content: 
  
  
  __all__ = ['Inception_Block_V1', 'FFT_for_Period', 'TimesBlock', 'TimesNet']
  
  
  from typing import Optional
  
  import torch
  import torch.fft
  import torch.nn as nn
  import torch.nn.functional as F
  
  from ..common._base_model import BaseModel
  from ..common._modules import DataEmbedding
  from ..losses.pytorch import MAE
  
  
  class Inception_Block_V1(nn.Module):
      """
      Inception_Block_V1
      """
  
      def __init__(self, in_channels, out_channels, num_kernels=6, init_weight=True):
          super(Inception_Block_V1, self).__init__()
          self.in_channels = in_channels
          self.out_channels = out_channels
          self.num_kernels = num_kernels
          kernels = []
          for i in range(self.num_kernels):
              kernels.append(
                  nn.Conv2d(in_channels, out_channels, kernel_size=2 * i + 1, padding=i)
              )
          self.kernels = nn.ModuleList(kernels)
          if init_weight:
              self._initialize_weights()
  
      def _initialize_weights(self):
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
                  if m.bias is not None:
                      nn.init.constant_(m.bias, 0)
  
      def forward(self, x):
          res_list = []
          for i in range(self.num_kernels):
              res_list.append(self.kernels[i](x))
          res = torch.stack(res_list, dim=-1).mean(-1)
          return res
  
  
  def FFT_for_Period(x, k=2):
      # [B, T, C]
      xf = torch.fft.rfft(x, dim=1)
      # find period by amplitudes
      frequency_list = abs(xf).mean(0).mean(-1)
      frequency_list[0] = 0
      _, top_list = torch.topk(frequency_list, k)
      top_list = top_list.detach().cpu().numpy()
      period = x.shape[1] // top_list
      return period, abs(xf).mean(-1)[:, top_list]
  
  
  class TimesBlock(nn.Module):
      """
      TimesBlock
      """
  
      def __init__(self, input_size, h, k, hidden_size, conv_hidden_size, num_kernels):
          super(TimesBlock, self).__init__()
          self.input_size = input_size
          self.h = h
          self.k = k
          # parameter-efficient design
          self.conv = nn.Sequential(
              Inception_Block_V1(hidden_size, conv_hidden_size, num_kernels=num_kernels),
              nn.GELU(),
              Inception_Block_V1(conv_hidden_size, hidden_size, num_kernels=num_kernels),
          )
  
      def forward(self, x):
          B, T, N = x.size()
          period_list, period_weight = FFT_for_Period(x, self.k)
  
          res = []
          for i in range(self.k):
              period = period_list[i]
              # padding
              if (self.input_size + self.h) % period != 0:
                  length = (((self.input_size + self.h) // period) + 1) * period
                  padding = torch.zeros(
                      [x.shape[0], (length - (self.input_size + self.h)), x.shape[2]],
                      device=x.device,
                  )
                  out = torch.cat([x, padding], dim=1)
              else:
                  length = self.input_size + self.h
                  out = x
              # reshape
              out = (
                  out.reshape(B, length // period, period, N)
                  .permute(0, 3, 1, 2)
                  .contiguous()
              )
              # 2D conv: from 1d Variation to 2d Variation
              out = self.conv(out)
              # reshape back
              out = out.permute(0, 2, 3, 1).reshape(B, -1, N)
              res.append(out[:, : (self.input_size + self.h), :])
          res = torch.stack(res, dim=-1)
          # adaptive aggregation
          period_weight = F.softmax(period_weight, dim=1)
          period_weight = period_weight.unsqueeze(1).unsqueeze(1).repeat(1, T, N, 1)
          res = torch.sum(res * period_weight, -1)
          # residual connection
          res = res + x
          return res
  
  
  class TimesNet(BaseModel):
      """TimesNet
  
      The TimesNet univariate model tackles the challenge of modeling multiple intraperiod and interperiod temporal variations.
  
