"""
Syllable Core — hyphenator init, word validation, pipe autocorrect.

Extracted from cv_syllable_detect.py for modularity.

Lizenz: Apache 2.0 (kommerziell nutzbar)
DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
"""

import logging
import re
from typing import Any, Dict, List, Optional, Tuple

logger = logging.getLogger(__name__)

# IPA/phonetic characters -- skip cells containing these
_IPA_RE = re.compile(r'[\[\]\u02c8\u02cc\u02d0\u0283\u0292\u03b8\u00f0\u014b\u0251\u0252\u00e6\u0254\u0259\u025b\u025c\u026a\u028a\u028c]')

# Common German words that should NOT be merged with adjacent tokens.
_STOP_WORDS = frozenset([
    # Articles
    'der', 'die', 'das', 'dem', 'den', 'des',
    'ein', 'eine', 'einem', 'einen', 'einer',
    # Pronouns
    'du', 'er', 'es', 'sie', 'wir', 'ihr', 'ich', 'man', 'sich',
    'dich', 'dir', 'mich', 'mir', 'uns', 'euch', 'ihm', 'ihn',
    # Prepositions
    'mit', 'von', 'zu', 'f\u00fcr', 'auf', 'in', 'an', 'um', 'am', 'im',
    'aus', 'bei', 'nach', 'vor', 'bis', 'durch', '\u00fcber', 'unter',
    'zwischen', 'ohne', 'gegen',
    # Conjunctions
    'und', 'oder', 'als', 'wie', 'wenn', 'dass', 'weil', 'aber',
    # Adverbs
    'auch', 'noch', 'nur', 'schon', 'sehr', 'nicht',
    # Verbs
    'ist', 'hat', 'wird', 'kann', 'soll', 'muss', 'darf',
    'sein', 'haben',
    # Other
    'kein', 'keine', 'keinem', 'keinen', 'keiner',
])

# Cached hyphenators
_hyph_de = None
_hyph_en = None

# Cached spellchecker (for autocorrect_pipe_artifacts)
_spell_de = None


def _get_hyphenators():
    """Lazy-load pyphen hyphenators (cached across calls)."""
    global _hyph_de, _hyph_en
    if _hyph_de is not None:
        return _hyph_de, _hyph_en
    try:
        import pyphen
    except ImportError:
        return None, None
    _hyph_de = pyphen.Pyphen(lang='de_DE')
    _hyph_en = pyphen.Pyphen(lang='en_US')
    return _hyph_de, _hyph_en


def _get_spellchecker():
    """Lazy-load German spellchecker (cached across calls)."""
    global _spell_de
    if _spell_de is not None:
        return _spell_de
    try:
        from spellchecker import SpellChecker
    except ImportError:
        return None
    _spell_de = SpellChecker(language='de')
    return _spell_de


def _is_known_word(word: str, hyph_de, hyph_en) -> bool:
    """Check whether pyphen recognises a word (DE or EN)."""
    if len(word) < 2:
        return False
    return ('|' in hyph_de.inserted(word, hyphen='|')
            or '|' in hyph_en.inserted(word, hyphen='|'))


def _is_real_word(word: str) -> bool:
    """Check whether spellchecker knows this word (case-insensitive)."""
    spell = _get_spellchecker()
    if spell is None:
        return False
    return word.lower() in spell


def _hyphenate_word(word: str, hyph_de, hyph_en) -> Optional[str]:
    """Try to hyphenate a word using DE then EN dictionary.

    Returns word with | separators, or None if not recognized.
    """
    hyph = hyph_de.inserted(word, hyphen='|')
    if '|' in hyph:
        return hyph
    hyph = hyph_en.inserted(word, hyphen='|')
    if '|' in hyph:
        return hyph
    return None


def _autocorrect_piped_word(word_with_pipes: str) -> Optional[str]:
    """Try to correct a word that has OCR pipe artifacts.

    Printed syllable divider lines on dictionary pages confuse OCR:
    the vertical stroke is often read as an extra character (commonly
    ``l``, ``I``, ``1``, ``i``) adjacent to where the pipe appears.

