content: 
  
  
  __all__ = ['ReplicationPad1d', 'TokenEmbedding', 'PatchEmbedding', 'FlattenHead', 'ReprogrammingLayer', 'TimeLLM']
  
  
  import math
  from typing import Optional
  
  import torch
  import torch.nn as nn
  
  import neuralforecast.losses.pytorch as losses
  
  from ..common._base_model import BaseModel
  from ..common._modules import RevIN
  from ..losses.pytorch import MAE
  
  try:
      from transformers import AutoConfig, AutoModel, AutoTokenizer
  
      IS_TRANSFORMERS_INSTALLED = True
  except ImportError:
      IS_TRANSFORMERS_INSTALLED = False
  
  import warnings
  
  
  class ReplicationPad1d(nn.Module):
      """
      ReplicationPad1d
      """
  
      def __init__(self, padding):
          super(ReplicationPad1d, self).__init__()
          self.padding = padding
  
      def forward(self, input):
          replicate_padding = input[:, :, -1].unsqueeze(-1).repeat(1, 1, self.padding[-1])
          output = torch.cat([input, replicate_padding], dim=-1)
          return output
  
  
  class TokenEmbedding(nn.Module):
      """
      TokenEmbedding
      """
  
      def __init__(self, c_in, d_model):
          super(TokenEmbedding, self).__init__()
          padding = 1 if torch.__version__ >= "1.5.0" else 2
          self.tokenConv = nn.Conv1d(
              in_channels=c_in,
              out_channels=d_model,
              kernel_size=3,
              padding=padding,
              padding_mode="circular",
              bias=False,
          )
          for m in self.modules():
              if isinstance(m, nn.Conv1d):
                  nn.init.kaiming_normal_(
                      m.weight, mode="fan_in", nonlinearity="leaky_relu"
                  )
  
      def forward(self, x):
          x = self.tokenConv(x.permute(0, 2, 1)).transpose(1, 2)
          return x
  
  
  class PatchEmbedding(nn.Module):
      """
      PatchEmbedding
      """
  
      def __init__(self, d_model, patch_len, stride, dropout):
          super(PatchEmbedding, self).__init__()
          # Patching
          self.patch_len = patch_len
          self.stride = stride
          self.padding_patch_layer = ReplicationPad1d((0, stride))
  
          # Backbone, Input encoding: projection of feature vectors onto a d-dim vector space
          self.value_embedding = TokenEmbedding(patch_len, d_model)
  
          # Positional embedding
          # self.position_embedding = PositionalEmbedding(d_model)
  
          # Residual dropout
          self.dropout = nn.Dropout(dropout)
  
      def forward(self, x):
          # do patching
          n_vars = x.shape[1]
          x = self.padding_patch_layer(x)
          x = x.unfold(dimension=-1, size=self.patch_len, step=self.stride)
          x = torch.reshape(x, (x.shape[0] * x.shape[1], x.shape[2], x.shape[3]))
          # Input encoding
          x = self.value_embedding(x)
          return self.dropout(x), n_vars
  
  
  class FlattenHead(nn.Module):
      """
      FlattenHead
      """
  
      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 = self.flatten(x)
          x = self.linear(x)
          x = self.dropout(x)
          return x
  
  
  class ReprogrammingLayer(nn.Module):
      """
      ReprogrammingLayer
      """
  
      def __init__(
          self, d_model, n_heads, d_keys=None, d_llm=None, attention_dropout=0.1
      ):
          super(ReprogrammingLayer, self).__init__()
  
          d_keys = d_keys or (d_model // n_heads)
  
          self.query_projection = nn.Linear(d_model, d_keys * n_heads)
          self.key_projection = nn.Linear(d_llm, d_keys * n_heads)
          self.value_projection = nn.Linear(d_llm, d_keys * n_heads)
          self.out_projection = nn.Linear(d_keys * n_heads, d_llm)
          self.n_heads = n_heads
          self.dropout = nn.Dropout(attention_dropout)
  
      def forward(self, target_embedding, source_embedding, value_embedding):
          B, L, _ = target_embedding.shape
          S, _ = source_embedding.shape
          H = self.n_heads
  
