


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_   :				List[str]						      = logging.getLogger(__name__)
class 				SCREAMING_SNAKE_CASE__						(       lowercase__    ):
      def __init__( self       : str  ,							SCREAMING_SNAKE_CASE__       : Dict  ,							SCREAMING_SNAKE_CASE__       : List[str]  ,							SCREAMING_SNAKE_CASE__       : Any  ,							SCREAMING_SNAKE_CASE__       : str=None  )	->    List[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__  ,							)
             a_      :	Dict							=						None
      def  SCREAMING_SNAKE_CASE							( self       : int  ,							SCREAMING_SNAKE_CASE__       : 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
                    a_      :	Union[str, Any]							=						self._infer_socket_ifname()
                    # avoid clash with the NCCL port
                    a_      :	Optional[Any]							=						str(distributed_port + 1  )
                    a_      :	Tuple							=						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  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Optional[Any]:
             return dist.get_rank(group=self.process_group  ) == 0
      def  SCREAMING_SNAKE_CASE							( self       : Dict  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : str=torch.floataa  )	->    int:
             a_      :	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  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    List[str]:
             a_      :	Union[str, Any]							=						psutil.net_if_addrs()
             # a hacky way to deal with varying network interface names
             a_      :	str							=						next((addr for addr in addrs if addr.startswith('e'  ))  ,							SCREAMING_SNAKE_CASE__  )
             return ifname

      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  ,							SCREAMING_SNAKE_CASE__       : np.ndarray  ,							SCREAMING_SNAKE_CASE__       : int  )	->    Tuple[np.ndarray, List[dict]]:

             # single GPU training
             if not dist.is_initialized():
                    a_							,	a_      :	Optional[Any]							=						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
             a_      :	List[str]							=						dist.get_world_size(group=self.process_group  )

             # gather logic
             a_      :	Tuple							=						None
             if self._is_main():
                    a_      :	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
             a_      :	Union[str, Any]							=						question_hidden_states.shape[0]
             a_      :	List[str]							=						[]
             a_      :	Any							=						[]
             if self._is_main():
                    assert len(SCREAMING_SNAKE_CASE__  ) == world_size
                    a_							,	a_      :	Tuple							=						self._main_retrieve(torch.cat(SCREAMING_SNAKE_CASE__  ).numpy()  ,							SCREAMING_SNAKE_CASE__  )
                    a_							,	a_      :	List[Any]							=						torch.tensor(SCREAMING_SNAKE_CASE__  ), torch.tensor(SCREAMING_SNAKE_CASE__  )
                    a_      :	Optional[int]							=						self._chunk_tensor(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
                    a_      :	List[Any]							=						self._chunk_tensor(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
             a_      :	str							=						self._scattered(SCREAMING_SNAKE_CASE__  ,							[n_queries, n_docs]  ,							target_type=torch.intaa  )
             a_      :	List[Any]							=						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__  )






import unittest

import numpy as np







def  SCREAMING_SNAKE_CASE_		(    __A				:    np.ndarray						,					__A				:    np.ndarray						,					__A				:    np.ndarray						,					__A				:    np.ndarray | None = None						,					)			->	np.ndarray:

       """simple docstring"""

       a_      :	List[str]							=						np.shape(__A       )
       a_      :	Any							=						np.shape(__A       )
       a_      :	List[Any]							=						np.shape(__A       )

       if shape_a[0] != shape_b[0]:
              a_      :	List[Any]							=						(
                  'Expected the same number of rows for A and B. '
                  F"""Instead found A of size {shape_a} and B of size {shape_b}"""
              )
              raise ValueError(__A       )

       if shape_b[1] != shape_c[1]:
              a_      :	int							=						(
                  'Expected the same number of columns for B and C. '
                  F"""Instead found B of size {shape_b} and C of size {shape_c}"""
              )
              raise ValueError(__A       )

       a_      :	Any							=						pseudo_inv
       if a_inv is None:
              try:
                     a_      :	List[str]							=						np.linalg.inv(__A       )
              except np.linalg.LinAlgError:
                     raise ValueError(
                         'Input matrix A is not invertible. Cannot compute Schur complement.'       )

       return mat_c - mat_b.T @ a_inv @ mat_b
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    None:
             a_      :	str							=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]  )
             a_      :	Any							=						np.array([[0, 3], [3, 0], [2, 3]]  )
             a_      :	List[str]							=						np.array([[2, 1], [6, 3]]  )

             a_      :	int							=						schur_complement(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

             a_      :	Optional[Any]							=						np.block([[a, b], [b.T, c]]  )

             a_      :	str							=						np.linalg.det(SCREAMING_SNAKE_CASE__  )
             a_      :	List[Any]							=						np.linalg.det(SCREAMING_SNAKE_CASE__  )
             a_      :	str							=						np.linalg.det(SCREAMING_SNAKE_CASE__  )

             self.assertAlmostEqual(SCREAMING_SNAKE_CASE__  ,							det_a * det_s  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    None:
             a_      :	Optional[int]							=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]  )
             a_      :	Dict							=						np.array([[0, 3], [3, 0], [2, 3]]  )
             a_      :	str							=						np.array([[2, 1], [6, 3]]  )

             with self.assertRaises(SCREAMING_SNAKE_CASE__  ):
                    schur_complement(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )

      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    None:
             a_      :	Tuple							=						np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]  )
             a_      :	Dict							=						np.array([[0, 3], [3, 0], [2, 3]]  )
             a_      :	List[str]							=						np.array([[2, 1, 3], [6, 3, 5]]  )

             with self.assertRaises(SCREAMING_SNAKE_CASE__  ):
                    schur_complement(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   unittest.main()


