





import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer



def   __lowercase  ( _SCREAMING_SNAKE_CASE      )					->  Any:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE						=		args.serialization_dir
      SCREAMING_SNAKE_CASE						=		args.pruning_method
      SCREAMING_SNAKE_CASE						=		args.threshold

      SCREAMING_SNAKE_CASE						=		torch.load(os.path.join(_SCREAMING_SNAKE_CASE					,		"""pytorch_model.bin"""      )					,		map_location="""cpu"""      )

      SCREAMING_SNAKE_CASE						=		0  # Number of remaining (not pruned) params in the encoder
      SCREAMING_SNAKE_CASE						=		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":
                        SCREAMING_SNAKE_CASE						=		TopKBinarizer.apply(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      ).sum().item()
                  elif pruning_method == "sigmoied_threshold":
                        SCREAMING_SNAKE_CASE						=		ThresholdBinarizer.apply(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE      ).sum().item()
                  elif pruning_method == "l0":
                        SCREAMING_SNAKE_CASE,   SCREAMING_SNAKE_CASE						=		-0.1, 1.1
                        SCREAMING_SNAKE_CASE						=		torch.sigmoid(_SCREAMING_SNAKE_CASE      )
                        SCREAMING_SNAKE_CASE						=		s * (r - l) + l
                        SCREAMING_SNAKE_CASE						=		s_bar.clamp(min=0.0					,		max=1.0      )
                        SCREAMING_SNAKE_CASE						=		(mask > 0.0).sum().item()
                  else:
                        raise ValueError("""Unknown pruning method"""      )
                  remaining_count += mask_ones
                  print(name.ljust(60					,		""" """      )					,		str(round(1_00 * mask_ones / param.numel()					,		3      )      ).ljust(20					,		""" """      )					,		str(_SCREAMING_SNAKE_CASE      )      )
            else:
                  encoder_count += param.numel()
                  if "bias" in name or "LayerNorm" in name:
                        remaining_count += param.numel()

      print(""""""      )
      print("""Remaining Weights (global) %: """					,		1_00 * remaining_count / encoder_count      )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_		  =			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_		  =			parser.parse_args()

      main(args)









import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput

@dataclass
class 						UpperCamelCase__					( lowerCAmelCase_  ):



     '''simple docstring'''

     __snake_case       :      float
     __snake_case       :      Optional[float]						      =     None
     __snake_case       :      Optional[float]						      =     None

class 						UpperCamelCase__					( unittest.TestCase  ):



     '''simple docstring'''

     def 							SCREAMING_SNAKE_CASE__    (				self		:       Any			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30			)
           self.assertEqual(x.a		,30			)
           self.assertIsNone(x.b			)
           self.assertIsNone(x.c			)
           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		x.d






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[Any]			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30		,b=10			)
           self.assertEqual(x[0]		,30			)
           self.assertEqual(x[1]		,10			)
           self.assertEqual(x[:2]		,(30, 10)			)
           self.assertEqual(x[:]		,(30, 10)			)

           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30		,c=10			)
           self.assertEqual(x[0]		,30			)
           self.assertEqual(x[1]		,10			)
           self.assertEqual(x[:2]		,(30, 10)			)
           self.assertEqual(x[:]		,(30, 10)			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       str			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30		,b=10			)
           self.assertEqual(x["""a"""]		,30			)
           self.assertEqual(x["""b"""]		,10			)
           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		x["""c"""]

           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30		,c=10			)
           self.assertEqual(x["""a"""]		,30			)
           self.assertEqual(x["""c"""]		,10			)
           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		x["""b"""]






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30			)
           self.assertEqual(list(x.keys()			)		,["""a"""]			)
           self.assertEqual(list(x.values()			)		,[30]			)
           self.assertEqual(list(x.items()			)		,[("""a""", 30)]			)
           self.assertEqual(list(lowerCamelCase__			)		,["""a"""]			)

           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30		,b=10			)
           self.assertEqual(list(x.keys()			)		,["""a""", """b"""]			)
           self.assertEqual(list(x.values()			)		,[30, 10]			)
           self.assertEqual(list(x.items()			)		,[("""a""", 30), ("""b""", 10)]			)
           self.assertEqual(list(lowerCamelCase__			)		,["""a""", """b"""]			)

           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30		,c=10			)
           self.assertEqual(list(x.keys()			)		,["""a""", """c"""]			)
           self.assertEqual(list(x.values()			)		,[30, 10]			)
           self.assertEqual(list(x.items()			)		,[("""a""", 30), ("""c""", 10)]			)
           self.assertEqual(list(lowerCamelCase__			)		,["""a""", """c"""]			)

           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		x.update({"""d""": 20}			)
           with self.assertRaises(lowerCamelCase__			):
                 del x["a"]
           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		x.pop("""a"""			)
           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		x.setdefault("""d"""		,32			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       int			)	->						List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30			)
           SCREAMING_SNAKE_CASE						=		10
           self.assertEqual(x.a		,10			)
           self.assertEqual(x["""a"""]		,10			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[str]			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30			)
           SCREAMING_SNAKE_CASE						=		10
           self.assertEqual(x.a		,10			)
           self.assertEqual(x["""a"""]		,10			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest({"""a""": 30, """b""": 10}			)
           self.assertEqual(list(x.keys()			)		,["""a""", """b"""]			)
           self.assertEqual(x.a		,30			)
           self.assertEqual(x.b		,10			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[int]			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest([("""a""", 30), ("""b""", 10)]			)
           self.assertEqual(list(x.keys()			)		,["""a""", """b"""]			)
           self.assertEqual(x.a		,30			)
           self.assertEqual(x.b		,10			)

           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		ModelOutputTest([("""a""", 30), (10, 10)]			)

           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=(30, 30)			)
           self.assertEqual(list(x.keys()			)		,["""a"""]			)
           self.assertEqual(x.a		,(30, 30)			)


