content: 
  
  
  __all__ = ['TemporalMixing', 'FeatureMixing', 'MixingLayer', 'MixingLayerWithStaticExogenous', 'TSMixerx']
  
  
  from typing import Optional
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  from ..common._base_model import BaseModel
  from ..common._modules import RevINMultivariate
  from ..losses.pytorch import MAE
  
  
  class TemporalMixing(nn.Module):
      """
      TemporalMixing
      """
  
      def __init__(self, num_features, h, dropout):
          super().__init__()
          self.temporal_norm = nn.LayerNorm(normalized_shape=(h, num_features))
          self.temporal_lin = nn.Linear(h, h)
          self.temporal_drop = nn.Dropout(dropout)
  
      def forward(self, input):
          x = input.permute(0, 2, 1)  # [B, h, C] -> [B, C, h]
          x = F.relu(self.temporal_lin(x))  # [B, C, h] -> [B, C, h]
          x = x.permute(0, 2, 1)  # [B, C, h] -> [B, h, C]
          x = self.temporal_drop(x)  # [B, h, C] -> [B, h, C]
  
          return self.temporal_norm(x + input)
  
  
  class FeatureMixing(nn.Module):
      """
      FeatureMixing
      """
  
      def __init__(self, in_features, out_features, h, dropout, ff_dim):
          super().__init__()
          self.feature_lin_1 = nn.Linear(in_features=in_features, out_features=ff_dim)
          self.feature_lin_2 = nn.Linear(in_features=ff_dim, out_features=out_features)
          self.feature_drop_1 = nn.Dropout(p=dropout)
          self.feature_drop_2 = nn.Dropout(p=dropout)
          self.linear_project_residual = False
          if in_features != out_features:
              self.project_residual = nn.Linear(
                  in_features=in_features, out_features=out_features
              )
              self.linear_project_residual = True
  
          self.feature_norm = nn.LayerNorm(normalized_shape=(h, out_features))
  
      def forward(self, input):
          x = F.relu(self.feature_lin_1(input))  # [B, h, C_in] -> [B, h, ff_dim]
          x = self.feature_drop_1(x)  # [B, h, ff_dim] -> [B, h, ff_dim]
          x = self.feature_lin_2(x)  # [B, h, ff_dim] -> [B, h, C_out]
          x = self.feature_drop_2(x)  # [B, h, C_out] -> [B, h, C_out]
          if self.linear_project_residual:
              input = self.project_residual(input)  # [B, h, C_in] -> [B, h, C_out]
  
          return self.feature_norm(x + input)
  
  
  class MixingLayer(nn.Module):
      """
      MixingLayer
      """
  
      def __init__(self, in_features, out_features, h, dropout, ff_dim):
          super().__init__()
          # Mixing layer consists of a temporal and feature mixer
          self.temporal_mixer = TemporalMixing(
              num_features=in_features, h=h, dropout=dropout
          )
          self.feature_mixer = FeatureMixing(
              in_features=in_features,
              out_features=out_features,
              h=h,
              dropout=dropout,
              ff_dim=ff_dim,
          )
  
      def forward(self, input):
          x = self.temporal_mixer(input)  # [B, h, C_in] -> [B, h, C_in]
          x = self.feature_mixer(x)  # [B, h, C_in] -> [B, h, C_out]
          return x
  
  
  class MixingLayerWithStaticExogenous(nn.Module):
      """
      MixingLayerWithStaticExogenous
      """
  
      def __init__(self, h, dropout, ff_dim, stat_input_size):
          super().__init__()
          # Feature mixer for the static exogenous variables
          self.feature_mixer_stat = FeatureMixing(
              in_features=stat_input_size,
              out_features=ff_dim,
              h=h,
              dropout=dropout,
              ff_dim=ff_dim,
          )
          # Mixing layer consists of a temporal and feature mixer
          self.temporal_mixer = TemporalMixing(
              num_features=2 * ff_dim, h=h, dropout=dropout
          )
          self.feature_mixer = FeatureMixing(
              in_features=2 * ff_dim,
              out_features=ff_dim,
              h=h,
              dropout=dropout,
              ff_dim=ff_dim,
          )
  
