



from __future__ import annotations

import time
from math import sqrt

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

UpperCAmelCase_							:      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],
]

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

UpperCAmelCase_							:      Tuple      =  tuple[int, int]






class      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	):
							A__							=       pos_x
							A__							=       pos_y
							A__							=       (pos_y, pos_x)
							A__							=       goal_x
							A__							=       goal_y
							A__							=       g_cost
							A__							=       parent
							A__							=       self.calculate_heuristic()
							A__							=       self.g_cost + self.h_cost



	def 			__A							(       self		):
							A__							=       self.pos_x - self.goal_x
							A__							=       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__		):
							return self.f_cost < other.f_cost






class      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       Node(start[1]			,	start[0]			,	goal[1]			,	goal[0]			,	0			,	UpperCAmelCase__		)
							A__							=       Node(goal[1]			,	goal[0]			,	goal[1]			,	goal[0]			,	99_999			,	UpperCAmelCase__		)

							A__							=       [self.start]
							A__							=       []

							A__							=       False



	def 			__A							(       self		):
							while self.open_nodes:
													# Open Nodes are sorted using __lt__
													self.open_nodes.sort()
													A__							=       self.open_nodes.pop(0		)

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

													self.closed_nodes.append(UpperCAmelCase__		)
													A__							=       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__							=       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			,	UpperCAmelCase__		):
							A__							=       []
							for action in delta:
													A__							=       parent.pos_x + action[1]
													A__							=       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			,	UpperCAmelCase__		):
							A__							=       node
							A__							=       []
							while current_node is not None:
													path.append((current_node.pos_y, current_node.pos_x)		)
													A__							=       current_node.parent
							path.reverse()
							return path






class      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       AStar(UpperCAmelCase__			,	UpperCAmelCase__		)
							A__							=       AStar(UpperCAmelCase__			,	UpperCAmelCase__		)
							A__							=       False



	def 			__A							(       self		):
							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__							=       self.fwd_astar.open_nodes.pop(0		)
													A__							=       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__							=       current_bwd_node
													A__							=       current_fwd_node

													A__							=       {
													    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__							=       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			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       self.fwd_astar.retrace_path(UpperCAmelCase__		)
							A__							=       self.bwd_astar.retrace_path(UpperCAmelCase__		)
							bwd_path.pop()
							bwd_path.reverse()
							A__							=       fwd_path + bwd_path
							return path


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

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

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










import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


UpperCAmelCase_							:      Optional[Any]      =  logging.get_logger(__name__)






class      UpperCamelCase    (						_UpperCAmelCase     ):
	lowerCAmelCase	:   List[str]				=	"""linear"""
	lowerCAmelCase	:   int				=	"""cosine"""
	lowerCAmelCase	:   Dict				=	"""cosine_with_restarts"""
	lowerCAmelCase	:   Optional[Any]				=	"""polynomial"""
	lowerCAmelCase	:   Dict				=	"""constant"""
	lowerCAmelCase	:   Any				=	"""constant_with_warmup"""
	lowerCAmelCase	:   Union[str, Any]				=	"""piecewise_constant"""





def  UpperCamelCase						(       _A			:				Optimizer			,							_A			:				int = -1    )->       Dict:





						"""simple docstring"""





						return LambdaLR(_A			,							lambda _A    : 1			,							last_epoch=_A    )
def  UpperCamelCase						(       _A			:				Optimizer			,							_A			:				int			,							_A			:				int = -1    )->       Optional[Any]:





						"""simple docstring"""





						def lr_lambda(_A			:				int    ):
												if current_step < num_warmup_steps:
																		return float(_A    ) / float(max(1.0			,							_A    )    )
												return 1.0

						return LambdaLR(_A			,							_A			,							last_epoch=_A    )
def  UpperCamelCase						(       _A			:				Optimizer			,							_A			:				str			,							_A			:				int = -1    )->       Dict:





						"""simple docstring"""





						A__							=       {}
						A__							=       step_rules.split(","    )
						for rule_str in rule_list[:-1]:
												A__						,       A__							=       rule_str.split(":"    )
												A__							=       int(_A    )
												A__							=       float(_A    )
												A__							=       value
						A__							=       float(rule_list[-1]    )

						def create_rules_function(_A			:				Any			,							_A			:				Optional[int]    ):
												def rule_func(_A			:				int    ) -> float:
																		A__							=       sorted(rules_dict.keys()    )
																		for i, sorted_step in enumerate(_A    ):
																								if steps < sorted_step:
																														return rules_dict[sorted_steps[i]]
																		return last_lr_multiple

												return rule_func

						A__							=       create_rules_function(_A			,							_A    )

						return LambdaLR(_A			,							_A			,							last_epoch=_A    )
def  UpperCamelCase						(       _A			:				Any			,							_A			:				Union[str, Any]			,							_A			:				str			,							_A			:				str=-1    )->       Tuple:





