# -*- coding: utf-8 -*-
import tensorflow as tf
import numpy as np
import pickle
import os
import jieba
from util import data_split_by_label
def separate_items_answers(path):
    """
    test paper have 3 types:1.answer is at the final  sign:#########################
                            2.answer is in the paper
                            3.no answer  sign:************************************
    :param path:where your txt_file is
    :return:list:items;list:answers << one line content is one element
    """
    items = []
    answers = []
    with open(path,'r',encoding='utf-8') as f:
        # print('txt的所有内容: %s' % f.read())
        content = f.readlines()
        # print(content)
        answers_start = 0
        if '####################' in ''.join(content):  # if txt has no ##############,has no answers or answers among test_paper,do not deal with this kind of txt
            for i,line in enumerate(content):
                Nline = line.replace(' ','').replace('#','')
                if line == '' or line.rstrip() == '':  #  \n
#                    items.append('')
                    pass
                elif Nline.rstrip() != '': # not :################### not \n
                    items.append(line)
                elif '####################' in line and Nline.rstrip() == '':   # only get data before ################
                    answers_start = i+1
                    break

            for line in content[answers_start:]:
                Nline = line.replace(' ', '').replace('#', '')
                if line == '' or line.rstrip() == '':  # \n
                    answers.append('')
                # elif '#####' in line and Nline.rstrip() != '':  # not :################### not \n
                else:
                    answers.append(line)

        elif '*******************' in ''.join(content): # no answer
            for i,line in enumerate(content):
                Nline = line.replace(' ', '').replace('#', '')
                if line == '' or line.rstrip() == '':  #  \n
#                    items.append('')
                    pass
                elif line.rstrip() != '': # not :################### not \n
                    items.append(line)
                elif '*************************' in line and Nline.rstrip() == '':   # only get data before ################
                    answers_start = i+1
                    break
    return items, answers

def load_data(x_data,x_vec_path):
    """
    :param x_vec: during trainging,haved saved this file as a pkl
    :return: the data to predict which transformed array
    """
    with open(x_vec_path,'rb') as f:
        vocab_processor = pickle.load(f)
    x_test = np.array(list(vocab_processor.fit_transform(x_data)))
    return x_test

def load_model(checkpoint_path, x_test, model_index):
    """
    :param checkpoint_path:
    :return: checkpoint file:meta,index..about DL model structure ans parameters
    """
    tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement")
    FLAGS = tf.flags.FLAGS
    FLAGS._parse_flags()
    gragh = tf.Graph()    
    with gragh.as_default():
        session_conf = tf.ConfigProto(
            allow_soft_placement=FLAGS.allow_soft_placement)
        sess = tf.Session(config=session_conf)      
        with sess.as_default():
            saver = tf.train.import_meta_graph(os.path.join(checkpoint_path,'model-'+str(model_index)+'.meta'))
            saver.restore(sess, os.path.join(checkpoint_path,'model-'+str(model_index)))
            input_x = gragh.get_operation_by_name("input_x").outputs[0]
            dropout_keep_prob = gragh.get_operation_by_name("dropout_keep_prob").outputs[0]
            predictions = gragh.get_operation_by_name("output/predictions").outputs[0]
            label = []
            for each_line in x_test:
                each_line_T = each_line.reshape(-1,len(each_line))
                prediction = sess.run(predictions, {input_x: each_line_T, dropout_keep_prob: 1.0})                
                label.append(prediction.tolist()[0])
    sess.close()          
    return label

def lable_predict(x_origin, x_vec_path, checkpoint_path, model_index):
#    x_data = []
#    x_origin = []
#    x_origin, ans = separate_items_answers(txt_file_path)
    x_data = [' '.join(jieba.cut(i)) for i in x_origin]
    x_test = load_data(x_data,x_vec_path)
    label = load_model(checkpoint_path,x_test, model_index)
    return label

if __name__ == '__main__':
    txt_file_path = r'test_paper.txt'
    x_vec_path = r'./des_content_label_predict/x_vec'
    checkpoint_path = r'./des_content_label_predict/checkpoints'
    x_origin, ans = separate_items_answers(txt_file_path)
    label = lable_predict(x_origin, x_vec_path, checkpoint_path,5550)
    one_file_split = data_split_by_label(x_origin, label) 
    item_type = ['听力','Na','听力','听力','听力','单项填空','完形填空','阅读理解','Na','Na','Na','短文改错','书面表达']                                                  #题型分类
#    type_content_dict = dict(zip(item_type,one_file_split))
    
    #single_filling
    single_filling = one_file_split[item_type.index('单项填空')]
    x_vec_path = r'./single_filling_stem_opt_predict/x_vec'
    checkpoint_path = r'./single_filling_stem_opt_predict/checkpoints'    
    stem_opt_label = lable_predict(x_origin, x_vec_path, checkpoint_path,2280)
    single_filling_stem_opt_split = data_split_by_label(single_filling[1], label)