content: 
  
  
  __all__ = ['NHITS']
  
  
  from typing import Optional, Tuple
  
  import numpy as np
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  from ..common._base_model import BaseModel
  from ..losses.pytorch import MAE
  
  
  class _IdentityBasis(nn.Module):
      def __init__(
          self,
          backcast_size: int,
          forecast_size: int,
          interpolation_mode: str,
          out_features: int = 1,
      ):
          super().__init__()
          assert (interpolation_mode in ["linear", "nearest"]) or (
              "cubic" in interpolation_mode
          )
          self.forecast_size = forecast_size
          self.backcast_size = backcast_size
          self.interpolation_mode = interpolation_mode
          self.out_features = out_features
  
      def forward(self, theta: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
  
          backcast = theta[:, : self.backcast_size]
          knots = theta[:, self.backcast_size :]
  
          # Interpolation is performed on default dim=-1 := H
          knots = knots.reshape(len(knots), self.out_features, -1)
          if self.interpolation_mode in ["nearest", "linear"]:
              # knots = knots[:,None,:]
              forecast = F.interpolate(
                  knots, size=self.forecast_size, mode=self.interpolation_mode
              )
              # forecast = forecast[:,0,:]
          elif "cubic" in self.interpolation_mode:
              if self.out_features > 1:
                  raise Exception(
                      "Cubic interpolation not available with multiple outputs."
                  )
              batch_size = len(backcast)
              knots = knots[:, None, :, :]
              forecast = torch.zeros(
                  (len(knots), self.forecast_size), device=knots.device
              )
              n_batches = int(np.ceil(len(knots) / batch_size))
              for i in range(n_batches):
                  forecast_i = F.interpolate(
                      knots[i * batch_size : (i + 1) * batch_size],
                      size=self.forecast_size,
                      mode="bicubic",
                  )
                  forecast[i * batch_size : (i + 1) * batch_size] += forecast_i[
                      :, 0, 0, :
                  ]  # [B,None,H,H] -> [B,H]
              forecast = forecast[:, None, :]  # [B,H] -> [B,None,H]
  
          # [B,Q,H] -> [B,H,Q]
          forecast = forecast.permute(0, 2, 1)
          return backcast, forecast
  
  
  ACTIVATIONS = ["ReLU", "Softplus", "Tanh", "SELU", "LeakyReLU", "PReLU", "Sigmoid"]
  
  POOLING = ["MaxPool1d", "AvgPool1d"]
  
  
  class NHITSBlock(nn.Module):
      """
      NHITS block which takes a basis function as an argument.
      """
  
      def __init__(
          self,
          input_size: int,
          h: int,
          n_theta: int,
          mlp_units: list,
          basis: nn.Module,
          futr_input_size: int,
          hist_input_size: int,
          stat_input_size: int,
          n_pool_kernel_size: int,
          pooling_mode: str,
          dropout_prob: float,
          activation: str,
      ):
          super().__init__()
  
          pooled_hist_size = int(np.ceil(input_size / n_pool_kernel_size))
          pooled_futr_size = int(np.ceil((input_size + h) / n_pool_kernel_size))
  
          input_size = (
              pooled_hist_size
              + hist_input_size * pooled_hist_size
              + futr_input_size * pooled_futr_size
              + stat_input_size
          )
  
          self.dropout_prob = dropout_prob
          self.futr_input_size = futr_input_size
          self.hist_input_size = hist_input_size
          self.stat_input_size = stat_input_size
  
          assert activation in ACTIVATIONS, f"{activation} is not in {ACTIVATIONS}"
          assert pooling_mode in POOLING, f"{pooling_mode} is not in {POOLING}"
  
          activ = getattr(nn, activation)()
  
          self.pooling_layer = getattr(nn, pooling_mode)(
              kernel_size=n_pool_kernel_size, stride=n_pool_kernel_size, ceil_mode=True
          )
  
          # Block MLPs
          hidden_layers = [
              nn.Linear(in_features=input_size, out_features=mlp_units[0][0])
          ]
          for layer in mlp_units:
              hidden_layers.append(nn.Linear(in_features=layer[0], out_features=layer[1]))
              hidden_layers.append(activ)
  
              if self.dropout_prob > 0:
                  # raise NotImplementedError('dropout')
                  hidden_layers.append(nn.Dropout(p=self.dropout_prob))
  
          output_layer = [nn.Linear(in_features=mlp_units[-1][1], out_features=n_theta)]
          layers = hidden_layers + output_layer
          self.layers = nn.Sequential(*layers)
          self.basis = basis
  
      def forward(
          self,
          insample_y: torch.Tensor,
          futr_exog: torch.Tensor,
          hist_exog: torch.Tensor,
          stat_exog: torch.Tensor,
      ) -> Tuple[torch.Tensor, torch.Tensor]:
  
