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 Config class. """
  
  import re
  from collections import OrderedDict
  
  from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig
  from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig
  from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig
  from .configuration_bert_generation import BertGenerationConfig
  from .configuration_blenderbot import BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP, BlenderbotConfig
  from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig
  from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
  from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig
  from .configuration_distilbert import DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, DistilBertConfig
  from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
  from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig
  from .configuration_encoder_decoder import EncoderDecoderConfig
  from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig
  from .configuration_fsmt import FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP, FSMTConfig
  from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
  from .configuration_gpt2 import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPT2Config
  from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig
  from .configuration_longformer import LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, LongformerConfig
  from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
  from .configuration_marian import MarianConfig
  from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig
  from .configuration_mobilebert import MobileBertConfig
  from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
  from .configuration_pegasus import PegasusConfig
  from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
  from .configuration_rag import RagConfig
  from .configuration_reformer import ReformerConfig
  from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
  from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig
  from .configuration_squeezebert import SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, SqueezeBertConfig
  from .configuration_t5 import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, T5Config
  from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
  from .configuration_utils import PretrainedConfig
  from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig
  from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig
  from .configuration_xlm_roberta import XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMRobertaConfig
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig
  
  
  ALL_PRETRAINED_CONFIG_ARCHIVE_MAP = dict(
      (key, value)
      for pretrained_map in [
          BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          BART_PRETRAINED_CONFIG_ARCHIVE_MAP,
          BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          MBART_PRETRAINED_CONFIG_ARCHIVE_MAP,
          OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
          GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
          CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
          XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
          XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
          ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
          XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
          FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          FSMT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
          LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP,
          LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DPR_PRETRAINED_CONFIG_ARCHIVE_MAP,
          DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
          PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
      ]
      for key, value, in pretrained_map.items()
  )
  
  
  CONFIG_MAPPING = OrderedDict(
      [
          ("retribert", RetriBertConfig),
          ("t5", T5Config),
          ("mobilebert", MobileBertConfig),
          ("distilbert", DistilBertConfig),
          ("albert", AlbertConfig),
          ("bert-generation", BertGenerationConfig),
          ("camembert", CamembertConfig),
          ("xlm-roberta", XLMRobertaConfig),
          ("pegasus", PegasusConfig),
          ("marian", MarianConfig),
          ("mbart", MBartConfig),
          ("bart", BartConfig),
          ("blenderbot", BlenderbotConfig),
          ("reformer", ReformerConfig),
          ("longformer", LongformerConfig),
          ("roberta", RobertaConfig),
          ("deberta", DebertaConfig),
          ("flaubert", FlaubertConfig),
          ("fsmt", FSMTConfig),
          ("squeezebert", SqueezeBertConfig),
          ("bert", BertConfig),
          ("openai-gpt", OpenAIGPTConfig),
          ("gpt2", GPT2Config),
          ("transfo-xl", TransfoXLConfig),
          ("xlnet", XLNetConfig),
          ("xlm-prophetnet", XLMProphetNetConfig),
          ("prophetnet", ProphetNetConfig),
          ("xlm", XLMConfig),
          ("ctrl", CTRLConfig),
          ("electra", ElectraConfig),
          ("encoder-decoder", EncoderDecoderConfig),
          ("funnel", FunnelConfig),
          ("lxmert", LxmertConfig),
          ("dpr", DPRConfig),
          ("layoutlm", LayoutLMConfig),
          ("rag", RagConfig),
      ]
  )
  
