from typing import Dict, List

import torch
import torch.nn as nn
import torch.nn.functional as F
import torchaudio.transforms as T


class MultiResolutionSTFTLoss(nn.Module):
    """
    Computes a loss based on multiple STFT resolutions, including both
    spectral convergence and log STFT magnitude components.
    """

    def __init__(
        self,
        fft_sizes: List[int] = [1024, 2048, 512],
        hop_sizes: List[int] = [120, 240, 50],
        win_lengths: List[int] = [600, 1200, 240],
        eps: float = 1e-7,
    ):
        super().__init__()
        self.stft_transforms = nn.ModuleList(
            [
                T.Spectrogram(
                    n_fft=n_fft, win_length=win_len, hop_length=hop_len, power=None
                )
                for n_fft, hop_len, win_len in zip(fft_sizes, hop_sizes, win_lengths)
            ]
        )
        self.eps = eps

    def forward(
        self, y_true: torch.Tensor, y_pred: torch.Tensor
    ) -> Dict[str, torch.Tensor]:
        sc_loss = 0.0  # Spectral Convergence Loss
        mag_loss = 0.0  # Log STFT Magnitude Loss

        for stft in self.stft_transforms:
            stft.to(y_pred.device)  # Ensure transform is on the correct device

            # Get complex STFTs
            stft_true = stft(y_true)
            stft_pred = stft(y_pred)

            # Get magnitudes
            stft_mag_true = torch.abs(stft_true)
            stft_mag_pred = torch.abs(stft_pred)

            # --- Spectral Convergence Loss ---
            # || |S_true| - |S_pred| ||_F  /  || |S_true| ||_F
            norm_true = torch.linalg.norm(stft_mag_true, dim=(-2, -1))
            norm_diff = torch.linalg.norm(stft_mag_true - stft_mag_pred, dim=(-2, -1))
            sc_loss += torch.mean(norm_diff / (norm_true + self.eps))

            # --- Log STFT Magnitude Loss ---
            mag_loss += F.l1_loss(
                torch.log(stft_mag_pred + self.eps), torch.log(stft_mag_true + self.eps)
            )

        total_loss = sc_loss + mag_loss
        return {"total": total_loss, "sc": sc_loss, "mag": mag_loss}