import torch
import torch.nn as nn


class mean_squared_error(nn.Module):
    """
    Mean squared error / L1 loss
    """

    def __init__(self, reduction="mean"):
        super().__init__()

        self.loss_fun = nn.MSELoss(reduction=reduction)
        self.higher_better = False

    def forward(self, prediction: torch.Tensor, target: torch.Tensor):

        if prediction.size() != target.size() or target.ndim < 3:
            raise TypeError(
                f"""Inputs must be of the same shape (batch_size,channels,samples) 
                            got {prediction.size()} and {target.size()} instead"""
            )

        return self.loss_fun(prediction, target)


class mean_absolute_error(nn.Module):
    """
    Mean absolute error / L2 loss
    """

    def __init__(self, reduction="mean"):
        super().__init__()

        self.loss_fun = nn.L1Loss(reduction=reduction)
        self.higher_better = False

    def forward(self, prediction: torch.Tensor, target: torch.Tensor):

        if prediction.size() != target.size() or target.ndim < 3:
            raise TypeError(
                f"""Inputs must be of the same shape (batch_size,channels,samples) 
                            got {prediction.size()} and {target.size()} instead"""
            )

        return self.loss_fun(prediction, target)


class Si_SDR(nn.Module):
    """
    SI-SDR metric based on SDR – HALF-BAKED OR WELL DONE?(https://arxiv.org/pdf/1811.02508.pdf)
    """

    def __init__(self, reduction: str = "mean"):
        super().__init__()
        if reduction in ["sum", "mean", None]:
            self.reduction = reduction
        else:
            raise TypeError(
                "Invalid reduction, valid options are sum, mean, None"
            )
        self.higher_better = False

    def forward(self, prediction: torch.Tensor, target: torch.Tensor):

        if prediction.size() != target.size() or target.ndim < 3:
            raise TypeError(
                f"""Inputs must be of the same shape (batch_size,channels,samples) 
                            got {prediction.size()} and {target.size()} instead"""
            )

        target_energy = torch.sum(target**2, keepdim=True, dim=-1)
        scaling_factor = (
            torch.sum(prediction * target, keepdim=True, dim=-1) / target_energy
        )
        target_projection = target * scaling_factor
        noise = prediction - target_projection
        ratio = torch.sum(target_projection**2, dim=-1) / torch.sum(
            noise**2, dim=-1
        )
        si_sdr = 10 * torch.log10(ratio).mean(dim=-1)

        if self.reduction == "sum":
            si_sdr = si_sdr.sum()
        elif self.reduction == "mean":
            si_sdr = si_sdr.mean()
        else:
            pass

        return si_sdr


class Avergeloss(nn.Module):
    """
    Combine multiple metics of same nature.
    for example, ["mea","mae"]
    parameters:
        losses : loss function names to be combined
    """

    def __init__(self, losses):
        super().__init__()

        self.valid_losses = nn.ModuleList()

        direction = [
            getattr(LOSS_MAP[loss](), "higher_better") for loss in losses
        ]
        if len(set(direction)) > 1:
            raise ValueError(
                "all cost functions should be of same nature, maximize or minimize!"
            )

        self.higher_better = direction[0]
        for loss in losses:
            loss = self.validate_loss(loss)
            self.valid_losses.append(loss())

    def validate_loss(self, loss: str):
        if loss not in LOSS_MAP.keys():
            raise ValueError(
                f"Invalid loss function {loss}, available loss functions are {tuple([loss for loss in LOSS_MAP.keys()])}"
            )
        else:
            return LOSS_MAP[loss]

    def forward(self, prediction: torch.Tensor, target: torch.Tensor):
        loss = 0.0
        for loss_fun in self.valid_losses:
            loss += loss_fun(prediction, target)

        return loss


LOSS_MAP = {
    "mae": mean_absolute_error,
    "mse": mean_squared_error,
    "SI-SDR": Si_SDR,
}