# 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.

# coding=UTF-8

import argparse
import os
from functools import partial

import paddle
from ann_util import build_index
from base_model import SemanticIndexBase
from data import convert_example, create_dataloader, gen_id2corpus, gen_text_file

from paddlenlp.data import Pad, Tuple
from paddlenlp.datasets import MapDataset
from paddlenlp.transformers import AutoModel, AutoTokenizer
from paddlenlp.utils.log import logger

# fmt: off
parser = argparse.ArgumentParser()
parser.add_argument("--corpus_file", type=str, required=True, help="The full path of input file")
parser.add_argument("--similar_text_pair_file", type=str, required=True, help="The full path of similar text pair file")
parser.add_argument("--recall_result_dir", type=str, default='recall_result', help="The full path of recall result file to save")
parser.add_argument("--recall_result_file", type=str, default='recall_result_file', help="The file name of recall result")
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("--recall_num", default=10, type=int, help="Recall number for each query from Ann index.")

parser.add_argument("--hnsw_m", default=100, type=int, help="Recall number for each query from Ann index.")
parser.add_argument("--hnsw_ef", default=100, type=int, help="Recall number for each query from Ann index.")
parser.add_argument("--hnsw_max_elements", default=1000000, type=int, help="Recall number for each query from Ann index.")

parser.add_argument('--device', choices=['cpu', 'gpu'], default="gpu", help="Select which device to train model, defaults to gpu.")
args = parser.parse_args()
# fmt: on

if __name__ == "__main__":
    paddle.set_device(args.device)
    rank = paddle.distributed.get_rank()
    if paddle.distributed.get_world_size() > 1:
        paddle.distributed.init_parallel_env()

    tokenizer = AutoTokenizer.from_pretrained("ernie-3.0-medium-zh")

    trans_func = partial(convert_example, tokenizer=tokenizer, max_seq_length=args.max_seq_length)

    batchify_fn = lambda samples, fn=Tuple(
        Pad(axis=0, pad_val=tokenizer.pad_token_id),  # text_input
        Pad(axis=0, pad_val=tokenizer.pad_token_type_id),  # text_segment
    ): [data for data in fn(samples)]

    pretrained_model = AutoModel.from_pretrained("ernie-3.0-medium-zh")

    model = SemanticIndexBase(pretrained_model, output_emb_size=args.output_emb_size)
    model = paddle.DataParallel(model)

    # Load pretrained semantic model
    if args.params_path and os.path.isfile(args.params_path):
        state_dict = paddle.load(args.params_path)
        model.set_dict(state_dict)
        logger.info("Loaded parameters from %s" % args.params_path)
    else:
        raise ValueError("Please set --params_path with correct pretrained model file")

    id2corpus = gen_id2corpus(args.corpus_file)

    # conver_example function's input must be dict
    corpus_list = [{idx: text} for idx, text in id2corpus.items()]
    corpus_ds = MapDataset(corpus_list)

    corpus_data_loader = create_dataloader(
        corpus_ds, mode="predict", batch_size=args.batch_size, batchify_fn=batchify_fn, trans_fn=trans_func
    )

    # Need better way to get inner model of DataParallel
    inner_model = model._layers

    final_index = build_index(args, corpus_data_loader, inner_model)

    text_list, text2similar_text = gen_text_file(args.similar_text_pair_file)

    query_ds = MapDataset(text_list)

    query_data_loader = create_dataloader(
        query_ds, mode="predict", batch_size=args.batch_size, batchify_fn=batchify_fn, trans_fn=trans_func
    )

    query_embedding = inner_model.get_semantic_embedding(query_data_loader)

    if not os.path.exists(args.recall_result_dir):
        os.mkdir(args.recall_result_dir)

    recall_result_file = os.path.join(args.recall_result_dir, args.recall_result_file)
    with open(recall_result_file, "w", encoding="utf-8") as f:
        for batch_index, batch_query_embedding in enumerate(query_embedding):
            recalled_idx, cosine_sims = final_index.knn_query(batch_query_embedding.numpy(), args.recall_num)

            batch_size = len(cosine_sims)

            for row_index in range(batch_size):
                text_index = args.batch_size * batch_index + row_index
                for idx, doc_idx in enumerate(recalled_idx[row_index]):
                    f.write(
                        "{}\t{}\t{}\n".format(
                            text_list[text_index]["text"], id2corpus[doc_idx], 1.0 - cosine_sims[row_index][idx]
                        )
                    )
