# Copyright (c) 2021 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 os
from functools import partial

import numpy as np
import paddle
from base_model import SemanticIndexBase
from data import convert_example, create_dataloader, read_text_pair

from paddlenlp.data import Pad, Tuple
from paddlenlp.datasets import load_dataset
from paddlenlp.transformers import AutoModel, AutoTokenizer

# fmt: off
parser = argparse.ArgumentParser()
parser.add_argument("--text_pair_file", type=str, required=True, help="The full path of input file")
parser.add_argument("--params_path", type=str, required=True, help="The path to model parameters to be loaded.")
parser.add_argument("--max_seq_length", default=64, type=int, help="The maximum total input sequence length after tokenization. Sequences longer than this will be truncated, sequences shorter will be padded.")
parser.add_argument("--batch_size", default=32, type=int, help="Batch size per GPU/CPU for training.")
parser.add_argument("--output_emb_size", default=None, type=int, help="output_embedding_size")
parser.add_argument('--device', choices=['cpu', 'gpu'], default="gpu", help="Select which device to train model, defaults to gpu.")
parser.add_argument("--pad_to_max_seq_len", action="store_true", help="Whether to pad to max seq length.")
parser.add_argument("--model_name_or_path", default='rocketqa-zh-dureader-query-encoder', type=str, help='The pretrained model used for training')
args = parser.parse_args()
# fmt: on


def predict(model, data_loader):
    """
    Predicts the data labels.

    Args:
        model (obj:`SemanticIndexBase`): A model to extract text embedding or calculate similarity of text pair.
        data_loader (obj:`List(Example)`): The processed data ids of text pair: [query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids]
    Returns:
        results(obj:`List`): cosine similarity of text pairs.
    """
    cosine_sims = []
    model.eval()
    with paddle.no_grad():
        for batch_data in data_loader:
            query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids = batch_data
            batch_cosine_sim = model.cosine_sim(
                query_input_ids=query_input_ids,
                title_input_ids=title_input_ids,
                query_token_type_ids=query_token_type_ids,
                title_token_type_ids=title_token_type_ids,
            ).numpy()
            cosine_sims.append(batch_cosine_sim)
        cosine_sims = np.concatenate(cosine_sims, axis=0)
        return cosine_sims


if __name__ == "__main__":
    paddle.set_device(args.device)

    tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path)
    trans_func = partial(
        convert_example,
        tokenizer=tokenizer,
        max_seq_length=args.max_seq_length,
        pad_to_max_seq_len=args.pad_to_max_seq_len,
    )
    batchify_fn = lambda samples, fn=Tuple(
        Pad(axis=0, pad_val=tokenizer.pad_token_id, dtype="int64"),  # query_input
        Pad(axis=0, pad_val=tokenizer.pad_token_type_id, dtype="int64"),  # query_segment
        Pad(axis=0, pad_val=tokenizer.pad_token_id, dtype="int64"),  # title_input
        Pad(axis=0, pad_val=tokenizer.pad_token_type_id, dtype="int64"),  # title_segment
    ): [data for data in fn(samples)]
    valid_ds = load_dataset(read_text_pair, data_path=args.text_pair_file, lazy=False)
    valid_data_loader = create_dataloader(
        valid_ds, mode="predict", batch_size=args.batch_size, batchify_fn=batchify_fn, trans_fn=trans_func
    )
    pretrained_model = AutoModel.from_pretrained(args.model_name_or_path)
    model = SemanticIndexBase(pretrained_model, output_emb_size=args.output_emb_size)
    if args.params_path and os.path.isfile(args.params_path):
        state_dict = paddle.load(args.params_path)
        model.set_dict(state_dict)
        print("Loaded parameters from %s" % args.params_path)
    else:
        raise ValueError("Please set --params_path with correct pretrained model file")
    cosin_sim = predict(model, valid_data_loader)
    for idx, cosine in enumerate(cosin_sim):
        print("{}".format(cosine))
        if idx > 5:
            break