      def forward(self, inputs):
          input, stat_exog = inputs
          x_stat = self.feature_mixer_stat(stat_exog)  # [B, h, S] -> [B, h, ff_dim]
          x = torch.cat(
              (input, x_stat), dim=2
          )  # [B, h, ff_dim] + [B, h, ff_dim] -> [B, h, 2 * ff_dim]
          x = self.temporal_mixer(x)  # [B, h, 2 * ff_dim] -> [B, h, 2 * ff_dim]
          x = self.feature_mixer(x)  # [B, h, 2 * ff_dim] -> [B, h, ff_dim]
          return (x, stat_exog)
  
  
  class ReversibleInstanceNorm1d(nn.Module):
      def __init__(self, n_series, eps=1e-5):
          super().__init__()
          self.weight = nn.Parameter(torch.ones((1, 1, 1, n_series)))
          self.bias = nn.Parameter(torch.zeros((1, 1, 1, n_series)))
          self.eps = eps
  
      def forward(self, x):
          # Batch statistics
          self.batch_mean = torch.mean(x, axis=2, keepdim=True).detach()
          self.batch_std = torch.sqrt(
              torch.var(x, axis=2, keepdim=True, unbiased=False) + self.eps
          ).detach()
  
          # Instance normalization
          x = x - self.batch_mean
          x = x / self.batch_std
          x = x * self.weight
          x = x + self.bias
  
          return x
  
      def reverse(self, x):
          # Reverse the normalization
          x = x - self.bias
          x = x / self.weight
          x = x * self.batch_std
          x = x + self.batch_mean
  
          return x
  
  
  class TSMixerx(BaseModel):
      """TSMixerx
  
      Time-Series Mixer exogenous (`TSMixerx`) is a MLP-based multivariate time-series forecasting model, with capability for additional exogenous inputs. `TSMixerx` jointly learns temporal and cross-sectional representations of the time-series by repeatedly combining time- and feature information using stacked mixing layers. A mixing layer consists of a sequential time- and feature Multi Layer Perceptron (`MLP`).
  
      Args:
          h (int): forecast horizon.
          input_size (int): considered autorregresive inputs (lags), y=[1,2,3,4] input_size=2 -> lags=[1,2].
          n_series (int): number of time-series.
          futr_exog_list (str list): future exogenous columns.
          hist_exog_list (str list): historic exogenous columns.
          stat_exog_list (str list): static exogenous columns.
          exclude_insample_y (bool): if True excludes insample_y from the model.
          n_block (int): number of mixing layers in the model.
          ff_dim (int): number of units for the second feed-forward layer in the feature MLP.
          dropout (float): dropout rate between (0, 1) .
          revin (bool): if True uses Reverse Instance Normalization on `insample_y` and applies it to the outputs.
          loss (PyTorch module): instantiated train loss class from [losses collection](./losses.pytorch).
          valid_loss (PyTorch module): instantiated valid loss class from [losses collection](./losses.pytorch).
          max_steps (int): maximum number of training steps.
          learning_rate (float): Learning rate between (0, 1).
          num_lr_decays (int): Number of learning rate decays, evenly distributed across max_steps.
          early_stop_patience_steps (int): Number of validation iterations before early stopping.
          val_check_steps (int): Number of training steps between every validation loss check.
          batch_size (int): number of different series in each batch.
          valid_batch_size (int): number of different series in each validation and test batch, if None uses batch_size.
          windows_batch_size (int): number of windows to sample in each training batch.
          inference_windows_batch_size (int): number of windows to sample in each inference batch, -1 uses all.
          start_padding_enabled (bool): if True, the model will pad the time series with zeros at the beginning, by input size.
          training_data_availability_threshold (Union[float, List[float]]): minimum fraction of valid data points required for training windows. Single float applies to both insample and outsample; list of two floats specifies [insample_fraction, outsample_fraction]. Default 0.0 allows windows with only 1 valid data point (current behavior).
          step_size (int): step size between each window of temporal data.
          scaler_type (str): type of scaler for temporal inputs normalization see [temporal scalers](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/common/_scalers.py).
          random_seed (int): random_seed for pytorch initializer and numpy generators.
          drop_last_loader (bool): if True `TimeSeriesDataLoader` drops last non-full batch.
          alias (str): optional,  Custom name of the model.
          optimizer (Subclass of 'torch.optim.Optimizer'): optional, user specified optimizer instead of the default choice (Adam).
          optimizer_kwargs (dict): optional, list of parameters used by the user specified `optimizer`.
          lr_scheduler (Subclass of 'torch.optim.lr_scheduler.LRScheduler'): optional, user specified lr_scheduler instead of the default choice (StepLR).
          lr_scheduler_kwargs (dict): optional, list of parameters used by the user specified `lr_scheduler`.
          dataloader_kwargs (dict): optional, list of parameters passed into the PyTorch Lightning dataloader by the `TimeSeriesDataLoader`.
          **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).
  
