code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  import logging
  import random
  
  import ray
  
  from transformers import RagConfig, RagRetriever, RagTokenizer
  from transformers.models.rag.retrieval_rag import CustomHFIndex
  
  
  _lowerCamelCase				:	Tuple				    =       logging.getLogger(__name__)
  
  
  
  class 							__UpperCAmelCase  :
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self						:      Union[str, Any]      ):
     A							  =      False
  
  
  
  
  
  
  
    def        A     (self						:      Any      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      Any      ):
     if not self.initialized:
      A							  =      RagRetriever(
          _lowerCAmelCase      ,      question_encoder_tokenizer=_lowerCAmelCase      ,      generator_tokenizer=_lowerCAmelCase      ,      index=_lowerCAmelCase      ,      init_retrieval=_lowerCAmelCase      ,      )
      A							  =      True
  
  
  
  
  
  
  
    def        A     (self						:      Optional[int]      ):
     self.retriever.index.init_index()
  
  
  
  
  
  
  
    def        A     (self						:      Optional[int]      ):
     # delete the old self.retriever object before assigning the new index
     del self.retriever
     A							  =      False
  
  
  
  
  
  
  
    def        A     (self						:      List[str]      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      List[str]      ):
     A  ,    A							  =      self.retriever._main_retrieve(_lowerCAmelCase      ,      _lowerCAmelCase      )
     A							  =      self.retriever.index.get_doc_dicts(_lowerCAmelCase      )
     return doc_ids, retrieved_doc_embeds, doc_dicts
  
  
  
  
  
  
  class 							__UpperCAmelCase  (       A__						):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self						:      Tuple      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      Tuple      ,      _lowerCAmelCase						:      str=None      ):
     if index is not None and index.is_initialized() and len(_lowerCAmelCase      ) > 0:
      raise ValueError(
          """When using Ray for distributed fine-tuning, """
          """you'll need to provide the paths instead, """
          """as the dataset and the index are loaded """
          """separately. More info in examples/rag/use_own_knowledge_dataset.py """      )
  
     super().__init__(
         _lowerCAmelCase      ,      question_encoder_tokenizer=_lowerCAmelCase      ,      generator_tokenizer=_lowerCAmelCase      ,      index=_lowerCAmelCase      ,      init_retrieval=_lowerCAmelCase      ,      )
  
     A							  =      retrieval_workers
     A							  =      question_encoder_tokenizer
     A							  =      generator_tokenizer
     if len(self.retrieval_workers      ) > 0:
      ray.get(
          [
              worker.create_rag_retriever.remote(_lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      ,      _lowerCAmelCase      )
              for worker in self.retrieval_workers
          ]      )
  
  
  
  
  
  
  
    def        A     (self						:      List[Any]      ):
     logger.info("""initializing retrieval"""      )
  
     if len(self.retrieval_workers      ) > 0:
      ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]      )
     else:
      # Non-distributed training. Load index into this same process.
      self.index.init_index()
  
  
  
  
  
  
  
    def        A     (self						:      str      ,      _lowerCAmelCase						:      Dict      ,      _lowerCAmelCase						:      Tuple      ):
     if len(self.retrieval_workers      ) > 0:
      # Select a random retrieval actor.
      A							  =      self.retrieval_workers[random.randint(0      ,      len(self.retrieval_workers      ) - 1      )]
      A  ,    A  ,    A							  =      ray.get(
          random_worker.retrieve.remote(_lowerCAmelCase      ,      _lowerCAmelCase      )      )
     else:
      A  ,    A							  =      self._main_retrieve(_lowerCAmelCase      ,      _lowerCAmelCase      )
      A							  =      self.index.get_doc_dicts(_lowerCAmelCase      )
     return retrieved_doc_embeds, doc_ids, doc_dicts
  
  
  
  
  
  
  
