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

in_file = 'train/in.tsv.xz'
out_file = 'train/expected.tsv'

X_train = pd.read_csv(in_file, sep='\t',  header=None, quoting=csv.QUOTE_NONE, nrows=70000, on_bad_lines='skip')
Y_train = pd.read_csv(out_file, sep='\t', header=None, quoting=csv.QUOTE_NONE, nrows=70000, on_bad_lines='skip')

X_train = X_train[[6, 7]]
X_train = pd.concat([X_train, Y_train], axis=1)
X_train['row'] = X_train[6] + X_train[0] + X_train[7]

def train(X_train, Y_train, alpha):
    model = defaultdict(lambda: defaultdict(lambda: 0))
    vocabulary = set()
    for _, (_, row) in enumerate(X_train.iterrows()):
        text = preprocess(str(row['row']))
        words = word_tokenize(text)
        for w1, w2, w3 in trigrams(words, pad_right=True, pad_left=True):
            if w1 and w2 and w3:
                model[(w1, w3)][w2] += 1
                vocabulary.add(w1)
                vocabulary.add(w2)
                vocabulary.add(w3)

    for _, w13 in enumerate(model):
        count = float(sum(model[w13].values()))
        denominator = count + alpha * len(vocabulary)
        for w2 in model[w13]:
            nominator = model[w13][w2] + alpha
            model[w13][w2] = nominator / denominator 
    return model

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

def predict_word(before, after, model):
    output = ''
    p = 0.0
    Y_pred = dict(Counter(dict(model[before, after])).most_common(7))
    for key, value in Y_pred.items():
        p += value
        output += f'{key}:{value} '
    if p == 0.0:
        output = 'the:0.04 be:0.04 to:0.04 and:0.02 not:0.02 or:0.02 a:0.02 :0.8'
        return output
    output += f':{max(1 - p, 0.01)}'
    return output

def word_gap_prediction(file, model):
    X_test = pd.read_csv(f'{file}/in.tsv.xz', sep='\t', header=None, quoting=csv.QUOTE_NONE,  on_bad_lines='skip')
    with open(f'{file}/out.tsv', 'w', encoding='utf-8') as output_file:
        for _, row in X_test.iterrows():
            before, after = word_tokenize(preprocess(str(row[6]))), word_tokenize(preprocess(str(row[7])))
            if len(before) < 2 or len(after) < 2:
                output = 'the:0.04 be:0.04 to:0.04 and:0.02 not:0.02 or:0.02 a:0.02 :0.8'
            else:
                output = predict_word(before[-1], after[0],model)
            output_file.write(output + '\n')
            

alpha = 0.00002
model = train(X_train, Y_train, alpha)
word_gap_prediction('dev-0', model)
word_gap_prediction('test-A',model)