content: 
  
  
  __all__ = ['iTransformer']
  
  
  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,
      TransEncoder,
      TransEncoderLayer,
  )
  
  from ..common._base_model import BaseModel
  from ..losses.pytorch import MAE
  
  
  class iTransformer(BaseModel):
      """iTransformer
  
      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.
          exclude_insample_y (bool): the model skips the autoregressive features y[t-input_size:t] if True.
          hidden_size (int): dimension of the model.
          n_heads (int): number of heads.
          e_layers (int): number of encoder layers.
          d_layers (int): number of decoder 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 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:
          - [Yong Liu, Tengge Hu, Haoran Zhang, Haixu Wu, Shiyu Wang, Lintao Ma, Mingsheng Long. "iTransformer: Inverted Transformers Are Effective for Time Series Forecasting"](https://arxiv.org/abs/2310.06625)
      """
  
      # 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,
          n_heads: int = 8,
          e_layers: int = 2,
          d_layers: int = 1,
          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(iTransformer, 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.dec_in = n_series
          self.c_out = n_series
          self.hidden_size = hidden_size
          self.n_heads = n_heads
          self.e_layers = e_layers
          self.d_layers = d_layers
          self.d_ff = d_ff
          self.factor = factor
          self.dropout = dropout
          self.use_norm = use_norm
  
          # Architecture
          self.enc_embedding = DataEmbedding_inverted(
              input_size, self.hidden_size, self.dropout
          )
  
          self.encoder = TransEncoder(
              [
                  TransEncoderLayer(
                      AttentionLayer(
                          FullAttention(
                              False, self.factor, attention_dropout=self.dropout
                          ),
                          self.hidden_size,
                          self.n_heads,
                      ),
                      self.hidden_size,
                      self.d_ff,
                      dropout=self.dropout,
                      activation=F.gelu,
                  )
                  for l in range(self.e_layers)
              ],
              norm_layer=torch.nn.LayerNorm(self.hidden_size),
          )
  
          self.projector = nn.Linear(
              self.hidden_size, h * self.loss.outputsize_multiplier, bias=True
          )
  
      def forecast(self, x_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  # B L N
          # B: batch_size;       E: hidden_size;
          # L: input_size;       S: horizon(h);
          # N: number of variate (tokens), can also includes covariates
  
          # Embedding
          # B L N -> B N E                (B L N -> B L E in the vanilla Transformer)
          enc_out = self.enc_embedding(
              x_enc, None
          )  # covariates (e.g timestamp) can be also embedded as tokens
  
          # B N E -> B N E                (B L E -> B L E in the vanilla Transformer)
          # the dimensions of embedded time series has been inverted, and then processed by native attn, layernorm and ffn modules
          enc_out, attns = self.encoder(enc_out, attn_mask=None)
  
          # B N E -> B N S -> B S N
          dec_out = self.projector(enc_out).permute(0, 2, 1)[
              :, :, :N
          ]  # filter the covariates
  
          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"]
  
          y_pred = self.forecast(insample_y)
          y_pred = y_pred.reshape(insample_y.shape[0], self.h, -1)
  
          return y_pred
  