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from __future__ import annotations
import bisect
import re
from dataclasses import dataclass
from pathlib import Path
from typing import Iterable, Mapping, Sequence
from strata.tokenization.special_tokens import DEFAULT_SPECIAL_TOKENS
from strata.types import AnchoredSpan
class TokenizerError(ValueError):
"""Raised when tokenizer inputs or outputs violate STRATA invariants."""
@dataclass(frozen=True, slots=True)
class TokenSpan:
"""A token with reversible text offsets."""
token_index: int
token_id: int
piece: str
byte_start: int
byte_end: int
char_start: int
char_end: int
is_special: bool = False
@property
def byte_length(self) -> int:
return self.byte_end - self.byte_start
@property
def char_length(self) -> int:
return self.char_end - self.char_start
@property
def anchor(self) -> AnchoredSpan:
return AnchoredSpan.from_offsets(
char_start=self.char_start,
char_end=self.char_end,
byte_start=self.byte_start,
byte_end=self.byte_end,
)
@dataclass(frozen=True, slots=True)
class WordSpan:
"""Whitespace-delimited surface word span used for graph alignment."""
word_index: int
text: str
byte_start: int
byte_end: int
char_start: int
char_end: int
@property
def anchor(self) -> AnchoredSpan:
return AnchoredSpan.from_offsets(
char_start=self.char_start,
char_end=self.char_end,
byte_start=self.byte_start,
byte_end=self.byte_end,
)
@dataclass(frozen=True, slots=True)
class LatticeEncoding:
"""Encoded text plus the span lattice needed by graph supervision."""
text: str
input_ids: tuple[int, ...]
token_spans: tuple[TokenSpan, ...]
word_spans: tuple[WordSpan, ...]
char_to_byte: tuple[int, ...]
tokenizer_name: str
@property
def attention_mask(self) -> tuple[int, ...]:
return tuple(1 for _ in self.input_ids)
@property
def byte_length(self) -> int:
return len(self.text.encode("utf-8"))
@property
def char_length(self) -> int:
return len(self.text)
def visible_tokens(self, *, byte_end: int) -> tuple[TokenSpan, ...]:
"""Return non-special tokens fully visible at a prefix byte boundary."""
if byte_end < 0 or byte_end > self.byte_length:
raise TokenizerError(
f"byte_end must be within [0, {self.byte_length}], got {byte_end}"
)
return tuple(
span
for span in self.token_spans
if not span.is_special and span.byte_end <= byte_end
)
def token_indices_for_anchor(self, anchor: AnchoredSpan) -> tuple[int, ...]:
"""Return token indices whose byte spans overlap an anchored graph node."""
return tuple(
span.token_index
for span in self.token_spans
if not span.is_special
and span.byte_start < anchor.byte.end
and anchor.byte.start < span.byte_end
)
def _validate_special_tokens(special_tokens: Sequence[str]) -> None:
if len(set(special_tokens)) != len(special_tokens):
raise TokenizerError("special tokens must be unique")
for token in special_tokens:
if not token or not token.startswith("<") or not token.endswith(">"):
raise TokenizerError(
f"special token {token!r} must use angle-bracket namespace"
)
def _char_to_byte_offsets(text: str) -> tuple[int, ...]:
offsets = [0]
byte_position = 0
for char in text:
byte_position += len(char.encode("utf-8"))
offsets.append(byte_position)
return tuple(offsets)
def _char_span_to_byte_span(
char_start: int,
char_end: int,
char_to_byte: Sequence[int],
) -> tuple[int, int]:
text_length = len(char_to_byte) - 1
if char_start < 0 or char_end < char_start or char_end > text_length:
raise TokenizerError(
f"invalid char span [{char_start}, {char_end}) for text length "
f"{text_length}"
)
return char_to_byte[char_start], char_to_byte[char_end]
def _byte_span_to_char_span(
byte_start: int,
byte_end: int,
char_to_byte: Sequence[int],
) -> tuple[int, int]:
if byte_start < 0 or byte_end < byte_start or byte_end > char_to_byte[-1]:
raise TokenizerError(
f"invalid byte span [{byte_start}, {byte_end}) for byte length "
f"{char_to_byte[-1]}"
)
if byte_start == byte_end:
char = bisect.bisect_right(char_to_byte, byte_start) - 1
char = max(0, min(char, len(char_to_byte) - 1))
return char, char
char_start = bisect.bisect_right(char_to_byte, byte_start) - 1
char_end = bisect.bisect_left(char_to_byte, byte_end)
if char_end <= char_start:
char_end = char_start + 1
return char_start, min(char_end, len(char_to_byte) - 1)
_WORD_PATTERN = re.compile(r"\S+")
def _word_spans(text: str, char_to_byte: Sequence[int]) -> tuple[WordSpan, ...]:
spans: list[WordSpan] = []
for word_index, match in enumerate(_WORD_PATTERN.finditer(text)):
byte_start, byte_end = _char_span_to_byte_span(
match.start(), match.end(), char_to_byte
)
spans.append(
WordSpan(
word_index=word_index,
text=match.group(0),
byte_start=byte_start,
byte_end=byte_end,
char_start=match.start(),
char_end=match.end(),
)
)
return tuple(spans)
class ByteLatticeTokenizer:
"""UTF-8 byte tokenizer that always roundtrips and preserves spans.
