huggingface / smollm Conditional Complexity

Intro

Conditional complexity (also called cyclomatic complexity) is a term used to measure the complexity of software. The term refers to the number of possible paths through a program function. A higher value ofter means higher maintenance and testing costs (infosecinstitute.com).
Conditional complexity is calculated by counting all conditions in the program that can affect the execution path (e.g. if statement, loops, switches, and/or operators, try and catch blocks...).
Conditional complexity is measured at the unit level (methods, functions...).
Units are classified in four categories based on the measured McCabe index: 1-5 (simple units), 6-10 (medium complex units), 11-25 (complex units), 26+ (very complex units).

Learn more...

Conditional Complexity Overall

There are 1,733 units with 25,401 lines of code in units (32.1% of code).

2 very complex units (386 lines of code)
17 complex units (1,949 lines of code)
90 medium complex units (4,098 lines of code)
173 simple units (4,639 lines of code)
1,451 very simple units (14,329 lines of code)

Legend:

51+

26-50

11-25

6-10

1-5

Alternative Visuals

3D view of all units

Conditional Complexity per Extension

51+

26-50

11-25

6-10

1-5

Conditional Complexity per Logical Component

primary logical decomposition

51+

26-50

11-25

6-10

1-5

Most Complex Units

Top 50 most complex units

Unit	# lines	McCabe index	# params
def prepare_dataset() in vision/m4/models/vgpt2/evaluation_classification_in_context_vgpt2.py	226	71	3
def _compute() in vision/m4/evaluation/custom_metrics/open_ended_vqa_metrics.py	160	53	5
def prepare_dataset() in vision/m4/models/vgpt2/evaluation_open_ended_vqa_in_context_vgpt2.py	160	49	3
def _get_item() in vision/smolvlm2/smolvlm/datasets/dataset.py	103	48	2
def main() in vision/m4/evaluation/scripts/sync_evaluations_on_wandb.py	164	48	0
def get_prompt() in vision/smolvlm2/smolvlm/conversation.py	110	47	1
def __call__() in vision/m4/sourcing/data_collection/processors/web_document_filtering.py	158	47	2
def _get_item() in vision/smolvlm2/smolvlm/datasets/dataset_clip_sampling.py	105	46	2
def __post_init__() in vision/m4/training/config.py	112	40	2
def prepare_dataset() in vision/m4/models/vgpt2/evaluation_captioning_in_context_vgpt2.py	114	36	3
def __iter__() in vision/m4/training/dataset.py	95	35	1
def __iter__() in vision/m4/training/dataset.py	99	35	1
def __call__() in vision/m4/sourcing/data_collection/processors/pair_filtering.py	70	34	2
def prepare_webdoc_ds() in vision/m4/models/vgpt2/evaluation_perplexity_in_context_vgpt2.py	95	33	2
def get_media_src() in vision/m4/sourcing/data_collection/utils/simplification_utils.py	49	29	1
def strip_string() in text/evaluation/math_utils.py	81	28	2
def extraction_mode() in vision/m4/sourcing/data_collection/visualization/wikipedia/global_visualization.py	171	28	1
def extraction_mode() in vision/m4/sourcing/data_collection/visualization/global_visualization.py	170	28	1
def __call__() in vision/m4/sourcing/data_collection/processors/web_document_filtering.py	93	27	2
def prepare_dataset() in vision/m4/models/vgpt2/evaluation_classification_vqa_in_context_vgpt2.py	97	25	3
def extract_answer() in text/evaluation/math_utils.py	60	24	3
def _compute() in vision/m4/evaluation/custom_metrics/image_caption_matching_metrics.py	54	24	5
def _compute() in vision/m4/evaluation/custom_metrics/unfolded_image_captioning_metrics.py	64	23	4
def in_contexter() in vision/m4/evaluation/evaluators/in_contexter.py	83	23	4
def __call__() in vision/smolvlm2/smolvlm/datasets/builder.py	71	22	3
def setup_batch_size_related_configs() in vision/m4/training/trainer.py	77	22	1
def load_state_dict_into_model() in vision/m4/training/utils.py	39	22	3
def _compute() in vision/m4/evaluation/custom_metrics/classification_vqa_metrics.py	53	22	6
def __init__() in vision/m4/training/trainer.py	57	21	7
def filtering() in vision/m4/sourcing/data_collection/visualization/pair_visualization.py	44	21	1
def from_pretrained() in vision/m4/models/custom_modules.py	41	20	5
def _init_weights() in vision/m4/models/vllama3/modeling_vllama3.py	31	20	2
def _merge_consecutive_text_nodes() in vision/m4/sourcing/data_collection/processors/pre_extraction_simplificator.py	48	20	2
def _do_batch() in vision/m4/training/trainer.py	99	19	6
def train() in vision/smolvlm2/smolvlm/train/train.py	71	18	0
def create_optimizer() in vision/smolvlm2/smolvlm/train/smolvlm_trainer.py	62	18	1
def map_transform_raven() in vision/data/datasets_processing_scripts/build_concatenation_datasets_sft/build_ds_sft.py	85	18	1
def _log_training() in vision/m4/training/trainer.py	66	18	4
def get_domain_to_duplicated_texts() in vision/data/datasets_processing_scripts/build_webdocs_dataset/python_scripts/09_04_get_domain_to_duplicated_texts.py	27	17	1
def display_examples() in vision/m4/evaluation/scripts/visualize_generations.py	53	17	5
def simplify_media_node() in vision/m4/sourcing/data_collection/utils/simplification_utils.py	38	17	2
def urls_to_images() in vision/m4/sourcing/data_collection/processors/web_document_extractor.py	50	17	5
def train() in vision/m4/training/trainer.py	258	16	2
def _compute() in vision/m4/evaluation/custom_metrics/doc_vqa_metrics.py	42	16	4
def get_statistics_on_extracted_pairs() in vision/m4/sourcing/data_collection/visualization/pair_visualization.py	102	16	1
def __getitem__() in vision/smolvlm2/smolvlm/datasets/builder.py	65	15	2
def _log_activations() in vision/m4/training/trainer.py	22	15	2
def prepare_dataset() in vision/m4/models/vgpt2/evaluation_image_caption_matching_vgpt2.py	67	15	3
def _extract_images_not_in_simplified_dom_tree() in vision/m4/sourcing/data_collection/processors/pair_extractor.py	26	15	4
def pytest_terminal_summary_main() in vision/m4/testing_utils.py	77	15	2