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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).
We normalize moves by stripping special suffixes:
- (x)=capture, (+)=check, (+*)=checkmate, (o)/(O)=castling
Example normalization:
WBb5c6(x) -> WBb5c6
BPd7c6(x) -> BPd7c6
... (x)(+) -> ... (both removed)
"""
from __future__ import annotations
import json
import os
from typing import Dict, List, Optional
from transformers import PreTrainedTokenizer
class ChessTokenizer(PreTrainedTokenizer):
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]"
# Suffixes to strip from move tokens
_SPECIAL_SUFFIXES = ("(x)", "(+*)", "(+)", "(o)", "(O)")
def __init__(
self,
vocab_file: Optional[str] = None,
vocab: Optional[Dict[str, int]] = None,
**kwargs,
):
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 duplicates passed through kwargs
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._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]
return {token: idx for idx, token in enumerate(special_tokens)}
@classmethod
def _normalize_move(cls, move: str) -> str:
"""
Strip known special suffixes from the end of a move token.
Handles stacked suffixes like "...(x)(+)" by stripping repeatedly.
"""
move = move.strip()
# Repeatedly remove known suffixes if they appear at the end
changed = True
while changed:
changed = False
for suf in cls._SPECIAL_SUFFIXES:
if move.endswith(suf):
move = move[: -len(suf)]
changed = True
return move
@classmethod
def build_vocab_from_iterator(
cls,
iterator,
min_frequency: int = 1,
) -> "ChessTokenizer":
from collections import Counter
token_counts = Counter()
for game in iterator:
raw_moves = game.strip().split()
moves = [cls._normalize_move(m) for m in raw_moves if m.strip()]
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)
@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)
@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]:
raw_moves = text.strip().split()
return [self._normalize_move(m) for m in raw_moves if m.strip()]
def _convert_token_to_id(self, token: str) -> int:
# Extra safety: normalize here too, in case someone passes raw "(x)" tokens.
token = self._normalize_move(token)
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:
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]:
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:
raw_moves = example[column].strip().split()
moves = [ChessTokenizer._normalize_move(m) for m in raw_moves if m.strip()]
token_counts.update(moves)
return dict(token_counts)
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