text: import sys
  import warnings
  
  if not sys.warnoptions:
      warnings.simplefilter('ignore')
  
  import numpy as np
  import json
  import re
  from fuzzywuzzy import fuzz
  from unidecode import unidecode
  from ._utils._utils import (
      load_graph,
      check_file,
      check_available,
      generate_session,
  )
  from .texts._text_functions import (
      normalizer_textcleaning,
      stemmer_str_idx,
      pad_sentence_batch,
      ENGLISH_WORDS,
  )
  from .texts._tatabahasa import (
      rules_normalizer,
      consonants,
      vowels,
      sounds,
      GO,
      PAD,
      EOS,
      UNK,
      hujung_malaysian,
      calon_dictionary,
  )
  from .num2word import to_cardinal
  from .word2num import word2num
  from .preprocessing import _tokenizer
  
  ignore_words = ['ringgit', 'sen']
  ignore_postfix = ['adalah']
  
  
  def _remove_postfix(word):
      if word in ignore_postfix:
          return word, ''
      for p in hujung_malaysian:
          if word.endswith(p):
              return word[: -len(p)], ' lah'
      return word, ''
  
  
  def _normalize_ke(word):
      # kesebelas -> ke-sebelas
      # ke-21 -> ke-dua puluh satu
      if word.startswith('ke'):
          original = word
          word = word.replace('-', '')
          word = word.split('ke')
          try:
              num = word2num(word[1])
          except:
              pass
          try:
              num = int(word[1])
          except:
              return original
          return 'ke-' + to_cardinal(num)
      return word
  
  
  def _normalize_title(word):
      if word.istitle() or word.isupper():
          return calon_dictionary.get(word, word)
      return word
  
  
  def _is_number_regex(s):
      if re.match('^\d+?\.\d+?$', s) is None:
          return s.isdigit()
      return True
  
  
  def _string_to_num(word):
      if '.' in word:
          return float(word)
      else:
          return int(word)
  
  
  def _normalized_money(word):
      original = word
      word = word.lower()
      if word[:2] == 'rm' and _is_number_regex(word[2:]):
          return to_cardinal(_string_to_num(word[2:])) + ' ringgit'
      elif word[-3:] == 'sen':
          return to_cardinal(_string_to_num(word[:-3])) + ' sen'
      else:
          return original
  
  
  class _SPELL_NORMALIZE:
      def __init__(self, speller):
          self._speller = speller
  
      def normalize(self, string, assume_wrong = True, check_english = True):
          """
          Normalize a string
  
          Parameters
          ----------
          string : str
          assume_wrong: bool, (default=True)
              force speller to predict.
          check_english: bool, (default=True)
              check a word in english dictionary.
  
          Returns
          -------
          string: normalized string
          """
          if not isinstance(string, str):
              raise ValueError('input must be a string')
          if not isinstance(check_english, bool):
              raise ValueError('check_english must be a boolean')
          if not isinstance(assume_wrong, bool):
              raise ValueError('assume_wrong must be a boolean')
  
