219 lines
12 KiB
Python
Executable File
219 lines
12 KiB
Python
Executable File
# coding=utf-8
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# Copyright 2018 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Auto Model class. """
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import logging
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from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig
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from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig
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from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig
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from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
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from .configuration_distilbert import DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DistilBertConfig
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from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config
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from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
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from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig
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from .configuration_t5 import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, T5Config
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from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
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from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig
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from .configuration_xlm_roberta import XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMRobertaConfig
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from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig
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logger = logging.getLogger(__name__)
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ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = dict(
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(key, value)
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for pretrained_map in [
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BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
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OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
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TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
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GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
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CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
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XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
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XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
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ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
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DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
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ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
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CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
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T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
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XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
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]
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for key, value, in pretrained_map.items()
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)
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class AutoConfig(object):
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r""":class:`~transformers.AutoConfig` is a generic configuration class
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that will be instantiated as one of the configuration classes of the library
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when created with the `AutoConfig.from_pretrained(pretrained_model_name_or_path)`
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class method.
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The `from_pretrained()` method take care of returning the correct model class instance
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using pattern matching on the `pretrained_model_name_or_path` string.
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The base model class to instantiate is selected as the first pattern matching
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in the `pretrained_model_name_or_path` string (in the following order):
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- contains `distilbert`: DistilBertConfig (DistilBERT model)
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- contains `albert`: AlbertConfig (ALBERT model)
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- contains `camembert`: CamembertConfig (CamemBERT model)
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- contains `xlm-roberta`: XLMRobertaConfig (XLM-RoBERTa model)
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- contains `roberta`: RobertaConfig (RoBERTa model)
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- contains `bert`: BertConfig (Bert model)
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- contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model)
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- contains `gpt2`: GPT2Config (OpenAI GPT-2 model)
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- contains `transfo-xl`: TransfoXLConfig (Transformer-XL model)
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- contains `xlnet`: XLNetConfig (XLNet model)
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- contains `xlm`: XLMConfig (XLM model)
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- contains `ctrl` : CTRLConfig (CTRL model)
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This class cannot be instantiated using `__init__()` (throw an error).
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"""
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def __init__(self):
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raise EnvironmentError(
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"AutoConfig is designed to be instantiated "
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"using the `AutoConfig.from_pretrained(pretrained_model_name_or_path)` method."
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)
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@classmethod
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def for_model(cls, model_type, *args, **kwargs):
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if "distilbert" in model_type:
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return DistilBertConfig(*args, **kwargs)
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elif "roberta" in model_type:
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return RobertaConfig(*args, **kwargs)
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elif "bert" in model_type:
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return BertConfig(*args, **kwargs)
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elif "openai-gpt" in model_type:
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return OpenAIGPTConfig(*args, **kwargs)
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elif "gpt2" in model_type:
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return GPT2Config(*args, **kwargs)
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elif "transfo-xl" in model_type:
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return TransfoXLConfig(*args, **kwargs)
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elif "xlnet" in model_type:
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return XLNetConfig(*args, **kwargs)
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elif "xlm" in model_type:
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return XLMConfig(*args, **kwargs)
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elif "ctrl" in model_type:
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return CTRLConfig(*args, **kwargs)
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elif "albert" in model_type:
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return AlbertConfig(*args, **kwargs)
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elif "camembert" in model_type:
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return CamembertConfig(*args, **kwargs)
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raise ValueError(
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"Unrecognized model identifier in {}. Should contains one of "
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"'distilbert', 'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
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"'xlm', 'roberta', 'ctrl', 'camembert', 'albert'".format(model_type)
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)
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@classmethod
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def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
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r""" Instantiate a one of the configuration classes of the library
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from a pre-trained model configuration.
