tokenizer_utils.py

"""Shared construction and loading helpers for the project's tokenizer."""

from __future__ import annotations

from dataclasses import dataclass, field
import json
from pathlib import Path
import re
from typing import Any, Iterable


SPECIAL_TOKENS = [
    "<|pad|>",
    "<|bos|>",
    "<|eos|>",
    "<|unk|>",
    "<|endoftext|>",
]
EOT_ID = SPECIAL_TOKENS.index("<|endoftext|>")
ARITHMETIC_TOKENS = ("+", "-", "*", "/", "=", "(", ")")
MAX_PLACE_ID = 64
PLACE_OVERFLOW_ID = MAX_PLACE_ID + 1
PLACE_VOCAB_SIZE = PLACE_OVERFLOW_ID + 1
RESULT_ROLE_ID = 10
SPACE_ROLE_ID = 11
ROLE_VOCAB_SIZE = SPACE_ROLE_ID + 1
MAX_OPERAND_ROLES = 9


@dataclass(frozen=True)
class FusionEncoding:
    ids: list[int]
    place_ids: list[int]
    role_ids: list[int]
    tokens: list[str] = field(default_factory=list)

    @property
    def input_ids(self) -> list[int]:
        return self.ids

    def __len__(self) -> int:
        return len(self.ids)

    def __iter__(self):
        return iter(self.ids)

    def __post_init__(self) -> None:
        if not (len(self.ids) == len(self.place_ids) == len(self.role_ids)):
            raise ValueError("Fusion tokenizer streams must have equal length")


def build_tokenizer() -> Any:
    """Build a byte-level BPE tokenizer with explicit lossless boundaries."""
    from tokenizers import Regex, Tokenizer, decoders, models, pre_tokenizers

    tokenizer = Tokenizer(models.BPE(unk_token="<|unk|>"))
    tokenizer.pre_tokenizer = pre_tokenizers.Sequence(
        [
            pre_tokenizers.Split(
                Regex(r"\s+|\d|[+\-*/=()]|[^\s\d+\-*/=()]+"),
                behavior="isolated",
            ),
            pre_tokenizers.ByteLevel(add_prefix_space=False, use_regex=False),
        ]
    )
    tokenizer.decoder = decoders.ByteLevel()
    return tokenizer


class FusionTokenizer:
    """Runtime wrapper adding LSD-first digit streams to a trained BPE tokenizer."""

    _digit_span_re = re.compile(r"\d+")

    def __init__(self, tokenizer: Any):
        self.tokenizer = tokenizer
        self._digit_token_ids = frozenset(
            token_id
            for digit in "0123456789"
            if (token_id := self.tokenizer.token_to_id(digit)) is not None
        )
        self._digit_id_to_text = {
            int(self.tokenizer.token_to_id(digit)): digit
            for digit in "0123456789"
            if self.tokenizer.token_to_id(digit) is not None
        }
        self._equals_id = self.tokenizer.token_to_id("=")
        self._special_token_ids = frozenset(
            token_id
            for token in SPECIAL_TOKENS
            if (token_id := self.tokenizer.token_to_id(token)) is not None
        )
        if len(self._digit_token_ids) != 10:
            raise ValueError("Tokenizer vocabulary must contain atomic digit tokens 0-9")
        if self._equals_id is None:
            raise ValueError("Tokenizer vocabulary must contain an atomic '=' token")

    def __getattr__(self, name: str) -> Any:
        return getattr(self.tokenizer, name)

    @property
    def digit_token_ids(self) -> frozenset[int]:
        return self._digit_token_ids

    @property
    def special_token_ids(self) -> frozenset[int]:
        return self._special_token_ids

    def get_vocab_size(self, with_added_tokens: bool = True) -> int:
        return int(self.tokenizer.get_vocab_size(with_added_tokens=with_added_tokens))

    def get_vocab(self, with_added_tokens: bool = True) -> dict[str, int]:
        return self.tokenizer.get_vocab(with_added_tokens=with_added_tokens)

    def token_to_id(self, token: str) -> int | None:
        return self.tokenizer.token_to_id(token)

    def id_to_token(self, token_id: int) -> str | None:
        return self.tokenizer.id_to_token(int(token_id))

    @classmethod
    def _reverse_digit_spans(cls, text: str) -> str:
        return cls._digit_span_re.sub(lambda match: match.group(0)[::-1], text)

    def _decode_token_piece(self, token_id: int) -> str:
        return self.tokenizer.decode([int(token_id)], skip_special_tokens=False)

    @staticmethod
    def _is_equation_whitespace(piece: str) -> bool:
        return bool(piece) and piece.isspace() and "\n" not in piece and "\r" not in piece

    def _is_equation_piece(self, token_id: int, piece: str) -> bool:
        if token_id in self._special_token_ids:
            return False
        if token_id in self._digit_token_ids:
            return True
        if self._is_equation_whitespace(piece):
            return True
        return len(piece) == 1 and piece in set(ARITHMETIC_TOKENS)

    def _annotate_equation_span(
        self,
        ids: list[int],
        pieces: list[str],
        start: int,
        end: int,
        role_ids: list[int],
    ) -> None:
        equals_positions = [
            index
            for index in range(start, end)
            if ids[index] == self._equals_id
        ]
        if len(equals_positions) != 1:
            return
        equals_position = equals_positions[0]

        digit_runs: list[tuple[int, int]] = []
        index = start
        while index < end:
            if ids[index] not in self._digit_token_ids:
                index += 1
                continue
            run_start = index
            while index < end and ids[index] in self._digit_token_ids:
                index += 1
            digit_runs.append((run_start, index))