This tokenizer is intentionally simple and production-safe. It is a reliable
fallback before a trained SentencePiece model exists, and it is useful for
debugging graph alignment because every byte position is represented.
"""
name = "strata-byte-lattice"
def __init__(self, special_tokens: Sequence[str] = DEFAULT_SPECIAL_TOKENS) -> None:
_validate_special_tokens(special_tokens)
self.special_tokens = tuple(special_tokens)
self.special_to_id = {token: idx for idx, token in enumerate(special_tokens)}
self.id_to_special = {idx: token for token, idx in self.special_to_id.items()}
self.byte_offset = len(self.special_tokens)
self.vocab_size = self.byte_offset + 256
@property
def pad_token_id(self) -> int:
return self.special_to_id["<PAD>"]
@property
def bos_token_id(self) -> int:
return self.special_to_id["<BOS>"]
@property
def eos_token_id(self) -> int:
return self.special_to_id["<EOS>"]
def token_to_id(self, token: str) -> int:
if token in self.special_to_id:
return self.special_to_id[token]
if re.fullmatch(r"<0x[0-9A-Fa-f]{2}>", token):
return self.byte_offset + int(token[3:5], 16)
raise TokenizerError(f"unknown token {token!r}")
def id_to_token(self, token_id: int) -> str:
if token_id in self.id_to_special:
return self.id_to_special[token_id]
if self.byte_offset <= token_id < self.byte_offset + 256:
return f"<0x{token_id - self.byte_offset:02X}>"
raise TokenizerError(f"token id {token_id} is outside vocab size {self.vocab_size}")
def encode(
self,
text: str,
*,
add_bos: bool = False,
add_eos: bool = False,
) -> LatticeEncoding:
if not isinstance(text, str):
raise TypeError(f"text must be str, got {type(text).__name__}")
char_to_byte = _char_to_byte_offsets(text)
data = text.encode("utf-8")
input_ids: list[int] = []
token_spans: list[TokenSpan] = []
def append_special(token: str) -> None:
token_id = self.special_to_id[token]
token_spans.append(
TokenSpan(
token_index=len(input_ids),
token_id=token_id,
piece=token,
byte_start=0,
byte_end=0,
char_start=0,
char_end=0,
is_special=True,
)
)
input_ids.append(token_id)
if add_bos:
append_special("<BOS>")
for byte_index, byte_value in enumerate(data):
char_start, char_end = _byte_span_to_char_span(
byte_index, byte_index + 1, char_to_byte
)
token_id = self.byte_offset + byte_value
token_spans.append(
TokenSpan(
token_index=len(input_ids),
token_id=token_id,
piece=f"<0x{byte_value:02X}>",
byte_start=byte_index,
byte_end=byte_index + 1,
char_start=char_start,
char_end=char_end,
)
)
input_ids.append(token_id)
if add_eos:
append_special("<EOS>")
return LatticeEncoding(
text=text,
input_ids=tuple(input_ids),
token_spans=tuple(token_spans),
word_spans=_word_spans(text, char_to_byte),
char_to_byte=char_to_byte,
tokenizer_name=self.name,
)
def decode(
self,
input_ids: Iterable[int],
*,
skip_special_tokens: bool = True,
errors: str = "strict",
) -> str:
data = bytearray()
parts: list[str] = []
def flush_data() -> None:
if data:
parts.append(bytes(data).decode("utf-8", errors=errors))
data.clear()
for token_id in input_ids:
if self.byte_offset <= token_id < self.byte_offset + 256:
data.append(token_id - self.byte_offset)
elif token_id in self.id_to_special:
if not skip_special_tokens:
flush_data()
parts.append(self.id_to_special[token_id])
else:
raise TokenizerError(
f"token id {token_id} is outside vocab size {self.vocab_size}"
)
flush_data()
return "".join(parts)
class SentencePieceLatticeTokenizer:
"""SentencePiece tokenizer wrapper that preserves proto-provided offsets."""
