



"""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


UpperCamelCase     =							logging.getLogger(__name__)






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			def __init__(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=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__						,	)
				A__											=					None


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__		)      ->	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
					A__											=					self._infer_socket_ifname()
					# avoid clash with the NCCL port
					A__											=					str(distributed_port + 1		)
					A__											=					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   snake_case__	(      self		)      ->	str:
				return dist.get_rank(group=self.process_group		) == 0


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=torch.floataa		)      ->	int:
				A__											=					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   snake_case__	(      self		)      ->	List[Any]:
				A__											=					psutil.net_if_addrs()
				# a hacky way to deal with varying network interface names
				A__											=					next((addr for addr in addrs if addr.startswith("e"		))						,	SCREAMING_SNAKE_CASE__		)
				return ifname







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

				# single GPU training
				if not dist.is_initialized():
					A__							,							A__											=					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__											=					dist.get_world_size(group=self.process_group		)

				# gather logic
				A__											=					None
				if self._is_main():
					A__											=					[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__											=					question_hidden_states.shape[0]
				A__											=					[]
				A__											=					[]
				if self._is_main():
					assert len(SCREAMING_SNAKE_CASE__		) == world_size
					A__							,							A__											=					self._main_retrieve(torch.cat(SCREAMING_SNAKE_CASE__		).numpy()						,	SCREAMING_SNAKE_CASE__		)
					A__							,							A__											=					torch.tensor(SCREAMING_SNAKE_CASE__		), torch.tensor(SCREAMING_SNAKE_CASE__		)
					A__											=					self._chunk_tensor(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
					A__											=					self._chunk_tensor(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)
				A__											=					self._scattered(SCREAMING_SNAKE_CASE__						,	[n_queries, n_docs]						,	target_type=torch.intaa		)
				A__											=					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"""


import numpy as np




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_ = 1e-12		,lowercase_ = 100		,)       ->		tuple[float, np.ndarray]:




      """simple docstring"""

      assert np.shape(lowercase_      )[0] == np.shape(lowercase_      )[1]
      # Ensure proper dimensionality.
      assert np.shape(lowercase_      )[0] == np.shape(lowercase_      )[0]
      # Ensure inputs are either both complex or both real
      assert np.iscomplexobj(lowercase_      ) == np.iscomplexobj(lowercase_      )
      _UpperCamelCase   :						Dict    =							np.iscomplexobj(lowercase_      )
      if is_complex:
            # Ensure complex input_matrix is Hermitian
            assert np.array_equal(lowercase_		,input_matrix.conj().T      )

      # Set convergence to False. Will define convergence when we exceed max_iterations
      # or when we have small changes from one iteration to next.

      _UpperCamelCase   :						List[Any]    =							False
      _UpperCamelCase   :						Optional[int]    =							0
      _UpperCamelCase   :						int    =							0
      _UpperCamelCase   :						Optional[int]    =							1e12

      while not convergence:
            # Multiple matrix by the vector.
            _UpperCamelCase   :						Optional[int]    =							np.dot(lowercase_		,lowercase_      )
            # Normalize the resulting output vector.
            _UpperCamelCase   :						List[Any]    =							w / np.linalg.norm(lowercase_      )
            # Find rayleigh quotient
            # (faster than usual b/c we know vector is normalized already)
            _UpperCamelCase   :						Optional[Any]    =							vector.conj().T if is_complex else vector.T
            _UpperCamelCase   :						Dict    =							np.dot(lowercase_		,np.dot(lowercase_		,lowercase_      )      )

            # Check convergence.
            _UpperCamelCase   :						Optional[int]    =							np.abs(lambda_ - lambda_previous      ) / lambda_
            iterations += 1

            if error <= error_tol or iterations >= max_iterations:
                  _UpperCamelCase   :						List[Any]    =							True

            _UpperCamelCase   :						Tuple    =							lambda_

      if is_complex:
            _UpperCamelCase   :						Tuple    =							np.real(lambda_      )

      return lambda_, vector




def        lowercase__     (       )       ->		None:




      """simple docstring"""

      _UpperCamelCase   :						Dict    =							np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]      )
      _UpperCamelCase   :						Optional[int]    =							np.array([41, 4, 20]      )
      _UpperCamelCase   :						Union[str, Any]    =							real_input_matrix.astype(np.complexaaa      )
      _UpperCamelCase   :						int    =							np.triu(1J * complex_input_matrix		,1      )
      complex_input_matrix += imag_matrix
      complex_input_matrix += -1 * imag_matrix.T
      _UpperCamelCase   :						Union[str, Any]    =							np.array([41, 4, 20]      ).astype(np.complexaaa      )

      for problem_type in ["real", "complex"]:
            if problem_type == "real":
                  _UpperCamelCase   :						Dict    =							real_input_matrix
                  _UpperCamelCase   :						Any    =							real_vector
            elif problem_type == "complex":
                  _UpperCamelCase   :						int    =							complex_input_matrix
                  _UpperCamelCase   :						Union[str, Any]    =							complex_vector

            # Our implementation.
            _UpperCamelCase,	_UpperCamelCase   :						int    =							power_iteration(lowercase_		,lowercase_      )

            # Numpy implementation.

            # Get eigenvalues and eigenvectors using built-in numpy
            # eigh (eigh used for symmetric or hermetian matrices).
            _UpperCamelCase,	_UpperCamelCase   :						str    =							np.linalg.eigh(lowercase_      )
            # Last eigenvalue is the maximum one.
            _UpperCamelCase   :						Any    =							eigen_values[-1]
            # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
            _UpperCamelCase   :						Tuple    =							eigen_vectors[:, -1]

            # Check our implementation and numpy gives close answers.
            assert np.abs(eigen_value - eigen_value_max      ) <= 1e-6
            # Take absolute values element wise of each eigenvector.
            # as they are only unique to a minus sign.
            assert np.linalg.norm(np.abs(lowercase_      ) - np.abs(lowercase_      )      ) <= 1e-6


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  test_power_iteration()
