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.
  # ==============================================================================
  
  """Module for constructing RNN Cells.
  
  ## Base interface for all RNN Cells
  
  @@RNNCell
  
  ## RNN Cells for use with TensorFlow's core RNN methods
  
  @@BasicRNNCell
  @@BasicLSTMCell
  @@GRUCell
  @@LSTMCell
  
  ## Classes storing split `RNNCell` state
  
  @@LSTMStateTuple
  
  ## RNN Cell wrappers (RNNCells that wrap other RNNCells)
  
  @@MultiRNNCell
  @@DropoutWrapper
  @@EmbeddingWrapper
  @@InputProjectionWrapper
  @@OutputProjectionWrapper
  """
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import collections
  import math
  
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import clip_ops
  from tensorflow.python.ops import embedding_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 variable_scope as vs
  
  from tensorflow.python.ops.math_ops import sigmoid
  from tensorflow.python.ops.math_ops import tanh
  
  from tensorflow.python.platform import tf_logging as logging
  from tensorflow.python.util import nest
  
  
  def _state_size_with_prefix(state_size, prefix=None):
    """Helper function that enables int or TensorShape shape specification.
  
    This function takes a size specification, which can be an integer or a
    TensorShape, and converts it into a list of integers. One may specify any
    additional dimensions that precede the final state size specification.
  
    Args:
      state_size: TensorShape or int that specifies the size of a tensor.
      prefix: optional additional list of dimensions to prepend.
  
    Returns:
      result_state_size: list of dimensions the resulting tensor size.
    """
    result_state_size = tensor_shape.as_shape(state_size).as_list()
    if prefix is not None:
      if not isinstance(prefix, list):
        raise TypeError("prefix of _state_size_with_prefix should be a list.")
      result_state_size = prefix + result_state_size
    return result_state_size
  
  
  class RNNCell(object):
    """Abstract object representing an RNN cell.
  
    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
    tuple of integers, then it results in a tuple of `len(state_size)` state
    matrices, each with the a column size corresponding to values in `state_size`.
  
    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 add dropouts, projections, or embeddings for inputs.
    Constructing multi-layer cells is supported by the class `MultiRNNCell`,
    or by calling the `rnn` ops several times. Every `RNNCell` must have the
    properties below and and implement `__call__` with the following signature.
    """
  
    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 x input_size]`.
        state: if `self.state_size` is an integer, this should be a `2-D Tensor`
          with shape `[batch_size x self.state_size]`.  Otherwise, if
          `self.state_size` is a tuple of integers, this should be a tuple
          with shapes `[batch_size x 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 x self.output_size]`.
        - New state: Either a single `2-D` tensor, or a tuple of tensors matching
          the arity and shapes of `state`.
      """
      raise NotImplementedError("Abstract method")
  
    @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 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 x 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 x s]` for each s in `state_size`.
      """
      state_size = self.state_size
      if nest.is_sequence(state_size):
        state_size_flat = nest.flatten(state_size)
        zeros_flat = [
            array_ops.zeros(
                array_ops.pack(_state_size_with_prefix(s, prefix=[batch_size])),
                dtype=dtype)
            for s in state_size_flat]
        for s, z in zip(state_size_flat, zeros_flat):
          z.set_shape(_state_size_with_prefix(s, prefix=[None]))
        zeros = nest.pack_sequence_as(structure=state_size,
                                      flat_sequence=zeros_flat)
      else:
        zeros_size = _state_size_with_prefix(state_size, prefix=[batch_size])
        zeros = array_ops.zeros(array_ops.pack(zeros_size), dtype=dtype)
        zeros.set_shape(_state_size_with_prefix(state_size, prefix=[None]))
  
      return zeros
  
  
  class BasicRNNCell(RNNCell):
    """The most basic RNN cell."""
  
    def __init__(self, num_units, input_size=None, activation=tanh):
      if input_size is not None:
        logging.warn("%s: The input_size parameter is deprecated." % self)
      self._num_units = num_units
      self._activation = activation
  
