"""Populate `text_encoder/` with the flan-t5-large encoder for upload.

MiniT2I conditions on the encoder of `google/flan-t5-large`. This script saves
just the encoder weights + tokenizer into this folder so it can be uploaded to
`<user>/text_encoder` and loaded with `T5EncoderModel.from_pretrained(repo)`
(pure transformers, no diffusers). Weights are intentionally not committed to the
source repo; run this once before uploading.

    python minit2i_hf/text_encoder/prepare_text_encoder.py
"""
import argparse
from pathlib import Path

from transformers import AutoTokenizer, T5EncoderModel

HERE = Path(__file__).resolve().parent


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--source", default="google/flan-t5-large", help="Source HF model id")
    args = ap.parse_args()

    print(f"Downloading {args.source} encoder + tokenizer ...")
    tokenizer = AutoTokenizer.from_pretrained(args.source)
    encoder = T5EncoderModel.from_pretrained(args.source)

    tokenizer.save_pretrained(HERE)
    encoder.save_pretrained(HERE)
    print(f"Saved encoder + tokenizer -> {HERE}")


if __name__ == "__main__":
    main()