


"""simple docstring"""





import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer





def     lowercase__	(  lowerCamelCase     :  int				)					->     Optional[int]:
  lowerCAmelCase__    :    List[str]								=					args.serialization_dir
  lowerCAmelCase__    :    Dict								=					args.pruning_method
  lowerCAmelCase__    :    List[Any]								=					args.threshold

  lowerCAmelCase__    :    Optional[int]								=					torch.load(os.path.join(lowerCamelCase				,	"pytorch_model.bin"				)				,	map_location="cpu"				)

  lowerCAmelCase__    :    List[str]								=					0  # Number of remaining (not pruned) params in the encoder
  lowerCAmelCase__    :    List[str]								=					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":
        lowerCAmelCase__    :    Any								=					TopKBinarizer.apply(lowerCamelCase				,	lowerCamelCase				).sum().item()
      elif pruning_method == "sigmoied_threshold":
        lowerCAmelCase__    :    Any								=					ThresholdBinarizer.apply(lowerCamelCase				,	lowerCamelCase				,	lowerCamelCase				).sum().item()
      elif pruning_method == "l0":
        lowerCAmelCase__    :    int								=					-0.1, 1.1
        lowerCAmelCase__    :    Dict								=					torch.sigmoid(lowerCamelCase				)
        lowerCAmelCase__    :    List[Any]								=					s * (r - l) + l
        lowerCAmelCase__    :    int								=					s_bar.clamp(min=0.0				,	max=1.0				)
        lowerCAmelCase__    :    int								=					(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(lowerCamelCase				)				)
    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__":
      __UpperCAmelCase    				=      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",
      )

      __UpperCAmelCase    				=      parser.parse_args()

      main(args)




"""simple docstring"""





from math import factorial





def     lowercase__	(  lowerCamelCase     :  Dict				,	lowerCamelCase     :  int				)					->     Tuple:
  return factorial(lowerCamelCase				) / (factorial(lowerCamelCase				) * factorial(n - r				))





def     lowercase__	(  )					->     Optional[Any]:
  lowerCAmelCase__    :    List[Any]								=					0

  for i in range(1				,	1_0_1				):
    for j in range(1				,	i + 1				):
      if combinations(lowerCamelCase				,	lowerCamelCase				) > 1e6:
        total += 1
  return total


if __name__ == "__main__":
   print(solution())
