





"""simple docstring"""
import logging
import random

import ray

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


__UpperCamelCase			:		Optional[Any]		    =   logging.getLogger(__name__)







class 	a				:



 def __init__(			self			):







  """simple docstring"""






  lowerCAmelCase  							=      False



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  if not self.initialized:
   lowerCAmelCase  							=      RagRetriever(
       _snake_case							,    question_encoder_tokenizer=_snake_case							,    generator_tokenizer=_snake_case							,    index=_snake_case							,    init_retrieval=_snake_case							,    )
   lowerCAmelCase  							=      True



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  self.retriever.index.init_index()



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  del self.retriever
  lowerCAmelCase  							=      False



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  lowerCAmelCase							,lowerCAmelCase  							=      self.retriever._main_retrieve(_snake_case							,    _snake_case			)
  lowerCAmelCase  							=      self.retriever.index.get_doc_dicts(_snake_case			)
  return doc_ids, retrieved_doc_embeds, doc_dicts







class 	a				(					a__   ):



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







  """simple docstring"""






  if index is not None and index.is_initialized() and len(_snake_case			) > 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__(
      _snake_case							,    question_encoder_tokenizer=_snake_case							,    generator_tokenizer=_snake_case							,    index=_snake_case							,    init_retrieval=_snake_case							,    )

  lowerCAmelCase  							=      retrieval_workers
  lowerCAmelCase  							=      question_encoder_tokenizer
  lowerCAmelCase  							=      generator_tokenizer
  if len(self.retrieval_workers			) > 0:
   ray.get(
       [
           worker.create_rag_retriever.remote(_snake_case							,    _snake_case							,    _snake_case							,    _snake_case			)
           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							,    _snake_case							,    _snake_case			):







  """simple docstring"""






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



 @classmethod
 def 					UpperCamelCase__   (			cls							,    _snake_case							,    _snake_case=None							,    **_snake_case			):







  """simple docstring"""






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



 @classmethod
 def 					UpperCamelCase__   (			cls							,    _snake_case							,    _snake_case							,    _snake_case=None							,    **_snake_case			):







  """simple docstring"""






  lowerCAmelCase  							=      kwargs.pop('config'							,    _snake_case			) or RagConfig.from_pretrained(_snake_case							,    **_snake_case			)
  lowerCAmelCase  							=      RagTokenizer.from_pretrained(_snake_case							,    config=_snake_case			)
  lowerCAmelCase  							=      rag_tokenizer.question_encoder
  lowerCAmelCase  							=      rag_tokenizer.generator

  if indexed_dataset is not None:
   lowerCAmelCase  							=      'custom'
   lowerCAmelCase  							=      CustomHFIndex(config.retrieval_vector_size							,    _snake_case			)
  else:
   lowerCAmelCase  							=      cls._build_index(_snake_case			)

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



 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
  lowerCAmelCase  							=      self._build_index(self.config			)

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






'''simple docstring'''




import math





def      _UpperCAmelCase    (							_UpperCamelCase    :	int   )	->      float:
    A_     =   math.log(2,   10   ) * number
    A_     =   round((log_number - int(_UpperCamelCase   )),   15   )

    return difference





def      _UpperCAmelCase    (							_UpperCamelCase    :	int = 67_89_10   )	->      int:
    A_     =   90
    A_     =   0

    A_     =   math.log(1.2_3,   10   )
    A_     =   math.log(1.2_4,   10   )
    A_     =   0

    while position < number:
        A_     =   log_difference(_UpperCamelCase   )

        if difference >= upper_limit:
            power_iterator += 93

        elif difference < lower_limit:
            power_iterator += 1_96

        else:
            A_     =   power_iterator
            power_iterator += 1_96
            position += 1

    return previous_power


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(F"""{solution() = }""")






