# 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 sys

import paddle

from paddlenlp.data import Pad, Stack, Tuple, Vocab
from paddlenlp.datasets import load_dataset

sys.path.append("../../..")
from model import SimNet  # noqa: E402
from utils import CharTokenizer, preprocess_data  # noqa: E402

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=1, help="Total examples' number of a batch for training.")
parser.add_argument("--vocab_path", type=str, default="./vocab.char", 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."
)
parser.add_argument("--language", type=str, required=True, help="Language that this model based on")
args = parser.parse_args()


def interpret(model, data, label_map, batch_size=1, pad_token_id=0, vocab=None):
    """
    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
        Stack(dtype="int64"),
    ): [data for data in fn(samples)]

    model.train()
    results = []
    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)
        probs, addiational_info = model.forward_interpreter(query_ids, title_ids, query_seq_lens, title_seq_lens)
        query_emb = addiational_info["embedded"][0]
        title_emb = addiational_info["embedded"][1]

        predicted_class_probs = paddle.max(probs, axis=-1)
        predicted_class_probs = predicted_class_probs.sum()
        paddle.autograd.backward([predicted_class_probs])
        q_gradients = ((query_emb * query_emb.grad).sum(-1).detach()).abs()  # gradients: (1, seq_len)
        q_grad_output = q_gradients / q_gradients.sum(-1, keepdim=True)
        t_gradients = ((title_emb * title_emb.grad).sum(-1).detach()).abs()  # gradients: (1, seq_len)
        t_grad_output = t_gradients / t_gradients.sum(-1, keepdim=True)

        model.clear_gradients()
        for query_id, title_id in zip(query_ids.numpy().tolist(), title_ids.numpy().tolist()):
            query = [vocab._idx_to_token[idx] for idx in query_id]
            title = [vocab._idx_to_token[idx] for idx in title_id]
        results.append([q_grad_output, query, t_grad_output, title])
        print([q_grad_output, query, t_grad_output, title])

    return results


if __name__ == "__main__":
    paddle.set_device(args.device + ":1")
    # Loads vocab.
    vocab = Vocab.load_vocabulary(args.vocab_path, unk_token="[UNK]", pad_token="[PAD]")
    tokenizer = CharTokenizer(vocab, args.language)
    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.

    dev_ds, test_ds = load_dataset("lcqmc", splits=["dev", "test"])

    dev_examples = preprocess_data(dev_ds.data, tokenizer, args.language)
    test_examples = preprocess_data(test_ds.data, tokenizer, args.language)
    results = interpret(
        model,
        dev_examples,
        label_map=label_map,
        batch_size=args.batch_size,
        pad_token_id=vocab.token_to_idx.get("[PAD]", 0),
        vocab=vocab,
    )

    # for idx, text in enumerate(data):
    #     print('Data: {} \t Label: {}'.format(text, results[idx]))
