huggingface / transformers 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 34,154 units with 336,951 lines of code in units (46.8% of code).

8 very complex units (1,573 lines of code)
93 complex units (8,957 lines of code)
760 medium complex units (31,374 lines of code)
2,032 simple units (46,004 lines of code)
31,261 very simple units (249,043 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 __post_init__() in src/transformers/training_args.py	466	135	1
def process_words() in src/transformers/models/whisper/english_normalizer.py	200	94	2
def validate() in src/transformers/generation/configuration_utils.py	201	69	2
def rename_key() in src/transformers/models/zoedepth/convert_zoedepth_to_hf.py	121	60	1
def convert_processors() in utils/create_dummy_models.py	172	58	4
def rename_key() in src/transformers/models/dpt/convert_dpt_hybrid_to_pytorch.py	104	58	1
def from_pretrained() in src/transformers/models/auto/image_processing_auto.py	144	56	4
def rename_keys() in src/transformers/models/perceiver/convert_perceiver_haiku_to_pytorch.py	165	51	2
def convert_checkpoint_from_transformers_to_megatron() in src/transformers/models/megatron_gpt2/checkpoint_reshaping_and_interoperability.py	230	47	1
def load_tf_weights_in_tapas() in src/transformers/models/tapas/modeling_tapas.py	110	47	3
def rename_key() in src/transformers/models/dpt/convert_dpt_to_pytorch.py	86	45	1
def load_tf_weights_in_big_bird() in src/transformers/models/big_bird/modeling_big_bird.py	133	45	3
def _parse_dataclass_field() in src/transformers/hf_argparser.py	75	45	2
def input_processing() in src/transformers/modeling_tf_utils.py	128	45	3
def convert_beit_checkpoint() in src/transformers/models/beit/convert_beit_unilm_to_pytorch.py	159	44	2
def _load_from_checkpoint() in src/transformers/trainer.py	125	44	3
def load_gguf_checkpoint() in src/transformers/modeling_gguf_pytorch_utils.py	97	42	3
def auto_class_docstring() in src/transformers/utils/args_doc.py	91	42	4
def convert_llava_to_hf() in src/transformers/models/llava_next/convert_llava_next_weights_to_hf.py	212	41	3
def convert_convnextv2_checkpoint() in src/transformers/models/convnextv2/convert_convnextv2_to_pytorch.py	99	41	4
def load_tf_weights_in_imagegpt() in src/transformers/models/imagegpt/modeling_imagegpt.py	89	40	3
def load_tf2_weights_in_bert() in src/transformers/models/bert/convert_bert_original_tf2_checkpoint_to_pytorch.py	139	40	3
def write_model() in src/transformers/models/chameleon/convert_chameleon_weights_to_hf.py	306	39	4
def build_processor() in utils/create_dummy_models.py	87	38	3
def rename_key() in src/transformers/models/mobilevit/convert_mlcvnets_to_pytorch.py	68	38	2
def create_rename_keys() in src/transformers/models/rt_detr/convert_rt_detr_original_pytorch_checkpoint_to_hf.py	371	38	1
def load_flax_weights_in_pytorch_model() in src/transformers/modeling_flax_pytorch_utils.py	105	38	2
def convert_tf_gptsan_to_pt() in src/transformers/models/deprecated/gptsan_japanese/convert_gptsan_tf_checkpoint_to_pytorch.py	144	36	1
def convert_segformer_checkpoint() in src/transformers/models/segformer/convert_segformer_original_to_pytorch.py	217	35	3
def load_tf_weights_in_t5() in src/transformers/models/t5/modeling_t5.py	95	35	3
def load_tf_weights_in_realm() in src/transformers/models/deprecated/realm/modeling_realm.py	89	35	3
def preprocess() in src/transformers/pipelines/question_answering.py	93	34	6
def __call__() in src/transformers/data/data_collator.py	76	34	3
def create_examples_from_document() in src/transformers/data/datasets/language_modeling.py	63	34	5
def rename_key() in src/transformers/models/clipseg/convert_clipseg_original_pytorch_to_hf.py	55	34	1
def load_tf_weights_in_mt5() in src/transformers/models/mt5/modeling_mt5.py	96	34	3
def from_pretrained() in src/transformers/models/auto/tokenization_auto.py	130	34	4
def check_docstrings() in utils/check_docstrings.py	69	33	2
def wrapper() in src/transformers/utils/deprecation.py	38	33	1
def _decode_asr() in src/transformers/models/whisper/tokenization_whisper.py	161	33	7
def create_rename_keys() in src/transformers/models/mobilevitv2/convert_mlcvnets_to_pytorch.py	73	33	2
def rename_key() in src/transformers/models/swin2sr/convert_swin2sr_original_to_pytorch.py	64	33	2
def load_tf_weights_in_canine() in src/transformers/models/canine/modeling_canine.py	81	32	3
def _load_best_model() in src/transformers/trainer.py	105	32	1
def set_module_quantized_tensor_to_device() in src/transformers/integrations/bitsandbytes.py	82	32	5
def rename_keys() in src/transformers/models/glpn/convert_glpn_to_pytorch.py	58	31	1
def convert_checkpoint_to_huggingface() in src/transformers/models/deprecated/mega/convert_mega_original_pytorch_checkpoint_to_pytorch.py	109	31	3
def convert_convnext_checkpoint() in src/transformers/models/convnext/convert_convnext_to_pytorch.py	82	31	2
def _get_layer_device_map_for_cache_init() in src/transformers/generation/utils.py	56	30	1
def on_epoch_end() in src/transformers/keras_callbacks.py	65	30	3