# -*- coding: utf-8 -*-
from __future__ import absolute_import
import numpy as np

from .. import backend as K
from .. import activations
from .. import initializers
from .. import regularizers
from .. import constraints
from ..engine import Layer
from ..engine import InputSpec
from ..legacy import interfaces


def _time_distributed_dense(x, w, b=None, dropout=None,
                            input_dim=None, output_dim=None,
                            timesteps=None, training=None):
    """Apply `y . w + b` for every temporal slice y of x.

    # Arguments
        x: input tensor.
        w: weight matrix.
        b: optional bias vector.
        dropout: wether to apply dropout (same dropout mask
            for every temporal slice of the input).
        input_dim: integer; optional dimensionality of the input.
        output_dim: integer; optional dimensionality of the output.
        timesteps: integer; optional number of timesteps.
        training: training phase tensor or boolean.

    # Returns
        Output tensor.
    """
    if not input_dim:
        input_dim = K.shape(x)[2]
    if not timesteps:
        timesteps = K.shape(x)[1]
    if not output_dim:
        output_dim = K.shape(w)[1]

    if dropout is not None and 0. < dropout < 1.:
        # apply the same dropout pattern at every timestep
        ones = K.ones_like(K.reshape(x[:, 0, :], (-1, input_dim)))
        dropout_matrix = K.dropout(ones, dropout)
        expanded_dropout_matrix = K.repeat(dropout_matrix, timesteps)
        x = K.in_train_phase(x * expanded_dropout_matrix, x, training=training)

    # collapse time dimension and batch dimension together
    x = K.reshape(x, (-1, input_dim))
    x = K.dot(x, w)
    if b is not None:
        x = K.bias_add(x, b)
    # reshape to 3D tensor
    if K.backend() == 'tensorflow':
        x = K.reshape(x, K.stack([-1, timesteps, output_dim]))
        x.set_shape([None, None, output_dim])
    else:
        x = K.reshape(x, (-1, timesteps, output_dim))
    return x


class Recurrent(Layer):
    """Abstract base class for recurrent layers.

    Do not use in a model -- it's not a valid layer!
    Use its children classes `LSTM`, `GRU` and `SimpleRNN` instead.

    All recurrent layers (`LSTM`, `GRU`, `SimpleRNN`) also
    follow the specifications of this class and accept
    the keyword arguments listed below.

    # Example

    ```python
        # as the first layer in a Sequential model
        model = Sequential()
        model.add(LSTM(32, input_shape=(10, 64)))
        # now model.output_shape == (None, 32)
        # note: `None` is the batch dimension.

        # the following is identical:
        model = Sequential()
        model.add(LSTM(32, input_dim=64, input_length=10))

        # for subsequent layers, not need to specify the input size:
        model.add(LSTM(16))
    ```

    # Arguments
        weights: list of Numpy arrays to set as initial weights.
            The list should have 3 elements, of shapes:
            `[(input_dim, output_dim), (output_dim, output_dim), (output_dim,)]`.
        return_sequences: Boolean. Whether to return the last output
            in the output sequence, or the full sequence.
        go_backwards: Boolean (default False).
            If True, process the input sequence backwards.
        stateful: Boolean (default False). If True, the last state
            for each sample at index i in a batch will be used as initial
            state for the sample of index i in the following batch.
        unroll: Boolean (default False).
            If True, the network will be unrolled,
            else a symbolic loop will be used.
            Unrolling can speed-up a RNN,
            although it tends to be more memory-intensive.
            Unrolling is only suitable for short sequences.
        implementation: one of {0, 1, or 2}.
            If set to 0, the RNN will use
            an implementation that uses fewer, larger matrix products,
            thus running faster on CPU but consuming more memory.
            If set to 1, the RNN will use more matrix products,
            but smaller ones, thus running slower
            (may actually be faster on GPU) while consuming less memory.
            If set to 2 (LSTM/GRU only),
            the RNN will combine the input gate,
            the forget gate and the output gate into a single matrix,
            enabling more time-efficient parallelization on the GPU.
            Note: RNN dropout must be shared for all gates,
            resulting in a slightly reduced regularization.
        input_dim: dimensionality of the input (integer).
            This argument (or alternatively, the keyword argument `input_shape`)
            is required when using this layer as the first layer in a model.
        input_length: Length of input sequences, to be specified
            when it is constant.
            This argument is required if you are going to connect
            `Flatten` then `Dense` layers upstream
            (without it, the shape of the dense outputs cannot be computed).
            Note that if the recurrent layer is not the first layer
            in your model, you would need to specify the input length
            at the level of the first layer
            (e.g. via the `input_shape` argument)

    # Input shapes
        3D tensor with shape `(batch_size, timesteps, input_dim)`,
        (Optional) 2D tensors with shape `(batch_size, output_dim)`.

    # Output shape
        - if `return_sequences`: 3D tensor with shape
            `(batch_size, timesteps, units)`.
        - else, 2D tensor with shape `(batch_size, units)`.

    # Masking
        This layer supports masking for input data with a variable number
        of timesteps. To introduce masks to your data,
        use an [Embedding](embeddings.md) layer with the `mask_zero` parameter
        set to `True`.

    # Note on using statefulness in RNNs
        You can set RNN layers to be 'stateful', which means that the states
        computed for the samples in one batch will be reused as initial states
        for the samples in the next batch. This assumes a one-to-one mapping
        between samples in different successive batches.

