# 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.
# ==============================================================================
"""Module implementing RNN Cells.

This module provides a number of basic commonly used RNN cells, such as LSTM
(Long Short Term Memory) or GRU (Gated Recurrent Unit), and a number of
operators that allow adding dropouts, projections, or embeddings for inputs.
Constructing multi-layer cells is supported by the class `MultiRNNCell`, or by
calling the `rnn` ops several times.
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import hashlib
import numbers

from tensorflow.python.eager import context
from tensorflow.python.framework import constant_op
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.keras import activations
from tensorflow.python.keras import initializers
from tensorflow.python.keras import layers as keras_layer
from tensorflow.python.keras.engine import input_spec
from tensorflow.python.keras.utils import tf_utils
from tensorflow.python.layers import base as base_layer
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import clip_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import partitioned_variables
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import tensor_array_ops
from tensorflow.python.ops import variable_scope as vs
from tensorflow.python.ops import variables as tf_variables
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.training.tracking import base as trackable
from tensorflow.python.util import nest
from tensorflow.python.util.deprecation import deprecated
from tensorflow.python.util.tf_export import tf_export

_BIAS_VARIABLE_NAME = "bias"
_WEIGHTS_VARIABLE_NAME = "kernel"

# This can be used with self.assertRaisesRegexp for assert_like_rnncell.
ASSERT_LIKE_RNNCELL_ERROR_REGEXP = "is not an RNNCell"


def _hasattr(obj, attr_name):
  try:
    getattr(obj, attr_name)
  except AttributeError:
    return False
  else:
    return True


def assert_like_rnncell(cell_name, cell):
  """Raises a TypeError if cell is not like an RNNCell.

  NOTE: Do not rely on the error message (in particular in tests) which can be
  subject to change to increase readability. Use
  ASSERT_LIKE_RNNCELL_ERROR_REGEXP.

  Args:
    cell_name: A string to give a meaningful error referencing to the name of
      the functionargument.
    cell: The object which should behave like an RNNCell.

  Raises:
    TypeError: A human-friendly exception.
  """
  conditions = [
      _hasattr(cell, "output_size"),
      _hasattr(cell, "state_size"),
      _hasattr(cell, "get_initial_state") or _hasattr(cell, "zero_state"),
      callable(cell),
  ]
  errors = [
      "'output_size' property is missing", "'state_size' property is missing",
      "either 'zero_state' or 'get_initial_state' method is required",
      "is not callable"
  ]

  if not all(conditions):

    errors = [error for error, cond in zip(errors, conditions) if not cond]
    raise TypeError("The argument {!r} ({}) is not an RNNCell: {}.".format(
        cell_name, cell, ", ".join(errors)))


def _concat(prefix, suffix, static=False):
  """Concat that enables int, Tensor, or TensorShape values.

  This function takes a size specification, which can be an integer, a
  TensorShape, or a Tensor, and converts it into a concatenated Tensor
  (if static = False) or a list of integers (if static = True).

  Args:
    prefix: The prefix; usually the batch size (and/or time step size).
      (TensorShape, int, or Tensor.)
    suffix: TensorShape, int, or Tensor.
    static: If `True`, return a python list with possibly unknown dimensions.
      Otherwise return a `Tensor`.

  Returns:
    shape: the concatenation of prefix and suffix.

  Raises:
    ValueError: if `suffix` is not a scalar or vector (or TensorShape).
    ValueError: if prefix or suffix was `None` and asked for dynamic
      Tensors out.
  """
  if isinstance(prefix, ops.Tensor):
    p = prefix
    p_static = tensor_util.constant_value(prefix)
    if p.shape.ndims == 0:
      p = array_ops.expand_dims(p, 0)
    elif p.shape.ndims != 1:
      raise ValueError("prefix tensor must be either a scalar or vector, "
                       "but saw tensor: %s" % p)
  else:
    p = tensor_shape.as_shape(prefix)
    p_static = p.as_list() if p.ndims is not None else None
    p = (
        constant_op.constant(p.as_list(), dtype=dtypes.int32)
        if p.is_fully_defined() else None)
  if isinstance(suffix, ops.Tensor):
    s = suffix
    s_static = tensor_util.constant_value(suffix)
    if s.shape.ndims == 0:
      s = array_ops.expand_dims(s, 0)
    elif s.shape.ndims != 1:
      raise ValueError("suffix tensor must be either a scalar or vector, "
                       "but saw tensor: %s" % s)
  else:
    s = tensor_shape.as_shape(suffix)
    s_static = s.as_list() if s.ndims is not None else None
    s = (
        constant_op.constant(s.as_list(), dtype=dtypes.int32)
        if s.is_fully_defined() else None)

  if static:
    shape = tensor_shape.as_shape(p_static).concatenate(s_static)
    shape = shape.as_list() if shape.ndims is not None else None
  else:
    if p is None or s is None:
      raise ValueError("Provided a prefix or suffix of None: %s and %s" %
                       (prefix, suffix))
    shape = array_ops.concat((p, s), 0)
  return shape


def _zero_state_tensors(state_size, batch_size, dtype):
  """Create tensors of zeros based on state_size, batch_size, and dtype."""

  def get_state_shape(s):
    """Combine s with batch_size to get a proper tensor shape."""
    c = _concat(batch_size, s)
    size = array_ops.zeros(c, dtype=dtype)
    if not context.executing_eagerly():
      c_static = _concat(batch_size, s, static=True)
      size.set_shape(c_static)
    return size

  return nest.map_structure(get_state_shape, state_size)


@tf_export(v1=["nn.rnn_cell.RNNCell"])
class RNNCell(base_layer.Layer):
  """Abstract object representing an RNN cell.

  Every `RNNCell` must have the properties below and implement `call` with
  the signature `(output, next_state) = call(input, state)`.  The optional
  third input argument, `scope`, is allowed for backwards compatibility
  purposes; but should be left off for new subclasses.

  This definition of cell differs from the definition used in the literature.
  In the literature, 'cell' refers to an object with a single scalar output.
  This definition refers to a horizontal array of such units.

  An RNN cell, in the most abstract setting, is anything that has
  a state and performs some operation that takes a matrix of inputs.
  This operation results in an output matrix with `self.output_size` columns.
  If `self.state_size` is an integer, this operation also results in a new
  state matrix with `self.state_size` columns.  If `self.state_size` is a
  (possibly nested tuple of) TensorShape object(s), then it should return a
  matching structure of Tensors having shape `[batch_size].concatenate(s)`
  for each `s` in `self.batch_size`.
  """

  def __init__(self, trainable=True, name=None, dtype=None, **kwargs):
    super(RNNCell, self).__init__(
        trainable=trainable, name=name, dtype=dtype, **kwargs)
    # Attribute that indicates whether the cell is a TF RNN cell, due the slight
    # difference between TF and Keras RNN cell. Notably the state is not wrapped
    # in a list for TF cell where they are single tensor state, whereas keras
    # cell will wrap the state into a list, and call() will have to unwrap them.
    self._is_tf_rnn_cell = True

  def __call__(self, inputs, state, scope=None):
    """Run this RNN cell on inputs, starting from the given state.

    Args:
      inputs: `2-D` tensor with shape `[batch_size, input_size]`.
      state: if `self.state_size` is an integer, this should be a `2-D Tensor`
        with shape `[batch_size, self.state_size]`.  Otherwise, if
        `self.state_size` is a tuple of integers, this should be a tuple with
        shapes `[batch_size, s] for s in self.state_size`.
      scope: VariableScope for the created subgraph; defaults to class name.

