import os
import math
import time
import torch
import random
import logging
import argparse
import datetime
from network import create_model
from options.yaml_opt import parse, dict2str
from dataset.data_sampler import EnlargedSampler
from dataset import create_dataset, create_dataloader
from base_utils.logger import get_root_logger, get_env_info, init_tb_logger, MessageLogger
from base_utils.utils import set_random_seed, get_time_str, check_resume, make_exp_dirs, mkdir_and_rename

torch.cuda.is_available()

def parse_options(is_train=True):
    format_str = 'train' if is_train  else 'test'
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '-opt', type=str, default='options/{}_option.yml'.format(format_str), help='Path to option YAML file.')
    parser.add_argument('--local_rank', type=int, default=0)
    args = parser.parse_args()
    opt = parse(args.opt, is_train=is_train)

    opt['dist'] = False

    # random seed
    seed = opt.get('manual_seed')
    if seed is None:
        seed = random.randint(1, 10000)
        opt['manual_seed'] = seed
    set_random_seed(seed + opt['rank'])

    return opt


def init_loggers(opt):
    log_path = opt['path'].get('log_path')
    if log_path == None:
        log_path = opt['path']['experiments_root']
    log_file = os.path.join(log_path,
                        f"train_{opt['name']}_{get_time_str()}.log")
    if os.path.exists(log_path) == False:
            os.makedirs(log_path)
    logger = get_root_logger(
        logger_name='relighting', log_level=logging.INFO, log_file=log_file)
    logger.info(get_env_info())
    logger.info(dict2str(opt))

    # initialize tensorboard logger and wandb logger
    tb_logger = None
    if opt['logger'].get('use_tb_logger') and 'debug' not in opt['name']:
        tb_logger = init_tb_logger(log_dir=opt['path']['tb_logger'])
    return logger, tb_logger


def create_train_val_dataloader(opt, logger):
    # create train and val dataloaders
    train_loader, val_loader = None, None
    for phase, dataset_opt in opt['datasets'].items():
        if phase == 'train':
            dataset_enlarge_ratio = dataset_opt.get('dataset_enlarge_ratio', 1)
            train_set = create_dataset(dataset_opt)
            train_sampler = EnlargedSampler(train_set, opt['world_size'],
                                            opt['rank'], dataset_enlarge_ratio)
            train_loader = create_dataloader(
                train_set,
                dataset_opt,
                num_gpu=opt['num_gpu'],
                dist=opt['dist'],
                sampler=train_sampler,
                seed=opt['manual_seed'])

            num_iter_per_epoch = math.ceil(
                len(train_set) * dataset_enlarge_ratio /
                (dataset_opt['batch_size_per_gpu'] * opt['num_gpu'] * opt['world_size']))
            total_iters = int(opt['train']['total_iter'])
            total_epochs = math.ceil(total_iters / (num_iter_per_epoch))
            logger.info(
                'Training statistics:'
                f'\n\tNumber of train images: {len(train_set)}'
                f'\n\tDataset enlarge ratio: {dataset_enlarge_ratio}'
                f'\n\tBatch size per gpu: {dataset_opt["batch_size_per_gpu"]}'
                f'\n\tWorld size (gpu number): {opt["world_size"]}'
                f'\n\tRequire iter number per epoch: {num_iter_per_epoch}'
                f'\n\tTotal epochs: {total_epochs}; iters: {total_iters}.')

        elif phase == 'val':
            val_set = create_dataset(dataset_opt)
            val_loader = create_dataloader(
                val_set,
                dataset_opt,
                num_gpu=opt['num_gpu'],
                dist=opt['dist'],
                sampler=None,
                seed=opt['manual_seed'])
            logger.info(
                f'Number of val images/folders in {dataset_opt["name"]}: '
                f'{len(val_set)}')
        else:
            raise ValueError(f'Dataset phase {phase} is not recognized.')

    return train_loader, train_sampler, val_loader, total_epochs, total_iters


def main():
    # parse options, set distributed setting, set ramdom seed
    opt = parse_options(is_train=True)
    os.environ["CUDA_VISIBLE_DEVICES"] = opt['gpu_id']
    torch.backends.cudnn.benchmark = True
    # torch.backends.cudnn.deterministic = True

    # load resume states if necessary
    if opt['path'].get('resume_state'):
        device_id = torch.cuda.current_device()
        resume_state = torch.load(
            opt['path']['resume_state'],
            map_location=lambda storage, loc: storage.cuda(device_id))
    else:
        resume_state = None

    # mkdir for experiments and logger
    if resume_state is None:
        make_exp_dirs(opt)

    # initialize loggers
    logger, tb_logger = init_loggers(opt)

    # create train and validation dataloaders
    result = create_train_val_dataloader(opt, logger)
    train_loader, train_sampler, val_loader, total_epochs, total_iters = result

    # create model
    if resume_state:  # resume training
        check_resume(opt, resume_state['iter'])
        model = create_model(opt)
        model.resume_training(resume_state)  # handle optimizers and schedulers
        logger.info(f"Resuming training from epoch: {resume_state['epoch']}, "
                    f"iter: {resume_state['iter']}.")
        start_epoch = resume_state['epoch']
        current_iter = resume_state['iter']
    else:
        model = create_model(opt)
        start_epoch = 0
        current_iter = 0

    # create message logger (formatted outputs)
    msg_logger = MessageLogger(opt, current_iter, tb_logger)

    # training
    logger.info(
        f'Start training from epoch: {start_epoch}, iter: {current_iter}')
    data_time, iter_time = time.time(), time.time()
    start_time = time.time()

    for epoch in range(start_epoch, total_epochs + 1):
        train_sampler.set_epoch(epoch)
        iter_trainloader = iter(train_loader)
        train_data = iter_trainloader.next()
        while train_data is not None:
            data_time = time.time() - data_time

            current_iter += 1
            if current_iter > total_iters:
                break
            model.lr_decay(current_iter)
            # training
            model.feed_data(train_data)
            model.optimize_parameters(current_iter)
            iter_time = time.time() - iter_time
            # log
            if current_iter % opt['logger']['print_freq'] == 0:
                log_vars = {'epoch': epoch, 'iter': current_iter}
                log_vars.update({'lrs': model.get_current_learning_rate()})
                log_vars.update({'time': iter_time, 'data_time': data_time})
                log_vars.update(model.get_current_log())
                msg_logger(log_vars)

            # save models and training states
            if current_iter % opt['logger']['save_checkpoint_freq'] == 0:
                logger.info('Saving models and training states.')
                model.save(epoch, current_iter)

            # validation
            if opt.get('val') is not None and (current_iter %
                                               opt['val']['val_freq'] == 0):
                                               
                model.validation(val_loader, current_iter, tb_logger,
                                 opt['val']['save_img'])

            data_time = time.time()
            iter_time = time.time()
            try:
                train_data = iter_trainloader.next()
            except StopIteration:
                train_data = None
        # end of iter

    # end of epoch

    consumed_time = str(
        datetime.timedelta(seconds=int(time.time() - start_time)))
    logger.info(f'End of training. Time consumed: {consumed_time}')
    logger.info('Save the latest model.')
    model.save(epoch=-1, current_iter=-1)  # -1 stands for the latest
    if opt.get('val') is not None:
        model.validation(val_loader, current_iter, tb_logger,
                         opt['val']['save_img'])
    if tb_logger:
        tb_logger.close()


if __name__ == '__main__':
    main()
