




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  _lowerCAmelCase     :

      def __init__(   self		):
        lowerCAmelCase__				:  Union[str, Any]		       =     False

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        if not self.initialized:
          lowerCAmelCase__				:  Optional[Any]		       =     RagRetriever(
              __UpperCAmelCase      ,				question_encoder_tokenizer=__UpperCAmelCase      ,				generator_tokenizer=__UpperCAmelCase      ,				index=__UpperCAmelCase      ,				init_retrieval=__UpperCAmelCase      ,				)
          lowerCAmelCase__				:  List[Any]		       =     True

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







      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  Tuple		       =     self.retriever._main_retrieve(__UpperCAmelCase      ,				__UpperCAmelCase		)
        return doc_ids, retrieved_doc_embeds






class  _lowerCAmelCase     (  _lowercase	):

      def __init__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase=None		):
        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__				:  int		       =     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 	__magic_name__(   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 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        if len(self.retrieval_workers		) > 0:
          # Select a random retrieval actor.
          lowerCAmelCase__				:  Dict		       =     self.retrieval_workers[random.randint(0      ,				len(self.retrieval_workers		) - 1		)]
          lowerCAmelCase__       ,			lowerCAmelCase__				:  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 	__magic_name__(   cls      ,				__UpperCAmelCase      ,				__UpperCAmelCase=None      ,				**__UpperCAmelCase		):
        return super(__UpperCAmelCase      ,				cls		).get_tokenizers(__UpperCAmelCase      ,				__UpperCAmelCase      ,				**__UpperCAmelCase		)







      @classmethod
      def 	__magic_name__(   cls      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase=None      ,				**__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[Any]		       =     kwargs.pop('''config'''      ,				__UpperCAmelCase		) or RagConfig.from_pretrained(__UpperCAmelCase      ,				**__UpperCAmelCase		)
        lowerCAmelCase__				:  Tuple		       =     RagTokenizer.from_pretrained(__UpperCAmelCase      ,				config=__UpperCAmelCase		)
        lowerCAmelCase__				:  str		       =     rag_tokenizer.question_encoder
        lowerCAmelCase__				:  Any		       =     rag_tokenizer.generator
        if indexed_dataset is not None:
          lowerCAmelCase__				:  Any		       =     '''custom'''
          lowerCAmelCase__				:  str		       =     CustomHFIndex(config.retrieval_vector_size      ,				__UpperCAmelCase		)
        else:
          lowerCAmelCase__				:  Dict		       =     cls._build_index(__UpperCAmelCase		)
        return cls(
            __UpperCAmelCase      ,				question_encoder_tokenizer=__UpperCAmelCase      ,				generator_tokenizer=__UpperCAmelCase      ,				retrieval_workers=__UpperCAmelCase      ,				index=__UpperCAmelCase      ,				)







from collections.abc import Callable






class  _lowerCAmelCase     :

      def __init__(   self      ,				__UpperCAmelCase = None		):
        # Stores actual heap items.
        lowerCAmelCase__				:  list		       =     []
        # Stores indexes of each item for supporting updates and deletion.
        lowerCAmelCase__				:  dict		       =     {}
        # Stores current size of heap.
        lowerCAmelCase__				:  Any		       =     0
        # Stores function used to evaluate the score of an item on which basis ordering
        # will be done.
        lowerCAmelCase__				:  Union[str, Any]		       =     key or (lambda __UpperCAmelCase		: x)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        return int((i - 1) / 2		) if i > 0 else None

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[Any]		       =     int(2 * i + 1		)
        return left if 0 < left < self.size else None

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  List[str]		       =     int(2 * i + 2		)
        return right if 0 < right < self.size else None

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__       ,			lowerCAmelCase__				:  List[str]		       =     (
            self.pos_map[self.arr[j][0]],
            self.pos_map[self.arr[i][0]],
        )
        # Then swap the items in the list.
        lowerCAmelCase__       ,			lowerCAmelCase__				:  Any		       =     self.arr[j], self.arr[i]

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        return self.arr[i][1] < self.arr[j][1]

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[int]		       =     self._left(__UpperCAmelCase		)
        lowerCAmelCase__				:  Any		       =     self._right(__UpperCAmelCase		)
        lowerCAmelCase__				:  str		       =     i

        if left is not None and not self._cmp(__UpperCAmelCase      ,				__UpperCAmelCase		):
          lowerCAmelCase__				:  Dict		       =     left
        if right is not None and not self._cmp(__UpperCAmelCase      ,				__UpperCAmelCase		):
          lowerCAmelCase__				:  List[str]		       =     right

        return valid_parent

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Tuple		       =     self._parent(__UpperCAmelCase		)
        while parent is not None and not self._cmp(__UpperCAmelCase      ,				__UpperCAmelCase		):
          self._swap(__UpperCAmelCase      ,				__UpperCAmelCase		)
          lowerCAmelCase__       ,			lowerCAmelCase__				:  Dict		       =     parent, self._parent(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  str		       =     self._get_valid_parent(__UpperCAmelCase		)
        while valid_parent != index:
          self._swap(__UpperCAmelCase      ,				__UpperCAmelCase		)
          lowerCAmelCase__       ,			lowerCAmelCase__				:  List[Any]		       =     valid_parent, self._get_valid_parent(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        if item not in self.pos_map:
          return
        lowerCAmelCase__				:  Tuple		       =     self.pos_map[item]
        lowerCAmelCase__				:  Any		       =     [item, self.key(__UpperCAmelCase		)]
        # Make sure heap is right in both up and down direction.
        # Ideally only one of them will make any change.
        self._heapify_up(__UpperCAmelCase		)
        self._heapify_down(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        if item not in self.pos_map:
          return
        lowerCAmelCase__				:  Tuple		       =     self.pos_map[item]
        del self.pos_map[item]
        lowerCAmelCase__				:  Tuple		       =     self.arr[self.size - 1]
        lowerCAmelCase__				:  int		       =     index
        self.size -= 1
        # Make sure heap is right in both up and down direction. Ideally only one
        # of them will make any change- so no performance loss in calling both.
        if self.size > index:
          self._heapify_up(__UpperCAmelCase		)
          self._heapify_down(__UpperCAmelCase		)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Optional[int]		       =     len(self.arr		)
        if arr_len == self.size:
          self.arr.append([item, self.key(__UpperCAmelCase		)]		)
        else:
          lowerCAmelCase__				:  str		       =     [item, self.key(__UpperCAmelCase		)]
        lowerCAmelCase__				:  Optional[Any]		       =     self.size
        self.size += 1
        self._heapify_up(self.size - 1		)

      def 	__magic_name__(   self		):
        return self.arr[0] if self.size else None







      def 	__magic_name__(   self		):
        lowerCAmelCase__				:  int		       =     self.get_top()
        if top_item_tuple:
          self.delete_item(top_item_tuple[0]		)
        return top_item_tuple



def      __lowerCAmelCase			(					)     ->	None:
  pass


if __name__ == "__main__":
     import doctest

     doctest.testmod()

