content: 
  
  
  __all__ = ['VanillaTransformer']
  
  
  from typing import Optional
  
  import numpy as np
  import torch
  import torch.nn as nn
  
  from neuralforecast.common._modules import (
      AttentionLayer,
      DataEmbedding,
      FullAttention,
      TransDecoder,
      TransDecoderLayer,
      TransEncoder,
      TransEncoderLayer,
  )
  
  from ..common._base_model import BaseModel
  from ..losses.pytorch import MAE
  
  
  class VanillaTransformer(BaseModel):
      """VanillaTransformer
  
      Vanilla Transformer, following implementation of the Informer paper, used as baseline.
  
      The architecture has three distinctive features:
      - Full-attention mechanism with O(L^2) time and memory complexity.
      - An MLP multi-step decoder that predicts long time-series sequences in a single forward operation rather than step-by-step.
  
      The Vanilla Transformer model utilizes a three-component approach to define its embedding:
      - It employs encoded autoregressive features obtained from a convolution network.
      - It uses window-relative positional embeddings derived from harmonic functions.
      - Absolute positional embeddings obtained from calendar features are utilized.
  
      Args:
          h (int): forecast horizon.
          input_size (int): maximum sequence length for truncated train backpropagation.
          stat_exog_list (str list): static exogenous columns.
          hist_exog_list (str list): historic exogenous columns.
          futr_exog_list (str list): future exogenous columns.
          exclude_insample_y (bool): whether to exclude the target variable from the input.
          decoder_input_size_multiplier (float): .
          hidden_size (int): units of embeddings and encoders.
          dropout (float): dropout throughout Informer architecture.
          n_head (int): controls number of multi-head's attention.
          conv_hidden_size (int): channels of the convolutional encoder.
          activation (str): activation from ['ReLU', 'Softplus', 'Tanh', 'SELU', 'LeakyReLU', 'PReLU', 'Sigmoid', 'GELU'].
          encoder_layers (int): number of layers for the TCN encoder.
          decoder_layers (int): number of layers for the MLP decoder.
          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.
          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:
          - [Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, Wancai Zhang. "Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting"](https://arxiv.org/abs/2012.07436)
      """
  
      # Class attributes
      EXOGENOUS_FUTR = True
      EXOGENOUS_HIST = False
      EXOGENOUS_STAT = False
      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,
          stat_exog_list=None,
          hist_exog_list=None,
          futr_exog_list=None,
          exclude_insample_y=False,
          decoder_input_size_multiplier: float = 0.5,
          hidden_size: int = 128,
          dropout: float = 0.05,
          n_head: int = 4,
          conv_hidden_size: int = 32,
          activation: str = "gelu",
          encoder_layers: int = 2,
          decoder_layers: int = 1,
          loss=MAE(),
          valid_loss=None,
          max_steps: int = 5000,
          learning_rate: float = 1e-4,
          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=1024,
          inference_windows_batch_size: int = 1024,
          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(VanillaTransformer, self).__init__(
              h=h,
              input_size=input_size,
              stat_exog_list=stat_exog_list,
              hist_exog_list=hist_exog_list,
              futr_exog_list=futr_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,
              drop_last_loader=drop_last_loader,
              alias=alias,
              random_seed=random_seed,
              optimizer=optimizer,
              optimizer_kwargs=optimizer_kwargs,
              lr_scheduler=lr_scheduler,
              lr_scheduler_kwargs=lr_scheduler_kwargs,
              dataloader_kwargs=dataloader_kwargs,
              **trainer_kwargs,
          )
  
          # Architecture
          self.label_len = int(np.ceil(input_size * decoder_input_size_multiplier))
          if (self.label_len >= input_size) or (self.label_len <= 0):
              raise Exception(
                  f"Check decoder_input_size_multiplier={decoder_input_size_multiplier}, range (0,1)"
              )
  
          if activation not in ["relu", "gelu"]:
              raise Exception(f"Check activation={activation}")
  
          self.c_out = self.loss.outputsize_multiplier
          self.output_attention = False
          self.enc_in = 1
          self.dec_in = 1
  
          # Embedding
          self.enc_embedding = DataEmbedding(
              c_in=self.enc_in,
              exog_input_size=self.futr_exog_size,
              hidden_size=hidden_size,
              pos_embedding=True,
              dropout=dropout,
          )
          self.dec_embedding = DataEmbedding(
              self.dec_in,
              exog_input_size=self.futr_exog_size,
              hidden_size=hidden_size,
              pos_embedding=True,
              dropout=dropout,
          )
  
          # Encoder
          self.encoder = TransEncoder(
              [
                  TransEncoderLayer(
                      AttentionLayer(
                          FullAttention(
                              mask_flag=False,
                              attention_dropout=dropout,
                              output_attention=self.output_attention,
                          ),
                          hidden_size,
                          n_head,
                      ),
                      hidden_size,
                      conv_hidden_size,
                      dropout=dropout,
                      activation=activation,
                  )
                  for l in range(encoder_layers)
              ],
              norm_layer=torch.nn.LayerNorm(hidden_size),
          )
          # Decoder
          self.decoder = TransDecoder(
              [
                  TransDecoderLayer(
                      AttentionLayer(
                          FullAttention(
                              mask_flag=True,
                              attention_dropout=dropout,
                              output_attention=False,
                          ),
                          hidden_size,
                          n_head,
                      ),
                      AttentionLayer(
                          FullAttention(
                              mask_flag=False,
                              attention_dropout=dropout,
                              output_attention=False,
                          ),
                          hidden_size,
                          n_head,
                      ),
                      hidden_size,
                      conv_hidden_size,
                      dropout=dropout,
                      activation=activation,
                  )
                  for l in range(decoder_layers)
              ],
              norm_layer=torch.nn.LayerNorm(hidden_size),
              projection=nn.Linear(hidden_size, self.c_out, bias=True),
          )
  
      def forward(self, windows_batch):
          # Parse windows_batch
          insample_y = windows_batch["insample_y"]
          futr_exog = windows_batch["futr_exog"]
  
          if self.futr_exog_size > 0:
              x_mark_enc = futr_exog[:, : self.input_size, :]
              x_mark_dec = futr_exog[:, -(self.label_len + self.h) :, :]
          else:
              x_mark_enc = None
              x_mark_dec = None
  
          x_dec = torch.zeros(size=(len(insample_y), self.h, 1), device=insample_y.device)
          x_dec = torch.cat([insample_y[:, -self.label_len :, :], x_dec], dim=1)
  
          enc_out = self.enc_embedding(insample_y, x_mark_enc)
          enc_out, _ = self.encoder(enc_out, attn_mask=None)  # attns visualization
  
          dec_out = self.dec_embedding(x_dec, x_mark_dec)
          dec_out = self.decoder(dec_out, enc_out, x_mask=None, cross_mask=None)
  
          forecast = dec_out[:, -self.h :]
          return forecast
  