





"""simple docstring"""





import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


_lowerCamelCase  					=     logging.getLogger(__name__)





class       snake_case		:

      def __init__(      self       :Any						):
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	False

      def 						SCREAMING_SNAKE_CASE_      (      self       :List[str]						,							_lowerCamelCase       :Optional[Any]						,							_lowerCamelCase       :Union[str, Any]						,							_lowerCamelCase       :List[str]						,							_lowerCamelCase       :Optional[int]						):
       if not self.initialized:
        __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	RagRetriever(
            _lowerCamelCase						,							question_encoder_tokenizer=_lowerCamelCase						,							generator_tokenizer=_lowerCamelCase						,							index=_lowerCamelCase						,							init_retrieval=_lowerCamelCase						,							)
        __SCREAMING_SNAKE_CASE			:      List[str] 							=	True

      def 						SCREAMING_SNAKE_CASE_      (      self       :int						):
       self.retriever.index.init_index()

      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[int]						):
       # delete the old self.retriever object before assigning the new index
       del self.retriever
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	False



      def 						SCREAMING_SNAKE_CASE_      (      self       :str						,							_lowerCamelCase       :Optional[int]						,							_lowerCamelCase       :Optional[Any]						):
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	self.retriever._main_retrieve(_lowerCamelCase						,							_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	self.retriever.index.get_doc_dicts(_lowerCamelCase						)
       return doc_ids, retrieved_doc_embeds, doc_dicts







class       snake_case		(       __UpperCAmelCase       ):

      def __init__(      self       :List[str]						,							_lowerCamelCase       :List[str]						,							_lowerCamelCase       :List[str]						,							_lowerCamelCase       :List[Any]						,							_lowerCamelCase       :Optional[int]						,							_lowerCamelCase       :List[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						,							)

       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	retrieval_workers
       __SCREAMING_SNAKE_CASE			:      int 							=	question_encoder_tokenizer
       __SCREAMING_SNAKE_CASE			:      List[str] 							=	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 						SCREAMING_SNAKE_CASE_      (      self       :str						):
       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 						SCREAMING_SNAKE_CASE_      (      self       :Union[str, Any]						,							_lowerCamelCase       :str						,							_lowerCamelCase       :Union[str, Any]						):
       if len(self.retrieval_workers						) > 0:
        # Select a random retrieval actor.
        __SCREAMING_SNAKE_CASE			:      List[Any] 							=	self.retrieval_workers[random.randint(0						,							len(self.retrieval_workers						) - 1						)]
        __SCREAMING_SNAKE_CASE			:      Any 							=	ray.get(
            random_worker.retrieve.remote(_lowerCamelCase						,							_lowerCamelCase						)						)
       else:
        __SCREAMING_SNAKE_CASE			:      int 							=	self._main_retrieve(_lowerCamelCase						,							_lowerCamelCase						)
        __SCREAMING_SNAKE_CASE			:      Any 							=	self.index.get_doc_dicts(_lowerCamelCase						)
       return retrieved_doc_embeds, doc_ids, doc_dicts

      @classmethod
      def 						SCREAMING_SNAKE_CASE_      (      cls       :Optional[int]						,							_lowerCamelCase       :Optional[int]						,							_lowerCamelCase       :Union[str, Any]=None						,							**_lowerCamelCase       :str						):
       return super(_lowerCamelCase						,							cls						).get_tokenizers(_lowerCamelCase						,							_lowerCamelCase						,							**_lowerCamelCase						)

      @classmethod
      def 						SCREAMING_SNAKE_CASE_      (      cls       :Optional[int]						,							_lowerCamelCase       :str						,							_lowerCamelCase       :Tuple						,							_lowerCamelCase       :List[Any]=None						,							**_lowerCamelCase       :List[Any]						):
       __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	kwargs.pop('''config'''						,							_lowerCamelCase						) or RagConfig.from_pretrained(_lowerCamelCase						,							**_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	RagTokenizer.from_pretrained(_lowerCamelCase						,							config=_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	rag_tokenizer.question_encoder
       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	rag_tokenizer.generator

       if indexed_dataset is not None:
        __SCREAMING_SNAKE_CASE			:      Dict 							=	'''custom'''
        __SCREAMING_SNAKE_CASE			:      List[Any] 							=	CustomHFIndex(config.retrieval_vector_size						,							_lowerCamelCase						)
       else:
        __SCREAMING_SNAKE_CASE			:      str 							=	cls._build_index(_lowerCamelCase						)

       return cls(
           _lowerCamelCase						,							question_encoder_tokenizer=_lowerCamelCase						,							generator_tokenizer=_lowerCamelCase						,							retrieval_workers=_lowerCamelCase						,							index=_lowerCamelCase						,							)



      def 						SCREAMING_SNAKE_CASE_      (      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
       __SCREAMING_SNAKE_CASE			:      int 							=	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
           ]						)








"""simple docstring"""





def 		lowerCAmelCase_     (  lowercase_       :							int = 30		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      str 							=	exponent + 2
 __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	(1 + 5**0.5) / 2
 __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5

 return int(lowercase_		)


if __name__ == "__main__":
    print(f'{solution() = }')

