
'''simple docstring'''





import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer





def 							snake_case				(		UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      __A													=      args.serialization_dir
      __A													=      args.pruning_method
      __A													=      args.threshold

      __A													=      torch.load(os.path.join(UpperCAmelCase   ,	'pytorch_model.bin'						)   ,	map_location='cpu'						)

      __A													=      0  # Number of remaining (not pruned) params in the encoder
      __A													=      0  # Number of params in the encoder

      print('name'.ljust(6_0   ,	' '						)   ,	'Remaining Weights %'   ,	'Remaining Weight'						)
      for name, param in st.items():
            if "encoder" not in name:
                  continue

            if "mask_scores" in name:
                  if pruning_method == "topK":
                        __A													=      TopKBinarizer.apply(UpperCAmelCase   ,	UpperCAmelCase						).sum().item()
                  elif pruning_method == "sigmoied_threshold":
                        __A													=      ThresholdBinarizer.apply(UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						).sum().item()
                  elif pruning_method == "l0":
                        __A						,   __A													=      -0.1, 1.1
                        __A													=      torch.sigmoid(UpperCAmelCase						)
                        __A													=      s * (r - l) + l
                        __A													=      s_bar.clamp(min=0.0   ,	max=1.0						)
                        __A													=      (mask > 0.0).sum().item()
                  else:
                        raise ValueError('Unknown pruning method'						)
                  remaining_count += mask_ones
                  print(name.ljust(6_0   ,	' '						)   ,	str(round(1_0_0 * mask_ones / param.numel()   ,	3						)						).ljust(2_0   ,	' '						)   ,	str(UpperCAmelCase						)						)
            else:
                  encoder_count += param.numel()
                  if "bias" in name or "LayerNorm" in name:
                        remaining_count += param.numel()

      print(''						)
      print('Remaining Weights (global) %: '   ,	1_0_0 * remaining_count / encoder_count						)


if __name__ == "__main__":
 a__		: Dict  							=							argparse.ArgumentParser()

 parser.add_argument(
     "--pruning_method",
     choices=["l0", "topK", "sigmoied_threshold"],
     type=str,
     required=True,
     help=(
         "Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement"
         " pruning)"
     ),
 )
 parser.add_argument(
     "--threshold",
     type=float,
     required=False,
     help=(
         "For `topK`, it is the level of remaining weights (in %) in the fine-pruned model."
         "For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."
         "Not needed for `l0`"
     ),
 )
 parser.add_argument(
     "--serialization_dir",
     type=str,
     required=True,
     help="Folder containing the model that was previously fine-pruned",
 )

 a__		: Tuple  							=							parser.parse_args()

 main(args)













'''simple docstring'''







import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
					snake_case_  :							Union[str, Any]            =	[]

					snake_case_  :							List[Any]            =	os.path.join("tests", "models", "**", "test_modeling_*.py")
					snake_case_  :							int            =	glob.glob(pattern)
					# TODO: deal with TF/Flax too
					snake_case_  :							Optional[int]            =	[
					    x for x in test_files if not (x.startswith("test_modeling_tf_") or x.startswith("test_modeling_flax_"))
					]

					for test_file in test_files:
										snake_case_  :							Any            =	get_tester_classes(test_file)
										for tester_class in tester_classes:
															# A few tester classes don't have `parent` parameter in `__init__`.
															# TODO: deal this better
															try:
																				snake_case_  :							Tuple            =	tester_class(parent=None)
															except Exception:
																				continue
															if hasattr(tester, "get_config"):
																				snake_case_  :							Any            =	tester.get_config()
																				for k, v in config.to_dict().items():
																									if isinstance(v, int):
																														snake_case_  :							int            =	None
																														if k in ["vocab_size"]:
																																			snake_case_  :							Optional[Any]            =	1_00
																														elif k in ["max_position_embeddings"]:
																																			snake_case_  :							Any            =	1_28
																														elif k in ["hidden_size", "d_model"]:
																																			snake_case_  :							str            =	40
																														elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
																																			snake_case_  :							str            =	5
																														if target is not None and v > target:
																																			failures.append(
																																			    f'''{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`'''
																																			    f''' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller'''
																																			    f''' than {target}.'''
																																			)

					if len(failures) > 0:
										raise Exception(f'''There were {len(failures)} failures:\n''' + "\n".join(failures))





