# 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 re
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_bert_generation import BertGenerationConfig
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_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config
from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig
from .configuration_longformer import LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, LongformerConfig
from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
from .configuration_marian import MarianConfig
from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig
from .configuration_mobilebert import MobileBertConfig
from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
from .configuration_pegasus import PegasusConfig
from .configuration_reformer import ReformerConfig
from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
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
ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = dict(
(key, value)
for pretrained_map in [
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_CONFIG_ARCHIVE_MAP,
MBART_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,
FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP,
ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
]
for key, value, in pretrained_map.items()
)
CONFIG_MAPPING = OrderedDict(
[
("retribert", RetriBertConfig),
("t5", T5Config),
("mobilebert", MobileBertConfig),
("distilbert", DistilBertConfig),
("albert", AlbertConfig),
("bert-generation", BertGenerationConfig),
("camembert", CamembertConfig),
("xlm-roberta", XLMRobertaConfig),
("pegasus", PegasusConfig),
("marian", MarianConfig),
("mbart", MBartConfig),
("bart", BartConfig),
("reformer", ReformerConfig),
("longformer", LongformerConfig),
("roberta", RobertaConfig),
("flaubert", FlaubertConfig),
("fsmt", FSMTConfig),
("bert", BertConfig),
("openai-gpt", OpenAIGPTConfig),
("gpt2", GPT2Config),
("transfo-xl", TransfoXLConfig),
("xlnet", XLNetConfig),
("xlm", XLMConfig),
("ctrl", CTRLConfig),
("electra", ElectraConfig),
("encoder-decoder", EncoderDecoderConfig),
("funnel", FunnelConfig),
("lxmert", LxmertConfig),
("layoutlm", LayoutLMConfig),
]
)
MODEL_NAMES_MAPPING = OrderedDict(
[
("retribert", "RetriBERT"),
("t5", "T5"),
("mobilebert", "MobileBERT"),
("distilbert", "DistilBERT"),
("albert", "ALBERT"),
("bert-generation", "Bert Generation"),
("camembert", "CamemBERT"),
("xlm-roberta", "XLM-RoBERTa"),
("pegasus", "Pegasus"),
("marian", "Marian"),
("mbart", "mBART"),
("bart", "BART"),
("reformer", "Reformer"),
("longformer", "Longformer"),
("roberta", "RoBERTa"),
("flaubert", "FlauBERT"),
("fsmt", "FairSeq Machine-Translation"),
("bert", "BERT"),
("openai-gpt", "OpenAI GPT"),
("gpt2", "OpenAI GPT-2"),
("transfo-xl", "Transformer-XL"),
("xlnet", "XLNet"),
("xlm", "XLM"),
("ctrl", "CTRL"),
("electra", "ELECTRA"),
("encoder-decoder", "Encoder decoder"),
("funnel", "Funnel Transformer"),
("lxmert", "LXMERT"),
("layoutlm", "LayoutLM"),
]
)
def _list_model_options(indent, config_to_class=None, use_model_types=True):
if config_to_class is None and not use_model_types:
raise ValueError("Using `use_model_types=False` requires a `config_to_class` dictionary.")
