




"""simple docstring"""

from __future__ import annotations

from sys import maxsize
from typing import Generic, TypeVar

a         =      TypeVar('''T''')






def 						_snake_case					( _snake_case							: int							)   -> int:






      '''simple docstring'''


      return (position - 1) // 2






def 						_snake_case					( _snake_case							: int							)   -> int:






      '''simple docstring'''


      return (2 * position) + 1






def 						_snake_case					( _snake_case							: int							)   -> int:






      '''simple docstring'''


      return (2 * position) + 2





class     lowercase_	(					Generic[T]							):



 '''simple docstring'''

 def __init__(			self	:	int		):
       _A        =       []
       _A        =       {}
       _A        =       0

 def __len__(			self	:	str		):
       return self.elements

 def __repr__(			self	:	List[str]		):
       return str(self.heap		)

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       # Check if the priority queue is empty
       return self.elements == 0

 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	T   ,							_UpperCAmelCase	:	int		):
       # Add an element with given priority to the queue
       self.heap.append((elem, weight)		)
       _A        =       self.elements
       self.elements += 1
       self._bubble_up(_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       # Remove and return the element with lowest weight (highest priority)
       if self.elements > 1:
             self._swap_nodes(0   ,							self.elements - 1		)
       _A       ,   _A        =       self.heap.pop()
       del self.position_map[elem]
       self.elements -= 1
       if self.elements > 0:
             _A       ,   _A        =       self.heap[0]
             self._bubble_down(_UpperCAmelCase		)
       return elem

 def       lowerCAmelCase_							(			self	:	int   ,							_UpperCAmelCase	:	T   ,							_UpperCAmelCase	:	int		):
       # Update the weight of the given key
       _A        =       self.position_map[elem]
       _A        =       (elem, weight)
       if position > 0:
             _A        =       get_parent_position(_UpperCAmelCase		)
             _A       ,   _A        =       self.heap[parent_position]
             if parent_weight > weight:
                   self._bubble_up(_UpperCAmelCase		)
             else:
                   self._bubble_down(_UpperCAmelCase		)
       else:
             self._bubble_down(_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	T		):
       # Place a node at the proper position (upward movement) [to be used internally
       # only]
       _A        =       self.position_map[elem]
       if curr_pos == 0:
             return None
       _A        =       get_parent_position(_UpperCAmelCase		)
       _A       ,   _A        =       self.heap[curr_pos]
       _A       ,   _A        =       self.heap[parent_position]
       if parent_weight > weight:
             self._swap_nodes(_UpperCAmelCase   ,							_UpperCAmelCase		)
             return self._bubble_up(_UpperCAmelCase		)
       return None

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	T		):
       # Place a node at the proper position (downward movement) [to be used
       # internally only]
       _A        =       self.position_map[elem]
       _A       ,   _A        =       self.heap[curr_pos]
       _A        =       get_child_left_position(_UpperCAmelCase		)
       _A        =       get_child_right_position(_UpperCAmelCase		)
       if child_left_position < self.elements and child_right_position < self.elements:
             _A       ,   _A        =       self.heap[child_left_position]
             _A       ,   _A        =       self.heap[child_right_position]
             if child_right_weight < child_left_weight and child_right_weight < weight:
                   self._swap_nodes(_UpperCAmelCase   ,							_UpperCAmelCase		)
                   return self._bubble_down(_UpperCAmelCase		)
       if child_left_position < self.elements:
             _A       ,   _A        =       self.heap[child_left_position]
             if child_left_weight < weight:
                   self._swap_nodes(_UpperCAmelCase   ,							_UpperCAmelCase		)
                   return self._bubble_down(_UpperCAmelCase		)
       else:
             return None
       if child_right_position < self.elements:
             _A       ,   _A        =       self.heap[child_right_position]
             if child_right_weight < weight:
                   self._swap_nodes(_UpperCAmelCase   ,							_UpperCAmelCase		)
                   return self._bubble_down(_UpperCAmelCase		)
       return None



 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	int		):
       # Swap the nodes at the given positions
       _A        =       self.heap[nodea_pos][0]
       _A        =       self.heap[nodea_pos][0]
       _A       ,   _A        =       (
           self.heap[nodea_pos],
           self.heap[nodea_pos],
       )
       _A        =       nodea_pos
       _A        =       nodea_pos





class     lowercase_	(					Generic[T]							):



 '''simple docstring'''

 def __init__(			self	:	Union[str, Any]		):
       _A        =       {}
       _A        =       0

 def __repr__(			self	:	List[str]		):
       return str(self.connections		)

 def __len__(			self	:	Union[str, Any]		):
       return self.nodes

 def       lowerCAmelCase_							(			self	:	List[Any]   ,							_UpperCAmelCase	:	T		):
       # Add a node in the graph if it is not in the graph
       if node not in self.connections:
             _A        =       {}
             self.nodes += 1



 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	T   ,							_UpperCAmelCase	:	T   ,							_UpperCAmelCase	:	int		):
       # Add an edge between 2 nodes in the graph
       self.add_node(_UpperCAmelCase		)
       self.add_node(_UpperCAmelCase		)
       _A        =       weight
       _A        =       weight






def 						_snake_case					( _snake_case							: GraphUndirectedWeighted[T]    ,  )   -> tuple[dict[T, int], dict[T, T | None]]:






      '''simple docstring'''


      _A        =       {node: maxsize for node in graph.connections}
      _A        =       {node: None for node in graph.connections}

      _A        =       MinPriorityQueue()
      for node, weight in dist.items():
            priority_queue.push(_snake_case    ,  _snake_case							)

      if priority_queue.is_empty():
            return dist, parent

      # initialization
      _A        =       priority_queue.extract_min()
      _A        =       0
      for neighbour in graph.connections[node]:
            if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                  _A        =       dist[node] + graph.connections[node][neighbour]
                  priority_queue.update_key(_snake_case    ,  dist[neighbour]							)
                  _A        =       node

    # running prim's algorithm
      while not priority_queue.is_empty():
            _A        =       priority_queue.extract_min()
            for neighbour in graph.connections[node]:
                  if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                        _A        =       dist[node] + graph.connections[node][neighbour]
                        priority_queue.update_key(_snake_case    ,  dist[neighbour]							)
                        _A        =       node
      return dist, parent







"""simple docstring"""

from sklearn.metrics import fa_score

import datasets


a         =      '''
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)
'''


a         =      '''
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. ])}
'''


a         =      '''
@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     lowercase_	(					datasets.Metric							):



 '''simple docstring'''

 def       lowerCAmelCase_							(			self	:	Any		):
       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   ,							_UpperCAmelCase	:	Any   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	List[str]=None   ,							_UpperCAmelCase	:	Dict=1   ,							_UpperCAmelCase	:	Union[str, Any]="binary"   ,							_UpperCAmelCase	:	List[str]=None		):
       _A        =       fa_score(
           _UpperCAmelCase   ,							_UpperCAmelCase   ,							labels=_UpperCAmelCase   ,							pos_label=_UpperCAmelCase   ,							average=_UpperCAmelCase   ,							sample_weight=_UpperCAmelCase		)
       return {"f1": float(_UpperCAmelCase		) if score.size == 1 else score}

