






'''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


_A			:    str					  =logging.getLogger(__name__)

class _lowercase			(       __lowerCamelCase	):




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




       def 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       Tuple  ):
              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__      :			Union[str, Any] 						= self._infer_socket_ifname()
                     # avoid clash with the NCCL port
                     lowerCamelCase__      :			Tuple 						= str(distributed_port + 1  )
                     lowerCamelCase__      :			List[Any] 						= dist.new_group(ranks=SCREAMING_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 	lowerCamelCase_       (							self:       int  ):
              return dist.get_rank(group=self.process_group  ) == 0




       def 	lowerCamelCase_       (							self:       str    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       Any=torch.floataa  ):
              lowerCamelCase__      :			List[Any] 						= torch.empty(SCREAMING_SNAKE_CASE_    ,							dtype=SCREAMING_SNAKE_CASE_  )
              dist.scatter(SCREAMING_SNAKE_CASE_    ,							src=0    ,							scatter_list=SCREAMING_SNAKE_CASE_    ,							group=self.process_group  )
              return target_tensor




       def 	lowerCamelCase_       (							self:       Dict  ):
              lowerCamelCase__      :			Dict 						= psutil.net_if_addrs()
              # a hacky way to deal with varying network interface names
              lowerCamelCase__      :			Optional[Any] 						= next((addr for addr in addrs if addr.startswith("""e"""  ))    ,							SCREAMING_SNAKE_CASE_  )
              return ifname




       def 	lowerCamelCase_       (							self:       Tuple    ,							UpperCamelCase__:       Optional[Any]    ,							UpperCamelCase__:       List[Any]  ):

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

              # distributed training
              lowerCamelCase__      :			Optional[int] 						= dist.get_world_size(group=self.process_group  )

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

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

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









'''simple docstring'''







# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Optional[Any]:
							lowerCamelCase__      :			str 						= [False] * len(UpperCamelCase   )
							lowerCamelCase__      :			str 						= [-1] * len(UpperCamelCase   )

							def dfs(UpperCamelCase       ,    UpperCamelCase   ):
														lowerCamelCase__      :			Optional[int] 						= True
														lowerCamelCase__      :			Union[str, Any] 						= c
														for u in graph[v]:
																					if not visited[u]:
																												dfs(UpperCamelCase       ,    1 - c   )

							for i in range(len(UpperCamelCase   )   ):
														if not visited[i]:
																					dfs(UpperCamelCase       ,    0   )

							for i in range(len(UpperCamelCase   )   ):
														for j in graph[i]:
																					if color[i] == color[j]:
																												return False

							return True


# Adjacency list of graph
_A			:    int					  ={0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}



print(check_bipartite_dfs(graph))

