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


DEFAULT_PREDICTION = 'the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1'


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


def predict(word_before, word_after):
    prediction = dict(Counter(dict(model[word_before, word_after])).most_common(6))
    result = []
    prob = 0.0
    for key, value in prediction.items():
        prob += value
        result.append(f'{key}:{value}')
    if prob == 0.0:
        return DEFAULT_PREDICTION
    result.append(f':{max(1 - prob, 0.01)}')
    return ' '.join(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():
            words_before, words_after = word_tokenize(preprocess(str(row[6]))), word_tokenize(preprocess(str(row[7])))
            if len(words_before) < 3 or len(words_after) < 3:
                prediction = DEFAULT_PREDICTION
            else:
                prediction = predict(words_before[-1], words_after[0])
            file_out.write(prediction + '\n')


file_in = pd.read_csv('train/in.tsv.xz', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE, nrows=200000)
file_expected = pd.read_csv('train/expected.tsv', sep='\t', on_bad_lines='skip', header=None, quoting=csv.QUOTE_NONE, nrows=200000)
file_in = file_in[[6, 7]]
file_concat = pd.concat([file_in, file_expected], axis=1)
file_concat['text'] = file_concat[6] + file_concat[0] + file_concat[7]
file_concat = file_concat[['text']]
trigrams_list = []
model = defaultdict(lambda: defaultdict(lambda: 0))

rows = file_concat.iterrows()
rows_len = len(file_concat)
for index, (_, row) in enumerate(rows):
    if index % 1000 == 0:
        print(f'uczenie modelu: {index / rows_len}')
    words = word_tokenize(preprocess(str(row['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:
            model[(word_1, word_3)][word_2] += 1

alpha = 0.25
model_len = len(model)
for index, words_1_3 in enumerate(model):
    if index % 100000 == 0:
        print(f'normalizacja: {index / model_len}')
    occurences = sum(model[words_1_3].values())
    for word_2 in model[words_1_3]:
        model[words_1_3][word_2] += alpha
        model[words_1_3][word_2] /= float(occurences + alpha + len(word_2))


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

print('koniec')