text: # Copyright 2015 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.
  # ==============================================================================
  # pylint: disable=protected-access
  """Wrapper layers: layers that augment the functionality of another layer.
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import copy
  
  from tensorflow.python.eager import context
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.keras import backend as K
  from tensorflow.python.keras.engine.base_layer import Layer
  from tensorflow.python.keras.engine.input_spec import InputSpec
  from tensorflow.python.keras.layers.recurrent import _standardize_args
  from tensorflow.python.keras.utils import generic_utils
  from tensorflow.python.keras.utils import layer_utils
  from tensorflow.python.keras.utils import tf_inspect
  from tensorflow.python.keras.utils import tf_utils
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.util import nest
  from tensorflow.python.util.tf_export import keras_export
  
  
  @keras_export('keras.layers.Wrapper')
  class Wrapper(Layer):
    """Abstract wrapper base class.
  
    Wrappers take another layer and augment it in various ways.
    Do not use this class as a layer, it is only an abstract base class.
    Two usable wrappers are the `TimeDistributed` and `Bidirectional` wrappers.
  
    Arguments:
      layer: The layer to be wrapped.
    """
  
    def __init__(self, layer, **kwargs):
      assert isinstance(layer, Layer)
      self.layer = layer
      super(Wrapper, self).__init__(**kwargs)
  
    def build(self, input_shape=None):
      if not self.layer.built:
        self.layer.build(input_shape)
        self.layer.built = True
      self.built = True
  
    @property
    def activity_regularizer(self):
      if hasattr(self.layer, 'activity_regularizer'):
        return self.layer.activity_regularizer
      else:
        return None
  
    def get_config(self):
      config = {'layer': generic_utils.serialize_keras_object(self.layer)}
      base_config = super(Wrapper, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
    @classmethod
    def from_config(cls, config, custom_objects=None):
      from tensorflow.python.keras.layers import deserialize as deserialize_layer  # pylint: disable=g-import-not-at-top
      # Avoid mutating the input dict
      config = copy.deepcopy(config)
      layer = deserialize_layer(
          config.pop('layer'), custom_objects=custom_objects)
      return cls(layer, **config)
  
  
  @keras_export('keras.layers.TimeDistributed')
  class TimeDistributed(Wrapper):
    """This wrapper allows to apply a layer to every temporal slice of an input.
  
    Every input should be at least 3D, and the dimension of index one of the
    first input will be considered to be the temporal dimension.
  
    Consider a batch of 32 video samples, where each sample is a 128x128 RGB image
    with `channels_last` data format, across 10 timesteps.
    The batch input shape is `(32, 10, 128, 128, 3)`.
  
    You can then use `TimeDistributed` to apply the same `Conv2D` layer to each
    of the 10 timesteps, independently:
  
    >>> inputs = tf.keras.Input(shape=(10, 128, 128, 3))
    >>> conv_2d_layer = tf.keras.layers.Conv2D(64, (3, 3))
    >>> outputs = tf.keras.layers.TimeDistributed(conv_2d_layer)(inputs)
    >>> outputs.shape
    TensorShape([None, 10, 126, 126, 64])
  
    Because `TimeDistributed` applies the same instance of `Conv2D` to each of the
    timestamps, the same set of weights are used at each timestamp.
  
    Arguments:
      layer: a `tf.keras.layers.Layer` instance.
  
    Call arguments:
      inputs: Input tensor of shape (batch, time, ...) or nested tensors,
        and each of which has shape (batch, time, ...).
      training: Python boolean indicating whether the layer should behave in
        training mode or in inference mode. This argument is passed to the
        wrapped layer (only if the layer supports this argument).
      mask: Binary tensor of shape `(samples, timesteps)` indicating whether
        a given timestep should be masked. This argument is passed to the
        wrapped layer (only if the layer supports this argument).
  
