#!/usr/bin/env python
"""
 Copyright 2018 Johns Hopkins University  (Author: Jesus Villalba)
 Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
"""
import sys
import os
from pathlib import Path
from jsonargparse import ArgumentParser, ActionConfigFile, ActionParser, namespace_to_dict
import time
import logging
import multiprocessing 

import numpy as np

import torch
import torch.nn as nn

from hyperion.hyp_defs import config_logger, set_float_cpu
from hyperion.torch.utils import open_device
from hyperion.torch.utils import ddp
from hyperion.torch.models import XVector as XVec
from hyperion.torch.trainers import XVectorAdvTrainerFromWav as Trainer
from hyperion.torch.data import AudioDataset as AD
from hyperion.torch.data import ClassWeightedSeqSampler as Sampler
from hyperion.torch.metrics import CategoricalAccuracy
from hyperion.torch.narchs import AudioFeatsMVN as AF
from hyperion.torch.adv_attacks import AttackFactory
from hyperion.torch import TorchModelLoader as TML


def init_data(audio_path, train_list, val_list, 
              train_aug_cfg, val_aug_cfg, num_workers, 
              num_gpus, rank, **kwargs):

    ad_args = AD.filter_args(**kwargs)
    sampler_args = Sampler.filter_args(**kwargs)
    if rank == 0:
        logging.info('audio dataset args={}'.format(ad_args))
        logging.info('sampler args={}'.format(sampler_args))
        logging.info('init datasets')

    train_data = AD(audio_path, train_list, aug_cfg=train_aug_cfg, **ad_args)
    val_data = AD(audio_path, val_list, aug_cfg=val_aug_cfg, is_val=True, **ad_args)

    if rank == 0:
        logging.info('init samplers')
    train_sampler = Sampler(train_data, **sampler_args)
    val_sampler = Sampler(val_data, **sampler_args)

    num_workers_per_gpu = int((num_workers + num_gpus - 1) / num_gpus)
    largs = {'num_workers': num_workers_per_gpu, 'pin_memory': True} if num_gpus > 0 else {}

    train_loader = torch.utils.data.DataLoader(
        train_data, batch_sampler = train_sampler, **largs)

    test_loader = torch.utils.data.DataLoader(
        val_data, batch_sampler = val_sampler, **largs)

    return train_loader, test_loader


def init_feats(rank, **kwargs):
    feat_args = AF.filter_args(**kwargs['feats'])
    if rank == 0:
        logging.info('feat args={}'.format(feat_args))
        logging.info('initializing feature extractor')
    feat_extractor = AF(trans=True, **feat_args)
    if rank == 0:
        logging.info('feat-extractor={}'.format(feat_extractor))
    return feat_extractor


def init_xvector(num_classes, in_model_path, rank, train_mode, **kwargs):
    xvec_args = XVec.filter_finetune_args(**kwargs)    
    if rank == 0:
        logging.info('xvector network ft args={}'.format(xvec_args))
    xvec_args['num_classes'] = num_classes
    model = TML.load(in_model_path)
    model.rebuild_output_layer(**xvec_args)
    if train_mode == 'ft-embed-affine':
        model.freeze_preembed_layers()
    if rank == 0:
        logging.info('x-vector-model={}'.format(model))
    return model


def init_attack(feat_extractor, model, wav_scale, **kwargs):
    victim_model = nn.Sequential(
        feat_extractor,
        model)
    attack_args = AttackFactory.filter_args(**kwargs['attack'])
    extra_args = {'eps_scale': wav_scale,
                  'loss': nn.functional.cross_entropy,
                  'time_dim': 1}
    attack_args.update(extra_args)
    logging.info('attacks args={}'.format(attack_args))
    attack = AttackFactory.create(victim_model, **attack_args)
    return attack


def train_xvec(gpu_id, args):

    config_logger(args.verbose)
    del args.verbose
    logging.debug(args)

