content: 
  
  
  __all__ = ['FlattenHead', 'Encoder', 'EncoderLayer', 'EnEmbedding', 'TimeXer']
  
  
  from typing import Optional
  
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  from neuralforecast.common._modules import (
      AttentionLayer,
      DataEmbedding_inverted,
      FullAttention,
      PositionalEmbedding,
  )
  
  from ..common._base_model import BaseModel
  from ..losses.pytorch import MAE
  
  
  class FlattenHead(nn.Module):
      def __init__(self, n_vars, nf, target_window, head_dropout=0):
          super().__init__()
          self.n_vars = n_vars
          self.flatten = nn.Flatten(start_dim=-2)
          self.linear = nn.Linear(nf, target_window)
          self.dropout = nn.Dropout(head_dropout)
  
      def forward(self, x):  # x: [bs x nvars x d_model x patch_num]
          x = self.flatten(x)
          x = self.linear(x)
          x = self.dropout(x)
          return x
  
  
  class Encoder(nn.Module):
      def __init__(self, layers, norm_layer=None, projection=None):
          super(Encoder, self).__init__()
          self.layers = nn.ModuleList(layers)
          self.norm = norm_layer
          self.projection = projection
  
      def forward(self, x, cross, x_mask=None, cross_mask=None, tau=None, delta=None):
          for layer in self.layers:
              x = layer(
                  x, cross, x_mask=x_mask, cross_mask=cross_mask, tau=tau, delta=delta
              )
  
          if self.norm is not None:
              x = self.norm(x)
  
          if self.projection is not None:
              x = self.projection(x)
          return x
  
  
  class EncoderLayer(nn.Module):
      def __init__(
          self,
          self_attention,
          cross_attention,
          d_model,
          d_ff=None,
          dropout=0.1,
          activation="relu",
      ):
          super(EncoderLayer, self).__init__()
          d_ff = d_ff or 4 * d_model
          self.self_attention = self_attention
          self.cross_attention = cross_attention
          self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=1)
          self.conv2 = nn.Conv1d(in_channels=d_ff, out_channels=d_model, kernel_size=1)
          self.norm1 = nn.LayerNorm(d_model)
          self.norm2 = nn.LayerNorm(d_model)
          self.norm3 = nn.LayerNorm(d_model)
          self.dropout = nn.Dropout(dropout)
          self.activation = F.relu if activation == "relu" else F.gelu
  
      def forward(self, x, cross, x_mask=None, cross_mask=None, tau=None, delta=None):
          B, L, D = cross.shape
          x = x + self.dropout(
              self.self_attention(x, x, x, attn_mask=x_mask, tau=tau, delta=None)[0]
          )
          x = self.norm1(x)
  
          x_glb_ori = x[:, -1, :].unsqueeze(1)
          x_glb = torch.reshape(x_glb_ori, (B, -1, D))
          x_glb_attn = self.dropout(
              self.cross_attention(
                  x_glb, cross, cross, attn_mask=cross_mask, tau=tau, delta=delta
              )[0]
          )
          x_glb_attn = torch.reshape(
              x_glb_attn, (x_glb_attn.shape[0] * x_glb_attn.shape[1], x_glb_attn.shape[2])
          ).unsqueeze(1)
          x_glb = x_glb_ori + x_glb_attn
          x_glb = self.norm2(x_glb)
  
          y = x = torch.cat([x[:, :-1, :], x_glb], dim=1)
  
          y = self.dropout(self.activation(self.conv1(y.transpose(-1, 1))))
          y = self.dropout(self.conv2(y).transpose(-1, 1))
  
          return self.norm3(x + y)
  
  
  class EnEmbedding(nn.Module):
      def __init__(self, n_vars, d_model, patch_len, dropout):
          super(EnEmbedding, self).__init__()
          # Patching
          self.patch_len = patch_len
  
          self.value_embedding = nn.Linear(patch_len, d_model, bias=False)
          self.glb_token = nn.Parameter(torch.randn(1, n_vars, 1, d_model))
          self.position_embedding = PositionalEmbedding(d_model)
  
          self.dropout = nn.Dropout(dropout)
  
      def forward(self, x):
          # do patching
          n_vars = x.shape[1]
          glb = self.glb_token.repeat((x.shape[0], 1, 1, 1))
  
          x = x.unfold(dimension=-1, size=self.patch_len, step=self.patch_len)
          x = torch.reshape(x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3]))
          # Input encoding
          x = self.value_embedding(x) + self.position_embedding(x)
          x = torch.reshape(x, (-1, n_vars, x.shape[-2], x.shape[-1]))
          x = torch.cat([x, glb], dim=2)
          x = torch.reshape(x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3]))
          return self.dropout(x), n_vars
  
  
  class TimeXer(BaseModel):
      """
      TimeXer
  
      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.
          patch_len (int): length of patches.
          hidden_size (int): dimension of the model.
          n_heads (int): number of heads.
          e_layers (int): number of encoder layers.
          d_ff (int): dimension of fully-connected layer.
          factor (int): attention factor.
          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 in each batch.
          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:
          - [Yuxuan Wang, Haixu Wu, Jiaxiang Dong, Guo Qin, Haoran Zhang, Yong Liu, Yunzhong Qiu, Jianmin Wang, Mingsheng Long. "TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables"](https://arxiv.org/abs/2402.19072)
      """
  
