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

The export stores each input as typed segments (``header``, ``target_text``, ``context_text`` with a
``relation``, ``facts``) whose concatenation is the input (format ``evidence-v1``). Each segment is tokenised
on its own and the ids are concatenated, followed by the label suffix; training and prediction both go
through ``encode``, so the two always agree. Truncation never parses post text: when a row is over budget it
shortens quoted text, then parent text (each from its end, last segment first), and the target text only as
a last resort. Headers, the FACTS block and the suffix are never cut.
"""
from __future__ import annotations

SUFFIX = '\n### LABELS'
FORMAT = 'evidence-v1'
CUT_ORDER = (('context_text', 'quoted'), ('context_text', 'self_thread_parent'), ('context_text', 'reply_parent'),
             ('target_text', None))


def joined(row: dict) -> str:
    return ''.join(s['text'] for s in row['segments'])


def _cuttable(seg: dict, kind: str, relation) -> bool:
    return seg['kind'] == kind and (relation is None or seg.get('relation') == relation)


def encode(row: dict, tokenize, max_len: int) -> tuple[list[int], dict]:
    """Token ids for one export row, and a record of what was cut.

    ``tokenize(text) -> list[int]`` must not add special tokens and must not parse special-token strings in
    the text (post text is untrusted). The record is ``{"n_tokens", "cut": {segment label: tokens removed}}``.
    """
    pieces = [list(tokenize(s['text'])) if s['text'] else [] for s in row['segments']]
    suffix = list(tokenize(SUFFIX))
    excess = sum(len(p) for p in pieces) + len(suffix) - max_len
    cut = {}
    for kind, relation in CUT_ORDER:
        if excess <= 0:
            break
        for i in range(len(pieces) - 1, -1, -1):
            if excess <= 0:
                break
            if not _cuttable(row['segments'][i], kind, relation) or not pieces[i]:
                continue
            take = min(excess, len(pieces[i]))
            pieces[i] = pieces[i][:len(pieces[i]) - take]
            label = kind if relation is None else '%s:%s' % (kind, relation)
            cut[label] = cut.get(label, 0) + take
            excess -= take
    if excess > 0:
        raise ValueError('Headers, FACTS and suffix alone exceed max_len for row %s' % row.get('id'))
    ids = [t for p in pieces for t in p] + suffix
    return ids, {'n_tokens': len(ids), 'cut': cut}