    Raises:
      ValueError: If not initialized with a `tf.keras.layers.Layer` instance.
    """
  
    def __init__(self, layer, **kwargs):
      if not isinstance(layer, Layer):
        raise ValueError(
            'Please initialize `TimeDistributed` layer with a '
            '`tf.keras.layers.Layer` instance. You passed: {input}'.format(
                input=layer))
      super(TimeDistributed, self).__init__(layer, **kwargs)
      self.supports_masking = True
  
      # It is safe to use the fast, reshape-based approach with all of our
      # built-in Layers.
      self._always_use_reshape = (
          layer_utils.is_builtin_layer(layer) and
          not getattr(layer, 'stateful', False))
  
    def _get_shape_tuple(self, init_tuple, tensor, start_idx, int_shape=None):
      """Finds non-specific dimensions in the static shapes.
  
      The static shapes are replaced with the corresponding dynamic shapes of the
      tensor.
      Arguments:
        init_tuple: a tuple, the first part of the output shape
        tensor: the tensor from which to get the (static and dynamic) shapes
          as the last part of the output shape
        start_idx: int, which indicate the first dimension to take from
          the static shape of the tensor
        int_shape: an alternative static shape to take as the last part
          of the output shape
      Returns:
        The new int_shape with the first part from init_tuple
        and the last part from either `int_shape` (if provided)
        or `tensor.shape`, where every `None` is replaced by
        the corresponding dimension from `tf.shape(tensor)`.
      """
      # replace all None in int_shape by K.shape
      if int_shape is None:
        int_shape = K.int_shape(tensor)[start_idx:]
      if isinstance(int_shape, tensor_shape.TensorShape):
        int_shape = int_shape.as_list()
      if not any(not s for s in int_shape):
        return init_tuple + tuple(int_shape)
      shape = K.shape(tensor)
      int_shape = list(int_shape)
      for i, s in enumerate(int_shape):
        if not s:
          int_shape[i] = shape[start_idx + i]
      return init_tuple + tuple(int_shape)
  
    def _remove_timesteps(self, dims):
      dims = dims.as_list()
      return tensor_shape.TensorShape([dims[0]] + dims[2:])
  
    def build(self, input_shape):
      input_shape = tf_utils.convert_shapes(input_shape, to_tuples=False)
      input_dims = nest.flatten(
          nest.map_structure(lambda x: x.ndims, input_shape))
      if any(dim < 3 for dim in input_dims):
        raise ValueError(
            '`TimeDistributed` Layer should be passed an `input_shape ` '
            'with at least 3 dimensions, received: ' + str(input_shape))
      # Don't enforce the batch or time dimension.
      self.input_spec = nest.map_structure(
          lambda x: InputSpec(shape=[None, None] + x.as_list()[2:]), input_shape)
      child_input_shape = nest.map_structure(self._remove_timesteps, input_shape)
      child_input_shape = tf_utils.convert_shapes(child_input_shape)
      super(TimeDistributed, self).build(tuple(child_input_shape))
      self.built = True
  
    def compute_output_shape(self, input_shape):
      input_shape = tf_utils.convert_shapes(input_shape, to_tuples=False)
  
      child_input_shape = nest.map_structure(self._remove_timesteps, input_shape)
      child_output_shape = self.layer.compute_output_shape(child_input_shape)
      child_output_shape = tf_utils.convert_shapes(
          child_output_shape, to_tuples=False)
      timesteps = tf_utils.convert_shapes(input_shape)
      timesteps = nest.flatten(timesteps)[1]
  
      def insert_timesteps(dims):
        dims = dims.as_list()
        return tensor_shape.TensorShape([dims[0], timesteps] + dims[1:])
  
      return nest.map_structure(insert_timesteps, child_output_shape)
  
    def call(self, inputs, training=None, mask=None):
      kwargs = {}
      if generic_utils.has_arg(self.layer.call, 'training'):
        kwargs['training'] = training
  
      input_shape = nest.map_structure(
          lambda x: tensor_shape.TensorShape(K.int_shape(x)), inputs)
      batch_size = tf_utils.convert_shapes(input_shape)
      batch_size = nest.flatten(batch_size)[0]
      if batch_size and not self._always_use_reshape:
        inputs, row_lengths = K.convert_inputs_if_ragged(inputs)
        is_ragged_input = row_lengths is not None
        input_length = tf_utils.convert_shapes(input_shape)
        input_length = nest.flatten(input_length)[1]
  
