text: # 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. """
  
  
  import logging
  
  from .configuration_auto import (
      AlbertConfig,
      BertConfig,
      CamembertConfig,
      CTRLConfig,
      DistilBertConfig,
      GPT2Config,
      OpenAIGPTConfig,
      RobertaConfig,
      TransfoXLConfig,
      XLMConfig,
      XLMRobertaConfig,
      XLNetConfig,
  )
  from .modeling_albert import (
      ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
      AlbertForMaskedLM,
      AlbertForQuestionAnswering,
      AlbertForSequenceClassification,
      AlbertModel,
  )
  from .modeling_bert import (
      BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
      BertForMaskedLM,
      BertForQuestionAnswering,
      BertForSequenceClassification,
      BertForTokenClassification,
      BertModel,
  )
  from .modeling_camembert import (
      CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
      CamembertForMaskedLM,
      CamembertForSequenceClassification,
      CamembertForTokenClassification,
      CamembertModel,
  )
  from .modeling_ctrl import CTRL_PRETRAINED_MODEL_ARCHIVE_MAP, CTRLLMHeadModel, CTRLModel
  from .modeling_distilbert import (
      DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
      DistilBertForMaskedLM,
      DistilBertForQuestionAnswering,
      DistilBertForSequenceClassification,
      DistilBertForTokenClassification,
      DistilBertModel,
  )
  from .modeling_gpt2 import GPT2_PRETRAINED_MODEL_ARCHIVE_MAP, GPT2LMHeadModel, GPT2Model
  from .modeling_openai import OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP, OpenAIGPTLMHeadModel, OpenAIGPTModel
  from .modeling_roberta import (
      ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
      RobertaForMaskedLM,
      RobertaForSequenceClassification,
      RobertaForTokenClassification,
      RobertaModel,
  )
  from .modeling_t5 import T5_PRETRAINED_MODEL_ARCHIVE_MAP, T5Model, T5WithLMHeadModel
  from .modeling_transfo_xl import TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP, TransfoXLLMHeadModel, TransfoXLModel
  from .modeling_xlm import (
      XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
      XLMForQuestionAnswering,
      XLMForSequenceClassification,
      XLMModel,
      XLMWithLMHeadModel,
  )
  from .modeling_xlm_roberta import (
      XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
      XLMRobertaForMaskedLM,
      XLMRobertaForSequenceClassification,
      XLMRobertaForTokenClassification,
      XLMRobertaModel,
  )
  from .modeling_xlnet import (
      XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
      XLNetForQuestionAnswering,
      XLNetForSequenceClassification,
      XLNetForTokenClassification,
      XLNetLMHeadModel,
      XLNetModel,
  )
  
  
  logger = logging.getLogger(__name__)
  
  
  ALL_PRETRAINED_MODEL_ARCHIVE_MAP = dict(
      (key, value)
      for pretrained_map in [
          BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
          OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP,
          TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP,
          GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
          CTRL_PRETRAINED_MODEL_ARCHIVE_MAP,
          XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
          XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
          ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
          DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
          ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
          CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
          T5_PRETRAINED_MODEL_ARCHIVE_MAP,
          XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
      ]
      for key, value, in pretrained_map.items()
  )
  
  
  class AutoModel(object):
      r"""
          :class:`~transformers.AutoModel` is a generic model class
          that will be instantiated as one of the base model classes of the library
          when created with the `AutoModel.from_pretrained(pretrained_model_name_or_path)`
          or the `AutoModel.from_config(config)` class methods.
  
          The `from_pretrained()` method takes 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 `t5`: T5Model (T5 model)
              - contains `distilbert`: DistilBertModel (DistilBERT model)
              - contains `albert`: AlbertModel (ALBERT model)
              - contains `camembert`: CamembertModel (CamemBERT model)
              - contains `xlm-roberta`: XLMRobertaModel (XLM-RoBERTa model)
              - contains `roberta`: RobertaModel (RoBERTa model)
              - contains `bert`: BertModel (Bert model)
              - contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model)
              - contains `gpt2`: GPT2Model (OpenAI GPT-2 model)
              - contains `transfo-xl`: TransfoXLModel (Transformer-XL model)
              - contains `xlnet`: XLNetModel (XLNet model)
              - contains `xlm`: XLMModel (XLM model)
              - contains `ctrl`: CTRLModel (Salesforce CTRL  model)
  
          This class cannot be instantiated using `__init__()` (throws an error).
      """
  
      def __init__(self):
          raise EnvironmentError(
              "AutoModel is designed to be instantiated "
              "using the `AutoModel.from_pretrained(pretrained_model_name_or_path)` or "
              "`AutoModel.from_config(config)` methods."
          )
  
      @classmethod
      def from_config(cls, config):
          r""" Instantiates one of the base model classes of the library
          from a configuration.
  
