text: import itertools
  import json
  import pickle
  import logging
  from io import TextIOWrapper
  from typing import Dict
  from ipdb import set_trace as bp
  
  import torch
  import numpy as np
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.file_utils import cached_path
  from allennlp.data.dataset_readers.dataset_reader import DatasetReader
  from allennlp.data.fields import (ArrayField, Field, LabelField, ListField,
                                    MetadataField, TextField)
  from allennlp.data.instance import Instance
  from overrides import overrides
  
  from neural_persona.common.util import load_sparse, load_named_sparse
  
  logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
  
  
  @DatasetReader.register("vampire_entities_reader")
  class VampireEntitiesReader(DatasetReader):
      """ 
      Reads bag of word vectors from a sparse matrices representing training and validation data.
  
      Expects a sparse matrix of size N documents x vocab size, which can be created via 
      the scripts/preprocess_data.py file.
  
      The output of ``read`` is a list of ``Instances`` with the field:
          vec: ``ArrayField``
  
      Parameters
      ----------
      lazy : ``bool``, optional, (default = ``False``)
          Whether or not instances can be read lazily.
      """
      def __init__(self, lazy: bool = False) -> None:
          super().__init__(lazy=lazy)
  
      @overrides
      def _read(self, file_path):
          examples = np.load(file_path)
          for ix, example in enumerate(examples):
              # bp()
              instance = self.text_to_instance(example)
              # bp()
              if instance is not None:
                  yield instance
  
      @overrides
      def text_to_instance(self, vec: np.array) -> Instance:  # type: ignore
          """
          Parameters
          ----------
          vec : ``np.array``, required.
              The text to classify
  
          Returns
          -------
          An ``Instance`` containing the following fields:
              tokens : ``TextField``
                  The tokens in the sentence or phrase.
              label : ``LabelField``
                  The label label of the sentence or phrase.
          """
          # pylint: disable=arguments-differ
          fields: Dict[str, Field] = {}
          fields['tokens'] = ArrayField(vec)
          return Instance(fields)
  