# Imports
# =============================================================================
import os
from distutils.util import strtobool
from pathlib import Path
from argparse import ArgumentParser

import warnings

from pytorch_lightning import Trainer
from torch.utils.data import DataLoader

from dataset.dataset import TrajData
from lib.utils.config import Config
from lib.utils.logging import Logger

warnings.filterwarnings('ignore', category=FutureWarning)
warnings.filterwarnings('ignore', category=UserWarning)

_ROOT = Path(__file__).parent

# Paramter Configuration
# =============================================================================
# Argument Parser
main_arg_parser = ArgumentParser(description="parser for fast-neural-style")

# Main Parameters
main_arg_parser.add_argument("--main_debug", type=strtobool, default=False, help="")
main_arg_parser.add_argument("--main_eval", type=strtobool, default=False, help="")
main_arg_parser.add_argument("--main_seed", type=int, default=69, help="")

# Data Parameters
main_arg_parser.add_argument("--data_worker", type=int, default=10, help="")
main_arg_parser.add_argument("--data_batchsize", type=int, default=100, help="")
main_arg_parser.add_argument("--data_root", type=str, default='../data/rpoot', help="")

# Transformations
main_arg_parser.add_argument("--transformations_to_tensor", type=strtobool, default=False, help="")

# Transformations
main_arg_parser.add_argument("--train_outpath", type=str, default="output", help="")
main_arg_parser.add_argument("--train_version", type=strtobool, required=False, help="")
main_arg_parser.add_argument("--train_epochs", type=int, default=10, help="")
main_arg_parser.add_argument("--train_batch_size", type=int, default=512, help="")
main_arg_parser.add_argument("--train_lr", type=float, default=0.002, help="")

# Model
main_arg_parser.add_argument("--model_type", type=str, default="LeNetAE", help="")
main_arg_parser.add_argument("--model_activation", type=str, default="relu", help="")
main_arg_parser.add_argument("--model_filters", type=str, default="[32, 16, 4]", help="")
main_arg_parser.add_argument("--model_use_bias", type=strtobool, default=True, help="")
main_arg_parser.add_argument("--model_use_norm", type=strtobool, default=True, help="")
main_arg_parser.add_argument("--model_dropout", type=float, default=0.00, help="")

# Project
main_arg_parser.add_argument("--project_name", type=str, default='traj-gen', help="")
main_arg_parser.add_argument("--project_owner", type=str, default='si11ium', help="")
main_arg_parser.add_argument("--project_neptune_key", type=str, default=os.getenv('NEPTUNE_KEY'), help="")

# Parse it
args = main_arg_parser.parse_args()
config = Config.read_namespace(args)

################
# TESTING ONLY #
# =============================================================================
hparams = config.model_paramters
dataset = TrajData('data', mapname='tate', alternatives=100, trajectories=10000)
dataloader = DataLoader(dataset=dataset.train_dataset, shuffle=True,
                        batch_size=hparams.data_param.batchsize,
                        num_workers=hparams.data_param.worker)

# Logger
# =============================================================================
logger = Logger(config, debug=True)

# Trainer
# =============================================================================
trainer = Trainer(logger=logger)

# Model
# =============================================================================
model = None


if __name__ == '__main__':
    next(iter(dataloader))
    pass