text: # coding=utf-8
  # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
  # Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
  #
  # 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.
  """ Configuration base class and utilities."""
  
  
  import copy
  import json
  import os
  from typing import Any, Dict, Tuple
  
  from .file_utils import CONFIG_NAME, cached_path, hf_bucket_url, is_remote_url
  from .utils import logging
  
  
  logger = logging.get_logger(__name__)
  
  
  class PretrainedConfig(object):
      r"""Base class for all configuration classes.
      Handles a few parameters common to all models' configurations as well as methods for loading/downloading/saving
      configurations.
  
      Note:
          A configuration file can be loaded and saved to disk. Loading the configuration file and using this file to
          initialize a model does **not** load the model weights.
          It only affects the model's configuration.
  
      Class attributes (overridden by derived classes)
          - **model_type** (:obj:`str`): An identifier for the model type, serialized into the JSON file, and used to
            recreate the correct object in :class:`~transformers.AutoConfig`.
          - **is_composition** (:obj:`bool`): Whether the config class is composed of multiple
            sub-configs. In this case the config has to be initialized from two or more configs of
            type :class:`~transformers.PretrainedConfig` like: :class:`~transformers.EncoderDecoderConfig` or
            :class:`~RagConfig`.
  
      Args:
          name_or_path (:obj:`str`, `optional`, defaults to :obj:`""`):
              Store the string that was passed to :func:`~transformers.PreTrainedModel.from_pretrained` or :func:`~transformers.TFPreTrainedModel.from_pretrained`
              as ``pretrained_model_name_or_path`` if the configuration was created with such a method.
          output_hidden_states (:obj:`bool`, `optional`, defaults to :obj:`False`):
              Whether or not the model should return all hidden-states.
          output_attentions (:obj:`bool`, `optional`, defaults to :obj:`False`):
              Whether or not the model should returns all attentions.
          use_cache (:obj:`bool`, `optional`, defaults to :obj:`True`):
              Whether or not the model should return the last key/values attentions (not used by all models).
          return_dict (:obj:`bool`, `optional`, defaults to :obj:`False`):
              Whether or not the model should return a :class:`~transformers.file_utils.ModelOutput` instead of a
              plain tuple.
          is_encoder_decoder (:obj:`bool`, `optional`, defaults to :obj:`False`):
              Whether the model is used as an encoder/decoder or not.
          is_decoder (:obj:`bool`, `optional`, defaults to :obj:`False`):
              Whether the model is used as decoder or not (in which case it's used as an encoder).
          add_cross_attention (:obj:`bool`, `optional`, defaults to :obj:`False`):
              Whether cross-attention layers should be added to the model. Note, this option is only relevant for models that can be used as decoder models within the `:class:~transformers.EncoderDecoderModel` class, which consists of all models in ``AUTO_MODELS_FOR_CAUSAL_LM``.
          tie_encoder_decoder (:obj:`bool`, `optional`, defaults to :obj:`False`)
              Whether all encoder weights should be tied to their equivalent decoder weights. This requires the encoder and decoder model to have the exact same parameter names.
          prune_heads (:obj:`Dict[int, List[int]]`, `optional`, defaults to :obj:`{}`):
              Pruned heads of the model. The keys are the selected layer indices and the associated values, the list
              of heads to prune in said layer.
  
              For instance ``{1: [0, 2], 2: [2, 3]}`` will prune heads 0 and 2 on layer 1 and heads 2 and 3 on layer
              2.
          xla_device (:obj:`bool`, `optional`):
              A flag to indicate if TPU are available or not.
          chunk_size_feed_forward (:obj:`int`, `optional`, defaults to :obj:`0`):
              The chunk size of all feed forward layers in the residual attention blocks.
              A chunk size of :obj:`0` means that the feed forward layer is not chunked.
              A chunk size of n means that the feed forward layer processes :obj:`n` < sequence_length embeddings at a time.
              For more information on feed forward chunking, see `How does Feed Forward Chunking work? <../glossary.html#feed-forward-chunking>`__ .
  