        # batch size matters, use rnn-based implementation
        def step(x, _):
          output = self.layer(x, **kwargs)
          return output, []
  
        _, outputs, _ = K.rnn(
            step,
            inputs,
            initial_states=[],
            input_length=row_lengths[0] if is_ragged_input else input_length,
            mask=mask,
            unroll=False)
        # pylint: disable=g-long-lambda
        y = nest.map_structure(
            lambda output: K.maybe_convert_to_ragged(is_ragged_input, output,
                                                     row_lengths), outputs)
      else:
        # No batch size specified, therefore the layer will be able
        # to process batches of any size.
        # We can go with reshape-based implementation for performance.
        is_ragged_input = nest.map_structure(
            lambda x: isinstance(x, ragged_tensor.RaggedTensor), inputs)
        is_ragged_input = nest.flatten(is_ragged_input)
        if all(is_ragged_input):
          input_values = nest.map_structure(lambda x: x.values, inputs)
          input_row_lenghts = nest.map_structure(
              lambda x: x.nested_row_lengths()[0], inputs)
          y = self.layer(input_values, **kwargs)
          y = nest.map_structure(ragged_tensor.RaggedTensor.from_row_lengths, y,
                                 input_row_lenghts)
        elif any(is_ragged_input):
          raise ValueError('All inputs has to be either ragged or not, '
                           'but not mixed. You passed: {}'.format(inputs))
        else:
          input_length = tf_utils.convert_shapes(input_shape)
          input_length = nest.flatten(input_length)[1]
          if not input_length:
            input_length = nest.map_structure(lambda x: array_ops.shape(x)[1],
                                              inputs)
            input_length = generic_utils.to_list(nest.flatten(input_length))[0]
  
          inner_input_shape = nest.map_structure(
              lambda x: self._get_shape_tuple((-1,), x, 2), inputs)
          # Shape: (num_samples * timesteps, ...). And track the
          # transformation in self._input_map.
          inputs = nest.map_structure_up_to(inputs, array_ops.reshape, inputs,
                                            inner_input_shape)
          # (num_samples * timesteps, ...)
          if generic_utils.has_arg(self.layer.call, 'mask') and mask is not None:
            inner_mask_shape = self._get_shape_tuple((-1,), mask, 2)
            kwargs['mask'] = K.reshape(mask, inner_mask_shape)
  
          y = self.layer(inputs, **kwargs)
  
          # Shape: (num_samples, timesteps, ...)
          output_shape = self.compute_output_shape(input_shape)
          # pylint: disable=g-long-lambda
          output_shape = nest.map_structure(
              lambda tensor, int_shape: self._get_shape_tuple(
                  (-1, input_length), tensor, 1, int_shape[2:]), y, output_shape)
          y = nest.map_structure_up_to(y, array_ops.reshape, y, output_shape)
          if not context.executing_eagerly():
            # Set the static shape for the result since it might be lost during
            # array_ops reshape, eg, some `None` dim in the result could be
            # inferred.
            nest.map_structure_up_to(
                y, lambda tensor, shape: tensor.set_shape(shape), y,
                self.compute_output_shape(input_shape))
  
      return y
  
    def compute_mask(self, inputs, mask=None):
      """Computes an output mask tensor for Embedding layer.
  
      This is based on the inputs, mask, and the inner layer.
      If batch size is specified:
      Simply return the input `mask`. (An rnn-based implementation with
      more than one rnn inputs is required but not supported in tf.keras yet.)
      Otherwise we call `compute_mask` of the inner layer at each time step.
      If the output mask at each time step is not `None`:
      (E.g., inner layer is Masking or RNN)
      Concatenate all of them and return the concatenation.
      If the output mask at each time step is `None` and the input mask is not
      `None`:(E.g., inner layer is Dense)
      Reduce the input_mask to 2 dimensions and return it.
      Otherwise (both the output mask and the input mask are `None`):
      (E.g., `mask` is not used at all)
      Return `None`.
  
