import os
from config.predict_t5_config import MultipleResultsDropT5Config
t5config = MultipleResultsDropT5Config()
from config.predict_sim_config import DropSimBertConfig
simbertconfig = DropSimBertConfig()
os.environ["CUDA_VISIBLE_DEVICES"] = t5config.cuda_id
from flask import Flask, jsonify
from flask import request
from predict_t5 import (GenerateModel as T5GenerateModel,
                        AutoTitle as T5AutoTitle)
from predict_sim import (GenerateModel as SimBertGenerateModel,
                        AutoTitle as SimBertT5AutoTitle)
import json
from threading import Thread
import time
import re
import requests


app = Flask(__name__)
app.config["JSON_AS_ASCII"] = False

import logging
pattern = r"[。]"
RE_DIALOG = re.compile(r"\".*?\"|\'.*?\'|“.*?”")
fuhao_end_sentence = ["。","，","？","！","…"]

t5generatemodel = T5GenerateModel(t5config.config_path,
                              t5config.checkpoint_path,
                              t5config.spm_path,
                              t5config.keep_tokens_path,
                              t5config.maxlen,
                              t5config.savemodel_path)

encoder, decoder, model, tokenizer = t5generatemodel.device_setup()
t5autotitle = T5AutoTitle(encoder, decoder, model, tokenizer, start_id=0, end_id=tokenizer._token_end_id, maxlen=120)

simbertgeneratemodel = SimBertGenerateModel(simbertconfig.config_path,
                              simbertconfig.checkpoint_path,
                              simbertconfig.dict_path,
                              simbertconfig.maxlen,
                              simbertconfig.savemodel_path)
encoder, seq2seq, tokenizer = simbertgeneratemodel.device_setup()
simbertautotitle = SimBertT5AutoTitle(seq2seq, tokenizer, start_id=None, end_id=tokenizer._token_end_id, maxlen=120)


def requests_chatGPT(data):
    res = requests.post('http://98.142.138.229:9999/chatgpt', data=data)
    return res.json()['res']

def get_dialogs_index(line: str):
    """
    获取对话及其索引
    :param line 文本
    :return dialogs 对话内容
            dialogs_index: 对话位置索引
            other_index: 其他内容位置索引
    """
    dialogs = re.finditer(RE_DIALOG, line)
    dialogs_text = re.findall(RE_DIALOG, line)
    dialogs_index = []
    for dialog in dialogs:
        all_ = [i for i in range(dialog.start(), dialog.end())]
        dialogs_index.extend(all_)
    other_index = [i for i in range(len(line)) if i not in dialogs_index]

    return dialogs_text, dialogs_index, other_index


def chulichangju_1(text, chulipangban_return_list, short_num):
    fuhao = ["，","？","！","…"]
    dialogs_text, dialogs_index, other_index = get_dialogs_index(text)
    text_1 = text[:120]
    text_2 = text[120:]
    text_1_new = ""
    if text_2 == "":
        chulipangban_return_list.append([text_1, short_num])
        return chulipangban_return_list
    for i in range(len(text_1)-1, -1, -1):
        if text_1[i] in fuhao:
            if i in dialogs_index:
                continue
            text_1_new = text_1[:i]
            text_1_new += text_1[i]
            chulipangban_return_list.append([text_1_new, short_num])
            if text_2 != "":
                if i+1 != 120:
                    text_2 = text_1[i+1:] + text_2
            break
        # else:
        #     chulipangban_return_list.append(text_1)
    if text_1_new == "":
        chulipangban_return_list.append([text_1, short_num])
    if text_2 != "":
        short_num += 1
        chulipangban_return_list = chulichangju_1(text_2, chulipangban_return_list, short_num)
    return chulipangban_return_list


def chulipangban_test_1(text):
    # 引号处理

    dialogs_text, dialogs_index, other_index = get_dialogs_index(text)
    for dialogs_text_dan in dialogs_text:
        text_dan_list = text.split(dialogs_text_dan)
        if "。" in dialogs_text_dan:
            dialogs_text_dan = str(dialogs_text_dan).replace("。", "&")
        text = dialogs_text_dan.join(text_dan_list)

    # text_new_str = "".join(text_new)

    sentence_list = text.split("。")
    # sentence_list_new = []
    # for i in sentence_list:
    #     if i != "":
    #         sentence_list_new.append(i)
    # sentence_list = sentence_list_new
    sentence_batch_list = []
    sentence_batch_one = []
    sentence_batch_length = 0
    return_list = []
    for sentence in sentence_list:
        if len(sentence) < 120:
            sentence_batch_length += len(sentence)
            sentence_batch_list.append([sentence, 0])
            # sentence_pre = autotitle.gen_synonyms_short(sentence)
            # return_list.append(sentence_pre)
        else:

            sentence_split_list = chulichangju_1(sentence,[], 0)
            for sentence_short in sentence_split_list:
                sentence_batch_list.append(sentence_short)
    return sentence_batch_list


def paragraph_test(texts:str):


    text_list = chulipangban_test_1(texts)