      Parameters for sequence generation
          - **max_length** (:obj:`int`, `optional`, defaults to 20) -- Maximum length that will be used by
            default in the :obj:`generate` method of the model.
          - **min_length** (:obj:`int`, `optional`, defaults to 10) -- Minimum length that will be used by
            default in the :obj:`generate` method of the model.
          - **do_sample** (:obj:`bool`, `optional`, defaults to :obj:`False`) -- Flag that will be used by default in
            the :obj:`generate` method of the model. Whether or not to use sampling ; use greedy decoding otherwise.
          - **early_stopping** (:obj:`bool`, `optional`, defaults to :obj:`False`) -- Flag that will be used by
            default in the :obj:`generate` method of the model. Whether to stop the beam search when at least
            ``num_beams`` sentences are finished per batch or not.
          - **num_beams** (:obj:`int`, `optional`, defaults to 1) -- Number of beams for beam search that will be
            used by default in the :obj:`generate` method of the model. 1 means no beam search.
          - **temperature** (:obj:`float`, `optional`, defaults to 1) -- The value used to module the next token
            probabilities that will be used by default in the :obj:`generate` method of the model. Must be strictly
            positive.
          - **top_k** (:obj:`int`, `optional`, defaults to 50) -- Number of highest probability vocabulary tokens to
            keep for top-k-filtering that will be used by default in the :obj:`generate` method of the model.
          - **top_p** (:obj:`float`, `optional`, defaults to 1) --  Value that will be used by default in the
            :obj:`generate` method of the model for ``top_p``. If set to float < 1, only the most probable tokens
            with probabilities that add up to ``top_p`` or higher are kept for generation.
          - **repetition_penalty** (:obj:`float`, `optional`, defaults to 1) -- Parameter for repetition penalty
            that will be used by default in the :obj:`generate` method of the model. 1.0 means no penalty.
          - **length_penalty** (:obj:`float`, `optional`, defaults to 1) -- Exponential penalty to the length that
            will be used by default in the :obj:`generate` method of the model.
          - **no_repeat_ngram_size** (:obj:`int`, `optional`, defaults to 0) -- Value that will be used by default
            in the :obj:`generate` method of the model for ``no_repeat_ngram_size``. If set to int > 0, all ngrams of
            that size can only occur once.
          - **bad_words_ids** (:obj:`List[int]`, `optional`) -- List of token ids that are not allowed to be
            generated that will be used by default in the :obj:`generate` method of the model. In order to get the
            tokens of the words that should not appear in the generated text, use
            :obj:`tokenizer.encode(bad_word, add_prefix_space=True)`.
          - **num_return_sequences** (:obj:`int`, `optional`, defaults to 1) -- Number of independently computed
            returned sequences for each element in the batch that will be used by default in the :obj:`generate`
            method of the model.
  
      Parameters for fine-tuning tasks
          - **architectures** (:obj:`List[str]`, `optional`) -- Model architectures that can be used with the
            model pretrained weights.
          - **finetuning_task** (:obj:`str`, `optional`) -- Name of the task used to fine-tune the model. This can be
            used when converting from an original (TensorFlow or PyTorch) checkpoint.
          - **id2label** (:obj:`Dict[int, str]`, `optional`) -- A map from index (for instance prediction index, or
            target index) to label.
          - **label2id** (:obj:`Dict[str, int]`, `optional`) -- A map from label to index for the model.
          - **num_labels** (:obj:`int`, `optional`) -- Number of labels to use in the last layer added to the model,
            typically for a classification task.
          - **task_specific_params** (:obj:`Dict[str, Any]`, `optional`) -- Additional keyword arguments to store for
            the current task.
  
      Parameters linked to the tokenizer
          - **prefix** (:obj:`str`, `optional`) -- A specific prompt that should be added at the beginning of each
            text before calling the model.
          - **bos_token_id** (:obj:`int`, `optional`)) -- The id of the `beginning-of-stream` token.
          - **pad_token_id** (:obj:`int`, `optional`)) -- The id of the `padding` token.
          - **eos_token_id** (:obj:`int`, `optional`)) -- The id of the `end-of-stream` token.
          - **decoder_start_token_id** (:obj:`int`, `optional`)) -- If an encoder-decoder model starts decoding with
            a different token than `bos`, the id of that token.
          - **sep_token_id** (:obj:`int`, `optional`)) -- The id of the `separation` token.
  
