
'''simple docstring'''





a__		: Union[str, Any]  							=							float("inf")


class 			UpperCamelCase__	:






 def __init__(      self	:Optional[Any]						,					_A	:Any      )					->      Optional[int]:







       '''simple docstring'''



       __A													=      [0] * n
       __A													=      [0] * n
       __A													=      [0] * n
       __A													=      [[] for _ in range(_A      )]






 def 						lowercase_    (      self	:Optional[int]						,					_A	:Dict						,					_A	:Any						,					_A	:Any						,					_A	:str=0      )					->      Dict:







       '''simple docstring'''



       self.adj[a].append([b, len(self.adj[b]      ), c, 0]      )
       self.adj[b].append([a, len(self.adj[a]      ) - 1, rcap, 0]      )






 def 						lowercase_    (      self	:str						,					_A	:Union[str, Any]						,					_A	:Optional[Any]						,					_A	:Any      )					->      Optional[int]:







       '''simple docstring'''



       if vertex == sink or not flow:
             return flow

       for i in range(self.ptr[vertex]						,					len(self.adj[vertex]      )      ):
             __A													=      self.adj[vertex][i]
             if self.lvl[e[0]] == self.lvl[vertex] + 1:
                   __A													=      self.depth_first_search(e[0]						,					_A						,					min(_A						,					e[2] - e[3]      )      )
                   if p:
                         self.adj[vertex][i][3] += p
                         self.adj[e[0]][e[1]][3] -= p
                         return p
             __A													=      self.ptr[vertex] + 1
       return 0






 def 						lowercase_    (      self	:Dict						,					_A	:Optional[int]						,					_A	:str      )					->      str:







       '''simple docstring'''



       __A						,   __A													=      0, source
       for l in range(31      ):  # noqa: E741  l = 30 maybe faster for random data
             while True:
                   __A						,   __A													=      [0] * len(self.q      ), [0] * len(self.q      )
                   __A						,   __A						,   __A													=      0, 1, 1
                   while qi < qe and not self.lvl[sink]:
                         __A													=      self.q[qi]
                         qi += 1
                         for e in self.adj[v]:
                               if not self.lvl[e[0]] and (e[2] - e[3]) >> (30 - l):
                                     __A													=      e[0]
                                     qe += 1
                                     __A													=      self.lvl[v] + 1

                   __A													=      self.depth_first_search(_A						,					_A						,					_A      )
                   while p:
                         flow += p
                         __A													=      self.depth_first_search(_A						,					_A						,					_A      )

                   if not self.lvl[sink]:
                         break

       return flow
a__		: List[str]  							=							Dinic(1_0)
a__		: Optional[Any]  							=							0
a__		: Any  							=							9
for vertex in range(1, 5):
 graph.add_edge(source, vertex, 1)
for vertex in range(5, 9):
 graph.add_edge(vertex, sink, 1)
for vertex in range(1, 5):
 graph.add_edge(vertex, vertex + 4, 1)

# Now we can know that is the maximum flow(source -> sink)
print(graph.max_flow(source, sink))











'''simple docstring'''


import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     a_									=args.serialization_dir
     a_									=args.pruning_method
     a_									=args.threshold

     a_									=torch.load(os.path.join(lowercase__							,						"pytorch_model.bin"							)							,						map_location="cpu"							)

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

     print("name".ljust(6_0							,						" "							)							,						"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":
                    a_									=TopKBinarizer.apply(lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    a_									=ThresholdBinarizer.apply(lowercase__							,						lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "l0":
                    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_									=(mask > 0.0).sum().item()
               else:
                    raise ValueError("Unknown pruning method"							)
               remaining_count += mask_ones
               print(name.ljust(6_0							,						" "							)							,						str(round(1_0_0 * mask_ones / param.numel()							,						3							)							).ljust(2_0							,						" "							)							,						str(lowercase__							)							)
          else:
               encoder_count += param.numel()
               if "bias" in name or "LayerNorm" in name:
                    remaining_count += param.numel()

     print(""							)
     print("Remaining Weights (global) %: "							,						1_0_0 * remaining_count / encoder_count							)


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

  lowercase											=      parser.parse_args()

  main(args)


