


import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


a					:				List[Any]       =      logging.getLogger(__name__)

class   lowerCamelCase						:

       def __init__(      self       ):






        '''simple docstring'''






        snake_case :			Union[str, Any]							=   False

       def 					UpperCAmelCase					(      self						,    a__						,    a__						,    a__						,    a__       ):






        '''simple docstring'''






        if not self.initialized:
         snake_case :			Optional[Any]							=   RagRetriever(
             a__						,    question_encoder_tokenizer=a__						,    generator_tokenizer=a__						,    index=a__						,    init_retrieval=a__						,    )
         snake_case :			Union[str, Any]							=   True

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        self.retriever.index.init_index()

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        del self.retriever
        snake_case :			str							=   False



       def 					UpperCAmelCase					(      self						,    a__						,    a__       ):






        '''simple docstring'''






        snake_case	,       snake_case :			Optional[int]							=   self.retriever._main_retrieve(a__						,    a__       )
        snake_case :			str							=   self.retriever.index.get_doc_dicts(a__       )
        return doc_ids, retrieved_doc_embeds, doc_dicts







class   lowerCamelCase						(	SCREAMING_SNAKE_CASE  ):

       def __init__(      self						,    a__						,    a__						,    a__						,    a__						,    a__=None       ):






        '''simple docstring'''






        if index is not None and index.is_initialized() and len(a__       ) > 0:
         raise ValueError(
             '''When using Ray for distributed fine-tuning, '''
             '''you\'ll need to provide the paths instead, '''
             '''as the dataset and the index are loaded '''
             '''separately. More info in examples/rag/use_own_knowledge_dataset.py '''       )

        super().__init__(
            a__						,    question_encoder_tokenizer=a__						,    generator_tokenizer=a__						,    index=a__						,    init_retrieval=a__						,    )

        snake_case :			Tuple							=   retrieval_workers
        snake_case :			Any							=   question_encoder_tokenizer
        snake_case :			int							=   generator_tokenizer
        if len(self.retrieval_workers       ) > 0:
         ray.get(
             [
                 worker.create_rag_retriever.remote(a__						,    a__						,    a__						,    a__       )
                 for worker in self.retrieval_workers
             ]       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        logger.info('''initializing retrieval'''       )

        if len(self.retrieval_workers       ) > 0:
         ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]       )
        else:
         # Non-distributed training. Load index into this same process.
         self.index.init_index()

       def 					UpperCAmelCase					(      self						,    a__						,    a__       ):






        '''simple docstring'''






        if len(self.retrieval_workers       ) > 0:
         # Select a random retrieval actor.
         snake_case :			Optional[Any]							=   self.retrieval_workers[random.randint(0						,    len(self.retrieval_workers       ) - 1       )]
         snake_case	,       snake_case	,       snake_case :			List[Any]							=   ray.get(
             random_worker.retrieve.remote(a__						,    a__       )       )
        else:
         snake_case	,       snake_case :			Optional[Any]							=   self._main_retrieve(a__						,    a__       )
         snake_case :			Any							=   self.index.get_doc_dicts(a__       )
        return retrieved_doc_embeds, doc_ids, doc_dicts

       @classmethod
       def 					UpperCAmelCase					(      cls						,    a__						,    a__=None						,    **a__       ):






        '''simple docstring'''






        return super(a__						,    cls       ).get_tokenizers(a__						,    a__						,    **a__       )

       @classmethod
       def 					UpperCAmelCase					(      cls						,    a__						,    a__						,    a__=None						,    **a__       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   kwargs.pop('''config'''						,    a__       ) or RagConfig.from_pretrained(a__						,    **a__       )
        snake_case :			Dict							=   RagTokenizer.from_pretrained(a__						,    config=a__       )
        snake_case :			Optional[int]							=   rag_tokenizer.question_encoder
        snake_case :			List[Any]							=   rag_tokenizer.generator

        if indexed_dataset is not None:
         snake_case :			Union[str, Any]							=   '''custom'''
         snake_case :			int							=   CustomHFIndex(config.retrieval_vector_size						,    a__       )
        else:
         snake_case :			int							=   cls._build_index(a__       )

        return cls(
            a__						,    question_encoder_tokenizer=a__						,    generator_tokenizer=a__						,    retrieval_workers=a__						,    index=a__						,    )



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        logger.info('''re-loading the new dataset with embeddings'''       )
        # access from the training loop

        ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]       )

