


"""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       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       def __init__(						self						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							False

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         if not self.initialized:
           lowerCAmelCase     :     Union[str, Any]								=							RagRetriever(
               snake_case__		,     question_encoder_tokenizer=snake_case__		,     generator_tokenizer=snake_case__		,     index=snake_case__		,     init_retrieval=snake_case__		,     )
           lowerCAmelCase     :     Optional[int]								=							True

       def        lowercase__							(						self						):






         """simple docstring"""





         self.retriever.index.init_index()







       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase     :     Dict								=							self.retriever._main_retrieve(snake_case__		,     snake_case__						)
         return doc_ids, retrieved_doc_embeds


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       def __init__(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__=None						):






         """simple docstring"""





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

       def        lowercase__							(						self						):






         """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        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """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     :     List[Any]								=							ray.get(random_worker.retrieve.remote(snake_case__		,     snake_case__						)						)
         else:
           lowerCAmelCase						,  lowerCAmelCase     :     List[str]								=							self._main_retrieve(snake_case__		,     snake_case__						)
         return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(snake_case__						)

       @classmethod
       def        lowercase__							(						cls		,     snake_case__		,     snake_case__=None		,     **snake_case__						):






         """simple docstring"""





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







       @classmethod
       def        lowercase__							(						cls		,     snake_case__		,     snake_case__		,     snake_case__=None		,     **snake_case__						):






         """simple docstring"""





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









"""simple docstring"""







def 					a__			(				SCREAMING_SNAKE_CASE    :    int				,      SCREAMING_SNAKE_CASE    :    List[Any]				,      SCREAMING_SNAKE_CASE    :    str				,      SCREAMING_SNAKE_CASE    :    List[Any]				):


  '''simple docstring'''







  if height >= 1:
    move_tower(height - 1				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
    move_disk(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
    move_tower(height - 1				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
def 					a__			(				SCREAMING_SNAKE_CASE    :    Tuple				,      SCREAMING_SNAKE_CASE    :    Optional[int]				):


  '''simple docstring'''







  print("moving disk from"				,      SCREAMING_SNAKE_CASE				,      "to"				,      SCREAMING_SNAKE_CASE				)
def 					a__			(				):


  '''simple docstring'''







  lowerCAmelCase     :     Optional[int]								=							int(input("Height of hanoi: "				).strip()				)
  move_tower(SCREAMING_SNAKE_CASE				,      "A"				,      "B"				,      "C"				)


if __name__ == "__main__":
     main()





