"""
SmartSpellChecker — Language-aware OCR post-correction without LLMs.

Uses pyspellchecker (MIT) with dual EN+DE dictionaries for:
- Automatic language detection per word (dual-dictionary heuristic)
- OCR error correction (digit↔letter, umlauts, transpositions)
- Context-based disambiguation (a/I, l/I) via bigram lookup
- Mixed-language support for example sentences

Lizenz: Apache 2.0 (kommerziell nutzbar)
"""

import logging
import re
from dataclasses import dataclass, field
from typing import Dict, List, Literal, Optional, Set, Tuple

logger = logging.getLogger(__name__)

# ---------------------------------------------------------------------------
# Init
# ---------------------------------------------------------------------------

try:
    from spellchecker import SpellChecker as _SpellChecker
    _en_spell = _SpellChecker(language='en', distance=1)
    _de_spell = _SpellChecker(language='de', distance=1)
    _AVAILABLE = True
except ImportError:
    _AVAILABLE = False
    logger.warning("pyspellchecker not installed — SmartSpellChecker disabled")

Lang = Literal["en", "de", "both", "unknown"]

# ---------------------------------------------------------------------------
# Bigram context for a/I disambiguation
# ---------------------------------------------------------------------------

# Words that commonly follow "I" (subject pronoun → verb/modal)
_I_FOLLOWERS: frozenset = frozenset({
    "am", "was", "have", "had", "do", "did", "will", "would", "can",
    "could", "should", "shall", "may", "might", "must",
    "think", "know", "see", "want", "need", "like", "love", "hate",
    "go", "went", "come", "came", "say", "said", "get", "got",
    "make", "made", "take", "took", "give", "gave", "tell", "told",
    "feel", "felt", "find", "found", "believe", "hope", "wish",
    "remember", "forget", "understand", "mean", "meant",
    "don't", "didn't", "can't", "won't", "couldn't", "wouldn't",
    "shouldn't", "haven't", "hadn't", "isn't", "wasn't",
    "really", "just", "also", "always", "never", "often", "sometimes",
})

# Words that commonly follow "a" (article → noun/adjective)
_A_FOLLOWERS: frozenset = frozenset({
    "lot", "few", "little", "bit", "good", "bad", "great", "new", "old",
    "long", "short", "big", "small", "large", "huge", "tiny",
    "nice", "beautiful", "wonderful", "terrible", "horrible",
    "man", "woman", "boy", "girl", "child", "dog", "cat", "bird",
    "book", "car", "house", "room", "school", "teacher", "student",
    "day", "week", "month", "year", "time", "place", "way",
    "friend", "family", "person", "problem", "question", "story",
    "very", "really", "quite", "rather", "pretty", "single",
})

# Digit→letter substitutions (OCR confusion)
_DIGIT_SUBS: Dict[str, List[str]] = {
    '0': ['o', 'O'],
    '1': ['l', 'I'],
    '5': ['s', 'S'],
    '6': ['g', 'G'],
    '8': ['b', 'B'],
    '|': ['I', 'l'],
}
_SUSPICIOUS_CHARS = frozenset(_DIGIT_SUBS.keys())

# Umlaut confusion: OCR drops dots (ü→u, ä→a, ö→o)
_UMLAUT_MAP = {
    'a': 'ä', 'o': 'ö', 'u': 'ü', 'i': 'ü',
    'A': 'Ä', 'O': 'Ö', 'U': 'Ü', 'I': 'Ü',
}

# Tokenizer
_TOKEN_RE = re.compile(r"([A-Za-zÄÖÜäöüß'|]+)([^A-Za-zÄÖÜäöüß'|]*)")


# ---------------------------------------------------------------------------
# Data types
# ---------------------------------------------------------------------------

@dataclass
class CorrectionResult:
    original: str
    corrected: str
    lang_detected: Lang
    changed: bool
    changes: List[str] = field(default_factory=list)


# ---------------------------------------------------------------------------
# Core class
# ---------------------------------------------------------------------------

class SmartSpellChecker:
    """Language-aware OCR spell checker using pyspellchecker (no LLM)."""

