content: 
  
  
  __all__ = ['xLSTM']
  
  
  import warnings
  from typing import Optional
  
  import torch
  import torch.nn as nn
  
  from ..common._base_model import BaseModel
  from ..common._modules import MLP
  from ..losses.pytorch import MAE
  
  try:
      from xlstm.blocks.mlstm.block import mLSTMBlockConfig
      from xlstm.blocks.slstm.block import sLSTMBlockConfig
      from xlstm.xlstm_block_stack import xLSTMBlockStack, xLSTMBlockStackConfig
  
      IS_XLSTM_INSTALLED = True
  except ImportError:
      IS_XLSTM_INSTALLED = False
  
  
  class xLSTM(BaseModel):
      """xLSTM
  
      xLSTM encoder, with MLP decoder.
  
      Args:
          h (int): forecast horizon.
          input_size (int): considered autorregresive inputs (lags), y=[1,2,3,4] input_size=2 -> lags=[1,2].
          encoder_n_blocks (int): number of blocks for the xLSTM.
          encoder_hidden_size (int): units for the xLSTM's hidden state size.
          encoder_bias (bool): whether or not to use biases within xLSTM blocks.
          encoder_dropout (float): dropout regularization applied within xLSTM blocks.
          decoder_hidden_size (int): size of hidden layer for the MLP decoder.
          decoder_layers (int): number of layers for the MLP decoder.
          decoder_dropout (float): dropout regularization applied within the MLP decoder.
          decoder_activation (str): activation function for the MLP decoder, see [activations collection](https://docs.pytorch.org/docs/stable/nn.html#non-linear-activations-weighted-sum-nonlinearity).
          backbone (str): backbone for the xLSTM, either 'sLSTM' or 'mLSTM'.
          futr_exog_list (List[str]): future exogenous columns.
          hist_exog_list (list): historic exogenous columns.
          stat_exog_list (list): static exogenous columns.
          exclude_insample_y (bool): whether to exclude the target variable from the input.
          recurrent (bool): whether to produce forecasts recursively (True) or direct (False).
          loss (nn.Module): instantiated train loss class from [losses collection](./losses.pytorch).
          valid_loss (nn.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).
  
      References:
          - [Maximilian Beck, Korbinian Pöppel, Markus Spanring, Andreas Auer, Oleksandra Prudnikova, Michael Kopp, Günter Klambauer, Johannes Brandstetter, Sepp Hochreiter (2024). "xLSTM: Extended Long Short-Term Memory"](https://arxiv.org/abs/2405.04517)
  
      """
  
      # 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,
          h_train: int = 1,
          encoder_n_blocks: int = 2,
          encoder_hidden_size: int = 128,
          encoder_bias: bool = True,
          encoder_dropout: float = 0.1,
          decoder_hidden_size: int = 128,
          decoder_layers: int = 1,
          decoder_dropout: float = 0.0,
          decoder_activation: str = "GELU",
          backbone: str = "mLSTM",
          futr_exog_list=None,
          hist_exog_list=None,
          stat_exog_list=None,
          exclude_insample_y=False,
          recurrent=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=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=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 recurrent:
              warnings.warn(
                  "xLSTM does not support recurrent forecasts. Setting recurrent=False."
              )
              recurrent = False
  
          super(xLSTM, self).__init__(
              h=h,
              input_size=input_size,
              inference_input_size=inference_input_size,
              h_train=h_train,
              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
          )
  
          if not IS_XLSTM_INSTALLED:
              raise ImportError(
                  "Please install `xlstm`. You also need to install `mlstm_kernels` for backend='mLSTM' and `ninja` for backend='sLSTM'."
              )
  
          # xLSTM input size (1 for target variable y)
          input_encoder = (
              1 + self.hist_exog_size + self.stat_exog_size + self.futr_exog_size
          )
  