      Args:
          h (int): Forecast horizon.
          input_size (int): Length of input window (lags).
          stat_exog_list (list of str): optional (default=None), Static exogenous columns.
          hist_exog_list (list of str): optional (default=None), Historic exogenous columns.
          futr_exog_list (list of str): optional (default=None), Future exogenous columns.
          exclude_insample_y (bool): The model skips the autoregressive features y[t-input_size:t] if True.
          hidden_size (int): Size of embedding for embedding and encoders.
          dropout (float): Dropout for embeddings.
          conv_hidden_size (int): Channels of the Inception block.
          top_k (int): Number of periods.
          num_kernels (int): Number of kernels for the Inception block.
          encoder_layers (int): Number of encoder layers.
          loss (PyTorch module): Instantiated train loss class from [losses collection](./losses.pytorch).
          valid_loss (PyTorch module): Instantiated validation loss class from [losses collection](./losses.pytorch).
          max_steps (int): Maximum number of training steps.
          learning_rate (float): Learning rate.
          num_lr_decays (int): Number of learning rate decays, evenly distributed across max_steps. If -1, no learning rate decay is performed.
          early_stop_patience_steps (int): Number of validation iterations before early stopping. If -1, no early stopping is performed.
          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.
          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 (default=None), Custom name of the model.
          optimizer (Subclass of 'torch.optim.Optimizer'): optional (default=None), User specified optimizer instead of the default choice (Adam).
          optimizer_kwargs (dict): optional (defualt=None), 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 (default=None), 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:
          - [Haixu Wu and Tengge Hu and Yong Liu and Hang Zhou and Jianmin Wang and Mingsheng Long. TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis. https://openreview.net/pdf?id=ju_Uqw384Oq](https://openreview.net/pdf?id=ju_Uqw384Oq)
      """
  
      # 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,
          hidden_size: int = 64,
          dropout: float = 0.1,
          conv_hidden_size: int = 64,
          top_k: int = 5,
          num_kernels: int = 6,
          encoder_layers: int = 2,
          loss=MAE(),
          valid_loss=None,
          max_steps: int = 1000,
          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=64,
          inference_windows_batch_size=256,
          start_padding_enabled=False,
          training_data_availability_threshold=0.0,
          step_size: int = 1,
          scaler_type: str = "standard",
          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(TimesNet, self).__init__(
              h=h,
              input_size=input_size,
              hist_exog_list=hist_exog_list,
              stat_exog_list=stat_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,
              windows_batch_size=windows_batch_size,
              valid_batch_size=valid_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.c_out = self.loss.outputsize_multiplier
          self.enc_in = 1
          self.dec_in = 1
  
          self.model = nn.ModuleList(
              [
                  TimesBlock(
                      input_size=input_size,
                      h=h,
                      k=top_k,
                      hidden_size=hidden_size,
                      conv_hidden_size=conv_hidden_size,
                      num_kernels=num_kernels,
                  )
                  for _ in range(encoder_layers)
              ]
          )
  
          self.enc_embedding = DataEmbedding(
              c_in=self.enc_in,
              exog_input_size=self.futr_exog_size,
              hidden_size=hidden_size,
              pos_embedding=True,  # Original implementation uses true
              dropout=dropout,
          )
          self.encoder_layers = encoder_layers
          self.layer_norm = nn.LayerNorm(hidden_size)
          self.predict_linear = nn.Linear(self.input_size, self.h + self.input_size)
          self.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"]
  
          # Parse inputs
          if self.futr_exog_size > 0:
              x_mark_enc = futr_exog[:, : self.input_size, :]
          else:
              x_mark_enc = None
  
          # embedding
          enc_out = self.enc_embedding(insample_y, x_mark_enc)
          enc_out = self.predict_linear(enc_out.permute(0, 2, 1)).permute(
              0, 2, 1
          )  # align temporal dimension
          # TimesNet
          for i in range(self.encoder_layers):
              enc_out = self.layer_norm(self.model[i](enc_out))
          # porject back
          dec_out = self.projection(enc_out)
  
          forecast = dec_out[:, -self.h :]
          return forecast
  