content: 
  
  
  __all__ = ['MLPResidual', 'TiDE']
  
  
  from typing import Optional
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  from ..common._base_model import BaseModel
  from ..losses.pytorch import MAE
  
  
  class MLPResidual(nn.Module):
      """
      MLPResidual
      """
  
      def __init__(self, input_dim, hidden_size, output_dim, dropout, layernorm):
          super().__init__()
          self.layernorm = layernorm
          if layernorm:
              self.norm = nn.LayerNorm(output_dim)
  
          self.drop = nn.Dropout(dropout)
          self.lin1 = nn.Linear(input_dim, hidden_size)
          self.lin2 = nn.Linear(hidden_size, output_dim)
          self.skip = nn.Linear(input_dim, output_dim)
  
      def forward(self, input):
          # MLP dense
          x = F.relu(self.lin1(input))
          x = self.lin2(x)
          x = self.drop(x)
  
          # Skip connection
          x_skip = self.skip(input)
  
          # Combine
          x = x + x_skip
  
          if self.layernorm:
              return self.norm(x)
  
          return x
  
  
  class TiDE(BaseModel):
      """TiDE
  
      Time-series Dense Encoder (`TiDE`) is a MLP-based univariate time-series forecasting model. `TiDE` uses Multi-layer Perceptrons (MLPs) in an encoder-decoder model for long-term time-series forecasting.
  
      Args:
          h (int): forecast horizon.
          input_size (int): considered autorregresive inputs (lags), y=[1,2,3,4] input_size=2 -> lags=[1,2].
          hidden_size (int): number of units for the dense MLPs.
          decoder_output_dim (int): number of units for the output of the decoder.
          temporal_decoder_dim (int): number of units for the hidden sizeof the temporal decoder.
          dropout (float): dropout rate between (0, 1) .
          layernorm (bool): if True uses Layer Normalization on the MLP residual block outputs.
          num_encoder_layers (int): number of encoder layers.
          num_decoder_layers (int): number of decoder layers.
          temporal_width (int): lower temporal projected dimension.
          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): whether to exclude the target variable from the historic exogenous data.
          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.
          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:
          - [Das, Abhimanyu, Weihao Kong, Andrew Leach, Shaan Mathur, Rajat Sen, and Rose Yu (2024). "Long-term Forecasting with TiDE: Time-series Dense Encoder."](http://arxiv.org/abs/2304.08424)
  
      """
  
      # 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,
          input_size,
          hidden_size=512,
          decoder_output_dim=32,
          temporal_decoder_dim=128,
          dropout=0.3,
          layernorm=True,
          num_encoder_layers=1,
          num_decoder_layers=1,
          temporal_width=4,
          futr_exog_list=None,
          hist_exog_list=None,
          stat_exog_list=None,
          exclude_insample_y=False,
          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=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
      ):
  
          # Inherit BaseWindows class
          super(TiDE, self).__init__(
              h=h,
              input_size=input_size,
              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
          )
          self.h = h
  
          if self.hist_exog_size > 0 or self.futr_exog_size > 0:
              self.hist_exog_projection = MLPResidual(
                  input_dim=self.hist_exog_size,
                  hidden_size=hidden_size,
                  output_dim=temporal_width,
                  dropout=dropout,
                  layernorm=layernorm,
              )
          if self.futr_exog_size > 0:
              self.futr_exog_projection = MLPResidual(
                  input_dim=self.futr_exog_size,
                  hidden_size=hidden_size,
                  output_dim=temporal_width,
                  dropout=dropout,
                  layernorm=layernorm,
              )
  
          # Encoder
          dense_encoder_input_size = (
              input_size
              + input_size * (self.hist_exog_size > 0) * temporal_width
              + (input_size + h) * (self.futr_exog_size > 0) * temporal_width
              + (self.stat_exog_size > 0) * self.stat_exog_size
          )
  
          dense_encoder_layers = [
              MLPResidual(
                  input_dim=dense_encoder_input_size if i == 0 else hidden_size,
                  hidden_size=hidden_size,
                  output_dim=hidden_size,
                  dropout=dropout,
                  layernorm=layernorm,
              )
              for i in range(num_encoder_layers)
          ]
          self.dense_encoder = nn.Sequential(*dense_encoder_layers)
  
