from . import pauls_networks
from . import renes_networks


def get_models_by_params(params: dict):
    # decomposing param section
    # mainly embedding model
    network_type = params.get("type")
    vocab_size = params.get("vocab_size")
    embedding_size = params.get("embedding_size")
    input_length = params.get("input_length")
    filter_embedding = params.get("filter_embedding")
    kernel_embedding = params.get("kernel_embedding")
    hidden_embedding = params.get("hidden_embedding")
    dropout = params.get("dropout")
    # mainly prediction model
    flow_features = params.get("flow_features")
    domain_features = params.get("domain_features")
    window_size = params.get("window_size")
    domain_length = params.get("domain_length")
    filter_main = params.get("filter_main")
    kernel_main = params.get("kernels_main")
    dense_dim = params.get("dense_main")
    # create models
    networks = renes_networks if network_type == "rene" else pauls_networks
    embedding_model = networks.get_embedding(vocab_size, embedding_size, input_length,
                                             filter_embedding, kernel_embedding, hidden_embedding, drop_out=dropout)

    predict_model = networks.get_model(dropout, flow_features, domain_features, window_size, domain_length,
                                       filter_main, kernel_main, dense_dim, embedding_model)

    return embedding_model, predict_model