
from __future__ import annotations

import time

A		: int								=     list[tuple[int, int]]

A		: 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		: Union[str, Any]								=     [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right



class    a_ :






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase  ):
									_lowercase					       =					pos_x
									_lowercase					       =					pos_y
									_lowercase					       =					(pos_y, pos_x)
									_lowercase					       =					goal_x
									_lowercase					       =					goal_y
									_lowercase					       =					parent







class    a_ :






			def __init__(      self				,			__UpperCamelCase				,			__UpperCamelCase  ):
									_lowercase					       =					Node(start[1]				,			start[0]				,			goal[1]				,			goal[0]				,			__UpperCamelCase  )
									_lowercase					       =					Node(goal[1]				,			goal[0]				,			goal[1]				,			goal[0]				,			__UpperCamelCase  )

									_lowercase					       =					[self.start]
									_lowercase					       =					False






			def    UpperCamelCase_				(      self  ):
									while self.node_queue:
															_lowercase					       =					self.node_queue.pop(0  )

															if current_node.pos == self.target.pos:
																					_lowercase					       =					True
																					return self.retrace_path(__UpperCamelCase  )

															_lowercase					       =					self.get_successors(__UpperCamelCase  )

															for node in successors:
																					self.node_queue.append(__UpperCamelCase  )

									if not self.reached:
															return [self.start.pos]
									return None






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									_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				,			__UpperCamelCase  )  )
									return successors






			def    UpperCamelCase_				(      self				,			__UpperCamelCase  ):
									_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				,			__UpperCamelCase				,			__UpperCamelCase  ):
									_lowercase					       =					BreadthFirstSearch(__UpperCamelCase				,			__UpperCamelCase  )
									_lowercase					       =					BreadthFirstSearch(__UpperCamelCase				,			__UpperCamelCase  )
									_lowercase					       =					False






			def    UpperCamelCase_				(      self  ):
									while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
															_lowercase					       =					self.fwd_bfs.node_queue.pop(0  )
															_lowercase					       =					self.bwd_bfs.node_queue.pop(0  )

															if current_bwd_node.pos == current_fwd_node.pos:
																					_lowercase					       =					True
																					return self.retrace_bidirectional_path(
																					    __UpperCamelCase				,			__UpperCamelCase  )

															_lowercase					       =					current_bwd_node
															_lowercase					       =					current_fwd_node

															_lowercase					       =					{
															    self.fwd_bfs: self.fwd_bfs.get_successors(__UpperCamelCase  ),
															    self.bwd_bfs: self.bwd_bfs.get_successors(__UpperCamelCase  ),
															}

															for bfs in [self.fwd_bfs, self.bwd_bfs]:
																					for node in successors[bfs]:
																											bfs.node_queue.append(__UpperCamelCase  )

									if not self.reached:
															return [self.fwd_bfs.start.pos]
									return None






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase  ):
									_lowercase					       =					self.fwd_bfs.retrace_path(__UpperCamelCase  )
									_lowercase					       =					self.bwd_bfs.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]
		import doctest

		doctest.testmod()
		A		: List[Any]								=     (0, 0)
		A		: Tuple								=     (len(grid) - 1, len(grid[0]) - 1)
		for elem in grid:
				print(elem)

		A		: List[Any]								=     time.time()
		A		: str								=     BreadthFirstSearch(init, goal)
		A		: Tuple								=     bfs.search()
		A		: Dict								=     time.time() - start_bfs_time

		print('''Unidirectional BFS computation time : ''', bfs_time)

		A		: Dict								=     time.time()
		A		: Optional[int]								=     BidirectionalBreadthFirstSearch(init, goal)
		A		: List[str]								=     bd_bfs.search()
		A		: str								=     time.time() - start_bd_bfs_time

		print('''Bidirectional BFS computation time : ''', bd_bfs_time)

import datasets


A		: Optional[int]								=     '''\
@InProceedings{conneau2018xnli,
  author = "Conneau, Alexis
                 and Rinott, Ruty
                 and Lample, Guillaume
                 and Williams, Adina
                 and Bowman, Samuel R.
                 and Schwenk, Holger
                 and Stoyanov, Veselin",
  title = "XNLI: Evaluating Cross-lingual Sentence Representations",
  booktitle = "Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing",
  year = "2018",
  publisher = "Association for Computational Linguistics",
  location = "Brussels, Belgium",
}
'''

A		: Optional[int]								=     '''\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
'''

A		: str								=     '''
Computes XNLI score which is just simple accuracy.
Args:
    predictions: Predicted labels.
    references: Ground truth labels.
Returns:
    \'accuracy\': accuracy
Examples:

    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> xnli_metric = datasets.load_metric("xnli")
    >>> results = xnli_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}
'''




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[str]		,   SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     str:
						return (preds == labels).mean()



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,			_KWARGS_DESCRIPTION    )
class    a_ (			datasets.Metric    ):






			def    UpperCamelCase_				(      self  ):
									return datasets.MetricInfo(
									    description=_DESCRIPTION				,			citation=_CITATION				,			inputs_description=_KWARGS_DESCRIPTION				,			features=datasets.Features(
									        {
									            """predictions""": datasets.Value("""int64""" if self.config_name != """sts-b""" else """float32"""  ),
									            """references""": datasets.Value("""int64""" if self.config_name != """sts-b""" else """float32"""  ),
									        }  )				,			codebase_urls=[]				,			reference_urls=[]				,			format="""numpy"""				,			)






			def    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase  ):
									return {"accuracy": simple_accuracy(__UpperCamelCase				,			__UpperCamelCase  )}