





'''simple docstring'''




from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
lowercase__       : str         =       0

lowercase__       : Optional[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],
]

lowercase__       : List[str]         =       [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

lowercase__       : List[Any]         =       tuple[int, int]


class 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      ):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			self.pos_x - self.goal_x
          __A		:					Tuple						=			self.pos_y - self.goal_y
          if HEURISTIC == 1:
               return abs(_UpperCAmelCase) + abs(_UpperCAmelCase)
          else:
               return sqrt(dy**2 + dx**2)


     def __lt__(		self		,      _UpperCAmelCase):

          '''simple docstring'''






          return self.f_cost < other.f_cost




class 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Dict						=			Node(start[1]		,      start[0]		,      goal[1]		,      goal[0]		,      0		,      _UpperCAmelCase)
          __A		:					List[str]						=			Node(goal[1]		,      goal[0]		,      goal[1]		,      goal[0]		,      9_9999		,      _UpperCAmelCase)

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

          __A		:					Optional[Any]						=			False


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          while self.open_nodes:
               # Open Nodes are sorted using __lt__
               self.open_nodes.sort()
               __A		:					List[str]						=			self.open_nodes.pop(0)

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

               self.closed_nodes.append(_UpperCAmelCase)
               __A		:					Tuple						=			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
                         __A		:					str						=			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 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Tuple						=			[]
          for action in delta:
               __A		:					Union[str, Any]						=			parent.pos_x + action[1]
               __A		:					List[str]						=			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 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






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




class 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			AStar(_UpperCAmelCase		,      _UpperCAmelCase)
          __A		:					List[Any]						=			AStar(_UpperCAmelCase		,      _UpperCAmelCase)
          __A		:					int						=			False


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
               self.fwd_astar.open_nodes.sort()
               self.bwd_astar.open_nodes.sort()
               __A		:					Tuple						=			self.fwd_astar.open_nodes.pop(0)
               __A		:					Union[str, Any]						=			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)

               __A		:					str						=			current_bwd_node
               __A		:					str						=			current_fwd_node

               __A		:					str						=			{
                   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
                              __A		:					List[Any]						=			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 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Optional[Any]						=			self.fwd_astar.retrace_path(_UpperCAmelCase)
          __A		:					Optional[int]						=			self.bwd_astar.retrace_path(_UpperCAmelCase)
          bwd_path.pop()
          bwd_path.reverse()
          __A		:					Tuple						=			fwd_path + bwd_path
          return path


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

  lowercase__       : Dict         =       time.time()
  lowercase__       : Optional[int]         =       AStar(init, goal)
  lowercase__       : List[Any]         =       a_star.search()
  lowercase__       : Dict         =       time.time() - start_time
  print(f"""AStar execution time = {end_time:f} seconds""")

  lowercase__       : Optional[Any]         =       time.time()
  lowercase__       : Optional[int]         =       BidirectionalAStar(init, goal)
  lowercase__       : int         =       time.time() - bd_start_time
  print(f"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")






'''simple docstring'''




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


lowercase__       : List[Any]         =       logging.get_logger(__name__)

lowercase__       : Optional[int]         =       {
    '''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json''',
}


class 					SCREAMING_SNAKE_CASE       (a__				):
     lowerCAmelCase						     =			'''lxmert'''
     lowerCAmelCase						     =			{}


     def __init__(		self		,      _UpperCAmelCase=3_0522		,      _UpperCAmelCase=768		,      _UpperCAmelCase=12		,      _UpperCAmelCase=9500		,      _UpperCAmelCase=1600		,      _UpperCAmelCase=400		,      _UpperCAmelCase=3072		,      _UpperCAmelCase="gelu"		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=512		,      _UpperCAmelCase=2		,      _UpperCAmelCase=0.02		,      _UpperCAmelCase=1e-1_2		,      _UpperCAmelCase=9		,      _UpperCAmelCase=5		,      _UpperCAmelCase=5		,      _UpperCAmelCase=2048		,      _UpperCAmelCase=4		,      _UpperCAmelCase=6.67		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      **_UpperCAmelCase		,      ):

          '''simple docstring'''






          __A		:					Tuple						=			vocab_size
          __A		:					int						=			hidden_size
          __A		:					str						=			num_attention_heads
          __A		:					Tuple						=			hidden_act
          __A		:					int						=			intermediate_size
          __A		:					str						=			hidden_dropout_prob
          __A		:					Optional[int]						=			attention_probs_dropout_prob
          __A		:					Optional[Any]						=			max_position_embeddings
          __A		:					Tuple						=			type_vocab_size
          __A		:					Optional[int]						=			initializer_range
          __A		:					Any						=			layer_norm_eps
          __A		:					Optional[Any]						=			num_qa_labels
          __A		:					Optional[int]						=			num_object_labels
          __A		:					Any						=			num_attr_labels
          __A		:					Union[str, Any]						=			l_layers
          __A		:					Optional[int]						=			x_layers
          __A		:					List[Any]						=			r_layers
          __A		:					Tuple						=			visual_feat_dim
          __A		:					Tuple						=			visual_pos_dim
          __A		:					Optional[int]						=			visual_loss_normalizer
          __A		:					int						=			task_matched
          __A		:					List[Any]						=			task_mask_lm
          __A		:					Optional[Any]						=			task_obj_predict
          __A		:					str						=			task_qa
          __A		:					List[Any]						=			visual_obj_loss
          __A		:					Optional[Any]						=			visual_attr_loss
          __A		:					Union[str, Any]						=			visual_feat_loss
          __A		:					Union[str, Any]						=			{'vision': r_layers, 'cross_encoder': x_layers, 'language': l_layers}
          super().__init__(**_UpperCAmelCase)