text: import random
  
  import numpy as np
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  
  from .. import BaseModel, register_model
  from cogdl.utils import spmm, symmetric_normalization
  
  
  def mix_hidden_state(feat, target, train_index, alpha):
      if alpha > 0:
          lamb = np.random.beta(alpha, alpha)
      else:
          lamb = 1
      permuted_index = train_index[torch.randperm(train_index.size(0))]
      feat[train_index] = lamb * feat[train_index] + (1 - lamb) * feat[permuted_index]
      return feat, target[train_index], target[permuted_index], lamb
  
  
  def sharpen(prob, temperature):
      prob = torch.pow(prob, 1.0 / temperature)
      row_sum = torch.sum(prob, dim=1).reshape(-1, 1)
      return prob / row_sum
  
  
  def get_one_hot_label(labels, index):
      num_classes = int(torch.max(labels) + 1)
      target = torch.zeros(labels.shape[0], num_classes).to(labels.device)
  
      target[index, labels[index]] = 1
      return target
  
  
  def get_current_consistency_weight(final_consistency_weight, rampup_starts, rampup_ends, epoch):
      # Consistency ramp-up from https://arxiv.org/abs/1610.02242
      rampup_length = rampup_ends - rampup_starts
      rampup = 1.0
      epoch = epoch - rampup_starts
      if rampup_length != 0:
          current = np.clip(epoch, 0.0, rampup_length)
          phase = 1.0 - current / rampup_length
          rampup = float(np.exp(-5.0 * phase * phase))
      return final_consistency_weight * rampup
  
  
  class GCNConv(nn.Module):
      def __init__(self, in_feats, out_feats):
          super(GCNConv, self).__init__()
          self.weight = nn.Linear(in_features=in_feats, out_features=out_feats)
          self.edge_index = None
          self.edge_attr = None
  
      def forward(self, graph, x):
          h = self.weight(x)
          h = spmm(graph, h)
          return h
  
      def forward_aux(self, x):
          return self.weight(x)
  
  
  class BaseGNNMix(BaseModel):
      def __init__(self, in_feat, hidden_size, num_classes, k, temperature, alpha, dropout):
          super(BaseGNNMix, self).__init__()
          self.dropout = dropout
          self.alpha = alpha
          self.k = k
          self.temperature = temperature
  
          self.input_gnn = GCNConv(in_feat, hidden_size)
          self.hidden_gnn = GCNConv(hidden_size, num_classes)
          self.loss_f = nn.BCELoss()
  
      def forward(self, graph):
          graph.sym_norm()
          x = graph.x
          h = F.dropout(x, p=self.dropout, training=self.training)
          h = self.input_gnn(graph, h)
          h = F.relu(h)
          h = F.dropout(h, p=self.dropout, training=self.training)
          h = self.hidden_gnn(graph, h)
          return h
  
      def forward_aux(self, x, label, train_index, mix_hidden=True, layer_mix=1):
          h = F.dropout(x, p=self.dropout, training=self.training)
          assert layer_mix in (0, 1)
          if layer_mix == 0:
              h, target, target_mix, lamb = mix_hidden_state(h, label, train_index, self.alpha)
          h = self.input_gnn.forward_aux(h)
          h = F.relu(h)
          if layer_mix == 1:
              h, target, target_mix, lamb = mix_hidden_state(h, label, train_index, self.alpha)
          h = F.dropout(h, p=self.dropout, training=self.training)
          h = self.hidden_gnn.forward_aux(h)
          target_label = lamb * target + (1 - lamb) * target_mix
          return h, target_label
  
      def update_aux(self, data, vector_labels, train_index, opt):
          device = data.x.device
          train_unlabelled = torch.where(data.train_mask == False)[0].to(device)  # noqa E712
          temp_labels = torch.zeros(self.k, vector_labels.shape[0], vector_labels.shape[1]).to(device)
          with torch.no_grad():
              for i in range(self.k):
                  temp_labels[i, :, :] = self.predict_noise(data)
  
          target_labels = temp_labels.mean(dim=0)
          target_labels = sharpen(target_labels, self.temperature)
          vector_labels[train_unlabelled] = target_labels[train_unlabelled]
          sampled_unlabelled = torch.randint(0, train_unlabelled.shape[0], size=(train_index.shape[0],))
          train_unlabelled = train_unlabelled[sampled_unlabelled]
  
