#!/usr/bin/env python3
"""Compare fertility of our Polish BPE vs Bielik / Llama-3 / GPT-2 on the same
held-out Polish sample."""
import pyarrow.parquet as pq
from tokenizers import Tokenizer
import tiktoken

DATA = "/home/ubuntu/dynaword/data"
sample = []
for b in pq.ParquetFile(f"{DATA}/wikipedia/wikipedia.parquet").iter_batches(columns=["text"], batch_size=1000):
    for x in b.column("text"):
        sample.append(x.as_py())
    if len(sample) >= 6000:
        break
sample = sample[4000:6000]
words = sum(len(s.split()) for s in sample)
chars = sum(len(s) for s in sample)
print(f"held-out: {len(sample)} docs, {words:,} words, {chars:,} chars\n")

rows = []

def add(name, vocab, ntok):
    rows.append((name, vocab, ntok / words, ntok / chars))

ours = Tokenizer.from_file("/home/ubuntu/dynaword/polish_bpe_32k.json")
add("polish-32k (ours)", ours.get_vocab_size(),
    sum(len(e.ids) for e in ours.encode_batch(sample)))

g2 = tiktoken.get_encoding("gpt2")
add("gpt2-50k", 50257, sum(len(x) for x in g2.encode_ordinary_batch(sample)))

from transformers import AutoTokenizer
for label, repo in [("Bielik-11B-v3", "speakleash/Bielik-11B-v3.0-Instruct"),
                    ("Llama-3", "NousResearch/Meta-Llama-3-8B")]:
    try:
        t = AutoTokenizer.from_pretrained(repo)
        enc = t(sample, add_special_tokens=False)["input_ids"]
        add(label, t.vocab_size, sum(len(x) for x in enc))
    except Exception as e:
        print(f"  {label}: FAILED {str(e)[:140]}")

rows.sort(key=lambda r: r[2])
base = next(r[2] for r in rows if r[0].startswith("polish-32k"))
print(f"{'tokenizer':<22}{'vocab':>8}{'tok/word':>11}{'tok/char':>11}{'vs ours':>10}")
for name, vocab, tpw, tpc in rows:
    print(f"{name:<22}{vocab:>8}{tpw:>11.3f}{tpc:>11.3f}{tpw/base:>9.2f}x")