# Copyright 2020 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.
# ==============================================================================
"""Implement Seq2Seq Transformer model by TF official NLP library.

Model paper: https://arxiv.org/pdf/1706.03762.pdf
"""
import math

import tensorflow as tf
from official.modeling import tf_utils
from official.nlp.modeling import layers
from official.nlp.modeling.ops import beam_search
from official.nlp.transformer import metrics
from official.nlp.transformer import model_utils

EOS_ID = 1
# pylint: disable=g-classes-have-attributes


def create_model(params, is_train):
  """Creates transformer model."""

  encdec_kwargs = dict(
      num_layers=params["num_hidden_layers"],
      num_attention_heads=params["num_heads"],
      intermediate_size=params["filter_size"],
      activation="relu",
      dropout_rate=params["relu_dropout"],
      attention_dropout_rate=params["attention_dropout"],
      use_bias=False,
      norm_first=True,
      norm_epsilon=1e-6,
      intermediate_dropout=params["relu_dropout"])
  encoder_layer = TransformerEncoder(**encdec_kwargs)
  decoder_layer = TransformerDecoder(**encdec_kwargs)

  model_kwargs = dict(
      vocab_size=params["vocab_size"],
      hidden_size=params["hidden_size"],
      dropout_rate=params["layer_postprocess_dropout"],
      padded_decode=params["padded_decode"],
      num_replicas=params["num_replicas"],
      decode_batch_size=params["decode_batch_size"],
      decode_max_length=params["decode_max_length"],
      dtype=params["dtype"],
      extra_decode_length=params["extra_decode_length"],
      num_heads=params["num_heads"],
      num_layers=params["num_hidden_layers"],
      beam_size=params["beam_size"],
      alpha=params["alpha"],
      encoder_layer=encoder_layer,
      decoder_layer=decoder_layer,
      name="transformer_v2")

  with tf.name_scope("model"):
    if is_train:
      inputs = tf.keras.layers.Input((None,), dtype="int64", name="inputs")
      targets = tf.keras.layers.Input((None,), dtype="int64", name="targets")
      internal_model = Seq2SeqTransformer(**model_kwargs)
      logits = internal_model([inputs, targets], training=is_train)
      vocab_size = params["vocab_size"]
      label_smoothing = params["label_smoothing"]
      if params["enable_metrics_in_training"]:
        logits = metrics.MetricLayer(vocab_size)([logits, targets])
      logits = tf.keras.layers.Lambda(
          lambda x: x, name="logits", dtype=tf.float32)(
              logits)
      model = tf.keras.Model([inputs, targets], logits)
      loss = metrics.transformer_loss(logits, targets, label_smoothing,
                                      vocab_size)
      model.add_loss(loss)
      return model

    else:
      inputs = tf.keras.layers.Input((None,), dtype="int64", name="inputs")
      internal_model = Seq2SeqTransformer(**model_kwargs)
      ret = internal_model([inputs], training=is_train)
      outputs, scores = ret["outputs"], ret["scores"]
      return tf.keras.Model(inputs, [outputs, scores])


@tf.keras.utils.register_keras_serializable(package="Text")
class Seq2SeqTransformer(tf.keras.Model):
  """Transformer model with Keras.

  Implemented as described in: https://arxiv.org/pdf/1706.03762.pdf

  The Transformer model consists of an encoder and decoder. The input is an int
  sequence (or a batch of sequences). The encoder produces a continuous
  representation, and the decoder uses the encoder output to generate
  probabilities for the output sequence.
  """

  def __init__(self,
               vocab_size=33708,
               hidden_size=512,
               dropout_rate=0.0,
               padded_decode=False,
               num_replicas=1,
               decode_batch_size=2048,
               decode_max_length=97,
               dtype=tf.float32,
               extra_decode_length=0,
               num_heads=8,
               num_layers=6,
               beam_size=4,
               alpha=0.6,
               encoder_layer=None,
               decoder_layer=None,
               name=None,
               **kwargs):
    """Initialize layers to build Transformer model.

