docstr: Initializes the training arguments.
docstr_tok: ["Initializes","the","training","arguments","."]
code: def init_training_args(self) -> None:
      """
      Initializes the training arguments.
      """
      self.training_args_kwargs = {}
      self.trainer_kwargs = {}
  
      if isinstance(self._task, TrainingTaskForPPO):
          from trl import PPOConfig
  
          self._logger.warning(
              "The PPOTrainer must be initialized by passing `reward_model`, `length_sampler_kwargs`, `generation_kwargs` as kwargs to the `update_config()`-method."
          )
          self.trainer_kwargs["config"] = PPOConfig()
          self.training_args_kwargs["reward_model"] = None
          self.training_args_kwargs["length_sampler_kwargs"] = {
              "min_value": 1,
              "max_value": 10,
          }
          self.training_args_kwargs["generation_kwargs"] = {
              "min_length": -1,
              "top_k": 0.0,
              "top_p": 1.0,
              "do_sample": True,
          }
      else:
          self.training_args_kwargs["evaluation_strategy"] = (
              "no" if self._eval_dataset is None else "epoch"
          )
          self.training_args_kwargs["logging_steps"] = 30
          self.training_args_kwargs["logging_steps"] = 1
          self.training_args_kwargs["num_train_epochs"] = 1
  
file_path: argilla/src/argilla/client/feedback/training/frameworks/trl.py
imports: import logging
  from typing import TYPE_CHECKING, Dict, List, Optional, Union
  from argilla.client.feedback.training.base import ArgillaTrainerSkeleton
  from argilla.client.feedback.training.schemas.base import TrainingTaskForDPO, TrainingTaskForPPO, TrainingTaskForRM, TrainingTaskForSFT
  from argilla.training.utils import filter_allowed_args
  from argilla.utils.dependency import require_dependencies, requires_dependencies
label_desc: Use image node_id 2 for calling the ArgillaTRLTrainer obj's underlying member method code with example usage: obj.init_training_args() without return types