#!/usr/bin/env python3
"""Convert modded-nanogpt's validation loss into paper-ready, tokenizer-agnostic
metrics: perplexity and bits-per-byte (bpb).

Perplexity depends on the tokenizer, so it is NOT comparable across models with
different vocabularies. Bits-per-byte normalises by raw UTF-8 bytes and IS
comparable (this is what to report against Bielik / Llama-based models).

  bpb = val_loss[nats/token] / ln(2) * (tokens / bytes)

Usage:
  python3 src/eval_bpb.py --val-loss 3.21   # val loss from the training log
"""
import argparse, struct
import numpy as np
from tokenizers import Tokenizer

VAL = "/home/ubuntu/dynaword/shards/polish_val_000000.bin"
TOK = "/home/ubuntu/dynaword/polish_bpe_32k.json"

def load_shard(path):
    with open(path, "rb") as f:
        header = np.frombuffer(f.read(256 * 4), dtype=np.int32)
        assert header[0] == 20240520 and header[1] == 1, "bad shard header"
        ntok = int(header[2])
        toks = np.frombuffer(f.read(ntok * 2), dtype=np.uint16)
    return toks

def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--val-loss", type=float, required=True, help="nats/token from training log")
    ap.add_argument("--vocab", type=int, default=32768)
    args = ap.parse_args()

    toks = load_shard(VAL)
    tok = Tokenizer.from_file(TOK)
    # decode in chunks -> UTF-8 bytes (held-out reconstructs to the original text)
    nbytes = 0
    step = 1_000_000
    for i in range(0, len(toks), step):
        nbytes += len(tok.decode(toks[i:i+step].tolist()).encode("utf-8"))
    ntok = len(toks)

    ln2 = np.log(2)
    ppl = float(np.exp(args.val_loss))
    bpb = args.val_loss / ln2 * (ntok / nbytes)
    bpt = nbytes / ntok
    rand_bpb = np.log(args.vocab) / ln2 * (ntok / nbytes)   # uniform baseline

    print(f"held-out val: {ntok:,} tokens | {nbytes:,} bytes | {bpt:.3f} bytes/token")
    print(f"val loss     : {args.val_loss:.4f} nats/token")
    print(f"perplexity   : {ppl:.2f}   (tokenizer-specific; NOT cross-model comparable)")
    print(f"bits-per-byte: {bpb:.4f}   (tokenizer-AGNOSTIC; report this)")
    print(f"  (uniform-{args.vocab} baseline bpb = {rand_bpb:.3f})")

if __name__ == "__main__":
    main()