      PyTorch specific parameters
          - **torchscript** (:obj:`bool`, `optional`, defaults to :obj:`False`) -- Whether or not the model should be
            used with Torchscript.
          - **tie_word_embeddings** (:obj:`bool`, `optional`, defaults to :obj:`True`) -- Whether the model's input and output word embeddings should be tied. Note that this is only relevant if the model has a output word embedding layer.
  
      TensorFlow specific parameters
          - **use_bfloat16** (:obj:`bool`, `optional`, defaults to :obj:`False`) -- Whether or not the model should
            use BFloat16 scalars (only used by some TensorFlow models).
      """
      model_type: str = ""
      is_composition: bool = False
  
      def __init__(self, **kwargs):
          # Attributes with defaults
          self.return_dict = kwargs.pop("return_dict", False)
          self.output_hidden_states = kwargs.pop("output_hidden_states", False)
          self.output_attentions = kwargs.pop("output_attentions", False)
          self.use_cache = kwargs.pop("use_cache", True)  # Not used by all models
          self.torchscript = kwargs.pop("torchscript", False)  # Only used by PyTorch models
          self.use_bfloat16 = kwargs.pop("use_bfloat16", False)
          self.pruned_heads = kwargs.pop("pruned_heads", {})
          self.tie_word_embeddings = kwargs.pop(
              "tie_word_embeddings", True
          )  # Whether input and output word embeddings should be tied for all MLM, LM and Seq2Seq models.
  
          # Is decoder is used in encoder-decoder models to differentiate encoder from decoder
          self.is_encoder_decoder = kwargs.pop("is_encoder_decoder", False)
          self.is_decoder = kwargs.pop("is_decoder", False)
          self.add_cross_attention = kwargs.pop("add_cross_attention", False)
          self.tie_encoder_decoder = kwargs.pop("tie_encoder_decoder", False)
  
          # Parameters for sequence generation
          self.max_length = kwargs.pop("max_length", 20)
          self.min_length = kwargs.pop("min_length", 0)
          self.do_sample = kwargs.pop("do_sample", False)
          self.early_stopping = kwargs.pop("early_stopping", False)
          self.num_beams = kwargs.pop("num_beams", 1)
          self.temperature = kwargs.pop("temperature", 1.0)
          self.top_k = kwargs.pop("top_k", 50)
          self.top_p = kwargs.pop("top_p", 1.0)
          self.repetition_penalty = kwargs.pop("repetition_penalty", 1.0)
          self.length_penalty = kwargs.pop("length_penalty", 1.0)
          self.no_repeat_ngram_size = kwargs.pop("no_repeat_ngram_size", 0)
          self.bad_words_ids = kwargs.pop("bad_words_ids", None)
          self.num_return_sequences = kwargs.pop("num_return_sequences", 1)
          self.chunk_size_feed_forward = kwargs.pop("chunk_size_feed_forward", 0)
  
          # Fine-tuning task arguments
          self.architectures = kwargs.pop("architectures", None)
          self.finetuning_task = kwargs.pop("finetuning_task", None)
          self.id2label = kwargs.pop("id2label", None)
          self.label2id = kwargs.pop("label2id", None)
          if self.id2label is not None:
              kwargs.pop("num_labels", None)
              self.id2label = dict((int(key), value) for key, value in self.id2label.items())
              # Keys are always strings in JSON so convert ids to int here.
          else:
              self.num_labels = kwargs.pop("num_labels", 2)
  
          # Tokenizer arguments TODO: eventually tokenizer and models should share the same config
          self.tokenizer_class = kwargs.pop("tokenizer_class", None)
          self.prefix = kwargs.pop("prefix", None)
          self.bos_token_id = kwargs.pop("bos_token_id", None)
          self.pad_token_id = kwargs.pop("pad_token_id", None)
          self.eos_token_id = kwargs.pop("eos_token_id", None)
          self.sep_token_id = kwargs.pop("sep_token_id", None)
  
          self.decoder_start_token_id = kwargs.pop("decoder_start_token_id", None)
  
