from __future__ import annotations

import json
import os
import re
import shutil
from typing import Dict, List, Optional

from transformers import PreTrainedTokenizer


REGEX_CASE = re.compile(r"([a-h][1-8])")

REGEX_PROMO = re.compile(r"[=\(]?([qrbnQRBN])[\)]?$")

class ChessTokenizer(PreTrainedTokenizer):
    """
    Tokenizer qui traite le jeu d'échecs case par case.
    Vocabulaire déterministe : Spéciaux + Cases (a1..h8) + Promotions.
    """
    

    vocab_files_names = {"vocab_file": "vocab.json"}
    model_input_names = ["input_ids", "attention_mask"]

    # Tokens
    PAD_TOKEN = "[PAD]"
    BOS_TOKEN = "[BOS]"
    EOS_TOKEN = "[EOS]"
    UNK_TOKEN = "[UNK]"

    def __init__(
        self,
        vocab_file: Optional[str] = None,
        vocab: Optional[Dict[str, int]] = None,
        **kwargs,
    ):
        self._pad_token = self.PAD_TOKEN
        self._bos_token = self.BOS_TOKEN
        self._eos_token = self.EOS_TOKEN
        self._unk_token = self.UNK_TOKEN

        for cle in ["pad_token", "bos_token", "eos_token", "unk_token"]:
            kwargs.pop(cle, None)

        if vocab:
            self.map_token_id = vocab
        elif vocab_file and os.path.exists(vocab_file):
            with open(vocab_file, "r", encoding="utf-8") as f:
                self.map_token_id = json.load(f)
        else:
            self.map_token_id = self._generer_vocabulaire()


        self.map_id_token = {i: t for t, i in self.map_token_id.items()}

        super().__init__(
            pad_token=self._pad_token,
            bos_token=self._bos_token,
            eos_token=self._eos_token,
            unk_token=self._unk_token,
            **kwargs,
        )

    def _generer_vocabulaire(self) -> Dict[str, int]:
        """Génère la liste fixe des tokens nécessaires."""
        liste_tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
        
        colonnes = "abcdefgh"
        lignes = "12345678"
        cases = [f"{c}{l}" for c in colonnes for l in lignes]
        liste_tokens.extend(cases)
        
        pieces_promo = ["q", "r", "b", "n"]
        liste_tokens.extend(pieces_promo)
        

        return {t: i for i, t in enumerate(liste_tokens)}

    @property
    def vocab_size(self) -> int:
        return len(self.map_token_id)

    def get_vocab(self) -> Dict[str, int]:
        return dict(self.map_token_id)

    def _tokenize(self, text: str) -> List[str]:
        """
        Transforme une phrase de coups en liste de tokens.
        """
        resultat = []
        

        mouvements = text.strip().split()
        
        for mv in mouvements:

            cases_trouvees = REGEX_CASE.findall(mv)
            

            if len(cases_trouvees) >= 2:

                resultat.extend(cases_trouvees[:2])
                

                match_promo = REGEX_PROMO.search(mv)
                if match_promo:

                    resultat.append(match_promo.group(1).lower())
            

            elif mv in self.map_token_id:
                resultat.append(mv)
            else:

                resultat.append(self.UNK_TOKEN)
                
        return resultat

    def _convert_token_to_id(self, token: str) -> int:
        return self.map_token_id.get(token, self.map_token_id[self.UNK_TOKEN])

    def _convert_id_to_token(self, index: int) -> str:
        return self.map_id_token.get(index, self.UNK_TOKEN)

    def convert_tokens_to_string(self, tokens: List[str]) -> str:
        """
        Reconstruit la chaine de caractères depuis les tokens.
        Logique : on assemble les paires de cases.
        """
        sortie = []
        tampon_cases = []
        
        exclus = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
        promotions = {"q", "r", "b", "n"}
        
        for t in tokens:
            if t in exclus:
                continue
                
            if t in promotions:
                if sortie:
                    sortie[-1] += t
            else:
                tampon_cases.append(t)

                if len(tampon_cases) == 2:
                    coup_complet = "".join(tampon_cases)
                    sortie.append(coup_complet)
                    tampon_cases = []
        
        return " ".join(sortie)

    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple:
        """Sauvegarde le vocabulaire sur le disque."""
        if not os.path.exists(save_directory):
            os.makedirs(save_directory)
            
        nom_fichier = "vocab.json"
        if filename_prefix:
            nom_fichier = f"{filename_prefix}-{nom_fichier}"
            
        chemin_complet = os.path.join(save_directory, nom_fichier)
        
        with open(chemin_complet, "w", encoding="utf-8") as f:
            json.dump(self.map_token_id, f, ensure_ascii=False, indent=2)
            
        return (chemin_complet,)


    def save_pretrained(self, save_directory: str, **kwargs):
        """
        Sauvegarde standard + Copie du script tokenizer.py pour Hugging Face.
        """
        super().save_pretrained(save_directory, **kwargs)
        
        source = os.path.abspath(__file__)
        dest = os.path.join(save_directory, "tokenizer.py")
        if source != dest:
            shutil.copy(source, dest)
            
        chem_config = os.path.join(save_directory, "tokenizer_config.json")
        if os.path.exists(chem_config):
            with open(chem_config, "r") as f:
                cfg = json.load(f)
            cfg["auto_map"] = {"AutoTokenizer": "tokenizer.ChessTokenizer"}
            with open(chem_config, "w") as f:
                json.dump(cfg, f, indent=2)


from transformers import AutoTokenizer
try:
    ChessTokenizer.register_for_auto_class("AutoTokenizer")
except:
    pass