          target_embedding = self.query_projection(target_embedding).view(B, L, H, -1)
          source_embedding = self.key_projection(source_embedding).view(S, H, -1)
          value_embedding = self.value_projection(value_embedding).view(S, H, -1)
  
          out = self.reprogramming(target_embedding, source_embedding, value_embedding)
  
          out = out.reshape(B, L, -1)
  
          return self.out_projection(out)
  
      def reprogramming(self, target_embedding, source_embedding, value_embedding):
          B, L, H, E = target_embedding.shape
  
          scale = 1.0 / math.sqrt(E)
  
          scores = torch.einsum("blhe,she->bhls", target_embedding, source_embedding)
  
          A = self.dropout(torch.softmax(scale * scores, dim=-1))
          reprogramming_embedding = torch.einsum("bhls,she->blhe", A, value_embedding)
  
          return reprogramming_embedding
  
  
  class TimeLLM(BaseModel):
      """TimeLLM
  
      Time-LLM is a reprogramming framework to repurpose an off-the-shelf LLM for time series forecasting.
  
      It trains a reprogramming layer that translates the observed series into a language task. This is fed to the LLM and an output
      projection layer translates the output back to numerical predictions.
  
      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].
          patch_len (int): length of patch. Default: 16
          stride (int): stride of patch. Default: 8
          d_ff (int): dimension of fcn. Default: 128
          top_k (int): top tokens to consider. Default: 5
          d_llm (int): hidden dimension of LLM. Default: 768 # LLama7b:4096; GPT2-small:768; BERT-base:768
          d_model (int): dimension of model. Default: 32
          n_heads (int): number of heads in attention layer. Default: 8
          enc_in (int): encoder input size. Default: 7
          dec_in (int): decoder input size. Default: 7
          llm (str): Path to pretrained LLM model to use. If not specified, it will use GPT-2 from https://huggingface.co/openai-community/gpt2"
          llm_config (dict): Deprecated, configuration of LLM. If not specified, it will use the configuration of GPT-2 from https://huggingface.co/openai-community/gpt2"
          llm_tokenizer (str): Deprecated, tokenizer of LLM. If not specified, it will use the GPT-2 tokenizer from https://huggingface.co/openai-community/gpt2"
          llm_num_hidden_layers (int): hidden layers in LLM. Default: 32
          llm_output_attention (bool): whether to output attention in encoder. Default: True
          llm_output_hidden_states (bool): whether to output hidden states. Default: True
          prompt_prefix (str): prompt to inform the LLM about the dataset. Default: None
          dropout (float): dropout rate. Default: 0.1
          stat_exog_list (list): static exogenous columns.
          hist_exog_list (list): historic exogenous columns.
          futr_exog_list (list): future exogenous columns.
          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).
          learning_rate (float): Learning rate between (0, 1). Default: 1e-3
          max_steps (int): maximum number of training steps. Default: 1000
          val_check_steps (int): Number of training steps between every validation loss check. Default: 100
          batch_size (int): number of different series in each batch. Default: 32
          valid_batch_size (int): number of different series in each validation and test batch, if None uses batch_size. Default: None
          windows_batch_size (int): number of windows to sample in each training batch, default uses all. Default: 1024
          inference_windows_batch_size (int): number of windows to sample in each inference batch. Default: 1024
          start_padding_enabled (bool): if True, the model will pad the time series with zeros at the beginning, by input size. Default: False
          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. Default: 1
          num_lr_decays (int): Number of learning rate decays, evenly distributed across max_steps. Default: -1
          early_stop_patience_steps (int): Number of validation iterations before early stopping. Default: -1
          scaler_type (str): type of scaler for temporal inputs normalization see [temporal scalers](https://github.com/Nixtla/neuralforecast/blob/main/neuralforecast/common/_scalers.py). Default: 'identity'
          random_seed (int): random_seed for pytorch initializer and numpy generators. Default: 1
          drop_last_loader (bool): if True `TimeSeriesDataLoader` drops last non-full batch. Default: False
          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:
          - [Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y. Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, Qingsong Wen. "Time-LLM: Time Series Forecasting by Reprogramming Large Language Models"](https://arxiv.org/abs/2310.01728)
  
