# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""A decoder that performs beam search."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import numpy as np

from tensorflow.contrib.seq2seq.python.ops import beam_search_ops
from tensorflow.contrib.seq2seq.python.ops import decoder
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_util
from tensorflow.python.layers import base as layers_base
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import embedding_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import rnn_cell_impl
from tensorflow.python.ops import tensor_array_ops
from tensorflow.python.util import nest

__all__ = [
    "BeamSearchDecoderOutput",
    "BeamSearchDecoderState",
    "BeamSearchDecoder",
    "FinalBeamSearchDecoderOutput",
    "tile_batch",
]


class BeamSearchDecoderState(
    collections.namedtuple("BeamSearchDecoderState",
                           ("cell_state", "log_probs", "finished", "lengths"))):
  pass


class BeamSearchDecoderOutput(
    collections.namedtuple("BeamSearchDecoderOutput",
                           ("scores", "predicted_ids", "parent_ids"))):
  pass


class FinalBeamSearchDecoderOutput(
    collections.namedtuple("FinalBeamDecoderOutput",
                           ["predicted_ids", "beam_search_decoder_output"])):
  """Final outputs returned by the beam search after all decoding is finished.

  Args:
    predicted_ids: The final prediction. A tensor of shape
      `[batch_size, T, beam_width]` (or `[T, batch_size, beam_width]` if
      `output_time_major` is True). Beams are ordered from best to worst.
    beam_search_decoder_output: An instance of `BeamSearchDecoderOutput` that
      describes the state of the beam search.
  """
  pass


def _tile_batch(t, multiplier):
  """Core single-tensor implementation of tile_batch."""
  t = ops.convert_to_tensor(t, name="t")
  shape_t = array_ops.shape(t)
  if t.shape.ndims is None or t.shape.ndims < 1:
    raise ValueError("t must have statically known rank")
  tiling = [1] * (t.shape.ndims + 1)
  tiling[1] = multiplier
  tiled_static_batch_size = (
      t.shape[0].value * multiplier if t.shape[0].value is not None else None)
  tiled = array_ops.tile(array_ops.expand_dims(t, 1), tiling)
  tiled = array_ops.reshape(tiled,
                            array_ops.concat(
                                ([shape_t[0] * multiplier], shape_t[1:]), 0))
  tiled.set_shape(
      tensor_shape.TensorShape([tiled_static_batch_size]).concatenate(
          t.shape[1:]))
  return tiled


def tile_batch(t, multiplier, name=None):
  """Tile the batch dimension of a (possibly nested structure of) tensor(s) t.

  For each tensor t in a (possibly nested structure) of tensors,
  this function takes a tensor t shaped `[batch_size, s0, s1, ...]` composed of
  minibatch entries `t[0], ..., t[batch_size - 1]` and tiles it to have a shape
  `[batch_size * multiplier, s0, s1, ...]` composed of minibatch entries
  `t[0], t[0], ..., t[1], t[1], ...` where each minibatch entry is repeated
  `multiplier` times.

  Args:
    t: `Tensor` shaped `[batch_size, ...]`.
    multiplier: Python int.
    name: Name scope for any created operations.

  Returns:
    A (possibly nested structure of) `Tensor` shaped
    `[batch_size * multiplier, ...]`.

  Raises:
    ValueError: if tensor(s) `t` do not have a statically known rank or
    the rank is < 1.
  """
  flat_t = nest.flatten(t)
  with ops.name_scope(name, "tile_batch", flat_t + [multiplier]):
    return nest.map_structure(lambda t_: _tile_batch(t_, multiplier), t)


def _check_maybe(t):
  if isinstance(t, tensor_array_ops.TensorArray):
    raise TypeError(
        "TensorArray state is not supported by BeamSearchDecoder: %s" % t.name)
  if t.shape.ndims is None:
    raise ValueError(
        "Expected tensor (%s) to have known rank, but ndims == None." % t)


class BeamSearchDecoder(decoder.Decoder):
  """BeamSearch sampling decoder.

    **NOTE** If you are using the `BeamSearchDecoder` with a cell wrapped in
    `AttentionWrapper`, then you must ensure that:

    - The encoder output has been tiled to `beam_width` via
      @{tf.contrib.seq2seq.tile_batch} (NOT `tf.tile`).
    - The `batch_size` argument passed to the `zero_state` method of this
      wrapper is equal to `true_batch_size * beam_width`.
    - The initial state created with `zero_state` above contains a
      `cell_state` value containing properly tiled final state from the
      encoder.

