import tensorflow as tf

from dice import dice
from tensorflow.python.ops.rnn_cell import GRUCell
# from tensorflow.python.ops.rnn import dynamic_rn
from rnn import dynamic_rnn
USE_DICE = True
USE_RNN = False #True

class Model(object):

  def __init__(self, user_count, item_count, cate_count, cate_list, predict_batch_size, predict_ads_num, reuse):

    with tf.variable_scope('DinNet', reuse=reuse):
        self.u = tf.placeholder(tf.int32, [None,]) # [B]
        self.i = tf.placeholder(tf.int32, [None,]) # [B]
        self.j = tf.placeholder(tf.int32, [None,]) # [B]
        self.y = tf.placeholder(tf.int32, [None,]) # [B]
        self.hist_i = tf.placeholder(tf.int32, [None, None]) # [B, T]
        self.sl = tf.placeholder(tf.int32, [None,]) # [B]
        self.lr = tf.placeholder(tf.float64, [])

        hidden_units = 128

        item_emb_w = tf.get_variable("item_emb_w", [item_count, hidden_units // 2])
        item_b = tf.get_variable("item_b", [item_count], initializer=tf.constant_initializer(0.0))
        cate_emb_w = tf.get_variable("cate_emb_w", [cate_count, hidden_units // 2])
        cate_list = tf.convert_to_tensor(cate_list, dtype=tf.int64)

        ic = tf.gather(cate_list, self.i)
        i_emb = tf.concat(values = [
            tf.nn.embedding_lookup(item_emb_w, self.i),
            tf.nn.embedding_lookup(cate_emb_w, ic),
            ], axis=1)
        i_b = tf.gather(item_b, self.i)

        jc = tf.gather(cate_list, self.j)
        j_emb = tf.concat([
            tf.nn.embedding_lookup(item_emb_w, self.j),
            tf.nn.embedding_lookup(cate_emb_w, jc),
            ], axis=1)
        j_b = tf.gather(item_b, self.j)

        hc = tf.gather(cate_list, self.hist_i)
        h_emb = tf.concat([
            tf.nn.embedding_lookup(item_emb_w, self.hist_i),
            tf.nn.embedding_lookup(cate_emb_w, hc),
            ], axis=2)

        if USE_RNN:
            rnn_outputs, _ = dynamic_rnn(GRUCell(hidden_units), inputs=h_emb, sequence_length=self.sl, dtype=tf.float32, scope='gru1')
            hist_i =attention(i_emb, rnn_outputs, self.sl)
        else:
            hist_i =attention(i_emb, h_emb, self.sl)
        #-- attention end ---

        hist_i = tf.layers.batch_normalization(inputs = hist_i)
        hist_i = tf.reshape(hist_i, [-1, hidden_units], name='hist_bn')
        hist_i = tf.layers.dense(hist_i, hidden_units, name='hist_fcn')

        u_emb_i = hist_i

        if USE_RNN:
            hist_j =attention(j_emb, rnn_outputs, self.sl)
        else:
            hist_j =attention(j_emb, h_emb, self.sl)
        #-- attention end ---

        hist_j = tf.layers.batch_normalization(inputs = hist_j, reuse=True)
        hist_j = tf.reshape(hist_j, [-1, hidden_units], name='hist_bn')
        hist_j = tf.layers.dense(hist_j, hidden_units, name='hist_fcn', reuse=True)

        u_emb_j = hist_j
        print('shapes:')
        print(f'(u_emb_i, u_emb_j, i_emb, j_emb) -> ({u_emb_i.get_shape().as_list()}, {u_emb_j.get_shape().as_list()}, {i_emb.get_shape().as_list()}, {j_emb.get_shape().as_list()})')

        #-- fcn begin -------
        din_i = tf.concat([u_emb_i, i_emb], axis=-1)
        din_i = tf.layers.batch_normalization(inputs=din_i, name='b1')

        if USE_DICE:
            d_layer_1_i = tf.layers.dense(din_i, 80, activation=None, name='f1')
            d_layer_1_i = dice(d_layer_1_i, name='dice_1')
            d_layer_2_i = tf.layers.dense(d_layer_1_i, 40, activation=None, name='f2')
            d_layer_2_i = dice(d_layer_2_i, name='dice_2')
        else:
            d_layer_1_i = tf.layers.dense(din_i, 80, activation=tf.nn.sigmoid, name='f1')
            d_layer_2_i = tf.layers.dense(d_layer_1_i, 40, activation=tf.nn.sigmoid, name='f2')