        operand_runs = [(a, b) for a, b in digit_runs if b <= equals_position]
        result_runs = [(a, b) for a, b in digit_runs if a > equals_position]
        if not operand_runs or not result_runs or len(operand_runs) > MAX_OPERAND_ROLES:
            return

        for index in range(start, end):
            if self._is_equation_whitespace(pieces[index]):
                role_ids[index] = SPACE_ROLE_ID

        for role, (run_start, run_end) in enumerate(operand_runs, start=1):
            for index in range(run_start, run_end):
                role_ids[index] = role
        for run_start, run_end in result_runs:
            for index in range(run_start, run_end):
                role_ids[index] = RESULT_ROLE_ID

    def annotate_ids(self, ids: Iterable[int]) -> tuple[list[int], list[int]]:
        input_ids = [int(token_id) for token_id in ids]
        place_ids = [0] * len(input_ids)
        role_ids = [0] * len(input_ids)
        pieces = [self._decode_token_piece(token_id) for token_id in input_ids]

        index = 0
        while index < len(input_ids):
            if input_ids[index] not in self._digit_token_ids:
                index += 1
                continue
            run_start = index
            while index < len(input_ids) and input_ids[index] in self._digit_token_ids:
                offset = index - run_start + 1
                place_ids[index] = min(offset, PLACE_OVERFLOW_ID)
                index += 1

        span_start: int | None = None
        for index, (token_id, piece) in enumerate(zip(input_ids, pieces, strict=True)):
            if self._is_equation_piece(token_id, piece):
                if span_start is None:
                    span_start = index
                continue
            if span_start is not None:
                self._annotate_equation_span(input_ids, pieces, span_start, index, role_ids)
                span_start = None
        if span_start is not None:
            self._annotate_equation_span(input_ids, pieces, span_start, len(input_ids), role_ids)

        return place_ids, role_ids

    def encode(self, text: str, *args, **kwargs) -> FusionEncoding:
        transformed = self._reverse_digit_spans(text)
        encoding = self.tokenizer.encode(transformed, *args, **kwargs)
        ids = [int(token_id) for token_id in encoding.ids]
        place_ids, role_ids = self.annotate_ids(ids)
        return FusionEncoding(
            ids=ids,
            place_ids=place_ids,
            role_ids=role_ids,
            tokens=list(getattr(encoding, "tokens", [])),
        )

    def encode_batch(self, texts: list[str], *args, **kwargs) -> list[FusionEncoding]:
        return [self.encode(text, *args, **kwargs) for text in texts]

    def decode(
        self,
        token_ids: Iterable[int],
        skip_special_tokens: bool = True,
    ) -> str:
        pieces: list[str] = []
        text_ids: list[int] = []
        digit_buffer: list[str] = []

        def flush_text() -> None:
            if text_ids:
                pieces.append(
                    self.tokenizer.decode(
                        text_ids,
                        skip_special_tokens=skip_special_tokens,
                    )
                )
                text_ids.clear()

        def flush_digits() -> None:
            if digit_buffer:
                pieces.extend(reversed(digit_buffer))
                digit_buffer.clear()

        for raw_id in token_ids:
            token_id = int(raw_id)
            if token_id in self._digit_token_ids:
                flush_text()
                digit_buffer.append(self._digit_id_to_text[token_id])
                continue

            flush_digits()
            text_ids.append(token_id)

        flush_text()
        flush_digits()
        return "".join(pieces)


def build_trainer(vocab_size: int, min_frequency: int) -> Any:
    from tokenizers import pre_tokenizers, trainers

    return trainers.BpeTrainer(
        vocab_size=vocab_size,
        min_frequency=min_frequency,
        special_tokens=SPECIAL_TOKENS,
        initial_alphabet=pre_tokenizers.ByteLevel.alphabet(),
    )


def tokenizer_files(tokenizer_dir: Path) -> tuple[Path, Path, Path]:
    return (
        tokenizer_dir / "tokenizer.json",
        tokenizer_dir / "vocab.json",
        tokenizer_dir / "merges.txt",
    )


def validate_tokenizer(tokenizer_dir: Path) -> None:
    tokenizer_json, vocab_path, merges_path = tokenizer_files(tokenizer_dir)
    if not tokenizer_json.exists():
        raise FileNotFoundError(
            f"Missing {tokenizer_json}. Retrain with train_tokenizer.py so the "
            "whitespace and digit boundary rules are preserved."
        )
    if vocab_path.exists():
        with vocab_path.open("r", encoding="utf-8") as f:
            vocab = json.load(f)
    else:
        with tokenizer_json.open("r", encoding="utf-8") as f:
            tokenizer_data = json.load(f)
        vocab = tokenizer_data.get("model", {}).get("vocab")
        if not isinstance(vocab, dict):
            raise FileNotFoundError(f"Missing vocab.json and no embedded vocab in {tokenizer_json}")

    max_id = max(vocab.values())
    if max_id > 65_535:
        raise ValueError(f"Tokenizer max id {max_id} does not fit in uint16")
    if vocab.get("<|endoftext|>") != EOT_ID:
        raise ValueError(
            f"Expected <|endoftext|> id {EOT_ID}, "
            f"got {vocab.get('<|endoftext|>')}"
        )
    missing = [
        token
        for token in (*[str(value) for value in range(10)], *ARITHMETIC_TOKENS)
        if token not in vocab
    ]
    if missing:
        raise ValueError(f"Tokenizer missing required atomic tokens: {missing}")


def load_tokenizer(tokenizer_dir: Path) -> Any:
    from tokenizers import Tokenizer

    validate_tokenizer(tokenizer_dir)
    tokenizer_json, _, _ = tokenizer_files(tokenizer_dir)
    return FusionTokenizer(Tokenizer.from_file(str(tokenizer_json)))