
"""simple docstring"""

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


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




def 			lowercase__  (    lowerCAmelCase :    str       )       ->					CheckpointState:






    """simple docstring"""




    print(F"Reading saved model from {model_file}"       )
    UpperCAmelCase  	=    torch.load(lowerCAmelCase       , map_location=lambda lowerCAmelCase       , lowerCAmelCase       : default_restore_location(lowerCAmelCase       , 'cpu'       )       )
    return CheckpointState(**lowerCAmelCase       )
class    _UpperCAmelCase							:




      def __init__(							self      ,				lowercase_							)  ->    int:
          UpperCAmelCase  	=    src_file




      def 			a_					(							self							)  ->    Dict:
          raise NotImplementedError




      @staticmethod
      def 			a_					(							lowercase_      ,				*lowercase_      ,				**lowercase_							)  ->    Optional[Any]:





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

class    _UpperCAmelCase							(						UpperCAmelCase_							):




      def 			a_					(							self							)  ->    Dict:
          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  	=    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(lowercase_							):
                  UpperCAmelCase  	=    key[len(lowercase_							) :]
                  if not key.startswith('encode_proj.'							):
                      UpperCAmelCase  	=    'bert_model.' + key
                  UpperCAmelCase  	=    value
          encoder.load_state_dict(lowercase_							)
          return model

class    _UpperCAmelCase							(						UpperCAmelCase_							):




      def 			a_					(							self							)  ->    Optional[int]:
          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  	=    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(lowercase_							):
                  UpperCAmelCase  	=    key[len(lowercase_							) :]
                  if not key.startswith('encode_proj.'							):
                      UpperCAmelCase  	=    'bert_model.' + key
                  UpperCAmelCase  	=    value
          encoder.load_state_dict(lowercase_							)
          return model

class    _UpperCAmelCase							(						UpperCAmelCase_							):




      def 			a_					(							self							)  ->    Any:
          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(lowercase_							)
          return model




def 			lowercase__  (    lowerCAmelCase :    str       , lowerCAmelCase :    Path       , lowerCAmelCase :    Path       )       ->					int:






    """simple docstring"""




    UpperCAmelCase  	=    Path(lowerCAmelCase       )
    dest_dir.mkdir(exist_ok=lowerCAmelCase       )

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


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

   SCREAMING_SNAKE_CASE_			    =  Path(args.src)
   SCREAMING_SNAKE_CASE_			    =  F'converted-{src_file.name}' if args.dest is None else args.dest
   SCREAMING_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)










def 			lowerCamelCase_		(					lowerCAmelCase:		str			,							lowerCAmelCase:		str )->						int:
  # The longer word should come first
  if len(lowerCAmelCase ) < len(lowerCAmelCase ):
    return levenshtein_distance(lowerCAmelCase			,							lowerCAmelCase )

  if len(lowerCAmelCase ) == 0:
    return len(lowerCAmelCase )

  _snake_case      : Dict														=		list(range(len(lowerCAmelCase ) + 1 ) )

  for i, ca in enumerate(lowerCAmelCase ):
    _snake_case      : Optional[int]														=		[i + 1]

    for j, ca in enumerate(lowerCAmelCase ):
      # Calculate insertions, deletions and substitutions
      _snake_case      : Optional[Any]														=		previous_row[j + 1] + 1
      _snake_case      : Optional[int]														=		current_row[j] + 1
      _snake_case      : Union[str, Any]														=		previous_row[j] + (ca != ca)

      # Get the minimum to append to the current row
      current_row.append(min(lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase ) )

    # Store the previous row
    _snake_case      : str														=		current_row

  # Returns the last element (distance)
  return previous_row[-1]


if __name__ == "__main__":
      lowerCAmelCase_        =			input("""Enter the first word:\n""").strip()
      lowerCAmelCase_        =			input("""Enter the second word:\n""").strip()

      lowerCAmelCase_        =			levenshtein_distance(first_word, second_word)
      print(F"""Levenshtein distance between {first_word} and {second_word} is {result}""")