        #if u want try dice change sigmoid to None and add dice layer like following two lines. You can also find model_dice.py in this folder.
        d_layer_3_i = tf.layers.dense(d_layer_2_i, 1, activation=None, name='f3')
        din_j = tf.concat([u_emb_j, j_emb], axis=-1)
        din_j = tf.layers.batch_normalization(inputs=din_j, name='b1', reuse=True)

        if USE_DICE:
            d_layer_1_j = tf.layers.dense(din_j, 80, activation=None, name='f1', reuse=True)
            d_layer_1_j = dice(d_layer_1_j, name='dice_1')
            d_layer_2_j = tf.layers.dense(d_layer_1_j, 40, activation=None, name='f2', reuse=True)
            d_layer_2_j = dice(d_layer_2_j, name='dice_2')
        else:
            d_layer_1_j = tf.layers.dense(din_j, 80, activation=tf.nn.sigmoid, name='f1', reuse=True)
            d_layer_2_j = tf.layers.dense(d_layer_1_j, 40, activation=tf.nn.sigmoid, name='f2', reuse=True)

        d_layer_3_j = tf.layers.dense(d_layer_2_j, 1, activation=None, name='f3', reuse=True)
        d_layer_3_i = tf.reshape(d_layer_3_i, [-1])
        d_layer_3_j = tf.reshape(d_layer_3_j, [-1])
        x = i_b - j_b + d_layer_3_i - d_layer_3_j # [B]
        self.logits = i_b + d_layer_3_i

        # prediciton for selected items
        # logits for selected item:
        item_emb_all = tf.concat([
            item_emb_w,
            tf.nn.embedding_lookup(cate_emb_w, cate_list)
            ], axis=1)
        item_emb_sub = item_emb_all[:predict_ads_num,:]
        item_emb_sub = tf.expand_dims(item_emb_sub, 0)
        item_emb_sub = tf.tile(item_emb_sub, [predict_batch_size, 1, 1])
        hist_sub =attention_multi_items(item_emb_sub, h_emb, self.sl)
        #-- attention end ---

        hist_sub = tf.layers.batch_normalization(inputs = hist_sub, name='hist_bn', reuse=tf.AUTO_REUSE)
        hist_sub = tf.reshape(hist_sub, [-1, hidden_units])
        hist_sub = tf.layers.dense(hist_sub, hidden_units, name='hist_fcn', reuse=tf.AUTO_REUSE)

        u_emb_sub = hist_sub
        item_emb_sub = tf.reshape(item_emb_sub, [-1, hidden_units])
        din_sub = tf.concat([u_emb_sub, item_emb_sub], axis=-1)
        din_sub = tf.layers.batch_normalization(inputs=din_sub, name='b1', reuse=True)
        d_layer_1_sub = tf.layers.dense(din_sub, 80, activation=tf.nn.sigmoid, name='f1', reuse=True)
        d_layer_2_sub = tf.layers.dense(d_layer_1_sub, 40, activation=tf.nn.sigmoid, name='f2', reuse=True)
        d_layer_3_sub = tf.layers.dense(d_layer_2_sub, 1, activation=None, name='f3', reuse=True)
        d_layer_3_sub = tf.reshape(d_layer_3_sub, [-1, predict_ads_num])
        self.logits_sub = tf.sigmoid(item_b[:predict_ads_num] + d_layer_3_sub)
        self.logits_sub = tf.reshape(self.logits_sub, [-1, predict_ads_num, 1])
        #-- fcn end -------


        self.mf_auc = tf.reduce_mean(tf.to_float(x > 0))
        self.score_i = tf.sigmoid(i_b + d_layer_3_i)
        self.score_j = tf.sigmoid(j_b + d_layer_3_j)
        self.score_i = tf.reshape(self.score_i, [-1, 1])
        self.score_j = tf.reshape(self.score_j, [-1, 1])
        self.p_and_n = tf.concat([self.score_i, self.score_j], axis=-1)
        print(f'p_and_n -> {self.p_and_n.get_shape().as_list()}')

