




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


lowerCamelCase__			:       int         =							logging.getLogger(__name__)





class 	lowerCamelCase_							(				_SCREAMING_SNAKE_CASE ):


	'''simple docstring'''







	def __init__(							self  :		Optional[Any]    ,    _lowerCAmelCase  :		str    ,    _lowerCAmelCase  :		Optional[int]    ,    _lowerCAmelCase  :		int    ,    _lowerCAmelCase  :		Optional[int]=None						):
							super().__init__(
							    _lowerCAmelCase    ,    question_encoder_tokenizer=_lowerCAmelCase    ,    generator_tokenizer=_lowerCAmelCase    ,    index=_lowerCAmelCase    ,    init_retrieval=_lowerCAmelCase    ,    )
							SCREAMING_SNAKE_CASE_     						=      None



	def 				lowerCAmelCase_  (							self  :		Any    ,    _lowerCAmelCase  :		int						):
							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
													SCREAMING_SNAKE_CASE_     						=      self._infer_socket_ifname()
													# avoid clash with the NCCL port
													SCREAMING_SNAKE_CASE_     						=      str(distributed_port + 1						)
													SCREAMING_SNAKE_CASE_     						=      dist.new_group(ranks=_lowerCAmelCase    ,    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 				lowerCAmelCase_  (							self  :		Any						):
							return dist.get_rank(group=self.process_group						) == 0



	def 				lowerCAmelCase_  (							self  :		Optional[Any]    ,    _lowerCAmelCase  :		Dict    ,    _lowerCAmelCase  :		Any    ,    _lowerCAmelCase  :		Any=torch.floataa						):
							SCREAMING_SNAKE_CASE_     						=      torch.empty(_lowerCAmelCase    ,    dtype=_lowerCAmelCase						)
							dist.scatter(_lowerCAmelCase    ,    src=0    ,    scatter_list=_lowerCAmelCase    ,    group=self.process_group						)
							return target_tensor



	def 				lowerCAmelCase_  (							self  :		List[str]						):
							SCREAMING_SNAKE_CASE_     						=      psutil.net_if_addrs()
							# a hacky way to deal with varying network interface names
							SCREAMING_SNAKE_CASE_     						=      next((addr for addr in addrs if addr.startswith('e'						))    ,    _lowerCAmelCase						)
							return ifname




	def 				lowerCAmelCase_  (							self  :		Optional[int]    ,    _lowerCAmelCase  :		np.ndarray    ,    _lowerCAmelCase  :		int						):

							# single GPU training
							if not dist.is_initialized():
													SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_     						=      self._main_retrieve(_lowerCAmelCase    ,    _lowerCAmelCase						)
													return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_lowerCAmelCase						)

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

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

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

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





def  UpperCAmelCase_							(				__UpperCAmelCase						:     str							) ->			str:
      SCREAMING_SNAKE_CASE_     						=      ''
      for ch in key:
            if ch == " " or ch not in key_no_dups and ch.isalpha():
                  key_no_dups += ch
      return key_no_dups

def  UpperCAmelCase_							(				__UpperCAmelCase						:     str							) ->			dict[str, str]:
      SCREAMING_SNAKE_CASE_     						=      [chr(i + 65							) for i in range(26							)]
      # Remove duplicate characters from key
      SCREAMING_SNAKE_CASE_     						=      remove_duplicates(key.upper()							)
      SCREAMING_SNAKE_CASE_     						=      len(__UpperCAmelCase							)
      # First fill cipher with key characters
      SCREAMING_SNAKE_CASE_     						=      {alphabet[i]: char for i, char in enumerate(__UpperCAmelCase							)}
      # Then map remaining characters in alphabet to
      # the alphabet from the beginning
      for i in range(len(__UpperCAmelCase							)      ,      26							):
            SCREAMING_SNAKE_CASE_     						=      alphabet[i - offset]
            # Ensure we are not mapping letters to letters previously mapped
            while char in key:
                  offset -= 1
                  SCREAMING_SNAKE_CASE_     						=      alphabet[i - offset]
            SCREAMING_SNAKE_CASE_     						=      char
      return cipher_alphabet

def  UpperCAmelCase_							(				__UpperCAmelCase						:     str      ,      __UpperCAmelCase						:     dict[str, str]							) ->			str:
      return "".join(cipher_map.get(__UpperCAmelCase      ,      __UpperCAmelCase							) for ch in message.upper()							)

def  UpperCAmelCase_							(				__UpperCAmelCase						:     str      ,      __UpperCAmelCase						:     dict[str, str]							) ->			str:
      SCREAMING_SNAKE_CASE_     						=      {v: k for k, v in cipher_map.items()}
      return "".join(rev_cipher_map.get(__UpperCAmelCase      ,      __UpperCAmelCase							) for ch in message.upper()							)

def  UpperCAmelCase_							(				) ->			None:
      SCREAMING_SNAKE_CASE_     						=      input('Enter message to encode or decode: '							).strip()
      SCREAMING_SNAKE_CASE_     						=      input('Enter keyword: '							).strip()
      SCREAMING_SNAKE_CASE_     						=      input('Encipher or decipher? E/D:'							).strip()[0].lower()
      try:
            SCREAMING_SNAKE_CASE_     						=      {'e': encipher, 'd': decipher}[option]
      except KeyError:
            raise KeyError('invalid input option'							)
      SCREAMING_SNAKE_CASE_     						=      create_cipher_map(__UpperCAmelCase							)
      print(func(__UpperCAmelCase      ,      __UpperCAmelCase							)							)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()