



'''simple docstring'''




from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
a     :    Any      						=					0

a     :    Union[str, 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],
]

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

a     :    str      						=					tuple[int, int]

class     UpperCamelCase_				:





	def __init__(						self	,	A	,	A	,	A	,	A	,	A	,	A	,	)    ->    None:
								UpperCAmelCase  :	str							  =	pos_x
								UpperCAmelCase  :	Dict							  =	pos_y
								UpperCAmelCase  :	Optional[Any]							  =	(pos_y, pos_x)
								UpperCAmelCase  :	List[Any]							  =	goal_x
								UpperCAmelCase  :	Tuple							  =	goal_y
								UpperCAmelCase  :	Union[str, Any]							  =	g_cost
								UpperCAmelCase  :	str							  =	parent
								UpperCAmelCase  :	List[str]							  =	self.calculate_heuristic()
								UpperCAmelCase  :	Any							  =	self.g_cost + self.h_cost





	def 				_lowercase(						self					)    ->    float:
								UpperCAmelCase  :	int							  =	self.pos_x - self.goal_x
								UpperCAmelCase  :	Dict							  =	self.pos_y - self.goal_y
								if HEURISTIC == 1:
															return abs(A					) + abs(A					)
								else:
															return sqrt(dy**2 + dx**2					)






	def __lt__(						self	,	A					)    ->    bool:
								return self.f_cost < other.f_cost




class     UpperCamelCase_				:





	def __init__(						self	,	A	,	A					)    ->    Optional[Any]:
								UpperCAmelCase  :	Tuple							  =	Node(start[1]	,	start[0]	,	goal[1]	,	goal[0]	,	0	,	A					)
								UpperCAmelCase  :	List[Any]							  =	Node(goal[1]	,	goal[0]	,	goal[1]	,	goal[0]	,	99999	,	A					)

								UpperCAmelCase  :	List[Any]							  =	[self.start]
								UpperCAmelCase  :	list[Node]							  =	[]

								UpperCAmelCase  :	Dict							  =	False





	def 				_lowercase(						self					)    ->    list[TPosition]:
								while self.open_nodes:
															# Open Nodes are sorted using __lt__
															self.open_nodes.sort()
															UpperCAmelCase  :	List[str]							  =	self.open_nodes.pop(0					)

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

															self.closed_nodes.append(A					)
															UpperCAmelCase  :	Any							  =	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
																													UpperCAmelCase  :	int							  =	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 				_lowercase(						self	,	A					)    ->    list[Node]:
								UpperCAmelCase  :	str							  =	[]
								for action in delta:
															UpperCAmelCase  :	Dict							  =	parent.pos_x + action[1]
															UpperCAmelCase  :	str							  =	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 				_lowercase(						self	,	A					)    ->    list[TPosition]:
								UpperCAmelCase  :	Any							  =	node
								UpperCAmelCase  :	Dict							  =	[]
								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     UpperCamelCase_				:





	def __init__(						self	,	A	,	A					)    ->    None:
								UpperCAmelCase  :	List[Any]							  =	AStar(A	,	A					)
								UpperCAmelCase  :	Dict							  =	AStar(A	,	A					)
								UpperCAmelCase  :	Tuple							  =	False





	def 				_lowercase(						self					)    ->    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()
															UpperCAmelCase  :	str							  =	self.fwd_astar.open_nodes.pop(0					)
															UpperCAmelCase  :	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					)

															UpperCAmelCase  :	Union[str, Any]							  =	current_bwd_node
															UpperCAmelCase  :	str							  =	current_fwd_node

															UpperCAmelCase  :	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
																																				UpperCAmelCase  :	Optional[Any]							  =	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 				_lowercase(						self	,	A	,	A					)    ->    list[TPosition]:
								UpperCAmelCase  :	Optional[Any]							  =	self.fwd_astar.retrace_path(A					)
								UpperCAmelCase  :	List[Any]							  =	self.bwd_astar.retrace_path(A					)
								bwd_path.pop()
								bwd_path.reverse()
								UpperCAmelCase  :	int							  =	fwd_path + bwd_path
								return path


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

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

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






'''simple docstring'''




# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
							import jax
							import jaxlib

a     :    Any      						=					get_logger()

a     :    Optional[dict]      						=					None

class     UpperCamelCase_				(							TensorFormatter[Mapping, 'jax.Array', Mapping]    ):





	def __init__(						self	,	A=None	,	A=None	,	**A					)    ->    str:
								super().__init__(features=A					)
								import jax
								from jaxlib.xla_client import Device

								if isinstance(A	,	A					):
															raise ValueError(
															    f'''Expected {device} to be a `str` not {type(A					)}, as `jaxlib.xla_extension.Device` '''
															    """is not serializable neither with `pickle` nor with `dill`. Instead you can surround """
															    """the device with `str()` to get its string identifier that will be internally mapped """
															    """to the actual `jaxlib.xla_extension.Device`."""					)
								UpperCAmelCase  :	Optional[int]							  =	device if isinstance(A	,	A					) else str(jax.devices()[0]					)
								# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
								# with `pickle` nor with `dill`, so we need to use a global variable instead
								global DEVICE_MAPPING
								if DEVICE_MAPPING is None:
															UpperCAmelCase  :	Any							  =	self._map_devices_to_str()
								if self.device not in list(DEVICE_MAPPING.keys()					):
															logger.warning(
															    f'''Device with string identifier {self.device} not listed among the available '''
															    f'''devices: {list(DEVICE_MAPPING.keys()					)}, so falling back to the default '''
															    f'''device: {str(jax.devices()[0]					)}.'''					)
															UpperCAmelCase  :	List[Any]							  =	str(jax.devices()[0]					)
								UpperCAmelCase  :	Union[str, Any]							  =	jnp_array_kwargs





