text: import torch
  # import matplotlib
  
  # matplotlib.use('Agg')
  from sklearn.manifold import TSNE
  import numpy as np
  # import matplotlib.pyplot as plt
  # import matplotlib.colors as col
  
  from torch.utils.data import TensorDataset
  import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from torch.utils.data import DataLoader
  from torch.optim import SGD
  from dassl.utils.meters import AverageMeter
  import pandas as pd
  import seaborn as sns
  # def visualize(source_feature: torch.Tensor, target_feature: torch.Tensor,
  #               filename: str, source_color='r', target_color='b'):
  #     """
  #     Visualize features from different domains using t-SNE.
  #     Args:
  #         source_feature (tensor): features from source domain in shape :math:`(minibatch, F)`
  #         target_feature (tensor): features from target domain in shape :math:`(minibatch, F)`
  #         filename (str): the file name to save t-SNE
  #         source_color (str): the color of the source features. Default: 'r'
  #         target_color (str): the color of the target features. Default: 'b'
  #     """
  #     source_feature = source_feature.numpy()
  #     target_feature = target_feature.numpy()
  #     features = np.concatenate([source_feature, target_feature], axis=0)
  #
  #     # map features to 2-d using TSNE
  #     X_tsne = TSNE(n_components=2, random_state=33).fit_transform(features)
  #
  #     # domain labels, 1 represents source while 0 represents target
  #     domains = np.concatenate((np.ones(len(source_feature)), np.zeros(len(target_feature))))
  #
  #     # visualize using matplotlib
  #     plt.figure(figsize=(10, 10))
  #     plt.scatter(X_tsne[:, 0], X_tsne[:, 1], c=domains, cmap=col.ListedColormap([source_color, target_color]), s=2)
  #     plt.savefig(filename)
  
  def visualize(features,labels,domain_names,filename: str,source_color='r', target_color='b'):
      """
      Visualize features from different domains using t-SNE.
      Args:
          source_feature (numpy): features from source domain in shape :math:`(minibatch, F)`
          target_feature (numpy): features from target domain in shape :math:`(minibatch, F)`
          source_label (numpy): label from source domain in shape : (minibatch,)
          target_label (numpy): label from target domain in shape : (minibatch,)
          source_domain (str): name of the source domain
          target_domain (str): name of target domain
          filename (str): the file name to save t-SNE
          source_color (str): the color of the source features. Default: 'r'
          target_color (str): the color of the target features. Default: 'b'
      """
  
      # map features to 2-d using TSNE
      X_tsne = TSNE(n_components=2, random_state=33,perplexity=50,init='pca').fit_transform(features)
  
      # domain labels, 1 represents source while 0 represents target
      # domains = np.concatenate((np.ones(len(source_feature)), np.zeros(len(target_feature))))
      table = pd.DataFrame({"dim_1":X_tsne[:, 0],
                    "dim_2":X_tsne[:, 1],
                    "classes":labels,
                    "domain":domain_names})
  
  
      # # visualize using matplotlib
      # plt.figure(figsize=(10, 10))
      # plt.scatter(X_tsne[:, 0], X_tsne[:, 1], c=domains, cmap=col.ListedColormap([source_color, target_color]), s=2)
      # plt.savefig(filename)
      # a = sns.scatterplot(data=table,x='dim_1',y='dim_2',hue='domain',style='classes')
      a = sns.scatterplot(data=table,x='dim_1',y='dim_2',hue='classes')
  
      fig = a.get_figure()
      fig.savefig(filename)
      fig.clf()
  
  
  class ANet(nn.Module):
      def __init__(self, in_feature):
          super(ANet, self).__init__()
          self.layer = nn.Linear(in_feature, 1)
          self.sigmoid = nn.Sigmoid()
  
      def forward(self, x):
          x = self.layer(x)
          x = self.sigmoid(x)
          return x
  
  
  def calculate(feature: torch.Tensor, domain_label: torch.tensor,
                device, progress=True, training_epochs=10):
      """
      Calculate the :math:`\mathcal{A}`-distance, which is a measure for distribution discrepancy.
      The definition is :math:`dist_\mathcal{A} = 2 (1-2\epsilon)`, where :math:`\epsilon` is the
      test error of a classifier trained to discriminate the source from the target.
      Args:
          source_feature (tensor): features from source domain in shape :math:`(minibatch, F)`
          target_feature (tensor): features from target domain in shape :math:`(minibatch, F)`
          device (torch.device)
          progress (bool): if True, displays a the progress of training A-Net
          training_epochs (int): the number of epochs when training the classifier
      Returns:
          :math:`\mathcal{A}`-distance
      """
      # source_label = torch.ones((source_feature.shape[0], 1))
      # target_label = torch.zeros((target_feature.shape[0], 1))
      # feature = torch.cat([source_feature, target_feature], dim=0)
      # label = torch.cat([source_label, target_label], dim=0)
  
      dataset = TensorDataset(feature, domain_label)
      length = len(dataset)
      train_size = int(0.8 * length)
      val_size = length - train_size
      train_set, val_set = torch.utils.data.random_split(dataset, [train_size, val_size])
      train_loader = DataLoader(train_set, batch_size=2, shuffle=True)
      val_loader = DataLoader(val_set, batch_size=8, shuffle=False)
  
      anet = ANet(feature.shape[1]).to(device)
      optimizer = SGD(anet.parameters(), lr=0.01)
      a_distance = 2.0
      for epoch in range(training_epochs):
          anet.train()
          for (x, label) in train_loader:
              x = x.to(device)
              label = label.to(device)
              anet.zero_grad()
              y = anet(x)
              loss = F.binary_cross_entropy(y, label)
              loss.backward()
              optimizer.step()
  
          anet.eval()
          meter = AverageMeter()
          with torch.no_grad():
              for (x, label) in val_loader:
                  x = x.to(device)
                  label = label.to(device)
                  y = anet(x)
                  acc = binary_accuracy(y, label)
                  meter.update(acc)
          error = 1 - meter.avg / 100
          a_distance = 2 * (1 - 2 * error)
          if progress:
              print("epoch {} accuracy: {} A-dist: {}".format(epoch, meter.avg, a_distance))
  
      return a_distance
  def binary_accuracy(output: torch.Tensor, target: torch.Tensor) -> float:
      """Computes the accuracy for binary classification"""
      with torch.no_grad():
          batch_size = target.size(0)
          pred = (output >= 0.5).float().t().view(-1)
          correct = pred.eq(target.view(-1)).float().sum()
          correct.mul_(100. / batch_size)
          return correct
  
  
  
  import tqdm
  
  def collect_feature(data_loader: DataLoader, feature_extractor: nn.Module,
                                     device: torch.device, max_num_features=None) -> torch.Tensor:
      """
      Fetch data from `data_loader`, and then use `feature_extractor` to collect features
      Args:
          data_loader (torch.utils.data.DataLoader): Data loader.
          feature_extractor (torch.nn.Module): A feature extractor.
          device (torch.device)
          max_num_features (int): The max number of features to return
      Returns:
          Features in shape (min(len(data_loader), max_num_features), :math:`|\mathcal{F}|`).
      """
      feature_extractor.eval()
      all_features = []
      with torch.no_grad():
          for i, (images, target) in enumerate(tqdm.tqdm(data_loader)):
              images = images.to(device)
              feature = feature_extractor(images).cpu()
              all_features.append(feature)
              if max_num_features is not None and i >= max_num_features:
                  break
      return torch.cat(all_features, dim=0)