codesage/codesage-small

1import json2import os3from functools import lru_cache4from typing import List, Optional, Tuple5 6import regex as re7 8from transformers import AddedToken, PreTrainedTokenizer9import logging10 11 12logger = logging.getLogger(__name__)13 14VOCAB_FILES_NAMES = {15    "vocab_file": "vocab.json",16    "merges_file": "merges.txt",17}18 19# Taken from20# https://github.com/huggingface/transformers/blob/8aca43bdb3cb9a5020f6d57589d85679dc873b1c/src/transformers/models/gpt2/tokenization_gpt2.py#L62-L8421@lru_cache()22def bytes_to_unicode():23    """24    Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control25    characters the bpe code barfs on.26 27    The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab28    if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for29    decent coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup30    tables between utf-8 bytes and unicode strings.31    """32    bs = (33        list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))34    )35    cs = bs[:]36    n = 037    for b in range(2**8):38        if b not in bs:39            bs.append(b)40            cs.append(2**8 + n)41            n += 142    cs = [chr(n) for n in cs]43    return dict(zip(bs, cs))44 45 46def get_pairs(word):47    """48    Return set of symbol pairs in a word.49 50    Word is represented as tuple of symbols (symbols being variable-length strings).51    """52    pairs = set()53    prev_char = word[0]54    for char in word[1:]:55        pairs.add((prev_char, char))56        prev_char = char57    return pairs58 59 60class CodeSageTokenizer(PreTrainedTokenizer):61    """A thin wrapper of the starcoder tokenizer.62    See HuggingFace for further documentation on general tokenizer methods.63    """64 65    vocab_files_names = VOCAB_FILES_NAMES66    model_input_names = ["input_ids", "attention_mask"]67 68    def __init__(69        self,70        vocab_file,71        merges_file,72        errors="replace",73        unk_token="<|endoftext|>",74        bos_token="<|endoftext|>",75        eos_token="<|endoftext|>",76        pad_token=None,77        add_prefix_space=False,78        add_bos_token=False,79        add_eos_token=True,80        **kwargs,81    ):82        bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token83        eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token84        unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token85        pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token86 87        self.add_bos_token = add_bos_token88        self.add_eos_token = add_eos_token89 90        with open(vocab_file, encoding="utf-8") as vocab_handle:91            self.encoder = json.load(vocab_handle)92        self.decoder = {v: k for k, v in self.encoder.items()}93        self.errors = errors  # how to handle errors in decoding94        self.byte_encoder = bytes_to_unicode()95        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}96        with open(merges_file, encoding="utf-8") as merges_handle:97            bpe_merges = merges_handle.read().split("\n")[1:-1]98        bpe_merges = [tuple(merge.split()) for merge in bpe_merges]99        self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))100        self.cache = {}101        self.add_prefix_space = add_prefix_space102 103        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions104        self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")105 106        super().__init__(107            errors=errors,108            unk_token=unk_token,109            bos_token=bos_token,110            eos_token=eos_token,111            pad_token=pad_token,112            add_prefix_space=add_prefix_space,113            add_bos_token=add_bos_token,114            add_eos_token=add_eos_token,115            **kwargs,116        )117 118    @property119    def vocab_size(self):120        return len(self.encoder)121 122    def get_vocab(self):123        return dict(self.encoder, **self.added_tokens_encoder)124 125    def bpe(self, token):126        if token in self.cache:127            return self.cache[token]128        word = tuple(token)129        pairs = get_pairs(word)130 131        if not pairs:132            return token133 134        while True:135            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))136            if bigram not in self.bpe_ranks:137                break138            first, second = bigram139            new_word = []140            i = 0141            while i < len(word):142                try:143                    j = word.index(first, i)144                except ValueError:145                    new_word.extend(word[i:])146                    break147                else:148                    new_word.extend(word[i:j])149                    i = j150 151                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:152                    new_word.append(first + second)153                    i += 2154                else:155                    new_word.append(word[i])156                    i += 1157            new_word = tuple(new_word)158            word = new_word159            if len(word) == 1:160                break161            else:162                pairs = get_pairs(word)163        word = " ".join(word)164        self.cache[token] = word165        return word166 167    def build_inputs_with_special_tokens(168            self,169            token_ids_0: List[int],170            token_ids_1: Optional[List[int]] = None) -> List[int]:171        bos_token_id = [self.bos_token_id] if self.add_bos_token else []172        eos_token_id = [self.eos_token_id] if self.add_eos_token else []173 174        output = bos_token_id + token_ids_0 + eos_token_id175 176        if token_ids_1 is not None:177            output = output + bos_token_id + token_ids_1 + eos_token_id178 179        return output180 181    def get_special_tokens_mask(182        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False183    ) -> List[int]:184        """185        Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding186        special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods.187 188        Args:189            token_ids_0 (`List[int]`):190                List of IDs.191            token_ids_1 (`List[int]`, *optional*):192                Optional second list of IDs for sequence pairs.193            already_has_special_tokens (`bool`, *optional*, defaults to `False`):194                Whether or not the token list is already formatted with special tokens for the model.195 196        Returns:197            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.198        """199        if already_has_special_tokens:200            return super().get_special_tokens_mask(201                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True202            )203 204        if not self.add_bos_token:205            return super().get_special_tokens_mask(206                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=False207            )208 209        if token_ids_1 is None:210            return [1] + ([0] * len(token_ids_0))211        return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1))212 213    def _tokenize(self, text):214        """Tokenize a string."""215        bpe_tokens = []216        for token in re.findall(self.pat, text):217            token = "".join(218                self.byte_encoder[b] for b in token.encode("utf-8")219            )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)220            bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))221        return bpe_tokens222 223    def _convert_token_to_id(self, token):224        """Converts a token (str) in an id using the vocab."""225        return self.encoder.get(token, self.encoder.get(self.unk_token))226 227    def _convert_id_to_token(self, index):228        """Converts an index (integer) in a token (str) using the vocab."""229        return self.decoder.get(index)230 231    def convert_tokens_to_string(self, tokens):232        """Converts a sequence of tokens (string) in a single string."""233        text = "".join(tokens)234        text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)235        return text236 237    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:238        if not os.path.isdir(save_directory):239            logger.error(f"Vocabulary path ({save_directory}) should be a directory")240            return241        vocab_file = os.path.join(242            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]243        )244        merge_file = os.path.join(245            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]246        )247 248        with open(vocab_file, "w", encoding="utf-8") as f:249            f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")250 251        index = 0252        with open(merge_file, "w", encoding="utf-8") as writer:253            writer.write("#version: 0.2\n")254            for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):255                if index != token_index:256                    logger.warning(257                        f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."258                        " Please check that the tokenizer is not corrupted!"259                    )260                    index = token_index261                writer.write(" ".join(bpe_tokens) + "\n")262                index += 1263 264        return vocab_file, merge_file265 266    def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):267        add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)268        if is_split_into_words or add_prefix_space:269            text = " " + text270        return (text, kwargs)271 272    @property273    def default_chat_template(self):274        """275        A simple chat template that ignores role information and just concatenates messages with EOS tokens.276        """277        return "{% for message in messages %}" "{{ message.content }}{{ eos_token }}" "{% endfor %}"