"""Tokenization classes for Lingxi.""" import json import os import unicodedata from functools import lru_cache from typing import Optional, Tuple import regex as re from transformers import AddedToken, PreTrainedTokenizer from transformers.utils import logging logger = logging.get_logger(__name__) VOCAB_FILES_NAMES = { "vocab_file": "vocab.json", "merges_file": "merges.txt", } MAX_MODEL_INPUT_SIZES = {"lingxi/lingxi-tokenizer": 1024} PRETOKENIZE_REGEX = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+""" @lru_cache() # Copied from transformers.models.gpt2.tokenization_gpt2.bytes_to_unicode def bytes_to_unicode(): bs = ( list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1)) ) cs = bs[:] n = 0 for b in range(2**8): if b not in bs: bs.append(b) cs.append(2**8 + n) n += 1 cs = [chr(n) for n in cs] return dict(zip(bs, cs)) # Copied from transformers.models.gpt2.tokenization_gpt2.get_pairs def get_pairs(word): """ Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). """ pairs = set() prev_char = word[0] for char in word[1:]: pairs.add((prev_char, char)) prev_char = char return pairs class LingxiTokenizer(PreTrainedTokenizer): vocab_files_names = VOCAB_FILES_NAMES model_input_names = ["input_ids", "attention_mask"] def __init__( self, vocab_file, merges_file, errors="replace", unk_token="<|endoftext|>", bos_token=None, eos_token="<|im_end|>", pad_token="<|endoftext|>", clean_up_tokenization_spaces=False, split_special_tokens=False, **kwargs, ): bos_token = ( AddedToken(bos_token, lstrip=False, rstrip=False, special=True, normalized=False) if isinstance(bos_token, str) else bos_token ) eos_token = ( AddedToken(eos_token, lstrip=False, rstrip=False, special=True, normalized=False) if isinstance(eos_token, str) else eos_token ) unk_token = ( AddedToken(unk_token, lstrip=False, rstrip=False, special=True, normalized=False) if isinstance(unk_token, str) else unk_token ) pad_token = ( AddedToken(pad_token, lstrip=False, rstrip=False, special=True, normalized=False) if isinstance(pad_token, str) else pad_token ) with open(vocab_file, encoding="utf-8") as vocab_handle: self.encoder = json.load(vocab_handle) self.decoder = {v: k for k, v in self.encoder.items()} self.errors = errors # how to handle errors in decoding self.byte_encoder = bytes_to_unicode() self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} bpe_merges = [] with open(merges_file, encoding="utf-8") as merges_handle: for i, line in enumerate(merges_handle): line = line.strip() if (i == 0 and line.startswith("#version:")) or not line: continue bpe_merges.append(tuple(line.split())) self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges)))) # NOTE: the cache can grow without bound and will get really large for long running processes # (esp. for texts of language that do not use space between word, e.g. Chinese); technically # not a memory leak but appears as one. # GPT2Tokenizer has the same problem, so let's be consistent. self.cache = {} self.pat = re.compile(PRETOKENIZE_REGEX) if kwargs.get("add_prefix_space", False): logger.warning_once( f"{self.__class__.__name} does not support `add_prefix_space`, setting it to True has no effect." ) super().__init__( errors=errors, bos_token=bos_token, eos_token=eos_token, pad_token=pad_token, unk_token=unk_token, clean_up_tokenization_spaces=clean_up_tokenization_spaces, split_special_tokens=split_special_tokens, **kwargs, ) @property def vocab_size(self) -> int: return len(self.encoder) # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.get_vocab def get_vocab(self): return dict(self.encoder, **self.added_tokens_encoder) # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.bpe @lru_cache(maxsize=100) # 设置缓存大小为100 def bpe(self, token): # if token in self.cache: # return self.cache[token] word = tuple(token) pairs = get_pairs(word) if not pairs: return token while True: bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) if bigram not in self.bpe_ranks: break first, second = bigram new_word = [] i = 0 while i < len(word): try: j = word.index(first, i) except ValueError: new_word.extend(word[i:]) break else: new_word.extend(word[i:j]) i = j if word[i] == first and i < len(word) - 1 and word[i + 1] == second: new_word.append(first + second) i += 2 else: new_word.append(word[i]) i += 1 new_word = tuple(new_word) word = new_word if len(word) == 1: break else: pairs = get_pairs(word) word = " ".join(word) # self.cache[token] = word return word # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer._tokenize def _tokenize(self, text): """Tokenize a string.""" bpe_tokens = [] for token in re.findall(self.pat, text): token = "".join( self.byte_encoder[b] for b in token.encode("utf-8") ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case) bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" ")) return bpe_tokens # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer._convert_token_to_id def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" return self.encoder.get(token, self.encoder.get(self.unk_token)) # Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer._convert_id_to_token def _convert_id_to_token(self, index): """Converts an index (integer) in a token (str) using the vocab.""" return self.decoder.get(index) def convert_tokens_to_string(self, tokens): """Converts a sequence of tokens (string) in a single string.""" text = "".join(tokens) text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors) return text def decode( self, token_ids, skip_special_tokens: bool = False, clean_up_tokenization_spaces: Optional[bool] = False, spaces_between_special_tokens: bool = False, **kwargs, ) -> str: return super().decode( token_ids, skip_special_tokens=skip_special_tokens, clean_up_tokenization_spaces=clean_up_tokenization_spaces, spaces_between_special_tokens=spaces_between_special_tokens, **kwargs, ) def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return vocab_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] ) merge_file = os.path.join( save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] ) with open(vocab_file, "w", encoding="utf-8") as f: f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n") index = 0 with open(merge_file, "w", encoding="utf-8") as writer: writer.write("#version: 0.2\n") for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]): if index != token_index: logger.warning( f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive." " Please check that the tokenizer is not corrupted!" ) index = token_index writer.write(" ".join(bpe_tokens) + "\n") index += 1 return vocab_file, merge_file def prepare_for_tokenization(self, text, **kwargs): text = unicodedata.normalize("NFC", text) return (text, kwargs)