Source code for transformers.tokenization_auto

# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
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# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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""" Auto Tokenizer class. """


from collections import OrderedDict

from .configuration_auto import (
    AlbertConfig,
    AutoConfig,
    BartConfig,
    BertConfig,
    BertGenerationConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    EncoderDecoderConfig,
    FlaubertConfig,
    FSMTConfig,
    FunnelConfig,
    GPT2Config,
    LayoutLMConfig,
    LongformerConfig,
    LxmertConfig,
    MarianConfig,
    MBartConfig,
    MobileBertConfig,
    OpenAIGPTConfig,
    PegasusConfig,
    RagConfig,
    ReformerConfig,
    RetriBertConfig,
    RobertaConfig,
    T5Config,
    TransfoXLConfig,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    replace_list_option_in_docstrings,
)
from .configuration_utils import PretrainedConfig
from .tokenization_albert import AlbertTokenizer
from .tokenization_bart import BartTokenizer, BartTokenizerFast
from .tokenization_bert import BertTokenizer, BertTokenizerFast
from .tokenization_bert_generation import BertGenerationTokenizer
from .tokenization_bert_japanese import BertJapaneseTokenizer
from .tokenization_bertweet import BertweetTokenizer
from .tokenization_camembert import CamembertTokenizer
from .tokenization_ctrl import CTRLTokenizer
from .tokenization_distilbert import DistilBertTokenizer, DistilBertTokenizerFast
from .tokenization_dpr import DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from .tokenization_electra import ElectraTokenizer, ElectraTokenizerFast
from .tokenization_flaubert import FlaubertTokenizer
from .tokenization_fsmt import FSMTTokenizer
from .tokenization_funnel import FunnelTokenizer, FunnelTokenizerFast
from .tokenization_gpt2 import GPT2Tokenizer, GPT2TokenizerFast
from .tokenization_layoutlm import LayoutLMTokenizer, LayoutLMTokenizerFast
from .tokenization_longformer import LongformerTokenizer, LongformerTokenizerFast
from .tokenization_lxmert import LxmertTokenizer, LxmertTokenizerFast
from .tokenization_marian import MarianTokenizer
from .tokenization_mbart import MBartTokenizer
from .tokenization_mobilebert import MobileBertTokenizer, MobileBertTokenizerFast
from .tokenization_openai import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from .tokenization_pegasus import PegasusTokenizer
from .tokenization_phobert import PhobertTokenizer
from .tokenization_rag import RagTokenizer
from .tokenization_reformer import ReformerTokenizer
from .tokenization_retribert import RetriBertTokenizer, RetriBertTokenizerFast
from .tokenization_roberta import RobertaTokenizer, RobertaTokenizerFast
from .tokenization_t5 import T5Tokenizer
from .tokenization_transfo_xl import TransfoXLTokenizer, TransfoXLTokenizerFast
from .tokenization_xlm import XLMTokenizer
from .tokenization_xlm_roberta import XLMRobertaTokenizer
from .tokenization_xlnet import XLNetTokenizer
from .utils import logging


logger = logging.get_logger(__name__)


TOKENIZER_MAPPING = OrderedDict(
    [
        (RetriBertConfig, (RetriBertTokenizer, RetriBertTokenizerFast)),
        (T5Config, (T5Tokenizer, None)),
        (MobileBertConfig, (MobileBertTokenizer, MobileBertTokenizerFast)),
        (DistilBertConfig, (DistilBertTokenizer, DistilBertTokenizerFast)),
        (AlbertConfig, (AlbertTokenizer, None)),
        (CamembertConfig, (CamembertTokenizer, None)),
        (PegasusConfig, (PegasusTokenizer, None)),
        (MBartConfig, (MBartTokenizer, None)),
        (XLMRobertaConfig, (XLMRobertaTokenizer, None)),
        (MarianConfig, (MarianTokenizer, None)),
        (BartConfig, (BartTokenizer, BartTokenizerFast)),
        (LongformerConfig, (LongformerTokenizer, LongformerTokenizerFast)),
        (RobertaConfig, (BertweetTokenizer, None)),
        (RobertaConfig, (PhobertTokenizer, None)),
        (RobertaConfig, (RobertaTokenizer, RobertaTokenizerFast)),
        (ReformerConfig, (ReformerTokenizer, None)),
        (ElectraConfig, (ElectraTokenizer, ElectraTokenizerFast)),
        (FunnelConfig, (FunnelTokenizer, FunnelTokenizerFast)),
        (LxmertConfig, (LxmertTokenizer, LxmertTokenizerFast)),
        (LayoutLMConfig, (LayoutLMTokenizer, LayoutLMTokenizerFast)),
        (DPRConfig, (DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast)),
        (BertConfig, (BertTokenizer, BertTokenizerFast)),
        (OpenAIGPTConfig, (OpenAIGPTTokenizer, OpenAIGPTTokenizerFast)),
        (GPT2Config, (GPT2Tokenizer, GPT2TokenizerFast)),
        (TransfoXLConfig, (TransfoXLTokenizer, TransfoXLTokenizerFast)),
        (XLNetConfig, (XLNetTokenizer, None)),
        (FlaubertConfig, (FlaubertTokenizer, None)),
        (XLMConfig, (XLMTokenizer, None)),
        (CTRLConfig, (CTRLTokenizer, None)),
        (FSMTConfig, (FSMTTokenizer, None)),
        (BertGenerationConfig, (BertGenerationTokenizer, None)),
        (LayoutLMConfig, (LayoutLMTokenizer, None)),
        (RagConfig, (RagTokenizer, None)),
    ]
)

