# -*- coding:utf-8 -*-
"""
Author:
    Tingyi Tan, 5636374@qq.com

Reference:
    [1] Chen W, Zhan L, Ci Y, Lin C. FLEN: Leveraging Field for Scalable CTR Prediction . arXiv preprint arXiv:1911.04690, 2019.(https://arxiv.org/pdf/1911.04690)

"""

from itertools import chain

import tensorflow as tf

from ..feature_column import build_input_features, get_linear_logit, input_from_feature_columns
from ..layers.core import PredictionLayer, DNN
from ..layers.interaction import FieldWiseBiInteraction
from ..layers.utils import concat_func, add_func, combined_dnn_input


def FLEN(linear_feature_columns,
         dnn_feature_columns,
         dnn_hidden_units=(256, 128, 64),
         l2_reg_linear=0.00001,
         l2_reg_embedding=0.00001,
         l2_reg_dnn=0,
         seed=1024,
         dnn_dropout=0.0,
         dnn_activation='relu',
         dnn_use_bn=False,
         task='binary'):
    """Instantiates the FLEN Network architecture.

    :param linear_feature_columns: An iterable containing all the features used by linear part of the model.
    :param dnn_feature_columns: An iterable containing all the features used by deep part of the model.
    :param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of deep net
    :param l2_reg_linear: float. L2 regularizer strength applied to linear part
    :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector
    :param l2_reg_dnn: float. L2 regularizer strength applied to DNN
    :param seed: integer ,to use as random seed.
    :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
    :param dnn_activation: Activation function to use in DNN
    :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not in DNN
    :param task: str, ``"binary"`` for  binary logloss or  ``"regression"`` for regression loss
    :return: A Keras model instance.
    """

    features = build_input_features(linear_feature_columns +
                                    dnn_feature_columns)

    inputs_list = list(features.values())

    group_embedding_dict, dense_value_list = input_from_feature_columns(
        features,
        dnn_feature_columns,
        l2_reg_embedding,
        seed,
        support_group=True)

    linear_logit = get_linear_logit(features,
                                    linear_feature_columns,
                                    seed=seed,
                                    prefix='linear',
                                    l2_reg=l2_reg_linear)

    fm_mf_out = FieldWiseBiInteraction(seed=seed)(
        [concat_func(v, axis=1) for k, v in group_embedding_dict.items()])

    dnn_input = combined_dnn_input(
        list(chain.from_iterable(group_embedding_dict.values())),
        dense_value_list)
    dnn_output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed)(dnn_input)

    dnn_logit = tf.keras.layers.Dense(1, use_bias=False, kernel_initializer=tf.keras.initializers.glorot_normal(seed))(
        concat_func([fm_mf_out, dnn_output]))

    final_logit = add_func([linear_logit, dnn_logit])
    output = PredictionLayer(task)(final_logit)

    model = tf.keras.models.Model(inputs=inputs_list, outputs=output)
    return model
