# Utilities
import string
import nltk
import re

nltk.download("stopwords")
# Constants
TASK_1_MODEL = " models/TASK_A_model_final.pkl"
TASK_2_MODEL = " models/TASK_B_model_final.pkl"
TASK_1_MAP = {
    0: "NAG - Non Aggressive Content",
    1: "CAG - Covertly Aggressive Content",
    2: "OAG - Overtly Aggressive Content",
}
TASK_2_MAP = {
    0: "NGEN - Non Misogynistic Content",
    1: "GEN - Misogynistic Content",
}

# Cleans one text
def clean_one_text(text: str) -> str:
    # Cleans one text and returns it

    # remove punctuation
    filter_str = string.punctuation.replace("'", "")

    new_string = text.translate(str.maketrans("", "", filter_str))
    tk = nltk.TweetTokenizer()

    s = set(nltk.corpus.stopwords.words("english"))
    # n't words
    rexp_1 = re.compile(r"n't")
    not_words = set(filter(rexp_1.findall, s))
    not_words.update(("against", "no", "nor", "not"))

    s.difference_update(not_words)

    stmr = nltk.stem.porter.PorterStemmer()
    tokens = [token for token in tk.tokenize(new_string) if token.lower() not in s]
    clean_tokens = [stmr.stem(token) for token in tokens]
    text = " ".join(clean_tokens)
    return text