# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import logging
import os
import io
import random
from functools import partial

import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid.layers.utils import flatten
from paddle.fluid.io import DataLoader
from paddle.static import InputSpec as Input

from args import parse_args
from seq2seq_base import BaseInferModel
from seq2seq_attn import AttentionInferModel
from reader import Seq2SeqDataset, Seq2SeqBatchSampler, SortType, prepare_infer_input


def post_process_seq(seq, bos_idx, eos_idx, output_bos=False,
                     output_eos=False):
    """
    Post-process the decoded sequence.
    """
    eos_pos = len(seq) - 1
    for i, idx in enumerate(seq):
        if idx == eos_idx:
            eos_pos = i
            break
    seq = [
        idx for idx in seq[:eos_pos + 1]
        if (output_bos or idx != bos_idx) and (output_eos or idx != eos_idx)
    ]
    return seq


def do_predict(args):
    device = paddle.set_device("gpu" if args.use_gpu else "cpu")
    fluid.enable_dygraph(device) if args.eager_run else None

    # define model
    inputs = [
        Input(
            [None, None], "int64", name="src_word"),
        Input(
            [None], "int64", name="src_length"),
    ]

    # def dataloader
    dataset = Seq2SeqDataset(
        fpattern=args.infer_file,
        src_vocab_fpath=args.vocab_prefix + "." + args.src_lang,
        trg_vocab_fpath=args.vocab_prefix + "." + args.tar_lang,
        token_delimiter=None,
        start_mark="<s>",
        end_mark="</s>",
        unk_mark="<unk>")
    trg_idx2word = Seq2SeqDataset.load_dict(
        dict_path=args.vocab_prefix + "." + args.tar_lang, reverse=True)
    (args.src_vocab_size, args.trg_vocab_size, bos_id, eos_id,
     unk_id) = dataset.get_vocab_summary()
    batch_sampler = Seq2SeqBatchSampler(
        dataset=dataset, use_token_batch=False, batch_size=args.batch_size)
    data_loader = DataLoader(
        dataset=dataset,
        batch_sampler=batch_sampler,
        places=device,
        collate_fn=partial(
            prepare_infer_input, bos_id=bos_id, eos_id=eos_id, pad_id=eos_id),
        num_workers=0,
        return_list=True)

    model_maker = AttentionInferModel if args.attention else BaseInferModel
    model = paddle.Model(
        model_maker(
            args.src_vocab_size,
            args.tar_vocab_size,
            args.hidden_size,
            args.hidden_size,
            args.num_layers,
            args.dropout,
            bos_id=bos_id,
            eos_id=eos_id,
            beam_size=args.beam_size,
            max_out_len=256),
        inputs=inputs)

    model.prepare()

    # load the trained model
    assert args.reload_model, (
        "Please set reload_model to load the infer model.")
    model.load(args.reload_model)

    # TODO(guosheng): use model.predict when support variant length
    with io.open(args.infer_output_file, 'w', encoding='utf-8') as f:
        for data in data_loader():
            finished_seq = model.test_batch(inputs=flatten(data))[0]
            finished_seq = finished_seq[:, :, np.newaxis] if len(
                finished_seq.shape) == 2 else finished_seq
            finished_seq = np.transpose(finished_seq, [0, 2, 1])
            for ins in finished_seq:
                for beam_idx, beam in enumerate(ins):
                    id_list = post_process_seq(beam, bos_id, eos_id)
                    word_list = [trg_idx2word[id] for id in id_list]
                    sequence = " ".join(word_list) + "\n"
                    f.write(sequence)
                    break


if __name__ == "__main__":
    args = parse_args()
    do_predict(args)