#!/usr/bin/env python
# coding: utf-8

# In[2]:


from nltk import trigrams, word_tokenize
import pandas as pd
import csv
import regex as re
from collections import Counter, defaultdict
import kenlm
from english_words import english_words_alpha_set
from math import log10


# In[3]:


train_set = pd.read_csv(
    'train/in.tsv.xz',
    sep='\t',
    header=None,
    quoting=csv.QUOTE_NONE,
    nrows=35000)

train_labels = pd.read_csv(
    'train/expected.tsv',
    sep='\t',
    header=None,
    quoting=csv.QUOTE_NONE,
    nrows=35000)


# In[4]:


data = pd.concat([train_set, train_labels], axis=1)


# In[5]:


data = train_set[6] + train_set[0] + train_set[7]


# In[6]:


def data_preprocessing(text):
    return re.sub(r'\p{P}', '', text.lower().replace('-\\n', '').replace('\\n', ' ').replace("'ll", " will").replace("-", "").replace("'ve", " have").replace("'s", " is"))


# In[8]:


data = data.apply(data_preprocessing)
prediction = 'the:0.03 be:0.03 to:0.03 of:0.025 and:0.025 a:0.025 in:0.020 that:0.020 have:0.015 I:0.010 it:0.010 for:0.010 not:0.010 on:0.010 with:0.010 he:0.010 as:0.010 you:0.010 do:0.010 at:0.010 :0.77'


# In[25]:


with open("train_file.txt", "w+") as f:
    for text in data:
        f.write(text + "\n")


# In[27]:


KENLM_BUILD_PATH='../kenlm/build/bin/lmplz'


# In[28]:


get_ipython().system('$KENLM_BUILD_PATH -o 4 < train_file.txt > kenlm_model.arpa')


# In[29]:


import os
print(os.getcwd())
model = kenlm.Model('kenlm_model.arpa')


# In[30]:


def predict(before, after):
    result = ''
    prob = 0.0
    best = []
    for word in english_words_alpha_set:
        text = ' '.join([before, word, after])
        text_score = model.score(text, bos=False, eos=False)
        if len(best) < 12:
            best.append((word, text_score))
        else:
            is_better = False
            worst_score = None
            for score in best:
                if not worst_score:
                    worst_score = score
                else:
                    if worst_score[1] > score[1]:
                        worst_score = score
            if worst_score[1] < text_score:
                best.remove(worst_score)
                best.append((word, text_score))
    probs = sorted(best, key=lambda tup: tup[1], reverse=True)
    pred_str = ''
    for word, prob in probs:
        pred_str += f'{word}:{prob} '
    pred_str += f':{log10(0.99)}'
    return pred_str


# In[31]:


def make_prediction(path, result_path):
    data = pd.read_csv(path, sep='\t', header=None, quoting=csv.QUOTE_NONE)
    with open(result_path, '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) < 2 or len(after) < 2:
                pred = prediction
            else:
                pred = predict(before[-1], after[0])
            file_out.write(pred + '\n')


# In[32]:


make_prediction("dev-0/in.tsv.xz", "dev-0/out.tsv")


# In[33]:


make_prediction("test-A/in.tsv.xz", "test-A/out.tsv")