import pandas as pd
import csv
import regex as re
import kenlm
from english_words import english_words_alpha_set
from nltk import trigrams, word_tokenize

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 = ""

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))
    

# create_train_file()
# train_model()