


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           =		logging.getLogger(__name__)





class       A    (   A_ ):


							def __init__(self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase=None					):
									super().__init__(
									    lowerCAmelCase      ,				question_encoder_tokenizer=lowerCAmelCase      ,				generator_tokenizer=lowerCAmelCase      ,				index=lowerCAmelCase      ,				init_retrieval=lowerCAmelCase      ,				)
									__lowercase=			None


							def 			_A       (self      ,				lowerCAmelCase					):
									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=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 			_A       (self					):
									return dist.get_rank(group=self.process_group					) == 0


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase=torch.floataa					):
									__lowercase=			torch.empty(lowerCAmelCase      ,				dtype=lowerCAmelCase					)
									dist.scatter(lowerCAmelCase      ,				src=0      ,				scatter_list=lowerCAmelCase      ,				group=self.process_group					)
									return target_tensor


							def 			_A       (self					):
									__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'					))      ,				lowerCAmelCase					)
									return ifname







							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase					):

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

									# 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(lowerCAmelCase					)]
									dist.gather(torch.tensor(lowerCAmelCase					)      ,				dst=0      ,				gather_list=lowerCAmelCase      ,				group=self.process_group					)

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










from __future__ import annotations







def        _lowerCamelCase(	lowercase__	)						->	list[int]:

		'''simple docstring'''







		__lowercase=			2
		__lowercase=			[]
		while i * i <= n:
				if n % i:
						i += 1
				else:
						n //= i
						factors.append(lowercase__	)
		if n > 1:
				factors.append(lowercase__	)
		return factors


if __name__ == "__main__":
	import doctest

	doctest.testmod()