      """
  
      EXOGENOUS_FUTR = False
      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,
          input_size,
          patch_len: int = 16,
          stride: int = 8,
          d_ff: int = 128,
          top_k: int = 5,
          d_llm: int = 768,
          d_model: int = 32,
          n_heads: int = 8,
          enc_in: int = 7,
          dec_in: int = 7,
          llm=None,
          llm_config=None,
          llm_tokenizer=None,
          llm_num_hidden_layers=32,
          llm_output_attention: bool = True,
          llm_output_hidden_states: bool = True,
          prompt_prefix: Optional[str] = None,
          dropout: float = 0.1,
          stat_exog_list=None,
          hist_exog_list=None,
          futr_exog_list=None,
          loss=MAE(),
          valid_loss=None,
          learning_rate: float = 1e-4,
          max_steps: int = 5,
          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 = 1024,
          start_padding_enabled: bool = False,
          training_data_availability_threshold=0.0,
          step_size: int = 1,
          num_lr_decays: int = 0,
          early_stop_patience_steps: 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(TimeLLM, 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,
              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,
          )
          if loss.outputsize_multiplier > 1:
              raise Exception(
                  "TimeLLM only supports point loss functions (MAE, MSE, etc) as loss function."
              )
  
          if valid_loss is not None and not isinstance(valid_loss, losses.BasePointLoss):
              raise Exception(
                  "TimeLLM only supports point loss functions (MAE, MSE, etc) as valid loss function."
              )
  
          # Architecture
          self.patch_len = patch_len
          self.stride = stride
          self.d_ff = d_ff
          self.top_k = top_k
          self.d_llm = d_llm
          self.d_model = d_model
          self.dropout = dropout
          self.n_heads = n_heads
          self.enc_in = enc_in
          self.dec_in = dec_in
  
          DEFAULT_MODEL = "openai-community/gpt2"
  
          if llm is None:
              if not IS_TRANSFORMERS_INSTALLED:
                  raise ImportError(
                      "Please install `transformers` to use the default LLM."
                  )
  
              print(f"Using {DEFAULT_MODEL} as default.")
              model_name = DEFAULT_MODEL
          else:
              model_name = llm
  
          if llm_config is not None or llm_tokenizer is not None:
              warnings.warn(
                  "'llm_config' and 'llm_tokenizer' parameters are deprecated and will be ignored. "
                  "The config and tokenizer will be automatically loaded from the specified model.",
                  DeprecationWarning,
              )
  
          try:
              self.llm_config = AutoConfig.from_pretrained(model_name)
              self.llm = AutoModel.from_pretrained(model_name, config=self.llm_config)
              self.llm_tokenizer = AutoTokenizer.from_pretrained(model_name)
              print(f"Successfully loaded model: {model_name}")
          except EnvironmentError:
              print(
                  f"Failed to load {model_name}. Loading the default model ({DEFAULT_MODEL})..."
              )
              self.llm_config = AutoConfig.from_pretrained(DEFAULT_MODEL)
              self.llm = AutoModel.from_pretrained(DEFAULT_MODEL, config=self.llm_config)
              self.llm_tokenizer = AutoTokenizer.from_pretrained(DEFAULT_MODEL)
  
          self.llm_num_hidden_layers = llm_num_hidden_layers
          self.llm_output_attention = llm_output_attention
          self.llm_output_hidden_states = llm_output_hidden_states
          self.prompt_prefix = prompt_prefix
  
          if self.llm_tokenizer.eos_token:
              self.llm_tokenizer.pad_token = self.llm_tokenizer.eos_token
          else:
              pad_token = "[PAD]"
              self.llm_tokenizer.add_special_tokens({"pad_token": pad_token})
              self.llm_tokenizer.pad_token = pad_token
  
          for param in self.llm.parameters():
              param.requires_grad = False
  
          self.patch_embedding = PatchEmbedding(
              self.d_model, self.patch_len, self.stride, self.dropout
          )
  
          self.word_embeddings = self.llm.get_input_embeddings().weight
          self.vocab_size = self.word_embeddings.shape[0]
          self.num_tokens = 1024
          self.mapping_layer = nn.Linear(self.vocab_size, self.num_tokens)
  
