content: 
  
  
  __all__ = ['TemporalMixing', 'FeatureMixing', 'MixingLayer', 'TSMixer']
  
  
  from typing import Optional
  
  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, n_series, input_size, dropout):
          super().__init__()
          self.temporal_norm = nn.BatchNorm1d(
              num_features=n_series * input_size, eps=0.001, momentum=0.01
          )
          self.temporal_lin = nn.Linear(input_size, input_size)
          self.temporal_drop = nn.Dropout(dropout)
  
      def forward(self, input):
          # Get shapes
          batch_size = input.shape[0]
          input_size = input.shape[1]
          n_series = input.shape[2]
  
          # Temporal MLP
          x = input.permute(0, 2, 1)  # [B, L, N] -> [B, N, L]
          x = x.reshape(batch_size, -1)  # [B, N, L] -> [B, N * L]
          x = self.temporal_norm(x)  # [B, N * L] -> [B, N * L]
          x = x.reshape(batch_size, n_series, input_size)  # [B, N * L] -> [B, N, L]
          x = F.relu(self.temporal_lin(x))  # [B, N, L] -> [B, N, L]
          x = x.permute(0, 2, 1)  # [B, N, L] -> [B, L, N]
          x = self.temporal_drop(x)  # [B, L, N] -> [B, L, N]
  
          return x + input
  
  
  class FeatureMixing(nn.Module):
      """
      FeatureMixing
      """
  
      def __init__(self, n_series, input_size, dropout, ff_dim):
          super().__init__()
          self.feature_norm = nn.BatchNorm1d(
              num_features=n_series * input_size, eps=0.001, momentum=0.01
          )
          self.feature_lin_1 = nn.Linear(n_series, ff_dim)
          self.feature_lin_2 = nn.Linear(ff_dim, n_series)
          self.feature_drop_1 = nn.Dropout(dropout)
          self.feature_drop_2 = nn.Dropout(dropout)
  
      def forward(self, input):
          # Get shapes
          batch_size = input.shape[0]
          input_size = input.shape[1]
          n_series = input.shape[2]
  
          # Feature MLP
          x = input.reshape(batch_size, -1)  # [B, L, N] -> [B, L * N]
          x = self.feature_norm(x)  # [B, L * N] -> [B, L * N]
          x = x.reshape(batch_size, input_size, n_series)  # [B, L * N] -> [B, L, N]
          x = F.relu(self.feature_lin_1(x))  # [B, L, N] -> [B, L, ff_dim]
          x = self.feature_drop_1(x)  # [B, L, ff_dim] -> [B, L, ff_dim]
          x = self.feature_lin_2(x)  # [B, L, ff_dim] -> [B, L, N]
          x = self.feature_drop_2(x)  # [B, L, N] -> [B, L, N]
  
          return x + input
  
  
  class MixingLayer(nn.Module):
      """
      MixingLayer
      """
  
      def __init__(self, n_series, input_size, dropout, ff_dim):
          super().__init__()
          # Mixing layer consists of a temporal and feature mixer
          self.temporal_mixer = TemporalMixing(n_series, input_size, dropout)
          self.feature_mixer = FeatureMixing(n_series, input_size, dropout, ff_dim)
  
      def forward(self, input):
          x = self.temporal_mixer(input)
          x = self.feature_mixer(x)
          return x
  
  
  class TSMixer(BaseModel):
      """TSMixer
  
      Time-Series Mixer (`TSMixer`) is a MLP-based multivariate time-series forecasting model. `TSMixer` 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 the target variable from the input features.
          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 to process inputs and 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, default uses all.
          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 = False
      EXOGENOUS_HIST = False
      EXOGENOUS_STAT = False
      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=0.9,
          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 BaseMultivariate class
          super(TSMixer, 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)
  
          # Mixing layers
          mixing_layers = [
              MixingLayer(
                  n_series=n_series, input_size=input_size, dropout=dropout, ff_dim=ff_dim
              )
              for _ in range(n_block)
          ]
          self.mixing_layers = nn.Sequential(*mixing_layers)
  
          # Linear output with Loss dependent dimensions
          self.out = nn.Linear(
              in_features=input_size, out_features=h * self.loss.outputsize_multiplier
          )
  
      def forward(self, windows_batch):
          # Parse batch
          x = windows_batch["insample_y"]  # x: [batch_size, input_size, n_series]
          batch_size = x.shape[0]
  
          # TSMixer: InstanceNorm + Mixing layers + Dense output layer + ReverseInstanceNorm
          if self.revin:
              x = self.norm(x, "norm")
          x = self.mixing_layers(x)
          x = x.permute(0, 2, 1)
          x = self.out(x)
          x = x.permute(0, 2, 1)
          if self.revin:
              x = self.norm(x, "denorm")
  
          x = x.reshape(
              batch_size, self.h, self.loss.outputsize_multiplier * self.n_series
          )
  
          return x
  