'''simple docstring'''




import logging
import random

import ray

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


_a   :	Optional[int] 							=   logging.getLogger(__name__)






class 		__A       :






       def __init__(      self      ):
              __UpperCAmelCase  :				int       				=		False






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ):
              if not self.initialized:
                     __UpperCAmelCase  :				Optional[int]       				=		RagRetriever(
                         A__				,     question_encoder_tokenizer=A__				,     generator_tokenizer=A__				,     index=A__				,     init_retrieval=A__				,     )
                     __UpperCAmelCase  :				Any       				=		True






       def 			_snake_case						(      self      ):
              self.retriever.index.init_index()







       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase				, __UpperCAmelCase  :				Optional[int]       				=		self.retriever._main_retrieve(A__				,     A__      )
              return doc_ids, retrieved_doc_embeds






class 		__A       (__a   ):






       def __init__(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_=None      ):
              if index is not None and index.is_initialized() and len(A__      ) > 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__(
                  A__				,     question_encoder_tokenizer=A__				,     generator_tokenizer=A__				,     index=A__				,     init_retrieval=A__				,     )
              __UpperCAmelCase  :				Optional[int]       				=		retrieval_workers
              if len(self.retrieval_workers      ) > 0:
                     ray.get(
                         [
                             worker.create_rag_retriever.remote(A__				,     A__				,     A__				,     A__      )
                             for worker in self.retrieval_workers
                         ]      )






       def 			_snake_case						(      self      ):
              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 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_      ):
              if len(self.retrieval_workers      ) > 0:
                     # Select a random retrieval actor.
                     __UpperCAmelCase  :				Any       				=		self.retrieval_workers[random.randint(0				,     len(self.retrieval_workers      ) - 1      )]
                     __UpperCAmelCase				, __UpperCAmelCase  :				Dict       				=		ray.get(random_worker.retrieve.remote(A__				,     A__      )      )
              else:
                     __UpperCAmelCase				, __UpperCAmelCase  :				Tuple       				=		self._main_retrieve(A__				,     A__      )
              return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(A__      )






       @classmethod
       def 			_snake_case						(      cls				,     UpperCamelCase_				,     UpperCamelCase_=None				,     **UpperCamelCase_      ):
              return super(A__				,     cls      ).get_tokenizers(A__				,     A__				,     **A__      )







       @classmethod
       def 			_snake_case						(      cls				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_=None				,     **UpperCamelCase_      ):
              __UpperCAmelCase  :				List[Any]       				=		kwargs.pop("config"				,     A__      ) or RagConfig.from_pretrained(A__				,     **A__      )
              __UpperCAmelCase  :				Union[str, Any]       				=		RagTokenizer.from_pretrained(A__				,     config=A__      )
              __UpperCAmelCase  :				str       				=		rag_tokenizer.question_encoder
              __UpperCAmelCase  :				Any       				=		rag_tokenizer.generator
              if indexed_dataset is not None:
                     __UpperCAmelCase  :				Optional[int]       				=		"custom"
                     __UpperCAmelCase  :				Dict       				=		CustomHFIndex(config.retrieval_vector_size				,     A__      )
              else:
                     __UpperCAmelCase  :				Optional[int]       				=		cls._build_index(A__      )
              return cls(
                  A__				,     question_encoder_tokenizer=A__				,     generator_tokenizer=A__				,     retrieval_workers=A__				,     index=A__				,     )







'''simple docstring'''




def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       int:





       """simple docstring"""


       return number | (1 << position)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       int:





       """simple docstring"""


       return number & ~(1 << position)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       int:





       """simple docstring"""


       return number ^ (1 << position)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       bool:





       """simple docstring"""


       return ((number >> position) & 1) == 1

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       int:





       """simple docstring"""


       return int((number & (1 << position)) != 0						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()






