# 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.
"""
Training example of adaClip-mechanism differential privacy.
"""
import os

import mindspore.nn as nn
from mindspore import context
from mindspore.train.callback import ModelCheckpoint
from mindspore.train.callback import CheckpointConfig
from mindspore.train.callback import LossMonitor
from mindspore.nn.metrics import Accuracy
from mindspore.train.serialization import load_checkpoint, load_param_into_net
import mindspore.dataset as ds
import mindspore.dataset.transforms.vision.c_transforms as CV
import mindspore.dataset.transforms.c_transforms as C
from mindspore.dataset.transforms.vision import Inter
import mindspore.common.dtype as mstype

from mindarmour.diff_privacy import DPModel
from mindarmour.diff_privacy import PrivacyMonitorFactory
from mindarmour.diff_privacy import NoiseMechanismsFactory
from mindarmour.diff_privacy import ClipMechanismsFactory
from mindarmour.utils.logger import LogUtil
from lenet5_net import LeNet5
from lenet5_config import mnist_cfg as cfg

LOGGER = LogUtil.get_instance()
LOGGER.set_level('INFO')
TAG = 'Lenet5_train'


def generate_mnist_dataset(data_path, batch_size=32, repeat_size=1,
                           num_parallel_workers=1, sparse=True):
    """
    create dataset for training or testing
    """
    # define dataset
    ds1 = ds.MnistDataset(data_path)

    # define operation parameters
    resize_height, resize_width = 32, 32
    rescale = 1.0 / 255.0
    shift = 0.0

    # define map operations
    resize_op = CV.Resize((resize_height, resize_width),
                          interpolation=Inter.LINEAR)
    rescale_op = CV.Rescale(rescale, shift)
    hwc2chw_op = CV.HWC2CHW()
    type_cast_op = C.TypeCast(mstype.int32)

    # apply map operations on images
    if not sparse:
        one_hot_enco = C.OneHot(10)
        ds1 = ds1.map(input_columns="label", operations=one_hot_enco,
                      num_parallel_workers=num_parallel_workers)
        type_cast_op = C.TypeCast(mstype.float32)
    ds1 = ds1.map(input_columns="label", operations=type_cast_op,
                  num_parallel_workers=num_parallel_workers)
    ds1 = ds1.map(input_columns="image", operations=resize_op,
                  num_parallel_workers=num_parallel_workers)
    ds1 = ds1.map(input_columns="image", operations=rescale_op,
                  num_parallel_workers=num_parallel_workers)
    ds1 = ds1.map(input_columns="image", operations=hwc2chw_op,
                  num_parallel_workers=num_parallel_workers)

    # apply DatasetOps
    buffer_size = 10000
    ds1 = ds1.shuffle(buffer_size=buffer_size)
    ds1 = ds1.batch(batch_size, drop_remainder=True)
    ds1 = ds1.repeat(repeat_size)

    return ds1


if __name__ == "__main__":
    # This configure can run both in pynative mode and graph mode
    context.set_context(mode=context.GRAPH_MODE,
                        device_target=cfg.device_target)
    network = LeNet5()
    net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True,
                                                reduction="mean")
    config_ck = CheckpointConfig(
        save_checkpoint_steps=cfg.save_checkpoint_steps,
        keep_checkpoint_max=cfg.keep_checkpoint_max)
    ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet",
                                 directory='./trained_ckpt_file/',
                                 config=config_ck)

    # get training dataset
    ds_train = generate_mnist_dataset(os.path.join(cfg.data_path, "train"),
                                      cfg.batch_size)

    if cfg.micro_batches and cfg.batch_size % cfg.micro_batches != 0:
        raise ValueError(
            "Number of micro_batches should divide evenly batch_size")
    # Create a factory class of DP noise mechanisms, this method is adding noise
    # in gradients while training. Initial_noise_multiplier is suggested to be
    # greater than 1.0, otherwise the privacy budget would be huge, which means
    # that the privacy protection effect is weak. Mechanisms can be 'Gaussian'
    # or 'AdaGaussian', in which noise would be decayed with 'AdaGaussian'
    # mechanism while be constant with 'Gaussian' mechanism.
    noise_mech = NoiseMechanismsFactory().create(cfg.noise_mechanisms,
                                                 norm_bound=cfg.norm_bound,
                                                 initial_noise_multiplier=cfg.initial_noise_multiplier,
                                                 decay_policy=None)
    # Create a factory class of clip mechanisms, this method is to adaptive clip
    # gradients while training, decay_policy support 'Linear' and 'Geometric',
    # learning_rate is the learning rate to update clip_norm,
    # target_unclipped_quantile is the target quantile of norm clip,
    # fraction_stddev is the stddev of Gaussian normal which used in
    # empirical_fraction, the formula is
    # $empirical_fraction + N(0, fraction_stddev)$.
    clip_mech = ClipMechanismsFactory().create(cfg.clip_mechanisms,
                                               decay_policy=cfg.clip_decay_policy,
                                               learning_rate=cfg.clip_learning_rate,
                                               target_unclipped_quantile=cfg.target_unclipped_quantile,
                                               fraction_stddev=cfg.fraction_stddev)
    net_opt = nn.Momentum(params=network.trainable_params(),
                          learning_rate=cfg.lr, momentum=cfg.momentum)
    # Create a monitor for DP training. The function of the monitor is to
    # compute and print the privacy budget(eps and delta) while training.
    rdp_monitor = PrivacyMonitorFactory.create('rdp',
                                               num_samples=60000,
                                               batch_size=cfg.batch_size,
                                               initial_noise_multiplier=cfg.initial_noise_multiplier,
                                               per_print_times=234,
                                               noise_decay_mode=None)
    # Create the DP model for training.
    model = DPModel(micro_batches=cfg.micro_batches,
                    norm_bound=cfg.norm_bound,
                    noise_mech=noise_mech,
                    clip_mech=clip_mech,
                    network=network,
                    loss_fn=net_loss,
                    optimizer=net_opt,
                    metrics={"Accuracy": Accuracy()})

    LOGGER.info(TAG, "============== Starting Training ==============")
    model.train(cfg['epoch_size'], ds_train,
                callbacks=[ckpoint_cb, LossMonitor(), rdp_monitor],
                dataset_sink_mode=cfg.dataset_sink_mode)

    LOGGER.info(TAG, "============== Starting Testing ==============")
    ckpt_file_name = 'trained_ckpt_file/checkpoint_lenet-10_234.ckpt'
    param_dict = load_checkpoint(ckpt_file_name)
    load_param_into_net(network, param_dict)
    ds_eval = generate_mnist_dataset(os.path.join(cfg.data_path, 'test'),
                                     batch_size=cfg.batch_size)
    acc = model.eval(ds_eval, dataset_sink_mode=False)
    LOGGER.info(TAG, "============== Accuracy: %s  ==============", acc)
