# MIT License
#
# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2019
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated
# documentation files (the "Software"), to deal in the Software without restriction, including without limitation the
# rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit
# persons to whom the Software is furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all copies or substantial portions of the
# Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
# TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
from __future__ import absolute_import, division, print_function, unicode_literals

import logging
import unittest

import numpy as np
import GPy

from art.exceptions import EstimatorError
from art.attacks.evasion.hclu import HighConfidenceLowUncertainty
from art.estimators.classification.GPy import GPyGaussianProcessClassifier

from tests.utils import TestBase
from tests.attacks.utils import backend_test_classifier_type_check_fail

logger = logging.getLogger(__name__)


class TestHCLU(TestBase):
    @classmethod
    def setUpClass(cls):
        super().setUpClass()

        # change iris to a binary problem
        cls.x_train = cls.x_train_iris[0:35]
        cls.y_train = cls.y_train_iris[0:35, 1]
        cls.x_test = cls.x_train
        cls.y_test = cls.y_train

    def test_GPy(self):
        x_test_original = self.x_test.copy()
        # set up GPclassifier
        gpkern = GPy.kern.RBF(np.shape(self.x_train)[1])
        m = GPy.models.GPClassification(self.x_train, self.y_train.reshape(-1, 1), kernel=gpkern)
        m.inference_method = GPy.inference.latent_function_inference.laplace.Laplace()
        m.optimize(messages=True, optimizer="lbfgs")
        # get ART classifier + clean accuracy
        m_art = GPyGaussianProcessClassifier(m)
        clean_acc = np.mean(np.argmin(m_art.predict(self.x_test), axis=1) == self.y_test)
        # get adversarial examples, accuracy, and uncertainty
        attack = HighConfidenceLowUncertainty(m_art, conf=0.9, min_val=-0.0, max_val=1.0, verbose=False)
        adv = attack.generate(self.x_test)
        adv_acc = np.mean(np.argmin(m_art.predict(adv), axis=1) == self.y_test)
        unc_f = m_art.predict_uncertainty(adv)
        # not all attacks succeed due to the decision surface landscape of GP, some should
        self.assertGreater(clean_acc, adv_acc)

        # now take into account uncertainty
        attack = HighConfidenceLowUncertainty(
            m_art, unc_increase=0.9, conf=0.9, min_val=0.0, max_val=1.0, verbose=False
        )
        adv = attack.generate(self.x_test)
        adv_acc = np.mean(np.argmin(m_art.predict(adv), axis=1) == self.y_test)
        unc_o = m_art.predict_uncertainty(adv)
        # same above
        self.assertGreater(clean_acc, adv_acc)
        # uncertainty should indeed be lower when used as a constraint
        # however, same as above, crafting might fail
        self.assertGreater(np.mean(unc_f > unc_o), 0.6)

        # Check that x_test has not been modified by attack and classifier
        self.assertAlmostEqual(float(np.max(np.abs(x_test_original - self.x_test))), 0.0, delta=0.00001)

    def test_check_params(self):
        gpkern = GPy.kern.RBF(np.shape(self.x_train)[1])
        m = GPy.models.GPClassification(self.x_train, self.y_train.reshape(-1, 1), kernel=gpkern)
        m_art = GPyGaussianProcessClassifier(m)

        with self.assertRaises(EstimatorError):
            _ = HighConfidenceLowUncertainty(
                "test", conf=0.9, unc_increase=100.0, min_val=-0.0, max_val=1.0, verbose=False
            )

        with self.assertRaises(ValueError):
            _ = HighConfidenceLowUncertainty(
                m_art, conf=0.1, unc_increase=-100.0, min_val=-0.0, max_val=1.0, verbose=False
            )

        with self.assertRaises(ValueError):
            _ = HighConfidenceLowUncertainty(
                m_art, conf=0.1, unc_increase=100.0, min_val=2.0, max_val=1.0, verbose=False
            )

        with self.assertRaises(ValueError):
            _ = HighConfidenceLowUncertainty(
                m_art, conf=0.1, unc_increase=100.0, min_val=-0.0, max_val=1.0, verbose="False"
            )

    def test_classifier_type_check_fail(self):
        backend_test_classifier_type_check_fail(HighConfidenceLowUncertainty, [GPyGaussianProcessClassifier])


if __name__ == "__main__":
    unittest.main()
