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import torch
import argparse
import os
import wandb
import pytorch_lightning as pl
from pytorch_lightning.loggers import WandbLogger
from transformers import BartTokenizer
from idiomify.datamodules import IdiomifyDataModule
from idiomify.fetchers import fetch_config, fetch_idiomifier
from idiomify.paths import ROOT_DIR
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--num_workers", type=int, default=os.cpu_count())
parser.add_argument("--fast_dev_run", action="store_true", default=False)
args = parser.parse_args()
config = fetch_config()['idiomifier']
config.update(vars(args))
tokenizer = BartTokenizer.from_pretrained(config['bart'])
# prepare the datamodule
with wandb.init(entity="eubinecto", project="idiomify", config=config) as run:
model = fetch_idiomifier(config['ver'], run) # fetch a pre-trained model
datamodule = IdiomifyDataModule(config, tokenizer, run)
logger = WandbLogger(log_model=False)
trainer = pl.Trainer(fast_dev_run=config['fast_dev_run'],
gpus=torch.cuda.device_count(),
default_root_dir=str(ROOT_DIR),
logger=logger)
trainer.test(model, datamodule)
if __name__ == '__main__':
main()
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