from collections import namedtuple

from keras.models import Model

from . import networks
from .metrics import *

NetworkParameters = namedtuple("NetworkParameters", [
    "type", "flow_features", "window_size", "domain_length", "output",
    "embedding_size",
    "domain_filter", "domain_kernel", "domain_dense", "domain_dropout",
    "main_filter", "main_kernel", "main_dense", "main_dropout",
])


def create_model(model, output_type):
    if output_type == "both":
        return Model(inputs=[model.in_domains, model.in_flows], outputs=(model.out_client, model.out_server))
    elif output_type == "client":
        return Model(inputs=[model.in_domains, model.in_flows], outputs=(model.out_client,))
    else:
        raise Exception("unknown model output")


def get_models_by_params(params: dict):
    K.clear_session()
    # decomposing param section
    # mainly embedding model
    network_type = params.get("type")
    # network_depth = params.get("depth")
    embedding_size = params.get("embedding")
    filter_embedding = params.get("filter_embedding")
    kernel_embedding = params.get("kernel_embedding")
    hidden_embedding = params.get("dense_embedding")
    # dropout = params.get("dropout")
    # mainly prediction model
    flow_features = params.get("flow_features")
    window_size = params.get("window_size")
    domain_length = params.get("domain_length")
    filter_main = params.get("filter_main")
    kernel_main = params.get("kernel_main")
    dense_dim = params.get("dense_main")
    model_output = params.get("model_output", "both")

    domain_cnn = networks.get_domain_embedding_model(embedding_size, domain_length, filter_embedding, kernel_embedding,
                                                     hidden_embedding, 0.5)
    
    if network_type == "final":
        model = networks.get_final_model(0.25, flow_features, window_size, domain_length,
                                         filter_main, kernel_main, dense_dim, domain_cnn)
        model = create_model(model, model_output)
    elif network_type in ("inter", "staggered"):
        model = networks.get_inter_model(0.25, flow_features, window_size, domain_length,
                                         filter_main, kernel_main, dense_dim, domain_cnn)
        model = create_model(model, model_output)
    elif network_type == "long":
        model = networks.get_long_model(0.25, flow_features, window_size, domain_length,
                                        filter_main, kernel_main, dense_dim, domain_cnn)
        model = create_model(model, model_output)
    elif network_type == "soft":
        model = networks.get_long_model(0.25, flow_features, window_size, domain_length,
                                        filter_main, kernel_main, dense_dim, domain_cnn)
        model = create_model(model, model_output)
        conv_server = model.get_layer("conv_server").trainable_weights
        conv_client = model.get_layer("conv_client").trainable_weights
        l1 = [0.001 * K.sum(K.abs(x - y)) for (x, y) in zip(conv_server, conv_client)]
        model.add_loss(l1)
    
        dense_server = model.get_layer("dense_server").trainable_weights
        dense_client = model.get_layer("dense_client").trainable_weights
        l2 = [0.001 * K.sum(K.abs(x - y)) for (x, y) in zip(dense_server, dense_client)]
        model.add_loss(l2)
    else:
        raise ValueError("network type not found")

    return model


def get_server_model_by_params(params: dict):
    # decomposing param section
    # mainly embedding model
    network_depth = params.get("depth")
    embedding_size = params.get("embedding")
    input_length = params.get("input_length")
    filter_embedding = params.get("filter_embedding")
    kernel_embedding = params.get("kernel_embedding")
    hidden_embedding = params.get("dense_embedding")
    # mainly prediction model
    flow_features = params.get("flow_features")
    domain_length = params.get("domain_length")
    dense_dim = params.get("dense_main")

    embedding_model = networks.get_domain_embedding_model(embedding_size, input_length, filter_embedding,
                                                          kernel_embedding,
                                                          hidden_embedding, 0.5)
    
    return networks.get_server_model(flow_features, domain_length, dense_dim, embedding_model)