    An example:

    ```
    tiled_encoder_outputs = tf.contrib.seq2seq.tile_batch(
        encoder_outputs, multiplier=beam_width)
    tiled_encoder_final_state = tf.conrib.seq2seq.tile_batch(
        encoder_final_state, multiplier=beam_width)
    tiled_sequence_length = tf.contrib.seq2seq.tile_batch(
        sequence_length, multiplier=beam_width)
    attention_mechanism = MyFavoriteAttentionMechanism(
        num_units=attention_depth,
        memory=tiled_inputs,
        memory_sequence_length=tiled_sequence_length)
    attention_cell = AttentionWrapper(cell, attention_mechanism, ...)
    decoder_initial_state = attention_cell.zero_state(
        dtype, batch_size=true_batch_size * beam_width)
    decoder_initial_state = decoder_initial_state.clone(
        cell_state=tiled_encoder_final_state)
    ```
  """

  def __init__(self,
               cell,
               embedding,
               start_tokens,
               end_token,
               initial_state,
               beam_width,
               output_layer=None,
               length_penalty_weight=0.0):
    """Initialize the BeamSearchDecoder.

    Args:
      cell: An `RNNCell` instance.
      embedding: A callable that takes a vector tensor of `ids` (argmax ids),
        or the `params` argument for `embedding_lookup`.
      start_tokens: `int32` vector shaped `[batch_size]`, the start tokens.
      end_token: `int32` scalar, the token that marks end of decoding.
      initial_state: A (possibly nested tuple of...) tensors and TensorArrays.
      beam_width:  Python integer, the number of beams.
      output_layer: (Optional) An instance of `tf.layers.Layer`, i.e.,
        `tf.layers.Dense`.  Optional layer to apply to the RNN output prior
        to storing the result or sampling.
      length_penalty_weight: Float weight to penalize length. Disabled with 0.0.

    Raises:
      TypeError: if `cell` is not an instance of `RNNCell`,
        or `output_layer` is not an instance of `tf.layers.Layer`.
      ValueError: If `start_tokens` is not a vector or
        `end_token` is not a scalar.
    """
    if not rnn_cell_impl._like_rnncell(cell):  # pylint: disable=protected-access
      raise TypeError("cell must be an RNNCell, received: %s" % type(cell))
    if (output_layer is not None and
        not isinstance(output_layer, layers_base.Layer)):
      raise TypeError(
          "output_layer must be a Layer, received: %s" % type(output_layer))
    self._cell = cell
    self._output_layer = output_layer

    if callable(embedding):
      self._embedding_fn = embedding
    else:
      self._embedding_fn = (
          lambda ids: embedding_ops.embedding_lookup(embedding, ids))

    self._start_tokens = ops.convert_to_tensor(
        start_tokens, dtype=dtypes.int32, name="start_tokens")
    if self._start_tokens.get_shape().ndims != 1:
      raise ValueError("start_tokens must be a vector")
    self._end_token = ops.convert_to_tensor(
        end_token, dtype=dtypes.int32, name="end_token")
    if self._end_token.get_shape().ndims != 0:
      raise ValueError("end_token must be a scalar")

    self._batch_size = array_ops.size(start_tokens)
    self._beam_width = beam_width
    self._length_penalty_weight = length_penalty_weight
    self._initial_cell_state = nest.map_structure(
        self._maybe_split_batch_beams, initial_state, self._cell.state_size)
    self._start_tokens = array_ops.tile(
        array_ops.expand_dims(self._start_tokens, 1), [1, self._beam_width])
    self._start_inputs = self._embedding_fn(self._start_tokens)

    self._finished = array_ops.one_hot(
        array_ops.zeros([self._batch_size], dtype=dtypes.int32),
        depth=self._beam_width,
        on_value=False,
        off_value=True,
        dtype=dtypes.bool)

  @property
  def batch_size(self):
    return self._batch_size

  def _rnn_output_size(self):
    size = self._cell.output_size
    if self._output_layer is None:
      return size
    else:
      # To use layer's compute_output_shape, we need to convert the
      # RNNCell's output_size entries into shapes with an unknown
      # batch size.  We then pass this through the layer's
      # compute_output_shape and read off all but the first (batch)
      # dimensions to get the output size of the rnn with the layer
      # applied to the top.
      output_shape_with_unknown_batch = nest.map_structure(
          lambda s: tensor_shape.TensorShape([None]).concatenate(s), size)
      layer_output_shape = self._output_layer.compute_output_shape(
          output_shape_with_unknown_batch)
      return nest.map_structure(lambda s: s[1:], layer_output_shape)

  @property
  def tracks_own_finished(self):
    """The BeamSearchDecoder shuffles its beams and their finished state.

