import argparse
from pathlib import Path
from uuid import uuid4

parser = argparse.ArgumentParser("train")
parser.add_argument("--training_data", type=str, help="Path to training data")
parser.add_argument("--max_epochs", type=int, help="Max # of epochs for the training")
parser.add_argument("--learning_rate", type=float, help="Learning rate")
parser.add_argument("--model_output", type=str, help="Path of output model")

args = parser.parse_args()

lines = [
    f"Training data path: {args.training_data}",
    f"Max epochs: {args.max_epochs}",
    f"Learning rate: {args.learning_rate}",
    f"Model output path: {args.model_output}",
]

for line in lines:
    print(line)

# Do the train and save the trained model as a file into the output folder.
# Here only output a dummy data for demo.
model = str(uuid4())
(Path(args.model_output) / "model").write_text(model)
