text: import logging, re
  from typing import Dict, List, Iterable
  from collections import defaultdict
  from overrides import overrides
  
  from allennlp.common.file_utils import cached_path
  from allennlp.data.dataset_readers.dataset_reader import DatasetReader
  from allennlp.data.fields import Field, TextField, SequenceLabelField, MetadataField
  from allennlp.data.instance import Instance
  from allennlp.data.token_indexers import SingleIdTokenIndexer, TokenIndexer
  from allennlp.data.tokenizers import Token
  from allennlp.data.dataset_readers.dataset_utils import Ontonotes, OntonotesSentence
  from itertools import repeat
  
  
  logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
  
  def clean_arabic(word: str):
      """
      This is to handle the strange Arabic text in Ontonotes, which has many
      more diacritics than most Arabic text found "in the wild".
      """
      diacritics = [chr(1614),chr(1615),chr(1616),chr(1617),chr(1618),chr(1761),chr(1619),chr(1648),chr(1649),chr(1611),chr(1612),chr(1613)]
      for dia in diacritics:
          word = re.sub(dia,'',word)
      return word
  
  def _normalize_word(word: str, lang: str):
      if lang == 'ara':
          word = word.split('#')[0]
          word = clean_arabic(word)
      if word == "/." or word == "/?":
          return word[1:]
      else:
          return word
  
  @DatasetReader.register("srl")
  class SrlReader(DatasetReader):
      """
      This DatasetReader is designed to read in the English OntoNotes v5.0 data
      for semantic role labelling. It returns a dataset of instances with the
      following fields:
  
      tokens : ``TextField``
          The tokens in the sentence.
      verb_indicator : ``SequenceLabelField``
          A sequence of binary indicators for whether the word is the verb for this frame.
      tags : ``SequenceLabelField``
          A sequence of Propbank tags for the given verb in a BIO format.
  
      Parameters
      ----------
      token_indexers : ``Dict[str, TokenIndexer]``, optional
          We similarly use this for both the premise and the hypothesis.  See :class:`TokenIndexer`.
          Default is ``{"tokens": SingleIdTokenIndexer()}``.
      domain_identifier: ``str``, (default = None)
          A string denoting a sub-domain of the Ontonotes 5.0 dataset to use. If present, only
          conll files under paths containing this domain identifier will be processed.
  
      Returns
      -------
      A ``Dataset`` of ``Instances`` for Semantic Role Labelling.
  
      """
      def __init__(self,
                   token_indexers: Dict[str, TokenIndexer] = None,
                   domain_identifier: str = None,
                   lazy: bool = False) -> None:
          super().__init__(lazy)
          self._token_indexers = token_indexers or {"tokens": SingleIdTokenIndexer()}
          self._domain_identifier = domain_identifier
  
      @overrides
      def _read(self, file_path: str):
          # if `file_path` is a URL, redirect to the cache
          file_path = cached_path(file_path)
          ontonotes_reader = Ontonotes()
          logger.info("Reading SRL instances from dataset files at: %s", file_path)
          if self._domain_identifier is not None:
              logger.info("Filtering to only include file paths containing the %s domain", self._domain_identifier)
          lang_to_instances = defaultdict(list)
          for sentence, lang in self._ontonotes_subset(ontonotes_reader, file_path, self._domain_identifier):
              tokens = [Token(_normalize_word(t, lang)) for t in sentence.words]
              if not sentence.srl_frames:
                  # Sentence contains no predicates.
                  tags = ["O" for _ in tokens]
                  verb_label = [0 for _ in tokens]
                  yield self.text_to_instance(tokens, verb_label, tags, lang)
              else:
                  for (_, tags) in sentence.srl_frames:
                      verb_indicator = [1 if label[-2:] == "-V" else 0 for label in tags]
                      yield self.text_to_instance(tokens, verb_indicator, tags, lang)
  
  
      @staticmethod
      def _ontonotes_subset(ontonotes_reader: Ontonotes,
                            file_path: str,
                            domain_identifier: str) -> Iterable[OntonotesSentence]:
          """
          Iterates over the Ontonotes 5.0 dataset using an optional domain identifier.
          If the domain identifier is present, only examples which contain the domain
          identifier in the file path are yielded.
          """
          for conll_file in ontonotes_reader.dataset_path_iterator(file_path):
              if domain_identifier is None or f"/{domain_identifier}/" in conll_file:
                  path_components = conll_file.split('/')
                  lang = None
                  if 'arabic' in path_components or 'ara' in path_components:
                      lang = 'ara'
                  elif 'english' in path_components or 'eng' in path_components:
                      lang = 'eng'
                  elif 'chinese' in path_components or 'cmn' in path_components:
                      lang = 'cmn'
                  if lang is None:
                      raise NameError("Lang name could not be automatically detected from file path. "
                                      "Check universal_dependencies.py to add language names or "
                                      "change how they are detected.")
                  yield from zip(ontonotes_reader.sentence_iterator(conll_file), repeat(lang))
  
  
      def text_to_instance(self,  # type: ignore
                           tokens: List[Token],
                           verb_label: List[int],
                           tags: List[str] = None,
                           lang: str = 'eng') -> Instance:
          """
          We take `pre-tokenized` input here, along with a verb label.  The verb label should be a
          one-hot binary vector, the same length as the tokens, indicating the position of the verb
          to find arguments for.
          """
          # pylint: disable=arguments-differ
          fields: Dict[str, Field] = {}
          text_field = TextField(tokens, token_indexers=self._token_indexers)
          fields['tokens'] = text_field
          fields['verb_indicator'] = SequenceLabelField(verb_label, text_field)
          if tags:
              fields['tags'] = SequenceLabelField(tags, text_field)
  
          if all([x == 0 for x in verb_label]):
              verb = None
          else:
              verb = tokens[verb_label.index(1)].text
          fields["metadata"] = MetadataField({"words": [x.text for x in tokens],
                                              "verb": verb, 'lang': lang})
          return Instance(fields)
  