import argparse
import sys
import torch
import rdkit
import pandas as pd
from tqdm.auto import tqdm
from models.models_storage import ModelsStorage
from utils.script_utils import add_sample_args, set_seed

lg = rdkit.RDLogger.logger()
lg.setLevel(rdkit.RDLogger.CRITICAL)

MODELS = ModelsStorage()


def get_parser():
    parser = argparse.ArgumentParser()
    subparsers = parser.add_subparsers(
        title='Models sampler script', description='available models')
    for model in MODELS.get_model_names():
        add_sample_args(subparsers.add_parser(model))
    return parser


def main(model, config):
    set_seed(config.seed)
    device = torch.device(config.device)

    # For CUDNN to work properly:
    if device.type.startswith('cuda'):
        torch.cuda.set_device(device.index or 0)

    model_config = torch.load(config.config_load)
    model_vocab = torch.load(config.vocab_load)
    model_state = torch.load(config.model_load)

    model = MODELS.get_model_class(model)(model_vocab, model_config)
    model.load_state_dict(model_state)
    model = model.to(device)
    model.eval()

    samples = []
    n = config.n_samples
    with tqdm(total=config.n_samples, desc='Generating samples') as T:
        while n > 0:
            current_samples = model.sample(min(n, config.n_batch))
            samples.extend(current_samples)

            n -= len(current_samples)
            T.update(len(current_samples))

    samples = pd.DataFrame(samples, columns=['SMILES'])
    samples.to_csv(config.gen_save, index=False)


if __name__ == '__main__':
    parser = get_parser()
    config = parser.parse_args()
    model = sys.argv[1]
    main(model, config)