    Uses ``spellchecker`` (frequency-based word list) for validation.

    Strategy:
        1. Strip ``|`` -- if spellchecker knows the result, done.
        2. Try deleting each pipe-like character (l, I, 1, i, t).
        3. Fall back to spellchecker's own ``correction()`` method.
        4. Preserve the original casing of the first letter.
    """
    stripped = word_with_pipes.replace('|', '')
    if not stripped or len(stripped) < 3:
        return stripped  # too short to validate

    # Step 1: if the stripped word is already a real word, done
    if _is_real_word(stripped):
        return stripped

    # Step 2: try deleting pipe-like characters (most likely artifacts)
    _PIPE_LIKE = frozenset('lI1it')
    for idx in range(len(stripped)):
        if stripped[idx] not in _PIPE_LIKE:
            continue
        candidate = stripped[:idx] + stripped[idx + 1:]
        if len(candidate) >= 3 and _is_real_word(candidate):
            return candidate

    # Step 3: use spellchecker's built-in correction
    spell = _get_spellchecker()
    if spell is not None:
        suggestion = spell.correction(stripped.lower())
        if suggestion and suggestion != stripped.lower():
            # Preserve original first-letter case
            if stripped[0].isupper():
                suggestion = suggestion[0].upper() + suggestion[1:]
            return suggestion

    return None  # could not fix


def autocorrect_pipe_artifacts(
    zones_data: List[Dict], session_id: str,
) -> int:
    """Strip OCR pipe artifacts and correct garbled words in-place.

    Printed syllable divider lines on dictionary scans are read by OCR
    as ``|`` characters embedded in words (e.g. ``Zel|le``, ``Ze|plpe|lin``).
    This function:

    1. Strips ``|`` from every word in content cells.
    2. Validates with spellchecker (real dictionary lookup).
    3. If not recognised, tries deleting pipe-like characters or uses
       spellchecker's correction (e.g. ``Zeplpelin`` -> ``Zeppelin``).
    4. Updates both word-box texts and cell text.

    Returns the number of cells modified.
    """
    spell = _get_spellchecker()
    if spell is None:
        logger.warning("spellchecker not available -- pipe autocorrect limited")
        # Fall back: still strip pipes even without spellchecker
        pass

    modified = 0
    for z in zones_data:
        for cell in z.get("cells", []):
            ct = cell.get("col_type", "")
            if not ct.startswith("column_"):
                continue

            cell_changed = False

            # --- Fix word boxes ---
            for wb in cell.get("word_boxes", []):
                wb_text = wb.get("text", "")
                if "|" not in wb_text:
                    continue

                # Separate trailing punctuation
                m = re.match(
                    r'^([^a-zA-Z\u00e4\u00f6\u00fc\u00c4\u00d6\u00dc\u00df\u1e9e]*)'
                    r'(.*?)'
                    r'([^a-zA-Z\u00e4\u00f6\u00fc\u00c4\u00d6\u00dc\u00df\u1e9e]*)$',
                    wb_text,
                )
                if not m:
                    continue
                lead, core, trail = m.group(1), m.group(2), m.group(3)
                if "|" not in core:
                    continue

                corrected = _autocorrect_piped_word(core)
                if corrected is not None and corrected != core:
                    wb["text"] = lead + corrected + trail
                    cell_changed = True

            # --- Rebuild cell text from word boxes ---
            if cell_changed:
                wbs = cell.get("word_boxes", [])
                if wbs:
                    cell["text"] = " ".join(
                        (wb.get("text") or "") for wb in wbs
                    )
                modified += 1

            # --- Fallback: strip residual | from cell text ---
            text = cell.get("text", "")
            if "|" in text:
                clean = text.replace("|", "")
                if clean != text:
                    cell["text"] = clean
                    if not cell_changed:
                        modified += 1

    if modified:
        logger.info(
            "build-grid session %s: autocorrected pipe artifacts in %d cells",
            session_id, modified,
        )
    return modified