



"""simple docstring"""

import argparse
import os
import shutil

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





def       __magic_name__    (   _lowerCamelCase				: Optional[int]					):
       __a		:		Tuple									=       args.pruning_method
       __a		:		Optional[Any]									=       args.threshold

       __a		:		Tuple									=       args.model_name_or_path.rstrip("""/"""					)
       __a		:		List[Any]									=       args.target_model_path

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

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

       if target_model_path is None:
              __a		:		Union[str, Any]									=       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__":
 lowercase__			   =  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",
 )

 lowercase__			   =  parser.parse_args()

 main(args)






"""simple docstring"""

import unittest

from knapsack import knapsack as k




class SCREAMING_SNAKE_CASE__					(		unittest.TestCase      ):



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		str									=       0
													__a		:		Optional[Any]									=       [0]
													__a		:		int									=       [0]
													__a		:		str									=       len(_lowercase							)
													self.assertEqual(k.knapsack(_lowercase						,     _lowercase						,     _lowercase						,     _lowercase							)						,     0							)

													__a		:		int									=       [60]
													__a		:		Union[str, Any]									=       [10]
													__a		:		Tuple									=       len(_lowercase							)
													self.assertEqual(k.knapsack(_lowercase						,     _lowercase						,     _lowercase						,     _lowercase							)						,     0							)



						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		int									=       3
													__a		:		str									=       [1, 2, 3]
													__a		:		Optional[Any]									=       [3, 2, 1]
													__a		:		int									=       len(_lowercase							)
													self.assertEqual(k.knapsack(_lowercase						,     _lowercase						,     _lowercase						,     _lowercase							)						,     5							)





						def     lowerCAmelCase__(self							):





													'''simple docstring'''

													__a		:		Dict									=       50
													__a		:		Tuple									=       [60, 100, 120]
													__a		:		List[str]									=       [10, 20, 30]
													__a		:		Union[str, Any]									=       len(_lowercase							)
													self.assertEqual(k.knapsack(_lowercase						,     _lowercase						,     _lowercase						,     _lowercase							)						,     220							)


if __name__ == "__main__":
	unittest.main()

