text: #!/usr/bin/env python3
  
  import fire
  import json
  import os
  import numpy as np
  import tensorflow.compat.v1 as tf
  
  import model, sample, encoder
  
  def interact_model(
      model_name='117M',
      seed=None,
      nsamples=1,
      batch_size=1,
      length=None,
      temperature=1,
      top_k=0,
      top_p=0.0
  ):
      """
      Interactively run the model
      :model_name=117M : String, which model to use
      :seed=None : Integer seed for random number generators, fix seed to reproduce
       results
      :nsamples=1 : Number of samples to return total
      :batch_size=1 : Number of batches (only affects speed/memory).  Must divide nsamples.
      :length=None : Number of tokens in generated text, if None (default), is
       determined by model hyperparameters
      :temperature=1 : Float value controlling randomness in boltzmann
       distribution. Lower temperature results in less random completions. As the
       temperature approaches zero, the model will become deterministic and
       repetitive. Higher temperature results in more random completions.
      :top_k=0 : Integer value controlling diversity. 1 means only 1 word is
       considered for each step (token), resulting in deterministic completions,
       while 40 means 40 words are considered at each step. 0 (default) is a
       special setting meaning no restrictions. 40 generally is a good value.
      :top_p=0.0 : Float value controlling diversity. Implements nucleus sampling,
       overriding top_k if set to a value > 0. A good setting is 0.9.
      """
      if batch_size is None:
          batch_size = 1
      assert nsamples % batch_size == 0
  
      enc = encoder.get_encoder(model_name)
      hparams = model.default_hparams()
      with open(os.path.join('models', model_name, 'hparams.json')) as f:
          hparams.override_from_dict(json.load(f))
  
      if length is None:
          length = hparams.n_ctx // 2
      elif length > hparams.n_ctx:
          raise ValueError("Can't get samples longer than window size: %s" % hparams.n_ctx)
  
      with tf.Session(graph=tf.Graph()) as sess:
          context = tf.placeholder(tf.int32, [batch_size, None])
          np.random.seed(seed)
          tf.set_random_seed(seed)
          output = sample.sample_sequence(
              hparams=hparams, length=length,
              context=context,
              batch_size=batch_size,
              temperature=temperature, top_k=top_k, top_p=top_p
          )
  
          saver = tf.train.Saver()
          ckpt = tf.train.latest_checkpoint(os.path.join('models', model_name))
          saver.restore(sess, ckpt)
  
          while True:
              raw_text = input("Model prompt >>> ")
              while not raw_text:
                  print('Prompt should not be empty!')
                  raw_text = input("Model prompt >>> ")
              context_tokens = enc.encode(raw_text)
              generated = 0
              for _ in range(nsamples // batch_size):
                  out = sess.run(output, feed_dict={
                      context: [context_tokens for _ in range(batch_size)]
                  })[:, len(context_tokens):]
                  for i in range(batch_size):
                      generated += 1
                      text = enc.decode(out[i])
                      print("=" * 40 + " SAMPLE " + str(generated) + " " + "=" * 40)
                      print(text)
              print("=" * 80)
  
  if __name__ == '__main__':
      fire.Fire(interact_model)
  