




'''simple docstring'''






import unittest
from timeit import timeit




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->int:






       '''simple docstring'''




       lowercase	:		Optional[int]    =   first_num if first_num >= second_num else second_num
       lowercase	:		List[str]    =   max_num
       while (common_mult % first_num > 0) or (common_mult % second_num > 0):
              common_mult += max_num
       return common_mult




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->int:






       '''simple docstring'''




       return b if a == 0 else greatest_common_divisor(b % a ,					lowerCamelCase   )




def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->int:






       '''simple docstring'''




       return first_num // greatest_common_divisor(lowerCamelCase ,					lowerCamelCase   ) * second_num




def 		a_(			)					->Any:






       '''simple docstring'''




       lowercase	:		str    =   (
           '''from __main__ import least_common_multiple_slow, least_common_multiple_fast'''
       )
       print(
           '''least_common_multiple_slow():''' ,					timeit('''least_common_multiple_slow(1000, 999)''' ,					setup=lowerCamelCase   ) ,					)
       print(
           '''least_common_multiple_fast():''' ,					timeit('''least_common_multiple_fast(1000, 999)''' ,					setup=lowerCamelCase   ) ,					)

class 	SCREAMING_SNAKE_CASE_		(							unittest.TestCase					):
 __snake_case						:  Tuple       =   (
     (1_0, 2_0),
     (1_3, 1_5),
     (4, 3_1),
     (1_0, 4_2),
     (4_3, 3_4),
     (5, 1_2),
     (1_2, 2_5),
     (1_0, 2_5),
     (6, 9),
 )
 __snake_case						:  List[Any]       =   (2_0, 1_9_5, 1_2_4, 2_1_0, 1_4_6_2, 6_0, 3_0_0, 5_0, 1_8)



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        for i, (first_num, second_num) in enumerate(self.test_inputs			):
               lowercase	:		List[Any]    =   least_common_multiple_slow(lowercase_ ,		lowercase_			)
               lowercase	:		Any    =   least_common_multiple_fast(lowercase_ ,		lowercase_			)
               with self.subTest(i=lowercase_			):
                      self.assertEqual(lowercase_ ,		self.expected_results[i]			)
                      self.assertEqual(lowercase_ ,		self.expected_results[i]			)


if __name__ == "__main__":
      benchmark()
      unittest.main()







'''simple docstring'''

import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def      __magic_name__							(			A				)      ->		str:
	snake_case							=					args.serialization_dir
	snake_case							=					args.pruning_method
	snake_case							=					args.threshold

	snake_case							=					torch.load(os.path.join(A       , 'pytorch_model.bin'				)       , map_location='cpu'				)

	snake_case							=					0  # Number of remaining (not pruned) params in the encoder
	snake_case							=					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":
				snake_case							=					TopKBinarizer.apply(A       , A				).sum().item()
			elif pruning_method == "sigmoied_threshold":
				snake_case							=					ThresholdBinarizer.apply(A       , A       , A				).sum().item()
			elif pruning_method == "l0":
				snake_case			,			snake_case							=					-0.1, 1.1
				snake_case							=					torch.sigmoid(A				)
				snake_case							=					s * (r - l) + l
				snake_case							=					s_bar.clamp(min=0.0       , max=1.0				)
				snake_case							=					(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(A				)				)
		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__":
						lowerCAmelCase_     =      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",
						)

						lowerCAmelCase_     =      parser.parse_args()

						main(args)





