
from sklearn.metrics import fa_score

import datasets


lowercase						:      Tuple									= '\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n'


lowercase						:      Any									= '\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    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.\n    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n    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\'`.\n\n        - \'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.\n        - \'micro\': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n        - \'macro\': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n        - \'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.\n        - \'samples\': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    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.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {\'f1\': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results[\'f1\'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> f1_metric = datasets.load_metric("f1")\n        >>> 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])\n        >>> print(round(results[\'f1\'], 2))\n        0.35\n\n    Example 4-A multiclass example, with different values for the `average` input.\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average="macro")\n        >>> print(round(results[\'f1\'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average="micro")\n        >>> print(round(results[\'f1\'], 2))\n        0.33\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average="weighted")\n        >>> print(round(results[\'f1\'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {\'f1\': array([0.8, 0. , 0. ])}\n'


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




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




	'''simple docstring'''




	def      _lowerCamelCase       (      self	:Optional[int]		)	->				Dict:
								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      _lowerCamelCase       (      self	:int  ,     a	:List[Any]  ,     a	:Optional[int]  ,     a	:int=None  ,     a	:Tuple=1  ,     a	:Any="binary"  ,     a	:List[str]=None		)	->				str:
								__UpperCamelCase			:     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}

from __future__ import annotations

lowercase						:      str									= list[tuple[int, int]]

lowercase						:      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],
]

lowercase						:      int									= ([-1, 0], [0, -1], [1, 0], [0, 1])  # up, left, down, right




class 				lowerCamelCase__							:




	'''simple docstring'''




	def __init__(      self	:Optional[int]  ,     a	:int  ,     a	:int  ,     a	:int  ,     a	:int  ,     a	:float  ,     a	:Node | None  ,     )	->				List[Any]:
								__UpperCamelCase			:     List[Any]         =      pos_x
								__UpperCamelCase			:     List[str]         =      pos_y
								__UpperCamelCase			:     str         =      (pos_y, pos_x)
								__UpperCamelCase			:     Optional[int]         =      goal_x
								__UpperCamelCase			:     str         =      goal_y
								__UpperCamelCase			:     int         =      g_cost
								__UpperCamelCase			:     Dict         =      parent
								__UpperCamelCase			:     str         =      self.calculate_heuristic()







	def      _lowerCamelCase       (      self	:List[Any]		)	->				float:
								__UpperCamelCase			:     Any         =      abs(self.pos_x - self.goal_x		)
								__UpperCamelCase			:     Tuple         =      abs(self.pos_y - self.goal_y		)
								return dx + dy







	def __lt__(      self	:Union[str, Any]  ,     a	:Dict		)	->				bool:
								return self.f_cost < other.f_cost




class 				lowerCamelCase__							:




	'''simple docstring'''




	def __init__(      self	:List[str]  ,     a	:tuple[int, int]  ,     a	:tuple[int, int]		)	->				List[str]:
								__UpperCamelCase			:     Any         =      Node(start[1]  ,     start[0]  ,     goal[1]  ,     goal[0]  ,     0  ,     a		)
								__UpperCamelCase			:     List[str]         =      Node(goal[1]  ,     goal[0]  ,     goal[1]  ,     goal[0]  ,     9_9_9_9_9  ,     a		)

								__UpperCamelCase			:     Optional[int]         =      [self.start]
								__UpperCamelCase			:     list[Node]         =      []

								__UpperCamelCase			:     Any         =      False







	def      _lowerCamelCase       (      self	:List[str]		)	->				Path | None:
								while self.open_nodes:
															# Open Nodes are sorted using __lt__
															self.open_nodes.sort()
															__UpperCamelCase			:     Optional[int]         =      self.open_nodes.pop(0		)

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

															self.closed_nodes.append(a		)
															__UpperCamelCase			:     Union[str, 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			:     Union[str, Any]         =      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		)

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







	def      _lowerCamelCase       (      self	:str  ,     a	:Node		)	->				list[Node]:
								__UpperCamelCase			:     List[Any]         =      []
								for action in delta:
															__UpperCamelCase			:     Optional[Any]         =      parent.pos_x + action[1]
															__UpperCamelCase			:     List[Any]         =      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      _lowerCamelCase       (      self	:Optional[Any]  ,     a	:Node | None		)	->				Path:
								__UpperCamelCase			:     str         =      node
								__UpperCamelCase			:     int         =      []
								while current_node is not None:
															path.append((current_node.pos_y, current_node.pos_x)		)
															__UpperCamelCase			:     str         =      current_node.parent
								path.reverse()
								return path


if __name__ == "__main__":
		lowercase						:      List[str]									= (0, 0)
		lowercase						:      int									= (len(grid) - 1, len(grid[0]) - 1)
		for elem in grid:
				print(elem)

		print('------')

		lowercase						:      Any									= GreedyBestFirst(init, goal)
		lowercase						:      List[str]									= greedy_bf.search()
		if path:
				for pos_x, pos_y in path:
						lowercase						:      Optional[int]									= 2

				for elem in grid:
						print(elem)