old_contents: import spacy
  import json
  import os
  from initialize import spacy_nlp
  from interfaces.SentenceOperation import SentenceOperation
  from tasks.TaskTypes import TaskType
  
  
  class CorrectCommonMisspellings(SentenceOperation):
      tasks = [
          TaskType.TEXT_CLASSIFICATION,
          TaskType.TEXT_TO_TEXT_GENERATION,
          TaskType.TEXT_TAGGING,
      ]
      languages = ["en"]
      keywords = [
          "lexical",
          "external-knowledge-based",
          "tokenizer-required",
          "highly-meaning-preserving",
          "high-coverage",
          "low-generations",
      ]
  
      def __init__(self):
          super().__init__()
          self.COMMON_MISSPELLINGS_DICT = get_common_misspellings_dict()
          self.nlp = spacy_nlp if spacy_nlp else spacy.load("en_core_web_sm")
  
      def generate(self, sentence: str):
          doc = self.nlp(sentence)
          perturbed_text = [
              self.COMMON_MISSPELLINGS_DICT.get(token.text, token.text) + " "
              if token.whitespace_
              else self.COMMON_MISSPELLINGS_DICT.get(token.text, token.text)
              for token in doc
          ]
          return ["".join(perturbed_text)]
  
  
  def get_common_misspellings_dict():
      spell_corrections = os.path.join(
          "transformations", "replace_spelling", "spell_corrections.json"
      )
      with open(spell_corrections, "r") as fp:
          spell_corrections = json.load(fp)
      return spell_corrections
  
  
  """
  # code to process machine-readable text into this list (first save from wikipedia into dict.txt)
  import numpy as np
  
  # read everything in
  d = np.loadtxt("dict.txt", delimiter="->", dtype=str)
  
  # prune anything with commas
  unique_idxs = np.array([len(x.split()) == 1 for x in d[:, 1]])
  d = d[unique_idxs]
  
  # print as dict
  d = {d[i, 0]: d[i, 1] for i in range(d.shape[0])}
  print(d)
  """
  
new_contents: import spacy
  import json
  import os
  from initialize import spacy_nlp
  from interfaces.SentenceOperation import SentenceOperation
  from tasks.TaskTypes import TaskType
  
  
  class CorrectCommonMisspellings(SentenceOperation):
      tasks = [
          TaskType.TEXT_CLASSIFICATION,
          TaskType.TEXT_TO_TEXT_GENERATION,
          TaskType.TEXT_TAGGING,
      ]
      languages = ["en"]
      keywords = [
          "lexical",
          "external-knowledge-based",
          "tokenizer-required",
          "highly-meaning-preserving",
          "high-coverage",
          "low-generations",
      ]
  
      def __init__(self):
          super().__init__()
          self.COMMON_MISSPELLINGS_DICT = get_common_misspellings_dict()
          self.nlp = spacy_nlp if spacy_nlp else spacy.load("en_core_web_sm")
  
      def generate(self, sentence: str):
          doc = self.nlp(sentence)
          perturbed_text = [
              self.COMMON_MISSPELLINGS_DICT.get(token.text, token.text) + " "
              if token.whitespace_
              else self.COMMON_MISSPELLINGS_DICT.get(token.text, token.text)
              for token in doc
          ]
          return ["".join(perturbed_text)]
  
  
  def get_common_misspellings_dict():
      spell_corrections = os.path.join(
          "transformations", "correct_common_misspellings", "spell_corrections.json"
      )
      with open(spell_corrections, "r") as fp:
          spell_corrections = json.load(fp)
      return spell_corrections
  
  
  """
  # code to process machine-readable text into this list (first save from wikipedia into dict.txt)
  import numpy as np
  
  # read everything in
  d = np.loadtxt("dict.txt", delimiter="->", dtype=str)
  
  # prune anything with commas
  unique_idxs = np.array([len(x.split()) == 1 for x in d[:, 1]])
  d = d[unique_idxs]
  
  # print as dict
  d = {d[i, 0]: d[i, 1] for i in range(d.shape[0])}
  print(d)
  """
  
subject: fix dict link