# MIT License
#
# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2020
#
# 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.
"""
This module implements the classifier `TensorFlowGenerator` for TensorFlow models.
"""
from __future__ import absolute_import, division, print_function, unicode_literals

import logging
from typing import Any, Dict, List, Optional, Union, TYPE_CHECKING

from art.estimators.generation.generator import GeneratorMixin
from art.estimators.tensorflow import TensorFlowEstimator

if TYPE_CHECKING:
    import numpy as np
    import tensorflow as tf

    from art.config import CLIP_VALUES_TYPE, PREPROCESSING_TYPE
    from art.defences.preprocessor import Preprocessor
    from art.defences.postprocessor import Postprocessor

logger = logging.getLogger(__name__)


class TensorFlowGenerator(GeneratorMixin, TensorFlowEstimator):  # lgtm [py/missing-call-to-init]
    """
    This class implements a GAN with the TensorFlow framework.
    """

    def __init__(
        self,
        input_ph: "tf.Placeholder",
        model: "tf.Tensor",
        loss: Optional["tf.Tensor"] = None,
        sess: Optional["tf.compat.v1.Session"] = None,
        channels_first=False,
        clip_values: Optional["CLIP_VALUES_TYPE"] = None,
        preprocessing_defences: Union["Preprocessor", List["Preprocessor"], None] = None,
        postprocessing_defences: Union["Postprocessor", List["Postprocessor"], None] = None,
        preprocessing: "PREPROCESSING_TYPE" = (0, 1),
        feed_dict: Dict[Any, Any] = {},
    ):
        """
        Initialization specific to TensorFlow generator implementations.

        :param input_ph: The input placeholder.
        :param model: tensorflow model, neural network or other.
        :param loss: The loss function for which to compute gradients. This parameter is necessary when training the
            model and when computing gradients w.r.t. the loss function.
        :param sess: Computation session.
        :param channels_first: Set channels first or last.
        :param clip_values: Tuple of the form `(min, max)` of floats or `np.ndarray` representing the minimum and
            maximum values allowed for features. If floats are provided, these will be used as the range of all
            features. If arrays are provided, each value will be considered the bound for a feature, thus
            the shape of clip values needs to match the total number of features.
        :param preprocessing_defences: Preprocessing defence(s) to be applied by the classifier.
        :param postprocessing_defences: Postprocessing defence(s) to be applied by the classifier.
        :param preprocessing: Tuple of the form `(subtractor, divider)` of floats or `np.ndarray` of values to be
            used for data preprocessing. The first value will be subtracted from the input. The input will then
            be divided by the second one.
        :param feed_dict: A feed dictionary for the session run evaluating the classifier. This dictionary includes all
                          additionally required placeholders except the placeholders defined in this class.
        """
        import tensorflow as tf

        super().__init__(
            clip_values=clip_values,
            channels_first=channels_first,
            preprocessing_defences=preprocessing_defences,
            postprocessing_defences=postprocessing_defences,
            preprocessing=preprocessing,
        )

        self._input_ph = input_ph
        self._encoding_length = self._input_ph.shape[1]
        self._model = model
        self._loss = loss
        if self._loss is not None:
            self._grad = tf.gradients(self._loss, self._input_ph)
        self._feed_dict = feed_dict

        # Assign session
        if sess is None:
            raise ValueError("A session cannot be None.")
            # TODO do the same thing for all not None variables
        self._sess = sess

    def loss(self, z, image_adv):
        pass

    def predict(self, x: "np.ndarray", batch_size: int = 128, **kwargs) -> "np.ndarray":
        """
        Perform projections over a batch of encodings.

        :param x: Encodings.
        :param batch_size: Batch size.
        :return: Array of prediction projections of shape `(num_inputs, nb_classes)`.
        """
        logging.info("Projecting new sample from z value")
        y = self._sess.run(self._model, feed_dict={self._input_ph: x})
        return y

    def loss_gradient(self, x, y, **kwargs) -> "np.ndarray":
        raise NotImplementedError

    def fit(self, x, y, batch_size=128, nb_epochs=10, **kwargs):
        """
        Do nothing.
        """
        raise NotImplementedError

    def get_activations(
        self, x: "np.ndarray", layer: Union[int, str], batch_size: int, framework: bool = False
    ) -> "np.ndarray":
        """
        Do nothing.
        """
        raise NotImplementedError

    def set_learning_phase(self, train: bool) -> None:
        """
        do nothing.
        """
        raise NotImplementedError

    @property
    def model(self) -> "tf.Tensor":
        """
        Returns the generator tensor.

        :return: The generator tensor.
        """
        return self._model

    @property
    def input_ph(self) -> "tf.Placeholder":
        """
        Returns the encoding seed input of the generator of shape `(batch_size, encoding_length)`.

        :return: The encoding seed input of the generator.
        """
        return self._input_ph

    @property
    def encoding_length(self) -> int:
        """
        Returns the length of the encoding size output.

        :return: The length of the encoding size output.
        """
        return self._encoding_length