              config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
                  The model class to instantiate is selected based on the configuration class:
                      - isInstance of `distilbert` configuration class: DistilBertModel (DistilBERT model)
                      - isInstance of `roberta` configuration class: RobertaModel (RoBERTa model)
                      - isInstance of `bert` configuration class: BertModel (Bert model)
                      - isInstance of `openai-gpt` configuration class: OpenAIGPTModel (OpenAI GPT model)
                      - isInstance of `gpt2` configuration class: GPT2Model (OpenAI GPT-2 model)
                      - isInstance of `ctrl` configuration class: CTRLModel (Salesforce CTRL  model)
                      - isInstance of `transfo-xl` configuration class: TransfoXLModel (Transformer-XL model)
                      - isInstance of `xlnet` configuration class: XLNetModel (XLNet model)
                      - isInstance of `xlm` configuration class: XLMModel (XLM model)
  
          Examples::
  
              config = BertConfig.from_pretrained('bert-base-uncased')    # Download configuration from S3 and cache.
              model = AutoModel.from_config(config)  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
          """
          if isinstance(config, DistilBertConfig):
              return DistilBertModel(config)
          elif isinstance(config, RobertaConfig):
              return RobertaModel(config)
          elif isinstance(config, BertConfig):
              return BertModel(config)
          elif isinstance(config, OpenAIGPTConfig):
              return OpenAIGPTModel(config)
          elif isinstance(config, GPT2Config):
              return GPT2Model(config)
          elif isinstance(config, TransfoXLConfig):
              return TransfoXLModel(config)
          elif isinstance(config, XLNetConfig):
              return XLNetModel(config)
          elif isinstance(config, XLMConfig):
              return XLMModel(config)
          elif isinstance(config, CTRLConfig):
              return CTRLModel(config)
          elif isinstance(config, AlbertConfig):
              return AlbertModel(config)
          elif isinstance(config, CamembertConfig):
              return CamembertModel(config)
          elif isinstance(config, XLMRobertaConfig):
              return XLMRobertaModel(config)
          raise ValueError("Unrecognized configuration class {}".format(config))
  
      @classmethod
      def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
          r""" Instantiates one of the base model classes of the library
          from a pre-trained model configuration.
  
          The model class to instantiate is selected as the first pattern matching
          in the `pretrained_model_name_or_path` string (in the following order):
              - contains `t5`: T5Model (T5 model)
              - contains `distilbert`: DistilBertModel (DistilBERT model)
              - contains `albert`: AlbertModel (ALBERT model)
              - contains `camembert`: CamembertModel (CamemBERT model)
              - contains `xlm-roberta`: XLMRobertaModel (XLM-RoBERTa model)
              - contains `roberta`: RobertaModel (RoBERTa model)
              - contains `bert`: BertModel (Bert model)
              - contains `openai-gpt`: OpenAIGPTModel (OpenAI GPT model)
              - contains `gpt2`: GPT2Model (OpenAI GPT-2 model)
              - contains `transfo-xl`: TransfoXLModel (Transformer-XL model)
              - contains `xlnet`: XLNetModel (XLNet model)
              - contains `xlm`: XLMModel (XLM model)
              - contains `ctrl`: CTRLModel (Salesforce CTRL model)
  
              The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
              To train the model, you should first set it back in training mode with `model.train()`
  
          Params:
              pretrained_model_name_or_path: either:
  
                  - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
                  - a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
                  - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
                  - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
  
              model_args: (`optional`) Sequence of positional arguments:
                  All remaning positional arguments will be passed to the underlying model's ``__init__`` method
  
              config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
                  Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
  
                  - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
                  - the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
                  - the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
  
              state_dict: (`optional`) dict:
                  an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
                  This option can be used if you want to create a model from a pretrained configuration but load your own weights.
                  In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
  
              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.
  
              force_download: (`optional`) boolean, default False:
                  Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
  
              resume_download: (`optional`) boolean, default False:
                  Do not delete incompletely recieved file. Attempt to resume the download if such a file 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.
  
              output_loading_info: (`optional`) boolean:
                  Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
  
              kwargs: (`optional`) Remaining dictionary of keyword arguments:
                  Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
  
                  - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
                  - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
  
          Examples::
  
              model = AutoModel.from_pretrained('bert-base-uncased')    # Download model and configuration from S3 and cache.
              model = AutoModel.from_pretrained('./test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
              model = AutoModel.from_pretrained('bert-base-uncased', output_attention=True)  # Update configuration during loading
              assert model.config.output_attention == True
              # Loading from a TF checkpoint file instead of a PyTorch model (slower)
              config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
              model = AutoModel.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
  
