# 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
from functools import partial

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

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

from reader import prepare_infer_input, Seq2SeqDataset, Seq2SeqBatchSampler
from transformer import InferTransformer


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_cuda else "cpu")
    fluid.enable_dygraph(device) if args.eager_run else None

    inputs = [
        Input(
            [None, None], "int64", name="src_word"),
        Input(
            [None, None], "int64", name="src_pos"),
        Input(
            [None, args.n_head, None, None],
            "float32",
            name="src_slf_attn_bias"),
        Input(
            [None, args.n_head, None, None],
            "float32",
            name="trg_src_attn_bias"),
    ]

    # define data
    dataset = Seq2SeqDataset(
        fpattern=args.predict_file,
        src_vocab_fpath=args.src_vocab_fpath,
        trg_vocab_fpath=args.trg_vocab_fpath,
        token_delimiter=args.token_delimiter,
        start_mark=args.special_token[0],
        end_mark=args.special_token[1],
        unk_mark=args.special_token[2],
        byte_data=True)
    args.src_vocab_size, args.trg_vocab_size, args.bos_idx, args.eos_idx, \
        args.unk_idx = dataset.get_vocab_summary()
    trg_idx2word = Seq2SeqDataset.load_dict(
        dict_path=args.trg_vocab_fpath, reverse=True, byte_data=True)
    batch_sampler = Seq2SeqBatchSampler(
        dataset=dataset,
        use_token_batch=False,
        batch_size=args.batch_size,
        max_length=args.max_length)
    data_loader = DataLoader(
        dataset=dataset,
        batch_sampler=batch_sampler,
        places=device,
        collate_fn=partial(
            prepare_infer_input,
            bos_idx=args.bos_idx,
            eos_idx=args.eos_idx,
            src_pad_idx=args.eos_idx,
            n_head=args.n_head),
        num_workers=0,
        return_list=True)

    # define model
    model = paddle.Model(
        InferTransformer(
            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,
            beam_size=args.beam_size,
            max_out_len=args.max_out_len),
        inputs)
    model.prepare()

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

    # TODO: use model.predict when support variant length
    f = open(args.output_file, "wb")
    for data in data_loader():
        finished_seq = model.test_batch(inputs=flatten(data))[0]
        finished_seq = np.transpose(finished_seq, [0, 2, 1])
        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)