text: # Copyright (c) Microsoft Corporation.
  # Licensed under the MIT License.
  
  
  from __future__ import division
  from __future__ import print_function
  
  import os
  import numpy as np
  import pandas as pd
  from typing import Text, Union
  import copy
  import math
  from ...utils import get_or_create_path
  from ...log import get_module_logger
  
  import torch
  import torch.nn as nn
  import torch.optim as optim
  from torch.utils.data import DataLoader
  
  from .pytorch_utils import count_parameters
  from ...model.base import Model
  from ...data.dataset import DatasetH, TSDatasetH
  from ...data.dataset.handler import DataHandlerLP
  from torch.nn.modules.container import ModuleList
  
  # qrun examples/benchmarks/Localformer/workflow_config_localformer_Alpha360.yaml ”
  
  
  class LocalformerModel(Model):
      def __init__(
          self,
          d_feat: int = 20,
          d_model: int = 64,
          batch_size: int = 2048,
          nhead: int = 2,
          num_layers: int = 2,
          dropout: float = 0,
          n_epochs=100,
          lr=0.0001,
          metric="",
          early_stop=5,
          loss="mse",
          optimizer="adam",
          reg=1e-3,
          n_jobs=10,
          GPU=0,
          seed=None,
          **kwargs
      ):
  
          # set hyper-parameters.
          self.d_model = d_model
          self.dropout = dropout
          self.n_epochs = n_epochs
          self.lr = lr
          self.reg = reg
          self.metric = metric
          self.batch_size = batch_size
          self.early_stop = early_stop
          self.optimizer = optimizer.lower()
          self.loss = loss
          self.n_jobs = n_jobs
          self.device = torch.device("cuda:%d" % GPU if torch.cuda.is_available() and GPU >= 0 else "cpu")
          self.seed = seed
          self.logger = get_module_logger("TransformerModel")
          self.logger.info("Naive Transformer:" "\nbatch_size : {}" "\ndevice : {}".format(self.batch_size, self.device))
  
          if self.seed is not None:
              np.random.seed(self.seed)
              torch.manual_seed(self.seed)
  
          self.model = Transformer(d_feat, d_model, nhead, num_layers, dropout, self.device)
          if optimizer.lower() == "adam":
              self.train_optimizer = optim.Adam(self.model.parameters(), lr=self.lr, weight_decay=self.reg)
          elif optimizer.lower() == "gd":
              self.train_optimizer = optim.SGD(self.model.parameters(), lr=self.lr, weight_decay=self.reg)
          else:
              raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
  
          self.fitted = False
          self.model.to(self.device)
  
      @property
      def use_gpu(self):
          return self.device != torch.device("cpu")
  
      def mse(self, pred, label):
          loss = (pred.float() - label.float()) ** 2
          return torch.mean(loss)
  
      def loss_fn(self, pred, label):
          mask = ~torch.isnan(label)
  
          if self.loss == "mse":
              return self.mse(pred[mask], label[mask])
  
          raise ValueError("unknown loss `%s`" % self.loss)
  
      def metric_fn(self, pred, label):
  
          mask = torch.isfinite(label)
  
          if self.metric == "" or self.metric == "loss":
              return -self.loss_fn(pred[mask], label[mask])
  
          raise ValueError("unknown metric `%s`" % self.metric)
  
      def train_epoch(self, x_train, y_train):
  
          x_train_values = x_train.values
          y_train_values = np.squeeze(y_train.values)
  
          self.model.train()
  
          indices = np.arange(len(x_train_values))
          np.random.shuffle(indices)
  
          for i in range(len(indices))[:: self.batch_size]:
  
              if len(indices) - i < self.batch_size:
                  break
  
              feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
              label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
  
              pred = self.model(feature)
              loss = self.loss_fn(pred, label)
  
              self.train_optimizer.zero_grad()
              loss.backward()
              torch.nn.utils.clip_grad_value_(self.model.parameters(), 3.0)
              self.train_optimizer.step()
  
      def test_epoch(self, data_x, data_y):
  
          # prepare training data
          x_values = data_x.values
          y_values = np.squeeze(data_y.values)
  
          self.model.eval()
  
          scores = []
          losses = []
  
          indices = np.arange(len(x_values))
  
          for i in range(len(indices))[:: self.batch_size]:
  
              if len(indices) - i < self.batch_size:
                  break
  
              feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float().to(self.device)
              label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float().to(self.device)
  
              with torch.no_grad():
                  pred = self.model(feature)
                  loss = self.loss_fn(pred, label)
                  losses.append(loss.item())
  
                  score = self.metric_fn(pred, label)
                  scores.append(score.item())
  
          return np.mean(losses), np.mean(scores)
  
      def fit(
          self,
          dataset: DatasetH,
          evals_result=dict(),
          save_path=None,
      ):
  
          df_train, df_valid, df_test = dataset.prepare(
              ["train", "valid", "test"],
              col_set=["feature", "label"],
              data_key=DataHandlerLP.DK_L,
          )
  
          x_train, y_train = df_train["feature"], df_train["label"]
          x_valid, y_valid = df_valid["feature"], df_valid["label"]
  
          save_path = get_or_create_path(save_path)
          stop_steps = 0
          train_loss = 0
          best_score = -np.inf
          best_epoch = 0
          evals_result["train"] = []
          evals_result["valid"] = []
  
