custom_tokenizer_adapters.py

#!/usr/bin/env python3
"""Load and evaluate the non-Hugging-Face tokenizer JSONs in this dataset.

The adapters deliberately do not pretend that missing configuration is known.
``load_custom_tokenizer`` returns a usable byte-level BPE core, plus a fidelity
classification describing whether its intended pre-tokenization is reproducible
from the artifact alone.
"""

from __future__ import annotations

import json
from dataclasses import dataclass
from pathlib import Path
from typing import Callable

try:
    import regex
except ImportError:  # pragma: no cover - surfaced only for regex tokenizers
    regex = None


GPT2_PATTERN = r"(?i:'s|'t|'re|'ve|'m|'ll|'d)| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"


def bytes_to_unicode() -> dict[int, str]:
    """The reversible byte alphabet used by GPT-2/minBPE-style artifacts."""
    visible = list(range(ord("!"), ord("~") + 1))
    visible += list(range(ord("¡"), ord("¬") + 1))
    visible += list(range(ord("®"), ord("ÿ") + 1))
    chars = visible[:]
    extra = 0
    for byte in range(256):
        if byte not in visible:
            visible.append(byte)
            chars.append(256 + extra)
            extra += 1
    return dict(zip(visible, map(chr, chars)))


@dataclass
class AdaptedTokenizer:
    source_format: str
    fidelity: str
    fidelity_note: str
    vocab_size: int
    merge_count: int
    merge_ranks: dict[tuple[int, int], int]
    token_bytes: dict[int, bytes]
    pretokenizer: Callable[[str], list[str]] | None = None

    def _encode_bytes(self, data: bytes) -> list[int]:
        ids = list(data)
        while len(ids) >= 2:
            candidate = min(
                ((self.merge_ranks[pair], pair) for pair in zip(ids, ids[1:]) if pair in self.merge_ranks),
                default=None,
            )
            if candidate is None:
                break
            new_id, pair = candidate
            out: list[int] = []
            i = 0
            while i < len(ids):
                if i + 1 < len(ids) and (ids[i], ids[i + 1]) == pair:
                    out.append(new_id)
                    i += 2
                else:
                    out.append(ids[i])
                    i += 1
            ids = out
        return ids

    def encode(self, text: str) -> list[int]:
        chunks = self.pretokenizer(text) if self.pretokenizer else [text]
        return [token for chunk in chunks for token in self._encode_bytes(chunk.encode("utf-8"))]

    def decode(self, ids: list[int]) -> str:
        return b"".join(self.token_bytes[token] for token in ids).decode("utf-8")


def _regex_split(pattern: str) -> Callable[[str], list[str]]:
    if regex is None:
        raise RuntimeError("The 'regex' package is required by this tokenizer")
    compiled = regex.compile(pattern)
    return lambda text: compiled.findall(text)


def _from_symbol_bpe(document: dict, path: Path) -> AdaptedTokenizer:
    model = document["model"]
    vocab: dict[str, int] = model["vocab"]
    byte_for_symbol = {symbol: byte for byte, symbol in bytes_to_unicode().items()}
    token_bytes: dict[int, bytes] = {}
    for symbol, token_id in vocab.items():
        token_bytes[token_id] = bytes(byte_for_symbol[c] for c in symbol)

    ranks = {}
    for line in model["merges"]:
        left, right = line.split(" ")
        ranks[(vocab[left], vocab[right])] = vocab[left + right]

    meta = document.get("meta", {})
    variant = str(meta.get("variant", ""))
    if "naive" in path.name:
        pretok, fidelity, note = None, "exact", "Artifact specifies no pre-tokenization."
    elif "slayer-v2" in path.name:
        # README specifies cl100k + full digit runs, but the exact cl100k
        # expression is not serialized. This is enough to inspect the BPE core,
        # not enough to claim benchmark parity.
        pretok, fidelity, note = None, "core_only", "Exact cl100k pre-tokenizer is absent from JSON; BPE core is lossless but intended boundaries are not reproducible from the artifact alone."
    elif isinstance(meta.get("regex_pretok"), str):
        pretok, fidelity, note = _regex_split(meta["regex_pretok"]), "exact", "Exact pre-tokenizer regex is serialized in artifact metadata."
    elif variant == "fast":
        pretok, fidelity, note = _regex_split(GPT2_PATTERN), "documented", "README identifies GPT-2 pre-tokenization, but the exact expression is not serialized."
    else:
        pretok, fidelity, note = None, "core_only", "Pre-tokenization is not fully specified."
    return AdaptedTokenizer("symbol_bpe", fidelity, note, len(vocab), len(ranks), ranks, token_bytes, pretok)


def _from_integer_bpe(document: dict) -> AdaptedTokenizer:
    merges = document.get("merges") or document.get("reguly_merge")
    vocab = document["vocab"]
    ranks = {(int(left), int(right)): int(new) for left, right, new in merges}
    # Merge triples are the authoritative lossless representation. Some early
    # Kasia artifacts rendered invalid standalone UTF-8 bytes as U+FFFD in
    # ``vocab``; reconstructing recursively avoids inheriting that display loss.
    token_bytes = {token_id: bytes([token_id]) for token_id in range(256)}
    for left, right, new in merges:
        token_bytes[int(new)] = token_bytes[int(left)] + token_bytes[int(right)]

    pattern = document.get("pretokenizer_regex")
    if pattern:
        pretok, fidelity, note = _regex_split(pattern), "exact", "Pre-tokenizer regex is serialized in the artifact."
    else:
        pretok, fidelity, note = None, "exact", "Artifact defines raw-stream byte BPE without pre-tokenization."
    return AdaptedTokenizer("integer_bpe", fidelity, note, len(vocab), len(ranks), ranks, token_bytes, pretok)


def _from_vocab_export(document: dict) -> AdaptedTokenizer:
    inverse = {symbol: byte for byte, symbol in bytes_to_unicode().items()}
    vocab: dict[str, int] = document["token_to_id"]
    token_bytes = {token_id: bytes(inverse[c] for c in symbol) for symbol, token_id in vocab.items()}
    ranks = {(int(left), int(right)): int(new) for left, right, new in document["merges"]}
    return AdaptedTokenizer(
        "vocab_export", "core_only",
        "Vocabulary and merge ranks are complete, but the intended Polish regex pre-tokenizer is documented only in the write-up, not serialized in JSON.",
        len(vocab), len(ranks), ranks, token_bytes, None,
    )


def load_custom_tokenizer(path: str | Path) -> AdaptedTokenizer:
    path = Path(path)
    document = json.loads(path.read_text(encoding="utf-8"))
    return load_custom_tokenizer_document(document, path)


def load_custom_tokenizer_document(document: dict, source_path: str | Path) -> AdaptedTokenizer:
    """Load an artifact already parsed from JSON, retaining its source filename hints."""
    path = Path(source_path)
    if isinstance(document.get("model"), dict) and isinstance(document["model"].get("merges"), list):
        return _from_symbol_bpe(document, path)
    if "token_to_id" in document and "merges" in document:
        return _from_vocab_export(document)
    if ("merges" in document or "reguly_merge" in document) and "vocab" in document:
        return _from_integer_bpe(document)
    raise ValueError(f"Unsupported custom tokenizer schema: {path}")