        # build the index object again
        snake_case :			List[Any]							=   self._build_index(self.config       )

        ray.get(
            [
                worker.create_rag_retriever.remote(
                    self.config						,    self.question_encoder_tokenizer						,    self.generator_tokenizer						,    a__       )
                for worker in self.retrieval_workers
            ]       )










from typing import Any

import numpy as np






def  _UpperCAmelCase						( __lowerCamelCase							)		->bool:


 '''simple docstring'''
 assert np.shape(__lowerCamelCase							)[0] == np.shape(__lowerCamelCase							)[1]

 # If matrix not symmetric, exit right away.
 if np.allclose(__lowerCamelCase			,  matrix.T							) is False:
  return False

 # Get eigenvalues and eignevectors for a symmetric matrix.
 snake_case	,       snake_case :			int							=   np.linalg.eigh(__lowerCamelCase							)

 # Check sign of all eigenvalues.
 # np.all returns a value of type np.bool_
 return bool(np.all(eigen_values > 0							)							)






def  _UpperCAmelCase						( __lowerCamelCase							)		->Any:


 '''simple docstring'''
 snake_case :			Dict							=   np.random.randn(__lowerCamelCase			,  __lowerCamelCase							)
 snake_case :			List[Any]							=   np.dot(__lowerCamelCase			,  random_matrix.T							)
 assert _is_matrix_spd(__lowerCamelCase							)
 return spd_matrix






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase = 1000			,  __lowerCamelCase = 1E-8			,  )		->Any:


 '''simple docstring'''
 assert np.shape(__lowerCamelCase							)[0] == np.shape(__lowerCamelCase							)[1]
 assert np.shape(__lowerCamelCase							)[0] == np.shape(__lowerCamelCase							)[0]
 assert _is_matrix_spd(__lowerCamelCase							)

 # Initialize solution guess, residual, search direction.
 snake_case :			Any							=   np.zeros((np.shape(__lowerCamelCase							)[0], 1)							)
 snake_case :			Optional[int]							=   np.copy(__lowerCamelCase							)
 snake_case :			Optional[Any]							=   np.copy(__lowerCamelCase							)

 # Set initial errors in solution guess and residual.
 snake_case :			Optional[Any]							=   1E9
 snake_case :			List[Any]							=   1E9
 snake_case :			List[str]							=   1E9

 # Set iteration counter to threshold number of iterations.
 snake_case :			List[str]							=   0

 while error > tol:
  # Save this value so we only calculate the matrix-vector product once.
  snake_case :			str							=   np.dot(__lowerCamelCase			,  __lowerCamelCase							)

  # The main algorithm.

  # Update search direction magnitude.
  snake_case :			List[str]							=   np.dot(ra.T			,  __lowerCamelCase							) / np.dot(pa.T			,  __lowerCamelCase							)
  # Update solution guess.
  snake_case :			Optional[int]							=   xa + alpha * pa
  # Calculate new residual.
  snake_case :			str							=   ra - alpha * w
  # Calculate new Krylov subspace scale.
  snake_case :			List[str]							=   np.dot(r.T			,  __lowerCamelCase							) / np.dot(ra.T			,  __lowerCamelCase							)
  # Calculate new A conjuage search direction.
  snake_case :			List[Any]							=   r + beta * pa

  # Calculate errors.
  snake_case :			List[Any]							=   np.linalg.norm(r - ra							)
  snake_case :			Any							=   np.linalg.norm(x - xa							)
  snake_case :			str							=   np.maximum(__lowerCamelCase			,  __lowerCamelCase							)

  # Update variables.
  snake_case :			Tuple							=   np.copy(__lowerCamelCase							)
  snake_case :			Tuple							=   np.copy(__lowerCamelCase							)
  snake_case :			List[str]							=   np.copy(__lowerCamelCase							)

  # Update number of iterations.
  iterations += 1
  if iterations > max_iterations:
   break

 return x






def  _UpperCAmelCase						( )		->None:


 '''simple docstring'''
 snake_case :			str							=   3
 snake_case :			Dict							=   _create_spd_matrix(__lowerCamelCase							)
 snake_case :			str							=   np.random.randn(__lowerCamelCase			,  1							)
 snake_case :			str							=   np.dot(__lowerCamelCase			,  __lowerCamelCase							)

 # Numpy solution.
 snake_case :			Any							=   np.linalg.solve(__lowerCamelCase			,  __lowerCamelCase							)

 # Our implementation.
 snake_case :			List[str]							=   conjugate_gradient(__lowerCamelCase			,  __lowerCamelCase							)

 # Ensure both solutions are close to x_true (and therefore one another).
 assert np.linalg.norm(x_numpy - x_true							) <= 1E-6
 assert np.linalg.norm(x_conjugate_gradient - x_true							) <= 1E-6


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  test_conjugate_gradient()