    @property
    def state_size(self):
      return self._num_units
  
    @property
    def output_size(self):
      return self._num_units
  
    def __call__(self, inputs, state, scope=None):
      """Most basic RNN: output = new_state = activation(W * input + U * state + B)."""
      with vs.variable_scope(scope or type(self).__name__):  # "BasicRNNCell"
        output = self._activation(_linear([inputs, state], self._num_units, True))
      return output, output
  
  
  class GRUCell(RNNCell):
    """Gated Recurrent Unit cell (cf. http://arxiv.org/abs/1406.1078)."""
  
    def __init__(self, num_units, input_size=None, activation=tanh):
      if input_size is not None:
        logging.warn("%s: The input_size parameter is deprecated." % self)
      self._num_units = num_units
      self._activation = activation
  
    @property
    def state_size(self):
      return self._num_units
  
    @property
    def output_size(self):
      return self._num_units
  
    def __call__(self, inputs, state, scope=None):
      """Gated recurrent unit (GRU) with nunits cells."""
      with vs.variable_scope(scope or type(self).__name__):  # "GRUCell"
        with vs.variable_scope("Gates"):  # Reset gate and update gate.
          # We start with bias of 1.0 to not reset and not update.
          r, u = array_ops.split(1, 2, _linear([inputs, state],
                                               2 * self._num_units, True, 1.0))
          r, u = sigmoid(r), sigmoid(u)
        with vs.variable_scope("Candidate"):
          c = self._activation(_linear([inputs, r * state],
                                       self._num_units, True))
        new_h = u * state + (1 - u) * c
      return new_h, new_h
  
  
  _LSTMStateTuple = collections.namedtuple("LSTMStateTuple", ("c", "h"))
  
  
  class LSTMStateTuple(_LSTMStateTuple):
    """Tuple used by LSTM Cells for `state_size`, `zero_state`, and output state.
  
    Stores two elements: `(c, h)`, in that order.
  
    Only used when `state_is_tuple=True`.
    """
    __slots__ = ()
  
  
  class BasicLSTMCell(RNNCell):
    """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 LSTMCell that follows.
    """
  
    def __init__(self, num_units, forget_bias=1.0, input_size=None,
                 state_is_tuple=False, activation=tanh):
      """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).
        input_size: Deprecated and unused.
        state_is_tuple: If True, accepted and returned states are 2-tuples of
          the `c_state` and `m_state`.  By default (False), they are concatenated
          along the column axis.  This default behavior will soon be deprecated.
        activation: Activation function of the inner states.
      """
      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 input_size is not None:
        logging.warn("%s: The input_size parameter is deprecated." % self)
      self._num_units = num_units
      self._forget_bias = forget_bias
      self._state_is_tuple = state_is_tuple
      self._activation = activation
  
    @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
  
    def __call__(self, inputs, state, scope=None):
      """Long short-term memory cell (LSTM)."""
      with vs.variable_scope(scope or type(self).__name__):  # "BasicLSTMCell"
        # Parameters of gates are concatenated into one multiply for efficiency.
        if self._state_is_tuple:
          c, h = state
        else:
          c, h = array_ops.split(1, 2, state)
        concat = _linear([inputs, h], 4 * self._num_units, True)
  
        # i = input_gate, j = new_input, f = forget_gate, o = output_gate
        i, j, f, o = array_ops.split(1, 4, concat)
  
        new_c = (c * sigmoid(f + self._forget_bias) + sigmoid(i) *
                 self._activation(j))
        new_h = self._activation(new_c) * sigmoid(o)
  
        if self._state_is_tuple:
          new_state = LSTMStateTuple(new_c, new_h)
        else:
          new_state = array_ops.concat(1, [new_c, new_h])
        return new_h, new_state
  
  
  def _get_concat_variable(name, shape, dtype, num_shards):
    """Get a sharded variable concatenated into one tensor."""
    sharded_variable = _get_sharded_variable(name, shape, dtype, num_shards)
    if len(sharded_variable) == 1:
      return sharded_variable[0]
  
