import string, re, nltk
nltk.download('stopwords')
nltk.download('averaged_perceptron_tagger')
from string import punctuation
from nltk.tokenize import word_tokenize, RegexpTokenizer
from nltk.corpus import stopwords
# from num2words import num2words
# from spellchecker import SpellChecker
# from nltk.stem.porter import PorterStemmer
import spacy
from nltk.stem import WordNetLemmatizer
import pandas as pd

# RegexpTokenizer
regexp = RegexpTokenizer("[\w']+")

# Converting to lowercase
def convert_to_lowercase(text):
    return text.lower()

# Removing whitespaces
def remove_whitespace(text):
    return text.strip()

# Removing punctuations
def remove_punctuation(text):
    punct_str = string.punctuation
    punct_str = punct_str.replace("'", "").replace("%", "") # discarding apostrophe from the string to keep the contractions intact
    return text.translate(str.maketrans("", "", punct_str))

# Removing HTML tags
def remove_html(text):
    html = re.compile(r'<.*?>')
    return html.sub(r'', text)

# Removing emojis
def remove_emoji(text):
    emoji_pattern = re.compile("["
                           u"\U0001F600-\U0001F64F"  # emoticons
                           u"\U0001F300-\U0001F5FF"  # symbols & pictographs
                           u"\U0001F680-\U0001F6FF"  # transport & map symbols
                           u"\U0001F1E0-\U0001F1FF"  # flags (iOS)
                           u"\U00002702-\U000027B0"
                           u"\U000024C2-\U0001F251"
                           "]+", flags = re.UNICODE)
    return emoji_pattern.sub(r'', text)

# Removing other unicode characters
def remove_http(text):
    http = "https?://\S+|www\.\S+" # matching strings beginning with http (but not just "http")
    pattern = r"({})".format(http) # creating pattern
    return re.sub(pattern, "", text)

# Dictionary of acronyms
acronyms_url = 'https://raw.githubusercontent.com/sugatagh/E-commerce-Text-Classification/main/JSON/english_acronyms.json'
acronyms_dict = pd.read_json(acronyms_url, typ = 'series')
acronyms_list = list(acronyms_dict.keys())

# Function to convert contractions in a text
def convert_acronyms(text):
    words = []
    for word in regexp.tokenize(text):
        if word in acronyms_list:
            words = words + acronyms_dict[word].split()
        else:
            words = words + word.split()
    
    text_converted = " ".join(words)
    return text_converted

# Dictionary of contractions
contractions_url = 'https://raw.githubusercontent.com/sugatagh/E-commerce-Text-Classification/main/JSON/english_contractions.json'
contractions_dict = pd.read_json(contractions_url, typ = 'series')
contractions_list = list(contractions_dict.keys())

# Function to convert contractions in a text
def convert_contractions(text):
    words = []
    for word in regexp.tokenize(text):
        if word in contractions_list:
            words = words + contractions_dict[word].split()
        else:
            words = words + word.split()
    
    text_converted = " ".join(words)
    return text_converted

# Stopwords
stops = stopwords.words("english") # stopwords
addstops = ["among", "onto", "shall", "thrice", "thus", "twice", "unto", "us", "would"] # additional stopwords
allstops = stops + addstops

# Function to remove stopwords from a list of texts
def remove_stopwords(text):
    return " ".join([word for word in regexp.tokenize(text) if word not in allstops])

# pyspellchecker
# spell = SpellChecker()

# def pyspellchecker(text):
#     word_list = regexp.tokenize(text)
#     word_list_corrected = []
#     for word in word_list:
#         if word in spell.unknown(word_list):
#             word_corrected = spell.correction(word)
#             if word_corrected == None:
#                 word_list_corrected.append(word)
#             else:
#                 word_list_corrected.append(word_corrected)
#         else:
#             word_list_corrected.append(word)
#     text_corrected = " ".join(word_list_corrected)
#     return text_corrected

# Lemmatization
spacy_lemmatizer = spacy.load("en_core_web_sm", disable = ['parser', 'ner'])

def text_lemmatizer(text):
    text_spacy = " ".join([token.lemma_ for token in spacy_lemmatizer(text)])
    return text_spacy

def keep_pos(text):
    tokens = regexp.tokenize(text)
    tokens_tagged = nltk.pos_tag(tokens)
    #keep_tags = ['NN', 'NNS', 'NNP', 'NNPS', 'FW']
    keep_tags = ['NN', 'NNS', 'NNP', 'NNPS', 'FW', 'PRP', 'PRPS', 'RB', 'RBR', 'RBS', 'VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ', 'WDT', 'WP', 'WPS', 'WRB', 'CD']
    keep_words = [x[0] for x in tokens_tagged if x[1] in keep_tags]
    return " ".join(keep_words)

# Additional stopwords

alphabets = ["a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
prepositions = ["about", "above", "across", "after", "against", "among", "around", "at", "before", "behind", "below", "beside", "between", "by", "down", "during", "for", "from", "in", "inside", "into", "near", "of", "off", "on", "out", "over", "through", "to", "toward", "under", "up", "with"]
prepositions_less_common = ["aboard", "along", "amid", "as", "beneath", "beyond", "but", "concerning", "considering", "despite", "except", "following", "like", "minus", "onto", "outside", "per", "plus", "regarding", "round", "since", "than", "till", "underneath", "unlike", "until", "upon", "versus", "via", "within", "without"]
coordinating_conjunctions = ["and", "but", "for", "nor", "or", "so", "and", "yet"]
correlative_conjunctions = ["both", "and", "either", "or", "neither", "nor", "not", "only", "but", "whether", "or"]
subordinating_conjunctions = ["after", "although", "as", "as if", "as long as", "as much as", "as soon as", "as though", "because", "before", "by the time", "even if", "even though", "if", "in order that", "in case", "in the event that", "lest", "now that", "once", "only", "only if", "provided that", "since", "so", "supposing", "that", "than", "though", "till", "unless", "until", "when", "whenever", "where", "whereas", "wherever", "whether or not", "while"]
others = ["ã", "å", "ì", "û", "ûªm", "ûó", "ûò", "ìñ", "ûªre", "ûªve", "ûª", "ûªs", "ûówe"]
additional_stops = prepositions + prepositions_less_common + coordinating_conjunctions + correlative_conjunctions + subordinating_conjunctions + others


def remove_additional_stopwords(text):
    return " ".join([word for word in regexp.tokenize(text) if word not in additional_stops])

def text_normalizer(text):
    text = convert_to_lowercase(text)
    text = remove_whitespace(text)
    text = re.sub('\n' , ' ', text) # converting text to one line
    text = re.sub('\[.*?\]', '', text) # removing square brackets
    text = remove_http(text)
    text = remove_punctuation(text)
    text = remove_html(text)
    text = remove_emoji(text)
    text = convert_acronyms(text)
    text = convert_contractions(text)
    text = remove_stopwords(text)
    # if include_spellchecker:
    #   text = pyspellchecker(text)
    text = text_lemmatizer(text) # text = text_stemmer(text)
    # text = keep_pos(text)
    text = remove_additional_stopwords(text)
    return text