    def __init__(self):
        if not _AVAILABLE:
            raise RuntimeError("pyspellchecker not installed")
        self.en = _en_spell
        self.de = _de_spell

    # --- Language detection ---

    def detect_word_lang(self, word: str) -> Lang:
        """Detect language of a single word using dual-dict heuristic."""
        w = word.lower().strip(".,;:!?\"'()")
        if not w:
            return "unknown"
        in_en = bool(self.en.known([w]))
        in_de = bool(self.de.known([w]))
        if in_en and in_de:
            return "both"
        if in_en:
            return "en"
        if in_de:
            return "de"
        return "unknown"

    def detect_text_lang(self, text: str) -> Lang:
        """Detect dominant language of a text string (sentence/phrase)."""
        words = re.findall(r"[A-Za-zÄÖÜäöüß]+", text)
        if not words:
            return "unknown"

        en_count = 0
        de_count = 0
        for w in words:
            lang = self.detect_word_lang(w)
            if lang == "en":
                en_count += 1
            elif lang == "de":
                de_count += 1
            # "both" doesn't count for either

        if en_count > de_count:
            return "en"
        if de_count > en_count:
            return "de"
        if en_count == de_count and en_count > 0:
            return "both"
        return "unknown"

    # --- Single-word correction ---

    def _known(self, word: str) -> bool:
        """True if word is known in EN or DE dictionary."""
        w = word.lower()
        return bool(self.en.known([w])) or bool(self.de.known([w]))

    def _known_in(self, word: str, lang: str) -> bool:
        """True if word is known in a specific language dictionary."""
        w = word.lower()
        spell = self.en if lang == "en" else self.de
        return bool(spell.known([w]))

    def correct_word(self, word: str, lang: str = "en",
                     prev_word: str = "", next_word: str = "") -> Optional[str]:
        """Correct a single word for the given language.

        Returns None if no correction needed, or the corrected string.

        Args:
            word: The word to check/correct
            lang: Expected language ("en" or "de")
            prev_word: Previous word (for context)
            next_word: Next word (for context)
        """
        if not word or not word.strip():
            return None

        # Skip numbers, abbreviations with dots, very short tokens
        if word.isdigit() or '.' in word:
            return None

        has_suspicious = any(ch in _SUSPICIOUS_CHARS for ch in word)

        # 1. Already known → no fix
        if self._known(word):
            # But check a/I disambiguation for single-char words
            if word.lower() in ('l', '|') and next_word:
                return self._disambiguate_a_I(word, next_word)
            return None

        # 2. Digit/pipe substitution
        if has_suspicious:
            if word == '|':
                return 'I'
            # Try single-char substitutions
            for i, ch in enumerate(word):
                if ch not in _DIGIT_SUBS:
                    continue
                for replacement in _DIGIT_SUBS[ch]:
                    candidate = word[:i] + replacement + word[i + 1:]
                    if self._known(candidate):
                        return candidate
            # Try multi-char substitution (e.g., "sch00l" → "school")
            multi = self._try_multi_digit_sub(word)
            if multi:
                return multi

        # 3. Umlaut correction (German)
        if lang == "de" and len(word) >= 3 and word.isalpha():
            umlaut_fix = self._try_umlaut_fix(word)
            if umlaut_fix:
                return umlaut_fix

        # 4. General spell correction
        if not has_suspicious and len(word) >= 3 and word.isalpha():
            # Safety: don't correct if the word is valid in the OTHER language
            # (either directly or via umlaut fix)
            other_lang = "de" if lang == "en" else "en"
            if self._known_in(word, other_lang):
                return None
            if other_lang == "de" and self._try_umlaut_fix(word):
                return None  # has a valid DE umlaut variant → don't touch

            spell = self.en if lang == "en" else self.de
            correction = spell.correction(word.lower())
            if correction and correction != word.lower():
                if word[0].isupper():
                    correction = correction[0].upper() + correction[1:]
                if self._known(correction):
                    return correction

        return None

    # --- Multi-digit substitution ---

    def _try_multi_digit_sub(self, word: str) -> Optional[str]:
        """Try replacing multiple digits simultaneously."""
        positions = [(i, ch) for i, ch in enumerate(word) if ch in _DIGIT_SUBS]
        if len(positions) < 1 or len(positions) > 4:
            return None

