#!/usr/bin/env python
# coding: utf-8
from __future__ import print_function, absolute_import, division
import paddle.fluid as fluid
from collections import OrderedDict
"""
DCN network
"""


class DCN(object):
    def __init__(self,
                 cross_num=2,
                 dnn_hidden_units=(128, 128),
                 l2_reg_cross=1e-5,
                 dnn_use_bn=False,
                 clip_by_norm=None):
        self.cross_num = cross_num
        self.dnn_hidden_units = dnn_hidden_units
        self.l2_reg_cross = l2_reg_cross
        self.dnn_use_bn = dnn_use_bn
        self.clip_by_norm = clip_by_norm

        self.cat_feat_dims_dict = OrderedDict()

        self.dense_feat_names = ['I' + str(i) for i in range(1, 14)]
        self.sparse_feat_names = ['C' + str(i) for i in range(1, 27)]
        target = ['label']

        for line in open('data/cat_feature_num.txt'):
            spls = line.strip().split()
            assert len(spls) == 2
            self.cat_feat_dims_dict[spls[0]] = int(spls[1])

        # {feat_name: dims}
        self.feat_dims_dict = OrderedDict(
            [(feat_name, 1) for feat_name in self.dense_feat_names])
        self.feat_dims_dict.update(self.cat_feat_dims_dict)

        self.net_input = None
        self.loss = None

    def build_network(self, is_test=False):
        # data input
        self.target_input = fluid.layers.data(
            name='label', shape=[1], dtype='float32')

        data_dict = OrderedDict()
        for feat_name in self.feat_dims_dict:
            data_dict[feat_name] = fluid.layers.data(
                name=feat_name, shape=[1], dtype='float32')

        self.net_input = self._create_embedding_input(data_dict)

        deep_out = self._deep_net(self.net_input, self.dnn_hidden_units,
                                  self.dnn_use_bn, is_test)
        cross_out, l2_reg_cross_loss = self._cross_net(self.net_input,
                                                       self.cross_num)
        last_out = fluid.layers.concat([deep_out, cross_out], axis=-1)
        logit = fluid.layers.fc(last_out, 1)
        self.prob = fluid.layers.sigmoid(logit)
        self.data_list = [self.target_input] + [
            data_dict[dense_name] for dense_name in self.dense_feat_names
        ] + [data_dict[sparse_name] for sparse_name in self.sparse_feat_names]

        # auc
        prob_2d = fluid.layers.concat([1 - self.prob, self.prob], 1)
        label_int = fluid.layers.cast(self.target_input, 'int64')
        auc_var, batch_auc_var, auc_states = fluid.layers.auc(input=prob_2d,
                                                              label=label_int,
                                                              slide_steps=0)
        self.auc_var = auc_var

        # logloss
        logloss = fluid.layers.log_loss(self.prob, self.target_input)
        self.avg_logloss = fluid.layers.reduce_mean(logloss)

        # reg_coeff * l2_reg_cross
        l2_reg_cross_loss = self.l2_reg_cross * l2_reg_cross_loss
        self.loss = self.avg_logloss + l2_reg_cross_loss

    def backward(self, lr):
        p_g_clip = fluid.backward.append_backward(loss=self.loss)
        fluid.clip.set_gradient_clip(
            fluid.clip.GradientClipByGlobalNorm(clip_norm=self.clip_by_norm))
        p_g_clip = fluid.clip.append_gradient_clip_ops(p_g_clip)

        optimizer = fluid.optimizer.Adam(learning_rate=lr)
        # params_grads = optimizer.backward(self.loss)
        optimizer.apply_gradients(p_g_clip)

    def _deep_net(self, input, hidden_units, use_bn=False, is_test=False):
        for units in hidden_units:
            input = fluid.layers.fc(input=input, size=units)
            if use_bn:
                input = fluid.layers.batch_norm(input, is_test=is_test)
            input = fluid.layers.relu(input)
        return input

    def _cross_layer(self, x0, x, prefix):
        input_dim = x0.shape[-1]
        w = fluid.layers.create_parameter(
            [input_dim], dtype='float32', name=prefix + "_w")
        b = fluid.layers.create_parameter(
            [input_dim], dtype='float32', name=prefix + "_b")
        xw = fluid.layers.reduce_sum(x * w, dim=1, keep_dim=True)  # (N, 1)
        return x0 * xw + b + x, w

    def _cross_net(self, input, num_corss_layers):
        x = x0 = input
        l2_reg_cross_list = []
        for i in range(num_corss_layers):
            x, w = self._cross_layer(x0, x, "cross_layer_{}".format(i))
            l2_reg_cross_list.append(self._l2_loss(w))
        l2_reg_cross_loss = fluid.layers.reduce_sum(
            fluid.layers.concat(
                l2_reg_cross_list, axis=-1))
        return x, l2_reg_cross_loss

    def _l2_loss(self, w):
        return fluid.layers.reduce_sum(fluid.layers.square(w))

    def _create_embedding_input(self, data_dict):
        # sparse embedding
        sparse_emb_dict = OrderedDict((
            name, fluid.layers.embedding(
                input=fluid.layers.cast(
                    data_dict[name], dtype='int64'),
                size=[
                    self.feat_dims_dict[name] + 1,
                    6 * int(pow(self.feat_dims_dict[name], 0.25))
                ]))
            for name in self.sparse_feat_names
        )

        # combine dense and sparse_emb
        dense_input_list = [
            data_dict[name] for name in data_dict if name.startswith('I')
        ]
        sparse_emb_list = list(sparse_emb_dict.values())

        sparse_input = fluid.layers.concat(sparse_emb_list, axis=-1)
        sparse_input = fluid.layers.flatten(sparse_input)

        dense_input = fluid.layers.concat(dense_input_list, axis=-1)
        dense_input = fluid.layers.flatten(dense_input)
        dense_input = fluid.layers.cast(dense_input, 'float32')

        net_input = fluid.layers.concat([dense_input, sparse_input], axis=-1)

        return net_input