





import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer



def    SCREAMING_SNAKE_CASE							(			snake_case       )		->						Any:
		__lowercase					=    args.serialization_dir
		__lowercase					=    args.pruning_method
		__lowercase					=    args.threshold

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

		__lowercase					=    0  # Number of remaining (not pruned) params in the encoder
		__lowercase					=    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":
								__lowercase					=    TopKBinarizer.apply(snake_case	,      snake_case       ).sum().item()
						elif pruning_method == "sigmoied_threshold":
								__lowercase					=    ThresholdBinarizer.apply(snake_case	,      snake_case	,      snake_case       ).sum().item()
						elif pruning_method == "l0":
								__lowercase	,			__lowercase					=    -0.1, 1.1
								__lowercase					=    torch.sigmoid(snake_case       )
								__lowercase					=    s * (r - l) + l
								__lowercase					=    s_bar.clamp(min=0.0	,      max=1.0       )
								__lowercase					=    (mask > 0.0).sum().item()
						else:
								raise ValueError('Unknown pruning method'       )
						remaining_count += mask_ones
						print(name.ljust(60	,      ' '       )	,      str(round(100 * mask_ones / param.numel()	,      3       )       ).ljust(20	,      ' '       )	,      str(snake_case       )       )
				else:
						encoder_count += param.numel()
						if "bias" in name or "LayerNorm" in name:
								remaining_count += param.numel()

		print(''       )
		print('Remaining Weights (global) %: '	,      100 * remaining_count / encoder_count       )


if __name__ == "__main__":
		SCREAMING_SNAKE_CASE_      :			List[str]		  =      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''',
		)

		SCREAMING_SNAKE_CASE_      :			List[Any]		  =      parser.parse_args()

		main(args)











from __future__ import annotations



def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case	,      snake_case	,      snake_case       )		->						None:
		__lowercase	,			__lowercase					=    m - 1, 0
		while i >= 0 and j < n:
				if key == mat[i][j]:
						print(F"Key {key} found at row- {i + 1} column- {j + 1}"       )
						return
				if key < mat[i][j]:
						i -= 1
				else:
						j += 1
		print(F"Key {key} not found"       )



def    SCREAMING_SNAKE_CASE							(			)		->						None:
		__lowercase					=    [[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
		__lowercase					=    int(input('Enter the element to be searched:'       )       )
		print(snake_case       )
		search_in_a_sorted_matrix(snake_case	,      len(snake_case       )	,      len(mat[0]       )	,      snake_case       )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()