          """
          if "t5" in pretrained_model_name_or_path:
              return T5Model.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "distilbert" in pretrained_model_name_or_path:
              return DistilBertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "albert" in pretrained_model_name_or_path:
              return AlbertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "camembert" in pretrained_model_name_or_path:
              return CamembertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "xlm-roberta" in pretrained_model_name_or_path:
              return XLMRobertaModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "roberta" in pretrained_model_name_or_path:
              return RobertaModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "bert" in pretrained_model_name_or_path:
              return BertModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "openai-gpt" in pretrained_model_name_or_path:
              return OpenAIGPTModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "gpt2" in pretrained_model_name_or_path:
              return GPT2Model.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "transfo-xl" in pretrained_model_name_or_path:
              return TransfoXLModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "xlnet" in pretrained_model_name_or_path:
              return XLNetModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "xlm" in pretrained_model_name_or_path:
              return XLMModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "ctrl" in pretrained_model_name_or_path:
              return CTRLModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          raise ValueError(
              "Unrecognized model identifier in {}. Should contains one of "
              "'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
              "'xlm-roberta', 'xlm', 'roberta, 'ctrl', 'distilbert', 'camembert', 'albert'".format(
                  pretrained_model_name_or_path
              )
          )
  
  
  class AutoModelWithLMHead(object):
      r"""
          :class:`~transformers.AutoModelWithLMHead` is a generic model class
          that will be instantiated as one of the language modeling model classes of the library
          when created with the `AutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)`
          class method.
  
          The `from_pretrained()` method takes care of returning the correct model class instance
          using pattern matching on the `pretrained_model_name_or_path` string.
  
          The model class to instantiate is selected as the first pattern matching
          in the `pretrained_model_name_or_path` string (in the following order):
              - contains `t5`: T5ModelWithLMHead (T5 model)
              - contains `distilbert`: DistilBertForMaskedLM (DistilBERT model)
              - contains `albert`: AlbertForMaskedLM (ALBERT model)
              - contains `camembert`: CamembertForMaskedLM (CamemBERT model)
              - contains `xlm-roberta`: XLMRobertaForMaskedLM (XLM-RoBERTa model)
              - contains `roberta`: RobertaForMaskedLM (RoBERTa model)
              - contains `bert`: BertForMaskedLM (Bert model)
              - contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model)
              - contains `gpt2`: GPT2LMHeadModel (OpenAI GPT-2 model)
              - contains `transfo-xl`: TransfoXLLMHeadModel (Transformer-XL model)
              - contains `xlnet`: XLNetLMHeadModel (XLNet model)
              - contains `xlm`: XLMWithLMHeadModel (XLM model)
              - contains `ctrl`: CTRLLMHeadModel (Salesforce CTRL model)
  
          This class cannot be instantiated using `__init__()` (throws an error).
      """
  
      def __init__(self):
          raise EnvironmentError(
              "AutoModelWithLMHead is designed to be instantiated "
              "using the `AutoModelWithLMHead.from_pretrained(pretrained_model_name_or_path)` or "
              "`AutoModelWithLMHead.from_config(config)` methods."
          )
  
      @classmethod
      def from_config(cls, config):
          r""" Instantiates one of the base model classes of the library
          from a configuration.
  
              config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
                  The model class to instantiate is selected based on the configuration class:
                      - isInstance of `distilbert` configuration class: DistilBertModel (DistilBERT model)
                      - isInstance of `roberta` configuration class: RobertaModel (RoBERTa model)
                      - isInstance of `bert` configuration class: BertModel (Bert model)
                      - isInstance of `openai-gpt` configuration class: OpenAIGPTModel (OpenAI GPT model)
                      - isInstance of `gpt2` configuration class: GPT2Model (OpenAI GPT-2 model)
                      - isInstance of `ctrl` configuration class: CTRLModel (Salesforce CTRL  model)
                      - isInstance of `transfo-xl` configuration class: TransfoXLModel (Transformer-XL model)
                      - isInstance of `xlnet` configuration class: XLNetModel (XLNet model)
                      - isInstance of `xlm` configuration class: XLMModel (XLM model)
  
          Examples::
  
              config = BertConfig.from_pretrained('bert-base-uncased')    # Download configuration from S3 and cache.
              model = AutoModelWithLMHead.from_config(config)  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
          """
          if isinstance(config, DistilBertConfig):
              return DistilBertForMaskedLM(config)
          elif isinstance(config, RobertaConfig):
              return RobertaForMaskedLM(config)
          elif isinstance(config, BertConfig):
              return BertForMaskedLM(config)
          elif isinstance(config, OpenAIGPTConfig):
              return OpenAIGPTLMHeadModel(config)
          elif isinstance(config, GPT2Config):
              return GPT2LMHeadModel(config)
          elif isinstance(config, TransfoXLConfig):
              return TransfoXLLMHeadModel(config)
          elif isinstance(config, XLNetConfig):
              return XLNetLMHeadModel(config)
          elif isinstance(config, XLMConfig):
              return XLMWithLMHeadModel(config)
          elif isinstance(config, CTRLConfig):
              return CTRLLMHeadModel(config)
          elif isinstance(config, XLMRobertaConfig):
              return XLMRobertaForMaskedLM(config)
          raise ValueError("Unrecognized configuration class {}".format(config))
  
      @classmethod
      def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
          r""" Instantiates one of the language modeling model classes of the library
          from a pre-trained model configuration.
  