    concat_name = name + "/concat"
    concat_full_name = vs.get_variable_scope().name + "/" + concat_name + ":0"
    for value in ops.get_collection(ops.GraphKeys.CONCATENATED_VARIABLES):
      if value.name == concat_full_name:
        return value
  
    concat_variable = array_ops.concat(0, sharded_variable, name=concat_name)
    ops.add_to_collection(ops.GraphKeys.CONCATENATED_VARIABLES,
                          concat_variable)
    return concat_variable
  
  
  def _get_sharded_variable(name, shape, dtype, num_shards):
    """Get a list of sharded variables with the given dtype."""
    if num_shards > shape[0]:
      raise ValueError("Too many shards: shape=%s, num_shards=%d" %
                       (shape, num_shards))
    unit_shard_size = int(math.floor(shape[0] / num_shards))
    remaining_rows = shape[0] - unit_shard_size * num_shards
  
    shards = []
    for i in range(num_shards):
      current_size = unit_shard_size
      if i < remaining_rows:
        current_size += 1
      shards.append(vs.get_variable(name + "_%d" % i, [current_size] + shape[1:],
                                    dtype=dtype))
    return shards
  
  
  class LSTMCell(RNNCell):
    """Long short-term memory unit (LSTM) recurrent network cell.
  
    The default non-peephole implementation is based on:
  
      http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf
  
    S. Hochreiter and J. Schmidhuber.
    "Long Short-Term Memory". Neural Computation, 9(8):1735-1780, 1997.
  
    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.
    """
  
    def __init__(self, num_units, input_size=None,
                 use_peepholes=False, cell_clip=None,
                 initializer=None, num_proj=None,
                 num_unit_shards=1, num_proj_shards=1,
                 forget_bias=1.0, state_is_tuple=False,
                 activation=tanh):
      """Initialize the parameters for an LSTM cell.
  
      Args:
        num_units: int, The number of units in the LSTM cell
        input_size: Deprecated and unused.
        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.
        num_unit_shards: How to split the weight matrix.  If >1, the weight
          matrix is stored across num_unit_shards.
        num_proj_shards: How to split the projection matrix.  If >1, the
          projection matrix is stored across num_proj_shards.
        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.
        state_is_tuple: If True, accepted and returned states are 2-tuples of
          the `c_state` and `m_state`.  By default (False), they are concatenated
          along the column axis.  This default behavior will soon be deprecated.
        activation: Activation function of the inner states.
      """
      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 input_size is not None:
        logging.warn("%s: The input_size parameter is deprecated." % self)
      self._num_units = num_units
      self._use_peepholes = use_peepholes
      self._cell_clip = cell_clip
      self._initializer = initializer
      self._num_proj = num_proj
      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
      self._activation = activation
  
      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
  
    def __call__(self, inputs, state, scope=None):
      """Run one step of LSTM.
  
      Args:
        inputs: input Tensor, 2D, batch x num_units.
        state: if `state_is_tuple` is False, this must be a state Tensor,
          `2-D, batch x 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`.
        scope: VariableScope for the created subgraph; defaults to "LSTMCell".
  
      Returns:
        A tuple containing:
        - A `2-D, [batch x 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.
      """
      num_proj = self._num_units if self._num_proj is None else self._num_proj
  
      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])
  
      dtype = inputs.dtype
      input_size = inputs.get_shape().with_rank(2)[1]
      if input_size.value is None:
        raise ValueError("Could not infer input size from inputs.get_shape()[-1]")
      with vs.variable_scope(scope or type(self).__name__,
                             initializer=self._initializer):  # "LSTMCell"
        concat_w = _get_concat_variable(
            "W", [input_size.value + num_proj, 4 * self._num_units],
            dtype, self._num_unit_shards)
  
        b = vs.get_variable(
            "B", shape=[4 * self._num_units],
            initializer=array_ops.zeros_initializer, dtype=dtype)
  
        # i = input_gate, j = new_input, f = forget_gate, o = output_gate
        cell_inputs = array_ops.concat(1, [inputs, m_prev])
        lstm_matrix = nn_ops.bias_add(math_ops.matmul(cell_inputs, concat_w), b)
        i, j, f, o = array_ops.split(1, 4, lstm_matrix)
  