          result = []
          tokenized = _tokenizer(string)
          index = 0
          while index < len(tokenized):
              word = tokenized[index]
              if len(word) < 2 and word not in sounds:
                  result.append(word)
                  index += 1
                  continue
              if word.lower() in ignore_words:
                  result.append(word)
                  index += 1
                  continue
              if word.istitle() or word.isupper():
                  result.append(_normalize_title(word))
                  index += 1
                  continue
              if check_english:
                  if word.lower() in ENGLISH_WORDS:
                      result.append(word)
                      index += 1
                      continue
              if len(word) > 2:
                  if word[-2] in consonants and word[-1] == 'e':
                      word = word[:-1] + 'a'
              if word[0] == 'x' and len(word) > 1:
                  result_string = 'tak '
                  word = word[1:]
              else:
                  result_string = ''
              if word.lower() == 'ke' and index < (len(tokenized) - 2):
                  if tokenized[index + 1] == '-' and _is_number_regex(
                      tokenized[index + 2]
                  ):
                      result.append(
                          _normalize_ke(
                              word + tokenized[index + 1] + tokenized[index + 2]
                          )
                      )
                      index += 3
                      continue
              normalized_ke = _normalize_ke(word)
              if normalized_ke != word:
                  result.append(normalized_ke)
                  index += 1
                  continue
              if _is_number_regex(word) and index < (len(tokenized) - 2):
                  if tokenized[index + 1] == '-' and _is_number_regex(
                      tokenized[index + 2]
                  ):
                      result.append(
                          to_cardinal(_string_to_num(word))
                          + ' hingga '
                          + to_cardinal(_string_to_num(tokenized[index + 2]))
                      )
                      index += 3
                      continue
              if word.lower() == 'pada' and index < (len(tokenized) - 3):
                  if (
                      _is_number_regex(tokenized[index + 1])
                      and tokenized[index + 2] in '/-'
                      and _is_number_regex(tokenized[index + 3])
                  ):
                      result.append(
                          'pada %s hari bulan %s'
                          % (
                              to_cardinal(_string_to_num(tokenized[index + 1])),
                              to_cardinal(_string_to_num(tokenized[index + 3])),
                          )
                      )
                      index += 4
                      continue
              money = _normalized_money(word)
              if money != word:
                  result.append(money)
                  index += 1
                  continue
  
              word, end_result_string = _remove_postfix(word)
              if word in sounds:
                  result.append(result_string + sounds[word] + end_result_string)
                  index += 1
                  continue
              if word in rules_normalizer:
                  result.append(
                      result_string + rules_normalizer[word] + end_result_string
                  )
                  index += 1
                  continue
              selected = self._speller.correct(
                  word, debug = False, assume_wrong = assume_wrong
              )
              result.append(result_string + selected + end_result_string)
              index += 1
          return ' '.join(result)
  
  
  class _FUZZY_NORMALIZE:
      def __init__(self, normalized, corpus):
          self.normalized = normalized
          self.corpus = corpus
  
      def normalize(self, string, fuzzy_ratio = 70, check_english = True):
          """
          Normalize a string
  
          Parameters
          ----------
          string : str
          fuzzy_ratio: int, (default=70)
              ratio of similar characters by positions, if 90, means 90%.
          check_english: bool, (default=True)
              check a word in english dictionary.
  
          Returns
          -------
          string: normalized string
          """
          if not isinstance(string, str):
              raise ValueError('input must be a string')
          if not isinstance(check_english, bool):
              raise ValueError('check_english must be a boolean')
          if not isinstance(fuzzy_ratio, int):
              raise ValueError('fuzzy_ratio must be an integer')
  
          result = []
          tokenized = _tokenizer(string)
          index = 0
          while index < len(tokenized):
              word = tokenized[index]
              if len(word) < 2 and word not in sounds:
                  result.append(word)
                  index += 1
                  continue
              if word.lower() in ignore_words:
                  result.append(word)
                  index += 1
                  continue
              if word.istitle() or word.isupper():
                  result.append(_normalize_title(word))
                  index += 1
                  continue
              if check_english:
                  if word.lower() in ENGLISH_WORDS:
                      result.append(word)
                      index += 1
                      continue
              if len(word) > 2:
                  if word[-2] in consonants and word[-1] == 'e':
                      word = word[:-1] + 'a'
              if word[0] == 'x' and len(word) > 1:
                  result_string = 'tak '
                  word = word[1:]
              else:
                  result_string = ''
              if word.lower() == 'ke' and index < (len(tokenized) - 2):
                  if tokenized[index + 1] == '-' and _is_number_regex(
                      tokenized[index + 2]
                  ):
                      result.append(
                          _normalize_ke(
                              word + tokenized[index + 1] + tokenized[index + 2]
                          )
                      )
                      index += 3
                      continue
              normalized_ke = _normalize_ke(word)
              if normalized_ke != word:
                  result.append(normalized_ke)
                  index += 1
                  continue
              if _is_number_regex(word) and index < (len(tokenized) - 2):
                  if tokenized[index + 1] == '-' and _is_number_regex(
                      tokenized[index + 2]
                  ):
                      result.append(
                          to_cardinal(_string_to_num(word))
                          + ' hingga '
                          + to_cardinal(_string_to_num(tokenized[index + 2]))
                      )
                      index += 3
                      continue
              if word.lower() == 'pada' and index < (len(tokenized) - 3):
                  if (
                      _is_number_regex(tokenized[index + 1])
                      and tokenized[index + 2] in '/-'
                      and _is_number_regex(tokenized[index + 3])
                  ):
                      result.append(
                          'pada %s hari bulan %s'
                          % (
                              to_cardinal(_string_to_num(tokenized[index + 1])),
                              to_cardinal(_string_to_num(tokenized[index + 3])),
                          )
                      )
                      index += 4
                      continue
              money = _normalized_money(word)
              if money != word:
                  result.append(money)
                  index += 1
                  continue
  
