text: # -*- coding: utf-8 -*-
  """ This module provides interface for splitting compound words and/or
  possible misspellings in LDT.
  
      Todo:
  
          * options for choosing the best analysis by sem similarity, ngrams
              and frequency
  
  """
  import os
  import ruamel.yaml as yaml
  
  from ldt.dicts.base.custom.en import BaseCustomDict
  from ldt.load_config import config
  from ldt.helpers.formatting import rreplace
  from ldt.helpers.formatting import _check_res
  from ldt.helpers.exceptions import ResourceError
  
  
  class Compounds(BaseCustomDict):
      """This class implements a generic interface for custom
      language-specific derivational dictionaries."""
  
      def __init__(self, language=config["default_language"], dictionary=None,
                   morph_dictionary=None):
          """ Initializing the base class.
  
          Args:
              language (str): the language of the dictionary (only
                  English currently supported)
              dictionary (base dictionary object): an LDT dictionary inheriting
                  from one of the classes in ``ldt.dicts.base``.
              morph_dictionary (morph dictionary object): an LDT dictionary
                  inheriting from one of the classes in ``ldt.dicts.morphology``.
  
          Note:
  
              Using WordNets as a base dictionary for derivational analysis is
              not recommended, as they do not include many closed-class parts
              of speech that may occur in compounds.
  
          """
          super(Compounds, self).__init__(language=language)
  
          self.dictionary = dictionary
          self.morph_dictionary = morph_dictionary
  
          dir_path = os.path.dirname(os.path.realpath(__file__))
          resources_path = os.path.join(dir_path, self.language,
                                        self.language+".yaml")
          # print(resources_path)
          if not os.path.isfile(resources_path):
              raise ResourceError(self.language+".yaml not found.")
  
          with open(resources_path) as stream:
              try:
                  resources = yaml.safe_load(stream)
              except yaml.YAMLError:
                  raise ResourceError("Something is wrong with the .yaml file "
                                      "for this language.")
  
              self.replacements_in_compounds = resources[
                  "replacements_in_compounds"]
              self.insertions_in_compounds = resources[
                  "insertions_in_compounds"]
  
      def split_on_dash(self, word, res=None):
          """Splitting dashed compounds, attempting to lemmatize all the parts
  
          Example:
              >>> test_dict.split_on_dash("god-knows-what")
              {'suffixes': [], 'prefixes': [], 'roots': ['god', 'know', 'what'],
              'other': [], 'original_word': 'god-knows-what'}
  
          Args:
              word (str): a potential nonce-word
  
          Returns:
              (dict): updated or newly created dictionary with derivational data
          """
  
          if not res:
              res = _check_res(res)
  
          if not "-" in word:
              return res
  
          else:
  
              words = word.split("-")
              checked = []
              for subword in words:
                  lemmas = self.morph_dictionary.lemmatize(subword)
                  if lemmas:
                      checked += lemmas
                  else:
                      if self.dictionary.is_a_word(subword):
                          checked.append(subword)
          if len(checked) >= len(words):
              res["roots"] += checked
              res["original_word"] = word
          return res
  
  
      def _in_vocab(self, word):
          """Returning a normalized version of a root: lemmatization, regular
          insertions and replacements (if any are defined for a language).
  
          Args:
              word (str): the word to split
  
          Returns:
              (str): a normalized word found in dictionaries, or *False* if none
                  can be found
          """
  
          lemmas = self.morph_dictionary.lemmatize(word)
          for lemma in lemmas:
              if self.dictionary.is_a_word(lemma):
                  return lemma
  
          if self.dictionary.is_a_word(word):
              return word
  
          for insertion in self.insertions_in_compounds:
              candidate = word.rstrip(insertion)
              if self.dictionary.is_a_word(candidate):
                  return candidate
  
          for replacement in self.replacements_in_compounds:
              candidate = rreplace(word, replacement[1], replacement[0])
              if self.dictionary.is_a_word(candidate):
                  return candidate
          return False
  
      def split_compound(self, word, min_length=3, filtering="min_split_3"):
  
          """ Recursive splitting of compounds, with lemmatization and optional
          language-specific replacement and insertion patterns (see
          :meth:`_in_vocab`). By default filters out analyses with overly
          short subwords, and does not handle short words.
  
