




from ..utils import DummyObject, requires_backends


class 				UpperCamelCase_    ( metaclass=UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      ['''flax''', '''transformers''']


    def __init__(			self      :	List[str]					, *UpperCAmelCase__      :	str					, **UpperCAmelCase__      :	Optional[Any])						->str:





           '''simple docstring'''

           requires_backends(self					, ['''flax''', '''transformers'''])


    @classmethod
    def        SCREAMING_SNAKE_CASE							(			cls      :	int					, *UpperCAmelCase__      :	List[Any]					, **UpperCAmelCase__      :	Tuple)						->Dict:





           '''simple docstring'''

           requires_backends(cls					, ['''flax''', '''transformers'''])





    @classmethod
    def        SCREAMING_SNAKE_CASE							(			cls      :	Optional[Any]					, *UpperCAmelCase__      :	Any					, **UpperCAmelCase__      :	List[str])						->Union[str, Any]:





           '''simple docstring'''

           requires_backends(cls					, ['''flax''', '''transformers'''])


class 				UpperCamelCase_    ( metaclass=UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      ['''flax''', '''transformers''']


    def __init__(			self      :	Tuple					, *UpperCAmelCase__      :	Tuple					, **UpperCAmelCase__      :	Optional[int])						->List[Any]:





           '''simple docstring'''

           requires_backends(self					, ['''flax''', '''transformers'''])


    @classmethod
    def        SCREAMING_SNAKE_CASE							(			cls      :	int					, *UpperCAmelCase__      :	Dict					, **UpperCAmelCase__      :	List[Any])						->Dict:





           '''simple docstring'''

           requires_backends(cls					, ['''flax''', '''transformers'''])





    @classmethod
    def        SCREAMING_SNAKE_CASE							(			cls      :	List[str]					, *UpperCAmelCase__      :	List[Any]					, **UpperCAmelCase__      :	Union[str, Any])						->List[Any]:





           '''simple docstring'''

           requires_backends(cls					, ['''flax''', '''transformers'''])


class 				UpperCamelCase_    ( metaclass=UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      ['''flax''', '''transformers''']


    def __init__(			self      :	Union[str, Any]					, *UpperCAmelCase__      :	Tuple					, **UpperCAmelCase__      :	Any)						->Union[str, Any]:





           '''simple docstring'''

           requires_backends(self					, ['''flax''', '''transformers'''])


    @classmethod
    def        SCREAMING_SNAKE_CASE							(			cls      :	int					, *UpperCAmelCase__      :	str					, **UpperCAmelCase__      :	Optional[int])						->Any:





           '''simple docstring'''

           requires_backends(cls					, ['''flax''', '''transformers'''])





    @classmethod
    def        SCREAMING_SNAKE_CASE							(			cls      :	Optional[Any]					, *UpperCAmelCase__      :	Tuple					, **UpperCAmelCase__      :	Union[str, Any])						->List[Any]:





           '''simple docstring'''

           requires_backends(cls					, ['''flax''', '''transformers'''])


class 				UpperCamelCase_    ( metaclass=UpperCAmelCase__     ):

    '''simple docstring'''






    UpperCAmelCase__							  =      ['''flax''', '''transformers''']


    def __init__(			self      :	int					, *UpperCAmelCase__      :	Dict					, **UpperCAmelCase__      :	int)						->Union[str, Any]:





           '''simple docstring'''

           requires_backends(self					, ['''flax''', '''transformers'''])


    @classmethod
    def        SCREAMING_SNAKE_CASE							(			cls      :	Any					, *UpperCAmelCase__      :	List[Any]					, **UpperCAmelCase__      :	Optional[int])						->Dict:





           '''simple docstring'''

           requires_backends(cls					, ['''flax''', '''transformers'''])





    @classmethod
    def        SCREAMING_SNAKE_CASE							(			cls      :	List[str]					, *UpperCAmelCase__      :	Optional[int]					, **UpperCAmelCase__      :	str)						->Optional[Any]:





           '''simple docstring'''

           requires_backends(cls					, ['''flax''', '''transformers'''])








import argparse
import os
import shutil

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







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Dict:



       """simple docstring"""

       A__       =    args.pruning_method
       A__       =    args.threshold

       A__       =    args.model_name_or_path.rstrip('''/'''						)
       A__       =    args.target_model_path

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

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

       if target_model_path is None:
              A__       =    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	:		Optional[Any]							       =   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	:		int							       =   parser.parse_args()

  main(args)