    Arguments:
      vocab_size: Size of vocabulary.
      hidden_size: Size of hidden layer for embedding.
      dropout_rate: Dropout probability.
      padded_decode: Whether to max_sequence_length padding is used. If set
        False, max_sequence_length padding is not used.
      num_replicas: Number of replicas for distribution strategy.
      decode_batch_size: batch_size for decoding.
      decode_max_length: maximum number of steps to decode a sequence.
      dtype: data type.
      extra_decode_length: Beam search will run extra steps to decode.
      num_heads: Number of attention heads.
      num_layers: Number of identical layers for Transformer architecture.
      beam_size: Number of beams for beam search
      alpha: The strength of length normalization for beam search.
      encoder_layer: An initialized encoder layer.
      decoder_layer: An initialized decoder layer.
      name: name of the model.
      **kwargs: other keyword arguments.
    """
    super(Seq2SeqTransformer, self).__init__(**kwargs)
    self._vocab_size = vocab_size
    self._hidden_size = hidden_size
    self._dropout_rate = dropout_rate
    self._padded_decode = padded_decode
    self._num_replicas = num_replicas
    self._decode_batch_size = decode_batch_size
    self._decode_max_length = decode_max_length
    self._dtype = dtype
    self._extra_decode_length = extra_decode_length
    self._num_heads = num_heads
    self._num_layers = num_layers
    self._beam_size = beam_size
    self._alpha = alpha
    self.embedding_lookup = layers.OnDeviceEmbedding(
        vocab_size=self._vocab_size,
        embedding_width=self._hidden_size,
        initializer=tf.random_normal_initializer(
            mean=0., stddev=self._hidden_size**-0.5),
        use_scale=True)
    self.encoder_layer = encoder_layer
    self.decoder_layer = decoder_layer
    self.position_embedding = layers.RelativePositionEmbedding(
        hidden_size=self._hidden_size)
    self.encoder_dropout = tf.keras.layers.Dropout(rate=self._dropout_rate)
    self.decoder_dropout = tf.keras.layers.Dropout(rate=self._dropout_rate)

  def get_config(self):
    config = {
        "vocab_size": self._vocab_size,
        "hidden_size": self._hidden_size,
        "dropout_rate": self._dropout_rate,
        "padded_decode": self._padded_decode,
        "num_replicas": self._num_replicas,
        "decode_batch_size": self._decode_batch_size,
        "decode_max_length": self._decode_max_length,
        "dtype": self._dtype,
        "extra_decode_length": self._extra_decode_length,
        "num_heads": self._num_heads,
        "num_layers": self._num_layers,
        "beam_size": self._beam_size,
        "alpha": self._alpha,
        "encoder_layer": self.encoder_layer,
        "decoder_layer": self.decoder_layer
    }
    base_config = super(Seq2SeqTransformer, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))

  def call(self, inputs):
    """Calculate target logits or inferred target sequences.

    Args:
      inputs: input tensor list of size 1 or 2.
        First item, inputs: int tensor with shape [batch_size, input_length].
        Second item (optional), targets: None or int tensor with shape
          [batch_size, target_length].

    Returns:
      If targets is defined, then return logits for each word in the target
      sequence. float tensor with shape [batch_size, target_length, vocab_size]
      If target is none, then generate output sequence one token at a time.
        returns a dictionary {
          outputs: [batch_size, decoded length]
          scores: [batch_size, float]}
      Even when float16 is used, the output tensor(s) are always float32.

    Raises:
      NotImplementedError: If try to use padded decode method on CPU/GPUs.
    """
    if len(inputs) == 2:
      inputs, targets = inputs[0], inputs[1]
    else:
      # Decoding path.
      inputs, targets = inputs[0], None

      # TODO(hongkuny): The check is not necessary. Fix this part.
      if self._padded_decode:
        if not self._num_replicas:
          raise NotImplementedError(
              "Padded decoding on CPU/GPUs is not supported.")
        decode_batch_size = int(self._decode_batch_size / self._num_replicas)
        inputs.set_shape([decode_batch_size, self._decode_max_length])

    with tf.name_scope("Transformer"):
      attention_bias = model_utils.get_padding_bias(inputs)
      attention_bias = tf.cast(attention_bias, self._dtype)
      with tf.name_scope("encode"):
        # Prepare inputs to the layer stack by adding positional encodings and
        # applying dropout.
        embedded_inputs = self.embedding_lookup(inputs)
        embedding_mask = tf.cast(
            tf.not_equal(inputs, 0), self.embedding_lookup.embeddings.dtype)
        embedded_inputs *= tf.expand_dims(embedding_mask, -1)
        embedded_inputs = tf.cast(embedded_inputs, self._dtype)

