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from typing import Tuple

from torch import nn, Tensor


class Tacotron2Loss(nn.Module):
    """Tacotron2 loss function modified from:
    https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/SpeechSynthesis/Tacotron2/tacotron2/loss_function.py
    """

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

        self.mse_loss = nn.MSELoss(reduction="mean")
        self.bce_loss = nn.BCEWithLogitsLoss(reduction="mean")

    def forward(
        self,
        model_outputs: Tuple[Tensor, Tensor, Tensor],
        targets: Tuple[Tensor, Tensor],
    ) -> Tuple[Tensor, Tensor, Tensor]:
        r"""Pass the input through the Tacotron2 loss.

        The original implementation was introduced in
        *Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions*
        [:footcite:`shen2018natural`].

        Args:
            model_outputs (tuple of three Tensors): The outputs of the
                Tacotron2. These outputs should include three items:
                (1) the predicted mel spectrogram before the postnet (``mel_specgram``)
                    with shape (batch, mel, time).
                (2) predicted mel spectrogram after the postnet (``mel_specgram_postnet``)
                    with shape (batch, mel, time), and
                (3) the stop token prediction (``gate_out``) with shape (batch, ).
            targets (tuple of two Tensors): The ground truth mel spectrogram (batch, mel, time) and
                stop token with shape (batch, ).

        Returns:
            mel_loss (Tensor): The mean MSE of the mel_specgram and ground truth mel spectrogram
                with shape ``torch.Size([])``.
            mel_postnet_loss (Tensor): The mean MSE of the mel_specgram_postnet and
                ground truth mel spectrogram with shape ``torch.Size([])``.
            gate_loss (Tensor): The mean binary cross entropy loss of
                the prediction on the stop token with shape ``torch.Size([])``.
        """
        mel_target, gate_target = targets[0], targets[1]
        gate_target = gate_target.view(-1, 1)

        mel_specgram, mel_specgram_postnet, gate_out = model_outputs
        gate_out = gate_out.view(-1, 1)
        mel_loss = self.mse_loss(mel_specgram, mel_target)
        mel_postnet_loss = self.mse_loss(mel_specgram_postnet, mel_target)
        gate_loss = self.bce_loss(gate_out, gate_target)
        return mel_loss, mel_postnet_loss, gate_loss