


import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer







def   lowerCamelCase(				a__):
				_SCREAMING_SNAKE_CASE     					=args.serialization_dir
				_SCREAMING_SNAKE_CASE     					=args.pruning_method
				_SCREAMING_SNAKE_CASE     					=args.threshold

				_SCREAMING_SNAKE_CASE     					=torch.load(os.path.join(_lowerCAmelCase		,'''pytorch_model.bin''')		,map_location='''cpu''')

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

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

							main(args)










from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class   A__							:




						def __init__(    self   : List[Any]			,      _a   : Any					)							->       None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=data
										_SCREAMING_SNAKE_CASE     					=None




						def __str__(    self   : List[Any]					)							->       str:


										"""simple docstring"""





										return f"{self.data}"

class   A__							:




						def __init__(    self   : Optional[Any]					)							->       None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=None
										_SCREAMING_SNAKE_CASE     					=None




						def __iter__(    self   : List[str]					)							->       Iterator[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=self.front
										while node:
														yield node.data
														_SCREAMING_SNAKE_CASE     					=node.next




						def __len__(    self   : List[Any]					)							->       int:


										"""simple docstring"""





										return len(tuple(iter(self					)					)					)




						def __str__(    self   : int					)							->       str:


										"""simple docstring"""





										return " <- ".join(str(_a					) for item in self					)




						def 							__UpperCamelCase			(    self   : Union[str, Any]					)							->       bool:


										"""simple docstring"""





										return len(self					) == 0




						def 							__UpperCamelCase			(    self   : int			,      _a   : Any					)							->       None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=Node(_a					)
										if self.is_empty():
														_SCREAMING_SNAKE_CASE     					=_SCREAMING_SNAKE_CASE     					=node
										else:
														assert isinstance(self.rear			,      _a					)
														_SCREAMING_SNAKE_CASE     					=node
														_SCREAMING_SNAKE_CASE     					=node




						def 							__UpperCamelCase			(    self   : Dict					)							->       Any:


										"""simple docstring"""





										if self.is_empty():
														raise IndexError('''dequeue from empty queue'''					)
										assert isinstance(self.front			,      _a					)
										_SCREAMING_SNAKE_CASE     					=self.front
										_SCREAMING_SNAKE_CASE     					=self.front.next
										if self.front is None:
														_SCREAMING_SNAKE_CASE     					=None
										return node.data




						def 							__UpperCamelCase			(    self   : Union[str, Any]					)							->       None:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=_SCREAMING_SNAKE_CASE     					=None


if __name__ == "__main__":
							from doctest import testmod

							testmod()