






'''simple docstring'''





from sklearn.metrics import mean_absolute_error

import datasets


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


a_      = '\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 		SCREAMING_SNAKE_CASE__   (     datasets.Metric    ):



  def _lowerCAmelCase     (    self:    Optional[int])  ->int:






        '''simple docstring'''


        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:    int)  ->str:






        '''simple docstring'''


        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:    List[str]				,   a:    Any				,   a:    Optional[Any]				,   a:    Dict=None				,   a:    List[str]="uniform_average")  ->Dict:






        '''simple docstring'''


        a_				     =   mean_absolute_error(a				,   a				,   sample_weight=a				,   multioutput=a)

        return {"mae": mae_score}











'''simple docstring'''





from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				torch.Tensor:

						'''simple docstring'''


						if not inplace:
												a_				     =   ma + ma
						else:
												ma += ma

						return ma



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				torch.Tensor:

						'''simple docstring'''


						a_				     =   -1 * len(lowercase__				)
						a_				     =   list(range(len(tensor.shape[:zero_index]				)				)				)
						return tensor.permute(first_inds + [zero_index + i for i in inds]				)



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				torch.Tensor:

						'''simple docstring'''


						return t.reshape(t.shape[:-no_dims] + (-1,)				)



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__	,lowercase__ = 1e-4				)		->				torch.Tensor:

						'''simple docstring'''


						a_				     =   mask.expand(*value.shape				)
						return torch.sum(mask * value	,dim=lowercase__				) / (eps + torch.sum(lowercase__	,dim=lowercase__				))



def 			__UpperCAmelCase				(lowercase__	,lowercase__ = 2.3125	,lowercase__ = 21.6875	,lowercase__ = 64				)		->				torch.Tensor:

						'''simple docstring'''


						a_				     =   torch.linspace(lowercase__	,lowercase__	,no_bins - 1	,device=pts.device				)
						a_				     =   torch.sqrt(torch.sum((pts.unsqueeze(-2				) - pts.unsqueeze(-3				)) ** 2	,dim=-1				)				)
						return torch.bucketize(lowercase__	,lowercase__				)



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				dict:

						'''simple docstring'''


						a_				     =   dicts[0]
						a_				     =   {}
						for k, v in first.items():
												a_				     =   [d[k] for d in dicts]
												if isinstance(lowercase__	,lowercase__				):
																		a_				     =   dict_multimap(lowercase__	,lowercase__				)
												else:
																		a_				     =   fn(lowercase__				)

						return new_dict



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				torch.Tensor:

						'''simple docstring'''


						a_				     =   v_bins.view(((1,) * len(x.shape				)) + (len(lowercase__				),)				)
						a_				     =   x[..., None] - reshaped_bins
						a_				     =   torch.argmin(torch.abs(lowercase__				)	,dim=-1				)
						return nn.functional.one_hot(lowercase__	,num_classes=len(lowercase__				)				).float()



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__ = 0	,lowercase__ = 0				)		->				torch.Tensor:

						'''simple docstring'''


						a_				     =   []
						for i, s in enumerate(data.shape[:no_batch_dims]				):
												a_				     =   torch.arange(lowercase__				)
												a_				     =   r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape				) - i - 1)))				)
												ranges.append(lowercase__				)

						a_				     =   [slice(lowercase__				) for _ in range(len(data.shape				) - no_batch_dims				)]
						a_				     =   inds
						ranges.extend(lowercase__				)
						# Matt note: Editing this to get around the behaviour of using a list as an array index changing
						# in recent Numpy versions
						return data[tuple(lowercase__				)]


a_      = TypeVar('T')



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				Dict[Any, Union[dict, list, tuple, Any]]:

						'''simple docstring'''


						a_				     =   {}
						for k, v in dic.items():
												if isinstance(lowercase__	,lowercase__				):
																		a_				     =   dict_map(lowercase__	,lowercase__	,lowercase__				)
												else:
																		a_				     =   tree_map(lowercase__	,lowercase__	,lowercase__				)

						return new_dict



@overload
def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				Any:

						'''simple docstring'''


						...



@overload
def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				dict:

						'''simple docstring'''


						...



@overload
def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				list:

						'''simple docstring'''


						...



@overload
def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				tuple:

						'''simple docstring'''


						...



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__				)		->				List[Any]:

						'''simple docstring'''


						if isinstance(lowercase__	,lowercase__				):
												return dict_map(lowercase__	,lowercase__	,lowercase__				)
						elif isinstance(lowercase__	,lowercase__				):
												return [tree_map(lowercase__	,lowercase__	,lowercase__				) for x in tree]
						elif isinstance(lowercase__	,lowercase__				):
												return tuple(tree_map(lowercase__	,lowercase__	,lowercase__				) for x in tree				)
						elif isinstance(lowercase__	,lowercase__				):
												return fn(lowercase__				)
						else:
												print(type(lowercase__				)				)
												raise ValueError("Not supported"				)
a_      = partial(tree_map, leaf_type=torch.Tensor)



