

from __future__ import annotations

from math import pi, pow


def 			_UpperCAmelCase							(		a__):

			'''simple docstring'''



			if side_length < 0:
						raise ValueError("""vol_cube() only accepts non-negative values""")
			return pow(a__       ,					3)


def 			_UpperCAmelCase							(		a__       ,					a__):

			'''simple docstring'''



			if height < 0 or radius < 0:
						raise ValueError("""vol_spherical_cap() only accepts non-negative values""")
			# Volume is 1/3 pi * height squared * (3 * radius - height)
			return 1 / 3 * pi * pow(a__       ,					2) * (3 * radius - height)


def 			_UpperCAmelCase							(		a__       ,					a__       ,					a__):

			'''simple docstring'''



			if radius_a < 0 or radius_a < 0 or centers_distance < 0:
						raise ValueError("""vol_spheres_intersect() only accepts non-negative values""")
			if centers_distance == 0:
						return vol_sphere(min(a__       ,					a__))

			a_		:		Optional[Any]				    =				(
			    (radius_a - radius_a + centers_distance)
			    * (radius_a + radius_a - centers_distance)
			    / (2 * centers_distance)
			)
			a_		:		Dict				    =				(
			    (radius_a - radius_a + centers_distance)
			    * (radius_a + radius_a - centers_distance)
			    / (2 * centers_distance)
			)

			return vol_spherical_cap(a__       ,					a__) + vol_spherical_cap(a__       ,					a__)


def 			_UpperCAmelCase							(		a__       ,					a__       ,					a__):

			'''simple docstring'''



			if radius_a <= 0 or radius_a <= 0 or centers_distance < 0:
						raise ValueError(
						    """vol_spheres_union() only accepts non-negative values, non-zero radius""")

			if centers_distance == 0:
						return vol_sphere(max(a__       ,					a__))

			return (
			    vol_sphere(a__)
			    + vol_sphere(a__)
			    - vol_spheres_intersect(a__       ,					a__       ,					a__)
			)


def 			_UpperCAmelCase							(		a__       ,					a__       ,					a__):

			'''simple docstring'''



			if width < 0 or height < 0 or length < 0:
						raise ValueError("""vol_cuboid() only accepts non-negative values""")
			return float(width * height * length)


def 			_UpperCAmelCase							(		a__       ,					a__):

			'''simple docstring'''



			if height < 0 or area_of_base < 0:
						raise ValueError("""vol_cone() only accepts non-negative values""")
			return area_of_base * height / 3.0


def 			_UpperCAmelCase							(		a__       ,					a__):

			'''simple docstring'''



			if height < 0 or radius < 0:
						raise ValueError("""vol_right_circ_cone() only accepts non-negative values""")
			return pi * pow(a__       ,					2) * height / 3.0


def 			_UpperCAmelCase							(		a__       ,					a__):

			'''simple docstring'''



			if height < 0 or area_of_base < 0:
						raise ValueError("""vol_prism() only accepts non-negative values""")
			return float(area_of_base * height)


def 			_UpperCAmelCase							(		a__       ,					a__):

			'''simple docstring'''



			if height < 0 or area_of_base < 0:
						raise ValueError("""vol_pyramid() only accepts non-negative values""")
			return area_of_base * height / 3.0


def 			_UpperCAmelCase							(		a__):

			'''simple docstring'''



			if radius < 0:
						raise ValueError("""vol_sphere() only accepts non-negative values""")
			# Volume is 4/3 * pi * radius cubed
			return 4 / 3 * pi * pow(a__       ,					3)


def 			_UpperCAmelCase							(		a__):

			'''simple docstring'''



			if radius < 0:
						raise ValueError("""vol_hemisphere() only accepts non-negative values""")
			# Volume is radius cubed * pi * 2/3
			return pow(a__       ,					3) * pi * 2 / 3


def 			_UpperCAmelCase							(		a__       ,					a__):