        # Attention_mask generation.
        input_shape = tf_utils.get_shape_list(inputs, expected_rank=2)
        attention_mask = tf.cast(
            tf.reshape(
                tf.not_equal(inputs, 0), [input_shape[0], 1, input_shape[1]]),
            dtype=inputs.dtype)
        broadcast_ones = tf.ones(
            shape=[input_shape[0], input_shape[1], 1], dtype=inputs.dtype)
        attention_mask = broadcast_ones * attention_mask

        with tf.name_scope("add_pos_encoding"):
          pos_encoding = self.position_embedding(inputs=embedded_inputs)
          pos_encoding = tf.cast(pos_encoding, self._dtype)
          encoder_inputs = embedded_inputs + pos_encoding

        encoder_inputs = self.encoder_dropout(encoder_inputs)

        encoder_outputs = self.encoder_layer(
            encoder_inputs, attention_mask=attention_mask)

      if targets is None:
        encoder_decoder_attention_bias = attention_bias
        encoder_outputs = tf.cast(encoder_outputs, self._dtype)
        if self._padded_decode:
          batch_size = encoder_outputs.shape.as_list()[0]
          input_length = encoder_outputs.shape.as_list()[1]
        else:
          batch_size = tf.shape(encoder_outputs)[0]
          input_length = tf.shape(encoder_outputs)[1]
        max_decode_length = input_length + self._extra_decode_length
        encoder_decoder_attention_bias = tf.cast(encoder_decoder_attention_bias,
                                                 self._dtype)

        symbols_to_logits_fn = self._get_symbols_to_logits_fn(max_decode_length)

        # Create initial set of IDs that will be passed to symbols_to_logits_fn.
        initial_ids = tf.zeros([batch_size], dtype=tf.int32)

        # Create cache storing decoder attention values for each layer.
        # pylint: disable=g-complex-comprehension
        init_decode_length = (max_decode_length if self._padded_decode else 0)
        num_heads = self._num_heads
        dim_per_head = self._hidden_size // num_heads

        cache = {
            str(layer): {
                "key":
                    tf.zeros([
                        batch_size, init_decode_length, num_heads, dim_per_head
                    ],
                             dtype=self._dtype),
                "value":
                    tf.zeros([
                        batch_size, init_decode_length, num_heads, dim_per_head
                    ],
                             dtype=self._dtype)
            } for layer in range(self._num_layers)
        }

        # pylint: enable=g-complex-comprehension

        # Add encoder output and attention bias to the cache.
        cache["encoder_outputs"] = encoder_outputs
        cache["encoder_decoder_attention_bias"] = encoder_decoder_attention_bias

        # Use beam search to find the top beam_size sequences and scores.
        decoded_ids, scores = beam_search.sequence_beam_search(
            symbols_to_logits_fn=symbols_to_logits_fn,
            initial_ids=initial_ids,
            initial_cache=cache,
            vocab_size=self._vocab_size,
            beam_size=self._beam_size,
            alpha=self._alpha,
            max_decode_length=max_decode_length,
            eos_id=EOS_ID,
            padded_decode=self._padded_decode,
            dtype=self._dtype)

        # Get the top sequence for each batch element
        top_decoded_ids = decoded_ids[:, 0, 1:]
        top_scores = scores[:, 0]

        return {"outputs": top_decoded_ids, "scores": top_scores}

      else:
        with tf.name_scope("decode"):
          decoder_inputs = self.embedding_lookup(targets)
          embedding_mask = tf.cast(
              tf.not_equal(targets, 0), self.embedding_lookup.embeddings.dtype)
          decoder_inputs *= tf.expand_dims(embedding_mask, -1)
          decoder_inputs = tf.cast(decoder_inputs, self._dtype)
          with tf.name_scope("shift_targets"):
            # Shift targets to the right, and remove the last element
            decoder_inputs = tf.pad(decoder_inputs,
                                    [[0, 0], [1, 0], [0, 0]])[:, :-1, :]
          with tf.name_scope("add_pos_encoding"):
            length = tf.shape(decoder_inputs)[1]
            pos_encoding = self.position_embedding(decoder_inputs)
            pos_encoding = tf.cast(pos_encoding, self._dtype)
            decoder_inputs += pos_encoding

          decoder_inputs = self.decoder_dropout(decoder_inputs)

          decoder_shape = tf_utils.get_shape_list(
              decoder_inputs, expected_rank=3)
          batch_size = decoder_shape[0]
          decoder_length = decoder_shape[1]

          self_attention_mask = tf.linalg.band_part(
              tf.ones([length, length], dtype=tf.float32), -1, 0)
          self_attention_mask = tf.reshape(self_attention_mask,
                                           [1, length, length])
          self_attention_mask = tf.tile(self_attention_mask, [batch_size, 1, 1])

