# AUTOGENERATED! DO NOT EDIT! File to edit: ../../nbs/models.nbeatsx.ipynb.

# %% auto 0
__all__ = ['NBEATSx']

# %% ../../nbs/models.nbeatsx.ipynb 5
from typing import Tuple

import numpy as np
import torch
import torch.nn as nn

from ..losses.pytorch import MAE
from ..common._base_windows import BaseWindows

# %% ../../nbs/models.nbeatsx.ipynb 8
class IdentityBasis(nn.Module):
    def __init__(self, backcast_size: int, forecast_size: int, out_features: int = 1):
        super().__init__()
        self.out_features = out_features
        self.forecast_size = forecast_size
        self.backcast_size = backcast_size

    def forward(self, theta: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        backcast = theta[:, : self.backcast_size]
        forecast = theta[:, self.backcast_size :]
        forecast = forecast.reshape(len(forecast), -1, self.out_features)
        return backcast, forecast


class TrendBasis(nn.Module):
    def __init__(
        self,
        degree_of_polynomial: int,
        backcast_size: int,
        forecast_size: int,
        out_features: int = 1,
    ):
        super().__init__()
        self.out_features = out_features
        polynomial_size = degree_of_polynomial + 1
        self.backcast_basis = nn.Parameter(
            torch.tensor(
                np.concatenate(
                    [
                        np.power(
                            np.arange(backcast_size, dtype=float) / backcast_size, i
                        )[None, :]
                        for i in range(polynomial_size)
                    ]
                ),
                dtype=torch.float32,
            ),
            requires_grad=False,
        )
        self.forecast_basis = nn.Parameter(
            torch.tensor(
                np.concatenate(
                    [
                        np.power(
                            np.arange(forecast_size, dtype=float) / forecast_size, i
                        )[None, :]
                        for i in range(polynomial_size)
                    ]
                ),
                dtype=torch.float32,
            ),
            requires_grad=False,
        )

    def forward(self, theta: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        polynomial_size = self.forecast_basis.shape[0]  # [polynomial_size, L+H]
        backcast_theta = theta[:, :polynomial_size]
        forecast_theta = theta[:, polynomial_size:]
        forecast_theta = forecast_theta.reshape(
            len(forecast_theta), polynomial_size, -1
        )
        backcast = torch.einsum("bp,pt->bt", backcast_theta, self.backcast_basis)
        forecast = torch.einsum("bpq,pt->btq", forecast_theta, self.forecast_basis)
        return backcast, forecast


class SeasonalityBasis(nn.Module):
    def __init__(
        self,
        harmonics: int,
        backcast_size: int,
        forecast_size: int,
        out_features: int = 1,
    ):
        super().__init__()
        self.out_features = out_features
        frequency = np.append(
            np.zeros(1, dtype=float),
            np.arange(harmonics, harmonics / 2 * forecast_size, dtype=float)
            / harmonics,
        )[None, :]
        backcast_grid = (
            -2
            * np.pi
            * (np.arange(backcast_size, dtype=float)[:, None] / forecast_size)
            * frequency
        )
        forecast_grid = (
            2
            * np.pi
            * (np.arange(forecast_size, dtype=float)[:, None] / forecast_size)
            * frequency
        )

        backcast_cos_template = torch.tensor(
            np.transpose(np.cos(backcast_grid)), dtype=torch.float32
        )
        backcast_sin_template = torch.tensor(
            np.transpose(np.sin(backcast_grid)), dtype=torch.float32
        )
        backcast_template = torch.cat(
            [backcast_cos_template, backcast_sin_template], dim=0
        )

        forecast_cos_template = torch.tensor(
            np.transpose(np.cos(forecast_grid)), dtype=torch.float32
        )
        forecast_sin_template = torch.tensor(
            np.transpose(np.sin(forecast_grid)), dtype=torch.float32
        )
        forecast_template = torch.cat(
            [forecast_cos_template, forecast_sin_template], dim=0
        )

        self.backcast_basis = nn.Parameter(backcast_template, requires_grad=False)
        self.forecast_basis = nn.Parameter(forecast_template, requires_grad=False)

    def forward(self, theta: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
        harmonic_size = self.forecast_basis.shape[0]  # [harmonic_size, L+H]
        backcast_theta = theta[:, :harmonic_size]
        forecast_theta = theta[:, harmonic_size:]
        forecast_theta = forecast_theta.reshape(len(forecast_theta), harmonic_size, -1)
        backcast = torch.einsum("bp,pt->bt", backcast_theta, self.backcast_basis)
        forecast = torch.einsum("bpq,pt->btq", forecast_theta, self.forecast_basis)
        return backcast, forecast

