"""Audited segmented formatting; tokenizer is supplied by the backend. Derived from Apache-2.0 Decision 2.0 Lux native segments/encode. See NOTICE. Labels, provenance and rationale are never accepted by this interface. """ from __future__ import annotations import json from dataclasses import dataclass from typing import Protocol from .contracts import DecisionInput def canonical(value: object) -> str: return json.dumps(value, sort_keys=True, ensure_ascii=False, separators=(",", ":"), allow_nan=False) class Tokenizer(Protocol): def encode(self, text: str, *, add_special_tokens: bool = False) -> list[int]: ... def segments(item: DecisionInput) -> tuple[str, list[str], str]: state = item.state if isinstance(item.state, str) else canonical(item.state) prefix = f"Context:\n{state}\n\nTask type: {item.task_type}\nQuestion:\n{item.instructions}\nOptions:" options = ["\n" for o in item.options] suffix = "\n\nSelect the single option best supported by the context and instructions.\nDecision:" return prefix, options, suffix @dataclass(frozen=True) class EncodedInput: ids: tuple[int, ...] candidate_positions: tuple[int, ...] query_position: int keys: tuple[str, ...] def encode(item: DecisionInput, tokenizer: Tokenizer, max_length: int) -> EncodedInput: prefix, options, suffix = segments(item) ids = list(tokenizer.encode(prefix, add_special_tokens=False)) positions = [] for option in options: part = tokenizer.encode(option, add_special_tokens=False) if not part: raise ValueError("empty tokenized option") ids.extend(part) positions.append(len(ids) - 1) tail = tokenizer.encode(suffix, add_special_tokens=False) if not tail: raise ValueError("empty tokenized query") ids.extend(tail) if len(ids) > max_length: raise ValueError(f"rendered input has {len(ids)} tokens; limit is {max_length}; no truncation") return EncodedInput(tuple(ids), tuple(positions), len(ids) - 1, tuple(o.key for o in item.options))