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 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, MBartConfig
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_longformer import LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, LongformerConfig
from .configuration_marian import MarianConfig
from .configuration_mobilebert import MobileBertConfig
from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
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

logger = logging.getLogger(__name__)

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

        ("retribert", RetriBertConfig,),
        ("t5", T5Config,),
        ("mobilebert", MobileBertConfig,),
        ("distilbert", DistilBertConfig,),
        ("albert", AlbertConfig,),
        ("camembert", CamembertConfig,),
        ("xlm-roberta", XLMRobertaConfig,),
        ("marian", MarianConfig,),
        ("mbart", MBartConfig,),
        ("bart", BartConfig,),
        ("reformer", ReformerConfig,),
        ("longformer", LongformerConfig,),
        ("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: 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 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: - `t5`: :class:`~transformers.T5Config` (T5 model) - `distilbert`: :class:`~transformers.DistilBertConfig` (DistilBERT model) - `albert`: :class:`~transformers.AlbertConfig` (ALBERT model) - `camembert`: :class:`~transformers.CamembertConfig` (CamemBERT model) - `xlm-roberta`: :class:`~transformers.XLMRobertaConfig` (XLM-RoBERTa model) - `longformer`: :class:`~transformers.LongformerConfig` (Longformer model) - `roberta`: :class:`~transformers.RobertaConfig` (RoBERTa model) - `reformer`: :class:`~transformers.ReformerConfig` (Reformer model) - `bert`: :class:`~transformers.BertConfig` (Bert model) - `openai-gpt`: :class:`~transformers.OpenAIGPTConfig` (OpenAI GPT model) - `gpt2`: :class:`~transformers.GPT2Config` (OpenAI GPT-2 model) - `transfo-xl`: :class:`~transformers.TransfoXLConfig` (Transformer-XL model) - `xlnet`: :class:`~transformers.XLNetConfig` (XLNet model) - `xlm`: :class:`~transformers.XLMConfig` (XLM model) - `ctrl` : :class:`~transformers.CTRLConfig` (CTRL model) - `flaubert` : :class:`~transformers.FlaubertConfig` (Flaubert model) - `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': '', 'http://hostname': ''}`. The proxies are used on each request. See `the requests documentation <>`__ 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, **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())) )