src/explicit_learning/training/answers.py

"""Strict final-answer parsing and deterministic answer normalisation."""

from __future__ import annotations

import re
import unicodedata
from collections.abc import Mapping, Sequence
from dataclasses import dataclass
from fractions import Fraction
from typing import Any

ANSWER_OPEN = "<answer>"
ANSWER_CLOSE = "</answer>"
UNANSWERABLE_TOKEN = "<UNANSWERABLE>"

# Anything that looks like an answer tag but is not one of the two exact tags
# is malformed.  This rejects case variants, attributes, and partially relaxed
# XML rather than silently changing the public output contract.
_ANSWER_LIKE = re.compile(r"<\s*/?\s*answer\b[^>]*>", flags=re.IGNORECASE)
_SHORT_TEXT_PUNCT = re.compile(r"[^\w\s]", flags=re.UNICODE)
_SHORT_TEXT_SPACE = re.compile(r"\s+")
_ARTICLES = frozenset({"a", "an", "the"})


@dataclass(frozen=True)
class AnswerParse:
    """Result of checking the exact ``<answer>...</answer>`` contract."""

    valid: bool
    content: str | None
    error: str | None
    reasoning_prefix: str = ""

    def require_content(self) -> str:
        if not self.valid or self.content is None:
            raise ValueError(self.error or "invalid answer")
        return self.content


class NormalizationError(ValueError):
    """Raised when an answer cannot be normalized for its declared type."""


def parse_answer(response: str) -> AnswerParse:
    """Parse exactly one non-empty final answer tag at response end.

    Trailing whitespace is permitted.  Multiple, nested, case-variant,
    attributed, unmatched, or trailing-text tags are rejected.  The function
    returns a structured failure instead of raising so reward code can trace the
    precise parser branch.
    """

    if not isinstance(response, str):
        return AnswerParse(False, None, "response_not_string")
    if "\x00" in response:
        return AnswerParse(False, None, "nul_byte")

    answer_like = list(_ANSWER_LIKE.finditer(response))
    if any(match.group(0) not in {ANSWER_OPEN, ANSWER_CLOSE} for match in answer_like):
        return AnswerParse(False, None, "malformed_answer_tag")
    if response.count(ANSWER_OPEN) != 1 or response.count(ANSWER_CLOSE) != 1:
        if response.count(ANSWER_OPEN) + response.count(ANSWER_CLOSE) == 0:
            return AnswerParse(False, None, "missing_answer_tag")
        return AnswerParse(False, None, "multiple_or_unmatched_answer_tags")

    start = response.find(ANSWER_OPEN)
    close = response.find(ANSWER_CLOSE)
    if close < start:
        return AnswerParse(False, None, "closing_tag_before_opening_tag")
    content_start = start + len(ANSWER_OPEN)
    content = response[content_start:close]
    if ANSWER_OPEN in content or ANSWER_CLOSE in content:
        return AnswerParse(False, None, "nested_answer_tag")
    if not content.strip():
        return AnswerParse(False, None, "empty_answer")

    end = close + len(ANSWER_CLOSE)
    if response[end:].strip():
        return AnswerParse(False, None, "trailing_text")
    return AnswerParse(True, content.strip(), None, response[:start])


def _choice_records(choices: Sequence[Mapping[str, Any]] | None) -> list[tuple[str, str]]:
    records: list[tuple[str, str]] = []
    for choice in choices or ():
        if "key" not in choice or "text" not in choice:
            raise NormalizationError("choice requires key and text")
        records.append((str(choice["key"]).strip(), str(choice["text"]).strip()))
    return records


def normalize_short_text(value: Any) -> str:
    """Normalizer-v1 short-text branch: case, punctuation, articles, whitespace."""

    text = unicodedata.normalize("NFKC", str(value)).casefold()
    text = _SHORT_TEXT_PUNCT.sub(" ", text)
    tokens = [token for token in _SHORT_TEXT_SPACE.split(text.strip()) if token]
    return " ".join(token for token in tokens if token not in _ARTICLES)