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The configuration class to instantiate is selected as the first pattern matching
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in the `pretrained_model_name_or_path` string (in the following order):
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- contains `t5`: T5Config (T5 model)
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- contains `distilbert`: DistilBertConfig (DistilBERT model)
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- contains `albert`: AlbertConfig (ALBERT model)
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- contains `camembert`: CamembertConfig (CamemBERT model)
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- contains `xlm-roberta`: XLMRobertaConfig (XLM-RoBERTa model)
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- contains `roberta`: RobertaConfig (RoBERTa model)
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- contains `bert`: BertConfig (Bert model)
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- contains `openai-gpt`: OpenAIGPTConfig (OpenAI GPT model)
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- contains `gpt2`: GPT2Config (OpenAI GPT-2 model)
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- contains `transfo-xl`: TransfoXLConfig (Transformer-XL model)
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- contains `xlnet`: XLNetConfig (XLNet model)
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- contains `xlm`: XLMConfig (XLM model)
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- contains `ctrl` : CTRLConfig (CTRL model)
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Params:
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pretrained_model_name_or_path: either:
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- a string with the `shortcut name` of a pre-trained model configuration to load from cache or download, e.g.: ``bert-base-uncased``.
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- 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``.
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- a path to a `directory` containing a configuration file saved using the :func:`~transformers.PretrainedConfig.save_pretrained` method, e.g.: ``./my_model_directory/``.
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- a path or url to a saved configuration JSON `file`, e.g.: ``./my_model_directory/configuration.json``.
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cache_dir: (`optional`) string:
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Path to a directory in which a downloaded pre-trained model
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configuration should be cached if the standard cache should not be used.
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kwargs: (`optional`) dict: key/value pairs with which to update the configuration object after loading.
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- The values in kwargs of any keys which are configuration attributes will be used to override the loaded values.
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- Behavior concerning key/value pairs whose keys are *not* configuration attributes is controlled by the `return_unused_kwargs` keyword parameter.
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force_download: (`optional`) boolean, default False:
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Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
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resume_download: (`optional`) boolean, default False:
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Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
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proxies: (`optional`) dict, default None:
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A dictionary of proxy servers to use by protocol or endpoint, e.g.: {'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}.
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The proxies are used on each request.
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return_unused_kwargs: (`optional`) bool:
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- If False, then this function returns just the final configuration object.
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- 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.
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Examples::
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config = AutoConfig.from_pretrained('bert-base-uncased') # Download configuration from S3 and cache.
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config = AutoConfig.from_pretrained('./test/bert_saved_model/') # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
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config = AutoConfig.from_pretrained('./test/bert_saved_model/my_configuration.json')
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config = AutoConfig.from_pretrained('bert-base-uncased', output_attention=True, foo=False)
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assert config.output_attention == True
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config, unused_kwargs = AutoConfig.from_pretrained('bert-base-uncased', output_attention=True,
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foo=False, return_unused_kwargs=True)
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assert config.output_attention == True
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assert unused_kwargs == {'foo': False}
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"""
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if "t5" in pretrained_model_name_or_path:
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return T5Config.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif "distilbert" in pretrained_model_name_or_path:
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return DistilBertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif "albert" in pretrained_model_name_or_path:
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return AlbertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif "camembert" in pretrained_model_name_or_path:
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return CamembertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif "xlm-roberta" in pretrained_model_name_or_path:
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return XLMRobertaConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif "roberta" in pretrained_model_name_or_path:
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return RobertaConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif "bert" in pretrained_model_name_or_path:
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return BertConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif "openai-gpt" in pretrained_model_name_or_path:
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return OpenAIGPTConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif "gpt2" in pretrained_model_name_or_path:
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return GPT2Config.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif "transfo-xl" in pretrained_model_name_or_path:
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return TransfoXLConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif "xlnet" in pretrained_model_name_or_path:
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return XLNetConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif "xlm" in pretrained_model_name_or_path:
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return XLMConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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elif "ctrl" in pretrained_model_name_or_path:
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return CTRLConfig.from_pretrained(pretrained_model_name_or_path, **kwargs)
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raise ValueError(
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"Unrecognized model identifier in {}. Should contains one of "
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"'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
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"'xlm-roberta', 'xlm', 'roberta', 'distilbert', 'camembert', 'ctrl', 'albert'".format(
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pretrained_model_name_or_path
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)
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)
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