






"""simple docstring"""





import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer



def      __lowercase       (     a			:					Union[str, Any]			)       ->       Optional[int]:
      __snake_case   :     str	      =args.serialization_dir
      __snake_case   :     Union[str, Any]	      =args.pruning_method
      __snake_case   :     List[Any]	      =args.threshold

      __snake_case   :     Optional[int]	      =torch.load(os.path.join(SCREAMING_SNAKE_CASE_							,		'''pytorch_model.bin'''			)							,		map_location='''cpu'''			)

      __snake_case   :     str	      =0  # Number of remaining (not pruned) params in the encoder
      __snake_case   :     Optional[int]	      =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":
                        __snake_case   :     Optional[int]	      =TopKBinarizer.apply(SCREAMING_SNAKE_CASE_							,		SCREAMING_SNAKE_CASE_			).sum().item()
                  elif pruning_method == "sigmoied_threshold":
                        __snake_case   :     List[Any]	      =ThresholdBinarizer.apply(SCREAMING_SNAKE_CASE_							,		SCREAMING_SNAKE_CASE_							,		SCREAMING_SNAKE_CASE_			).sum().item()
                  elif pruning_method == "l0":
                        __snake_case   :     Optional[Any]	      =-0.1, 1.1
                        __snake_case   :     Tuple	      =torch.sigmoid(SCREAMING_SNAKE_CASE_			)
                        __snake_case   :     Union[str, Any]	      =s * (r - l) + l
                        __snake_case   :     Tuple	      =s_bar.clamp(min=0.0							,		max=1.0			)
                        __snake_case   :     List[str]	      =(mask > 0.0).sum().item()
                  else:
                        raise ValueError('''Unknown pruning method'''			)
                  remaining_count += mask_ones
                  print(name.ljust(60							,		''' '''			)							,		str(round(100 * 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) %: '''							,		100 * remaining_count / encoder_count			)


if __name__ == "__main__":
    UpperCamelCase_						:					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_						:					Optional[int]      				=			parser.parse_args()

    main(args)



'''simple docstring'''




def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     return " ".join([word[::-1] for word in input_str.split()]				)


if __name__ == "__main__":
 import doctest

 doctest.testmod()