              word, end_result_string = _remove_postfix(word)
              if word in sounds:
                  result.append(result_string + sounds[word] + end_result_string)
                  index += 1
                  continue
              if word in rules_normalizer:
                  result.append(
                      result_string + rules_normalizer[word] + end_result_string
                  )
                  index += 1
                  continue
              results = []
              for i in range(len(self.normalized)):
                  results.append(
                      np.mean([fuzz.ratio(word, k) for k in self.normalized[i]])
                  )
              if len(np.where(np.array(results) > fuzzy_ratio)[0]) < 1:
                  result.append(result_string + word + end_result_string)
                  continue
              result.append(
                  result_string
                  + self.corpus[np.argmax(results)]
                  + end_result_string
              )
              index += 1
          return ' '.join(result)
  
  
  def fuzzy(corpus):
      """
      Train a fuzzy logic Normalizer
  
      Parameters
      ----------
      corpus : list of strings. Prefer to feed with malaya.load_malay_dictionary().
  
      Returns
      -------
      _FUZZY_NORMALIZE: Trained malaya.normalizer._FUZZY_NORMALIZE class
      """
      if not isinstance(corpus, list):
          raise ValueError('corpus must be a list')
      if not isinstance(corpus[0], str):
          raise ValueError('corpus must be list of strings')
      corpus = [unidecode(w) for w in corpus]
      transform = []
      for i in corpus:
          i = i.lower()
          result = []
          result.append(i)
          result.append(''.join(char for char in i if char not in vowels))
          if i[0] in consonants and i[-1] in consonants:
              inner = i[0] + i[-1]
              result.append(inner)
          if i[-1] == 'a':
              inner = i[:-1]
              result.append(i[:-1] + 'e')
              inner = i + 'k'
              result.append(inner)
          if i[1] in vowels and i[0] in consonants:
              inner = i[0] + i[2:]
              result.append(inner)
          if i[-2] in vowels and i[-1] in consonants:
              inner = i[:-2] + i[-1]
              result.append(inner)
          result.append(i[0] + i[-1])
          if i[-2:] == 'ar':
              result.append(i[:-2] + 'o')
          if i[:2] == 'ha':
              result.append(i[1:])
          result = filter(None, result)
          transform.append(list(set(result)))
      return _FUZZY_NORMALIZE(transform, corpus)
  
  
  def spell(speller):
      """
      Train a Spelling Normalizer
  
      Parameters
      ----------
      speller : Malaya spelling correction object
  
      Returns
      -------
      _SPELL_NORMALIZE: malaya.normalizer._SPELL_NORMALIZE class
      """
      if not hasattr(speller, 'correct') and not hasattr(
          speller, 'normalize_elongated'
      ):
          raise ValueError(
              'speller must has `correct` or `normalize_elongated` method'
          )
      return _SPELL_NORMALIZE(speller)
  
  
  def basic(string):
      """
      Use basic rules-based to normalize a string.
  
      Parameters
      ----------
      string: str
  
      Returns
      -------
      string: normalized string
      """
      if not isinstance(string, str):
          ValueError('input must be a string')
      result = []
      for word in normalizer_textcleaning(string).split():
          if word.istitle():
              result.append(word)
              continue
          if word in sounds:
              result.append(sounds[word])
          elif word[-1] == '2':
              result.append(word[:-1])
          else:
              result.append(word)
      return ' '.join(result)
  