"""
E2E tests for llama pretrain
"""

import logging
import os
import unittest

from axolotl.cli.args import TrainerCliArgs
from axolotl.common.datasets import load_datasets
from axolotl.train import train
from axolotl.utils.config import normalize_config
from axolotl.utils.dict import DictDefault

from .utils import check_model_output_exists, with_temp_dir

LOG = logging.getLogger("axolotl.tests.e2e")
os.environ["WANDB_DISABLED"] = "true"


class TestPretrainLlama(unittest.TestCase):
    """
    Test case for Llama models w pretraining
    """

    @with_temp_dir
    def test_pretrain_w_sample_packing(self, temp_dir):
        # pylint: disable=duplicate-code
        cfg = DictDefault(
            {
                "base_model": "JackFram/llama-68m",
                "tokenizer_type": "LlamaTokenizer",
                "flash_attention": True,
                "sequence_len": 1024,
                "sample_packing": True,
                "special_tokens": {
                    "unk_token": "<unk>",
                    "bos_token": "<s>",
                    "eos_token": "</s>",
                },
                "pretraining_dataset": [
                    {
                        "path": "allenai/c4",
                        "name": "en",
                        "type": "pretrain",
                    }
                ],
                "max_steps": 5,
                "num_epochs": 1,
                "micro_batch_size": 1,
                "gradient_accumulation_steps": 1,
                "val_set_size": 0.0,
                "output_dir": temp_dir,
                "learning_rate": 0.00001,
                "optimizer": "adamw_torch",
                "lr_scheduler": "cosine",
                "save_safetensors": True,
                "bf16": "auto",
            }
        )
        normalize_config(cfg)
        cli_args = TrainerCliArgs()
        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)

        train(cfg=cfg, dataset_meta=dataset_meta)
        check_model_output_exists(temp_dir, cfg)