

import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
       snake_case_			  = 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''')
       snake_case_			  = parser.parse_args()

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

       snake_case_			  = model.state_dict()
       snake_case_			  = {}

       for w in ["word_embeddings", "position_embeddings"]:
              snake_case_			  = state_dict[F'''{prefix}.embeddings.{w}.weight''']
       for w in ["weight", "bias"]:
              snake_case_			  = state_dict[F'''{prefix}.embeddings.LayerNorm.{w}''']

       snake_case_			  = 0
       for teacher_idx in [0, 2, 4, 7, 9, 11]:
              for w in ["weight", "bias"]:
                     snake_case_			  = state_dict[
                         F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
                     ]
                     snake_case_			  = state_dict[
                         F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
                     ]
                     snake_case_			  = state_dict[
                         F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
                     ]

                     snake_case_			  = state_dict[
                         F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
                     ]
                     snake_case_			  = state_dict[
                         F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
                     ]

                     snake_case_			  = state_dict[
                         F'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
                     ]
                     snake_case_			  = state_dict[
                         F'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
                     ]
                     snake_case_			  = state_dict[
                         F'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
                     ]
              std_idx += 1

       snake_case_			  = state_dict['cls.predictions.decoder.weight']
       snake_case_			  = state_dict['cls.predictions.bias']
       if args.vocab_transform:
              for w in ["weight", "bias"]:
                     snake_case_			  = state_dict[F'''cls.predictions.transform.dense.{w}''']
                     snake_case_			  = 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)








from __future__ import annotations



def _a				(	lowerCamelCase:   list[float]					,				lowerCamelCase:   Tuple )	->						List[str]:







      '''simple docstring'''






      print(F"""Vertex\tShortest Distance from vertex {src}""" )
      for i, d in enumerate(lowerCamelCase ):
            print(F"""{i}\t\t{d}""" )



def _a				(	lowerCamelCase:   list[dict[str, int]]					,				lowerCamelCase:   list[float]					,				lowerCamelCase:   int )	->						Union[str, Any]:







      '''simple docstring'''






      for j in range(lowerCamelCase ):
            __A   ,						__A   ,						__A          =				(graph[j][k] for k in ['''src''', '''dst''', '''weight'''])
            if distance[u] != float('''inf''' ) and distance[u] + w < distance[v]:
                  return True
      return False



def _a				(	lowerCamelCase:   list[dict[str, int]]					,				lowerCamelCase:   int					,				lowerCamelCase:   int					,				lowerCamelCase:   int )	->						list[float]:







      '''simple docstring'''






      __A          =				[float('''inf''' )] * vertex_count
      __A          =				0.0

      for _ in range(vertex_count - 1 ):
            for j in range(lowerCamelCase ):
                  __A   ,						__A   ,						__A          =				(graph[j][k] for k in ['''src''', '''dst''', '''weight'''])

                  if distance[u] != float('''inf''' ) and distance[u] + w < distance[v]:
                        __A          =				distance[u] + w

      __A          =				check_negative_cycle(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )
      if negative_cycle_exists:
            raise Exception('''Negative cycle found''' )

      return distance


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       snake_case__					:							Dict													=     int(input('Enter number of vertices: ').strip())
       snake_case__					:							Optional[int]													=     int(input('Enter number of edges: ').strip())

       snake_case__					:							list[dict[str, int]]													=     [{} for _ in range(E)]

       for i in range(E):
              print('Edge ', i + 1)
              snake_case__						,    snake_case__						,    snake_case__					:							Dict													=     (
                  int(x)
                  for x in input('Enter source, destination, weight: ').strip().split(' ')
              )
              snake_case__					:							List[Any]													=     {'src': src, 'dst': dest, 'weight': weight}

       snake_case__					:							Union[str, Any]													=     int(input('\nEnter shortest path source:').strip())
       snake_case__					:							List[Any]													=     bellman_ford(graph, V, E, source)
       print_distance(shortest_distance, 0)





