import logging
import json
import re

import torch
from pathlib import Path
from unicodedata import category
from tokenizers import Tokenizer
from huggingface_hub import hf_hub_download


# Special tokens
SOT = "[START]"
EOT = "[STOP]"
UNK = "[UNK]"
SPACE = "[SPACE]"
SPECIAL_TOKENS = [SOT, EOT, UNK, SPACE, "[PAD]", "[SEP]", "[CLS]", "[MASK]"]

logger = logging.getLogger(__name__)

class EnTokenizer:
    def __init__(self, vocab_file_path):
        self.tokenizer: Tokenizer = Tokenizer.from_file(vocab_file_path)
        self.check_vocabset_sot_eot()

    def check_vocabset_sot_eot(self):
        voc = self.tokenizer.get_vocab()
        assert SOT in voc
        assert EOT in voc

    def text_to_tokens(self, text: str):
        text_tokens = self.encode(text)
        text_tokens = torch.IntTensor(text_tokens).unsqueeze(0)
        return text_tokens

    def encode( self, txt: str, verbose=False):
        """
        clean_text > (append `lang_id`) > replace SPACE > encode text using Tokenizer
        """
        txt = txt.replace(' ', SPACE)
        code = self.tokenizer.encode(txt)
        ids = code.ids
        return ids

    def decode(self, seq):
        if isinstance(seq, torch.Tensor):
            seq = seq.cpu().numpy()

        txt: str = self.tokenizer.decode(seq,
        skip_special_tokens=False)
        txt = txt.replace(' ', '')
        txt = txt.replace(SPACE, ' ')
        txt = txt.replace(EOT, '')
        txt = txt.replace(UNK, '')
        return txt


# Model repository
REPO_ID = "ResembleAI/chatterbox"

# Global instances for optional dependencies
_kakasi = None
_dicta = None


def is_kanji(c: str) -> bool:
    """Check if character is kanji."""
    return 19968 <= ord(c) <= 40959


def is_katakana(c: str) -> bool:
    """Check if character is katakana."""
    return 12449 <= ord(c) <= 12538


def hiragana_normalize(text: str) -> str:
    """Japanese text normalization: converts kanji to hiragana; katakana remains the same."""
    global _kakasi
    
    try:
        if _kakasi is None:
            import pykakasi
            _kakasi = pykakasi.kakasi()
        
        result = _kakasi.convert(text)
        out = []
        
        for r in result:
            inp = r['orig']
            hira = r["hira"]

            # Any kanji in the phrase
            if any([is_kanji(c) for c in inp]):
                if hira and hira[0] in ["は", "へ"]:  # Safety check for empty hira
                    hira = " " + hira
                out.append(hira)

            # All katakana
            elif all([is_katakana(c) for c in inp]) if inp else False:  # Safety check for empty inp
                out.append(r['orig'])

            else:
                out.append(inp)
        
        normalized_text = "".join(out)
        
        # Decompose Japanese characters for tokenizer compatibility
        import unicodedata
        normalized_text = unicodedata.normalize('NFKD', normalized_text)
        
        return normalized_text
        
    except ImportError:
        logger.warning("pykakasi not available - Japanese text processing skipped")
        return text


def add_hebrew_diacritics(text: str) -> str:
    """Hebrew text normalization: adds diacritics to Hebrew text."""
    global _dicta
    
    try:
        if _dicta is None:
            from dicta_onnx import Dicta
            _dicta = Dicta()
        
        return _dicta.add_diacritics(text)
        
    except ImportError:
        logger.warning("dicta_onnx not available - Hebrew text processing skipped")
        return text
    except Exception as e:
        logger.warning(f"Hebrew diacritization failed: {e}")
        return text


def korean_normalize(text: str) -> str:
    """Korean text normalization: decompose syllables into Jamo for tokenization."""
    
    def decompose_hangul(char):
        """Decompose Korean syllable into Jamo components."""
        if not ('\uac00' <= char <= '\ud7af'):
            return char
        
