

"""simple docstring"""




from sklearn.metrics import mean_absolute_error

import datasets


__A	:       Any        =						'\\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'

__A	:       int        =						'\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n'


__A	:       Tuple        =						'\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      __UpperCamelCase					(  datasets.Metric		):


		def    SCREAMING_SNAKE_CASE__  (self					:						Any):
							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    SCREAMING_SNAKE_CASE__  (self					:						Union[str, 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    SCREAMING_SNAKE_CASE__  (self					:						List[Any]	,	__SCREAMING_SNAKE_CASE					:						str	,	__SCREAMING_SNAKE_CASE					:						Optional[Any]	,	__SCREAMING_SNAKE_CASE					:						str=None	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]="uniform_average"):
							A     =   mean_absolute_error(__SCREAMING_SNAKE_CASE	,	__SCREAMING_SNAKE_CASE	,	sample_weight=__SCREAMING_SNAKE_CASE	,	multioutput=__SCREAMING_SNAKE_CASE)

							return {"mae": mae_score}








"""simple docstring"""




from __future__ import annotations
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   {}
					for index, val in enumerate(lowercase__ ):
										A     =   target - val
										if compl in chk_map:
															return [chk_map[compl], index]
										A     =   index
					return []


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(f"""{two_sum([2, 7, 11, 15], 9) = }""")





