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dS )    )AnyN)Tensortensor)'_mean_absolute_percentage_error_compute&_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 )MeanAbsolutePercentageErrora  Computes `Mean Absolute Percentage Error`_ (MAPE):

    .. math:: \text{MAPE} = \frac{1}{n}\sum_{i=1}^n\frac{|   y_i - \hat{y_i} |}{\max(\epsilon, | 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_abs_percentage_error`` (:class:`~torch.Tensor`): A tensor with the mean absolute percentage error over
      state

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

    Note:
        MAPE output is a non-negative floating point. Best result is ``0.0`` . But it is important to note that,
        bad predictions, can lead to arbitarily large values. Especially when some ``target`` values are close to 0.
        This `MAPE implementation returns`_ a very large number instead of ``inf``.

    Example:
        >>> from torchmetrics import MeanAbsolutePercentageError
        >>> target = torch.tensor([1, 10, 1e6])
        >>> preds = torch.tensor([0.9, 15, 1.2e6])
        >>> mean_abs_percentage_error = MeanAbsolutePercentageError()
        >>> mean_abs_percentage_error(preds, target)
        tensor(0.2667)
    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   P/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/regression/mape.pyr   A   s   z$MeanAbsolutePercentageError.__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   updateJ   s   z"MeanAbsolutePercentageError.updatec                 C   s   t | j| jS )z3Computes mean absolute percentage error over state.)r   r   r   )r   r   r   r   computeQ   s   z#MeanAbsolutePercentageError.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.maper   r   torchmetrics.metricr   r   r   r   r   r   <module>   s   