import random

import itertools
import numpy as np
import tensorflow as tf
from tensorflow.contrib.rnn import BasicLSTMCell

from basic.read_data import DataSet
from my.tensorflow import get_initializer
from my.tensorflow.nn import softsel, get_logits, highway_network, multi_conv1d
from my.tensorflow.rnn import bidirectional_dynamic_rnn
from my.tensorflow.rnn_cell import SwitchableDropoutWrapper, AttentionCell


def get_multi_gpu_models(config):
    models = []
    with tf.variable_scope(tf.get_variable_scope()):
        for gpu_idx in range(config.num_gpus):
            with tf.name_scope("model_{}".format(gpu_idx)) as scope, tf.device("/{}:{}".format(config.device_type, gpu_idx)):
                if gpu_idx > 0:
                    tf.get_variable_scope().reuse_variables()
                model = Model(config, scope, rep=gpu_idx == 0)
                models.append(model)

    # update the summary in a different scope to avoid reuse issue
    with tf.variable_scope('loss_summary', reuse=False):
        for gpu_idx in range(config.num_gpus):
            with tf.name_scope("model_{}".format(gpu_idx)) as scope, tf.device("/{}:{}".format(config.device_type, gpu_idx)):
                model = models[gpu_idx]
                rep = gpu_idx == 0
                if rep:
                    model._build_var_ema()
                if config.mode == 'train':
                    model._build_ema();
                model.summary = tf.summary.merge_all()
                model.summary = tf.summary.merge(tf.get_collection("summaries", scope=model.scope))

    return models


class Model(object):
    def __init__(self, config, scope, rep=True):
        self.scope = scope
        self.config = config
        self.global_step = tf.get_variable('global_step', shape=[], dtype='int32',
                                           initializer=tf.constant_initializer(0), trainable=False)

        # Define forward inputs here
        N, M, JX, JQ, VW, VC, W = \
            config.batch_size, config.max_num_sents, config.max_sent_size, \
            config.max_ques_size, config.word_vocab_size, config.char_vocab_size, config.max_word_size
        self.x = tf.placeholder('int32', [N, None, None], name='x')
        self.cx = tf.placeholder('int32', [N, None, None, W], name='cx')
        self.x_mask = tf.placeholder('bool', [N, None, None], name='x_mask')
        self.q = tf.placeholder('int32', [N, None], name='q')
        self.cq = tf.placeholder('int32', [N, None, W], name='cq')
        self.q_mask = tf.placeholder('bool', [N, None], name='q_mask')
        self.y = tf.placeholder('bool', [N, None, None], name='y')
        self.y2 = tf.placeholder('bool', [N, None, None], name='y2')
        self.wy = tf.placeholder('bool', [N, None, None], name='wy')
        self.is_train = tf.placeholder('bool', [], name='is_train')
        self.new_emb_mat = tf.placeholder('float', [None, config.word_emb_size], name='new_emb_mat')
        self.na = tf.placeholder('bool', [N], name='na')

        # Define misc
        self.tensor_dict = {}

        # Forward outputs / loss inputs
        self.logits = None
        self.yp = None
        self.var_list = None
        self.na_prob = None

        # Loss outputs
        self.loss = None

        self._build_forward()
        self._build_loss()
        self.var_ema = None
        # if rep:
        #     self._build_var_ema()
        # if config.mode == 'train':
        #     self._build_ema()

        # self.summary = tf.summary.merge_all()
        # self.summary = tf.summary.merge(tf.get_collection("summaries", scope=self.scope))

    def _build_forward(self):
        config = self.config
        N, M, JX, JQ, VW, VC, d, W = \
            config.batch_size, config.max_num_sents, config.max_sent_size, \
            config.max_ques_size, config.word_vocab_size, config.char_vocab_size, config.hidden_size, \
            config.max_word_size
        JX = tf.shape(self.x)[2]
        JQ = tf.shape(self.q)[1]
        M = tf.shape(self.x)[1]
        dc, dw, dco = config.char_emb_size, config.word_emb_size, config.char_out_size