          The `from_pretrained()` method takes care of returning the correct model class instance
          using pattern matching on the `pretrained_model_name_or_path` string.
  
          The model class to instantiate is selected as the first pattern matching
          in the `pretrained_model_name_or_path` string (in the following order):
              - contains `t5`: T5ModelWithLMHead (T5 model)
              - contains `distilbert`: DistilBertForMaskedLM (DistilBERT model)
              - contains `albert`: AlbertForMaskedLM (ALBERT model)
              - contains `camembert`: CamembertForMaskedLM (CamemBERT model)
              - contains `xlm-roberta`: XLMRobertaForMaskedLM (XLM-RoBERTa model)
              - contains `roberta`: RobertaForMaskedLM (RoBERTa model)
              - contains `bert`: BertForMaskedLM (Bert model)
              - contains `openai-gpt`: OpenAIGPTLMHeadModel (OpenAI GPT model)
              - contains `gpt2`: GPT2LMHeadModel (OpenAI GPT-2 model)
              - contains `transfo-xl`: TransfoXLLMHeadModel (Transformer-XL model)
              - contains `xlnet`: XLNetLMHeadModel (XLNet model)
              - contains `xlm`: XLMWithLMHeadModel (XLM model)
              - contains `ctrl`: CTRLLMHeadModel (Salesforce CTRL model)
  
          The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
          To train the model, you should first set it back in training mode with `model.train()`
  
          Params:
              pretrained_model_name_or_path: either:
  
                  - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
                  - a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
                  - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
                  - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
  
              model_args: (`optional`) Sequence of positional arguments:
                  All remaning positional arguments will be passed to the underlying model's ``__init__`` method
  
              config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
                  Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
  
                  - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
                  - the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
                  - the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
  
              state_dict: (`optional`) dict:
                  an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
                  This option can be used if you want to create a model from a pretrained configuration but load your own weights.
                  In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
  
              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.
  
              force_download: (`optional`) boolean, default False:
                  Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
              resume_download: (`optional`) boolean, default False:
                  Do not delete incompletely recieved file. Attempt to resume the download if such a file 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.
  
              output_loading_info: (`optional`) boolean:
                  Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
  
              kwargs: (`optional`) Remaining dictionary of keyword arguments:
                  Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
  
                  - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
                  - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
  
          Examples::
  
              model = AutoModelWithLMHead.from_pretrained('bert-base-uncased')    # Download model and configuration from S3 and cache.
              model = AutoModelWithLMHead.from_pretrained('./test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
              model = AutoModelWithLMHead.from_pretrained('bert-base-uncased', output_attention=True)  # Update configuration during loading
              assert model.config.output_attention == True
              # Loading from a TF checkpoint file instead of a PyTorch model (slower)
              config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
              model = AutoModelWithLMHead.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
  
          """
          if "t5" in pretrained_model_name_or_path:
              return T5WithLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "distilbert" in pretrained_model_name_or_path:
              return DistilBertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "albert" in pretrained_model_name_or_path:
              return AlbertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "camembert" in pretrained_model_name_or_path:
              return CamembertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "xlm-roberta" in pretrained_model_name_or_path:
              return XLMRobertaForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "roberta" in pretrained_model_name_or_path:
              return RobertaForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "bert" in pretrained_model_name_or_path:
              return BertForMaskedLM.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "openai-gpt" in pretrained_model_name_or_path:
              return OpenAIGPTLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "gpt2" in pretrained_model_name_or_path:
              return GPT2LMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "transfo-xl" in pretrained_model_name_or_path:
              return TransfoXLLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "xlnet" in pretrained_model_name_or_path:
              return XLNetLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "xlm" in pretrained_model_name_or_path:
              return XLMWithLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "ctrl" in pretrained_model_name_or_path:
              return CTRLLMHeadModel.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          raise ValueError(
              "Unrecognized model identifier in {}. Should contains one of "
              "'bert', 'openai-gpt', 'gpt2', 'transfo-xl', 'xlnet', "
              "'xlm-roberta', 'xlm', 'roberta','ctrl', 'distilbert', 'camembert', 'albert'".format(
                  pretrained_model_name_or_path
              )
          )
  
  
  class AutoModelForSequenceClassification(object):
      r"""
          :class:`~transformers.AutoModelForSequenceClassification` is a generic model class
          that will be instantiated as one of the sequence classification model classes of the library
          when created with the `AutoModelForSequenceClassification.from_pretrained(pretrained_model_name_or_path)`
          class method.
  
