class_docstr: Processor for the WSC data set.
class_docstr_tok: ["Processor","for","the","WSC","data","set","."]
code: def get_examples(
      self, data_dir: str, split: str
  ) -> List[InputExample]:
      examples = []
      path = os.path.join(data_dir, "{}.jsonl".format(split))
      with open(path, encoding="utf8") as f:
          for line in f:
              example_json = json.loads(line)
              idx = example_json["idx"]
              label = self.get_label_id(example_json["label"])
              guid = "%s-%s" % (split, idx)
              text_a = example_json["text"]
              meta = {
                  "span1_text": example_json["target"]["span1_text"],
                  "span2_text": example_json["target"]["span2_text"],
                  "span1_index": example_json["target"]["span1_index"],
                  "span2_index": example_json["target"]["span2_index"],
              }
  
              # the indices in the dataset are wrong for some examples, so we manually fix them
              span1_index, span1_text = (
                  meta["span1_index"],
                  meta["span1_text"],
              )
              span2_index, span2_text = (
                  meta["span2_index"],
                  meta["span2_text"],
              )
              words_a = text_a.split()
              words_a_lower = text_a.lower().split()
              words_span1_text = span1_text.lower().split()
              span1_len = len(words_span1_text)
  
              if (
                  words_a_lower[span1_index : span1_index + span1_len]
                  != words_span1_text
              ):
                  for offset in [-1, +1]:
                      if (
                          words_a_lower[
                              span1_index
                              + offset : span1_index
                              + span1_len
                              + offset
                          ]
                          == words_span1_text
                      ):
                          span1_index += offset
  
              if (
                  words_a_lower[span1_index : span1_index + span1_len]
                  != words_span1_text
              ):
                  logger.warning(
                      f"Got '{words_a_lower[span1_index:span1_index + span1_len]}' but expected "
                      f"'{words_span1_text}' at index {span1_index} for '{words_a}'"
                  )
  
              if words_a[span2_index] != span2_text:
                  for offset in [-1, +1]:
                      if words_a[span2_index + offset] == span2_text:
                          span2_index += offset
  
                  if words_a[span2_index] != span2_text and words_a[
                      span2_index
                  ].startswith(span2_text):
                      words_a = (
                          words_a[:span2_index]
                          + [
                              words_a[span2_index][: len(span2_text)],
                              words_a[span2_index][len(span2_text) :],
                          ]
                          + words_a[span2_index + 1 :]
                      )
  
              assert (
                  words_a[span2_index] == span2_text
              ), f"Got '{words_a[span2_index]}' but expected '{span2_text}' at index {span2_index} for '{words_a}'"
  
              text_a = " ".join(words_a)
              meta["span1_index"], meta["span2_index"] = (
                  span1_index,
                  span2_index,
              )
  
              example = InputExample(
                  guid=guid,
                  text_a=text_a,
                  label=label,
                  meta=meta,
                  idx=idx,
              )
              if split == "train" and label != "True":
                  continue
              examples.append(example)
  
      return examples
  
imports: import csv
  import json
  import os
  import random
  from abc import ABC, abstractmethod
  from collections import defaultdict, Counter
  from typing import List, Dict, Callable, Sequence
  from torch.utils.data import dataset
  from openprompt.utils.logging import logger
  from openprompt.data_utils.utils import InputExample
  from openprompt.data_utils.data_processor import DataProcessor
label_desc: Use image node_id 2 for calling the WscProcessor obj's underlying member method code with example usage: obj.get_examples(data_dir, split) and returns: examples