import time
from odps import ODPS
from transformers import BertModel, BertTokenizer
import dashvector
from dashvector import Doc
from typing import List

# 阿里云向量数据库连接
client = dashvector.Client(
    api_key='sk-TbWSOiwIcp9FZkx0fyM9JRomTxmOtD796E4626C1411EEB3525A6F9FFB919B')
# 索引集合
collection = client.get('video_title_performance_01')
assert collection


# 阿里云odps连接
access_id = 'LTAIWYUujJAm7CbH'
access_key = 'RfSjdiWwED1sGFlsjXv0DlfTnZTG1P'
endpoint = 'http://service.cn.maxcompute.aliyun.com/api'
project_name = 'loghubods'

odps = ODPS(
    access_id=access_id,
    secret_access_key=access_key,
    project=project_name,
    endpoint=endpoint
)

# 加载预训练的BERT模型和对应的tokenizer
model_name = 'bert-base-chinese'
model = BertModel.from_pretrained(model_name)
tokenizer = BertTokenizer.from_pretrained(model_name)


def insert_vector(docs: List[Doc]):
    if len(docs) == 0:
        return
    # 通过dashvector.Doc对象，批量插入
    resp = collection.insert(docs=docs)
    print(resp)


def text_to_vector(text) -> List[float]:
    # 使用tokenizer将文本转化为模型需要的格式，这里我们只取一个文本所以使用encode而非batch_encode
    inputs = tokenizer(text, return_tensors='pt')

    # 用BERT模型处理输入数据
    outputs = model(**inputs)

    # 提取嵌入向量
    embeddings = outputs.last_hidden_state  # 最后一层的隐藏状态

    # 将嵌入向量转为NumPy数组
    embeddings = embeddings.detach().numpy().tolist()[0][0]

    return embeddings


# 查询视频标题的表现（从阿里云odps中查询）
def query_video_title_perfermance(start_idx, limit):
    sql = f"SELECT * FROM video_perfermance_info_3 WHERE title is not NULL AND title != '' ORDER BY videoid LIMIT {start_idx}, {limit};"
    result = []
    with odps.execute_sql(sql).open_reader() as reader:
        for record in reader:
            # 处理查询结果
            result.append(record)
    return result

# 将标题表现拼接为向量数据库的向量对象


def video_title_perfermance_to_vector(startIdx, limit) -> List[Doc]:
    records = query_video_title_perfermance(startIdx, limit)
    docs = []
    for record in records:
        # 获取字段值
        videoid = str(record.videoid)
        title = record.title
        if title is None:
            continue
        rntCount = record.回流次数
        rntHeadCount = record.回流人数
        shareCount = record.分享次数
        shareHeadCount = record.分享人数
        exposureCount = record.曝光次数
        exposureHeadCount = record.曝光人数
        playCount = record.播放次数
        playHeadCount = record.播放人数

        # 将文本转化为向量
        vector = text_to_vector(title)
        # 将向量和标题表现拼接为Doc对象
        doc = Doc(id=videoid, vector=vector, fields={
            'title': title, 'rntCount': rntCount, 'rntHeadCount': rntHeadCount,
            'shareCount': shareCount, 'shareHeadCount': shareHeadCount,
            'exposureCount': exposureCount, 'exposureHeadCount': exposureHeadCount, 'playCount': playCount, 'playHeadCount': playHeadCount
        })
        docs.append(doc)
    return docs


def batchInsert():
    for i in range(100000, 185000, 500):
        print(i)
        # 计算耗时
        start = time.time()
        docs = video_title_perfermance_to_vector(i, 500)
        insert_vector(docs)
        end = time.time()
        print(f'{i} done in {end - start}s')