        with tf.variable_scope("emb"):
            if config.use_char_emb:
                with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
                    char_emb_mat = tf.get_variable("char_emb_mat", shape=[VC, dc], dtype='float')

                with tf.variable_scope("char"):
                    Acx = tf.nn.embedding_lookup(char_emb_mat, self.cx)  # [N, M, JX, W, dc]
                    Acq = tf.nn.embedding_lookup(char_emb_mat, self.cq)  # [N, JQ, W, dc]
                    Acx = tf.reshape(Acx, [-1, JX, W, dc])
                    Acq = tf.reshape(Acq, [-1, JQ, W, dc])

                    filter_sizes = list(map(int, config.out_channel_dims.split(',')))
                    heights = list(map(int, config.filter_heights.split(',')))
                    assert sum(filter_sizes) == dco, (filter_sizes, dco)
                    with tf.variable_scope("conv"):
                        xx = multi_conv1d(Acx, filter_sizes, heights, "VALID",  self.is_train, config.keep_prob, scope="xx")
                        if config.share_cnn_weights:
                            tf.get_variable_scope().reuse_variables()
                            qq = multi_conv1d(Acq, filter_sizes, heights, "VALID", self.is_train, config.keep_prob, scope="xx")
                        else:
                            qq = multi_conv1d(Acq, filter_sizes, heights, "VALID", self.is_train, config.keep_prob, scope="qq")
                        xx = tf.reshape(xx, [-1, M, JX, dco])
                        qq = tf.reshape(qq, [-1, JQ, dco])

            if config.use_word_emb:
                with tf.variable_scope("emb_var"), tf.device("/cpu:0"):
                    if config.mode == 'train':
                        word_emb_mat = tf.get_variable("word_emb_mat", dtype='float', shape=[VW, dw], initializer=get_initializer(config.emb_mat))
                    else:
                        word_emb_mat = tf.get_variable("word_emb_mat", shape=[VW, dw], dtype='float')
                    if config.use_glove_for_unk:
                        word_emb_mat = tf.concat(axis=0, values=[word_emb_mat, self.new_emb_mat])

                with tf.name_scope("word"):
                    Ax = tf.nn.embedding_lookup(word_emb_mat, self.x)  # [N, M, JX, d]
                    Aq = tf.nn.embedding_lookup(word_emb_mat, self.q)  # [N, JQ, d]
                    self.tensor_dict['x'] = Ax
                    self.tensor_dict['q'] = Aq
                if config.use_char_emb:
                    xx = tf.concat(axis=3, values=[xx, Ax])  # [N, M, JX, di]
                    qq = tf.concat(axis=2, values=[qq, Aq])  # [N, JQ, di]
                else:
                    xx = Ax
                    qq = Aq

        # highway network
        if config.highway:
            with tf.variable_scope("highway"):
                xx = highway_network(xx, config.highway_num_layers, True, wd=config.wd, is_train=self.is_train)
                tf.get_variable_scope().reuse_variables()
                qq = highway_network(qq, config.highway_num_layers, True, wd=config.wd, is_train=self.is_train)

