

"""simple docstring"""





import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer



def 							_lowerCAmelCase   (    UpperCAmelCase  :				str					):


					'''simple docstring'''






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

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

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

				main(args)





"""simple docstring"""





from __future__ import annotations



def 							_lowerCAmelCase   (    UpperCAmelCase  :				list[int]   ,				UpperCAmelCase  :				int					):


					'''simple docstring'''






					UpperCamelCase__		:     dict[int, int]								={}
					for index, val in enumerate(UpperCAmelCase					):
										UpperCamelCase__		:     List[Any]								=target - val
										if compl in chk_map:
															return [chk_map[compl], index]
										UpperCamelCase__		:     Any								=index
					return []


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				print(F'''{two_sum([2, 7, 1_1, 1_5], 9) = }''')


