Module audiocraft.solvers.watermark

def compute_FNR(positive):
    N = (positive[:, 0, :].mean(dim=1) > 0.5).sum()
    fpr = N / (positive.size(0))
    return fpr

def compute_FPR(negative):
    N = (negative[:, 1, :].mean(dim=1) > 0.5).sum()
    fpr = N / (negative.size(0))
    return fpr

def compute_accuracy(positive, negative):
    N = (positive[:, 1, :].mean(dim=1) > 0.5).sum() + (
        negative[:, 0, :].mean(dim=1) > 0.5
    ).sum()
    acc = N / (2 * positive.size(0))
    return acc

def compute_bit_acc(positive, original, mask=None):
    """Compute bit accuracy.
    Args:
        positive: detector outputs [bsz, 2+nbits, time_steps]
        original: original message (0 or 1) [bsz, nbits]
        mask: mask of the watermark [bsz, 1, time_steps]
    """
    decoded = positive[:, 2:, :]  # b 2+nbits t -> b nbits t
    if mask is not None:
        # cut last dim of positive to keep only where mask is 1
        new_shape = [*decoded.shape[:-1], -1]  # b nbits t -> b nbits -1
        decoded = torch.masked_select(decoded, mask == 1).reshape(new_shape)
    # average decision over time, then threshold
    decoded = decoded.mean(dim=-1) > 0  # b nbits
    return _bit_acc(decoded, original)

def evaluate_audio_watermark(
    y_pred: torch.Tensor,
    y: torch.Tensor,
    cfg: DictConfig,
) -> dict:
    """Audio reconstruction evaluation method that can be conveniently pickled."""
    metrics = {}
    if cfg.evaluate.metrics.visqol:
        visqol = builders.get_visqol(cfg.metrics.visqol)
        metrics["visqol"] = visqol(y_pred, y, cfg.sample_rate)
    sisnr = ScaleInvariantSignalNoiseRatio().to(y.device)
    stoi = ShortTimeObjectiveIntelligibility(fs=cfg.sample_rate)
    metrics["sisnr"] = sisnr(y_pred, y)
    metrics["stoi"] = stoi(y_pred, y)
    metrics["pesq"] = tensor_pesq(y_pred, y, sr=cfg.sample_rate)
    return metrics

def evaluate_augmentations(
    positive: torch.Tensor,
    negative: torch.Tensor,
    augmentation_name: str,
    message: torch.Tensor,
) -> dict:
    """calculating evaluation metrics but take name of the augmentation
    method that has been done before getting positive and negative results"""
    metrics = {}
    metrics[f"aug_{augmentation_name}_acc"] = compute_accuracy(positive, negative)
    metrics[f"aug_{augmentation_name}_fpr"] = compute_FPR(negative)
    metrics[f"aug_{augmentation_name}_fnr"] = compute_FNR(positive)
    if message.shape[0] != 0:
        metrics[f"aug_{augmentation_name}_bit_acc"] = compute_bit_acc(positive, message)

    # add one metric which is average overall score of all augmentations
    metrics["all_aug_acc"] = compute_accuracy(positive, negative)

    return metrics

def evaluate_localizations(predictions, true_predictions, name):
    metrics = {}
    # predictions are output of the detector shape [bsz, 2, frames]
    # true_predictions is output of the mix method shape [bsz, 2, frames]
    metrics[f"localization_acc_{name}"] = (
        ((predictions[:, 1, :] > 0.5) == true_predictions[:, 1, :])
        .float()
        .mean()
        .item()
    )
    metrics[f"localization_miou_{name}"] = calculate_miou(
        predictions[:, 1, :], true_predictions[:, 1, :]
    )
    return metrics

def get_encodec_audio_effect(encodec_cfg: DictConfig, sr: int) -> tp.Dict:
    """
    Construct encodec-based compression data agumentation. This method is
    is put here instead of in `audiocraft.utils.audio_effects` because
    it depends on the package `audiocraft.solvers`, which is one layer
    higher than `audiocraft.utils`, so we avoid the circle dependency
    from any solvers using `audiocraft.utils.audio_effects` to do the
    augmentation
    """
    from ..solvers.compression import CompressionSolver