# %% ../../nbs/models.nbeatsx.ipynb 9
ACTIVATIONS = ["ReLU", "Softplus", "Tanh", "SELU", "LeakyReLU", "PReLU", "Sigmoid"]


class NBEATSBlock(nn.Module):
    """
    N-BEATS block which takes a basis function as an argument.
    """

    def __init__(
        self,
        input_size: int,
        h: int,
        futr_input_size: int,
        hist_input_size: int,
        stat_input_size: int,
        n_theta: int,
        mlp_units: list,
        basis: nn.Module,
        dropout_prob: float,
        activation: str,
    ):
        """ """
        super().__init__()

        self.dropout_prob = dropout_prob
        self.futr_input_size = futr_input_size
        self.hist_input_size = hist_input_size
        self.stat_input_size = stat_input_size

        assert activation in ACTIVATIONS, f"{activation} is not in {ACTIVATIONS}"
        activ = getattr(nn, activation)()

        # Input vector for the block is
        # y_lags (input_size) + historical exogenous (hist_input_size*input_size) +
        # future exogenous (futr_input_size*input_size) + static exogenous (stat_input_size)
        # [ Y_[t-L:t], X_[t-L:t], F_[t-L:t+H], S ]
        input_size = (
            input_size
            + hist_input_size * input_size
            + futr_input_size * (input_size + h)
            + stat_input_size
        )

        hidden_layers = [
            nn.Linear(in_features=input_size, out_features=mlp_units[0][0])
        ]
        for layer in mlp_units:
            hidden_layers.append(nn.Linear(in_features=layer[0], out_features=layer[1]))
            hidden_layers.append(activ)

            if self.dropout_prob > 0:
                raise NotImplementedError("dropout")
                # hidden_layers.append(nn.Dropout(p=self.dropout_prob))

        output_layer = [nn.Linear(in_features=mlp_units[-1][1], out_features=n_theta)]
        layers = hidden_layers + output_layer
        self.layers = nn.Sequential(*layers)
        self.basis = basis

    def forward(
        self,
        insample_y: torch.Tensor,
        futr_exog: torch.Tensor,
        hist_exog: torch.Tensor,
        stat_exog: torch.Tensor,
    ) -> Tuple[torch.Tensor, torch.Tensor]:

        # Flatten MLP inputs [B, L+H, C] -> [B, (L+H)*C]
        # Contatenate [ Y_t, | X_{t-L},..., X_{t} | F_{t-L},..., F_{t+H} | S ]
        batch_size = len(insample_y)
        if self.hist_input_size > 0:
            insample_y = torch.cat(
                (insample_y, hist_exog.reshape(batch_size, -1)), dim=1
            )

        if self.futr_input_size > 0:
            insample_y = torch.cat(
                (insample_y, futr_exog.reshape(batch_size, -1)), dim=1
            )

        if self.stat_input_size > 0:
            insample_y = torch.cat(
                (insample_y, stat_exog.reshape(batch_size, -1)), dim=1
            )

        # Compute local projection weights and projection
        theta = self.layers(insample_y)
        backcast, forecast = self.basis(theta)
        return backcast, forecast

# %% ../../nbs/models.nbeatsx.ipynb 10
class NBEATSx(BaseWindows):
    """NBEATSx

    The Neural Basis Expansion Analysis with Exogenous variables (NBEATSx) is a simple
    and effective deep learning architecture. It is built with a deep stack of MLPs with
    doubly residual connections. The NBEATSx architecture includes additional exogenous
    blocks, extending NBEATS capabilities and interpretability. With its interpretable
    version, NBEATSx decomposes its predictions on seasonality, trend, and exogenous effects.