        self.tensor_dict['xx'] = xx
        self.tensor_dict['qq'] = qq

        cell_fw = BasicLSTMCell(d, state_is_tuple=True)
        cell_bw = BasicLSTMCell(d, state_is_tuple=True)
        d_cell_fw = SwitchableDropoutWrapper(cell_fw, self.is_train, input_keep_prob=config.input_keep_prob)
        d_cell_bw = SwitchableDropoutWrapper(cell_bw, self.is_train, input_keep_prob=config.input_keep_prob)
        cell2_fw = BasicLSTMCell(d, state_is_tuple=True)
        cell2_bw = BasicLSTMCell(d, state_is_tuple=True)
        d_cell2_fw = SwitchableDropoutWrapper(cell2_fw, self.is_train, input_keep_prob=config.input_keep_prob)
        d_cell2_bw = SwitchableDropoutWrapper(cell2_bw, self.is_train, input_keep_prob=config.input_keep_prob)
        cell3_fw = BasicLSTMCell(d, state_is_tuple=True)
        cell3_bw = BasicLSTMCell(d, state_is_tuple=True)
        d_cell3_fw = SwitchableDropoutWrapper(cell3_fw, self.is_train, input_keep_prob=config.input_keep_prob)
        d_cell3_bw = SwitchableDropoutWrapper(cell3_bw, self.is_train, input_keep_prob=config.input_keep_prob)
        cell4_fw = BasicLSTMCell(d, state_is_tuple=True)
        cell4_bw = BasicLSTMCell(d, state_is_tuple=True)
        d_cell4_fw = SwitchableDropoutWrapper(cell4_fw, self.is_train, input_keep_prob=config.input_keep_prob)
        d_cell4_bw = SwitchableDropoutWrapper(cell4_bw, self.is_train, input_keep_prob=config.input_keep_prob)
        x_len = tf.reduce_sum(tf.cast(self.x_mask, 'int32'), 2)  # [N, M]
        q_len = tf.reduce_sum(tf.cast(self.q_mask, 'int32'), 1)  # [N]

        with tf.variable_scope("prepro"):
            (fw_u, bw_u), ((_, fw_u_f), (_, bw_u_f)) = bidirectional_dynamic_rnn(d_cell_fw, d_cell_bw, qq, q_len, dtype='float', scope='u1')  # [N, J, d], [N, d]
            u = tf.concat(axis=2, values=[fw_u, bw_u])
            if config.share_lstm_weights:
                tf.get_variable_scope().reuse_variables()
                (fw_h, bw_h), _ = bidirectional_dynamic_rnn(cell_fw, cell_bw, xx, x_len, dtype='float', scope='u1')  # [N, M, JX, 2d]
                h = tf.concat(axis=3, values=[fw_h, bw_h])  # [N, M, JX, 2d]
            else:
                (fw_h, bw_h), _ = bidirectional_dynamic_rnn(cell_fw, cell_bw, xx, x_len, dtype='float', scope='h1')  # [N, M, JX, 2d]
                h = tf.concat(axis=3, values=[fw_h, bw_h])  # [N, M, JX, 2d]
            self.tensor_dict['u'] = u
            self.tensor_dict['h'] = h

        with tf.variable_scope("main"):
            if config.dynamic_att:
                p0 = h
                u = tf.reshape(tf.tile(tf.expand_dims(u, 1), [1, M, 1, 1]), [N * M, JQ, 2 * d])
                q_mask = tf.reshape(tf.tile(tf.expand_dims(self.q_mask, 1), [1, M, 1]), [N * M, JQ])
                first_cell_fw = AttentionCell(cell2_fw, u, mask=q_mask, mapper='sim',
                                              input_keep_prob=self.config.input_keep_prob, is_train=self.is_train)
                first_cell_bw = AttentionCell(cell2_bw, u, mask=q_mask, mapper='sim',
                                              input_keep_prob=self.config.input_keep_prob, is_train=self.is_train)
                second_cell_fw = AttentionCell(cell3_fw, u, mask=q_mask, mapper='sim',
                                            input_keep_prob=self.config.input_keep_prob, is_train=self.is_train)
                second_cell_bw = AttentionCell(cell3_bw, u, mask=q_mask, mapper='sim',
                                               input_keep_prob=self.config.input_keep_prob, is_train=self.is_train)
            else:
                p0 = attention_layer(config, self.is_train, h, u, h_mask=self.x_mask, u_mask=self.q_mask, scope="p0", tensor_dict=self.tensor_dict)
                first_cell_fw = d_cell2_fw
                second_cell_fw = d_cell3_fw
                first_cell_bw = d_cell2_bw
                second_cell_bw = d_cell3_bw

