import re
from typing import Any, Dict, List, Text ,Optional
from rasa.nlu.config import RasaNLUModelConfig
import pyarabic.araby as araby
import json
from itertools import chain
import jellyfish  #correctionPackage"
import unicodedata
from pyarabic.unshape import unshaping_line

from itertools import *
from rasa.nlu.components import Component
from custom_func.remove_stop_words import is_stop_words_fr
from rasa.nlu.config import RasaNLUModelConfig
from rasa.nlu.tokenizers.tokenizer_fr import Token, Tokenizer
from rasa.nlu.training_data import Message, TrainingData
from custom_func.clean_data_eng import clean_data_eng
from custom_func.clean_data_eng_training import clean_data_eng_training
from custom_func.generateur_oncf_eng import generateur_oncf_eng
from rasa.nlu.training_data import Message


from rasa.nlu.constants import (
    TOKENS_NAMES,
    MESSAGE_ATTRIBUTES,
    TEXT,
    INTENT,
)

class WhitespaceTokenizer_oncf_eng_v2(Tokenizer):

    defaults = {
        # Flag to check whether to split intents
        "intent_tokenization_flag": False,
        # Symbol on which intent should be split
        "intent_split_symbol": "_",
        # Text will be tokenized with case sensitive as default
        "case_sensitive": True,
    }

    def __init__(self, component_config: Dict[Text, Any] = None) -> None:
        """Construct a new tokenizer using the WhitespaceTokenizer framework."""

        super().__init__(component_config)

        self.case_sensitive = self.component_config["case_sensitive"]
    
    def process(self, message: Message, **kwargs: Any) -> None:
        """Tokenize the incoming message."""

        
        txt = message.text
        
        tokens = self.tokenize_pr(message, TEXT)
            
        tokens = self.add_cls_token(tokens, TEXT)
        message.set(TOKENS_NAMES[TEXT], tokens)

    def train(
        self,
        training_data: TrainingData,
        config: Optional[RasaNLUModelConfig] = None,
        **kwargs: Any,
    ) -> None:
        generateur_oncf_eng('data_oncf_eng_v2',)
        """Tokenize all training data."""

        for example in training_data.training_examples:
            for attribute in MESSAGE_ATTRIBUTES:
                if example.get(attribute) is not None:
                    if attribute == INTENT:
                        tokens = self._split_intent(example)
                    else:
                        tokens = self.tokenize(example, attribute)
                        tokens = self.add_cls_token(tokens, attribute)
                    example.set(TOKENS_NAMES[attribute], tokens)

    def tokenize(self, message: Message, attribute: Text) -> List[Token]:
        text = message.get(attribute)

        
        text = text.lower()
        
        # remove 'not a word character' if
        words = re.sub(
            # there is a space or an end of a string after it
            r"[^\w#@&]+(?=\s|$)|"
            # there is a space or beginning of a string before it
            # not followed by a number
            r"(\s|^)[^\w#@&]+(?=[^0-9\s])|"
            # not in between numbers and not . or @ or & or - or #
            # e.g. 10'000.00 or blabla@gmail.com
            # and not url characters
            r"(?<=[^0-9\s])[^\w._~:/?#\[\]()@!$&*+,;=-]+(?=[^0-9\s])",
            " ",
            text,
        ).split()
        words2 =[]
        words2 = clean_data_eng_training(words)
        # if we removed everything like smiles `:)`, use the whole text as 1 token
        if not words:
            words = [text]
        

        return self._convert_words_to_tokens(words, text)


    def tokenize_pr(self, message: Message, attribute: Text ) -> List[Token]:
        text = message.get(attribute)
        
        
        text = text.lower()
        
        

        toks = ''
        toks = text.split()
        
        numbs = []
        numb = []

        mot = ''
        
        for S in text.split():
            hours1 = re.search(r'(([0-9]+)+(h|heure|heures))',S)
            hours2 = re.search(r'(([0-9]+)+(h|heure|heures)).([0-9]+)',S)
            hours3 = re.search(r'(([0-9]+):([0-9]+))',S)
            if S.isdigit():
                numb.append(S)
            elif hours1 != None :
                print("date found")
                
            elif hours2 != None :
                print("date found")
                
            elif hours3 != None :
                print("date found")

        new_text = ''
        

        words = text.split()

        with open("dictionaries/dict_oncf_eng.txt", 'r',encoding="utf8") as f:
            list = set(chain(*(line.split() for line in f if line)))
        
        dicto = {}
        
        tokens = []
        running_offset = 0
        
        words2 = []
        
        words2 = clean_data_eng(list,words)
        words2 = words
        text = "" 
        for w in words2 :
            text = text  +  w     

        if words2 == []:
            words2.append('hdjfhj')
            text = text + 'hdjfhj'

        for word in words2:
            

            if word.isnumeric()  or hours1 != None or hours2 != None or hours3 != None :
                min_dist = word
            
            else :
                for mot in list:
                    dicto[mot]=jellyfish.levenshtein_distance(mot, word)
                min_dist = min(dicto.keys(), key=(lambda k: dicto[k]))
            if jellyfish.levenshtein_distance(min_dist, word) > 2:
                    min_dist = word 
            

            
            
            word_corr = min_dist
            word_offset = text.index(word, running_offset)
            word_len = len(word)
            running_offset = word_offset + word_len
            tokens.append(Token(word_corr, word_offset))

        return tokens