        To enable statefulness:
            - specify `stateful=True` in the layer constructor.
            - specify a fixed batch size for your model, by passing
                if sequential model:
                  `batch_input_shape=(...)` to the first layer in your model.
                else for functional model with 1 or more Input layers:
                  `batch_shape=(...)` to all the first layers in your model.
                This is the expected shape of your inputs
                *including the batch size*.
                It should be a tuple of integers, e.g. `(32, 10, 100)`.
            - specify `shuffle=False` when calling fit().

        To reset the states of your model, call `.reset_states()` on either
        a specific layer, or on your entire model.

    # Note on specifying initial states in RNNs
        You can specify the initial state of RNN layers by calling them with
        the keyword argument `initial_state`. The value of `initial_state`
        should be a tensor or list of tensors representing the initial state
        of the RNN layer.
    """

    def __init__(self, return_sequences=False,
                 go_backwards=False,
                 stateful=False,
                 unroll=False,
                 implementation=0,
                 **kwargs):
        super(Recurrent, self).__init__(**kwargs)
        self.return_sequences = return_sequences
        self.go_backwards = go_backwards
        self.stateful = stateful
        self.unroll = unroll
        self.implementation = implementation
        self.supports_masking = True
        self.input_spec = InputSpec(ndim=3)
        self.state_spec = None
        self.dropout = 0
        self.recurrent_dropout = 0

    def compute_output_shape(self, input_shape):
        if isinstance(input_shape, list):
            input_shape = input_shape[0]
        if self.return_sequences:
            return (input_shape[0], input_shape[1], self.units)
        else:
            return (input_shape[0], self.units)

    def compute_mask(self, inputs, mask):
        if self.return_sequences:
            return mask
        else:
            return None

    def step(self, inputs, states):
        raise NotImplementedError

    def get_constants(self, inputs, training=None):
        return []

    def get_initial_states(self, inputs):
        # build an all-zero tensor of shape (samples, output_dim)
        initial_state = K.zeros_like(inputs)  # (samples, timesteps, input_dim)
        initial_state = K.sum(initial_state, axis=(1, 2))  # (samples,)
        initial_state = K.expand_dims(initial_state)  # (samples, 1)
        initial_state = K.tile(initial_state, [1, self.units])  # (samples, output_dim)
        initial_states = [initial_state for _ in range(len(self.states))]
        return initial_states

    def preprocess_input(self, inputs, training=None):
        return inputs

    def __call__(self, inputs, initial_state=None, **kwargs):
        # If `initial_state` is specified,
        # and if it a Keras tensor,
        # then add it to the inputs and temporarily
        # modify the input spec to include the state.
        if initial_state is not None:
            if hasattr(initial_state, '_keras_history'):
                # Compute the full input spec, including state
                input_spec = self.input_spec
                state_spec = self.state_spec
                if not isinstance(state_spec, list):
                    state_spec = [state_spec]
                self.input_spec = [input_spec] + state_spec

                # Compute the full inputs, including state
                if not isinstance(initial_state, (list, tuple)):
                    initial_state = [initial_state]
                inputs = [inputs] + list(initial_state)

                # Perform the call
                output = super(Recurrent, self).__call__(inputs, **kwargs)

                # Restore original input spec
                self.input_spec = input_spec
                return output
            else:
                kwargs['initial_state'] = initial_state
        return super(Recurrent, self).__call__(inputs, **kwargs)

    def call(self, inputs, mask=None, initial_state=None, training=None):
        # input shape: `(samples, time (padded with zeros), input_dim)`
        # note that the .build() method of subclasses MUST define
        # self.input_spec and self.state_spec with complete input shapes.
        if initial_state is not None:
            if not isinstance(initial_state, (list, tuple)):
                initial_states = [initial_state]
            else:
                initial_states = list(initial_state)
        if isinstance(inputs, list):
            initial_states = inputs[1:]
            inputs = inputs[0]
        elif self.stateful:
            initial_states = self.states
        else:
            initial_states = self.get_initial_states(inputs)

        if len(initial_states) != len(self.states):
            raise ValueError('Layer has ' + str(len(self.states)) +
                             ' states but was passed ' +
                             str(len(initial_states)) +
                             ' initial states.')
        input_shape = K.int_shape(inputs)
        if self.unroll and input_shape[1] is None:
            raise ValueError('Cannot unroll a RNN if the '
                             'time dimension is undefined. \n'
                             '- If using a Sequential model, '
                             'specify the time dimension by passing '
                             'an `input_shape` or `batch_input_shape` '
                             'argument to your first layer. If your '
                             'first layer is an Embedding, you can '
                             'also use the `input_length` argument.\n'
                             '- If using the functional API, specify '
                             'the time dimension by passing a `shape` '
                             'or `batch_shape` argument to your Input layer.')
        constants = self.get_constants(inputs, training=None)
        preprocessed_input = self.preprocess_input(inputs, training=None)
        last_output, outputs, states = K.rnn(self.step,
                                             preprocessed_input,
                                             initial_states,
                                             go_backwards=self.go_backwards,
                                             mask=mask,
                                             constants=constants,
                                             unroll=self.unroll,
                                             input_length=input_shape[1])
        if self.stateful:
            updates = []
            for i in range(len(states)):
                updates.append((self.states[i], states[i]))
            self.add_update(updates, inputs)

