# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""Library for computing privacy values for DP-SGD."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import math
import sys

from scipy.optimize import bisect

from federated_gbdt.core.moments_accountant.rdp_accountant import compute_rdp, get_privacy_spent # pylint: disable=g-import-not-at-top

# Opting out of loading all sibling packages and their dependencies.
sys.skip_tf_privacy_import = True


def apply_dp_sgd_analysis(q, sigma, steps, orders, delta):
    """Compute and print results of DP-SGD analysis."""

    # compute_rdp requires that sigma be the ratio of the standard deviation of
    # the Gaussian noise to the l2-sensitivity of the function to which it is
    # added. Hence, sigma here corresponds to the `noise_multiplier` parameter
    # in the DP-SGD implementation found in privacy.optimizers.dp_optimizer
    rdp = compute_rdp(q, sigma, steps, orders)

    eps, _, opt_order = get_privacy_spent(orders, rdp, target_delta=delta)
    return eps, opt_order


def compute_noise(n, batch_size, target_epsilon, epochs, delta, noise_lbd, verbose=False):
    """Compute noise based on the given hyperparameters."""
    q = batch_size / n  # q - the sampling ratio.
    if q > 1:
        raise Exception('n must be larger than the batch size.')
    orders = ([1.25, 1.5, 1.75, 2., 2.25, 2.5, 3., 3.5, 4., 4.5] +
              list(range(5, 64)) + [128, 256, 512])
    steps = int(math.ceil(epochs * n / batch_size))

    init_noise = noise_lbd  # minimum possible noise
    init_epsilon, _ = apply_dp_sgd_analysis(q, init_noise, steps, orders, delta)

    if init_epsilon < target_epsilon:  # noise_lbd was an overestimate
        print('min_noise too large for target epsilon.')
        return 0

    cur_epsilon = init_epsilon
    max_noise, min_noise = init_noise, 0

    # doubling to find the right range
    while cur_epsilon > target_epsilon:  # until noise is large enough
        max_noise, min_noise = max_noise * 2, max_noise
        cur_epsilon, _ = apply_dp_sgd_analysis(q, max_noise, steps, orders, delta)
        # print(cur_epsilon)

    def epsilon_fn(noise):  # should return 0 if guess_epsilon==target_epsilon
        guess_epsilon, opt_order = apply_dp_sgd_analysis(q, noise, steps, orders, delta)
        if verbose:
            print("Optimal Alpha", opt_order)
        return guess_epsilon - target_epsilon

    target_noise, res = bisect(epsilon_fn, min_noise, max_noise, full_output=True)
    final_eps, opt_order = apply_dp_sgd_analysis(q, target_noise, steps, orders, delta)

    if verbose:
        print(res)
        print(
            'DP-SGD with sampling rate = {:.3g}% and noise_multiplier = {} iterated'
            ' over {} steps satisfies'.format(100 * q, target_noise, steps),
            end=' ')
        print('differential privacy with eps = {:.3g} and delta = {}.'.format(
            target_epsilon, delta))

    return target_noise, opt_order