          self.reprogramming_layer = ReprogrammingLayer(
              self.d_model, self.n_heads, self.d_ff, self.d_llm
          )
  
          self.patch_nums = int((input_size - self.patch_len) / self.stride + 2)
          self.head_nf = self.d_ff * self.patch_nums
  
          self.output_projection = FlattenHead(
              self.enc_in, self.head_nf, self.h, head_dropout=self.dropout
          )
  
          self.normalize_layers = RevIN(self.enc_in, affine=False)
  
      def forecast(self, x_enc):
  
          x_enc = self.normalize_layers(x_enc, "norm")
  
          B, T, N = x_enc.size()
          x_enc = x_enc.permute(0, 2, 1).contiguous().reshape(B * N, T, 1)
  
          min_values = torch.min(x_enc, dim=1)[0]
          max_values = torch.max(x_enc, dim=1)[0]
          medians = torch.median(x_enc, dim=1).values
          lags = self.calcute_lags(x_enc)
          trends = x_enc.diff(dim=1).sum(dim=1)
  
          prompt = []
          for b in range(x_enc.shape[0]):
              min_values_str = str(min_values[b].tolist()[0])
              max_values_str = str(max_values[b].tolist()[0])
              median_values_str = str(medians[b].tolist()[0])
              lags_values_str = str(lags[b].tolist())
              prompt_ = (
                  f"<|start_prompt|>{self.prompt_prefix}"
                  f"Task description: forecast the next {str(self.h)} steps given the previous {str(self.input_size)} steps information; "
                  "Input statistics: "
                  f"min value {min_values_str}, "
                  f"max value {max_values_str}, "
                  f"median value {median_values_str}, "
                  f"the trend of input is {'upward' if trends[b] > 0 else 'downward'}, "
                  f"top 5 lags are : {lags_values_str}<|<end_prompt>|>"
              )
  
              prompt.append(prompt_)
  
          x_enc = x_enc.reshape(B, N, T).permute(0, 2, 1).contiguous()
  
          prompt = self.llm_tokenizer(
              prompt, return_tensors="pt", padding=True, truncation=True, max_length=2048
          ).input_ids
          prompt_embeddings = self.llm.get_input_embeddings()(
              prompt.to(x_enc.device)
          )  # (batch, prompt_token, dim)
  
          source_embeddings = self.mapping_layer(
              self.word_embeddings.permute(1, 0)
          ).permute(1, 0)
  
          x_enc = x_enc.permute(0, 2, 1).contiguous()
          enc_out, n_vars = self.patch_embedding(x_enc.to(torch.float32))
          enc_out = self.reprogramming_layer(
              enc_out, source_embeddings, source_embeddings
          )
          llm_enc_out = torch.cat([prompt_embeddings, enc_out], dim=1)
          dec_out = self.llm(inputs_embeds=llm_enc_out).last_hidden_state
          dec_out = dec_out[:, :, : self.d_ff]
  
          dec_out = torch.reshape(
              dec_out, (-1, n_vars, dec_out.shape[-2], dec_out.shape[-1])
          )
          dec_out = dec_out.permute(0, 1, 3, 2).contiguous()
  
          dec_out = self.output_projection(dec_out[:, :, :, -self.patch_nums :])
          dec_out = dec_out.permute(0, 2, 1).contiguous()
  
          dec_out = self.normalize_layers(dec_out, "denorm")
  
          return dec_out
  
      def calcute_lags(self, x_enc):
          q_fft = torch.fft.rfft(x_enc.permute(0, 2, 1).contiguous(), dim=-1)
          k_fft = torch.fft.rfft(x_enc.permute(0, 2, 1).contiguous(), dim=-1)
          res = q_fft * torch.conj(k_fft)
          corr = torch.fft.irfft(res, dim=-1)
          mean_value = torch.mean(corr, dim=1)
          _, lags = torch.topk(mean_value, self.top_k, dim=-1)
          return lags
  
      def forward(self, windows_batch):
          x = windows_batch["insample_y"]
  
          y_pred = self.forecast(x)
          y_pred = y_pred[:, -self.h :, :]
  
          return y_pred
  