        # Diagonal connections
        if self._use_peepholes:
          w_f_diag = vs.get_variable(
              "W_F_diag", shape=[self._num_units], dtype=dtype)
          w_i_diag = vs.get_variable(
              "W_I_diag", shape=[self._num_units], dtype=dtype)
          w_o_diag = vs.get_variable(
              "W_O_diag", shape=[self._num_units], dtype=dtype)
  
        if self._use_peepholes:
          c = (sigmoid(f + self._forget_bias + w_f_diag * c_prev) * c_prev +
               sigmoid(i + 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 + w_o_diag * c) * self._activation(c)
        else:
          m = sigmoid(o) * self._activation(c)
  
        if self._num_proj is not None:
          concat_w_proj = _get_concat_variable(
              "W_P", [self._num_units, self._num_proj],
              dtype, self._num_proj_shards)
  
          m = math_ops.matmul(m, concat_w_proj)
  
      new_state = (LSTMStateTuple(c, m) if self._state_is_tuple
                   else array_ops.concat(1, [c, m]))
      return m, new_state
  
  
  class OutputProjectionWrapper(RNNCell):
    """Operator adding an output projection to the given cell.
  
    Note: in many cases it may be more efficient to not use this wrapper,
    but instead concatenate the whole sequence of your outputs in time,
    do the projection on this batch-concatenated sequence, then split it
    if needed or directly feed into a softmax.
    """
  
    def __init__(self, cell, output_size):
      """Create a cell with output projection.
  
      Args:
        cell: an RNNCell, a projection to output_size is added to it.
        output_size: integer, the size of the output after projection.
  
      Raises:
        TypeError: if cell is not an RNNCell.
        ValueError: if output_size is not positive.
      """
      if not isinstance(cell, RNNCell):
        raise TypeError("The parameter cell is not RNNCell.")
      if output_size < 1:
        raise ValueError("Parameter output_size must be > 0: %d." % output_size)
      self._cell = cell
      self._output_size = output_size
  
    @property
    def state_size(self):
      return self._cell.state_size
  
    @property
    def output_size(self):
      return self._output_size
  
    def __call__(self, inputs, state, scope=None):
      """Run the cell and output projection on inputs, starting from state."""
      output, res_state = self._cell(inputs, state)
      # Default scope: "OutputProjectionWrapper"
      with vs.variable_scope(scope or type(self).__name__):
        projected = _linear(output, self._output_size, True)
      return projected, res_state
  
  
  class InputProjectionWrapper(RNNCell):
    """Operator adding an input projection to the given cell.
  
    Note: in many cases it may be more efficient to not use this wrapper,
    but instead concatenate the whole sequence of your inputs in time,
    do the projection on this batch-concatenated sequence, then split it.
    """
  
    def __init__(self, cell, num_proj, input_size=None):
      """Create a cell with input projection.
  
      Args:
        cell: an RNNCell, a projection of inputs is added before it.
        num_proj: Python integer.  The dimension to project to.
        input_size: Deprecated and unused.
  
      Raises:
        TypeError: if cell is not an RNNCell.
      """
      if input_size is not None:
        logging.warn("%s: The input_size parameter is deprecated." % self)
      if not isinstance(cell, RNNCell):
        raise TypeError("The parameter cell is not RNNCell.")
      self._cell = cell
      self._num_proj = num_proj
  
    @property
    def state_size(self):
      return self._cell.state_size
  
    @property
    def output_size(self):
      return self._cell.output_size
  
    def __call__(self, inputs, state, scope=None):
      """Run the input projection and then the cell."""
      # Default scope: "InputProjectionWrapper"
      with vs.variable_scope(scope or type(self).__name__):
        projected = _linear(inputs, self._num_proj, True)
      return self._cell(projected, state)
  
  
  class DropoutWrapper(RNNCell):
    """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,
                 seed=None):
      """Create a cell with added input and/or output dropout.
  
