





import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer
def  __UpperCamelCase    (						snake_case      )    ->		List[str]:


   '''simple docstring'''







   __A    =  args.serialization_dir
   __A    =  args.pruning_method
   __A    =  args.threshold

   __A    =  torch.load(os.path.join(snake_case							,			'''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(snake_case							,			snake_case      ).sum().item()
         elif pruning_method == "sigmoied_threshold":
            __A    =  ThresholdBinarizer.apply(snake_case							,			snake_case							,			snake_case      ).sum().item()
         elif pruning_method == "l0":
            __A   ,		__A    =  -0.1, 1.1
            __A    =  torch.sigmoid(snake_case      )
            __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(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_0_0 * remaining_count / encoder_count      )


if __name__ == "__main__":
    _UpperCamelCase       :				List[Any]           =			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""",
    )

    _UpperCamelCase       :				Tuple           =			parser.parse_args()

    main(args)








import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


_UpperCamelCase       :				Tuple           =			logging.get_logger(__name__)







class 		_lowerCAmelCase(		_a):

  """simple docstring"""







  def __init__(   self    ,							*UpperCAmelCase    ,							**UpperCAmelCase      )->   None:
     warnings.warn(
         '''The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
         ''' Please use DetrImageProcessor instead.'''    ,							UpperCAmelCase    ,							)
     super().__init__(*UpperCAmelCase    ,							**UpperCAmelCase      )

