"""
Syllable Merge — word gap merging, syllabification, divider insertion.

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

import numpy as np

from cv_syllable_core import (
    _get_hyphenators,
    _hyphenate_word,
    _IPA_RE,
    _STOP_WORDS,
)

logger = logging.getLogger(__name__)


def _try_merge_pipe_gaps(text: str, hyph_de) -> str:
    """Merge fragments separated by single spaces where OCR split at a pipe.

    Example: "Kaf fee" -> "Kaffee" (pyphen recognizes the merged word).
    Multi-step: "Ka bel jau" -> "Kabel jau" -> "Kabeljau".

    Guards against false merges:
    - The FIRST token must be pure alpha (word start -- no attached punctuation)
    - The second token may have trailing punctuation (comma, period) which
      stays attached to the merged word: "Ka" + "fer," -> "Kafer,"
    - Common German function words (der, die, das, ...) are never merged
    - At least one fragment must be very short (<=3 alpha chars)
    """
    parts = text.split(' ')
    if len(parts) < 2:
        return text

    result = [parts[0]]
    i = 1
    while i < len(parts):
        prev = result[-1]
        curr = parts[i]

        # Extract alpha-only core for lookup
        prev_alpha = re.sub(r'[^a-zA-Z\u00e4\u00f6\u00fc\u00c4\u00d6\u00dc\u00df\u1e9e]', '', prev)
        curr_alpha = re.sub(r'[^a-zA-Z\u00e4\u00f6\u00fc\u00c4\u00d6\u00dc\u00df\u1e9e]', '', curr)

        # Guard 1: first token must be pure alpha (word-start fragment)
        #          second token may have trailing punctuation
        # Guard 2: neither alpha core can be a common German function word
        # Guard 3: the shorter fragment must be <= 3 chars (pipe-gap signal)
        # Guard 4: combined length must be >= 4
        should_try = (
            prev == prev_alpha  # first token: pure alpha (word start)
            and prev_alpha and curr_alpha
            and prev_alpha.lower() not in _STOP_WORDS
            and curr_alpha.lower() not in _STOP_WORDS
            and min(len(prev_alpha), len(curr_alpha)) <= 3
            and len(prev_alpha) + len(curr_alpha) >= 4
        )

        if should_try:
            merged_alpha = prev_alpha + curr_alpha
            hyph = hyph_de.inserted(merged_alpha, hyphen='-')
            if '-' in hyph:
                # pyphen recognizes merged word -- collapse the space
                result[-1] = prev + curr
                i += 1
                continue

        result.append(curr)
        i += 1

    return ' '.join(result)


def merge_word_gaps_in_zones(zones_data: List[Dict], session_id: str) -> int:
    """Merge OCR word-gap fragments in cell texts using pyphen validation.

    OCR often splits words at syllable boundaries into separate word_boxes,
    producing text like "zerknit tert" instead of "zerknittert".  This
    function tries to merge adjacent fragments in every content cell.

    More permissive than ``_try_merge_pipe_gaps`` (threshold 5 instead of 3)
    but still guarded by pyphen dictionary lookup and stop-word exclusion.

    Returns the number of cells modified.
    """
    hyph_de, _ = _get_hyphenators()
    if hyph_de is None:
        return 0

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

            # Skip IPA cells
            text_no_brackets = re.sub(r'\[[^\]]*\]', '', text)
            if _IPA_RE.search(text_no_brackets):
                continue

            new_text = _try_merge_word_gaps(text, hyph_de)
            if new_text != text:
                cell["text"] = new_text
                modified += 1

    if modified:
        logger.info(
            "build-grid session %s: merged word gaps in %d cells",
            session_id, modified,
        )
    return modified


def _try_merge_word_gaps(text: str, hyph_de) -> str:
    """Merge OCR word fragments with relaxed threshold (max_short=5).

    Similar to ``_try_merge_pipe_gaps`` but allows slightly longer fragments
    (max_short=5 instead of 3).  Still requires pyphen to recognize the
    merged word.
    """
    parts = text.split(' ')
    if len(parts) < 2:
        return text

    result = [parts[0]]
    i = 1
    while i < len(parts):
        prev = result[-1]
        curr = parts[i]

        prev_alpha = re.sub(r'[^a-zA-Z\u00e4\u00f6\u00fc\u00c4\u00d6\u00dc\u00df\u1e9e]', '', prev)
        curr_alpha = re.sub(r'[^a-zA-Z\u00e4\u00f6\u00fc\u00c4\u00d6\u00dc\u00df\u1e9e]', '', curr)

        should_try = (
            prev == prev_alpha
            and prev_alpha and curr_alpha
            and prev_alpha.lower() not in _STOP_WORDS
            and curr_alpha.lower() not in _STOP_WORDS
            and min(len(prev_alpha), len(curr_alpha)) <= 5
            and len(prev_alpha) + len(curr_alpha) >= 4
        )

        if should_try:
            merged_alpha = prev_alpha + curr_alpha
            hyph = hyph_de.inserted(merged_alpha, hyphen='-')
            if '-' in hyph:
                result[-1] = prev + curr
                i += 1
                continue

        result.append(curr)
        i += 1

    return ' '.join(result)


def _syllabify_text(text: str, hyph_de, hyph_en) -> str:
    """Syllabify all significant words in a text string.

