

'''simple docstring'''

import logging
import os
from typing import List, Tuple

import numpy as np
import psutil
import torch
import torch.distributed as dist

from transformers import RagRetriever


UpperCAmelCase					 =	logging.getLogger(__name__)


class 	lowerCAmelCase  (   A ):







       def __init__(				self      :			List[Any]    ,   __lowercase      :			List[Any]    ,   __lowercase      :			Any    ,   __lowercase      :			List[str]    ,   __lowercase      :			str=None   ):







            """simple docstring"""







            super().__init__(
                __lowercase    ,   question_encoder_tokenizer=__lowercase    ,   generator_tokenizer=__lowercase    ,   index=__lowercase    ,   init_retrieval=__lowercase    ,   )
            __lowercase      		=None







       def       snake_case (				self      :			Tuple    ,   __lowercase      :			int   ):







            """simple docstring"""







            logger.info('initializing retrieval'   )

            # initializing a separate process group for retrieval as the default
            # nccl backend doesn't support gather/scatter operations while gloo
            # is too slow to replace nccl for the core gpu communication
            if dist.is_initialized():
                 logger.info('dist initialized'   )
                 # needs to be set manually
                 __lowercase      		=self._infer_socket_ifname()
                 # avoid clash with the NCCL port
                 __lowercase      		=str(distributed_port + 1   )
                 __lowercase      		=dist.new_group(ranks=__lowercase    ,   backend='gloo'   )

            # initialize retriever only on the main worker
            if not dist.is_initialized() or self._is_main():
                 logger.info('dist not initialized / main'   )
                 self.index.init_index()

            # all processes wait untill the retriever is initialized by the main process
            if dist.is_initialized():
                 torch.distributed.barrier(group=self.process_group   )







       def       snake_case (				self      :			Any   ):







            """simple docstring"""







            return dist.get_rank(group=self.process_group   ) == 0







       def       snake_case (				self      :			int    ,   __lowercase      :			Optional[int]    ,   __lowercase      :			Optional[int]    ,   __lowercase      :			List[str]=torch.floataa   ):







            """simple docstring"""







            __lowercase      		=torch.empty(__lowercase    ,   dtype=__lowercase   )
            dist.scatter(__lowercase    ,   src=0    ,   scatter_list=__lowercase    ,   group=self.process_group   )
            return target_tensor







       def       snake_case (				self      :			Optional[int]   ):







            """simple docstring"""







            __lowercase      		=psutil.net_if_addrs()
            # a hacky way to deal with varying network interface names
            __lowercase      		=next((addr for addr in addrs if addr.startswith('e'   ))    ,   __lowercase   )
            return ifname







       def       snake_case (				self      :			int    ,   __lowercase      :			np.ndarray    ,   __lowercase      :			int   ):







            """simple docstring"""







            if not dist.is_initialized():
                 __lowercase		,  __lowercase      		=self._main_retrieve(__lowercase    ,   __lowercase   )
                 return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__lowercase   )

            # distributed training
            __lowercase      		=dist.get_world_size(group=self.process_group   )

            # gather logic
            __lowercase      		=None
            if self._is_main():
                 __lowercase      		=[torch.empty(question_hidden_states.shape    ,   dtype=torch.floataa   ) for _ in range(__lowercase   )]
            dist.gather(torch.tensor(__lowercase   )    ,   dst=0    ,   gather_list=__lowercase    ,   group=self.process_group   )

            # scatter logic
            __lowercase      		=question_hidden_states.shape[0]
            __lowercase      		=[]
            __lowercase      		=[]
            if self._is_main():
                 assert len(__lowercase   ) == world_size
                 __lowercase		,  __lowercase      		=self._main_retrieve(torch.cat(__lowercase   ).numpy()    ,   __lowercase   )
                 __lowercase		,  __lowercase      		=torch.tensor(__lowercase   ), torch.tensor(__lowercase   )
                 __lowercase      		=self._chunk_tensor(__lowercase    ,   __lowercase   )
                 __lowercase      		=self._chunk_tensor(__lowercase    ,   __lowercase   )
            __lowercase      		=self._scattered(__lowercase    ,   [n_queries, n_docs]    ,   target_type=torch.intaa   )
            __lowercase      		=self._scattered(__lowercase    ,   [n_queries, n_docs, question_hidden_states.shape[1]]   )

            return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(__lowercase   )





'''simple docstring'''

import string

def 					__UpperCamelCase				(						lowercase__    :	str			):






     '''simple docstring'''





     for key in range(len(string.ascii_uppercase			)			):
          __lowercase      		=''
          for symbol in message:
               if symbol in string.ascii_uppercase:
                    __lowercase      		=string.ascii_uppercase.find(lowercase__			)
                    __lowercase      		=num - key
                    if num < 0:
                         __lowercase      		=num + len(string.ascii_uppercase			)
                    __lowercase      		=translated + string.ascii_uppercase[num]
               else:
                    __lowercase      		=translated + symbol
          print(F'''Decryption using Key #{key}: {translated}'''			)

def 					__UpperCamelCase				(						):






     '''simple docstring'''





     __lowercase      		=input('Encrypted message: '			)
     __lowercase      		=message.upper()
     decrypt(lowercase__			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       main()


