





'''simple docstring'''




import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
  lowercase__       : Optional[int]         =       argparse.ArgumentParser(
      description=(
          '''Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned'''
          ''' Distillation'''
      )
  )
  parser.add_argument('''--model_type''', default='''bert''', choices=['''bert'''])
  parser.add_argument('''--model_name''', default='''bert-base-uncased''', type=str)
  parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_bert-base-uncased_0247911.pth''', type=str)
  parser.add_argument('''--vocab_transform''', action='''store_true''')
  lowercase__       : Dict         =       parser.parse_args()

  if args.model_type == "bert":
    lowercase__       : Dict         =       BertForMaskedLM.from_pretrained(args.model_name)
    lowercase__       : int         =       '''bert'''
  else:
    raise ValueError('''args.model_type should be "bert".''')

  lowercase__       : List[Any]         =       model.state_dict()
  lowercase__       : Optional[Any]         =       {}

  for w in ["word_embeddings", "position_embeddings"]:
    lowercase__       : Dict         =       state_dict[f"""{prefix}.embeddings.{w}.weight"""]
  for w in ["weight", "bias"]:
    lowercase__       : Any         =       state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]

  lowercase__       : List[Any]         =       0
  for teacher_idx in [0, 2, 4, 7, 9, 11]:
    for w in ["weight", "bias"]:
      lowercase__       : Optional[Any]         =       state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
      ]
      lowercase__       : str         =       state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
      ]
      lowercase__       : Optional[int]         =       state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
      ]

      lowercase__       : Dict         =       state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
      ]
      lowercase__       : List[Any]         =       state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
      ]

      lowercase__       : Dict         =       state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
      ]
      lowercase__       : List[str]         =       state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
      ]
      lowercase__       : List[Any]         =       state_dict[
          f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
      ]
    std_idx += 1

  lowercase__       : int         =       state_dict['''cls.predictions.decoder.weight''']
  lowercase__       : List[Any]         =       state_dict['''cls.predictions.bias''']
  if args.vocab_transform:
    for w in ["weight", "bias"]:
      lowercase__       : Optional[Any]         =       state_dict[f"""cls.predictions.transform.dense.{w}"""]
      lowercase__       : Union[str, Any]         =       state_dict[f"""cls.predictions.transform.LayerNorm.{w}"""]

  print(f"""N layers selected for distillation: {std_idx}""")
  print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

  print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
  torch.save(compressed_sd, args.dump_checkpoint)





'''simple docstring'''





from __future__ import annotations

from typing import Any

class   _a							:






     def __init__(					self   ,_SCREAMING_SNAKE_CASE       )				->     None:
          _snake_case				= num_of_nodes
          _snake_case				= []
          _snake_case				= {}






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     None:
          self.m_edges.append([u_node, v_node, weight]       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     int:

          if self.m_component[u_node] == u_node:
               return u_node
          return self.find_component(self.m_component[u_node]       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE       )				->     None:

          if self.m_component[u_node] != u_node:
               for k in self.m_component:
                    _snake_case				= self.find_component(_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )				->     None:

          if component_size[u_node] <= component_size[v_node]:
               _snake_case				= v_node
               component_size[v_node] += component_size[u_node]
               self.set_component(_SCREAMING_SNAKE_CASE       )

          elif component_size[u_node] >= component_size[v_node]:
               _snake_case				= self.find_component(_SCREAMING_SNAKE_CASE       )
               component_size[u_node] += component_size[v_node]
               self.set_component(_SCREAMING_SNAKE_CASE       )






     def 							_lowercase      (					self       )				->     None:
          _snake_case				= []
          _snake_case				= 0

          _snake_case				= [-1] * self.m_num_of_nodes

          # A list of components (initialized to all of the nodes)
          for node in range(self.m_num_of_nodes       ):
               self.m_component.update({node: node}       )
               component_size.append(1       )

          _snake_case				= self.m_num_of_nodes

          while num_of_components > 1:
               for edge in self.m_edges:
                    _snake_case				,   _snake_case				,   _snake_case				= edge

                    _snake_case				= self.m_component[u]
                    _snake_case				= self.m_component[v]

                    if u_component != v_component:

                         for component in (u_component, v_component):
                              if (
                                  minimum_weight_edge[component] == -1
                                  or minimum_weight_edge[component][2] > w
                              ):
                                   _snake_case				= [u, v, w]

               for edge in minimum_weight_edge:
                    if isinstance(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       ):
                         _snake_case				,   _snake_case				,   _snake_case				= edge

                         _snake_case				= self.m_component[u]
                         _snake_case				= self.m_component[v]

                         if u_component != v_component:
                              mst_weight += w
                              self.union(_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE   ,_SCREAMING_SNAKE_CASE       )
                              print(f"""Added edge [{u} - {v}]\nAdded weight: {w}\n"""       )
                              num_of_components -= 1

               _snake_case				= [-1] * self.m_num_of_nodes
          print(f"""The total weight of the minimal spanning tree is: {mst_weight}"""       )







def       __a      (  )		->					None:

     """simple docstring"""






if __name__ == "__main__":
   import doctest

   doctest.testmod()


