"""
Minimal end-to-end example for the Nordic Translator.

Usage:
    python run_example.py "Hello, how are you?" sv
    python run_example.py "Var bor du?" en

Two ways to run are shown:
  1. Standalone (pure torch, fast KV-cached)  -> NordicTranslator.translate()
  2. HuggingFace AutoModel (trust_remote_code) -> model.generate()
"""
import sys
import torch
import sentencepiece as spm
from modeling_nordic import NordicTranslator

HERE = __import__("os").path.dirname(__file__)
WEIGHTS = HERE + "/model.safetensors"
TOKENIZER = HERE + "/nordic_unigram_65k.model"

BOS, EOS, EOS_SRC = 1, 2, 65007
LANG = {"en": 65000, "sv": 65001, "da": 65002, "nb": 65003,
        "nn": 65004, "fi": 65005, "is": 65006}


def main():
    text = sys.argv[1] if len(sys.argv) > 1 else "Hello, how are you today?"
    tgt = sys.argv[2] if len(sys.argv) > 2 else "sv"
    assert tgt in LANG, f"target must be one of {list(LANG)}"

    device = "cuda" if torch.cuda.is_available() else "cpu"
    sp = spm.SentencePieceProcessor()
    sp.load(TOKENIZER)

    model = NordicTranslator.from_checkpoint(WEIGHTS, device=device, dtype=torch.bfloat16)
    src_ids = sp.encode(text, out_type=int)
    out_ids = model.translate(src_ids, LANG[tgt], bos=BOS, eos=EOS, eos_src=EOS_SRC)
    print(f"[{tgt}] {sp.decode(out_ids)}")


if __name__ == "__main__":
    main()