





from scipy.stats import spearmanr

import datasets


lowercase    : Tuple    	=  """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
"""

lowercase    : List[str]    	=  """
Args:
    predictions (`List[float]`): Predicted labels, as returned by a model.
    references (`List[float]`): Ground truth labels.
    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
            only the spearmanr score. Defaults to `False`.
Returns:
    spearmanr (`float`): Spearman correlation coefficient.
    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
    Example 1:
        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
        >>> print(results)
        {'spearmanr': -0.7}

    Example 2:
        >>> spearmanr_metric = datasets.load_metric(\"spearmanr\")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results['spearmanr'])
        -0.7
        >>> print(round(results['spearmanr_pvalue'], 2))
        0.19
"""

lowercase    : int    	=  R"""\
@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}
@article{2020SciPy-NMeth,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
            Computing in Python}},
  journal = {Nature Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
  adsurl  = {https://rdcu.be/b08Wh},
  doi     = {10.1038/s41592-019-0686-2},
}
"""

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,			_KWARGS_DESCRIPTION )
class  __A(						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.spearmanr.html'''],       )



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





            """simple docstring"""

            _UpperCamelCase              =						spearmanr(A,       A      )
            if return_pvalue:
                   return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
            else:
                   return {"spearmanr": results[0]}













from __future__ import annotations

import math


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):

       if 1 < number < 4:
              # 2 and 3 are primes
              return True
       elif number < 2 or number % 2 == 0 or number % 3 == 0:
              # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
              return False

       # All primes number are in format of 6k +/- 1
       for i in range(5    ,       int(math.sqrt(lowerCAmelCase					) + 1					)    ,       6					):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True


lowercase    : Tuple    	=  [num for num in range(3, 10_0001, 2) if not is_prime(num)]


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase					):
       if not isinstance(lowerCAmelCase    ,       lowerCAmelCase					):
              raise ValueError('''n must be an integer'''					)
       if n <= 0:
              raise ValueError('''n must be >= 0'''					)

       _UpperCamelCase              =						[]
       for num in range(len(lowerCAmelCase					)					):
              _UpperCamelCase              =						0
              while 2 * i * i <= odd_composites[num]:
                     _UpperCamelCase              =						odd_composites[num] - 2 * i * i
                     if is_prime(lowerCAmelCase					):
                            break
                     i += 1
              else:
                     list_nums.append(odd_composites[num]					)
                     if len(lowerCAmelCase					) == n:
                            return list_nums

       return []


def     SCREAMING_SNAKE_CASE			(      ):
       return compute_nums(1					)[0]


if __name__ == "__main__":
       print(F'''{solution() = }''')






