

__A	:   str	=					[0.33, 0.44, 0.55, 0.44, 0.33]
__A	:   Any	=					3
__A	:   Any	=					5

# Buffer Space (with Parameters Space)
__A	:   Union[str, Any]	=					[]
__A	:   List[str]	=					[]

# Machine Time
__A	:   List[str]	=					0




def        __UpperCamelCase     (      _A					:							List[Any]		)				->Dict:



							"""simple docstring"""
							global buffer_space, params_space, machine_time, K, m, t

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

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

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

														# Modification (Transition Function) - Jumps
														lowerCamelCase_								=round(float(r * value * (1 - value)		)      ,  10		)
														lowerCamelCase_								=r  # Saving to Parameters Space

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

							# Machine Time
							machine_time += 1




def        __UpperCamelCase     (      )				->Optional[Any]:



							"""simple docstring"""
							global buffer_space, params_space, machine_time, K, m, t

							# PRNG (Xorshift by George Marsaglia)
							def xorshift(_A					:							Optional[Any]      ,  _A					:							List[str]		):
														x ^= y >> 13
														y ^= x << 17
														x ^= y >> 5
														return x

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

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

														# Modification (Transition Function) - Flow
														lowerCamelCase_								=round(float(r * value * (1 - value)		)      ,  10		)
														lowerCamelCase_								=(machine_time * 0.0_1 + r * 1.0_1) % 1 + 3

							# Choosing Chaotic Data
							lowerCamelCase_								=int(buffer_space[(key + 2) % m] * (10**10)		)
							lowerCamelCase_								=int(buffer_space[(key - 2) % m] * (10**10)		)

							# Machine Time
							machine_time += 1

							return xorshift(lowercase__      ,  lowercase__		) % 0XFFFFFFFF




def        __UpperCamelCase     (      )				->int:



							"""simple docstring"""
							global buffer_space, params_space, machine_time, K, m, t

							lowerCamelCase_								=K
							lowerCamelCase_								=[0] * m
							lowerCamelCase_								=0


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

	# Pushing Data (Input)
	import random

	__A	:   Any	=					random.sample(range(0Xffffffff), 1_00)
	for chunk in message:
		push(chunk)

	# for controlling
	__A	:   Dict	=					''

	# Pulling Data (Output)
	while inp in ("e", "E"):
		print(F"""{format(pull(), "#04x")}""")
		print(buffer_space)
		print(params_space)
		__A	:   Union[str, Any]	=					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)