        # Properly set learning phase
        if 0 < self.dropout + self.recurrent_dropout:
            last_output._uses_learning_phase = True
            outputs._uses_learning_phase = True

        if self.return_sequences:
            return outputs
        else:
            return last_output

    def reset_states(self, states_value=None):
        if not self.stateful:
            raise AttributeError('Layer must be stateful.')
        if not self.input_spec:
            raise RuntimeError('Layer has never been called '
                               'and thus has no states.')
        batch_size = self.input_spec.shape[0]
        if not batch_size:
            raise ValueError('If a RNN is stateful, it needs to know '
                             'its batch size. Specify the batch size '
                             'of your input tensors: \n'
                             '- If using a Sequential model, '
                             'specify the batch size by passing '
                             'a `batch_input_shape` '
                             'argument to your first layer.\n'
                             '- If using the functional API, specify '
                             'the time dimension by passing a '
                             '`batch_shape` argument to your Input layer.')
        if states_value is not None:
            if not isinstance(states_value, (list, tuple)):
                states_value = [states_value]
            if len(states_value) != len(self.states):
                raise ValueError('The layer has ' + str(len(self.states)) +
                                 ' states, but the `states_value` '
                                 'argument passed '
                                 'only has ' + str(len(states_value)) +
                                 ' entries')
        if self.states[0] is None:
            self.states = [K.zeros((batch_size, self.units))
                           for _ in self.states]
            if not states_value:
                return
        for i, state in enumerate(self.states):
            if states_value:
                value = states_value[i]
                if value.shape != (batch_size, self.units):
                    raise ValueError(
                        'Expected state #' + str(i) +
                        ' to have shape ' + str((batch_size, self.units)) +
                        ' but got array with shape ' + str(value.shape))
            else:
                value = np.zeros((batch_size, self.units))
            K.set_value(state, value)

    def get_config(self):
        config = {'return_sequences': self.return_sequences,
                  'go_backwards': self.go_backwards,
                  'stateful': self.stateful,
                  'unroll': self.unroll,
                  'implementation': self.implementation}
        base_config = super(Recurrent, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))


class SimpleRNN(Recurrent):
    """Fully-connected RNN where the output is to be fed back to input.

    # Arguments
        units: Positive integer, dimensionality of the output space.
        activation: Activation function to use
            (see [activations](../activations.md)).
            If you don't specify anything, no activation is applied
            (ie. "linear" activation: `a(x) = x`).
        use_bias: Boolean, whether the layer uses a bias vector.
        kernel_initializer: Initializer for the `kernel` weights matrix,
            used for the linear transformation of the inputs.
            (see [initializers](../initializers.md)).
        recurrent_initializer: Initializer for the `recurrent_kernel`
            weights matrix,
            used for the linear transformation of the recurrent state.
            (see [initializers](../initializers.md)).
        bias_initializer: Initializer for the bias vector
            (see [initializers](../initializers.md)).
        kernel_regularizer: Regularizer function applied to
            the `kernel` weights matrix
            (see [regularizer](../regularizers.md)).
        recurrent_regularizer: Regularizer function applied to
            the `recurrent_kernel` weights matrix
            (see [regularizer](../regularizers.md)).
        bias_regularizer: Regularizer function applied to the bias vector
            (see [regularizer](../regularizers.md)).
        activity_regularizer: Regularizer function applied to
            the output of the layer (its "activation").
            (see [regularizer](../regularizers.md)).
        kernel_constraint: Constraint function applied to
            the `kernel` weights matrix
            (see [constraints](../constraints.md)).
        recurrent_constraint: Constraint function applied to
            the `recurrent_kernel` weights matrix
            (see [constraints](../constraints.md)).
        bias_constraint: Constraint function applied to the bias vector
            (see [constraints](../constraints.md)).
        dropout: Float between 0 and 1.
            Fraction of the units to drop for
            the linear transformation of the inputs.
        recurrent_dropout: Float between 0 and 1.
            Fraction of the units to drop for
            the linear transformation of the recurrent state.

    # References
        - [A Theoretically Grounded Application of Dropout in Recurrent Neural Networks](http://arxiv.org/abs/1512.05287)
    """

    @interfaces.legacy_recurrent_support
    def __init__(self, units,
                 activation='tanh',
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 recurrent_initializer='orthogonal',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 recurrent_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 recurrent_constraint=None,
                 bias_constraint=None,
                 dropout=0.,
                 recurrent_dropout=0.,
                 **kwargs):
        super(SimpleRNN, self).__init__(**kwargs)
        self.units = units
        self.activation = activations.get(activation)
        self.use_bias = use_bias

        self.kernel_initializer = initializers.get(kernel_initializer)
        self.recurrent_initializer = initializers.get(recurrent_initializer)
        self.bias_initializer = initializers.get(bias_initializer)

        self.kernel_regularizer = regularizers.get(kernel_regularizer)
        self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
        self.bias_regularizer = regularizers.get(bias_regularizer)
        self.activity_regularizer = regularizers.get(activity_regularizer)

        self.kernel_constraint = constraints.get(kernel_constraint)
        self.recurrent_constraint = constraints.get(recurrent_constraint)
        self.bias_constraint = constraints.get(bias_constraint)

        self.dropout = min(1., max(0., dropout))
        self.recurrent_dropout = min(1., max(0., recurrent_dropout))

    def build(self, input_shape):
        if isinstance(input_shape, list):
            input_shape = input_shape[0]

        batch_size = input_shape[0] if self.stateful else None
        self.input_dim = input_shape[2]
        self.input_spec = InputSpec(shape=(batch_size, None, self.input_dim))
        self.state_spec = InputSpec(shape=(batch_size, self.units))

