
'''simple docstring'''





import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer





def 							snake_case				(		UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      __A													=      args.serialization_dir
      __A													=      args.pruning_method
      __A													=      args.threshold

      __A													=      torch.load(os.path.join(UpperCAmelCase   ,	'pytorch_model.bin'						)   ,	map_location='cpu'						)

      __A													=      0  # Number of remaining (not pruned) params in the encoder
      __A													=      0  # Number of params in the encoder

      print('name'.ljust(6_0   ,	' '						)   ,	'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":
                        __A													=      TopKBinarizer.apply(UpperCAmelCase   ,	UpperCAmelCase						).sum().item()
                  elif pruning_method == "sigmoied_threshold":
                        __A													=      ThresholdBinarizer.apply(UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						).sum().item()
                  elif pruning_method == "l0":
                        __A						,   __A													=      -0.1, 1.1
                        __A													=      torch.sigmoid(UpperCAmelCase						)
                        __A													=      s * (r - l) + l
                        __A													=      s_bar.clamp(min=0.0   ,	max=1.0						)
                        __A													=      (mask > 0.0).sum().item()
                  else:
                        raise ValueError('Unknown pruning method'						)
                  remaining_count += mask_ones
                  print(name.ljust(6_0   ,	' '						)   ,	str(round(1_0_0 * mask_ones / param.numel()   ,	3						)						).ljust(2_0   ,	' '						)   ,	str(UpperCAmelCase						)						)
            else:
                  encoder_count += param.numel()
                  if "bias" in name or "LayerNorm" in name:
                        remaining_count += param.numel()

      print(''						)
      print('Remaining Weights (global) %: '   ,	1_0_0 * remaining_count / encoder_count						)


if __name__ == "__main__":
 a__		: Dict  							=							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",
 )

 a__		: Tuple  							=							parser.parse_args()

 main(args)








'''simple docstring'''





import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

a__		: Tuple  							=							[(1,)]





def 							snake_case				(		)->      Union[str, Any]:







      """simple docstring"""
      __A													=      bool(os.getenv('RUN_SLOW'   ,	0						)						)
      if run_slow:
            assert True
      else:
            assert False, "should have been skipped"





def 							snake_case				(		)->      Tuple:







      """simple docstring"""
      __A													=      bool(os.getenv('RUN_SLOW'   ,	0						)						)
      if run_slow and torch_device == "cuda":
            assert True
      else:
            assert False, "should have been skipped"


@require_torch
class 			UpperCamelCase__	(     unittest.TestCase):






 @slow
 @require_torch_gpu
 def 						lowercase_    (      self	:Dict      )					->      List[Any]:







       '''simple docstring'''



       check_slow_torch_cuda()






 @require_torch_gpu
 @slow
 def 						lowercase_    (      self	:int      )					->      int:







       '''simple docstring'''



       check_slow_torch_cuda()






 @parameterized.expand(_A      )
 @slow
 def 						lowercase_    (      self	:Dict						,					_A	:Union[str, Any]=None      )					->      Dict:







       '''simple docstring'''



       check_slow()





@slow
@require_torch_gpu
def 							snake_case				(		)->      Optional[int]:







      """simple docstring"""
      check_slow_torch_cuda()





@require_torch_gpu
@slow
def 							snake_case				(		)->      List[str]:







      """simple docstring"""
      check_slow_torch_cuda()





@slow
@pytest.mark.parametrize('param'   ,	[1]						)
def 							snake_case				(		UpperCAmelCase						)->      int:







      """simple docstring"""
      check_slow()





@pytest.mark.parametrize('param'   ,	[1]						)
@slow
def 							snake_case				(		UpperCAmelCase						)->      Any:







      """simple docstring"""
      check_slow()






