





import logging
import random

import ray

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


_UpperCAmelCase :    Union[str, Any]             =							logging.getLogger(__name__)






class        snake_case_    :






  def __init__(							self			:					str      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       False






  def 				UpperCAmelCase__						(							self			:					Tuple       ,  _snake_case			:					List[Any]       ,  _snake_case			:					Union[str, Any]       ,  _snake_case			:					Optional[Any]       ,  _snake_case			:					List[str]      )->List[str]:






   '''simple docstring'''

   if not self.initialized:
    __lowerCAmelCase					:	str      =       RagRetriever(
        __UpperCAmelCase       ,  question_encoder_tokenizer=__UpperCAmelCase       ,  generator_tokenizer=__UpperCAmelCase       ,  index=__UpperCAmelCase       ,  init_retrieval=__UpperCAmelCase       ,  )
    __lowerCAmelCase					:	int      =       True






  def 				UpperCAmelCase__						(							self			:					Any      )->Tuple:






   '''simple docstring'''

   self.retriever.index.init_index()






  def 				UpperCAmelCase__						(							self			:					Union[str, Any]       ,  _snake_case			:					str       ,  _snake_case			:					Tuple      )->List[str]:






   '''simple docstring'''

   __lowerCAmelCase ,					__lowerCAmelCase					:	int      =       self.retriever._main_retrieve(__UpperCAmelCase       ,  __UpperCAmelCase      )
   return doc_ids, retrieved_doc_embeds






class        snake_case_    (		__lowercase						):






  def __init__(							self			:					int       ,  _snake_case			:					Tuple       ,  _snake_case			:					Tuple       ,  _snake_case			:					Dict       ,  _snake_case			:					Dict       ,  _snake_case			:					Dict=None      )->Optional[Any]:






   '''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       ,  )
   __lowerCAmelCase					:	Dict      =       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 				UpperCAmelCase__						(							self			:					List[Any]      )->Tuple:






   '''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 				UpperCAmelCase__						(							self			:					Optional[Any]       ,  _snake_case			:					Any       ,  _snake_case			:					Tuple      )->Dict:






   '''simple docstring'''

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






  @classmethod
  def 				UpperCAmelCase__						(							cls			:					List[str]       ,  _snake_case			:					Union[str, Any]       ,  _snake_case			:					Tuple=None       ,  **_snake_case			:					Optional[int]      )->List[Any]:






   '''simple docstring'''

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






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






   '''simple docstring'''

   __lowerCAmelCase					:	Union[str, Any]      =       kwargs.pop("""config"""       ,  __UpperCAmelCase      ) or RagConfig.from_pretrained(__UpperCAmelCase       ,  **__UpperCAmelCase      )
   __lowerCAmelCase					:	str      =       RagTokenizer.from_pretrained(__UpperCAmelCase       ,  config=__UpperCAmelCase      )
   __lowerCAmelCase					:	List[Any]      =       rag_tokenizer.question_encoder
   __lowerCAmelCase					:	Tuple      =       rag_tokenizer.generator
   if indexed_dataset is not None:
    __lowerCAmelCase					:	List[Any]      =       """custom"""
    __lowerCAmelCase					:	str      =       CustomHFIndex(config.retrieval_vector_size       ,  __UpperCAmelCase      )
   else:
    __lowerCAmelCase					:	Tuple      =       cls._build_index(__UpperCAmelCase      )
   return cls(
       __UpperCAmelCase       ,  question_encoder_tokenizer=__UpperCAmelCase       ,  generator_tokenizer=__UpperCAmelCase       ,  retrieval_workers=__UpperCAmelCase       ,  index=__UpperCAmelCase       ,  )






import random






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:Dict   )      ->   tuple:
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	List[str]      =       [], [], []
 for element in data:
  if element < pivot:
   less.append(SCREAMING_SNAKE_CASE   )
  elif element > pivot:
   greater.append(SCREAMING_SNAKE_CASE   )
  else:
   equal.append(SCREAMING_SNAKE_CASE   )
 return less, equal, greater







def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:int   )      ->   Dict:
 # index = len(items) // 2 when trying to find the median
 #   (value of index when items is sorted)

 # invalid input
 if index >= len(SCREAMING_SNAKE_CASE   ) or index < 0:
  return None

 __lowerCAmelCase					:	Union[str, Any]      =       items[random.randint(0   ,   len(SCREAMING_SNAKE_CASE   ) - 1   )]
 __lowerCAmelCase					:	int      =       0
 __lowerCAmelCase ,					__lowerCAmelCase ,					__lowerCAmelCase					:	Tuple      =       _partition(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	Optional[int]      =       len(SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	List[str]      =       len(SCREAMING_SNAKE_CASE   )

 # index is the pivot
 if m <= index < m + count:
  return pivot
 # must be in smaller
 elif m > index:
  return quick_select(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 # must be in larger
 else:
  return quick_select(SCREAMING_SNAKE_CASE   ,   index - (m + count)   )