ai-content-maker/.venv/Lib/site-packages/tokenizers/implementations/bert_wordpiece.py

from typing import Dict, Iterator, List, Optional, Union

from tokenizers import AddedToken, Tokenizer, decoders, trainers
from tokenizers.models import WordPiece
from tokenizers.normalizers import BertNormalizer
from tokenizers.pre_tokenizers import BertPreTokenizer
from tokenizers.processors import BertProcessing

from .base_tokenizer import BaseTokenizer


class BertWordPieceTokenizer(BaseTokenizer):
    """Bert WordPiece Tokenizer"""

    def __init__(
        self,
        vocab: Optional[Union[str, Dict[str, int]]] = None,
        unk_token: Union[str, AddedToken] = "[UNK]",
        sep_token: Union[str, AddedToken] = "[SEP]",
        cls_token: Union[str, AddedToken] = "[CLS]",
        pad_token: Union[str, AddedToken] = "[PAD]",
        mask_token: Union[str, AddedToken] = "[MASK]",
        clean_text: bool = True,
        handle_chinese_chars: bool = True,
        strip_accents: Optional[bool] = None,
        lowercase: bool = True,
        wordpieces_prefix: str = "##",
    ):
        if vocab is not None:
            tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(unk_token)))
        else:
            tokenizer = Tokenizer(WordPiece(unk_token=str(unk_token)))

        # Let the tokenizer know about special tokens if they are part of the vocab
        if tokenizer.token_to_id(str(unk_token)) is not None:
            tokenizer.add_special_tokens([str(unk_token)])
        if tokenizer.token_to_id(str(sep_token)) is not None:
            tokenizer.add_special_tokens([str(sep_token)])
        if tokenizer.token_to_id(str(cls_token)) is not None:
            tokenizer.add_special_tokens([str(cls_token)])
        if tokenizer.token_to_id(str(pad_token)) is not None:
            tokenizer.add_special_tokens([str(pad_token)])
        if tokenizer.token_to_id(str(mask_token)) is not None:
            tokenizer.add_special_tokens([str(mask_token)])

        tokenizer.normalizer = BertNormalizer(
            clean_text=clean_text,
            handle_chinese_chars=handle_chinese_chars,
            strip_accents=strip_accents,
            lowercase=lowercase,
        )
        tokenizer.pre_tokenizer = BertPreTokenizer()

        if vocab is not None:
            sep_token_id = tokenizer.token_to_id(str(sep_token))
            if sep_token_id is None:
                raise TypeError("sep_token not found in the vocabulary")
            cls_token_id = tokenizer.token_to_id(str(cls_token))
            if cls_token_id is None:
                raise TypeError("cls_token not found in the vocabulary")

            tokenizer.post_processor = BertProcessing((str(sep_token), sep_token_id), (str(cls_token), cls_token_id))
        tokenizer.decoder = decoders.WordPiece(prefix=wordpieces_prefix)

        parameters = {
            "model": "BertWordPiece",
            "unk_token": unk_token,
            "sep_token": sep_token,
            "cls_token": cls_token,
            "pad_token": pad_token,
            "mask_token": mask_token,
            "clean_text": clean_text,
            "handle_chinese_chars": handle_chinese_chars,
            "strip_accents": strip_accents,
            "lowercase": lowercase,
            "wordpieces_prefix": wordpieces_prefix,
        }

        super().__init__(tokenizer, parameters)

    @staticmethod
    def from_file(vocab: str, **kwargs):
        vocab = WordPiece.read_file(vocab)
        return BertWordPieceTokenizer(vocab, **kwargs)

    def train(
        self,
        files: Union[str, List[str]],
        vocab_size: int = 30000,
        min_frequency: int = 2,
        limit_alphabet: int = 1000,
        initial_alphabet: List[str] = [],
        special_tokens: List[Union[str, AddedToken]] = [
            "[PAD]",
            "[UNK]",
            "[CLS]",
            "[SEP]",
            "[MASK]",
        ],
        show_progress: bool = True,
        wordpieces_prefix: str = "##",
    ):
        """Train the model using the given files"""

        trainer = trainers.WordPieceTrainer(
            vocab_size=vocab_size,
            min_frequency=min_frequency,
            limit_alphabet=limit_alphabet,
            initial_alphabet=initial_alphabet,
            special_tokens=special_tokens,
            show_progress=show_progress,
            continuing_subword_prefix=wordpieces_prefix,
        )
        if isinstance(files, str):
            files = [files]
        self._tokenizer.train(files, trainer=trainer)

    def train_from_iterator(
        self,
        iterator: Union[Iterator[str], Iterator[Iterator[str]]],
        vocab_size: int = 30000,
        min_frequency: int = 2,
        limit_alphabet: int = 1000,
        initial_alphabet: List[str] = [],
        special_tokens: List[Union[str, AddedToken]] = [
            "[PAD]",
            "[UNK]",
            "[CLS]",
            "[SEP]",
            "[MASK]",
        ],
        show_progress: bool = True,
        wordpieces_prefix: str = "##",
        length: Optional[int] = None,
    ):
        """Train the model using the given iterator"""

        trainer = trainers.WordPieceTrainer(
            vocab_size=vocab_size,
            min_frequency=min_frequency,
            limit_alphabet=limit_alphabet,
            initial_alphabet=initial_alphabet,
            special_tokens=special_tokens,
            show_progress=show_progress,
            continuing_subword_prefix=wordpieces_prefix,
        )
        self._tokenizer.train_from_iterator(
            iterator,
            trainer=trainer,
            length=length,
        )