  MODEL_NAMES_MAPPING = OrderedDict(
      [
          ("retribert", "RetriBERT"),
          ("t5", "T5"),
          ("mobilebert", "MobileBERT"),
          ("distilbert", "DistilBERT"),
          ("albert", "ALBERT"),
          ("bert-generation", "Bert Generation"),
          ("camembert", "CamemBERT"),
          ("xlm-roberta", "XLM-RoBERTa"),
          ("pegasus", "Pegasus"),
          ("blenderbot", "Blenderbot"),
          ("marian", "Marian"),
          ("mbart", "mBART"),
          ("bart", "BART"),
          ("reformer", "Reformer"),
          ("longformer", "Longformer"),
          ("roberta", "RoBERTa"),
          ("flaubert", "FlauBERT"),
          ("fsmt", "FairSeq Machine-Translation"),
          ("squeezebert", "SqueezeBERT"),
          ("bert", "BERT"),
          ("openai-gpt", "OpenAI GPT"),
          ("gpt2", "OpenAI GPT-2"),
          ("transfo-xl", "Transformer-XL"),
          ("xlnet", "XLNet"),
          ("xlm", "XLM"),
          ("ctrl", "CTRL"),
          ("electra", "ELECTRA"),
          ("encoder-decoder", "Encoder decoder"),
          ("funnel", "Funnel Transformer"),
          ("lxmert", "LXMERT"),
          ("deberta", "DeBERTa"),
          ("layoutlm", "LayoutLM"),
          ("dpr", "DPR"),
          ("rag", "RAG"),
          ("xlm-prophetnet", "XLMProphetNet"),
          ("prophetnet", "ProphetNet"),
      ]
  )
  
  
  def _list_model_options(indent, config_to_class=None, use_model_types=True):
      if config_to_class is None and not use_model_types:
          raise ValueError("Using `use_model_types=False` requires a `config_to_class` dictionary.")
      if use_model_types:
          if config_to_class is None:
              model_type_to_name = {model_type: config.__name__ for model_type, config in CONFIG_MAPPING.items()}
          else:
              model_type_to_name = {
                  model_type: config_to_class[config].__name__
                  for model_type, config in CONFIG_MAPPING.items()
                  if config in config_to_class
              }
          lines = [
              f"{indent}- **{model_type}** -- :class:`~transformers.{cls_name}` ({MODEL_NAMES_MAPPING[model_type]} model)"
              for model_type, cls_name in model_type_to_name.items()
          ]
      else:
          config_to_name = {config.__name__: clas.__name__ for config, clas in config_to_class.items()}
          config_to_model_name = {
              config.__name__: MODEL_NAMES_MAPPING[model_type] for model_type, config in CONFIG_MAPPING.items()
          }
          lines = [
              f"{indent}- :class:`~transformers.{config_name}` configuration class: :class:`~transformers.{cls_name}` ({config_to_model_name[config_name]} model)"
              for config_name, cls_name in config_to_name.items()
          ]
      return "\n".join(lines)
  
  
  def replace_list_option_in_docstrings(config_to_class=None, use_model_types=True):
      def docstring_decorator(fn):
          docstrings = fn.__doc__
          lines = docstrings.split("\n")
          i = 0
          while i < len(lines) and re.search(r"^(\s*)List options\s*$", lines[i]) is None:
              i += 1
          if i < len(lines):
              indent = re.search(r"^(\s*)List options\s*$", lines[i]).groups()[0]
              if use_model_types:
                  indent = f"{indent}    "
              lines[i] = _list_model_options(indent, config_to_class=config_to_class, use_model_types=use_model_types)
              docstrings = "\n".join(lines)
          else:
              raise ValueError(
                  f"The function {fn} should have an empty 'List options' in its docstring as placeholder, current docstring is:\n{docstrings}"
              )
          fn.__doc__ = docstrings
          return fn
  
      return docstring_decorator
  
  
  class AutoConfig:
      r"""
      This is a generic configuration class that will be instantiated as one of the configuration classes of the library
      when created with the :meth:`~transformers.AutoConfig.from_pretrained` class method.
  
      This class cannot be instantiated directly using ``__init__()`` (throws an error).
      """
  
      def __init__(self):
          raise EnvironmentError(
              "AutoConfig is designed to be instantiated "
              "using the `AutoConfig.from_pretrained(pretrained_model_name_or_path)` method."
          )
  
      @classmethod
      def for_model(cls, model_type: str, *args, **kwargs):
          if model_type in CONFIG_MAPPING:
              config_class = CONFIG_MAPPING[model_type]
              return config_class(*args, **kwargs)
          raise ValueError(
              "Unrecognized model identifier: {}. Should contain one of {}".format(
                  model_type, ", ".join(CONFIG_MAPPING.keys())
              )
          )
  
      @classmethod
      @replace_list_option_in_docstrings()
      def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
          r"""
          Instantiate one of the configuration classes of the library from a pretrained model configuration.
  
