"""Streaming encode of a large raw-text corpus to a uint16 .bin file.

Memory-safe: encodes line-by-line, flushes chunks of ~16M tokens.
Usage:
  .venv/bin/python data/encode_full.py --raw data/TinyStoriesV2-GPT4-train.txt \
      --out data/train_full.bin --tok data/tokenizer.json
"""
import argparse, time
from pathlib import Path
import numpy as np
from data.tokenizer import load_tokenizer

def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--raw", default="data/TinyStoriesV2-GPT4-train.txt")
    ap.add_argument("--out", default="data/train_full.bin")
    ap.add_argument("--tok", default="data/tokenizer.json")
    ap.add_argument("--chunk", type=int, default=16_000_000)
    ap.add_argument("--max-tokens", type=int, default=600_000_000)
    args = ap.parse_args()

    tok = load_tokenizer(args.tok)
    eot = tok.token_to_id("<|endoftext|>")
    out = Path(args.out)
    out.parent.mkdir(parents=True, exist_ok=True)
    total, buf = 0, []
    t0 = time.time()
    with open(args.raw, "rb") as f, open(out, "wb") as g:
        for raw in f:
            line = raw.decode("utf-8", errors="replace").strip()
            if not line:
                continue
            ids = tok.encode(line).ids
            buf.extend(ids)
            buf.append(eot)
            total += len(ids) + 1
            if len(buf) >= args.chunk or total >= args.max_tokens:
                np.asarray(buf, dtype=np.uint16).tofile(g)
                buf.clear()
                print(f"encoded {total:,} tokens in {time.time()-t0:.0f}s "
                      f"({total/(time.time()-t0):,.0f} tok/s)", flush=True)
            if total >= args.max_tokens:
                break
    if buf:
        np.asarray(buf, dtype=np.uint16).tofile(g)
    print(f"done: {total:,} tokens -> {out} ({out.stat().st_size/1e9:.2f} GB)", flush=True)

if __name__ == "__main__":
    main()