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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 .configuration_bert import BertConfig
- from .configuration_openai import OpenAIGPTConfig
- from .configuration_gpt2 import GPT2Config
- from .configuration_transfo_xl import TransfoXLConfig
- from .configuration_xlnet import XLNetConfig
- from .configuration_xlm import XLMConfig
- from .configuration_roberta import RobertaConfig
- from .configuration_distilbert import DistilBertConfig
-
- logger = logging.getLogger(__name__)
-
-
- class AutoConfig(object):
- r""":class:`~pytorch_transformers.AutoConfig` is a generic configuration class
- that will be instantiated as one of the configuration classes of the library
- when created with the `AutoConfig.from_pretrained(pretrained_model_name_or_path)`
- class method.
-
- The `from_pretrained()` method take care of returning the correct model class instance
- using pattern matching on the `pretrained_model_name_or_path` string.
-
- The base model class to instantiate is selected as the first pattern matching
- in the `pretrained_model_name_or_path` string (in the following order):
- - contains `distilbert`: DistilBertConfig (DistilBERT model)
- - contains `bert`: BertConfig (Bert model)
- - contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model)
- - contains `gpt2`: GPT2Config (OpenAI GPT-2 model)
- - contains `transfo-xl`: TransfoXLConfig (Transformer-XL model)
- - contains `xlnet`: XLNetConfig (XLNet model)
- - contains `xlm`: XLMConfig (XLM model)
- - contains `roberta`: RobertaConfig (RoBERTa model)
-
- This class cannot be instantiated using `__init__()` (throw 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 from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
- r""" Instantiate a one of the configuration classes of the library
- from a pre-trained model configuration.
-
- The configuration class to instantiate is selected as the first pattern matching
- in the `pretrained_model_name_or_path` string (in the following order):
- - contains `distilbert`: DistilBertConfig (DistilBERT model)
- - contains `bert`: BertConfig (Bert model)
- - contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model)
- - contains `gpt2`: GPT2Config (OpenAI GPT-2 model)
- - contains `transfo-xl`: TransfoXLConfig (Transformer-XL model)
- - contains `xlnet`: XLNetConfig (XLNet model)
- - contains `xlm`: XLMConfig (XLM model)
- - contains `roberta`: RobertaConfig (RoBERTa model)
-
- Params:
- pretrained_model_name_or_path: 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 path to a `directory` containing a configuration file saved using the :func:`~pytorch_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: (`optional`) string:
- Path to a directory in which a downloaded pre-trained model
- configuration should be cached if the standard cache should not be used.
-
- kwargs: (`optional`) dict: 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.
-
- force_download: (`optional`) boolean, default False:
- Force to (re-)download the model weights and configuration 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.
-
- return_unused_kwargs: (`optional`) bool:
-
- - 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.
-
- 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}
-
- """
- if 'distilbert' in pretrained_model_name_or_path:
- return DistilBertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
- elif 'roberta' in pretrained_model_name_or_path:
- return RobertaConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
- elif 'bert' in pretrained_model_name_or_path:
- return BertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
- elif 'openai-gpt' in pretrained_model_name_or_path:
- return OpenAIGPTConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
- elif 'gpt2' in pretrained_model_name_or_path:
- return GPT2Config.from_pretrained(pretrained_model_name_or_path, **kwargs)
- elif 'transfo-xl' in pretrained_model_name_or_path:
- return TransfoXLConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
- elif 'xlnet' in pretrained_model_name_or_path:
- return XLNetConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
- elif 'xlm' in pretrained_model_name_or_path:
- return XLMConfig.from_pretrained(pretrained_model_name_or_path, **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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