# Largest decimal-exponent magnitude we will let ``Fraction`` materialize.
# A model prediction such as ``1e100000000`` would otherwise make the
# ``Fraction`` constructor build a 100-million-digit integer -- CPU-bound
# big-int arithmetic that wedges the reward path for minutes (looks like a
# stall with one GPU idle, but is live compute, not a deadlock). Values up to
# ~1e10000 construct in milliseconds and are still rejected downstream by
# ``_format_rational`` (Python 3.12's 4300-digit int->str cap, CVE-2020-10735),
# so this guard only intercepts the truly degenerate exponents that would hang
# construction itself; such predictions are scored as invalid (zero-reward).
_MAX_RATIONAL_EXPONENT = 10000
_RATIONAL_EXPONENT = re.compile(r"[eE]([-+]?\d+)")


def _fraction(value: Any) -> Fraction:
    if isinstance(value, bool):
        raise NormalizationError("boolean is not numeric")
    if isinstance(value, Fraction):
        return value
    if isinstance(value, int):
        return Fraction(value)
    if isinstance(value, float):
        # Decimal text avoids the implementation-specific binary expansion.
        return Fraction(str(value))
    text = unicodedata.normalize("NFKC", str(value)).strip().replace(",", "")
    # Bound degenerate huge-magnitude text BEFORE constructing the Fraction,
    # so a prediction like ``1e100000000`` raises instead of wedging on the
    # giant-integer allocation. Both reward paths catch NormalizationError and
    # score the completion as an incorrect (zero-reward) answer.
    exp_match = _RATIONAL_EXPONENT.search(text)
    if exp_match is not None and abs(int(exp_match.group(1))) > _MAX_RATIONAL_EXPONENT:
        raise NormalizationError(f"numeric answer exponent too large: {value!r}")
    try:
        return Fraction(text)
    except (ValueError, ZeroDivisionError) as exc:
        raise NormalizationError(f"not an exact rational: {value!r}") from exc


def _format_rational(number: Fraction) -> str:
    """Stringify a ``Fraction``, rejecting integers too large to convert.

    Python 3.12 caps int->str conversion at 4300 digits (CVE-2020-10735). A
    model prediction such as ``1e5000`` parses (via :func:`_fraction`) into a
    >4300-digit numerator; ``str(number.numerator)`` then raises ``ValueError``.
    Treat such degenerate values as un-normalizable so the reward path scores
    them as an invalid/incorrect answer instead of crashing the whole run.
    """
    try:
        if number.denominator == 1:
            return str(number.numerator)
        return f"{number.numerator}/{number.denominator}"
    except ValueError as exc:
        raise NormalizationError("numeric answer has too many digits to represent") from exc


def normalize_answer(
    value: Any,
    answer_type: str,
    *,
    choices: Sequence[Mapping[str, Any]] | None = None,
) -> str:
    """Normalize one prediction or gold value using deterministic v1 branches."""

    text = str(value).strip()
    if text == UNANSWERABLE_TOKEN:
        return UNANSWERABLE_TOKEN

    if answer_type == "multiple_choice":
        records = _choice_records(choices)
        key_matches = [key for key, _ in records if text == key]
        if len(key_matches) == 1:
            return key_matches[0]
        if len(key_matches) > 1:
            raise NormalizationError("duplicate matching choice key")
        normalized_text = normalize_short_text(text)
        text_matches = [
            key
            for key, choice_text in records
            if normalize_short_text(choice_text) == normalized_text
        ]
        if len(text_matches) == 1:
            return text_matches[0]
        if not text_matches:
            raise NormalizationError("answer does not map to a choice")
        raise NormalizationError("choice text mapping is ambiguous")

    if answer_type == "integer":
        number = _fraction(value)
        if number.denominator != 1:
            raise NormalizationError("integer answer has a fractional value")
        return _format_rational(number)
    if answer_type == "number":
        number = _fraction(value)
        return _format_rational(number)
    if answer_type == "expression":
        try:
            number = _fraction(value)
        except NormalizationError:
            expression = "".join(unicodedata.normalize("NFKC", text).split())
            if not expression:
                raise NormalizationError("empty expression") from None
            return expression
        return _format_rational(number)
    if answer_type == "boolean":
        if isinstance(value, bool):
            return "true" if value else "false"
        lowered = unicodedata.normalize("NFKC", text).casefold()
        mapping = {
            "true": "true",
            "yes": "true",
            "false": "false",
            "no": "false",
        }
        try:
            return mapping[lowered]
        except KeyError:
            raise NormalizationError("boolean answer must be true/false or yes/no") from None
    if answer_type == "short_text":
        normalized = normalize_short_text(value)
        if not normalized:
            raise NormalizationError("empty normalized short-text answer")
        return normalized
    raise NormalizationError(f"unsupported answer_type: {answer_type!r}")


def answers_equal(
    prediction: Any,
    gold: Any,
    answer_type: str,
    *,
    choices: Sequence[Mapping[str, Any]] | None = None,
) -> bool:
    """Return normalized equality; invalid predictions are simply incorrect."""

    try:
        predicted = normalize_answer(prediction, answer_type, choices=choices)
        expected = normalize_answer(gold, answer_type, choices=choices)
    except NormalizationError:
        return False
    return predicted == expected