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 logging
  import os
  from typing import Dict, Optional, Tuple
  
  from transformers.file_utils import CONFIG_NAME, cached_path, hf_bucket_url, is_remote_url
  
  
  logger = logging.getLogger(__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):
              - ``pretrained_config_archive_map``: a python ``dict`` with `shortcut names` (string) as keys and `url` (string) of associated pretrained model configurations as values.
              - ``model_type``: a string that identifies the model type, that we serialize into the JSON file, and that we use to recreate the correct object in :class:`~transformers.AutoConfig`.
  
          Args:
              finetuning_task (:obj:`string` or :obj:`None`, `optional`, defaults to :obj:`None`):
                  Name of the task used to fine-tune the model. This can be used when converting from an original (TensorFlow or PyTorch) checkpoint.
              num_labels (:obj:`int`, `optional`, defaults to `2`):
                  Number of classes to use when the model is a classification model (sequences/tokens)
              output_attentions (:obj:`bool`, `optional`, defaults to :obj:`False`):
                  Should the model returns attentions weights.
              output_hidden_states (:obj:`string`, `optional`, defaults to :obj:`False`):
                  Should the model returns all hidden-states.
              torchscript (:obj:`bool`, `optional`, defaults to :obj:`False`):
                  Is the model used with Torchscript (for PyTorch models).
      """
      pretrained_config_archive_map = {}  # type: Dict[str, str]
      model_type = ""  # type: str
  
      def __init__(self, **kwargs):
          # Attributes with defaults
          self.output_attentions = kwargs.pop("output_attentions", False)
          self.output_hidden_states = kwargs.pop("output_hidden_states", False)
          self.output_past = kwargs.pop("output_past", 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", {})
  
          # 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)
  
          # 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.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.length_penalty = kwargs.pop("length_penalty", 1.0)
          self.no_repeat_ngram_size = kwargs.pop("no_repeat_ngram_size", 0)
          self.num_return_sequences = kwargs.pop("num_return_sequences", 1)
  
          # Fine-tuning task arguments
          self.architectures = kwargs.pop("architectures", None)
          self.finetuning_task = kwargs.pop("finetuning_task", None)
          self.num_labels = kwargs.pop("num_labels", 2)
          self.id2label = kwargs.pop("id2label", {i: "LABEL_{}".format(i) for i in range(self.num_labels)})
          self.id2label = dict((int(key), value) for key, value in self.id2label.items())
          self.label2id = kwargs.pop("label2id", dict(zip(self.id2label.values(), self.id2label.keys())))
          self.label2id = dict((key, int(value)) for key, value in self.label2id.items())
  
          # 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 num_labels(self):
          return self._num_labels
  
      @num_labels.setter
      def num_labels(self, num_labels):
          self._num_labels = num_labels
          self.id2label = {i: "LABEL_{}".format(i) for i in range(self.num_labels)}
          self.id2label = dict((int(key), value) for key, value in self.id2label.items())
          self.label2id = dict(zip(self.id2label.values(), self.id2label.keys()))
          self.label2id = dict((key, int(value)) for key, value in self.label2id.items())
  
      def save_pretrained(self, save_directory):
          """
          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:`string`):
                  Directory where the configuration JSON file will be saved.
          """
          assert os.path.isdir(
              save_directory
          ), "Saving path should be a directory where the model and configuration can be saved"
  
          # 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)
          logger.info("Configuration saved in {}".format(output_config_file))
  
      @classmethod
      def from_pretrained(cls, pretrained_model_name_or_path, **kwargs) -> "PretrainedConfig":
          r"""
  
          Instantiate a :class:`~transformers.PretrainedConfig` (or a derived class) from a pre-trained model configuration.
  
          Args:
              pretrained_model_name_or_path (:obj:`string`):
                  either:
                    - a string with the `shortcut name` of a pre-trained model configuration to load from cache or
                      download, e.g.: ``bert-base-uncased``.
                    - a string with the `identifier name` of a pre-trained model configuration that was user-uploaded to
                      our S3, e.g.: ``dbmdz/bert-base-german-cased``.
                    - 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:`string`, `optional`):
                  Path to a directory in which a downloaded pre-trained model
                  configuration should be cached if the standard cache should not be used.
              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.
              force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
                  Force to (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`):
                  Do not delete incompletely recieved file. Attempt to resume the download if such a file exists.
              proxies (:obj:`Dict`, `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: (`optional`) bool:
                  If False, then this function returns just the final configuration object.
                  If 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: ie the part
                  of kwargs which has not been used to update `config` and is otherwise ignored.
  
          Returns:
              :class:`PretrainedConfig`: An instance of a configuration object
  
          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_attention=True, foo=False)
              assert config.output_attention == True
              config, unused_kwargs = BertConfig.from_pretrained('bert-base-uncased', output_attention=True,
                                                                 foo=False, return_unused_kwargs=True)
              assert config.output_attention == 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, pretrained_config_archive_map: Optional[Dict] = None, **kwargs
      ) -> Tuple[Dict, Dict]:
          """
          From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used
          for instantiating a Config using `from_dict`.
  
          Parameters:
              pretrained_model_name_or_path (:obj:`string`):
                  The identifier of the pre-trained checkpoint from which we want the dictionary of parameters.
              pretrained_config_archive_map: (:obj:`Dict[str, str]`, `optional`) Dict:
                  A map of `shortcut names` to `url`. By default, will use the current class attribute.
  
          Returns:
              :obj:`Tuple[Dict, Dict]`: The dictionary 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 pretrained_config_archive_map is None:
              pretrained_config_archive_map = cls.pretrained_config_archive_map
  
          if pretrained_model_name_or_path in pretrained_config_archive_map:
              config_file = pretrained_config_archive_map[pretrained_model_name_or_path]
          elif 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, postfix=CONFIG_NAME)
  
          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:
              if pretrained_model_name_or_path in pretrained_config_archive_map:
                  msg = "Couldn't reach server at '{}' to download pretrained model configuration file.".format(
                      config_file
                  )
              else:
                  msg = (
                      "Can't load '{}'. Make sure that:\n\n"
                      "- '{}' is a correct model identifier listed on 'https://huggingface.co/models'\n\n"
                      "- or '{}' is the correct path to a directory containing a '{}' file\n\n".format(
                          pretrained_model_name_or_path,
                          pretrained_model_name_or_path,
                          pretrained_model_name_or_path,
                          CONFIG_NAME,
                      )
                  )
              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, **kwargs) -> "PretrainedConfig":
          """
          Constructs a `Config` 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 pre-trained 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`: An instance of a configuration object
          """
          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":
          """
          Constructs a `Config` from the path to a json file of parameters.
  
          Args:
              json_file (:obj:`string`):
                  Path to the JSON file containing the parameters.
  
          Returns:
              :class:`PretrainedConfig`: An instance of a configuration object
  
          """
          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_dict(self):
          """
          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):
          """
          Serializes this instance to a JSON string.
  
          Returns:
              :obj:`string`: String containing all the attributes that make up this configuration instance in JSON format.
          """
          return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
  
      def to_json_file(self, json_file_path):
          """
          Save this instance to a json file.
  
          Args:
              json_file_path (:obj:`string`):
                  Path to the JSON file in which this configuration instance's parameters will be saved.
          """
          with open(json_file_path, "w", encoding="utf-8") as writer:
              writer.write(self.to_json_string())