Spaces:
Sleeping
Sleeping
from llm_studio.python_configs.text_causal_classification_modeling_config import ( | |
ConfigProblemBase as CausalClassificationConfigProblemBase, | |
) | |
from llm_studio.python_configs.text_causal_language_modeling_config import ( | |
ConfigProblemBase as CausalConfigProblemBase, | |
) | |
from llm_studio.python_configs.text_causal_regression_modeling_config import ( | |
ConfigProblemBase as CausalRegressionConfigProblemBase, | |
) | |
from llm_studio.python_configs.text_sequence_to_sequence_modeling_config import ( | |
ConfigProblemBase as Seq2SeqConfigProblemBase, | |
) | |
from llm_studio.src.utils.config_utils import ( | |
NON_GENERATION_PROBLEM_TYPES, | |
convert_cfg_base_to_nested_dictionary, | |
) | |
def test_from_dict(): | |
for cfg_class in [ | |
CausalConfigProblemBase, | |
Seq2SeqConfigProblemBase, | |
CausalClassificationConfigProblemBase, | |
CausalRegressionConfigProblemBase, | |
]: | |
cfg = cfg_class() | |
cfg_dict = convert_cfg_base_to_nested_dictionary(cfg) | |
cfg2 = cfg_class.from_dict(cfg_dict) # type: ignore | |
cfg_dict_2 = convert_cfg_base_to_nested_dictionary(cfg2) | |
for k, v in cfg_dict.items(): | |
if isinstance(v, dict): | |
for k2, v2 in v.items(): | |
assert cfg_dict_2[k][k2] == v2 | |
assert cfg_dict_2[k] == v | |
def test_classification_config_is_in_non_generating_problem_types(): | |
cfg = CausalClassificationConfigProblemBase() | |
assert cfg.problem_type in NON_GENERATION_PROBLEM_TYPES | |
def test_regression_config_is_in_non_generating_problem_types(): | |
cfg = CausalRegressionConfigProblemBase() | |
assert cfg.problem_type in NON_GENERATION_PROBLEM_TYPES | |