        self.states = [None]
        if self.stateful:
            self.reset_states()

        self.kernel = self.add_weight((self.input_dim, self.units),
                                      name='kernel',
                                      initializer=self.kernel_initializer,
                                      regularizer=self.kernel_regularizer,
                                      constraint=self.kernel_constraint)
        self.recurrent_kernel = self.add_weight(
            (self.units, self.units),
            name='recurrent_kernel',
            initializer=self.recurrent_initializer,
            regularizer=self.recurrent_regularizer,
            constraint=self.recurrent_constraint)
        if self.use_bias:
            self.bias = self.add_weight((self.units,),
                                        name='bias',
                                        initializer=self.bias_initializer,
                                        regularizer=self.bias_regularizer,
                                        constraint=self.bias_constraint)
        else:
            self.bias = None
        self.built = True

    def preprocess_input(self, inputs, training=None):
        if self.implementation > 0:
            return inputs
        else:
            input_shape = K.int_shape(inputs)
            input_dim = input_shape[2]
            timesteps = input_shape[1]
            return _time_distributed_dense(inputs,
                                           self.kernel,
                                           self.bias,
                                           self.dropout,
                                           input_dim,
                                           self.units,
                                           timesteps,
                                           training=training)

    def step(self, inputs, states):
        if self.implementation == 0:
            h = inputs
        else:
            if 0 < self.dropout < 1:
                h = K.dot(inputs * states[1], self.kernel)
            else:
                h = K.dot(inputs, self.kernel)
            if self.bias is not None:
                h = K.bias_add(h, self.bias)

        prev_output = states[0]
        if 0 < self.recurrent_dropout < 1:
            prev_output *= states[2]
        output = h + K.dot(prev_output, self.recurrent_kernel)
        if self.activation is not None:
            output = self.activation(output)

        # Properly set learning phase on output tensor.
        if 0 < self.dropout + self.recurrent_dropout:
            output._uses_learning_phase = True
        return output, [output]

    def get_constants(self, inputs, training=None):
        constants = []
        if self.implementation == 0 and 0 < self.dropout < 1:
            input_shape = K.int_shape(inputs)
            input_dim = input_shape[-1]
            ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1)))
            ones = K.tile(ones, (1, int(input_dim)))

            def dropped_inputs():
                return K.dropout(ones, self.dropout)

            dp_mask = K.in_train_phase(dropped_inputs,
                                       ones,
                                       training=training)
            constants.append(dp_mask)
        else:
            constants.append(K.cast_to_floatx(1.))

        if 0 < self.recurrent_dropout < 1:
            ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1)))
            ones = K.tile(ones, (1, self.units))

            def dropped_inputs():
                return K.dropout(ones, self.recurrent_dropout)
            rec_dp_mask = K.in_train_phase(dropped_inputs,
                                           ones,
                                           training=training)
            constants.append(rec_dp_mask)
        else:
            constants.append(K.cast_to_floatx(1.))
        return constants

    def get_config(self):
        config = {'units': self.units,
                  'activation': activations.serialize(self.activation),
                  'use_bias': self.use_bias,
                  'kernel_initializer': initializers.serialize(self.kernel_initializer),
                  'recurrent_initializer': initializers.serialize(self.recurrent_initializer),
                  'bias_initializer': initializers.serialize(self.bias_initializer),
                  'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
                  'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer),
                  'bias_regularizer': regularizers.serialize(self.bias_regularizer),
                  'activity_regularizer': regularizers.serialize(self.activity_regularizer),
                  'kernel_constraint': constraints.serialize(self.kernel_constraint),
                  'recurrent_constraint': constraints.serialize(self.recurrent_constraint),
                  'bias_constraint': constraints.serialize(self.bias_constraint),
                  'dropout': self.dropout,
                  'recurrent_dropout': self.recurrent_dropout}
        base_config = super(SimpleRNN, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))


class GRU(Recurrent):
    """Gated Recurrent Unit - Cho et al. 2014.

    # Arguments
        units: Positive integer, dimensionality of the output space.
        activation: Activation function to use
            (see [activations](../activations.md)).
            If you don't specify anything, no activation is applied
            (ie. "linear" activation: `a(x) = x`).
        recurrent_activation: Activation function to use
            for the recurrent step
            (see [activations](../activations.md)).
        use_bias: Boolean, whether the layer uses a bias vector.
        kernel_initializer: Initializer for the `kernel` weights matrix,
            used for the linear transformation of the inputs.
            (see [initializers](../initializers.md)).
        recurrent_initializer: Initializer for the `recurrent_kernel`
            weights matrix,
            used for the linear transformation of the recurrent state.
            (see [initializers](../initializers.md)).
        bias_initializer: Initializer for the bias vector
            (see [initializers](../initializers.md)).
        kernel_regularizer: Regularizer function applied to
            the `kernel` weights matrix
            (see [regularizer](../regularizers.md)).
        recurrent_regularizer: Regularizer function applied to
            the `recurrent_kernel` weights matrix
            (see [regularizer](../regularizers.md)).
        bias_regularizer: Regularizer function applied to the bias vector
            (see [regularizer](../regularizers.md)).
        activity_regularizer: Regularizer function applied to
            the output of the layer (its "activation").
            (see [regularizer](../regularizers.md)).
        kernel_constraint: Constraint function applied to
            the `kernel` weights matrix
            (see [constraints](../constraints.md)).
        recurrent_constraint: Constraint function applied to
            the `recurrent_kernel` weights matrix
            (see [constraints](../constraints.md)).
        bias_constraint: Constraint function applied to the bias vector
            (see [constraints](../constraints.md)).
        dropout: Float between 0 and 1.
            Fraction of the units to drop for
            the linear transformation of the inputs.
        recurrent_dropout: Float between 0 and 1.
            Fraction of the units to drop for
            the linear transformation of the recurrent state.

