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 							a_					:






 '''simple docstring'''






 def __init__(     self   :    str):







        '''simple docstring'''



        lowerCAmelCase__	      =   False




 def 						__snake_case		(     self   :    Union[str, Any]     ,       lowercase__   :    Tuple     ,       lowercase__   :    int     ,       lowercase__   :    Optional[int]     ,       lowercase__   :    Optional[int]):







        '''simple docstring'''



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




 def 						__snake_case		(     self   :    int):







        '''simple docstring'''



        self.retriever.index.init_index()




 def 						__snake_case		(     self   :    Optional[int]):







        '''simple docstring'''



        del self.retriever
        lowerCAmelCase__	      =   False




 def 						__snake_case		(     self   :    Optional[Any]     ,       lowercase__   :    Any     ,       lowercase__   :    Tuple):







        '''simple docstring'''



        lowerCAmelCase__      ,   lowerCAmelCase__	      =   self.retriever._main_retrieve(lowercase__     ,       lowercase__)
        lowerCAmelCase__	      =   self.retriever.index.get_doc_dicts(lowercase__)
        return doc_ids, retrieved_doc_embeds, doc_dicts






class 							a_					(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 def __init__(     self   :    Tuple     ,       lowercase__   :    Optional[int]     ,       lowercase__   :    str     ,       lowercase__   :    int     ,       lowercase__   :    Dict     ,       lowercase__   :    Dict=None):







        '''simple docstring'''



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

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




 def 						__snake_case		(     self   :    Optional[int]):







        '''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 						__snake_case		(     self   :    Tuple     ,       lowercase__   :    int     ,       lowercase__   :    List[str]):







        '''simple docstring'''



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




 @classmethod
 def 						__snake_case		(     cls   :    List[str]     ,       lowercase__   :    Union[str, Any]     ,       lowercase__   :    List[str]=None     ,       **lowercase__   :    Any):







        '''simple docstring'''



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




 @classmethod
 def 						__snake_case		(     cls   :    Any     ,       lowercase__   :    Optional[int]     ,       lowercase__   :    Dict     ,       lowercase__   :    Tuple=None     ,       **lowercase__   :    Optional[Any]):







        '''simple docstring'''



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

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

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




 def 						__snake_case		(     self   :    Optional[Any]):







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

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

import random
import sys

from . import cryptomath_module as cryptomath

lowerCAmelCase__      		=					(
    r' !"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`'
    r'abcdefghijklmnopqrstuvwxyz{|}~'
)






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ,    lowerCAmelCase__      ):
       if mode == "encrypt":
              if key_a == 1:
                     sys.exit(
                         'The affine cipher becomes weak when key '
                         'A is set to 1. Choose different key'      )
              if key_b == 0:
                     sys.exit(
                         'The affine cipher becomes weak when key '
                         'B is set to 0. Choose different key'      )
       if key_a < 0 or key_b < 0 or key_b > len(lowerCAmelCase__      ) - 1:
              sys.exit(
                  'Key A must be greater than 0 and key B must '
                  F"""be between 0 and {len(lowerCAmelCase__      ) - 1}."""      )
       if cryptomath.gcd(lowerCAmelCase__      ,    len(lowerCAmelCase__      )      ) != 1:
              sys.exit(
                  F"""Key A {key_a} and the symbol set size {len(lowerCAmelCase__      )} """
                  'are not relatively prime. Choose a different key.'      )






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       lowerCAmelCase__      ,   lowerCAmelCase__	      =   divmod(lowerCAmelCase__      ,    len(lowerCAmelCase__      )      )
       check_keys(lowerCAmelCase__      ,    lowerCAmelCase__      ,    'encrypt'      )
       lowerCAmelCase__	      =   ''
       for symbol in message:
              if symbol in SYMBOLS:
                     lowerCAmelCase__	      =   SYMBOLS.find(lowerCAmelCase__      )
                     cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(lowerCAmelCase__      )]
              else:
                     cipher_text += symbol
       return cipher_text






def  __lowerCamelCase						(		lowerCAmelCase__      ,    lowerCAmelCase__      ):
       lowerCAmelCase__      ,   lowerCAmelCase__	      =   divmod(lowerCAmelCase__      ,    len(lowerCAmelCase__      )      )
       check_keys(lowerCAmelCase__      ,    lowerCAmelCase__      ,    'decrypt'      )
       lowerCAmelCase__	      =   ''
       lowerCAmelCase__	      =   cryptomath.find_mod_inverse(lowerCAmelCase__      ,    len(lowerCAmelCase__      )      )
       for symbol in message:
              if symbol in SYMBOLS:
                     lowerCAmelCase__	      =   SYMBOLS.find(lowerCAmelCase__      )
                     plain_text += SYMBOLS[
                         (sym_index - key_b) * mod_inverse_of_key_a % len(lowerCAmelCase__      )
                     ]
              else:
                     plain_text += symbol
       return plain_text






def  __lowerCamelCase						(		):
       while True:
              lowerCAmelCase__	      =   random.randint(2      ,    len(lowerCAmelCase__      )      )
              lowerCAmelCase__	      =   random.randint(2      ,    len(lowerCAmelCase__      )      )
              if cryptomath.gcd(lowerCAmelCase__      ,    len(lowerCAmelCase__      )      ) == 1 and key_b % len(lowerCAmelCase__      ) != 0:
                     return key_b * len(lowerCAmelCase__      ) + key_b






def  __lowerCamelCase						(		):
       lowerCAmelCase__	      =   input('Enter message: '      ).strip()
       lowerCAmelCase__	      =   int(input('Enter key [2000 - 9000]: '      ).strip()      )
       lowerCAmelCase__	      =   input('Encrypt/Decrypt [E/D]: '      ).strip().lower()

       if mode.startswith('e'      ):
              lowerCAmelCase__	      =   'encrypt'
              lowerCAmelCase__	      =   encrypt_message(lowerCAmelCase__      ,    lowerCAmelCase__      )
       elif mode.startswith('d'      ):
              lowerCAmelCase__	      =   'decrypt'
              lowerCAmelCase__	      =   decrypt_message(lowerCAmelCase__      ,    lowerCAmelCase__      )
       print(F"""\n{mode.title()}ed text: \n{translated}"""      )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    # main()