    Returns:
      A pair containing:

      - Output: A `2-D` tensor with shape `[batch_size, self.output_size]`.
      - New state: Either a single `2-D` tensor, or a tuple of tensors matching
        the arity and shapes of `state`.
    """
    if scope is not None:
      with vs.variable_scope(
          scope, custom_getter=self._rnn_get_variable) as scope:
        return super(RNNCell, self).__call__(inputs, state, scope=scope)
    else:
      scope_attrname = "rnncell_scope"
      scope = getattr(self, scope_attrname, None)
      if scope is None:
        scope = vs.variable_scope(
            vs.get_variable_scope(), custom_getter=self._rnn_get_variable)
        setattr(self, scope_attrname, scope)
      with scope:
        return super(RNNCell, self).__call__(inputs, state)

  def _rnn_get_variable(self, getter, *args, **kwargs):
    variable = getter(*args, **kwargs)
    if context.executing_eagerly():
      trainable = variable._trainable  # pylint: disable=protected-access
    else:
      trainable = (
          variable in tf_variables.trainable_variables() or
          (isinstance(variable, tf_variables.PartitionedVariable) and
           list(variable)[0] in tf_variables.trainable_variables()))
    if trainable and variable not in self._trainable_weights:
      self._trainable_weights.append(variable)
    elif not trainable and variable not in self._non_trainable_weights:
      self._non_trainable_weights.append(variable)
    return variable

  @property
  def state_size(self):
    """size(s) of state(s) used by this cell.

    It can be represented by an Integer, a TensorShape or a tuple of Integers
    or TensorShapes.
    """
    raise NotImplementedError("Abstract method")

  @property
  def output_size(self):
    """Integer or TensorShape: size of outputs produced by this cell."""
    raise NotImplementedError("Abstract method")

  def build(self, _):
    # This tells the parent Layer object that it's OK to call
    # self.add_variable() inside the call() method.
    pass

  def get_initial_state(self, inputs=None, batch_size=None, dtype=None):
    if inputs is not None:
      # Validate the given batch_size and dtype against inputs if provided.
      inputs = ops.convert_to_tensor(inputs, name="inputs")
      if batch_size is not None:
        if tensor_util.is_tensor(batch_size):
          static_batch_size = tensor_util.constant_value(
              batch_size, partial=True)
        else:
          static_batch_size = batch_size
        if inputs.shape.dims[0].value != static_batch_size:
          raise ValueError(
              "batch size from input tensor is different from the "
              "input param. Input tensor batch: {}, batch_size: {}".format(
                  inputs.shape.dims[0].value, batch_size))

      if dtype is not None and inputs.dtype != dtype:
        raise ValueError(
            "dtype from input tensor is different from the "
            "input param. Input tensor dtype: {}, dtype: {}".format(
                inputs.dtype, dtype))

      batch_size = inputs.shape.dims[0].value or array_ops.shape(inputs)[0]
      dtype = inputs.dtype
    if None in [batch_size, dtype]:
      raise ValueError(
          "batch_size and dtype cannot be None while constructing initial "
          "state: batch_size={}, dtype={}".format(batch_size, dtype))
    return self.zero_state(batch_size, dtype)

  def zero_state(self, batch_size, dtype):
    """Return zero-filled state tensor(s).

    Args:
      batch_size: int, float, or unit Tensor representing the batch size.
      dtype: the data type to use for the state.

    Returns:
      If `state_size` is an int or TensorShape, then the return value is a
      `N-D` tensor of shape `[batch_size, state_size]` filled with zeros.

      If `state_size` is a nested list or tuple, then the return value is
      a nested list or tuple (of the same structure) of `2-D` tensors with
      the shapes `[batch_size, s]` for each s in `state_size`.
    """
    # Try to use the last cached zero_state. This is done to avoid recreating
    # zeros, especially when eager execution is enabled.
    state_size = self.state_size
    is_eager = context.executing_eagerly()
    if is_eager and _hasattr(self, "_last_zero_state"):
      (last_state_size, last_batch_size, last_dtype,
       last_output) = getattr(self, "_last_zero_state")
      if (last_batch_size == batch_size and last_dtype == dtype and
          last_state_size == state_size):
        return last_output
    with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
      output = _zero_state_tensors(state_size, batch_size, dtype)
    if is_eager:
      self._last_zero_state = (state_size, batch_size, dtype, output)
    return output


class LayerRNNCell(RNNCell):
  """Subclass of RNNCells that act like proper `tf.Layer` objects.

  For backwards compatibility purposes, most `RNNCell` instances allow their
  `call` methods to instantiate variables via `tf.compat.v1.get_variable`.  The
  underlying
  variable scope thus keeps track of any variables, and returning cached
  versions.  This is atypical of `tf.layer` objects, which separate this
  part of layer building into a `build` method that is only called once.

  Here we provide a subclass for `RNNCell` objects that act exactly as
  `Layer` objects do.  They must provide a `build` method and their
  `call` methods do not access Variables `tf.compat.v1.get_variable`.
  """

  def __call__(self, inputs, state, scope=None, *args, **kwargs):
    """Run this RNN cell on inputs, starting from the given state.

    Args:
      inputs: `2-D` tensor with shape `[batch_size, input_size]`.
      state: if `self.state_size` is an integer, this should be a `2-D Tensor`
        with shape `[batch_size, self.state_size]`.  Otherwise, if
        `self.state_size` is a tuple of integers, this should be a tuple with
        shapes `[batch_size, s] for s in self.state_size`.
      scope: optional cell scope.
      *args: Additional positional arguments.
      **kwargs: Additional keyword arguments.

    Returns:
      A pair containing:

      - Output: A `2-D` tensor with shape `[batch_size, self.output_size]`.
      - New state: Either a single `2-D` tensor, or a tuple of tensors matching
        the arity and shapes of `state`.
    """
    # Bypass RNNCell's variable capturing semantics for LayerRNNCell.
    # Instead, it is up to subclasses to provide a proper build
    # method.  See the class docstring for more details.
    return base_layer.Layer.__call__(
        self, inputs, state, scope=scope, *args, **kwargs)


@tf_export(v1=["nn.rnn_cell.BasicRNNCell"])
class BasicRNNCell(LayerRNNCell):
  """The most basic RNN cell.

  Note that this cell is not optimized for performance. Please use
  `tf.contrib.cudnn_rnn.CudnnRNNTanh` for better performance on GPU.

  Args:
    num_units: int, The number of units in the RNN cell.
    activation: Nonlinearity to use.  Default: `tanh`. It could also be string
      that is within Keras activation function names.
    reuse: (optional) Python boolean describing whether to reuse variables in an
      existing scope.  If not `True`, and the existing scope already has the
      given variables, an error is raised.
    name: String, the name of the layer. Layers with the same name will share
      weights, but to avoid mistakes we require reuse=True in such cases.
    dtype: Default dtype of the layer (default of `None` means use the type of
      the first input). Required when `build` is called before `call`.
    **kwargs: Dict, keyword named properties for common layer attributes, like
      `trainable` etc when constructing the cell from configs of get_config().
  """

  @deprecated(None, "This class is equivalent as tf.keras.layers.SimpleRNNCell,"
              " and will be replaced by that in Tensorflow 2.0.")
  def __init__(self,
               num_units,
               activation=None,
               reuse=None,
               name=None,
               dtype=None,
               **kwargs):
    super(BasicRNNCell, self).__init__(
        _reuse=reuse, name=name, dtype=dtype, **kwargs)
    _check_supported_dtypes(self.dtype)
    if context.executing_eagerly() and context.num_gpus() > 0:
      logging.warn(
          "%s: Note that this cell is not optimized for performance. "
          "Please use tf.contrib.cudnn_rnn.CudnnRNNTanh for better "
          "performance on GPU.", self)

    # Inputs must be 2-dimensional.
    self.input_spec = input_spec.InputSpec(ndim=2)

    self._num_units = num_units
    if activation:
      self._activation = activations.get(activation)
    else:
      self._activation = math_ops.tanh

  @property
  def state_size(self):
    return self._num_units

  @property
  def output_size(self):
    return self._num_units

  @tf_utils.shape_type_conversion
  def build(self, inputs_shape):
    if inputs_shape[-1] is None:
      raise ValueError("Expected inputs.shape[-1] to be known, saw shape: %s" %
                       str(inputs_shape))
    _check_supported_dtypes(self.dtype)

    input_depth = inputs_shape[-1]
    self._kernel = self.add_variable(
        _WEIGHTS_VARIABLE_NAME,
        shape=[input_depth + self._num_units, self._num_units])
    self._bias = self.add_variable(
        _BIAS_VARIABLE_NAME,
        shape=[self._num_units],
        initializer=init_ops.zeros_initializer(dtype=self.dtype))

    self.built = True

  def call(self, inputs, state):
    """Most basic RNN: output = new_state = act(W * input + U * state + B)."""
    _check_rnn_cell_input_dtypes([inputs, state])
    gate_inputs = math_ops.matmul(
        array_ops.concat([inputs, state], 1), self._kernel)
    gate_inputs = nn_ops.bias_add(gate_inputs, self._bias)
    output = self._activation(gate_inputs)
    return output, output

  def get_config(self):
    config = {
        "num_units": self._num_units,
        "activation": activations.serialize(self._activation),
        "reuse": self._reuse,
    }
    base_config = super(BasicRNNCell, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))


@tf_export(v1=["nn.rnn_cell.GRUCell"])
class GRUCell(LayerRNNCell):
  """Gated Recurrent Unit cell (cf.

  http://arxiv.org/abs/1406.1078).

