

'''simple docstring'''






from math import floor, sqrt


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	int    )							->   int:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       0.0
      SCREAMING_SNAKE_CASE_      :			Tuple     =       1.0
      SCREAMING_SNAKE_CASE_      :			Any     =       int(sqrt(SCREAMING_SNAKE_CASE_    )    )
      SCREAMING_SNAKE_CASE_      :			List[str]     =       root
      SCREAMING_SNAKE_CASE_      :			str     =       0
      while integer_part != 2 * root:
            SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       denominator * integer_part - numerator
            SCREAMING_SNAKE_CASE_      :			Dict     =       (n - numerator**2) / denominator
            SCREAMING_SNAKE_CASE_      :			Dict     =       int((root + numerator) / denominator    )
            period += 1
      return period


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	int = 1_0_0_0_0    )							->   int:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			Any     =       0
      for i in range(2  ,  n + 1    ):
            SCREAMING_SNAKE_CASE_      :			List[str]     =       sqrt(SCREAMING_SNAKE_CASE_    )
            if sr - floor(SCREAMING_SNAKE_CASE_    ) != 0 and continuous_fraction_period(SCREAMING_SNAKE_CASE_    ) % 2 == 1:
                  count_odd_periods += 1
      return count_odd_periods


if __name__ == "__main__":
      print(F'''{solution(int(input().strip()))}''')




'''simple docstring'''






import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Optional[int]    )							->   str:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			Any     =       args.serialization_dir
      SCREAMING_SNAKE_CASE_      :			List[str]     =       args.pruning_method
      SCREAMING_SNAKE_CASE_      :			str     =       args.threshold

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

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

      snake_case_       =       parser.parse_args()

      main(args)

