




'''simple docstring'''



import logging
import random

import ray

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


UpperCAmelCase_				:     Any     			=						logging.getLogger(__name__)





class 			lowerCAmelCase      :


       def __init__(      self     )					->     Any:



            '''simple docstring'''




            __snake_case			    =     False


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     Any:



            '''simple docstring'''




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


       def 					lowerCAmelCase					(      self     )					->     Optional[int]:



            '''simple docstring'''




            self.retriever.index.init_index()


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



            '''simple docstring'''




            del self.retriever
            __snake_case			    =     False




       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     Optional[int]:



            '''simple docstring'''




            __snake_case    ,			__snake_case			    =     self.retriever._main_retrieve(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )
            __snake_case			    =     self.retriever.index.get_doc_dicts(__SCREAMING_SNAKE_CASE     )
            return doc_ids, retrieved_doc_embeds, doc_dicts





class 			lowerCAmelCase      (       __lowerCAmelCase):


       def __init__(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=None     )					->     Tuple:



            '''simple docstring'''




            if index is not None and index.is_initialized() and len(__SCREAMING_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__(
                __SCREAMING_SNAKE_CASE			,     question_encoder_tokenizer=__SCREAMING_SNAKE_CASE			,     generator_tokenizer=__SCREAMING_SNAKE_CASE			,     index=__SCREAMING_SNAKE_CASE			,     init_retrieval=__SCREAMING_SNAKE_CASE			,     )

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


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



            '''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 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     Any:



            '''simple docstring'''




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


       @classmethod
       def 					lowerCAmelCase					(      cls			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=None			,     **__SCREAMING_SNAKE_CASE     )					->     int:



            '''simple docstring'''




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


       @classmethod
       def 					lowerCAmelCase					(      cls			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=None			,     **__SCREAMING_SNAKE_CASE     )					->     Union[str, Any]:



            '''simple docstring'''




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

            if indexed_dataset is not None:
                 __snake_case			    =     '''custom'''
                 __snake_case			    =     CustomHFIndex(config.retrieval_vector_size			,     __SCREAMING_SNAKE_CASE     )
            else:
                 __snake_case			    =     cls._build_index(__SCREAMING_SNAKE_CASE     )

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




       def 					lowerCAmelCase					(      self     )					->     Tuple:



            '''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
            __snake_case			    =     self._build_index(self.config     )

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












'''simple docstring'''



from __future__ import annotations

import math
from itertools import permutations







def 	_UpperCamelCase       (_lowerCamelCase							:   int		)->      bool:




     '''simple docstring'''





     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5      ,		int(math.sqrt(_lowerCamelCase		) + 1		)      ,		6		):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True







def 	_UpperCamelCase       (_lowerCamelCase							:   int = 7		)->      int:




     '''simple docstring'''





     __snake_case			    =     ''''''.join(str(_lowerCamelCase		) for i in range(1      ,		n + 1		)		)
     __snake_case			    =     [int(''''''.join(_lowerCamelCase		)		) for i in permutations(_lowerCamelCase      ,		_lowerCamelCase		)]
     __snake_case			    =     [num for num in perm_list if is_prime(_lowerCamelCase		)]
     return max(_lowerCamelCase		) if pandigitals else 0


if __name__ == "__main__":
     print(F"""{solution() = }""")