          Example:
              >>> print(split_compound("tomcat", min_length=3))
              [['tom', 'cat']]
  
          Args:
              word (str): the word to analyze
              min_length (int): do not attempt to break up words shorter than that.
              filter (str): what filtering function to use.
  
                  * *min_split_length*: reject analyses that contain over 50%
                    items shorter than the final number in the parameter
                    (e.g. min_split_2)
  
          Todo:
  
              * if lemmatized word form is found, discard the ending
                ("tortoiseshell" > "tortoises + hell"
  
          Returns:
              (list): list of lists of stems, one per an alternative analysis
  
          """
  
          if len(word) < min_length:
              return [[word]]
  
          def build_stem_tree(compound):
              """Helper for building the tree-like dictionary structure with
                  all possible splits of the word
  
              Args:
                  compound (str): the word to analyze
  
              Returns:
                  (dict): nested dictionaries with keys for split nodes
  
              Todo:
                  * filtering by frequency
                  * filtering by embedding similarity
                  * filtering by ngrams
  
              """
  
              if not isinstance(compound, dict):
                  res = {compound + " ": {}}
              for i in res:
                  for k in range(1, len(i)):
                      if self._in_vocab(i[:k]) and i[:k] != i:
                          res[i][i[:k]] = build_stem_tree(i[k:].strip())
              return res
  
          tree = build_stem_tree(word)
  
          lists = []
          #pylint: disable=dangerous-default-value
          def tree_traverse_iter(tree, prefix=[]):
              """Helper for flattening the dictionary tree structure into a
              list of lists with all possible splits"""
              for key in tree:
                  if tree[key] == {}:
                      lists.append(prefix + [key])
                  else:
                      tree_traverse_iter(tree[key], prefix + [key])
  
          tree_traverse_iter(tree)
          cleaned = []
          for i in range(len(lists)):
              cleaned_list = [lists[i][ind] for ind in range(len(lists[i])) if
                              int(ind/2) != ind/2]
              cleaned.insert(i, cleaned_list)
  
          cleaned = [i for i in cleaned if i != [word]]
  
          if filtering:
              if filtering.startswith("min_split"):
                  min_length = int(filtering.strip("min_split_"))
                  cleaned = filter_by_min_length(cleaned, min_length)
  
          return cleaned
  
  
      def decompose_compound(self, word, res=None, split_known_words=True):
  
          """ Combined analysis of compounds: dashed words and recursive
          splitting, with lemmatization and optional language-specific
          replacement and insertion patterns (see :meth:`_in_vocab`).
  
          Example:
              >>> test_dict.split_on_dash("god-knows-what")
              {'suffixes': [], 'prefixes': [], 'roots': ['god', 'know', 'what'],
              'other': [], 'original_word': 'god-knows-what'}
  
          Args:
              word (str): the word to analyze
  
          Returns:
              (dict): updated or newly created dictionary with derivational data
  
          """
  
          res = _check_res(res)
  
          if "-" in word:
              return self.split_on_dash(word, res)
  
          if len(word) < 6:
              return res
  
          if not split_known_words:
              lemmas = self.morph_dictionary.lemmatize(word)
              for lemma in lemmas:
                  if self.dictionary.is_a_word(lemma):
                      res["original_word"] = word
                      return res
  
          splits = self.split_compound(word)
          if splits:
              for split in splits:
                  res["roots"] += split
          return res
  
  
  def filter_by_min_length(splits, min_length, threshold=0.1):
      """Filtering out those compound analyses that contain over a
      threshold ratio of words over specified length.
  
          Example:
              >>> splits = [['t', 'o', 'm', 'c', 'a', 't'],\
                            ['t', 'o', 'm', 'c', 'at'],\
                            ["tom", "cat"]]
              >>> print(filter_by_min_length(splits, min_length=3))
              [['tom', 'cat']]
  
      Args:
          splits (list): list of lists
          min_length (int): the minimum length of words in an acceptable
              analysis
          threshold: the acceptable ratio of short to longer words in the
              analysis
  
      Returns:
          (list): filtered list of analyses
      """
      filtered = []
      for split in splits:
          too_short = [w for w in split if len(w) < min_length]
          score = len(too_short)/len(split)
          if score < threshold:
              filtered.append(split)
      return filtered
  