# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from warnings import warn

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"""Signal-to-noise ratio (SNR_):

    .. 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:
            shape ``[...,time]``
        target:
            shape ``[...,time]``
        zero_mean:
            if to zero mean target and preds or not

    Returns:
        snr value of 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)

    References:
        [1] <NAME>, Jonathan, et al. "SDR half-baked or well done." IEEE International Conference on Acoustics, Speech
         and Signal Processing (ICASSP) 2019.

    """
    _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 snr(preds: Tensor, target: Tensor, zero_mean: bool = False) -> Tensor:
    r"""Signal-to-noise ratio (SNR_)

    .. deprecated:: v0.7
        Use :func:`torchmetrics.functional.signal_noise_ratio`. Will be removed in v0.8.

    Example:
        >>> snr(torch.tensor([2.5, 0.0, 2.0, 8.0]), torch.tensor([3.0, -0.5, 2.0, 7.0]))
        tensor(16.1805)

    """
    warn("`snr` was renamed to `signal_noise_ratio` in v0.7 and it will be removed in v0.8", DeprecationWarning)
    return signal_noise_ratio(preds, target, zero_mean)


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

    Args:
        preds:
            shape ``[...,time]``
        target:
            shape ``[...,time]``

    Returns:
        si-snr value of 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)

    References:
        [1] <NAME> and <NAME>, "TaSNet: Time-Domain Audio Separation Network for Real-Time, Single-Channel Speech
        Separation," 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, pp.
        696-700, doi: 10.1109/ICASSP.2018.8462116.
    """
    return scale_invariant_signal_distortion_ratio(preds=preds, target=target, zero_mean=True)

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from warnings import warn

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"""Signal-to-noise ratio (SNR_):

    .. 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:
            shape ``[...,time]``
        target:
            shape ``[...,time]``
        zero_mean:
            if to zero mean target and preds or not

    Returns:
        snr value of 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)

    References:
        [1] <NAME>, Jonathan, et al. "SDR half-baked or well done." IEEE International Conference on Acoustics, Speech
         and Signal Processing (ICASSP) 2019.

    """
    _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 snr(preds: Tensor, target: Tensor, zero_mean: bool = False) -> Tensor:
    r"""Signal-to-noise ratio (SNR_)

    .. deprecated:: v0.7
        Use :func:`torchmetrics.functional.signal_noise_ratio`. Will be removed in v0.8.

    Example:
        >>> snr(torch.tensor([2.5, 0.0, 2.0, 8.0]), torch.tensor([3.0, -0.5, 2.0, 7.0]))
        tensor(16.1805)

    """
    warn("`snr` was renamed to `signal_noise_ratio` in v0.7 and it will be removed in v0.8", DeprecationWarning)
    return signal_noise_ratio(preds, target, zero_mean)


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

    Args:
        preds:
            shape ``[...,time]``
        target:
            shape ``[...,time]``

    Returns:
        si-snr value of 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)

    References:
        [1] <NAME> and <NAME>, "TaSNet: Time-Domain Audio Separation Network for Real-Time, Single-Channel Speech
        Separation," 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, pp.
        696-700, doi: 10.1109/ICASSP.2018.8462116.
    """
    return scale_invariant_signal_distortion_ratio(preds=preds, target=target, zero_mean=True)

# Copyright The PyTorch Lightning team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # #     http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. Signal-to-noise ratio (SNR_):      .. 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:             shape ``[...,time]``         target:             shape ``[...,time]``         zero_mean:             if to zero mean target and preds or not      Returns:         snr value of 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)      References:         [1] <NAME>, Jonathan, et al. "SDR half-baked or well done." IEEE International Conference on Acoustics, Speech          and Signal Processing (ICASSP) 2019. Signal-to-noise ratio (SNR_)      .. deprecated:: v0.7         Use :func:`torchmetrics.functional.signal_noise_ratio`. Will be removed in v0.8.      Example:         >>> snr(torch.tensor([2.5, 0.0, 2.0, 8.0]), torch.tensor([3.0, -0.5, 2.0, 7.0]))         tensor(16.1805) Scale-invariant signal-to-noise ratio (SI-SNR).      Args:         preds:             shape ``[...,time]``         target:             shape ``[...,time]``      Returns:         si-snr value of 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)      References:         [1] <NAME> and <NAME>, "TaSNet: Time-Domain Audio Separation Network for Real-Time, Single-Channel Speech         Separation," 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, pp.         696-700, doi: 10.1109/ICASSP.2018.8462116.