      References:
          - [Chen, Si-An, Chun-Liang Li, Nate Yoder, Sercan O. Arik, and Tomas Pfister (2023). "TSMixer: An All-MLP Architecture for Time Series Forecasting."](http://arxiv.org/abs/2303.06053)
  
      """
  
      # Class attributes
      EXOGENOUS_FUTR = True
      EXOGENOUS_HIST = True
      EXOGENOUS_STAT = True
      MULTIVARIATE = True  # If the model produces multivariate forecasts (True) or univariate (False)
      RECURRENT = (
          False  # If the model produces forecasts recursively (True) or direct (False)
      )
  
      def __init__(
          self,
          h,
          input_size,
          n_series,
          futr_exog_list=None,
          hist_exog_list=None,
          stat_exog_list=None,
          exclude_insample_y=False,
          n_block=2,
          ff_dim=64,
          dropout=0.0,
          revin=True,
          loss=MAE(),
          valid_loss=None,
          max_steps: int = 1000,
          learning_rate: float = 1e-3,
          num_lr_decays: int = -1,
          early_stop_patience_steps: int = -1,
          val_check_steps: int = 100,
          batch_size: int = 32,
          valid_batch_size: Optional[int] = None,
          windows_batch_size=32,
          inference_windows_batch_size=32,
          start_padding_enabled=False,
          training_data_availability_threshold=0.0,
          step_size: int = 1,
          scaler_type: str = "identity",
          random_seed: int = 1,
          drop_last_loader: bool = False,
          alias: Optional[str] = None,
          optimizer=None,
          optimizer_kwargs=None,
          lr_scheduler=None,
          lr_scheduler_kwargs=None,
          dataloader_kwargs=None,
          **trainer_kwargs
      ):
  
          # Inherit BaseMultvariate class
          super(TSMixerx, self).__init__(
              h=h,
              input_size=input_size,
              n_series=n_series,
              futr_exog_list=futr_exog_list,
              hist_exog_list=hist_exog_list,
              stat_exog_list=stat_exog_list,
              exclude_insample_y=exclude_insample_y,
              loss=loss,
              valid_loss=valid_loss,
              max_steps=max_steps,
              learning_rate=learning_rate,
              num_lr_decays=num_lr_decays,
              early_stop_patience_steps=early_stop_patience_steps,
              val_check_steps=val_check_steps,
              batch_size=batch_size,
              valid_batch_size=valid_batch_size,
              windows_batch_size=windows_batch_size,
              inference_windows_batch_size=inference_windows_batch_size,
              start_padding_enabled=start_padding_enabled,
              training_data_availability_threshold=training_data_availability_threshold,
              step_size=step_size,
              scaler_type=scaler_type,
              random_seed=random_seed,
              drop_last_loader=drop_last_loader,
              alias=alias,
              optimizer=optimizer,
              optimizer_kwargs=optimizer_kwargs,
              lr_scheduler=lr_scheduler,
              lr_scheduler_kwargs=lr_scheduler_kwargs,
              dataloader_kwargs=dataloader_kwargs,
              **trainer_kwargs
          )
          # Reversible InstanceNormalization layer
          self.revin = revin
          if self.revin:
              self.norm = RevINMultivariate(num_features=n_series, affine=True)
  