    # References
        - [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://arxiv.org/abs/1409.1259)
        - [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](http://arxiv.org/abs/1412.3555v1)
        - [A Theoretically Grounded Application of Dropout in Recurrent Neural Networks](http://arxiv.org/abs/1512.05287)
    """

    @interfaces.legacy_recurrent_support
    def __init__(self, units,
                 activation='tanh',
                 recurrent_activation='hard_sigmoid',
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 recurrent_initializer='orthogonal',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 recurrent_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 recurrent_constraint=None,
                 bias_constraint=None,
                 dropout=0.,
                 recurrent_dropout=0.,
                 **kwargs):
        super(GRU, self).__init__(**kwargs)
        self.units = units
        self.activation = activations.get(activation)
        self.recurrent_activation = activations.get(recurrent_activation)
        self.use_bias = use_bias

        self.kernel_initializer = initializers.get(kernel_initializer)
        self.recurrent_initializer = initializers.get(recurrent_initializer)
        self.bias_initializer = initializers.get(bias_initializer)

        self.kernel_regularizer = regularizers.get(kernel_regularizer)
        self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
        self.bias_regularizer = regularizers.get(bias_regularizer)
        self.activity_regularizer = regularizers.get(activity_regularizer)

        self.kernel_constraint = constraints.get(kernel_constraint)
        self.recurrent_constraint = constraints.get(recurrent_constraint)
        self.bias_constraint = constraints.get(bias_constraint)

        self.dropout = min(1., max(0., dropout))
        self.recurrent_dropout = min(1., max(0., recurrent_dropout))

    def build(self, input_shape):
        if isinstance(input_shape, list):
            input_shape = input_shape[0]

        batch_size = input_shape[0] if self.stateful else None
        self.input_dim = input_shape[2]
        self.input_spec = InputSpec(shape=(batch_size, None, self.input_dim))
        self.state_spec = InputSpec(shape=(batch_size, self.units))

        self.states = [None]
        if self.stateful:
            self.reset_states()

        self.kernel = self.add_weight((self.input_dim, self.units * 3),
                                      name='kernel',
                                      initializer=self.kernel_initializer,
                                      regularizer=self.kernel_regularizer,
                                      constraint=self.kernel_constraint)
        self.recurrent_kernel = self.add_weight(
            (self.units, self.units * 3),
            name='recurrent_kernel',
            initializer=self.recurrent_initializer,
            regularizer=self.recurrent_regularizer,
            constraint=self.recurrent_constraint)

        if self.use_bias:
            self.bias = self.add_weight((self.units * 3,),
                                        name='bias',
                                        initializer='zero',
                                        regularizer=self.bias_regularizer,
                                        constraint=self.bias_constraint)
        else:
            self.bias = None

        self.kernel_z = self.kernel[:, :self.units]
        self.recurrent_kernel_z = self.recurrent_kernel[:, :self.units]
        self.kernel_r = self.kernel[:, self.units: self.units * 2]
        self.recurrent_kernel_r = self.recurrent_kernel[:,
                                                        self.units:
                                                        self.units * 2]
        self.kernel_h = self.kernel[:, self.units * 2:]
        self.recurrent_kernel_h = self.recurrent_kernel[:, self.units * 2:]

        if self.use_bias:
            self.bias_z = self.bias[:self.units]
            self.bias_r = self.bias[self.units: self.units * 2]
            self.bias_h = self.bias[self.units * 2:]
        else:
            self.bias_z = None
            self.bias_r = None
            self.bias_h = None
        self.built = True

    def preprocess_input(self, inputs, training=None):
        if self.implementation == 0:
            input_shape = K.int_shape(inputs)
            input_dim = input_shape[2]
            timesteps = input_shape[1]

            x_z = _time_distributed_dense(inputs, self.kernel_z, self.bias_z,
                                          self.dropout, input_dim, self.units,
                                          timesteps, training=training)
            x_r = _time_distributed_dense(inputs, self.kernel_r, self.bias_r,
                                          self.dropout, input_dim, self.units,
                                          timesteps, training=training)
            x_h = _time_distributed_dense(inputs, self.kernel_h, self.bias_h,
                                          self.dropout, input_dim, self.units,
                                          timesteps, training=training)
            return K.concatenate([x_z, x_r, x_h], axis=2)
        else:
            return inputs

    def get_constants(self, inputs, training=None):
        constants = []
        if self.implementation == 0 and 0 < self.dropout < 1:
            input_shape = K.int_shape(inputs)
            input_dim = input_shape[-1]
            ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1)))
            ones = K.tile(ones, (1, int(input_dim)))

            def dropped_inputs():
                return K.dropout(ones, self.dropout)

            dp_mask = [K.in_train_phase(dropped_inputs,
                                        ones,
                                        training=training) for _ in range(3)]
            constants.append(dp_mask)
        else:
            constants.append([K.cast_to_floatx(1.) for _ in range(3)])

        if 0 < self.recurrent_dropout < 1:
            ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1)))
            ones = K.tile(ones, (1, self.units))

