from megnet.layers.graph import GraphNetworkLayer
import tensorflow as tf
import keras.backend as kb
from megnet.utils.layer import repeat_with_index

__author__ = "Chi Chen"
__copyright__ = "Copyright 2018, Materials Virtual Lab "
__version__ = "0.1"
__date__ = "Dec 1, 2018"


class MEGNetLayer(GraphNetworkLayer):
    """
    The MEGNet graph implementation as described in the paper

    Chen, Chi; Ye, Weike Ye; Zuo, Yunxing; Zheng, Chen; Ong, Shyue Ping.
    Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals,
    2018, arXiv preprint. [arXiv:1812.05055](https://arxiv.org/abs/1812.05055)

    Args:
        units_v (list of integers): the hidden layer sizes for node update neural network
        units_e (list of integers): the hidden layer sizes for edge update neural network
        units_u (list of integers): the hidden layer sizes for state update neural network
        pool_method (str): 'mean' or 'sum', determines how information is gathered to nodes from neighboring edges
        activation (str): Default: None. The activation function used for each sub-neural network. Examples include
            'relu', 'softmax', 'tanh', 'sigmoid' and etc.
        use_bias (bool): Default: True. Whether to use the bias term in the neural network.
        kernel_initializer (str): Default: 'glorot_uniform'. Initialization function for the layer kernel weights,
        bias_initializer (str): Default: 'zeros'
        activity_regularizer (str): Default: None. The regularization function for the output
        kernel_constraint (str): Default: None. Keras constraint for kernel values
        bias_constraint (str): Default: None .Keras constraint for bias values

    Methods:
        call(inputs, mask=None): the logic of the layer, returns the final graph
        compute_output_shape(input_shape): compute static output shapes, returns list of tuple shapes
        build(input_shape): initialize the weights and biases for each function
        phi_e(inputs): update function for bonds and returns updated bond attribute e_p
        rho_e_v(e_p, inputs): aggregate updated bonds e_p to per atom attributes, b_e_p
        phi_v(b_e_p, inputs): update the atom attributes by the results from previous step b_e_p and all the inputs
            returns v_p.
        rho_e_u(e_p, inputs): aggregate bonds to global attribute
        rho_v_u(v_p, inputs): aggregate atom to global attributes
        get_config(): part of keras interface for serialization

    """

    def __init__(self,
                 units_v,
                 units_e,
                 units_u,
                 pool_method='mean',
                 activation=None,
                 use_bias=True,
                 kernel_initializer='glorot_uniform',
                 bias_initializer='zeros',
                 kernel_regularizer=None,
                 bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None,
                 bias_constraint=None,
                 **kwargs):

        super().__init__(activation=activation,
                         use_bias=use_bias,
                         kernel_initializer=kernel_initializer,
                         bias_initializer=bias_initializer,
                         kernel_regularizer=kernel_regularizer,
                         bias_regularizer=bias_regularizer,
                         activity_regularizer=activity_regularizer,
                         kernel_constraint=kernel_constraint,
                         bias_constraint=bias_constraint,
                         **kwargs)
        self.units_v = units_v
        self.units_e = units_e
        self.units_u = units_u
        self.pool_method = pool_method
        if pool_method == 'mean':
            self.reduce_method = tf.reduce_mean
            self.seg_method = tf.math.segment_mean
        elif pool_method == 'sum':
            self.reduce_method = tf.reduce_sum
            self.seg_method = tf.math.segment_sum
        else:
            raise ValueError('Pool method: ' + pool_method + ' not understood!')

    def build(self, input_shapes):
        vdim = input_shapes[0][2]
        edim = input_shapes[1][2]
        udim = input_shapes[2][2]

        with kb.name_scope(self.name):
            with kb.name_scope('phi_v'):
                v_shapes = [self.units_e[-1] + vdim + udim] + self.units_v
                v_shapes = list(zip(v_shapes[:-1], v_shapes[1:]))
                self.phi_v_weights = [self.add_weight(shape=i,
                                                      initializer=self.kernel_initializer,
                                                      name='weight_v_%d' % j,
                                                      regularizer=self.kernel_regularizer,
                                                      constraint=self.kernel_constraint)
                                      for j, i in enumerate(v_shapes)]
                if self.use_bias:
                    self.phi_v_biases = [self.add_weight(shape=(i[-1],),
                                                         initializer=self.bias_initializer,
                                                         name='bias_v_%d' % j,
                                                         regularizer=self.bias_regularizer,
                                                         constraint=self.bias_constraint)
                                         for j, i in enumerate(v_shapes)]
                else:
                    self.phi_v_biases = None

