


from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
lowerCAmelCase       =							0

lowerCAmelCase       =							[
    [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       =							[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

lowerCAmelCase       =							tuple[int, int]


class        _a							:

							def __init__(							self:	Optional[Any]    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	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       lowerCamelCase_ (							self:	Union[str, Any]							)			->   float:





									"""simple docstring"""







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


							def __lt__(							self:	Optional[Any]    ,						UpperCamelCase_:	Node							)			->   bool:





									"""simple docstring"""







									return self.f_cost < other.f_cost


class        _a							:

							def __init__(							self:	List[Any]    ,						UpperCamelCase_:	TPosition    ,						UpperCamelCase_:	TPosition							)			->   Dict:





									"""simple docstring"""







									lowercase__            =      Node(start[1]    ,						start[0]    ,						goal[1]    ,						goal[0]    ,						0    ,						UpperCamelCase_							)
									lowercase__            =      Node(goal[1]    ,						goal[0]    ,						goal[1]    ,						goal[0]    ,						99_999    ,						UpperCamelCase_							)

									lowercase__            =      [self.start]
									lowercase__            =      []

									lowercase__            =      False

							def       lowerCamelCase_ (							self:	Any							)			->   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(UpperCamelCase_							)

											self.closed_nodes.append(UpperCamelCase_							)
											lowercase__            =      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
															lowercase__            =      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       lowerCamelCase_ (							self:	List[Any]    ,						UpperCamelCase_:	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(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       lowerCamelCase_ (							self:	Optional[int]    ,						UpperCamelCase_:	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        _a							:

							def __init__(							self:	Optional[int]    ,						UpperCamelCase_:	TPosition    ,						UpperCamelCase_:	TPosition							)			->   None:





									"""simple docstring"""







									lowercase__            =      AStar(UpperCamelCase_    ,						UpperCamelCase_							)
									lowercase__            =      AStar(UpperCamelCase_    ,						UpperCamelCase_							)
									lowercase__            =      False

							def       lowerCamelCase_ (							self:	int							)			->   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(
													    UpperCamelCase_    ,						UpperCamelCase_							)

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

											lowercase__            =      current_bwd_node
											lowercase__            =      current_fwd_node

											lowercase__            =      {
											    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
																	lowercase__            =      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       lowerCamelCase_ (							self:	Tuple    ,						UpperCamelCase_:	Node    ,						UpperCamelCase_:	Node							)			->   list[TPosition]:





									"""simple docstring"""







									lowercase__            =      self.fwd_astar.retrace_path(UpperCamelCase_							)
									lowercase__            =      self.bwd_astar.retrace_path(UpperCamelCase_							)
									bwd_path.pop()
									bwd_path.reverse()
									lowercase__            =      fwd_path + bwd_path
									return path


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

			lowerCAmelCase       =							time.time()
			lowerCAmelCase       =							AStar(init, goal)
			lowerCAmelCase       =							a_star.search()
			lowerCAmelCase       =							time.time() - start_time
			print(f"""AStar execution time = {end_time:f} seconds""")

			lowerCAmelCase       =							time.time()
			lowerCAmelCase       =							BidirectionalAStar(init, goal)
			lowerCAmelCase       =							time.time() - bd_start_time
			print(f"""BidirectionalAStar execution time = {bd_end_time:f} seconds""")








import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
					import jax.numpy as jnp

if is_tf_available():
					import tensorflow as tf

if is_torch_available():
					import torch







class     __lowerCAmelCase (       unittest.TestCase ):

			"""simple docstring"""







			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    {
							    'task_specific_params': {
							        'summarization': {'length_penalty': 1.0, 'max_length': 128, 'min_length': 12, 'num_beams': 4},
							        'summarization_cnn': {'length_penalty': 2.0, 'max_length': 142, 'min_length': 56, 'num_beams': 4},
							        'summarization_xsum': {'length_penalty': 1.0, 'max_length': 62, 'min_length': 11, 'num_beams': 6},
							    }
							}
							__lowerCamelCase		       =    {
							    'task_specific_params.summarization.length_penalty': 1.0,
							    'task_specific_params.summarization.max_length': 128,
							    'task_specific_params.summarization.min_length': 12,
							    'task_specific_params.summarization.num_beams': 4,
							    'task_specific_params.summarization_cnn.length_penalty': 2.0,
							    'task_specific_params.summarization_cnn.max_length': 142,
							    'task_specific_params.summarization_cnn.min_length': 56,
							    'task_specific_params.summarization_cnn.num_beams': 4,
							    'task_specific_params.summarization_xsum.length_penalty': 1.0,
							    'task_specific_params.summarization_xsum.max_length': 62,
							    'task_specific_params.summarization_xsum.min_length': 11,
							    'task_specific_params.summarization_xsum.num_beams': 6,
							}

							self.assertEqual(flatten_dict(lowerCamelCase__							)			,						lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    np.random.randn(3			,						4							)
							self.assertTrue(np.allclose(transpose(lowerCamelCase__							)			,						x.transpose()							)							)

