# Copyright 2020 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.
# ============================================================================
"""Robustness."""

import numpy as np

import mindspore as ms
import mindspore.nn as nn
from mindspore import log
from .metric import LabelSensitiveMetric
from ...explanation._attribution._perturbation.replacement import RandomPerturb


class Robustness(LabelSensitiveMetric):
    """
    Robustness perturbs the inputs by adding random noise and choose the maximum sensitivity as evaluation score from
    the perturbations.

    Args:
        num_labels (int): Number of classes in the dataset.

    Examples:
        >>> from mindspore.explainer.benchmark import Robustness
        >>> num_labels = 100
        >>> robustness = Robustness(num_labels)
    """

    def __init__(self, num_labels, activation_fn=nn.Softmax()):
        super().__init__(num_labels)

        self._perturb = RandomPerturb()
        self._num_perturbations = 100  # number of perturbations used in evaluation
        self._threshold = 0.1  # threshold to generate perturbation
        self._activation_fn = activation_fn

    def evaluate(self, explainer, inputs, targets, saliency=None):
        """
        Evaluate robustness on single sample.

        Note:
            Currently only single sample (:math:`N=1`) at each call is supported.

        Args:
            explainer (Explanation): The explainer to be evaluated, see `mindspore.explainer.explanation`.
            inputs (Tensor): A data sample, a 4D tensor of shape :math:`(N, C, H, W)`.
            targets (Tensor, int): The label of interest. It should be a 1D or 0D tensor, or an integer.
                If `targets` is a 1D tensor, its length should be the same as `inputs`.
            saliency (Tensor, optional): The saliency map to be evaluated, a 4D tensor of shape :math:`(N, 1, H, W)`.
                If it is None, the parsed `explainer` will generate the saliency map with `inputs` and `targets` and
                continue the evaluation. Default: None.

        Returns:
            numpy.ndarray, 1D array of shape :math:`(N,)`, result of localization evaluated on `explainer`.

        Raises:
            ValueError: If batch_size is larger than 1.

        Examples:
            >>> # init an explainer, the network should contain the output activation function.
            >>> from mindspore.explainer.explanation import Gradient
            >>> from mindspore.explainer.benchmark import Robustness
            >>> gradient = Gradient(network)
            >>> input_x = ms.Tensor(np.random.rand(1, 3, 224, 224), ms.float32)
            >>> target_label = 5
            >>> robustness = Robustness(num_labels=10)
            >>> res = robustness.evaluate(gradient, input_x, target_label)
        """

        self._check_evaluate_param(explainer, inputs, targets, saliency)
        if inputs.shape[0] > 1:
            raise ValueError('Robustness only support a sample each time, but receive {}'.format(inputs.shape[0]))

        inputs_np = inputs.asnumpy()
        if isinstance(targets, int):
            targets = ms.Tensor(targets, ms.int32)
        if saliency is None:
            saliency = explainer(inputs, targets)
        saliency_np = saliency.asnumpy()
        norm = np.sqrt(np.sum(np.square(saliency_np), axis=tuple(range(1, len(saliency_np.shape)))))
        if norm == 0:
            log.warning('Get saliency norm equals 0, robustness return NaN for zero-norm saliency currently.')
            return np.array([np.nan])

        perturbations = []
        for sample in inputs_np:
            sample = np.expand_dims(sample, axis=0)
            perturbations_per_input = []
            for _ in range(self._num_perturbations):
                perturbation = self._perturb(sample)
                perturbations_per_input.append(perturbation)
            perturbations_per_input = np.vstack(perturbations_per_input)
            perturbations.append(perturbations_per_input)
        perturbations = np.stack(perturbations, axis=0)

        perturbations = np.reshape(perturbations, (-1,) + inputs_np.shape[1:])
        perturbations = ms.Tensor(perturbations, ms.float32)

        repeated_targets = np.repeat(targets.asnumpy(), repeats=self._num_perturbations, axis=0)
        repeated_targets = ms.Tensor(repeated_targets, ms.int32)
        saliency_of_perturbations = explainer(perturbations, repeated_targets)
        perturbations_saliency = saliency_of_perturbations.asnumpy()

        repeated_saliency = np.repeat(saliency_np, repeats=self._num_perturbations, axis=0)

        sensitivities = np.sum((repeated_saliency - perturbations_saliency) ** 2,
                               axis=tuple(range(1, len(repeated_saliency.shape))))

        max_sensitivity = np.max(sensitivities.reshape((norm.shape[0], -1)), axis=1) / norm
        robustness_res = 1 / np.exp(max_sensitivity)
        return robustness_res