import tensorflow as tf


  return filename


class TranslateDistillProblem(TranslateProblem):
  """Base class for translation problems."""

  def is_generate_per_split(self):
    return True

  def example_reading_spec(self):
    data_fields = {"dist_targets": tf.VarLenFeature(tf.int64)}

    if self.has_inputs:
      data_fields["inputs"] = tf.VarLenFeature(tf.int64)

    # hack: ignoring true targets and putting dist_targets in targets
    data_items_to_decoders = {
        "inputs": tf.contrib.slim.tfexample_decoder.Tensor("inputs"),
        "targets": tf.contrib.slim.tfexample_decoder.Tensor("dist_targets"),
    }

    return (data_fields, data_items_to_decoders)

  def get_or_create_vocab(self, data_dir, tmp_dir, force_get=False):
    """Get vocab for distill problems."""
    # We assume that vocab file is present in data_dir directory where the
    # data generated will be stored.