          # task specific arguments
          self.task_specific_params = kwargs.pop("task_specific_params", None)
  
          # TPU arguments
          self.xla_device = kwargs.pop("xla_device", None)
  
          # Name or path to the pretrained checkpoint
          self._name_or_path = str(kwargs.pop("name_or_path", ""))
  
          # Additional attributes without default values
          for key, value in kwargs.items():
              try:
                  setattr(self, key, value)
              except AttributeError as err:
                  logger.error("Can't set {} with value {} for {}".format(key, value, self))
                  raise err
  
      @property
      def name_or_path(self) -> str:
          return self._name_or_path
  
      @name_or_path.setter
      def name_or_path(self, value):
          self._name_or_path = str(value)  # Make sure that name_or_path is a string (for JSON encoding)
  
      @property
      def use_return_dict(self) -> bool:
          """
          :obj:`bool`: Whether or not return :class:`~transformers.file_utils.ModelOutput` instead of tuples.
          """
          # If torchscript is set, force `return_dict=False` to avoid jit errors
          return self.return_dict and not self.torchscript
  
      @property
      def num_labels(self) -> int:
          """
          :obj:`int`: The number of labels for classification models.
          """
          return len(self.id2label)
  
      @num_labels.setter
      def num_labels(self, num_labels: int):
          self.id2label = {i: "LABEL_{}".format(i) for i in range(num_labels)}
          self.label2id = dict(zip(self.id2label.values(), self.id2label.keys()))
  
      def save_pretrained(self, save_directory: str):
          """
          Save a configuration object to the directory ``save_directory``, so that it can be re-loaded using the
          :func:`~transformers.PretrainedConfig.from_pretrained` class method.
  
          Args:
              save_directory (:obj:`str`):
                  Directory where the configuration JSON file will be saved (will be created if it does not exist).
          """
          if os.path.isfile(save_directory):
              raise AssertionError("Provided path ({}) should be a directory, not a file".format(save_directory))
          os.makedirs(save_directory, exist_ok=True)
          # If we save using the predefined names, we can load using `from_pretrained`
          output_config_file = os.path.join(save_directory, CONFIG_NAME)
  
          self.to_json_file(output_config_file, use_diff=True)
          logger.info("Configuration saved in {}".format(output_config_file))
  
      @classmethod
      def from_pretrained(cls, pretrained_model_name_or_path: str, **kwargs) -> "PretrainedConfig":
          r"""
          Instantiate a :class:`~transformers.PretrainedConfig` (or a derived class) from a pretrained model
          configuration.
  
          Args:
              pretrained_model_name_or_path (:obj:`str`):
                  This can be either:
  
                  - the `shortcut name` of a pretrained model configuration to load from cache or download, e.g.,
                    ``bert-base-uncased``.
                  - the `identifier name` of a pretrained model configuration that was uploaded to our S3 by any user,
                    e.g., ``dbmdz/bert-base-german-cased``.
                  - a path to a `directory` containing a configuration file saved using the
                    :func:`~transformers.PretrainedConfig.save_pretrained` method, e.g., ``./my_model_directory/``.
                  - a path or url to a saved configuration JSON `file`, e.g.,
                    ``./my_model_directory/configuration.json``.
              cache_dir (:obj:`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 to (re-)download the 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 file. Attempts 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 (:obj:`Dict[str, Any]`, `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.
  
          Returns:
              :class:`PretrainedConfig`: The configuration object instantiated from this pretrained model.
  
