# Copyright 2019 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys

import numpy as np
from mindspore import Tensor
from mindspore import context
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from scipy.special import softmax

from lenet5_net import LeNet5
from mindarmour.attacks.black.black_model import BlackModel
from mindarmour.attacks.black.salt_and_pepper_attack import SaltAndPepperNoiseAttack
from mindarmour.evaluations.attack_evaluation import AttackEvaluate
from mindarmour.utils.logger import LogUtil

sys.path.append("..")
from data_processing import generate_mnist_dataset

LOGGER = LogUtil.get_instance()
TAG = 'Salt_and_Pepper_Attack'
LOGGER.set_level('DEBUG')


class ModelToBeAttacked(BlackModel):
    """model to be attack"""

    def __init__(self, network):
        super(ModelToBeAttacked, self).__init__()
        self._network = network

    def predict(self, inputs):
        """predict"""
        if len(inputs.shape) == 3:
            inputs = inputs[np.newaxis, :]
        result = self._network(Tensor(inputs.astype(np.float32)))
        return result.asnumpy()


def test_salt_and_pepper_attack_on_mnist():
    """
    Salt-and-Pepper-Attack test
    """
    # upload trained network
    ckpt_name = './trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
    net = LeNet5()
    load_dict = load_checkpoint(ckpt_name)
    load_param_into_net(net, load_dict)

    # get test data
    data_list = "./MNIST_unzip/test"
    batch_size = 32
    ds = generate_mnist_dataset(data_list, batch_size=batch_size)

    # prediction accuracy before attack
    model = ModelToBeAttacked(net)
    batch_num = 3  # the number of batches of attacking samples
    test_images = []
    test_labels = []
    predict_labels = []
    i = 0
    for data in ds.create_tuple_iterator():
        i += 1
        images = data[0].astype(np.float32)
        labels = data[1]
        test_images.append(images)
        test_labels.append(labels)
        pred_labels = np.argmax(model.predict(images), axis=1)
        predict_labels.append(pred_labels)
        if i >= batch_num:
            break
    LOGGER.debug(TAG, 'model input image shape is: {}'.format(np.array(test_images).shape))
    predict_labels = np.concatenate(predict_labels)
    true_labels = np.concatenate(test_labels)
    accuracy = np.mean(np.equal(predict_labels, true_labels))
    LOGGER.info(TAG, "prediction accuracy before attacking is : %g", accuracy)

    # attacking
    is_target = False
    attack = SaltAndPepperNoiseAttack(model=model,
                                      is_targeted=is_target,
                                      sparse=True)
    if is_target:
        targeted_labels = np.random.randint(0, 10, size=len(true_labels))
        for i, true_l in enumerate(true_labels):
            if targeted_labels[i] == true_l:
                targeted_labels[i] = (targeted_labels[i] + 1) % 10
    else:
        targeted_labels = true_labels
    LOGGER.debug(TAG, 'input shape is: {}'.format(np.concatenate(test_images).shape))
    success_list, adv_data, query_list = attack.generate(
        np.concatenate(test_images), targeted_labels)
    success_list = np.arange(success_list.shape[0])[success_list]
    LOGGER.info(TAG, 'success_list: %s', success_list)
    LOGGER.info(TAG, 'average of query times is : %s', np.mean(query_list))
    adv_preds = []
    for ite_data in adv_data:
        pred_logits_adv = model.predict(ite_data)
        # rescale predict confidences into (0, 1).
        pred_logits_adv = softmax(pred_logits_adv, axis=1)
        adv_preds.extend(pred_logits_adv)
    accuracy_adv = np.mean(np.equal(np.max(adv_preds, axis=1), true_labels))
    LOGGER.info(TAG, "prediction accuracy after attacking is : %g",
                accuracy_adv)
    test_labels_onehot = np.eye(10)[true_labels]
    attack_evaluate = AttackEvaluate(np.concatenate(test_images),
                                     test_labels_onehot, adv_data,
                                     adv_preds, targeted=is_target,
                                     target_label=targeted_labels)
    LOGGER.info(TAG, 'mis-classification rate of adversaries is : %s',
                attack_evaluate.mis_classification_rate())
    LOGGER.info(TAG, 'The average confidence of adversarial class is : %s',
                attack_evaluate.avg_conf_adv_class())
    LOGGER.info(TAG, 'The average confidence of true class is : %s',
                attack_evaluate.avg_conf_true_class())
    LOGGER.info(TAG, 'The average distance (l0, l2, linf) between original '
                     'samples and adversarial samples are: %s',
                attack_evaluate.avg_lp_distance())


if __name__ == '__main__':
    # device_target can be "CPU", "GPU" or "Ascend"
    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
    test_salt_and_pepper_attack_on_mnist()
