text: """
  This module contains various text-comparison algorithms
  designed to compare one statement to another.
  """
  
  # Use python-Levenshtein if available
  try:
      from Levenshtein.StringMatcher import StringMatcher as SequenceMatcher
  except ImportError:
      from difflib import SequenceMatcher
  
  
  class Comparator:
  
      def __call__(self, statement_a, statement_b):
          return self.compare(statement_a, statement_b)
  
      def compare(self, statement_a, statement_b):
          return 0
  
      def get_initialization_functions(self):
          """
          Return all initialization methods for the comparison algorithm.
          Initialization methods must start with 'initialize_' and
          take no parameters.
          """
          initialization_methods = [
              (
                  method,
                  getattr(self, method),
              ) for method in dir(self) if method.startswith('initialize_')
          ]
  
          return {
              key: value for (key, value) in initialization_methods
          }
  
  
  class LevenshteinDistance(Comparator):
      """
      Compare two statements based on the Levenshtein distance
      of each statement's text.
  
      For example, there is a 65% similarity between the statements
      "where is the post office?" and "looking for the post office"
      based on the Levenshtein distance algorithm.
      """
  
      def compare(self, statement, other_statement):
          """
          Compare the two input statements.
  
          :return: The percent of similarity between the text of the statements.
          :rtype: float
          """
  
          # Return 0 if either statement has a falsy text value
          if not statement.text or not other_statement.text:
              return 0
  
          # Get the lowercase version of both strings
          statement_text = str(statement.text.lower())
          other_statement_text = str(other_statement.text.lower())
  
          similarity = SequenceMatcher(
              None,
              statement_text,
              other_statement_text
          )
  
          # Calculate a decimal percent of the similarity
          percent = round(similarity.ratio(), 2)
  
          return percent
  
  
  class SynsetDistance(Comparator):
      """
      Calculate the similarity of two statements.
      This is based on the total maximum synset similarity between each word in each sentence.
  
      This algorithm uses the `wordnet`_ functionality of `NLTK`_ to determine the similarity
      of two statements based on the path similarity between each token of each statement.
      This is essentially an evaluation of the closeness of synonyms.
      """
  
      def initialize_nltk_wordnet(self):
          """
          Download required NLTK corpora if they have not already been downloaded.
          """
          from chatterbot.utils import nltk_download_corpus
  
          nltk_download_corpus('corpora/wordnet')
  
      def initialize_nltk_punkt(self):
          """
          Download required NLTK corpora if they have not already been downloaded.
          """
          from chatterbot.utils import nltk_download_corpus
  
          nltk_download_corpus('tokenizers/punkt')
  
      def initialize_nltk_stopwords(self):
          """
          Download required NLTK corpora if they have not already been downloaded.
          """
          from chatterbot.utils import nltk_download_corpus
  
          nltk_download_corpus('corpora/stopwords')
  
      def compare(self, statement, other_statement):
          """
          Compare the two input statements.
  
          :return: The percent of similarity between the closest synset distance.
          :rtype: float
  
          .. _wordnet: http://www.nltk.org/howto/wordnet.html
          .. _NLTK: http://www.nltk.org/
          """
          from nltk.corpus import wordnet
          from nltk import word_tokenize
          from chatterbot import utils
          import itertools
  
          tokens1 = word_tokenize(statement.text.lower())
          tokens2 = word_tokenize(other_statement.text.lower())
  
          # Remove all stop words from the list of word tokens
          tokens1 = utils.remove_stopwords(tokens1, language='english')
          tokens2 = utils.remove_stopwords(tokens2, language='english')
  
          # The maximum possible similarity is an exact match
          # Because path_similarity returns a value between 0 and 1,
          # max_possible_similarity is the number of words in the longer
          # of the two input statements.
          max_possible_similarity = min(
              len(statement.text.split()),
              len(other_statement.text.split())
          ) / max(
              len(statement.text.split()),
              len(other_statement.text.split())
          )
  
          max_similarity = 0.0
  
          # Get the highest matching value for each possible combination of words
          for combination in itertools.product(*[tokens1, tokens2]):
  
              synset1 = wordnet.synsets(combination[0])
              synset2 = wordnet.synsets(combination[1])
  
              if synset1 and synset2:
  
                  # Get the highest similarity for each combination of synsets
                  for synset in itertools.product(*[synset1, synset2]):
                      similarity = synset[0].path_similarity(synset[1])
  
                      if similarity and (similarity > max_similarity):
                          max_similarity = similarity
  
          if max_possible_similarity == 0:
              return 0
  
          return max_similarity / max_possible_similarity
  
  
  class SentimentComparison(Comparator):
      """
      Calculate the similarity of two statements based on the closeness of
      the sentiment value calculated for each statement.
      """
  
      def initialize_nltk_vader_lexicon(self):
          """
          Download the NLTK vader lexicon for sentiment analysis
          that is required for this algorithm to run.
          """
          from chatterbot.utils import nltk_download_corpus
  
          nltk_download_corpus('sentiment/vader_lexicon')
  
      def compare(self, statement, other_statement):
          """
          Return the similarity of two statements based on
          their calculated sentiment values.
  