          Examples::
  
              # We can't instantiate directly the base class `PretrainedConfig` so let's show the examples on a
              # derived class: BertConfig
              config = BertConfig.from_pretrained('bert-base-uncased')    # Download configuration from S3 and cache.
              config = BertConfig.from_pretrained('./test/saved_model/')  # E.g. config (or model) was saved using `save_pretrained('./test/saved_model/')`
              config = BertConfig.from_pretrained('./test/saved_model/my_configuration.json')
              config = BertConfig.from_pretrained('bert-base-uncased', output_attentions=True, foo=False)
              assert config.output_attentions == True
              config, unused_kwargs = BertConfig.from_pretrained('bert-base-uncased', output_attentions=True,
                                                                 foo=False, return_unused_kwargs=True)
              assert config.output_attentions == True
              assert unused_kwargs == {'foo': False}
  
          """
          config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
          return cls.from_dict(config_dict, **kwargs)
  
      @classmethod
      def get_config_dict(cls, pretrained_model_name_or_path: str, **kwargs) -> Tuple[Dict[str, Any], Dict[str, Any]]:
          """
          From a ``pretrained_model_name_or_path``, resolve to a dictionary of parameters, to be used
          for instantiating a :class:`~transformers.PretrainedConfig` using ``from_dict``.
  
          Parameters:
              pretrained_model_name_or_path (:obj:`str`):
                  The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.
  
          Returns:
              :obj:`Tuple[Dict, Dict]`: The dictionary(ies) that will be used to instantiate the configuration object.
  
          """
          cache_dir = kwargs.pop("cache_dir", None)
          force_download = kwargs.pop("force_download", False)
          resume_download = kwargs.pop("resume_download", False)
          proxies = kwargs.pop("proxies", None)
          local_files_only = kwargs.pop("local_files_only", False)
  
          if os.path.isdir(pretrained_model_name_or_path):
              config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
          elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
              config_file = pretrained_model_name_or_path
          else:
              config_file = hf_bucket_url(
                  pretrained_model_name_or_path, filename=CONFIG_NAME, use_cdn=False, mirror=None
              )
  
          try:
              # Load from URL or cache if already cached
              resolved_config_file = cached_path(
                  config_file,
                  cache_dir=cache_dir,
                  force_download=force_download,
                  proxies=proxies,
                  resume_download=resume_download,
                  local_files_only=local_files_only,
              )
              # Load config dict
              if resolved_config_file is None:
                  raise EnvironmentError
              config_dict = cls._dict_from_json_file(resolved_config_file)
  
          except EnvironmentError:
              msg = (
                  f"Can't load config for '{pretrained_model_name_or_path}'. Make sure that:\n\n"
                  f"- '{pretrained_model_name_or_path}' is a correct model identifier listed on 'https://huggingface.co/models'\n\n"
                  f"- or '{pretrained_model_name_or_path}' is the correct path to a directory containing a {CONFIG_NAME} file\n\n"
              )
              raise EnvironmentError(msg)
  
          except json.JSONDecodeError:
              msg = (
                  "Couldn't reach server at '{}' to download configuration file or "
                  "configuration file is not a valid JSON file. "
                  "Please check network or file content here: {}.".format(config_file, resolved_config_file)
              )
              raise EnvironmentError(msg)
  
          if resolved_config_file == config_file:
              logger.info("loading configuration file {}".format(config_file))
          else:
              logger.info("loading configuration file {} from cache at {}".format(config_file, resolved_config_file))
  
          return config_dict, kwargs
  
      @classmethod
      def from_dict(cls, config_dict: Dict[str, Any], **kwargs) -> "PretrainedConfig":
          """
          Instantiates a :class:`~transformers.PretrainedConfig` from a Python dictionary of parameters.
  
          Args:
              config_dict (:obj:`Dict[str, Any]`):
                  Dictionary that will be used to instantiate the configuration object. Such a dictionary can be
                  retrieved from a pretrained checkpoint by leveraging the
                  :func:`~transformers.PretrainedConfig.get_config_dict` method.
              kwargs (:obj:`Dict[str, Any]`):
                  Additional parameters from which to initialize the configuration object.
  