          # Decoder
          decoder_output_size = decoder_output_dim * h
          dense_decoder_layers = [
              MLPResidual(
                  input_dim=hidden_size,
                  hidden_size=hidden_size,
                  output_dim=(
                      decoder_output_size if i == num_decoder_layers - 1 else hidden_size
                  ),
                  dropout=dropout,
                  layernorm=layernorm,
              )
              for i in range(num_decoder_layers)
          ]
          self.dense_decoder = nn.Sequential(*dense_decoder_layers)
  
          # Temporal decoder with loss dependent dimensions
          self.temporal_decoder = MLPResidual(
              input_dim=decoder_output_dim + (self.futr_exog_size > 0) * temporal_width,
              hidden_size=temporal_decoder_dim,
              output_dim=self.loss.outputsize_multiplier,
              dropout=dropout,
              layernorm=layernorm,
          )
  
          # Global skip connection
          self.global_skip = nn.Linear(
              in_features=input_size, out_features=h * self.loss.outputsize_multiplier
          )
  
      def forward(self, windows_batch):
          # Parse windows_batch
          x = windows_batch["insample_y"]  #   [B, L, 1]
          hist_exog = windows_batch["hist_exog"]  #   [B, L, X]
          futr_exog = windows_batch["futr_exog"]  #   [B, L + h, F]
          stat_exog = windows_batch["stat_exog"]  #   [B, S]
          batch_size, seq_len = x.shape[:2]  #   B = batch_size, L = seq_len
  
          # Flatten insample_y
          x = x.reshape(batch_size, -1)  #   [B, L, 1] -> [B, L]
  
          # Global skip connection
          x_skip = self.global_skip(x)  #   [B, L] -> [B, h * n_outputs]
          x_skip = x_skip.reshape(
              batch_size, self.h, -1
          )  #   [B, h * n_outputs] -> [B, h, n_outputs]
  
          # Concatenate x with flattened historical exogenous
          if self.hist_exog_size > 0:
              x_hist_exog = self.hist_exog_projection(
                  hist_exog
              )  #   [B, L, X] -> [B, L, temporal_width]
              x_hist_exog = x_hist_exog.reshape(
                  batch_size, -1
              )  #   [B, L, temporal_width] -> [B, L * temporal_width]
              x = torch.cat(
                  (x, x_hist_exog), dim=1
              )  #   [B, L] + [B, L * temporal_width] -> [B, L * (1 + temporal_width)]
  
          # Concatenate x with flattened future exogenous
          if self.futr_exog_size > 0:
              x_futr_exog = self.futr_exog_projection(
                  futr_exog
              )  #   [B, L + h, F] -> [B, L + h, temporal_width]
              x_futr_exog_flat = x_futr_exog.reshape(
                  batch_size, -1
              )  #   [B, L + h, temporal_width] -> [B, (L + h) * temporal_width]
              x = torch.cat(
                  (x, x_futr_exog_flat), dim=1
              )  #   [B, L * (1 + temporal_width)] + [B, (L + h) * temporal_width] -> [B, L * (1 + 2 * temporal_width) + h * temporal_width]
  
          # Concatenate x with static exogenous
          if self.stat_exog_size > 0:
              x = torch.cat(
                  (x, stat_exog), dim=1
              )  #   [B, L * (1 + 2 * temporal_width) + h * temporal_width] + [B, S] -> [B, L * (1 + 2 * temporal_width) + h * temporal_width + S]
  
          # Dense encoder
          x = self.dense_encoder(
              x
          )  #   [B, L * (1 + 2 * temporal_width) + h * temporal_width + S] -> [B, hidden_size]
  
          # Dense decoder
          x = self.dense_decoder(x)  #   [B, hidden_size] ->  [B, decoder_output_dim * h]
          x = x.reshape(
              batch_size, self.h, -1
          )  #   [B, decoder_output_dim * h] -> [B, h, decoder_output_dim]
  
          # Stack with futr_exog for horizon part of futr_exog
          if self.futr_exog_size > 0:
              x_futr_exog_h = x_futr_exog[
                  :, seq_len:
              ]  #  [B, L + h, temporal_width] -> [B, h, temporal_width]
              x = torch.cat(
                  (x, x_futr_exog_h), dim=2
              )  #  [B, h, decoder_output_dim] + [B, h, temporal_width] -> [B, h, temporal_width + decoder_output_dim]
  
          # Temporal decoder
          x = self.temporal_decoder(
              x
          )  #  [B, h, temporal_width + decoder_output_dim] -> [B, h, n_outputs]
  
          forecast = x + x_skip
  
          return forecast
  