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# 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 Model class. """
from __future__ import absolute_import, division, print_function, unicode_literals
import logging
from .tokenization_bert import BertTokenizer
from .tokenization_openai import OpenAIGPTTokenizer
from .tokenization_gpt2 import GPT2Tokenizer
from .tokenization_transfo_xl import TransfoXLTokenizer
from .tokenization_xlnet import XLNetTokenizer
from .tokenization_xlm import XLMTokenizer
from .tokenization_roberta import RobertaTokenizer
from .tokenization_distilbert import DistilBertTokenizer
logger = logging.getLogger(__name__)
class AutoTokenizer(object):
r""":class:`~pytorch_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
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 `distilbert`: DistilBertTokenizer (DistilBert 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)
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 a 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 `distilbert`: DistilBertTokenizer (DistilBert model)
- contains `roberta`: RobertaTokenizer (XLM 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)
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 path to a `directory` containing vocabulary files required by the tokenizer, for instance saved using the :func:`~pytorch_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.
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.
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:`~pytorch_transformers.PreTrainedTokenizer` for details.
Examples::
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') # Download vocabulary from S3 and cache.
tokenizer = AutoTokenizer.from_pretrained('./test/bert_saved_model/') # E.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`
"""
if 'distilbert' in pretrained_model_name_or_path:
return DistilBertTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
elif 'roberta' in pretrained_model_name_or_path:
return RobertaTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
elif 'bert' in pretrained_model_name_or_path:
return BertTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
elif 'openai-gpt' in pretrained_model_name_or_path:
return OpenAIGPTTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
elif 'gpt2' in pretrained_model_name_or_path:
return GPT2Tokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
elif 'transfo-xl' in pretrained_model_name_or_path:
return TransfoXLTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
elif 'xlnet' in pretrained_model_name_or_path:
return XLNetTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
elif 'xlm' in pretrained_model_name_or_path:
return XLMTokenizer.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
raise ValueError("Unrecognized model identifier in {}. Should contains one of "
"'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
"'xlm', 'roberta'".format(pretrained_model_name_or_path))
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