    **Parameters:**<br>
    `h`: int, Forecast horizon. <br>
    `input_size`: int, autorregresive inputs size, y=[1,2,3,4] input_size=2 -> y_[t-2:t]=[1,2].<br>
    `stat_exog_list`: str list, static exogenous columns.<br>
    `hist_exog_list`: str list, historic exogenous columns.<br>
    `futr_exog_list`: str list, future exogenous columns.<br>
    `n_harmonics`: int, Number of harmonic terms for trend stack type. Note that len(n_harmonics) = len(stack_types). Note that it will only be used if a trend stack is used.<br>
    `n_polynomials`: int, Number of polynomial terms for seasonality stack type. Note that len(n_polynomials) = len(stack_types). Note that it will only be used if a seasonality stack is used.<br>
    `stack_types`: List[str], List of stack types. Subset from ['seasonality', 'trend', 'identity'].<br>
    `n_blocks`: List[int], Number of blocks for each stack. Note that len(n_blocks) = len(stack_types).<br>
    `mlp_units`: List[List[int]], Structure of hidden layers for each stack type. Each internal list should contain the number of units of each hidden layer. Note that len(n_hidden) = len(stack_types).<br>
    `dropout_prob_theta`: float, Float between (0, 1). Dropout for N-BEATS basis.<br>
    `shared_weights`: bool, If True, all blocks within each stack will share parameters.<br>
    `activation`: str, activation from ['ReLU', 'Softplus', 'Tanh', 'SELU', 'LeakyReLU', 'PReLU', 'Sigmoid'].<br>
    `loss`: PyTorch module, instantiated train loss class from [losses collection](https://nixtla.github.io/neuralforecast/losses.pytorch.html).<br>
    `learning_rate`: float, initial optimization learning rate.<br>
    `batch_size`: int, number of different series in each batch.<br>
    `windows_batch_size`: int=None, windows sampled from rolled data, default uses all.<br>
    `step_size`: int=1, step size between each window of temporal data.<br>
    `scaler_type`: str='identity', type of scaler for temporal inputs normalization see [temporal scalers](https://nixtla.github.io/neuralforecast/common.scalers.html).<br>
    `random_seed`: int, random seed initialization for replicability.<br>
    `num_workers_loader`: int=os.cpu_count(), workers to be used by `TimeSeriesDataLoader`.<br>
    `drop_last_loader`: bool=False, if True `TimeSeriesDataLoader` drops last non-full batch.<br>
    `**trainer_kwargs`: int,  keyword trainer arguments inherited from [PyTorch Lighning's trainer](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).<br>

    **References:**<br>
    -[Kin G. Olivares, Cristian Challu, Grzegorz Marcjasz, Rafał Weron, Artur Dubrawski (2021).
    "Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx".](https://arxiv.org/abs/2104.05522)
    """

    def __init__(
        self,
        h,
        input_size,
        futr_exog_list=None,
        hist_exog_list=None,
        stat_exog_list=None,
        n_harmonics=2,
        n_polynomials=2,
        stack_types: list = ["identity", "trend", "seasonality"],
        n_blocks: list = [1, 1, 1],
        mlp_units: list = 3 * [[512, 512]],
        dropout_prob_theta=0.0,
        activation="ReLU",
        shared_weights=False,
        loss=MAE(),
        learning_rate=1e-3,
        batch_size=32,
        windows_batch_size: int = 1024,
        step_size: int = 1,
        scaler_type="identity",
        random_seed=1,
        num_workers_loader=0,
        drop_last_loader=False,
        **trainer_kwargs,
    ):

        # Inherit BaseWindows class
        super(NBEATSx, self).__init__(
            h=h,
            input_size=input_size,
            futr_exog_list=futr_exog_list,
            hist_exog_list=hist_exog_list,
            stat_exog_list=stat_exog_list,
            loss=loss,
            learning_rate=learning_rate,
            batch_size=batch_size,
            windows_batch_size=windows_batch_size,
            step_size=step_size,
            scaler_type=scaler_type,
            num_workers_loader=num_workers_loader,
            drop_last_loader=drop_last_loader,
            random_seed=random_seed,
            **trainer_kwargs,
        )