          Returns:
              :class:`PretrainedConfig`: The configuration object instantiated from those parameters.
          """
          return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)
  
          config = cls(**config_dict)
  
          if hasattr(config, "pruned_heads"):
              config.pruned_heads = dict((int(key), value) for key, value in config.pruned_heads.items())
  
          # Update config with kwargs if needed
          to_remove = []
          for key, value in kwargs.items():
              if hasattr(config, key):
                  setattr(config, key, value)
                  to_remove.append(key)
          for key in to_remove:
              kwargs.pop(key, None)
  
          logger.info("Model config %s", str(config))
          if return_unused_kwargs:
              return config, kwargs
          else:
              return config
  
      @classmethod
      def from_json_file(cls, json_file: str) -> "PretrainedConfig":
          """
          Instantiates a :class:`~transformers.PretrainedConfig` from the path to a JSON file of parameters.
  
          Args:
              json_file (:obj:`str`):
                  Path to the JSON file containing the parameters.
  
          Returns:
              :class:`PretrainedConfig`: The configuration object instantiated from that JSON file.
  
          """
          config_dict = cls._dict_from_json_file(json_file)
          return cls(**config_dict)
  
      @classmethod
      def _dict_from_json_file(cls, json_file: str):
          with open(json_file, "r", encoding="utf-8") as reader:
              text = reader.read()
          return json.loads(text)
  
      def __eq__(self, other):
          return self.__dict__ == other.__dict__
  
      def __repr__(self):
          return "{} {}".format(self.__class__.__name__, self.to_json_string())
  
      def to_diff_dict(self) -> Dict[str, Any]:
          """
          Removes all attributes from config which correspond to the default
          config attributes for better readability and serializes to a Python
          dictionary.
  
          Returns:
              :obj:`Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance,
          """
          config_dict = self.to_dict()
  
          # get the default config dict
          default_config_dict = PretrainedConfig().to_dict()
  
          # get class specific config dict
          class_config_dict = self.__class__().to_dict() if not self.is_composition else {}
  
          serializable_config_dict = {}
  
          # only serialize values that differ from the default config
          for key, value in config_dict.items():
              if (
                  key not in default_config_dict
                  or value != default_config_dict[key]
                  or (key in class_config_dict and value != class_config_dict[key])
              ):
                  serializable_config_dict[key] = value
  
          return serializable_config_dict
  
      def to_dict(self) -> Dict[str, Any]:
          """
          Serializes this instance to a Python dictionary.
  
          Returns:
              :obj:`Dict[str, Any]`: Dictionary of all the attributes that make up this configuration instance.
          """
          output = copy.deepcopy(self.__dict__)
          if hasattr(self.__class__, "model_type"):
              output["model_type"] = self.__class__.model_type
          return output
  
      def to_json_string(self, use_diff: bool = True) -> str:
          """
          Serializes this instance to a JSON string.
  
          Args:
              use_diff (:obj:`bool`, `optional`, defaults to :obj:`True`):
                  If set to ``True``, only the difference between the config instance and the default
                  ``PretrainedConfig()`` is serialized to JSON string.
  
          Returns:
              :obj:`str`: String containing all the attributes that make up this configuration instance in JSON format.
          """
          if use_diff is True:
              config_dict = self.to_diff_dict()
          else:
              config_dict = self.to_dict()
          return json.dumps(config_dict, indent=2, sort_keys=True) + "\n"
  
      def to_json_file(self, json_file_path: str, use_diff: bool = True):
          """
          Save this instance to a JSON file.
  
          Args:
              json_file_path (:obj:`str`):
                  Path to the JSON file in which this configuration instance's parameters will be saved.
              use_diff (:obj:`bool`, `optional`, defaults to :obj:`True`):
                  If set to ``True``, only the difference between the config instance and the default
                  ``PretrainedConfig()`` is serialized to JSON file.
          """
          with open(json_file_path, "w", encoding="utf-8") as writer:
              writer.write(self.to_json_string(use_diff=use_diff))
  
      def update(self, config_dict: Dict[str, Any]):
          """
          Updates attributes of this class with attributes from ``config_dict``.
  
          Args:
              config_dict (:obj:`Dict[str, Any]`): Dictionary of attributes that shall be updated for this class.
          """
          for key, value in config_dict.items():
              setattr(self, key, value)
  