#!/usr/bin/env python
"""
 Copyright 2020 Johns Hopkins University  (Author: Jesus Villalba)
 Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)
"""
import sys
import os
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.trainers import XVectorTrainerFromWav as Trainer
from hyperion.torch.models import TDNNXVector as XVec
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


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, rank, **kwargs):
    xvec_args = XVec.filter_args(**kwargs)
    if rank == 0:
        logging.info('xvector network args={}'.format(xvec_args))
    xvec_args['num_classes'] = num_classes
    model = XVec(**xvec_args)
    if rank == 0:
        logging.info('x-vector-model={}'.format(model))
    return model


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')

    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)

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

    ddp.ddp_cleanup()


if __name__ == '__main__':

    parser = ArgumentParser(
        description='Train XVector with ResNet encoder from audio files')

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

    AD.add_class_args(parser)
    Sampler.add_class_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')
    XVec.add_class_args(parser)
    Trainer.add_class_args(parser)

    ddp.add_ddp_args(parser)
    parser.add_argument('--seed', type=int, default=1123581321, 
                        help='random seed')
    parser.add_argument('--resume', action='store_true', default=False,
                        help='resume training from checkpoint')
    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)

