code: def test_grammar_dataset(
      step_lr,
      optimizer,
      get_model,
      tokenizer,
      train,
      mocker,
      setup_tokenizer,
  ):
      from llama_recipes.finetuning import main
  
      setup_tokenizer(tokenizer)
  
      BATCH_SIZE = 8
      kwargs = {
          "model_name": "meta-llama/Llama-2-7b-hf",
          "batch_size_training": BATCH_SIZE,
          "val_batch_size": 1,
          "use_peft": False,
          "dataset": "grammar_dataset",
          "batching_strategy": "padding",
      }
  
      main(**kwargs)
  
      assert train.call_count == 1
  
      args, kwargs = train.call_args
      train_dataloader = args[1]
      eval_dataloader = args[2]
  
      VAL_SAMPLES = 2988
      TRAIN_SAMPLES = 13016
  
      assert len(train_dataloader) == TRAIN_SAMPLES // BATCH_SIZE
      assert len(eval_dataloader) == VAL_SAMPLES
  
      batch = next(iter(train_dataloader))
  
      assert "labels" in batch.keys()
      assert "input_ids" in batch.keys()
      assert "attention_mask" in batch.keys()
  
      assert batch["labels"][0][31] == -100
      assert batch["labels"][0][32] == 1152
  
      assert batch["input_ids"][0][0] == 1
      assert batch["labels"][0][-1] == 2
      assert batch["input_ids"][0][-1] == 2
  
imports: import pytest
  from unittest.mock import patch
  from transformers import LlamaTokenizer
label_desc: Use image node_id 1 for calling a global function with example usage: test_grammar_dataset(step_lr, optimizer, get_model, tokenizer, train, mocker, setup_tokenizer) without return types