






from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
UpperCAmelCase_ :						Dict      =      0

UpperCAmelCase_ :						List[str]      =      [
    [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],
]

UpperCAmelCase_ :						int      =      [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

UpperCAmelCase_ :						str      =      tuple[int, int]

class     _SCREAMING_SNAKE_CASE       :





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





	def 						_A (		self				:					List[Any]	):
		UpperCamelCase     :List[str]          =	self.pos_x - self.goal_x
		UpperCamelCase     :Optional[int]          =	self.pos_y - self.goal_y
		if HEURISTIC == 1:
			return abs(__lowerCamelCase	) + abs(__lowerCamelCase	)
		else:
			return sqrt(dy**2 + dx**2	)





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

class     _SCREAMING_SNAKE_CASE       :





	def __init__(		self				:					List[Any] ,  __lowerCamelCase				:					TPosition ,  __lowerCamelCase				:					TPosition	):
		UpperCamelCase     :str          =	Node(start[1] ,  start[0] ,  goal[1] ,  goal[0] ,  0 ,  __lowerCamelCase	)
		UpperCamelCase     :int          =	Node(goal[1] ,  goal[0] ,  goal[1] ,  goal[0] ,  99_999 ,  __lowerCamelCase	)

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

		UpperCamelCase     :Optional[int]          =	False





	def 						_A (		self				:					Optional[Any]	):
		while self.open_nodes:
			# Open Nodes are sorted using __lt__
			self.open_nodes.sort()
			UpperCamelCase     :Dict          =	self.open_nodes.pop(0	)

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

			self.closed_nodes.append(__lowerCamelCase	)
			UpperCamelCase     :List[Any]          =	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
					UpperCamelCase     :List[Any]          =	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	)

		return [self.start.pos]





	def 						_A (		self				:					Tuple ,  __lowerCamelCase				:					Node	):
		UpperCamelCase     :List[Any]          =	[]
		for action in delta:
			UpperCamelCase     :Union[str, Any]          =	parent.pos_x + action[1]
			UpperCamelCase     :List[Any]          =	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				:					Any ,  __lowerCamelCase				:					Node | None	):
		UpperCamelCase     :Union[str, Any]          =	node
		UpperCamelCase     :List[str]          =	[]
		while current_node is not None:
			path.append((current_node.pos_y, current_node.pos_x)	)
			UpperCamelCase     :Optional[Any]          =	current_node.parent
		path.reverse()
		return path

class     _SCREAMING_SNAKE_CASE       :





	def __init__(		self				:					Union[str, Any] ,  __lowerCamelCase				:					TPosition ,  __lowerCamelCase				:					TPosition	):
		UpperCamelCase     :Any          =	AStar(__lowerCamelCase ,  __lowerCamelCase	)
		UpperCamelCase     :List[str]          =	AStar(__lowerCamelCase ,  __lowerCamelCase	)
		UpperCamelCase     :Dict          =	False





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

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

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

			UpperCamelCase     :int          =	current_bwd_node
			UpperCamelCase     :str          =	current_fwd_node

			UpperCamelCase     :List[Any]          =	{
			    self.fwd_astar: self.fwd_astar.get_successors(__lowerCamelCase	),
			    self.bwd_astar: self.bwd_astar.get_successors(__lowerCamelCase	),
			}

			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(__lowerCamelCase	)
					else:
						# retrieve the best current path
						UpperCamelCase     :Optional[int]          =	astar.open_nodes.pop(
						    astar.open_nodes.index(__lowerCamelCase	)	)

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

		return [self.fwd_astar.start.pos]





	def 						_A (		self				:					List[Any] ,  __lowerCamelCase				:					Node ,  __lowerCamelCase				:					Node	):
		UpperCamelCase     :List[str]          =	self.fwd_astar.retrace_path(__lowerCamelCase	)
		UpperCamelCase     :Tuple          =	self.bwd_astar.retrace_path(__lowerCamelCase	)
		bwd_path.pop()
		bwd_path.reverse()
		UpperCamelCase     :List[Any]          =	fwd_path + bwd_path
		return path


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

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

	UpperCAmelCase_ :						Union[str, Any]      =      time.time()
	UpperCAmelCase_ :						List[str]      =      BidirectionalAStar(init, goal)
	UpperCAmelCase_ :						Union[str, Any]      =      time.time() - bd_start_time
	print(F'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_ :						str      =      {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_ :						Tuple      =      [
	    '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''OPTForCausalLM''',
	    '''OPTModel''',
	    '''OPTPreTrainedModel''',
	    '''OPTForSequenceClassification''',
	    '''OPTForQuestionAnswering''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_ :						Any      =      ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_ :						Optional[Any]      =      [
	    '''FlaxOPTForCausalLM''',
	    '''FlaxOPTModel''',
	    '''FlaxOPTPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_opt import (
		    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OPTForCausalLM,
		    OPTForQuestionAnswering,
		    OPTForSequenceClassification,
		    OPTModel,
		    OPTPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
	import sys

	UpperCAmelCase_ :						List[Any]      =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