    # text_new_str = "".join(text_new)
    return text_list


def batch_data_process(text_list):
    sentence_batch_length = 0
    sentence_batch_one = []
    sentence_batch_list = []

    for sentence in text_list:
        sentence_batch_length += len(sentence[0])
        sentence_batch_one.append(sentence)
        if sentence_batch_length > 500:
            sentence_batch_length = 0
            sentence_ = sentence_batch_one.pop(-1)
            sentence_batch_list.append(sentence_batch_one)
            sentence_batch_one = []
            sentence_batch_one.append(sentence_)
    sentence_batch_list.append(sentence_batch_one)
    return sentence_batch_list

def batch_predict(batch_data_list):
    '''
    一个bacth数据预测
    @param data_text:
    @return:
    '''
    batch_data_list_new = []
    batch_data_text_list = []
    batch_data_snetence_id_list = []
    for i in batch_data_list:
        batch_data_text_list.append(i[0])
        batch_data_snetence_id_list.append(i[1:])
    # batch_pre_data_list = autotitle.generate_beam_search_batch(batch_data_text_list)
    batch_pre_data_list = batch_data_text_list
    for text,sentence_id in zip(batch_pre_data_list,batch_data_snetence_id_list):
        batch_data_list_new.append([text] + sentence_id)

    return batch_data_list_new


def one_predict(data_text):
    '''
    一个条数据预测
    @param data_text:
    @return:
    '''
    return_data_list = []
    if data_text[0] != "":
        data_inputs = data_text[0].replace("&", "。")
        prompt_list = ["请帮我改写一下这个句子", "请帮美化一下下面句子", "请帮我修改下面句子让这句话更完美"]
        pre_data_list = []
        for i in prompt_list:
            pre_data = requests_chatGPT(
                data={
                    'prompt':i,
                    'text':data_inputs
                }
            )
            pre_data_list.append(pre_data)
        modelclass_list = [t5autotitle, simbertautotitle]
        for model in modelclass_list:
             pre_data_list.append(model.generate(data_inputs))
    else:
        pre_data_list = [""] * 5
    for pre_data in pre_data_list:
        return_data_list.append([pre_data] + data_text[1:])

    return return_data_list


def predict_data_post_processing(text_list, index):
    text_list_sentence = []
    # text_list_sentence.append([text_list[0][0], text_list[0][1]])

    for i in range(len(text_list)):
        if text_list[i][index][2] != 0:
            text_list_sentence[-1][0] += text_list[i][index][0]
        else:
            text_list_sentence.append([text_list[i][0], text_list[i][1]])

    return_list = {}
    sentence_one = []
    sentence_id = text_list_sentence[0][1]
    for i in text_list_sentence:
        if i[1] == sentence_id:
            sentence_one.append(i[0])
        else:
            return_list[sentence_id] = "。".join(sentence_one)
            sentence_id = i[1]
            sentence_one = []
            sentence_one.append(i[0])
    if sentence_one != []:
        return_list[sentence_id] = "。".join(sentence_one)
    return return_list


# def main(text:list):
#     # text_list = paragraph_test(text)
#     # batch_data = batch_data_process(text_list)
#     # text_list = []
#     # for i in batch_data:
#     #     text_list.extend(i)
#     # return_list = predict_data_post_processing(text_list)
#     # return return_list

def main(text: str):
    text_list = paragraph_test(text)
    text_list_new = []
    return_list = []
    for i in text_list:
        pre_list = one_predict(i)
        text_list_new.append(pre_list)

    for index in range(len(text_list_new[0])):
        return_list.append(predict_data_post_processing(text_list_new, index))
    return return_list

@app.route('/multiple_results_droprepeat/', methods=['POST'])
def sentence():
    print(request.remote_addr)
    texts = request.json["texts"]
    print("原始语句" + str(texts))
    # question = question.strip('。、！？?')


    if isinstance(texts, str):
        texts_list = []
        y_pred_label_list = []
        position_list = []

        # texts = texts.replace('\'', '\"')
        if texts is None:
            return_text = {"texts": "输入了空值", "probabilities": None, "status_code": False}
            return jsonify(return_text)
        else:
            texts_list = main(texts)
            return_text = {"texts": texts_list, "probabilities": None, "status_code": True}
    else:
        return_text = {"texts":"输入格式应该为list", "probabilities": None, "status_code":False}
    return jsonify(return_text)


if __name__ == "__main__":
    fh = logging.FileHandler(mode='a', encoding='utf-8', filename='chitchat.log')
    logging.basicConfig(
        handlers=[fh],
        level=logging.DEBUG,
        format='%(asctime)s - %(levelname)s - %(message)s',
        datefmt='%a, %d %b %Y %H:%M:%S',
    )
    app.run(host="0.0.0.0", port=14000, threaded=True, debug=False)