from argparse import ArgumentParser
from funnelling import *
from view_generators import *
from data.dataset_builder import MultilingualDataset
from util.common import MultilingualIndex
from util.evaluation import evaluate
from time import time


def main(args):
    N_JOBS = 8
    print('Running refactored...')

    # _DATASET = '/homenfs/a.pedrotti1/datasets/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle'
    # EMBEDDINGS_PATH = '/homenfs/a.pedrotti1/embeddings/MUSE'

    _DATASET = '/home/moreo/CLESA/rcv2/rcv1-2_doclist_trByLang1000_teByLang1000_processed_run0.pickle'
    EMBEDDINGS_PATH = '/home/andreapdr/gfun/embeddings'
    data = MultilingualDataset.load(_DATASET)
    data.set_view(languages=['it', 'fr'])
    lX, ly = data.training()
    lXte, lyte = data.test()

    # Init multilingualIndex - mandatory when deploying Neural View Generators...
    multilingualIndex = MultilingualIndex()
    lMuse = MuseLoader(langs=sorted(lX.keys()), cache=EMBEDDINGS_PATH)
    multilingualIndex.index(lX, ly, lXte, lyte, l_pretrained_vocabulary=lMuse.vocabulary())

    # posteriorEmbedder = VanillaFunGen(base_learner=get_learner(calibrate=True), n_jobs=N_JOBS)
    museEmbedder = MuseGen(muse_dir=EMBEDDINGS_PATH, n_jobs=N_JOBS)
    wceEmbedder = WordClassGen(n_jobs=N_JOBS)
    # rnnEmbedder = RecurrentGen(multilingualIndex, pretrained_embeddings=lMuse, wce=False, batch_size=256,
    #                     nepochs=250, gpus=args.gpus, n_jobs=N_JOBS)
    # bertEmbedder = BertGen(multilingualIndex, batch_size=4, nepochs=1, gpus=args.gpus, n_jobs=N_JOBS)

    docEmbedders = DocEmbedderList([museEmbedder, wceEmbedder])

    gfun = Funnelling(first_tier=docEmbedders)

    # Training ---------------------------------------
    print('\n[Training Generalized Funnelling]')
    time_init = time()
    time_tr = time()
    gfun.fit(lX, ly)
    time_tr = round(time() - time_tr, 3)
    print(f'Training completed in {time_tr} seconds!')

    # Testing ----------------------------------------
    print('\n[Testing Generalized Funnelling]')
    time_te = time()
    ly_ = gfun.predict(lXte)

    l_eval = evaluate(ly_true=ly, ly_pred=ly_)
    print(l_eval)

    time_te = round(time() - time_te, 3)
    print(f'Testing completed in {time_te} seconds!')

    overall_time = round(time() - time_init, 3)
    exit(f'\nExecuted in: {overall_time } seconds!')


if __name__ == '__main__':
    parser = ArgumentParser()
    parser.add_argument('--gpus', default=None)
    args = parser.parse_args()
    main(args)