codingutf8 2022 the huggingface team and microsoft research ai4science licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache orglicenseslicense2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license tokenization classes for biogpt import json import os from typing import list optional tuple from tokenizationutils import pretrainedtokenizer from utils import logging logger logging getloggername vocabfilesnames vocabfile vocab json mergesfile merges txt pretrainedvocabfilesmap vocabfile microsoftbiogpt https huggingface comicrosoftbiogptresolvemainvocab json mergesfile microsoftbiogpt https huggingface comicrosoftbiogptresolvemainmerges txt pretrainedpositionalembeddingssizes microsoftbiogpt 1024 def getpairsword pairs set prevchar word0 for char in word1 pairs addprevchar char prevchar char return pairs class biogpttokenizerpretrainedtokenizer vocabfilesnames vocabfilesnames pretrainedvocabfilesmap pretrainedvocabfilesmap maxmodelinputsizes pretrainedpositionalembeddingssizes modelinputnames inputids attentionmask def init self vocabfile mergesfile unktokenunk bostokens eostokens septokens padtokenpad kwargs try import sacremoses except importerror raise importerror you need to install sacremoses to use biogpttokenizer see https pypi orgprojectsacremoses for installation self lang en self sm sacremoses cache of sm mosestokenizer instance self cachemosestokenizer self cachemosesdetokenizer returns vocab size return lenself encoder def getvocabself return dictself encoder self addedtokensencoder def mosestokenizeself text lang if lang not in self cachemosestokenizer mosestokenizer self sm mosestokenizerlanglang self cachemosestokenizerlang mosestokenizer return self cachemosestokenizerlang tokenize text aggressivedashsplitstrue returnstrfalse escapetrue def mosesdetokenizeself tokens lang if lang not in self cachemosesdetokenizer mosesdetokenizer self sm mosesdetokenizerlanglang self cachemosesdetokenizerlang mosesdetokenizer return self cachemosesdetokenizerlang detokenizetokens def bpeself token word tupletoken 1 token1 w if token in self cache return self cachetoken pairs getpairsword if not pairs return token w while true bigram minpairs keylambda pair self bperanks getpair floatinf if bigram not in self bperanks break first second bigram newword i 0 while i lenword try j word indexfirst i except valueerror newword extendwordi break else newword extendwordi j i j if wordi first and i lenword 1 and wordi 1 second newword appendfirst second i 2 else newword appendwordi i 1 newword tuplenewword word newword if lenword 1 break else pairs getpairsword word joinword if word n w word nw self cachetoken word return word def tokenizeself text bypasstokenizerfalse converts a token str in an id using the vocab return self encoder gettoken self encoder getself unktoken def convertidtotokenself index converts a sequence of tokens string in a single string remove bpe tokens t replace replacew for t in tokens tokens jointokens split detokenize text self mosesdetokenizetokens self lang return text def buildinputswithspecialtokens self tokenids0 listint tokenids1 optionallistint none listint if tokenids1 is none return self septokenid tokenids0 sep self septokenid return sep tokenids0 sep tokenids1 def getspecialtokensmask self tokenids0 listint tokenids1 optionallistint none alreadyhasspecialtokens bool false listint if alreadyhasspecialtokens return super getspecialtokensmask tokenids0tokenids0 tokenids1tokenids1 alreadyhasspecialtokenstrue no bos used in fairseq if tokenids1 is not none return 1 0 lentokenids0 1 0 lentokenids1 return 1 0 lentokenids0 def createtokentypeidsfromsequences self tokenids0 listint tokenids1 optionallistint none listint sep self septokenid no bos used in fairseq if tokenids1 is none return lentokenids0 sep 0 return lentokenids0 sep 0 lentokenids1 sep 1 def savevocabularyself savedirectory str filenameprefix optionalstr none tuplestr if not os path isdirsavedirectory logger errorfvocabulary path savedirectory should be a directory return vocabfile os path join savedirectory filenameprefix if filenameprefix else vocabfilesnamesvocabfile mergefile os path join savedirectory filenameprefix if filenameprefix else vocabfilesnamesmergesfile with openvocabfile w encodingutf8 as f f writejson dumpsself encoder indent2 sortkeystrue ensureasciifalse n index 0 with