      # Class attributes
      EXOGENOUS_FUTR = True
      EXOGENOUS_HIST = True
      EXOGENOUS_STAT = True
      MULTIVARIATE = True  # 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,
          n_series,
          futr_exog_list=None,
          hist_exog_list=None,
          stat_exog_list=None,
          exclude_insample_y: bool = False,
          patch_len: int = 16,
          hidden_size: int = 512,
          n_heads: int = 8,
          e_layers: int = 2,
          d_ff: int = 2048,
          factor: int = 1,
          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(TimeXer, 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.enc_in = n_series
          self.hidden_size = hidden_size
          self.n_heads = n_heads
          self.e_layers = e_layers
          self.d_ff = d_ff
          self.dropout = dropout
          self.factor = factor
          self.patch_len = patch_len
          self.use_norm = use_norm
          self.patch_num = int(input_size // self.patch_len)
  
          # Architecture
          self.en_embedding = EnEmbedding(
              n_series, self.hidden_size, self.patch_len, self.dropout
          )
          self.ex_embedding = DataEmbedding_inverted(
              input_size, self.hidden_size, self.dropout
          )
  
          self.encoder = Encoder(
              [
                  EncoderLayer(
                      AttentionLayer(
                          FullAttention(
                              False,
                              self.factor,
                              attention_dropout=self.dropout,
                              output_attention=False,
                          ),
                          self.hidden_size,
                          self.n_heads,
                      ),
                      AttentionLayer(
                          FullAttention(
                              False,
                              self.factor,
                              attention_dropout=self.dropout,
                              output_attention=False,
                          ),
                          self.hidden_size,
                          self.n_heads,
                      ),
                      self.hidden_size,
                      self.d_ff,
                      dropout=self.dropout,
                      activation="relu",
                  )
                  for l in range(self.e_layers)
              ],
              norm_layer=torch.nn.LayerNorm(self.hidden_size),
          )
          self.head_nf = self.hidden_size * (self.patch_num + 1)
          self.head = FlattenHead(
              self.enc_in,
              self.head_nf,
              h * self.loss.outputsize_multiplier,
              head_dropout=self.dropout,
          )
  
      def forecast(self, x_enc, x_mark_enc):
          if self.use_norm:
              # Normalization from Non-stationary Transformer
              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
  
          en_embed, n_vars = self.en_embedding(x_enc.permute(0, 2, 1))
          ex_embed = self.ex_embedding(x_enc, x_mark_enc)
  
          enc_out = self.encoder(en_embed, ex_embed)
          enc_out = torch.reshape(
              enc_out, (-1, n_vars, enc_out.shape[-2], enc_out.shape[-1])
          )
          # z: [bs x nvars x d_model x patch_num]
          enc_out = enc_out.permute(0, 1, 3, 2)
  
          dec_out = self.head(enc_out)  # z: [bs x nvars x h * n_outputs]
          dec_out = dec_out.permute(0, 2, 1)
  
          if self.use_norm:
              # De-Normalization from Non-stationary Transformer
              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"]  # [B, L, N]
          hist_exog = windows_batch["hist_exog"]    # [B, X, L, N]
          futr_exog = windows_batch["futr_exog"]    # [B, F, L+h, N]
          stat_exog = windows_batch["stat_exog"]    # [N, S]
          
          B, L, N = insample_y.shape
          
          # Build exogenous features for the input sequence
          exog_list = []
          
          # Add historical exogenous
          if self.hist_exog_size > 0:
              # hist_exog: [B, X, L, N] -> [B, L, X, N] -> [B, L, X*N]
              hist_exog_input = hist_exog.permute(0, 2, 1, 3)  # [B, L, X, N]
              hist_exog_input = hist_exog_input.reshape(B, L, -1)  # [B, L, X*N]
              exog_list.append(hist_exog_input)
          
          # Add future exogenous
          if self.futr_exog_size > 0:
              # Take only the input sequence part of future exogenous
              futr_exog_input = futr_exog[:, :, :L, :]  # [B, F, L, N]
              futr_exog_input = futr_exog_input.permute(0, 2, 1, 3)  # [B, L, F, N]
              futr_exog_input = futr_exog_input.reshape(B, L, -1)  # [B, L, F*N]
              exog_list.append(futr_exog_input)
          
          # Combine all exogenous features
          if len(exog_list) > 0:
              x_mark_enc = torch.cat(exog_list, dim=-1)  # [B, L, (X+F)*N]
          else:
              x_mark_enc = None
          
          # Add static exogenous to the cross-attention context
          if self.stat_exog_size > 0 and x_mark_enc is not None:
              # stat_exog: [N, S] -> [B, L, N*S]
              stat_exog_expanded = stat_exog.reshape(-1).unsqueeze(0).unsqueeze(0)  # [1, 1, N*S]
              stat_exog_expanded = stat_exog_expanded.repeat(B, L, 1)  # [B, L, N*S]
              x_mark_enc = torch.cat([x_mark_enc, stat_exog_expanded], dim=-1)  # [B, L, (X+F)*N + N*S]
          elif self.stat_exog_size > 0:
              # Only static exogenous available
              stat_exog_expanded = stat_exog.reshape(-1).unsqueeze(0).unsqueeze(0)
              x_mark_enc = stat_exog_expanded.repeat(B, L, 1)
  
          y_pred = self.forecast(insample_y, x_mark_enc)
          y_pred = y_pred.reshape(B, self.h, -1)
          return y_pred
  