

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							:  Optional[int]   	=						logging.getLogger(__name__)



class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







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



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]   ,						lowerCAmelCase__  :     int):
									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
															SCREAMING_SNAKE_CASE_: Dict  		=       self._infer_socket_ifname()
															# avoid clash with the NCCL port
															SCREAMING_SNAKE_CASE_: List[Any]  		=       str(distributed_port + 1)
															SCREAMING_SNAKE_CASE_: int  		=       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[int]):
									return dist.get_rank(group=self.process_group) == 0



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     Union[str, Any]   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     Optional[int]=torch.floataa):
									SCREAMING_SNAKE_CASE_: str  		=       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  :     List[Any]):
									SCREAMING_SNAKE_CASE_: int  		=       psutil.net_if_addrs()
									# a hacky way to deal with varying network interface names
									SCREAMING_SNAKE_CASE_: Any  		=       next((addr for addr in addrs if addr.startswith("e"))   ,						lowerCAmelCase__)
									return ifname




			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     np.ndarray   ,						lowerCAmelCase__  :     int):

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

									# distributed training
									SCREAMING_SNAKE_CASE_: Dict  		=       dist.get_world_size(group=self.process_group)

									# gather logic
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       None
									if self._is_main():
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       [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
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       question_hidden_states.shape[0]
									SCREAMING_SNAKE_CASE_: Dict  		=       []
									SCREAMING_SNAKE_CASE_: List[Any]  		=       []
									if self._is_main():
															assert len(lowerCAmelCase__) == world_size
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       self._main_retrieve(torch.cat(lowerCAmelCase__).numpy()   ,						lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: int  		=       torch.tensor(lowerCAmelCase__), torch.tensor(lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self._chunk_tensor(lowerCAmelCase__   ,						lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       self._chunk_tensor(lowerCAmelCase__   ,						lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       self._scattered(lowerCAmelCase__   ,						[n_queries, n_docs]   ,						target_type=torch.intaa)
									SCREAMING_SNAKE_CASE_: int  		=       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 queue import PriorityQueue
from typing import Any

import numpy as np







def 	_a      (						SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				set      ,      SCREAMING_SNAKE_CASE_							:				set      ,      SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				PriorityQueue      ,      SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				float | int      ,      ):
					for nxt, d in graph[v]:
										if nxt in visited_forward:
															continue
										__lowerCAmelCase				   =       cst_fwd.get(SCREAMING_SNAKE_CASE_      ,      np.inf     )
										__lowerCAmelCase				   =       cst_fwd[v] + d
										if new_cost_f < old_cost_f:
															queue.put((new_cost_f, nxt)     )
															__lowerCAmelCase				   =       new_cost_f
															__lowerCAmelCase				   =       v
										if nxt in visited_backward:
															if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																				__lowerCAmelCase				   =       cst_fwd[v] + d + cst_bwd[nxt]
					return shortest_distance







def 	_a      (						SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				dict      ,      SCREAMING_SNAKE_CASE_							:				dict     ):
					__lowerCAmelCase				   =       -1

					__lowerCAmelCase				   =       set()
					__lowerCAmelCase				   =       set()
					__lowerCAmelCase				   =       {source: 0}
					__lowerCAmelCase				   =       {destination: 0}
					__lowerCAmelCase				   =       {source: None}
					__lowerCAmelCase				   =       {destination: None}
					__lowerCAmelCase				   =       PriorityQueue()
					__lowerCAmelCase				   =       PriorityQueue()

					__lowerCAmelCase				   =       np.inf

					queue_forward.put((0, source)     )
					queue_backward.put((0, destination)     )

					if source == destination:
										return 0

					while not queue_forward.empty() and not queue_backward.empty():
										__lowerCAmelCase   ,							__lowerCAmelCase				   =       queue_forward.get()
										visited_forward.add(SCREAMING_SNAKE_CASE_     )

										__lowerCAmelCase   ,							__lowerCAmelCase				   =       queue_backward.get()
										visited_backward.add(SCREAMING_SNAKE_CASE_     )

										__lowerCAmelCase				   =       pass_and_relaxation(
										    SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      )

										__lowerCAmelCase				   =       pass_and_relaxation(
										    SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      )

										if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
															break

					if shortest_distance != np.inf:
										__lowerCAmelCase				   =       shortest_distance
					return shortest_path_distance


UpperCamelCase__    			=			{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}
UpperCamelCase__    			=			{
    """B""": [["""E""", 1]],
    """C""": [["""B""", 1]],
    """D""": [["""C""", 1]],
    """F""": [["""D""", 1], ["""G""", 1]],
    """E""": [[None, np.inf]],
    """G""": [["""E""", 2]],
}

if __name__ == "__main__":
					import doctest

					doctest.testmod()