          # Pooling
          # Pool1d needs 3D input, (B,C,L), adding C dimension
          insample_y = insample_y.unsqueeze(1)
          insample_y = self.pooling_layer(insample_y)
          insample_y = insample_y.squeeze(1)
  
          # Flatten MLP inputs [B, L+H, C] -> [B, (L+H)*C]
          # Contatenate [ Y_t, | X_{t-L},..., X_{t} | F_{t-L},..., F_{t+H} | S ]
          batch_size = len(insample_y)
          if self.hist_input_size > 0:
              hist_exog = hist_exog.permute(0, 2, 1)  # [B, L, C] -> [B, C, L]
              hist_exog = self.pooling_layer(hist_exog)
              hist_exog = hist_exog.permute(0, 2, 1)  # [B, C, L] -> [B, L, C]
              insample_y = torch.cat(
                  (insample_y, hist_exog.reshape(batch_size, -1)), dim=1
              )
  
          if self.futr_input_size > 0:
              futr_exog = futr_exog.permute(0, 2, 1)  # [B, L, C] -> [B, C, L]
              futr_exog = self.pooling_layer(futr_exog)
              futr_exog = futr_exog.permute(0, 2, 1)  # [B, C, L] -> [B, L, C]
              insample_y = torch.cat(
                  (insample_y, futr_exog.reshape(batch_size, -1)), dim=1
              )
  
          if self.stat_input_size > 0:
              insample_y = torch.cat(
                  (insample_y, stat_exog.reshape(batch_size, -1)), dim=1
              )
  
          # Compute local projection weights and projection
          theta = self.layers(insample_y)
          backcast, forecast = self.basis(theta)
          return backcast, forecast
  
  
  class NHITS(BaseModel):
      """NHITS
  
      The Neural Hierarchical Interpolation for Time Series (NHITS), is an MLP-based deep
      neural architecture with backward and forward residual links. NHITS tackles volatility and
      memory complexity challenges, by locally specializing its sequential predictions into
      the signals frequencies with hierarchical interpolation and pooling.
  
      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].
          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.
          stack_types (List[str]): stacks list in the form N * ['identity'], to be deprecated in favor of `n_stacks`. Note that len(stack_types)=len(n_freq_downsample)=len(n_pool_kernel_size).
          n_blocks (List[int]): Number of blocks for each stack. Note that len(n_blocks) = len(stack_types).
          mlp_units (List[List[int]]): Structure of hidden layers for each stack type. Each internal list should contain the number of units of each hidden layer. Note that len(n_hidden) = len(stack_types).
          n_pool_kernel_size (List[int]): list with the size of the windows to take a max/avg over. Note that len(stack_types)=len(n_freq_downsample)=len(n_pool_kernel_size).
          n_freq_downsample (List[int]): list with the stack's coefficients (inverse expressivity ratios). Note that len(stack_types)=len(n_freq_downsample)=len(n_pool_kernel_size).
          pooling_mode (str): input pooling module from ['MaxPool1d', 'AvgPool1d'].
          interpolation_mode (str): interpolation basis from ['linear', 'nearest', 'cubic'].
          dropout_prob_theta (float): Float between (0, 1). Dropout for NHITS basis.
          activation (str): activation from ['ReLU', 'Softplus', 'Tanh', 'SELU', 'LeakyReLU', 'PReLU', 'Sigmoid'].
          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:
          - [Cristian Challu, Kin G. Olivares, Boris N. Oreshkin, Federico Garza, Max Mergenthaler-Canseco, Artur Dubrawski (2023). "NHITS: Neural Hierarchical Interpolation for Time Series Forecasting". Accepted at the Thirty-Seventh AAAI Conference on Artificial Intelligence.](https://arxiv.org/abs/2201.12886)
      """
  