    codec_model = CompressionSolver.model_from_checkpoint(encodec_cfg.ckpt)
    codec_model.train()
    return {
        f"encodec_nq={n_q}": partial(
            compress_with_encodec,
            model=codec_model,
            n_q=n_q,
            sample_rate=sr,
        )
        for n_q in encodec_cfg.n_qs
    }

def random_message(nbits: int, batch_size: int) -> torch.Tensor:
    """Return random message as 0/1 tensor."""
    if nbits == 0:
        return torch.tensor([])
    return torch.randint(0, 2, (batch_size, nbits))

def tensor_pesq(y_pred: torch.Tensor, y: torch.Tensor, sr: int):
    # pesq returns error if no speech is detected, so we catch it
    return PesqMetric(sr)(y_pred, y).item()

class WatermarkSolver(base.StandardSolver):
    """Solver for different watermarking models"""

    def __init__(self, cfg: DictConfig):
        super().__init__(cfg)
        self.rng: torch.Generator  # set at each epoch
        self.model: WMModel
        if hasattr(cfg, "fsdp"):
            assert not getattr(
                cfg.fsdp, "use", False
            ), "FSDP not supported by WatermarkSolver."
        self._init_losses()
        self._init_augmentations()
        self.balancer = builders.get_balancer(self.loss_weights, self.cfg.balancer)
        self.path_specs = os.path.join(self.folder, "spectrograms")
        os.makedirs(self.path_specs, exist_ok=True)

    def _init_losses(self):
        assert hasattr(self.cfg, "losses") and isinstance(
            self.cfg.losses, (DictConfig, tp.Mapping)
        ), "WatermarkSolver must declare training losses in the config"

        self.adv_losses = builders.get_adversarial_losses(self.cfg)  # noqa
        self.register_stateful("adv_losses")

        self.aux_losses = nn.ModuleDict()  # noqa
        self.info_losses = nn.ModuleDict()  # noqa
        self.wm_losses = nn.ModuleDict()  # noqa
        loss_weights = {}
        for loss_name, weight in self.cfg.losses.items():

            # explicitly skip this loss calculation by setting a -1 as weight
            # if weight == 0 it will be calculated but kept as info
            if weight == -1:
                continue

            if loss_name in ["adv", "feat"]:
                for adv_name, _ in self.adv_losses.items():
                    loss_weights[f"{loss_name}_{adv_name}"] = weight
            elif weight > 0:
                if loss_name[:3] == "wm_":
                    self.wm_losses[loss_name] = builders.get_loss(
                        loss_name, self.cfg
                    ).to(self.device)
                    loss_weights[loss_name] = weight
                else:
                    self.aux_losses[loss_name] = builders.get_loss(
                        loss_name, self.cfg
                    ).to(self.device)
                    loss_weights[loss_name] = weight
            else:
                self.info_losses[loss_name] = builders.get_loss(loss_name, self.cfg).to(
                    self.device
                )

        self.loss_weights = loss_weights  # noqa

    def _init_augmentations(self):
        if not hasattr(self.cfg, "aug_weights") or not hasattr(
            self.cfg, "audio_effects"
        ):
            return

        aug_weights = {}
        cfg_audio_effects = dict(self.cfg.audio_effects)

        # Handle `encodec` augmentation separately as this requires loading a
        # CompressionSolver checkpoint
        encodec_cfg = cfg_audio_effects.pop("encodec", None)
        if encodec_cfg:
            encodec_effects = get_encodec_audio_effect(
                encodec_cfg, self.cfg.sample_rate
            )
            for aug_name in encodec_effects.keys():
                aug_weights[aug_name] = getattr(self.cfg.aug_weights, "encodec", -1)
        else:
            encodec_effects = {}

        other_effects = get_audio_effects(self.cfg)  # noqa
        for name in other_effects.keys():
            aug_weights[name] = self.cfg.aug_weights.get(name, -1)

        self.aug_weights = aug_weights  # noqa
        self.augmentations = {**encodec_effects, **other_effects}  # noqa