          The `from_pretrained()` method takes care of returning the correct model class instance
          using pattern matching on the `pretrained_model_name_or_path` string.
  
          The 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`: DistilBertForSequenceClassification (DistilBERT model)
              - contains `albert`: AlbertForSequenceClassification (ALBERT model)
              - contains `camembert`: CamembertForSequenceClassification (CamemBERT model)
              - contains `xlm-roberta`: XLMRobertaForSequenceClassification (XLM-RoBERTa model)
              - contains `roberta`: RobertaForSequenceClassification (RoBERTa model)
              - contains `bert`: BertForSequenceClassification (Bert model)
              - contains `xlnet`: XLNetForSequenceClassification (XLNet model)
              - contains `xlm`: XLMForSequenceClassification (XLM model)
  
          This class cannot be instantiated using `__init__()` (throws an error).
      """
  
      def __init__(self):
          raise EnvironmentError(
              "AutoModelForSequenceClassification is designed to be instantiated "
              "using the `AutoModelForSequenceClassification.from_pretrained(pretrained_model_name_or_path)` or "
              "`AutoModelForSequenceClassification.from_config(config)` methods."
          )
  
      @classmethod
      def from_config(cls, config):
          r""" Instantiates one of the base model classes of the library
          from a configuration.
  
              config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
                  The model class to instantiate is selected based on the configuration class:
                      - isInstance of `distilbert` configuration class: DistilBertModel (DistilBERT model)
                      - isInstance of `roberta` configuration class: RobertaModel (RoBERTa model)
                      - isInstance of `bert` configuration class: BertModel (Bert model)
                      - isInstance of `xlnet` configuration class: XLNetModel (XLNet model)
                      - isInstance of `xlm` configuration class: XLMModel (XLM model)
  
          Examples::
  
              config = BertConfig.from_pretrained('bert-base-uncased')    # Download configuration from S3 and cache.
              model = AutoModelForSequenceClassification.from_config(config)  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
          """
          if isinstance(config, AlbertConfig):
              return AlbertForSequenceClassification(config)
          elif isinstance(config, CamembertConfig):
              return CamembertForSequenceClassification(config)
          elif isinstance(config, DistilBertConfig):
              return DistilBertForSequenceClassification(config)
          elif isinstance(config, RobertaConfig):
              return RobertaForSequenceClassification(config)
          elif isinstance(config, BertConfig):
              return BertForSequenceClassification(config)
          elif isinstance(config, XLNetConfig):
              return XLNetForSequenceClassification(config)
          elif isinstance(config, XLMConfig):
              return XLMForSequenceClassification(config)
          elif isinstance(config, XLMRobertaConfig):
              return XLMRobertaForSequenceClassification(config)
          raise ValueError("Unrecognized configuration class {}".format(config))
  
      @classmethod
      def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
          r""" Instantiates one of the sequence classification model classes of the library
          from a pre-trained model configuration.
  
          The `from_pretrained()` method takes care of returning the correct model class instance
          using pattern matching on the `pretrained_model_name_or_path` string.
  
          The 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`: DistilBertForSequenceClassification (DistilBERT model)
              - contains `albert`: AlbertForSequenceClassification (ALBERT model)
              - contains `camembert`: CamembertForSequenceClassification (CamemBERT model)
              - contains `xlm-roberta`: XLMRobertaForSequenceClassification (XLM-RoBERTa model)
              - contains `roberta`: RobertaForSequenceClassification (RoBERTa model)
              - contains `bert`: BertForSequenceClassification (Bert model)
              - contains `xlnet`: XLNetForSequenceClassification (XLNet model)
              - contains `xlm`: XLMForSequenceClassification (XLM model)
  
          The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
          To train the model, you should first set it back in training mode with `model.train()`
  
          Params:
              pretrained_model_name_or_path: either:
  
                  - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
                  - a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
                  - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
                  - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
  
              model_args: (`optional`) Sequence of positional arguments:
                  All remaning positional arguments will be passed to the underlying model's ``__init__`` method
  
              config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
                  Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
  
                  - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
                  - the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
                  - the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
  
              state_dict: (`optional`) dict:
                  an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
                  This option can be used if you want to create a model from a pretrained configuration but load your own weights.
                  In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
  
              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.
  
              force_download: (`optional`) boolean, default False:
                  Force to (re-)download the model weights and configuration files and override the cached versions if they exists.
  
              resume_download: (`optional`) boolean, default False:
                  Do not delete incompletely recieved file. Attempt to resume the download if such a file 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.
  