        # Hangul decomposition formula
        base = ord(char) - 0xAC00
        initial = chr(0x1100 + base // (21 * 28))
        medial = chr(0x1161 + (base % (21 * 28)) // 28)
        final = chr(0x11A7 + base % 28) if base % 28 > 0 else ''
        
        return initial + medial + final
    
    # Decompose syllables and normalize punctuation
    result = ''.join(decompose_hangul(char) for char in text)    
    return result.strip()


class ChineseCangjieConverter:
    """Converts Chinese characters to Cangjie codes for tokenization."""
    
    def __init__(self, model_dir=None):
        self.word2cj = {}
        self.cj2word = {}
        self.segmenter = None
        self._load_cangjie_mapping(model_dir)
        self._init_segmenter()
    
    def _load_cangjie_mapping(self, model_dir=None):
        """Load Cangjie mapping from HuggingFace model repository."""        
        try:
            cangjie_file = hf_hub_download(
                repo_id=REPO_ID,
                filename="Cangjie5_TC.json",
                cache_dir=model_dir
            )
            
            with open(cangjie_file, "r", encoding="utf-8") as fp:
                data = json.load(fp)
            
            for entry in data:
                word, code = entry.split("\t")[:2]
                self.word2cj[word] = code
                if code not in self.cj2word:
                    self.cj2word[code] = [word]
                else:
                    self.cj2word[code].append(word)
                    
        except Exception as e:
            logger.warning(f"Could not load Cangjie mapping: {e}")
    
    def _init_segmenter(self):
        """Initialize pkuseg segmenter."""
        try:
            from pkuseg import pkuseg
            self.segmenter = pkuseg()
        except ImportError:
            logger.warning("pkuseg not available - Chinese segmentation will be skipped")
            self.segmenter = None
    
    def _cangjie_encode(self, glyph: str):
        """Encode a single Chinese glyph to Cangjie code."""
        normed_glyph = glyph
        code = self.word2cj.get(normed_glyph, None)
        if code is None:  # e.g. Japanese hiragana
            return None
        index = self.cj2word[code].index(normed_glyph)
        index = str(index) if index > 0 else ""
        return code + str(index)
    

    
    def __call__(self, text):
        """Convert Chinese characters in text to Cangjie tokens."""
        output = []
        if self.segmenter is not None:
            segmented_words = self.segmenter.cut(text)
            full_text = " ".join(segmented_words)
        else:
            full_text = text
        
        for t in full_text:
            if category(t) == "Lo":
                cangjie = self._cangjie_encode(t)
                if cangjie is None:
                    output.append(t)
                    continue
                code = []
                for c in cangjie:
                    code.append(f"[cj_{c}]")
                code.append("[cj_.]")
                code = "".join(code)
                output.append(code)
            else:
                output.append(t)
        return "".join(output)


class MTLTokenizer:
    def __init__(self, vocab_file_path):
        self.tokenizer: Tokenizer = Tokenizer.from_file(vocab_file_path)
        model_dir = Path(vocab_file_path).parent
        self.cangjie_converter = ChineseCangjieConverter(model_dir)
        self.check_vocabset_sot_eot()

    def check_vocabset_sot_eot(self):
        voc = self.tokenizer.get_vocab()
        assert SOT in voc
        assert EOT in voc

    def text_to_tokens(self, text: str, language_id: str = None):
        text_tokens = self.encode(text, language_id=language_id)
        text_tokens = torch.IntTensor(text_tokens).unsqueeze(0)
        return text_tokens

    def encode(self, txt: str, language_id: str = None):
        # Language-specific text processing
        if language_id == 'zh':
            txt = self.cangjie_converter(txt)
        elif language_id == 'ja':
            txt = hiragana_normalize(txt)
        elif language_id == 'he':
            txt = add_hebrew_diacritics(txt)
        elif language_id == 'ko':
            txt = korean_normalize(txt)
        
        # Prepend language token
        if language_id:
            txt = f"[{language_id.lower()}]{txt}"
        
        txt = txt.replace(' ', SPACE)
        return self.tokenizer.encode(txt).ids

    def decode(self, seq):
        if isinstance(seq, torch.Tensor):
            seq = seq.cpu().numpy()

        txt = self.tokenizer.decode(seq, skip_special_tokens=False)
        txt = txt.replace(' ', '').replace(SPACE, ' ').replace(EOT, '').replace(UNK, '')
        return txt