          The configuration class to instantiate is selected based on the :obj:`model_type` property of the config
          object that is loaded, or when it's missing, by falling back to using pattern matching on
          :obj:`pretrained_model_name_or_path`:
  
          List options
  
          Args:
              pretrained_model_name_or_path (:obj:`str`):
                  Can be either:
  
                      - A string with the `shortcut name` of a pretrained model configuration to load from cache or
                        download, e.g., ``bert-base-uncased``.
                      - A string with the `identifier name` of a pretrained model configuration that was user-uploaded to
                        our S3, e.g., ``dbmdz/bert-base-german-cased``.
                      - A path to a `directory` containing a configuration file saved using the
                        :meth:`~transformers.PretrainedConfig.save_pretrained` method, or the
                        :meth:`~transformers.PreTrainedModel.save_pretrained` method, e.g., ``./my_model_directory/``.
                      - A path or url to a saved configuration JSON `file`, e.g.,
                        ``./my_model_directory/configuration.json``.
              cache_dir (:obj:`str`, `optional`):
                  Path to a directory in which a downloaded pretrained model configuration should be cached if the
                  standard cache should not be used.
              force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
                  Whether or not to force the (re-)download the model weights and configuration files and override the
                  cached versions if they exist.
              resume_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
                  Whether or not to delete incompletely received files. Will attempt to resume the download if such a
                  file exists.
              proxies (:obj:`Dict[str, str]`, `optional`):
                  A dictionary of proxy servers to use by protocol or endpoint, e.g.,
                  :obj:`{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each
                  request.
              return_unused_kwargs (:obj:`bool`, `optional`, defaults to :obj:`False`):
                  If :obj:`False`, then this function returns just the final configuration object.
  
                  If :obj:`True`, then this functions returns a :obj:`Tuple(config, unused_kwargs)` where `unused_kwargs`
                  is a dictionary consisting of the key/value pairs whose keys are not configuration attributes: i.e.,
                  the part of ``kwargs`` which has not been used to update ``config`` and is otherwise ignored.
              kwargs(additional keyword arguments, `optional`):
                  The values in kwargs of any keys which are configuration attributes will be used to override the loaded
                  values. Behavior concerning key/value pairs whose keys are *not* configuration attributes is
                  controlled by the ``return_unused_kwargs`` keyword parameter.
  
          Examples::
  
              >>> from transformers import AutoConfig
  
              >>> # Download configuration from S3 and cache.
              >>> config = AutoConfig.from_pretrained('bert-base-uncased')
  
              >>> # Download configuration from S3 (user-uploaded) and cache.
              >>> config = AutoConfig.from_pretrained('dbmdz/bert-base-german-cased')
  
              >>> # If configuration file is in a directory (e.g., was saved using `save_pretrained('./test/saved_model/')`).
              >>> config = AutoConfig.from_pretrained('./test/bert_saved_model/')
  
              >>> # Load a specific configuration file.
              >>> config = AutoConfig.from_pretrained('./test/bert_saved_model/my_configuration.json')
  
              >>> # Change some config attributes when loading a pretrained config.
              >>> config = AutoConfig.from_pretrained('bert-base-uncased', output_attentions=True, foo=False)
              >>> config.output_attentions
              True
              >>> config, unused_kwargs = AutoConfig.from_pretrained('bert-base-uncased', output_attentions=True, foo=False, return_unused_kwargs=True)
              >>> config.output_attentions
              True
              >>> config.unused_kwargs
              {'foo': False}
          """
          config_dict, _ = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)
  
          if "model_type" in config_dict:
              config_class = CONFIG_MAPPING[config_dict["model_type"]]
              return config_class.from_dict(config_dict, **kwargs)
          else:
              # Fallback: use pattern matching on the string.
              for pattern, config_class in CONFIG_MAPPING.items():
                  if pattern in pretrained_model_name_or_path:
                      return config_class.from_dict(config_dict, **kwargs)
  
          raise ValueError(
              "Unrecognized model in {}. "
              "Should have a `model_type` key in its config.json, or contain one of the following strings "
              "in its name: {}".format(pretrained_model_name_or_path, ", ".join(CONFIG_MAPPING.keys()))
          )
  