content: 
  
  
  __all__ = ['DataEmbedding_inverted', 'STAD', 'SOFTS']
  
  
  from typing import Optional
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  from ..common._base_model import BaseModel
  from ..common._modules import TransEncoder, TransEncoderLayer
  from ..losses.pytorch import MAE
  
  
  class DataEmbedding_inverted(nn.Module):
      """
      Data Embedding
      """
  
      def __init__(self, c_in, d_model, dropout=0.1):
          super(DataEmbedding_inverted, self).__init__()
          self.value_embedding = nn.Linear(c_in, d_model)
          self.dropout = nn.Dropout(p=dropout)
  
      def forward(self, x, x_mark):
          x = x.permute(0, 2, 1)
          # x: [Batch Variate Time]
          if x_mark is None:
              x = self.value_embedding(x)
          else:
              # the potential to take covariates (e.g. timestamps) as tokens
              x = self.value_embedding(torch.cat([x, x_mark.permute(0, 2, 1)], 1))
          # x: [Batch Variate d_model]
          return self.dropout(x)
  
  
  class STAD(nn.Module):
      """
      STar Aggregate Dispatch Module
      """
  
      def __init__(self, d_series, d_core):
          super(STAD, self).__init__()
  
          self.gen1 = nn.Linear(d_series, d_series)
          self.gen2 = nn.Linear(d_series, d_core)
          self.gen3 = nn.Linear(d_series + d_core, d_series)
          self.gen4 = nn.Linear(d_series, d_series)
  
      def forward(self, input, *args, **kwargs):
          batch_size, channels, d_series = input.shape
  
          # set FFN
          combined_mean = F.gelu(self.gen1(input))
          combined_mean = self.gen2(combined_mean)
  
          # stochastic pooling
          if self.training:
              ratio = F.softmax(torch.nan_to_num(combined_mean), dim=1)
              ratio = ratio.permute(0, 2, 1)
              ratio = ratio.reshape(-1, channels)
              indices = torch.multinomial(ratio, 1)
              indices = indices.view(batch_size, -1, 1).permute(0, 2, 1)
              combined_mean = torch.gather(combined_mean, 1, indices)
              combined_mean = combined_mean.repeat(1, channels, 1)
          else:
              weight = F.softmax(combined_mean, dim=1)
              combined_mean = torch.sum(
                  combined_mean * weight, dim=1, keepdim=True
              ).repeat(1, channels, 1)
  
          # mlp fusion
          combined_mean_cat = torch.cat([input, combined_mean], -1)
          combined_mean_cat = F.gelu(self.gen3(combined_mean_cat))
          combined_mean_cat = self.gen4(combined_mean_cat)
          output = combined_mean_cat
  
          return output, None
  
  
  class SOFTS(BaseModel):
      """SOFTS
  
      Args:
          h (int): Forecast horizon.
          input_size (int): autorregresive inputs size, y=[1,2,3,4] input_size=2 -> y_[t-2:t]=[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.
          hidden_size (int): dimension of the model.
          d_core (int): dimension of core in STAD.
          e_layers (int): number of encoder layers.
          d_ff (int): dimension of fully-connected layer.
          dropout (float): dropout rate.
          use_norm (bool): whether to normalize or not.
          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:
          - [Lu Han, Xu-Yang Chen, Han-Jia Ye, De-Chuan Zhan. "SOFTS: Efficient Multivariate Time Series Forecasting with Series-Core Fusion"](https://arxiv.org/pdf/2404.14197)
      """
  
      # Class attributes
      EXOGENOUS_FUTR = False
      EXOGENOUS_HIST = False
      EXOGENOUS_STAT = False
      MULTIVARIATE = True
      RECURRENT = 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,
          hidden_size: int = 512,
          d_core: int = 512,
          e_layers: int = 2,
          d_ff: int = 2048,
          dropout: float = 0.1,
          use_norm: bool = 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
      ):
  
          super(SOFTS, 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
          )
  
          self.h = h
          self.enc_in = n_series
          self.dec_in = n_series
          self.c_out = n_series
          self.use_norm = use_norm
  
          # Architecture
          self.enc_embedding = DataEmbedding_inverted(input_size, hidden_size, dropout)
  
          self.encoder = TransEncoder(
              [
                  TransEncoderLayer(
                      STAD(hidden_size, d_core),
                      hidden_size,
                      d_ff,
                      dropout=dropout,
                      activation=F.gelu,
                  )
                  for l in range(e_layers)
              ]
          )
  
          self.projection = nn.Linear(
              hidden_size, self.h * self.loss.outputsize_multiplier, bias=True
          )
  
      def forecast(self, x_enc):
          # Normalization from Non-stationary Transformer
          if self.use_norm:
              means = x_enc.mean(1, keepdim=True).detach()
              x_enc = x_enc - means
              stdev = torch.sqrt(
                  torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5
              )
              x_enc /= stdev
  
          _, _, N = x_enc.shape
          enc_out = self.enc_embedding(x_enc, None)
          enc_out, attns = self.encoder(enc_out, attn_mask=None)
          dec_out = self.projection(enc_out).permute(0, 2, 1)[:, :, :N]
  
          # De-Normalization from Non-stationary Transformer
          if self.use_norm:
              dec_out = dec_out * (
                  stdev[:, 0, :]
                  .unsqueeze(1)
                  .repeat(1, self.h * self.loss.outputsize_multiplier, 1)
              )
              dec_out = dec_out + (
                  means[:, 0, :]
                  .unsqueeze(1)
                  .repeat(1, self.h * self.loss.outputsize_multiplier, 1)
              )
          return dec_out
  
      def forward(self, windows_batch):
          insample_y = windows_batch["insample_y"]
  
          y_pred = self.forecast(insample_y)
          y_pred = y_pred.reshape(insample_y.shape[0], self.h, -1)
  
          return y_pred
  