    @classmethod
    def        A     (cls						:      Union[str, Any]      ,      _lowerCAmelCase						:      int      ,      _lowerCAmelCase						:      Optional[Any]=None      ,      **_lowerCAmelCase						:      Union[str, Any]      ):
     return super(_lowerCAmelCase      ,      cls      ).get_tokenizers(_lowerCAmelCase      ,      _lowerCAmelCase      ,      **_lowerCAmelCase      )
  
  
  
  
  
  
  
    @classmethod
    def        A     (cls						:      List[Any]      ,      _lowerCAmelCase						:      Optional[int]      ,      _lowerCAmelCase						:      List[str]      ,      _lowerCAmelCase						:      str=None      ,      **_lowerCAmelCase						:      List[str]      ):
     A							  =      kwargs.pop("""config"""      ,      _lowerCAmelCase      ) or RagConfig.from_pretrained(_lowerCAmelCase      ,      **_lowerCAmelCase      )
     A							  =      RagTokenizer.from_pretrained(_lowerCAmelCase      ,      config=_lowerCAmelCase      )
     A							  =      rag_tokenizer.question_encoder
     A							  =      rag_tokenizer.generator
  
     if indexed_dataset is not None:
      A							  =      """custom"""
      A							  =      CustomHFIndex(config.retrieval_vector_size      ,      _lowerCAmelCase      )
     else:
      A							  =      cls._build_index(_lowerCAmelCase      )
  
     return cls(
         _lowerCAmelCase      ,      question_encoder_tokenizer=_lowerCAmelCase      ,      generator_tokenizer=_lowerCAmelCase      ,      retrieval_workers=_lowerCAmelCase      ,      index=_lowerCAmelCase      ,      )
  
  
  
  
  
  
  
    def        A     (self						:      List[Any]      ):
     logger.info("""re-loading the new dataset with embeddings"""      )
     # access from the training loop
  
     ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]      )
  
     # build the index object again
     A							  =      self._build_index(self.config      )
  
     ray.get(
         [
             worker.create_rag_retriever.remote(
                 self.config      ,      self.question_encoder_tokenizer      ,      self.generator_tokenizer      ,      _lowerCAmelCase      )
             for worker in self.retrieval_workers
         ]      )
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  from typing import Dict, List, Optional, Tuple
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  _lowerCamelCase				:	int				    =       logging.get_logger(__name__)
  
  
  
  class 							__UpperCAmelCase  (       A__						):
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowerCAmelCase 				=    ['''input_ids''', '''attention_mask''']
  
  
  
  
  
  
  
  		def __init__(self						:      Tuple      ,      _lowerCAmelCase						:      str="[PAD]"      ,      _lowerCAmelCase						:      Dict="[BOS]"      ,      _lowerCAmelCase						:      Any="[EOS]"      ,      _lowerCAmelCase						:      List[Any]="[MASK]"      ,      _lowerCAmelCase						:      int="[CLS]"      ,      _lowerCAmelCase						:      Optional[Any]="[SEP]"      ,      _lowerCAmelCase						:      List[str]=2048      ,      **_lowerCAmelCase						:      Dict      ,      ):
  			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else pad_token
  			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else bos_token
  			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else eos_token
  			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else mask_token
  			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else cls_token
  			A							  =      AddedToken(_lowerCAmelCase      ,      lstrip=_lowerCAmelCase      ,      rstrip=_lowerCAmelCase      ) if isinstance(_lowerCAmelCase      ,      _lowerCAmelCase      ) else sep_token
  
  			super().__init__(
  			    pad_token=_lowerCAmelCase      ,      bos_token=_lowerCAmelCase      ,      eos_token=_lowerCAmelCase      ,      mask_token=_lowerCAmelCase      ,      cls_token=_lowerCAmelCase      ,      sep_token=_lowerCAmelCase      ,      model_max_length=_lowerCAmelCase      ,      **_lowerCAmelCase      ,      )
  
  			A							  =      2**8  # utf is 8 bits
  
  			# define special tokens dict
  			A							  =      {
  			    self.pad_token: 0,
  			    self.bos_token: 1,
  			    self.eos_token: 2,
  			    self.mask_token: 3,
  			    self.cls_token: 4,
  			    self.sep_token: 5,
  			}
  			A							  =      len(self.special_tokens_encoder      )
  			A							  =      {v: k for k, v in self.special_tokens_encoder.items()}
  
