# 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 Config class. """
import logging
from collections import OrderedDict
from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig
from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig
from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig
from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig
from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
from .configuration_distilbert import DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DistilBertConfig
from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig
from .configuration_encoder_decoder import EncoderDecoderConfig
from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig
from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config
from .configuration_marian import MarianConfig
from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
from .configuration_reformer import ReformerConfig
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig
from .configuration_t5 import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, T5Config
from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
from .configuration_utils import PretrainedConfig
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig
from .configuration_xlm_roberta import XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMRobertaConfig
from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig
logger = logging.getLogger(__name__)
ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = dict(
(key, value)
for pretrained_map in [
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_CONFIG_ARCHIVE_MAP,
OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
]
for key, value, in pretrained_map.items()
)
CONFIG_MAPPING = OrderedDict(
[
("t5", T5Config,),
("distilbert", DistilBertConfig,),
("albert", AlbertConfig,),
("camembert", CamembertConfig,),
("xlm-roberta", XLMRobertaConfig,),
("marian", MarianConfig,),
("bart", BartConfig,),
("reformer", ReformerConfig,),
("roberta", RobertaConfig,),
("flaubert", FlaubertConfig,),
("bert", BertConfig,),
("openai-gpt", OpenAIGPTConfig,),
("gpt2", GPT2Config,),
("transfo-xl", TransfoXLConfig,),
("xlnet", XLNetConfig,),
("xlm", XLMConfig,),
("ctrl", CTRLConfig,),
("electra", ElectraConfig,),
("encoder-decoder", EncoderDecoderConfig,),
]
)
[docs]class AutoConfig:
r"""
:class:`~transformers.AutoConfig` is a generic configuration class
that will be instantiated as one of the configuration classes of the library
when created with the :func:`~transformers.AutoConfig.from_pretrained` class method.
The :func:`~transformers.AutoConfig.from_pretrained` method takes care of returning the correct model 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.
"""
def __init__(self):
raise EnvironmentError(
"AutoConfig is designed to be instantiated "
"using the `AutoConfig.from_pretrained(pretrained_model_name_or_path)` method."
)
@classmethod
def for_model(cls, model_type, *args, **kwargs):
for pattern, config_class in CONFIG_MAPPING.items():
if pattern in model_type:
return config_class(*args, **kwargs)
raise ValueError(
"Unrecognized model identifier in {}. Should contain one of {}".format(
model_type, ", ".join(CONFIG_MAPPING.keys())
)
)
[docs] @classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
r""" Instantiates one of the configuration classes of the library
from a pre-trained model configuration.
The configuration 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.
- contains `t5`: :class:`~transformers.T5Config` (T5 model)
- contains `distilbert`: :class:`~transformers.DistilBertConfig` (DistilBERT model)
- contains `albert`: :class:`~transformers.AlbertConfig` (ALBERT model)
- contains `camembert`: :class:`~transformers.CamembertConfig` (CamemBERT model)
- contains `xlm-roberta`: :class:`~transformers.XLMRobertaConfig` (XLM-RoBERTa model)
- contains `roberta`: :class:`~transformers.RobertaConfig` (RoBERTa model)
- contains `reformer`: :class:`~transformers.ReformerConfig` (Reformer model)
- contains `bert`: :class:`~transformers.BertConfig` (Bert model)
- contains `openai-gpt`: :class:`~transformers.OpenAIGPTConfig` (OpenAI GPT model)
- contains `gpt2`: :class:`~transformers.GPT2Config` (OpenAI GPT-2 model)
- contains `transfo-xl`: :class:`~transformers.TransfoXLConfig` (Transformer-XL model)
- contains `xlnet`: :class:`~transformers.XLNetConfig` (XLNet model)
- contains `xlm`: :class:`~transformers.XLMConfig` (XLM model)
- contains `ctrl` : :class:`~transformers.CTRLConfig` (CTRL model)
- contains `flaubert` : :class:`~transformers.FlaubertConfig` (Flaubert model)
- contains `electra` : :class:`~transformers.ElectraConfig` (ELECTRA model)
Args:
pretrained_model_name_or_path (:obj:`string`):
Is either: \
- a string with the `shortcut name` of a pre-trained model configuration to load from cache or download, e.g.: ``bert-base-uncased``.
- a string with the `identifier name` of a pre-trained model configuration that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
- a path to a `directory` containing a configuration file saved using the :func:`~transformers.PretrainedConfig.save_pretrained` method, e.g.: ``./my_model_directory/``.
- a path or url to a saved configuration JSON `file`, e.g.: ``./my_model_directory/configuration.json``.
cache_dir (:obj:`string`, optional, defaults to `None`):
Path to a directory in which a downloaded pre-trained model
configuration should be cached if the standard cache should not be used.
force_download (:obj:`boolean`, optional, defaults to `False`):
Force to (re-)download the model weights and configuration files and override the cached versions if they exist.
resume_download (:obj:`boolean`, optional, defaults to `False`):
Do not delete incompletely received file. Attempt to resume the download if such a file exists.
proxies (:obj:`Dict[str, str]`, optional, defaults to `None`):
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. See `the requests documentation <https://requests.readthedocs.io/en/master/user/advanced/#proxies>`__ for usage.
return_unused_kwargs (:obj:`boolean`, optional, defaults to `False`):
- If False, then this function returns just the final configuration object.
- If True, then this functions returns a tuple `(config, unused_kwargs)` where `unused_kwargs` is a dictionary consisting of the key/value pairs whose keys are not configuration attributes: ie the part of kwargs which has not been used to update `config` and is otherwise ignored.
kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`): key/value pairs with which to update the configuration object after loading.
- The values in kwargs of any keys which are configuration attributes will be used to override the loaded values.
- Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled by the `return_unused_kwargs` keyword parameter.
Examples::
config = AutoConfig.from_pretrained('bert-base-uncased') # Download configuration from S3 and cache.
config = AutoConfig.from_pretrained('./test/bert_saved_model/') # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
config = AutoConfig.from_pretrained('./test/bert_saved_model/my_configuration.json')
config = AutoConfig.from_pretrained('bert-base-uncased', output_attention=True, foo=False)
assert config.output_attention == True
config, unused_kwargs = AutoConfig.from_pretrained('bert-base-uncased', output_attention=True,
foo=False, return_unused_kwargs=True)
assert config.output_attention == True
assert unused_kwargs == {'foo': False}
"""
config_dict, _ = PretrainedConfig.get_config_dict(
pretrained_model_name_or_path, pretrained_config_archive_map=ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, **kwargs
)
if "model_type" in config_dict:
config_class = CONFIG_MAPPING[config_dict["model_type"]]
return config_class.from_dict(config_dict, **kwargs)
else:
# Fallback: use pattern matching on the string.
for pattern, config_class in CONFIG_MAPPING.items():
if pattern in pretrained_model_name_or_path:
return config_class.from_dict(config_dict, **kwargs)
raise ValueError(
"Unrecognized model in {}. "
"Should have a `model_type` key in its config.json, or contain one of the following strings "
"in its name: {}".format(pretrained_model_name_or_path, ", ".join(CONFIG_MAPPING.keys()))
)