            def dropped_inputs():
                return K.dropout(ones, self.recurrent_dropout)
            rec_dp_mask = [K.in_train_phase(dropped_inputs,
                                            ones,
                                            training=training) for _ in range(3)]
            constants.append(rec_dp_mask)
        else:
            constants.append([K.cast_to_floatx(1.) for _ in range(3)])
        return constants

    def step(self, inputs, states):
        h_tm1 = states[0]  # previous memory
        dp_mask = states[1]  # dropout matrices for recurrent units
        rec_dp_mask = states[2]

        if self.implementation == 2:
            matrix_x = K.dot(inputs * dp_mask[0], self.kernel)
            if self.use_bias:
                matrix_x = K.bias_add(matrix_x, self.bias)
            matrix_inner = K.dot(h_tm1 * rec_dp_mask[0],
                                 self.recurrent_kernel[:, :2 * self.units])

            x_z = matrix_x[:, :self.units]
            x_r = matrix_x[:, self.units: 2 * self.units]
            recurrent_z = matrix_inner[:, :self.units]
            recurrent_r = matrix_inner[:, self.units: 2 * self.units]

            z = self.recurrent_activation(x_z + recurrent_z)
            r = self.recurrent_activation(x_r + recurrent_r)

            x_h = matrix_x[:, 2 * self.units:]
            recurrent_h = K.dot(r * h_tm1 * rec_dp_mask[0],
                                self.recurrent_kernel[:, 2 * self.units:])
            hh = self.activation(x_h + recurrent_h)
        else:
            if self.implementation == 0:
                x_z = inputs[:, :self.units]
                x_r = inputs[:, self.units: 2 * self.units]
                x_h = inputs[:, 2 * self.units:]
            elif self.implementation == 1:
                x_z = K.dot(inputs * dp_mask[0], self.kernel_z)
                x_r = K.dot(inputs * dp_mask[1], self.kernel_r)
                x_h = K.dot(inputs * dp_mask[2], self.kernel_h)
                if self.use_bias:
                    x_z = K.bias_add(x_z, self.bias_z)
                    x_r = K.bias_add(x_r, self.bias_r)
                    x_h = K.bias_add(x_r, self.bias_h)
            else:
                raise ValueError('Unknown `implementation` mode.')
            z = self.recurrent_activation(x_z + K.dot(h_tm1 * rec_dp_mask[0],
                                                      self.recurrent_kernel_z))
            r = self.recurrent_activation(x_r + K.dot(h_tm1 * rec_dp_mask[1],
                                                      self.recurrent_kernel_r))

            hh = self.activation(x_h + K.dot(r * h_tm1 * rec_dp_mask[2],
                                             self.recurrent_kernel_h))
        h = z * h_tm1 + (1 - z) * hh
        if 0 < self.dropout + self.recurrent_dropout:
            h._uses_learning_phase = True
        return h, [h]

    def get_config(self):
        config = {'units': self.units,
                  'activation': activations.serialize(self.activation),
                  'recurrent_activation': activations.serialize(self.recurrent_activation),
                  'use_bias': self.use_bias,
                  'kernel_initializer': initializers.serialize(self.kernel_initializer),
                  'recurrent_initializer': initializers.serialize(self.recurrent_initializer),
                  'bias_initializer': initializers.serialize(self.bias_initializer),
                  'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
                  'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer),
                  'bias_regularizer': regularizers.serialize(self.bias_regularizer),
                  'activity_regularizer': regularizers.serialize(self.activity_regularizer),
                  'kernel_constraint': constraints.serialize(self.kernel_constraint),
                  'recurrent_constraint': constraints.serialize(self.recurrent_constraint),
                  'bias_constraint': constraints.serialize(self.bias_constraint),
                  'dropout': self.dropout,
                  'recurrent_dropout': self.recurrent_dropout}
        base_config = super(GRU, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))


class LSTM(Recurrent):
    """Long-Short Term Memory unit - Hochreiter 1997.

    For a step-by-step description of the algorithm, see
    [this tutorial](http://deeplearning.net/tutorial/lstm.html).

    # Arguments
        units: Positive integer, dimensionality of the output space.
        activation: Activation function to use
            (see [activations](../activations.md)).
            If you don't specify anything, no activation is applied
            (ie. "linear" activation: `a(x) = x`).
        recurrent_activation: Activation function to use
            for the recurrent step
            (see [activations](../activations.md)).
        use_bias: Boolean, whether the layer uses a bias vector.
        kernel_initializer: Initializer for the `kernel` weights matrix,
            used for the linear transformation of the inputs.
            (see [initializers](../initializers.md)).
        recurrent_initializer: Initializer for the `recurrent_kernel`
            weights matrix,
            used for the linear transformation of the recurrent state.
            (see [initializers](../initializers.md)).
        bias_initializer: Initializer for the bias vector
            (see [initializers](../initializers.md)).
        unit_forget_bias: Boolean.
            If True, add 1 to the bias of the forget gate at initialization.
            Setting it to true will also force `bias_initializer="zeros"`.
            This is recommended in [Jozefowicz et al.](http://www.jmlr.org/proceedings/papers/v37/jozefowicz15.pdf)
        kernel_regularizer: Regularizer function applied to
            the `kernel` weights matrix
            (see [regularizer](../regularizers.md)).
        recurrent_regularizer: Regularizer function applied to
            the `recurrent_kernel` weights matrix
            (see [regularizer](../regularizers.md)).
        bias_regularizer: Regularizer function applied to the bias vector
            (see [regularizer](../regularizers.md)).
        activity_regularizer: Regularizer function applied to
            the output of the layer (its "activation").
            (see [regularizer](../regularizers.md)).
        kernel_constraint: Constraint function applied to
            the `kernel` weights matrix
            (see [constraints](../constraints.md)).
        recurrent_constraint: Constraint function applied to
            the `recurrent_kernel` weights matrix
            (see [constraints](../constraints.md)).
        bias_constraint: Constraint function applied to the bias vector
            (see [constraints](../constraints.md)).
        dropout: Float between 0 and 1.
            Fraction of the units to drop for
            the linear transformation of the inputs.
        recurrent_dropout: Float between 0 and 1.
            Fraction of the units to drop for
            the linear transformation of the recurrent state.

