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dS )    )Any)Tensortensor)1_symmetric_mean_absolute_percentage_error_compute0_symmetric_mean_absolute_percentage_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 )$SymmetricMeanAbsolutePercentageErrora<  Computes symmetric mean absolute percentage error (`SMAPE`_).

    .. math:: \text{SMAPE} = \frac{2}{n}\sum_1^n\frac{|   y_i - \hat{y_i} |}{\max(| y_i | + | \hat{y_i} |, \epsilon)}

    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:

    - ``smape`` (:class:`~torch.Tensor`): A tensor with non-negative floating point smape value between 0 and 1

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

    Example:
        >>> from torchmetrics import SymmetricMeanAbsolutePercentageError
        >>> target = tensor([1, 10, 1e6])
        >>> preds = tensor([0.9, 15, 1.2e6])
        >>> smape = SymmetricMeanAbsolutePercentageError()
        >>> smape(preds, target)
        tensor(0.2290)
    Tis_differentiableFhigher_is_betterfull_state_updatesum_abs_per_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    )super__init__	add_stater   )selfr   	__class__r   Z/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/regression/symmetric_mape.pyr   :   s   z-SymmetricMeanAbsolutePercentageError.__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   num_obsr   r   r   updateC   s   z+SymmetricMeanAbsolutePercentageError.updatec                 C   s   t | j| jS )z3Computes mean absolute percentage error over state.)r   r   r   )r   r   r   r   computeJ   s   z,SymmetricMeanAbsolutePercentageError.compute)__name__
__module____qualname____doc__r	   bool__annotations__r
   r   r   r   r   r   r   __classcell__r   r   r   r   r      s   
 	r   N)typingr   torchr   r   1torchmetrics.functional.regression.symmetric_maper   r   torchmetrics.metricr   r   r   r   r   r   <module>   s
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