    For this reason, it conflicts with the `dynamic_decode` function's
    tracking of finished states.  Setting this property to true avoids
    early stopping of decoding due to mismanagement of the finished state
    in `dynamic_decode`.

    Returns:
      `True`.
    """
    return True

  @property
  def output_size(self):
    # Return the cell output and the id
    return BeamSearchDecoderOutput(
        scores=tensor_shape.TensorShape([self._beam_width]),
        predicted_ids=tensor_shape.TensorShape([self._beam_width]),
        parent_ids=tensor_shape.TensorShape([self._beam_width]))

  @property
  def output_dtype(self):
    # Assume the dtype of the cell is the output_size structure
    # containing the input_state's first component's dtype.
    # Return that structure and int32 (the id)
    dtype = nest.flatten(self._initial_cell_state)[0].dtype
    return BeamSearchDecoderOutput(
        scores=nest.map_structure(lambda _: dtype, self._rnn_output_size()),
        predicted_ids=dtypes.int32,
        parent_ids=dtypes.int32)

  def initialize(self, name=None):
    """Initialize the decoder.

    Args:
      name: Name scope for any created operations.

    Returns:
      `(finished, start_inputs, initial_state)`.
    """
    finished, start_inputs = self._finished, self._start_inputs

    log_probs = array_ops.one_hot(  # shape(batch_sz, beam_sz)
        array_ops.zeros([self._batch_size], dtype=dtypes.int32),
        depth=self._beam_width,
        on_value=0.0,
        off_value=-np.Inf,
        dtype=nest.flatten(self._initial_cell_state)[0].dtype)

    initial_state = BeamSearchDecoderState(
        cell_state=self._initial_cell_state,
        log_probs=log_probs,
        finished=finished,
        lengths=array_ops.zeros(
            [self._batch_size, self._beam_width], dtype=dtypes.int64))

    return (finished, start_inputs, initial_state)

  def finalize(self, outputs, final_state, sequence_lengths):
    """Finalize and return the predicted_ids.

    Args:
      outputs: An instance of BeamSearchDecoderOutput.
      final_state: An instance of BeamSearchDecoderState. Passed through to the
        output.
      sequence_lengths: An `int64` tensor shaped `[batch_size, beam_width]`.
        The sequence lengths determined for each beam during decode.
        **NOTE** These are ignored; the updated sequence lengths are stored in
        `final_state.lengths`.

    Returns:
      outputs: An instance of `FinalBeamSearchDecoderOutput` where the
        predicted_ids are the result of calling _gather_tree.
      final_state: The same input instance of `BeamSearchDecoderState`.
    """
    del sequence_lengths
    # Get max_sequence_length across all beams for each batch.
    max_sequence_lengths = math_ops.to_int32(
        math_ops.reduce_max(final_state.lengths, axis=1))
    predicted_ids = beam_search_ops.gather_tree(
        outputs.predicted_ids,
        outputs.parent_ids,
        max_sequence_lengths=max_sequence_lengths,
        end_token=self._end_token)
    outputs = FinalBeamSearchDecoderOutput(
        beam_search_decoder_output=outputs, predicted_ids=predicted_ids)
    return outputs, final_state

  def _merge_batch_beams(self, t, s=None):
    """Merges the tensor from a batch of beams into a batch by beams.

    More exactly, t is a tensor of dimension [batch_size, beam_width, s]. We
    reshape this into [batch_size*beam_width, s]

    Args:
      t: Tensor of dimension [batch_size, beam_width, s]
      s: (Possibly known) depth shape.

    Returns:
      A reshaped version of t with dimension [batch_size * beam_width, s].
    """
    if isinstance(s, ops.Tensor):
      s = tensor_shape.as_shape(tensor_util.constant_value(s))
    else:
      s = tensor_shape.TensorShape(s)
    t_shape = array_ops.shape(t)
    static_batch_size = tensor_util.constant_value(self._batch_size)
    batch_size_beam_width = (
        None
        if static_batch_size is None else static_batch_size * self._beam_width)
    reshaped_t = array_ops.reshape(
        t,
        array_ops.concat(([self._batch_size * self._beam_width], t_shape[2:]),
                         0))
    reshaped_t.set_shape(
        (tensor_shape.TensorShape([batch_size_beam_width]).concatenate(s)))
    return reshaped_t

  def _split_batch_beams(self, t, s=None):
    """Splits the tensor from a batch by beams into a batch of beams.

