"""Model input from typed segments, with segment-wise truncation to a token budget (pure Python).

Bundle rows (and the inference worker's requests) carry the renderer's typed segments
(``[{"kind": "header"|"title"|"body"|..., "text": ...}]``) whose ``\\n``-join is the ``render_input()`` text
(classifier-plan §2.3, §4.1). Each segment is tokenised on its own (``add_special_tokens=False``,
``split_special_tokens=True``: post text is untrusted), segments are joined with the ids of ``"\\n"``, and the
suffix ``"\\n### LABELS"`` closes the input; Gemma inputs start with exactly one BOS. Training, ``predict`` and
the node12 server all go through ``encode``, so their inputs are identical.

Truncation never parses post text: when a row is over budget the body is cut from its end (body tail first);
headers, title and the suffix are never cut (a row whose fixed parts alone exceed the budget is an error).
"""
from __future__ import annotations

import hashlib
from typing import Callable, Iterable, Mapping

SUFFIX = "\n### LABELS"
SEPARATOR = "\n"
FORMAT = "nsb-heads-v1"
CUTTABLE = ("body",)

Tokenize = Callable[[str], list[int]]


def joined(segments: Iterable[Mapping[str, str]]) -> str:
    """The rendered text: segments joined with newlines (== render_input() output)."""
    return SEPARATOR.join(s["text"] for s in segments)


def input_hash(segments: Iterable[Mapping[str, str]], render_version: str) -> str:
    """``sha256(render_version + "\\n" + text)``: the labelling §3.2 / ``post_labels.input_hash`` definition."""
    return hashlib.sha256((render_version + "\n" + joined(segments)).encode("utf-8")).hexdigest()


def encode(segments: list[Mapping[str, str]], tokenize: Tokenize, max_len: int, bos_id: int | None = None
           ) -> tuple[list[int], dict]:
    """Token ids for one input and a record of what was cut: ``{"n_tokens", "cut": {kind: tokens removed}}``."""
    if not segments:
        raise ValueError("empty input: no segments")
    pieces = [list(tokenize(s["text"])) if s["text"] else [] for s in segments]
    sep = list(tokenize(SEPARATOR))
    suffix = list(tokenize(SUFFIX))
    fixed = len(suffix) + len(sep) * (len(segments) - 1) + (1 if bos_id is not None else 0)
    excess = fixed + sum(len(p) for p in pieces) - max_len
    cut: dict[str, int] = {}
    for kind in CUTTABLE:
        for i in range(len(pieces) - 1, -1, -1):
            if excess <= 0:
                break
            if segments[i]["kind"] != kind or not pieces[i]:
                continue
            take = min(excess, len(pieces[i]))
            pieces[i] = pieces[i][:len(pieces[i]) - take]
            cut[kind] = cut.get(kind, 0) + take
            excess -= take
    if excess > 0:
        raise ValueError("header, title and suffix alone exceed max_len")
    ids: list[int] = [] if bos_id is None else [bos_id]
    for i, p in enumerate(pieces):
        if i:
            ids += sep
        ids += p
    ids += suffix
    return ids, {"n_tokens": len(ids), "cut": cut}