          # train
          self.logger.info("training...")
          self.fitted = True
  
          for step in range(self.n_epochs):
              self.logger.info("Epoch%d:", step)
              self.logger.info("training...")
              self.train_epoch(x_train, y_train)
              self.logger.info("evaluating...")
              train_loss, train_score = self.test_epoch(x_train, y_train)
              val_loss, val_score = self.test_epoch(x_valid, y_valid)
              self.logger.info("train %.6f, valid %.6f" % (train_score, val_score))
              evals_result["train"].append(train_score)
              evals_result["valid"].append(val_score)
  
              if val_score > best_score:
                  best_score = val_score
                  stop_steps = 0
                  best_epoch = step
                  best_param = copy.deepcopy(self.model.state_dict())
              else:
                  stop_steps += 1
                  if stop_steps >= self.early_stop:
                      self.logger.info("early stop")
                      break
  
          self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch))
          self.model.load_state_dict(best_param)
          torch.save(best_param, save_path)
  
          if self.use_gpu:
              torch.cuda.empty_cache()
  
      def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
          if not self.fitted:
              raise ValueError("model is not fitted yet!")
  
          x_test = dataset.prepare(segment, col_set="feature", data_key=DataHandlerLP.DK_I)
          index = x_test.index
          self.model.eval()
          x_values = x_test.values
          sample_num = x_values.shape[0]
          preds = []
  
          for begin in range(sample_num)[:: self.batch_size]:
  
              if sample_num - begin < self.batch_size:
                  end = sample_num
              else:
                  end = begin + self.batch_size
  
              x_batch = torch.from_numpy(x_values[begin:end]).float().to(self.device)
  
              with torch.no_grad():
                  pred = self.model(x_batch).detach().cpu().numpy()
  
              preds.append(pred)
  
          return pd.Series(np.concatenate(preds), index=index)
  
  
  class PositionalEncoding(nn.Module):
      def __init__(self, d_model, max_len=1000):
          super(PositionalEncoding, self).__init__()
          pe = torch.zeros(max_len, d_model)
          position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
          div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
          pe[:, 0::2] = torch.sin(position * div_term)
          pe[:, 1::2] = torch.cos(position * div_term)
          pe = pe.unsqueeze(0).transpose(0, 1)
          self.register_buffer("pe", pe)
  
      def forward(self, x):
          # [T, N, F]
          return x + self.pe[: x.size(0), :]
  
  
  def _get_clones(module, N):
      return ModuleList([copy.deepcopy(module) for i in range(N)])
  
  
  class LocalformerEncoder(nn.Module):
      __constants__ = ["norm"]
  
      def __init__(self, encoder_layer, num_layers, d_model):
          super(LocalformerEncoder, self).__init__()
          self.layers = _get_clones(encoder_layer, num_layers)
          self.conv = _get_clones(nn.Conv1d(d_model, d_model, 3, 1, 1), num_layers)
          self.num_layers = num_layers
  
      def forward(self, src, mask):
          output = src
          out = src
  
          for i, mod in enumerate(self.layers):
              # [T, N, F] --> [N, T, F] --> [N, F, T]
              out = output.transpose(1, 0).transpose(2, 1)
              out = self.conv[i](out).transpose(2, 1).transpose(1, 0)
  
              output = mod(output + out, src_mask=mask)
  
          return output + out
  
  
  class Transformer(nn.Module):
      def __init__(self, d_feat=6, d_model=8, nhead=4, num_layers=2, dropout=0.5, device=None):
          super(Transformer, self).__init__()
          self.rnn = nn.GRU(
              input_size=d_model,
              hidden_size=d_model,
              num_layers=num_layers,
              batch_first=False,
              dropout=dropout,
          )
          self.feature_layer = nn.Linear(d_feat, d_model)
          self.pos_encoder = PositionalEncoding(d_model)
          self.encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead, dropout=dropout)
          self.transformer_encoder = LocalformerEncoder(self.encoder_layer, num_layers=num_layers, d_model=d_model)
          self.decoder_layer = nn.Linear(d_model, 1)
          self.device = device
          self.d_feat = d_feat
  
      def forward(self, src):
          # src [N, F*T] --> [N, T, F]
          src = src.reshape(len(src), self.d_feat, -1).permute(0, 2, 1)
          src = self.feature_layer(src)
  
          # src [N, T, F] --> [T, N, F], [60, 512, 8]
          src = src.transpose(1, 0)  # not batch first
  
          mask = None
  
          src = self.pos_encoder(src)
          output = self.transformer_encoder(src, mask)  # [60, 512, 8]
  
          output, _ = self.rnn(output)
  
          # [T, N, F] --> [N, T*F]
          output = self.decoder_layer(output.transpose(1, 0)[:, -1, :])  # [512, 1]
  
          return output.squeeze()
  