      Dropout is never used on the state.
  
      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 float 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 float and 1, no output dropout will be added.
        seed: (optional) integer, the randomness seed.
  
      Raises:
        TypeError: if cell is not an RNNCell.
        ValueError: if keep_prob is not between 0 and 1.
      """
      if not isinstance(cell, RNNCell):
        raise TypeError("The parameter cell is not a RNNCell.")
      if (isinstance(input_keep_prob, float) and
          not (input_keep_prob >= 0.0 and input_keep_prob <= 1.0)):
        raise ValueError("Parameter input_keep_prob must be between 0 and 1: %d"
                         % input_keep_prob)
      if (isinstance(output_keep_prob, float) and
          not (output_keep_prob >= 0.0 and output_keep_prob <= 1.0)):
        raise ValueError("Parameter input_keep_prob must be between 0 and 1: %d"
                         % output_keep_prob)
      self._cell = cell
      self._input_keep_prob = input_keep_prob
      self._output_keep_prob = output_keep_prob
      self._seed = seed
  
    @property
    def state_size(self):
      return self._cell.state_size
  
    @property
    def output_size(self):
      return self._cell.output_size
  
    def __call__(self, inputs, state, scope=None):
      """Run the cell with the declared dropouts."""
      if (not isinstance(self._input_keep_prob, float) or
          self._input_keep_prob < 1):
        inputs = nn_ops.dropout(inputs, self._input_keep_prob, seed=self._seed)
      output, new_state = self._cell(inputs, state, scope)
      if (not isinstance(self._output_keep_prob, float) or
          self._output_keep_prob < 1):
        output = nn_ops.dropout(output, self._output_keep_prob, seed=self._seed)
      return output, new_state
  
  
  class EmbeddingWrapper(RNNCell):
    """Operator adding input embedding to the given cell.
  
    Note: in many cases it may be more efficient to not use this wrapper,
    but instead concatenate the whole sequence of your inputs in time,
    do the embedding on this batch-concatenated sequence, then split it and
    feed into your RNN.
    """
  
    def __init__(self, cell, embedding_classes, embedding_size, initializer=None):
      """Create a cell with an added input embedding.
  
      Args:
        cell: an RNNCell, an embedding will be put before its inputs.
        embedding_classes: integer, how many symbols will be embedded.
        embedding_size: integer, the size of the vectors we embed into.
        initializer: an initializer to use when creating the embedding;
          if None, the initializer from variable scope or a default one is used.
  
      Raises:
        TypeError: if cell is not an RNNCell.
        ValueError: if embedding_classes is not positive.
      """
      if not isinstance(cell, RNNCell):
        raise TypeError("The parameter cell is not RNNCell.")
      if embedding_classes <= 0 or embedding_size <= 0:
        raise ValueError("Both embedding_classes and embedding_size must be > 0: "
                         "%d, %d." % (embedding_classes, embedding_size))
      self._cell = cell
      self._embedding_classes = embedding_classes
      self._embedding_size = embedding_size
      self._initializer = initializer
  
    @property
    def state_size(self):
      return self._cell.state_size
  
    @property
    def output_size(self):
      return self._cell.output_size
  
    def __call__(self, inputs, state, scope=None):
      """Run the cell on embedded inputs."""
      with vs.variable_scope(scope or type(self).__name__):  # "EmbeddingWrapper"
        with ops.device("/cpu:0"):
          if self._initializer:
            initializer = self._initializer
          elif vs.get_variable_scope().initializer:
            initializer = vs.get_variable_scope().initializer
          else:
            # Default initializer for embeddings should have variance=1.
            sqrt3 = math.sqrt(3)  # Uniform(-sqrt(3), sqrt(3)) has variance=1.
            initializer = init_ops.random_uniform_initializer(-sqrt3, sqrt3)
          embedding = vs.get_variable("embedding", [self._embedding_classes,
                                                    self._embedding_size],
                                      initializer=initializer)
          embedded = embedding_ops.embedding_lookup(
              embedding, array_ops.reshape(inputs, [-1]))
      return self._cell(embedded, state)
  
  
  class MultiRNNCell(RNNCell):
    """RNN cell composed sequentially of multiple simple cells."""
  
    def __init__(self, cells, state_is_tuple=False):
      """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)`.  By default (False), the states are all
          concatenated along the column axis.
  
