from nltk import trigrams, word_tokenize
import pandas as pd
import csv
import regex as re
from collections import Counter, defaultdict


train_set = pd.read_csv(
    'train/in.tsv.xz',
    sep='\t',
    on_bad_lines='skip',
    header=None,
    uoting=csv.QUOTE_NONE,
    nrows=50000)


train_labels = pd.read_csv(
    'train/expected.tsv',
    sep='\t',
    on_bad_lines='skip',
    header=None,
    quoting=csv.QUOTE_NONE,
    nrows=50000)


def data_preprocessing(text):
    return re.sub(r'\p{P}', '', text.lower().replace('-\\n', '').replace('\\n', ' '))


def predict(before, after):
    prediction = dict(Counter(dict(trigram[before, after])).most_common(5))
    result = ''
    prob = 0.0
    for key, value in prediction.items():
        prob += value
        result += f'{key}:{value} '
    if prob == 0.0:
        return 'to:0.02 be:0.02 the:0.02 or:0.01 not:0.01 and:0.01 a:0.01 :0.9'
    result += f':{max(1 - prob, 0.01)}'
    return result


def make_prediction(file):
    data = pd.read_csv(f'{file}/in.tsv.xz', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE)
    with open(f'{file}/out.tsv', 'w', encoding='utf-8') as file_out:
        for _, row in data.iterrows():
            before, after = word_tokenize(data_preprocessing(str(row[6]))), word_tokenize(data_preprocessing(str(row[7])))
            if len(before) < 3 or len(after) < 3:
                prediction = 'to:0.02 be:0.02 the:0.02 or:0.01 not:0.01 and:0.01 a:0.01 :0.9'
            else:
                prediction = predict(before[-1], after[0])
            file_out.write(prediction + '\n')


train_set = train_set[[6, 7]]
train_set = pd.concat([train_set, train_labels], axis=1)
train_set['line'] = train_set[6] + train_set[0] + train_set[7]


trigram = defaultdict(lambda: defaultdict(lambda: 0))

rows = train_set.iterrows()
rows_len = len(train_set)
for index, (_, row) in enumerate(rows):
    text = data_preprocessing(str(row['line']))
    words = word_tokenize(text)
    for word_1, word_2, word_3 in trigrams(words, pad_right=True, pad_left=True):
        if word_1 and word_2 and word_3:
            trigram[(word_1, word_3)][word_2] += 1

model_len = len(trigram)
for index, words_1_3 in enumerate(trigram):
    count = sum(trigram[words_1_3].values())
    for word_2 in trigram[words_1_3]:
        trigram[words_1_3][word_2] += 0.25
        trigram[words_1_3][word_2] /= float(count + 0.25 + len(word_2))


make_prediction('test-A')
make_prediction('dev-0')