"""

"""

# Created by Wenjie Du <wenjay.du@gmail.com>
# License: BSD-3-Clause

from ...nn.functional import (
    nonstationary_norm,
    nonstationary_denorm,
)
from ...nn.modules import ModelCore
from ...nn.modules.loss import Criterion
from ...nn.modules.timemixerpp import BackboneTimeMixerPP


class _TimeMixerPP(ModelCore):
    def __init__(
        self,
        n_steps: int,
        n_features: int,
        n_layers: int,
        d_model: int,
        d_ffn: int,
        n_heads: int,
        dropout: float,
        top_k: int,
        n_kernels: int,
        channel_mixing: bool,
        channel_independence: bool,
        downsampling_layers: int,
        downsampling_window: int,
        apply_nonstationary_norm: bool,
        training_loss: Criterion,
        validation_metric: Criterion,
        task_name: str,
    ):
        super().__init__()

        self.apply_nonstationary_norm = apply_nonstationary_norm
        self.training_loss = training_loss
        if validation_metric.__class__.__name__ == "Criterion":
            # in this case, we need validation_metric.lower_better in _train_model() so only pass Criterion()
            # we use training_loss as validation_metric for concrete calculation process
            self.validation_metric = self.training_loss
        else:
            self.validation_metric = validation_metric

        self.model = BackboneTimeMixerPP(
            task_name=task_name,
            n_steps=n_steps,
            n_features=n_features,
            n_pred_steps=0,
            n_pred_features=n_features,
            n_layers=n_layers,
            d_model=d_model,
            d_ffn=d_ffn,
            n_heads=n_heads,
            dropout=dropout,
            top_k=top_k,
            n_kernels=n_kernels,
            channel_mixing=channel_mixing,
            channel_independence=channel_independence,
            downsampling_layers=downsampling_layers,
            downsampling_window=downsampling_window,
            downsampling_method="avg",
            use_future_temporal_feature=False,
        )

    def forward(
        self,
        inputs: dict,
        calc_criterion: bool = False,
    ) -> dict:
        X, missing_mask = inputs["X"], inputs["missing_mask"]

        if self.apply_nonstationary_norm:
            # Normalization from Non-stationary Transformer
            X, means, stdev = nonstationary_norm(X, missing_mask)

        # TimesMixer processing
        reconstruction = self.model.imputation(X, None)

        if self.apply_nonstationary_norm:
            # De-Normalization from Non-stationary Transformer
            reconstruction = nonstationary_denorm(reconstruction, means, stdev)

        imputed_data = missing_mask * X + (1 - missing_mask) * reconstruction
        results = {
            "imputation": imputed_data,
            "reconstruction": reconstruction,
        }

        if calc_criterion:
            X_ori, indicating_mask = inputs["X_ori"], inputs["indicating_mask"]
            if self.training:  # if in the training mode (the training stage), return loss result from training_loss
                # `loss` is always the item for backward propagating to update the model
                loss, ORT_loss, MIT_loss = self.training_loss(reconstruction, X_ori, missing_mask, indicating_mask)
                results["loss"] = loss
                results["ORT_loss"] = ORT_loss
                results["MIT_loss"] = MIT_loss
            else:  # if in the eval mode (the validation stage), return metric result from validation_metric
                results["metric"] = self.validation_metric(reconstruction, X_ori, indicating_mask)

        return results
