





"""simple docstring"""
import argparse
import os
import shutil

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

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[Any]			):
 lowerCAmelCase    				= args.pruning_method
 lowerCAmelCase    				= args.threshold

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

 print(F'Load fine-pruned model from {model_name_or_path}'			)
 lowerCAmelCase    				= torch.load(os.path.join(_UpperCAmelCase  ,     'pytorch_model.bin'			)			)
 lowerCAmelCase    				= {}

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

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

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

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


if __name__ == "__main__":
       __UpperCamelCase     :						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''',
       )

       __UpperCamelCase     :						List[Any]						       =  parser.parse_args()

       main(args)








UpperCAmelCase_							:					dict[str, float]		 =   {
    "joule": 1.0,
    "kilojoule": 1_000,
    "megajoule": 1_000_000,
    "gigajoule": 1_000_000_000,
    "wattsecond": 1.0,
    "watthour": 3_600,
    "kilowatthour": 3_600_000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_186.8,
    "kilocalorie_nutr": 4_186_800.00,
    "electronvolt": 1.6_0217_6634e-19,
    "britishthermalunit_it": 1_055.05_585,
    "footpound": 1.35_5818,
}
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   str			,a__    :   float       )					->				float:
	if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
		__A :			Optional[int]		    =			(
		    f"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
		    f"""Valid values are: {", ".join(a__       )}"""
		)
		raise ValueError(a__       )
	return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
			import doctest

			doctest.testmod()





