from sklearn.metrics import mean_squared_error

import datasets


SCREAMING_SNAKE_CASE :	Tuple							     =		"""\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
"""

SCREAMING_SNAKE_CASE :	str							     =		"""\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


SCREAMING_SNAKE_CASE :	Tuple							     =		"""
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        \"raw_values\" : Returns a full set of errors in case of multioutput input.

        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric(\"mse\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {'mse': 0.6123724356957945}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mse': array([0.41666667, 1. ])}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,       _KWARGS_DESCRIPTION )
class    A_ (       datasets.Metric ):

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   return datasets.MetricInfo(
       description=_DESCRIPTION     ,							citation=_CITATION     ,							inputs_description=_KWARGS_DESCRIPTION     ,							features=datasets.Features(self._get_feature_types()       )     ,							reference_urls=[
           "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
       ]     ,							)

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   if self.config_name == "multilist":
    return {
        "predictions": datasets.Sequence(datasets.Value("float"       )       ),
        "references": datasets.Sequence(datasets.Value("float"       )       ),
    }
   else:
    return {
        "predictions": datasets.Value("float"       ),
        "references": datasets.Value("float"       ),
    }

  def 		_UpperCAmelCase   (		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      str=None     ,							__SCREAMING_SNAKE_CASE :      Tuple="uniform_average"     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=True       ):
   __a       =  mean_squared_error(
       __SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							sample_weight=__SCREAMING_SNAKE_CASE     ,							multioutput=__SCREAMING_SNAKE_CASE     ,							squared=__SCREAMING_SNAKE_CASE       )

   return {"mse": mse}



def   __A		(			_A					):



 """simple docstring"""


 __a       =  []
 for data in source_data:
  for i, el in enumerate(_A					):
   if len(_A					) < i + 1:
    data_lists.append([]					)
   data_lists[i].append(float(_A					)					)
 return data_lists



def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  []
 for dlist, weight in zip(_A							,				_A					):
  __a       =  min(_A					)
  __a       =  max(_A					)

  __a       =  []
  # for weight 0 score is 1 - actual score
  if weight == 0:
   for item in dlist:
    try:
     score.append(1 - ((item - mind) / (maxd - mind))					)
    except ZeroDivisionError:
     score.append(1					)

  elif weight == 1:
   for item in dlist:
    try:
     score.append((item - mind) / (maxd - mind)					)
    except ZeroDivisionError:
     score.append(0					)

        # weight not 0 or 1
  else:
   __a       =  f"""Invalid weight of {weight:f} provided"""
   raise ValueError(_A					)

  score_lists.append(_A					)

 return score_lists



def   __A		(			_A					):



 """simple docstring"""


 __a       =  [0 for i in range(len(score_lists[0]					)					)]

 for slist in score_lists:
  for j, ele in enumerate(_A					):
   __a       =  final_scores[j] + ele

 return final_scores






def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  get_data(_A					)
 __a       =  calculate_each_score(_A							,				_A					)
 __a       =  generate_final_scores(_A					)

 # append scores to source data
 for i, ele in enumerate(_A					):
  source_data[i].append(_A					)

 return source_data


