# Path: keyphrase/dataset/dataset_utils.py
# SENTENCEDELIMITER = '<eos>'
# DIGIT = '<digit>'
#         A = [word2idx[w] if w in word2idx else word2idx['<unk>'] for w in source]
#         B = [[word2idx[w] if w in word2idx else word2idx['<unk>'] for w in p] for p in target]
#         C = [[0 if w not in source else source.index(w) + Lmax for w in p] for p in target]
# def prepare_text(record, process_type=1):
# def get_tokens(text, process_type=1):
# def process_keyphrase(keyword_str):
# def build_data(data, idx2word, word2idx):
# def load_pairs(records, process_type=1 ,do_filter=False):
# def get_none_phrases(source_text, source_postag, max_len):


import json
import sys
import time
import nltk
import numpy
import numpy as np
import re
import dataset_utils as utils
from keyphrase.config import *
from emolga.dataset.build_dataset import *
from keyphrase.dataset import dataset_utils
from keyphrase_test_dataset import DataLoader,testing_data_loader
            keyphrases = re.sub(r'\(.*?\)', ' ', r['keyword'])
            keyphrases = re.split('[,;]',keyphrases)
            keyphrase_count += len(keyphrases)
            f.write('\n'.join(keyphrases))

    print('length of testing ids: %d' % len(testing_ids))
    print('length of actually testing samples: %d' % len(testing_records['kp20k']))
    print('average number of keyphrases: %f' % (float(keyphrase_count)/ float(len(testing_records['kp20k']))))
    # exit()

    test_pairs                = dict([(k, dataset_utils.load_pairs(v, do_filter=False)[1]) for (k,v) in testing_records.items()])

    print('Building dicts')
    # if voc exists and is assigned, load it, overwrite the wordfreq
    if 'voc' in config and os.path.exists(config['voc']):
        print('Loading dicts from %s' % config['voc'])
        wordfreq = dict(deserialize_from_file(config['voc']))
    else:
        # export vocabulary to file for manual check
        sorted_wordfreq = sorted(wordfreq.items(), key=lambda a: a[1], reverse=True)
        serialize_to_file(sorted_wordfreq, config['voc'])
        serialize_to_file_json(sorted_wordfreq, config['voc'].replace('pkl', 'json'))

        with open(config['path'] + '/dataset/keyphrase/'+config['data_process_name']+'/voc_list.txt', 'w') as f_:
            for (k,f) in sorted_wordfreq:
                f_.write('%s\t%d\n' % (k,f))

    idx2word, word2idx = build_dict(wordfreq)

    print('Mapping tokens to indexes')

# Path: keyphrase/dataset/dataset_utils.py
# SENTENCEDELIMITER = '<eos>'
# DIGIT = '<digit>'
#         A = [word2idx[w] if w in word2idx else word2idx['<unk>'] for w in source]
#         B = [[word2idx[w] if w in word2idx else word2idx['<unk>'] for w in p] for p in target]
#         C = [[0 if w not in source else source.index(w) + Lmax for w in p] for p in target]
# def prepare_text(record, process_type=1):
# def get_tokens(text, process_type=1):
# def process_keyphrase(keyword_str):
# def build_data(data, idx2word, word2idx):
# def load_pairs(records, process_type=1 ,do_filter=False):
# def get_none_phrases(source_text, source_postag, max_len):


# Path: keyphrase/dataset/keyphrase_train_dataset.py
import json
import sys
import time
import nltk
import numpy
import numpy as np
import re
import dataset_utils as utils
from keyphrase.config import *
from emolga.dataset.build_dataset import *
from keyphrase.dataset import dataset_utils
from keyphrase_test_dataset import DataLoader,testing_data_loader

            keyphrases = re.sub(r'\(.*?\)', ' ', r['keyword'])
            keyphrases = re.split('[,;]',keyphrases)
            keyphrase_count += len(keyphrases)
            f.write('\n'.join(keyphrases))

    print('length of testing ids: %d' % len(testing_ids))
    print('length of actually testing samples: %d' % len(testing_records['kp20k']))
    print('average number of keyphrases: %f' % (float(keyphrase_count)/ float(len(testing_records['kp20k']))))
    # exit()

    test_pairs                = dict([(k, dataset_utils.load_pairs(v, do_filter=False)[1]) for (k,v) in testing_records.items()])

    print('Building dicts')
    # if voc exists and is assigned, load it, overwrite the wordfreq
    if 'voc' in config and os.path.exists(config['voc']):
        print('Loading dicts from %s' % config['voc'])
        wordfreq = dict(deserialize_from_file(config['voc']))
    else:
        # export vocabulary to file for manual check
        sorted_wordfreq = sorted(wordfreq.items(), key=lambda a: a[1], reverse=True)
        serialize_to_file(sorted_wordfreq, config['voc'])
        serialize_to_file_json(sorted_wordfreq, config['voc'].replace('pkl', 'json'))

        with open(config['path'] + '/dataset/keyphrase/'+config['data_process_name']+'/voc_list.txt', 'w') as f_:
            for (k,f) in sorted_wordfreq:
                f_.write('%s\t%d\n' % (k,f))

    idx2word, word2idx = build_dict(wordfreq)

    print('Mapping tokens to indexes')

    train_set           = dataset_utils.build_data(train_pairs, idx2word, word2idx)