# encoding=utf-8

import os 
os.environ["CUDA_VISIBLE_DEVICES"] = "2"

"""
基于命令行的在线预测方法
@Author: Macan (<EMAIL>) 
"""


import re
import tensorflow as tf
import numpy as np
import codecs
import pickle
import os
from datetime import datetime

from flask import Flask,request

app = Flask(__name__)

from bert_base.bert import tokenization, modeling
from bert_base.train.models import create_classification_model


## some parameters
DO_LOWER_CASE = False
MAX_SEQ_LENGTH = 256


model_dir = '/data/leo/Projects/bert/outputs/Cla_wende_2_v1'
bert_dir = '/data/leo/Projects/bert/models/chinese_L-12_H-768_A-12'


is_training=False
use_one_hot_embeddings=False
batch_size=1

gpu_config = tf.ConfigProto()
gpu_config.gpu_options.allow_growth = True
sess=tf.Session(config=gpu_config)
model=None

global graph
input_ids_p, input_mask_p, label_ids_p, segment_ids_p = None, None, None, None


print('checkpoint path:{}'.format(os.path.join(model_dir, "checkpoint")))
if not os.path.exists(os.path.join(model_dir, "checkpoint")):
    raise Exception("failed to get checkpoint. going to return ")

label2id = {"0":0, "1":1}
id2label = {value: key for key, value in label2id.items()}
label_list = label2id.keys()
num_labels = len(label_list)


graph = tf.get_default_graph()
with graph.as_default():
    print("going to restore checkpoint")
    #sess.run(tf.global_variables_initializer())
    input_ids_p = tf.placeholder(tf.int32, [batch_size, MAX_SEQ_LENGTH], name="input_ids")
    input_mask_p = tf.placeholder(tf.int32, [batch_size, MAX_SEQ_LENGTH], name="input_mask")

    bert_config = modeling.BertConfig.from_json_file(os.path.join(bert_dir, 'bert_config.json'))

    loss, per_example_loss, logits, probabilities = create_classification_model(bert_config=bert_config, is_training=False,
        input_ids=input_ids_p, input_mask=input_mask_p, segment_ids=None, labels=None, num_labels=num_labels)
    # pred_ids = tf.argmax(probabilities, axis=-1, output_type=tf.int32, name='pred_ids')
    # pred_ids = tf.identity(pred_ids, 'pred_ids')
    # probabilities = tf.identity(probabilities, 'pred_prob')
    saver = tf.train.Saver()


    # (total_loss, logits, trans, pred_ids) = create_model(
    #     bert_config=bert_config, is_training=False, input_ids=input_ids_p, input_mask=input_mask_p, segment_ids=None,
    #     labels=None, num_labels=num_labels, use_one_hot_embeddings=False, dropout_rate=1.0)

    saver = tf.train.Saver()

    print("model_dir: ",model_dir)

    saver.restore(sess, tf.train.latest_checkpoint(model_dir))


tokenizer = tokenization.FullTokenizer(
        vocab_file=os.path.join(bert_dir, 'vocab.txt'), do_lower_case=DO_LOWER_CASE)


class InputFeatures(object):
    """A single set of features of data."""

    def __init__(self, input_ids, input_mask, segment_ids, ):
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        # self.label_ids = label_ids
        # self.label_mask = label_mask

def convert_single_example(ex_index, example, label_list, max_seq_length, tokenizer, mode):
    """
    将一个样本进行分析，然后将字转化为id, 标签转化为id,然后结构化到InputFeatures对象中
    :param ex_index: index
    :param example: 一个样本
    :param label_list: 标签列表
    :param max_seq_length:
    :param tokenizer:
    :param mode:
    :return:
    """
    label_map = {}
    # 1表示从1开始对label进行index化
    for (i, label) in enumerate(label_list, 1):
        label_map[label] = i
    # 保存label->index 的map
    if not os.path.exists(os.path.join(model_dir, 'label2id.pkl')):
        with codecs.open(os.path.join(model_dir, 'label2id.pkl'), 'wb') as w:
            pickle.dump(label_map, w)

    tokens = example
    # tokens = tokenizer.tokenize(example.text)
    # 序列截断
    if len(tokens) >= max_seq_length - 1:
        tokens = tokens[0:(max_seq_length - 2)]  # -2 的原因是因为序列需要加一个句首和句尾标志
    ntokens = []
    segment_ids = []
    # label_ids = []
    ntokens.append("[CLS]")  # 句子开始设置CLS 标志
    segment_ids.append(0)
    # append("O") or append("[CLS]") not sure!
    # label_ids.append(label_map["[CLS]"])  # O OR CLS 没有任何影响，不过我觉得O 会减少标签个数,不过拒收和句尾使用不同的标志来标注，使用LCS 也没毛病
    for i, token in enumerate(tokens):
        ntokens.append(token)
        segment_ids.append(0)
        # label_ids.append(0)
    ntokens.append("[SEP]")  # 句尾添加[SEP] 标志
    segment_ids.append(0)
    # append("O") or append("[SEP]") not sure!
    # label_ids.append(label_map["[SEP]"])
    input_ids = tokenizer.convert_tokens_to_ids(ntokens)  # 将序列中的字(ntokens)转化为ID形式
    input_mask = [1] * len(input_ids)

