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eZdS )    )AnyDictListOptionalSequenceUnion)Tensor)relative_average_spectral_error)Metric)dim_zero_cat)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE!RelativeAverageSpectralError.plotc                       s   e Zd ZU dZdZeed< dZeed< dZeed< dZ	e
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< deeeef dd fddZeeddddZedddZdeeeee f  ee edddZ  ZS )RelativeAverageSpectralErrora  Computes Relative Average Spectral Error (RASE) (RelativeAverageSpectralError_).

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

    - ``preds`` (:class:`~torch.Tensor`): Predictions from model of shape ``(N,C,H,W)``
    - ``target`` (:class:`~torch.Tensor`): Ground truth values of shape ``(N,C,H,W)``

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

    - ``rase`` (:class:`~torch.Tensor`): returns float scalar tensor with average RASE value over sample

    Args:
        window_size: Sliding window used for rmse calculation
        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

    Return:
        Relative Average Spectral Error (RASE)

    Example:
        >>> import torch
        >>> from torchmetrics.image import RelativeAverageSpectralError
        >>> g = torch.manual_seed(22)
        >>> preds = torch.rand(4, 3, 16, 16)
        >>> target = torch.rand(4, 3, 16, 16)
        >>> rase = RelativeAverageSpectralError()
        >>> rase(preds, target)
        tensor(5114.6641)

    Raises:
        ValueError: If ``window_size`` is not a positive integer.

    Fhigher_is_betterTis_differentiablefull_state_updateg        plot_lower_boundpredstarget   N)window_sizekwargsreturnc                    sf   t  jf i | t|tr.t|tr<|dk r<td| || _| jdg dd | jdg dd d S )N   zEArgument `window_size` is expected to be a positive integer, but got r   cat)defaultZdist_reduce_fxr   )super__init__
isinstanceint
ValueErrorr   Z	add_state)selfr   r   	__class__ `/var/www/html/stable-diffusion-webui/venv/lib/python3.9/site-packages/torchmetrics/image/rase.pyr   G   s    z%RelativeAverageSpectralError.__init__)r   r   r   c                 C   s   | j | | j| dS )z*Update state with predictions and targets.N)r   appendr   r#   r   r   r&   r&   r'   updateU   s    z#RelativeAverageSpectralError.update)r   c                 C   s"   t | j}t | j}t||| jS )z/Compute Relative Average Spectral Error (RASE).)r   r   r   r	   r   r)   r&   r&   r'   computeZ   s    

z$RelativeAverageSpectralError.compute)valaxr   c                 C   s   |  ||S )a  Plot a single or multiple values from the metric.

        Args:
            val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
                If no value is provided, will automatically call `metric.compute` and plot that result.
            ax: An matplotlib axis object. If provided will add plot to that axis

        Returns:
            Figure and Axes object

        Raises:
            ModuleNotFoundError:
                If `matplotlib` is not installed

        .. plot::
            :scale: 75

            >>> # Example plotting a single value
            >>> import torch
            >>> from torchmetrics.image import RelativeAverageSpectralError
            >>> metric = RelativeAverageSpectralError()
            >>> metric.update(torch.rand(4, 3, 16, 16), torch.rand(4, 3, 16, 16))
            >>> fig_, ax_ = metric.plot()

        .. plot::
            :scale: 75

            >>> # Example plotting multiple values
            >>> import torch
            >>> _ = torch.manual_seed(42)
            >>> from torchmetrics.image import RelativeAverageSpectralError
            >>> metric = RelativeAverageSpectralError()
            >>> values = [ ]
            >>> for _ in range(10):
            ...     values.append(metric(torch.rand(4, 3, 16, 16), torch.rand(4, 3, 16, 16)))
            >>> fig_, ax_ = metric.plot(values)

        )Z_plot)r#   r,   r-   r&   r&   r'   plot`   s    )r   )r   )NN)__name__
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