

import logging
import random

import ray

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


lowercase_									=   logging.getLogger(__name__)





class  __lowerCAmelCase     :




  def __init__(				self					)  -> List[Any]:
        '''simple docstring'''






        _lowercase	       =False




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					)  -> Dict:
        '''simple docstring'''






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




  def   A__     (				self					)  -> Tuple:
        '''simple docstring'''






        self.retriever.index.init_index()




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase					)  -> Tuple:
        '''simple docstring'''






        _lowercase       ,       _lowercase	       =self.retriever._main_retrieve(lowerCAmelCase							,   lowerCAmelCase					)
        return doc_ids, retrieved_doc_embeds





class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):




  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase=None					)  -> Any:
        '''simple docstring'''






        if index is not None and index.is_initialized() and len(lowerCAmelCase					) > 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__(
            lowerCAmelCase							,   question_encoder_tokenizer=lowerCAmelCase							,   generator_tokenizer=lowerCAmelCase							,   index=lowerCAmelCase							,   init_retrieval=lowerCAmelCase							,   )
        _lowercase	       =retrieval_workers
        if len(self.retrieval_workers					) > 0:
              ray.get(
                  [
                      worker.create_rag_retriever.remote(lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					)
                      for worker in self.retrieval_workers
                  ]					)




  def   A__     (				self					)  -> Optional[Any]:
        '''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   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase					)  -> List[str]:
        '''simple docstring'''






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




  @classmethod
  def   A__     (				cls							,   lowerCAmelCase							,   lowerCAmelCase=None							,   **lowerCAmelCase					)  -> Optional[Any]:
        '''simple docstring'''






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




  @classmethod
  def   A__     (				cls							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase=None							,   **lowerCAmelCase					)  -> List[Any]:
        '''simple docstring'''






        _lowercase	       =kwargs.pop('config'							,   lowerCAmelCase					) or RagConfig.from_pretrained(lowerCAmelCase							,   **lowerCAmelCase					)
        _lowercase	       =RagTokenizer.from_pretrained(lowerCAmelCase							,   config=lowerCAmelCase					)
        _lowercase	       =rag_tokenizer.question_encoder
        _lowercase	       =rag_tokenizer.generator
        if indexed_dataset is not None:
              _lowercase	       ='custom'
              _lowercase	       =CustomHFIndex(config.retrieval_vector_size							,   lowerCAmelCase					)
        else:
              _lowercase	       =cls._build_index(lowerCAmelCase					)
        return cls(
            lowerCAmelCase							,   question_encoder_tokenizer=lowerCAmelCase							,   generator_tokenizer=lowerCAmelCase							,   retrieval_workers=lowerCAmelCase							,   index=lowerCAmelCase							,   )





def      a      (			A__	:   str      ,   A__	:   bool = False     )    ->      str:





      """simple docstring"""



      if not isinstance(A__      ,   A__     ):
            _lowercase	       =F'''Expected string as input, found {type(A__     )}'''
            raise ValueError(A__     )
      if not isinstance(A__      ,   A__     ):
            _lowercase	       =F'''Expected boolean as use_pascal parameter, found {type(A__     )}'''
            raise ValueError(A__     )

      _lowercase	       =input_str.split('_'     )

      _lowercase	       =0 if use_pascal else 1

      _lowercase	       =words[start_index:]

      _lowercase	       =[word[0].upper() + word[1:] for word in words_to_capitalize]

      _lowercase	       ='' if use_pascal else words[0]

      return "".join([initial_word, *capitalized_words]     )


if __name__ == "__main__":
  from doctest import testmod

  testmod()


