


import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Dict							):
     __UpperCamelCase       				=args.serialization_dir
     __UpperCamelCase       				=args.pruning_method
     __UpperCamelCase       				=args.threshold

     __UpperCamelCase       				=torch.load(os.path.join(SCREAMING_SNAKE_CASE__     ,	'pytorch_model.bin'							)     ,	map_location='cpu'							)

     __UpperCamelCase       				=0  # Number of remaining (not pruned) params in the encoder
     __UpperCamelCase       				=0  # Number of params in the encoder

     print('name'.ljust(60     ,	' '							)     ,	'Remaining Weights %'     ,	'Remaining Weight'							)
     for name, param in st.items():
          if "encoder" not in name:
               continue

          if "mask_scores" in name:
               if pruning_method == "topK":
                    __UpperCamelCase       				=TopKBinarizer.apply(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    __UpperCamelCase       				=ThresholdBinarizer.apply(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							).sum().item()
               elif pruning_method == "l0":
                    __UpperCamelCase				, __UpperCamelCase       				=-0.1, 1.1
                    __UpperCamelCase       				=torch.sigmoid(SCREAMING_SNAKE_CASE__							)
                    __UpperCamelCase       				=s * (r - l) + l
                    __UpperCamelCase       				=s_bar.clamp(min=0.0     ,	max=1.0							)
                    __UpperCamelCase       				=(mask > 0.0).sum().item()
               else:
                    raise ValueError('Unknown pruning method'							)
               remaining_count += mask_ones
               print(name.ljust(60     ,	' '							)     ,	str(round(1_00 * mask_ones / param.numel()     ,	3							)							).ljust(20     ,	' '							)     ,	str(SCREAMING_SNAKE_CASE__							)							)
          else:
               encoder_count += param.numel()
               if "bias" in name or "LayerNorm" in name:
                    remaining_count += param.numel()

     print(''							)
     print('Remaining Weights (global) %: '     ,	1_00 * remaining_count / encoder_count							)


if __name__ == "__main__":
   _A									=		argparse.ArgumentParser()

   parser.add_argument(
       '--pruning_method',
       choices=['l0', 'topK', 'sigmoied_threshold'],
       type=str,
       required=True,
       help=(
           'Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'
           ' pruning)'
       ),
   )
   parser.add_argument(
       '--threshold',
       type=float,
       required=False,
       help=(
           'For `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(
       '--serialization_dir',
       type=str,
       required=True,
       help='Folder containing the model that was previously fine-pruned',
   )

   _A									=		parser.parse_args()

   main(args)









_A									=		{
    0: 'underwear',
    1: 'pants',
    2: 'belt',
    3: 'suit',
    4: 'shoe',
    5: 'socks',
    6: 'shirt',
    7: 'tie',
    8: 'watch',
}

_A									=		[[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

_A									=		[0 for x in range(len(graph))]
_A									=		[]

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Optional[Any]     ,	SCREAMING_SNAKE_CASE__     :   Tuple							):
     __UpperCamelCase       				=1
     while stack:
          __UpperCamelCase       				=stack.pop()
          print(SCREAMING_SNAKE_CASE__     ,	clothes[current_clothing]							)
          order += 1

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Any     ,	SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   Tuple							):
     __UpperCamelCase       				=1
     for v in graph[u]:
          if not visited[v]:
               depth_first_search(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

     stack.append(SCREAMING_SNAKE_CASE__							)

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   int     ,	SCREAMING_SNAKE_CASE__     :   List[str]							):
     for v in range(len(SCREAMING_SNAKE_CASE__							)							):
          if not visited[v]:
               depth_first_search(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
   topological_sort(graph, visited)
   print(stack)
   print_stack(stack, clothes)