      Arguments:
        inputs: Tensor with shape [batch size, timesteps, ...] indicating the
          input to TimeDistributed. If static shape information is available for
          "batch size", `mask` is returned unmodified.
        mask: Either None (indicating no masking) or a Tensor indicating the
          input mask for TimeDistributed. The shape can be static or dynamic.
  
      Returns:
        Either None (no masking), or a [batch size, timesteps, ...] Tensor with
        an output mask for the TimeDistributed layer with the shape beyond the
        second dimension being the value of the input mask shape(if the computed
        output mask is none), an output mask with the shape beyond the first
        dimension being the value of the mask shape(if mask is not None) or
        output mask with the shape beyond the first dimension being the
        value of the computed output shape.
  
      """
      # cases need to call the layer.compute_mask when input_mask is None:
      # Masking layer and Embedding layer with mask_zero
      input_shape = nest.map_structure(
          lambda x: tensor_shape.TensorShape(K.int_shape(x)), inputs)
      input_shape = tf_utils.convert_shapes(input_shape, to_tuples=False)
      batch_size = tf_utils.convert_shapes(input_shape)
      batch_size = nest.flatten(batch_size)[0]
      is_ragged_input = nest.map_structure(
          lambda x: isinstance(x, ragged_tensor.RaggedTensor), inputs)
      is_ragged_input = generic_utils.to_list(nest.flatten(is_ragged_input))
      if batch_size and not self._always_use_reshape or any(is_ragged_input):
        # batch size matters, we currently do not handle mask explicitly, or if
        # the layer always uses reshape approach, or the input is a ragged tensor.
        return mask
      inner_mask = mask
      if inner_mask is not None:
        inner_mask_shape = self._get_shape_tuple((-1,), mask, 2)
        inner_mask = K.reshape(inner_mask, inner_mask_shape)
      inner_input_shape = nest.map_structure(
          lambda tensor: self._get_shape_tuple((-1,), tensor, 2), inputs)
      inner_inputs = nest.map_structure_up_to(inputs, array_ops.reshape, inputs,
                                              inner_input_shape)
      output_mask = self.layer.compute_mask(inner_inputs, inner_mask)
      if output_mask is None:
        if mask is None:
          return None
        # input_mask is not None, and output_mask is None:
        # we should return a not-None mask
        output_mask = mask
        for _ in range(2, len(K.int_shape(mask))):
          output_mask = K.any(output_mask, axis=-1)
      else:
        # output_mask is not None. We need to reshape it
        input_length = tf_utils.convert_shapes(input_shape)
        input_length = nest.flatten(input_length)[1]
        if not input_length:
          input_length = nest.map_structure(lambda x: K.shape(x)[1], inputs)
          input_length = nest.flatten(input_length)[0]
        output_mask_int_shape = K.int_shape(output_mask)
        if output_mask_int_shape is None:
          # if the output_mask does not have a static shape,
          # its shape must be the same as mask's
          if mask is not None:
            output_mask_int_shape = K.int_shape(mask)
          else:
            input_shape = generic_utils.to_list(nest.flatten(input_shape))[0]
            output_mask_int_shape = K.compute_output_shape(input_shape)[:-1]
        output_mask_shape = self._get_shape_tuple(
            (-1, input_length), output_mask, 1, output_mask_int_shape[1:])
        output_mask = K.reshape(output_mask, output_mask_shape)
      return output_mask
  
  
  @keras_export('keras.layers.Bidirectional')
  class Bidirectional(Wrapper):
    """Bidirectional wrapper for RNNs.
  
