'''simple docstring'''




import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer






def __lowerCAmelCase				(							UpperCamelCase__       )			->					List[Any]:
    __lowerCamelCase						=						args.serialization_dir
    __lowerCamelCase						=						args.pruning_method
    __lowerCamelCase						=						args.threshold

    __lowerCamelCase						=						torch.load(os.path.join(UpperCamelCase__       ,						'''pytorch_model.bin'''       )       ,						map_location='''cpu'''       )

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

    print('''name'''.ljust(60       ,						''' '''       )       ,						'''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						=						TopKBinarizer.apply(UpperCamelCase__       ,						UpperCamelCase__       ).sum().item()
            elif pruning_method == "sigmoied_threshold":
                __lowerCamelCase						=						ThresholdBinarizer.apply(UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__       ).sum().item()
            elif pruning_method == "l0":
                __lowerCamelCase			,						__lowerCamelCase						=						-0.1, 1.1
                __lowerCamelCase						=						torch.sigmoid(UpperCamelCase__       )
                __lowerCamelCase						=						s * (r - l) + l
                __lowerCamelCase						=						s_bar.clamp(min=0.0       ,						max=1.0       )
                __lowerCamelCase						=						(mask > 0.0).sum().item()
            else:
                raise ValueError('''Unknown pruning method'''       )
            remaining_count += mask_ones
            print(name.ljust(60       ,						''' '''       )       ,						str(round(1_00 * mask_ones / param.numel()       ,						3       )       ).ljust(20       ,						''' '''       )       ,						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_00 * 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'''




__UpperCAmelCase  							=1_0**8






def __lowerCAmelCase				(							UpperCamelCase__       )			->					bool:
    if n % 10 == 0:
        return False
    __lowerCamelCase						=						str(UpperCamelCase__       )
    return s == s[::-1]






def __lowerCAmelCase				(							)			->					int:
    __lowerCamelCase						=						set()
    __lowerCamelCase						=						1
    __lowerCamelCase						=						5
    while sum_squares < LIMIT:
        __lowerCamelCase						=						first_square + 1
        while sum_squares < LIMIT:
            if is_palindrome(UpperCamelCase__       ):
                answer.add(UpperCamelCase__       )
            last_square += 1
            sum_squares += last_square**2
        first_square += 1
        __lowerCamelCase						=						first_square**2 + (first_square + 1) ** 2

    return sum(UpperCamelCase__       )


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