  Note that this cell is not optimized for performance. Please use
  `tf.contrib.cudnn_rnn.CudnnGRU` for better performance on GPU, or
  `tf.contrib.rnn.GRUBlockCellV2` for better performance on CPU.

  Args:
    num_units: int, The number of units in the GRU cell.
    activation: Nonlinearity to use.  Default: `tanh`.
    reuse: (optional) Python boolean describing whether to reuse variables in an
      existing scope.  If not `True`, and the existing scope already has the
      given variables, an error is raised.
    kernel_initializer: (optional) The initializer to use for the weight and
      projection matrices.
    bias_initializer: (optional) The initializer to use for the bias.
    name: String, the name of the layer. Layers with the same name will share
      weights, but to avoid mistakes we require reuse=True in such cases.
    dtype: Default dtype of the layer (default of `None` means use the type of
      the first input). Required when `build` is called before `call`.
    **kwargs: Dict, keyword named properties for common layer attributes, like
      `trainable` etc when constructing the cell from configs of get_config().
  """

  @deprecated(None, "This class is equivalent as tf.keras.layers.GRUCell,"
              " and will be replaced by that in Tensorflow 2.0.")
  def __init__(self,
               num_units,
               activation=None,
               reuse=None,
               kernel_initializer=None,
               bias_initializer=None,
               name=None,
               dtype=None,
               **kwargs):
    super(GRUCell, self).__init__(
        _reuse=reuse, name=name, dtype=dtype, **kwargs)
    _check_supported_dtypes(self.dtype)

    if context.executing_eagerly() and context.num_gpus() > 0:
      logging.warn(
          "%s: Note that this cell is not optimized for performance. "
          "Please use tf.contrib.cudnn_rnn.CudnnGRU for better "
          "performance on GPU.", self)
    # Inputs must be 2-dimensional.
    self.input_spec = input_spec.InputSpec(ndim=2)

    self._num_units = num_units
    if activation:
      self._activation = activations.get(activation)
    else:
      self._activation = math_ops.tanh
    self._kernel_initializer = initializers.get(kernel_initializer)
    self._bias_initializer = initializers.get(bias_initializer)

  @property
  def state_size(self):
    return self._num_units

  @property
  def output_size(self):
    return self._num_units

  @tf_utils.shape_type_conversion
  def build(self, inputs_shape):
    if inputs_shape[-1] is None:
      raise ValueError("Expected inputs.shape[-1] to be known, saw shape: %s" %
                       str(inputs_shape))
    _check_supported_dtypes(self.dtype)
    input_depth = inputs_shape[-1]
    self._gate_kernel = self.add_variable(
        "gates/%s" % _WEIGHTS_VARIABLE_NAME,
        shape=[input_depth + self._num_units, 2 * self._num_units],
        initializer=self._kernel_initializer)
    self._gate_bias = self.add_variable(
        "gates/%s" % _BIAS_VARIABLE_NAME,
        shape=[2 * self._num_units],
        initializer=(self._bias_initializer
                     if self._bias_initializer is not None else
                     init_ops.constant_initializer(1.0, dtype=self.dtype)))
    self._candidate_kernel = self.add_variable(
        "candidate/%s" % _WEIGHTS_VARIABLE_NAME,
        shape=[input_depth + self._num_units, self._num_units],
        initializer=self._kernel_initializer)
    self._candidate_bias = self.add_variable(
        "candidate/%s" % _BIAS_VARIABLE_NAME,
        shape=[self._num_units],
        initializer=(self._bias_initializer
                     if self._bias_initializer is not None else
                     init_ops.zeros_initializer(dtype=self.dtype)))

    self.built = True

  def call(self, inputs, state):
    """Gated recurrent unit (GRU) with nunits cells."""
    _check_rnn_cell_input_dtypes([inputs, state])

    gate_inputs = math_ops.matmul(
        array_ops.concat([inputs, state], 1), self._gate_kernel)
    gate_inputs = nn_ops.bias_add(gate_inputs, self._gate_bias)

    value = math_ops.sigmoid(gate_inputs)
    r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1)

    r_state = r * state

    candidate = math_ops.matmul(
        array_ops.concat([inputs, r_state], 1), self._candidate_kernel)
    candidate = nn_ops.bias_add(candidate, self._candidate_bias)

    c = self._activation(candidate)
    new_h = u * state + (1 - u) * c
    return new_h, new_h

  def get_config(self):
    config = {
        "num_units": self._num_units,
        "kernel_initializer": initializers.serialize(self._kernel_initializer),
        "bias_initializer": initializers.serialize(self._bias_initializer),
        "activation": activations.serialize(self._activation),
        "reuse": self._reuse,
    }
    base_config = super(GRUCell, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))


_LSTMStateTuple = collections.namedtuple("LSTMStateTuple", ("c", "h"))


@tf_export(v1=["nn.rnn_cell.LSTMStateTuple"])
class LSTMStateTuple(_LSTMStateTuple):
  """Tuple used by LSTM Cells for `state_size`, `zero_state`, and output state.

  Stores two elements: `(c, h)`, in that order. Where `c` is the hidden state
  and `h` is the output.

  Only used when `state_is_tuple=True`.
  """
  __slots__ = ()

  @property
  def dtype(self):
    (c, h) = self
    if c.dtype != h.dtype:
      raise TypeError("Inconsistent internal state: %s vs %s" %
                      (str(c.dtype), str(h.dtype)))
    return c.dtype


@tf_export(v1=["nn.rnn_cell.BasicLSTMCell"])
class BasicLSTMCell(LayerRNNCell):
  """DEPRECATED: Please use `tf.compat.v1.nn.rnn_cell.LSTMCell` instead.

  Basic LSTM recurrent network cell.

  The implementation is based on: http://arxiv.org/abs/1409.2329.

  We add forget_bias (default: 1) to the biases of the forget gate in order to
  reduce the scale of forgetting in the beginning of the training.

  It does not allow cell clipping, a projection layer, and does not
  use peep-hole connections: it is the basic baseline.

  For advanced models, please use the full `tf.compat.v1.nn.rnn_cell.LSTMCell`
  that follows.

  Note that this cell is not optimized for performance. Please use
  `tf.contrib.cudnn_rnn.CudnnLSTM` for better performance on GPU, or
  `tf.contrib.rnn.LSTMBlockCell` and `tf.contrib.rnn.LSTMBlockFusedCell` for
  better performance on CPU.
  """

  @deprecated(None, "This class is equivalent as tf.keras.layers.LSTMCell,"
              " and will be replaced by that in Tensorflow 2.0.")
  def __init__(self,
               num_units,
               forget_bias=1.0,
               state_is_tuple=True,
               activation=None,
               reuse=None,
               name=None,
               dtype=None,
               **kwargs):
    """Initialize the basic LSTM cell.

