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dS )    )AnyN)Tensortensor)_mean_absolute_error_compute_mean_absolute_error_update)Metricc                       s   e Zd ZU dZdZeed< dZeed< dZeed< e	ed< e	ed< d	e
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e	fddZ  ZS )MeanAbsoluteErrora  `Computes Mean Absolute Error`_ (MAE):

    .. math:: \text{MAE} = \frac{1}{N}\sum_i^N | y_i - \hat{y_i} |

    Where :math:`y` is a tensor of target values, and :math:`\hat{y}` is a tensor of predictions.

    As input to ``forward`` and ``update`` the metric accepts the following input:

    - ``preds`` (:class:`~torch.Tensor`): Predictions from model
    - ``target`` (:class:`~torch.Tensor`): Ground truth values

    As output of ``forward`` and ``compute`` the metric returns the following output:

    - ``mean_absolute_error`` (:class:`~torch.Tensor`): A tensor with the mean absolute error over the state

    Args:
        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

    Example:
        >>> from torchmetrics import MeanAbsoluteError
        >>> target = torch.tensor([3.0, -0.5, 2.0, 7.0])
        >>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0])
        >>> mean_absolute_error = MeanAbsoluteError()
        >>> mean_absolute_error(preds, target)
        tensor(0.5000)
    Tis_differentiableFhigher_is_betterfull_state_updatesum_abs_errortotalkwargsreturnNc                    s>   t  jdi | | jdtddd | jdtddd d S )Nr   g        sum)defaultdist_reduce_fxr   r    )super__init__	add_stater   )selfr   	__class__r   O/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/regression/mae.pyr   8   s   zMeanAbsoluteError.__init__predstargetc                 C   s.   t ||\}}|  j|7  _|  j|7  _dS )z*Update state with predictions and targets.N)r   r   r   )r   r   r   r   n_obsr   r   r   updateA   s   zMeanAbsoluteError.updatec                 C   s   t | j| jS )z(Computes mean absolute error over state.)r   r   r   )r   r   r   r   computeH   s   zMeanAbsoluteError.compute)__name__
__module____qualname____doc__r	   bool__annotations__r
   r   r   r   r   r   r   __classcell__r   r   r   r   r      s   
 	r   )typingr   torchr   r   &torchmetrics.functional.regression.maer   r   torchmetrics.metricr   r   r   r   r   r   <module>   s   