# coding:utf-8
import tensorflow as tf

from .Model import Model


class TransH(Model):
    r'''
    To preserve the mapping propertities of 1-N/N-1/N-N relations,
    TransH inperprets a relation as a translating operation on a hyperplane.
    '''
    key = 'transh'

    def _transfer(self, e, n):
        n = tf.nn.l2_normalize(n, -1)
        return e - tf.reduce_sum(e * n, -1, keepdims=True) * n

    def _calc(self, h, t, r, flag=True):
        h = tf.nn.l2_normalize(h, -1)
        t = tf.nn.l2_normalize(t, -1)
        r = tf.nn.l2_normalize(r, -1)
        return abs(h + r - t)

    def embedding_def(self):
        # Obtaining the initial configuration of the model
        config = self.get_config()
        # Defining required parameters of the model, including embeddings of entities and relations, and normal vectors of planes
        initializer = tf.initializers.GlorotUniform()
        self.ent_embeddings = tf.compat.v1.get_variable(name="ent_embeddings", shape=[config.entTotal, config.hidden_size], initializer=initializer, trainable=True)
        self.rel_embeddings = tf.compat.v1.get_variable(name="rel_embeddings", shape=[config.relTotal, config.hidden_size], initializer=initializer, trainable=True)
        self.normal_vectors = tf.compat.v1.get_variable(name="normal_vectors", shape=[config.relTotal, config.hidden_size], initializer=initializer, trainable=True)
        self.parameter_lists = {"ent_embeddings": self.ent_embeddings, "rel_embeddings": self.rel_embeddings, "normal_vectors": self.normal_vectors}

    def loss_def(self):
        # Obtaining the initial configuration of the model
        config = self.get_config()
        # To get positive triples and negative triples for training
        # The shapes of pos_h, pos_t, pos_r are (batch_size, 1)
        # The shapes of neg_h, neg_t, neg_r are (batch_size, negative_ent + negative_rel)
        pos_h, pos_t, pos_r = self.get_positive_instance(in_batch=True)
        neg_h, neg_t, neg_r = self.get_negative_instance(in_batch=True)
        # Embedding entities and relations of triples, e.g. pos_h_e, pos_t_e and pos_r_e are embeddings for positive triples
        pos_h_e = tf.nn.embedding_lookup(self.ent_embeddings, pos_h)
        pos_t_e = tf.nn.embedding_lookup(self.ent_embeddings, pos_t)
        pos_r_e = tf.nn.embedding_lookup(self.rel_embeddings, pos_r)
        neg_h_e = tf.nn.embedding_lookup(self.ent_embeddings, neg_h)
        neg_t_e = tf.nn.embedding_lookup(self.ent_embeddings, neg_t)
        neg_r_e = tf.nn.embedding_lookup(self.rel_embeddings, neg_r)
        # Getting the required normal vectors of planes to transfer entity embeddings
        pos_norm = tf.nn.embedding_lookup(self.normal_vectors, pos_r)
        neg_norm = tf.nn.embedding_lookup(self.normal_vectors, neg_r)

        # Calculating score functions for all positive triples and negative triples
        p_h = self._transfer(pos_h_e, pos_norm)
        p_t = self._transfer(pos_t_e, pos_norm)
        p_r = pos_r_e
        n_h = self._transfer(neg_h_e, neg_norm)
        n_t = self._transfer(neg_t_e, neg_norm)
        n_r = neg_r_e
        # Calculating score functions for all positive triples and negative triples
        # The shape of _p_score is (batch_size, 1, hidden_size)
        # The shape of _n_score is (batch_size, negative_ent + negative_rel, hidden_size)
        _p_score = self._calc(p_h, p_t, p_r)
        _n_score = self._calc(n_h, n_t, n_r)
        # The shape of p_score is (batch_size, 1, 1)
        # The shape of n_score is (batch_size, negative_ent + negative_rel, 1)
        p_score = tf.reduce_sum(_p_score, -1, keepdims=True)
        n_score = tf.reduce_sum(_n_score, -1, keepdims=True)
        # Calculating loss to get what the framework will optimize
        self.loss = tf.reduce_mean(tf.maximum(p_score - n_score + config.margin, 0))

    def predict_def(self):
        config = self.get_config()
        predict_h, predict_t, predict_r = self.get_predict_instance()
        predict_h_e = tf.nn.embedding_lookup(self.ent_embeddings, predict_h)
        predict_t_e = tf.nn.embedding_lookup(self.ent_embeddings, predict_t)
        predict_r_e = tf.nn.embedding_lookup(self.rel_embeddings, predict_r)
        predict_norm = tf.nn.embedding_lookup(self.normal_vectors, predict_r)
        h_e = self._transfer(predict_h_e, predict_norm)
        t_e = self._transfer(predict_t_e, predict_norm)
        r_e = predict_r_e
        self.predict = tf.reduce_sum(self._calc(h_e, t_e, r_e), -1, keepdims=True)
