Source code for transformers.configuration_auto

# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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

    (key, value)
    for pretrained_map in [
    for key, value, in pretrained_map.items()

        ("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),

        ("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()}
            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()
        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"^(\s*)List options\s*$", lines[i]) is None:
            i += 1
        if i < len(lines):
            indent ="^(\s*)List options\s*$", 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)
            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': '', 'http://hostname': ''}`. 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())) )