    @property
    def best_metric_name(self) -> tp.Optional[str]:
        # best model is the last for the watermark model for now
        return None

    def build_model(self):
        """Instantiate model and optimizer."""
        # Model and optimizer
        self.model = get_watermark_model(self.cfg)
        # Need two optimizers ?
        self.optimizer = builders.get_optimizer(self.model.parameters(), self.cfg.optim)
        self.register_stateful("model", "optimizer")
        self.register_best_state("model")
        self.register_ema("model")

    def build_dataloaders(self):
        """Instantiate audio dataloaders for each stage."""
        self.dataloaders = builders.get_audio_datasets(self.cfg)

    def show(self):
        """Show the Watermark model and employed adversarial loss."""
        self.log_model_summary(self.model)
        self.logger.info("Sould print losses here:")

    def crop(
        self, signal: torch.Tensor, watermark: torch.Tensor
    ) -> tp.Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """
        Applies a transformation to modify the watermarked signal to train localization.
        It can be one of the following:
            - zero padding: add zeros at the begining and the end of the signal
            - crop: crop the watermark apply a watermark only on some parts of the signal
            - shuffle: replace some part of the audio with other non watermarked parts
                from the batch
        In every cases the function returns a mask that contains indicates the parts that are or
        not watermarked

        Args:
            watermark (torch.Tensor): The watermark to apply on the signal.
            signal (torch.Tensor): clean signal
        Returns:
            watermark (torch.Tensor): modified watermark
            signal (torch.Tensor): modified signal
            mask (torch.Tensor): mask indicating which portion is still watermarked
        """
        assert (
            self.cfg.crop.prob + self.cfg.crop.shuffle_prob + self.cfg.crop.pad_prob
            <= 1
        ), f"The sum of the probabilities {self.cfg.crop.prob=} {self.cfg.crop.shuffle_prob=} \
                {self.cfg.crop.pad_prob=} should be less than 1"
        mask = torch.ones_like(watermark)
        p = torch.rand(1)
        if p < self.cfg.crop.pad_prob:  # Pad with some probability
            start = int(torch.rand(1) * 0.33 * watermark.size(-1))
            finish = int((0.66 + torch.rand(1) * 0.33) * watermark.size(-1))
            mask[:, :, :start] = 0
            mask[:, :, finish:] = 0
            if torch.rand(1) > 0.5:
                mask = 1 - mask
            signal *= mask  # pad signal

        elif (
            p < self.cfg.crop.prob + self.cfg.crop.pad_prob + self.cfg.crop.shuffle_prob
        ):
            # Define a mask, then crop or shuffle
            mask_size = round(watermark.shape[-1] * self.cfg.crop.size)
            n_windows = int(
                torch.randint(1, self.cfg.crop.max_n_windows + 1, (1,)).item()
            )
            window_size = int(mask_size / n_windows)
            for _ in range(n_windows):  # Create multiple windows in the mask
                mask_start = torch.randint(0, watermark.shape[-1] - window_size, (1,))
                mask[:, :, mask_start: mask_start + window_size] = (
                    0  # Apply window to mask
                )
            # inverse the mask half the time
            if torch.rand(1) > 0.5:
                mask = 1 - mask

            if p < self.cfg.crop.pad_prob + self.cfg.crop.shuffle_prob:  # shuffle
                # shuffle
                signal_cloned = signal.clone().detach()  # detach to be sure
                shuffle_idx = torch.randint(0, signal.size(0), (signal.size(0),))
                signal = signal * mask + signal_cloned[shuffle_idx] * (
                    1 - mask
                )  # shuffle signal where not wm

        watermark *= mask  # Apply mask to the watermark
        return signal, watermark, mask

    def run_step(self, idx: int, batch: torch.Tensor, metrics: dict):
        """Perform one training or valid step on a given batch."""
        x = batch.to(self.device)
        y = x.clone()
        nbits = getattr(self.model, "nbits")
        message = random_message(nbits, y.shape[0]).to(self.device)
        watermark = self.model.get_watermark(x, message=message)
        y, watermark, mask = self.crop(y, watermark)

