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"""
Custom Chess Tokenizer for the Chess Challenge.
This tokenizer decomposes moves into atomic tokens:
Piece -> Source Square -> Target Square -> Suffixes.
Example: "WPe2e4" -> ['P', 'e2', 'e4'] (Color is implicit to save context)
Example: "Bxb7+" -> ['B', 'c8', 'b7', '(x)', '(+)']
"""
from __future__ import annotations
import json
import os
import re
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]"
# Atomic components
PIECES = ["P", "N", "B", "R", "Q", "K"]
FILES = "abcdefgh"
RANKS = "12345678"
SUFFIXES = ["(x)", "(+)", "(+*)", "(o)", "(O)", "(=)"]
def __init__(
self,
vocab_file: Optional[str] = None,
vocab: Optional[Dict[str, int]] = None,
**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
# Clean kwargs
kwargs.pop("pad_token", None)
kwargs.pop("bos_token", None)
kwargs.pop("eos_token", None)
kwargs.pop("unk_token", None)
# Load or create FIXED vocabulary
if vocab is not None:
self._vocab = vocab
else:
self._vocab = self._create_fixed_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_fixed_vocab(self) -> Dict[str, int]:
"""Creates the fixed vocabulary of ~80 atomic tokens."""
vocab = {}
idx = 0
# 1. Special Tokens
for token in [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]:
vocab[token] = idx
idx += 1
# 2. Pieces
for p in self.PIECES:
vocab[p] = idx
idx += 1
# 3. Squares (a1...h8)
# We treat squares as atomic tokens for better spatial learning
for f in self.FILES:
for r in self.RANKS:
vocab[f"{f}{r}"] = idx
idx += 1
# 4. Suffixes
for s in self.SUFFIXES:
vocab[s] = idx
idx += 1
return vocab
@classmethod
def build_vocab_from_dataset(
cls,
dataset_name: str = "dlouapre/lichess_2025-01_1M",
**kwargs
) -> "ChessTokenizer":
"""
Override: Returns the tokenizer with the fixed vocabulary immediately.
We do not need to scan the dataset anymore.
"""
print("Initializing Fixed Vocabulary Tokenizer (Deconstructed Strategy)...")
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]:
"""
Decomposes move strings into atomic tokens.
Input: "[BOS] WPe2e4 BNg8f6"
Output: ['[BOS]', 'P', 'e2', 'e4', 'N', 'g8', 'f6']
"""
tokens = []
moves = text.strip().split()
# Set of special tokens for quick lookup
special_tokens = {self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN}
for move in moves:
# Skip empty strings
if not move: continue
# 1. Handle Special Tokens (Important for data.py compatibility)
if move in special_tokens:
tokens.append(move)
continue
# 2. Regex to parse Lichess format: WPe2e4(x)
# Group 1: Color (W/B) - Ignored
# Group 2: Piece (P/N/B/R/Q/K)
# Group 3: Source (e.g. e2)
# Group 4: Target (e.g. e4)
# Group 5: Suffix (optional)
match = re.match(r"([WB])([PNBRQK])([a-h][1-8])([a-h][1-8])(.*)", move)
if match:
_, piece, src, dst, suffix = match.groups()
tokens.extend([piece, src, dst])
if suffix:
if suffix in self._vocab:
tokens.append(suffix)
else:
# Fallback for unexpected formats
found_any = False
# Check for piece
for p in self.PIECES:
if p in move:
tokens.append(p)
found_any = True
break
# Check for squares
squares = re.findall(r"[a-h][1-8]", move)
tokens.extend(squares)
if squares: found_any = True
# Check for suffixes
for s in self.SUFFIXES:
if s in move:
tokens.append(s)
found_any = True
if not found_any:
tokens.append(self.UNK_TOKEN)
return tokens
def _convert_token_to_id(self, token: str) -> int:
return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN))
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:
# Reconstructs a readable string (space separated for clarity)
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,) |