

import argparse
import collections
from pathlib import Path

import torch
from torch.serialization import default_restore_location

from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader


_UpperCamelCase     =    collections.namedtuple(
    '''CheckpointState''', ['''model_dict''', '''optimizer_dict''', '''scheduler_dict''', '''offset''', '''epoch''', '''encoder_params''']
)
def 		UpperCamelCase_(    snake_case__:						str     ) ->				CheckpointState:
 print(f"Reading saved model from {model_file}"     )
 UpperCAmelCase__           =       torch.load(snake_case__						,					map_location=lambda snake_case__						,					snake_case__     : default_restore_location(snake_case__						,					'cpu'     )     )
 return CheckpointState(**snake_case__     )



class 					lowercase		:



 '''simple docstring'''


 def __init__(self , __a				)				->      int:






  """simple docstring"""





  UpperCAmelCase__           =       src_file




 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  raise NotImplementedError




 @staticmethod
 def 						UpperCamelCase__							(__a , *__a , **__a				)				->      "DPRState":






  """simple docstring"""





  if comp_type.startswith('c'				):
   return DPRContextEncoderState(*__a , **__a				)
  if comp_type.startswith('q'				):
   return DPRQuestionEncoderState(*__a , **__a				)
  if comp_type.startswith('r'				):
   return DPRReaderState(*__a , **__a				)
  else:
   raise ValueError('Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'				)







class 					lowercase		( _UpperCamelCase      ):



 '''simple docstring'''


 def 						UpperCamelCase__							(self				)				->      Dict:






  """simple docstring"""





  UpperCAmelCase__           =       DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'				)[0]				)				)
  print(F"Loading DPR biencoder from {self.src_file}"				)
  UpperCAmelCase__           =       load_states_from_checkpoint(self.src_file				)
  UpperCAmelCase__       ,					UpperCAmelCase__           =       model.ctx_encoder, 'ctx_model.'
  # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
  UpperCAmelCase__           =       {'bert_model.embeddings.position_ids': model.ctx_encoder.bert_model.embeddings.position_ids}
  for key, value in saved_state.model_dict.items():
   if key.startswith(__a				):
    UpperCAmelCase__           =       key[len(__a				) :]
    if not key.startswith('encode_proj.'				):
     UpperCAmelCase__           =       'bert_model.' + key
    UpperCAmelCase__           =       value
  encoder.load_state_dict(__a				)
  return model







class 					lowercase		( _UpperCamelCase      ):



 '''simple docstring'''


 def 						UpperCamelCase__							(self				)				->      Optional[Any]:






  """simple docstring"""





  UpperCAmelCase__           =       DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'				)[0]				)				)
  print(F"Loading DPR biencoder from {self.src_file}"				)
  UpperCAmelCase__           =       load_states_from_checkpoint(self.src_file				)
  UpperCAmelCase__       ,					UpperCAmelCase__           =       model.question_encoder, 'question_model.'
  # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
  UpperCAmelCase__           =       {'bert_model.embeddings.position_ids': model.question_encoder.bert_model.embeddings.position_ids}
  for key, value in saved_state.model_dict.items():
   if key.startswith(__a				):
    UpperCAmelCase__           =       key[len(__a				) :]
    if not key.startswith('encode_proj.'				):
     UpperCAmelCase__           =       'bert_model.' + key
    UpperCAmelCase__           =       value
  encoder.load_state_dict(__a				)
  return model







class 					lowercase		( _UpperCamelCase      ):



 '''simple docstring'''


 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  UpperCAmelCase__           =       DPRReader(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'				)[0]				)				)
  print(F"Loading DPR reader from {self.src_file}"				)
  UpperCAmelCase__           =       load_states_from_checkpoint(self.src_file				)
  # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
  UpperCAmelCase__           =       {
      'encoder.bert_model.embeddings.position_ids': model.span_predictor.encoder.bert_model.embeddings.position_ids
  }
  for key, value in saved_state.model_dict.items():
   if key.startswith('encoder.'				) and not key.startswith('encoder.encode_proj'				):
    UpperCAmelCase__           =       'encoder.bert_model.' + key[len('encoder.'				) :]
   UpperCAmelCase__           =       value
  model.span_predictor.load_state_dict(__a				)
  return model







def 		UpperCamelCase_(    snake_case__:						str						,					snake_case__:						Path						,					snake_case__:						Path     ) ->				Union[str, Any]:
 UpperCAmelCase__           =       Path(snake_case__     )
 dest_dir.mkdir(exist_ok=snake_case__     )

