


"""simple docstring"""


from __future__ import annotations

import time
from math import sqrt

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

__lowerCamelCase      :Optional[int]	    =		[
    [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],
]

__lowerCamelCase      :Tuple	    =		[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

__lowerCamelCase      :Tuple	    =		tuple[int, int]


class 			A__	:


     """simple docstring"""

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

     def 		a__						(	self:						List[str]	)->    float:
           lowerCamelCase					:					List[str]        =	self.pos_x - self.goal_x
           lowerCamelCase					:					Optional[Any]        =	self.pos_y - self.goal_y
           if HEURISTIC == 1:
                 return abs(__a	) + abs(__a	)
           else:
                 return sqrt(dy**2 + dx**2	)







     def __lt__(	self:						List[Any]     ,		__a:						Node	)->    bool:
           return self.f_cost < other.f_cost


class 			A__	:


     """simple docstring"""

     def __init__(	self:						Any     ,		__a:						TPosition     ,		__a:						TPosition	)->    Optional[Any]:
           lowerCamelCase					:					List[Any]        =	Node(start[1]     ,		start[0]     ,		goal[1]     ,		goal[0]     ,		0     ,		__a	)
           lowerCamelCase					:					Tuple        =	Node(goal[1]     ,		goal[0]     ,		goal[1]     ,		goal[0]     ,		99_999     ,		__a	)

           lowerCamelCase					:					Optional[Any]        =	[self.start]
           lowerCamelCase					:					list[Node]        =	[]

           lowerCamelCase					:					Tuple        =	False

     def 		a__						(	self:						int	)->    list[TPosition]:
           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:
                       return self.retrace_path(__a	)

                 self.closed_nodes.append(__a	)
                 lowerCamelCase					:					Dict        =	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					:					Any        =	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	)

           return [self.start.pos]

     def 		a__						(	self:						Union[str, Any]     ,		__a:						Node	)->    list[Node]:
           lowerCamelCase					:					str        =	[]
           for action in delta:
                 lowerCamelCase					:					Union[str, 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:						Union[str, Any]     ,		__a:						Node | None	)->    list[TPosition]:
           lowerCamelCase					:					Dict        =	node
           lowerCamelCase					:					Tuple        =	[]
           while current_node is not None:
                 path.append((current_node.pos_y, current_node.pos_x)	)
                 lowerCamelCase					:					str        =	current_node.parent
           path.reverse()
           return path


class 			A__	:


     """simple docstring"""

     def __init__(	self:						Union[str, Any]     ,		__a:						TPosition     ,		__a:						TPosition	)->    None:
           lowerCamelCase					:					Dict        =	AStar(__a     ,		__a	)
           lowerCamelCase					:					str        =	AStar(__a     ,		__a	)
           lowerCamelCase					:					int        =	False

     def 		a__						(	self:						str	)->    list[TPosition]:
           while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
                 self.fwd_astar.open_nodes.sort()
                 self.bwd_astar.open_nodes.sort()
                 lowerCamelCase					:					Tuple        =	self.fwd_astar.open_nodes.pop(0	)
                 lowerCamelCase					:					List[Any]        =	self.bwd_astar.open_nodes.pop(0	)

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

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

                 lowerCamelCase					:					int        =	current_bwd_node
                 lowerCamelCase					:					List[Any]        =	current_fwd_node

                 lowerCamelCase					:					Optional[int]        =	{
                     self.fwd_astar: self.fwd_astar.get_successors(__a	),
                     self.bwd_astar: self.bwd_astar.get_successors(__a	),
                 }

                 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(__a	)
                             else:
                                   # retrieve the best current path
                                   lowerCamelCase					:					str        =	astar.open_nodes.pop(
                                       astar.open_nodes.index(__a	)	)

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

           return [self.fwd_astar.start.pos]







     def 		a__						(	self:						List[str]     ,		__a:						Node     ,		__a:						Node	)->    list[TPosition]:
           lowerCamelCase					:					str        =	self.fwd_astar.retrace_path(__a	)
           lowerCamelCase					:					Any        =	self.bwd_astar.retrace_path(__a	)
           bwd_path.pop()
           bwd_path.reverse()
           lowerCamelCase					:					Any        =	fwd_path + bwd_path
           return path


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

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

    __lowerCamelCase      :List[Any]	    =		time.time()
    __lowerCamelCase      :List[str]	    =		BidirectionalAStar(init, goal)
    __lowerCamelCase      :List[str]	    =		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


__lowerCamelCase      :Tuple	    =		logging.get_logger(__name__)

__lowerCamelCase      :Any	    =		{
    'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Tuple													='''glpn'''

     def __init__(	self:						Dict     ,		__a:						List[str]=3     ,		__a:						Optional[int]=4     ,		__a:						Dict=[2, 2, 2, 2]     ,		__a:						str=[8, 4, 2, 1]     ,		__a:						Optional[int]=[32, 64, 160, 256]     ,		__a:						Dict=[7, 3, 3, 3]     ,		__a:						Dict=[4, 2, 2, 2]     ,		__a:						Optional[Any]=[1, 2, 5, 8]     ,		__a:						Tuple=[4, 4, 4, 4]     ,		__a:						int="gelu"     ,		__a:						Union[str, Any]=0.0     ,		__a:						str=0.0     ,		__a:						Union[str, Any]=0.02     ,		__a:						str=0.1     ,		__a:						Union[str, Any]=1e-6     ,		__a:						Any=64     ,		__a:						Dict=10     ,		__a:						Union[str, Any]=-1     ,		**__a:						Optional[Any]     ,		)->    Dict:
           super().__init__(**__a	)

           lowerCamelCase					:					Dict        =	num_channels
           lowerCamelCase					:					Any        =	num_encoder_blocks
           lowerCamelCase					:					Dict        =	depths
           lowerCamelCase					:					List[str]        =	sr_ratios
           lowerCamelCase					:					Dict        =	hidden_sizes
           lowerCamelCase					:					Tuple        =	patch_sizes
           lowerCamelCase					:					Optional[int]        =	strides
           lowerCamelCase					:					Optional[Any]        =	mlp_ratios
           lowerCamelCase					:					Union[str, Any]        =	num_attention_heads
           lowerCamelCase					:					List[str]        =	hidden_act
           lowerCamelCase					:					Any        =	hidden_dropout_prob
           lowerCamelCase					:					Optional[int]        =	attention_probs_dropout_prob
           lowerCamelCase					:					List[Any]        =	initializer_range
           lowerCamelCase					:					Dict        =	drop_path_rate
           lowerCamelCase					:					Any        =	layer_norm_eps
           lowerCamelCase					:					Optional[Any]        =	decoder_hidden_size
           lowerCamelCase					:					Tuple        =	max_depth
           lowerCamelCase					:					Optional[Any]        =	head_in_index