              output_loading_info: (`optional`) boolean:
                  Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
  
              kwargs: (`optional`) Remaining dictionary of keyword arguments:
                  Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
  
                  - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
                  - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
  
          Examples::
  
              model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased')    # Download model and configuration from S3 and cache.
              model = AutoModelForSequenceClassification.from_pretrained('./test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
              model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', output_attention=True)  # Update configuration during loading
              assert model.config.output_attention == True
              # Loading from a TF checkpoint file instead of a PyTorch model (slower)
              config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
              model = AutoModelForSequenceClassification.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
  
          """
          if "distilbert" in pretrained_model_name_or_path:
              return DistilBertForSequenceClassification.from_pretrained(
                  pretrained_model_name_or_path, *model_args, **kwargs
              )
          elif "albert" in pretrained_model_name_or_path:
              return AlbertForSequenceClassification.from_pretrained(
                  pretrained_model_name_or_path, *model_args, **kwargs
              )
          elif "camembert" in pretrained_model_name_or_path:
              return CamembertForSequenceClassification.from_pretrained(
                  pretrained_model_name_or_path, *model_args, **kwargs
              )
          elif "xlm-roberta" in pretrained_model_name_or_path:
              return XLMRobertaForSequenceClassification.from_pretrained(
                  pretrained_model_name_or_path, *model_args, **kwargs
              )
          elif "roberta" in pretrained_model_name_or_path:
              return RobertaForSequenceClassification.from_pretrained(
                  pretrained_model_name_or_path, *model_args, **kwargs
              )
          elif "bert" in pretrained_model_name_or_path:
              return BertForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "xlnet" in pretrained_model_name_or_path:
              return XLNetForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "xlm" in pretrained_model_name_or_path:
              return XLMForSequenceClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
  
          raise ValueError(
              "Unrecognized model identifier in {}. Should contains one of "
              "'bert', 'xlnet', 'xlm-roberta', 'xlm', 'roberta', 'distilbert', 'camembert', 'albert'".format(
                  pretrained_model_name_or_path
              )
          )
  
  
  class AutoModelForQuestionAnswering(object):
      r"""
          :class:`~transformers.AutoModelForQuestionAnswering` is a generic model class
          that will be instantiated as one of the question answering model classes of the library
          when created with the `AutoModelForQuestionAnswering.from_pretrained(pretrained_model_name_or_path)`
          class method.
  
          The `from_pretrained()` method takes care of returning the correct model class instance
          using pattern matching on the `pretrained_model_name_or_path` string.
  
          The 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`: DistilBertForQuestionAnswering (DistilBERT model)
              - contains `albert`: AlbertForQuestionAnswering (ALBERT model)
              - contains `bert`: BertForQuestionAnswering (Bert model)
              - contains `xlnet`: XLNetForQuestionAnswering (XLNet model)
              - contains `xlm`: XLMForQuestionAnswering (XLM model)
  
          This class cannot be instantiated using `__init__()` (throws an error).
      """
  
      def __init__(self):
          raise EnvironmentError(
              "AutoModelForQuestionAnswering is designed to be instantiated "
              "using the `AutoModelForQuestionAnswering.from_pretrained(pretrained_model_name_or_path)` or "
              "`AutoModelForQuestionAnswering.from_config(config)` methods."
          )
  
      @classmethod
      def from_config(cls, config):
          r""" Instantiates one of the base model classes of the library
          from a configuration.
  
              config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
                  The model class to instantiate is selected based on the configuration class:
                      - isInstance of `distilbert` configuration class: DistilBertModel (DistilBERT model)
                      - isInstance of `bert` configuration class: BertModel (Bert model)
                      - isInstance of `xlnet` configuration class: XLNetModel (XLNet model)
                      - isInstance of `xlm` configuration class: XLMModel (XLM model)
  
          Examples::
  
              config = BertConfig.from_pretrained('bert-base-uncased')    # Download configuration from S3 and cache.
              model = AutoModelForSequenceClassification.from_config(config)  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
          """
          if isinstance(config, AlbertConfig):
              return AlbertForQuestionAnswering(config)
          elif isinstance(config, DistilBertConfig):
              return DistilBertForQuestionAnswering(config)
          elif isinstance(config, BertConfig):
              return BertForQuestionAnswering(config)
          elif isinstance(config, XLNetConfig):
              return XLNetForQuestionAnswering(config)
          elif isinstance(config, XLMConfig):
              return XLMForQuestionAnswering(config)
          raise ValueError("Unrecognized configuration class {}".format(config))
  
      @classmethod
      def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
          r""" Instantiates one of the question answering model classes of the library
          from a pre-trained model configuration.
  
          The `from_pretrained()` method takes care of returning the correct model class instance
          using pattern matching on the `pretrained_model_name_or_path` string.
  
