




"""simple docstring"""


import argparse
import os
import shutil

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






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _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(a_   ,"""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=a_   ,threshold=a_		)
        _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(a_   ,a_		)
        _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(a_   ,a_   ,a_		)
        _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						 =      -0.1, 1.1
        _UpperCAmelCase						 =      torch.sigmoid(a_		)
        _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(a_		)   ,f'''bertarized_{os.path.basename(a_		)}'''		)

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

  torch.save(a_   ,os.path.join(a_   ,"""pytorch_model.bin"""		)		)
  print("""\nPruned model saved! See you later!"""		)


if __name__ == "__main__":
    UpperCAmelCase__  		=						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""",
    )

    UpperCAmelCase__  		=						parser.parse_args()

    main(args)

"""simple docstring"""


from __future__ import annotations






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return [ord(lowercase		) - 96 for elem in plain]






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return "".join(chr(elem + 96		) for elem in encoded		)






def     __UpperCAmelCase					(			):







  """simple docstring"""
  _UpperCAmelCase						 =      encode(input("""-> """		).strip().lower()		)
  print("""Encoded: """   ,lowercase		)
  print("""Decoded:"""   ,decode(lowercase		)		)


if __name__ == "__main__":
    main()
