




"""simple docstring"""






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

import sentencepiece as spm

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


lowercase__     						=						logging.get_logger(__name__)


lowercase__     						=						'▁'

lowercase__     						=						{'vocab_file': 'spiece.model'}

lowercase__     						=						{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    }
}

lowercase__     						=						{
    'google/reformer-crime-and-punishment': 52_4288,
}






class lowerCAmelCase__	(     lowerCAmelCase__						):






			'''simple docstring'''



			lowerCamelCase__      =			VOCAB_FILES_NAMES
			lowerCamelCase__      =			PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__      =			["""input_ids""", """attention_mask"""]
			def __init__(  self  ,      lowercase  ,      lowercase="</s>"  ,      lowercase="<unk>"  ,      lowercase=[]  ,      lowercase = None  ,      **lowercase  ,      ):
							_lowerCamelCase     :			List[Any]    =    {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    eos_token=UpperCamelCase__  ,      unk_token=UpperCamelCase__  ,      additional_special_tokens=UpperCamelCase__  ,      sp_model_kwargs=self.sp_model_kwargs  ,      **UpperCamelCase__  ,      )

							_lowerCamelCase     :			str    =    vocab_file
							_lowerCamelCase     :			List[Any]    =    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
							self.sp_model.Load(UpperCamelCase__  )
			@property
			def 					A_    (  self  ):
							return self.sp_model.get_piece_size()
			def 					A_    (  self  ):
							_lowerCamelCase     :			Optional[Any]    =    {self.convert_ids_to_tokens(UpperCamelCase__  ): i for i in range(self.vocab_size  )}
							vocab.update(self.added_tokens_encoder  )
							return vocab
			def __getstate__(  self  ):
							_lowerCamelCase     :			Optional[int]    =    self.__dict__.copy()
							_lowerCamelCase     :			Dict    =    None
							return state
			def __setstate__(  self  ,      lowercase  ):
							_lowerCamelCase     :			Any    =    d

							# for backward compatibility
							if not hasattr(self  ,      'sp_model_kwargs'  ):
											_lowerCamelCase     :			Any    =    {}

							_lowerCamelCase     :			int    =    spm.SentencePieceProcessor(**self.sp_model_kwargs  )
							self.sp_model.Load(self.vocab_file  )
			def 					A_    (  self  ,      lowercase  ):
							return self.sp_model.encode(UpperCamelCase__  ,      out_type=UpperCamelCase__  )
			def 					A_    (  self  ,      lowercase  ):
							return self.sp_model.piece_to_id(UpperCamelCase__  )
			def 					A_    (  self  ,      lowercase  ):
							if index < self.sp_model.get_piece_size():
											_lowerCamelCase     :			Dict    =    self.sp_model.IdToPiece(UpperCamelCase__  )
							return token
			def 					A_    (  self  ,      lowercase  ):
							_lowerCamelCase     :			Any    =    []
							_lowerCamelCase     :			str    =    ""
							for token in tokens:
											# make sure that special tokens are not decoded using sentencepiece model
											if token in self.all_special_tokens:
															out_string += self.sp_model.decode(UpperCamelCase__  ) + token
															_lowerCamelCase     :			Union[str, Any]    =    []
											else:
															current_sub_tokens.append(UpperCamelCase__  )
							out_string += self.sp_model.decode(UpperCamelCase__  )
							return out_string.strip()






			def 					A_    (  self  ,      lowercase  ,      lowercase = None  ):
							if not os.path.isdir(UpperCamelCase__  ):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''  )
											return
							_lowerCamelCase     :			Optional[int]    =    os.path.join(
							    UpperCamelCase__  ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

							if os.path.abspath(self.vocab_file  ) != os.path.abspath(UpperCamelCase__  ) and os.path.isfile(self.vocab_file  ):
											copyfile(self.vocab_file  ,      UpperCamelCase__  )
							elif not os.path.isfile(self.vocab_file  ):
											with open(UpperCamelCase__  ,      'wb'  ) as fi:
															_lowerCamelCase     :			Tuple    =    self.sp_model.serialized_model_proto()
															fi.write(UpperCamelCase__  )

							return (out_vocab_file,)





import argparse
from pathlib import Path

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

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = None ,)	-> List[str]:
       if config_name_or_path is None:
              lowerCamelCase   :      Any			 =					"facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

       if generator_tokenizer_name_or_path is None:
              lowerCamelCase   :      Dict			 =					generator_name_or_path

       if question_encoder_tokenizer_name_or_path is None:
              lowerCamelCase   :      Any			 =					question_encoder_name_or_path

       lowerCamelCase   :      str			 =					RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration

       # Save model.
       lowerCamelCase   :      List[Any]			 =					RagConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Union[str, Any]			 =					AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Optional[int]			 =					AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )

       lowerCamelCase   :      Optional[Any]			 =					gen_config
       lowerCamelCase   :      Optional[Any]			 =					question_encoder_config

       lowerCamelCase   :      List[Any]			 =					model_class.from_pretrained_question_encoder_generator(
           _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,config=_SCREAMING_SNAKE_CASE    )
       rag_model.save_pretrained(_SCREAMING_SNAKE_CASE    )

       # Sanity check.
       model_class.from_pretrained(_SCREAMING_SNAKE_CASE    )

       # Save tokenizers.
       lowerCamelCase   :      List[str]			 =					AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
       gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"    )
       lowerCamelCase   :      int			 =					AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE    )
       question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Any  =   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``'
           ),
       )

       SCREAMING_SNAKE_CASE__       :      Union[str, Any]  =   parser.parse_args()

       SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   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,
       )






