content: 
  
  
  __all__ = ['TCN']
  
  
  from typing import List, Optional
  
  import torch
  import torch.nn as nn
  
  from ..common._base_model import BaseModel
  from ..common._modules import MLP, TemporalConvolutionEncoder
  from ..losses.pytorch import MAE
  
  
  class TCN(BaseModel):
      """TCN
  
      Temporal Convolution Network (TCN), with MLP decoder.
      The historical encoder uses dilated skip connections to obtain efficient long memory,
      while the rest of the architecture allows for future exogenous alignment.
  
      Args:
          h (int): forecast horizon.
          input_size (int): maximum sequence length for truncated train backpropagation. Default -1 uses 3 * horizon
          inference_input_size (int): maximum sequence length for truncated inference. Default None uses input_size history.
          kernel_size (int): size of the convolving kernel.
          dilations (int list): controls the temporal spacing between the kernel points; also known as the à trous algorithm.
          encoder_hidden_size (int): units for the TCN's hidden state size.
          encoder_activation (str): type of TCN activation from `tanh` or `relu`.
          context_size (int): size of context vector for each timestamp on the forecasting window.
          decoder_hidden_size (int): size of hidden layer for the MLP decoder.
          decoder_layers (int): number of layers for the MLP decoder.
          futr_exog_list (str list): future exogenous columns.
          hist_exog_list (str list): historic exogenous columns.
          stat_exog_list (str list): static exogenous columns.
          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 differentseries in each batch.
          valid_batch_size (int): number of different series in each validation and test batch.
          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).
      """
  
      # Class attributes
      EXOGENOUS_FUTR = True
      EXOGENOUS_HIST = True
      EXOGENOUS_STAT = True
      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 = -1,
          inference_input_size: Optional[int] = None,
          kernel_size: int = 2,
          dilations: List[int] = [1, 2, 4, 8, 16],
          encoder_hidden_size: int = 128,
          encoder_activation: str = "ReLU",
          context_size: int = 10,
          decoder_hidden_size: int = 128,
          decoder_layers: int = 2,
          futr_exog_list=None,
          hist_exog_list=None,
          stat_exog_list=None,
          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=128,
          inference_windows_batch_size=1024,
          start_padding_enabled=False,
          training_data_availability_threshold=0.0,
          step_size: int = 1,
          scaler_type: str = "robust",
          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
      ):
          super(TCN, self).__init__(
              h=h,
              input_size=input_size,
              inference_input_size=inference_input_size,
              futr_exog_list=futr_exog_list,
              hist_exog_list=hist_exog_list,
              stat_exog_list=stat_exog_list,
              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
          )
  
          # ----------------------------------- Parse dimensions -----------------------------------#
          # TCN
          self.kernel_size = kernel_size
          self.dilations = dilations
          self.encoder_hidden_size = encoder_hidden_size
          self.encoder_activation = encoder_activation
  
          # Context adapter
          self.context_size = context_size
  
          # MLP decoder
          self.decoder_hidden_size = decoder_hidden_size
          self.decoder_layers = decoder_layers
  
          # TCN input size (1 for target variable y)
          input_encoder = (
              1 + self.hist_exog_size + self.stat_exog_size + self.futr_exog_size
          )
  
          # ---------------------------------- Instantiate Model -----------------------------------#
          # Instantiate historic encoder
          self.hist_encoder = TemporalConvolutionEncoder(
              in_channels=input_encoder,
              out_channels=self.encoder_hidden_size,
              kernel_size=self.kernel_size,  # Almost like lags
              dilations=self.dilations,
              activation=self.encoder_activation,
          )
  
          # Context adapter
          self.context_adapter = nn.Linear(in_features=self.input_size, out_features=h)
  
          # Decoder MLP
          self.mlp_decoder = MLP(
              in_features=self.encoder_hidden_size + self.futr_exog_size,
              out_features=self.loss.outputsize_multiplier,
              hidden_size=self.decoder_hidden_size,
              num_layers=self.decoder_layers,
              activation="ReLU",
              dropout=0.0,
          )
  
      def forward(self, windows_batch):
  
          # Parse windows_batch
          encoder_input = windows_batch["insample_y"]  # [B, L, 1]
          futr_exog = windows_batch["futr_exog"]  # [B, L + h, F]
          hist_exog = windows_batch["hist_exog"]  # [B, L, X]
          stat_exog = windows_batch["stat_exog"]  # [B, S]
  
          # Concatenate y, historic and static inputs
          batch_size, input_size = encoder_input.shape[:2]
          if self.hist_exog_size > 0:
              encoder_input = torch.cat(
                  (encoder_input, hist_exog), dim=2
              )  # [B, L, 1] + [B, L, X] -> [B, L, 1 + X]
  
          if self.stat_exog_size > 0:
              # print(encoder_input.shape)
              stat_exog = stat_exog.unsqueeze(1).repeat(
                  1, input_size, 1
              )  # [B, S] -> [B, L, S]
              encoder_input = torch.cat(
                  (encoder_input, stat_exog), dim=2
              )  # [B, L, 1 + X] + [B, L, S] -> [B, L, 1 + X + S]
  
          if self.futr_exog_size > 0:
              encoder_input = torch.cat(
                  (encoder_input, futr_exog[:, :input_size]), dim=2
              )  # [B, L, 1 + X + S] + [B, L, F] -> [B, L, 1 + X + S + F]
  
          # TCN forward
          hidden_state = self.hist_encoder(encoder_input)  # [B, L, C]
  
          # Context adapter
          hidden_state = hidden_state.permute(0, 2, 1)  # [B, L, C] -> [B, C, L]
          context = self.context_adapter(hidden_state)  # [B, C, L] -> [B, C, h]
  
          # Residual connection with futr_exog
          if self.futr_exog_size > 0:
              futr_exog_futr = futr_exog[:, input_size:].swapaxes(
                  1, 2
              )  # [B, L + h, F] -> [B, F, h]
              context = torch.cat(
                  (context, futr_exog_futr), dim=1
              )  # [B, C, h] + [B, F, h] = [B, C + F, h]
  
          context = context.swapaxes(1, 2)  # [B, C + F, h] -> [B, h, C + F]
  
          # Final forecast
          output = self.mlp_decoder(context)  # [B, h, C + F] -> [B, h, n_output]
  
          return output
  