    Args:
      num_units: int, The number of units in the LSTM cell.
      forget_bias: float, The bias added to forget gates (see above). Must set
        to `0.0` manually when restoring from CudnnLSTM-trained checkpoints.
      state_is_tuple: If True, accepted and returned states are 2-tuples of the
        `c_state` and `m_state`.  If False, they are concatenated along the
        column axis.  The latter behavior will soon be deprecated.
      activation: Activation function of the inner states.  Default: `tanh`. It
        could also be string that is within Keras activation function names.
      reuse: (optional) Python boolean describing whether to reuse variables in
        an existing scope.  If not `True`, and the existing scope already has
        the given variables, an error is raised.
      name: String, the name of the layer. Layers with the same name will share
        weights, but to avoid mistakes we require reuse=True in such cases.
      dtype: Default dtype of the layer (default of `None` means use the type of
        the first input). Required when `build` is called before `call`.
      **kwargs: Dict, keyword named properties for common layer attributes, like
        `trainable` etc when constructing the cell from configs of get_config().
        When restoring from CudnnLSTM-trained checkpoints, must use
        `CudnnCompatibleLSTMCell` instead.
    """
    super(BasicLSTMCell, self).__init__(
        _reuse=reuse, name=name, dtype=dtype, **kwargs)
    _check_supported_dtypes(self.dtype)
    if not state_is_tuple:
      logging.warn(
          "%s: Using a concatenated state is slower and will soon be "
          "deprecated.  Use state_is_tuple=True.", self)
    if context.executing_eagerly() and context.num_gpus() > 0:
      logging.warn(
          "%s: Note that this cell is not optimized for performance. "
          "Please use tf.contrib.cudnn_rnn.CudnnLSTM for better "
          "performance on GPU.", self)

    # Inputs must be 2-dimensional.
    self.input_spec = input_spec.InputSpec(ndim=2)

    self._num_units = num_units
    self._forget_bias = forget_bias
    self._state_is_tuple = state_is_tuple
    if activation:
      self._activation = activations.get(activation)
    else:
      self._activation = math_ops.tanh

  @property
  def state_size(self):
    return (LSTMStateTuple(self._num_units, self._num_units)
            if self._state_is_tuple else 2 * self._num_units)

  @property
  def output_size(self):
    return self._num_units

  @tf_utils.shape_type_conversion
  def build(self, inputs_shape):
    if inputs_shape[-1] is None:
      raise ValueError("Expected inputs.shape[-1] to be known, saw shape: %s" %
                       str(inputs_shape))
    _check_supported_dtypes(self.dtype)
    input_depth = inputs_shape[-1]
    h_depth = self._num_units
    self._kernel = self.add_variable(
        _WEIGHTS_VARIABLE_NAME,
        shape=[input_depth + h_depth, 4 * self._num_units])
    self._bias = self.add_variable(
        _BIAS_VARIABLE_NAME,
        shape=[4 * self._num_units],
        initializer=init_ops.zeros_initializer(dtype=self.dtype))

    self.built = True

  def call(self, inputs, state):
    """Long short-term memory cell (LSTM).

    Args:
      inputs: `2-D` tensor with shape `[batch_size, input_size]`.
      state: An `LSTMStateTuple` of state tensors, each shaped `[batch_size,
        num_units]`, if `state_is_tuple` has been set to `True`.  Otherwise, a
        `Tensor` shaped `[batch_size, 2 * num_units]`.

    Returns:
      A pair containing the new hidden state, and the new state (either a
        `LSTMStateTuple` or a concatenated state, depending on
        `state_is_tuple`).
    """
    _check_rnn_cell_input_dtypes([inputs, state])

    sigmoid = math_ops.sigmoid
    one = constant_op.constant(1, dtype=dtypes.int32)
    # Parameters of gates are concatenated into one multiply for efficiency.
    if self._state_is_tuple:
      c, h = state
    else:
      c, h = array_ops.split(value=state, num_or_size_splits=2, axis=one)

    gate_inputs = math_ops.matmul(
        array_ops.concat([inputs, h], 1), self._kernel)
    gate_inputs = nn_ops.bias_add(gate_inputs, self._bias)

    # i = input_gate, j = new_input, f = forget_gate, o = output_gate
    i, j, f, o = array_ops.split(
        value=gate_inputs, num_or_size_splits=4, axis=one)

    forget_bias_tensor = constant_op.constant(self._forget_bias, dtype=f.dtype)
    # Note that using `add` and `multiply` instead of `+` and `*` gives a
    # performance improvement. So using those at the cost of readability.
    add = math_ops.add
    multiply = math_ops.multiply
    new_c = add(
        multiply(c, sigmoid(add(f, forget_bias_tensor))),
        multiply(sigmoid(i), self._activation(j)))
    new_h = multiply(self._activation(new_c), sigmoid(o))

    if self._state_is_tuple:
      new_state = LSTMStateTuple(new_c, new_h)
    else:
      new_state = array_ops.concat([new_c, new_h], 1)
    return new_h, new_state

  def get_config(self):
    config = {
        "num_units": self._num_units,
        "forget_bias": self._forget_bias,
        "state_is_tuple": self._state_is_tuple,
        "activation": activations.serialize(self._activation),
        "reuse": self._reuse,
    }
    base_config = super(BasicLSTMCell, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))


@tf_export(v1=["nn.rnn_cell.LSTMCell"])
class LSTMCell(LayerRNNCell):
  """Long short-term memory unit (LSTM) recurrent network cell.

  The default non-peephole implementation is based on:

    https://pdfs.semanticscholar.org/1154/0131eae85b2e11d53df7f1360eeb6476e7f4.pdf

  Felix Gers, Jurgen Schmidhuber, and Fred Cummins.
  "Learning to forget: Continual prediction with LSTM." IET, 850-855, 1999.

  The peephole implementation is based on:

    https://research.google.com/pubs/archive/43905.pdf

  Hasim Sak, Andrew Senior, and Francoise Beaufays.
  "Long short-term memory recurrent neural network architectures for
   large scale acoustic modeling." INTERSPEECH, 2014.

  The class uses optional peep-hole connections, optional cell clipping, and
  an optional projection layer.

  Note that this cell is not optimized for performance. Please use
  `tf.contrib.cudnn_rnn.CudnnLSTM` for better performance on GPU, or
  `tf.contrib.rnn.LSTMBlockCell` and `tf.contrib.rnn.LSTMBlockFusedCell` for
  better performance on CPU.
  """

  @deprecated(None, "This class is equivalent as tf.keras.layers.LSTMCell,"
              " and will be replaced by that in Tensorflow 2.0.")
  def __init__(self,
               num_units,
               use_peepholes=False,
               cell_clip=None,
               initializer=None,
               num_proj=None,
               proj_clip=None,
               num_unit_shards=None,
               num_proj_shards=None,
               forget_bias=1.0,
               state_is_tuple=True,
               activation=None,
               reuse=None,
               name=None,
               dtype=None,
               **kwargs):
    """Initialize the parameters for an LSTM cell.

    Args:
      num_units: int, The number of units in the LSTM cell.
      use_peepholes: bool, set True to enable diagonal/peephole connections.
      cell_clip: (optional) A float value, if provided the cell state is clipped
        by this value prior to the cell output activation.
      initializer: (optional) The initializer to use for the weight and
        projection matrices.
      num_proj: (optional) int, The output dimensionality for the projection
        matrices.  If None, no projection is performed.
      proj_clip: (optional) A float value.  If `num_proj > 0` and `proj_clip` is
        provided, then the projected values are clipped elementwise to within
        `[-proj_clip, proj_clip]`.
      num_unit_shards: Deprecated, will be removed by Jan. 2017. Use a
        variable_scope partitioner instead.
      num_proj_shards: Deprecated, will be removed by Jan. 2017. Use a
        variable_scope partitioner instead.
      forget_bias: Biases of the forget gate are initialized by default to 1 in
        order to reduce the scale of forgetting at the beginning of the
        training. Must set it manually to `0.0` when restoring from CudnnLSTM
        trained checkpoints.
      state_is_tuple: If True, accepted and returned states are 2-tuples of the
        `c_state` and `m_state`.  If False, they are concatenated along the
        column axis.  This latter behavior will soon be deprecated.
      activation: Activation function of the inner states.  Default: `tanh`. It
        could also be string that is within Keras activation function names.
      reuse: (optional) Python boolean describing whether to reuse variables in
        an existing scope.  If not `True`, and the existing scope already has
        the given variables, an error is raised.
      name: String, the name of the layer. Layers with the same name will share
        weights, but to avoid mistakes we require reuse=True in such cases.
      dtype: Default dtype of the layer (default of `None` means use the type of
        the first input). Required when `build` is called before `call`.
      **kwargs: Dict, keyword named properties for common layer attributes, like
        `trainable` etc when constructing the cell from configs of get_config().
        When restoring from CudnnLSTM-trained checkpoints, use
        `CudnnCompatibleLSTMCell` instead.
    """
    super(LSTMCell, self).__init__(
        _reuse=reuse, name=name, dtype=dtype, **kwargs)
    _check_supported_dtypes(self.dtype)
    if not state_is_tuple:
      logging.warn(
          "%s: Using a concatenated state is slower and will soon be "
          "deprecated.  Use state_is_tuple=True.", self)
    if num_unit_shards is not None or num_proj_shards is not None:
      logging.warn(
          "%s: The num_unit_shards and proj_unit_shards parameters are "
          "deprecated and will be removed in Jan 2017.  "
          "Use a variable scope with a partitioner instead.", self)
    if context.executing_eagerly() and context.num_gpus() > 0:
      logging.warn(
          "%s: Note that this cell is not optimized for performance. "
          "Please use tf.contrib.cudnn_rnn.CudnnLSTM for better "
          "performance on GPU.", self)

