
'''simple docstring'''






from scipy.stats import pearsonr

import datasets


A			      =      "\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.\nThe p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.\n"


A			      =      "\nArgs:\n    predictions (`list` of `int`): Predicted class labels, as returned by a model.\n    references (`list` of `int`): Ground truth labels.\n    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.\n\nReturns:\n    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.\n    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.\n\nExamples:\n\n    Example 1-A simple example using only predictions and references.\n        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])\n        >>> print(round(results['pearsonr'], 2))\n        -0.74\n\n    Example 2-The same as Example 1, but that also returns the `p-value`.\n        >>> pearsonr_metric = datasets.load_metric(\"pearsonr\")\n        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)\n        >>> print(sorted(list(results.keys())))\n        ['p-value', 'pearsonr']\n        >>> print(round(results['pearsonr'], 2))\n        -0.74\n        >>> print(round(results['p-value'], 2))\n        0.15\n"


A			      =      "\n@article{2020SciPy-NMeth,\nauthor  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n      Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n      Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n      Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n      Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n      Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n      Kern, Robert and Larson, Eric and Carey, C J and\n      Polat, Ilhan and Feng, Yu and Moore, Eric W. and\n      {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n      Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n      Harris, Charles R. and Archibald, Anne M. and\n      Ribeiro, Antonio H. and Pedregosa, Fabian and\n      {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\ntitle   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n      Computing in Python}},\njournal = {Nature Methods},\nyear    = {2020},\nvolume  = {17},\npages   = {261--272},\nadsurl  = {https://rdcu.be/b08Wh},\ndoi = {10.1038/s41592-019-0686-2},\n}\n"




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,   _KWARGS_DESCRIPTION)
class     __snake_case		(   datasets.Metric):
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return datasets.MetricInfo(
									    description=_DESCRIPTION,    citation=_CITATION,    inputs_description=_KWARGS_DESCRIPTION,    features=datasets.Features(
									        {
									            'predictions': datasets.Value('float'      ),
									            'references': datasets.Value('float'      ),
									        }      ),    reference_urls=['https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.pearsonr.html'],    )




					def     UpperCAmelCase_							(     self,    A,    A,    A=False      ):

									"""simple docstring"""


									if return_pvalue:
													lowerCamelCase					:			Optional[Any]									= pearsonr(lowercase_,    lowercase_      )
													return {"pearsonr": results[0], "p-value": results[1]}
									else:
													return {"pearsonr": float(pearsonr(lowercase_,    lowercase_      )[0]      )}


'''simple docstring'''






A			      =      {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
A			      =      ['a', 'b', 'c', 'd', 'e']





def    UpperCAmelCase							(  UpperCAmelCase__							:							Union[str, Any]       ,							UpperCAmelCase__							:							Any       ,							UpperCAmelCase__							:							List[Any]):
				lowerCamelCase					:			List[Any]									= start
				# add current to visited
				visited.append(UpperCAmelCase__)
				lowerCamelCase					:			List[str]									= edges[current]
				for neighbor in neighbors:
								# if neighbor not in visited, visit
								if neighbor not in visited:
												lowerCamelCase					:			Any									= topological_sort(UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)
    # if all neighbors visited add current to sort
				sort.append(UpperCAmelCase__)
				# if all vertices haven't been visited select a new one to visit
				if len(UpperCAmelCase__) != len(UpperCAmelCase__):
								for vertice in vertices:
												if vertice not in visited:
																lowerCamelCase					:			List[str]									= topological_sort(UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)
    # return sort
				return sort


if __name__ == "__main__":
					A			      =      topological_sort('a', [], [])
					print(sort)
