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Custom Chess Tokenizer for the Chess Challenge.
This tokenizer treats each move as a single token using the extended UCI notation
from the Lichess dataset (e.g., WPe2e4, BNg8f6).
The dataset format uses:
- W/B prefix for White/Black
- Piece letter: P=Pawn, N=Knight, B=Bishop, R=Rook, Q=Queen, K=King
- Source and destination squares (e.g., e2e4)
- Special suffixes: (x)=capture, (+)=check, (+*)=checkmate, (o)/(O)=castling
"""
from __future__ import annotations
import json
import os
from pathlib import Path
from typing import Dict, List, Optional
from transformers import PreTrainedTokenizer
class ChessTokenizer(PreTrainedTokenizer):
"""
A custom tokenizer for chess moves using extended UCI notation.
"""
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: Additional arguments passed to PreTrainedTokenizer.
"""
# 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
# 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
# The full vocabulary should be built from the dataset
self._vocab = self._create_fixed_vocab()
# Create reverse mapping
self._ids_to_tokens = {v: k for k, v in self._vocab.items()}
# 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_fixed_vocab(self) -> Dict[str, int]:
vocab_list = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
# Separate moves
vocab_list.append(" ")
# Colors
vocab_list.extend(["W", "B"])
# Pieces
vocab_list.extend(["P", "N", "B", "R", "Q", "K"])
# 3. Squares
files = "abcdefgh"
ranks = "12345678"
squares = [f"{f}{r}" for f in files for r in ranks]
vocab_list.extend(sorted(squares))
# Suffixes
suffixes = ["(x)", "(+)", "(+*)", "(o)", "(O)", "(Q)", "(K)", "(x*)", "(x+*)"]
vocab_list.extend(suffixes)
vocab_list = list(dict.fromkeys(vocab_list))
return {token: idx for idx, token in enumerate(vocab_list)}
@property
def vocab_size(self) -> int:
"""Return the size of the vocabulary."""
return len(self._vocab)
def get_vocab(self) -> Dict[str, int]:
"""Return the vocabulary as a dictionary."""
return dict(self._vocab)
def _tokenize(self, text: str) -> List[str]:
"""
Tokenize a string of moves into atomic components.
"""
import re
tokens = []
moves = text.strip().split()
pattern = re.compile(r"^([WB])([PNBRQK])([a-h][1-8])([a-h][1-8])(.*)$")
for move in moves:
match = pattern.match(move)
if match:
for i in range(1,6):
if match.group(i) in self._vocab:
tokens.append(match.group(i))
tokens.append(' ')
else:
tokens.append(self.UNK_TOKEN)
return tokens
def _convert_token_to_id(self, token: str) -> int:
"""Convert a token to its ID."""
return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN, 0))
def _convert_id_to_token(self, index: int) -> str:
"""Convert an ID to its token."""
return self._ids_to_tokens.get(index, self.UNK_TOKEN)
def convert_tokens_to_string(self, tokens: List[str]) -> str:
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:
"""
Save the vocabulary to a JSON file.
Args:
save_directory: Directory to save the vocabulary.
filename_prefix: Optional prefix for the filename.
Returns:
Tuple containing the path to the saved vocabulary file.
"""
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,)
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