        y_wm = y + watermark

        if (
            self.cfg.losses.adv != 0 or self.cfg.losses.feat != 0
        ) and self.is_training:  # train quality adv
            d_losses: dict = {}
            if (
                len(self.adv_losses) > 0
                and torch.rand(1, generator=self.rng).item()
                <= 1 / self.cfg.adversarial.every
            ):
                for adv_name, adversary in self.adv_losses.items():
                    disc_loss = adversary.train_adv(y_wm, y)
                    d_losses[f"d_{adv_name}"] = disc_loss
                metrics["d_loss"] = torch.sum(torch.stack(list(d_losses.values())))
            metrics.update(d_losses)

        balanced_losses: dict = {}
        other_losses: dict = {}

        # adversarial losses
        if self.cfg.losses.adv != 0 or self.cfg.losses.feat != 0:
            for adv_name, adversary in self.adv_losses.items():
                adv_loss, feat_loss = adversary(y_wm, y)
                balanced_losses[f"adv_{adv_name}"] = adv_loss
                balanced_losses[f"feat_{adv_name}"] = feat_loss

        # auxiliary losses on quality/similarity
        for loss_name, criterion in self.aux_losses.items():
            loss = criterion(y_wm, y)
            balanced_losses[loss_name] = loss

        # apply augmentations
        mode = "all" if self.cfg.select_aug_mode == "all" else "weighted"
        selected_augs = select_audio_effects(
            self.augmentations,
            self.aug_weights,
            mode=mode,
            max_length=self.cfg.n_max_aug,
        )
        N_augs = len(selected_augs)
        for (
            augmentation_name,
            augmentation_method,
        ) in selected_augs.items():
            # concatenate to use the augmentation function only once
            y_y_wm = torch.cat([y, y_wm], dim=0)
            aug_cat, mask_aug = augmentation_method(y_y_wm, mask=mask)
            aug_y = aug_cat[: y.size(0)]
            aug_y_wm = aug_cat[y.size(0):]
            positive = self.model.detect_watermark(aug_y_wm)
            negative = self.model.detect_watermark(aug_y)
            for loss_name, criterion in self.wm_losses.items():
                loss = criterion(positive, negative, mask_aug, message)
                other_losses[f"{loss_name}_{augmentation_name}"] = loss

        # weighted losses
        metrics.update(balanced_losses)
        metrics.update(other_losses)
        if self.is_training:  # something is weird about the loss balancer not
            other_loss = torch.tensor(0.0, device=self.device)
            for name, o_loss in other_losses.items():
                if "wm_detection" in name:
                    # here we include the detection losses for augmentation
                    other_loss += (self.loss_weights["wm_detection"] / N_augs) * o_loss
                elif "wm_mb" in name:
                    other_loss += (self.loss_weights["wm_mb"] / N_augs) * o_loss
                else:
                    other_loss += self.loss_weights[name] * o_loss
            if other_loss.requires_grad:
                other_loss.backward(retain_graph=True)
                ratio1 = sum(
                    p.grad.data.norm(p=2).pow(2)
                    for p in self.model.parameters()
                    if p.grad is not None
                )
                assert isinstance(ratio1, torch.Tensor)
                metrics["ratio1"] = ratio1.sqrt()

            # balancer losses backward, returns effective training loss
            # with effective weights at the current batch.
            metrics["g_loss"] = self.balancer.backward(balanced_losses, y_wm)
            # add metrics corresponding to weight ratios
            metrics.update(self.balancer.metrics)
            ratio2 = sum(
                p.grad.data.norm(p=2).pow(2)
                for p in self.model.parameters()
                if p.grad is not None
            )
            assert isinstance(ratio2, torch.Tensor)
            metrics["ratio2"] = ratio2.sqrt()

            # optim
            flashy.distrib.sync_model(self.model)
            if self.cfg.optim.max_norm:
                torch.nn.utils.clip_grad_norm_(
                    self.model.parameters(), self.cfg.optim.max_norm
                )

            self.optimizer.step()
            self.optimizer.zero_grad()