    kwargs = namespace_to_dict(args)
    torch.manual_seed(args.seed)
    set_float_cpu('float32')
    
    train_mode = kwargs['train_mode']

    ddp_args = ddp.filter_ddp_args(**kwargs)
    device, rank, world_size = ddp.ddp_init(gpu_id, **ddp_args)
    kwargs['rank'] = rank

    train_loader, test_loader = init_data(**kwargs)
    feat_extractor = init_feats(**kwargs)
    model = init_xvector(train_loader.dataset.num_classes, **kwargs)
    kwargs['wav_scale'] = train_loader.dataset.wav_scale
    attack = init_attack(feat_extractor, model, **kwargs)

    trn_args = Trainer.filter_args(**kwargs)
    if rank == 0:
        logging.info('trainer args={}'.format(trn_args))
    metrics = { 'acc': CategoricalAccuracy() }
    trainer = Trainer(model, feat_extractor, attack,
                      device=device, metrics=metrics, 
                      ddp=world_size>1, train_mode=train_mode, **trn_args)
    if args.resume:
        trainer.load_last_checkpoint()
    trainer.fit(train_loader, test_loader)

    ddp.ddp_cleanup()



if __name__ == '__main__':

    parser = ArgumentParser(
        description='Fine-tune x-vector model with adv attacks on wav domain')

    parser.add_argument('--cfg', action=ActionConfigFile)
    parser.add_argument('--audio-path', required=True)
    parser.add_argument('--train-list', dest='train_list', required=True)
    parser.add_argument('--val-list', dest='val_list', required=True)

    AD.add_argparse_args(parser)
    Sampler.add_argparse_args(parser)

    parser.add_argument('--train-aug-cfg', default=None)
    parser.add_argument('--val-aug-cfg', default=None)

    parser.add_argument('--num-workers', type=int, default=5, 
                        help='num_workers of data loader')

    AF.add_class_args(parser, prefix='feats')
    parser.add_argument('--in-model-path', required=True)

    XVec.add_finetune_args(parser)
    AttackFactory.add_class_args(parser, prefix='attack')

    Trainer.add_class_args(parser)
    ddp.add_ddp_args(parser)

    # parser.add_argument('--num-gpus', type=int, default=1,
    #                     help='number of gpus, if 0 it uses cpu')
    parser.add_argument('--seed', type=int, default=1123581321, 
                        help='random seed (default: 1)')
    parser.add_argument('--resume', action='store_true', default=False,
                        help='resume training from checkpoint')
    parser.add_argument('--train-mode', default='ft-full',
                        choices=['ft-full', 'ft-embed-affine'],
                        help=('ft-full: adapt full x-vector network'
                              'ft-embed-affine: adapt affine transform before embedding'))

    # parser.add_argument('--attack-eps', required=True, type=float,
    #                    help='epsilon adversarial attack')
    # parser.add_argument('--attack-eps-step', required=True, type=float,
    #                    help='eps step adversarial attack')
    # parser.add_argument('--attack-random-eps', default=False, 
    #                    action='store_true',
    #                    help='use random eps in adv. attack')

    # parser.add_argument('--attack-max-iter', default=10, type=int, 
    #                    help='number of iterations for adversarial optimization')

    # parser.add_argument('--p-attack', default=0.5, type=float, 
    #                    help='ratio of batches with adv attack')

    parser.add_argument('-v', '--verbose', dest='verbose', default=1, choices=[0, 1, 2, 3], type=int)
    parser.add_argument('--local_rank', default=0, type=int)

    args = parser.parse_args()
    gpu_id = args.local_rank
    del args.local_rank

    if gpu_id == 0:
        try:
            config_file = Path(args.exp_path) / 'config.yaml'
            parser.save(args, str(config_file), format='yaml', overwrite=True)
        except:
            pass

    # torch docs recommend using forkserver
    multiprocessing.set_start_method('forkserver')
    train_xvec(gpu_id, args)


