# Copyright (c) 2019 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 six
import sys
import time

import numpy as np
import paddle
import paddle.fluid as fluid

from utils.configure import PDConfig
from utils.check import check_gpu, check_version

# include task-specific libs
import reader
from model import Transformer, position_encoding_init


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):
    if args.use_cuda:
        place = fluid.CUDAPlace(0)
    else:
        place = fluid.CPUPlace()

    # define the data generator
    processor = reader.DataProcessor(fpattern=args.predict_file,
                                     src_vocab_fpath=args.src_vocab_fpath,
                                     trg_vocab_fpath=args.trg_vocab_fpath,
                                     token_delimiter=args.token_delimiter,
                                     use_token_batch=False,
                                     batch_size=args.batch_size,
                                     device_count=1,
                                     pool_size=args.pool_size,
                                     sort_type=reader.SortType.NONE,
                                     shuffle=False,
                                     shuffle_batch=False,
                                     start_mark=args.special_token[0],
                                     end_mark=args.special_token[1],
                                     unk_mark=args.special_token[2],
                                     max_length=args.max_length,
                                     n_head=args.n_head)
    batch_generator = processor.data_generator(phase="predict", place=place)
    args.src_vocab_size, args.trg_vocab_size, args.bos_idx, args.eos_idx, \
        args.unk_idx = processor.get_vocab_summary()
    trg_idx2word = reader.DataProcessor.load_dict(
        dict_path=args.trg_vocab_fpath, reverse=True)

    args.src_vocab_size, args.trg_vocab_size, args.bos_idx, args.eos_idx, \
        args.unk_idx = processor.get_vocab_summary()

    with fluid.dygraph.guard(place):
        # define data loader
        test_loader = fluid.io.DataLoader.from_generator(capacity=10)
        test_loader.set_batch_generator(batch_generator, places=place)

        # define model
        transformer = Transformer(
            args.src_vocab_size, args.trg_vocab_size, args.max_length + 1,
            args.n_layer, args.n_head, args.d_key, args.d_value, args.d_model,
            args.d_inner_hid, args.prepostprocess_dropout,
            args.attention_dropout, args.relu_dropout, args.preprocess_cmd,
            args.postprocess_cmd, args.weight_sharing, args.bos_idx,
            args.eos_idx)

        # load the trained model
        assert args.init_from_params, (
            "Please set init_from_params to load the infer model.")
        model_dict, _ = fluid.load_dygraph(
            os.path.join(args.init_from_params, "transformer"))
        # to avoid a longer length than training, reset the size of position
        # encoding to max_length
        model_dict["encoder.pos_encoder.weight"] = position_encoding_init(
            args.max_length + 1, args.d_model)
        model_dict["decoder.pos_encoder.weight"] = position_encoding_init(
            args.max_length + 1, args.d_model)
        transformer.load_dict(model_dict)

        # set evaluate mode
        transformer.eval()

        f = open(args.output_file, "wb")
        for input_data in test_loader():
            (src_word, src_pos, src_slf_attn_bias, trg_word,
             trg_src_attn_bias) = input_data
            finished_seq, finished_scores = transformer.beam_search(
                src_word,
                src_pos,
                src_slf_attn_bias,
                trg_word,
                trg_src_attn_bias,
                bos_id=args.bos_idx,
                eos_id=args.eos_idx,
                beam_size=args.beam_size,
                max_len=args.max_out_len)
            finished_seq = finished_seq.numpy()
            finished_scores = finished_scores.numpy()
            for ins in finished_seq:
                for beam_idx, beam in enumerate(ins):
                    if beam_idx >= args.n_best: break
                    id_list = post_process_seq(beam, args.bos_idx, args.eos_idx)
                    word_list = [trg_idx2word[id] for id in id_list]
                    sequence = b" ".join(word_list) + b"\n"
                    f.write(sequence)


if __name__ == "__main__":
    args = PDConfig(yaml_file="./transformer.yaml")
    args.build()
    args.Print()
    check_gpu(args.use_cuda)
    check_version()

    do_predict(args)