# MIT License
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# Copyright (C) The Adversarial Robustness Toolbox (ART) Authors 2018
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"""
This module implements the fast generalized subset scan based detector.
"""

from __future__ import absolute_import, division, print_function, unicode_literals

import logging
from typing import List, Optional, Tuple, Union, TYPE_CHECKING

# pylint: disable=E0001
import numpy as np
from sklearn import metrics
from tqdm.auto import trange, tqdm

from myart.defences.detector.evasion.subsetscanning.scanner import Scanner
from myart.estimators.classification.classifier import ClassifierNeuralNetwork


if TYPE_CHECKING:
    from myart.utils import CLIP_VALUES_TYPE
    from myart.data_generators import DataGenerator

logger = logging.getLogger(__name__)


class SubsetScanningDetector(ClassifierNeuralNetwork):
    """
    Fast generalized subset scan based detector by McFowland, E., Speakman, S., and Neill, D. B. (2013).

    | Paper link: https://www.cs.cmu.edu/~neill/papers/mcfowland13a.pdf
    """

    estimator_params = ClassifierNeuralNetwork.estimator_params + ["classifier", "bgd_data", "layer", "verbose"]

    def __init__(
        self, classifier: ClassifierNeuralNetwork, bgd_data: np.ndarray, layer: Union[int, str], verbose: bool = True
    ) -> None:
        """
        Create a `SubsetScanningDetector` instance which is used to the detect the presence of adversarial samples.

        :param classifier: The model being evaluated for its robustness to anomalies (e.g. adversarial samples).
        :param bgd_data: The background data used to learn a null model. Typically dataset used to train the classifier.
        :param layer: The layer from which to extract activations to perform scan.
        :param verbose: Show progress bars.
        """
        super().__init__(
            model=None,
            clip_values=classifier.clip_values,
            channels_first=classifier.channels_first,
            preprocessing_defences=classifier.preprocessing_defences,
            preprocessing=classifier.preprocessing,
        )
        self.detector = classifier
        self.bgd_data = bgd_data
        self.verbose = verbose
        self.layer = layer

        # Ensure that layer is well-defined
        if classifier.layer_names is None:
            raise ValueError("No layer names identified.")

        if isinstance(layer, 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:
            if layer not in classifier.layer_names:
                raise ValueError("Layer name %s is not part of the graph." % layer)
            self._layer_name = layer

        bgd_activations = classifier.get_activations(bgd_data, self._layer_name, batch_size=128)
        if len(bgd_activations.shape) == 4:
            dim2 = bgd_activations.shape[1] * bgd_activations.shape[2] * bgd_activations.shape[3]
            bgd_activations = np.reshape(bgd_activations, (bgd_activations.shape[0], dim2))

        self.sorted_bgd_activations = np.sort(bgd_activations, axis=0)

    def calculate_pvalue_ranges(self, eval_x: np.ndarray) -> np.ndarray:
        """
        Returns computed p-value ranges.

        :param eval_x: Data being evaluated for anomalies.
        :return: P-value ranges.
        """
        bgd_activations = self.sorted_bgd_activations
        eval_activations = self.detector.get_activations(eval_x, self._layer_name, batch_size=128)

        if len(eval_activations.shape) == 4:
            dim2 = eval_activations.shape[1] * eval_activations.shape[2] * eval_activations.shape[3]
            eval_activations = np.reshape(eval_activations, (eval_activations.shape[0], dim2))

        bgrecords_n = bgd_activations.shape[0]
        records_n = eval_activations.shape[0]
        atrr_n = eval_activations.shape[1]

        pvalue_ranges = np.empty((records_n, atrr_n, 2))

        for j in range(atrr_n):
            pvalue_ranges[:, j, 0] = np.searchsorted(bgd_activations[:, j], eval_activations[:, j], side="right")
            pvalue_ranges[:, j, 1] = np.searchsorted(bgd_activations[:, j], eval_activations[:, j], side="left")

        pvalue_ranges = bgrecords_n - pvalue_ranges

        pvalue_ranges[:, :, 0] = np.divide(pvalue_ranges[:, :, 0], bgrecords_n + 1)
        pvalue_ranges[:, :, 1] = np.divide(pvalue_ranges[:, :, 1] + 1, bgrecords_n + 1)

        return pvalue_ranges

    def scan(
        self,
        clean_x: np.ndarray,
        adv_x: np.ndarray,
        clean_size: Optional[int] = None,
        advs_size: Optional[int] = None,
        run: int = 10,
    ) -> Tuple[list, list, float]:
        """
        Returns scores of highest scoring subsets.

