


'''simple docstring'''






from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
__A	:	int     =     0

__A	:	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],
]

__A	:	Dict     =     [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

__A	:	str     =     tuple[int, int]
class 						__UpperCamelCase	:





							def __init__(  self		:int  ,_UpperCamelCase		:int  ,_UpperCamelCase		:int  ,_UpperCamelCase		:int  ,_UpperCamelCase		:int  ,_UpperCamelCase		:int  ,_UpperCamelCase		:Node | None  ,):
									snake_case_		:							Optional[Any]			       =						pos_x
									snake_case_		:							Optional[Any]			       =						pos_y
									snake_case_		:							Optional[int]			       =						(pos_y, pos_x)
									snake_case_		:							Optional[int]			       =						goal_x
									snake_case_		:							Union[str, Any]			       =						goal_y
									snake_case_		:							List[Any]			       =						g_cost
									snake_case_		:							Tuple			       =						parent
									snake_case_		:							Optional[int]			       =						self.calculate_heuristic()
									snake_case_		:							Tuple			       =						self.g_cost + self.h_cost





							def 		a__   (  self		:Any			):
									snake_case_		:							Union[str, Any]			       =						self.pos_x - self.goal_x
									snake_case_		:							int			       =						self.pos_y - self.goal_y
									if HEURISTIC == 1:
											return abs(_UpperCamelCase			) + abs(_UpperCamelCase			)
									else:
											return sqrt(dy**2 + dx**2			)





							def __lt__(  self		:List[Any]  ,_UpperCamelCase		:Node			):
									return self.f_cost < other.f_cost





class 						__UpperCamelCase	:





							def __init__(  self		:str  ,_UpperCamelCase		:TPosition  ,_UpperCamelCase		:TPosition			):
									snake_case_		:							List[str]			       =						Node(start[1]  ,start[0]  ,goal[1]  ,goal[0]  ,0  ,_UpperCamelCase			)
									snake_case_		:							Optional[int]			       =						Node(goal[1]  ,goal[0]  ,goal[1]  ,goal[0]  ,9_9_9_9_9  ,_UpperCamelCase			)

									snake_case_		:							Dict			       =						[self.start]
									snake_case_		:							list[Node]			       =						[]

									snake_case_		:							Optional[int]			       =						False





							def 		a__   (  self		:List[Any]			):
									while self.open_nodes:
											# Open Nodes are sorted using __lt__
											self.open_nodes.sort()
											snake_case_		:							Any			       =						self.open_nodes.pop(0			)

											if current_node.pos == self.target.pos:
													return self.retrace_path(_UpperCamelCase			)

											self.closed_nodes.append(_UpperCamelCase			)
											snake_case_		:							Union[str, Any]			       =						self.get_successors(_UpperCamelCase			)

											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(_UpperCamelCase			)
													else:
															# retrieve the best current path
															snake_case_		:							int			       =						self.open_nodes.pop(self.open_nodes.index(_UpperCamelCase			)			)

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

									return [self.start.pos]





							def 		a__   (  self		:int  ,_UpperCamelCase		:Node			):
									snake_case_		:							Union[str, Any]			       =						[]
									for action in delta:
											snake_case_		:							Union[str, Any]			       =						parent.pos_x + action[1]
											snake_case_		:							List[Any]			       =						parent.pos_y + action[0]
											if not (0 <= pos_x <= len(grid[0]			) - 1 and 0 <= pos_y <= len(_UpperCamelCase			) - 1):
													continue

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

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





							def 		a__   (  self		:List[str]  ,_UpperCamelCase		:Node | None			):
									snake_case_		:							int			       =						node
									snake_case_		:							Tuple			       =						[]
									while current_node is not None:
											path.append((current_node.pos_y, current_node.pos_x)			)
											snake_case_		:							List[str]			       =						current_node.parent
									path.reverse()
									return path





class 						__UpperCamelCase	:





							def __init__(  self		:Union[str, Any]  ,_UpperCamelCase		:TPosition  ,_UpperCamelCase		:TPosition			):
									snake_case_		:							Optional[Any]			       =						AStar(_UpperCamelCase  ,_UpperCamelCase			)
									snake_case_		:							List[str]			       =						AStar(_UpperCamelCase  ,_UpperCamelCase			)
									snake_case_		:							str			       =						False





							def 		a__   (  self		:int			):
									while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
											self.fwd_astar.open_nodes.sort()
											self.bwd_astar.open_nodes.sort()
											snake_case_		:							Tuple			       =						self.fwd_astar.open_nodes.pop(0			)
											snake_case_		:							str			       =						self.bwd_astar.open_nodes.pop(0			)

											if current_bwd_node.pos == current_fwd_node.pos:
													return self.retrace_bidirectional_path(
													    _UpperCamelCase  ,_UpperCamelCase			)

											self.fwd_astar.closed_nodes.append(_UpperCamelCase			)
											self.bwd_astar.closed_nodes.append(_UpperCamelCase			)

											snake_case_		:							List[str]			       =						current_bwd_node
											snake_case_		:							int			       =						current_fwd_node

