content: 
  
  
  __all__ = ['GRU']
  
  
  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
  
  
  class GRU(BaseModel):
      """GRU
  
      Multi Layer Recurrent Network with Gated Units (GRU), and
      MLP decoder. The network has non-linear activation functions, it is trained
      using ADAM stochastic gradient descent. The network accepts static, historic
      and future exogenous data, flattens the inputs.
  
      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.
          h_train (int): maximum sequence length for truncated train backpropagation. Default 1.
          encoder_n_layers (int): number of layers for the GRU.
          encoder_hidden_size (int): units for the GRU's hidden state size.
          encoder_activation (Optional[str]): Deprecated. Activation function in GRU is frozen in PyTorch.
          encoder_bias (bool): whether or not to use biases b_ih, b_hh within GRU units.
          encoder_dropout (float): dropout regularization applied to GRU outputs.
          context_size (Optional[int]): deprecated.
          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.
          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 (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:
          - [Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, Yoshua Bengio (2014). "Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling".](https://arxiv.org/abs/1412.3555)
          - [Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, Yoshua Bengio (2014). "On the Properties of Neural Machine Translation: Encoder-Decoder Approaches".](https://arxiv.org/abs/1409.1259)
      """
  
      # Class attributes
      EXOGENOUS_FUTR = True
      EXOGENOUS_HIST = True
      EXOGENOUS_STAT = True
      MULTIVARIATE = False  # If the model produces multivariate forecasts (True) or univariate (False)
      RECURRENT = (
          True  # 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_layers: int = 2,
          encoder_hidden_size: int = 200,
          encoder_activation: Optional[str] = None,
          encoder_bias: bool = True,
          encoder_dropout: float = 0.0,
          context_size: Optional[int] = None,
          decoder_hidden_size: int = 128,
          decoder_layers: int = 2,
          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
      ):
  
          self.RECURRENT = recurrent
  
          super(GRU, 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 encoder_activation is not None:
              warnings.warn(
                  "The 'encoder_activation' argument is deprecated and will be removed in "
                  "future versions. The activation function in GRU is frozen in PyTorch and "
                  "it cannot be modified.",
                  DeprecationWarning,
              )
  
          # RNN
          self.encoder_n_layers = encoder_n_layers
          self.encoder_hidden_size = encoder_hidden_size
          self.encoder_bias = encoder_bias
          self.encoder_dropout = encoder_dropout
  
          # Context adapter
          if context_size is not None:
              warnings.warn(
                  "context_size is deprecated and will be removed in future versions."
              )
  
          # MLP decoder
          self.decoder_hidden_size = decoder_hidden_size
          self.decoder_layers = decoder_layers
  
          # RNN input size (1 for target variable y)
          input_encoder = (
              1 + self.hist_exog_size + self.stat_exog_size + self.futr_exog_size
          )
  
          # Instantiate model
          self.rnn_state = None
          self.maintain_state = False
          self.hist_encoder = nn.GRU(
              input_size=input_encoder,
              hidden_size=self.encoder_hidden_size,
              num_layers=self.encoder_n_layers,
              bias=self.encoder_bias,
              dropout=self.encoder_dropout,
              batch_first=True,
          )
  
          # Decoder MLP
          if self.RECURRENT:
              self.proj = nn.Linear(
                  self.encoder_hidden_size, self.loss.outputsize_multiplier
              )
          else:
              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,
              )
              if self.h > self.input_size:
                  self.upsample_sequence = 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]
  
          if self.RECURRENT:
              if self.maintain_state:
                  rnn_state = self.rnn_state
              else:
                  rnn_state = None
  
              output, rnn_state = self.hist_encoder(
                  encoder_input, rnn_state
              )  # [B, seq_len, rnn_hidden_state]
              output = self.proj(
                  output
              )  # [B, seq_len, rnn_hidden_state] -> [B, seq_len, n_output]
              if self.maintain_state:
                  self.rnn_state = rnn_state
          else:
              hidden_state, _ = self.hist_encoder(
                  encoder_input, None
              )  # [B, seq_len, rnn_hidden_state]
              if self.h > self.input_size:
                  hidden_state = hidden_state.permute(
                      0, 2, 1
                  )  # [B, seq_len, rnn_hidden_state] -> [B, rnn_hidden_state, seq_len]
                  hidden_state = self.upsample_sequence(
                      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]
              else:
                  hidden_state = hidden_state[
                      :, -self.h :
                  ]  # [B, seq_len, rnn_hidden_state] -> [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, seq_len, n_output]
  
          return output[:, -self.h :]
  