          # Forecast horizon
          self.h = h
  
          # Temporal projection and feature mixing of historical variables
          self.temporal_projection = nn.Linear(in_features=input_size, out_features=h)
  
          self.feature_mixer_hist = FeatureMixing(
              in_features=n_series * (1 + self.hist_exog_size + self.futr_exog_size),
              out_features=ff_dim,
              h=h,
              dropout=dropout,
              ff_dim=ff_dim,
          )
          first_mixing_ff_dim_multiplier = 1
  
          # Feature mixing of future variables
          if self.futr_exog_size > 0:
              self.feature_mixer_futr = FeatureMixing(
                  in_features=n_series * self.futr_exog_size,
                  out_features=ff_dim,
                  h=h,
                  dropout=dropout,
                  ff_dim=ff_dim,
              )
              first_mixing_ff_dim_multiplier += 1
  
          # Feature mixing of static variables
          if self.stat_exog_size > 0:
              self.feature_mixer_stat = FeatureMixing(
                  in_features=self.stat_exog_size * n_series,
                  out_features=ff_dim,
                  h=h,
                  dropout=dropout,
                  ff_dim=ff_dim,
              )
              first_mixing_ff_dim_multiplier += 1
  
          # First mixing layer
          self.first_mixing = MixingLayer(
              in_features=first_mixing_ff_dim_multiplier * ff_dim,
              out_features=ff_dim,
              h=h,
              dropout=dropout,
              ff_dim=ff_dim,
          )
  
          # Mixing layer block
          if self.stat_exog_size > 0:
              mixing_layers = [
                  MixingLayerWithStaticExogenous(
                      h=h,
                      dropout=dropout,
                      ff_dim=ff_dim,
                      stat_input_size=self.stat_exog_size * n_series,
                  )
                  for _ in range(n_block)
              ]
          else:
              mixing_layers = [
                  MixingLayer(
                      in_features=ff_dim,
                      out_features=ff_dim,
                      h=h,
                      dropout=dropout,
                      ff_dim=ff_dim,
                  )
                  for _ in range(n_block)
              ]
  
          self.mixing_block = nn.Sequential(*mixing_layers)
  
          # Linear output with Loss dependent dimensions
          self.out = nn.Linear(
              in_features=ff_dim, out_features=self.loss.outputsize_multiplier * n_series
          )
  
      def forward(self, windows_batch):
          # Parse batch
          x = windows_batch[
              "insample_y"
          ]  #   [batch_size (B), input_size (L), n_series (N)]
          hist_exog = windows_batch["hist_exog"]  #   [B, hist_exog_size (X), L, N]
          futr_exog = windows_batch["futr_exog"]  #   [B, futr_exog_size (F), L + h, N]
          stat_exog = windows_batch["stat_exog"]  #   [N, stat_exog_size (S)]
          batch_size, input_size = x.shape[:2]
  
          # Apply revin to x
          if self.revin:
              x = self.norm(x, mode="norm")  #   [B, L, N] -> [B, L, N]
  
          # Add channel dimension to x
          x = x.unsqueeze(1)  #   [B, L, N] -> [B, 1, L, N]
  
          # Concatenate x with historical exogenous
          if self.hist_exog_size > 0:
              x = torch.cat(
                  (x, hist_exog), dim=1
              )  #   [B, 1, L, N] + [B, X, L, N] -> [B, 1 + X, L, N]
  