        # Step variable
        self.global_step = tf.Variable(0, trainable=False, name='global_step')
        self.global_epoch_step = tf.Variable(0, trainable=False, name='global_epoch_step')
        self.global_epoch_step_op = tf.assign(self.global_epoch_step, self.global_epoch_step+1)

        self.loss = tf.reduce_mean(
            tf.nn.sigmoid_cross_entropy_with_logits(
                logits=self.logits,
                # labels=self.y)
                labels=tf.cast(self.y, tf.float32))
            )

        self.trainable_params = tf.trainable_variables()
        self.opt = tf.train.GradientDescentOptimizer(learning_rate=self.lr)
        self.gradients = tf.gradients(self.loss, self.trainable_params)
        self.clip_gradients, _ = tf.clip_by_global_norm(self.gradients, 5)
        self.train_op = self.opt.apply_gradients(
            zip(self.clip_gradients, self.trainable_params), global_step=self.global_step)


  def train(self, sess, uij, l):
    loss, _ = sess.run([self.loss, self.train_op], feed_dict={
        self.u: uij[0],
        self.i: uij[1],
        self.y: uij[2],
        self.hist_i: uij[3],
        self.sl: uij[4],
        self.lr: l,
        })
    return loss
    # return loss, gradients

  def eval(self, sess, uij):
    u_auc, socre_p_and_n = sess.run([self.mf_auc, self.p_and_n], feed_dict={
        self.u: uij[0],
        self.i: uij[1],
        self.j: uij[2],
        self.hist_i: uij[3],
        self.sl: uij[4],
        })
    return u_auc, socre_p_and_n
  
  def eval_logdata(self, sess, uij):
      score_i = sess.run([self.score_i], feed_dict={
          self.u: uij[0],
          self.i: uij[1],
          self.hist_i: uij[3],
          self.sl: uij[4],
      })
      return score_i

  def test(self, sess, uij):
    return sess.run(self.logits_sub, feed_dict={
        self.u: uij[0],
        self.i: uij[1],
        self.j: uij[2],
        self.hist_i: uij[3],
        self.sl: uij[4],
        })

  def save(self, sess, path):
    saver = tf.train.Saver()
    saver.save(sess, save_path=path)

  def restore(self, sess, path):
    saver = tf.train.Saver()
    saver.restore(sess, save_path=path)


def extract_axis_1(data, ind):
  batch_range = tf.range(tf.shape(data)[0])
  indices = tf.stack([batch_range, ind], axis=1)
  res = tf.gather_nd(data, indices)
  return res


def attention(queries, keys, keys_length):
  '''
    queries:     [B, H]
    keys:        [B, T, H]
    keys_length: [B]
  '''
  queries_hidden_units = queries.get_shape().as_list()[-1]
  queries = tf.tile(queries, [1, tf.shape(keys)[1]])
  queries = tf.reshape(queries, [-1, tf.shape(keys)[1], queries_hidden_units])
  din_all = tf.concat([queries, keys, queries-keys, queries*keys], axis=-1)
  d_layer_1_all = tf.layers.dense(din_all, 80, activation=tf.nn.sigmoid, name='f1_att', reuse=tf.AUTO_REUSE)
  d_layer_2_all = tf.layers.dense(d_layer_1_all, 40, activation=tf.nn.sigmoid, name='f2_att', reuse=tf.AUTO_REUSE)
  d_layer_3_all = tf.layers.dense(d_layer_2_all, 1, activation=None, name='f3_att', reuse=tf.AUTO_REUSE)
  d_layer_3_all = tf.reshape(d_layer_3_all, [-1, 1, tf.shape(keys)[1]])
  outputs = d_layer_3_all 
  # Mask
  key_masks = tf.sequence_mask(keys_length, tf.shape(keys)[1])   # [B, T]
  key_masks = tf.expand_dims(key_masks, 1) # [B, 1, T]
  paddings = tf.ones_like(outputs) * (-2 ** 32 + 1)
  outputs = tf.where(key_masks, outputs, paddings)  # [B, 1, T]

