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


__snake_case			      =    collections.namedtuple(
    '''CheckpointState''', ['''model_dict''', '''optimizer_dict''', '''scheduler_dict''', '''offset''', '''epoch''', '''encoder_params''']
)





def   lowerCAmelCase_ (				__lowerCAmelCase						)->  CheckpointState:






      '''simple docstring'''





      print(f'''Reading saved model from {model_file}'''						)
      UpperCAmelCase    : int      =torch.load(__lowerCAmelCase		,					map_location=lambda __lowerCAmelCase		,					__lowerCAmelCase						: default_restore_location(__lowerCAmelCase		,					'''cpu'''						)						)
      return CheckpointState(**__lowerCAmelCase						)







class 			__snake_case   :
 def __init__(  self		,   snake_case__						)	->							Dict:
       '''simple docstring'''





       UpperCAmelCase    : Optional[int]      =src_file
 def 	UpperCAmelCase__       (  self						)	->							Optional[Any]:
       '''simple docstring'''





       raise NotImplementedError






 @staticmethod
 def 	UpperCAmelCase__       (  snake_case__		,   *snake_case__		,   **snake_case__						)	->							"DPRState":
       '''simple docstring'''





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







class 			__snake_case   (     lowerCamelCase__   ):
 def 	UpperCAmelCase__       (  self						)	->							str:
       '''simple docstring'''





       UpperCAmelCase    : List[str]      =DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''						)[0]						)						)
       print(f'''Loading DPR biencoder from {self.src_file}'''						)
       UpperCAmelCase    : Dict      =load_states_from_checkpoint(self.src_file						)
       UpperCAmelCase  ,						UpperCAmelCase    : Any      =model.ctx_encoder, '''ctx_model.'''
       # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
       UpperCAmelCase    : List[Any]      ={'''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(snake_case__						):
                   UpperCAmelCase    : Optional[Any]      =key[len(snake_case__						) :]
                   if not key.startswith('''encode_proj.'''						):
                         UpperCAmelCase    : Optional[Any]      ='''bert_model.''' + key
                   UpperCAmelCase    : Dict      =value
       encoder.load_state_dict(snake_case__						)
       return model







class 			__snake_case   (     lowerCamelCase__   ):
 def 	UpperCAmelCase__       (  self						)	->							Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase    : Any      =DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''						)[0]						)						)
       print(f'''Loading DPR biencoder from {self.src_file}'''						)
       UpperCAmelCase    : Dict      =load_states_from_checkpoint(self.src_file						)
       UpperCAmelCase  ,						UpperCAmelCase    : Optional[int]      =model.question_encoder, '''question_model.'''
       # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
       UpperCAmelCase    : str      ={'''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(snake_case__						):
                   UpperCAmelCase    : List[str]      =key[len(snake_case__						) :]
                   if not key.startswith('''encode_proj.'''						):
                         UpperCAmelCase    : List[Any]      ='''bert_model.''' + key
                   UpperCAmelCase    : str      =value
       encoder.load_state_dict(snake_case__						)
       return model







class 			__snake_case   (     lowerCamelCase__   ):
 def 	UpperCAmelCase__       (  self						)	->							Optional[int]:
       '''simple docstring'''





       UpperCAmelCase    : Dict      =DPRReader(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''						)[0]						)						)
       print(f'''Loading DPR reader from {self.src_file}'''						)
       UpperCAmelCase    : str      =load_states_from_checkpoint(self.src_file						)
       # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
       UpperCAmelCase    : Any      ={
           '''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    : Any      ='''encoder.bert_model.''' + key[len('''encoder.'''						) :]
             UpperCAmelCase    : Dict      =value
       model.span_predictor.load_state_dict(snake_case__						)
       return model





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase		,					__lowerCAmelCase						)->  List[str]:






      '''simple docstring'''





      UpperCAmelCase    : Dict      =Path(__lowerCAmelCase						)
      dest_dir.mkdir(exist_ok=__lowerCAmelCase						)

      UpperCAmelCase    : Any      =DPRState.from_type(__lowerCAmelCase		,					src_file=__lowerCAmelCase						)
      UpperCAmelCase    : Optional[Any]      =dpr_state.load_dpr_model()
      model.save_pretrained(__lowerCAmelCase						)
      model.from_pretrained(__lowerCAmelCase						)  # sanity check


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

     __snake_case			      =    Path(args.src)
     __snake_case			      =    f'converted-{src_file.name}' if args.dest is None else args.dest
     __snake_case			      =    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)

__snake_case			      =    [[0, 1, 1], [0, 0, 1], [1, 0, 0]]







class 			__snake_case   :
 def __init__(  self		,   snake_case__						)	->							Any:
       '''simple docstring'''





       UpperCAmelCase    : List[Any]      =name
       UpperCAmelCase    : Tuple      =[]
       UpperCAmelCase    : str      =[]
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							int:
       '''simple docstring'''





       self.inbound.append(snake_case__						)
 def 	UpperCAmelCase__       (  self		,   snake_case__						)	->							Dict:
       '''simple docstring'''





       self.outbound.append(snake_case__						)






 def __repr__(  self						)	->							Union[str, Any]:
       '''simple docstring'''





       return f'''<node={self.name} inbound={self.inbound} outbound={self.outbound}>'''





def   lowerCAmelCase_ (				__lowerCAmelCase		,					__lowerCAmelCase=3		,					__lowerCAmelCase=0.85						)->  Any:






      '''simple docstring'''





      UpperCAmelCase    : List[Any]      ={}
      for node in nodes:
            UpperCAmelCase    : str      =1

      UpperCAmelCase    : str      ={}
      for node in nodes:
            UpperCAmelCase    : int      =len(node.outbound						)

      for i in range(__lowerCAmelCase						):
            print(f'''======= Iteration {i + 1} ======='''						)
            for _, node in enumerate(__lowerCAmelCase						):
                  UpperCAmelCase    : Optional[int]      =(1 - d) + d * sum(
                      ranks[ib] / outbounds[ib] for ib in node.inbound						)
            print(__lowerCAmelCase						)





def   lowerCAmelCase_ (				)->  Union[str, Any]:






      '''simple docstring'''





      UpperCAmelCase    : Union[str, Any]      =list(input('''Enter Names of the Nodes: '''						).split()						)

      UpperCAmelCase    : str      =[Node(__lowerCAmelCase						) for name in names]

      for ri, row in enumerate(__lowerCAmelCase						):
            for ci, col in enumerate(__lowerCAmelCase						):
                  if col == 1:
                        nodes[ci].add_inbound(names[ri]						)
                        nodes[ri].add_outbound(names[ci]						)

      print('''======= Nodes ======='''						)
      for node in nodes:
            print(__lowerCAmelCase						)

      page_rank(__lowerCAmelCase						)


if __name__ == "__main__":
     main()
