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# Version (Player (Color + Piece), Source_S, Destination_D, Suffix)
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):
"""
Sub-move tokenizer for chess moves using extended UCI notation.
This tokenizer splits each move into atomic components:
- Players (color + piece): WP, WN, WB, WR, WQ, WK, etc.
- Source square: e2
- Destination square: e4
- Optional suffixes: x (capture), + (check), * (checkmate), o/O (castling)
Example:
Move "WPe2e4(x+)" -> ["WP", "e2_S", "e4_D", "(x+)"]
"""
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]"
# Atomic suffix tokens for default vocab
SUFFIX_TOKENS = ["(x)", "(+)", "(*)", "(o)", "(O)", "(+*)", "(x+)"]
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
# Remove duplicates from kwargs
kwargs.pop("pad_token", None)
kwargs.pop("bos_token", None)
kwargs.pop("eos_token", None)
kwargs.pop("unk_token", None)
# Load or create vocab
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()
# Reverse mapping
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]:
"""
Build a fixed vocab based on chess grammar for sub-moves.
Useful for predefined grammar instead of dataset-based vocab.
"""
colors = ["W", "B"]
pieces = ["P", "N", "B", "R", "Q", "K"]
files = ["a", "b", "c", "d", "e", "f", "g", "h"]
ranks = ["1", "2", "3", "4", "5", "6", "7", "8"]
squares = [f + r for f in files for r in ranks]
players = [c + p for c in colors for p in pieces]
# Source and destination tokens
sources = [sq + "_S" for sq in squares]
dests = [sq + "_D" for sq in squares]
# Build all possible sub-tokens
vocab_tokens = players + sources + dests + self.SUFFIX_TOKENS
# Add special tokens at the start
special_tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]
vocab = {token: idx for idx, token in enumerate(special_tokens + vocab_tokens)}
return vocab
def _tokenize(self, text: str) -> List[str]:
"""
Convert a string of moves into sub-move tokens.
"""
tokens: List[str] = []
moves = text.strip().split()
for move in moves:
if not move:
continue
# Color + Piece
tokens.append(move[:2]) # WP, BN, etc.
# Source square with _S
tokens.append(move[2:4] + "_S")
# Destination square with _D
tokens.append(move[4:6] + "_D")
if (len(move)>6):
tokens.append(move[6:])
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 a list of tokens back to a string."""
# Filter out special tokens for cleaner output
special = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
clean_tokens = []
for t in tokens:
if t in special:
continue
# Remove everything from _ onward
if "_" in t:
clean_tokens.append(t.split("_")[0])
else:
clean_tokens.append(t)
result = ""
temp = "".join(token for token in clean_tokens)
for i, str in enumerate(temp):
if str in ["W", "B"]:
if result == "":
result += str
elif temp[i-1].isnumeric() or temp[i-1]==")":
result += " " + str
else :
result += str
else :
result += str
return result.split()[0]
@property
def vocab_size(self) -> int:
return len(self._vocab)
def get_vocab(self) -> Dict[str, int]:
return dict(self._vocab)
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,)
@classmethod
def build_vocab_from_iterator(cls, iterator, min_frequency: int = 1) -> "ChessTokenizer":
"""
Build vocab from dataset iterator using sub-move tokens.
"""
from collections import Counter
token_counts = Counter()
for game in iterator:
sub_tokens = cls()._tokenize(game)
token_counts.update(sub_tokens)
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)
@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":
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)
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 sub-move token frequencies in a dataset (useful for vocab 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()
# Use sub-tokenization
tokenizer = ChessTokenizer()
for move in moves:
token_counts.update(tokenizer._tokenize(move))
return dict(token_counts)
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