




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 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    def __init__(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Any       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     SCREAMING_SNAKE_CASE__					:					int       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=None       )				->	Optional[Any]:
         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__          =			None


    def        a				(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	Union[str, Any]:
         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=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        a				(	self					:					Optional[int]       )				->	Tuple:
         return dist.get_rank(group=self.process_group       ) == 0


    def        a				(	self					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					Tuple=torch.floataa       )				->	Optional[Any]:
         lowerCAmelCase__          =			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        a				(	self					:					Union[str, Any]       )				->	Any:
         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"       ))       ,     SCREAMING_SNAKE_CASE__       )
         return ifname


    def        a				(	self					:					Tuple       ,     SCREAMING_SNAKE_CASE__					:					np.ndarray       ,     SCREAMING_SNAKE_CASE__					:					int       )				->	Tuple[np.ndarray, List[dict]]:

         # single GPU training
         if not dist.is_initialized():
              lowerCAmelCase__     ,		lowerCAmelCase__          =			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__          =			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(SCREAMING_SNAKE_CASE__       )]
         dist.gather(torch.tensor(SCREAMING_SNAKE_CASE__       )       ,     dst=0       ,     gather_list=SCREAMING_SNAKE_CASE__       ,     group=self.process_group       )

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






from maths.prime_factors import prime_factors



def 							_A       (						lowerCAmelCase_					: int      ):
     """simple docstring"""

     if not isinstance(lowerCAmelCase_       ,    lowerCAmelCase_      ):
          lowerCAmelCase__          =			F'Input value of [number={number}] must be an integer'
          raise TypeError(lowerCAmelCase_      )
     if number < 1:
          raise ValueError("Input must be a positive integer"      )
     return -1 if len(prime_factors(lowerCAmelCase_      )      ) % 2 else 1


if __name__ == "__main__":
     import doctest

     doctest.testmod()