							__lowerCamelCase		       =    np.random.randn(3			,						4			,						5							)
							self.assertTrue(np.allclose(transpose(lowerCamelCase__			,						axes=(1, 2, 0)							)			,						x.transpose((1, 2, 0)							)							)							)


			@require_torch
			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase		       =    np.random.randn(3			,						4							)
							__lowerCamelCase		       =    torch.tensor(lowerCamelCase__							)
							self.assertTrue(np.allclose(transpose(lowerCamelCase__							)			,						transpose(lowerCamelCase__							).numpy()							)							)

							__lowerCamelCase		       =    np.random.randn(3			,						4			,						5							)
							__lowerCamelCase		       =    torch.tensor(lowerCamelCase__							)
							self.assertTrue(np.allclose(transpose(lowerCamelCase__			,						axes=(1, 2, 0)							)			,						transpose(lowerCamelCase__			,						axes=(1, 2, 0)							).numpy()							)							)


			@require_tf
			def       lowercase_    (      self							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    np.random.randn(3			,						4							)
							__lowerCamelCase		       =    tf.constant(lowerCamelCase__							)
							self.assertTrue(np.allclose(transpose(lowerCamelCase__							)			,						transpose(lowerCamelCase__							).numpy()							)							)

							__lowerCamelCase		       =    np.random.randn(3			,						4			,						5							)
							__lowerCamelCase		       =    tf.constant(lowerCamelCase__							)
							self.assertTrue(np.allclose(transpose(lowerCamelCase__			,						axes=(1, 2, 0)							)			,						transpose(lowerCamelCase__			,						axes=(1, 2, 0)							).numpy()							)							)


			@require_flax
			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    np.random.randn(3			,						4							)
							__lowerCamelCase		       =    jnp.array(lowerCamelCase__							)
							self.assertTrue(np.allclose(transpose(lowerCamelCase__							)			,						np.asarray(transpose(lowerCamelCase__							)							)							)							)

							__lowerCamelCase		       =    np.random.randn(3			,						4			,						5							)
							__lowerCamelCase		       =    jnp.array(lowerCamelCase__							)
							self.assertTrue(np.allclose(transpose(lowerCamelCase__			,						axes=(1, 2, 0)							)			,						np.asarray(transpose(lowerCamelCase__			,						axes=(1, 2, 0)							)							)							)							)


			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase		       =    np.random.randn(3			,						4							)
							self.assertTrue(np.allclose(reshape(lowerCamelCase__			,						(4, 3)							)			,						np.reshape(lowerCamelCase__			,						(4, 3)							)							)							)

							__lowerCamelCase		       =    np.random.randn(3			,						4			,						5							)
							self.assertTrue(np.allclose(reshape(lowerCamelCase__			,						(12, 5)							)			,						np.reshape(lowerCamelCase__			,						(12, 5)							)							)							)


			@require_torch
			def       lowercase_    (      self							)    ->      List[Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    np.random.randn(3			,						4							)
							__lowerCamelCase		       =    torch.tensor(lowerCamelCase__							)
							self.assertTrue(np.allclose(reshape(lowerCamelCase__			,						(4, 3)							)			,						reshape(lowerCamelCase__			,						(4, 3)							).numpy()							)							)

							__lowerCamelCase		       =    np.random.randn(3			,						4			,						5							)
							__lowerCamelCase		       =    torch.tensor(lowerCamelCase__							)
							self.assertTrue(np.allclose(reshape(lowerCamelCase__			,						(12, 5)							)			,						reshape(lowerCamelCase__			,						(12, 5)							).numpy()							)							)


			@require_tf
			def       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    np.random.randn(3			,						4							)
							__lowerCamelCase		       =    tf.constant(lowerCamelCase__							)
							self.assertTrue(np.allclose(reshape(lowerCamelCase__			,						(4, 3)							)			,						reshape(lowerCamelCase__			,						(4, 3)							).numpy()							)							)

							__lowerCamelCase		       =    np.random.randn(3			,						4			,						5							)
							__lowerCamelCase		       =    tf.constant(lowerCamelCase__							)
							self.assertTrue(np.allclose(reshape(lowerCamelCase__			,						(12, 5)							)			,						reshape(lowerCamelCase__			,						(12, 5)							).numpy()							)							)


