"""
Runs a model on a single node across N-gpus.
"""
import os
import sys
import numpy as np
from time import sleep
import torch

from test_tube import HyperOptArgumentParser, Experiment, SlurmCluster
from pytorch_lightning.models.trainer import Trainer
from pytorch_lightning.utils.arg_parse import add_default_args

from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint

SEED = 2334
torch.manual_seed(SEED)
np.random.seed(SEED)

from lightning_module_template import LightningTemplateModel


def main(hparams):
    """
    Main training routine specific for this project
    :param hparams:
    :return:
    """
    # ------------------------
    # 1 INIT LIGHTNING MODEL
    # ------------------------
    print('loading model...')
    model = LightningTemplateModel(hparams)
    print('model built')

    # ------------------------
    # 2 INIT TEST TUBE EXP
    # ------------------------

    # init experiment
    exp = Experiment(
        name=hyperparams.experiment_name,
        save_dir=hyperparams.test_tube_save_path,
        autosave=False,
        description='test demo'
    )

    exp.argparse(hparams)
    exp.save()

    # ------------------------
    # 3 DEFINE CALLBACKS
    # ------------------------
    model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
    early_stop = EarlyStopping(
        monitor='val_acc',
        patience=3,
        verbose=True,
        mode='max'
    )

    checkpoint = ModelCheckpoint(
        filepath=model_save_path,
        save_best_only=True,
        verbose=True,
        monitor='val_loss',
        mode='min'
    )

    # ------------------------
    # 4 INIT TRAINER
    # ------------------------
    trainer = Trainer(
        experiment=exp,
        checkpoint_callback=checkpoint,
        early_stop_callback=early_stop,
        gpus=hparams.gpus,
    )

    # ------------------------
    # 5 START TRAINING
    # ------------------------
    trainer.fit(model)


if __name__ == '__main__':

    # dirs
    root_dir = os.path.dirname(os.path.realpath(__file__))
    demo_log_dir = os.path.join(root_dir, 'pt_lightning_demo_logs')
    checkpoint_dir = os.path.join(demo_log_dir, 'model_weights')
    test_tube_dir = os.path.join(demo_log_dir, 'test_tube_data')

    # although we user hyperOptParser, we are using it only as argparse right now
    parent_parser = HyperOptArgumentParser(strategy='grid_search', add_help=False)

    # gpu args
    parent_parser.add_argument('--gpus', type=str, default='0', help='how many gpus to use in the node. -1 uses all the gpus on the node')
    parent_parser.add_argument('--test_tube_save_path', type=str, default=test_tube_dir, help='where to save logs')
    parent_parser.add_argument('--model_save_path', type=str, default=checkpoint_dir, help='where to save model')
    parent_parser.add_argument('--experiment_name', type=str, default='pt_lightning_exp_a', help='test tube exp name')

    # allow model to overwrite or extend args
    parser = LightningTemplateModel.add_model_specific_args(parent_parser, root_dir)
    hyperparams = parser.parse_args()

    # ---------------------
    # RUN TRAINING
    # ---------------------
    # run on HPC cluster
    print(f'RUNNING INTERACTIVE MODE ON GPUS. gpu ids: {hyperparams.gpus}')
    main(hyperparams)