





import argparse
import os
import shutil

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

def   a_    ( lowerCAmelCase_      :			Dict	):
   __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(lowerCAmelCase_,	'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=lowerCAmelCase_,	threshold=lowerCAmelCase_	)
            __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(lowerCAmelCase_,	lowerCAmelCase_	)
            __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(lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	)
            __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(lowerCAmelCase_	)
            __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(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__":
     _snake_case  :    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',
     )

     _snake_case  :    Union[str, Any]						    =  parser.parse_args()

     main(args)








from __future__ import annotations

import math

def   a_    ( lowerCAmelCase_      :			int,	lowerCAmelCase_      :			int,	lowerCAmelCase_      :			bool,	lowerCAmelCase_      :			list[int],	lowerCAmelCase_      :			float	):

   if depth < 0:
      raise ValueError('Depth cannot be less than 0'	)

   if len(lowerCAmelCase_	) == 0:
      raise ValueError('Scores cannot be empty'	)

   if depth == height:
      return scores[node_index]

   if is_max:
      return max(
          minimax(depth + 1,	node_index * 2,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	),	minimax(depth + 1,	node_index * 2 + 1,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	),	)

   return min(
       minimax(depth + 1,	node_index * 2,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	),	minimax(depth + 1,	node_index * 2 + 1,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	),	)

def   a_    ( ):
   __lowerCAmelCase								=	[90, 23, 6, 33, 21, 65, 123, 3_4423]
   __lowerCAmelCase								=	math.log(len(lowerCAmelCase_	),	2	)
   print('Optimal value : ',	end=''	)
   print(minimax(0,	0,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_	)	)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()

