tokenizer.py

# =========================
# CELLULE 1 — src/tokenizer.py
# ➜ Remplace TOUT le contenu du fichier par ce code
# =========================

"""
Factorized Chess Tokenizer for the Chess Challenge.

Instead of "1 move = 1 token", we represent a move as multiple tokens:
- Side: [W] / [B]
- Piece: [P], [N], [BISHOP], [R], [Q], [K]
- Squares: [e2], [e4], ...
- Optional suffix: [x], [+], [#], [O-O], [O-O-O]
- Optional promotion: [prom_Q], [prom_R], [prom_B], [prom_N]

Important:
- We KEEP squares as tokens so evaluation (regex [a-h][1-8]) can extract UCI moves.
- We decode squares to plain "e2" etc, and promotions to "q/r/b/n" so evaluate.py can detect promotions.
"""

from __future__ import annotations

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

from transformers import PreTrainedTokenizer


MOVE_RE = re.compile(
    r"^(?P<side>[WB])"
    r"(?P<piece>[PNBRQK])"
    r"(?P<src>[a-h][1-8])"
    r"(?P<dst>[a-h][1-8])"
    r"(?P<suffix>.*)$"
)

SQUARE_TOKEN_RE = re.compile(r"^\[[a-h][1-8]\]$")



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

    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,
    ):
        # 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

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

        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._create_default_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_default_vocab(self) -> Dict[str, int]:
        special_tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]

        side_tokens = ["[W]", "[B]"]
        piece_tokens = ["[P]", "[N]", "[BISHOP]", "[R]", "[Q]", "[K]"]

        square_tokens = [f"[{file}{rank}]" for rank in "12345678" for file in "abcdefgh"]

        suffix_tokens = [
            "[x]", "[+]", "[#]",
            "[O-O]", "[O-O-O]",
            "[prom_Q]", "[prom_R]", "[prom_B]", "[prom_N]",
        ]

        vocab_list = special_tokens + side_tokens + piece_tokens + square_tokens + suffix_tokens
        return {tok: i for i, tok in enumerate(vocab_list)}

    # IMPORTANT: prevent HF from auto-adding BOS/EOS on top of your text
    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
        if token_ids_1 is None:
            return token_ids_0
        return token_ids_0 + token_ids_1

    @classmethod
    def build_vocab_from_iterator(cls, iterator, min_frequency: int = 1) -> "ChessTokenizer":
        # Fixed vocab (we ignore dataset frequency)
        return cls()

    @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":
        # Fixed vocab (we ignore dataset frequency)
        return cls()

    @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]:
        tokens: List[str] = []
        parts = str(text).strip().split()

        specials = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}

        for p in parts:
            if p in specials:
                tokens.append(p)
                continue

            m = MOVE_RE.match(p)
            if not m:
                tokens.append(self.UNK_TOKEN)
                continue

            side = m.group("side")
            piece = m.group("piece")
            src = m.group("src")
            dst = m.group("dst")
            suffix = m.group("suffix") or ""

            tokens.append("[W]" if side == "W" else "[B]")

            if piece == "B":
                tokens.append("[BISHOP]")
            else:
                tokens.append(f"[{piece}]")

            tokens.append(f"[{src}]")
            tokens.append(f"[{dst}]")

            # capture/check/checkmate
            if "x" in suffix:
                tokens.append("[x]")
            if "*" in suffix:
                tokens.append("[#]")
            elif "+" in suffix:
                tokens.append("[+]")

            # castling annotation (optional, squares already encode it)
            if piece == "K":
                if (src, dst) in (("e1", "g1"), ("e8", "g8")) or "(o)" in suffix:
                    tokens.append("[O-O]")
                elif (src, dst) in (("e1", "c1"), ("e8", "c8")) or "(O)" in suffix:
                    tokens.append("[O-O-O]")

            # promotion
            if "=" in suffix:
                i = suffix.find("=")
                if i != -1 and i + 1 < len(suffix):
                    promo = suffix[i + 1].upper()
                    if promo in ("Q", "R", "B", "N"):
                        tokens.append(f"[prom_{promo}]")

        return tokens

    def _convert_token_to_id(self, token: str) -> int:
        return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN, 3))

    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:
        """
        Decode tokens into a compact string so evaluate.py can extract squares easily.

        Examples:
          [W] [P] [e2] [e4]          -> "WPe2e4"
          ... [e7] [e8] [prom_Q]     -> "WPe7e8q"   (promotion detectable)
        """
        out: List[str] = []
        for t in tokens:
            if t in (self.PAD_TOKEN,):
                continue

            # keep these literal so evaluator can compare EOS if needed
            if t in (self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN):
                out.append(t)
                continue

            if t == "[W]":
                out.append("W")
            elif t == "[B]":
                out.append("B")
            elif t == "[BISHOP]":
                out.append("B")
            elif t in ("[P]", "[N]", "[R]", "[Q]", "[K]"):
                out.append(t.strip("[]"))
            elif SQUARE_TOKEN_RE.match(t):
                out.append(t[1:-1])  # "[e2]" -> "e2"
            elif t == "[x]":
                out.append("(x)")
            elif t == "[+]":
                out.append("(+)")
            elif t == "[#]":
                out.append("(+*)")
            elif t == "[O-O]":
                out.append("(o)")
            elif t == "[O-O-O]":
                out.append("(O)")
            elif t == "[prom_Q]":
                out.append("q")
            elif t == "[prom_R]":
                out.append("r")
            elif t == "[prom_B]":
                out.append("b")
            elif t == "[prom_N]":
                out.append("n")
            else:
                out.append(t)

        return "".join(out)

    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,)