          The 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`: DistilBertForQuestionAnswering (DistilBERT model)
              - contains `albert`: AlbertForQuestionAnswering (ALBERT model)
              - contains `bert`: BertForQuestionAnswering (Bert model)
              - contains `xlnet`: XLNetForQuestionAnswering (XLNet model)
              - contains `xlm`: XLMForQuestionAnswering (XLM model)
  
          The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
          To train the model, you should first set it back in training mode with `model.train()`
  
          Params:
              pretrained_model_name_or_path: either:
  
                  - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
                  - a string with the `identifier name` of a pre-trained model that was user-uploaded to our S3, e.g.: ``dbmdz/bert-base-german-cased``.
                  - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
                  - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
  
              model_args: (`optional`) Sequence of positional arguments:
                  All remaning positional arguments will be passed to the underlying model's ``__init__`` method
  
              config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
                  Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
  
                  - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
                  - the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
                  - the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
  
              state_dict: (`optional`) dict:
                  an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
                  This option can be used if you want to create a model from a pretrained configuration but load your own weights.
                  In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
  
              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.
  
              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.
  
              output_loading_info: (`optional`) boolean:
                  Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
  
              kwargs: (`optional`) Remaining dictionary of keyword arguments:
                  Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
  
                  - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
                  - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
  
          Examples::
  
              model = AutoModelForQuestionAnswering.from_pretrained('bert-base-uncased')    # Download model and configuration from S3 and cache.
              model = AutoModelForQuestionAnswering.from_pretrained('./test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
              model = AutoModelForQuestionAnswering.from_pretrained('bert-base-uncased', output_attention=True)  # Update configuration during loading
              assert model.config.output_attention == True
              # Loading from a TF checkpoint file instead of a PyTorch model (slower)
              config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
              model = AutoModelForQuestionAnswering.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
  
          """
          if "distilbert" in pretrained_model_name_or_path:
              return DistilBertForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "albert" in pretrained_model_name_or_path:
              return AlbertForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "bert" in pretrained_model_name_or_path:
              return BertForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "xlnet" in pretrained_model_name_or_path:
              return XLNetForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "xlm" in pretrained_model_name_or_path:
              return XLMForQuestionAnswering.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
  
          raise ValueError(
              "Unrecognized model identifier in {}. Should contains one of "
              "'bert', 'xlnet', 'xlm', 'distilbert', 'albert'".format(pretrained_model_name_or_path)
          )
  
  
  class AutoModelForTokenClassification:
      def __init__(self):
          raise EnvironmentError(
              "AutoModelForTokenClassification is designed to be instantiated "
              "using the `AutoModelForTokenClassification.from_pretrained(pretrained_model_name_or_path)` or "
              "`AutoModelForTokenClassification.from_config(config)` methods."
          )
  
      @classmethod
      def from_config(cls, config):
          r""" Instantiates one of the base model classes of the library
          from a configuration.
  
              config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
                  The model class to instantiate is selected based on the configuration class:
                      - isInstance of `distilbert` configuration class: DistilBertModel (DistilBERT model)
                      - isInstance of `bert` configuration class: BertModel (Bert model)
                      - isInstance of `xlnet` configuration class: XLNetModel (XLNet model)
                      - isInstance of `camembert` configuration class: CamembertModel (Camembert model)
                      - isInstance of `roberta` configuration class: RobertaModel (Roberta model)
  
          Examples::
  
              config = BertConfig.from_pretrained('bert-base-uncased')    # Download configuration from S3 and cache.
              model = AutoModelForTokenClassification.from_config(config)  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
          """
          if isinstance(config, CamembertConfig):
              return CamembertForTokenClassification(config)
          elif isinstance(config, DistilBertConfig):
              return DistilBertForTokenClassification(config)
          elif isinstance(config, BertConfig):
              return BertForTokenClassification(config)
          elif isinstance(config, XLNetConfig):
              return XLNetForTokenClassification(config)
          elif isinstance(config, RobertaConfig):
              return RobertaForTokenClassification(config)
          elif isinstance(config, XLMRobertaConfig):
              return XLMRobertaForTokenClassification(config)
          raise ValueError("Unrecognized configuration class {}".format(config))
  
      @classmethod
      def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
          r""" Instantiates one of the question answering model classes of the library
          from a pre-trained model configuration.
  
          The `from_pretrained()` method takes care of returning the correct model class instance
          using pattern matching on the `pretrained_model_name_or_path` string.
  