						"""simple docstring"""





						def lr_lambda(_A			:				int    ):
												if current_step < num_warmup_steps:
																		return float(_A    ) / float(max(1			,							_A    )    )
												return max(
												    0.0			,							float(num_training_steps - current_step    ) / float(max(1			,							num_training_steps - num_warmup_steps    )    )    )

						return LambdaLR(_A			,							_A			,							_A    )
def  UpperCamelCase						(       _A			:				Optimizer			,							_A			:				int			,							_A			:				int			,							_A			:				float = 0.5			,							_A			:				int = -1    )->       Any:





						"""simple docstring"""





						def lr_lambda(_A			:				Tuple    ):
												if current_step < num_warmup_steps:
																		return float(_A    ) / float(max(1			,							_A    )    )
												A__							=       float(current_step - num_warmup_steps    ) / float(max(1			,							num_training_steps - num_warmup_steps    )    )
												return max(0.0			,							0.5 * (1.0 + math.cos(math.pi * float(_A    ) * 2.0 * progress    ))    )

						return LambdaLR(_A			,							_A			,							_A    )
def  UpperCamelCase						(       _A			:				Optimizer			,							_A			:				int			,							_A			:				int			,							_A			:				int = 1			,							_A			:				int = -1    )->       Any:





						"""simple docstring"""





						def lr_lambda(_A			:				Tuple    ):
												if current_step < num_warmup_steps:
																		return float(_A    ) / float(max(1			,							_A    )    )
												A__							=       float(current_step - num_warmup_steps    ) / float(max(1			,							num_training_steps - num_warmup_steps    )    )
												if progress >= 1.0:
																		return 0.0
												return max(0.0			,							0.5 * (1.0 + math.cos(math.pi * ((float(_A    ) * progress) % 1.0)    ))    )

						return LambdaLR(_A			,							_A			,							_A    )
def  UpperCamelCase						(       _A			:				Union[str, Any]			,							_A			:				Union[str, Any]			,							_A			:				List[str]			,							_A			:				Tuple=1E-7			,							_A			:				Dict=1.0			,							_A			:				Union[str, Any]=-1    )->       Any:





						"""simple docstring"""





						A__							=       optimizer.defaults["lr"]
						if not (lr_init > lr_end):
												raise ValueError(f"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"""    )

						def lr_lambda(_A			:				int    ):
												if current_step < num_warmup_steps:
																		return float(_A    ) / float(max(1			,							_A    )    )
												elif current_step > num_training_steps:
																		return lr_end / lr_init  # as LambdaLR multiplies by lr_init
												else:
																		A__							=       lr_init - lr_end
																		A__							=       num_training_steps - num_warmup_steps
																		A__							=       1 - (current_step - num_warmup_steps) / decay_steps
																		A__							=       lr_range * pct_remaining**power + lr_end
																		return decay / lr_init  # as LambdaLR multiplies by lr_init

						return LambdaLR(_A			,							_A			,							_A    )


UpperCAmelCase_							:      Any      =  {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}
def  UpperCamelCase						(       _A			:				Union[str, SchedulerType]			,							_A			:				Optimizer			,							_A			:				Optional[str] = None			,							_A			:				Optional[int] = None			,							_A			:				Optional[int] = None			,							_A			:				int = 1			,							_A			:				float = 1.0			,							_A			:				int = -1			,							)->       Union[str, Any]:





						"""simple docstring"""





						A__							=       SchedulerType(_A    )
						A__							=       TYPE_TO_SCHEDULER_FUNCTION[name]
						if name == SchedulerType.CONSTANT:
												return schedule_func(_A			,							last_epoch=_A    )

						if name == SchedulerType.PIECEWISE_CONSTANT:
												return schedule_func(_A			,							step_rules=_A			,							last_epoch=_A    )

						# All other schedulers require `num_warmup_steps`
						if num_warmup_steps is None:
												raise ValueError(f"""{name} requires `num_warmup_steps`, please provide that argument."""    )

						if name == SchedulerType.CONSTANT_WITH_WARMUP:
												return schedule_func(_A			,							num_warmup_steps=_A			,							last_epoch=_A    )

						# All other schedulers require `num_training_steps`
						if num_training_steps is None:
												raise ValueError(f"""{name} requires `num_training_steps`, please provide that argument."""    )

						if name == SchedulerType.COSINE_WITH_RESTARTS:
												return schedule_func(
												    _A			,							num_warmup_steps=_A			,							num_training_steps=_A			,							num_cycles=_A			,							last_epoch=_A			,							)

						if name == SchedulerType.POLYNOMIAL:
												return schedule_func(
												    _A			,							num_warmup_steps=_A			,							num_training_steps=_A			,							power=_A			,							last_epoch=_A			,							)

						return schedule_func(
						    _A			,							num_warmup_steps=_A			,							num_training_steps=_A			,							last_epoch=_A    )





