input: Write a function named `are_strings_similar` that takes two strings and returns True if they are similar up to a provided threshold, and False otherwise. The similarity is determined by dividing the edit distance of the two strings by the maximum length of the two strings and comparing it to the threshold.
  assert are_strings_similar("hello", "hallo", 0.25)==True
  assert are_strings_similar("hello", "world", 0.25)==False
  assert are_strings_similar("programming", "progtammor", 0.25)==True
  assert are_strings_similar("python", "pycharm", 0.25)==False
  assert are_strings_similar("algorithm", "rithm", 0.5)==False
output: def are_strings_similar(s1, s2, threshold):
      from difflib import ndiff
      def levenshtein_distance(str1, str2):
          counter = {"+": 0, "-": 0}
          distance = 0
          for edit_code, *_ in ndiff(str1, str2):
              if edit_code == " ":
                  distance += max(counter.values())
                  counter = {"+": 0, "-": 0}
              else:
                  counter[edit_code] += 1
          distance += max(counter.values())
          return distance
      
      max_len = max(len(s1), len(s2))
      if max_len == 0:
          return True
      edit_distance = levenshtein_distance(s1, s2)
      similarity_ratio = edit_distance / max_len
      return similarity_ratio <= threshold