





"""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     :						int						       =  logging.getLogger(__name__)







class       a							(   a__  ):



 def __init__(     self				,       _snake_case				,       _snake_case				,       _snake_case				,       _snake_case=None     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """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
   lowerCAmelCase    				= self._infer_socket_ifname()
   # avoid clash with the NCCL port
   lowerCAmelCase    				= str(distributed_port + 1     )
   lowerCAmelCase    				= dist.new_group(ranks=_snake_case				,       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    UpperCamelCase__  (     self     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case				,       _snake_case=torch.floataa     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case     ):







  """simple docstring"""






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

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

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

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

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










from __future__ import annotations

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase = None   )     ->     list[list[str]]:
					SCREAMING_SNAKE_CASE							:     Dict						=				word_bank or []
					# create a table
					SCREAMING_SNAKE_CASE							:     int						=				len(__lowerCAmelCase   ) + 1

					SCREAMING_SNAKE_CASE							:     list[list[list[str]]]						=				[]
					for _ in range(__lowerCAmelCase   ):
										table.append([]   )
					# seed value
					SCREAMING_SNAKE_CASE							:     Tuple						=				[[]]  # because empty string has empty combination

					# iterate through the indices
					for i in range(__lowerCAmelCase   ):
										# condition
										if table[i] != []:
															for word in word_bank:
																				# slice condition
																				if target[i : i + len(__lowerCAmelCase   )] == word:
																									SCREAMING_SNAKE_CASE							:     list[list[str]]						=				[
																									    [word, *way] for way in table[i]
																									]
																									# adds the word to every combination the current position holds
																									# now,push that combination to the table[i+len(word)]
																									table[i + len(__lowerCAmelCase   )] += new_combinations

    # combinations are in reverse order so reverse for better output
					for combination in table[len(__lowerCAmelCase   )]:
										combination.reverse()

					return table[len(__lowerCAmelCase   )]


if __name__ == "__main__":
			print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
			print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
			print(
			    all_construct(
			        """hexagonosaurus""",
			        ["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
			    )
			)