



'''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


lowerCamelCase							:List[str]       = logging.getLogger(__name__)







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	def __init__(self	,				lowercase	,				lowercase	,				lowercase	,				lowercase=None							):
				super().__init__(
				    lowercase	,				question_encoder_tokenizer=lowercase	,				generator_tokenizer=lowercase	,				index=lowercase	,				init_retrieval=lowercase	,				)
				A_     :						List[str]     =					None


	def    _a     (self	,				lowercase							):
				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
							A_     :						str     =					self._infer_socket_ifname()
							# avoid clash with the NCCL port
							A_     :						Tuple     =					str(distributed_port + 1							)
							A_     :						int     =					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    _a     (self							):
				return dist.get_rank(group=self.process_group							) == 0


	def    _a     (self	,				lowercase	,				lowercase	,				lowercase=torch.floataa							):
				A_     :						str     =					torch.empty(lowercase	,				dtype=lowercase							)
				dist.scatter(lowercase	,				src=0	,				scatter_list=lowercase	,				group=self.process_group							)
				return target_tensor


	def    _a     (self							):
				A_     :						Any     =					psutil.net_if_addrs()
				# a hacky way to deal with varying network interface names
				A_     :						int     =					next((addr for addr in addrs if addr.startswith("""e"""							))	,				lowercase							)
				return ifname


	def    _a     (self	,				lowercase	,				lowercase							):

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

				# distributed training
				A_     :						Any     =					dist.get_world_size(group=self.process_group							)

				# gather logic
				A_     :						str     =					None
				if self._is_main():
							A_     :						Any     =					[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
				A_     :						str     =					question_hidden_states.shape[0]
				A_     :						Optional[int]     =					[]
				A_     :						Dict     =					[]
				if self._is_main():
							assert len(lowercase							) == world_size
							A_,   A_     :						Union[str, Any]     =					self._main_retrieve(torch.cat(lowercase							).numpy()	,				lowercase							)
							A_,   A_     :						Optional[int]     =					torch.tensor(lowercase							), torch.tensor(lowercase							)
							A_     :						Tuple     =					self._chunk_tensor(lowercase	,				lowercase							)
							A_     :						Any     =					self._chunk_tensor(lowercase	,				lowercase							)
				A_     :						Tuple     =					self._scattered(lowercase	,				[n_queries, n_docs]	,				target_type=torch.intaa							)
				A_     :						Optional[int]     =					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'''




def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			return " ".join(input_str.split()[::-1]   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()