| """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<option>\n" + canonical(o.model_dump()) + "\n</option>" 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)) |
|
|
