


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


__UpperCamelCase							:							Any    							=					logging.get_logger(__name__)

__UpperCamelCase							:							List[str]    							=					{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__UpperCamelCase							:							Optional[Any]    							=					{
    "vocab_file": {
        "microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt",
    },
    "tokenizer_file": {
        "microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json",
    },
}

__UpperCamelCase							:							Optional[int]    							=					{
    "microsoft/mpnet-base": 512,
}

__UpperCamelCase							:							Union[str, Any]    							=					{
    "microsoft/mpnet-base": {"do_lower_case": True},
}

class    __magic_name__						(  __lowerCAmelCase):
     A:		Any								=   VOCAB_FILES_NAMES
     A:		Optional[Any]								=   PRETRAINED_VOCAB_FILES_MAP
     A:		Dict								=   PRETRAINED_INIT_CONFIGURATION
     A:		int								=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A:		Optional[Any]								=   MPNetTokenizer
     A:		Union[str, Any]								=   ["input_ids", "attention_mask"]


     def __init__(   self				:  Tuple				,						lowerCamelCase__				:  Tuple=None				,						lowerCamelCase__				:  Any=None				,						lowerCamelCase__				:  List[str]=True				,						lowerCamelCase__				:  str="<s>"				,						lowerCamelCase__				:  Optional[Any]="</s>"				,						lowerCamelCase__				:  List[str]="</s>"				,						lowerCamelCase__				:  Optional[int]="<s>"				,						lowerCamelCase__				:  Tuple="[UNK]"				,						lowerCamelCase__				:  int="<pad>"				,						lowerCamelCase__				:  Tuple="<mask>"				,						lowerCamelCase__				:  int=True				,						lowerCamelCase__				:  str=None				,						**lowerCamelCase__				:  Optional[int]				,						)	->   List[str]:





      '''simple docstring'''






      super().__init__(
          lowerCamelCase__				,						tokenizer_file=lowerCamelCase__				,						do_lower_case=lowerCamelCase__				,						bos_token=lowerCamelCase__				,						eos_token=lowerCamelCase__				,						sep_token=lowerCamelCase__				,						cls_token=lowerCamelCase__				,						unk_token=lowerCamelCase__				,						pad_token=lowerCamelCase__				,						mask_token=lowerCamelCase__				,						tokenize_chinese_chars=lowerCamelCase__				,						strip_accents=lowerCamelCase__				,						**lowerCamelCase__				,						)

      UpperCamelCase__       :     Optional[int] 							= json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
      if (
          pre_tok_state.get('''lowercase'''				,						lowerCamelCase__      ) != do_lower_case
          or pre_tok_state.get('''strip_accents'''				,						lowerCamelCase__      ) != strip_accents
      ):
       UpperCamelCase__       :     Optional[int] 							= getattr(lowerCamelCase__				,						pre_tok_state.pop('''type'''      )      )
       UpperCamelCase__       :     Dict 							= do_lower_case
       UpperCamelCase__       :     Tuple 							= strip_accents
       UpperCamelCase__       :     str 							= pre_tok_class(**lowerCamelCase__      )

      UpperCamelCase__       :     Optional[int] 							= do_lower_case


     @property
     def   UpperCAmelCase__						(   self				:  Tuple      )	->   str:





      '''simple docstring'''






      if self._mask_token is None:
       if self.verbose:
        logger.error('''Using mask_token, but it is not set yet.'''      )
       return None
      return str(self._mask_token      )


     @mask_token.setter
     def   UpperCAmelCase__						(   self				:  Optional[int]				,						lowerCamelCase__				:  int      )	->   Any:





      '''simple docstring'''






      UpperCamelCase__       :     Optional[Any] 							= AddedToken(lowerCamelCase__				,						lstrip=lowerCamelCase__				,						rstrip=lowerCamelCase__      ) if isinstance(lowerCamelCase__				,						lowerCamelCase__      ) else value
      UpperCamelCase__       :     Optional[Any] 							= value


     def   UpperCAmelCase__						(   self				:  int				,						lowerCamelCase__				:  int				,						lowerCamelCase__				:  Optional[int]=None      )	->   List[Any]:





      '''simple docstring'''






      UpperCamelCase__       :     List[Any] 							= [self.bos_token_id] + token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
       return output

      return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


     def   UpperCAmelCase__						(   self				:  Dict				,						lowerCamelCase__				:  List[int]				,						lowerCamelCase__				:  Optional[List[int]] = None      )	->   List[int]:





      '''simple docstring'''






      UpperCamelCase__       :     Optional[int] 							= [self.sep_token_id]
      UpperCamelCase__       :     str 							= [self.cls_token_id]

      if token_ids_a is None:
       return len(cls + token_ids_a + sep      ) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]


     def   UpperCAmelCase__						(   self				:  int				,						lowerCamelCase__				:  str				,						lowerCamelCase__				:  Optional[str] = None      )	->   Tuple[str]:





      '''simple docstring'''






      UpperCamelCase__       :     Tuple 							= self._tokenizer.model.save(lowerCamelCase__				,						name=lowerCamelCase__      )
      return tuple(lowerCamelCase__      )






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							:							Tuple    							=					collections.namedtuple(
    "CheckpointState", ["model_dict", "optimizer_dict", "scheduler_dict", "offset", "epoch", "encoder_params"]
)

def       _a	(					SCREAMING_SNAKE_CASE						:					str  ):







 """simple docstring"""



 print(F"Reading saved model from {model_file}"  )
 UpperCamelCase__       :     int 							= torch.load(SCREAMING_SNAKE_CASE     ,			map_location=lambda SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  : default_restore_location(SCREAMING_SNAKE_CASE     ,			'''cpu'''  )  )
 return CheckpointState(**SCREAMING_SNAKE_CASE  )

class    __magic_name__						:


     def __init__(   self				:  str				,						lowerCamelCase__				:  Path      )	->   Tuple:





      '''simple docstring'''






      UpperCamelCase__       :     List[str] 							= src_file


     def   UpperCAmelCase__						(   self				:  Dict      )	->   int:





      '''simple docstring'''






      raise NotImplementedError


     @staticmethod
     def   UpperCAmelCase__						(   lowerCamelCase__				:  str				,						*lowerCamelCase__				:  Union[str, Any]				,						**lowerCamelCase__				:  Optional[int]      )	->   "DPRState":





      '''simple docstring'''






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



class    __magic_name__						(  __lowerCAmelCase):


     def   UpperCAmelCase__						(   self				:  List[str]      )	->   Dict:





      '''simple docstring'''






      UpperCamelCase__       :     Union[str, Any] 							= DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''      )[0]      )      )
      print(F"Loading DPR biencoder from {self.src_file}"      )
      UpperCamelCase__       :     int 							= 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__       :     Union[str, 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(lowerCamelCase__      ):
        UpperCamelCase__       :     int 							= key[len(lowerCamelCase__      ) :]
        if not key.startswith('''encode_proj.'''      ):
         UpperCamelCase__       :     Union[str, Any] 							= '''bert_model.''' + key
        UpperCamelCase__       :     Dict 							= value
      encoder.load_state_dict(lowerCamelCase__      )
      return model



class    __magic_name__						(  __lowerCAmelCase):


     def   UpperCAmelCase__						(   self				:  Any      )	->   Dict:





      '''simple docstring'''






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



class    __magic_name__						(  __lowerCAmelCase):


     def   UpperCAmelCase__						(   self				:  int      )	->   Any:





      '''simple docstring'''






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



def       _a	(					SCREAMING_SNAKE_CASE						:					str     ,			SCREAMING_SNAKE_CASE						:					Path     ,			SCREAMING_SNAKE_CASE						:					Path  ):







 """simple docstring"""



 UpperCamelCase__       :     Any 							= Path(SCREAMING_SNAKE_CASE  )
 dest_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE  )

 UpperCamelCase__       :     str 							= DPRState.from_type(SCREAMING_SNAKE_CASE     ,			src_file=SCREAMING_SNAKE_CASE  )
 UpperCamelCase__       :     str 							= dpr_state.load_dpr_model()
 model.save_pretrained(SCREAMING_SNAKE_CASE  )
 model.from_pretrained(SCREAMING_SNAKE_CASE  )  # sanity check


if __name__ == "__main__":
       __UpperCamelCase							:							Optional[Any]    							=					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							:							Union[str, Any]    							=					parser.parse_args()

       __UpperCamelCase							:							Optional[int]    							=					Path(args.src)
       __UpperCamelCase							:							Dict    							=					f"converted-{src_file.name}" if args.dest is None else args.dest
       __UpperCamelCase							:							str    							=					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)


