import stanza

# download stanza german model
stanza.download('de')

  epochs = 10
  num_labels = 2
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

  data_path = {
      "train": "./datasets/hasoc_dataset/hasoc_german_train.csv",
      "dev": "./datasets/hasoc_dataset/hasoc_german_validation.csv",
      "test": "./datasets/hasoc_dataset/hasoc_german_test.csv",
  }
  model_name = "deepset/gbert-base"
  data_loaders = GermanDataLoader(
      data_path, model_name, do_cleansing=False, max_sequence_length=128, batch_size=8
  )
  model = BERTClassifier(num_labels=num_labels).get_model()
  optim_config = BertOptimConfig(
      model=model, train_dataloader=data_loaders.train_dataloader, epochs=epochs
  )

  ## execute the training routine
  model = train_model(
      model=model,
      optimizer=optim_config.optimizer,
      scheduler=optim_config.scheduler,
      train_dataloader=data_loaders.train_dataloader,
      validation_dataloader=data_loaders.validation_dataloader,
      epochs=epochs,
      device=device,
      model_name=model_name,
  )

  ## test model performance on unseen test set
  eval_model(model=model, test_dataloader=data_loaders.test_dataloader, device=device)

  logs_path = "./attack_logs"
  attack_config = [
        (
            "hasoc",
            "shahrukhx01/gbert-hasoc-german-2019",
            "data/hasoc_german_2019/hasoc_german_probing_set.csv",
        ),
        (
             "germeval",
             "shahrukhx01/gbert-germeval-2021",
             "data/hasoc_german_2019/germeval_probing_set.csv",
        ),
    ]
  for attack_name, model_name_path, dataset_path in attack_config:
    ## load dataset
    dataset = GermanDataset(
        filepath=dataset_path
    ).load_dataset()  ### sampling = False
    ## load model
    model_wrapper = GermanHateSpeechModelWrapper(model_name_path=model_name_path)
    ## define and build attacks
    blackbox_wordlevel_attack = BlackboxWordLevelAttack.build(model_wrapper)
    blackbox_charlevel_attack = BlackboxCharacterLevel.build(model_wrapper)
    attacks = [
            (
                f"{attack_name}_blackbox_wordlevel_attack",
                blackbox_wordlevel_attack,
            ),
            (
                f"{attack_name}_blackbox_charlevel_attack",
                blackbox_charlevel_attack,
            ),
        ]
     ## execute the attack
    ExecuteAttack.execute(dataset, attacks=attacks, logs_path=logs_path)