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""" |
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Custom Chess Tokenizer - Final Fix |
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""" |
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from __future__ import annotations |
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import json |
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import os |
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import shutil |
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import re |
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from typing import Dict, List, Optional |
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from transformers import PreTrainedTokenizer |
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MOVE_RE = re.compile(r"([a-h][1-8])([a-h][1-8])") |
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PROMO_RE = re.compile(r"=([NBRQ])") |
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def normalize_move(tok: str) -> str: |
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if tok.startswith("["): return tok |
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m = MOVE_RE.search(tok) |
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if not m: return tok |
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fr, to = m.group(1), m.group(2) |
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promo = "" |
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pm = PROMO_RE.search(tok) |
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if pm: promo = "=" + pm.group(1) |
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prefix = tok[:2] if len(tok) >= 2 else "WP" |
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return f"{prefix}{fr}{to}{promo}" |
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class ChessTokenizer(PreTrainedTokenizer): |
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model_input_names = ["input_ids", "attention_mask"] |
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vocab_files_names = {"vocab_file": "vocab.json"} |
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PAD_TOKEN = "[PAD]" |
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BOS_TOKEN = "[BOS]" |
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EOS_TOKEN = "[EOS]" |
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UNK_TOKEN = "[UNK]" |
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def __init__(self, vocab_file=None, vocab=None, **kwargs): |
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self._pad_token = self.PAD_TOKEN |
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self._bos_token = self.BOS_TOKEN |
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self._eos_token = self.EOS_TOKEN |
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self._unk_token = self.UNK_TOKEN |
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for t in ["pad_token", "bos_token", "eos_token", "unk_token"]: kwargs.pop(t, None) |
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if vocab is None: |
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if vocab_file is None: |
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vocab_file = os.path.join(os.path.dirname(__file__), "vocab.json") |
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self.vocab_file = vocab_file |
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if os.path.exists(vocab_file): |
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with open(vocab_file, "r", encoding="utf-8") as f: self._vocab = json.load(f) |
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else: self._vocab = self._create_default_vocab() |
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else: |
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self._vocab = vocab |
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self.vocab_file = vocab_file |
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self._ids_to_tokens = {v: k for k, v in self._vocab.items()} |
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super().__init__(pad_token=self.PAD_TOKEN, bos_token=self.BOS_TOKEN, eos_token=self.EOS_TOKEN, unk_token=self.UNK_TOKEN, **kwargs) |
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def save_pretrained(self, save_directory: str, **kwargs): |
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super().save_pretrained(save_directory, **kwargs) |
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src_path = os.path.abspath(__file__) |
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dst_path = os.path.join(save_directory, "tokenizer.py") |
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if src_path != dst_path: shutil.copy(src_path, dst_path) |
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config_path = os.path.join(save_directory, "tokenizer_config.json") |
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if os.path.exists(config_path): |
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with open(config_path, "r") as f: cfg = json.load(f) |
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cfg["auto_map"] = {"AutoTokenizer": "tokenizer.ChessTokenizer"} |
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with open(config_path, "w") as f: json.dump(cfg, f, indent=2) |
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def _create_default_vocab(self): |
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return {t: i for i, t in enumerate([self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN])} |
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@classmethod |
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def build_vocab_from_dataset(cls, dataset_name, split="train", column="text", min_frequency=2, max_vocab_size=1700, max_samples=100000): |
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from datasets import load_dataset |
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from collections import Counter |
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ds = load_dataset(dataset_name, split=split, streaming=True) |
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ds = ds.take(max_samples) |
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counter = Counter() |
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for ex in ds: |
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moves = [normalize_move(t) for t in ex[column].split()] |
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counter.update(moves) |
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special = [cls.PAD_TOKEN, cls.BOS_TOKEN, cls.EOS_TOKEN, cls.UNK_TOKEN] |
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most_common = counter.most_common(max_vocab_size - len(special)) |
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vocab = {t: i for i, t in enumerate(special + [t for t, c in most_common])} |
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return cls(vocab=vocab) |
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@property |
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def vocab_size(self): return len(self._vocab) |
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def get_vocab(self): return dict(self._vocab) |
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def _tokenize(self, text): return [normalize_move(t) for t in text.strip().split()] |
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def _convert_token_to_id(self, token): return self._vocab.get(token, self._vocab.get(self.UNK_TOKEN)) |
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def _convert_id_to_token(self, index): return self._ids_to_tokens.get(index, self.UNK_TOKEN) |
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def convert_tokens_to_string(self, tokens): return " ".join(t for t in tokens if t not in [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN]) |
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def save_vocabulary(self, save_directory, filename_prefix=None): |
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if not os.path.isdir(save_directory): os.makedirs(save_directory, exist_ok=True) |
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path = os.path.join(save_directory, (filename_prefix + "-" if filename_prefix else "") + "vocab.json") |
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with open(path, "w", encoding="utf-8") as f: json.dump(self._vocab, f, ensure_ascii=False, indent=2) |
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return (path,) |
