



'''simple docstring'''


lowercase											=      [0.33, 0.44, 0.55, 0.44, 0.33]
lowercase											=      3
lowercase											=      5

# Buffer Space (with Parameters Space)
lowercase											=      []
lowercase											=      []

# Machine Time
lowercase											=      0






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     global buffer_space, params_space, machine_time, K, m, t

     # Choosing Dynamical Systems (All)
     for key, value in enumerate(lowercase__							):
          # Evolution Parameter
          a_									=float(seed / value							)

          # Control Theory: Orbit Change
          a_									=(buffer_space[(key + 1) % m] + e) % 1

          # Control Theory: Trajectory Change
          a_									=(params_space[key] + e) % 1 + 3

          # Modification (Transition Function) - Jumps
          a_									=round(float(r * value * (1 - value)							)							,						1_0							)
          a_									=r  # Saving to Parameters Space

     # Logistic Map
     assert max(lowercase__							) < 1
     assert max(lowercase__							) < 4

     # Machine Time
     machine_time += 1






def 			UpperCAmelCase_							(		):


     '''simple docstring'''







     global buffer_space, params_space, machine_time, K, m, t

     # PRNG (Xorshift by George Marsaglia)
     def xorshift(lowercase__							,						lowercase__							):
          x ^= y >> 1_3
          y ^= x << 1_7
          x ^= y >> 5
          return x

     # Choosing Dynamical Systems (Increment)
     a_									=machine_time % m

     # Evolution (Time Length)
     for _ in range(0							,						lowercase__							):
          # Variables (Position + Parameters)
          a_									=params_space[key]
          a_									=buffer_space[key]

          # Modification (Transition Function) - Flow
          a_									=round(float(r * value * (1 - value)							)							,						1_0							)
          a_									=(machine_time * 0.01 + r * 1.01) % 1 + 3

     # Choosing Chaotic Data
     a_									=int(buffer_space[(key + 2) % m] * (1_0**1_0)							)
     a_									=int(buffer_space[(key - 2) % m] * (1_0**1_0)							)

     # Machine Time
     machine_time += 1

     return xorshift(lowercase__							,						lowercase__							) % 0Xff_fff_fff






def 			UpperCAmelCase_							(		):


     '''simple docstring'''







     global buffer_space, params_space, machine_time, K, m, t

     a_									=K
     a_									=[0] * m
     a_									=0


if __name__ == "__main__":
  # Initialization
  reset()

  # Pushing Data (Input)
  import random

  lowercase											=      random.sample(range(0xffffffff), 100)
  for chunk in message:
    push(chunk)

  # for controlling
  lowercase											=      ''''''

  # Pulling Data (Output)
  while inp in ("e", "E"):
    print(F"""{format(pull(), "#04x")}""")
    print(buffer_space)
    print(params_space)
    lowercase											=      input('''(e)exit? ''').strip()







'''simple docstring'''


import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     a_									=args.serialization_dir
     a_									=args.pruning_method
     a_									=args.threshold

     a_									=torch.load(os.path.join(lowercase__							,						"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(lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    a_									=ThresholdBinarizer.apply(lowercase__							,						lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "l0":
                    a_		,    a_									=-0.1, 1.1
                    a_									=torch.sigmoid(lowercase__							)
                    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(lowercase__							)							)
          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__":
  lowercase											=      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''',
  )

  lowercase											=      parser.parse_args()

  main(args)


