(model, inputs=None,
                          batch_size=1, temperature=1.0,
                          start_id=0, eos_id=1, max_length=100,
                          accelerate=True, eval_mode=False, eval_min_length=1)
return np.concatenate(result, axis=1)
Returns a batch of sequences created by autoregressive sampling.

  This function uses `model` to generate outputs one position at a time, with
  access to inputs for the current position and all preceding positions. The
  new output becomes the next position's input, and this loop repeats until
  either the model outputs the `eos_id` value or the output sequence reaches
  `max_length` items.

  Args:
    model: A layer object (subclass of `trax.layers.Layer`) created in
        `'predict'` mode and initialized from trained weights. The model
        must have a structure that allows it to run as autoregressive
        one-sample-at-a-time predictor (e.g., `trax.models.TransformerLM`),
        except if `eval_mode` is set -- any model can be sampled then,
        but the sampling process may be much slower.
    inputs: Sequence of symbols the model sees as input the first time it
        generates an output. If None, the model must generate the first output
        with no input to guide it.
    batch_size: Number of sequences to generate in parallel as a batch.
    temperature: Parameter that controls the sharpness of the softmax that
        feeds the sampling process. Values range from 0.0 (all probability mass
        goes to one candidate; like an argmax) to positive infinity (all
        candidates have equal probability).
    start_id: The start symbol (ID/integer) for the autoregressive process,
        or array of shape (`batch_size`, 1) of such integers.
    eos_id: The end-of-sequence symbol (ID/integer) for the autoregressive
        process.
    max_length: Maximum length for generated sequences.
    accelerate: If True, create an accelerated version of `model` and use it
        for generating outputs.
    eval_mode: If True, assume the model is created in `eval` mode and sample
        by collecting all previous outputs and passing the whole tensor.
    eval_min_length: If set, the minimum length to pad to in eval mode.

  Returns:
    Tensor of integers with shape (`batch_size`, output_length) representing
    a batch of output sequences. output_length is the maximum length of the
    output sequences, where each sequence can be no longer than `max_length`.
Returns a batch of sequences created by autoregressive sampling.
Returns a batch of sequences created by autoregressive sampling .
def autoregressive_sample(model, inputs=None,
                          batch_size=1, temperature=1.0,
                          start_id=0, eos_id=1, max_length=100,
                          accelerate=True, eval_mode=False, eval_min_length=1):
  """Returns a batch of sequences created by autoregressive sampling.

  This function uses `model` to generate outputs one position at a time, with
  access to inputs for the current position and all preceding positions. The
  new output becomes the next position's input, and this loop repeats until
  either the model outputs the `eos_id` value or the output sequence reaches
  `max_length` items.

  Args:
    model: A layer object (subclass of `trax.layers.Layer`) created in
        `'predict'` mode and initialized from trained weights. The model
        must have a structure that allows it to run as autoregressive
        one-sample-at-a-time predictor (e.g., `trax.models.TransformerLM`),
        except if `eval_mode` is set -- any model can be sampled then,
        but the sampling process may be much slower.
    inputs: Sequence of symbols the model sees as input the first time it
        generates an output. If None, the model must generate the first output
        with no input to guide it.
    batch_size: Number of sequences to generate in parallel as a batch.
    temperature: Parameter that controls the sharpness of the softmax that
        feeds the sampling process. Values range from 0.0 (all probability mass
        goes to one candidate; like an argmax) to positive infinity (all
        candidates have equal probability).
    start_id: The start symbol (ID/integer) for the autoregressive process,
        or array of shape (`batch_size`, 1) of such integers.
    eos_id: The end-of-sequence symbol (ID/integer) for the autoregressive
        process.
    max_length: Maximum length for generated sequences.
    accelerate: If True, create an accelerated version of `model` and use it
        for generating outputs.
    eval_mode: If True, assume the model is created in `eval` mode and sample
        by collecting all previous outputs and passing the whole tensor.
    eval_min_length: If set, the minimum length to pad to in eval mode.

  Returns:
    Tensor of integers with shape (`batch_size`, output_length) representing
    a batch of output sequences. output_length is the maximum length of the
    output sequences, where each sequence can be no longer than `max_length`.
  """
  result = []
  eos_seen = []
  counter = 0
  for sample in autoregressive_sample_stream(
      model, inputs, batch_size=batch_size, temperature=temperature,
      start_id=start_id, accelerate=accelerate, eval_mode=eval_mode,
      eval_min_length=eval_min_length):
    sample = sample[:, None]
    result.append(sample)
    counter += 1
    if counter >= max_length:
      return np.concatenate(result, axis=1)
    # Check at which batch positions have we already encountered EOS.
    for j in range(batch_size):
      if int(sample[j, 0]) == eos_id:
        eos_seen.append(j)
    # If EOS has been seen on all positions, stop.
    if all([j in eos_seen for j in range(batch_size)]):
      return np.concatenate(result, axis=1)
  return np.concatenate(result, axis=1)
def autoregressive_sample ( model , inputs = None , batch_size = 1 , temperature = 1.0 , start_id = 0 , eos_id = 1 , max_length = 100 , accelerate = True , eval_mode = False , eval_min_length = 1 ) : result = [ ] eos_seen = [ ] counter = 0 for sample in autoregressive_sample_stream ( model , inputs , batch_size = batch_size , temperature = temperature , start_id = start_id , accelerate = accelerate , eval_mode = eval_mode , eval_min_length = eval_min_length ) : sample = sample [ : , None ] result . append ( sample ) counter += 1 if counter >= max_length : return np . concatenate ( result , axis = 1 ) # Check at which batch positions have we already encountered EOS. for j in range ( batch_size ) : if int ( sample [ j , 0 ] ) == eos_id : eos_seen . append ( j ) # If EOS has been seen on all positions, stop. if all ( [ j in eos_seen for j in range ( batch_size ) ] ) : return np . concatenate ( result , axis = 1 ) return np . concatenate ( result , axis = 1 )