    More exactly, t is a tensor of dimension [batch_size*beam_width, s]. We
    reshape this into [batch_size, beam_width, s]

    Args:
      t: Tensor of dimension [batch_size*beam_width, s].
      s: (Possibly known) depth shape.

    Returns:
      A reshaped version of t with dimension [batch_size, beam_width, s].

    Raises:
      ValueError: If, after reshaping, the new tensor is not shaped
        `[batch_size, beam_width, s]` (assuming batch_size and beam_width
        are known statically).
    """
    if isinstance(s, ops.Tensor):
      s = tensor_shape.TensorShape(tensor_util.constant_value(s))
    else:
      s = tensor_shape.TensorShape(s)
    t_shape = array_ops.shape(t)
    reshaped_t = array_ops.reshape(
        t,
        array_ops.concat(([self._batch_size, self._beam_width], t_shape[1:]),
                         0))
    static_batch_size = tensor_util.constant_value(self._batch_size)
    expected_reshaped_shape = tensor_shape.TensorShape(
        [static_batch_size, self._beam_width]).concatenate(s)
    if not reshaped_t.shape.is_compatible_with(expected_reshaped_shape):
      raise ValueError("Unexpected behavior when reshaping between beam width "
                       "and batch size.  The reshaped tensor has shape: %s.  "
                       "We expected it to have shape "
                       "(batch_size, beam_width, depth) == %s.  Perhaps you "
                       "forgot to create a zero_state with "
                       "batch_size=encoder_batch_size * beam_width?" %
                       (reshaped_t.shape, expected_reshaped_shape))
    reshaped_t.set_shape(expected_reshaped_shape)
    return reshaped_t

  def _maybe_split_batch_beams(self, t, s):
    """Maybe splits the tensor from a batch by beams into a batch of beams.

    We do this so that we can use nest and not run into problems with shapes.

    Args:
      t: `Tensor`, either scalar or shaped `[batch_size * beam_width] + s`.
      s: `Tensor`, Python int, or `TensorShape`.

    Returns:
      If `t` is a matrix or higher order tensor, then the return value is
      `t` reshaped to `[batch_size, beam_width] + s`.  Otherwise `t` is
      returned unchanged.

    Raises:
      TypeError: If `t` is an instance of `TensorArray`.
      ValueError: If the rank of `t` is not statically known.
    """
    _check_maybe(t)
    if t.shape.ndims >= 1:
      return self._split_batch_beams(t, s)
    else:
      return t

  def _maybe_merge_batch_beams(self, t, s):
    """Splits the tensor from a batch by beams into a batch of beams.

    More exactly, `t` is a tensor of dimension `[batch_size * beam_width] + s`,
    then we reshape it to `[batch_size, beam_width] + s`.

    Args:
      t: `Tensor` of dimension `[batch_size * beam_width] + s`.
      s: `Tensor`, Python int, or `TensorShape`.

    Returns:
      A reshaped version of t with shape `[batch_size, beam_width] + s`.

    Raises:
      TypeError: If `t` is an instance of `TensorArray`.
      ValueError:  If the rank of `t` is not statically known.
    """
    _check_maybe(t)
    if t.shape.ndims >= 2:
      return self._merge_batch_beams(t, s)
    else:
      return t

  def step(self, time, inputs, state, name=None):
    """Perform a decoding step.

    Args:
      time: scalar `int32` tensor.
      inputs: A (structure of) input tensors.
      state: A (structure of) state tensors and TensorArrays.
      name: Name scope for any created operations.