            (fw_g0, bw_g0), _ = bidirectional_dynamic_rnn(first_cell_fw, first_cell_bw, p0, x_len, dtype='float', scope='g0')  # [N, M, JX, 2d]
            g0 = tf.concat(axis=3, values=[fw_g0, bw_g0])
            (fw_g1, bw_g1), _ = bidirectional_dynamic_rnn(second_cell_fw, second_cell_bw, g0, x_len, dtype='float', scope='g1')  # [N, M, JX, 2d]
            g1 = tf.concat(axis=3, values=[fw_g1, bw_g1])

            logits = get_logits([g1, p0], d, True, wd=config.wd, input_keep_prob=config.input_keep_prob,
                                mask=self.x_mask, is_train=self.is_train, func=config.answer_func, scope='logits1')
            a1i = softsel(tf.reshape(g1, [N, M * JX, 2 * d]), tf.reshape(logits, [N, M * JX]))
            a1i = tf.tile(tf.expand_dims(tf.expand_dims(a1i, 1), 1), [1, M, JX, 1])

            (fw_g2, bw_g2), _ = bidirectional_dynamic_rnn(d_cell4_fw, d_cell4_bw, tf.concat(axis=3, values=[p0, g1, a1i, g1 * a1i]),
                                                          x_len, dtype='float', scope='g2')  # [N, M, JX, 2d]
            g2 = tf.concat(axis=3, values=[fw_g2, bw_g2])
            logits2 = get_logits([g2, p0], d, True, wd=config.wd, input_keep_prob=config.input_keep_prob,
                                 mask=self.x_mask,
                                 is_train=self.is_train, func=config.answer_func, scope='logits2')

            flat_logits = tf.reshape(logits, [-1, M * JX])
            flat_yp = tf.nn.softmax(flat_logits)  # [-1, M*JX]
            flat_logits2 = tf.reshape(logits2, [-1, M * JX])
            flat_yp2 = tf.nn.softmax(flat_logits2)

            if config.na:
                na_bias = tf.get_variable("na_bias", shape=[], dtype='float')
                na_bias_tiled = tf.tile(tf.reshape(na_bias, [1, 1]), [N, 1])  # [N, 1]
                concat_flat_logits = tf.concat(axis=1, values=[na_bias_tiled, flat_logits])
                concat_flat_yp = tf.nn.softmax(concat_flat_logits)
                na_prob = tf.squeeze(tf.slice(concat_flat_yp, [0, 0], [-1, 1]), [1])
                flat_yp = tf.slice(concat_flat_yp, [0, 1], [-1, -1])

                concat_flat_logits2 = tf.concat(axis=1, values=[na_bias_tiled, flat_logits2])
                concat_flat_yp2 = tf.nn.softmax(concat_flat_logits2)
                na_prob2 = tf.squeeze(tf.slice(concat_flat_yp2, [0, 0], [-1, 1]), [1])  # [N]
                flat_yp2 = tf.slice(concat_flat_yp2, [0, 1], [-1, -1])

                self.concat_logits = concat_flat_logits
                self.concat_logits2 = concat_flat_logits2
                self.na_prob = na_prob * na_prob2

            yp = tf.reshape(flat_yp, [-1, M, JX])
            yp2 = tf.reshape(flat_yp2, [-1, M, JX])
            wyp = tf.nn.sigmoid(logits2)

            self.tensor_dict['g1'] = g1
            self.tensor_dict['g2'] = g2

            self.logits = flat_logits
            self.logits2 = flat_logits2
            self.yp = yp
            self.yp2 = yp2
            self.wyp = wyp

    def _build_loss(self):
        config = self.config
        JX = tf.shape(self.x)[2]
        M = tf.shape(self.x)[1]
        JQ = tf.shape(self.q)[1]