    # References
        - [Long short-term memory](http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf) (original 1997 paper)
        - [Learning to forget: Continual prediction with LSTM](http://www.mitpressjournals.org/doi/pdf/10.1162/089976600300015015)
        - [Supervised sequence labeling with recurrent neural networks](http://www.cs.toronto.edu/~graves/preprint.pdf)
        - [A Theoretically Grounded Application of Dropout in Recurrent Neural Networks](http://arxiv.org/abs/1512.05287)
    """
    @interfaces.legacy_recurrent_support
    def __init__(self, units,
                 activation='tanh',
                 recurrent_activation='hard_sigmoid',
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 recurrent_initializer='orthogonal',
                 bias_initializer='zeros',
                 unit_forget_bias=True,
                 kernel_regularizer=None,
                 recurrent_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 recurrent_constraint=None,
                 bias_constraint=None,
                 dropout=0.,
                 recurrent_dropout=0.,
                 **kwargs):
        super(LSTM, self).__init__(**kwargs)
        self.units = units
        self.activation = activations.get(activation)
        self.recurrent_activation = activations.get(recurrent_activation)
        self.use_bias = use_bias

        self.kernel_initializer = initializers.get(kernel_initializer)
        self.recurrent_initializer = initializers.get(recurrent_initializer)
        self.bias_initializer = initializers.get(bias_initializer)
        self.unit_forget_bias = unit_forget_bias

        self.kernel_regularizer = regularizers.get(kernel_regularizer)
        self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
        self.bias_regularizer = regularizers.get(bias_regularizer)
        self.activity_regularizer = regularizers.get(activity_regularizer)

        self.kernel_constraint = constraints.get(kernel_constraint)
        self.recurrent_constraint = constraints.get(recurrent_constraint)
        self.bias_constraint = constraints.get(bias_constraint)

        self.dropout = min(1., max(0., dropout))
        self.recurrent_dropout = min(1., max(0., recurrent_dropout))

    def build(self, input_shape):
        if isinstance(input_shape, list):
            input_shape = input_shape[0]

        batch_size = input_shape[0] if self.stateful else None
        self.input_dim = input_shape[2]
        self.input_spec = InputSpec(shape=(batch_size, None, self.input_dim))
        self.state_spec = [InputSpec(shape=(batch_size, self.units)),
                           InputSpec(shape=(batch_size, self.units))]

        self.states = [None, None]
        if self.stateful:
            self.reset_states()

        self.kernel = self.add_weight((self.input_dim, self.units * 4),
                                      name='kernel',
                                      initializer=self.kernel_initializer,
                                      regularizer=self.kernel_regularizer,
                                      constraint=self.kernel_constraint)
        self.recurrent_kernel = self.add_weight(
            (self.units, self.units * 4),
            name='recurrent_kernel',
            initializer=self.recurrent_initializer,
            regularizer=self.recurrent_regularizer,
            constraint=self.recurrent_constraint)

        if self.use_bias:
            self.bias = self.add_weight((self.units * 4,),
                                        name='bias',
                                        initializer=self.bias_initializer,
                                        regularizer=self.bias_regularizer,
                                        constraint=self.bias_constraint)
            if self.unit_forget_bias:
                bias_value = np.zeros((self.units * 4,))
                bias_value[self.units: self.units * 2] = 1.
                K.set_value(self.bias, bias_value)
        else:
            self.bias = None

        self.kernel_i = self.kernel[:, :self.units]
        self.kernel_f = self.kernel[:, self.units: self.units * 2]
        self.kernel_c = self.kernel[:, self.units * 2: self.units * 3]
        self.kernel_o = self.kernel[:, self.units * 3:]

        self.recurrent_kernel_i = self.recurrent_kernel[:, :self.units]
        self.recurrent_kernel_f = self.recurrent_kernel[:, self.units: self.units * 2]
        self.recurrent_kernel_c = self.recurrent_kernel[:, self.units * 2: self.units * 3]
        self.recurrent_kernel_o = self.recurrent_kernel[:, self.units * 3:]

        if self.use_bias:
            self.bias_i = self.bias[:self.units]
            self.bias_f = self.bias[self.units: self.units * 2]
            self.bias_c = self.bias[self.units * 2: self.units * 3]
            self.bias_o = self.bias[self.units * 3:]
        else:
            self.bias_i = None
            self.bias_f = None
            self.bias_c = None
            self.bias_o = None
        self.built = True

    def preprocess_input(self, inputs, training=None):
        if self.implementation == 0:
            input_shape = K.int_shape(inputs)
            input_dim = input_shape[2]
            timesteps = input_shape[1]

