docstr: Parse the dataset and assign scores using LLMs
  then save back the dataset with the uploaded scores
docstr_tok: ["Parse","the","dataset","and","assign","scores","using","LLMs","then","save","back","the","dataset","with","the","uploaded","scores"]
code: def distill(
      self,
      dataset_path: str,
  ) -> None:
      """Parse the dataset and assign scores using LLMs
      then save back the dataset with the uploaded scores
      """
  
      print("Assigning scores to the reward dataset...")
  
      # load the dataset
      with open(dataset_path, "r") as f:
          train_data = json.load(f)
  
      # for each element of the dataset, assing a score.
      for i, data in enumerate(train_data):
          if data.get("score", None) is None:
              user_input = data["user_input"]
              completion = data["completion"]
              print(
                  f"#### Data {i}:\n"
                  f"#### User_input:\n {user_input}\n"
                  f"#### Completion:\n {completion}\n"
              )
              prompt_tokens = (
                  data["user_input"]
                  + data["completion"]
                  + self.llm.prompt.template
              )
              prompt_len = int(len(prompt_tokens.split(" ")) / 0.75)
              # 80% of the max length as safety margin
              if prompt_len > self.llm_max_tokens * 0.8:
                  print(
                      f"The prompt of the data {i} is too long\n"
                      f"tokens: {prompt_len}\n"
                      f"max_tokens: {self.llm_max_tokens * 0.8}"
                  )
                  continue
              score = self.llm.run(
                  user_input=data["user_input"],
                  completion=data["completion"],
              ).strip()
              # TODO: extract from score the float value with a regex
              try:
                  score = float(score)
              except Exception:
                  print(
                      f"The score returned by the LLM for the"
                      f"data, {i}, is not a float float:\n{score}"
                  )
                  continue
              data["score"] = score
              print(f"### Score: {score} \n\n")
      # remove all the data that have no score
      train_data = [
          data for data in train_data if data.get("score", None)
      ]
      # save the dataset back
      print("Writing the updated dataset back to disk ... ")
      with open(dataset_path, "w") as f:
          json.dump(train_data, f)
  
      print("Score Assignment Completed")
  
imports: import argparse
  import json
  from langchain import OpenAI, LLMChain, PromptTemplate
label_desc: Use image node_id 2 for calling the ScoreGenerator obj's underlying member method code with example usage: obj.distill(dataset_path) without return types