





'''simple docstring'''




from string import ascii_uppercase

_A:					Optional[int]							=				{str(ord(c) - 55): c for c in ascii_uppercase}







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     str:
 if isinstance(_lowerCAmelCase       ,    _lowerCAmelCase    ):
  raise TypeError('int() can\'t convert non-string with explicit base'    )
 if num < 0:
  raise ValueError('parameter must be positive int'    )
 if isinstance(_lowerCAmelCase       ,    _lowerCAmelCase    ):
  raise TypeError('\'str\' object cannot be interpreted as an integer'    )
 if isinstance(_lowerCAmelCase       ,    _lowerCAmelCase    ):
  raise TypeError('\'float\' object cannot be interpreted as an integer'    )
 if base in (0, 1):
  raise ValueError('base must be >= 2'    )
 if base > 36:
  raise ValueError('base must be <= 36'    )
 __UpperCAmelCase					=   ''
 __UpperCAmelCase					=   0
 __UpperCAmelCase					=   0
 while div != 1:
  __UpperCAmelCase					,    __UpperCAmelCase					=   divmod(_lowerCAmelCase       ,    _lowerCAmelCase    )
  if base >= 11 and 9 < mod < 36:
   __UpperCAmelCase					=   ALPHABET_VALUES[str(_lowerCAmelCase    )]
  else:
   __UpperCAmelCase					=   str(_lowerCAmelCase    )
  new_value += actual_value
  __UpperCAmelCase					=   num // base
  __UpperCAmelCase					=   div
  if div == 0:
   return str(new_value[::-1]    )
  elif div == 1:
   new_value += str(_lowerCAmelCase    )
   return str(new_value[::-1]    )

 return new_value[::-1]


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    for base in range(2, 37):
        for num in range(1_000):
            assert int(decimal_to_any(num, base), base) == num, (
                num,
                base,
                decimal_to_any(num, base),
                int(decimal_to_any(num, base), base),
            )












'''simple docstring'''




import logging
import random

import ray

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


_A:					int							=				logging.getLogger(__name__)






class UpperCAmelCase							:



    def __init__(  self					):
     __UpperCAmelCase					=   False



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A					):
     if not self.initialized:
      __UpperCAmelCase					=   RagRetriever(
          __A     ,			question_encoder_tokenizer=__A     ,			generator_tokenizer=__A     ,			index=__A     ,			init_retrieval=__A     ,			)
      __UpperCAmelCase					=   True



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




    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     __UpperCAmelCase					,    __UpperCAmelCase					=   self.retriever._main_retrieve(__A     ,			__A					)
     return doc_ids, retrieved_doc_embeds






class UpperCAmelCase							(		UpperCAmelCase_							):



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



    @classmethod
    def 					__lowerCamelCase						(  cls     ,			__A     ,			__A=None     ,			**__A					):
     return super(__A     ,			cls					).get_tokenizers(__A     ,			__A     ,			**__A					)




    @classmethod
    def 					__lowerCamelCase						(  cls     ,			__A     ,			__A     ,			__A=None     ,			**__A					):
     __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     ,			)





