import asyncio
import string, re
import pandas as pd
from aiogoogletrans import Translator
from spellchecker import SpellChecker
from nltk.tokenize import RegexpTokenizer


class Normalizer:
    """
    A class for text normalization tasks such as converting to lowercase,
    removing whitespace, punctuation, HTML tags, emojis, etc.
    """

    def __init__(self):
        """
        Initializes the Normalizer object.
        """

        # Letter variations dictionary
        self._letter_variations = {
            "aàáâãäåāăą": "a",
            "cçćĉċč": "c",
            "eèéêëēĕėęě": "e",
            "gğ": "g",
            "hħĥ": "h",
            "iìíîïīĭįı": "i",
            "jĵ": "j",
            "nñńņň": "n",
            "oòóôõöøōŏő": "o",
            "ś": "s",
            "ß": "ss",
            "uùúûüūŭůűų": "u",
            "yýÿŷ": "y",
            "æ": "ae",
            "œ": "oe",
        }

        # Generate regex pattern including single characters
        pattern_parts = []
        for variation in self._letter_variations.keys():
            pattern_parts.append(variation)
            for char in variation:
                if len(char) == 1:
                    pattern_parts.append(re.escape(char))

        self._pattern = "|".join(pattern_parts)

        # RegexpTokenizer
        self._regexp = RegexpTokenizer("[\w']+")

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

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

        # Initialize translator for language detection
        self._translator = Translator()

    # Converting to lowercase
    def _convert_to_lowercase(self, text):
        """
        Convert the input text to lowercase.

        Args:
            text (str): The input text to be converted.

        Returns:
            str: The input text converted to lowercase.
        """
        try:
            return text.lower()
        except Exception as e:
            print(f"An error occurred during lowercase conversion: {e}")
            return text

    # Removing whitespaces
    def _remove_whitespace(self, text):
        """
        Remove leading and trailing whitespaces from the input text.

        Args:
            text (str): The input text to be processed.

        Returns:
            str: The input text with leading and trailing whitespaces removed.
        """
        try:
            return text.strip()
        except Exception as e:
            print(f"An error occurred during whitespace removal: {e}")
            return text

    # Removing punctuations
    def _remove_punctuation(self, text):
        """
        Remove punctuation marks from the input text, except for apostrophes and percent signs.

        Args:
            text (str): The input text to be processed.

        Returns:
            str: The input text with punctuation marks removed.
        """
        try:
            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))
        except Exception as e:
            print(f"An error occurred during punctuation removal: {e}")
            return text

    # Removing HTML tags
    def _remove_html(self, text):
        """
        Remove HTML tags from the input text.

        Args:
            text (str): The input text containing HTML tags.

        Returns:
            str: The input text with HTML tags removed.
        """
        try:
            html = re.compile(r"<.*?>")
            return html.sub(r"", text)
        except Exception as e:
            print(f"An error occurred during HTML tag removal: {e}")
            return text

    # Removing emojis
    def _remove_emoji(self, text):
        """
        Remove emojis from the input text.

        Args:
            text (str): The input text containing emojis.

        Returns:
            str: The input text with emojis removed.
        """
        try:
            emoji_pattern = re.compile(
                "["
                "\U0001F600-\U0001F64F"  # emoticons
                "\U0001F300-\U0001F5FF"  # symbols & pictographs
                "\U0001F680-\U0001F6FF"  # transport & map symbols
                "\U0001F1E0-\U0001F1FF"  # flags (iOS)
                "\U00002702-\U000027B0"
                "\U000024C2-\U0001F251"
                "]+",
                flags=re.UNICODE,
            )
            return emoji_pattern.sub(r"", text)
        except Exception as e:
            print(f"An error occurred during emoji removal: {e}")
            return text


    # Removing other unicode characters
    def _remove_http(self, text):
        """
        Remove HTTP links from the input text.

        Args:
            text (str): The input text containing HTTP links.

        Returns:
            str: The input text with HTTP links removed.
        """
        try:
            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)
        except Exception as e:
            print(f"An error occurred during HTTP link removal: {e}")
            return text

    # Function to convert contractions in a text
    def _convert_acronyms(self, text):
        """
        Convert acronyms in the text.

