






from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
__UpperCamelCase   :   List[Any]      =    0

__UpperCamelCase   :   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],
]

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

__UpperCamelCase   :   Dict      =    tuple[int, int]


class 							__UpperCamelCase     :







	def __init__(	self   :				Tuple							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				Node | None							,					)		->			None:
		"""simple docstring"""







		__lowercase   							=	pos_x
		__lowercase   							=	pos_y
		__lowercase   							=	(pos_y, pos_x)
		__lowercase   							=	goal_x
		__lowercase   							=	goal_y
		__lowercase   							=	g_cost
		__lowercase   							=	parent
		__lowercase   							=	self.calculate_heuristic()
		__lowercase   							=	self.g_cost + self.h_cost







	def 			_a (	self   :				Optional[Any]      )		->			float:
		"""simple docstring"""







		__lowercase   							=	self.pos_x - self.goal_x
		__lowercase   							=	self.pos_y - self.goal_y
		if HEURISTIC == 1:
			return abs(_lowerCAmelCase      ) + abs(_lowerCAmelCase      )
		else:
			return sqrt(dy**2 + dx**2      )







	def __lt__(	self   :				int							,					_lowerCAmelCase   :				Node      )		->			bool:
		"""simple docstring"""







		return self.f_cost < other.f_cost


class 							__UpperCamelCase     :







	def __init__(	self   :				Optional[Any]							,					_lowerCAmelCase   :				TPosition							,					_lowerCAmelCase   :				TPosition      )		->			List[Any]:
		"""simple docstring"""







		__lowercase   							=	Node(start[1]							,					start[0]							,					goal[1]							,					goal[0]							,					0							,					_lowerCAmelCase      )
		__lowercase   							=	Node(goal[1]							,					goal[0]							,					goal[1]							,					goal[0]							,					9_9999							,					_lowerCAmelCase      )

		__lowercase   							=	[self.start]
		__lowercase   							=	[]

		__lowercase   							=	False







	def 			_a (	self   :				Optional[int]      )		->			list[TPosition]:
		"""simple docstring"""







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

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

			self.closed_nodes.append(_lowerCAmelCase      )
			__lowercase   							=	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
					__lowercase   							=	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   :				Dict							,					_lowerCAmelCase   :				Node      )		->			list[Node]:
		"""simple docstring"""







		__lowercase   							=	[]
		for action in delta:
			__lowercase   							=	parent.pos_x + action[1]
			__lowercase   							=	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   :				List[Any]							,					_lowerCAmelCase   :				Node | None      )		->			list[TPosition]:
		"""simple docstring"""







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


class 							__UpperCamelCase     :







	def __init__(	self   :				List[Any]							,					_lowerCAmelCase   :				TPosition							,					_lowerCAmelCase   :				TPosition      )		->			None:
		"""simple docstring"""







		__lowercase   							=	AStar(_lowerCAmelCase							,					_lowerCAmelCase      )
		__lowercase   							=	AStar(_lowerCAmelCase							,					_lowerCAmelCase      )
		__lowercase   							=	False







	def 			_a (	self   :				Any      )		->			list[TPosition]:
		"""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()
			__lowercase   							=	self.fwd_astar.open_nodes.pop(0      )
			__lowercase   							=	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      )

			__lowercase   							=	current_bwd_node
			__lowercase   							=	current_fwd_node

			__lowercase   							=	{
			    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
						__lowercase   							=	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   :				int							,					_lowerCAmelCase   :				Node							,					_lowerCAmelCase   :				Node      )		->			list[TPosition]:
		"""simple docstring"""







		__lowercase   							=	self.fwd_astar.retrace_path(_lowerCAmelCase      )
		__lowercase   							=	self.bwd_astar.retrace_path(_lowerCAmelCase      )
		bwd_path.pop()
		bwd_path.reverse()
		__lowercase   							=	fwd_path + bwd_path
		return path


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

						__UpperCamelCase   :   Dict      =    time.time()
						__UpperCamelCase   :   Any      =    AStar(init, goal)
						__UpperCamelCase   :   Any      =    a_star.search()
						__UpperCamelCase   :   str      =    time.time() - start_time
						print(F'''AStar execution time = {end_time:f} seconds''')

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





# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


A				:       Union[str, Any]      =  logging.get_logger(__name__)

A				:       Dict[Optional[str], Type[Formatter]]      =  {}
A				:       Dict[Optional[str], str]      =  {}
A				:       Dict[Optional[str], Exception]      =  {}





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase = None   ,						)    ->						str:


						'''simple docstring'''






						__snake_case				=		aliases if aliases is not None else []
						if format_type in _FORMAT_TYPES:
												logger.warning(
												    F'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''			)
						__snake_case				=		formatter_cls
						for alias in set(aliases + [format_type]			):
												if alias in _FORMAT_TYPES_ALIASES:
																		logger.warning(
																		    F'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''			)
												__snake_case				=		format_type





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase = None			)    ->						List[Any]:


						'''simple docstring'''






						__snake_case				=		aliases if aliases is not None else []
						for alias in set(aliases + [format_type]			):
												__snake_case				=		unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['python'])
_register_formatter(ArrowFormatter, 'arrow', aliases=['pa', 'pyarrow'])
_register_formatter(NumpyFormatter, 'numpy', aliases=['np'])
_register_formatter(PandasFormatter, 'pandas', aliases=['pd'])
_register_formatter(CustomFormatter, 'custom')

if config.TORCH_AVAILABLE:
	from .torch_formatter import TorchFormatter

	_register_formatter(TorchFormatter, 'torch', aliases=['pt', 'pytorch'])
else:
	A				:       Any      =  ValueError('PyTorch needs to be installed to be able to return PyTorch tensors.')
	_register_unavailable_formatter(_torch_error, 'torch', aliases=['pt', 'pytorch'])

if config.TF_AVAILABLE:
	from .tf_formatter import TFFormatter

	_register_formatter(TFFormatter, 'tensorflow', aliases=['tf'])
else:
	A				:       Tuple      =  ValueError('Tensorflow needs to be installed to be able to return Tensorflow tensors.')
	_register_unavailable_formatter(_tf_error, 'tensorflow', aliases=['tf'])

if config.JAX_AVAILABLE:
	from .jax_formatter import JaxFormatter

	_register_formatter(JaxFormatter, 'jax', aliases=[])
else:
	A				:       Optional[int]      =  ValueError('JAX needs to be installed to be able to return JAX arrays.')
	_register_unavailable_formatter(_jax_error, 'jax', aliases=[])





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						Optional[str]:


						'''simple docstring'''






						if format_type in _FORMAT_TYPES_ALIASES:
												return _FORMAT_TYPES_ALIASES[format_type]
						else:
												return format_type





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						**_lowerCAmelCase			)    ->						Formatter:


						'''simple docstring'''






						__snake_case				=		get_format_type_from_alias(_lowerCAmelCase			)
						if format_type in _FORMAT_TYPES:
												return _FORMAT_TYPES[format_type](**_lowerCAmelCase			)
						if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
												raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
						else:
												raise ValueError(
												    F'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None			)}, but got \'{format_type}\''''			)