          # Concatenate x with future exogenous of input sequence
          if self.futr_exog_size > 0:
              futr_exog_hist = futr_exog[
                  :, :, :input_size
              ]  #   [B, F, L + h, N] -> [B, F, L, N]
              x = torch.cat(
                  (x, futr_exog_hist), dim=1
              )  #   [B, 1 + X, L, N] + [B, F, L, N] -> [B, 1 + X + F, L, N]
  
          # Temporal projection & feature mixing of x
          x = x.permute(0, 1, 3, 2)  #   [B, 1 + X + F, L, N] -> [B, 1 + X + F, N, L]
          x = self.temporal_projection(
              x
          )  #   [B, 1 + X + F, N, L] -> [B, 1 + X + F, N, h]
          x = x.permute(0, 3, 1, 2)  #   [B, 1 + X + F, N, h] -> [B, h, 1 + X + F, N]
          x = x.reshape(
              batch_size, self.h, -1
          )  #   [B, h, 1 + X + F, N] -> [B, h, (1 + X + F) * N]
          x = self.feature_mixer_hist(x)  #   [B, h, (1 + X + F) * N] -> [B, h, ff_dim]
  
          # Concatenate x with future exogenous of output horizon
          if self.futr_exog_size > 0:
              x_futr = futr_exog[:, :, input_size:]  #   [B, F, L + h, N] -> [B, F, h, N]
              x_futr = x_futr.permute(0, 2, 1, 3)  #   [B, F, h, N] -> [B, h, F, N]
              x_futr = x_futr.reshape(
                  batch_size, self.h, -1
              )  #   [B, h, N, F] -> [B, h, N * F]
              x_futr = self.feature_mixer_futr(
                  x_futr
              )  #   [B, h, N * F] -> [B, h, ff_dim]
              x = torch.cat(
                  (x, x_futr), dim=2
              )  #   [B, h, ff_dim] + [B, h, ff_dim] -> [B, h, 2 * ff_dim]
  
          # Concatenate x with static exogenous
          if self.stat_exog_size > 0:
              stat_exog = stat_exog.reshape(-1)  #   [N, S] -> [N * S]
              stat_exog = (
                  stat_exog.unsqueeze(0).unsqueeze(1).repeat(batch_size, self.h, 1)
              )  #   [N * S] -> [B, h, N * S]
              x_stat = self.feature_mixer_stat(
                  stat_exog
              )  #   [B, h, N * S] -> [B, h, ff_dim]
              x = torch.cat(
                  (x, x_stat), dim=2
              )  #   [B, h, 2 * ff_dim] + [B, h, ff_dim] -> [B, h, 3 * ff_dim]
  
          # First mixing layer
          x = self.first_mixing(x)  #   [B, h, 3 * ff_dim] -> [B, h, ff_dim]
  
          # N blocks of mixing layers
          if self.stat_exog_size > 0:
              x, _ = self.mixing_block(
                  (x, stat_exog)
              )  #   [B, h, ff_dim], [B, h, N * S] -> [B, h, ff_dim]
          else:
              x = self.mixing_block(x)  #   [B, h, ff_dim] -> [B, h, ff_dim]
  
          # Fully connected output layer
          forecast = self.out(x)  #   [B, h, ff_dim] -> [B, h, N * n_outputs]
  
          # Reverse Instance Normalization on output
          if self.revin:
              forecast = forecast.reshape(
                  batch_size, self.h * self.loss.outputsize_multiplier, -1
              )  #   [B, h, N * n_outputs] -> [B, h * n_outputs, N]
              forecast = self.norm(forecast, "denorm")
              forecast = forecast.reshape(
                  batch_size, self.h, -1
              )  #   [B, h * n_outputs, N] -> [B, h, n_outputs * N]
  
          return forecast
  