"""
Coordinate Chess Tokenizer (Vocab Size = 72).
Compatible with Hugging Face AutoTokenizer and existing Evaluation scripts.
"""

from __future__ import annotations

import json
import os
import re
from typing import Dict, List, Optional, Tuple, Union

from transformers import PreTrainedTokenizer

class ChessTokenizer(PreTrainedTokenizer):
    model_input_names = ["input_ids", "attention_mask"]
    vocab_files_names = {"vocab_file": "vocab.json"}

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

    # Regex to capture coordinates and promotions from any format (UCI, SAN, Extended)
    # Captures: "e2", "e4", "q" inside strings like "WPe2e4" or "e2e4q"
    MOVE_REGEX = re.compile(r"([a-h][1-8])([a-h][1-8])([qrbn])?")

    def __init__(
        self,
        vocab_file: Optional[str] = None,
        **kwargs,
    ):
        # Initialize special tokens
        self._pad_token = self.PAD_TOKEN
        self._bos_token = self.BOS_TOKEN
        self._eos_token = self.EOS_TOKEN
        self._unk_token = self.UNK_TOKEN

        # Clean kwargs to avoid duplication errors during loading
        kwargs.pop("pad_token", None)
        kwargs.pop("bos_token", None)
        kwargs.pop("eos_token", None)
        kwargs.pop("unk_token", None)

        # 1. Load or Create Vocabulary
        # If a vocab_file is provided (loading from HF), use it.
        # Otherwise, create the fixed 72-token vocabulary.
        if vocab_file is not None and os.path.exists(vocab_file):
            with open(vocab_file, "r", encoding="utf-8") as f:
                self._vocab = json.load(f)
        else:
            self._vocab = self._create_fixed_vocab()

        self._ids_to_tokens = {v: k for k, v in self._vocab.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 _create_fixed_vocab(self) -> Dict[str, int]:
        """Creates the deterministic 72-token vocabulary."""
        vocab = {}
        
        # 0-3: Special Tokens
        special_tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
        for idx, token in enumerate(special_tokens):
            vocab[token] = idx
            
        # 4-7: Promotions (q, r, b, n)
        promotions = ["q", "r", "b", "n"]
        for idx, token in enumerate(promotions):
            vocab[token] = len(vocab)
            
        # 8-71: Squares (a1...h8)
        files = "abcdefgh"
        ranks = "12345678"
        for r in ranks:
            for f in files:
                square = f + r
                vocab[square] = len(vocab)
                
        return vocab

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

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

    def _tokenize(self, text: str) -> List[str]:
        """
        Robust tokenization handling both raw coordinates and 'dirty' UCI extended strings.
        """
        tokens = []
        # Split by whitespace first
        raw_chunks = text.strip().split()
        
        # Set of exact match tokens to preserve special tokens
        special_set = {self.BOS_TOKEN, self.EOS_TOKEN, self.PAD_TOKEN, self.UNK_TOKEN}

        for chunk in raw_chunks:
            # If it's explicitly a special token, keep it
            if chunk in special_set:
                tokens.append(chunk)
                continue

            # Otherwise, use Regex to extract coordinates
            # This handles "WPe2e4" -> ["e2", "e4"]
            # And "e2e4" -> ["e2", "e4"]
            match = self.MOVE_REGEX.search(chunk)
            if match:
                start_sq, end_sq, promotion = match.groups()
                tokens.append(start_sq)
                tokens.append(end_sq)
                if promotion:
                    tokens.append(promotion)
            else:
                # If regex fails but it is in our vocab (e.g. isolated 'a1'), take it
                if chunk in self._vocab:
                    tokens.append(chunk)
                else:
                    tokens.append(self.UNK_TOKEN)
                
        return tokens

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

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

    def convert_tokens_to_string(self, tokens: List[str]) -> str:
        """
        Reconstructs string. Important: adds spaces between coordinates.
        Evaluate.py handles spaces fine via regex.
        """
        special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
        clean_tokens = [t for t in tokens if t not in special]
        return " ".join(clean_tokens)

    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        """
        Vital for Hugging Face: saves the vocab.json to the directory.
        """
        if not os.path.isdir(save_directory):
            os.makedirs(save_directory, exist_ok=True)
            
        vocab_file = os.path.join(
            save_directory, 
            (filename_prefix + "-" if filename_prefix else "") + "vocab.json"
        )
        
        with open(vocab_file, "w", encoding="utf-8") as f:
            json.dump(self._vocab, f, ensure_ascii=False, indent=2)
            
        return (vocab_file,)
    
    @classmethod
    def build_vocab_from_dataset(
        cls,
        dataset_name: str = "dlouapre/lichess_2025-01_1M",
        split: str = "train",
        column: str = "text",
        min_frequency: int = 500,  # Ignored
        max_samples: Optional[int] = 100000,  # Ignored
    ) -> "ChessTokenizer":
        """
        Mock implementation to satisfy train.py API.
        Ignores dataset scanning since vocab is fixed.
        """
        print(f"Coordinate Tokenizer: Using fixed vocabulary (size 72). Ignoring dataset scan.")
        return cls()