
import re, string, unicodedata
import nltk
import contractions
import inflect
from bs4 import BeautifulSoup
from nltk import word_tokenize, sent_tokenize
from nltk.corpus import stopwords
from nltk.stem import LancasterStemmer, WordNetLemmatizer

def remove_non_ascii(words):
    """Remove non-ASCII characters from list of tokenized words"""
    new_words = []
    for word in words:
        new_word = unicodedata.normalize('NFKD', word).encode('ascii', 'ignore').decode('utf-8', 'ignore')
        new_words.append(new_word)
    return new_words

def to_lowercase(words):
    """Convert all characters to lowercase from list of tokenized words"""
    new_words = []
    for word in words:
        new_word = word.lower()
        new_words.append(new_word)
    return new_words

def remove_punctuation(words):
    """Remove punctuation from list of tokenized words"""
    new_words = []
    for word in words:
        new_word = re.sub(r'[^\w\s]', '', word)
        if new_word != '':
            new_words.append(new_word)
    return new_words

def replace_numbers(words):
    """Replace all interger occurrences in list of tokenized words with textual representation"""
    p = inflect.engine()
    new_words = []
    for word in words:
        if word.isdigit():
            new_word = p.number_to_words(word)
            new_words.append(new_word)
        else:
            new_words.append(word)
    return new_words

def remove_stopwords(words):
    """Remove stop words from list of tokenized words"""
    new_words = []
    for word in words:
        # print(word)
        if word not in stopwords.words('english'):
            new_words.append(word)
    return new_words

def stem_words(words):
    """Stem words in list of tokenized words"""
    stemmer = LancasterStemmer()
    stems = []
    for word in words:
        stem = stemmer.stem(word)
        stems.append(stem)
    return stems

def lemmatize_verbs(words):
    """Lemmatize verbs in list of tokenized words"""
    lemmatizer = WordNetLemmatizer()
    lemmas = []
    for word in words:
        lemma = lemmatizer.lemmatize(word, pos='v')
        lemmas.append(lemma)
    return lemmas

def normalize(words):
    words = remove_non_ascii(words)
    words = to_lowercase(words)
    words = remove_punctuation(words)
    words = replace_numbers(words)
    words = remove_stopwords(words)
    words = stem_words(words)
    words = lemmatize_verbs(words)
    return words


words = str("jabscjbjb ")

words = nltk.word_tokenize(words)



words = normalize(words)
print(words)
words = ' '.join(map(str, words))
print(words)