# 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.

# 城市百科知识智能问答系统
import argparse
import os

from pipelines.document_stores import FAISSDocumentStore
from pipelines.nodes import DensePassageRetriever, ErnieRanker, ErnieReader
from pipelines.utils import (
    convert_files_to_dicts,
    fetch_archive_from_http,
    print_answers,
)

# yapf: disable
parser = argparse.ArgumentParser()
parser.add_argument('--device', choices=['cpu', 'gpu'], default="gpu", help="Select which device to run dense_qa system, defaults to gpu.")
parser.add_argument("--index_name", default='faiss_index', type=str, help="The ann index name of FAISS.")
parser.add_argument("--max_seq_len_query", default=64, type=int, help="The maximum total length of query after tokenization.")
parser.add_argument("--max_seq_len_passage", default=256, type=int, help="The maximum total length of passage after tokenization.")
parser.add_argument("--retriever_batch_size", default=16, type=int, help="The batch size of retriever to extract passage embedding for building ANN index.")
args = parser.parse_args()
# yapf: enable


def dense_qa_pipeline():

    use_gpu = True if args.device == "gpu" else False

    faiss_document_store = "faiss_document_store.db"
    if os.path.exists(args.index_name) and os.path.exists(faiss_document_store):
        # connect to existed FAISS Index
        document_store = FAISSDocumentStore.load(args.index_name)
        retriever = DensePassageRetriever(
            document_store=document_store,
            query_embedding_model="rocketqa-zh-dureader-query-encoder",
            passage_embedding_model="rocketqa-zh-dureader-query-encoder",
            max_seq_len_query=args.max_seq_len_query,
            max_seq_len_passage=args.max_seq_len_passage,
            batch_size=args.retriever_batch_size,
            use_gpu=use_gpu,
            embed_title=False,
        )
    else:
        doc_dir = "data/baike"
        city_data = "https://paddlenlp.bj.bcebos.com/applications/baike.zip"
        fetch_archive_from_http(url=city_data, output_dir=doc_dir)
        dicts = convert_files_to_dicts(dir_path=doc_dir, split_paragraphs=True, encoding="utf-8")

        if os.path.exists(args.index_name):
            os.remove(args.index_name)
        if os.path.exists(faiss_document_store):
            os.remove(faiss_document_store)

        document_store = FAISSDocumentStore(embedding_dim=768, faiss_index_factory_str="Flat")
        document_store.write_documents(dicts)

        retriever = DensePassageRetriever(
            document_store=document_store,
            query_embedding_model="rocketqa-zh-dureader-query-encoder",
            passage_embedding_model="rocketqa-zh-dureader-query-encoder",
            max_seq_len_query=args.max_seq_len_query,
            max_seq_len_passage=args.max_seq_len_passage,
            batch_size=args.retriever_batch_size,
            use_gpu=use_gpu,
            embed_title=False,
        )

        # update Embedding
        document_store.update_embeddings(retriever)

        # save index
        document_store.save(args.index_name)

    # Ranker
    ranker = ErnieRanker(model_name_or_path="rocketqa-zh-dureader-cross-encoder", use_gpu=use_gpu)

    reader = ErnieReader(
        model_name_or_path="ernie-gram-zh-finetuned-dureader-robust", use_gpu=use_gpu, num_processes=1
    )

    # Pipeline
    from pipelines import ExtractiveQAPipeline

    pipe = ExtractiveQAPipeline(reader, ranker, retriever)

    pipeline_params = {"Retriever": {"top_k": 50}, "Ranker": {"top_k": 1}, "Reader": {"top_k": 1}}

    prediction = pipe.run(query="北京市有多少个行政区？", params=pipeline_params)
    print_answers(prediction, details="minimum")

    prediction = pipe.run(query="上海常住人口有多少？", params=pipeline_params)
    print_answers(prediction, details="minimum")

    prediction = pipe.run(query="广州市总面积多大？", params=pipeline_params)
    print_answers(prediction, details="minimum")

    prediction = pipe.run(query="河北省的省会在哪里？", params=pipeline_params)
    print_answers(prediction, details="minimum")

    prediction = pipe.run(query="安徽省的简称是什么？", params=pipeline_params)
    print_answers(prediction, details="minimum")


if __name__ == "__main__":
    dense_qa_pipeline()
