




import numpy as np



def       UpperCAmelCase       (     _snake_case						, _snake_case							):
							return np.where(vector > 0						, _snake_case						, (alpha * (np.exp(_snake_case							) - 1))							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()









import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =		args.pruning_method
							lowerCAmelCase   =		args.threshold

							lowerCAmelCase   =		args.model_name_or_path.rstrip('''/'''							)
							lowerCAmelCase   =		args.target_model_path

							print(F"""Load fine-pruned model from {model_name_or_path}"""							)
							lowerCAmelCase   =		torch.load(os.path.join(_snake_case						, '''pytorch_model.bin'''							)							)
							lowerCAmelCase   =		{}

							for name, tensor in model.items():
														if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
																					lowerCAmelCase   =		tensor
																					print(F"""Copied layer {name}"""							)
														elif "classifier" in name or "qa_output" in name:
																					lowerCAmelCase   =		tensor
																					print(F"""Copied layer {name}"""							)
														elif "bias" in name:
																					lowerCAmelCase   =		tensor
																					print(F"""Copied layer {name}"""							)
														else:
																					if pruning_method == "magnitude":
																												lowerCAmelCase   =		MagnitudeBinarizer.apply(inputs=_snake_case						, threshold=_snake_case							)
																												lowerCAmelCase   =		tensor * mask
																												print(F"""Pruned layer {name}"""							)
																					elif pruning_method == "topK":
																												if "mask_scores" in name:
																																			continue
																												lowerCAmelCase   =		name[:-6]
																												lowerCAmelCase   =		model[F"""{prefix_}mask_scores"""]
																												lowerCAmelCase   =		TopKBinarizer.apply(_snake_case						, _snake_case							)
																												lowerCAmelCase   =		tensor * mask
																												print(F"""Pruned layer {name}"""							)
																					elif pruning_method == "sigmoied_threshold":
																												if "mask_scores" in name:
																																			continue
																												lowerCAmelCase   =		name[:-6]
																												lowerCAmelCase   =		model[F"""{prefix_}mask_scores"""]
																												lowerCAmelCase   =		ThresholdBinarizer.apply(_snake_case						, _snake_case						, _snake_case							)
																												lowerCAmelCase   =		tensor * mask
																												print(F"""Pruned layer {name}"""							)
																					elif pruning_method == "l0":
																												if "mask_scores" in name:
																																			continue
																												lowerCAmelCase   =		name[:-6]
																												lowerCAmelCase   =		model[F"""{prefix_}mask_scores"""]
																												lowerCAmelCase   ,     lowerCAmelCase   =		-0.1, 1.1
																												lowerCAmelCase   =		torch.sigmoid(_snake_case							)
																												lowerCAmelCase   =		s * (r - l) + l
																												lowerCAmelCase   =		s_bar.clamp(min=0.0						, max=1.0							)
																												lowerCAmelCase   =		tensor * mask
																												print(F"""Pruned layer {name}"""							)
																					else:
																												raise ValueError('''Unknown pruning method'''							)

							if target_model_path is None:
														lowerCAmelCase   =		os.path.join(
														    os.path.dirname(_snake_case							)						, F"""bertarized_{os.path.basename(_snake_case							)}"""							)

							if not os.path.isdir(_snake_case							):
														shutil.copytree(_snake_case						, _snake_case							)
														print(F"""\nCreated folder {target_model_path}"""							)

							torch.save(_snake_case						, os.path.join(_snake_case						, '''pytorch_model.bin'''							)							)
							print('''\nPruned model saved! See you later!'''							)


if __name__ == "__main__":
		UpperCAmelCase_						=argparse.ArgumentParser()

		parser.add_argument(
		    """--pruning_method""",
		    choices=["""l0""", """magnitude""", """topK""", """sigmoied_threshold"""],
		    type=str,
		    required=True,
		    help=(
		        """Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"""
		        """ sigmoied_threshold = Soft movement pruning)"""
		    ),
		)
		parser.add_argument(
		    """--threshold""",
		    type=float,
		    required=False,
		    help=(
		        """For `magnitude` and `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(
		    """--model_name_or_path""",
		    type=str,
		    required=True,
		    help="""Folder containing the model that was previously fine-pruned""",
		)
		parser.add_argument(
		    """--target_model_path""",
		    default=None,
		    type=str,
		    required=False,
		    help="""Folder containing the model that was previously fine-pruned""",
		)

		UpperCAmelCase_						=parser.parse_args()

		main(args)



