from collections import namedtuple

import keras
from keras.activations import elu
from keras.engine import Input, Model as KerasModel
from keras.layers import BatchNormalization, Conv1D, Dense, Dropout, Embedding, GlobalAveragePooling1D, \
    GlobalMaxPooling1D, TimeDistributed

import dataset


def selu(x):
    """Scaled Exponential Linear Unit. (Klambauer et al., 2017)
    # Arguments
        x: A tensor or variable to compute the activation function for.
    # References
        - [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
    # copied from keras.io
    """
    alpha = 1.6732632423543772848170429916717
    scale = 1.0507009873554804934193349852946
    return scale * elu(x, alpha)


Model = namedtuple("Model", ["in_domains", "in_flows", "out_client", "out_server"])


def get_embedding(embedding_size, input_length, filter_size, kernel_size, hidden_dims, drop_out=0.5) -> KerasModel:
    x = y = Input(shape=(input_length,))
    y = Embedding(input_dim=dataset.get_vocab_size(), output_dim=embedding_size)(y)
    y = Conv1D(filter_size,
               kernel_size,
               activation=selu)(y)
    y = GlobalAveragePooling1D()(y)
    y = Dense(hidden_dims, activation=selu)(y)
    return KerasModel(x, y)


def get_model(cnnDropout, flow_features, domain_features, window_size, domain_length, cnn_dims, kernel_size,
              dense_dim, cnn, model_output="both") -> Model:
    ipt_domains = Input(shape=(window_size, domain_length), name="ipt_domains")
    encoded = TimeDistributed(cnn, name="domain_cnn")(ipt_domains)
    ipt_flows = Input(shape=(window_size, flow_features), name="ipt_flows")
    ipt_flows = BatchNormalization()(ipt_flows)
    ipt_flows = Dense(dense_dim, activation=selu)(ipt_flows)
    merged = keras.layers.concatenate([encoded, ipt_flows], -1)
    # CNN processing a small slides of flow windows
    y = Conv1D(cnn_dims,
               kernel_size,
               activation=selu,
               input_shape=(window_size, domain_features + flow_features))(merged)
    # remove temporal dimension by global max pooling
    y = GlobalMaxPooling1D()(y)
    y = Dropout(cnnDropout)(y)
    y = Dense(dense_dim, activation=selu)(y)
    out_client = Dense(1, activation='sigmoid', name="client")(y)
    out_server = Dense(1, activation='sigmoid', name="server")(y)
    
    return Model(ipt_domains, ipt_flows, out_client, out_server)


def get_new_model(dropout, flow_features, domain_features, window_size, domain_length, cnn_dims, kernel_size,
                  dense_dim, cnn, model_output="both") -> Model:
    ipt_domains = Input(shape=(window_size, domain_length), name="ipt_domains")
    ipt_flows = Input(shape=(window_size, flow_features), name="ipt_flows")
    encoded = TimeDistributed(cnn, name="domain_cnn")(ipt_domains)
    merged = keras.layers.concatenate([encoded, ipt_flows], -1)
    y = Dense(dense_dim, activation=selu)(merged)
    out_server = Dense(1, activation="sigmoid", name="server")(y)
    merged = keras.layers.concatenate([merged, y], -1)
    # CNN processing a small slides of flow windows
    y = Conv1D(filters=cnn_dims,
               kernel_size=kernel_size,
               activation=selu,
               padding="same",
               input_shape=(window_size, domain_features + flow_features))(merged)
    # remove temporal dimension by global max pooling
    y = GlobalMaxPooling1D()(y)
    y = Dropout(dropout)(y)
    y = Dense(dense_dim,
              activation=selu,
              name="dense_client")(y)
    out_client = Dense(1, activation='sigmoid', name="client")(y)
    
    return Model(ipt_domains, ipt_flows, out_client, out_server)