header.py

import torch
import numpy as np
from torch.utils.data import Dataset, DataLoader
from torch.nn.utils import clip_grad_norm_
from torch.nn import DataParallel
from torch.optim import lr_scheduler
from torch.utils.tensorboard import SummaryWriter
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
from torchtext import vocab
from collections import Counter
from tqdm import tqdm
import networkx as nx
import os
import sys
import re
import math
from itertools import chain
import csv
import jieba
from jieba import analyse
import random
import json
import ijson
import time
import pprint
import hashlib
import pymongo
import logging
import gensim
import xml.etree.ElementTree as ET
from copy import deepcopy
from flask import Flask, jsonify, request, make_response, session
from bert_serving.client import BertClient
import ipdb
from transformers.modeling_gpt2 import GPT2Config, GPT2LMHeadModel
from transformers import BertTokenizer
import pickle
import argparse
from torch.nn.utils.rnn import pad_sequence
from data import generate_negative_samples
from models.model_utils import ESChat
from models.test import TestAgent
from elasticsearch import Elasticsearch
from multiview.diversity import *

logging.getLogger("elasticsearch").setLevel(logging.WARNING)
logging.getLogger("transformers").setLevel(logging.WARNING)
logging.getLogger("jieba").setLevel(logging.WARNING)