

'''simple docstring'''

class lowerCamelCase      :







				class lowerCamelCase      :
								snake_case_           =   '''_prev''', '''_data''', '''_next'''

								def __init__(					self,	lowercase_,	lowercase_,	lowercase_   )						->       Union[str, Any]:
									snake_case							 =				link_p
									snake_case							 =				element
									snake_case							 =				link_n






								def 				_lowerCamelCase						(					self   )						->       int:
									return (
									    F''' Prev -> {self._prev is not None}, Next -> {self._next is not None}'''
									)

				def __init__(					self   )						->       int:
					snake_case							 =				self._Node(lowercase_,	lowercase_,	lowercase_   )
					snake_case							 =				self._Node(lowercase_,	lowercase_,	lowercase_   )
					snake_case							 =				self._trailer
					snake_case							 =				self._header
					snake_case							 =				0

				def __len__(					self   )						->       List[Any]:
					return self._size

				def 				_lowerCamelCase						(					self   )						->       List[Any]:
					return self.__len__() == 0

				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_,	lowercase_   )						->       Optional[int]:
					# Create new_node by setting it's prev.link -> header
					# setting it's next.link -> trailer
					snake_case							 =				self._Node(lowercase_,	lowercase_,	lowercase_   )
					snake_case							 =				new_node
					snake_case							 =				new_node
					self._size += 1
					return self






				def 				_lowerCamelCase						(					self,	lowercase_   )						->       Optional[int]:
					snake_case							 =				node._prev
					snake_case							 =				node._next

					snake_case							 =				successor
					snake_case							 =				predecessor
					self._size -= 1
					snake_case							 =				node._data
					snake_case							 =				snake_case							 =				snake_case							 =				None
					del node
					return temp







class lowerCamelCase      (						_DoublyLinkedBase							):

				def 				_lowerCamelCase						(					self   )						->       List[str]:
					if self.is_empty():
						raise Exception('List is empty'   )
					return self._header._next._data

				def 				_lowerCamelCase						(					self   )						->       Any:
					if self.is_empty():
						raise Exception('List is empty'   )
					return self._trailer._prev._data

				def 				_lowerCamelCase						(					self,	lowercase_   )						->       Optional[Any]:
					return self._insert(self._header,	lowercase_,	self._header._next   )

				def 				_lowerCamelCase						(					self,	lowercase_   )						->       List[Any]:
					return self._insert(self._trailer._prev,	lowercase_,	self._trailer   )

				def 				_lowerCamelCase						(					self   )						->       Optional[Any]:
					if self.is_empty():
						raise IndexError('remove_first from empty list'   )
					return self._delete(self._header._next   )






				def 				_lowerCamelCase						(					self   )						->       Optional[int]:
					if self.is_empty():
						raise IndexError('remove_first from empty list'   )
					return self._delete(self._trailer._prev   )








'''simple docstring'''

import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def     __magic_name__       ( A							)			->		Dict:
	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)