    Returns:
      `(outputs, next_state, next_inputs, finished)`.
    """
    batch_size = self._batch_size
    beam_width = self._beam_width
    end_token = self._end_token
    length_penalty_weight = self._length_penalty_weight

    with ops.name_scope(name, "BeamSearchDecoderStep", (time, inputs, state)):
      cell_state = state.cell_state
      inputs = nest.map_structure(
          lambda inp: self._merge_batch_beams(inp, s=inp.shape[2:]), inputs)
      cell_state = nest.map_structure(self._maybe_merge_batch_beams, cell_state,
                                      self._cell.state_size)
      cell_outputs, next_cell_state = self._cell(inputs, cell_state)
      cell_outputs = nest.map_structure(
          lambda out: self._split_batch_beams(out, out.shape[1:]), cell_outputs)
      next_cell_state = nest.map_structure(
          self._maybe_split_batch_beams, next_cell_state, self._cell.state_size)

      if self._output_layer is not None:
        cell_outputs = self._output_layer(cell_outputs)

      beam_search_output, beam_search_state = _beam_search_step(
          time=time,
          logits=cell_outputs,
          next_cell_state=next_cell_state,
          beam_state=state,
          batch_size=batch_size,
          beam_width=beam_width,
          end_token=end_token,
          length_penalty_weight=length_penalty_weight)

      finished = beam_search_state.finished
      sample_ids = beam_search_output.predicted_ids
      next_inputs = control_flow_ops.cond(
          math_ops.reduce_all(finished), lambda: self._start_inputs,
          lambda: self._embedding_fn(sample_ids))

    return (beam_search_output, beam_search_state, next_inputs, finished)


def _beam_search_step(time, logits, next_cell_state, beam_state, batch_size,
                      beam_width, end_token, length_penalty_weight):
  """Performs a single step of Beam Search Decoding.

  Args:
    time: Beam search time step, should start at 0. At time 0 we assume
      that all beams are equal and consider only the first beam for
      continuations.
    logits: Logits at the current time step. A tensor of shape
      `[batch_size, beam_width, vocab_size]`
    next_cell_state: The next state from the cell, e.g. an instance of
      AttentionWrapperState if the cell is attentional.
    beam_state: Current state of the beam search.
      An instance of `BeamSearchDecoderState`.
    batch_size: The batch size for this input.
    beam_width: Python int.  The size of the beams.
    end_token: The int32 end token.
    length_penalty_weight: Float weight to penalize length. Disabled with 0.0.

  Returns:
    A new beam state.
  """
  static_batch_size = tensor_util.constant_value(batch_size)

  # Calculate the current lengths of the predictions
  prediction_lengths = beam_state.lengths
  previously_finished = beam_state.finished

  # Calculate the total log probs for the new hypotheses
  # Final Shape: [batch_size, beam_width, vocab_size]
  step_log_probs = nn_ops.log_softmax(logits)
  step_log_probs = _mask_probs(step_log_probs, end_token, previously_finished)
  total_probs = array_ops.expand_dims(beam_state.log_probs, 2) + step_log_probs

  # Calculate the continuation lengths by adding to all continuing beams.
  vocab_size = logits.shape[-1].value or array_ops.shape(logits)[-1]
  lengths_to_add = array_ops.one_hot(
      indices=array_ops.fill([batch_size, beam_width], end_token),
      depth=vocab_size,
      on_value=np.int64(0),
      off_value=np.int64(1),
      dtype=dtypes.int64)
  add_mask = math_ops.to_int64(math_ops.logical_not(previously_finished))
  lengths_to_add *= array_ops.expand_dims(add_mask, 2)
  new_prediction_lengths = (
      lengths_to_add + array_ops.expand_dims(prediction_lengths, 2))

  # Calculate the scores for each beam
  scores = _get_scores(
      log_probs=total_probs,
      sequence_lengths=new_prediction_lengths,
      length_penalty_weight=length_penalty_weight)

  time = ops.convert_to_tensor(time, name="time")
  # During the first time step we only consider the initial beam
  scores_shape = array_ops.shape(scores)
  scores_flat = array_ops.reshape(scores, [batch_size, -1])

  # Pick the next beams according to the specified successors function
  next_beam_size = ops.convert_to_tensor(
      beam_width, dtype=dtypes.int32, name="beam_width")
  next_beam_scores, word_indices = nn_ops.top_k(scores_flat, k=next_beam_size)

  next_beam_scores.set_shape([static_batch_size, beam_width])
  word_indices.set_shape([static_batch_size, beam_width])

