




import logging
import random

import ray

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


SCREAMING_SNAKE_CASE__	:				Optional[int]  =       logging.getLogger(__name__)




class 		UpperCamelCase__     :

    '''simple docstring'''



    def __init__(   self					)					->				Union[str, Any]:
           lowerCamelCase       :				List[Any]      			= False







    def 	_lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					)					->				Optional[Any]:
           if not self.initialized:
                  lowerCamelCase       :				int      			= RagRetriever(
                      UpperCamelCase__					,  question_encoder_tokenizer=UpperCamelCase__					,  generator_tokenizer=UpperCamelCase__					,  index=UpperCamelCase__					,  init_retrieval=UpperCamelCase__					,  )
                  lowerCamelCase       :				Optional[int]      			= True







    def 	_lowercase					(   self					)					->				Optional[int]:
           self.retriever.index.init_index()







    def 	_lowercase					(   self					)					->				List[Any]:
           # delete the old self.retriever object before assigning the new index
           del self.retriever
           lowerCamelCase       :				Optional[Any]      			= False







    def 	_lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					)					->				Any:
           lowerCamelCase       :				Optional[Any]      			= self.retriever._main_retrieve(UpperCamelCase__					,  UpperCamelCase__					)
           lowerCamelCase       :				Optional[Any]      			= self.retriever.index.get_doc_dicts(UpperCamelCase__					)
           return doc_ids, retrieved_doc_embeds, doc_dicts




class 		UpperCamelCase__     (lowerCAmelCase__					):

    '''simple docstring'''



    def __init__(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__=None					)					->				Any:
           if index is not None and index.is_initialized() and len(UpperCamelCase__					) > 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__(
               UpperCamelCase__					,  question_encoder_tokenizer=UpperCamelCase__					,  generator_tokenizer=UpperCamelCase__					,  index=UpperCamelCase__					,  init_retrieval=UpperCamelCase__					,  )

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







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







    @classmethod
    def 	_lowercase					(   cls					,  UpperCamelCase__					,  UpperCamelCase__=None					,  **UpperCamelCase__					)					->				Union[str, Any]:
           return super(UpperCamelCase__					,  cls					).get_tokenizers(UpperCamelCase__					,  UpperCamelCase__					,  **UpperCamelCase__					)







    @classmethod
    def 	_lowercase					(   cls					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__=None					,  **UpperCamelCase__					)					->				Tuple:
           lowerCamelCase       :				Any      			= kwargs.pop("config"					,  UpperCamelCase__					) or RagConfig.from_pretrained(UpperCamelCase__					,  **UpperCamelCase__					)
           lowerCamelCase       :				int      			= RagTokenizer.from_pretrained(UpperCamelCase__					,  config=UpperCamelCase__					)
           lowerCamelCase       :				str      			= rag_tokenizer.question_encoder
           lowerCamelCase       :				List[str]      			= rag_tokenizer.generator

           if indexed_dataset is not None:
                  lowerCamelCase       :				List[Any]      			= "custom"
                  lowerCamelCase       :				List[Any]      			= CustomHFIndex(config.retrieval_vector_size					,  UpperCamelCase__					)
           else:
                  lowerCamelCase       :				List[str]      			= cls._build_index(UpperCamelCase__					)

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







    def 	_lowercase					(   self					)					->				Optional[int]:
           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
           lowerCamelCase       :				str      			= self._build_index(self.config					)

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












import numpy as np

def 					A				(				_SCREAMING_SNAKE_CASE   )     ->			str:
       lowerCamelCase			,				lowerCamelCase       :				Dict      			= a.shape
       lowerCamelCase       :				Union[str, Any]      			= min(_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				Union[str, Any]      			= np.eye(_SCREAMING_SNAKE_CASE   )
       lowerCamelCase       :				List[Any]      			= a.copy()

       for k in range(t - 1   ):
              # select a column of modified matrix A':
              lowerCamelCase       :				Optional[Any]      			= r[k:, [k]]
              # construct first basis vector
              lowerCamelCase       :				Union[str, Any]      			= np.zeros_like(_SCREAMING_SNAKE_CASE   )
              lowerCamelCase       :				int      			= 1.0
              # determine scaling factor
              lowerCamelCase       :				Any      			= np.linalg.norm(_SCREAMING_SNAKE_CASE   )
              # construct vector v for Householder reflection
              lowerCamelCase       :				List[str]      			= x + np.sign(x[0]   ) * alpha * ea
              v /= np.linalg.norm(_SCREAMING_SNAKE_CASE   )

              # construct the Householder matrix
              lowerCamelCase       :				List[Any]      			= np.eye(m - k   ) - 2.0 * v @ v.T
              # pad with ones and zeros as necessary
              lowerCamelCase       :				Union[str, Any]      			= np.block([[np.eye(_SCREAMING_SNAKE_CASE   ), np.zeros((k, m - k)   )], [np.zeros((m - k, k)   ), q_k]]   )

              lowerCamelCase       :				Any      			= q @ q_k.T
              lowerCamelCase       :				List[Any]      			= q_k @ r

       return q, r


if __name__ == "__main__":
       import doctest

       doctest.testmod()