        # informative losses only
        info_losses: dict = {}
        with torch.no_grad():
            for loss_name, criterion in self.info_losses.items():
                loss = criterion(y_wm, y)
                info_losses[loss_name] = loss
            # pesq
            metrics["pesq"] = tensor_pesq(y_wm, y, sr=self.cfg.sample_rate)
            # max allocated memory
            metrics["max_mem"] = torch.cuda.max_memory_allocated() / 1e9

        metrics.update(info_losses)
        if self.cfg.losses.adv != 0 or self.cfg.losses.feat != 0:
            # aggregated GAN losses: this is useful to report adv and feat across different adversarial loss setups
            adv_losses = [
                loss
                for loss_name, loss in metrics.items()
                if loss_name.startswith("adv")
            ]
            if len(adv_losses) > 0:
                metrics["adv"] = torch.sum(torch.stack(adv_losses))
            feat_losses = [
                loss
                for loss_name, loss in metrics.items()
                if loss_name.startswith("feat")
            ]
            if len(feat_losses) > 0:
                metrics["feat"] = torch.sum(torch.stack(feat_losses))

        return metrics

    def run_epoch(self):
        # reset random seed at the beginning of the epoch
        self.rng = torch.Generator()
        self.rng.manual_seed(1234 + self.epoch)
        # run epoch
        super().run_epoch()

    def evaluate(self) -> dict:
        """Evaluate stage. Runs audio reconstruction evaluation."""
        self.model.eval()
        evaluate_stage_name = str(self.current_stage)

        loader = self.dataloaders["evaluate"]
        updates = len(loader)
        lp = self.log_progress(
            f"{evaluate_stage_name} inference",
            loader,
            total=updates,
            updates=self.log_updates,
        )
        average = flashy.averager()

        pendings = []
        ctx = multiprocessing.get_context("spawn")
        with get_pool_executor(self.cfg.evaluate.num_workers, mp_context=ctx) as pool:
            for batch in lp:
                x = batch.to(self.device)
                with torch.no_grad():
                    message = random_message(self.model.nbits, x.shape[0])
                    watermark = self.model.get_watermark(x, message)
                    x_wm = x + watermark
                y_pred = x_wm.cpu()
                y = batch.cpu()  # should already be on CPU but just in case
                pendings.append(
                    pool.submit(
                        evaluate_audio_watermark,
                        y_pred,
                        y,
                        self.cfg,
                    )
                )
                # evaluate augmentations
                # evaluation is run on all the augmentations
                for (
                    augmentation_name,
                    augmentation_method,
                ) in self.augmentations.items():
                    # if (
                    #     "mp3" in augmentation_name
                    #     and idx >= 8
                    #     and self.cfg.evaluate.every <= 2
                    # ):
                    #     # When evaluating often do not compute mp3 on the full eval dset to make things faster
                    #     continue
                    with torch.no_grad():
                        aug_positive = self.model.detect_watermark(
                            augmentation_method(x_wm)
                        )
                        aug_negative = self.model.detect_watermark(
                            augmentation_method(x)
                        )

                    pendings.append(
                        pool.submit(
                            evaluate_augmentations,
                            aug_positive.cpu(),
                            aug_negative.cpu(),
                            augmentation_name,
                            message.cpu(),
                        )
                    )
                # end eval of augmentations

                # evaluate localization cropping
                for window_size in np.linspace(0.1, 0.9, 9):

                    mixed, true_predictions = mix(x, x_wm, window_size=window_size)
                    model_predictions = self.model.detect_watermark(mixed)
                    pendings.append(
                        pool.submit(
                            evaluate_localizations,
                            model_predictions.cpu(),
                            true_predictions.cpu(),
                            f"crop_{window_size:0.1f}",
                        )
                    )
                    mixed, true_predictions = mix(
                        x, x_wm, window_size=window_size, shuffle=True
                    )
                    model_predictions = self.model.detect_watermark(mixed)
                    pendings.append(
                        pool.submit(
                            evaluate_localizations,
                            model_predictions.cpu(),
                            true_predictions.cpu(),
                            f"shuffle_{window_size:0.1f}",
                        )
                    )
                # evaluate localization padding
                mixed, true_predictions = pad(x_wm)
                model_predictions = self.model.detect_watermark(mixed)
                pendings.append(
                    pool.submit(
                        evaluate_localizations,
                        model_predictions.cpu(),
                        true_predictions.cpu(),
                        "padding",
                    )
                )
                mixed, true_predictions = pad(x_wm, central=True)
                model_predictions = self.model.detect_watermark(mixed)
                pendings.append(
                    pool.submit(
                        evaluate_localizations,
                        model_predictions.cpu(),
                        true_predictions.cpu(),
                        "central_padding",
                    )
                )
                # end of evaluate localization

