tokenizer.py

"""
Custom Chess Tokenizer for the Chess Challenge.

This tokenizer supports TWO tokenization modes:

1) tokenization_mode="move"  (original)
   - Each move is a single token using the extended UCI notation
     from the Lichess dataset (e.g., WPe2e4, BNg8f6, WPe7e8=Q(x+), ...).
   - Vocabulary is usually built from the dataset (frequency threshold).

2) tokenization_mode="uci_square"  (recommended for good legal-move performance with small vocab)
   - Each move is decomposed into 3 tokens:
       [from_square, to_square, promotion_or_-]
     Example:
       "WPe2e4"      -> ["e2", "e4", "-"]
       "WPe7e8=Q(+)" -> ["e7", "e8", "q"]
   - Fixed vocabulary that can express ANY UCI move:
       specials (4) + squares (64) + promo tokens (5) = 73 tokens.

Why uci_square helps:
- You can keep vocab tiny (70-150 range) WITHOUT losing expressivity,
  so the model can still output any move.
"""

from __future__ import annotations

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

from transformers import PreTrainedTokenizer


class ChessTokenizer(PreTrainedTokenizer):
    """
    A custom tokenizer for chess moves.

    - "move" mode: extended-uci move tokens like "WPe2e4"
    - "uci_square" mode: squares + promotion 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.

        Args:
            vocab_file: Path to a JSON file containing the vocabulary mapping.
            vocab: Dictionary mapping tokens to IDs (alternative to vocab_file).
            kwargs:
              - tokenization_mode: "move" (default) or "uci_square"
              - plus usual HF tokenizer 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

        # Read tokenization_mode from kwargs (and keep it for save/load)
        tokenization_mode = kwargs.pop("tokenization_mode", "move")
        if tokenization_mode not in ("move", "uci_square"):
            raise ValueError(f"Unknown tokenization_mode={tokenization_mode!r}")
        self.tokenization_mode = tokenization_mode

        # Remove any duplicate special-token entries passed through kwargs
        # to avoid "multiple values for keyword" errors when loading from disk.
        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:
            # Create a minimal vocabulary with just special tokens
            # (you should build from dataset or use build_uci_square_vocab)
            self._vocab = self._create_default_vocab()

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

        # Ensure tokenization_mode is saved in tokenizer_config.json
        kwargs["tokenization_mode"] = self.tokenization_mode

        # Call parent init AFTER setting up vocab
        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_default_vocab(self) -> Dict[str, int]:
        """
        Minimal default vocabulary with just special tokens.
        """
        special_tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
        vocab = {token: idx for idx, token in enumerate(special_tokens)}
        return vocab

    @classmethod
    def build_vocab_from_iterator(
        cls,
        iterator,
        min_frequency: int = 1,
    ) -> "ChessTokenizer":
        """
        Build a "move" tokenizer vocabulary from an iterator of game strings.

        Args:
            iterator: yields game strings (space-separated moves).
            min_frequency: minimum frequency for a token to be included.

        Returns:
            ChessTokenizer(tokenization_mode="move") with the built vocabulary.
        """
        from collections import Counter

        token_counts = Counter()

        for game in iterator:
            moves = game.strip().split()
            token_counts.update(moves)

        tokens = [token for token, count in token_counts.items() if count >= min_frequency]
        tokens = sorted(tokens)

        special_tokens = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]
        vocab = {token: idx for idx, token in enumerate(special_tokens + tokens)}

        return cls(vocab=vocab, tokenization_mode="move")

    @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,
        max_samples: Optional[int] = 100000,
    ) -> "ChessTokenizer":
        """
        Build a "move" tokenizer vocabulary from a Hugging Face dataset.

        Args:
            dataset_name: dataset on HF Hub.
            split: dataset split.
            column: column containing game strings.
            min_frequency: minimum frequency for a token to be included.
            max_samples: max number of samples to process.

        Returns:
            ChessTokenizer(tokenization_mode="move") with the built vocabulary.
        """
        from datasets import load_dataset

        dataset = load_dataset(dataset_name, split=split)

        if max_samples is not None:
            dataset = dataset.select(range(min(max_samples, len(dataset))))

        def game_iterator():
            for example in dataset:
                yield example[column]

        return cls.build_vocab_from_iterator(game_iterator(), min_frequency=min_frequency)

    @classmethod
    def build_uci_square_vocab(cls) -> "ChessTokenizer":
        """
        Build a fixed tiny vocab that can express ANY UCI move using 3 tokens:
        [from_square, to_square, promotion_or_-].

        Vocab:
          - 4 specials
          - 64 squares (a1..h8)
          - 5 promo tokens: "-", "q", "r", "b", "n"
        Total = 73 tokens.
        """
        special = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN]

        files = "abcdefgh"
        ranks = "12345678"
        squares = [f"{f}{r}" for r in ranks for f in files]  # 64

        promo = ["-", "q", "r", "b", "n"]  # 5

        vocab = {tok: i for i, tok in enumerate(special + squares + promo)}
        return cls(vocab=vocab, tokenization_mode="uci_square")

    @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 a string.

        - mode="move": split on spaces (original dataset tokens like "WPe2e4").
        - mode="uci_square": each dataset move token -> [from_sq, to_sq, promo_or_-]
          Example: "WPe2e4" -> ["e2", "e4", "-"]
                   "WPe7e8=Q" -> ["e7", "e8", "q"]
        """
        tokens = text.strip().split()

        if self.tokenization_mode != "uci_square":
            return tokens

        out: List[str] = []
        for tok in tokens:
            # Keep special tokens as-is if they appear in text
            if tok in self._vocab:
                out.append(tok)
                continue

            # Typical dataset format:
            #   [W|B][Piece][from_sq][to_sq]... possibly "(x)" "(+)" "(o)" "=Q" etc.
            # Examples:
            #   WPe2e4
            #   BNg8f6
            #   WPe7e8=Q(+)
            #   WPe5d6(x)
            if len(tok) >= 6 and tok[0] in ("W", "B"):
                from_sq = tok[2:4]
                to_sq = tok[4:6]

                if re.fullmatch(r"[a-h][1-8]", from_sq) and re.fullmatch(r"[a-h][1-8]", to_sq):
                    promo = "-"
                    if "=" in tok:
                        i = tok.index("=")
                        if i + 1 < len(tok):
                            p = tok[i + 1].lower()
                            if p in ("q", "r", "b", "n"):
                                promo = p
                    out.extend([from_sq, to_sq, promo])
                    continue

            # Fallback: find two squares anywhere in token
            squares = re.findall(r"[a-h][1-8]", tok)
            if len(squares) >= 2:
                promo = "-"
                m = re.search(r"[=]?([qrbnQRBN])", tok)
                if m:
                    promo = m.group(1).lower()
                out.extend([squares[0], squares[1], promo])
            else:
                out.append(self.UNK_TOKEN)

        return out

    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:
        # Filter out special tokens for cleaner output
        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,)


def count_vocab_from_dataset(
    dataset_name: str = "dlouapre/lichess_2025-01_1M",
    split: str = "train",
    column: str = "text",
    max_samples: Optional[int] = 10000,
) -> Dict[str, int]:
    """
    Count token frequencies in a dataset (useful for vocabulary analysis).
    """
    from collections import Counter
    from datasets import load_dataset

    dataset = load_dataset(dataset_name, split=split)

    if max_samples is not None:
        dataset = dataset.select(range(min(max_samples, len(dataset))))

    token_counts = Counter()

    for example in dataset:
        moves = example[column].strip().split()
        token_counts.update(moves)

    return dict(token_counts)