





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				:       Union[str, Any]	   =				logging.getLogger(__name__)

class    lowerCAmelCase							(					__UpperCamelCase			):
 def __init__(      self  :      Dict			,     UpperCAmelCase  :      List[Any]			,     UpperCAmelCase  :      int			,     UpperCAmelCase  :      Any			,     UpperCAmelCase  :      Any=None	)			->	Any:
     super().__init__(
         UpperCAmelCase			,     question_encoder_tokenizer=UpperCAmelCase			,     generator_tokenizer=UpperCAmelCase			,     index=UpperCAmelCase			,     init_retrieval=UpperCAmelCase			,     )
     lowerCamelCase__    :      Union[str, Any]					 =		None
 def      A_	(      self  :      Optional[Any]			,     UpperCAmelCase  :      int	)			->	Optional[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__    :      Dict					 =		self._infer_socket_ifname()
         # avoid clash with the NCCL port
         lowerCamelCase__    :      Union[str, Any]					 =		str(distributed_port + 1	)
         lowerCamelCase__    :      int					 =		dist.new_group(ranks=UpperCAmelCase			,     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  :      List[Any]	)			->	List[Any]:
     return dist.get_rank(group=self.process_group	) == 0
 def      A_	(      self  :      Dict			,     UpperCAmelCase  :      str			,     UpperCAmelCase  :      Union[str, Any]			,     UpperCAmelCase  :      List[str]=torch.floataa	)			->	List[Any]:
     lowerCamelCase__    :      Dict					 =		torch.empty(UpperCAmelCase			,     dtype=UpperCAmelCase	)
     dist.scatter(UpperCAmelCase			,     src=0			,     scatter_list=UpperCAmelCase			,     group=self.process_group	)
     return target_tensor
 def      A_	(      self  :      str	)			->	Union[str, Any]:
     lowerCamelCase__    :      Any					 =		psutil.net_if_addrs()
     # a hacky way to deal with varying network interface names
     lowerCamelCase__    :      Any					 =		next((addr for addr in addrs if addr.startswith('e'	))			,     UpperCAmelCase	)
     return ifname







 def      A_	(      self  :      Dict			,     UpperCAmelCase  :      np.ndarray			,     UpperCAmelCase  :      int	)			->	Tuple[np.ndarray, List[dict]]:

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

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

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

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

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












def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase = 100_0000 )			->     int:
    lowerCamelCase__    :      int					 =		limit + 1
    lowerCamelCase__    :      Optional[Any]					 =		[0] * limit
    for first_term in range(1						,	_UpperCAmelCase ):
        for n in range(_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase ):
            lowerCamelCase__    :      Optional[Any]					 =		first_term + n / first_term
            if common_difference % 4:  # d must be divisble by 4
                continue
            else:
                common_difference /= 4
                if (
                    first_term > common_difference
                    and first_term < 4 * common_difference
                ):  # since x,y,z are positive integers
                    frequency[n] += 1  # so z>0 and a>d ,also 4d<a

    lowerCamelCase__    :      List[str]					 =		sum(1 for x in frequency[1:limit] if x == 10 )

    return count


if __name__ == "__main__":
    print(F"""{solution() = }""")





