

import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


lowercase_					     =	{
    """<""": operator.lt,
    """<=""": operator.le,
    """==""": operator.eq,
    """!=""": operator.ne,
    """>=""": operator.ge,
    """>""": operator.gt,
}




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Optional[int]:
  if got_ver is None or want_ver is None:
    raise ValueError(
        F"""Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"""
        F""" reinstalling {pkg}."""	)
  if not ops[op](version.parse(_SCREAMING_SNAKE_CASE	)	,		version.parse(_SCREAMING_SNAKE_CASE	)	):
    raise ImportError(
        F"""{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}"""	)




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None	)       ->     None:
  lowercase__			    =						F"""\n{hint}""" if hint is not None else ''

  # non-versioned check
  if re.match(R'^[\w_\-\d]+$'	,		_SCREAMING_SNAKE_CASE	):
    lowercase__    ,					lowercase__    ,					lowercase__			    =						requirement, None, None
  else:
    lowercase__			    =						re.findall(R'^([^!=<>\s]+)([\s!=<>]{1,2}.+)'	,		_SCREAMING_SNAKE_CASE	)
    if not match:
      raise ValueError(
          'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'
          F""" got {requirement}"""	)
    lowercase__    ,					lowercase__			    =						match[0]
    lowercase__			    =						want_full.split(','	)  # there could be multiple requirements
    lowercase__			    =						{}
    for w in want_range:
      lowercase__			    =						re.findall(R'^([\s!=<>]{1,2})(.+)'	,		_SCREAMING_SNAKE_CASE	)
      if not match:
        raise ValueError(
            'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'
            F""" but got {requirement}"""	)
      lowercase__    ,					lowercase__			    =						match[0]
      lowercase__			    =						want_ver
      if op not in ops:
        raise ValueError(F"""{requirement}: need one of {list(ops.keys()	)}, but got {op}"""	)

    # special case
  if pkg == "python":
    lowercase__			    =						'.'.join([str(_SCREAMING_SNAKE_CASE	) for x in sys.version_info[:3]]	)
    for op, want_ver in wanted.items():
      _compare_versions(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)
    return

  # check if any version is installed
  try:
    lowercase__			    =						importlib.metadata.version(_SCREAMING_SNAKE_CASE	)
  except importlib.metadata.PackageNotFoundError:
    raise importlib.metadata.PackageNotFoundError(
        F"""The '{requirement}' distribution was not found and is required by this application. {hint}"""	)

  # check that the right version is installed if version number or a range was provided
  if want_ver is not None:
    for op, want_ver in wanted.items():
      _compare_versions(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Optional[Any]:
  lowercase__			    =						'Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'
  return require_version(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)









from scipy.stats import spearmanr

import datasets


lowercase_					     =	"""
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_					     =	"""
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_					     =	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        SCREAMING_SNAKE_CASE     (datasets.Metric   ):


   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     Tuple:





     """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  SCREAMING_SNAKE_CASE_							(					self    :       int    ,  a    :       str    ,  a    :       Any    ,  a    :       str=False )->     Optional[int]:





     """simple docstring"""







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