    # padding, 使用
    while len(input_ids) < max_seq_length:
        input_ids.append(0)
        input_mask.append(0)
        segment_ids.append(0)
        # we don't concerned about it!
        # label_ids.append(0)
        ntokens.append("**NULL**")
        # label_mask.append(0)
    # print(len(input_ids))
    assert len(input_ids) == max_seq_length
    assert len(input_mask) == max_seq_length
    assert len(segment_ids) == max_seq_length
    # assert len(label_ids) == max_seq_length
    # assert len(label_mask) == max_seq_length

    # 结构化为一个类
    feature = InputFeatures(
        input_ids=input_ids,
        input_mask=input_mask,
        segment_ids=segment_ids,
        # label_ids=label_ids,
        # label_mask = label_mask
    )
    return feature


def predict(text):
    """
    do online prediction. each time make prediction for one instance.
    you can change to a batch if you want.

    :param line: a list. element is: [dummy_label,text_a,text_b]
    :return:
    """
    def convert(line):
        feature = convert_single_example(0, line, label_list, MAX_SEQ_LENGTH, tokenizer, 'p')
        input_ids = np.reshape([feature.input_ids],(batch_size, MAX_SEQ_LENGTH))
        input_mask = np.reshape([feature.input_mask],(batch_size, MAX_SEQ_LENGTH))
        segment_ids = np.reshape([feature.segment_ids],(batch_size, MAX_SEQ_LENGTH))
        # label_ids =np.reshape([feature.label_ids],(batch_size, MAX_SEQ_LENGTH))
        return input_ids, input_mask, segment_ids

    global graph
    with graph.as_default():
        # print(id2label)
        sentence = text
        start = datetime.now()
        sentence = tokenizer.tokenize(sentence)
        # print('tokenized sentence:{}'.format(sentence))
        input_ids, input_mask, segment_ids = convert(sentence)

        feed_dict = {input_ids_p: input_ids,
                        input_mask_p: input_mask}
        # run session get current feed_dict result
        prob = sess.run([probabilities], feed_dict)
        # print("prob: ", prob)

        result = prob[0].tolist()[0]
        # print(result)
        if result[0] < result[1]:
            return '1',result[1]
        else:
            return '0',result[0]

        # exit()


        # pred_label_result = convert_id_to_label(pred_ids_result, id2label)
        # print(pred_label_result)
        # #todo: 组合策略
        # result = strage_combined_link_org_loc(sentence, pred_label_result[0])
        # print("研究问题：", result)
        # print('time used: {} sec'.format((datetime.now() - start).total_seconds()))
        # return result

@app.route('/Question_Word', methods=['GET'])
def Question_Word():
    if not request.args.get("data"):
        pass
    text = request.args.get("data")
    print(text)
    words = predict(text)

    print(words)

    return {'results':words}

def clean(text):
    text = re.sub(r"(回复)?(//)?\s*@\S*?\s*(:| |$)", " ", text)  # 去除正文中的@和回复/转发中的用户名
    text = re.sub(r"\[\S+\]", "", text)      # 去除表情符号
    text = re.sub(r"#\S+#", "", text)      # 去除话题内容
    text = re.sub(r"【\S+】", "", text)      # 去除标题
    URL_REGEX = re.compile(
        r'(?i)\b((?:https?://|www\d{0,3}[.]|[a-z0-9.\-]+[.][a-z]{2,4}/)(?:[^\s()<>]+|\(([^\s()<>]+|(\([^\s()<>]+\)))*\))+(?:\(([^\s()<>]+|(\([^\s()<>]+\)))*\)|[^\s`!()\[\]{};:\'".,<>?«»“”‘’]))',
        re.IGNORECASE)
    text = re.sub(URL_REGEX, "", text)       # 去除网址
    text = text.replace("转发微博", "")       # 去除无意义的词语
    text = text.replace("本报讯", "") 
    text = re.sub(r"\s+", " ", text) # 合并正文中过多的空格
    return text.strip()


if __name__ == "__main__":
    # app.run('0.0.0.0', port=7200,debug=False)

    # while True:
    #     text = input()
    #     predict(text)
    import pandas as pd

    df = pd.read_excel('/data/leo/Work/Wende/弹幕审核测试文案.xlsx')
    print(df.head())
    text = df['弹幕审核测试文案'].tolist()
    new_text = []
    for t in text:
        new_text.append(clean(t))

    df['涉政模型预测结果_v3'] = ''
    df['涉政模型预测得分_v3'] = ''


    from tqdm import tqdm

    i = 0
    with open('/data/leo/Work/Wende/test_2_v1.txt','w',encoding='utf-8') as f:
        for t in tqdm(new_text):
            c = ''
         
            c,prob = predict(t)
            f.write(c + '\t' + str(prob) + '\n')
            i += 1
            df.loc[i, '涉政模型预测结果'] = c
            df.loc[i, '涉政模型预测得分'] = str(prob)
            
    
    df.to_excel('/data/leo/Work/Wende/弹幕审核测试文案_2_v1.xlsx')

