import tensorflow as tf


def craft_adv(X, Y, gamma, learning_rate, model, loss_fn, md = 0):
  
  # 将测试数据转换为TensorFlow张量
    X_test_tensor = tf.convert_to_tensor(X, dtype=tf.float64)
    if md == 0:
      Y_test_tensor = tf.convert_to_tensor(Y, dtype=tf.int32)
    elif md == 1:
      Y_test_tensor = tf.convert_to_tensor(Y, dtype=tf.float64)

  # 初始化更新后的数据集
    X_train_updated = []

    for i in range(X_test_tensor.shape[0]):
        # 对每个样本使用GradientTape
        with tf.GradientTape() as tape:
            # 监视当前样本
            current_sample = X_test_tensor[i:i+1]
            tape.watch(current_sample)

            # 对当前样本进行预测并计算损失
            predictions = model(current_sample)
            loss = loss_fn(Y_test_tensor[i:i+1], predictions)

        # 计算关于输入的梯度
        gradients = tape.gradient(loss, current_sample)

        # 平坦化梯度以便进行处理
        flattened_gradients = tf.reshape(gradients, [-1])

        # 选择最大的γ * |X|个梯度
        num_gradients_to_select = int(gamma * tf.size(flattened_gradients, out_type=tf.dtypes.float32))
        top_gradients_indices = tf.argsort(flattened_gradients, direction='DESCENDING')[:num_gradients_to_select]

        # 创建新的梯度张量，初始值为原始梯度
        updated_gradients = tf.identity(flattened_gradients)

        # 创建布尔掩码，用于选择特定梯度
        mask = tf.ones_like(updated_gradients, dtype=bool)
        mask = tf.tensor_scatter_nd_update(mask, tf.expand_dims(top_gradients_indices, 1), tf.zeros_like(top_gradients_indices, dtype=bool))

        # 应用掩码更新梯度
        updated_gradients = tf.where(mask, tf.zeros_like(updated_gradients), updated_gradients)

        # 将梯度恢复到原始形状
        updated_gradients = tf.reshape(updated_gradients, tf.shape(gradients))

        # 应用学习率到梯度
        scaled_gradients = learning_rate * updated_gradients

        # 更新当前样本
        current_sample_updated = tf.add(current_sample, scaled_gradients)

        # 将更新后的样本添加到列表中
        X_train_updated.append(current_sample_updated.numpy())

    # 将列表转换为张量
    X_train_updated = tf.concat(X_train_updated, axis=0)

    # 评估更新后的模型
    if md == 1:
      loss, mape = model.evaluate(X_train_updated, Y)
      print(f"Accuracy gamma: {gamma},learning:{learning_rate}", loss)

      return X_train_updated, loss, mape
    elif md == 0:
      loss, accuracy = model.evaluate(X_train_updated, Y)
      print(f"Accuracy gamma: {gamma},learning:{learning_rate},accuracy{accuracy}" )

      return X_train_updated, accuracy