            with kb.name_scope('phi_e'):
                e_shapes = [2 * vdim + edim + udim] + self.units_e
                e_shapes = list(zip(e_shapes[:-1], e_shapes[1:]))
                self.phi_e_weights = [self.add_weight(shape=i,
                                                      initializer=self.kernel_initializer,
                                                      name='weight_e_%d' % j,
                                                      regularizer=self.kernel_regularizer,
                                                      constraint=self.kernel_constraint)
                                      for j, i in enumerate(e_shapes)]
                if self.use_bias:
                    self.phi_e_biases = [self.add_weight(shape=(i[-1],),
                                                         initializer=self.bias_initializer,
                                                         name='bias_e_%d' % j,
                                                         regularizer=self.bias_regularizer,
                                                         constraint=self.bias_constraint)
                                         for j, i in enumerate(e_shapes)]
                else:
                    self.phi_e_biases = None

            with kb.name_scope('phi_u'):
                u_shapes = [self.units_e[-1] + self.units_v[
                    -1] + udim] + self.units_u
                u_shapes = list(zip(u_shapes[:-1], u_shapes[1:]))
                self.phi_u_weights = [self.add_weight(shape=i,
                                                      initializer=self.kernel_initializer,
                                                      name='weight_u_%d' % j,
                                                      regularizer=self.kernel_regularizer,
                                                      constraint=self.kernel_constraint)
                                      for j, i in enumerate(u_shapes)]
                if self.use_bias:
                    self.phi_u_biases = [self.add_weight(shape=(i[-1],),
                                                         initializer=self.bias_initializer,
                                                         name='bias_u_%d' % j,
                                                         regularizer=self.bias_regularizer,
                                                         constraint=self.bias_constraint)
                                         for j, i in enumerate(u_shapes)]
                else:
                    self.phi_u_biases = None
        self.built = True

    def compute_output_shape(self, input_shape):
        node_feature_shape = input_shape[0]
        edge_feature_shape = input_shape[1]
        state_feature_shape = input_shape[2]
        output_shape = [
            (node_feature_shape[0], node_feature_shape[1], self.units_v[-1]),
            (edge_feature_shape[0], edge_feature_shape[1], self.units_e[-1]),
            (state_feature_shape[0], state_feature_shape[1], self.units_u[-1])]
        return output_shape

    def phi_e(self, inputs):
        nodes, edges, u, index1, index2, gnode, gbond = inputs
        index1 = tf.reshape(index1, (-1,))
        index2 = tf.reshape(index2, (-1,))
        fs = tf.gather(nodes, index1, axis=1)
        fr = tf.gather(nodes, index2, axis=1)
        concate_node = tf.concat([fs, fr], axis=-1)
        u_expand = repeat_with_index(u, gbond, axis=1)
        concated = tf.concat([concate_node, edges, u_expand], axis=-1)
        return self._mlp(concated, self.phi_e_weights, self.phi_e_biases)

    def rho_e_v(self, e_p, inputs):
        node, edges, u, index1, index2, gnode, gbond = inputs
        index1 = tf.reshape(index1, (-1,))
        return tf.expand_dims(self.seg_method(tf.squeeze(e_p), index1), axis=0)

    def phi_v(self, b_ei_p, inputs):
        nodes, edges, u, index1, index2, gnode, gbond = inputs
        u_expand = repeat_with_index(u, gnode, axis=1)
        concated = tf.concat([b_ei_p, nodes, u_expand], axis=-1)
        return self._mlp(concated, self.phi_v_weights, self.phi_v_biases)

    def rho_e_u(self, e_p, inputs):
        nodes, edges, u, index1, index2, gnode, gbond = inputs
        gbond = tf.reshape(gbond, (-1,))
        return tf.expand_dims(self.seg_method(tf.squeeze(e_p), gbond), axis=0)

    def rho_v_u(self, v_p, inputs):
        nodes, edges, u, index1, index2, gnode, gbond = inputs
        gnode = tf.reshape(gnode, (-1,))
        return tf.expand_dims(self.seg_method(tf.squeeze(v_p, axis=0), gnode), axis=0)

    def phi_u(self, b_e_p, b_v_p, inputs):
        concated = tf.concat([b_e_p, b_v_p, inputs[2]], axis=-1)
        return self._mlp(concated, self.phi_u_weights, self.phi_u_biases)

    def _mlp(self, input_, weights, biases):
        if biases is None:
            biases = [0] * len(weights)
        act = input_
        for w, b in zip(weights, biases):
            output = kb.dot(act, w) + b
            act = self.activation(output)
        return output

    def get_config(self):
        config = {
            'units_e': self.units_e,
            'units_v': self.units_v,
            'units_u': self.units_u,
            'pool_method': self.pool_method
        }

        base_config = super().get_config()
        return dict(list(base_config.items()) + list(config.items()))