    # Inputs must be 2-dimensional.
    self.input_spec = input_spec.InputSpec(ndim=2)

    self._num_units = num_units
    self._use_peepholes = use_peepholes
    self._cell_clip = cell_clip
    self._initializer = initializers.get(initializer)
    self._num_proj = num_proj
    self._proj_clip = proj_clip
    self._num_unit_shards = num_unit_shards
    self._num_proj_shards = num_proj_shards
    self._forget_bias = forget_bias
    self._state_is_tuple = state_is_tuple
    if activation:
      self._activation = activations.get(activation)
    else:
      self._activation = math_ops.tanh

    if num_proj:
      self._state_size = (
          LSTMStateTuple(num_units, num_proj) if state_is_tuple else num_units +
          num_proj)
      self._output_size = num_proj
    else:
      self._state_size = (
          LSTMStateTuple(num_units, num_units) if state_is_tuple else 2 *
          num_units)
      self._output_size = num_units

  @property
  def state_size(self):
    return self._state_size

  @property
  def output_size(self):
    return self._output_size

  @tf_utils.shape_type_conversion
  def build(self, inputs_shape):
    if inputs_shape[-1] is None:
      raise ValueError("Expected inputs.shape[-1] to be known, saw shape: %s" %
                       str(inputs_shape))
    _check_supported_dtypes(self.dtype)
    input_depth = inputs_shape[-1]
    h_depth = self._num_units if self._num_proj is None else self._num_proj
    maybe_partitioner = (
        partitioned_variables.fixed_size_partitioner(self._num_unit_shards)
        if self._num_unit_shards is not None else None)
    self._kernel = self.add_variable(
        _WEIGHTS_VARIABLE_NAME,
        shape=[input_depth + h_depth, 4 * self._num_units],
        initializer=self._initializer,
        partitioner=maybe_partitioner)
    if self.dtype is None:
      initializer = init_ops.zeros_initializer
    else:
      initializer = init_ops.zeros_initializer(dtype=self.dtype)
    self._bias = self.add_variable(
        _BIAS_VARIABLE_NAME,
        shape=[4 * self._num_units],
        initializer=initializer)
    if self._use_peepholes:
      self._w_f_diag = self.add_variable(
          "w_f_diag", shape=[self._num_units], initializer=self._initializer)
      self._w_i_diag = self.add_variable(
          "w_i_diag", shape=[self._num_units], initializer=self._initializer)
      self._w_o_diag = self.add_variable(
          "w_o_diag", shape=[self._num_units], initializer=self._initializer)

    if self._num_proj is not None:
      maybe_proj_partitioner = (
          partitioned_variables.fixed_size_partitioner(self._num_proj_shards)
          if self._num_proj_shards is not None else None)
      self._proj_kernel = self.add_variable(
          "projection/%s" % _WEIGHTS_VARIABLE_NAME,
          shape=[self._num_units, self._num_proj],
          initializer=self._initializer,
          partitioner=maybe_proj_partitioner)

    self.built = True

  def call(self, inputs, state):
    """Run one step of LSTM.

    Args:
      inputs: input Tensor, must be 2-D, `[batch, input_size]`.
      state: if `state_is_tuple` is False, this must be a state Tensor, `2-D,
        [batch, state_size]`.  If `state_is_tuple` is True, this must be a tuple
        of state Tensors, both `2-D`, with column sizes `c_state` and `m_state`.

    Returns:
      A tuple containing:

      - A `2-D, [batch, output_dim]`, Tensor representing the output of the
        LSTM after reading `inputs` when previous state was `state`.
        Here output_dim is:
           num_proj if num_proj was set,
           num_units otherwise.
      - Tensor(s) representing the new state of LSTM after reading `inputs` when
        the previous state was `state`.  Same type and shape(s) as `state`.

    Raises:
      ValueError: If input size cannot be inferred from inputs via
        static shape inference.
    """
    _check_rnn_cell_input_dtypes([inputs, state])

    num_proj = self._num_units if self._num_proj is None else self._num_proj
    sigmoid = math_ops.sigmoid

    if self._state_is_tuple:
      (c_prev, m_prev) = state
    else:
      c_prev = array_ops.slice(state, [0, 0], [-1, self._num_units])
      m_prev = array_ops.slice(state, [0, self._num_units], [-1, num_proj])

    input_size = inputs.get_shape().with_rank(2).dims[1].value
    if input_size is None:
      raise ValueError("Could not infer input size from inputs.get_shape()[-1]")

    # i = input_gate, j = new_input, f = forget_gate, o = output_gate
    lstm_matrix = math_ops.matmul(
        array_ops.concat([inputs, m_prev], 1), self._kernel)
    lstm_matrix = nn_ops.bias_add(lstm_matrix, self._bias)

    i, j, f, o = array_ops.split(
        value=lstm_matrix, num_or_size_splits=4, axis=1)
    # Diagonal connections
    if self._use_peepholes:
      c = (
          sigmoid(f + self._forget_bias + self._w_f_diag * c_prev) * c_prev +
          sigmoid(i + self._w_i_diag * c_prev) * self._activation(j))
    else:
      c = (
          sigmoid(f + self._forget_bias) * c_prev +
          sigmoid(i) * self._activation(j))

    if self._cell_clip is not None:
      # pylint: disable=invalid-unary-operand-type
      c = clip_ops.clip_by_value(c, -self._cell_clip, self._cell_clip)
      # pylint: enable=invalid-unary-operand-type
    if self._use_peepholes:
      m = sigmoid(o + self._w_o_diag * c) * self._activation(c)
    else:
      m = sigmoid(o) * self._activation(c)

    if self._num_proj is not None:
      m = math_ops.matmul(m, self._proj_kernel)

      if self._proj_clip is not None:
        # pylint: disable=invalid-unary-operand-type
        m = clip_ops.clip_by_value(m, -self._proj_clip, self._proj_clip)
        # pylint: enable=invalid-unary-operand-type

    new_state = (
        LSTMStateTuple(c, m)
        if self._state_is_tuple else array_ops.concat([c, m], 1))
    return m, new_state

  def get_config(self):
    config = {
        "num_units": self._num_units,
        "use_peepholes": self._use_peepholes,
        "cell_clip": self._cell_clip,
        "initializer": initializers.serialize(self._initializer),
        "num_proj": self._num_proj,
        "proj_clip": self._proj_clip,
        "num_unit_shards": self._num_unit_shards,
        "num_proj_shards": self._num_proj_shards,
        "forget_bias": self._forget_bias,
        "state_is_tuple": self._state_is_tuple,
        "activation": activations.serialize(self._activation),
        "reuse": self._reuse,
    }
    base_config = super(LSTMCell, self).get_config()
    return dict(list(base_config.items()) + list(config.items()))


def _enumerated_map_structure_up_to(shallow_structure, map_fn, *args, **kwargs):
  ix = [0]

  def enumerated_fn(*inner_args, **inner_kwargs):
    r = map_fn(ix[0], *inner_args, **inner_kwargs)
    ix[0] += 1
    return r

  return nest.map_structure_up_to(shallow_structure, enumerated_fn, *args,
                                  **kwargs)


def _default_dropout_state_filter_visitor(substate):
  if isinstance(substate, LSTMStateTuple):
    # Do not perform dropout on the memory state.
    return LSTMStateTuple(c=False, h=True)
  elif isinstance(substate, tensor_array_ops.TensorArray):
    return False
  return True


class _RNNCellWrapperV1(RNNCell):
  """Base class for cells wrappers V1 compatibility.