    Arguments:
      layer: `keras.layers.RNN` instance, such as `keras.layers.LSTM` or
        `keras.layers.GRU`. It could also be a `keras.layers.Layer` instance
        that meets the following criteria:
        1. Be a sequence-processing layer (accepts 3D+ inputs).
        2. Have a `go_backwards`, `return_sequences` and `return_state`
          attribute (with the same semantics as for the `RNN` class).
        3. Have an `input_spec` attribute.
        4. Implement serialization via `get_config()` and `from_config()`.
        Note that the recommended way to create new RNN layers is to write a
        custom RNN cell and use it with `keras.layers.RNN`, instead of
        subclassing `keras.layers.Layer` directly.
      merge_mode: Mode by which outputs of the forward and backward RNNs will be
        combined. One of {'sum', 'mul', 'concat', 'ave', None}. If None, the
        outputs will not be combined, they will be returned as a list. Default
        value is 'concat'.
      backward_layer: Optional `keras.layers.RNN`, or `keras.layers.Layer`
        instance to be used to handle backwards input processing.
        If `backward_layer` is not provided, the layer instance passed as the
        `layer` argument will be used to generate the backward layer
        automatically.
        Note that the provided `backward_layer` layer should have properties
        matching those of the `layer` argument, in particular it should have the
        same values for `stateful`, `return_states`, `return_sequence`, etc.
        In addition, `backward_layer` and `layer` should have different
        `go_backwards` argument values.
        A `ValueError` will be raised if these requirements are not met.
  
    Call arguments:
      The call arguments for this layer are the same as those of the wrapped RNN
        layer.
      Beware that when passing the `initial_state` argument during the call of
      this layer, the first half in the list of elements in the `initial_state`
      list will be passed to the forward RNN call and the last half in the list
      of elements will be passed to the backward RNN call.
  
    Raises:
      ValueError:
        1. If `layer` or `backward_layer` is not a `Layer` instance.
        2. In case of invalid `merge_mode` argument.
        3. If `backward_layer` has mismatched properties compared to `layer`.
  
    Examples:
  
    ```python
    model = Sequential()
    model.add(Bidirectional(LSTM(10, return_sequences=True), input_shape=(5, 10)))
    model.add(Bidirectional(LSTM(10)))
    model.add(Dense(5))
    model.add(Activation('softmax'))
    model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
  
     # With custom backward layer
     model = Sequential()
     forward_layer = LSTM(10, return_sequences=True)
     backward_layer = LSTM(10, activation='relu', return_sequences=True,
                           go_backwards=True)
     model.add(Bidirectional(forward_layer, backward_layer=backward_layer,
                             input_shape=(5, 10)))
     model.add(Dense(5))
     model.add(Activation('softmax'))
     model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
    ```
    """
  
    def __init__(self,
                 layer,
                 merge_mode='concat',
                 weights=None,
                 backward_layer=None,
                 **kwargs):
      if not isinstance(layer, Layer):
        raise ValueError(
            'Please initialize `Bidirectional` layer with a '
            '`Layer` instance. You passed: {input}'.format(input=layer))
      if backward_layer is not None and not isinstance(backward_layer, Layer):
        raise ValueError('`backward_layer` need to be a `Layer` instance. '
                         'You passed: {input}'.format(input=backward_layer))
      if merge_mode not in ['sum', 'mul', 'ave', 'concat', None]:
        raise ValueError('Invalid merge mode. '
                         'Merge mode should be one of '
                         '{"sum", "mul", "ave", "concat", None}')
      # We don't want to track `layer` since we're already tracking the two copies
      # of it we actually run.
      self._setattr_tracking = False
      super(Bidirectional, self).__init__(layer, **kwargs)
      self._setattr_tracking = True
  
      # Recreate the forward layer from the original layer config, so that it will
      # not carry over any state from the layer.
      self.forward_layer = self._recreate_layer_from_config(layer)
  
      if backward_layer is None:
        self.backward_layer = self._recreate_layer_from_config(
            layer, go_backwards=True)
      else:
        self.backward_layer = backward_layer
        # Keep the custom backward layer config, so that we can save it later. The
        # layer's name might be updated below with prefix 'backward_', and we want
        # to preserve the original config.
        self._backward_layer_config = generic_utils.serialize_keras_object(
            backward_layer)
  
      self.forward_layer._name = 'forward_' + self.forward_layer.name
      self.backward_layer._name = 'backward_' + self.backward_layer.name
  
