


"""simple docstring"""





from sklearn.metrics import mean_absolute_error

import datasets


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

__UpperCAmelCase    				=      "\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n"


__UpperCAmelCase    				=      "\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n                 \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\nReturns:\n    mae : mean absolute error.\n        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric(\"mae\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mae': 0.5}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mae': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results)\n    {'mae': array([0.5, 1. ])}\n"

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,							_KWARGS_DESCRIPTION						)
class  lowercase_  ( datasets.Metric						):







      def 				_lowerCAmelCase       (      self :							Dict    ):
        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_absolute_error.html"
            ]		,  )







      def 				_lowerCAmelCase       (      self :							Optional[Any]    ):
        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 				_lowerCAmelCase       (      self :							str		,  _lowercase :							Optional[int]		,  _lowercase :							Union[str, Any]		,  _lowercase :							int=None		,  _lowercase :							List[str]="uniform_average"    ):
        lowerCAmelCase__    :    List[str]								=					mean_absolute_error(_lowercase		,  _lowercase		,  sample_weight=_lowercase		,  multioutput=_lowercase    )

        return {"mae": mae_score}




"""simple docstring"""





from __future__ import annotations





def     lowercase__	(  lowerCamelCase     :  list				,	lowerCamelCase     :  list				)					->     list:
  lowerCAmelCase__    :    Dict								=					[None] * (len(lowerCamelCase				) + len(lowerCamelCase				))

  lowerCAmelCase__    :    Optional[Any]								=					0  # pointer to current index for left Half
  lowerCAmelCase__    :    Any								=					0  # pointer to current index for the right Half
  lowerCAmelCase__    :    Dict								=					0  # pointer to current index for the sorted array Half

  while pointera < len(lowerCamelCase				) and pointera < len(lowerCamelCase				):
    if left_half[pointera] < right_half[pointera]:
      lowerCAmelCase__    :    Tuple								=					left_half[pointera]
      pointera += 1
      index += 1
    else:
      lowerCAmelCase__    :    int								=					right_half[pointera]
      pointera += 1
      index += 1
  while pointera < len(lowerCamelCase				):
    lowerCAmelCase__    :    Tuple								=					left_half[pointera]
    pointera += 1
    index += 1

  while pointera < len(lowerCamelCase				):
    lowerCAmelCase__    :    Optional[int]								=					right_half[pointera]
    pointera += 1
    index += 1

  return sorted_array





def     lowercase__	(  lowerCamelCase     :  list				)					->     list:
  if len(lowerCamelCase				) <= 1:
    return array
  # the actual formula to calculate the middle element = left + (right - left) // 2
  # this avoids integer overflow in case of large N
  lowerCAmelCase__    :    Optional[int]								=					0 + (len(lowerCamelCase				) - 0) // 2

  # Split the array into halves till the array length becomes equal to One
  # merge the arrays of single length returned by mergeSort function and
  # pass them into the merge arrays function which merges the array
  lowerCAmelCase__    :    Union[str, Any]								=					array[:middle]
  lowerCAmelCase__    :    List[str]								=					array[middle:]

  return merge(merge_sort(lowerCamelCase				)				,	merge_sort(lowerCamelCase				)				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
