class_docstr: DatasetBuilder for wiki_auto dataset.
docstr: Yields examples.
docstr_tok: ["Yields","examples","."]
code: def _generate_examples(self, filepaths, split):
      """Yields examples."""
  
      if self.builder_config.name == "manual":
          keys = [
              "alignment_label",
              "simple_sentence_id",
              "normal_sentence_id",
              "simple_sentence",
              "normal_sentence",
              "GLEU-score",
          ]
  
          with tf.io.gfile.GFile(filepaths[split]) as f:
              for id_, line in enumerate(f):
                  values = line.strip().split("\t")
                  dict_ = {}
                  for k, v in zip(keys, values):
                      dict_[k] = v
                  yield id_, dict_
  
      elif (
          self.builder_config.name == "auto_acl"
          or self.builder_config.name == "auto_full_no_split"
          or self.builder_config.name == "auto_full_with_split"
      ):
          with tf.io.gfile.GFile(filepaths["normal"]) as fi:
              with tf.io.gfile.GFile(filepaths["simple"]) as fo:
                  for id_, (norm_se, simp_se) in enumerate(zip(fi, fo)):
                      yield id_, {
                          "normal_sentence": norm_se,
                          "simple_sentence": simp_se,
                      }
      else:
          dataset_dict = json.load(tf.io.gfile.GFile(filepaths[split]))
          for id_, (eid, example_dict) in enumerate(
              dataset_dict.items()
          ):
              res = {
                  "example_id": eid,
                  "normal": {
                      "normal_article_id": example_dict["normal"]["id"],
                      "normal_article_title": example_dict["normal"][
                          "title"
                      ],
                      "normal_article_url": example_dict["normal"][
                          "url"
                      ],
                      "normal_article_content": {
                          "normal_sentence_id": [
                              sen_id
                              for sen_id, sen_txt in example_dict[
                                  "normal"
                              ]["content"].items()
                          ],
                          "normal_sentence": [
                              sen_txt
                              for sen_id, sen_txt in example_dict[
                                  "normal"
                              ]["content"].items()
                          ],
                      },
                  },
                  "simple": {
                      "simple_article_id": example_dict["simple"]["id"],
                      "simple_article_title": example_dict["simple"][
                          "title"
                      ],
                      "simple_article_url": example_dict["simple"][
                          "url"
                      ],
                      "simple_article_content": {
                          "simple_sentence_id": [
                              sen_id
                              for sen_id, sen_txt in example_dict[
                                  "simple"
                              ]["content"].items()
                          ],
                          "simple_sentence": [
                              sen_txt
                              for sen_id, sen_txt in example_dict[
                                  "simple"
                              ]["content"].items()
                          ],
                      },
                  },
                  "paragraph_alignment": {
                      "normal_paragraph_id": [
                          norm_id
                          for simp_id, norm_id in example_dict.get(
                              "paragraph_alignment", []
                          )
                      ],
                      "simple_paragraph_id": [
                          simp_id
                          for simp_id, norm_id in example_dict.get(
                              "paragraph_alignment", []
                          )
                      ],
                  },
                  "sentence_alignment": {
                      "normal_sentence_id": [
                          norm_id
                          for simp_id, norm_id in example_dict.get(
                              "sentence_alignment", []
                          )
                      ],
                      "simple_sentence_id": [
                          simp_id
                          for simp_id, norm_id in example_dict.get(
                              "sentence_alignment", []
                          )
                      ],
                  },
              }
              yield id_, res
  
file_path: datasets/tensorflow_datasets/text_simplification/wiki_auto/wiki_auto.py
imports: import json
  import numpy
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  import tensorflow_datasets.public_api
label_desc: Use image node_id 3 for calling the WikiAuto obj's underlying member method code with example usage: obj._generate_examples(filepaths, split) without return types