





'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__SCREAMING_SNAKE_CASE     :Union[str, Any]       =				logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     :Tuple       =				{'''vocab_file''': '''sentencepiece.model'''}

__SCREAMING_SNAKE_CASE     :Union[str, Any]       =				{
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
}

__SCREAMING_SNAKE_CASE     :List[Any]       =				{
    '''google/rembert''': 256,
}







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Optional[Any]           =							VOCAB_FILES_NAMES
 _lowerCamelCase  : Union[str, Any]           =							PRETRAINED_VOCAB_FILES_MAP
 _lowerCamelCase  : Any           =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




 def __init__(					self       :    List[str]	,			snake_case_       :    Tuple	,			snake_case_       :    Any=False	,			snake_case_       :    Tuple=True	,			snake_case_       :    Tuple=True	,			snake_case_       :    int="[CLS]"	,			snake_case_       :    Optional[Any]="[SEP]"	,			snake_case_       :    int="[UNK]"	,			snake_case_       :    Optional[int]="[SEP]"	,			snake_case_       :    Any="[PAD]"	,			snake_case_       :    Any="[CLS]"	,			snake_case_       :    int="[MASK]"	,			**snake_case_       :    Optional[int]	,			):
      super().__init__(
          do_lower_case=snake_case_	,			remove_space=snake_case_	,			keep_accents=snake_case_	,			bos_token=snake_case_	,			eos_token=snake_case_	,			unk_token=snake_case_	,			sep_token=snake_case_	,			pad_token=snake_case_	,			cls_token=snake_case_	,			mask_token=snake_case_	,			**snake_case_	,			)

      _UpperCAmelCase     							=   do_lower_case
      _UpperCAmelCase     							=   remove_space
      _UpperCAmelCase     							=   keep_accents
      _UpperCAmelCase     							=   vocab_file

      _UpperCAmelCase     							=   spm.SentencePieceProcessor()
      self.sp_model.Load(snake_case_    )




 @property
 def      lowercase	(					self       :    Union[str, Any]    ):
      return len(self.sp_model    )




 def      lowercase	(					self       :    Union[str, Any]    ):
      _UpperCAmelCase     							=   {self.convert_ids_to_tokens(snake_case_    ): i for i in range(self.vocab_size    )}
      vocab.update(self.added_tokens_encoder    )
      return vocab




 def __getstate__(					self       :    Tuple    ):
      _UpperCAmelCase     							=   self.__dict__.copy()
      _UpperCAmelCase     							=   None
      return state




 def __setstate__(					self       :    Tuple	,			snake_case_       :    Dict    ):
      _UpperCAmelCase     							=   d
      _UpperCAmelCase     							=   spm.SentencePieceProcessor()
      self.sp_model.Load(self.vocab_file    )




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    Union[str, Any]	,			snake_case_       :    str=False    ):
      _UpperCAmelCase     							=   self.sp_model.EncodeAsPieces(snake_case_    )
      return pieces




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    Optional[Any]    ):
      return self.sp_model.PieceToId(snake_case_    )




 def      lowercase	(					self       :    Dict	,			snake_case_       :    Tuple    ):
      return self.sp_model.IdToPiece(snake_case_    )




 def      lowercase	(					self       :    Dict	,			snake_case_       :    Union[str, Any]    ):
      _UpperCAmelCase     							=   self.sp_model.decode_pieces(snake_case_    )
      return out_string




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    List[int]	,			snake_case_       :    Optional[List[int]] = None    ):
      _UpperCAmelCase     							=   [self.sep_token_id]
      _UpperCAmelCase     							=   [self.cls_token_id]
      if token_ids_a is None:
           return cls + token_ids_a + sep
      return cls + token_ids_a + sep + token_ids_a + sep