          attention_mask = tf.cast(
              tf.expand_dims(tf.not_equal(inputs, 0), axis=1),
              dtype=inputs.dtype)
          attention_mask = tf.tile(attention_mask, [1, decoder_length, 1])

          outputs = self.decoder_layer(
              decoder_inputs,
              encoder_outputs,
              memory_mask=self_attention_mask,
              target_mask=attention_mask)
          logits = embedding_linear(self.embedding_lookup.embeddings, outputs)
          logits = tf.cast(logits, tf.float32)

        return logits

  def _get_symbols_to_logits_fn(self, max_decode_length):
    """Returns a decoding function that calculates logits of the next tokens."""
    timing_signal = self.position_embedding(
        inputs=None, length=max_decode_length + 1)
    decoder_self_attention_bias = model_utils.get_decoder_self_attention_bias(
        max_decode_length, dtype=self._dtype)

    def symbols_to_logits_fn(ids, i, cache):
      """Generate logits for next potential IDs.

      Args:
        ids: Current decoded sequences. int tensor with shape [batch_size *
          beam_size, i + 1].
        i: Loop index.
        cache: dictionary of values storing the encoder output, encoder-decoder
          attention bias, and previous decoder attention values.

      Returns:
        Tuple of
          (logits with shape [batch_size * beam_size, vocab_size],
           updated cache values)
      """
      # Set decoder input to the last generated IDs
      decoder_input = ids[:, -1:]

      # Preprocess decoder input by getting embeddings and adding timing signal.
      # decoder_input = self.embedding_softmax_layer(decoder_input)
      source_decoder_input = decoder_input
      decoder_input = self.embedding_lookup(decoder_input)
      embedding_mask = tf.cast(
          tf.not_equal(source_decoder_input, 0),
          self.embedding_lookup.embeddings.dtype)
      decoder_input *= tf.expand_dims(embedding_mask, -1)

      if self._padded_decode:
        timing_signal_shape = timing_signal.shape.as_list()
        decoder_input += tf.slice(timing_signal, [i, 0],
                                  [1, timing_signal_shape[1]])

        bias_shape = decoder_self_attention_bias.shape.as_list()
        self_attention_bias = tf.slice(
            decoder_self_attention_bias, [0, 0, i, 0],
            [bias_shape[0], bias_shape[1], 1, bias_shape[3]])
      else:
        decoder_input += timing_signal[i:i + 1]

        self_attention_bias = decoder_self_attention_bias[:, :, i:i + 1, :i + 1]

      decoder_shape = tf_utils.get_shape_list(decoder_input, expected_rank=3)
      batch_size = decoder_shape[0]
      decoder_length = decoder_shape[1]

      attention_bias = cache.get("encoder_decoder_attention_bias")
      attention_bias = tf.where(attention_bias < 0,
                                tf.zeros_like(attention_bias),
                                tf.ones_like(attention_bias))
      attention_bias = tf.squeeze(attention_bias, axis=[1])
      attention_mask = tf.tile(attention_bias, [1, decoder_length, 1])

      self_attention_bias = tf.where(self_attention_bias < 0,
                                     tf.zeros_like(self_attention_bias),
                                     tf.ones_like(self_attention_bias))
      self_attention_bias = tf.squeeze(self_attention_bias, axis=[1])
      self_attention_mask = tf.tile(self_attention_bias, [batch_size, 1, 1])

      decoder_outputs = self.decoder_layer(
          decoder_input,
          cache.get("encoder_outputs"),
          memory_mask=self_attention_mask,
          target_mask=attention_mask,
          cache=cache,
          decode_loop_step=i if self._padded_decode else None)

      logits = embedding_linear(self.embedding_lookup.embeddings,
                                decoder_outputs)
      logits = tf.squeeze(logits, axis=[1])
      return logits, cache

    return symbols_to_logits_fn


class TransformerEncoder(tf.keras.layers.Layer):
  """Transformer encoder.

