text: from typing import List
  import json
  
  from allennlp.common import Registrable
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.util import JsonDict, sanitize
  from allennlp.data import DatasetReader, Instance
  from allennlp.models import Model
  from allennlp.models.archival import Archive, load_archive
  
  # a mapping from model `type` to the default Predictor for that type
  DEFAULT_PREDICTORS = {
          'srl': 'semantic-role-labeling',
          'decomposable_attention': 'textual-entailment',
          'bidaf': 'machine-comprehension',
          'bidaf-ensemble': 'machine-comprehension',
          'simple_tagger': 'sentence-tagger',
          'crf_tagger': 'sentence-tagger',
          'coref': 'coreference-resolution',
          'constituency_parser': 'constituency-parser',
          'dialog_qa': 'dialog_qa'
  }
  
  class Predictor(Registrable):
      """
      a ``Predictor`` is a thin wrapper around an AllenNLP model that handles JSON -> JSON predictions
      that can be used for serving models through the web API or making predictions in bulk.
      """
      def __init__(self, model: Model, dataset_reader: DatasetReader) -> None:
          self._model = model
          self._dataset_reader = dataset_reader
  
      def load_line(self, line: str) -> JsonDict:  # pylint: disable=no-self-use
          """
          If your inputs are not in JSON-lines format (e.g. you have a CSV)
          you can override this function to parse them correctly.
          """
          return json.loads(line)
  
      def dump_line(self, outputs: JsonDict) -> str:  # pylint: disable=no-self-use
          """
          If you don't want your outputs in JSON-lines format
          you can override this function to output them differently.
          """
          return json.dumps(outputs) + "\n"
  
      def predict_json(self, inputs: JsonDict) -> JsonDict:
          instance = self._json_to_instance(inputs)
          return self.predict_instance(instance)
  
      def predict_instance(self, instance: Instance) -> JsonDict:
          outputs = self._model.forward_on_instance(instance)
          return sanitize(outputs)
  
      def _json_to_instance(self, json_dict: JsonDict) -> Instance:
          """
          Converts a JSON object into an :class:`~allennlp.data.instance.Instance`
          and a ``JsonDict`` of information which the ``Predictor`` should pass through,
          such as tokenised inputs.
          """
          raise NotImplementedError
  
      def predict_batch_json(self, inputs: List[JsonDict]) -> List[JsonDict]:
          instances = self._batch_json_to_instances(inputs)
          return self.predict_batch_instance(instances)
  
      def predict_batch_instance(self, instances: List[Instance]) -> List[JsonDict]:
          outputs = self._model.forward_on_instances(instances)
          return sanitize(outputs)
  
      def _batch_json_to_instances(self, json_dicts: List[JsonDict]) -> List[Instance]:
          """
          Converts a list of JSON objects into a list of :class:`~allennlp.data.instance.Instance`s.
          By default, this expects that a "batch" consists of a list of JSON blobs which would
          individually be predicted by :func:`predict_json`. In order to use this method for
          batch prediction, :func:`_json_to_instance` should be implemented by the subclass, or
          if the instances have some dependency on each other, this method should be overridden
          directly.
          """
          instances = []
          for json_dict in json_dicts:
              instances.append(self._json_to_instance(json_dict))
          return instances
  
      @classmethod
      def from_path(cls, archive_path: str, predictor_name: str = None) -> 'Predictor':
          """
          Instantiate a :class:`Predictor` from an archive path.
  
          If you need more detailed configuration options, such as running the predictor on the GPU,
          please use `from_archive`.
  
          Parameters
          ----------
          archive_path The path to the archive.
  
          Returns
          -------
          A Predictor instance.
          """
          return Predictor.from_archive(load_archive(archive_path), predictor_name)
  
      @classmethod
      def from_archive(cls, archive: Archive, predictor_name: str = None) -> 'Predictor':
          """
          Instantiate a :class:`Predictor` from an :class:`~allennlp.models.archival.Archive`;
          that is, from the result of training a model. Optionally specify which `Predictor`
          subclass; otherwise, the default one for the model will be used.
          """
          # Duplicate the config so that the config inside the archive doesn't get consumed
          config = archive.config.duplicate()
  
          if not predictor_name:
              model_type = config.get("model").get("type")
              if not model_type in DEFAULT_PREDICTORS:
                  raise ConfigurationError(f"No default predictor for model type {model_type}.\n"\
                                           f"Please specify a predictor explicitly.")
              predictor_name = DEFAULT_PREDICTORS[model_type]
  
          dataset_reader_params = config["dataset_reader"]
          dataset_reader = DatasetReader.from_params(dataset_reader_params)
  
          model = archive.model
          model.eval()
  
          return Predictor.by_name(predictor_name)(model, dataset_reader)
  