          :return: The percent of similarity between the sentiment value.
          :rtype: float
          """
          from nltk.sentiment.vader import SentimentIntensityAnalyzer
  
          sentiment_analyzer = SentimentIntensityAnalyzer()
          statement_polarity = sentiment_analyzer.polarity_scores(statement.text.lower())
          statement2_polarity = sentiment_analyzer.polarity_scores(other_statement.text.lower())
  
          statement_greatest_polarity = 'neu'
          statement_greatest_score = -1
          for polarity in sorted(statement_polarity):
              if statement_polarity[polarity] > statement_greatest_score:
                  statement_greatest_polarity = polarity
                  statement_greatest_score = statement_polarity[polarity]
  
          statement2_greatest_polarity = 'neu'
          statement2_greatest_score = -1
          for polarity in sorted(statement2_polarity):
              if statement2_polarity[polarity] > statement2_greatest_score:
                  statement2_greatest_polarity = polarity
                  statement2_greatest_score = statement2_polarity[polarity]
  
          # Check if the polarity if of a different type
          if statement_greatest_polarity != statement2_greatest_polarity:
              return 0
  
          values = [statement_greatest_score, statement2_greatest_score]
          difference = max(values) - min(values)
  
          return 1.0 - difference
  
  
  class JaccardSimilarity(Comparator):
      """
      Calculates the similarity of two statements based on the Jaccard index.
  
      The Jaccard index is composed of a numerator and denominator.
      In the numerator, we count the number of items that are shared between the sets.
      In the denominator, we count the total number of items across both sets.
      Let's say we define sentences to be equivalent if 50% or more of their tokens are equivalent.
      Here are two sample sentences:
  
          The young cat is hungry.
          The cat is very hungry.
  
      When we parse these sentences to remove stopwords, we end up with the following two sets:
  
          {young, cat, hungry}
          {cat, very, hungry}
  
      In our example above, our intersection is {cat, hungry}, which has count of two.
      The union of the sets is {young, cat, very, hungry}, which has a count of four.
      Therefore, our `Jaccard similarity index`_ is two divided by four, or 50%.
      Given our similarity threshold above, we would consider this to be a match.
  
      .. _`Jaccard similarity index`: https://en.wikipedia.org/wiki/Jaccard_index
      """
  
      def initialize_nltk_wordnet(self):
          """
          Download the NLTK wordnet corpora that is required for this algorithm
          to run only if the corpora has not already been downloaded.
          """
          from chatterbot.utils import nltk_download_corpus
  
          nltk_download_corpus('corpora/wordnet')
  
      def initialize_nltk_averaged_perceptron_tagger(self):
          """
          Download the NLTK averaged perceptron tagger that is required for this algorithm
          to run only if the corpora has not already been downloaded.
          """
          from chatterbot.utils import nltk_download_corpus
  
          nltk_download_corpus('averaged_perceptron_tagger')
  
      def compare(self, statement, other_statement):
          """
          Return the calculated similarity of two
          statements based on the Jaccard index.
          """
          from nltk.corpus import wordnet
          import nltk
          import string
  
          # Get default English stopwords
          stopwords = nltk.corpus.stopwords.words('english')
          stopwords.append('')
  
          lemmatizer = nltk.stem.wordnet.WordNetLemmatizer()
  
          # Make both strings lowercase
          a = statement.text.lower()
          b = other_statement.text.lower()
  
          # Remove punctuation from each string
          table = str.maketrans(dict.fromkeys(string.punctuation))
          a = a.translate(table)
          b = b.translate(table)
  
          def get_wordnet_pos(pos_tag):
              if pos_tag[1].startswith('J'):
                  return (pos_tag[0], wordnet.ADJ)
              elif pos_tag[1].startswith('V'):
                  return (pos_tag[0], wordnet.VERB)
              elif pos_tag[1].startswith('N'):
                  return (pos_tag[0], wordnet.NOUN)
              elif pos_tag[1].startswith('R'):
                  return (pos_tag[0], wordnet.ADV)
              else:
                  return (pos_tag[0], wordnet.NOUN)
  
          pos_a = list(map(get_wordnet_pos, nltk.pos_tag(nltk.tokenize.word_tokenize(a))))
          pos_b = list(map(get_wordnet_pos, nltk.pos_tag(nltk.tokenize.word_tokenize(b))))
  
          lemma_a = [
              lemmatizer.lemmatize(
                  token, pos
              ) for token, pos in pos_a if token not in stopwords
          ]
          lemma_b = [
              lemmatizer.lemmatize(
                  token, pos
              ) for token, pos in pos_b if token not in stopwords
          ]
  
          # Calculate Jaccard similarity
          numerator = len(set(lemma_a).intersection(lemma_b))
          denominator = float(len(set(lemma_a).union(lemma_b)))
          ratio = numerator / denominator
  
          return ratio
  
  
  # ---------------------------------------- #
  
  
  levenshtein_distance = LevenshteinDistance()
  synset_distance = SynsetDistance()
  sentiment_comparison = SentimentComparison()
  jaccard_similarity = JaccardSimilarity()
  