  Transformer encoder is made up of N identical layers. Each layer is composed
  of the sublayers:
    1. Self-attention layer
    2. Feedforward network (which is 2 fully-connected layers)

  Arguments:
    num_layers: Number of layers.
    num_attention_heads: Number of attention heads.
    intermediate_size: Size of the intermediate (Feedforward) layer.
    activation: Activation for the intermediate layer.
    dropout_rate: Dropout probability.
    attention_dropout_rate: Dropout probability for attention layers.
    use_bias: Whether to enable use_bias in attention layer. If set False,
      use_bias in attention layer is disabled.
    norm_first: Whether to normalize inputs to attention and intermediate dense
      layers. If set False, output of attention and intermediate dense layers is
      normalized.
    norm_epsilon: Epsilon value to initialize normalization layers.
    intermediate_dropout: Dropout probability for intermediate_dropout_layer.
  """

  def __init__(self,
               num_layers=6,
               num_attention_heads=8,
               intermediate_size=2048,
               activation="relu",
               dropout_rate=0.0,
               attention_dropout_rate=0.0,
               use_bias=False,
               norm_first=True,
               norm_epsilon=1e-6,
               intermediate_dropout=0.0,
               **kwargs):
    super(TransformerEncoder, self).__init__(**kwargs)
    self._num_layers = num_layers
    self._num_attention_heads = num_attention_heads
    self._intermediate_size = intermediate_size
    self._activation = activation
    self._dropout_rate = dropout_rate
    self._attention_dropout_rate = attention_dropout_rate
    self._use_bias = use_bias
    self._norm_first = norm_first
    self._norm_epsilon = norm_epsilon
    self._intermediate_dropout = intermediate_dropout

  def build(self, input_shape):
    """Implements build() for the layer."""
    self.encoder_layers = []
    for i in range(self._num_layers):
      self.encoder_layers.append(
          layers.Transformer(
              num_attention_heads=self._num_attention_heads,
              intermediate_size=self._intermediate_size,
              intermediate_activation=self._activation,
              dropout_rate=self._dropout_rate,
              attention_dropout_rate=self._attention_dropout_rate,
              use_bias=self._use_bias,
              norm_first=self._norm_first,
              norm_epsilon=self._norm_epsilon,
              intermediate_dropout=self._intermediate_dropout,
              attention_initializer=attention_initializer(input_shape[2]),
              name=("layer_%d" % i)))
    self.output_normalization = tf.keras.layers.LayerNormalization(
        epsilon=self._norm_epsilon, dtype="float32")
    super(TransformerEncoder, self).build(input_shape)

  def get_config(self):
    config = {
        "num_layers": self._num_layers,
        "num_attention_heads": self._num_attention_heads,
        "intermediate_size": self._intermediate_size,
        "activation": self._activation,
        "dropout_rate": self._dropout_rate,
        "attention_dropout_rate": self._attention_dropout_rate,
        "use_bias": self._use_bias,
        "norm_first": self._norm_first,
        "norm_epsilon": self._norm_epsilon,
        "intermediate_dropout": self._intermediate_dropout
    }
    base_config = super(TransformerEncoder, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))

  def call(self, encoder_inputs, attention_mask=None):
    """Return the output of the encoder.

    Args:
      encoder_inputs: tensor with shape [batch_size, input_length, hidden_size]
      attention_mask: mask for the encoder self-attention layer. [batch_size,
        input_length, input_length]

    Returns:
      Output of encoder.
      float32 tensor with shape [batch_size, input_length, hidden_size]
    """
    for layer_idx in range(self._num_layers):
      encoder_inputs = self.encoder_layers[layer_idx](
          [encoder_inputs, attention_mask])

    output_tensor = encoder_inputs
    output_tensor = self.output_normalization(output_tensor)

    return output_tensor


class TransformerDecoder(tf.keras.layers.Layer):
  """Transformer decoder.

  Like the encoder, the decoder is made up of N identical layers.
  Each layer is composed of the sublayers:
    1. Self-attention layer
    2. Multi-headed attention layer combining encoder outputs with results from
       the previous self-attention layer.
    3. Feedforward network (2 fully-connected layers)

  Arguments:
    num_layers: Number of layers.
    num_attention_heads: Number of attention heads.
    intermediate_size: Size of the intermediate (Feedforward) layer.
    activation: Activation for the intermediate layer.
    dropout_rate: Dropout probability.
    attention_dropout_rate: Dropout probability for attention layers.
    use_bias: Whether to enable use_bias in attention layer. If set False,
      use_bias in attention layer is disabled.
    norm_first: Whether to normalize inputs to attention and intermediate dense
      layers. If set False, output of attention and intermediate dense layers is
      normalized.
    norm_epsilon: Epsilon value to initialize normalization layers.
    intermediate_dropout: Dropout probability for intermediate_dropout_layer.
  """