        Example of acronyms dictionary:
            {"LOL": "laugh out loud", "BRB": "be right back", "IDK": "I don't know"}

        Args:
            text (str): The input text containing acronyms.

        Returns:
            str: The input text with acronyms expanded.
        """
        try:
            words = []
            for word in self._regexp.tokenize(text):
                if word in self._acronyms_list:
                    words = words + self._acronyms_dict[word].split()
                else:
                    words = words + word.split()

            text_converted = " ".join(words)
            return text_converted
        except Exception as e:
            print(f"An error occurred during acronym conversion: {e}")
            return text

    # Function to convert contractions in a text
    def _convert_contractions(self, text):
        """
        Convert contractions in the text.

        Example of contractions dictionary:
            {"I'm": "I am", "he's": "he is", "won't": "will not"}

        Args:
            text (str): The input text containing contractions.

        Returns:
            str: The input text with contractions expanded.
        """
        try:
            words = []
            for word in self._regexp.tokenize(text):
                if word in self._contractions_list:
                    words = words + self._contractions_dict[word].split()
                else:
                    words = words + word.split()

            text_converted = " ".join(words)
            return text_converted
        except Exception as e:
            print(f"An error occurred during contraction conversion: {e}")
            return text

    def _fix_letter_variations(self, query):
        """
        Replace variations of letters with their original counterparts.

        Args:
            query (str): The input query containing variations of letters.

        Returns:
            str: The normalized query with variations replaced by their original counterparts.
        """

        def replace_variation(match):
            """
            Helper function to replace variations with original counterparts.

            Args:
                match (re.Match): The match object representing the found variation.

            Returns:
                str: The original character if match is not found in letter_variations, otherwise its original counterpart.
            """
            for key in self._letter_variations.keys():
                if match.group(0) in key:
                    return self._letter_variations[key]
            return match.group(0)

        try:
            # Fixing the query
            normalized_query = re.sub(self._pattern, replace_variation, query)
            return normalized_query
        except Exception as e:
            print(f"An error occurred during letter variation fixing: {e}")
            return query

    def _normalize_query(self, word: str):
        """
        Clean the input text by performing the following steps:
        1. Remove non-alphabetic characters and keep specific characters like spaces, dashes, asterisks, and Arabic characters.
        2. Remove non-alphabetic characters between alphabetic characters.
        3. Remove repeating characters.
        4. Remove preceding numbers (e.g. 123phone -> phone).
        5. Add space between numbers and letters.
        6. Remove extra spaces.

        Args:
            word (str): The input text to be cleaned.

        Returns:
            str: The cleaned text.
        """
        try:
            # Remove non-alphabetic characters and keep specific characters like spaces, dashes, asterisks, and Arabic characters
            word = re.sub(
                r"[^A-Za-z\s\-%*.$\u0621-\u064A0-9\u00E4\u00F6\u00FC\u00C4\u00D6\u00DC\u00df]",
                "",
                word,
                flags=re.UNICODE,
            )

            # Remove non-alphabetic characters between alphabetic characters
            clean_text = re.sub(
                r"(?<=[a-zA-Z])([^A-Za-z\u0621-\u064A\s]+)(?=[a-zA-Z])", "", word
            )
            # Remove non-alphabetic characters between alphabetic characters
            clean_text = re.sub(r"(?<=[a-zA-Z])([^A-Za-z\s]+)(?=[a-zA-Z])", "", clean_text)
            # Remove non-alphabetic characters between Arabic characters
            clean_text = re.sub(
                r"(?<=[\u0621-\u064A])([^\u0621-\u064A\s]+)(?=[\u0621-\u064A])",
                "",
                clean_text,
            )

            # Remove repeating characters
            clean_text = re.sub(r"(.)(\1+)", r"\1\1", clean_text)

            # Remove preceding non latin alpha (e.g. صصphone -> phone)
            clean_text = re.sub(r"([\u0621-\u064A]+)([a-zA-Z]+)", r"\2", clean_text)
            # Add space between numbers and letters
            clean_text = re.sub(r"([a-zA-Z]+)([\u0621-\u064A]+)", r"\1", clean_text)

