




"""simple docstring"""

import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






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

	A_	:					Optional[Any]						 =		torch.load(os.path.join(_UpperCAmelCase	,   '''pytorch_model.bin'''						)	,   map_location='''cpu'''						)

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

		main(args)











"""simple docstring"""

class 							lowercase   :







				def __init__(				self :       Union[str, Any]   ,  _lowerCamelCase :       int							):



					"""simple docstring"""

					A_	:					Optional[int]						 =		data
					A_	:					int
					A_	:					Node

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	A_	:					List[str]						 =		0
	A_	:					List[str]						 =		x

def 		lowercase_   (		_UpperCAmelCase	,   _UpperCAmelCase						):




	"""simple docstring"""






	A_ ,			A_	:					Any						 =		find_set(_UpperCAmelCase						), find_set(_UpperCAmelCase						)
	if x == y:
		return

	elif x.rank > y.rank:
		A_	:					Optional[int]						 =		x
	else:
		A_	:					Optional[int]						 =		y
		if x.rank == y.rank:
			y.rank += 1

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	if x != x.parent:
		A_	:					Union[str, Any]						 =		find_set(x.parent						)
	return x.parent

def 		lowercase_   (		_UpperCAmelCase						):




	"""simple docstring"""






	A_	:					str						 =		({0, 1, 2}, {3, 4, 5})
	for s in sets:
		if node.data in s:
			return s
	A_	:					Optional[int]						 =		f"""{node.data} is not in {sets}"""
	raise ValueError(_UpperCAmelCase						)

def 		lowercase_   (		):




	"""simple docstring"""






	A_	:					Dict						 =		[Node(_UpperCAmelCase						) for i in range(6						)]
	for v in vertex:
		make_set(_UpperCAmelCase						)

	union_set(vertex[0]	,   vertex[1]						)
	union_set(vertex[1]	,   vertex[2]						)
	union_set(vertex[3]	,   vertex[4]						)
	union_set(vertex[3]	,   vertex[5]						)

	for nodea in vertex:
		for nodea in vertex:
			if find_python_set(_UpperCAmelCase						).isdisjoint(find_python_set(_UpperCAmelCase						)						):
				assert find_set(_UpperCAmelCase						) != find_set(_UpperCAmelCase						)
			else:
				assert find_set(_UpperCAmelCase						) == find_set(_UpperCAmelCase						)


if __name__ == "__main__":
		test_disjoint_set()





