old_contents: import collections
  import re
  
  from data import crossword, warehouse
  from puzzle.problems import problem
  
  _CROSSWORD_REGEX = re.compile(r'^.*\(([\d\s,|]+)\)$')
  _INTS = re.compile(r'(\d+)')
  
  class CrosswordProblem(problem.Problem):
    def __init__(self, name, lines):
      super(CrosswordProblem, self).__init__(name, lines)
      self._conn = None
      self._cursor = None
      for line in lines:
        for match in _CROSSWORD_REGEX.finditer(line):
          lengths = _INTS.findall(match.group(1))
          if len(lengths) == 1:
            self.constrain(lambda x, _: len(x) == int(lengths[0]))
          else:
            target = sum(map(int, lengths))
            if '|' in line:
              self.constrain(lambda x, _: len(x) >= target)
            else:
              self.constrain(lambda x, _: len(x) == target)
          break
  
    @staticmethod
    def score(lines):
      if len(lines) > 1:
        return 0
      src = lines[0]
      # TODO: Look for common crossword expressions.
      if _CROSSWORD_REGEX.match(src):
        return 1
      # Something with a lot of words *might* be a crossword clue.
      return max(0.0, 0.5 * (min(5, len(src.split())) / 5))
  
    def _solve(self):
      clue = ''.join(self.lines)
      clue_keywords = crossword.clue_keywords(clue)
      cursor = warehouse.get('/phrases/crossword/cursor')
      results = crossword.query(cursor, clue)
      if not results:
        return {}
      max_frequency = max([f for _, f, _ in results])
      ranked = []
      for (solution, frequency, keywords) in results:
        score = 0.0
        for keyword in clue_keywords:
          # Increase score by how often the keyword appeared in other clues.
          score += keywords[keyword] / frequency
        # Normalize score based on how many keywords were considered.
        score /= len(clue_keywords)
        rank = score * frequency / max_frequency
        if rank:
          ranked.append((solution, rank))
      return collections.OrderedDict(
          sorted(ranked, key=lambda x: x[1], reverse=True))
  
new_contents: import collections
  import re
  
  from data import crossword, warehouse
  from puzzle.problems import problem
  
  _CROSSWORD_REGEX = re.compile(r'^.*\(([\d\s,|]+)\)$')
  _INTS = re.compile(r'(\d+)')
  
  class CrosswordProblem(problem.Problem):
    def __init__(self, name, lines):
      super(CrosswordProblem, self).__init__(name, lines)
      for line in lines:
        for match in _CROSSWORD_REGEX.finditer(line):
          lengths = _INTS.findall(match.group(1))
          if len(lengths) == 1:
            self.constrain(lambda x, _: len(x) == int(lengths[0]))
          else:
            target = sum(map(int, lengths))
            if '|' in line:
              self.constrain(lambda x, _: len(x) >= target)
            else:
              self.constrain(lambda x, _: len(x) == target)
          break
  
    @staticmethod
    def score(lines):
      if len(lines) > 1:
        return 0
      src = lines[0]
      # TODO: Look for common crossword expressions.
      if _CROSSWORD_REGEX.match(src):
        return 1
      # Something with a lot of words *might* be a crossword clue.
      return max(0.0, 0.5 * (min(5, len(src.split())) / 5))
  
    def _solve(self):
      clue = ''.join(self.lines)
      clue_keywords = crossword.clue_keywords(clue)
      cursor = warehouse.get('/phrases/crossword/cursor')
      results = crossword.query(cursor, clue)
      if not results:
        return {}
      max_frequency = max([f for _, f, _ in results])
      ranked = []
      for (solution, frequency, keywords) in results:
        score = 0.0
        for keyword in clue_keywords:
          # Increase score by how often the keyword appeared in other clues.
          score += keywords[keyword] / frequency
        # Normalize score based on how many keywords were considered.
        score /= len(clue_keywords)
        rank = score * frequency / max_frequency
        if rank:
          ranked.append((solution, rank))
      return collections.OrderedDict(
          sorted(ranked, key=lambda x: x[1], reverse=True))
  
subject: Delete unused private variables.