  # Pick out the probs, beam_ids, and states according to the chosen predictions
  next_beam_probs = _tensor_gather_helper(
      gather_indices=word_indices,
      gather_from=total_probs,
      batch_size=batch_size,
      range_size=beam_width * vocab_size,
      gather_shape=[-1],
      name="next_beam_probs")
  # Note: just doing the following
  #   math_ops.to_int32(word_indices % vocab_size,
  #       name="next_beam_word_ids")
  # would be a lot cleaner but for reasons unclear, that hides the results of
  # the op which prevents capturing it with tfdbg debug ops.
  raw_next_word_ids = math_ops.mod(
      word_indices, vocab_size, name="next_beam_word_ids")
  next_word_ids = math_ops.to_int32(raw_next_word_ids)
  next_beam_ids = math_ops.to_int32(
      word_indices / vocab_size, name="next_beam_parent_ids")

  # Append new ids to current predictions
  previously_finished = _tensor_gather_helper(
      gather_indices=next_beam_ids,
      gather_from=previously_finished,
      batch_size=batch_size,
      range_size=beam_width,
      gather_shape=[-1])
  next_finished = math_ops.logical_or(
      previously_finished,
      math_ops.equal(next_word_ids, end_token),
      name="next_beam_finished")

  # Calculate the length of the next predictions.
  # 1. Finished beams remain unchanged.
  # 2. Beams that are now finished (EOS predicted) have their length
  #    increased by 1.
  # 3. Beams that are not yet finished have their length increased by 1.
  lengths_to_add = math_ops.to_int64(math_ops.logical_not(previously_finished))
  next_prediction_len = _tensor_gather_helper(
      gather_indices=next_beam_ids,
      gather_from=beam_state.lengths,
      batch_size=batch_size,
      range_size=beam_width,
      gather_shape=[-1])
  next_prediction_len += lengths_to_add

  # Pick out the cell_states according to the next_beam_ids. We use a
  # different gather_shape here because the cell_state tensors, i.e.
  # the tensors that would be gathered from, all have dimension
  # greater than two and we need to preserve those dimensions.
  # pylint: disable=g-long-lambda
  next_cell_state = nest.map_structure(
      lambda gather_from: _maybe_tensor_gather_helper(
          gather_indices=next_beam_ids,
          gather_from=gather_from,
          batch_size=batch_size,
          range_size=beam_width,
          gather_shape=[batch_size * beam_width, -1]),
      next_cell_state)
  # pylint: enable=g-long-lambda

  next_state = BeamSearchDecoderState(
      cell_state=next_cell_state,
      log_probs=next_beam_probs,
      lengths=next_prediction_len,
      finished=next_finished)

  output = BeamSearchDecoderOutput(
      scores=next_beam_scores,
      predicted_ids=next_word_ids,
      parent_ids=next_beam_ids)

  return output, next_state


def _get_scores(log_probs, sequence_lengths, length_penalty_weight):
  """Calculates scores for beam search hypotheses.

  Args:
    log_probs: The log probabilities with shape
      `[batch_size, beam_width, vocab_size]`.
    sequence_lengths: The array of sequence lengths.
    length_penalty_weight: Float weight to penalize length. Disabled with 0.0.

  Returns:
    The scores normalized by the length_penalty.
  """
  length_penality_ = _length_penalty(
      sequence_lengths=sequence_lengths, penalty_factor=length_penalty_weight)
  return log_probs / length_penality_


def _length_penalty(sequence_lengths, penalty_factor):
  """Calculates the length penalty. See https://arxiv.org/abs/1609.08144.

  Returns the length penalty tensor:
  ```
  [(5+sequence_lengths)/6]**penalty_factor
  ```
  where all operations are performed element-wise.

  Args:
    sequence_lengths: `Tensor`, the sequence lengths of each hypotheses.
    penalty_factor: A scalar that weights the length penalty.

  Returns:
    If the penalty is `0`, returns the scalar `1.0`.  Otherwise returns
    the length penalty factor, a tensor with the same shape as
    `sequence_lengths`.
  """
  penalty_factor = ops.convert_to_tensor(penalty_factor, name="penalty_factor")
  penalty_factor.set_shape(())  # penalty should be a scalar.
  static_penalty = tensor_util.constant_value(penalty_factor)
  if static_penalty is not None and static_penalty == 0:
    return 1.0
  return math_ops.div((5. + math_ops.to_float(sequence_lengths))
                      **penalty_factor, (5. + 1.)**penalty_factor)


def _mask_probs(probs, eos_token, finished):
  """Masks log probabilities.

  The result is that finished beams allocate all probability mass to eos and
  unfinished beams remain unchanged.

