




'''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 						lowerCAmelCase_   :







  '''simple docstring'''







  def __init__(						self    :	int ):






   """simple docstring"""






   UpperCAmelCase__										=    False


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any						,		_UpperCAmelCase    :	Optional[Any]						,		_UpperCAmelCase    :	str						,		_UpperCAmelCase    :	Tuple						,		_UpperCAmelCase    :	Optional[Any] ):






   """simple docstring"""






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


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any] ):






   """simple docstring"""






   self.retriever.index.init_index()


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Union[str, Any] ):






   """simple docstring"""






   del self.retriever
   UpperCAmelCase__										=    False



  def 							SCREAMING_SNAKE_CASE__			(						self    :	Any						,		_UpperCAmelCase    :	Union[str, Any]						,		_UpperCAmelCase    :	Optional[int] ):






   """simple docstring"""






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

class 						lowerCAmelCase_   ( lowerCamelCase_   ):







  '''simple docstring'''







  def __init__(						self    :	Optional[Any]						,		_UpperCAmelCase    :	List[Any]						,		_UpperCAmelCase    :	Dict						,		_UpperCAmelCase    :	str						,		_UpperCAmelCase    :	Any						,		_UpperCAmelCase    :	List[Any]=None ):






   """simple docstring"""






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


  def 							SCREAMING_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 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple						,		_UpperCAmelCase    :	str						,		_UpperCAmelCase    :	Dict ):






   """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__							,			UpperCAmelCase__							,			UpperCAmelCase__										=    ray.get(
        random_worker.retrieve.remote(_UpperCAmelCase						,		_UpperCAmelCase ) )
   else:
    UpperCAmelCase__							,			UpperCAmelCase__										=    self._main_retrieve(_UpperCAmelCase						,		_UpperCAmelCase )
    UpperCAmelCase__										=    self.index.get_doc_dicts(_UpperCAmelCase )
   return retrieved_doc_embeds, doc_ids, doc_dicts


  @classmethod
  def 							SCREAMING_SNAKE_CASE__			(						cls    :	str						,		_UpperCAmelCase    :	Optional[Any]						,		_UpperCAmelCase    :	str=None						,		**_UpperCAmelCase    :	Tuple ):






   """simple docstring"""






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


  @classmethod
  def 							SCREAMING_SNAKE_CASE__			(						cls    :	int						,		_UpperCAmelCase    :	Optional[int]						,		_UpperCAmelCase    :	Optional[int]						,		_UpperCAmelCase    :	Union[str, Any]=None						,		**_UpperCAmelCase    :	int ):






   """simple docstring"""






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

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

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



  def 							SCREAMING_SNAKE_CASE__			(						self    :	Dict ):






   """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						,		_UpperCAmelCase )
           for worker in self.retrieval_workers
       ] )






'''simple docstring'''




from __future__ import annotations

import random
import string

class 						lowerCAmelCase_   :







  '''simple docstring'''







  def __init__(						self    :	int						,		_UpperCAmelCase    :	str | None = None ):






   """simple docstring"""






   UpperCAmelCase__										=    passcode or self.__passcode_creator()
   UpperCAmelCase__										=    self.__make_key_list()
   UpperCAmelCase__										=    self.__make_shift_key()


  def __str__(						self    :	int ):






   """simple docstring"""






   return "".join(self.__passcode )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str]						,		_UpperCAmelCase    :	list[int] ):






   """simple docstring"""






   for i in range(1						,		len(_UpperCAmelCase )						,		2 ):
    iterlist[i] *= -1
   return iterlist


  def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   UpperCAmelCase__										=    string.ascii_letters + string.digits
   UpperCAmelCase__										=    [random.choice(_UpperCAmelCase ) for _ in range(random.randint(10						,		20 ) )]
   return password


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple ):






   """simple docstring"""






   UpperCAmelCase__										=    (
       string.ascii_letters + string.digits + string.punctuation + """ \t\n"""
   )

   UpperCAmelCase__										=    []

   # creates points known as breakpoints to break the key_list_options at those
   # points and pivot each substring
   UpperCAmelCase__										=    sorted(set(self.__passcode ) )
   UpperCAmelCase__										=    []

   # algorithm for creating a new shuffled list, keys_l, out of key_list_options
   for i in key_list_options:
    temp_list.extend(_UpperCAmelCase )

    # checking breakpoints at which to pivot temporary sublist and add it into
    # keys_l
    if i in breakpoints or i == key_list_options[-1]:
     keys_l.extend(temp_list[::-1] )
     temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
   return keys_l


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple ):






   """simple docstring"""






   UpperCAmelCase__										=    sum(self.__neg_pos([ord(_UpperCAmelCase ) for x in self.__passcode] ) )
   return num if num > 0 else len(self.__passcode )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[Any]						,		_UpperCAmelCase    :	str ):






   """simple docstring"""






   UpperCAmelCase__										=    """"""

   # decoding shift like Caesar cipher algorithm implementing negative shift or
   # reverse shift or left shift
   for i in encoded_message:
    UpperCAmelCase__										=    self.__key_list.index(_UpperCAmelCase )
    decoded_message += self.__key_list[
        (position - self.__shift_key) % -len(self.__key_list )
    ]

   return decoded_message



  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str]						,		_UpperCAmelCase    :	str ):






   """simple docstring"""






   UpperCAmelCase__										=    """"""

   # encoding shift like Caesar cipher algorithm implementing positive shift or
   # forward shift or right shift
   for i in plaintext:
    UpperCAmelCase__										=    self.__key_list.index(_UpperCAmelCase )
    encoded_message += self.__key_list[
        (position + self.__shift_key) % len(self.__key_list )
    ]

   return encoded_message

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      str = "Hello, this is a modified Caesar cipher"       ):
 '''simple docstring'''





 UpperCAmelCase__										=    ShuffledShiftCipher()
 return cipa.decrypt(cipa.encrypt(SCREAMING_SNAKE_CASE__       )       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
