# Copyright (c) 2019-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
#
# Translate sentences from the input stream.
# The model will be faster is sentences are sorted by length.
# Input sentences must have the same tokenization and BPE codes than the ones used in the model.
#
# Usage:
#     cat source_sentences.bpe | \
#     python translate.py --exp_name translate \
#     --src_lang en --tgt_lang fr \
#     --model_path trained_model.pth --output_path output
#

import os
import io
import sys
import argparse
import torch

from xlm.utils import AttrDict
from xlm.utils import bool_flag, initialize_exp
from xlm.data.dictionary import Dictionary
from xlm.model.transformer import TransformerModel

from sub_by_dict import sub_list

def get_parser():
    """
    Generate a parameters parser.
    """
    # parse parameters
    parser = argparse.ArgumentParser(description="Translate sentences")

    # main parameters
    parser.add_argument("--dump_path", type=str, default="./dumped/", help="Experiment dump path")
    parser.add_argument("--exp_name", type=str, default="", help="Experiment name")
    parser.add_argument("--exp_id", type=str, default="", help="Experiment ID")
    parser.add_argument("--batch_size", type=int, default=32, help="Number of sentences per batch")

    # model / output paths
    parser.add_argument("--model_path", type=str, default="", help="Model path")
    parser.add_argument("--output_path", type=str, default="", help="Output path")

    parser.add_argument("--dict_path", default='', type=str, help="Bilingual dictionary path for models trained with AT objective")

    # parser.add_argument("--max_vocab", type=int, default=-1, help="Maximum vocabulary size (-1 to disable)")
    # parser.add_argument("--min_count", type=int, default=0, help="Minimum vocabulary count")

    # source language / target language
    parser.add_argument("--src_lang", type=str, default="", help="Source language")
    parser.add_argument("--tgt_lang", type=str, default="", help="Target language")

    return parser


def main(params):

    # initialize the experiment
    logger = initialize_exp(params)

    # generate parser / parse parameters
    parser = get_parser()
    params = parser.parse_args()
    reloaded = torch.load(params.model_path)
    model_params = AttrDict(reloaded['params'])
    logger.info("Supported languages: %s" % ", ".join(model_params.lang2id.keys()))

    # update dictionary parameters
    for name in ['n_words', 'bos_index', 'eos_index', 'pad_index', 'unk_index', 'mask_index']:
        setattr(params, name, getattr(model_params, name))

    # build dictionary / build encoder / build decoder / reload weights
    dico = Dictionary(reloaded['dico_id2word'], reloaded['dico_word2id'], reloaded['dico_counts'])
    encoder = TransformerModel(model_params, dico, is_encoder=True, with_output=True).cuda().eval()
    decoder = TransformerModel(model_params, dico, is_encoder=False, with_output=True).cuda().eval()
    encoder.load_state_dict(reloaded['encoder'])
    decoder.load_state_dict(reloaded['decoder'])
    params.src_id = model_params.lang2id[params.src_lang]
    params.tgt_id = model_params.lang2id[params.tgt_lang]

    # read sentences from stdin
    src_sent = []
    for line in sys.stdin.readlines():
        # assert len(line.strip().split()) > 0
        src_sent.append(line)

    if params.dict_path:
        src_sent = list(sub_list(src_sent, params.dict_path, sample=False))

    logger.info("Read %i sentences from stdin. Translating ..." % len(src_sent))

    f = io.open(params.output_path, 'w', encoding='utf-8')

    for i in range(0, len(src_sent), params.batch_size):

        # prepare batch
        word_ids = [torch.LongTensor([dico.index(w) for w in s.strip().split()])
                    for s in src_sent[i:i + params.batch_size]]
        lengths = torch.LongTensor([len(s) + 2 for s in word_ids])
        batch = torch.LongTensor(lengths.max().item(), lengths.size(0)).fill_(params.pad_index)
        batch[0] = params.eos_index
        for j, s in enumerate(word_ids):
            if lengths[j] > 2:  # if sentence not empty
                batch[1:lengths[j] - 1, j].copy_(s)
            batch[lengths[j] - 1, j] = params.eos_index
        langs = batch.clone().fill_(params.src_id)

        # encode source batch and translate it
        encoded = encoder('fwd', x=batch.cuda(), lengths=lengths.cuda(), langs=langs.cuda(), causal=False)
        encoded = encoded.transpose(0, 1)
        decoded, dec_lengths = decoder.generate(encoded, lengths.cuda(), params.tgt_id, max_len=int(1.5 * lengths.max().item() + 10))

        # convert sentences to words
        for j in range(decoded.size(1)):

            # remove delimiters
            sent = decoded[:, j]
            delimiters = (sent == params.eos_index).nonzero().view(-1)
            assert len(delimiters) >= 1 and delimiters[0].item() == 0
            sent = sent[1:] if len(delimiters) == 1 else sent[1:delimiters[1]]

            # output translation
            source = src_sent[i + j].strip()
            target = " ".join([dico[sent[k].item()] for k in range(len(sent))])
            sys.stderr.write("%i / %i: %s -> %s\n" % (i + j, len(src_sent), source, target))
            f.write(target + "\n")

    f.close()


if __name__ == '__main__':

    # generate parser / parse parameters
    parser = get_parser()
    params = parser.parse_args()

    # check parameters
    assert os.path.isfile(params.model_path)
    assert params.src_lang != '' and params.tgt_lang != '' and params.src_lang != params.tgt_lang; print(params.output_path)
    assert params.output_path and not os.path.isfile(params.output_path)

    # translate
    with torch.no_grad():
        main(params)