          # Architecture
          self.feature_projection = nn.Linear(
              in_features=input_encoder, out_features=encoder_hidden_size
          )
          if backbone == "sLSTM":
              block_stack_config = xLSTMBlockStackConfig(
                  slstm_block=sLSTMBlockConfig(),
                  context_length=input_size,
                  num_blocks=encoder_n_blocks,
                  embedding_dim=encoder_hidden_size,
                  bias=encoder_bias,
                  dropout=encoder_dropout,
              )
          elif backbone == "mLSTM":
              block_stack_config = xLSTMBlockStackConfig(
                  mlstm_block=mLSTMBlockConfig(),
                  context_length=input_size,
                  num_blocks=encoder_n_blocks,
                  embedding_dim=encoder_hidden_size,
                  bias=encoder_bias,
                  dropout=encoder_dropout,
              )
          self.hist_encoder = xLSTMBlockStack(block_stack_config)
  
          # Decoder MLP
          self.mlp_decoder = MLP(
              in_features=encoder_hidden_size + self.futr_exog_size,
              out_features=self.loss.outputsize_multiplier,
              hidden_size=decoder_hidden_size,
              num_layers=decoder_layers,
              activation=decoder_activation,
              dropout=decoder_dropout,
          )
          self.temporal_projection = nn.Linear(self.input_size, self.h)
  
      def forward(self, windows_batch):
  
          # Parse windows_batch
          encoder_input = windows_batch["insample_y"]  # [B, seq_len, 1]
          futr_exog = windows_batch["futr_exog"]  # [B, seq_len, F]
          hist_exog = windows_batch["hist_exog"]  # [B, seq_len, X]
          stat_exog = windows_batch["stat_exog"]  # [B, S]
  
          # Concatenate y, historic and static inputs
          batch_size, seq_len = encoder_input.shape[:2]
          if self.hist_exog_size > 0:
              encoder_input = torch.cat(
                  (encoder_input, hist_exog), dim=2
              )  # [B, seq_len, 1] + [B, seq_len, X] -> [B, seq_len, 1 + X]
  
          if self.stat_exog_size > 0:
              # print(encoder_input.shape)
              stat_exog = stat_exog.unsqueeze(1).repeat(
                  1, seq_len, 1
              )  # [B, S] -> [B, seq_len, S]
              encoder_input = torch.cat(
                  (encoder_input, stat_exog), dim=2
              )  # [B, seq_len, 1 + X] + [B, seq_len, S] -> [B, seq_len, 1 + X + S]
  
          if self.futr_exog_size > 0:
              encoder_input = torch.cat(
                  (encoder_input, futr_exog[:, :seq_len]), dim=2
              )  # [B, seq_len, 1 + X + S] + [B, seq_len, F] -> [B, seq_len, 1 + X + S + F]
  
          encoder_input = self.feature_projection(
              encoder_input
          )  # [B, seq_len, 1 + X + S + F] -> [B, seq_len, rnn_hidden_state]
          hidden_state = self.hist_encoder(
              encoder_input
          )  # [B, seq_len, rnn_hidden_state]
          hidden_state = hidden_state.permute(
              0, 2, 1
          )  # [B, seq_len, rnn_hidden_state] -> [B, rnn_hidden_state, seq_len]
          hidden_state = self.temporal_projection(
              hidden_state
          )  # [B, rnn_hidden_state, seq_len] -> [B, rnn_hidden_state, h]
          hidden_state = hidden_state.permute(
              0, 2, 1
          )  # [B, rnn_hidden_state, h] -> [B, h, rnn_hidden_state]
  
          if self.futr_exog_size > 0:
              futr_exog_futr = futr_exog[:, -self.h :]  # [B, h, F]
              hidden_state = torch.cat(
                  (hidden_state, futr_exog_futr), dim=-1
              )  # [B, h, rnn_hidden_state] + [B, h, F] -> [B, h, rnn_hidden_state + F]
  
          output = self.mlp_decoder(
              hidden_state
          )  # [B, h, rnn_hidden_state + F] -> [B, h, n_output]
  
          return output
  