code: def test_data_collator_chat_completion_lm_with_multiple_text(self):
      tokenizer = copy.deepcopy(self.tokenizer)
      tokenizer.padding_side = "left"
  
      instruction_template = "### Human:"
      assistant_template = "### Assistant:"
      data_collator = DataCollatorForCompletionOnlyLM(
          response_template=assistant_template,
          instruction_template=instruction_template,
          tokenizer=tokenizer,
          mlm=False,
      )
  
      text1 = """### Human: Hello all this should be masked.### Assistant: I should not be masked."""
      text2 = """### Human: Hello all this should be masked.### Assistant: I should not be masked.### Human: All this should be masked too.### Assistant: I should not be masked too."""
      encoded_text1 = tokenizer(text1)
      encoded_text2 = tokenizer(text2)
  
      examples = [encoded_text1, encoded_text2]
  
      batch = data_collator(examples)
      labels = batch["labels"]
      input_ids = batch["input_ids"]
  
      non_masked_tokens1 = input_ids[0][labels[0] != -100]
      result_text1 = tokenizer.decode(non_masked_tokens1)
      self.assertEqual(result_text1, " I should not be masked.")
  
      non_masked_tokens2 = input_ids[1][labels[1] != -100]
      result_text2 = tokenizer.decode(non_masked_tokens2)
      self.assertEqual(
          result_text2,
          " I should not be masked. I should not be masked too.",
      )
  
imports: import copy
  import os
  import tempfile
  import unittest
  import numpy
  import torch
  from datasets import Dataset
  from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments
  from trl import SFTTrainer
  from trl.import_utils import is_peft_available
  from trl.trainer import ConstantLengthDataset, DataCollatorForCompletionOnlyLM
  from .testing_utils import require_peft
label_desc: Use image node_id 11 for calling the SFTTrainerTester obj's underlying member method code with example usage: obj.test_data_collator_chat_completion_lm_with_multiple_text() without return types