






"""simple docstring"""
import argparse
import os
import shutil

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






def 			__UpperCamelCase	(		snake_case__					):

	'''simple docstring'''





	A_		:						List[Any]	    =   args.pruning_method
	A_		:						int	    =   args.threshold

	A_		:						Optional[Any]	    =   args.model_name_or_path.rstrip("""/"""					)
	A_		:						str	    =   args.target_model_path

	print(F"""Load fine-pruned model from {model_name_or_path}"""					)
	A_		:						Dict	    =   torch.load(os.path.join(lowercase_   ,   """pytorch_model.bin"""					)					)
	A_		:						Tuple	    =   {}

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

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

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

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


if __name__ == "__main__":
							_lowerCAmelCase         =							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",
							)

							_lowerCAmelCase         =							parser.parse_args()

							main(args)









"""simple docstring"""
import math






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	if (
	    not isinstance(snake_case__   ,   (int, float)					)
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError("""power_factor must be a valid float value between -1 and 1."""					)
	return apparent_power * power_factor






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	if (
	    not isinstance(snake_case__   ,   (int, float)					)
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError("""power_factor must be a valid float value between -1 and 1."""					)
	return apparent_power * math.sqrt(1 - power_factor**2					)


if __name__ == "__main__":
							import doctest

							doctest.testmod()