  This class along with `_RNNCellWrapperV2` allows to define cells wrappers that
  are compatible with V1 and V2, and defines helper methods for this purpose.
  """

  def __init__(self, cell):
    super(_RNNCellWrapperV1, self).__init__()
    self.cell = cell
    if isinstance(cell, trackable.Trackable):
      self._track_trackable(self.cell, name="cell")

  def _call_wrapped_cell(self, inputs, state, cell_call_fn, **kwargs):
    """Calls the wrapped cell and performs the wrapping logic.

    This method is called from the wrapper's `call` or `__call__` methods.

    Args:
      inputs: A tensor with wrapped cell's input.
      state: A tensor or tuple of tensors with wrapped cell's state.
      cell_call_fn: Wrapped cell's method to use for step computation (cell's
        `__call__` or 'call' method).
      **kwargs: Additional arguments.

    Returns:
      A pair containing:
      - Output: A tensor with cell's output.
      - New state: A tensor or tuple of tensors with new wrapped cell's state.
    """
    raise NotImplementedError

  def __call__(self, inputs, state, scope=None):
    """Runs the RNN cell step computation.

    We assume that the wrapped RNNCell is being built within its `__call__`
    method. We directly use the wrapped cell's `__call__` in the overridden
    wrapper `__call__` method.

    This allows to use the wrapped cell and the non-wrapped cell equivalently
    when using `__call__`.

    Args:
      inputs: A tensor with wrapped cell's input.
      state: A tensor or tuple of tensors with wrapped cell's state.
      scope: VariableScope for the subgraph created in the wrapped cells'
        `__call__`.

    Returns:
      A pair containing:

      - Output: A tensor with cell's output.
      - New state: A tensor or tuple of tensors with new wrapped cell's state.
    """
    return self._call_wrapped_cell(
        inputs, state, cell_call_fn=self.cell.__call__, scope=scope)


class _RNNCellWrapperV2(keras_layer.AbstractRNNCell):
  """Base class for cells wrappers V2 compatibility.

  This class along with `_RNNCellWrapperV1` allows to define cells wrappers that
  are compatible with V1 and V2, and defines helper methods for this purpose.
  """

  def __init__(self, cell, *args, **kwargs):
    super(_RNNCellWrapperV2, self).__init__(*args, **kwargs)
    self.cell = cell

  def call(self, inputs, state, **kwargs):
    """Runs the RNN cell step computation.

    When `call` is being used, we assume that the wrapper object has been built,
    and therefore the wrapped cells has been built via its `build` method and
    its `call` method can be used directly.

    This allows to use the wrapped cell and the non-wrapped cell equivalently
    when using `call` and `build`.

    Args:
      inputs: A tensor with wrapped cell's input.
      state: A tensor or tuple of tensors with wrapped cell's state.
      **kwargs: Additional arguments passed to the wrapped cell's `call`.

    Returns:
      A pair containing:

      - Output: A tensor with cell's output.
      - New state: A tensor or tuple of tensors with new wrapped cell's state.
    """
    return self._call_wrapped_cell(
        inputs, state, cell_call_fn=self.cell.call, **kwargs)

  def build(self, inputs_shape):
    """Builds the wrapped cell."""
    self.cell.build(inputs_shape)
    self.built = True


class DropoutWrapperBase(object):
  """Operator adding dropout to inputs and outputs of the given cell."""

  def __init__(self,
               cell,
               input_keep_prob=1.0,
               output_keep_prob=1.0,
               state_keep_prob=1.0,
               variational_recurrent=False,
               input_size=None,
               dtype=None,
               seed=None,
               dropout_state_filter_visitor=None):
    """Create a cell with added input, state, and/or output dropout.

    If `variational_recurrent` is set to `True` (**NOT** the default behavior),
    then the same dropout mask is applied at every step, as described in:
    [A Theoretically Grounded Application of Dropout in Recurrent
    Neural Networks. Y. Gal, Z. Ghahramani](https://arxiv.org/abs/1512.05287).

    Otherwise a different dropout mask is applied at every time step.

    Note, by default (unless a custom `dropout_state_filter` is provided),
    the memory state (`c` component of any `LSTMStateTuple`) passing through
    a `DropoutWrapper` is never modified.  This behavior is described in the
    above article.

    Args:
      cell: an RNNCell, a projection to output_size is added to it.
      input_keep_prob: unit Tensor or float between 0 and 1, input keep
        probability; if it is constant and 1, no input dropout will be added.
      output_keep_prob: unit Tensor or float between 0 and 1, output keep
        probability; if it is constant and 1, no output dropout will be added.
      state_keep_prob: unit Tensor or float between 0 and 1, output keep
        probability; if it is constant and 1, no output dropout will be added.
        State dropout is performed on the outgoing states of the cell. **Note**
        the state components to which dropout is applied when `state_keep_prob`
        is in `(0, 1)` are also determined by the argument
        `dropout_state_filter_visitor` (e.g. by default dropout is never applied
        to the `c` component of an `LSTMStateTuple`).
      variational_recurrent: Python bool.  If `True`, then the same dropout
        pattern is applied across all time steps per run call. If this parameter
        is set, `input_size` **must** be provided.
      input_size: (optional) (possibly nested tuple of) `TensorShape` objects
        containing the depth(s) of the input tensors expected to be passed in to
        the `DropoutWrapper`.  Required and used **iff** `variational_recurrent
        = True` and `input_keep_prob < 1`.
      dtype: (optional) The `dtype` of the input, state, and output tensors.
        Required and used **iff** `variational_recurrent = True`.
      seed: (optional) integer, the randomness seed.
      dropout_state_filter_visitor: (optional), default: (see below).  Function
        that takes any hierarchical level of the state and returns a scalar or
        depth=1 structure of Python booleans describing which terms in the state
        should be dropped out.  In addition, if the function returns `True`,
        dropout is applied across this sublevel.  If the function returns
        `False`, dropout is not applied across this entire sublevel.
        Default behavior: perform dropout on all terms except the memory (`c`)
          state of `LSTMCellState` objects, and don't try to apply dropout to
        `TensorArray` objects: ```
        def dropout_state_filter_visitor(s):
          if isinstance(s, LSTMCellState): # Never perform dropout on the c
            state. return LSTMCellState(c=False, h=True)
          elif isinstance(s, TensorArray): return False return True ```

    Raises:
      TypeError: if `cell` is not an `RNNCell`, or `keep_state_fn` is provided
        but not `callable`.
      ValueError: if any of the keep_probs are not between 0 and 1.
    """
    super(DropoutWrapperBase, self).__init__(cell)
    assert_like_rnncell("cell", cell)

    if (dropout_state_filter_visitor is not None and
        not callable(dropout_state_filter_visitor)):
      raise TypeError("dropout_state_filter_visitor must be callable")
    self._dropout_state_filter = (
        dropout_state_filter_visitor or _default_dropout_state_filter_visitor)
    with ops.name_scope("DropoutWrapperInit"):

      def tensor_and_const_value(v):
        tensor_value = ops.convert_to_tensor(v)
        const_value = tensor_util.constant_value(tensor_value)
        return (tensor_value, const_value)

      for prob, attr in [(input_keep_prob, "input_keep_prob"),
                         (state_keep_prob, "state_keep_prob"),
                         (output_keep_prob, "output_keep_prob")]:
        tensor_prob, const_prob = tensor_and_const_value(prob)
        if const_prob is not None:
          if const_prob < 0 or const_prob > 1:
            raise ValueError("Parameter %s must be between 0 and 1: %d" %
                             (attr, const_prob))
          setattr(self, "_%s" % attr, float(const_prob))
        else:
          setattr(self, "_%s" % attr, tensor_prob)