  Args:
    probs: Log probabiltiies of shape `[batch_size, beam_width, vocab_size]`
    eos_token: An int32 id corresponding to the EOS token to allocate
      probability to.
    finished: A boolean tensor of shape `[batch_size, beam_width]` that
      specifies which elements in the beam are finished already.

  Returns:
    A tensor of shape `[batch_size, beam_width, vocab_size]`, where unfinished
    beams stay unchanged and finished beams are replaced with a tensor with all
    probability on the EOS token.
  """
  vocab_size = array_ops.shape(probs)[2]
  # All finished examples are replaced with a vector that has all
  # probability on EOS
  finished_row = array_ops.one_hot(
      eos_token,
      vocab_size,
      dtype=probs.dtype,
      on_value=0.,
      off_value=probs.dtype.min)
  finished_probs = array_ops.tile(
      array_ops.reshape(finished_row, [1, 1, -1]),
      array_ops.concat([array_ops.shape(finished), [1]], 0))
  finished_mask = array_ops.tile(
      array_ops.expand_dims(finished, 2), [1, 1, vocab_size])

  return array_ops.where(finished_mask, finished_probs, probs)


def _maybe_tensor_gather_helper(gather_indices, gather_from, batch_size,
                                range_size, gather_shape):
  """Maybe applies _tensor_gather_helper.

  This applies _tensor_gather_helper when the gather_from dims is at least as
  big as the length of gather_shape. This is used in conjunction with nest so
  that we don't apply _tensor_gather_helper to inapplicable values like scalars.

  Args:
    gather_indices: The tensor indices that we use to gather.
    gather_from: The tensor that we are gathering from.
    batch_size: The batch size.
    range_size: The number of values in each range. Likely equal to beam_width.
    gather_shape: What we should reshape gather_from to in order to preserve the
      correct values. An example is when gather_from is the attention from an
      AttentionWrapperState with shape [batch_size, beam_width, attention_size].
      There, we want to preserve the attention_size elements, so gather_shape is
      [batch_size * beam_width, -1]. Then, upon reshape, we still have the
      attention_size as desired.

  Returns:
    output: Gathered tensor of shape tf.shape(gather_from)[:1+len(gather_shape)]
      or the original tensor if its dimensions are too small.
  """
  _check_maybe(gather_from)
  if gather_from.shape.ndims >= len(gather_shape):
    return _tensor_gather_helper(
        gather_indices=gather_indices,
        gather_from=gather_from,
        batch_size=batch_size,
        range_size=range_size,
        gather_shape=gather_shape)
  else:
    return gather_from


def _tensor_gather_helper(gather_indices,
                          gather_from,
                          batch_size,
                          range_size,
                          gather_shape,
                          name=None):
  """Helper for gathering the right indices from the tensor.

  This works by reshaping gather_from to gather_shape (e.g. [-1]) and then
  gathering from that according to the gather_indices, which are offset by
  the right amounts in order to preserve the batch order.

  Args:
    gather_indices: The tensor indices that we use to gather.
    gather_from: The tensor that we are gathering from.
    batch_size: The input batch size.
    range_size: The number of values in each range. Likely equal to beam_width.
    gather_shape: What we should reshape gather_from to in order to preserve the
      correct values. An example is when gather_from is the attention from an
      AttentionWrapperState with shape [batch_size, beam_width, attention_size].
      There, we want to preserve the attention_size elements, so gather_shape is
      [batch_size * beam_width, -1]. Then, upon reshape, we still have the
      attention_size as desired.
    name: The tensor name for set of operations. By default this is
      'tensor_gather_helper'. The final output is named 'output'.

  Returns:
    output: Gathered tensor of shape tf.shape(gather_from)[:1+len(gather_shape)]
  """
  with ops.name_scope(name, "tensor_gather_helper"):
    range_ = array_ops.expand_dims(math_ops.range(batch_size) * range_size, 1)
    gather_indices = array_ops.reshape(gather_indices + range_, [-1])
    output = array_ops.gather(
        array_ops.reshape(gather_from, gather_shape), gather_indices)
    final_shape = array_ops.shape(gather_from)[:1 + len(gather_shape)]
    static_batch_size = tensor_util.constant_value(batch_size)
    final_static_shape = (
        tensor_shape.TensorShape([static_batch_size]).concatenate(
            gather_from.shape[1:1 + len(gather_shape)]))
    output = array_ops.reshape(output, final_shape, name="output")
    output.set_shape(final_static_shape)
    return output
