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"""
CLI to run training on a model
"""
import logging
from pathlib import Path
import fire
import transformers
from colorama import Fore
from axolotl.cli import (
check_accelerate_default_config,
check_user_token,
load_cfg,
load_datasets,
print_axolotl_text_art,
)
from axolotl.common.cli import PreprocessCliArgs
from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
LOG = logging.getLogger("axolotl.cli.preprocess")
def do_cli(config: Path = Path("examples/"), **kwargs):
# pylint: disable=duplicate-code
print_axolotl_text_art()
parsed_cfg = load_cfg(config, **kwargs)
check_accelerate_default_config()
check_user_token()
parser = transformers.HfArgumentParser((PreprocessCliArgs))
parsed_cli_args, _ = parser.parse_args_into_dataclasses(
return_remaining_strings=True
)
if not parsed_cfg.dataset_prepared_path:
msg = (
Fore.RED
+ "preprocess CLI called without dataset_prepared_path set, "
+ f"using default path: {DEFAULT_DATASET_PREPARED_PATH}"
+ Fore.RESET
)
LOG.warning(msg)
parsed_cfg.dataset_prepared_path = DEFAULT_DATASET_PREPARED_PATH
_ = load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
LOG.info(
Fore.GREEN
+ f"Success! Preprocessed data path: `dataset_prepared_path: {parsed_cfg.dataset_prepared_path}`"
+ Fore.RESET
)
if __name__ == "__main__":
fire.Fire(do_cli)
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