        loss_mask = tf.reduce_max(tf.cast(self.q_mask, 'float'), 1)
        if config.wy:
            losses = tf.nn.sigmoid_cross_entropy_with_logits(
                logits=tf.reshape(self.logits2, [-1, M, JX]), labels=tf.cast(self.wy, 'float'))  # [N, M, JX]
            num_pos = tf.reduce_sum(tf.cast(self.wy, 'float'))
            num_neg = tf.reduce_sum(tf.cast(self.x_mask, 'float')) - num_pos
            damp_ratio = num_pos / num_neg
            dampened_losses = losses * (
                (tf.cast(self.x_mask, 'float') - tf.cast(self.wy, 'float')) * damp_ratio + tf.cast(self.wy, 'float'))
            new_losses = tf.reduce_sum(dampened_losses, [1, 2])
            ce_loss = tf.reduce_mean(loss_mask * new_losses)
            """
            if config.na:
                na = tf.reshape(self.na, [-1, 1])
                concat_y = tf.concat(1, [na, tf.reshape(self.wy, [-1, M * JX])])
                losses = tf.nn.softmax_cross_entropy_with_logits(
                    self.concat_logits, tf.cast(concat_y, 'float') / tf.reduce_sum(tf.cast(self.wy, 'float')))
            else:
                losses = tf.nn.softmax_cross_entropy_with_logits(
                    self.logits2, tf.cast(tf.reshape(self.wy, [-1, M * JX]), 'float') / tf.reduce_sum(tf.cast(self.wy, 'float')))
            ce_loss = tf.reduce_mean(loss_mask * losses)
            """
            tf.add_to_collection('losses', ce_loss)

        else:
            if config.na:
                na = tf.reshape(self.na, [-1, 1])
                concat_y = tf.concat(axis=1, values=[na, tf.reshape(self.y, [-1, M * JX])])
                losses = tf.nn.softmax_cross_entropy_with_logits(logits=self.concat_logits, labels=tf.cast(concat_y, 'float'))
                concat_y2 = tf.concat(axis=1, values=[na, tf.reshape(self.y2, [-1, M * JX])])
                losses2 = tf.nn.softmax_cross_entropy_with_logits(logits=self.concat_logits2, labels=tf.cast(concat_y2, 'float'))
            else:
                losses = tf.nn.softmax_cross_entropy_with_logits(
                    logits=self.logits, labels=tf.cast(tf.reshape(self.y, [-1, M * JX]), 'float'))
                losses2 = tf.nn.softmax_cross_entropy_with_logits(
                    logits=self.logits2, labels=tf.cast(tf.reshape(self.y2, [-1, M * JX]), 'float'))
            ce_loss = tf.reduce_mean(loss_mask * losses)
            ce_loss2 = tf.reduce_mean(loss_mask * losses2)
            tf.add_to_collection('losses', ce_loss)
            tf.add_to_collection("losses", ce_loss2)

        self.loss = tf.add_n(tf.get_collection('losses', scope=self.scope), name='loss')
        tf.summary.scalar(self.loss.op.name, self.loss)
        tf.add_to_collection('ema/scalar', self.loss)

    def _build_ema(self):
        self.ema = tf.train.ExponentialMovingAverage(self.config.decay)
        ema = self.ema
        tensors = tf.get_collection("ema/scalar", scope=self.scope) + tf.get_collection("ema/vector", scope=self.scope)
        ema_op = ema.apply(tensors)
        for var in tf.get_collection("ema/scalar", scope=self.scope):
            ema_var = ema.average(var)
            tf.summary.scalar(ema_var.op.name, ema_var)
        for var in tf.get_collection("ema/vector", scope=self.scope):
            ema_var = ema.average(var)
            tf.summary.histogram(ema_var.op.name, ema_var)

        with tf.control_dependencies([ema_op]):
            self.loss = tf.identity(self.loss)

    def _build_var_ema(self):
        self.var_ema = tf.train.ExponentialMovingAverage(self.config.var_decay)
        ema = self.var_ema
        ema_op = ema.apply(tf.trainable_variables())
        with tf.control_dependencies([ema_op]):
            self.loss = tf.identity(self.loss)

