



"""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        A__:







      def __init__(					self		:     Tuple			)							->		int:






            """simple docstring"""

            __SCREAMING_SNAKE_CASE       		=   False







      def  _a   (					self		:     Optional[int]				,				__SCREAMING_SNAKE_CASE		:     str				,				__SCREAMING_SNAKE_CASE		:     Optional[Any]				,				__SCREAMING_SNAKE_CASE		:     int				,				__SCREAMING_SNAKE_CASE		:     Optional[Any]			)							->		Any:






            """simple docstring"""

            if not self.initialized:
                  __SCREAMING_SNAKE_CASE       		=   RagRetriever(
                      UpperCamelCase__				,				question_encoder_tokenizer=UpperCamelCase__				,				generator_tokenizer=UpperCamelCase__				,				index=UpperCamelCase__				,				init_retrieval=UpperCamelCase__				,				)
                  __SCREAMING_SNAKE_CASE       		=   True







      def  _a   (					self		:     Any			)							->		List[Any]:






            """simple docstring"""

            self.retriever.index.init_index()







      def  _a   (					self		:     int				,				__SCREAMING_SNAKE_CASE		:     Optional[Any]				,				__SCREAMING_SNAKE_CASE		:     Dict			)							->		Optional[Any]:






            """simple docstring"""

            __SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE       		=   self.retriever._main_retrieve(UpperCamelCase__				,				UpperCamelCase__			)
            return doc_ids, retrieved_doc_embeds






class        A__(  _UpperCamelCase			):







      def __init__(					self		:     Optional[Any]				,				__SCREAMING_SNAKE_CASE		:     Optional[Any]				,				__SCREAMING_SNAKE_CASE		:     Optional[int]				,				__SCREAMING_SNAKE_CASE		:     Dict				,				__SCREAMING_SNAKE_CASE		:     Union[str, Any]				,				__SCREAMING_SNAKE_CASE		:     int=None			)							->		Optional[int]:






            """simple docstring"""

            if index is not None and index.is_initialized() and len(UpperCamelCase__			) > 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__(
                UpperCamelCase__				,				question_encoder_tokenizer=UpperCamelCase__				,				generator_tokenizer=UpperCamelCase__				,				index=UpperCamelCase__				,				init_retrieval=UpperCamelCase__				,				)
            __SCREAMING_SNAKE_CASE       		=   retrieval_workers
            if len(self.retrieval_workers			) > 0:
                  ray.get(
                      [
                          worker.create_rag_retriever.remote(UpperCamelCase__				,				UpperCamelCase__				,				UpperCamelCase__				,				UpperCamelCase__			)
                          for worker in self.retrieval_workers
                      ]			)







      def  _a   (					self		:     Dict			)							->		int:






            """simple docstring"""

            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		:     Optional[Any]				,				__SCREAMING_SNAKE_CASE		:     Dict				,				__SCREAMING_SNAKE_CASE		:     Tuple			)							->		Dict:






            """simple docstring"""

            if len(self.retrieval_workers			) > 0:
                  # Select a random retrieval actor.
                  __SCREAMING_SNAKE_CASE       		=   self.retrieval_workers[random.randint(0				,				len(self.retrieval_workers			) - 1			)]
                  __SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE       		=   ray.get(random_worker.retrieve.remote(UpperCamelCase__				,				UpperCamelCase__			)			)
            else:
                  __SCREAMING_SNAKE_CASE		,				__SCREAMING_SNAKE_CASE       		=   self._main_retrieve(UpperCamelCase__				,				UpperCamelCase__			)
            return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCamelCase__			)







      @classmethod
      def  _a   (					cls		:     List[str]				,				__SCREAMING_SNAKE_CASE		:     List[Any]				,				__SCREAMING_SNAKE_CASE		:     List[Any]=None				,				**__SCREAMING_SNAKE_CASE		:     Optional[int]			)							->		Optional[Any]:






            """simple docstring"""

            return super(UpperCamelCase__				,				cls			).get_tokenizers(UpperCamelCase__				,				UpperCamelCase__				,				**UpperCamelCase__			)







      @classmethod
      def  _a   (					cls		:     Any				,				__SCREAMING_SNAKE_CASE		:     Tuple				,				__SCREAMING_SNAKE_CASE		:     int				,				__SCREAMING_SNAKE_CASE		:     List[Any]=None				,				**__SCREAMING_SNAKE_CASE		:     List[str]			)							->		Tuple:






            """simple docstring"""

            __SCREAMING_SNAKE_CASE       		=   kwargs.pop('''config'''				,				UpperCamelCase__			) or RagConfig.from_pretrained(UpperCamelCase__				,				**UpperCamelCase__			)
            __SCREAMING_SNAKE_CASE       		=   RagTokenizer.from_pretrained(UpperCamelCase__				,				config=UpperCamelCase__			)
            __SCREAMING_SNAKE_CASE       		=   rag_tokenizer.question_encoder
            __SCREAMING_SNAKE_CASE       		=   rag_tokenizer.generator
            if indexed_dataset is not None:
                  __SCREAMING_SNAKE_CASE       		=   '''custom'''
                  __SCREAMING_SNAKE_CASE       		=   CustomHFIndex(config.retrieval_vector_size				,				UpperCamelCase__			)
            else:
                  __SCREAMING_SNAKE_CASE       		=   cls._build_index(UpperCamelCase__			)
            return cls(
                UpperCamelCase__				,				question_encoder_tokenizer=UpperCamelCase__				,				generator_tokenizer=UpperCamelCase__				,				retrieval_workers=UpperCamelCase__				,				index=UpperCamelCase__				,				)

'''simple docstring'''






def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			str   ,		__snake_case							:			str   ,		__snake_case							:			List[Any]=False				):

     if isinstance(__snake_case   ,		__snake_case				) and isinstance(__snake_case   ,		__snake_case				):
          _A    		=  len(set_a.intersection(__snake_case				)				)

          if alternative_union:
               _A    		=  len(__snake_case				) + len(__snake_case				)
          else:
               _A    		=  len(set_a.union(__snake_case				)				)

          return intersection / union

     if isinstance(__snake_case   ,		(list, tuple)				) and isinstance(__snake_case   ,		(list, tuple)				):
          _A    		=  [element for element in set_a if element in set_b]

          if alternative_union:
               _A    		=  len(__snake_case				) + len(__snake_case				)
               return len(__snake_case				) / union
          else:
               _A    		=  set_a + [element for element in set_b if element not in set_a]
               return len(__snake_case				) / len(__snake_case				)

          return len(__snake_case				) / len(__snake_case				)
     return None


if __name__ == "__main__":
   _UpperCAmelCase    :     Optional[Any]         =       {'''a''', '''b''', '''c''', '''d''', '''e'''}
   _UpperCAmelCase    :     str         =       {'''c''', '''d''', '''e''', '''f''', '''h''', '''i'''}
   print(jaccard_similarity(set_a, set_b))



