import numpy as np
import json
  
swap_dict = {"ई":"इ","श":"स","ष":"स","ू":"ु","ी":"ि","ं":"ँ"}


def get_valid_chars(file_path):
    '''-> valid_characters(list)
    '''
    valid_characters = list(json.load(open(file_path,'r',encoding="utf-8")).keys())
    return valid_characters


def get_word_arr_from_text(text):
    return text.split(" ")

def get_minimal_text(text):
    new_text = ''
    for chars in text:
        if chars in swap_dict.keys():
            new_text += swap_dict[chars]
        else:
            new_text += chars
    return new_text

def add_purnabiram(text,kriyapad,samyajoak):

    # print(kriyapad)
    text_arr = get_word_arr_from_text(text)
    new_text = ''
    # print(text_arr)
    for ind,words in enumerate(text_arr[:-1]):
        if len(words) <= 0:
            continue
        # print(words)
        new_text = new_text + words + ' '
        if get_minimal_text(words) in kriyapad:
            if text_arr[ind+1] not in samyajoak and get_minimal_text(text_arr[ind+1]) not in kriyapad:
                new_text = new_text + '। '
    new_text = new_text + text_arr[-1] + '। '
    return new_text 

def remove_useless_characters(text,valid_characters):
    '''text(string), valid_characters(list)  -> sentences text(string)
    '''
    valid_text = ''
    for chars in text:
        if chars in valid_characters:
            valid_text+=chars
    # print(valid_text)
    return valid_text


def get_sentences_as_arr(text):
    '''text(string)  -> sentences (1d-array)
    '''
    arr = text.split('।')
    return arr

def remove_stop_words_and_filter_word_arr(word_arr,word_endings,stop_words):
    '''word_arr (2d-array), word_endings(string), stop_words(string)  -> new_word_arr (2d-array)
    '''
    stop_words = stop_words.split('\n')
    new_word_arr = []
    word_endings = word_endings.split("\n")
    for sentences in word_arr:
        new_sentences = []
        for words in sentences:
            for endings in word_endings:
                if words.endswith(endings):
                    words = words[:-len(endings)]
                    # print(f"endings = {endings}, word = {words}")
                    break
            # print(words)
            for st_word in stop_words:
                if st_word == words:
                    # print(words)
                    break
            new_sentences.append(words)
        # print(new_sentences)
        new_word_arr.append(new_sentences)
    return new_word_arr


def get_words_as_arr(sentence_arr):
    ''' sentence_arr(1d-array) -> word_arr(1d-array)
    '''
    ret_arr = []
    for sentence in sentence_arr:
        word_arr = sentence.split(" ")
        ret_arr.append(word_arr)
    return ret_arr

def remove_repeating_sentences(sentence_arr):
    new_sentence_arr = []
    for sentence in sentence_arr:
        if len(sentence) > 1 and sentence not in new_sentence_arr:
            new_sentence_arr.append(sentence)
    return new_sentence_arr

def remove_empty_sentences(sentences,words_arr):
    ''' sentences(1d-array), word_arr(1d-arr)  -> sentences (1-D array), word_arr(1d-array) 
    '''
    new_sentences = []
    new_words_arr = []
    for (sent , sent_arr) in zip(sentences,words_arr):
        new_sent_arr = []
        for word in sent_arr:
            if len(word) > 0:
                new_sent_arr.append(word)
        if len(new_sent_arr) > 1:      # Removing Lonely word - sentence as they won't have association with other words. Set 0 to consider those lonely words
            new_sentences.append(sent)
            new_words_arr.append(new_sent_arr)
    return new_sentences,new_words_arr

def search_and_get_index(arr,val):
    for i,item in enumerate(arr):
        if item == val:
            return i
    return -1

def tokenize(word_arr):
    word_list = []
    tokenized_sentence = []
    count = 0
    for sentence in word_arr:
        token_words = []
        for word in sentence:
            ind = search_and_get_index(word_list,word)
            if ind == -1:
                word_list.append(word)
                token_words.append(count)
                count+=1
            else:
                token_words.append(ind)
        tokenized_sentence.append(token_words)
    return tokenized_sentence, word_list