    1. Strip existing | dividers
    2. Merge pipe-gap spaces where possible
    3. Apply pyphen to each word >= 3 alphabetic chars
    4. Words pyphen doesn't recognize stay as-is (no bad guesses)
    """
    if not text:
        return text

    # Skip cells that contain IPA transcription characters outside brackets.
    text_no_brackets = re.sub(r'\[[^\]]*\]', '', text)
    if _IPA_RE.search(text_no_brackets):
        return text

    # Phase 1: strip existing pipe dividers for clean normalization
    clean = text.replace('|', '')

    # Phase 2: merge pipe-gap spaces (OCR fragments from pipe splitting)
    clean = _try_merge_pipe_gaps(clean, hyph_de)

    # Phase 3: tokenize and syllabify each word
    # Split on whitespace and comma/semicolon sequences, keeping separators
    tokens = re.split(r'(\s+|[,;:]+\s*)', clean)

    result = []
    for tok in tokens:
        if not tok or re.match(r'^[\s,;:]+$', tok):
            result.append(tok)
            continue

        # Strip trailing/leading punctuation for pyphen lookup
        m = re.match(r'^([^a-zA-Z\u00e4\u00f6\u00fc\u00c4\u00d6\u00dc\u00df\u1e9e]*)(.*?)([^a-zA-Z\u00e4\u00f6\u00fc\u00c4\u00d6\u00dc\u00df\u1e9e]*)$', tok)
        if not m:
            result.append(tok)
            continue
        lead, word, trail = m.group(1), m.group(2), m.group(3)

        if len(word) < 3 or not re.search(r'[a-zA-Z\u00e4\u00f6\u00fc\u00c4\u00d6\u00dc\u00df]', word):
            result.append(tok)
            continue

        hyph = _hyphenate_word(word, hyph_de, hyph_en)
        if hyph:
            result.append(lead + hyph + trail)
        else:
            result.append(tok)

    return ''.join(result)


def insert_syllable_dividers(
    zones_data: List[Dict],
    img_bgr: np.ndarray,
    session_id: str,
    *,
    force: bool = False,
    col_filter: Optional[set] = None,
) -> int:
    """Insert pipe syllable dividers into dictionary cells.

    For dictionary pages: process all content column cells, strip existing
    pipes, merge pipe-gap spaces, and re-syllabify using pyphen.

    Pre-check: at least 1% of content cells must already contain ``|`` from
    OCR.  This guards against pages with zero pipe characters.

    Args:
        force: If True, skip the pipe-ratio pre-check and syllabify all
            content words regardless of whether the original has pipe dividers.
        col_filter: If set, only process cells whose col_type is in this set.
            None means process all content columns.

    Returns the number of cells modified.
    """
    hyph_de, hyph_en = _get_hyphenators()
    if hyph_de is None:
        logger.warning("pyphen not installed -- skipping syllable insertion")
        return 0

    # Pre-check: count cells that already have | from OCR.
    if not force:
        total_col_cells = 0
        cells_with_pipes = 0
        for z in zones_data:
            for cell in z.get("cells", []):
                if cell.get("col_type", "").startswith("column_"):
                    total_col_cells += 1
                    if "|" in cell.get("text", ""):
                        cells_with_pipes += 1

        if total_col_cells > 0:
            pipe_ratio = cells_with_pipes / total_col_cells
            if pipe_ratio < 0.01:
                logger.info(
                    "build-grid session %s: skipping syllable insertion -- "
                    "only %.1f%% of cells have existing pipes (need >=1%%)",
                    session_id, pipe_ratio * 100,
                )
                return 0

    insertions = 0
    for z in zones_data:
        for cell in z.get("cells", []):
            ct = cell.get("col_type", "")
            if not ct.startswith("column_"):
                continue
            if col_filter is not None and ct not in col_filter:
                continue
            text = cell.get("text", "")
            if not text:
                continue

            # In auto mode (force=False), only normalize cells that already
            # have | from OCR (i.e. printed syllable dividers on the original
            # scan).  Don't add new syllable marks to other words.
            if not force and "|" not in text:
                continue

            new_text = _syllabify_text(text, hyph_de, hyph_en)
            if new_text != text:
                cell["text"] = new_text
                insertions += 1

    if insertions:
        logger.info(
            "build-grid session %s: syllable dividers inserted/normalized "
            "in %d cells (pyphen)",
            session_id, insertions,
        )
    return insertions