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#     http://www.apache.org/licenses/LICENSE-2.0
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import torch
from torch import Tensor

from torchmetrics.functional.audio.sdr import scale_invariant_signal_distortion_ratio
from torchmetrics.utilities.checks import _check_same_shape


def signal_noise_ratio(preds: Tensor, target: Tensor, zero_mean: bool = False) -> Tensor:
    r"""Calculates `Signal-to-noise ratio`_ (SNR_) meric for evaluating quality of audio. It is defined as:

    .. 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.

    Args:
        preds: float tensor with shape ``(...,time)``
        target: float tensor with shape ``(...,time)``
        zero_mean: if to zero mean target and preds or not

    Returns:
        Float tensor with shape ``(...,)`` of SNR values per sample

    Raises:
        RuntimeError:
            If ``preds`` and ``target`` does not have the same shape

    Example:
        >>> from torchmetrics.functional.audio import signal_noise_ratio
        >>> target = torch.tensor([3.0, -0.5, 2.0, 7.0])
        >>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0])
        >>> signal_noise_ratio(preds, target)
        tensor(16.1805)
    """
    _check_same_shape(preds, target)
    eps = torch.finfo(preds.dtype).eps

    if zero_mean:
        target = target - torch.mean(target, dim=-1, keepdim=True)
        preds = preds - torch.mean(preds, dim=-1, keepdim=True)

    noise = target - preds

    snr_value = (torch.sum(target**2, dim=-1) + eps) / (torch.sum(noise**2, dim=-1) + eps)
    snr_value = 10 * torch.log10(snr_value)

    return snr_value


def scale_invariant_signal_noise_ratio(preds: Tensor, target: Tensor) -> Tensor:
    """`Scale-invariant signal-to-noise ratio`_ (SI-SNR).

    Args:
        preds: float tensor with shape ``(...,time)``
        target: float tensor with shape ``(...,time)``

    Returns:
         Float tensor with shape ``(...,)`` of SI-SNR values per sample

    Raises:
        RuntimeError:
            If ``preds`` and ``target`` does not have the same shape

    Example:
        >>> import torch
        >>> from torchmetrics.functional.audio import scale_invariant_signal_noise_ratio
        >>> target = torch.tensor([3.0, -0.5, 2.0, 7.0])
        >>> preds = torch.tensor([2.5, 0.0, 2.0, 8.0])
        >>> scale_invariant_signal_noise_ratio(preds, target)
        tensor(15.0918)
    """
    return scale_invariant_signal_distortion_ratio(preds=preds, target=target, zero_mean=True)