  # Scale
  outputs = outputs / (keys.get_shape().as_list()[-1] ** 0.5)

  # Activation
  outputs = tf.nn.softmax(outputs)  # [B, 1, T]

  # Weighted sum
  outputs = tf.matmul(outputs, keys)  # [B, 1, H]

  return outputs


def attention_multi_items(queries, keys, keys_length):
  '''
    queries:     [B, N, H] N is the number of ads
    keys:        [B, T, H] 
    keys_length: [B]
  '''
  queries_hidden_units = queries.get_shape().as_list()[-1]
  queries_nums = queries.get_shape().as_list()[1]
  queries = tf.tile(queries, [1, 1, tf.shape(keys)[1]])
  queries = tf.reshape(queries, [-1, queries_nums, tf.shape(keys)[1], queries_hidden_units]) # shape : [B, N, T, H]
  max_len = tf.shape(keys)[1]
  keys = tf.tile(keys, [1, queries_nums, 1])
  keys = tf.reshape(keys, [-1, queries_nums, max_len, queries_hidden_units]) # shape : [B, N, T, H]
  din_all = tf.concat([queries, keys, queries-keys, queries*keys], axis=-1)
  d_layer_1_all = tf.layers.dense(din_all, 80, activation=tf.nn.sigmoid, name='f1_att', reuse=tf.AUTO_REUSE)
  d_layer_2_all = tf.layers.dense(d_layer_1_all, 40, activation=tf.nn.sigmoid, name='f2_att', reuse=tf.AUTO_REUSE)
  d_layer_3_all = tf.layers.dense(d_layer_2_all, 1, activation=None, name='f3_att', reuse=tf.AUTO_REUSE)
  d_layer_3_all = tf.reshape(d_layer_3_all, [-1, queries_nums, 1, max_len])
  outputs = d_layer_3_all 
  # Mask
  key_masks = tf.sequence_mask(keys_length, max_len)   # [B, T]
  key_masks = tf.tile(key_masks, [1, queries_nums])
  key_masks = tf.reshape(key_masks, [-1, queries_nums, 1, max_len]) # shape : [B, N, 1, T]
  paddings = tf.ones_like(outputs) * (-2 ** 32 + 1)
  outputs = tf.where(key_masks, outputs, paddings)  # [B, N, 1, T]

  # Scale
  outputs = outputs / (keys.get_shape().as_list()[-1] ** 0.5)

  # Activation
  outputs = tf.nn.softmax(outputs)  # [B, N, 1, T]
  outputs = tf.reshape(outputs, [-1, 1, max_len])
  keys = tf.reshape(keys, [-1, max_len, queries_hidden_units])

  # Weighted sum
  outputs = tf.matmul(outputs, keys)
  outputs = tf.reshape(outputs, [-1, queries_nums, queries_hidden_units])  # [B, N, 1, H]
  print(f'outputs -> {outputs.get_shape().as_list()}')
  return outputs


def DIN(i, j, y, hist_i, sl, item_count, cate_count, cate_list, reuse, is_training):

    with tf.variable_scope('DinNet', reuse=reuse):

        hidden_units = 128

        item_emb_w = tf.get_variable("item_emb_w", [item_count, hidden_units // 2])
        item_b = tf.get_variable("item_b", [item_count], initializer=tf.constant_initializer(0.0))
        cate_emb_w = tf.get_variable("cate_emb_w", [cate_count, hidden_units // 2])
        cate_list = tf.convert_to_tensor(cate_list, dtype=tf.int64)

        ic = tf.gather(cate_list, i)
        i_emb = tf.concat(values = [
            tf.nn.embedding_lookup(item_emb_w, i),
            tf.nn.embedding_lookup(cate_emb_w, ic),
            ], axis=1)
        i_b = tf.gather(item_b, i)

        jc = tf.gather(cate_list, j)
        j_emb = tf.concat([
            tf.nn.embedding_lookup(item_emb_w, j),
            tf.nn.embedding_lookup(cate_emb_w, jc),
            ], axis=1)
        j_b = tf.gather(item_b, j)

