class_docstr: A wrapper class for testing PPOTrainer
class_docstr_tok: ["A","wrapper","class","for","testing","PPOTrainer"]
docstr: Test if the shape of the expected inputs are correct
code: def test_ppo_step_input_shape(self):
      """
      Test if the shape of the expected inputs are correct
      """
      # initialize dataset
      dummy_dataset = self._init_dummy_dataset()
  
      ppo_trainer = PPOTrainer(
          config=self.ppo_config,
          model=self.gpt2_model,
          ref_model=None,
          tokenizer=self.gpt2_tokenizer,
          dataset=dummy_dataset,
      )
      dummy_dataloader = ppo_trainer.dataloader
      # train model with ppo
      for query_tensor, response_tensor in dummy_dataloader:
          # define a reward for response
          # (this could be any reward such as human feedback or output from another model)
          reward = [torch.tensor([1.0]), torch.tensor([0.0])]
          # train model - this should raise an error
          bs = ppo_trainer.config.batch_size
  
          queries, responses, _, _ = ppo_trainer._step_safety_checker(
              bs,
              [q for q in query_tensor],
              [r for r in response_tensor],
              reward,
          )
  
          self.assertTrue(
              isinstance(queries, list),
              f"queries should be a list, got {type(queries)}",
          )
          self.assertTrue(
              isinstance(responses, list),
              f"responses should be a list, got {type(responses)}",
          )
  
          # check the shapes
          for i in range(bs):
              self.assertEqual(queries[i].shape, torch.Size([7]))
              self.assertEqual(responses[i].size(), torch.Size([7]))
          break
  
imports: import copy
  import fnmatch
  import gc
  import re
  import tempfile
  import unittest
  import pytest
  import torch
  from huggingface_hub import HfApi, HfFolder, delete_repo
  from parameterized import parameterized
  from pytest import mark
  from requests.exceptions import HTTPError
  from transformers import AutoTokenizer
  from trl import AutoModelForCausalLMWithValueHead, AutoModelForSeq2SeqLMWithValueHead, PPOConfig, PPOTrainer, set_seed
  from trl.core import respond_to_batch
  from .testing_constants import CI_HUB_ENDPOINT, CI_HUB_USER, CI_HUB_USER_TOKEN
  from .testing_utils import require_peft, require_torch_multi_gpu
label_desc: Use image node_id 15 for calling the PPOTrainerTester obj's underlying member method code with example usage: obj.test_ppo_step_input_shape() without return types