import string
import unicodedata

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):
    # remove punctuation
    text = text.translate(str.maketrans(' ', ' ', string.punctuation))
    # only alphabets and numerics
    text = re.sub('[^a-zA-Z]', ' ', text)
    # replace newline with space
    text = re.sub("\n", " ", text)
    # lower case
    text = text.lower()
    # split and join the words
    text = ' '.join(text.split())
    return text


def predict_probs(word1, word2):
    raw_prediction = dict(model[word1, word2])
    prediction = dict(Counter(raw_prediction).most_common(6))

    total_prob = 0.0
    str_prediction = ''

    for word, prob in prediction.items():
        total_prob += prob
        str_prediction += f'{word}:{prob} '

    if total_prob == 0.0:
        return DEFAULT_PREDICTION

    remaining_prob = 1 - total_prob

    if remaining_prob < 0.01:
        remaining_prob = 0.01

    str_prediction += f':{remaining_prob}'

    return str_prediction


def train_model(training_data):
    for index, row in training_data.iterrows():
        text = preprocess_text(str(row["final"]))
        words = word_tokenize(text)
        for w1, w2, w3 in trigrams(words, pad_right=True, pad_left=True):
            if w1 and w2 and w3:
                model[(w2, w3)][w1] += 1
                model[(w1, w2)][w3] += 1

    for word_pair in model:
        num_n_grams = float(sum(model[word_pair].values()))
        for word in model[word_pair]:
            model[word_pair][word] /= num_n_grams


data = pd.read_csv(
    "train/in.tsv.xz",
    sep="\t",
    error_bad_lines=False,
    warn_bad_lines=False,
    header=None,
    quoting=csv.QUOTE_NONE,
    nrows=100000,
)

train_labels = pd.read_csv(
    "train/expected.tsv",
    sep="\t",
    error_bad_lines=False,
    header=None,
    quoting=csv.QUOTE_NONE,
    nrows=100000,
)

train_data = data[[6, 7]]
train_data = pd.concat([train_data, train_labels], axis=1)
train_data["final"] = train_data[6] + train_data[0] + train_data[7]

model = defaultdict(lambda: defaultdict(lambda: 0))


dev_data = pd.read_csv('dev-0/in.tsv.xz', sep='\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)
test_data = pd.read_csv('test-A/in.tsv.xz', sep='\t', error_bad_lines=False, warn_bad_lines=False, header=None, quoting=csv.QUOTE_NONE)


train_model(train_data)

with open("dev-0/out.tsv", "w") as file:
    for _, row in dev_data.iterrows():
        text = preprocess_text(str(row[7]))
        words = word_tokenize(text)
        if len(words) < 3:
            prediction = "the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1"
        else:
            prediction = predict_probs(words[0], words[1])
        file.write(prediction + "\n")

with open("test-A/out.tsv", "w") as file:
    for _, row in test_data.iterrows():
        text = preprocess_text(str(row[7]))
        words = word_tokenize(text)
        if len(words) < 3:
            prediction = "the:0.2 be:0.2 to:0.2 of:0.1 and:0.1 a:0.1 :0.1"
        else:
            prediction = predict_probs(words[0], words[1])
        file.write(prediction + "\n")