openmergefile w encodingutf8 as writer for bpetokens tokenindex in sortedself bperanks items keylambda kv kv1 if index tokenindex logger warning fsaving vocabulary to mergefile bpe merge indices are not consecutive please check that the tokenizer is not corrupted index tokenindex writer write joinbpetokens n index 1 return vocabfile mergefile def getstateself state self dict copy statesm none return state def setstateself d self dict d try import sacremoses except importerror raise importerror you need to install sacremoses to use xlmtokenizer see https pypi orgprojectsacremoses for installation self sm sacremoses coding utf 8 2022 the huggingface team and microsoft research ai4science licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license tokenization classes for biogpt return set of symbol pairs in a word word is represented as tuple of symbols symbols being variable length strings construct an fairseq transformer tokenizer moses tokenization followed by byte pair encoding this tokenizer inherits from pretrainedtokenizer which contains most of the main methods users should refer to this superclass for more information regarding those methods args vocab_file str path to the vocabulary file merges_file str merges file unk_token str optional defaults to unk the unknown token a token that is not in the vocabulary cannot be converted to an id and is set to be this token instead bos_token str optional defaults to s the beginning of sequence token that was used during pretraining can be used a sequence classifier token tip when building a sequence using special tokens this is not the token that is used for the beginning of sequence the token used is the cls_token tip eos_token str optional defaults to s the end of sequence token tip when building a sequence using special tokens this is not the token that is used for the end of sequence the token used is the sep_token tip sep_token str optional defaults to s the separator token which is used when building a sequence from multiple sequences e g two sequences for sequence classification or for a text and a question for question answering it is also used as the last token of a sequence built with special tokens pad_token str optional defaults to pad the token used for padding for example when batching sequences of different lengths cache of sm mosestokenizer instance initialisation returns vocab size returns a tokenized string converts a token str in an id using the vocab converts an index integer in a token str using the vocab converts a sequence of tokens string in a single string remove bpe detokenize build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens a biogpt sequence has the following format single sequence s x pair of sequences s a s b args token_ids_0 list int list of ids to which the special tokens will be added token_ids_1 list int optional optional second list of ids for sequence pairs returns list int list of input ids glossary input ids with the appropriate special tokens retrieve sequence ids from a token list that has no special tokens added this method is called when adding special tokens using the tokenizer prepare_for_model method args token_ids_0 list int list of ids token_ids_1 list int optional optional second list of ids for sequence pairs already_has_special_tokens bool optional defaults to false whether or not the token list is already formatted with special tokens for the model returns list int a list of integers in the range 0 1 1 for a special token 0 for a sequence token no bos used in fairseq create a mask from the two sequences passed to be used in a sequence pair classification task a fairseq transformer sequence pair mask has the following format 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 first sequence second sequence if token_ids_1 is none this method only returns the first portion of the mask 0s args token_ids_0 list int list of ids token_ids_1 list int optional optional second list of ids for sequence pairs returns list int list of token type ids glossary token type ids according to the given sequence s no bos used in fairseq
import json
import os
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json",
    },
    "merges_file": {"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt"},
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "microsoft/biogpt": 1024,
}