        # Architecture
        self.futr_input_size = len(self.futr_exog_list)
        self.hist_input_size = len(self.hist_exog_list)
        self.stat_input_size = len(self.stat_exog_list)

        blocks = self.create_stack(
            h=h,
            input_size=input_size,
            futr_input_size=self.futr_input_size,
            hist_input_size=self.hist_input_size,
            stat_input_size=self.stat_input_size,
            stack_types=stack_types,
            n_blocks=n_blocks,
            mlp_units=mlp_units,
            dropout_prob_theta=dropout_prob_theta,
            activation=activation,
            shared_weights=shared_weights,
            n_polynomials=n_polynomials,
            n_harmonics=n_harmonics,
        )
        self.blocks = torch.nn.ModuleList(blocks)

        # Adapter with Loss dependent dimensions
        if self.loss.outputsize_multiplier > 1:
            self.out = nn.Linear(
                in_features=h, out_features=h * self.loss.outputsize_multiplier
            )

    def create_stack(
        self,
        h,
        input_size,
        stack_types,
        n_blocks,
        mlp_units,
        dropout_prob_theta,
        activation,
        shared_weights,
        n_polynomials,
        n_harmonics,
        futr_input_size,
        hist_input_size,
        stat_input_size,
    ):

        block_list = []
        for i in range(len(stack_types)):
            for block_id in range(n_blocks[i]):

                # Shared weights
                if shared_weights and block_id > 0:
                    nbeats_block = block_list[-1]
                else:
                    if stack_types[i] == "seasonality":
                        n_theta = (
                            2
                            * (self.loss.outputsize_multiplier + 1)
                            * int(np.ceil(n_harmonics / 2 * h) - (n_harmonics - 1))
                        )
                        basis = SeasonalityBasis(
                            harmonics=n_harmonics,
                            backcast_size=input_size,
                            forecast_size=h,
                            out_features=self.loss.outputsize_multiplier,
                        )

                    elif stack_types[i] == "trend":
                        n_theta = (self.loss.outputsize_multiplier + 1) * (
                            n_polynomials + 1
                        )
                        basis = TrendBasis(
                            degree_of_polynomial=n_polynomials,
                            backcast_size=input_size,
                            forecast_size=h,
                            out_features=self.loss.outputsize_multiplier,
                        )

                    elif stack_types[i] == "identity":
                        n_theta = input_size + self.loss.outputsize_multiplier * h
                        basis = IdentityBasis(
                            backcast_size=input_size,
                            forecast_size=h,
                            out_features=self.loss.outputsize_multiplier,
                        )
                    else:
                        raise ValueError(f"Block type {stack_types[i]} not found!")

                    nbeats_block = NBEATSBlock(
                        input_size=input_size,
                        h=h,
                        futr_input_size=futr_input_size,
                        hist_input_size=hist_input_size,
                        stat_input_size=stat_input_size,
                        n_theta=n_theta,
                        mlp_units=mlp_units,
                        basis=basis,
                        dropout_prob=dropout_prob_theta,
                        activation=activation,
                    )

                # Select type of evaluation and apply it to all layers of block
                block_list.append(nbeats_block)

        return block_list

    def forward(self, windows_batch):

        # Parse windows_batch
        insample_y = windows_batch["insample_y"]
        insample_mask = windows_batch["insample_mask"]
        futr_exog = windows_batch["futr_exog"]
        hist_exog = windows_batch["hist_exog"]
        stat_exog = windows_batch["stat_exog"]

        # NBEATSx' forward
        residuals = insample_y.flip(dims=(-1,))  # backcast init
        insample_mask = insample_mask.flip(dims=(-1,))

        forecast = insample_y[:, -1:, None]  # Level with Naive1
        block_forecasts = [forecast.repeat(1, self.h, 1)]
        for i, block in enumerate(self.blocks):
            backcast, block_forecast = block(
                insample_y=residuals,
                futr_exog=futr_exog,
                hist_exog=hist_exog,
                stat_exog=stat_exog,
            )
            residuals = (residuals - backcast) * insample_mask
            forecast = forecast + block_forecast

            if self.decompose_forecast:
                block_forecasts.append(block_forecast)

        # Adapting output's domain
        forecast = self.loss.domain_map(forecast)

        if self.decompose_forecast:
            # (n_batch, n_blocks, h)
            block_forecasts = torch.stack(block_forecasts)
            block_forecasts = block_forecasts.permute(1, 0, 2, 3)
            block_forecasts = block_forecasts.squeeze(-1)  # univariate output
            return block_forecasts
        else:
            return forecast