      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`.
      """
      if not cells:
        raise ValueError("Must specify at least one cell for MultiRNNCell.")
      self._cells = cells
      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 __call__(self, inputs, state, scope=None):
      """Run this multi-layer cell on inputs, starting from state."""
      with vs.variable_scope(scope or type(self).__name__):  # "MultiRNNCell"
        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(1, new_states))
      return cur_inp, new_states
  
  
  class _SlimRNNCell(RNNCell):
    """A simple wrapper for slim.rnn_cells."""
  
    def __init__(self, cell_fn):
      """Create a SlimRNNCell from a cell_fn.
  
      Args:
        cell_fn: a function which takes (inputs, state, scope) and produces the
          outputs and the new_state. Additionally when called with inputs=None and
          state=None it should return (initial_outputs, initial_state).
  
      Raises:
        TypeError: if cell_fn is not callable
        ValueError: if cell_fn cannot produce a valid initial state.
      """
      if not callable(cell_fn):
        raise TypeError("cell_fn %s needs to be callable", cell_fn)
      self._cell_fn = cell_fn
      self._cell_name = cell_fn.func.__name__
      init_output, init_state = self._cell_fn(None, None)
      output_shape = init_output.get_shape()
      state_shape = init_state.get_shape()
      self._output_size = output_shape.with_rank(2)[1].value
      self._state_size = state_shape.with_rank(2)[1].value
      if self._output_size is None:
        raise ValueError("Initial output created by %s has invalid shape %s" %
                         (self._cell_name, output_shape))
      if self._state_size is None:
        raise ValueError("Initial state created by %s has invalid shape %s" %
                         (self._cell_name, state_shape))
  
    @property
    def state_size(self):
      return self._state_size
  
    @property
    def output_size(self):
      return self._output_size
  
    def __call__(self, inputs, state, scope=None):
      scope = scope or self._cell_name
      output, state = self._cell_fn(inputs, state, scope=scope)
      return output, state
  
  
  def _linear(args, output_size, bias, bias_start=0.0, scope=None):
    """Linear map: sum_i(args[i] * W[i]), where W[i] is a variable.
  
    Args:
      args: a 2D Tensor or a list of 2D, batch x n, Tensors.
      output_size: int, second dimension of W[i].
      bias: boolean, whether to add a bias term or not.
      bias_start: starting value to initialize the bias; 0 by default.
      scope: VariableScope for the created subgraph; defaults to "Linear".
  
    Returns:
      A 2D Tensor with shape [batch x output_size] equal to
      sum_i(args[i] * W[i]), where W[i]s are newly created matrices.
  
    Raises:
      ValueError: if some of the arguments has unspecified or wrong shape.
    """
    if args is None or (nest.is_sequence(args) and not args):
      raise ValueError("`args` must be specified")
    if not nest.is_sequence(args):
      args = [args]
  
    # Calculate the total size of arguments on dimension 1.
    total_arg_size = 0
    shapes = [a.get_shape().as_list() for a in args]
    for shape in shapes:
      if len(shape) != 2:
        raise ValueError("Linear is expecting 2D arguments: %s" % str(shapes))
      if not shape[1]:
        raise ValueError("Linear expects shape[1] of arguments: %s" % str(shapes))
      else:
        total_arg_size += shape[1]
  
    # Now the computation.
    with vs.variable_scope(scope or "Linear"):
      matrix = vs.get_variable("Matrix", [total_arg_size, output_size])
      if len(args) == 1:
        res = math_ops.matmul(args[0], matrix)
      else:
        res = math_ops.matmul(array_ops.concat(1, args), matrix)
      if not bias:
        return res
      bias_term = vs.get_variable(
          "Bias", [output_size],
          initializer=init_ops.constant_initializer(bias_start))
    return res + bias_term
  