def _interactive_input_fn(hparams, decode_hp):
  """Generator that reads from the terminal and yields "interactive inputs".

  Due to temporary limitations in tf.learn, if we don't want to reload the
  whole graph, then we are stuck encoding all of the input as one fixed-size
  numpy array.

  We yield int32 arrays with shape [const_array_size].  The format is:
  [num_samples, decode_length, len(input ids), <input ids>, <padding>]

  Args:
    hparams: model hparams
    decode_hp: decode hparams
  Yields:
    numpy arrays

  Raises:
    Exception: when `input_type` is invalid.
  """
  num_samples = decode_hp.num_samples if decode_hp.num_samples > 0 else 1
  decode_length = decode_hp.extra_length
  input_type = "text"
  p_hparams = hparams.problem_hparams
  has_input = "inputs" in p_hparams.modality
  vocabulary = p_hparams.vocabulary["inputs" if has_input else "targets"]
  # This should be longer than the longest input.
  const_array_size = 10000
  # Import readline if available for command line editing and recall.
  try:
    import readline  # pylint: disable=g-import-not-at-top,unused-variable
  except ImportError:
    pass
  while True:
    prompt = ("INTERACTIVE MODE  num_samples=%d  decode_length=%d  \n"
              "  it=<input_type>     ('text' or 'image' or 'label', default: "
              "text)\n"
              "  ns=<num_samples>    (changes number of samples, default: 1)\n"
              "  dl=<decode_length>  (changes decode length, default: 100)\n"
              "  <%s>                (decode)\n"
              "  q                   (quit)\n"
              ">" % (num_samples, decode_length,
                     "source_string" if has_input else "target_prefix"))
    input_string = input(prompt)
    if input_string == "q":
      return
    elif input_string[:3] == "ns=":
      num_samples = int(input_string[3:])
    elif input_string[:3] == "dl=":
      decode_length = int(input_string[3:])
    elif input_string[:3] == "it=":
      input_type = input_string[3:]
    else:
      if input_type == "text":
        input_ids = vocabulary.encode(input_string)
        if has_input:
          input_ids.append(text_encoder.EOS_ID)
        x = [num_samples, decode_length, len(input_ids)] + input_ids
        assert len(x) < const_array_size
        x += [0] * (const_array_size - len(x))
        features = {
            "inputs": np.array(x).astype(np.int32),
        }
      elif input_type == "image":
        input_path = input_string
        img = vocabulary.encode(input_path)
        features = {
            "inputs": img.astype(np.int32),
        }
      elif input_type == "label":
        input_ids = [int(input_string)]
        x = [num_samples, decode_length, len(input_ids)] + input_ids
        features = {
            "inputs": np.array(x).astype(np.int32),
        }
      else:
        raise Exception("Unsupported input type.")
      for k, v in six.iteritems(
          problem_lib.problem_hparams_to_features(p_hparams)):
        features[k] = np.array(v).astype(np.int32)
      yield features
Generator that reads from the terminal and yields "interactive inputs".

  Due to temporary limitations in tf.learn, if we don't want to reload the
  whole graph, then we are stuck encoding all of the input as one fixed-size
  numpy array.

  We yield int32 arrays with shape [const_array_size].  The format is:
  [num_samples, decode_length, len(input ids), <input ids>, <padding>]

  Args:
    hparams: model hparams
    decode_hp: decode hparams
  Yields:
    numpy arrays

  Raises:
    Exception: when `input_type` is invalid.