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    .wi1                     @   s   d dl mZ d dlmZmZmZ d dlmZmZ d dl	m
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mZmZ d dlmZ d dlmZ d dlmZmZ es<g dZG d	d
 d
eZG dd deZG dd deZdS )    )Sequence)AnyOptionalUnion)Tensortensor)*complex_scale_invariant_signal_noise_ratio"scale_invariant_signal_noise_ratiosignal_noise_ratio)Metric)_MATPLOTLIB_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPE)SignalNoiseRatio.plot#ScaleInvariantSignalNoiseRatio.plot*ComplexScaleInvariantSignalNoiseRatio.plotc                       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	Z
ee ed
< d	Zee ed< 	ddededd	f fddZde	de	dd	fddZde	fddZ		ddeee	ee	 f  dee defddZ  ZS )SignalNoiseRatioa3  Calculate `Signal-to-noise ratio`_ (SNR_) meric for evaluating quality of audio.

    .. math::
        \text{SNR} = \frac{P_{signal}}{P_{noise}}

    where  :math:`P` denotes the power of each signal. The SNR metric compares the level of the desired signal to
    the level of background noise. Therefore, a high value of SNR means that the audio is clear.

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

    - ``preds`` (:class:`~torch.Tensor`): float tensor with shape ``(...,time)``
    - ``target`` (:class:`~torch.Tensor`): float tensor with shape ``(...,time)``

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

    - ``snr`` (:class:`~torch.Tensor`): float scalar tensor with average SNR value over samples

    Args:
        zero_mean: if to zero mean target and preds or not
        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

    Raises:
        TypeError:
            if target and preds have a different shape

    Example:
        >>> from torch import tensor
        >>> from torchmetrics.audio import SignalNoiseRatio
        >>> target = tensor([3.0, -0.5, 2.0, 7.0])
        >>> preds = tensor([2.5, 0.0, 2.0, 8.0])
        >>> snr = SignalNoiseRatio()
        >>> snr(preds, target)
        tensor(16.1805)

    Ffull_state_updateTis_differentiablehigher_is_bettersum_snrtotalNplot_lower_boundplot_upper_bound	zero_meankwargsreturnc                    sD   t  jdi | || _| jdtddd | jdtddd d S )Nr           sumdefaultdist_reduce_fxr   r    )super__init__r   	add_stater   selfr   r   	__class__r"   S/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/audio/snr.pyr$   Q   s   zSignalNoiseRatio.__init__predstargetc                 C   8   t ||| jd}|  j| 7  _|  j| 7  _dS *Update state with predictions and targets.)r+   r,   r   N)r
   r   r   r   r   numel)r'   r+   r,   	snr_batchr"   r"   r*   update\      zSignalNoiseRatio.updatec                 C      | j | j S zCompute metric.)r   r   r'   r"   r"   r*   computec      zSignalNoiseRatio.computevalaxc                 C      |  ||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.audio import SignalNoiseRatio
            >>> metric = SignalNoiseRatio()
            >>> metric.update(torch.rand(4), torch.rand(4))
            >>> fig_, ax_ = metric.plot()

        .. plot::
            :scale: 75

            >>> # Example plotting multiple values
            >>> import torch
            >>> from torchmetrics.audio import SignalNoiseRatio
            >>> metric = SignalNoiseRatio()
            >>> values = [ ]
            >>> for _ in range(10):
            ...     values.append(metric(torch.rand(4), torch.rand(4)))
            >>> fig_, ax_ = metric.plot(values)

        _plotr'   r9   r:   r"   r"   r*   plotg   s   (r   FNN)__name__
__module____qualname____doc__r   bool__annotations__r   r   r   r   r   floatr   r   r$   r2   r7   r   r   r   r   r?   __classcell__r"   r"   r(   r*   r   $   s6   
 $r   c                       s   e Zd ZU dZdZeed< eed< dZdZe	e
 ed< dZe	e
 ed< ded	df fd
dZdeded	dfddZd	efddZddeeee df de	e d	efddZ  ZS )ScaleInvariantSignalNoiseRatioa7  Calculate `Scale-invariant signal-to-noise ratio`_ (SI-SNR) metric for evaluating quality of audio.

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

    - ``preds`` (:class:`~torch.Tensor`): float tensor with shape ``(...,time)``
    - ``target`` (:class:`~torch.Tensor`): float tensor with shape ``(...,time)``

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

    - ``si_snr`` (:class:`~torch.Tensor`): float scalar tensor with average SI-SNR value over samples

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

    Raises:
        TypeError:
            if target and preds have a different shape

    Example:
        >>> import torch
        >>> from torch import tensor
        >>> from torchmetrics.audio import ScaleInvariantSignalNoiseRatio
        >>> target = tensor([3.0, -0.5, 2.0, 7.0])
        >>> preds = tensor([2.5, 0.0, 2.0, 8.0])
        >>> si_snr = ScaleInvariantSignalNoiseRatio()
        >>> si_snr(preds, target)
        tensor(15.0918)

