






import argparse
import os
import shutil

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





def 					lowercase_ (    __snake_case     :							Union[str, Any]      )     -> int:





				'''simple docstring'''


				snake_case__      :List[str]         =   args.pruning_method
				snake_case__      :Any         =   args.threshold

				snake_case__      :str         =   args.model_name_or_path.rstrip("/"      )
				snake_case__      :List[Any]         =   args.target_model_path

				print(F'Load fine-pruned model from {model_name_or_path}'      )
				snake_case__      :Optional[Any]         =   torch.load(os.path.join(lowerCAmelCase__	,	"pytorch_model.bin"      )      )
				snake_case__      :List[str]         =   {}

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

				if target_model_path is None:
								snake_case__      :Tuple         =   os.path.join(
								    os.path.dirname(lowerCAmelCase__      )	,	F'bertarized_{os.path.basename(lowerCAmelCase__      )}'      )

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

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


if __name__ == "__main__":
	__UpperCAmelCase							:   List[str]							=  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							:   str							=  parser.parse_args()

	main(args)







def 					lowercase_ (    __snake_case     :							int      )     -> bool:





				'''simple docstring'''


				if p < 2:
								raise ValueError("p should not be less than 2!"      )
				elif p == 2:
								return True

				snake_case__      :List[str]         =   4
				snake_case__      :Optional[int]         =   (1 << p) - 1
				for _ in range(p - 2      ):
								snake_case__      :List[Any]         =   ((s * s) - 2) % m
				return s == 0


if __name__ == "__main__":
	print(lucas_lehmer_test(7))
	print(lucas_lehmer_test(1_1))