codingutf8 2021 google ai google brain and the huggingface inc team 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 fnet model import os from shutil import copyfile from typing import list optional tuple from tokenizationutils import addedtoken from tokenizationutilsfast import pretrainedtokenizerfast from utils import issentencepieceavailable logging if issentencepieceavailable from tokenizationfnet import fnettokenizer else fnettokenizer none logger logging getloggername vocabfilesnames vocabfile spiece model tokenizerfile tokenizer json pretrainedvocabfilesmap vocabfile googlefnetbase https huggingface cogooglefnetbaseresolvemainspiece model googlefnetlarge https huggingface cogooglefnetlargeresolvemainspiece model tokenizerfile googlefnetbase https huggingface cogooglefnetbaseresolvemaintokenizer json googlefnetlarge https huggingface cogooglefnetlargeresolvemaintokenizer json pretrainedpositionalembeddingssizes googlefnetbase 512 googlefnetlarge 512 spieceunderline class fnettokenizerfastpretrainedtokenizerfast vocabfilesnames vocabfilesnames pretrainedvocabfilesmap pretrainedvocabfilesmap maxmodelinputsizes pretrainedpositionalembeddingssizes modelinputnames inputids tokentypeids slowtokenizerclass fnettokenizer def init self vocabfilenone tokenizerfilenone dolowercasefalse removespacetrue keepaccentstrue unktokenunk septokensep padtokenpad clstokencls masktokenmask kwargs mask token behave like a normal word i e include the space before it and is included in the raw text there should be a match in a nonnormalized sentence masktoken addedtokenmasktoken lstriptrue rstripfalse if isinstancemasktoken str else masktoken clstoken addedtokenclstoken lstripfalse rstripfalse if isinstanceclstoken str else clstoken septoken addedtokenseptoken lstripfalse rstripfalse if isinstanceseptoken str else septoken super init vocabfile tokenizerfiletokenizerfile dolowercasedolowercase removespaceremovespace keepaccentskeepaccents unktokenunktoken septokenseptoken padtokenpadtoken clstokenclstoken masktokenmasktoken kwargs self dolowercase dolowercase self removespace removespace self keepaccents keepaccents self vocabfile vocabfile property def cansaveslowtokenizerself bool return os path isfileself vocabfile if self vocabfile else false def buildinputswithspecialtokens self tokenids0 listint tokenids1 optionallistint none listint sep self septokenid cls self clstokenid if tokenids1 is none return cls tokenids0 sep return cls tokenids0 sep tokenids1 sep def createtokentypeidsfromsequences self tokenids0 listint tokenids1 optionallistint none listint sep self septokenid cls self clstokenid if tokenids1 is none return lencls tokenids0 sep 0 return lencls tokenids0 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 outvocabfile os path join savedirectory filenameprefix if filenameprefix else vocabfilesnamesvocabfile if os path abspathself vocabfile os path abspathoutvocabfile copyfileself vocabfile outvocabfile return outvocabfile coding utf 8 2021 google ai google brain and the huggingface inc team 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 fnet model construct a fast fnettokenizer backed by huggingface s tokenizers library adapted from alberttokenizerfast based on unigram https huggingface co docs tokenizers python latest components html highlight unigram models this tokenizer inherits from pretrainedtokenizerfast which contains most of the main methods users should refer to this superclass for more information regarding those methods args vocab_file str sentencepiece https github com google sentencepiece file generally has a spm extension that contains the vocabulary necessary to instantiate a tokenizer do_lower_case bool optional defaults to false whether or not to lowercase the input when tokenizing remove_space bool optional defaults to true whether or not to strip the text when tokenizing removing excess spaces before and after the string keep_accents bool optional defaults to true whether or not to keep accents when tokenizing 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 sep_token str optional defaults to sep 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 cls_token str optional defaults to cls the classifier token which is used when doing sequence classification classification of the whole sequence instead of per token classification it is the first token of the sequence when built with special tokens mask_token str optional defaults to mask the token used for masking values this is the token used when training this model with masked language modeling this is the token which the model will try to predict mask token behave like a normal word i e include the space before it and is included in the raw text there should be a match in a non normalized sentence build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens an fnet sequence has the following format single sequence cls x sep pair of sequences cls a sep b sep 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 creates a mask from the two sequences passed to be used in a sequence pair classification task an fnet 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 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
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_fnet import FNetTokenizer
else:
    FNetTokenizer = None

logger = logging.get_logger(__name__)
VOCAB_FILES_NAMES = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
    },
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

SPIECE_UNDERLINE = "▁"


class FNetTokenizerFast(PreTrainedTokenizerFast):
    

    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", "token_type_ids"]
    slow_tokenizer_class = FNetTokenizer

    def __init__(
        self,
        vocab_file=None,
        tokenizer_file=None,
        do_lower_case=False,
        remove_space=True,
        keep_accents=True,
        unk_token="<unk>",
        sep_token="[SEP]",
        pad_token="<pad>",
        cls_token="[CLS]",
        mask_token="[MASK]",
        **kwargs,
    ):
        
        
        mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token
        cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
        sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token

        super().__init__(
            vocab_file,
            tokenizer_file=tokenizer_file,
            do_lower_case=do_lower_case,
            remove_space=remove_space,
            keep_accents=keep_accents,
            unk_token=unk_token,
            sep_token=sep_token,
            pad_token=pad_token,
            cls_token=cls_token,
            mask_token=mask_token,
            **kwargs,
        )

        self.do_lower_case = do_lower_case
        self.remove_space = remove_space
        self.keep_accents = keep_accents
        self.vocab_file = vocab_file

    @property
    def can_save_slow_tokenizer(self) -> bool:
        return os.path.isfile(self.vocab_file) if self.vocab_file else False

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

    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]
        cls = [self.cls_token_id]

        if token_ids_1 is None:
            return len(cls + token_ids_0 + sep) * [0]
        return len(cls + 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
        out_vocab_file = os.path.join(
            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
        )

        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file):
            copyfile(self.vocab_file, out_vocab_file)

        return (out_vocab_file,)