import rerank_utils
import os
from fairseq import options
from examples.noisychannel import rerank_options


def score_lm(args):
    using_nbest = args.nbest_list is not None
    pre_gen, left_to_right_preprocessed_dir, right_to_left_preprocessed_dir, \
        backwards_preprocessed_dir, lm_preprocessed_dir = \
        rerank_utils.get_directories(args.data_dir_name, args.num_rescore, args.gen_subset,
                                     args.gen_model_name, args.shard_id, args.num_shards,
                                     args.sampling, args.prefix_len, args.target_prefix_frac,
                                     args.source_prefix_frac)

    predictions_bpe_file = pre_gen+"/generate_output_bpe.txt"
    if using_nbest:
        print("Using predefined n-best list from interactive.py")
        predictions_bpe_file = args.nbest_list

    gen_output = rerank_utils.BitextOutputFromGen(predictions_bpe_file, bpe_symbol=args.remove_bpe, nbest=using_nbest)

    if args.language_model is not None:
        lm_score_file = rerank_utils.rescore_file_name(pre_gen, args.prefix_len, args.lm_name, lm_file=True)

    if args.language_model is not None and not os.path.isfile(lm_score_file):
        print("STEP 4.5: language modeling for P(T)")
        if args.lm_bpe_code is None:
            bpe_status = "no bpe"
        elif args.lm_bpe_code == "shared":
            bpe_status = "shared"
        else:
            bpe_status = "different"

        rerank_utils.lm_scoring(lm_preprocessed_dir, bpe_status, gen_output, pre_gen,
                                args.lm_dict, args.lm_name, args.language_model,
                                args.lm_bpe_code, 128, lm_score_file, args.target_lang,
                                args.source_lang, prefix_len=args.prefix_len)


def cli_main():
    parser = rerank_options.get_reranking_parser()
    args = options.parse_args_and_arch(parser)
    score_lm(args)


if __name__ == '__main__':
    cli_main()
