# MIT License
#
# Copyright (C) IBM Corporation 2018
#
# 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 abc
import sys


# Ensure compatibility with Python 2 and 3 when using ABCMeta
if sys.version_info >= (3, 4):
    ABC = abc.ABC
else:
    ABC = abc.ABCMeta(str('ABC'), (), {})


class Detector(ABC):
    """
    Base abstract class for all detection methods.
    """
    def __init__(self):
        """
        Create a detector.
        """
        self._is_fitted = False

    @property
    def is_fitted(self):
        """
        Return the state of the detector.

        :return: `True` if the detection model has been fitted (if this applies).
        :rtype: `bool`
        """
        return self._is_fitted

    @abc.abstractmethod
    def fit(self, x, y=None, **kwargs):
        """
        Fit the detector using training data (if this applies).

        :param x: Training set to fit the detector.
        :type x: `np.ndarray`
        :param y: Labels for the training set.
        :type y: `np.ndarray`
        :param kwargs: Other parameters.
        :type kwargs: `dict`
        :return: None
        """
        self._is_fitted = True

    @abc.abstractmethod
    def __call__(self, x):
        """
        Perform detection of adversarial data and return preprocessed data as tuple.

        :param x: Data sample on which to perform detection.
        :type x: `np.ndarray`
        :return: Per-sample prediction whether data is adversarial or not, where `0` means non-adversarial.
                 Return variable has the same `batch_size` (first dimension) as `x`.
        :rtype: `np.ndarray`
        """
        raise NotImplementedError

    def set_params(self, **kwargs):
        """
        Take in a dictionary of parameters and apply checks before saving them as attributes.

        :return: `True` when parsing was successful.
        :rtype: `bool`
        """
        for key, value in kwargs.items():
            if key in self.params:
                setattr(self, key, value)
        return True


class BinaryInputDetector(Detector):

    def __init__(self, detector):
        """
        Create a `Detector` instance which performs binary classification on input data.

        :param detector: The detector architecture to be trained and applied for the binary classification.
        :type detector: `art.classifier.Classifier`
        """
        self._detector = detector
        self._is_fitted = False

    def fit(self, x, y, **kwargs):
        """
        Fit the detector using training data.

        :param x: Training set to fit the detector.
        :type x: `np.ndarray`
        :param y: Labels for the training set.
        :type y: `np.ndarray`
        :param kwargs: Other parameters.
        :type kwargs: `dict`
        :return: None
        """
        self._detector.fit(x, y, **kwargs)
        self._is_fitted = True

    def __call__(self, x):
        """
        Perform detection of adversarial data and return prediction as tuple.

        :param x: Data sample on which to perform detection.
        :type x: `np.ndarray`
        :return: Per-sample prediction whether data is adversarial or not, where `0` means non-adversarial.
                 Return variable has the same `batch_size` (first dimension) as `x`.
        :rtype: `np.ndarray`
        """
        return self._detector.predict(x)


class BinaryActivationDetector(Detector):

    def __init__(self, classifier, detector, layer):
        """
        Create a `Detector` instance which performs binary classification on activation information.

        :param classifier: The classifier of which the activation information is to be used for detection.
        :type classifier: `art.classifier.Classifier`
        :param detector: The detector architecture to be trained and applied for the binary classification.
        :type classifier: `art.classifier.Classifier`
        :param layer: Layer for computing the activations
        :type layer: `int` or `str`
        """
        self._classifier = classifier
        self._detector = detector

        # Ensure that layer is well-defined:
        if type(layer) is str:
            if layer not in classifier.layer_names:
                raise ValueError('Layer name %s is not part of the graph.' % layer)
            self._layer_name = layer
        elif type(layer) is int:
            if layer < 0 or layer >= len(classifier.layer_names):
                raise ValueError('Layer index %d is outside of range (0 to %d included).'
                                 % (layer, len(classifier.layer_names) - 1))
            self._layer_name = classifier.layer_names[layer]
        else:
            raise TypeError('Layer must be of type `str` or `int`.')

        self._is_fitted = False

    def fit(self, x, y, **kwargs):
        """
        Fit the detector using training data.

        :param x: Training set to fit the detector.
        :type x: `np.ndarray`
        :param y: Labels for the training set.
        :type y: `np.ndarray`
        :param kwargs: Other parameters.
        :type kwargs: `dict`
        :return: None
        """
        x_activations = self._classifier.get_activations(x, self._layer_name)
        self._detector.fit(x_activations, y, **kwargs)
        self._is_fitted = True

    def __call__(self, x):
        """
        Perform detection of adversarial data and return prediction as tuple.

        :param x: Data sample on which to perform detection.
        :type x: `np.ndarray`
        :return: Per-sample prediction whether data is adversarial or not, where `0` means non-adversarial.
                 Return variable has the same `batch_size` (first dimension) as `x`.
        :rtype: `np.ndarray`
        """
        return self._detector.predict(self._classifier.get_activations(x, self._layer_name))