            # Remove preceding latin alpha (from arabic words) (e.g. phoneصص -> phone)
            clean_text = re.sub(r"([a-zA-Z]+)([\u0621-\u064A]+)", r"\2", clean_text)
            # Add space between numbers and letters
            clean_text = re.sub(r"([\u0621-\u064A]+)([a-zA-Z]+)", r"\1", clean_text)

            # Remove preceding numbers (e.g. 123phone -> phone)
            clean_text = re.sub(r"(\d+)([a-zA-Z\u0621-\u064A]+)", r"\1 \2", clean_text)
            # Add space between numbers and letters
            clean_text = re.sub(r"([a-zA-Z\u0621-\u064A]+)(\d+)", r"\1 \2", clean_text)

            # Remove extra spaces
            clean_text = re.sub(r"\s+", " ", clean_text)

            return clean_text.strip()
        except Exception as e:
            print(f"An error occurred during query normalization: {e}")
            return word

    def keep_one_char(self, word: str) -> str:
        """
        Keep only one occurrence of consecutive repeated characters in the input word.

        Args:
        - word (str): The input word to modify.

        Returns:
        - str: The modified word with only one occurrence of consecutive repeated characters.
        """
        try:
            return re.sub(r"(.)(\1+)", r"\1", word)
        except Exception as e:
            print(f"An error occurred during character repetition removal: {e}")
            return word
        
    def translate_text(self, text: str) -> str:
        """
        Translate the given text to English and return the translated text.

        Args:
        - text (str): The text to translate.

        Returns:
        - str: The translated text.
        """
        try:
            loop = asyncio.get_event_loop()
            translated_text = (
                loop.run_until_complete(self._translator.translate(text))
                .text.lower()
                .strip()
            )
        except Exception as e:
            print(f"Text Translation failed: {e}")
            translated_text = (
                text.lower().strip()
            )  # Use original text if translation fails
        return translated_text

    def check_spelling(self, query: str) -> str:
        """
        Check the spelling of the input query and return the corrected version.

        Args:
        - query (str): The input query to check its spelling.

        Returns:
        - str: The corrected query.
        """
        try:
            # Detect the language of the input query using Google Translate API
            # input_language = self._translator.detect(query)
            input_language = "en" if query.encode().isalpha() else "ar"

            # Initialize SpellChecker with detected language, fallback to English if language detection fails
            try:
                spell_checker = SpellChecker(language=input_language)
            except:
                spell_checker = SpellChecker(language="en")

            # Initialize an empty string to store the corrected query
            result_query = ""

            # Iterate through each word in the query
            for word in query.split(" "):
                # Get the corrected version of the word
                corrected_word = spell_checker.correction(word)

                # If the corrected word is not found, try correcting with keeping one character
                if corrected_word is None:
                    corrected_word = spell_checker.correction(self.keep_one_char(word))

                    # If still not found, keep the original word
                    if corrected_word is None:
                        result_query += word + " "
                    else:
                        result_query += corrected_word + " "
                else:
                    result_query += corrected_word + " "

            # Remove trailing whitespace and return the corrected query
            return result_query.strip()
        except Exception as e:
            print(f"An error occurred during spelling check: {e}")
            return query

    def clean_text(self, text):
        """
        Normalize the input text.

        Args:
            text (str): The input text to be normalized.

        Returns:
            str: The normalized text.
        """
        try:
            # Convert text to lowercase
            text = self._convert_to_lowercase(text)

            # Remove whitespace
            text = self._remove_whitespace(text)

            # Convert text to one line
            text = re.sub("\n", " ", text)

            # Remove square brackets
            text = re.sub("\[.*?\]", "", text)

            # Remove HTTP links
            text = self._remove_http(text)

            # Remove HTML tags
            text = self._remove_html(text)

            # Remove emojis
            text = self._remove_emoji(text)

            # Fix letter variations
            text = self._fix_letter_variations(text)

            # Normalize queries
            text = self._normalize_query(text)

            return text
        except Exception as e:
            print(f"An error occurred during text cleaning: {e}")
            return text