    def get_loss(self):
        return self.loss

    def get_global_step(self):
        return self.global_step

    def get_var_list(self):
        return self.var_list

    def get_feed_dict(self, batch, is_train, supervised=True):
        assert isinstance(batch, DataSet)
        config = self.config
        N, M, JX, JQ, VW, VC, d, W = \
            config.batch_size, config.max_num_sents, config.max_sent_size, \
            config.max_ques_size, config.word_vocab_size, config.char_vocab_size, config.hidden_size, config.max_word_size
        feed_dict = {}

        if config.len_opt:
            """
            Note that this optimization results in variable GPU RAM usage (i.e. can cause OOM in the middle of training.)
            First test without len_opt and make sure no OOM, and use len_opt
            """
            if sum(len(sent) for para in batch.data['x'] for sent in para) == 0:
                new_JX = 1
            else:
                new_JX = max(len(sent) for para in batch.data['x'] for sent in para)
            JX = min(JX, new_JX)

            if sum(len(ques) for ques in batch.data['q']) == 0:
                new_JQ = 1
            else:
                new_JQ = max(len(ques) for ques in batch.data['q'])
            JQ = min(JQ, new_JQ)

        if config.cpu_opt:
            if sum(len(para) for para in batch.data['x']) == 0:
                new_M = 1
            else:
                new_M = max(len(para) for para in batch.data['x'])
            M = min(M, new_M)

        x = np.zeros([N, M, JX], dtype='int32')
        cx = np.zeros([N, M, JX, W], dtype='int32')
        x_mask = np.zeros([N, M, JX], dtype='bool')
        q = np.zeros([N, JQ], dtype='int32')
        cq = np.zeros([N, JQ, W], dtype='int32')
        q_mask = np.zeros([N, JQ], dtype='bool')

        feed_dict[self.x] = x
        feed_dict[self.x_mask] = x_mask
        feed_dict[self.cx] = cx
        feed_dict[self.q] = q
        feed_dict[self.cq] = cq
        feed_dict[self.q_mask] = q_mask
        feed_dict[self.is_train] = is_train
        if config.use_glove_for_unk:
            feed_dict[self.new_emb_mat] = batch.shared['new_emb_mat']

        X = batch.data['x']
        CX = batch.data['cx']

        if supervised:
            y = np.zeros([N, M, JX], dtype='bool')
            y2 = np.zeros([N, M, JX], dtype='bool')
            wy = np.zeros([N, M, JX], dtype='bool')
            na = np.zeros([N], dtype='bool')
            feed_dict[self.y] = y
            feed_dict[self.y2] = y2
            feed_dict[self.wy] = wy
            feed_dict[self.na] = na

            for i, (xi, cxi, yi, nai) in enumerate(zip(X, CX, batch.data['y'], batch.data['na'])):
                if nai:
                    na[i] = nai
                    continue
                start_idx, stop_idx = random.choice(yi)
                j, k = start_idx
                j2, k2 = stop_idx
                if config.single:
                    X[i] = [xi[j]]
                    CX[i] = [cxi[j]]
                    j, j2 = 0, 0
                if config.squash:
                    offset = sum(map(len, xi[:j]))
                    j, k = 0, k + offset
                    offset = sum(map(len, xi[:j2]))
                    j2, k2 = 0, k2 + offset
                y[i, j, k] = True
                y2[i, j2, k2-1] = True
                if j == j2:
                    wy[i, j, k:k2] = True
                else:
                    wy[i, j, k:len(batch.data['x'][i][j])] = True
                    wy[i, j2, :k2] = True

        def _get_word(word):
            d = batch.shared['word2idx']
            for each in (word, word.lower(), word.capitalize(), word.upper()):
                if each in d:
                    return d[each]
            if config.use_glove_for_unk:
                d2 = batch.shared['new_word2idx']
                for each in (word, word.lower(), word.capitalize(), word.upper()):
                    if each in d2:
                        return d2[each] + len(d)
            return 1

        def _get_char(char):
            d = batch.shared['char2idx']
            if char in d:
                return d[char]
            return 1