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    List[int]	,			snake_case_       :    Optional[List[int]] = None	,			snake_case_       :    bool = False    ):

      if already_has_special_tokens:
           if token_ids_a is not None:
                raise ValueError(
                    "You should not supply a second sequence if the provided sequence of "
                    "ids is already formatted with special tokens for the model."    )
           return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

      if token_ids_a is not None:
           return [1] + ([0] * len(snake_case_    )) + [1] + ([0] * len(snake_case_    )) + [1]
      return [1] + ([0] * len(snake_case_    )) + [1]




 def      lowercase	(					self       :    Tuple	,			snake_case_       :    List[int]	,			snake_case_       :    Optional[List[int]] = None    ):
      _UpperCAmelCase     							=   [self.sep_token_id]
      _UpperCAmelCase     							=   [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    ) * [0] + len(token_ids_a + sep    ) * [1]




 def      lowercase	(					self       :    Tuple	,			snake_case_       :    str	,			snake_case_       :    Optional[str] = None    ):
      if not os.path.isdir(snake_case_    ):
           logger.error("Vocabulary path ({}) should be a directory".format(snake_case_    )    )
           return
      _UpperCAmelCase     							=   os.path.join(
          snake_case_	,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )

      if os.path.abspath(self.vocab_file    ) != os.path.abspath(snake_case_    ):
           copyfile(self.vocab_file	,			snake_case_    )

      return (out_vocab_file,)









'''simple docstring'''





import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = None       ,_lowerCAmelCase = None       ,_lowerCAmelCase = None       ,)      ->      Optional[int]:
  if config_name_or_path is None:
    __lowerCamelCase       :       List[Any]           = 'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

  if generator_tokenizer_name_or_path is None:
    __lowerCamelCase       :       Optional[int]           = generator_name_or_path

  if question_encoder_tokenizer_name_or_path is None:
    __lowerCamelCase       :       Tuple           = question_encoder_name_or_path

  __lowerCamelCase       :       Union[str, Any]           = RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

  # Save model.
  __lowerCamelCase       :       Tuple           = RagConfig.from_pretrained(_lowerCAmelCase       )
  __lowerCamelCase       :       List[Any]           = AutoConfig.from_pretrained(_lowerCAmelCase       )
  __lowerCamelCase       :       Union[str, Any]           = AutoConfig.from_pretrained(_lowerCAmelCase       )

  __lowerCamelCase       :       Tuple           = gen_config
  __lowerCamelCase       :       List[Any]           = question_encoder_config

  __lowerCamelCase       :       str           = model_class.from_pretrained_question_encoder_generator(
      _lowerCAmelCase       ,_lowerCAmelCase       ,config=_lowerCAmelCase       )
  rag_model.save_pretrained(_lowerCAmelCase       )

  # Sanity check.
  model_class.from_pretrained(_lowerCAmelCase       )

  # Save tokenizers.
  __lowerCamelCase       :       List[str]           = AutoTokenizer.from_pretrained(_lowerCAmelCase       )
  gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'       )
  __lowerCamelCase       :       Optional[int]           = AutoTokenizer.from_pretrained(_lowerCAmelCase       )
  question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/'       )


if __name__ == "__main__":
   _UpperCamelCase											=						argparse.ArgumentParser()
   parser.add_argument(
       '--model_type',
       choices=['rag_sequence', 'rag_token'],
       required=True,
       type=str,
       help='RAG model type: rag_sequence, rag_token',
   )
   parser.add_argument('--dest', type=str, required=True, help='Path to the output checkpoint directory.')
   parser.add_argument('--generator_name_or_path', type=str, required=True, help='Generator model identifier')
   parser.add_argument(
       '--question_encoder_name_or_path', type=str, required=True, help='Question encoder model identifier'
   )

   parser.add_argument(
       '--generator_tokenizer_name_or_path',
       type=str,
       help='Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``',
   )
   parser.add_argument(
       '--question_encoder_tokenizer_name_or_path',
       type=str,
       help='Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``',
   )
   parser.add_argument(
       '--config_name_or_path',
       type=str,
       help=(
           'Identifier of the model config to use, if not provided, resolves to a base config for a given'
           ' ``model_type``'
       ),
   )

   _UpperCamelCase											=						parser.parse_args()

   _UpperCamelCase											=						Path(args.dest)
   dest_dir.mkdir(exist_ok=True)

   consolidate(
       args.model_type,
       args.generator_name_or_path,
       args.question_encoder_name_or_path,
       dest_dir,
       args.config_name_or_path,
       args.generator_tokenizer_name_or_path,
       args.question_encoder_tokenizer_name_or_path,
   )