        hc = tf.gather(cate_list, hist_i)
        h_emb = tf.concat([
            tf.nn.embedding_lookup(item_emb_w, hist_i),
            tf.nn.embedding_lookup(cate_emb_w, hc),
            ], axis=2)

        if USE_RNN:
            rnn_outputs, _ = dynamic_rnn(GRUCell(hidden_units), inputs=h_emb, sequence_length=sl, dtype=tf.float32, scope='gru1')
            hist_i =attention(i_emb, rnn_outputs, sl)
        else:
            hist_i =attention(i_emb, h_emb, sl)
        #-- attention end ---

        hist_i = tf.layers.batch_normalization(inputs = hist_i)
        hist_i = tf.reshape(hist_i, [-1, hidden_units], name='hist_bn')
        hist_i = tf.layers.dense(hist_i, hidden_units, name='hist_fcn')

        u_emb_i = hist_i

        if USE_RNN:
            hist_j =attention(j_emb, rnn_outputs, sl)
        else:
            hist_j =attention(j_emb, h_emb, sl)
        #-- attention end ---

        # hist_j = tf.layers.batch_normalization(inputs = hist_j)
        hist_j = tf.layers.batch_normalization(inputs = hist_j, reuse=True)
        hist_j = tf.reshape(hist_j, [-1, hidden_units], name='hist_bn')
        hist_j = tf.layers.dense(hist_j, hidden_units, name='hist_fcn', reuse=True)

        u_emb_j = hist_j
        print('shapes:')
        print(f'(u_emb_i, u_emb_j, i_emb, j_emb) -> ({u_emb_i.get_shape().as_list()}, {u_emb_j.get_shape().as_list()}, {i_emb.get_shape().as_list()}, {j_emb.get_shape().as_list()})')

        #-- fcn begin -------
        din_i = tf.concat([u_emb_i, i_emb], axis=-1)
        din_i = tf.layers.batch_normalization(inputs=din_i, name='b1')

        if USE_DICE:
            d_layer_1_i = tf.layers.dense(din_i, 80, activation=None, name='f1')
            d_layer_1_i = dice(d_layer_1_i, name='dice_1')
            d_layer_2_i = tf.layers.dense(d_layer_1_i, 40, activation=None, name='f2')
            d_layer_2_i = dice(d_layer_2_i, name='dice_2')
        else:
            d_layer_1_i = tf.layers.dense(din_i, 80, activation=tf.nn.sigmoid, name='f1')
            d_layer_2_i = tf.layers.dense(d_layer_1_i, 40, activation=tf.nn.sigmoid, name='f2')

        #if u want try dice change sigmoid to None and add dice layer like following two lines. You can also find model_dice.py in this folder.
        d_layer_3_i = tf.layers.dense(d_layer_2_i, 1, activation=None, name='f3')
        din_j = tf.concat([u_emb_j, j_emb], axis=-1)
        din_j = tf.layers.batch_normalization(inputs=din_j, name='b1', reuse=True)

        if USE_DICE:
            d_layer_1_j = tf.layers.dense(din_j, 80, activation=None, name='f1', reuse=True)
            d_layer_1_j = dice(d_layer_1_j, name='dice_1')
            d_layer_2_j = tf.layers.dense(d_layer_1_j, 40, activation=None, name='f2', reuse=True)
            d_layer_2_j = dice(d_layer_2_j, name='dice_2')
        else:
            d_layer_1_j = tf.layers.dense(din_j, 80, activation=tf.nn.sigmoid, name='f1', reuse=True)
            d_layer_2_j = tf.layers.dense(d_layer_1_j, 40, activation=tf.nn.sigmoid, name='f2', reuse=True)

        d_layer_3_j = tf.layers.dense(d_layer_2_j, 1, activation=None, name='f3', reuse=True)
        d_layer_3_i = tf.reshape(d_layer_3_i, [-1])
        d_layer_3_j = tf.reshape(d_layer_3_j, [-1])
        x = i_b - j_b + d_layer_3_i - d_layer_3_j # [B]
        logits = i_b + d_layer_3_i

        logits = tf.sigmoid(logits) if not is_training else logits

    return logits