        for i, xi in enumerate(X):
            if self.config.squash:
                xi = [list(itertools.chain(*xi))]
            for j, xij in enumerate(xi):
                if j == config.max_num_sents:
                    break
                for k, xijk in enumerate(xij):
                    if k == config.max_sent_size:
                        break
                    each = _get_word(xijk)
                    assert isinstance(each, int), each
                    x[i, j, k] = each
                    x_mask[i, j, k] = True

        for i, cxi in enumerate(CX):
            if self.config.squash:
                cxi = [list(itertools.chain(*cxi))]
            for j, cxij in enumerate(cxi):
                if j == config.max_num_sents:
                    break
                for k, cxijk in enumerate(cxij):
                    if k == config.max_sent_size:
                        break
                    for l, cxijkl in enumerate(cxijk):
                        if l == config.max_word_size:
                            break
                        cx[i, j, k, l] = _get_char(cxijkl)

        for i, qi in enumerate(batch.data['q']):
            for j, qij in enumerate(qi):
                q[i, j] = _get_word(qij)
                q_mask[i, j] = True

        for i, cqi in enumerate(batch.data['cq']):
            for j, cqij in enumerate(cqi):
                for k, cqijk in enumerate(cqij):
                    cq[i, j, k] = _get_char(cqijk)
                    if k + 1 == config.max_word_size:
                        break

        if supervised:
            assert np.sum(~(x_mask | ~wy)) == 0

        return feed_dict


def bi_attention(config, is_train, h, u, h_mask=None, u_mask=None, scope=None, tensor_dict=None):
    with tf.variable_scope(scope or "bi_attention"):
        JX = tf.shape(h)[2]
        M = tf.shape(h)[1]
        JQ = tf.shape(u)[1]
        h_aug = tf.tile(tf.expand_dims(h, 3), [1, 1, 1, JQ, 1])
        u_aug = tf.tile(tf.expand_dims(tf.expand_dims(u, 1), 1), [1, M, JX, 1, 1])
        if h_mask is None:
            hu_mask = None
        else:
            h_mask_aug = tf.tile(tf.expand_dims(h_mask, 3), [1, 1, 1, JQ])
            u_mask_aug = tf.tile(tf.expand_dims(tf.expand_dims(u_mask, 1), 1), [1, M, JX, 1])
            hu_mask = h_mask_aug & u_mask_aug

        u_logits = get_logits([h_aug, u_aug], None, True, wd=config.wd, mask=hu_mask,
                              is_train=is_train, func=config.logit_func, scope='u_logits')  # [N, M, JX, JQ]
        u_a = softsel(u_aug, u_logits)  # [N, M, JX, d]
        h_a = softsel(h, tf.reduce_max(u_logits, 3))  # [N, M, d]
        h_a = tf.tile(tf.expand_dims(h_a, 2), [1, 1, JX, 1])

        if tensor_dict is not None:
            a_u = tf.nn.softmax(u_logits)  # [N, M, JX, JQ]
            a_h = tf.nn.softmax(tf.reduce_max(u_logits, 3))
            tensor_dict['a_u'] = a_u
            tensor_dict['a_h'] = a_h
            variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=tf.get_variable_scope().name)
            for var in variables:
                tensor_dict[var.name] = var

        return u_a, h_a


def attention_layer(config, is_train, h, u, h_mask=None, u_mask=None, scope=None, tensor_dict=None):
    with tf.variable_scope(scope or "attention_layer"):
        JX = tf.shape(h)[2]
        M = tf.shape(h)[1]
        JQ = tf.shape(u)[1]
        if config.q2c_att or config.c2q_att:
            u_a, h_a = bi_attention(config, is_train, h, u, h_mask=h_mask, u_mask=u_mask, tensor_dict=tensor_dict)
        if not config.c2q_att:
            u_a = tf.tile(tf.expand_dims(tf.expand_dims(tf.reduce_mean(u, 1), 1), 1), [1, M, JX, 1])
        if config.q2c_att:
            p0 = tf.concat(axis=3, values=[h, u_a, h * u_a, h * h_a])
        else:
            p0 = tf.concat(axis=3, values=[h, u_a, h * u_a])
        return p0