content: 
  
  
  __all__ = ['MovingAvg', 'SeriesDecomp', 'DLinear']
  
  
  from typing import Optional
  
  import torch
  import torch.nn as nn
  
  from ..common._base_model import BaseModel
  from ..losses.pytorch import MAE
  
  
  class MovingAvg(nn.Module):
      """
      Moving average block to highlight the trend of time series
      """
  
      def __init__(self, kernel_size, stride):
          super(MovingAvg, self).__init__()
          self.kernel_size = kernel_size
          self.avg = nn.AvgPool1d(kernel_size=kernel_size, stride=stride, padding=0)
  
      def forward(self, x):
          # padding on the both ends of time series
          front = x[:, 0:1].repeat(1, (self.kernel_size - 1) // 2)
          end = x[:, -1:].repeat(1, (self.kernel_size - 1) // 2)
          x = torch.cat([front, x, end], dim=1)
          x = self.avg(x)
          return x
  
  
  class SeriesDecomp(nn.Module):
      """
      Series decomposition block
      """
  
      def __init__(self, kernel_size):
          super(SeriesDecomp, self).__init__()
          self.MovingAvg = MovingAvg(kernel_size, stride=1)
  
      def forward(self, x):
          moving_mean = self.MovingAvg(x)
          res = x - moving_mean
          return res, moving_mean
  
  
  class DLinear(BaseModel):
      """DLinear
  
      Args:
          h (int): forecast horizon.
          input_size (int): maximum sequence length for truncated train backpropagation.
          stat_exog_list (str list): static exogenous columns.
          hist_exog_list (str list): historic exogenous columns.
          futr_exog_list (str list): future exogenous columns.
          exclude_insample_y (bool): the model skips the autoregressive features y[t-input_size:t] if True.
          moving_avg_window (int): window size for trend-seasonality decomposition. Should be uneven.
          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.
          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:
          - [Zeng, Ailing, et al. "Are transformers effective for time series forecasting?." Proceedings of the AAAI conference on artificial intelligence. Vol. 37. No. 9. 2023."](https://ojs.aaai.org/index.php/AAAI/article/view/27695)
      """
  
      # Class attributes
      EXOGENOUS_FUTR = False
      EXOGENOUS_HIST = False
      EXOGENOUS_STAT = False
      MULTIVARIATE = False  # 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: int,
          input_size: int,
          stat_exog_list=None,
          hist_exog_list=None,
          futr_exog_list=None,
          exclude_insample_y=False,
          moving_avg_window: int = 25,
          loss=MAE(),
          valid_loss=None,
          max_steps: int = 5000,
          learning_rate: float = 1e-4,
          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=1024,
          inference_windows_batch_size=1024,
          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
      ):
          super(DLinear, self).__init__(
              h=h,
              input_size=input_size,
              hist_exog_list=hist_exog_list,
              stat_exog_list=stat_exog_list,
              futr_exog_list=futr_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,
              windows_batch_size=windows_batch_size,
              valid_batch_size=valid_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,
              drop_last_loader=drop_last_loader,
              alias=alias,
              random_seed=random_seed,
              optimizer=optimizer,
              optimizer_kwargs=optimizer_kwargs,
              lr_scheduler=lr_scheduler,
              lr_scheduler_kwargs=lr_scheduler_kwargs,
              dataloader_kwargs=dataloader_kwargs,
              **trainer_kwargs
          )
  
          # Architecture
          if moving_avg_window % 2 == 0:
              raise Exception("moving_avg_window should be uneven")
  
          self.c_out = self.loss.outputsize_multiplier
          self.output_attention = False
          self.enc_in = 1
          self.dec_in = 1
  
          # Decomposition
          self.decomp = SeriesDecomp(moving_avg_window)
  
          self.linear_trend = nn.Linear(
              self.input_size, self.loss.outputsize_multiplier * h, bias=True
          )
          self.linear_season = nn.Linear(
              self.input_size, self.loss.outputsize_multiplier * h, bias=True
          )
  
      def forward(self, windows_batch):
          # Parse windows_batch
          insample_y = windows_batch["insample_y"].squeeze(-1)
  
          # Parse inputs
          batch_size = len(insample_y)
          seasonal_init, trend_init = self.decomp(insample_y)
  
          trend_part = self.linear_trend(trend_init)
          seasonal_part = self.linear_season(seasonal_init)
  
          # Final
          forecast = trend_part + seasonal_part
          forecast = forecast.reshape(batch_size, self.h, self.loss.outputsize_multiplier)
          return forecast
  