        # Try all combinations (max 2^4 = 16 for 4 positions)
        chars = list(word)
        best = None
        self._multi_sub_recurse(chars, positions, 0, best_result=[None])
        return self._multi_sub_recurse_result

    _multi_sub_recurse_result: Optional[str] = None

    def _try_multi_digit_sub(self, word: str) -> Optional[str]:
        """Try replacing multiple digits simultaneously using BFS."""
        positions = [(i, ch) for i, ch in enumerate(word) if ch in _DIGIT_SUBS]
        if not positions or len(positions) > 4:
            return None

        # BFS over substitution combinations
        queue = [list(word)]
        for pos, ch in positions:
            next_queue = []
            for current in queue:
                # Keep original
                next_queue.append(current[:])
                # Try each substitution
                for repl in _DIGIT_SUBS[ch]:
                    variant = current[:]
                    variant[pos] = repl
                    next_queue.append(variant)
            queue = next_queue

        # Check which combinations produce known words
        for combo in queue:
            candidate = "".join(combo)
            if candidate != word and self._known(candidate):
                return candidate

        return None

    # --- Umlaut fix ---

    def _try_umlaut_fix(self, word: str) -> Optional[str]:
        """Try single-char umlaut substitutions for German words."""
        for i, ch in enumerate(word):
            if ch in _UMLAUT_MAP:
                candidate = word[:i] + _UMLAUT_MAP[ch] + word[i + 1:]
                if self._known(candidate):
                    return candidate
        return None

    # --- a/I disambiguation ---

    def _disambiguate_a_I(self, token: str, next_word: str) -> Optional[str]:
        """Disambiguate 'a' vs 'I' (and OCR variants like 'l', '|')."""
        nw = next_word.lower().strip(".,;:!?")
        if nw in _I_FOLLOWERS:
            return "I"
        if nw in _A_FOLLOWERS:
            return "a"
        # Fallback: check if next word is more commonly a verb (→I) or noun/adj (→a)
        # Simple heuristic: if next word starts with uppercase (and isn't first in sentence)
        # it's likely a German noun following "I"... but in English context, uppercase
        # after "I" is unusual.
        return None  # uncertain, don't change

    # --- Full text correction ---

    def correct_text(self, text: str, lang: str = "en") -> CorrectionResult:
        """Correct a full text string (field value).

        Args:
            text: The text to correct
            lang: Expected language ("en" or "de")
        """
        if not text or not text.strip():
            return CorrectionResult(text, text, "unknown", False)

        detected = self.detect_text_lang(text) if lang == "auto" else lang

        parts: List[str] = []
        changes: List[str] = []
        tokens = list(_TOKEN_RE.finditer(text))

        for idx, m in enumerate(tokens):
            token, sep = m.group(1), m.group(2)
            next_word = tokens[idx + 1].group(1) if idx + 1 < len(tokens) else ""
            prev_word = tokens[idx - 1].group(1) if idx > 0 else ""

            correction = self.correct_word(
                token, lang=detected if detected in ("en", "de") else "en",
                prev_word=prev_word, next_word=next_word,
            )
            if correction and correction != token:
                changes.append(f"{token}→{correction}")
                parts.append(correction)
            else:
                parts.append(token)
            parts.append(sep)

        # Append any trailing text
        last_end = tokens[-1].end() if tokens else 0
        if last_end < len(text):
            parts.append(text[last_end:])

        corrected = "".join(parts)
        return CorrectionResult(
            original=text,
            corrected=corrected,
            lang_detected=detected,
            changed=corrected != text,
            changes=changes,
        )

    # --- Vocabulary entry correction ---

    def correct_vocab_entry(self, english: str, german: str,
                            example: str = "") -> Dict[str, CorrectionResult]:
        """Correct a full vocabulary entry (EN + DE + example).

        Uses column position to determine language — the most reliable signal.
        """
        results = {}
        results["english"] = self.correct_text(english, lang="en")
        results["german"] = self.correct_text(german, lang="de")
        if example:
            # For examples, auto-detect language
            results["example"] = self.correct_text(example, lang="auto")
        return results