name = "strata-sentencepiece-lattice"
def __init__(
self,
model_file: str | Path,
*,
special_tokens: Sequence[str] = DEFAULT_SPECIAL_TOKENS,
require_exact_roundtrip: bool = True,
) -> None:
try:
import sentencepiece as spm
except ImportError as exc: # pragma: no cover - dependency declared.
raise TokenizerError("sentencepiece is required for this tokenizer") from exc
_validate_special_tokens(special_tokens)
self.model_file = Path(model_file)
if not self.model_file.exists():
raise TokenizerError(f"SentencePiece model does not exist: {self.model_file}")
self.processor = spm.SentencePieceProcessor(model_file=str(self.model_file))
self.special_tokens = tuple(special_tokens)
self.special_to_id = {token: idx for idx, token in enumerate(special_tokens)}
self.id_to_special = {idx: token for token, idx in self.special_to_id.items()}
self.sp_offset = len(self.special_tokens)
self.sp_vocab_size = int(self.processor.vocab_size())
self.vocab_size = self.sp_offset + self.sp_vocab_size
self.require_exact_roundtrip = require_exact_roundtrip
@property
def pad_token_id(self) -> int:
return self.special_to_id["<PAD>"]
@property
def bos_token_id(self) -> int:
return self.special_to_id["<BOS>"]
@property
def eos_token_id(self) -> int:
return self.special_to_id["<EOS>"]
def encode(
self,
text: str,
*,
add_bos: bool = False,
add_eos: bool = False,
) -> LatticeEncoding:
if not isinstance(text, str):
raise TypeError(f"text must be str, got {type(text).__name__}")
char_to_byte = _char_to_byte_offsets(text)
# SentencePiece 0.2.2 removed the legacy immutable-proto wrapper. Its
# offset mapping exposes the same IDs, pieces, and character spans
# without adding a protobuf runtime dependency.
try:
encoded = self.processor.Encode(text, return_type="offset_mapping")
proto_pieces = zip(
encoded["ids"], encoded["pieces"], encoded["offsets"], strict=True
)
except (TypeError, ValueError): # pragma: no cover - pre-0.2 compatibility.
proto = self.processor.EncodeAsImmutableProto(text)
proto_pieces = (
(piece.id, piece.piece, (piece.begin, piece.end)) for piece in proto.pieces
)
input_ids: list[int] = []
token_spans: list[TokenSpan] = []
def append_special(token: str) -> None:
token_id = self.special_to_id[token]
token_spans.append(
TokenSpan(
token_index=len(input_ids),
token_id=token_id,
piece=token,
byte_start=0,
byte_end=0,
char_start=0,
char_end=0,
is_special=True,
)
)
input_ids.append(token_id)
if add_bos:
append_special("<BOS>")
for piece_id, piece_text, offsets in proto_pieces:
char_start, char_end = (int(value) for value in offsets)
byte_start, byte_end = _char_span_to_byte_span(
char_start, char_end, char_to_byte
)
token_id = self.sp_offset + int(piece_id)
token_spans.append(
TokenSpan(
token_index=len(input_ids),
token_id=token_id,
piece=str(piece_text),
byte_start=byte_start,
byte_end=byte_end,
char_start=char_start,
char_end=char_end,
)
)
input_ids.append(token_id)
if add_eos:
append_special("<EOS>")
if self.require_exact_roundtrip:
decoded = self.decode(input_ids)
if decoded != text:
raise TokenizerError(
"SentencePiece model is not exact-roundtrip for this text; "
"train with identity normalization and preserved whitespace. "
f"decoded={decoded!r}, original={text!r}"
)
return LatticeEncoding(
text=text,
input_ids=tuple(input_ids),
token_spans=tuple(token_spans),
word_spans=_word_spans(text, char_to_byte),
char_to_byte=char_to_byte,
tokenizer_name=self.name,
)
def decode(
self,
input_ids: Iterable[int],
*,
skip_special_tokens: bool = True,
) -> str:
sp_ids: list[int] = []
parts: list[str] = []
def flush_sp_ids() -> None:
if sp_ids:
parts.append(self.processor.DecodeIds(sp_ids))
sp_ids.clear()
for token_id in input_ids:
if self.sp_offset <= token_id < self.sp_offset + self.sp_vocab_size:
sp_ids.append(token_id - self.sp_offset)
elif token_id in self.id_to_special:
if not skip_special_tokens:
flush_sp_ids()
parts.append(self.id_to_special[token_id])
else:
raise TokenizerError(
f"token id {token_id} is outside vocab size {self.vocab_size}"
)
flush_sp_ids()
return "".join(parts)
def special_token_ids(tokenizer: ByteLatticeTokenizer | SentencePieceLatticeTokenizer) -> Mapping[str, int]:
"""Return a copy of the tokenizer's reserved-token ID mapping."""
return dict(tokenizer.special_to_id)
|