      # Class attributes
      EXOGENOUS_FUTR = True
      EXOGENOUS_HIST = True
      EXOGENOUS_STAT = True
      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,
          input_size,
          futr_exog_list=None,
          hist_exog_list=None,
          stat_exog_list=None,
          exclude_insample_y=False,
          stack_types: list = ["identity", "identity", "identity"],
          n_blocks: list = [1, 1, 1],
          mlp_units: list = 3 * [[512, 512]],
          n_pool_kernel_size: list = [2, 2, 1],
          n_freq_downsample: list = [4, 2, 1],
          pooling_mode: str = "MaxPool1d",
          interpolation_mode: str = "linear",
          dropout_prob_theta=0.0,
          activation="ReLU",
          loss=MAE(),
          valid_loss=None,
          max_steps: int = 1000,
          learning_rate: float = 1e-3,
          num_lr_decays: int = 3,
          early_stop_patience_steps: int = -1,
          val_check_steps: int = 100,
          batch_size: int = 32,
          valid_batch_size: Optional[int] = None,
          windows_batch_size: int = 1024,
          inference_windows_batch_size: int = -1,
          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=False,
          alias: Optional[str] = None,
          optimizer=None,
          optimizer_kwargs=None,
          lr_scheduler=None,
          lr_scheduler_kwargs=None,
          dataloader_kwargs=None,
          **trainer_kwargs,
      ):
  
          # Inherit BaseWindows class
          super(NHITS, self).__init__(
              h=h,
              input_size=input_size,
              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,
          )
  
          # Architecture
          blocks = self.create_stack(
              h=h,
              input_size=input_size,
              stack_types=stack_types,
              futr_input_size=self.futr_exog_size,
              hist_input_size=self.hist_exog_size,
              stat_input_size=self.stat_exog_size,
              n_blocks=n_blocks,
              mlp_units=mlp_units,
              n_pool_kernel_size=n_pool_kernel_size,
              n_freq_downsample=n_freq_downsample,
              pooling_mode=pooling_mode,
              interpolation_mode=interpolation_mode,
              dropout_prob_theta=dropout_prob_theta,
              activation=activation,
          )
          self.blocks = torch.nn.ModuleList(blocks)
  
      def create_stack(
          self,
          h,
          input_size,
          stack_types,
          n_blocks,
          mlp_units,
          n_pool_kernel_size,
          n_freq_downsample,
          pooling_mode,
          interpolation_mode,
          dropout_prob_theta,
          activation,
          futr_input_size,
          hist_input_size,
          stat_input_size,
      ):
  
          block_list = []
          for i in range(len(stack_types)):
              for block_id in range(n_blocks[i]):
  
                  assert (
                      stack_types[i] == "identity"
                  ), f"Block type {stack_types[i]} not found!"
  
                  n_theta = input_size + self.loss.outputsize_multiplier * max(
                      h // n_freq_downsample[i], 1
                  )
                  basis = _IdentityBasis(
                      backcast_size=input_size,
                      forecast_size=h,
                      out_features=self.loss.outputsize_multiplier,
                      interpolation_mode=interpolation_mode,
                  )
  
                  nbeats_block = NHITSBlock(
                      h=h,
                      input_size=input_size,
                      futr_input_size=futr_input_size,
                      hist_input_size=hist_input_size,
                      stat_input_size=stat_input_size,
                      n_theta=n_theta,
                      mlp_units=mlp_units,
                      n_pool_kernel_size=n_pool_kernel_size[i],
                      pooling_mode=pooling_mode,
                      basis=basis,
                      dropout_prob=dropout_prob_theta,
                      activation=activation,
                  )
  
                  # Select type of evaluation and apply it to all layers of block
                  block_list.append(nbeats_block)
  
          return block_list
  
      def forward(self, windows_batch):
  
          # Parse windows_batch
          insample_y = windows_batch["insample_y"].squeeze(-1).contiguous()
          insample_mask = windows_batch["insample_mask"].squeeze(-1).contiguous()
          futr_exog = windows_batch["futr_exog"]
          hist_exog = windows_batch["hist_exog"]
          stat_exog = windows_batch["stat_exog"]
  
          # insample
          residuals = insample_y.flip(dims=(-1,))  # backcast init
          insample_mask = insample_mask.flip(dims=(-1,))
  
          forecast = insample_y[:, -1:, None]  # Level with Naive1
          block_forecasts = [forecast.repeat(1, self.h, 1)]
          for i, block in enumerate(self.blocks):
              backcast, block_forecast = block(
                  insample_y=residuals,
                  futr_exog=futr_exog,
                  hist_exog=hist_exog,
                  stat_exog=stat_exog,
              )
              residuals = (residuals - backcast) * insample_mask
              forecast = forecast + block_forecast
  
              if self.decompose_forecast:
                  block_forecasts.append(block_forecast)
  
          if self.decompose_forecast:
              # (n_batch, n_blocks, h, output_size)
              block_forecasts = torch.stack(block_forecasts)
              block_forecasts = block_forecasts.permute(1, 0, 2, 3)
              block_forecasts = block_forecasts.squeeze(-1)  # univariate output
              return block_forecasts
          else:
              return forecast
  