




def      _SCREAMING_SNAKE_CASE     (				lowercase      :			int = 10	):


      '''simple docstring'''



      if not isinstance(lowercase      ,				lowercase	) or n < 0:
            raise ValueError('Invalid input'	)
      lowerCamelCase_							=       10**n
      lowerCamelCase_							=       2_84_33 * (pow(2      ,				7_83_04_57      ,				lowercase	)) + 1
      return str(number % modulus	)


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 print(F"""{solution(10) = }""")





"""simple docstring"""



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 				a			:



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



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          if not self.initialized:
              UpperCAmelCase__	:      Optional[Any]     							=					RagRetriever(
                  UpperCamelCase_     ,	question_encoder_tokenizer=UpperCamelCase_     ,	generator_tokenizer=UpperCamelCase_     ,	index=UpperCamelCase_     ,	init_retrieval=UpperCamelCase_     ,	)
              UpperCAmelCase__	:      Union[str, Any]     							=					True



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



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          UpperCAmelCase__							,							UpperCAmelCase__	:      Union[str, Any]     							=					self.retriever._main_retrieve(UpperCamelCase_     ,	UpperCamelCase_      )
          return doc_ids, retrieved_doc_embeds





class 				a			(					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_     ,	)
          UpperCAmelCase__	:      Union[str, Any]     							=					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  __snake_case							(   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  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          if len(self.retrieval_workers      ) > 0:
              # Select a random retrieval actor.
              UpperCAmelCase__	:      Optional[Any]     							=					self.retrieval_workers[random.randint(0     ,	len(self.retrieval_workers      ) - 1      )]
              UpperCAmelCase__							,							UpperCAmelCase__	:      List[Any]     							=					ray.get(random_worker.retrieve.remote(UpperCamelCase_     ,	UpperCamelCase_      )      )
          else:
              UpperCAmelCase__							,							UpperCAmelCase__	:      Dict     							=					self._main_retrieve(UpperCamelCase_     ,	UpperCamelCase_      )
          return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCamelCase_      )



      @classmethod
      def  __snake_case							(   cls     ,	UpperCamelCase_     ,	UpperCamelCase_=None     ,	**UpperCamelCase_      ):
          return super(UpperCamelCase_     ,	cls      ).get_tokenizers(UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      )



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

