class_docstr: Tests the BestOfNSampler class
class_docstr_tok: ["Tests","the","BestOfNSampler","class"]
docstr: Tests if the different input types normalizer works
docstr_tok: ["Tests","if","the","different","input","types","normalizer","works"]
code: def test_different_input_types(self):
      r"""
      Tests if the different input types normalizer works
      """
  
      generation_config = GenerationConfig(
          min_length=-1,
          top_k=0.0,
          top_p=1.0,
          do_sample=True,
          pad_token_id=self.tokenizer.eos_token_id,
      )
  
      output_length_sampler = LengthSampler(2, 6)
  
      best_of_n = BestOfNSampler(
          self.model,
          self.tokenizer,
          queries_to_scores,
          length_sampler=output_length_sampler,
          generation_config=generation_config,
      )
  
      queries = ["hello world", "goodbye world"]
      tokenized_queries = [
          self.tokenizer.encode(query) for query in queries
      ]
  
      various_queries_formats = [
          (tokenized_queries[0], 1),
          (tokenized_queries, 2),
          (torch.tensor(tokenized_queries[1]), 1),
          ([torch.tensor(query) for query in tokenized_queries], 2),
      ]
  
      for q, expected_length in various_queries_formats:
          results = best_of_n.generate(q)
          self.assertIsInstance(results, list)
          assert len(results) == expected_length
  
file_path: trl/tests/test_best_of_n_sampler.py
imports: import unittest
  import torch
  from transformers import AutoTokenizer, GenerationConfig
  from trl import AutoModelForCausalLMWithValueHead
  from trl.core import LengthSampler
  from trl.extras import BestOfNSampler
label_desc: Use image node_id 1 for calling the BestOfNSamplerTester obj's underlying member method code with example usage: obj.test_different_input_types() without return types