

import logging
import random

import ray

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


_UpperCamelCase     =    logging.getLogger(__name__)



class 					lowercase		:



 '''simple docstring'''


 def __init__(self				)				->      Tuple:






  """simple docstring"""





  UpperCAmelCase__           =       False




 def 						UpperCamelCase__							(self , __a , __a , __a , __a				)				->      List[Any]:






  """simple docstring"""





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




 def 						UpperCamelCase__							(self				)				->      Tuple:






  """simple docstring"""





  self.retriever.index.init_index()




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






  """simple docstring"""





  del self.retriever
  UpperCAmelCase__           =       False




 def 						UpperCamelCase__							(self , __a , __a				)				->      Union[str, Any]:






  """simple docstring"""





  UpperCAmelCase__           =       self.retriever._main_retrieve(__a , __a				)
  UpperCAmelCase__           =       self.retriever.index.get_doc_dicts(__a				)
  return doc_ids, retrieved_doc_embeds, doc_dicts







class 					lowercase		( lowerCamelCase_      ):



 '''simple docstring'''


 def __init__(self , __a , __a , __a , __a , __a=None				)				->      Optional[Any]:






  """simple docstring"""





  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__           =       retrieval_workers
  UpperCAmelCase__           =       question_encoder_tokenizer
  UpperCAmelCase__           =       generator_tokenizer
  if len(self.retrieval_workers				) > 0:
   ray.get(
       [
           worker.create_rag_retriever.remote(__a , __a , __a , __a				)
           for worker in self.retrieval_workers
       ]				)




 def 						UpperCamelCase__							(self				)				->      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 , __a , __a				)				->      str:






  """simple docstring"""





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




 @classmethod
 def 						UpperCamelCase__							(cls , __a , __a=None , **__a				)				->      str:






  """simple docstring"""





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




 @classmethod
 def 						UpperCamelCase__							(cls , __a , __a , __a=None , **__a				)				->      int:






  """simple docstring"""





  UpperCAmelCase__           =       kwargs.pop('config' , __a				) or RagConfig.from_pretrained(__a , **__a				)
  UpperCAmelCase__           =       RagTokenizer.from_pretrained(__a , config=__a				)
  UpperCAmelCase__           =       rag_tokenizer.question_encoder
  UpperCAmelCase__           =       rag_tokenizer.generator

  if indexed_dataset is not None:
   UpperCAmelCase__           =       "custom"
   UpperCAmelCase__           =       CustomHFIndex(config.retrieval_vector_size , __a				)
  else:
   UpperCAmelCase__           =       cls._build_index(__a				)

  return cls(
      __a , question_encoder_tokenizer=__a , generator_tokenizer=__a , retrieval_workers=__a , index=__a , )




 def 						UpperCamelCase__							(self				)				->      int:






  """simple docstring"""





  logger.info('re-loading the new dataset with embeddings'				)
  # access from the training loop

  ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]				)

  # build the index object again
  UpperCAmelCase__           =       self._build_index(self.config				)

  ray.get(
      [
          worker.create_rag_retriever.remote(
              self.config , self.question_encoder_tokenizer , self.generator_tokenizer , __a				)
          for worker in self.retrieval_workers
      ]				)






def 		UpperCamelCase_(    snake_case__:						int     ) ->				int:
 if not isinstance(snake_case__						,					snake_case__     ):
  UpperCAmelCase__           =       f"Input value of [number={number}] must be an integer"
  raise TypeError(snake_case__     )
 if number < 1:
  raise ValueError('Input must be a positive integer'     )
 return number * (2 * number - 1)


if __name__ == "__main__":
 import doctest

 doctest.testmod()



