



"""simple docstring"""







from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

A    =	{
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       dict    ,		lowerCamelCase_:       str   ):
						"""simple docstring"""






						snake_case      : Union[str, Any]							=   {start}
						snake_case      : Tuple							=   [start]
						snake_case      : Queue							=   Queue()
						queue.put(lowerCamelCase_   )
						while not queue.empty():
												snake_case      : Any							=   queue.get()
												for w in graph[v]:
																		if w not in explored:
																								explored.add(lowerCamelCase_   )
																								result.append(lowerCamelCase_   )
																								queue.put(lowerCamelCase_   )
						return result

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       dict    ,		lowerCamelCase_:       str   ):
						"""simple docstring"""






						snake_case      : List[str]							=   {start}
						snake_case      : int							=   [start]
						snake_case      : int							=   deque([start]   )
						while queue:
												snake_case      : List[Any]							=   queue.popleft()
												for child in graph[v]:
																		if child not in visited:
																								visited.add(lowerCamelCase_   )
																								result.append(lowerCamelCase_   )
																								queue.append(lowerCamelCase_   )
						return result

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       str   ):
						"""simple docstring"""






						snake_case      : Tuple							=   f'''from __main__ import G, {name}'''
						snake_case      : List[Any]							=   1_0_0_0_0
						snake_case      : int							=   timeit(f'''{name}(G, \'A\')'''    ,		setup=lowerCamelCase_    ,		number=lowerCamelCase_   )
						print(f'''{name:<35} finished {number} runs in {res:.5f} seconds'''   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			benchmark_function('breadth_first_search')
			benchmark_function('breadth_first_search_with_deque')
			# breadth_first_search                finished 10000 runs in 0.20999 seconds
			# breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds








"""simple docstring"""







from sklearn.metrics import accuracy_score

import datasets


A    =	'\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n'


A    =	'\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {\'accuracy\': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {\'accuracy\': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric("accuracy")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {\'accuracy\': 0.8778625954198473}\n'


A    =	'\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 			_a     (   datasets.Metric):



			def 							__lowercase				( self       :							List[Any]		)       ->				List[str]:
									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.accuracy_score.html"]      ,  )



			def 							__lowercase				( self       :							str      ,  _lowercase       :							Optional[int]      ,  _lowercase       :							Tuple      ,  _lowercase       :							Union[str, Any]=True      ,  _lowercase       :							int=None		)       ->				Any:
									return {
									    "accuracy": float(
									        accuracy_score(_lowercase      ,  _lowercase      ,  normalize=_lowercase      ,  sample_weight=_lowercase		)		)
									}