SLOW_TOKENIZER_MAPPING = {k: v[0] for k, v in TOKENIZER_MAPPING.items()}


[docs]class AutoTokenizer: r""" This is a generic tokenizer class that will be instantiated as one of the tokenizer classes of the library when created with the :meth:`AutoTokenizer.from_pretrained` class method. This class cannot be instantiated directly using ``__init__()`` (throws an error). """ def __init__(self): raise EnvironmentError( "AutoTokenizer is designed to be instantiated " "using the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)` method." )
[docs] @classmethod @replace_list_option_in_docstrings(SLOW_TOKENIZER_MAPPING) def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs): r""" Instantiate one of the tokenizer classes of the library from a pretrained model vocabulary. The tokenizer class to instantiate is selected based on the :obj:`model_type` property of the config object (either passed as an argument or loaded from :obj:`pretrained_model_name_or_path` if possible), or when it's missing, by falling back to using pattern matching on :obj:`pretrained_model_name_or_path`: List options Params: pretrained_model_name_or_path (:obj:`str`): Can be either: - A string with the `shortcut name` of a predefined tokenizer to load from cache or download, e.g., ``bert-base-uncased``. - A string with the `identifier name` of a predefined tokenizer that was user-uploaded to our S3, e.g., ``dbmdz/bert-base-german-cased``. - A path to a `directory` containing vocabulary files required by the tokenizer, for instance saved using the :func:`~transformers.PreTrainedTokenizer.save_pretrained` method, e.g., ``./my_model_directory/``. - A path or url to a single saved vocabulary file if and only if the tokenizer only requires a single vocabulary file (like Bert or XLNet), e.g.: ``./my_model_directory/vocab.txt``. (Not applicable to all derived classes) inputs (additional positional arguments, `optional`): Will be passed along to the Tokenizer ``__init__()`` method. config (:class:`~transformers.PreTrainedConfig`, `optional`) The configuration object used to dertermine the tokenizer class to instantiate. cache_dir (:obj:`str`, `optional`): Path to a directory in which a downloaded pretrained model configuration should be cached if the standard cache should not be used. force_download (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to force the (re-)download the model weights and configuration files and override the cached versions if they exist. resume_download (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to delete incompletely received files. Will attempt to resume the download if such a file exists. proxies (:obj:`Dict[str, str]`, `optional`): A dictionary of proxy servers to use by protocol or endpoint, e.g., :obj:`{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request. use_fast (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether or not to try to load the fast version of the tokenizer. kwargs (additional keyword arguments, `optional`): Will be passed to the Tokenizer ``__init__()`` method. Can be used to set special tokens like ``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``, ``mask_token``, ``additional_special_tokens``. See parameters in the ``__init__()`` for more details. Examples:: >>> from transformers import AutoTokenizer >>> # Download vocabulary from S3 and cache. >>> tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') >>> # Download vocabulary from S3 (user-uploaded) and cache. >>> tokenizer = AutoTokenizer.from_pretrained('dbmdz/bert-base-german-cased') >>> # If vocabulary files are in a directory (e.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`) >>> tokenizer = AutoTokenizer.from_pretrained('./test/bert_saved_model/') """ config = kwargs.pop("config", None) if not isinstance(config, PretrainedConfig): config = AutoConfig.from_pretrained(pretrained_model_name_or_path, **kwargs) if "bert-base-japanese" in str(pretrained_model_name_or_path): return BertJapaneseTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) use_fast = kwargs.pop("use_fast", False) if config.tokenizer_class is not None: if use_fast and not config.tokenizer_class.endswith("Fast"): tokenizer_class_candidate = f"{config.tokenizer_class}Fast" else: tokenizer_class_candidate = config.tokenizer_class tokenizer_class = globals().get(tokenizer_class_candidate) if tokenizer_class is None: raise ValueError( "Tokenizer class {} does not exist or is not currently imported.".format(tokenizer_class_candidate) ) return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) # if model is an encoder decoder, the encoder tokenizer class is used by default if isinstance(config, EncoderDecoderConfig): if type(config.decoder) is not type(config.encoder): # noqa: E721 logger.warn( f"The encoder model config class: {config.encoder.__class__} is different from the decoder model " f"config class: {config.decoder.__class}. It is not recommended to use the " "`AutoTokenizer.from_pretrained()` method in this case. Please use the encoder and decoder " "specific tokenizer classes." ) config = config.encoder if type(config) in TOKENIZER_MAPPING.keys(): tokenizer_class_py, tokenizer_class_fast = TOKENIZER_MAPPING[type(config)] if tokenizer_class_fast and use_fast: return tokenizer_class_fast.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) else: return tokenizer_class_py.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) raise ValueError( "Unrecognized configuration class {} to build an AutoTokenizer.\n" "Model type should be one of {}.".format( config.__class__, ", ".join(c.__name__ for c in TOKENIZER_MAPPING.keys()) ) )