import logging

from keras.callbacks import CSVLogger, EarlyStopping, ModelCheckpoint

import arguments
import dataset
import models
# create logger
import visualize
from arguments import get_model_args
from utils import exists_or_make_path, load_model

logger = logging.getLogger('cisco_logger')

args = arguments.parse()


def train_server_only(params):
    logger.info(f"Create model path {args.model_path}")
    exists_or_make_path(args.model_path)
    logger.info(f"Use command line arguments: {args}")
    
    domain_tr, flow_tr, name_tr, client_tr, server_windows_tr = dataset.load_or_generate_h5data(args.data,
                                                                                                args.domain_length,
                                                                                                args.window)
    domain_tr = domain_tr.value.reshape(-1, 40)
    flow_tr = flow_tr.value.reshape(-1, 3)
    server_tr = server_windows_tr.value.reshape(-1)
    
    logger.info("define callbacks")
    callbacks = []
    callbacks.append(ModelCheckpoint(filepath=args.clf_model,
                                     monitor='loss',
                                     verbose=False,
                                     save_best_only=True))
    callbacks.append(CSVLogger(args.train_log))
    logger.info(f"Use early stopping: {args.stop_early}")
    if args.stop_early:
        callbacks.append(EarlyStopping(monitor='val_loss',
                                       patience=5,
                                       verbose=False))
    custom_metrics = models.get_metric_functions()
    
    model = models.get_server_model_by_params(params=params)
    
    features = {"ipt_domains": domain_tr, "ipt_flows": flow_tr}
    if args.model_output == "both":
        labels = {"client": client_tr, "server": server_tr}
    elif args.model_output == "client":
        labels = {"client": client_tr}
    elif args.model_output == "server":
        labels = {"server": server_tr}
    else:
        raise ValueError("unknown model output")
    
    logger.info("compile and train model")
    logger.info(model.get_config())
    model.compile(optimizer='adam',
                  loss='binary_crossentropy',
                  metrics=['accuracy'] + custom_metrics)
    
    model.summary()
    model.fit(features, labels,
              batch_size=args.batch_size,
              epochs=args.epochs,
              callbacks=callbacks)


def test_server_only():
    logger.info("start test: load data")
    domain_val, flow_val, _, _, _, _ = dataset.load_or_generate_raw_h5data(args.data, args.domain_length, args.window)
    domain_val = domain_val.value.reshape(-1, 40)
    flow_val = flow_val.value.reshape(-1, 3)
    domain_encs, _ = dataset.load_or_generate_domains(args.data, args.domain_length)
    
    for model_args in get_model_args(args):
        results = {}
        logger.info(f"process model {model_args['model_path']}")
        embd_model, clf_model = load_model(model_args["clf_model"], custom_objects=models.get_custom_objects())
        
        pred = clf_model.predict([domain_val, flow_val],
                                 batch_size=args.batch_size,
                                 verbose=1)
        
        results["server_pred"] = pred
        
        domain_embeddings = embd_model.predict(domain_encs, batch_size=args.batch_size, verbose=1)
        results["domain_embds"] = domain_embeddings
        
        dataset.save_predictions(model_args["model_path"], results)


def vis_server():
    def load_model(m, c):
        from keras.models import load_model
        clf = load_model(m, custom_objects=c)
        emdb = clf.layers[1]
        return emdb, clf
    
    domain_raw, flow_raw, name_raw, hits_vt_raw, hits_trusted_raw, server_raw = dataset.load_or_generate_raw_h5data(
            args.data, args.domain_length, args.window)
    
    results = dataset.load_predictions(args.clf_model)
    
    visualize.plot_clf()
    visualize.plot_precision_recall(server_raw.flatten(), results["server_pred"].flatten(), "server")
    visualize.plot_legend()
    visualize.plot_save("results/server_model/windows_prc.pdf")
    visualize.plot_clf()
    visualize.plot_precision_recall(server_raw.flatten(), results["server_pred"].flatten(), "server")
    visualize.plot_legend()
    visualize.plot_save("results/server_model/windows_prc.pdf")
    visualize.plot_clf()
    visualize.plot_roc_curve(server_raw.flatten(), results["server_pred"].flatten(), "server")
    visualize.plot_legend()
    visualize.plot_save("results/server_model/windows_roc.pdf")