# 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 transformers.configuration_mobilebert import MobileBertConfig
from .configuration_auto import (
AlbertConfig,
AutoConfig,
BartConfig,
BertConfig,
CamembertConfig,
CTRLConfig,
DistilBertConfig,
ElectraConfig,
FlaubertConfig,
GPT2Config,
LongformerConfig,
MBartConfig,
OpenAIGPTConfig,
ReformerConfig,
RetriBertConfig,
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, MBartTokenizer
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_longformer import LongformerTokenizer
from .tokenization_marian import MarianTokenizer
from .tokenization_mobilebert import MobileBertTokenizer, MobileBertTokenizerFast
from .tokenization_openai import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
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
logger = logging.getLogger(__name__)
TOKENIZER_MAPPING = OrderedDict(
[
(RetriBertConfig, (RetriBertTokenizer, RetriBertTokenizerFast)),
(T5Config, (T5Tokenizer, None)),
(MobileBertConfig, (MobileBertTokenizer, MobileBertTokenizerFast)),
(DistilBertConfig, (DistilBertTokenizer, DistilBertTokenizerFast)),
(AlbertConfig, (AlbertTokenizer, None)),
(CamembertConfig, (CamembertTokenizer, None)),
(MBartConfig, (MBartTokenizer, None)),
(XLMRobertaConfig, (XLMRobertaTokenizer, None)),
(MarianConfig, (MarianTokenizer, None)),
(BartConfig, (BartTokenizer, None)),
(LongformerConfig, (LongformerTokenizer, 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 takes 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:
- `t5`: T5Tokenizer (T5 model)
- `distilbert`: DistilBertTokenizer (DistilBert model)
- `albert`: AlbertTokenizer (ALBERT model)
- `camembert`: CamembertTokenizer (CamemBERT model)
- `xlm-roberta`: XLMRobertaTokenizer (XLM-RoBERTa model)
- `longformer`: LongformerTokenizer (AllenAI Longformer model)
- `roberta`: RobertaTokenizer (RoBERTa model)
- `bert`: BertTokenizer (Bert model)
- `openai-gpt`: OpenAIGPTTokenizer (OpenAI GPT model)
- `gpt2`: GPT2Tokenizer (OpenAI GPT-2 model)
- `transfo-xl`: TransfoXLTokenizer (Transformer-XL model)
- `xlnet`: XLNetTokenizer (XLNet model)
- `xlm`: XLMTokenizer (XLM model)
- `ctrl`: CTRLTokenizer (Salesforce CTRL model)
- `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
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:
- `t5`: T5Tokenizer (T5 model)
- `distilbert`: DistilBertTokenizer (DistilBert model)
- `albert`: AlbertTokenizer (ALBERT model)
- `camembert`: CamembertTokenizer (CamemBERT model)
- `xlm-roberta`: XLMRobertaTokenizer (XLM-RoBERTa model)
- `longformer`: LongformerTokenizer (AllenAI Longformer model)
- `roberta`: RobertaTokenizer (RoBERTa model)
- `bert-base-japanese`: BertJapaneseTokenizer (Bert model)
- `bert`: BertTokenizer (Bert model)
- `openai-gpt`: OpenAIGPTTokenizer (OpenAI GPT model)
- `gpt2`: GPT2Tokenizer (OpenAI GPT-2 model)
- `transfo-xl`: TransfoXLTokenizer (Transformer-XL model)
- `xlnet`: XLNetTokenizer (XLNet model)
- `xlm`: XLMTokenizer (XLM model)
- `ctrl`: CTRLTokenizer (Salesforce CTRL model)
- `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 str(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())
)
)