






def 		lowercase__  (				_UpperCamelCase) ->			Union[str, Any]:
      """simple docstring"""

      return 1 if digit in (0, 1) else (digit * factorial(digit - 1))
def 		lowercase__  (				_UpperCamelCase) ->			Any:
      """simple docstring"""

      UpperCamelCase					       =  0
      UpperCamelCase					       =  number
      while duplicate > 0:
            UpperCamelCase       ,  UpperCamelCase					       =  divmod(lowerCamelCase_       ,							10)
            fact_sum += factorial(lowerCamelCase_)
      return fact_sum == number


if __name__ == "__main__":
       print('''Program to check whether a number is a Krisnamurthy Number or not.''')
       __magic_name__   :  Any        =							int(input('''Enter number: ''').strip())
       print(
           F'{number} is {"" if krishnamurthy(number) else "not "}a Krishnamurthy Number.'
       )











import numpy as np

import datasets


A__      :     int       =					'\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. Mahalanobis in 1936\nand has been used in various statistical applications ever since\n[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]\n'

A__      :     Optional[int]       =					'\\n@article{de2000mahalanobis,\n  title={The mahalanobis distance},\n  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},\n  journal={Chemometrics and intelligent laboratory systems},\n  volume={50},\n  number={1},\n  pages={1--18},\n  year={2000},\n  publisher={Elsevier}\n}\n'

A__      :     Optional[int]       =					'\nArgs:\n    X: List of datapoints to be compared with the `reference_distribution`.\n    reference_distribution: List of datapoints from the reference distribution we want to compare to.\nReturns:\n    mahalanobis: The Mahalonobis distance for each datapoint in `X`.\nExamples:\n\n    >>> mahalanobis_metric = datasets.load_metric("mahalanobis")\n    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])\n    >>> print(results)\n    {\'mahalanobis\': array([0.5])}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,_KWARGS_DESCRIPTION      )
class  _UpperCAmelCase   (			datasets.Metric      ):

				"""simple docstring"""




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								return datasets.MetricInfo(
								    description=_DESCRIPTION,    citation=_CITATION,    inputs_description=_KWARGS_DESCRIPTION,    features=datasets.Features(
								        {
								            '''X''': datasets.Sequence(datasets.Value('''float''',    id='''sequence'''    ),    id='''X'''    ),
								        }    ),    )




				def 							lowercase__  (	self		:       Dict,    lowerCamelCase		:       int,    lowerCamelCase		:       Optional[int]    ):

								'''simple docstring'''
								# convert to numpy arrays
								lowercase__  					=     np.array(lowerCamelCase    )
								lowercase__  					=     np.array(lowerCamelCase    )

								# Assert that arrays are 2D
								if len(X.shape    ) != 2:
												raise ValueError('''Expected `X` to be a 2D vector'''    )
								if len(reference_distribution.shape    ) != 2:
												raise ValueError('''Expected `reference_distribution` to be a 2D vector'''    )
								if reference_distribution.shape[0] < 2:
												raise ValueError(
												    '''Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension'''    )

								# Get mahalanobis distance for each prediction
								lowercase__  					=     X - np.mean(lowerCamelCase    )
								lowercase__  					=     np.cov(reference_distribution.T    )
								try:
												lowercase__  					=     np.linalg.inv(lowerCamelCase    )
								except np.linalg.LinAlgError:
												lowercase__  					=     np.linalg.pinv(lowerCamelCase    )
								lowercase__  					=     np.dot(lowerCamelCase,    lowerCamelCase    )
								lowercase__  					=     np.dot(lowerCamelCase,    X_minus_mu.T    ).diagonal()

								return {"mahalanobis": mahal_dist}