  def __init__(self,
               num_layers=6,
               num_attention_heads=8,
               intermediate_size=2048,
               activation="relu",
               dropout_rate=0.0,
               attention_dropout_rate=0.0,
               use_bias=False,
               norm_first=True,
               norm_epsilon=1e-6,
               intermediate_dropout=0.0,
               **kwargs):
    super(TransformerDecoder, self).__init__(**kwargs)
    self._num_layers = num_layers
    self._num_attention_heads = num_attention_heads
    self._intermediate_size = intermediate_size
    self._activation = activation
    self._dropout_rate = dropout_rate
    self._attention_dropout_rate = attention_dropout_rate
    self._use_bias = use_bias
    self._norm_first = norm_first
    self._norm_epsilon = norm_epsilon
    self._intermediate_dropout = intermediate_dropout

  def build(self, input_shape):
    """Implements build() for the layer."""
    self.decoder_layers = []
    for i in range(self._num_layers):
      self.decoder_layers.append(
          layers.TransformerDecoderLayer(
              num_attention_heads=self._num_attention_heads,
              intermediate_size=self._intermediate_size,
              intermediate_activation=self._activation,
              dropout_rate=self._dropout_rate,
              attention_dropout_rate=self._attention_dropout_rate,
              use_bias=self._use_bias,
              norm_first=self._norm_first,
              norm_epsilon=self._norm_epsilon,
              intermediate_dropout=self._intermediate_dropout,
              attention_initializer=attention_initializer(input_shape[2]),
              name=("layer_%d" % i)))
    self.output_normalization = tf.keras.layers.LayerNormalization(
        epsilon=1e-6, dtype="float32")
    super(TransformerDecoder, self).build(input_shape)

  def get_config(self):
    config = {
        "num_layers": self._num_layers,
        "num_attention_heads": self._num_attention_heads,
        "intermediate_size": self._intermediate_size,
        "activation": self._activation,
        "dropout_rate": self._dropout_rate,
        "attention_dropout_rate": self._attention_dropout_rate,
        "use_bias": self._use_bias,
        "norm_first": self._norm_first,
        "norm_epsilon": self._norm_epsilon,
        "intermediate_dropout": self._intermediate_dropout
    }
    base_config = super(TransformerDecoder, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))

  def call(self,
           target,
           memory,
           memory_mask=None,
           target_mask=None,
           cache=None,
           decode_loop_step=None):
    """Return the output of the decoder layer stacks.

    Args:
      target: A tensor with shape [batch_size, target_length, hidden_size].
      memory: A tensor with shape [batch_size, input_length, hidden_size]
      memory_mask: A tensor with shape [batch_size, target_len, target_length],
        the mask for decoder self-attention layer.
      target_mask: A tensor with shape [batch_size, target_length, input_length]
        which is the mask for encoder-decoder attention layer.
      cache: (Used for fast decoding) A nested dictionary storing previous
        decoder self-attention values. The items are:
          {layer_n: {"k": A tensor with shape [batch_size, i, key_channels],
                     "v": A tensor with shape [batch_size, i, value_channels]},
                       ...}
      decode_loop_step: An integer, the step number of the decoding loop. Used
        only for autoregressive inference on TPU.

    Returns:
      Output of decoder.
      float32 tensor with shape [batch_size, target_length, hidden_size]
    """

    output_tensor = target
    for layer_idx in range(self._num_layers):
      transformer_inputs = [output_tensor, memory, target_mask, memory_mask]
      # Gets the cache for decoding.
      if cache is None:
        output_tensor, _ = self.decoder_layers[layer_idx](transformer_inputs)
      else:
        cache_layer_idx = str(layer_idx)
        output_tensor, cache[cache_layer_idx] = self.decoder_layers[layer_idx](
            transformer_inputs,
            cache=cache[cache_layer_idx],
            decode_loop_step=decode_loop_step)
    return self.output_normalization(output_tensor)


def embedding_linear(embedding_matrix, x):
  """Uses embeddings as linear transformation weights."""
  with tf.name_scope("presoftmax_linear"):
    batch_size = tf.shape(x)[0]
    length = tf.shape(x)[1]
    hidden_size = tf.shape(x)[2]
    vocab_size = tf.shape(embedding_matrix)[0]

    x = tf.reshape(x, [-1, hidden_size])
    logits = tf.matmul(x, embedding_matrix, transpose_b=True)

    return tf.reshape(logits, [batch_size, length, vocab_size])


def attention_initializer(hidden_size):
  """Initializer for attention layers in Seq2SeqTransformer."""
  limit = math.sqrt(6.0 / (hidden_size + hidden_size))
  return tf.keras.initializers.RandomUniform(minval=-limit, maxval=limit)