    # Set variational_recurrent, seed before running the code below
    self._variational_recurrent = variational_recurrent
    self._seed = seed

    self._recurrent_input_noise = None
    self._recurrent_state_noise = None
    self._recurrent_output_noise = None

    if variational_recurrent:
      if dtype is None:
        raise ValueError(
            "When variational_recurrent=True, dtype must be provided")

      def convert_to_batch_shape(s):
        # Prepend a 1 for the batch dimension; for recurrent
        # variational dropout we use the same dropout mask for all
        # batch elements.
        return array_ops.concat(([1], tensor_shape.TensorShape(s).as_list()), 0)

      def batch_noise(s, inner_seed):
        shape = convert_to_batch_shape(s)
        return random_ops.random_uniform(shape, seed=inner_seed, dtype=dtype)

      if (not isinstance(self._input_keep_prob, numbers.Real) or
          self._input_keep_prob < 1.0):
        if input_size is None:
          raise ValueError(
              "When variational_recurrent=True and input_keep_prob < 1.0 or "
              "is unknown, input_size must be provided")
        self._recurrent_input_noise = _enumerated_map_structure_up_to(
            input_size,
            lambda i, s: batch_noise(s, inner_seed=self._gen_seed("input", i)),
            input_size)
      self._recurrent_state_noise = _enumerated_map_structure_up_to(
          cell.state_size,
          lambda i, s: batch_noise(s, inner_seed=self._gen_seed("state", i)),
          cell.state_size)
      self._recurrent_output_noise = _enumerated_map_structure_up_to(
          cell.output_size,
          lambda i, s: batch_noise(s, inner_seed=self._gen_seed("output", i)),
          cell.output_size)

  def _gen_seed(self, salt_prefix, index):
    if self._seed is None:
      return None
    salt = "%s_%d" % (salt_prefix, index)
    string = (str(self._seed) + salt).encode("utf-8")
    return int(hashlib.md5(string).hexdigest()[:8], 16) & 0x7FFFFFFF

  @property
  def wrapped_cell(self):
    return self.cell

  @property
  def state_size(self):
    return self.cell.state_size

  @property
  def output_size(self):
    return self.cell.output_size

  def zero_state(self, batch_size, dtype):
    with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
      return self.cell.zero_state(batch_size, dtype)

  def _variational_recurrent_dropout_value(
      self, index, value, noise, keep_prob):
    """Performs dropout given the pre-calculated noise tensor."""
    # uniform [keep_prob, 1.0 + keep_prob)
    random_tensor = keep_prob + noise

    # 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob)
    binary_tensor = math_ops.floor(random_tensor)
    ret = math_ops.div(value, keep_prob) * binary_tensor
    ret.set_shape(value.get_shape())
    return ret

  def _dropout(self,
               values,
               salt_prefix,
               recurrent_noise,
               keep_prob,
               shallow_filtered_substructure=None):
    """Decides whether to perform standard dropout or recurrent dropout."""

    if shallow_filtered_substructure is None:
      # Put something so we traverse the entire structure; inside the
      # dropout function we check to see if leafs of this are bool or not.
      shallow_filtered_substructure = values

    if not self._variational_recurrent:

      def dropout(i, do_dropout, v):
        if not isinstance(do_dropout, bool) or do_dropout:
          return nn_ops.dropout_v2(
              v, rate=1. - keep_prob, seed=self._gen_seed(salt_prefix, i))
        else:
          return v

      return _enumerated_map_structure_up_to(
          shallow_filtered_substructure, dropout,
          *[shallow_filtered_substructure, values])
    else:

      def dropout(i, do_dropout, v, n):
        if not isinstance(do_dropout, bool) or do_dropout:
          return self._variational_recurrent_dropout_value(i, v, n, keep_prob)
        else:
          return v

      return _enumerated_map_structure_up_to(
          shallow_filtered_substructure, dropout,
          *[shallow_filtered_substructure, values, recurrent_noise])

  def _call_wrapped_cell(self, inputs, state, cell_call_fn, **kwargs):
    """Runs the wrapped cell and applies dropout.

    Args:
      inputs: A tensor with wrapped cell's input.
      state: A tensor or tuple of tensors with wrapped cell's state.
      cell_call_fn: Wrapped cell's method to use for step computation (cell's
        `__call__` or 'call' method).
      **kwargs: Additional arguments.

    Returns:
      A pair containing:

      - Output: A tensor with cell's output.
      - New state: A tensor or tuple of tensors with new wrapped cell's state.
    """

    def _should_dropout(p):
      return (not isinstance(p, float)) or p < 1

    if _should_dropout(self._input_keep_prob):
      inputs = self._dropout(inputs, "input", self._recurrent_input_noise,
                             self._input_keep_prob)
    output, new_state = cell_call_fn(inputs, state, **kwargs)
    if _should_dropout(self._state_keep_prob):
      # Identify which subsets of the state to perform dropout on and
      # which ones to keep.
      shallow_filtered_substructure = nest.get_traverse_shallow_structure(
          self._dropout_state_filter, new_state)
      new_state = self._dropout(new_state, "state", self._recurrent_state_noise,
                                self._state_keep_prob,
                                shallow_filtered_substructure)
    if _should_dropout(self._output_keep_prob):
      output = self._dropout(output, "output", self._recurrent_output_noise,
                             self._output_keep_prob)
    return output, new_state


@tf_export(v1=["nn.rnn_cell.DropoutWrapper"])
class DropoutWrapper(DropoutWrapperBase, _RNNCellWrapperV1):
  """Operator adding dropout to inputs and outputs of the given cell."""

  def __init__(self, *args, **kwargs):
    super(DropoutWrapper, self).__init__(*args, **kwargs)

  __init__.__doc__ = DropoutWrapperBase.__init__.__doc__


@tf_export("nn.RNNCellDropoutWrapper", v1=[])
class DropoutWrapperV2(DropoutWrapperBase, _RNNCellWrapperV2):
  """Operator adding dropout to inputs and outputs of the given cell."""

  def __init__(self, *args, **kwargs):
    super(DropoutWrapperV2, self).__init__(*args, **kwargs)

  __init__.__doc__ = DropoutWrapperBase.__init__.__doc__


class ResidualWrapperBase(object):
  """RNNCell wrapper that ensures cell inputs are added to the outputs."""

  def __init__(self, cell, residual_fn=None):
    """Constructs a `ResidualWrapper` for `cell`.

    Args:
      cell: An instance of `RNNCell`.
      residual_fn: (Optional) The function to map raw cell inputs and raw cell
        outputs to the actual cell outputs of the residual network.
        Defaults to calling nest.map_structure on (lambda i, o: i + o), inputs
          and outputs.
    """
    super(ResidualWrapperBase, self).__init__(cell)
    self._residual_fn = residual_fn

  @property
  def state_size(self):
    return self.cell.state_size

  @property
  def output_size(self):
    return self.cell.output_size

  def zero_state(self, batch_size, dtype):
    with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
      return self.cell.zero_state(batch_size, dtype)

  def _call_wrapped_cell(self, inputs, state, cell_call_fn, **kwargs):
    """Run the cell and then apply the residual_fn on its inputs to its outputs.

    Args:
      inputs: cell inputs.
      state: cell state.
      cell_call_fn: Wrapped cell's method to use for step computation (cell's
        `__call__` or 'call' method).
      **kwargs: Additional arguments passed to the wrapped cell's `call`.

    Returns:
      Tuple of cell outputs and new state.

