



"""simple docstring"""





import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def   a__      ( lowerCAmelCase__ ):
      UpperCAmelCase_     					=				args.pruning_method
      UpperCAmelCase_     					=				args.threshold

      UpperCAmelCase_     					=				args.model_name_or_path.rstrip("/" )
      UpperCAmelCase_     					=				args.target_model_path

      print(f"""Load fine-pruned model from {model_name_or_path}""" )
      UpperCAmelCase_     					=				torch.load(os.path.join(lowerCAmelCase__      ,     "pytorch_model.bin" ) )
      UpperCAmelCase_     					=				{}

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

      if target_model_path is None:
            UpperCAmelCase_     					=				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__":
  lowerCamelCase  			=		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""",
  )

  lowerCamelCase  			=		parser.parse_args()

  main(args)










"""simple docstring"""

from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *

