"""
Custom Chess Tokenizer for the Chess Challenge.

This tokenizer uses sub-token decomposition to achieve a minimal vocabulary
by breaking moves into atomic components (squares + modifiers).

Example:
    WPe2e4 → ["e2", "e4"]
    BNg8f6(+) → ["g8", "f6", "+"]
    
This approach trades sequence length (3x longer) for vocabulary size (77 vs 1800+).
"""

from __future__ import annotations
import json
import os
from typing import Dict, List, Optional
from transformers import PreTrainedTokenizer


class ChessTokenizer(PreTrainedTokenizer):
    """
    Sub-token chess tokenizer with minimal fixed vocabulary.

    Decomposes each move into:
    - Source square (e.g., e2)
    - Destination square (e.g., e4)
    - Optional modifiers (x, +, +*, Q/R/B/N, O/o)

    Vocabulary composition:
    - 64 squares (a1-h8)
    - 9 modifiers (x, +, +*, Q, R, B, N, O, o)
    - 4 special tokens ([PAD], [BOS], [EOS], [UNK])
    Total: 77 tokens
    """

    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]"

    def __init__(
        self,
        vocab_file: Optional[str] = None,
        vocab: Optional[Dict[str, int]] = None,
        **kwargs,
    ):
        """
        Initialize the chess tokenizer.
        """
        # 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

        kwargs.pop("pad_token", None)
        kwargs.pop("bos_token", None)
        kwargs.pop("eos_token", None)
        kwargs.pop("unk_token", None)

        # Load or create vocabulary
        if vocab is not None:
            self._vocab = vocab
        elif 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.build_minimal_vocab().get_vocab()

        # Reverse mapping
        self._ids_to_tokens = {v: k for k, v in self._vocab.items()}

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

    @classmethod
    def build_minimal_vocab(cls) -> "ChessTokenizer":
        """
        Build tokenizer with minimal fixed vocabulary (77 tokens).
        """
        files = "abcdefgh"
        ranks = "12345678"
        squares = [f + r for f in files for r in ranks]
        modifiers = ["x", "+", "+*", "Q", "R", "B", "N", "O", "o"]
        special_tokens = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]

        vocab_tokens = special_tokens + squares + modifiers
        vocab = {tok: i for i, tok in enumerate(vocab_tokens)}
        return cls(vocab=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]:
        """
        Tokenize moves into squares + modifiers.

        Examples:
            WPe2e4        -> ["e2", "e4"]
            BNg8f6(+)     -> ["g8", "f6", "+"]
            WKe1g1(O)     -> ["e1", "g1", "O"]
        """
        tokens = []

        for move in text.strip().split():
            if len(move) < 4:
                continue

            core = move[2:]  # Remove color + piece

            # Squares
            from_sq = core[0:2]
            to_sq = core[2:4]
            tokens.extend([from_sq, to_sq])

            # Modifiers
            suffix = core[4:]
            if "x" in suffix:
                tokens.append("x")
            if "+*" in suffix:
                tokens.append("+*")
            elif "+" in suffix:
                tokens.append("+")
            for promo in ["Q", "R", "B", "N"]:
                if f"({promo})" in suffix:
                    tokens.append(promo)
            # Castling
            if "O" in move or "o" in move:
                tokens.append("O")

        return tokens

    def _convert_token_to_id(self, token: str) -> int:
        return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN, 0))

    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:
        """Convert sub-tokens back to string representation."""
        special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
        return " ".join(t for t in tokens if t not in special)

    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple:
        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,)


# ===== Example usage =====
if __name__ == "__main__":
    tokenizer = ChessTokenizer.build_minimal_vocab()
    print(f"Vocabulary size: {tokenizer.vocab_size}")

    test_games = [
        "WPe2e4 BPe7e5",
        "WNg1f3 BNb8c6",
        "WBb5c6(x) BPd7d6",
        "WPe7e8(Q) BKe8d7",
        "WKe1g1(O) BKe8c8(o)",
    ]

    for game in test_games:
        print(f"\nOriginal: {game}")
        tokens = tokenizer._tokenize(game)
        print(f"Tokens: {tokens}")
        ids = tokenizer.convert_tokens_to_ids(tokens)
        print(f"IDs: {ids}")
        decoded = tokenizer.convert_ids_to_tokens(ids)
        print(f"Decoded: {decoded}")