"""simple docstring"""

from __future__ import annotations

import time
from math import sqrt

# 1 for manhattan, 0 for euclidean
UpperCAmelCase       =  0

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

UpperCAmelCase       =  tuple[int, int]







class      UpperCAmelCase_       :






							def __init__(		self :      int     ,						__UpperCamelCase :      str     ,						__UpperCamelCase :      int     ,						__UpperCamelCase :      Optional[Any]     ,						__UpperCamelCase :      Tuple     ,						__UpperCamelCase :      Any     ,						__UpperCamelCase :      Any     ,						)       ->       int:
												_UpperCamelCase      						=	pos_x
												_UpperCamelCase      						=	pos_y
												_UpperCamelCase      						=	(pos_y, pos_x)
												_UpperCamelCase      						=	goal_x
												_UpperCamelCase      						=	goal_y
												_UpperCamelCase      						=	g_cost
												_UpperCamelCase      						=	parent
												_UpperCamelCase      						=	self.calculate_heuristic()
												_UpperCamelCase      						=	self.g_cost + self.h_cost






							def       _UpperCamelCase			(		self :      Optional[Any]  )       ->       Optional[int]:
												_UpperCamelCase      						=	self.pos_x - self.goal_x
												_UpperCamelCase      						=	self.pos_y - self.goal_y
												if HEURISTIC == 1:
																	return abs(A_  ) + abs(A_  )
												else:
																	return sqrt(dy**2 + dx**2  )






							def __lt__(		self :      Optional[Any]     ,						__UpperCamelCase :      Any  )       ->       Any:
												return self.f_cost < other.f_cost







class      UpperCAmelCase_       :






							def __init__(		self :      List[str]     ,						__UpperCamelCase :      Optional[int]     ,						__UpperCamelCase :      int  )       ->       Union[str, Any]:
												_UpperCamelCase      						=	Node(start[1]     ,						start[0]     ,						goal[1]     ,						goal[0]     ,						0     ,						A_  )
												_UpperCamelCase      						=	Node(goal[1]     ,						goal[0]     ,						goal[1]     ,						goal[0]     ,						9_9999     ,						A_  )

												_UpperCamelCase      						=	[self.start]
												_UpperCamelCase      						=	[]

												_UpperCamelCase      						=	False






							def       _UpperCamelCase			(		self :      List[str]  )       ->       Dict:




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

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

																	self.closed_nodes.append(A_  )
																	_UpperCamelCase      						=	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      						=	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       _UpperCamelCase			(		self :      Optional[int]     ,						__UpperCamelCase :      Dict  )       ->       int:
												_UpperCamelCase      						=	[]
												for action in delta:
																	_UpperCamelCase      						=	parent.pos_x + action[1]
																	_UpperCamelCase      						=	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       _UpperCamelCase			(		self :      int     ,						__UpperCamelCase :      Dict  )       ->       str:
												_UpperCamelCase      						=	node
												_UpperCamelCase      						=	[]
												while current_node is not None:
																	path.append((current_node.pos_y, current_node.pos_x)  )
																	_UpperCamelCase      						=	current_node.parent
												path.reverse()
												return path







class      UpperCAmelCase_       :






							def __init__(		self :      Tuple     ,						__UpperCamelCase :      str     ,						__UpperCamelCase :      Optional[Any]  )       ->       List[Any]:
												_UpperCamelCase      						=	AStar(A_     ,						A_  )
												_UpperCamelCase      						=	AStar(A_     ,						A_  )
												_UpperCamelCase      						=	False






							def       _UpperCamelCase			(		self :      Optional[Any]  )       ->       Dict:




												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      						=	self.fwd_astar.open_nodes.pop(0  )
																	_UpperCamelCase      						=	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      						=	current_bwd_node
																	_UpperCamelCase      						=	current_fwd_node

																	_UpperCamelCase      						=	{
																	    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      						=	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       _UpperCamelCase			(		self :      int     ,						__UpperCamelCase :      str     ,						__UpperCamelCase :      Any  )       ->       Tuple:
												_UpperCamelCase      						=	self.fwd_astar.retrace_path(A_  )
												_UpperCamelCase      						=	self.bwd_astar.retrace_path(A_  )
												bwd_path.pop()
												bwd_path.reverse()
												_UpperCamelCase      						=	fwd_path + bwd_path
												return path


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

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

		UpperCAmelCase       =  time.time()
		UpperCAmelCase       =  BidirectionalAStar(init, goal)
		UpperCAmelCase       =  time.time() - bd_start_time
		print(F'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')







from sklearn.metrics import fa_score

import datasets


__lowerCamelCase       :     List[Any]   =     """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""


__lowerCamelCase       :     List[Any]   =     """
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.

        - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
        - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.
        - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
        - 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
        - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.

Examples:

    Example 1-A simple binary example
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
        >>> print(results)
        {'f1': 0.5}

    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
        >>> print(round(results['f1'], 2))
        0.67

    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
        >>> f1_metric = datasets.load_metric(\"f1\")
        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
        >>> print(round(results['f1'], 2))
        0.35

    Example 4-A multiclass example, with different values for the `average` input.
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")
        >>> print(round(results['f1'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")
        >>> print(round(results['f1'], 2))
        0.33
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")
        >>> print(round(results['f1'], 2))
        0.27
        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {'f1': array([0.8, 0. , 0. ])}
"""


__lowerCamelCase       :     str   =     """
@article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
}
"""



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





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return datasets.MetricInfo(
           description=_DESCRIPTION       ,							citation=_CITATION       ,							inputs_description=_KWARGS_DESCRIPTION       ,							features=datasets.Features(
               {
                   "predictions": datasets.Sequence(datasets.Value("int32"   )   ),
                   "references": datasets.Sequence(datasets.Value("int32"   )   ),
               }
               if self.config_name == "multilabel"
               else {
                   "predictions": datasets.Value("int32"   ),
                   "references": datasets.Value("int32"   ),
               }   )       ,							reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"]       ,							)





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_=None       ,							A_=1       ,							A_="binary"       ,							A_=None   ):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   fa_score(
           A_       ,							A_       ,							labels=A_       ,							pos_label=A_       ,							average=A_       ,							sample_weight=A_   )
       return {"f1": float(A_   ) if score.size == 1 else score}
