# -*- coding = utf-8 -*-
# @Time:  18:02
# @Author:ZYP
# @File：util.py
# @mail：zypsunshine1@gmail.com
# @Software: PyCharm
# =========================================================================================
# 工具类
# 用于加载停用词、数据库、word2vec、fasttext模型
# =========================================================================================
import os
import time
import math
import json
import jieba
import numpy as np
import requests
from collections import defaultdict
from textrank4zh import TextRank4Keyword

jieba.initialize()

stop_word_path = '/home/zc-nlp-zyp/work_file/ssd_data/program/check_paper/fasttext_train/data/total_stopwords.txt'

class Logging:
    def __init__(self):
        pass

    def log(*args, **kwargs):
        format = '%Y/%m/%d-%H:%M:%S'
        format_h = '%Y-%m-%d'
        value = time.localtime(int(time.time()))
        dt = time.strftime(format, value)
        dt_log_file = time.strftime(format_h, value)
        log_file = 'gunicornLogs/access-%s-%s' % (str(os.getpid()), dt_log_file) + ".log"
        if not os.path.exists(log_file):
            with open(os.path.join(log_file), 'w', encoding='utf-8') as f:
                print(dt, *args, file=f, **kwargs)
        else:
            with open(os.path.join(log_file), 'a+', encoding='utf-8') as f:
                print(dt, *args, file=f, **kwargs)


def load_stopwords(path=stop_word_path):
    """加载停用词"""
    with open(path, 'r', encoding='utf-8') as f:
        stop_words = {i.strip() for i in f.readlines()}
    return stop_words


def cut_text(text_str, tokenizer='jieba'):
    """使用相应的分词算法对文章进行分词，然后统计每个单词的词频，按照降序返回相应的字典"""
    word_dict = defaultdict(int)
    if tokenizer == 'jieba':
        all_word_list = jieba.cut(text_str)
        for word in all_word_list:
            if word not in stop_word:
                word_dict[word] += 1
    # elif tokenizer == 'hanlp':
    #     for i in HanLP.segment(text_str):
    #         if i.word not in stop_word and i.nature != 'w':
    #             word_dict[i.word] += 1
    else:
        print('您输入的 tokenizer 参数有误！')

    return {k: v for k, v in sorted(word_dict.items(), key=lambda x: x[1], reverse=True)}


def l2_normal(tf_idf_dict):
    """对计算出来的tf-idf字典进行归一化，归一到（0-1）之间"""
    l2_norm = math.sqrt(sum(map(lambda x: x ** 2, tf_idf_dict.values())))
    tf_idf_dict1 = sorted(tf_idf_dict.items(), key=lambda x: x[1] / l2_norm, reverse=True)
    tf_idf_dict2 = {key: value / l2_norm for key, value in tf_idf_dict1[:15]}
    return tf_idf_dict2


def get_word_vec(word):
    """根据相应的词语，使用模型进行提取词语向量，如果不存在词表中返回0，存在词表中返回对应向量"""
    vec = requests.post('http://192.168.31.74:50001/word2vec', data=json.dumps({'word': word}), timeout=100)
    if len(vec.text) < 100:
        vec = requests.post('http://192.168.31.74:50002/fasttext', data=json.dumps({'word': word}), timeout=100)
        if len(vec.text) < 100:
            vec = 0
            return vec
        else:
            json_dict = json.loads(vec.text)
            res_vec = np.array([float(j) for j in json_dict["vec"].split(',')], dtype=np.float64)
            return res_vec
    else:
        json_dict = json.loads(vec.text)
        res_vec = np.array([float(j) for j in json_dict["vec"].split(',')], dtype=np.float64)
        return res_vec


stop_word = load_stopwords()
tr4w = TextRank4Keyword(stop_words_file=stop_word_path)