    Raises:
      TypeError: If cell inputs and outputs have different structure (type).
      ValueError: If cell inputs and outputs have different structure (value).
    """
    outputs, new_state = cell_call_fn(inputs, state, **kwargs)

    # Ensure shapes match
    def assert_shape_match(inp, out):
      inp.get_shape().assert_is_compatible_with(out.get_shape())

    def default_residual_fn(inputs, outputs):
      nest.assert_same_structure(inputs, outputs)
      nest.map_structure(assert_shape_match, inputs, outputs)
      return nest.map_structure(lambda inp, out: inp + out, inputs, outputs)

    res_outputs = (self._residual_fn or default_residual_fn)(inputs, outputs)
    return (res_outputs, new_state)


@tf_export(v1=["nn.rnn_cell.ResidualWrapper"])
class ResidualWrapper(ResidualWrapperBase, _RNNCellWrapperV1):
  """RNNCell wrapper that ensures cell inputs are added to the outputs."""

  def __init__(self, *args, **kwargs):
    super(ResidualWrapper, self).__init__(*args, **kwargs)

  __init__.__doc__ = ResidualWrapperBase.__init__.__doc__


@tf_export("nn.RNNCellResidualWrapper", v1=[])
class ResidualWrapperV2(ResidualWrapperBase, _RNNCellWrapperV2):
  """RNNCell wrapper that ensures cell inputs are added to the outputs."""

  def __init__(self, *args, **kwargs):
    super(ResidualWrapperV2, self).__init__(*args, **kwargs)

  __init__.__doc__ = ResidualWrapperBase.__init__.__doc__


class DeviceWrapperBase(object):
  """Operator that ensures an RNNCell runs on a particular device."""

  def __init__(self, cell, device):
    """Construct a `DeviceWrapper` for `cell` with device `device`.

    Ensures the wrapped `cell` is called with `tf.device(device)`.

    Args:
      cell: An instance of `RNNCell`.
      device: A device string or function, for passing to `tf.device`.
    """
    super(DeviceWrapperBase, self).__init__(cell)
    self._device = device

  @property
  def state_size(self):
    return self.cell.state_size

  @property
  def output_size(self):
    return self.cell.output_size

  def zero_state(self, batch_size, dtype):
    with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
      with ops.device(self._device):
        return self.cell.zero_state(batch_size, dtype)

  def _call_wrapped_cell(self, inputs, state, cell_call_fn, **kwargs):
    """Run the cell on specified device."""
    with ops.device(self._device):
      return cell_call_fn(inputs, state, **kwargs)


@tf_export(v1=["nn.rnn_cell.DeviceWrapper"])
class DeviceWrapper(DeviceWrapperBase, _RNNCellWrapperV1):

  def __init__(self, *args, **kwargs):  # pylint: disable=useless-super-delegation
    super(DeviceWrapper, self).__init__(*args, **kwargs)

  __init__.__doc__ = DeviceWrapperBase.__init__.__doc__


@tf_export("nn.RNNCellDeviceWrapper", v1=[])
class DeviceWrapperV2(DeviceWrapperBase, _RNNCellWrapperV2):
  """Operator that ensures an RNNCell runs on a particular device."""

  def __init__(self, *args, **kwargs):  # pylint: disable=useless-super-delegation
    super(DeviceWrapperV2, self).__init__(*args, **kwargs)

  __init__.__doc__ = DeviceWrapperBase.__init__.__doc__


@tf_export(v1=["nn.rnn_cell.MultiRNNCell"])
class MultiRNNCell(RNNCell):
  """RNN cell composed sequentially of multiple simple cells.

  Example:

  ```python
  num_units = [128, 64]
  cells = [BasicLSTMCell(num_units=n) for n in num_units]
  stacked_rnn_cell = MultiRNNCell(cells)
  ```
  """

  @deprecated(None, "This class is equivalent as "
              "tf.keras.layers.StackedRNNCells, and will be replaced by "
              "that in Tensorflow 2.0.")
  def __init__(self, cells, state_is_tuple=True):
    """Create a RNN cell composed sequentially of a number of RNNCells.

    Args:
      cells: list of RNNCells that will be composed in this order.
      state_is_tuple: If True, accepted and returned states are n-tuples, where
        `n = len(cells)`.  If False, the states are all concatenated along the
        column axis.  This latter behavior will soon be deprecated.

    Raises:
      ValueError: if cells is empty (not allowed), or at least one of the cells
        returns a state tuple but the flag `state_is_tuple` is `False`.
    """
    super(MultiRNNCell, self).__init__()
    if not cells:
      raise ValueError("Must specify at least one cell for MultiRNNCell.")
    if not nest.is_sequence(cells):
      raise TypeError("cells must be a list or tuple, but saw: %s." % cells)

    if len(set([id(cell) for cell in cells])) < len(cells):
      logging.log_first_n(
          logging.WARN, "At least two cells provided to MultiRNNCell "
          "are the same object and will share weights.", 1)

    self._cells = cells
    for cell_number, cell in enumerate(self._cells):
      # Add Trackable dependencies on these cells so their variables get
      # saved with this object when using object-based saving.
      if isinstance(cell, trackable.Trackable):
        # TODO(allenl): Track down non-Trackable callers.
        self._track_trackable(cell, name="cell-%d" % (cell_number,))
    self._state_is_tuple = state_is_tuple
    if not state_is_tuple:
      if any(nest.is_sequence(c.state_size) for c in self._cells):
        raise ValueError("Some cells return tuples of states, but the flag "
                         "state_is_tuple is not set.  State sizes are: %s" %
                         str([c.state_size for c in self._cells]))

  @property
  def state_size(self):
    if self._state_is_tuple:
      return tuple(cell.state_size for cell in self._cells)
    else:
      return sum(cell.state_size for cell in self._cells)

  @property
  def output_size(self):
    return self._cells[-1].output_size

  def zero_state(self, batch_size, dtype):
    with ops.name_scope(type(self).__name__ + "ZeroState", values=[batch_size]):
      if self._state_is_tuple:
        return tuple(cell.zero_state(batch_size, dtype) for cell in self._cells)
      else:
        # We know here that state_size of each cell is not a tuple and
        # presumably does not contain TensorArrays or anything else fancy
        return super(MultiRNNCell, self).zero_state(batch_size, dtype)

  @property
  def trainable_weights(self):
    if not self.trainable:
      return []
    weights = []
    for cell in self._cells:
      if isinstance(cell, base_layer.Layer):
        weights += cell.trainable_weights
    return weights

  @property
  def non_trainable_weights(self):
    weights = []
    for cell in self._cells:
      if isinstance(cell, base_layer.Layer):
        weights += cell.non_trainable_weights
    if not self.trainable:
      trainable_weights = []
      for cell in self._cells:
        if isinstance(cell, base_layer.Layer):
          trainable_weights += cell.trainable_weights
      return trainable_weights + weights
    return weights

  def call(self, inputs, state):
    """Run this multi-layer cell on inputs, starting from state."""
    cur_state_pos = 0
    cur_inp = inputs
    new_states = []
    for i, cell in enumerate(self._cells):
      with vs.variable_scope("cell_%d" % i):
        if self._state_is_tuple:
          if not nest.is_sequence(state):
            raise ValueError(
                "Expected state to be a tuple of length %d, but received: %s" %
                (len(self.state_size), state))
          cur_state = state[i]
        else:
          cur_state = array_ops.slice(state, [0, cur_state_pos],
                                      [-1, cell.state_size])
          cur_state_pos += cell.state_size
        cur_inp, new_state = cell(cur_inp, cur_state)
        new_states.append(new_state)

    new_states = (
        tuple(new_states) if self._state_is_tuple else array_ops.concat(
            new_states, 1))

    return cur_inp, new_states


def _check_rnn_cell_input_dtypes(inputs):
  """Check whether the input tensors are with supported dtypes.

  Default RNN cells only support floats and complex as its dtypes since the
  activation function (tanh and sigmoid) only allow those types. This function
  will throw a proper error message if the inputs is not in a supported type.

  Args:
    inputs: tensor or nested structure of tensors that are feed to RNN cell as
      input or state.

  Raises:
    ValueError: if any of the input tensor are not having dtypes of float or
      complex.
  """
  for t in nest.flatten(inputs):
    _check_supported_dtypes(t.dtype)


def _check_supported_dtypes(dtype):
  if dtype is None:
    return
  dtype = dtypes.as_dtype(dtype)
  if not (dtype.is_floating or dtype.is_complex):
    raise ValueError("RNN cell only supports floating point inputs, "
                     "but saw dtype: %s" % dtype)