      self._verify_layer_config()
  
      def force_zero_output_for_mask(layer):
        # Force the zero_output_for_mask to be True if returning sequences.
        if getattr(layer, 'zero_output_for_mask', None) is not None:
          layer.zero_output_for_mask = layer.return_sequences
  
      force_zero_output_for_mask(self.forward_layer)
      force_zero_output_for_mask(self.backward_layer)
  
      self.merge_mode = merge_mode
      if weights:
        nw = len(weights)
        self.forward_layer.initial_weights = weights[:nw // 2]
        self.backward_layer.initial_weights = weights[nw // 2:]
      self.stateful = layer.stateful
      self.return_sequences = layer.return_sequences
      self.return_state = layer.return_state
      self.supports_masking = True
      self._trainable = True
      self._num_constants = 0
      self.input_spec = layer.input_spec
  
    def _verify_layer_config(self):
      """Ensure the forward and backward layers have valid common property."""
      if self.forward_layer.go_backwards == self.backward_layer.go_backwards:
        raise ValueError('Forward layer and backward layer should have different '
                         '`go_backwards` value.')
  
      common_attributes = ('stateful', 'return_sequences', 'return_state')
      for a in common_attributes:
        forward_value = getattr(self.forward_layer, a)
        backward_value = getattr(self.backward_layer, a)
        if forward_value != backward_value:
          raise ValueError(
              'Forward layer and backward layer are expected to have the same '
              'value for attribute {attr}, got {forward} and {backward}'.format(
                  attr=a, forward=forward_value, backward=backward_value))
  
    def _recreate_layer_from_config(self, layer, go_backwards=False):
      # When recreating the layer from its config, it is possible that the layer
      # is a RNN layer that contains custom cells. In this case we inspect the
      # layer and pass the custom cell class as part of the `custom_objects`
      # argument when calling `from_config`.
      # See https://github.com/tensorflow/tensorflow/issues/26581 for more detail.
      config = layer.get_config()
      if go_backwards:
        config['go_backwards'] = not config['go_backwards']
      if 'custom_objects' in tf_inspect.getfullargspec(
          layer.__class__.from_config).args:
        custom_objects = {}
        cell = getattr(layer, 'cell', None)
        if cell is not None:
          custom_objects[cell.__class__.__name__] = cell.__class__
          # For StackedRNNCells
          stacked_cells = getattr(cell, 'cells', [])
          for c in stacked_cells:
            custom_objects[c.__class__.__name__] = c.__class__
        return layer.__class__.from_config(config, custom_objects=custom_objects)
      else:
        return layer.__class__.from_config(config)
  
    @tf_utils.shape_type_conversion
    def compute_output_shape(self, input_shape):
      output_shape = self.forward_layer.compute_output_shape(input_shape)
      if self.return_state:
        state_shape = tf_utils.convert_shapes(output_shape[1:], to_tuples=False)
        output_shape = tf_utils.convert_shapes(output_shape[0], to_tuples=False)
      else:
        output_shape = tf_utils.convert_shapes(output_shape, to_tuples=False)
  
      if self.merge_mode == 'concat':
        output_shape = output_shape.as_list()
        output_shape[-1] *= 2
        output_shape = tensor_shape.TensorShape(output_shape)
      elif self.merge_mode is None:
        output_shape = [output_shape, copy.copy(output_shape)]
  
      if self.return_state:
        if self.merge_mode is None:
          return output_shape + state_shape + copy.copy(state_shape)
        return [output_shape] + state_shape + copy.copy(state_shape)
      return output_shape
  
    def __call__(self, inputs, initial_state=None, constants=None, **kwargs):
      """`Bidirectional.__call__` implements the same API as the wrapped `RNN`."""
      inputs, initial_state, constants = _standardize_args(
          inputs, initial_state, constants, self._num_constants)
  
      if isinstance(inputs, list):
        if len(inputs) > 1:
          initial_state = inputs[1:]
        inputs = inputs[0]
  
      if initial_state is None and constants is None:
        return super(Bidirectional, self).__call__(inputs, **kwargs)
  
