input: 
  def find_matches(query: str, choices: List[str], threshold: int) -> List[str]:
      """Generate a fuzzy search algorithm that can match strings with near similarities by 
      calculating Levenshtein distance and comparing it with a given threshold.
      
      >>> find_matches("fuzzy", ["fuzzy", "buzzing", "fuzz", "fog"], 2)
      ['fuzzy', 'buzzing', 'fuzz']
      >>> find_matches("apple", ["apples", "grape", "orange"], 1)
      ['apples']
      """
      # Convert query and choices to lower case
      query, choices = query.lower(), [choice.lower() for choice in choices]
      # Initialize empty list to store matches
      matches = []
      
      # Loop through each choice
      for choice in choices:
          # Calculate Levenshtein distance between query and choice
          distance = levenshtein_distance(query, choice)
          # If distance is less than threshold, append choice to matches
          if distance <= threshold:
              matches.append(choice)
      
      return matches
  
output:     # Function to calculate Levenshtein distance
      def levenshtein_distance(s1: str, s2: str) -> int:
          if len(s1) < len(s2):
              return levenshtein_distance(s2, s1)
  
          if len(s2) == 0:
              return len(s1)
  
          previous_row = range(len(s2) + 1)
          for i, c1 in enumerate(s1):
              current_row = [i + 1]
              for j, c2 in enumerate(s2):
                  insertions = previous_row[j + 1] + 1
                  deletions = current_row[j] + 1
                  substitutions = previous_row[j] + (c1 != c2)
                  current_row.append(min(insertions, deletions, substitutions))
              previous_row = current_row
  
          return previous_row[-1]