if use_model_types:
if config_to_class is None:
model_type_to_name = {model_type: config.__name__ for model_type, config in CONFIG_MAPPING.items()}
else:
model_type_to_name = {
model_type: config_to_class[config].__name__
for model_type, config in CONFIG_MAPPING.items()
if config in config_to_class
}
lines = [
f"{indent}- **{model_type}** -- :class:`~transformers.{cls_name}` ({MODEL_NAMES_MAPPING[model_type]} model)"
for model_type, cls_name in model_type_to_name.items()
]
else:
config_to_name = {config.__name__: clas.__name__ for config, clas in config_to_class.items()}
config_to_model_name = {
config.__name__: MODEL_NAMES_MAPPING[model_type] for model_type, config in CONFIG_MAPPING.items()
}
lines = [
f"{indent}- :class:`~transformers.{config_name}` configuration class: :class:`~transformers.{cls_name}` ({config_to_model_name[config_name]} model)"
for config_name, cls_name in config_to_name.items()
]
return "\n".join(lines)
def replace_list_option_in_docstrings(config_to_class=None, use_model_types=True):
def docstring_decorator(fn):
docstrings = fn.__doc__
lines = docstrings.split("\n")
i = 0
while i < len(lines) and re.search(r"^(\s*)List options\s*$", lines[i]) is None:
i += 1
if i < len(lines):
indent = re.search(r"^(\s*)List options\s*$", lines[i]).groups()[0]
if use_model_types:
indent = f"{indent} "
lines[i] = _list_model_options(indent, config_to_class=config_to_class, use_model_types=use_model_types)
docstrings = "\n".join(lines)
else:
raise ValueError(
f"The function {fn} should have an empty 'List options' in its docstring as placeholder, current docstring is:\n{docstrings}"
)
fn.__doc__ = docstrings
return fn
return docstring_decorator
[docs]class AutoConfig:
r"""
This is a generic configuration class that will be instantiated as one of the configuration classes of the library
when created with the :meth:`~transformers.AutoConfig.from_pretrained` class method.
This class cannot be instantiated directly using ``__init__()`` (throws an error).
"""
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: str, *args, **kwargs):
if model_type in CONFIG_MAPPING:
config_class = CONFIG_MAPPING[model_type]
return config_class(*args, **kwargs)
raise ValueError(
"Unrecognized model identifier: {}. Should contain one of {}".format(
model_type, ", ".join(CONFIG_MAPPING.keys())
)
)
[docs] @classmethod
@replace_list_option_in_docstrings()
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
r"""
Instantiate one of the configuration classes of the library from a pretrained model configuration.
The configuration class to instantiate is selected based on the :obj:`model_type` property of the config
object that is loaded, or when it's missing, by falling back to using pattern matching on
:obj:`pretrained_model_name_or_path`:
List options
Args:
pretrained_model_name_or_path (:obj:`str`):
Can be either:
- A string with the `shortcut name` of a pretrained model configuration to load from cache or
download, e.g., ``bert-base-uncased``.
- A string with the `identifier name` of a pretrained 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
:meth:`~transformers.PretrainedConfig.save_pretrained` method, or the
:meth:`~transformers.PretrainedModel.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:`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.
return_unused_kwargs (:obj:`bool`, `optional`, defaults to :obj:`False`):
If :obj:`False`, then this function returns just the final configuration object.
If :obj:`True`, then this functions returns a :obj:`Tuple(config, unused_kwargs)` where `unused_kwargs`
is a dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e.,
the part of ``kwargs`` which has not been used to update ``config`` and is otherwise ignored.
kwargs(additional keyword arguments, `optional`):
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::
>>> from transformers import AutoConfig
>>> # Download configuration from S3 and cache.
>>> config = AutoConfig.from_pretrained('bert-base-uncased')
>>> # Download configuration from S3 (user-uploaded) and cache.
>>> config = AutoConfig.from_pretrained('dbmdz/bert-base-german-cased')
>>> # If configuration file is in a directory (e.g., was saved using `save_pretrained('./test/saved_model/')`).
>>> config = AutoConfig.from_pretrained('./test/bert_saved_model/')
>>> # Load a specific configuration file.
>>> config = AutoConfig.from_pretrained('./test/bert_saved_model/my_configuration.json')
>>> # Change some config attributes when loading a pretrained config.
>>> config = AutoConfig.from_pretrained('bert-base-uncased', output_attentions=True, foo=False)
>>> config.output_attentions
True
>>> config, unused_kwargs = AutoConfig.from_pretrained('bert-base-uncased', output_attentions=True, foo=False, return_unused_kwargs=True)
>>> config.output_attentions
True
>>> config.unused_kwargs
{'foo': False}
"""
config_dict, _ = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **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()))
)