      # Applies the same workaround as in `RNN.__call__`
      additional_inputs = []
      additional_specs = []
      if initial_state is not None:
        # Check if `initial_state` can be splitted into half
        num_states = len(initial_state)
        if num_states % 2 > 0:
          raise ValueError(
              'When passing `initial_state` to a Bidirectional RNN, '
              'the state should be a list containing the states of '
              'the underlying RNNs. '
              'Found: ' + str(initial_state))
  
        kwargs['initial_state'] = initial_state
        additional_inputs += initial_state
        state_specs = [InputSpec(shape=K.int_shape(state))
                       for state in initial_state]
        self.forward_layer.state_spec = state_specs[:num_states // 2]
        self.backward_layer.state_spec = state_specs[num_states // 2:]
        additional_specs += state_specs
      if constants is not None:
        kwargs['constants'] = constants
        additional_inputs += constants
        constants_spec = [InputSpec(shape=K.int_shape(constant))
                          for constant in constants]
        self.forward_layer.constants_spec = constants_spec
        self.backward_layer.constants_spec = constants_spec
        additional_specs += constants_spec
  
        self._num_constants = len(constants)
        self.forward_layer._num_constants = self._num_constants
        self.backward_layer._num_constants = self._num_constants
  
      is_keras_tensor = K.is_keras_tensor(additional_inputs[0])
      for tensor in additional_inputs:
        if K.is_keras_tensor(tensor) != is_keras_tensor:
          raise ValueError('The initial state of a Bidirectional'
                           ' layer cannot be specified with a mix of'
                           ' Keras tensors and non-Keras tensors'
                           ' (a "Keras tensor" is a tensor that was'
                           ' returned by a Keras layer, or by `Input`)')
  
      if is_keras_tensor:
        # Compute the full input spec, including state
        full_input = [inputs] + additional_inputs
        # The original input_spec is None since there could be a nested tensor
        # input. Update the input_spec to match the inputs.
        full_input_spec = [None for _ in range(len(nest.flatten(inputs)))
                          ] + additional_specs
        # Removing kwargs since the value are passed with input list.
        kwargs['initial_state'] = None
        kwargs['constants'] = None
  
        # Perform the call with temporarily replaced input_spec
        original_input_spec = self.input_spec
        self.input_spec = full_input_spec
        output = super(Bidirectional, self).__call__(full_input, **kwargs)
        self.input_spec = original_input_spec
        return output
      else:
        return super(Bidirectional, self).__call__(inputs, **kwargs)
  
    def call(self,
             inputs,
             training=None,
             mask=None,
             initial_state=None,
             constants=None):
      """`Bidirectional.call` implements the same API as the wrapped `RNN`."""
      kwargs = {}
      if generic_utils.has_arg(self.layer.call, 'training'):
        kwargs['training'] = training
      if generic_utils.has_arg(self.layer.call, 'mask'):
        kwargs['mask'] = mask
      if generic_utils.has_arg(self.layer.call, 'constants'):
        kwargs['constants'] = constants
  
      if generic_utils.has_arg(self.layer.call, 'initial_state'):
        if isinstance(inputs, list) and len(inputs) > 1:
          # initial_states are keras tensors, which means they are passed in
          # together with inputs as list. The initial_states need to be split into
          # forward and backward section, and be feed to layers accordingly.
          forward_inputs = [inputs[0]]
          backward_inputs = [inputs[0]]
          pivot = (len(inputs) - self._num_constants) // 2 + 1
          # add forward initial state
          forward_inputs += inputs[1:pivot]
          if not self._num_constants:
            # add backward initial state
            backward_inputs += inputs[pivot:]
          else:
            # add backward initial state
            backward_inputs += inputs[pivot:-self._num_constants]
            # add constants for forward and backward layers
            forward_inputs += inputs[-self._num_constants:]
            backward_inputs += inputs[-self._num_constants:]
          forward_state, backward_state = None, None
          if 'constants' in kwargs:
            kwargs['constants'] = None
        elif initial_state is not None:
          # initial_states are not keras tensors, eg eager tensor from np array.
          # They are only passed in from kwarg initial_state, and should be passed
          # to forward/backward layer via kwarg initial_state as well.
          forward_inputs, backward_inputs = inputs, inputs
          half = len(initial_state) // 2
          forward_state = initial_state[:half]
          backward_state = initial_state[half:]
        else:
          forward_inputs, backward_inputs = inputs, inputs
          forward_state, backward_state = None, None
  
