| import json |
| import os |
| from transformers import PreTrainedTokenizer |
|
|
| class ChessIntelligentTokenizer(PreTrainedTokenizer): |
| model_input_names = ["input_ids", "attention_mask"] |
| vocab_files_names = {"vocab_file": "vocab.json"} |
| PAD_TOKEN = "[PAD]" |
| BOS_TOKEN = "[BOS]" |
| EOS_TOKEN = "[EOS]" |
| UNK_TOKEN = "[UNK]" |
| def __init__(self, vocab_file=None, vocab=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 |
| kwargs.pop("pad_token", None) |
| kwargs.pop("bos_token", None) |
| kwargs.pop("eos_token", None) |
| kwargs.pop("unk_token", None) |
| 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_chess_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_chess_vocab(self): |
| tokens = [self.PAD_TOKEN, self.BOS_TOKEN, self.EOS_TOKEN, self.UNK_TOKEN] |
| tokens.extend(["W", "B"]) |
| tokens.extend(["P", "N", "R", "Q", "K", "B"]) |
| files = "abcdefgh" |
| ranks = "12345678" |
| for f in files: |
| for r in ranks: |
| tokens.append(f + r) |
| tokens.extend(["(", ")", "x", "+", "*", "o", "=", " "]) |
| vocab = {token: idx for idx, token in enumerate(tokens)} |
| return vocab |
| @property |
| def vocab_size(self): |
| return len(self._vocab) |
| def get_vocab(self): |
| return dict(self._vocab) |
| def _tokenize(self, text): |
| tokens = [] |
| moves = text.strip().split() |
| for i, move in enumerate(moves): |
| if i > 0: |
| tokens.append(" ") |
| tokens.extend(self._parse_move(move)) |
| return tokens |
| def _parse_move(self, move): |
| tokens = [] |
| idx = 0 |
| if idx < len(move) and move[idx] in "WB": |
| tokens.append(move[idx]) |
| idx += 1 |
| if idx < len(move) and move[idx] in "PNRQKB": |
| tokens.append(move[idx]) |
| idx += 1 |
| while idx < len(move): |
| if idx + 1 < len(move) and move[idx] in "abcdefgh" and move[idx+1] in "12345678": |
| tokens.append(move[idx:idx+2]) |
| idx += 2 |
| elif move[idx] in "()+*xo=": |
| tokens.append(move[idx]) |
| idx += 1 |
| else: |
| idx += 1 |
| return tokens |
| def _convert_token_to_id(self, token): |
| return self._vocab.get(token, self._vocab[self.UNK_TOKEN]) |
| def _convert_id_to_token(self, index): |
| return self._ids_to_tokens.get(index, self.UNK_TOKEN) |
| def convert_tokens_to_string(self, tokens): |
| 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, filename_prefix=None): |
| 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,) |
|
|
