"""Runtime-identical assistant reply-prose extraction.

The Context Switch turn-end watcher classifies agent replies after stripping fenced code
and tail-biasing the remainder (`docs/CONTEXT_SWITCH.md` §3.2). Training the classifier on
raw replies would therefore train it on text the runtime never sees, so the assistant-prose
slice runs its student text through this module, which is a deliberate line-by-line port of
``HeuristicSummary.contextSwitchReplyProse`` in ``Sources/NucleicCore/Intelligence.swift``.

The two implementations are pinned together by
``Tests/NucleicCoreTests/Fixtures/context-switch-prose.json``, which
``tests/test_prose_extract.py`` and ``ContextSwitchTests`` both assert against. Change one
side and the other side's test fails.

Two Swift behaviours need explicit modelling in Python:

* ``String.count``/``suffix`` count **grapheme clusters**, not code points, so the
  character limit is applied over :func:`graphemes` rather than ``len``. That segmentation
  is a pragmatic subset of UAX #29 (CRLF, combining marks, variation selectors, emoji
  ZWJ sequences, skin-tone modifiers, regional-indicator pairs) — the cases that actually
  occur in agent prose. Anything it does not model degrades to one cluster per scalar,
  which is what Python would have done anyway.
* ``CharacterSet.whitespacesAndNewlines`` is the Unicode ``White_Space`` property, whereas
  ``str.strip()`` also strips U+001C–U+001F. :data:`WHITESPACE` spells the Swift set out so
  a stray information separator in a reply cannot make the two extractions disagree.
"""

from __future__ import annotations

import unicodedata


DEFAULT_CHARACTER_LIMIT = 1_200

#: Unicode ``White_Space``, matching Swift's ``CharacterSet.whitespacesAndNewlines`` and
#: ``Character.isWhitespace``. Deliberately excludes U+001C–U+001F, which ``str.strip()``
#: would otherwise remove.
WHITESPACE = frozenset(
    [chr(code) for code in range(0x0009, 0x000E)]  # tab, LF, VT, FF, CR
    + [chr(code) for code in range(0x2000, 0x200B)]  # en quad through hair space
    + [
        " ",  # space
        "…",  # next line
        " ",  # no-break space
        " ",  # ogham space mark
        "
",  # line separator
        "
",  # paragraph separator
        " ",  # narrow no-break space
        " ",  # medium mathematical space
        "　",  # ideographic space
    ]
)

_ZERO_WIDTH_JOINER = "‍"
_EXTEND_CATEGORIES = frozenset({"Mn", "Me", "Mc"})


def _is_extend(character: str) -> bool:
    """Scalars that attach to the preceding grapheme cluster."""

    if character in ("︎", "️"):  # variation selectors 15/16
        return True
    if "\U000e0100" <= character <= "\U000e01ef":  # variation selectors supplement
        return True
    if "\U0001f3fb" <= character <= "\U0001f3ff":  # emoji skin-tone modifiers
        return True
    return unicodedata.category(character) in _EXTEND_CATEGORIES


def _is_regional_indicator(character: str) -> bool:
    return "\U0001f1e6" <= character <= "\U0001f1ff"


def graphemes(text: str) -> list[str]:
    """Segment ``text`` the way Swift's ``Character`` view does, for the cases we see."""

    clusters: list[str] = []
    index, length = 0, len(text)
    while index < length:
        base = text[index]
        index += 1
        if base == "\r" and index < length and text[index] == "\n":
            clusters.append("\r\n")
            index += 1
            continue
        if unicodedata.category(base) == "Cc":
            # Controls never combine with what follows; CR LF above is the one exception.
            clusters.append(base)
            continue
        cluster = base
        if (
            _is_regional_indicator(base)
            and index < length
            and _is_regional_indicator(text[index])
        ):
            cluster += text[index]
            index += 1
        while index < length:
            following = text[index]
            if _is_extend(following):
                cluster += following
                index += 1
            elif following == _ZERO_WIDTH_JOINER and index + 1 < length:
                cluster += following + text[index + 1]
                index += 2
            else:
                break
        clusters.append(cluster)
    return clusters


def trim(text: str) -> str:
    """``trimmingCharacters(in: .whitespacesAndNewlines)``."""

    start, end = 0, len(text)
    while start < end and text[start] in WHITESPACE:
        start += 1
    while end > start and text[end - 1] in WHITESPACE:
        end -= 1
    return text[start:end]


def strip_fenced_code(reply: str) -> str:
    """Drop Markdown fenced blocks, including an unclosed trailing fence.

    Half-streamed code is code, not evidence that the chat changed purpose. Prose outside
    the fences keeps its line structure so sentence boundaries survive.
    """

    fence: str | None = None
    prose_lines: list[str] = []
    for line in reply.split("\n"):
        trimmed = line.lstrip(" \t")
        if trimmed.startswith("```"):
            marker: str | None = "`"
        elif trimmed.startswith("~~~"):
            marker = "~"
        else:
            marker = None
        if fence is not None:
            if marker == fence:
                fence = None
            continue
        if marker is not None:
            fence = marker
            continue
        prose_lines.append(line)
    return "\n".join(prose_lines)


def reply_prose(reply: str, character_limit: int = DEFAULT_CHARACTER_LIMIT) -> str | None:
    """The exact text the runtime hands the purpose classifier, or ``None`` for no signal."""

    if character_limit <= 0:
        return None
    prose = trim(strip_fenced_code(reply))
    if not prose:
        return None

    clusters = graphemes(prose)
    if len(clusters) <= character_limit:
        return prose

    tail = clusters[-character_limit:]
    # Prefer a whole-word start when the bounded suffix cut through one. If there is no
    # whitespace at all, retain the hard suffix rather than returning an empty signal.
    boundary = next(
        (offset for offset, cluster in enumerate(tail) if cluster[0] in WHITESPACE), None
    )
    if boundary is not None and boundary + 1 < len(tail):
        tail = tail[boundary + 1 :]
    bounded = trim("".join(tail))
    return bounded or None


def ends_in_question(prose: str | None) -> bool:
    """The runtime's ``replyEndsInQuestion`` signal — a question is not committed drift."""

    return prose is not None and prose.endswith("?")