    T
sum_si_snrr   Nr   r   r   r   c                    s>   t  jdi | | jdtddd | jdtddd d S )NrK   r   r   r   r   r   r"   )r#   r$   r%   r   )r'   r   r(   r"   r*   r$      s   z'ScaleInvariantSignalNoiseRatio.__init__r+   r,   c                 C   s4   t ||d}|  j| 7  _|  j| 7  _dS )r/   )r+   r,   N)r	   rK   r   r   r0   )r'   r+   r,   si_snr_batchr"   r"   r*   r2      s   z%ScaleInvariantSignalNoiseRatio.updatec                 C   r4   r5   )rK   r   r6   r"   r"   r*   r7      r8   z&ScaleInvariantSignalNoiseRatio.computer9   r:   c                 C   r;   )a6  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.audio import ScaleInvariantSignalNoiseRatio
            >>> metric = ScaleInvariantSignalNoiseRatio()
            >>> metric.update(torch.rand(4), torch.rand(4))
            >>> fig_, ax_ = metric.plot()

        .. plot::
            :scale: 75

            >>> # Example plotting multiple values
            >>> import torch
            >>> from torchmetrics.audio import ScaleInvariantSignalNoiseRatio
            >>> metric = ScaleInvariantSignalNoiseRatio()
            >>> values = [ ]
            >>> for _ in range(10):
            ...     values.append(metric(torch.rand(4), torch.rand(4)))
            >>> fig_, ax_ = metric.plot(values)

        r<   r>   r"   r"   r*   r?         &r   rA   )rB   rC   rD   rE   r   r   rG   r   r   r   rH   r   r   r$   r2   r7   r   r   r   r   r?   rI   r"   r"   r(   r*   rJ      s    
 	2rJ   c                       s   e Zd ZU dZdZeed< eed< dZdZe	e
 ed< dZe	e
 ed< 	dd	ed
eddf fddZdededdfddZdefddZddeeee df de	e defddZ  ZS )%ComplexScaleInvariantSignalNoiseRatioa  Calculate `Complex scale-invariant signal-to-noise ratio`_ (C-SI-SNR) metric for evaluating quality of audio.

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

    - ``preds`` (:class:`~torch.Tensor`): real float tensor with shape ``(...,frequency,time,2)`` or complex float
      tensor with shape ``(..., frequency,time)``

    - ``target`` (:class:`~torch.Tensor`): real float tensor with shape ``(...,frequency,time,2)`` or complex float
      tensor with shape ``(..., frequency,time)``

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

    - ``c_si_snr`` (:class:`~torch.Tensor`): float scalar tensor with average C-SI-SNR value over samples

    Args:
        zero_mean: if to zero mean target and preds or not
        kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.

    Raises:
        ValueError:
            If ``zero_mean`` is not an bool
        TypeError:
            If ``preds`` is not the shape (..., frequency, time, 2) (after being converted to real if it is complex).
            If ``preds`` and ``target`` does not have the same shape.

    Example:
        >>> from torch import randn
        >>> from torchmetrics.audio import ComplexScaleInvariantSignalNoiseRatio
        >>> preds = randn((1,257,100,2))
        >>> target = randn((1,257,100,2))
        >>> c_si_snr = ComplexScaleInvariantSignalNoiseRatio()
        >>> c_si_snr(preds, target)
        tensor(-38.8832)

    T
ci_snr_sumnumNr   r   Fr   r   r   c                    s\   t  jdi | t|tstd| || _| jdtddd | jdtddd d S )	Nz5Expected argument `zero_mean` to be an bool, but got rO   r   r   r   rP   r   r"   )r#   r$   
isinstancerF   
ValueErrorr   r%   r   r&   r(   r"   r*   r$   !  s   
z.ComplexScaleInvariantSignalNoiseRatio.__init__r+   r,   c                 C   r-   r.   )r   r   rO   r   rP   r0   )r'   r+   r,   vr"   r"   r*   r2   .  r3   z,ComplexScaleInvariantSignalNoiseRatio.updatec                 C   r4   r5   )rO   rP   r6   r"   r"   r*   r7   5  r8   z-ComplexScaleInvariantSignalNoiseRatio.computer9   r:   c                 C   r;   )az  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.audio import ComplexScaleInvariantSignalNoiseRatio
            >>> metric = ComplexScaleInvariantSignalNoiseRatio()
            >>> metric.update(torch.rand(1,257,100,2), torch.rand(1,257,100,2))
            >>> fig_, ax_ = metric.plot()

        .. plot::
            :scale: 75

            >>> # Example plotting multiple values
            >>> import torch
            >>> from torchmetrics.audio import ComplexScaleInvariantSignalNoiseRatio
            >>> metric = ComplexScaleInvariantSignalNoiseRatio()
            >>> values = [ ]
            >>> for _ in range(10):
            ...     values.append(metric(torch.rand(1,257,100,2), torch.rand(1,257,100,2)))
            >>> fig_, ax_ = metric.plot(values)

        r<   r>   r"   r"   r*   r?   9  rM   r   r@   rA   )rB   rC   rD   rE   r   r   rG   r   r   r   rH   r   rF   r   r$   r2   r7   r   r   r   r   r?   rI   r"   r"   r(   r*   rN      s&   
 $2rN   N)collections.abcr   typingr   r   r   torchr   r   !torchmetrics.functional.audio.snrr   r	   r
   torchmetrics.metricr   torchmetrics.utilities.importsr   torchmetrics.utilities.plotr   r   __doctest_skip__r   rJ   rN   r"   r"   r"   r*   <module>   s   nc