector

  return latent_states




class    lowerCAmelCase       (     nn.Module  ):


 def __init__(    self  : Optional[Any]			,							a__  : Optional[Any]    ):
  '''simple docstring'''

  super().__init__()
  lowerCAmelCase__     :   Optional[Any]     		=	(config.num_hidden_layers + 1) // 5
  lowerCAmelCase__     :   str     		=	config.hidden_size
  lowerCAmelCase__     :   Dict     		=	1
  lowerCAmelCase__     :   int     		=	nn.ModuleList(
      [
          BasicTransformerBlock(a__			,							a__			,							a__			,							activation_fn="gelu"			,							attention_bias=a__    )
          for _ in range(a__    )
      ]    )


 def    _A	(    self  : int			,							a__  : Union[str, Any]    ):
  '''simple docstring'''

  for block in self.blocks:
   lowerCAmelCase__     :   List[Any]     		=	block(a__    )

  return hidden_states





