# Copyright (c) 2022 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 argparse

import paddle
import paddle.nn.functional as F
from model import SimNet
from utils import preprocess_prediction_data

from paddlenlp.data import JiebaTokenizer, Pad, Stack, Tuple, Vocab

# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument('--device', choices=['cpu', 'gpu'], default="gpu", help="Select which device to train model, defaults to gpu.")
parser.add_argument("--batch_size", type=int, default=64, help="Total examples' number of a batch for training.")
parser.add_argument("--vocab_path", type=str, default="./simnet_vocab.txt", help="The path to vocabulary.")
parser.add_argument('--network', type=str, default="lstm", help="Which network you would like to choose bow, cnn, lstm or gru ?")
parser.add_argument("--params_path", type=str, default='./checkpoints/final.pdparams', help="The path of model parameter to be loaded.")
args = parser.parse_args()
# yapf: enable


def predict(model, data, label_map, batch_size=1, pad_token_id=0):
    """
    Predicts the data labels.

    Args:
        model (obj:`paddle.nn.Layer`): A model to classify texts.
        data (obj:`List(Example)`): The processed data whose each element is a Example (numedtuple) object.
            A Example object contains `text`(word_ids) and `seq_len`(sequence length).
        label_map(obj:`dict`): The label id (key) to label str (value) map.
        batch_size(obj:`int`, defaults to 1): The number of batch.
        pad_token_id(obj:`int`, optional, defaults to 0): The pad token index.

    Returns:
        results(obj:`dict`): All the predictions labels.
    """

    # Separates data into some batches.
    batches = [data[idx : idx + batch_size] for idx in range(0, len(data), batch_size)]

    batchify_fn = lambda samples, fn=Tuple(
        Pad(axis=0, pad_val=pad_token_id),  # query_ids
        Pad(axis=0, pad_val=pad_token_id),  # title_ids
        Stack(dtype="int64"),  # query_seq_lens
        Stack(dtype="int64"),  # title_seq_lens
    ): [data for data in fn(samples)]

    results = []
    model.eval()
    for batch in batches:
        query_ids, title_ids, query_seq_lens, title_seq_lens = batchify_fn(batch)
        query_ids = paddle.to_tensor(query_ids)
        title_ids = paddle.to_tensor(title_ids)
        query_seq_lens = paddle.to_tensor(query_seq_lens)
        title_seq_lens = paddle.to_tensor(title_seq_lens)
        logits = model(query_ids, title_ids, query_seq_lens, title_seq_lens)
        probs = F.softmax(logits, axis=1)
        idx = paddle.argmax(probs, axis=1).numpy()
        idx = idx.tolist()
        labels = [label_map[i] for i in idx]
        results.extend(labels)
    return results


if __name__ == "__main__":
    paddle.set_device(args.device)
    # Loads vocab.
    vocab = Vocab.load_vocabulary(args.vocab_path, unk_token="[UNK]", pad_token="[PAD]")
    tokenizer = JiebaTokenizer(vocab)
    label_map = {0: "dissimilar", 1: "similar"}

    # Constructs the newtork.
    model = SimNet(network=args.network, vocab_size=len(vocab), num_classes=len(label_map))

    # Loads model parameters.
    state_dict = paddle.load(args.params_path)
    model.set_dict(state_dict)
    print("Loaded parameters from %s" % args.params_path)

    # Firstly pre-processing prediction data  and then do predict.
    data = [
        ["世界上什么东西最小", "世界上什么东西最小？"],
        ["光眼睛大就好看吗", "眼睛好看吗？"],
        ["小蝌蚪找妈妈怎么样", "小蝌蚪找妈妈是谁画的"],
    ]
    examples = preprocess_prediction_data(data, tokenizer)
    results = predict(
        model,
        examples,
        label_map=label_map,
        batch_size=args.batch_size,
        pad_token_id=vocab.token_to_idx.get("[PAD]", 0),
    )

    for idx, text in enumerate(data):
        print("Data: {} \t Label: {}".format(text, results[idx]))
