#!/usr/bin/env python
"""
For Evaluation
Extended from ADNet code by Hansen et al.
"""
import os
import random
import logging
import shutil

import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim
from torch.utils.data import DataLoader
from torch.optim.lr_scheduler import MultiStepLR
from models.fewshot import FewShotSeg
from dataloaders.datasets import TrainDataset as TrainDataset
from utils import *
from config import ex


@ex.automain
def main(_run, _config, _log):
    if _run.observers:
        # Set up source folder
        os.makedirs(f'{_run.observers[0].dir}/snapshots', exist_ok=True)
        for source_file, _ in _run.experiment_info['sources']:
            os.makedirs(os.path.dirname(f'{_run.observers[0].dir}/source/{source_file}'),
                        exist_ok=True)
            _run.observers[0].save_file(source_file, f'source/{source_file}')
        shutil.rmtree(f'{_run.observers[0].basedir}/_sources')

        # Set up logger -> log to .txt
        file_handler = logging.FileHandler(os.path.join(f'{_run.observers[0].dir}', f'logger.log'))
        file_handler.setLevel('INFO')
        formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(name)s - %(message)s')
        file_handler.setFormatter(formatter)
        _log.handlers.append(file_handler)
        _log.info(f'Run "{_config["exp_str"]}" with ID "{_run.observers[0].dir[-1]}"')

    # Deterministic setting for reproduciablity.
    if _config['seed'] is not None:
        random.seed(_config['seed'])
        torch.manual_seed(_config['seed'])
        torch.cuda.manual_seed_all(_config['seed'])
        cudnn.deterministic = True

    # Enable cuDNN benchmark mode to select the fastest convolution algorithm.
    cudnn.enabled = True
    cudnn.benchmark = True
    torch.cuda.set_device(device=_config['gpu_id'])
    torch.set_num_threads(1)

    _log.info(f'Create model...')
    model = FewShotSeg()
    model = model.cuda()
    model.train()

    _log.info(f'Set optimizer...')
    optimizer = torch.optim.SGD(model.parameters(), **_config['optim'])
    lr_milestones = [(ii + 1) * _config['max_iters_per_load'] for ii in
                     range(_config['n_steps'] // _config['max_iters_per_load'] - 1)]
    scheduler = MultiStepLR(optimizer, milestones=lr_milestones, gamma=_config['lr_step_gamma'])

    my_weight = torch.FloatTensor([0.1, 1.0]).cuda()
    criterion = nn.NLLLoss(ignore_index=255, weight=my_weight)

    _log.info(f'Load data...')
    data_config = {
        'data_dir': _config['path'][_config['dataset']]['data_dir'],
        'dataset': _config['dataset'],
        'n_shot': _config['n_shot'],
        'n_way': _config['n_way'],
        'n_query': _config['n_query'],
        'n_sv': _config['n_sv'],
        'max_iter': _config['max_iters_per_load'],
        'eval_fold': _config['eval_fold'],
        'min_size': _config['min_size'],
        'max_slices': _config['max_slices'],
        'test_label': _config['test_label'],
        'exclude_label': _config['exclude_label'],
        'use_gt': _config['use_gt'],
    }
    train_dataset = TrainDataset(data_config)
    train_loader = DataLoader(train_dataset,
                              batch_size=_config['batch_size'],
                              shuffle=True,
                              num_workers=_config['num_workers'],
                              pin_memory=True,
                              drop_last=True)

    n_sub_epochs = _config['n_steps'] // _config['max_iters_per_load']  # number of times for reloading
    log_loss = {'total_loss': 0, 'query_loss': 0, 'align_loss': 0}

    i_iter = 0
    _log.info(f'Start training...')
    for sub_epoch in range(n_sub_epochs):
        _log.info(f'This is epoch "{sub_epoch}" of "{n_sub_epochs}" epochs.')
        for _, sample in enumerate(train_loader):
            # Prepare episode data.
            support_images = [[shot.float().cuda() for shot in way]
                              for way in sample['support_images']]
            support_fg_mask = [[shot.float().cuda() for shot in way]
                               for way in sample['support_fg_labels']]

            query_images = [query_image.float().cuda() for query_image in sample['query_images']]
            query_labels = torch.cat([query_label.long().cuda() for query_label in sample['query_labels']], dim=0)

            # Compute outputs and losses.
            query_pred, align_loss = model(support_images, support_fg_mask, query_images, train=True)

            query_loss = criterion(torch.log(torch.clamp(query_pred, torch.finfo(torch.float32).eps,
                                                         1 - torch.finfo(torch.float32).eps)), query_labels)
            loss = query_loss + align_loss

            # Compute gradient and do SGD step.
            for param in model.parameters():
                param.grad = None

            loss.backward()
            optimizer.step()
            scheduler.step()

            # Log loss
            query_loss = query_loss.detach().data.cpu().numpy()
            align_loss = align_loss.detach().data.cpu().numpy()

            _run.log_scalar('total_loss', loss.item())
            _run.log_scalar('query_loss', query_loss)
            _run.log_scalar('align_loss', align_loss)

            log_loss['total_loss'] += loss.item()
            log_loss['query_loss'] += query_loss
            log_loss['align_loss'] += align_loss

            # Print loss and take snapshots.
            if (i_iter + 1) % _config['print_interval'] == 0:
                total_loss = log_loss['total_loss'] / _config['print_interval']
                query_loss = log_loss['query_loss'] / _config['print_interval']
                align_loss = log_loss['align_loss'] / _config['print_interval']

                log_loss['total_loss'] = 0
                log_loss['query_loss'] = 0
                log_loss['align_loss'] = 0

                _log.info(f'step {i_iter + 1}: total_loss: {total_loss}, query_loss: {query_loss},'
                          f' align_loss: {align_loss}')

            if (i_iter + 1) % _config['save_snapshot_every'] == 0:
                _log.info('###### Taking snapshot ######')
                torch.save(model.state_dict(),
                           os.path.join(f'{_run.observers[0].dir}/snapshots', f'{i_iter + 1}.pth'))

            i_iter += 1
    _log.info('End of training.')
    return 1