        :param clean_x: Data presumably without anomalies.
        :param adv_x: Data presumably with anomalies (adversarial samples).
        :param clean_size:
        :param advs_size:
        :param run:
        :return: (clean_scores, adv_scores, detectionpower).
        """
        clean_pvalranges = self.calculate_pvalue_ranges(clean_x)
        adv_pvalranges = self.calculate_pvalue_ranges(adv_x)

        clean_scores = []
        adv_scores = []

        if clean_size is None and advs_size is None:
            # Individual scan
            with tqdm(
                total=len(clean_pvalranges) + len(adv_pvalranges), desc="Subset scanning", disable=not self.verbose
            ) as pbar:
                for _, c_p in enumerate(clean_pvalranges):
                    best_score, _, _, _ = Scanner.fgss_individ_for_nets(c_p)
                    clean_scores.append(best_score)
                    pbar.update(1)
                for _, a_p in enumerate(adv_pvalranges):
                    best_score, _, _, _ = Scanner.fgss_individ_for_nets(a_p)
                    adv_scores.append(best_score)
                    pbar.update(1)

        else:
            len_adv_x = len(adv_x)
            len_clean_x = len(clean_x)

            for _ in trange(run, desc="Subset scanning", disable=not self.verbose):
                np.random.seed()

                advchoice = np.random.choice(range(len_adv_x), advs_size, replace=False)
                cleanchoice = np.random.choice(range(len_clean_x), clean_size, replace=False)

                combined_pvals = np.concatenate((clean_pvalranges[cleanchoice], adv_pvalranges[advchoice]), axis=0)

                best_score, _, _, _ = Scanner.fgss_for_nets(clean_pvalranges[cleanchoice])
                clean_scores.append(best_score)
                best_score, _, _, _ = Scanner.fgss_for_nets(combined_pvals)
                adv_scores.append(best_score)

        y_true = np.append([np.ones(len(adv_scores))], [np.zeros(len(clean_scores))])
        all_scores = np.append([adv_scores], [clean_scores])

        fpr, tpr, _ = metrics.roc_curve(y_true, all_scores)
        roc_auc = metrics.auc(fpr, tpr)
        detectionpower = roc_auc

        return clean_scores, adv_scores, detectionpower

    def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: int = 20, **kwargs) -> None:
        """
        Fit the detector using training data. Assumes that the classifier is already trained.

        :raises `NotImplementedException`: This method is not supported for detectors.
        """
        raise NotImplementedError

    def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs) -> np.ndarray:
        """
        Perform detection of adversarial data and return prediction as tuple.

        :raises `NotImplementedException`: This method is not supported for detectors.
        """
        raise NotImplementedError

    def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, **kwargs) -> None:
        """
        Fit the classifier using the generator gen that yields batches as specified. This function is not supported
        for this detector.

        :raises `NotImplementedException`: This method is not supported for detectors.
        """
        raise NotImplementedError

    def compute_loss(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
        """
        Compute the loss of the neural network for samples `x`.

        :param x: Samples of shape (nb_samples, nb_features) or (nb_samples, nb_pixels_1, nb_pixels_2,
                  nb_channels) or (nb_samples, nb_channels, nb_pixels_1, nb_pixels_2).
        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices
                  of shape `(nb_samples,)`.
        :return: Loss values.
        :rtype: Format as expected by the `model`
        """
        raise NotImplementedError

    @property
    def nb_classes(self) -> int:
        return self.detector.nb_classes

    @property
    def input_shape(self) -> Tuple[int, ...]:
        return self.detector.input_shape

    @property
    def clip_values(self) -> Optional["CLIP_VALUES_TYPE"]:
        return self.detector.clip_values

    @property
    def channels_first(self) -> bool:
        """
        :return: Boolean to indicate index of the color channels in the sample `x`.
        """
        return self.channels_first

    @property
    def classifier(self) -> ClassifierNeuralNetwork:
        """
        :return: Classifier.
        """
        return self.detector

    def class_gradient(  # pylint: disable=W0221
        self, x: np.ndarray, label: Union[int, List[int], None] = None, training_mode: bool = False, **kwargs
    ) -> np.ndarray:
        """
        Compute per-class derivatives w.r.t. `x`.

        :param x: Sample input with shape as expected by the model.
        :param label: Index of a specific per-class derivative. If an integer is provided, the gradient of that class
                      output is computed for all samples. If multiple values as provided, the first dimension should
                      match the batch size of `x`, and each value will be used as target for its corresponding sample in
                      `x`. If `None`, then gradients for all classes will be computed for each sample.
        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
        :return: Array of gradients of input features w.r.t. each class in the form
                 `(batch_size, nb_classes, input_shape)` when computing for all classes, otherwise shape becomes
                 `(batch_size, 1, input_shape)` when `label` parameter is specified.
        """
        return self.detector.class_gradient(x=x, label=label, training_mode=training_mode, **kwargs)

    def loss_gradient(  # pylint: disable=W0221
        self, x: np.ndarray, y: np.ndarray, training_mode: bool = False, **kwargs
    ) -> np.ndarray:
        """
        Compute the gradient of the loss function w.r.t. `x`.

        :param x: Sample input with shape as expected by the model.
        :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)` or indices of shape
                  `(nb_samples,)`.
        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
        :return: Array of gradients of the same shape as `x`.
        """
        return self.detector.loss_gradient(x=x, y=y, training_mode=training_mode, **kwargs)

    def get_activations(
        self, x: np.ndarray, layer: Union[int, str], batch_size: int, framework: bool = False
    ) -> np.ndarray:
        """
        Return the output of the specified layer for input `x`. `layer` is specified by layer index (between 0 and
        `nb_layers - 1`) or by name. The number of layers can be determined by counting the results returned by
        calling `layer_names`. This function is not supported for this detector.

        :raises `NotImplementedException`: This method is not supported for detectors.
        """
        raise NotImplementedError

    def save(self, filename: str, path: Optional[str] = None) -> None:
        self.detector.save(filename, path)
