# -*- coding: utf-8 -*-
# Author: Runsheng Xu <rxx3386@ucla.edu>
# License: TDG-Attribution-NonCommercial-NoDistrib


import argparse
import os
import statistics

import torch
from torch.utils.data import DataLoader
from tensorboardX import SummaryWriter

import opencood.hypes_yaml.yaml_utils as yaml_utils
from opencood.tools import train_utils
from opencood.data_utils.datasets import build_dataset


def train_parser():
    parser = argparse.ArgumentParser(description="synthetic data generation")
    parser.add_argument("--hypes_yaml", type=str, required=True,
                        help='data generation yaml file needed ')
    parser.add_argument('--model_dir', default='',
                        help='Continued training path')
    opt = parser.parse_args()
    return opt


def main():
    opt = train_parser()
    hypes = yaml_utils.load_yaml(opt.hypes_yaml, opt)

    print('Dataset Building')
    opencood_train_dataset = build_dataset(hypes, visualize=False, train=True)
    opencood_validate_dataset = build_dataset(hypes,
                                              visualize=False,
                                              train=False)

    train_loader = DataLoader(opencood_train_dataset,
                              batch_size=hypes['train_params']['batch_size'],
                              num_workers=4,
                              collate_fn=opencood_train_dataset.collate_batch_train,
                              shuffle=True,
                              pin_memory=False,
                              drop_last=True)
    val_loader = DataLoader(opencood_validate_dataset,
                            batch_size=hypes['train_params']['batch_size'],
                            num_workers=4,
                            collate_fn=opencood_train_dataset.collate_batch_train,
                            shuffle=False,
                            pin_memory=False,
                            drop_last=True)

    print('Creating Model')
    model = train_utils.create_model(hypes)
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    # we assume gpu is necessary
    if torch.cuda.is_available():
        model.to(device)

    # define the loss
    criterion = train_utils.create_loss(hypes)

    # optimizer setup
    optimizer = train_utils.setup_optimizer(hypes, model)
    # lr scheduler setup
    scheduler = train_utils.setup_lr_schedular(hypes, optimizer)

    # if we want to train from last checkpoint.
    if opt.model_dir:
        saved_path = opt.model_dir
        init_epoch, model = train_utils.load_saved_model(saved_path, model)

    else:
        init_epoch = 0
        # if we train the model from scratch, we need to create a folder
        # to save the model,
        saved_path = train_utils.setup_train(hypes)

    # record training
    writer = SummaryWriter(saved_path)

    print('Training start')
    epoches = hypes['train_params']['epoches']
    # used to help schedule learning rate

    for epoch in range(init_epoch, max(epoches, init_epoch)):
        for param_group in optimizer.param_groups:
            print('learning rate %f' % param_group["lr"])
        for i, batch_data in enumerate(train_loader):
            # the model will be evaluation mode during validation
            model.train()
            model.zero_grad()
            optimizer.zero_grad()

            batch_data = train_utils.to_device(batch_data, device)

            # case1 : late fusion train --> only ego needed,
            # and ego is random selected
            # case2 : early fusion train --> all data projected to ego
            # case3 : intermediate fusion --> ['ego']['processed_lidar']
            # becomes a list, which containing all data from other cavs
            # as well
            ouput_dict = model(batch_data['ego'])
            # first argument is always your output dictionary,
            # second argument is always your label dictionary.
            final_loss = criterion(ouput_dict, batch_data['ego']['label_dict'])
            criterion.logging(epoch, i, len(train_loader), writer)

            # back-propagation
            final_loss.backward()
            optimizer.step()

        if epoch % hypes['train_params']['eval_freq'] == 0:
            valid_ave_loss = []

            with torch.no_grad():
                for i, batch_data in enumerate(val_loader):
                    model.eval()

                    batch_data = train_utils.to_device(batch_data, device)
                    ouput_dict = model(batch_data['ego'])

                    final_loss = criterion(ouput_dict,
                                           batch_data['ego']['label_dict'])
                    valid_ave_loss.append(final_loss.item())
            valid_ave_loss = statistics.mean(valid_ave_loss)
            print('At epoch %d, the validation loss is %f' % (epoch,
                                                              valid_ave_loss))
            writer.add_scalar('Validate_Loss', valid_ave_loss, epoch)

        if epoch % hypes['train_params']['save_freq'] == 0:
            torch.save(model.state_dict(),
                       os.path.join(saved_path,
                                    'net_epoch%d.pth' % (epoch + 1)))
        scheduler.step(epoch)

    print('Training Finished, checkpoints saved to %s' % saved_path)


if __name__ == '__main__':
    main()