  
  
  
  
  
  
  		def        A     (self						:      Tuple      ):
  			A							  =      self.special_tokens_encoder.copy()
  			vocab.update(self.added_tokens_encoder      )
  			for i in range(self._utf_vocab_size      ):
  				A							  =      chr(_lowerCAmelCase      )
  				A							  =      i + len(self.special_tokens_encoder      )
  			return vocab
  
  
  
  
  
  
  
  		@property
  		def        A     (self						:      Tuple      ):
  			return self._utf_vocab_size + self._num_special_tokens
  
  
  
  
  
  
  
  		def        A     (self						:      Union[str, Any]      ,      _lowerCAmelCase						:      List[int]      ,      _lowerCAmelCase						:      Optional[List[int]] = None      ,      _lowerCAmelCase						:      bool = False      ):
  			if already_has_special_tokens:
  				return super().get_special_tokens_mask(
  				    token_ids_a=_lowerCAmelCase      ,      token_ids_a=_lowerCAmelCase      ,      already_has_special_tokens=_lowerCAmelCase      )
  
  			# normal case: some special tokens
  			if token_ids_a is None:
  				return [1] + [0] * len(_lowerCAmelCase      ) + [1]
  			return [1] + ([0] * len(_lowerCAmelCase      )) + [1] + ([0] * len(_lowerCAmelCase      )) + [1]
  
  
  
  
  
  
  
  		def        A     (self						:      List[Any]      ,      _lowerCAmelCase						:      List[int]      ,      _lowerCAmelCase						:      Optional[List[int]] = None      ):
  			if token_ids_a is None:
  				return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  			else:
  				return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]
  
  
  
  
  
  
  
  		def        A     (self						:      str      ,      _lowerCAmelCase						:      str      ):
  			A							  =      [chr(_lowerCAmelCase      ) for i in text.encode("""utf-8"""      )]
  			return tokens
  
  
  
  
  
  
  
  		def        A     (self						:      int      ,      _lowerCAmelCase						:      List[str]      ):
  			if token in self.special_tokens_encoder:
  				A							  =      self.special_tokens_encoder[token]
  			elif token in self.added_tokens_encoder:
  				A							  =      self.added_tokens_encoder[token]
  			elif len(_lowerCAmelCase      ) != 1:
  				A							  =      self.unk_token_id
  			else:
  				A							  =      ord(_lowerCAmelCase      ) + self._num_special_tokens
  			return token_id
  
  
  
  
  
  
  
  		def        A     (self						:      Optional[int]      ,      _lowerCAmelCase						:      Any      ):
  			if index in self.special_tokens_decoder:
  				A							  =      self.special_tokens_decoder[index]
  			elif index in self.added_tokens_decoder:
  				A							  =      self.added_tokens_decoder[index]
  			else:
  				A							  =      chr(index - self._num_special_tokens      )
  			return token
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      List[str]      ):
  			A							  =      b""""""
  			for token in tokens:
  				if token in self.special_tokens_decoder:
  					A							  =      self.special_tokens_decoder[token].encode("""utf-8"""      )
  				elif token in self.added_tokens_decoder:
  					A							  =      self.special_tokens_decoder[token].encode("""utf-8"""      )
  				elif token in self.special_tokens_encoder:
  					A							  =      token.encode("""utf-8"""      )
  				elif token in self.added_tokens_encoder:
  					A							  =      token.encode("""utf-8"""      )
  				else:
  					A							  =      bytes([ord(_lowerCAmelCase      )]      )
  				bstring += tok_string
  			A							  =      bstring.decode("""utf-8"""      ,      errors="""replace"""      )
  			return string
  
  
  
  
  
  
  
  		def        A     (self						:      Tuple      ,      _lowerCAmelCase						:      str      ,      _lowerCAmelCase						:      Optional[str] = None      ):
  			return ()
  
  