          The 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`: DistilBertForTokenClassification (DistilBERT model)
              - contains `camembert`: CamembertForTokenClassification (Camembert model)
              - contains `bert`: BertForTokenClassification (Bert model)
              - contains `xlnet`: XLNetForTokenClassification (XLNet model)
              - contains `roberta`: RobertaForTokenClassification (Roberta model)
  
          The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated)
          To train the model, you should first set it back in training mode with `model.train()`
  
          Params:
              pretrained_model_name_or_path: either:
  
                  - a string with the `shortcut name` of a pre-trained model to load from cache or download, e.g.: ``bert-base-uncased``.
                  - a path to a `directory` containing model weights saved using :func:`~transformers.PreTrainedModel.save_pretrained`, e.g.: ``./my_model_directory/``.
                  - a path or url to a `tensorflow index checkpoint file` (e.g. `./tf_model/model.ckpt.index`). In this case, ``from_tf`` should be set to True and a configuration object should be provided as ``config`` argument. This loading path is slower than converting the TensorFlow checkpoint in a PyTorch model using the provided conversion scripts and loading the PyTorch model afterwards.
  
              model_args: (`optional`) Sequence of positional arguments:
                  All remaning positional arguments will be passed to the underlying model's ``__init__`` method
  
              config: (`optional`) instance of a class derived from :class:`~transformers.PretrainedConfig`:
                  Configuration for the model to use instead of an automatically loaded configuation. Configuration can be automatically loaded when:
  
                  - the model is a model provided by the library (loaded with the ``shortcut-name`` string of a pretrained model), or
                  - the model was saved using :func:`~transformers.PreTrainedModel.save_pretrained` and is reloaded by suppling the save directory.
                  - the model is loaded by suppling a local directory as ``pretrained_model_name_or_path`` and a configuration JSON file named `config.json` is found in the directory.
  
              state_dict: (`optional`) dict:
                  an optional state dictionnary for the model to use instead of a state dictionary loaded from saved weights file.
                  This option can be used if you want to create a model from a pretrained configuration but load your own weights.
                  In this case though, you should check if using :func:`~transformers.PreTrainedModel.save_pretrained` and :func:`~transformers.PreTrainedModel.from_pretrained` is not a simpler option.
  
              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.
  
              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.
  
              output_loading_info: (`optional`) boolean:
                  Set to ``True`` to also return a dictionnary containing missing keys, unexpected keys and error messages.
  
              kwargs: (`optional`) Remaining dictionary of keyword arguments:
                  Can be used to update the configuration object (after it being loaded) and initiate the model. (e.g. ``output_attention=True``). Behave differently depending on whether a `config` is provided or automatically loaded:
  
                  - If a configuration is provided with ``config``, ``**kwargs`` will be directly passed to the underlying model's ``__init__`` method (we assume all relevant updates to the configuration have already been done)
                  - If a configuration is not provided, ``kwargs`` will be first passed to the configuration class initialization function (:func:`~transformers.PretrainedConfig.from_pretrained`). Each key of ``kwargs`` that corresponds to a configuration attribute will be used to override said attribute with the supplied ``kwargs`` value. Remaining keys that do not correspond to any configuration attribute will be passed to the underlying model's ``__init__`` function.
  
          Examples::
  
              model = AutoModelForTokenClassification.from_pretrained('bert-base-uncased')    # Download model and configuration from S3 and cache.
              model = AutoModelForTokenClassification.from_pretrained('./test/bert_model/')  # E.g. model was saved using `save_pretrained('./test/saved_model/')`
              model = AutoModelForTokenClassification.from_pretrained('bert-base-uncased', output_attention=True)  # Update configuration during loading
              assert model.config.output_attention == True
              # Loading from a TF checkpoint file instead of a PyTorch model (slower)
              config = AutoConfig.from_json_file('./tf_model/bert_tf_model_config.json')
              model = AutoModelForTokenClassification.from_pretrained('./tf_model/bert_tf_checkpoint.ckpt.index', from_tf=True, config=config)
  
          """
          if "camembert" in pretrained_model_name_or_path:
              return CamembertForTokenClassification.from_pretrained(
                  pretrained_model_name_or_path, *model_args, **kwargs
              )
          elif "distilbert" in pretrained_model_name_or_path:
              return DistilBertForTokenClassification.from_pretrained(
                  pretrained_model_name_or_path, *model_args, **kwargs
              )
          elif "xlm-roberta" in pretrained_model_name_or_path:
              return XLMRobertaForTokenClassification.from_pretrained(
                  pretrained_model_name_or_path, *model_args, **kwargs
              )
          elif "roberta" in pretrained_model_name_or_path:
              return RobertaForTokenClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "bert" in pretrained_model_name_or_path:
              return BertForTokenClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
          elif "xlnet" in pretrained_model_name_or_path:
              return XLNetForTokenClassification.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
  
          raise ValueError(
              "Unrecognized model identifier in {}. Should contains one of "
              "'bert', 'xlnet', 'camembert', 'distilbert', 'xlm-roberta', 'roberta'".format(
                  pretrained_model_name_or_path
              )
          )
  