            x_i = _time_distributed_dense(inputs, self.kernel_i, self.bias_i,
                                          self.dropout, input_dim, self.units,
                                          timesteps, training=training)
            x_f = _time_distributed_dense(inputs, self.kernel_f, self.bias_f,
                                          self.dropout, input_dim, self.units,
                                          timesteps, training=training)
            x_c = _time_distributed_dense(inputs, self.kernel_c, self.bias_c,
                                          self.dropout, input_dim, self.units,
                                          timesteps, training=training)
            x_o = _time_distributed_dense(inputs, self.kernel_o, self.bias_o,
                                          self.dropout, input_dim, self.units,
                                          timesteps, training=training)
            return K.concatenate([x_i, x_f, x_c, x_o], axis=2)
        else:
            return inputs

    def get_constants(self, inputs, training=None):
        constants = []
        if self.implementation == 0 and 0 < self.dropout < 1:
            input_shape = K.int_shape(inputs)
            input_dim = input_shape[-1]
            ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1)))
            ones = K.tile(ones, (1, int(input_dim)))

            def dropped_inputs():
                return K.dropout(ones, self.dropout)

            dp_mask = [K.in_train_phase(dropped_inputs,
                                        ones,
                                        training=training) for _ in range(4)]
            constants.append(dp_mask)
        else:
            constants.append([K.cast_to_floatx(1.) for _ in range(4)])

        if 0 < self.recurrent_dropout < 1:
            ones = K.ones_like(K.reshape(inputs[:, 0, 0], (-1, 1)))
            ones = K.tile(ones, (1, self.units))

            def dropped_inputs():
                return K.dropout(ones, self.recurrent_dropout)
            rec_dp_mask = [K.in_train_phase(dropped_inputs,
                                            ones,
                                            training=training) for _ in range(4)]
            constants.append(rec_dp_mask)
        else:
            constants.append([K.cast_to_floatx(1.) for _ in range(4)])
        return constants

    def step(self, inputs, states):
        h_tm1 = states[0]
        c_tm1 = states[1]
        dp_mask = states[2]
        rec_dp_mask = states[3]

        if self.implementation == 2:
            z = K.dot(inputs * dp_mask[0], self.kernel)
            z += K.dot(h_tm1 * rec_dp_mask[0], self.recurrent_kernel)
            if self.use_bias:
                z = K.bias_add(z, self.bias)

            z0 = z[:, :self.units]
            z1 = z[:, self.units: 2 * self.units]
            z2 = z[:, 2 * self.units: 3 * self.units]
            z3 = z[:, 3 * self.units:]

            i = self.recurrent_activation(z0)
            f = self.recurrent_activation(z1)
            c = f * c_tm1 + i * self.activation(z2)
            o = self.recurrent_activation(z3)
        else:
            if self.implementation == 0:
                x_i = inputs[:, :self.units]
                x_f = inputs[:, self.units: 2 * self.units]
                x_c = inputs[:, 2 * self.units: 3 * self.units]
                x_o = inputs[:, 3 * self.units:]
            elif self.implementation == 1:
                x_i = K.dot(inputs * dp_mask[0], self.kernel_i) + self.bias_i
                x_f = K.dot(inputs * dp_mask[1], self.kernel_f) + self.bias_f
                x_c = K.dot(inputs * dp_mask[2], self.kernel_c) + self.bias_c
                x_o = K.dot(inputs * dp_mask[3], self.kernel_o) + self.bias_o
            else:
                raise ValueError('Unknown `implementation` mode.')

            i = self.recurrent_activation(x_i + K.dot(h_tm1 * rec_dp_mask[0],
                                                      self.recurrent_kernel_i))
            f = self.recurrent_activation(x_f + K.dot(h_tm1 * rec_dp_mask[1],
                                                      self.recurrent_kernel_f))
            c = f * c_tm1 + i * self.activation(x_c + K.dot(h_tm1 * rec_dp_mask[2],
                                                            self.recurrent_kernel_c))
            o = self.recurrent_activation(x_o + K.dot(h_tm1 * rec_dp_mask[3],
                                                      self.recurrent_kernel_o))
        h = o * self.activation(c)
        if 0 < self.dropout + self.recurrent_dropout:
            h._uses_learning_phase = True
        return h, [h, c]

    def get_config(self):
        config = {'units': self.units,
                  'activation': activations.serialize(self.activation),
                  'recurrent_activation': activations.serialize(self.recurrent_activation),
                  'use_bias': self.use_bias,
                  'kernel_initializer': initializers.serialize(self.kernel_initializer),
                  'recurrent_initializer': initializers.serialize(self.recurrent_initializer),
                  'bias_initializer': initializers.serialize(self.bias_initializer),
                  'unit_forget_bias': self.unit_forget_bias,
                  'kernel_regularizer': regularizers.serialize(self.kernel_regularizer),
                  'recurrent_regularizer': regularizers.serialize(self.recurrent_regularizer),
                  'bias_regularizer': regularizers.serialize(self.bias_regularizer),
                  'activity_regularizer': regularizers.serialize(self.activity_regularizer),
                  'kernel_constraint': constraints.serialize(self.kernel_constraint),
                  'recurrent_constraint': constraints.serialize(self.recurrent_constraint),
                  'bias_constraint': constraints.serialize(self.bias_constraint),
                  'dropout': self.dropout,
                  'recurrent_dropout': self.recurrent_dropout}
        base_config = super(LSTM, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))