        y = self.forward_layer(forward_inputs,
                               initial_state=forward_state, **kwargs)
        y_rev = self.backward_layer(backward_inputs,
                                    initial_state=backward_state, **kwargs)
      else:
        y = self.forward_layer(inputs, **kwargs)
        y_rev = self.backward_layer(inputs, **kwargs)
  
      if self.return_state:
        states = y[1:] + y_rev[1:]
        y = y[0]
        y_rev = y_rev[0]
  
      if self.return_sequences:
        time_dim = 0 if getattr(self.forward_layer, 'time_major', False) else 1
        y_rev = K.reverse(y_rev, time_dim)
      if self.merge_mode == 'concat':
        output = K.concatenate([y, y_rev])
      elif self.merge_mode == 'sum':
        output = y + y_rev
      elif self.merge_mode == 'ave':
        output = (y + y_rev) / 2
      elif self.merge_mode == 'mul':
        output = y * y_rev
      elif self.merge_mode is None:
        output = [y, y_rev]
      else:
        raise ValueError(
            'Unrecognized value for `merge_mode`: %s' % (self.merge_mode))
  
      if self.return_state:
        if self.merge_mode is None:
          return output + states
        return [output] + states
      return output
  
    def reset_states(self):
      self.forward_layer.reset_states()
      self.backward_layer.reset_states()
  
    def build(self, input_shape):
      with K.name_scope(self.forward_layer.name):
        self.forward_layer.build(input_shape)
      with K.name_scope(self.backward_layer.name):
        self.backward_layer.build(input_shape)
      self.built = True
  
    def compute_mask(self, inputs, mask):
      if isinstance(mask, list):
        mask = mask[0]
      if self.return_sequences:
        if not self.merge_mode:
          output_mask = [mask, mask]
        else:
          output_mask = mask
      else:
        output_mask = [None, None] if not self.merge_mode else None
  
      if self.return_state:
        states = self.forward_layer.states
        state_mask = [None for _ in states]
        if isinstance(output_mask, list):
          return output_mask + state_mask * 2
        return [output_mask] + state_mask * 2
      return output_mask
  
    @property
    def constraints(self):
      constraints = {}
      if hasattr(self.forward_layer, 'constraints'):
        constraints.update(self.forward_layer.constraints)
        constraints.update(self.backward_layer.constraints)
      return constraints
  
    def get_config(self):
      config = {'merge_mode': self.merge_mode}
      if self._num_constants:
        config['num_constants'] = self._num_constants
  
      if hasattr(self, '_backward_layer_config'):
        config['backward_layer'] = self._backward_layer_config
      base_config = super(Bidirectional, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
    @classmethod
    def from_config(cls, config, custom_objects=None):
      # Instead of updating the input, create a copy and use that.
      config = copy.deepcopy(config)
      num_constants = config.pop('num_constants', 0)
      # Handle forward layer instantiation (as would parent class).
      from tensorflow.python.keras.layers import deserialize as deserialize_layer  # pylint: disable=g-import-not-at-top
      config['layer'] = deserialize_layer(
          config['layer'], custom_objects=custom_objects)
      # Handle (optional) backward layer instantiation.
      backward_layer_config = config.pop('backward_layer', None)
      if backward_layer_config is not None:
        backward_layer = deserialize_layer(
            backward_layer_config, custom_objects=custom_objects)
        config['backward_layer'] = backward_layer
      # Instantiate the wrapper, adjust it and return it.
      layer = cls(**config)
      layer._num_constants = num_constants
      return layer
  