			'''simple docstring'''



			if height < 0 or radius < 0:
						raise ValueError("""vol_circular_cylinder() only accepts non-negative values""")
			# Volume is radius squared * height * pi
			return pow(a__       ,					2) * height * pi


def 			_UpperCAmelCase							(		a__       ,					a__       ,					a__):

			'''simple docstring'''



			if inner_radius < 0 or outer_radius < 0 or height < 0:
						raise ValueError(
						    """vol_hollow_circular_cylinder() only accepts non-negative values""")
			if outer_radius <= inner_radius:
						raise ValueError("""outer_radius must be greater than inner_radius""")
			return pi * (pow(a__       ,					2) - pow(a__       ,					2)) * height


def 			_UpperCAmelCase							(		a__       ,					a__       ,					a__):

			'''simple docstring'''



			if radius_a < 0 or radius_a < 0 or height < 0:
						raise ValueError("""vol_conical_frustum() only accepts non-negative values""")
			return (
			    1
			    / 3
			    * pi
			    * height
			    * (pow(a__       ,					2) + pow(a__       ,					2) + radius_a * radius_a)
			)


def 			_UpperCAmelCase							(		a__       ,					a__):

			'''simple docstring'''



			if torus_radius < 0 or tube_radius < 0:
						raise ValueError("""vol_torus() only accepts non-negative values""")
			return 2 * pow(a__       ,					2) * torus_radius * pow(a__       ,					2)


def 			_UpperCAmelCase							(		):

			'''simple docstring'''



			print("""Volumes:""")
			print(f'''Cube: {vol_cube(2) = }''')  # = 8
			print(f'''Cuboid: {vol_cuboid(2       ,					2       ,					2) = }''')  # = 8
			print(f'''Cone: {vol_cone(2       ,					2) = }''')  # ~= 1.33
			print(f'''Right Circular Cone: {vol_right_circ_cone(2       ,					2) = }''')  # ~= 8.38
			print(f'''Prism: {vol_prism(2       ,					2) = }''')  # = 4
			print(f'''Pyramid: {vol_pyramid(2       ,					2) = }''')  # ~= 1.33
			print(f'''Sphere: {vol_sphere(2) = }''')  # ~= 33.5
			print(f'''Hemisphere: {vol_hemisphere(2) = }''')  # ~= 16.75
			print(f'''Circular Cylinder: {vol_circular_cylinder(2       ,					2) = }''')  # ~= 25.1
			print(f'''Torus: {vol_torus(2       ,					2) = }''')  # ~= 157.9
			print(f'''Conical Frustum: {vol_conical_frustum(2       ,					2       ,					4) = }''')  # ~= 58.6
			print(f'''Spherical cap: {vol_spherical_cap(1       ,					2) = }''')  # ~= 5.24
			print(f'''Spheres intersetion: {vol_spheres_intersect(2       ,					2       ,					1) = }''')  # ~= 21.21
			print(f'''Spheres union: {vol_spheres_union(2       ,					2       ,					1) = }''')  # ~= 45.81
			print(
			    f'''Hollow Circular Cylinder: {vol_hollow_circular_cylinder(1       ,					2       ,					3) = }''')  # ~= 28.3


if __name__ == "__main__":
	main()








from sklearn.metrics import mean_absolute_error

import datasets


__snake_case							:     List[Any] 						=	"""\
@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}
}
"""

__snake_case							:     List[Any] 						=	"""\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


__snake_case							:     str 						=	"""
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 	UpperCamelCase__			(  self  )      ->				List[str]:
									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 	UpperCamelCase__			(  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 	UpperCamelCase__			(  self       ,     _lowercase       ,     _lowercase       ,     _lowercase=None       ,     _lowercase="uniform_average"  )      ->				Union[str, Any]:
									a_		:		Any				    =				mean_absolute_error(_lowercase       ,     _lowercase       ,     sample_weight=_lowercase       ,     multioutput=_lowercase  )

									return {"mae": mae_score}


