import pandas as pd
import csv
import regex as re
import kenlm
from english_words import english_words_alpha_set
from nltk import  word_tokenize
from math import log10
from pathlib import  Path
import os


KENLM_BUILD_PATH = Path("/home/bartek/Pulpit/challenging-america-word-gap-prediction/kenlm/build")
KENLM_LMPLZ_PATH = KENLM_BUILD_PATH / "bin" / "lmplz"
KENLM_BUILD_BINARY_PATH = KENLM_BUILD_PATH / "bin" / "build_binary"
SUDO_PASSWORD = ""
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'


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


def create_train_data():
    data = pd.read_csv(
        "train/in.tsv.xz",
        sep="\t",
        error_bad_lines=False,
        header=None,
        quoting=csv.QUOTE_NONE,
        nrows=10000
    )
    train_labels = pd.read_csv(
        "train/expected.tsv",
        sep="\t",
        error_bad_lines=False,
        header=None,
        quoting=csv.QUOTE_NONE,
        nrows=10000
    )

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

    return train_data[6] + train_data[0] + train_data[7]


def create_train_file(filename="train.txt"):
    with open(filename, "w") as f:
        for line in create_train_data():
            f.write(clean(line) + "\n")
    

def train_model():
    lmplz_command = f"{KENLM_LMPLZ_PATH} -o 4 < train.txt > model.arpa"
    build_binary_command = f"{KENLM_BUILD_BINARY_PATH} model.arpa model.binary"
    os.system('echo %s|sudo -S %s' % (SUDO_PASSWORD, lmplz_command))
    os.system('echo %s|sudo -S %s' % (SUDO_PASSWORD, build_binary_command))
    

def predict(model, before, after):
    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:
            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

def make_prediction(model, 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(clean(str(row[6]))), word_tokenize(clean(str(row[7])))
            if len(before) < 2 or len(after) < 2:
                pred = PREDICTION
            else:
                pred = predict(model, before[-1], after[0])
            file_out.write(pred + '\n')


if __name__ == "__main__":
    create_train_file()
    train_model()
    model = kenlm.Model('model.arpa')
    make_prediction(model, "dev-0/in.tsv.xz", "dev-0/out.tsv")
    make_prediction(model, "test-A/in.tsv.xz", "test-A/out.tsv")