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e	fddZ  ZS )MeanSquaredLogErrorad  Computes `mean squared logarithmic error`_ (MSLE):

    .. math:: \text{MSLE} = \frac{1}{N}\sum_i^N (\log_e(1 + y_i) - \log_e(1 + \hat{y_i}))^2

    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_squared_log_error`` (:class:`~torch.Tensor`): A tensor with the mean squared log error

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

    Example:
        >>> from torchmetrics import MeanSquaredLogError
        >>> target = torch.tensor([2.5, 5, 4, 8])
        >>> preds = torch.tensor([3, 5, 2.5, 7])
        >>> mean_squared_log_error = MeanSquaredLogError()
        >>> mean_squared_log_error(preds, target)
        tensor(0.0397)

    .. note::
        Half precision is only support on GPU for this metric
    Tis_differentiableFhigher_is_betterfull_state_updatesum_squared_log_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   S/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/regression/log_mse.pyr   ;   s   zMeanSquaredLogError.__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   updateD   s   zMeanSquaredLogError.updatec                 C   s   t | j| jS )z2Compute mean squared logarithmic error over state.)r   r   r   )r   r   r   r   computeK   s   zMeanSquaredLogError.compute)__name__
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 	r   )typingr   torchr   r   *torchmetrics.functional.regression.log_mser   r   torchmetrics.metricr   r   r   r   r   r   <module>   s   