


'''simple docstring'''






from __future__ import annotations

from typing import Generic, TypeVar

__UpperCAmelCase					       =							TypeVar('''T''')




class 	a__       (							Generic[T]						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_     ) ->							None:
														lowerCAmelCase__      =			data
														lowerCAmelCase__      =			self
														lowerCAmelCase__      =			0




class 	a__       (							Generic[T]						):





							'''simple docstring'''






							def __init__(	self     ) ->							None:
														# map from node name to the node object
														lowerCAmelCase__      =			{}







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							None:
														# create a new set with x as its member
														lowerCAmelCase__      =			DisjointSetTreeNode(lowerCamelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							DisjointSetTreeNode[T]:
														# find the set x belongs to (with path-compression)
														lowerCAmelCase__      =			self.map[data]
														if elem_ref != elem_ref.parent:
																					lowerCAmelCase__      =			self.find_set(elem_ref.parent.data     )
														return elem_ref.parent







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							None:
														# helper function for union operation
														if nodea.rank > nodea.rank:
																					lowerCAmelCase__      =			nodea
														else:
																					lowerCAmelCase__      =			nodea
																					if nodea.rank == nodea.rank:
																												nodea.rank += 1







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							None:
														# merge 2 disjoint sets
														self.link(self.find_set(lowerCamelCase_     )      ,					self.find_set(lowerCamelCase_     )     )




class 	a__       (							Generic[T]						):





							'''simple docstring'''






							def __init__(	self     ) ->							None:
														# connections: map from the node to the neighbouring nodes (with weights)
														lowerCAmelCase__      =			{}







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							None:
														# add a node ONLY if its not present in the graph
														if node not in self.connections:
																					lowerCAmelCase__      =			{}







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							None:
														# add an edge with the given weight
														self.add_node(lowerCamelCase_     )
														self.add_node(lowerCamelCase_     )
														lowerCAmelCase__      =			weight
														lowerCAmelCase__      =			weight







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							GraphUndirectedWeighted[T]:
														lowerCAmelCase__      =			[]
														lowerCAmelCase__      =			set()
														for start in self.connections:
																					for end in self.connections[start]:
																												if (start, end) not in seen:
																																			seen.add((end, start)     )
																																			edges.append((start, end, self.connections[start][end])     )
														edges.sort(key=lambda lowerCamelCase_     : x[2]     )

														# creating the disjoint set
														lowerCAmelCase__      =			DisjointSetTree[T]()
														for node in self.connections:
																					disjoint_set.make_set(lowerCamelCase_     )

														# MST generation
														lowerCAmelCase__      =			0
														lowerCAmelCase__      =			0
														lowerCAmelCase__      =			GraphUndirectedWeighted[T]()
														while num_edges < len(self.connections     ) - 1:
																					lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__      =			edges[index]
																					index += 1
																					lowerCAmelCase__      =			disjoint_set.find_set(lowerCamelCase_     )
																					lowerCAmelCase__      =			disjoint_set.find_set(lowerCamelCase_     )
																					if parent_u != parent_v:
																												num_edges += 1
																												graph.add_edge(lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_     )
																												disjoint_set.union(lowerCamelCase_      ,					lowerCamelCase_     )
														return graph



'''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 	a__       (							a__						):





							'''simple docstring'''






							def __init__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_=None     ) ->							Dict:
														super().__init__(
														    lowerCamelCase_      ,					question_encoder_tokenizer=lowerCamelCase_      ,					generator_tokenizer=lowerCamelCase_      ,					index=lowerCamelCase_      ,					init_retrieval=lowerCamelCase_      ,					)
														lowerCAmelCase__      =			None







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							str:
														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=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 						__SCREAMING_SNAKE_CASE							(	self     ) ->							Optional[Any]:
														return dist.get_rank(group=self.process_group     ) == 0







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_      ,					lowerCamelCase_=torch.floataa     ) ->							Union[str, Any]:
														lowerCAmelCase__      =			torch.empty(lowerCamelCase_      ,					dtype=lowerCamelCase_     )
														dist.scatter(lowerCamelCase_      ,					src=0      ,					scatter_list=lowerCamelCase_      ,					group=self.process_group     )
														return target_tensor







							def 						__SCREAMING_SNAKE_CASE							(	self     ) ->							str:
														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'''     ))      ,					lowerCamelCase_     )
														return ifname







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_     ) ->							Tuple[np.ndarray, List[dict]]:

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

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

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