"""
CV Review Spell — Rule-based OCR spell correction (no LLM).

Provides dictionary-backed digit-to-letter substitution, umlaut correction,
general spell correction, merged-word splitting, and page-ref normalization.

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

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

logger = logging.getLogger(__name__)

try:
    from spellchecker import SpellChecker as _SpellChecker
    _en_spell = _SpellChecker(language='en', distance=1)
    _de_spell = _SpellChecker(language='de', distance=1)
    _SPELL_AVAILABLE = True
    logger.info("pyspellchecker loaded (EN+DE)")
except ImportError:
    _SPELL_AVAILABLE = False
    _en_spell = None  # type: ignore[assignment]
    _de_spell = None  # type: ignore[assignment]
    logger.warning("pyspellchecker not installed")


# ---- Page-Ref Normalization ----
# Normalizes OCR variants like "p-60", "p 61", "p60" -> "p.60"
_PAGE_REF_RE = re.compile(r'\bp[\s\-]?(\d+)', re.IGNORECASE)


def _normalize_page_ref(text: str) -> str:
    """Normalize page references: 'p-60' / 'p 61' / 'p60' -> 'p.60'."""
    if not text:
        return text
    return _PAGE_REF_RE.sub(lambda m: f"p.{m.group(1)}", text)


# Suspicious OCR chars -> ordered list of most-likely correct replacements
_SPELL_SUBS: Dict[str, List[str]] = {
    '0': ['O', 'o'],
    '1': ['l', 'I'],
    '5': ['S', 's'],
    '6': ['G', 'g'],
    '8': ['B', 'b'],
    '|': ['I', 'l', '1'],
}
_SPELL_SUSPICIOUS = frozenset(_SPELL_SUBS.keys())

# Tokenizer: word tokens (letters + pipe) alternating with separators
_SPELL_TOKEN_RE = re.compile(r'([A-Za-z\u00c4\u00d6\u00dc\u00e4\u00f6\u00fc\u00df|]+)([^A-Za-z\u00c4\u00d6\u00dc\u00e4\u00f6\u00fc\u00df|]*)')


def _spell_dict_knows(word: str) -> bool:
    """True if word is known in EN or DE dictionary."""
    if not _SPELL_AVAILABLE:
        return False
    w = word.lower()
    return bool(_en_spell.known([w])) or bool(_de_spell.known([w]))


def _try_split_merged_word(token: str) -> Optional[str]:
    """Try to split a merged word like 'atmyschool' into 'at my school'.

    Uses dynamic programming to find the shortest sequence of dictionary
    words that covers the entire token. Only returns a result when the
    split produces at least 2 words and ALL parts are known dictionary words.

    Preserves original capitalisation by mapping back to the input string.
    """
    if not _SPELL_AVAILABLE or len(token) < 4:
        return None

    lower = token.lower()
    n = len(lower)

    # dp[i] = (word_lengths_list, score) for best split of lower[:i], or None
    dp: list = [None] * (n + 1)
    dp[0] = ([], 0)

    for i in range(1, n + 1):
        for j in range(max(0, i - 20), i):
            if dp[j] is None:
                continue
            candidate = lower[j:i]
            word_len = i - j
            if word_len == 1 and candidate not in ('a', 'i'):
                continue
            if _spell_dict_knows(candidate):
                prev_words, prev_sq = dp[j]
                new_words = prev_words + [word_len]
                new_sq = prev_sq + word_len * word_len
                new_key = (-len(new_words), new_sq)
                if dp[i] is None:
                    dp[i] = (new_words, new_sq)
                else:
                    old_key = (-len(dp[i][0]), dp[i][1])
                    if new_key >= old_key:
                        dp[i] = (new_words, new_sq)

    if dp[n] is None or len(dp[n][0]) < 2:
        return None

    result = []
    pos = 0
    for wlen in dp[n][0]:
        result.append(token[pos:pos + wlen])
        pos += wlen

    logger.debug("Split merged word: %r -> %r", token, " ".join(result))
    return " ".join(result)


def _spell_fix_token(token: str, field: str = "") -> Optional[str]:
    """Return corrected form of token, or None if no fix needed/possible.

    *field* is 'english' or 'german' -- used to pick the right dictionary.
    """
    has_suspicious = any(ch in _SPELL_SUSPICIOUS for ch in token)

    # 1. Already known word -> no fix needed
    if _spell_dict_knows(token):
        return None

    # 2. Digit/pipe substitution
    if has_suspicious:
        if token == '|':
            return 'I'
        for i, ch in enumerate(token):
            if ch not in _SPELL_SUBS:
                continue
            for replacement in _SPELL_SUBS[ch]:
                candidate = token[:i] + replacement + token[i + 1:]
                if _spell_dict_knows(candidate):
                    return candidate
        first = token[0]
        if first in _SPELL_SUBS and len(token) >= 2:
            rest = token[1:]
            if rest.isalpha() and rest.islower():
                candidate = _SPELL_SUBS[first][0] + rest
                if not candidate[0].isdigit():
                    return candidate

    # 3. OCR umlaut confusion
    if len(token) >= 3 and token.isalpha() and field == "german":
        _UMLAUT_SUBS = {'a': '\u00e4', 'o': '\u00f6', 'u': '\u00fc', 'i': '\u00fc',
                         'A': '\u00c4', 'O': '\u00d6', 'U': '\u00dc', 'I': '\u00dc'}
        for i, ch in enumerate(token):
            if ch in _UMLAUT_SUBS:
                candidate = token[:i] + _UMLAUT_SUBS[ch] + token[i + 1:]
                if _spell_dict_knows(candidate):
                    return candidate

