class_docstr: An abstract solver class that uses the OpenAI API through completion functions.
class_docstr_tok: ["An","abstract","solver","class","that","uses","the","OpenAI","API","through","completion","functions","."]
code: def __init__(
      self,
      completion_fn_options: Dict[str, Any] = {},
      valid_answers: Union[list[str], None] = None,
  ):
      self.completion_fn_options = completion_fn_options
  
      # If valid answers were provided, encode them into a logit bias dictionary.
      if valid_answers is not None and len(valid_answers) > 0:
          model = (
              completion_fn_options["model"]
              if "model" in completion_fn_options
              else None
          )
          if model is None:
              raise ValueError(
                  "OpenAISolver requires a model to be specified."
              )
          if model == "code-davinci-002":
              logging.info(
                  f"Attempting to use logit bias with model {model}, which does not support logit bias."
              )
  
          enc = tiktoken.encoding_for_model(model)
          token_ids = []
          for answer in valid_answers:
              encoded_answer = enc.encode(answer)
              if len(encoded_answer) > 1:
                  raise ValueError(
                      f"Answer {answer} was encoded to {encoded_answer}, but we expected a single token."
                  )
              token_ids.append(encoded_answer[0])
          self.completion_fn_options["extra_options"]["logit_bias"] = {
              token_id: 100 for token_id in token_ids
          }
  
imports: import json
  import logging
  from abc import ABC, abstractmethod
  from copy import deepcopy
  from typing import Any, Dict, TypeVar, Union
  import tiktoken
  from evals.api import CompletionFn
  from evals.task_state import TaskState
label_desc: Use image node_id 1 to create a new OpenAISolver object from inherited base classes: Solver with example: obj = OpenAISolver(completion_fn_options, valid_answers)