






"""simple docstring"""







import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer





def 						lowercase__(A						)				->List[str]:




						"""simple docstring"""



						lowercase__				:      Optional[int]=       args.serialization_dir
						lowercase__				:      Optional[Any]=       args.pruning_method
						lowercase__				:      int=       args.threshold

						lowercase__				:      List[Any]=       torch.load(os.path.join(A					,		"pytorch_model.bin"						)					,		map_location="cpu"						)

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

			a :				List[str]      					=		parser.parse_args()

			main(args)














"""simple docstring"""







from collections import Counter
from functools import total_ordering

from data_structures.heap.heap import Heap


@total_ordering
class     __UpperCAmelCase:




						"""simple docstring"""




						def __init__(   self       ,  snake_case__       ,  snake_case__			):
												'''simple docstring'''
												lowercase__				:      Optional[Any]=       word
												lowercase__				:      Any=       count




						def __eq__(   self       ,  snake_case__			):
												'''simple docstring'''
												if not isinstance(snake_case__       ,  snake_case__			):
																		return NotImplemented
												return self.count == other.count




						def __lt__(   self       ,  snake_case__			):
												'''simple docstring'''
												if not isinstance(snake_case__       ,  snake_case__			):
																		return NotImplemented
												return self.count < other.count





def 						lowercase__(A					,		A						)				->list[str]:




						"""simple docstring"""



						lowercase__				:      Heap[WordCount]=       Heap()
						lowercase__				:      str=       Counter(A						)
						heap.build_max_heap(
						    [WordCount(A					,		A						) for word, count in count_by_word.items()]						)
						return [heap.extract_max().word for _ in range(min(A					,		len(A						)						)						)]


if __name__ == "__main__":
			import doctest

			doctest.testmod()