            metrics_lp = self.log_progress(
                f"{evaluate_stage_name} metrics", pendings, updates=self.log_updates
            )
            for pending in metrics_lp:
                metrics = pending.result()
                metrics = average(metrics)

        metrics = flashy.distrib.average_metrics(metrics, len(loader))
        if self.cfg.select_aug_mode == "use_eval_acc":
            # Adjust augmentation weights based on evaluation loss.
            # Higher accuracy results in lower probability of selecting this augmentation.
            for name in self.augmentations.keys():
                if (
                    self.aug_weights[name] != -1
                ):  # keep weight to -1 for unwanted augmentations
                    # set to 0.05 to ensure that an augmentation is never completely removed during a full epoch.
                    self.aug_weights[name] = max(1 - metrics[f"aug_{name}_acc"], 0.05)
        return metrics

    def generate(self):
        """Generate stage."""
        self.model.eval()
        sample_manager = SampleManager(self.xp, map_reference_to_sample_id=True)
        generate_stage_name = str(self.current_stage)

        loader = self.dataloaders["generate"]
        updates = len(loader)
        lp = self.log_progress(
            generate_stage_name, loader, total=updates, updates=self.log_updates
        )
        path_dir = os.path.join(self.path_specs, f"epoch={self.epoch}")
        os.makedirs(path_dir, exist_ok=True)
        first_batch = True
        for batch in lp:
            reference, _ = batch
            reference = reference.to(self.device)
            with torch.no_grad():
                message = random_message(self.model.nbits, reference.shape[0])
                watermark = self.model.get_watermark(reference, message)
                x_wm = reference + watermark

            reference = reference.cpu()
            sample_manager.add_samples(
                x_wm.cpu(), self.epoch, ground_truth_wavs=reference
            )
            if first_batch and flashy.distrib.is_rank_zero():
                for i in range(reference.size(0)):
                    ys = [
                        reference.cpu()[i].squeeze(0).numpy(),
                        x_wm.cpu()[i].squeeze(0).numpy(),
                        watermark.cpu()[i].squeeze(0).numpy(),
                    ]
                    path = os.path.join(path_dir, f"spec_{i}.pdf")
                    save_spectrograms(
                        ys,
                        names=["Ground Truth", "Audio Watermarked", "Watermark"],
                        sr=self.cfg.sample_rate,
                        path=path,
                    )
                first_batch = False
        flashy.distrib.barrier()

    def load_from_pretrained(self, name: str) -> dict:
        raise ValueError("No pretrained model")

    @staticmethod
    def model_from_checkpoint(
        checkpoint_path: tp.Union[Path, str],
        device: tp.Union[torch.device, str] = "cpu",
    ) -> "WMModel":
        """Instantiate a WatermarkModel from a given checkpoint path or dora sig.

        Args:
            checkpoint_path (Path or str): Path to checkpoint or dora sig from where the checkpoint is resolved.
            device (torch.device or str): Device on which the model is loaded.
        """
        checkpoint_path = str(checkpoint_path)
        logger = logging.getLogger(__name__)
        logger.info(f"Loading WatermarkModel from checkpoint: {checkpoint_path}")
        _checkpoint_path = checkpoint.resolve_checkpoint_path(
            checkpoint_path, use_fsdp=False
        )
        assert (
            _checkpoint_path is not None
        ), f"Could not resolve WatermarkModel checkpoint path: {checkpoint_path}"
        state = checkpoint.load_checkpoint(_checkpoint_path)
        assert (
            state is not None and "xp.cfg" in state
        ), f"Could not load WatermarkModel from ckpt: {checkpoint_path}"
        cfg = state["xp.cfg"]
        cfg.device = device
        watermarking_model = get_watermark_model(cfg).to(device)

        assert "best_state" in state and state["best_state"] != {}
        assert (
            "exported" not in state
        ), "When loading an exported checkpoint, use the //pretrained/ prefix."
        watermarking_model.load_state_dict(state["best_state"]["model"])
        watermarking_model.eval()
        logger.info("Watermarking model loaded!")
        return watermarking_model