# coding=utf-8 # Copyright 2018 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. """ Auto Tokenizer class. """ import logging from collections import OrderedDict from .configuration_auto import ( AlbertConfig, AutoConfig, BartConfig, BertConfig, CamembertConfig, CTRLConfig, DistilBertConfig, ElectraConfig, FlaubertConfig, GPT2Config, OpenAIGPTConfig, ReformerConfig, RobertaConfig, T5Config, TransfoXLConfig, XLMConfig, XLMRobertaConfig, XLNetConfig, ) from .configuration_marian import MarianConfig from .configuration_utils import PretrainedConfig from .tokenization_albert import AlbertTokenizer from .tokenization_bart import BartTokenizer from .tokenization_bert import BertTokenizer, BertTokenizerFast from .tokenization_bert_japanese import BertJapaneseTokenizer from .tokenization_camembert import CamembertTokenizer from .tokenization_ctrl import CTRLTokenizer from .tokenization_distilbert import DistilBertTokenizer, DistilBertTokenizerFast from .tokenization_electra import ElectraTokenizer, ElectraTokenizerFast from .tokenization_flaubert import FlaubertTokenizer from .tokenization_gpt2 import GPT2Tokenizer, GPT2TokenizerFast from .tokenization_marian import MarianTokenizer from .tokenization_openai import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast from .tokenization_reformer import ReformerTokenizer 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 logger = logging.getLogger(__name__) TOKENIZER_MAPPING = OrderedDict( [ (T5Config, (T5Tokenizer, None)), (DistilBertConfig, (DistilBertTokenizer, DistilBertTokenizerFast)), (AlbertConfig, (AlbertTokenizer, None)), (CamembertConfig, (CamembertTokenizer, None)), (XLMRobertaConfig, (XLMRobertaTokenizer, None)), (MarianConfig, (MarianTokenizer, None)), (BartConfig, (BartTokenizer, None)), (RobertaConfig, (RobertaTokenizer, RobertaTokenizerFast)), (ReformerConfig, (ReformerTokenizer, None)), (ElectraConfig, (ElectraTokenizer, ElectraTokenizerFast)), (BertConfig, (BertTokenizer, BertTokenizerFast)), (OpenAIGPTConfig, (OpenAIGPTTokenizer, OpenAIGPTTokenizerFast)), (GPT2Config, (GPT2Tokenizer, GPT2TokenizerFast)), (TransfoXLConfig, (TransfoXLTokenizer, TransfoXLTokenizerFast)), (XLNetConfig, (XLNetTokenizer, None)), (FlaubertConfig, (FlaubertTokenizer, None)), (XLMConfig, (XLMTokenizer, None)), (CTRLConfig, (CTRLTokenizer, None)), ] ) class AutoTokenizer: r""":class:`~transformers.AutoTokenizer` is a generic tokenizer class that will be instantiated as one of the tokenizer classes of the library when created with the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)` class method. The `from_pretrained()` method take care of returning the correct tokenizer class instance based on the `model_type` property of the config object, or when it's missing, falling back to using pattern matching on the `pretrained_model_name_or_path` string. The tokenizer class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - contains `t5`: T5Tokenizer (T5 model) - contains `distilbert`: DistilBertTokenizer (DistilBert model) - contains `albert`: AlbertTokenizer (ALBERT model) - contains `camembert`: CamembertTokenizer (CamemBERT model) - contains `xlm-roberta`: XLMRobertaTokenizer (XLM-RoBERTa model) - contains `roberta`: RobertaTokenizer (RoBERTa model) - contains `bert`: BertTokenizer (Bert model) - contains `openai-gpt`: OpenAIGPTTokenizer (OpenAI GPT model) - contains `gpt2`: GPT2Tokenizer (OpenAI GPT-2 model) - contains `transfo-xl`: TransfoXLTokenizer (Transformer-XL model) - contains `xlnet`: XLNetTokenizer (XLNet model) - contains `xlm`: XLMTokenizer (XLM model) - contains `ctrl`: CTRLTokenizer (Salesforce CTRL model) - contains `electra`: ElectraTokenizer (Google ELECTRA model) This class cannot be instantiated using `__init__()` (throw an error). """ def __init__(self): raise EnvironmentError( "AutoTokenizer is designed to be instantiated " "using the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)` method." ) @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs): r""" Instantiate one of the tokenizer classes of the library from a pre-trained model vocabulary. The tokenizer class to instantiate is selected as the first pattern matching in the `pretrained_model_name_or_path` string (in the following order): - contains `t5`: T5Tokenizer (T5 model) - contains `distilbert`: DistilBertTokenizer (DistilBert model) - contains `albert`: AlbertTokenizer (ALBERT model) - contains `camembert`: CamembertTokenizer (CamemBERT model) - contains `xlm-roberta`: XLMRobertaTokenizer (XLM-RoBERTa model) - contains `roberta`: RobertaTokenizer (RoBERTa model) - contains `bert-base-japanese`: BertJapaneseTokenizer (Bert model) - contains `bert`: BertTokenizer (Bert model) - contains `openai-gpt`: OpenAIGPTTokenizer (OpenAI GPT model) - contains `gpt2`: GPT2Tokenizer (OpenAI GPT-2 model) - contains `transfo-xl`: TransfoXLTokenizer (Transformer-XL model) - contains `xlnet`: XLNetTokenizer (XLNet model) - contains `xlm`: XLMTokenizer (XLM model) - contains `ctrl`: CTRLTokenizer (Salesforce CTRL model) - contains `electra`: ElectraTokenizer (Google ELECTRA model) Params: pretrained_model_name_or_path: 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/``. - (not applicable to all derived classes) a path or url to a single saved vocabulary file if and only if the tokenizer only requires a single vocabulary file (e.g. Bert, XLNet), e.g.: ``./my_model_directory/vocab.txt``. cache_dir: (`optional`) string: Path to a directory in which a downloaded predefined tokenizer vocabulary files should be cached if the standard cache should not be used. force_download: (`optional`) boolean, default False: Force to (re-)download the vocabulary files and override the cached versions if they exists. resume_download: (`optional`) boolean, default False: Do not delete incompletely recieved file. Attempt to resume the download if such a file exists. proxies: (`optional`) dict, default None: A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}. The proxies are used on each request. use_fast: (`optional`) boolean, default False: Indicate if transformers should try to load the fast version of the tokenizer (True) or use the Python one (False). inputs: (`optional`) positional arguments: will be passed to the Tokenizer ``__init__`` method. kwargs: (`optional`) keyword arguments: 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 doc string of :class:`~transformers.PreTrainedTokenizer` for details. Examples:: # 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 pretrained_model_name_or_path: return BertJapaneseTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) use_fast = kwargs.pop("use_fast", False) for config_class, (tokenizer_class_py, tokenizer_class_fast) in TOKENIZER_MAPPING.items(): if isinstance(config, config_class): 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()) ) )