    # 4. General spell correction for unknown words (no digits/pipes)
    if not has_suspicious and len(token) >= 3 and token.isalpha():
        spell = _en_spell if field == "english" else _de_spell if field == "german" else None
        if spell is not None:
            correction = spell.correction(token.lower())
            if correction and correction != token.lower():
                if token[0].isupper():
                    correction = correction[0].upper() + correction[1:]
                if _spell_dict_knows(correction):
                    return correction

    # 5. Merged-word split
    if len(token) >= 4 and token.isalpha():
        split = _try_split_merged_word(token)
        if split:
            return split

    return None


def _spell_fix_field(text: str, field: str = "") -> Tuple[str, bool]:
    """Apply OCR corrections to a text field. Returns (fixed_text, was_changed)."""
    if not text:
        return text, False
    has_suspicious = any(ch in text for ch in _SPELL_SUSPICIOUS)
    if not has_suspicious and not any(c.isalpha() for c in text):
        return text, False
    # Pattern: | immediately before . or , -> numbered list prefix
    fixed = re.sub(r'(?<!\w)\|(?=[.,])', '1', text) if has_suspicious else text
    changed = fixed != text
    # Tokenize and fix word by word
    parts: List[str] = []
    pos = 0
    for m in _SPELL_TOKEN_RE.finditer(fixed):
        token, sep = m.group(1), m.group(2)
        correction = _spell_fix_token(token, field=field)
        if correction:
            parts.append(correction)
            changed = True
        else:
            parts.append(token)
        parts.append(sep)
        pos = m.end()
    if pos < len(fixed):
        parts.append(fixed[pos:])
    return ''.join(parts), changed


def spell_review_entries_sync(entries: List[Dict]) -> Dict:
    """Rule-based OCR correction: spell-checker + structural heuristics.

    Deterministic -- never translates, never touches IPA, never hallucinates.
    Uses SmartSpellChecker for language-aware corrections with context-based
    disambiguation (a/I), multi-digit substitution, and cross-language guard.
    """
    from cv_review_llm import _entry_needs_review

    t0 = time.time()
    changes: List[Dict] = []
    all_corrected: List[Dict] = []

    # Use SmartSpellChecker if available
    _smart = None
    try:
        from smart_spell import SmartSpellChecker
        _smart = SmartSpellChecker()
        logger.debug("spell_review: using SmartSpellChecker")
    except Exception:
        logger.debug("spell_review: SmartSpellChecker not available, using legacy")

    _LANG_MAP = {"english": "en", "german": "de", "example": "auto"}

    for i, entry in enumerate(entries):
        e = dict(entry)
        # Page-ref normalization
        old_ref = (e.get("source_page") or "").strip()
        if old_ref:
            new_ref = _normalize_page_ref(old_ref)
            if new_ref != old_ref:
                changes.append({
                    "row_index": e.get("row_index", i),
                    "field": "source_page",
                    "old": old_ref,
                    "new": new_ref,
                })
                e["source_page"] = new_ref
                e["llm_corrected"] = True
        if not _entry_needs_review(e):
            all_corrected.append(e)
            continue
        for field_name in ("english", "german", "example"):
            old_val = (e.get(field_name) or "").strip()
            if not old_val:
                continue

            if _smart:
                lang_code = _LANG_MAP.get(field_name, "en")
                result = _smart.correct_text(old_val, lang=lang_code)
                new_val = result.corrected
                was_changed = result.changed
            else:
                lang = "german" if field_name in ("german", "example") else "english"
                new_val, was_changed = _spell_fix_field(old_val, field=lang)

            if was_changed and new_val != old_val:
                changes.append({
                    "row_index": e.get("row_index", i),
                    "field": field_name,
                    "old": old_val,
                    "new": new_val,
                })
                e[field_name] = new_val
                e["llm_corrected"] = True
        all_corrected.append(e)
    duration_ms = int((time.time() - t0) * 1000)
    model_name = "smart-spell-checker" if _smart else "spell-checker"
    return {
        "entries_original": entries,
        "entries_corrected": all_corrected,
        "changes": changes,
        "skipped_count": 0,
        "model_used": model_name,
        "duration_ms": duration_ms,
    }


async def spell_review_entries_streaming(entries: List[Dict], batch_size: int = 50):
    """Async generator yielding SSE-compatible events for spell-checker review."""
    total = len(entries)
    yield {
        "type": "meta",
        "total_entries": total,
        "to_review": total,
        "skipped": 0,
        "model": "spell-checker",
        "batch_size": batch_size,
    }
    result = spell_review_entries_sync(entries)
    changes = result["changes"]
    yield {
        "type": "batch",
        "batch_index": 0,
        "entries_reviewed": [e.get("row_index", i) for i, e in enumerate(entries)],
        "changes": changes,
        "duration_ms": result["duration_ms"],
        "progress": {"current": total, "total": total},
    }
    yield {
        "type": "complete",
        "changes": changes,
        "model_used": "spell-checker",
        "duration_ms": result["duration_ms"],
        "total_entries": total,
        "reviewed": total,
        "skipped": 0,
        "corrections_found": len(changes),
        "entries_corrected": result["entries_corrected"],
    }