	@staticmethod
	def 				_lowercase(						)    ->    Dict[str, "jaxlib.xla_extension.Device"]:
								import jax

								return {str(A					): device for device in jax.devices()}





	def 				_lowercase(						self	,	A					)    ->    str:
								import jax
								import jax.numpy as jnp

								if isinstance(A	,	A					) and column:
															if all(
															    isinstance(A	,	jax.Array					) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column					):
																						return jnp.stack(A	,	axis=0					)
								return column





	def 				_lowercase(						self	,	A					)    ->    Tuple:
								import jax
								import jax.numpy as jnp

								if isinstance(A	,	(str, bytes, type(A					))					):
															return value
								elif isinstance(A	,	(np.character, np.ndarray)					) and np.issubdtype(value.dtype	,	np.character					):
															return value.tolist()

								UpperCAmelCase  :	List[str]							  =	{}

								if isinstance(A	,	(np.number, np.ndarray)					) and np.issubdtype(value.dtype	,	np.integer					):
															# the default int precision depends on the jax config
															# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
															if jax.config.jax_enable_xaa:
																						UpperCAmelCase  :	str							  =	{"""dtype""": jnp.intaa}
															else:
																						UpperCAmelCase  :	int							  =	{"""dtype""": jnp.intaa}
								elif isinstance(A	,	(np.number, np.ndarray)					) and np.issubdtype(value.dtype	,	np.floating					):
															UpperCAmelCase  :	Any							  =	{"""dtype""": jnp.floataa}
								elif config.PIL_AVAILABLE and "PIL" in sys.modules:
															import PIL.Image

															if isinstance(A	,	PIL.Image.Image					):
																						UpperCAmelCase  :	List[str]							  =	np.asarray(A					)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
								global DEVICE_MAPPING
								if DEVICE_MAPPING is None:
															UpperCAmelCase  :	Dict							  =	self._map_devices_to_str()

								with jax.default_device(DEVICE_MAPPING[self.device]					):
															# calling jnp.array on a np.ndarray does copy the data
															# see https://github.com/google/jax/issues/4486
															return jnp.array(A	,	**{**default_dtype, **self.jnp_array_kwargs}					)





	def 				_lowercase(						self	,	A					)    ->    Tuple:
								import jax

								# support for torch, tf, jax etc.
								if config.TORCH_AVAILABLE and "torch" in sys.modules:
															import torch

															if isinstance(A	,	torch.Tensor					):
																						return self._tensorize(data_struct.detach().cpu().numpy()[()]					)
								if hasattr(A	,	"""__array__"""					) and not isinstance(A	,	jax.Array					):
															UpperCAmelCase  :	Optional[int]							  =	data_struct.__array__()
								# support for nested types like struct of list of struct
								if isinstance(A	,	np.ndarray					):
															if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
																						return self._consolidate([self.recursive_tensorize(A					) for substruct in data_struct]					)
								elif isinstance(A	,	(list, tuple)					):
															return self._consolidate([self.recursive_tensorize(A					) for substruct in data_struct]					)
								return self._tensorize(A					)





	def 				_lowercase(						self	,	A					)    ->    Dict:
								return map_nested(self._recursive_tensorize	,	A	,	map_list=A					)





	def 				_lowercase(						self	,	A					)    ->    Mapping:
								UpperCAmelCase  :	Union[str, Any]							  =	self.numpy_arrow_extractor().extract_row(A					)
								UpperCAmelCase  :	Dict							  =	self.python_features_decoder.decode_row(A					)
								return self.recursive_tensorize(A					)





	def 				_lowercase(						self	,	A					)    ->    "jax.Array":
								UpperCAmelCase  :	int							  =	self.numpy_arrow_extractor().extract_column(A					)
								UpperCAmelCase  :	Optional[Any]							  =	self.python_features_decoder.decode_column(A	,	pa_table.column_names[0]					)
								UpperCAmelCase  :	Optional[int]							  =	self.recursive_tensorize(A					)
								UpperCAmelCase  :	Any							  =	self._consolidate(A					)
								return column






	def 				_lowercase(						self	,	A					)    ->    Mapping:
								UpperCAmelCase  :	Optional[int]							  =	self.numpy_arrow_extractor().extract_batch(A					)
								UpperCAmelCase  :	List[str]							  =	self.python_features_decoder.decode_batch(A					)
								UpperCAmelCase  :	Union[str, Any]							  =	self.recursive_tensorize(A					)
								for column_name in batch:
															UpperCAmelCase  :	Optional[Any]							  =	self._consolidate(batch[column_name]					)
								return batch