          def get_loss(index):
              mix_logits, target = self.forward_aux(data.x, vector_labels, index, mix_hidden=True)
              temp_loss = self.loss_f(F.softmax(mix_logits[index], -1), target)
              return temp_loss
  
          sup_loss = get_loss(train_index)
          unsup_loss = get_loss(train_unlabelled)
  
          mixup_weight = get_current_consistency_weight(
              opt["final_consistency_weight"], opt["rampup_starts"], opt["rampup_ends"], opt["epoch"]
          )
  
          loss_sum = sup_loss + mixup_weight * unsup_loss
          return loss_sum
  
      def update_soft(self, graph, labels, train_index):
          out = self.forward(graph)
          out = F.log_softmax(out, dim=-1)
          loss_sum = F.nll_loss(out[train_index], labels[train_index])
          return loss_sum
  
      def loss(self, data, opt):
          device = data.x.device
          train_index = torch.where(data.train_mask)[0].to(device)
          rand_n = random.randint(0, 1)
          if rand_n == 0:
              vector_labels = get_one_hot_label(data.y, train_index).to(device)
              return self.update_aux(data, vector_labels, train_index, opt)
          else:
              return self.update_soft(data, data.y, train_index)
  
      def predict_noise(self, data, tau=1):
          out = self.forward(data) / tau
          return out
  
  
  @register_model("gcnmix")
  class GCNMix(BaseModel):
      @staticmethod
      def add_args(parser):
          parser.add_argument("--dropout", type=float, default=0.5)
          parser.add_argument("--hidden-size", type=int, default=64)
          parser.add_argument("--alpha", type=float, default=1.0)
          parser.add_argument("--k", type=int, default=10)
          parser.add_argument("--temperature", type=float, default=0.1)
          parser.add_argument("--rampup-starts", type=int, default=500)
          parser.add_argument("--rampup_ends", type=int, default=1000)
          parser.add_argument("--mixup-consistency", type=float, default=10.0)
          parser.add_argument("--ema-decay", type=float, default=0.999)
          parser.add_argument("--tau", type=float, default=1.0)
  
      @classmethod
      def build_model_from_args(cls, args):
          return cls(
              in_feat=args.num_features,
              hidden_size=args.hidden_size,
              num_classes=args.num_classes,
              k=args.k,
              temperature=args.temperature,
              alpha=args.alpha,
              rampup_starts=args.rampup_starts,
              rampup_ends=args.rampup_ends,
              final_consistency_weight=args.mixup_consistency,
              ema_decay=args.ema_decay,
              dropout=args.dropout,
          )
  
      def __init__(
          self,
          in_feat,
          hidden_size,
          num_classes,
          k,
          temperature,
          alpha,
          rampup_starts,
          rampup_ends,
          final_consistency_weight,
          ema_decay,
          dropout,
      ):
          super(GCNMix, self).__init__()
          self.final_consistency_weight = final_consistency_weight
          self.rampup_starts = rampup_starts
          self.rampup_ends = rampup_ends
          self.ema_decay = ema_decay
  
          self.base_gnn = BaseGNNMix(in_feat, hidden_size, num_classes, k, temperature, alpha, dropout)
          self.ema_gnn = BaseGNNMix(in_feat, hidden_size, num_classes, k, temperature, alpha, dropout)
          for param in self.ema_gnn.parameters():
              param.detach_()
  
          self.epoch = 0
  
      def forward(self, graph):
          return self.base_gnn.forward(graph)
  
      def forward_ema(self, graph):
          return self.ema_gnn(graph)
  
      def node_classification_loss(self, data):
          opt = {
              "epoch": self.epoch,
              "final_consistency_weight": self.final_consistency_weight,
              "rampup_starts": self.rampup_starts,
              "rampup_ends": self.rampup_ends,
          }
          self.base_gnn.train()
          loss_n = self.base_gnn.loss(data, opt)
  
          alpha = min(1 - 1 / (self.epoch + 1), self.ema_decay)
          for ema_param, param in zip(self.ema_gnn.parameters(), self.base_gnn.parameters()):
              ema_param.data.mul_(alpha).add_((1 - alpha) * param.data)
          self.epoch += 1
          return loss_n
  
      def predict(self, data):
          prediction = self.forward_ema(data)
          return prediction
  