			@require_flax
			def       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    np.random.randn(3			,						4							)
							__lowerCamelCase		       =    jnp.array(lowerCamelCase__							)
							self.assertTrue(np.allclose(reshape(lowerCamelCase__			,						(4, 3)							)			,						np.asarray(reshape(lowerCamelCase__			,						(4, 3)							)							)							)							)

							__lowerCamelCase		       =    np.random.randn(3			,						4			,						5							)
							__lowerCamelCase		       =    jnp.array(lowerCamelCase__							)
							self.assertTrue(np.allclose(reshape(lowerCamelCase__			,						(12, 5)							)			,						np.asarray(reshape(lowerCamelCase__			,						(12, 5)							)							)							)							)


			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase		       =    np.random.randn(1			,						3			,						4							)
							self.assertTrue(np.allclose(squeeze(lowerCamelCase__							)			,						np.squeeze(lowerCamelCase__							)							)							)

							__lowerCamelCase		       =    np.random.randn(1			,						4			,						1			,						5							)
							self.assertTrue(np.allclose(squeeze(lowerCamelCase__			,						axis=2							)			,						np.squeeze(lowerCamelCase__			,						axis=2							)							)							)


			@require_torch
			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase		       =    np.random.randn(1			,						3			,						4							)
							__lowerCamelCase		       =    torch.tensor(lowerCamelCase__							)
							self.assertTrue(np.allclose(squeeze(lowerCamelCase__							)			,						squeeze(lowerCamelCase__							).numpy()							)							)

							__lowerCamelCase		       =    np.random.randn(1			,						4			,						1			,						5							)
							__lowerCamelCase		       =    torch.tensor(lowerCamelCase__							)
							self.assertTrue(np.allclose(squeeze(lowerCamelCase__			,						axis=2							)			,						squeeze(lowerCamelCase__			,						axis=2							).numpy()							)							)


			@require_tf
			def       lowercase_    (      self							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    np.random.randn(1			,						3			,						4							)
							__lowerCamelCase		       =    tf.constant(lowerCamelCase__							)
							self.assertTrue(np.allclose(squeeze(lowerCamelCase__							)			,						squeeze(lowerCamelCase__							).numpy()							)							)

							__lowerCamelCase		       =    np.random.randn(1			,						4			,						1			,						5							)
							__lowerCamelCase		       =    tf.constant(lowerCamelCase__							)
							self.assertTrue(np.allclose(squeeze(lowerCamelCase__			,						axis=2							)			,						squeeze(lowerCamelCase__			,						axis=2							).numpy()							)							)


			@require_flax
			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							__lowerCamelCase		       =    np.random.randn(1			,						3			,						4							)
							__lowerCamelCase		       =    jnp.array(lowerCamelCase__							)
							self.assertTrue(np.allclose(squeeze(lowerCamelCase__							)			,						np.asarray(squeeze(lowerCamelCase__							)							)							)							)

							__lowerCamelCase		       =    np.random.randn(1			,						4			,						1			,						5							)
							__lowerCamelCase		       =    jnp.array(lowerCamelCase__							)
							self.assertTrue(np.allclose(squeeze(lowerCamelCase__			,						axis=2							)			,						np.asarray(squeeze(lowerCamelCase__			,						axis=2							)							)							)							)


			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase		       =    np.random.randn(3			,						4							)
							self.assertTrue(np.allclose(expand_dims(lowerCamelCase__			,						axis=1							)			,						np.expand_dims(lowerCamelCase__			,						axis=1							)							)							)


			@require_torch
			def       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    np.random.randn(3			,						4							)
							__lowerCamelCase		       =    torch.tensor(lowerCamelCase__							)
							self.assertTrue(np.allclose(expand_dims(lowerCamelCase__			,						axis=1							)			,						expand_dims(lowerCamelCase__			,						axis=1							).numpy()							)							)


			@require_tf
			def       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    np.random.randn(3			,						4							)
							__lowerCamelCase		       =    tf.constant(lowerCamelCase__							)
							self.assertTrue(np.allclose(expand_dims(lowerCamelCase__			,						axis=1							)			,						expand_dims(lowerCamelCase__			,						axis=1							).numpy()							)							)






			@require_flax
			def       lowercase_    (      self							)    ->      List[Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    np.random.randn(3			,						4							)
							__lowerCamelCase		       =    jnp.array(lowerCamelCase__							)
							self.assertTrue(np.allclose(expand_dims(lowerCamelCase__			,						axis=1							)			,						np.asarray(expand_dims(lowerCamelCase__			,						axis=1							)							)							)							)