											snake_case_		:							Dict			       =						{
											    self.fwd_astar: self.fwd_astar.get_successors(_UpperCamelCase			),
											    self.bwd_astar: self.bwd_astar.get_successors(_UpperCamelCase			),
											}

											for astar in [self.fwd_astar, self.bwd_astar]:
													for child_node in successors[astar]:
															if child_node in astar.closed_nodes:
																	continue

															if child_node not in astar.open_nodes:
																	astar.open_nodes.append(_UpperCamelCase			)
															else:
																	# retrieve the best current path
																	snake_case_		:							int			       =						astar.open_nodes.pop(
																	    astar.open_nodes.index(_UpperCamelCase			)			)

																	if child_node.g_cost < better_node.g_cost:
																			astar.open_nodes.append(_UpperCamelCase			)
																	else:
																			astar.open_nodes.append(_UpperCamelCase			)

									return [self.fwd_astar.start.pos]





							def 		a__   (  self		:int  ,_UpperCamelCase		:Node  ,_UpperCamelCase		:Node			):
									snake_case_		:							Optional[int]			       =						self.fwd_astar.retrace_path(_UpperCamelCase			)
									snake_case_		:							Tuple			       =						self.bwd_astar.retrace_path(_UpperCamelCase			)
									bwd_path.pop()
									bwd_path.reverse()
									snake_case_		:							Union[str, Any]			       =						fwd_path + bwd_path
									return path


if __name__ == "__main__":
							# all coordinates are given in format [y,x]
							__A	:	Optional[int]     =     (0, 0)
							__A	:	List[str]     =     (len(grid) - 1, len(grid[0]) - 1)
							for elem in grid:
														print(elem)

							__A	:	Optional[Any]     =     time.time()
							__A	:	str     =     AStar(init, goal)
							__A	:	Union[str, Any]     =     a_star.search()
							__A	:	List[Any]     =     time.time() - start_time
							print(F'AStar execution time = {end_time:f} seconds')

							__A	:	int     =     time.time()
							__A	:	Dict     =     BidirectionalAStar(init, goal)
							__A	:	str     =     time.time() - bd_start_time
							print(F'BidirectionalAStar execution time = {bd_end_time:f} seconds')



'''simple docstring'''






import torch

from transformers import AutoModel
class 						__UpperCamelCase	(						torch.nn.Module							):





							def __init__(  self		:Union[str, Any]  ,_UpperCamelCase		:Tuple="sayef/fsner-bert-base-uncased"			):
									super(_UpperCamelCase  ,self			).__init__()

									snake_case_		:							Dict			       =						AutoModel.from_pretrained(_UpperCamelCase  ,return_dict=_UpperCamelCase			)
									snake_case_		:							Dict			       =						torch.nn.CosineSimilarity(3  ,1E-0_8			)
									snake_case_		:							int			       =						torch.nn.Softmax(dim=1			)





							def 		a__   (  self		:Optional[Any]  ,**_UpperCamelCase		:Optional[Any]			):
									return self.bert(**_UpperCamelCase			).last_hidden_state





							def 		a__   (  self		:List[Any]  ,_UpperCamelCase		:Dict			):
									return token_embeddings.sum(2  ,keepdim=_UpperCamelCase			)





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:List[str]=1			):
									return self.softmax(T * self.cos(_UpperCamelCase  ,_UpperCamelCase			)			)





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:int			):
									snake_case_		:							Union[str, Any]			       =						W_supports["""sizes"""].tolist()
									snake_case_		:							Dict			       =						W_supports["""start_token_id"""].item()
									snake_case_		:							int			       =						W_supports["""end_token_id"""].item()

									del W_supports["sizes"]
									del W_supports["start_token_id"]
									del W_supports["end_token_id"]

									snake_case_		:							int			       =						self.BERT(**_UpperCamelCase			)
									snake_case_		:							int			       =						self.BERT(**_UpperCamelCase			)

									snake_case_		:							Any			       =						None
									snake_case_		:							Any			       =						None

									snake_case_		:							Union[str, Any]			       =						W_supports["""input_ids"""] == start_token_id
									snake_case_		:							Tuple			       =						W_supports["""input_ids"""] == end_token_id

									for i, size in enumerate(_UpperCamelCase			):
											if i == 0:
													snake_case_		:							str			       =						0
											else:
													snake_case_		:							Optional[Any]			       =						support_sizes[i - 1]

											snake_case_		:							Dict			       =						S[s : s + size][start_token_masks[s : s + size]]
											snake_case_		:							Optional[Any]			       =						S[s : s + size][end_token_masks[s : s + size]]

											snake_case_		:							int			       =						torch.matmul(q[i]  ,s_start.T			).sum(1			).softmax(0			)
											snake_case_		:							List[str]			       =						torch.matmul(q[i]  ,s_end.T			).sum(1			).softmax(0			)

											if p_starts is not None:
													snake_case_		:							List[str]			       =						torch.vstack((p_starts, p_start)			)
													snake_case_		:							Tuple			       =						torch.vstack((p_ends, p_end)			)
											else:
													snake_case_		:							Any			       =						p_start
													snake_case_		:							List[str]			       =						p_end

									return p_starts, p_ends