def get_pairs(word):
    
    pairs = set()
    prev_char = word[0]
    for char in word[1:]:
        pairs.add((prev_char, char))
        prev_char = char
    return pairs


class BioGptTokenizer(PreTrainedTokenizer):
    

    vocab_files_names = VOCAB_FILES_NAMES
    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    model_input_names = ["input_ids", "attention_mask"]

    def __init__(
        self,
        vocab_file,
        merges_file,
        unk_token="<unk>",
        bos_token="<s>",
        eos_token="</s>",
        sep_token="</s>",
        pad_token="<pad>",
        **kwargs,
    ):
        try:
            import sacremoses
        except ImportError:
            raise ImportError(
                "You need to install sacremoses to use BioGptTokenizer. "
                "See https://pypi.org/project/sacremoses/ for installation."
            )

        self.lang = "en"
        self.sm = sacremoses
        
        self.cache_moses_tokenizer = {}
        self.cache_moses_detokenizer = {}

        
        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()}
        with open(merges_file, encoding="utf-8") as merges_handle:
            merges = merges_handle.read().split("\n")[:-1]
        merges = [tuple(merge.split()[:2]) for merge in merges]
        self.bpe_ranks = dict(zip(merges, range(len(merges))))
        self.cache = {}

        super().__init__(
            bos_token=bos_token,
            eos_token=eos_token,
            sep_token=sep_token,
            unk_token=unk_token,
            pad_token=pad_token,
            **kwargs,
        )

    @property
    def vocab_size(self):
        
        return len(self.encoder)

    def get_vocab(self):
        return dict(self.encoder, **self.added_tokens_encoder)

    def moses_tokenize(self, text, lang):
        if lang not in self.cache_moses_tokenizer:
            moses_tokenizer = self.sm.MosesTokenizer(lang=lang)
            self.cache_moses_tokenizer[lang] = moses_tokenizer
        return self.cache_moses_tokenizer[lang].tokenize(
            text, aggressive_dash_splits=True, return_str=False, escape=True
        )

    def moses_detokenize(self, tokens, lang):
        if lang not in self.cache_moses_detokenizer:
            moses_detokenizer = self.sm.MosesDetokenizer(lang=lang)
            self.cache_moses_detokenizer[lang] = moses_detokenizer
        return self.cache_moses_detokenizer[lang].detokenize(tokens)

    def bpe(self, token):
        word = tuple(token[:-1]) + (token[-1] + "</w>",)
        if token in self.cache:
            return self.cache[token]
        pairs = get_pairs(word)

        if not pairs:
            return token + "</w>"

        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)
        if word == "\n  </w>":
            word = "\n</w>"
        self.cache[token] = word
        return word

    def _tokenize(self, text, bypass_tokenizer=False):
        
        if bypass_tokenizer:
            text = text.split()
        else:
            text = self.moses_tokenize(text, self.lang)

        split_tokens = []
        for token in text:
            if token:
                split_tokens.extend(list(self.bpe(token).split(" ")))

        return split_tokens

    def _convert_token_to_id(self, token):
        
        return self.encoder.get(token, self.encoder.get(self.unk_token))

    def _convert_id_to_token(self, index):
        
        return self.decoder.get(index, self.unk_token)

    def convert_tokens_to_string(self, tokens):
        
        
        tokens = [t.replace(" ", "").replace("</w>", " ") for t in tokens]
        tokens = "".join(tokens).split()
        
        text = self.moses_detokenize(tokens, self.lang)
        return text

    def build_inputs_with_special_tokens(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        
        if token_ids_1 is None:
            return [self.sep_token_id] + token_ids_0
        sep = [self.sep_token_id]
        return sep + token_ids_0 + sep + token_ids_1

    def get_special_tokens_mask(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
    ) -> List[int]:
        
        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
            )
        
        if token_ids_1 is not None:
            return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1))
        return [1] + ([0] * len(token_ids_0))

    def create_token_type_ids_from_sequences(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        
        sep = [self.sep_token_id]

        
        if token_ids_1 is None:
            return len(token_ids_0 + sep) * [0]
        return len(token_ids_0 + sep) * [0] + len(token_ids_1 + sep) * [1]

    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:
            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 __getstate__(self):
        state = self.__dict__.copy()
        state["sm"] = None
        return state

    def __setstate__(self, d):
        self.__dict__ = d

        try:
            import sacremoses
        except ImportError:
            raise ImportError(
                "You need to install sacremoses to use XLMTokenizer. "
                "See https://pypi.org/project/sacremoses/ for installation."
            )

        self.sm = sacremoses