 UpperCAmelCase__           =       DPRState.from_type(snake_case__						,					src_file=snake_case__     )
 UpperCAmelCase__           =       dpr_state.load_dpr_model()
 model.save_pretrained(snake_case__     )
 model.from_pretrained(snake_case__     )  # sanity check


if __name__ == "__main__":
 _UpperCamelCase     =    argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--type''', type=str, help='''Type of the component to convert: \'ctx_encoder\', \'question_encoder\' or \'reader\'.'''
 )
 parser.add_argument(
     '''--src''',
     type=str,
     help=(
         '''Path to the dpr checkpoint file. They can be downloaded from the official DPR repo'''
         ''' https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the'''
         ''' \'retriever\' checkpoints.'''
     ),
 )
 parser.add_argument('''--dest''', type=str, default=None, help='''Path to the output PyTorch model directory.''')
 _UpperCamelCase     =    parser.parse_args()

 _UpperCamelCase     =    Path(args.src)
 _UpperCamelCase     =    F"""converted-{src_file.name}""" if args.dest is None else args.dest
 _UpperCamelCase     =    Path(dest_dir)
 assert src_file.exists()
 assert (
     args.type is not None
 ), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
 convert(args.type, src_file, dest_dir)






from __future__ import annotations



class 					lowercase		:



 '''simple docstring'''


 def __init__(self , __a				)				->      None:






  """simple docstring"""





  UpperCAmelCase__           =       vertices
  UpperCAmelCase__           =       [[0] * vertices for _ in range(__a				)]




 def 						UpperCamelCase__							(self , __a				)				->      None:






  """simple docstring"""





  print('Vertex \t Distance from Source'				)
  for vertex in range(self.vertices				):
   print(__a , '\t\t' , distances_from_source[vertex]				)




 def 						UpperCamelCase__							(self , __a , __a				)				->      int:






  """simple docstring"""





  UpperCAmelCase__           =       1E7
  UpperCAmelCase__           =       0

  # Search not nearest vertex not in the shortest path tree
  for vertex in range(self.vertices				):
   if distances_from_source[vertex] < minimum and visited[vertex] is False:
    UpperCAmelCase__           =       distances_from_source[vertex]
    UpperCAmelCase__           =       vertex
  return min_index




 def 						UpperCamelCase__							(self , __a				)				->      None:






  """simple docstring"""





  UpperCAmelCase__           =       [int(1E7				)] * self.vertices  # distances from the source
  UpperCAmelCase__           =       0
  UpperCAmelCase__           =       [False] * self.vertices

  for _ in range(self.vertices				):
   UpperCAmelCase__           =       self.minimum_distance(__a , __a				)
   UpperCAmelCase__           =       True

   # Update dist value of the adjacent vertices
   # of the picked vertex only if the current
   # distance is greater than new distance and
   # the vertex in not in the shortest path tree
   for v in range(self.vertices				):
    if (
        self.graph[u][v] > 0
        and visited[v] is False
        and distances[v] > distances[u] + self.graph[u][v]
    ):
     UpperCAmelCase__           =       distances[u] + self.graph[u][v]

  self.print_solution(__a				)


if __name__ == "__main__":
 _UpperCamelCase     =    Graph(9)
 _UpperCamelCase     =    [
     [0, 4, 0, 0, 0, 0, 0, 8, 0],
     [4, 0, 8, 0, 0, 0, 0, 11, 0],
     [0, 8, 0, 7, 0, 4, 0, 0, 2],
     [0, 0, 7, 0, 9, 14, 0, 0, 0],
     [0, 0, 0, 9, 0, 10, 0, 0, 0],
     [0, 0, 4, 14, 10, 0, 2, 0, 0],
     [0, 0, 0, 0, 0, 2, 0, 1, 6],
     [8, 11, 0, 0, 0, 0, 1, 0, 7],
     [0, 0, 2, 0, 0, 0, 6, 7, 0],
 ]
 graph.dijkstra(0)



