content: 
  
  
  __all__ = ['Decoder', 'DeepAR']
  
  
  from typing import Optional
  
  import torch
  import torch.nn as nn
  
  from ..common._base_model import BaseModel
  from ..losses.pytorch import MAE, DistributionLoss
  
  
  class Decoder(nn.Module):
      """Multi-Layer Perceptron Decoder
  
      Args:
          in_features (int): dimension of input.
          out_features (int): dimension of output.
          hidden_size (int): dimension of hidden layers.
          hidden_layers (int): number of hidden layers.
      """
  
      def __init__(self, in_features, out_features, hidden_size, hidden_layers):
          super().__init__()
  
          if hidden_layers == 0:
              # Input layer
              layers = [nn.Linear(in_features=in_features, out_features=out_features)]
          else:
              # Input layer
              layers = [
                  nn.Linear(in_features=in_features, out_features=hidden_size),
                  nn.ReLU(),
              ]
              # Hidden layers
              for i in range(hidden_layers - 2):
                  layers += [
                      nn.Linear(in_features=hidden_size, out_features=hidden_size),
                      nn.ReLU(),
                  ]
              # Output layer
              layers += [nn.Linear(in_features=hidden_size, out_features=out_features)]
  
          # Store in layers as ModuleList
          self.layers = nn.Sequential(*layers)
  
      def forward(self, x):
          return self.layers(x)
  
  
  class DeepAR(BaseModel):
      """DeepAR
  
      Args:
          h (int): Forecast horizon.
          input_size (int): maximum sequence length for truncated train backpropagation. Default -1 uses 3 * horizon
          h_train (int): maximum sequence length for truncated train backpropagation. Default 1.
          lstm_n_layers (int): number of LSTM layers.
          lstm_hidden_size (int): LSTM hidden size.
          lstm_dropout (float): LSTM dropout.
          decoder_hidden_layers (int): number of decoder MLP hidden layers. Default: 0 for linear layer.
          decoder_hidden_size (int): decoder MLP hidden size. Default: 0 for linear layer.
          trajectory_samples (int): number of Monte Carlo trajectories during inference.
          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.
          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:
          - [David Salinas, Valentin Flunkert, Jan Gasthaus, Tim Januschowski (2020). "DeepAR: Probabilistic forecasting with autoregressive recurrent networks". International Journal of Forecasting.](https://www.sciencedirect.com/science/article/pii/S0169207019301888)
          - [Alexander Alexandrov et. al (2020). "GluonTS: Probabilistic and Neural Time Series Modeling in Python". Journal of Machine Learning Research.](https://www.jmlr.org/papers/v21/19-820.html)
  
      """
  
      # Class attributes
      EXOGENOUS_FUTR = True
      EXOGENOUS_HIST = False
      EXOGENOUS_STAT = True
      MULTIVARIATE = False
      RECURRENT = True
  
      def __init__(
          self,
          h,
          input_size: int = -1,
          h_train: int = 1,
          lstm_n_layers: int = 2,
          lstm_hidden_size: int = 128,
          lstm_dropout: float = 0.1,
          decoder_hidden_layers: int = 0,
          decoder_hidden_size: int = 0,
          trajectory_samples: int = 100,
          stat_exog_list=None,
          hist_exog_list=None,
          futr_exog_list=None,
          exclude_insample_y=False,
          loss=DistributionLoss(
              distribution="StudentT", level=[80, 90], return_params=False
          ),
          valid_loss=MAE(),
          max_steps: int = 1000,
          learning_rate: float = 1e-3,
          num_lr_decays: int = 3,
          early_stop_patience_steps: int = -1,
          val_check_steps: int = 100,
          batch_size: int = 32,
          valid_batch_size: Optional[int] = None,
          windows_batch_size: int = 1024,
          inference_windows_batch_size: int = -1,
          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=False,
          alias: Optional[str] = None,
          optimizer=None,
          optimizer_kwargs=None,
          lr_scheduler=None,
          lr_scheduler_kwargs=None,
          dataloader_kwargs=None,
          **trainer_kwargs
      ):
  
          if exclude_insample_y:
              raise Exception("DeepAR has no possibility for excluding y.")
  
          # Inherit BaseWindows class
          super(DeepAR, self).__init__(
              h=h,
              input_size=input_size,
              h_train=h_train,
              stat_exog_list=stat_exog_list,
              hist_exog_list=hist_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,
              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
          )
  
          self.n_samples = trajectory_samples
  
          # LSTM
          self.encoder_n_layers = lstm_n_layers
          self.encoder_hidden_size = lstm_hidden_size
          self.encoder_dropout = lstm_dropout
  
          # LSTM input size (1 for target variable y)
          input_encoder = 1 + self.futr_exog_size + self.stat_exog_size
  
          # Instantiate model
          self.rnn_state = None
          self.maintain_state = False
          self.hist_encoder = nn.LSTM(
              input_size=input_encoder,
              hidden_size=self.encoder_hidden_size,
              num_layers=self.encoder_n_layers,
              dropout=self.encoder_dropout,
              batch_first=True,
          )
  
          # Decoder MLP
          self.decoder = Decoder(
              in_features=lstm_hidden_size,
              out_features=self.loss.outputsize_multiplier,
              hidden_size=decoder_hidden_size,
              hidden_layers=decoder_hidden_layers,
          )
  
      def forward(self, windows_batch):
  
          # Parse windows_batch
          encoder_input = windows_batch["insample_y"]  # <- [B, T, 1]
          futr_exog = windows_batch["futr_exog"]
          stat_exog = windows_batch["stat_exog"]
  
          _, input_size = encoder_input.shape[:2]
          if self.futr_exog_size > 0:
              encoder_input = torch.cat((encoder_input, futr_exog), dim=2)
  
          if self.stat_exog_size > 0:
              stat_exog = stat_exog.unsqueeze(1).repeat(
                  1, input_size, 1
              )  # [B, S] -> [B, input_size-1, S]
              encoder_input = torch.cat((encoder_input, stat_exog), dim=2)
  
          # RNN forward
          if self.maintain_state:
              rnn_state = self.rnn_state
          else:
              rnn_state = None
  
          hidden_state, rnn_state = self.hist_encoder(
              encoder_input, rnn_state
          )  # [B, input_size-1, rnn_hidden_state]
  
          if self.maintain_state:
              self.rnn_state = rnn_state
  
          # Decoder forward
          output = self.decoder(hidden_state)  # [B, input_size-1, output_size]
  
          # Return only horizon part
          return output[:, -self.h :]
  