


'''simple docstring'''
from sklearn.metrics import mean_absolute_error

import datasets


lowerCAmelCase__  =     """\
@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}
}
"""

lowerCAmelCase__  =     """\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


lowerCAmelCase__  =     """
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.

Returns:
    mae : mean absolute error.
        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.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric(\"mae\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.5}

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

    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results)
    {'mae': array([0.5, 1. ])}
"""







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



   '''simple docstring'''






   def   __lowerCAmelCase						(	self	:					List[str]						)->	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]						)->	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   __lowerCAmelCase						(	self	:					Optional[int]					, lowerCamelCase	:					Optional[Any]					, lowerCamelCase	:					Optional[int]					, lowerCamelCase	:					List[Any]=None					, lowerCamelCase	:					Any="uniform_average"						)->	Dict:
          snake_case__					:	int          =					mean_absolute_error(__lowercase					, __lowercase					, sample_weight=__lowercase					, multioutput=__lowercase						)

          return {"mae": mae_score}








import doctest

import projectq
from projectq.ops import H, Measure



def       lowerCAmelCase			(		lowerCAmelCase_			)->			int:
				lowerCAmelCase_			:   List[Any]     					=					quantum_engine.allocate_qubit()
				H | qubit
				Measure | qubit
				return int(lowerCAmelCase_			)


if __name__ == "__main__":
						doctest.testmod()

						# initialises a new quantum backend
						_UpperCAmelCase      :	str  =projectq.MainEngine()

						# Generate a list of 10 random numbers
						_UpperCAmelCase      :	Optional[int]  =[get_random_number(quantum_engine) for _ in range(10)]

						# Flushes the quantum engine from memory
						quantum_engine.flush()

						print("""Random numbers""", random_numbers_list)