





"""simple docstring"""


import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer




def 					lowercase__    (					lowercase_			)					->		List[str]:




      """simple docstring"""

      _UpperCamelCase      :	List[Any]						=       args.pruning_method
      _UpperCamelCase      :	int						=       args.threshold

      _UpperCamelCase      :	Optional[Any]						=       args.model_name_or_path.rstrip("/"			)
      _UpperCamelCase      :	str						=       args.target_model_path

      print(F'''Load fine-pruned model from {model_name_or_path}'''			)
      _UpperCamelCase      :	Dict						=       torch.load(os.path.join(lowercase_		,"pytorch_model.bin"			)			)
      _UpperCamelCase      :	Tuple						=       {}

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

      if target_model_path is None:
            _UpperCamelCase      :	int						=       os.path.join(
                os.path.dirname(lowercase_			)		,F'''bertarized_{os.path.basename(lowercase_			)}'''			)

      if not os.path.isdir(lowercase_			):
            shutil.copytree(lowercase_		,lowercase_			)
            print(F'''\nCreated folder {target_model_path}'''			)

      torch.save(lowercase_		,os.path.join(lowercase_		,"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"""


def 					lowercase__    (					lowercase_		,lowercase_			)					->		None:




      """simple docstring"""

      _UpperCamelCase      :	List[Any]						=       len(lowercase_			)
      print("The following activities are selected:"			)

      # The first activity is always selected
      _UpperCamelCase      :	List[Any]						=       0
      print(lowercase_		,end=","			)

      # Consider rest of the activities
      for j in range(lowercase_			):
            # If this activity has start time greater than
            # or equal to the finish time of previously
            # selected activity, then select it
            if start[j] >= finish[i]:
                  print(lowercase_		,end=","			)
                  _UpperCamelCase      :	Optional[Any]						=       j


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  lowerCamelCase__				  =   [1, 3, 0, 5, 8, 5]
  lowerCamelCase__				  =   [2, 4, 6, 7, 9, 9]
  print_max_activities(start, finish)
