





"""simple docstring"""






import logging
import random

import ray

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


lowerCAmelCase_      =				logging.getLogger(__name__)



class 						lowerCAmelCase       :






       def __init__( self  ):
              _UpperCAmelCase			     =  False






       def 				__A			( self     ,	a__     ,	a__     ,	a__     ,	a__  ):
              if not self.initialized:
                     _UpperCAmelCase			     =  RagRetriever(
                         a__     ,	question_encoder_tokenizer=a__     ,	generator_tokenizer=a__     ,	index=a__     ,	init_retrieval=a__     ,	)
                     _UpperCAmelCase			     =  True






       def 				__A			( self  ):
              self.retriever.index.init_index()






       def 				__A			( self  ):
              # delete the old self.retriever object before assigning the new index
              del self.retriever
              _UpperCAmelCase			     =  False






       def 				__A			( self     ,	a__     ,	a__  ):
              _UpperCAmelCase     ,				_UpperCAmelCase			     =  self.retriever._main_retrieve(a__     ,	a__  )
              _UpperCAmelCase			     =  self.retriever.index.get_doc_dicts(a__  )
              return doc_ids, retrieved_doc_embeds, doc_dicts



class 						lowerCAmelCase       (   snake_case				):






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

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






       @classmethod
       def 				__A			( cls     ,	a__     ,	a__=None     ,	**a__  ):
              return super(a__     ,	cls  ).get_tokenizers(a__     ,	a__     ,	**a__  )






       @classmethod
       def 				__A			( cls     ,	a__     ,	a__     ,	a__=None     ,	**a__  ):
              _UpperCAmelCase			     =  kwargs.pop('config'     ,	a__  ) or RagConfig.from_pretrained(a__     ,	**a__  )
              _UpperCAmelCase			     =  RagTokenizer.from_pretrained(a__     ,	config=a__  )
              _UpperCAmelCase			     =  rag_tokenizer.question_encoder
              _UpperCAmelCase			     =  rag_tokenizer.generator

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

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






       def 				__A			( self  ):
              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
              _UpperCAmelCase			     =  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
                  ]  )













"""simple docstring"""






def    __lowerCamelCase     (							SCREAMING_SNAKE_CASE				)		->							Dict:






       """simple docstring"""




       _UpperCAmelCase			     =  [701, 301, 132, 57, 23, 10, 4, 1]
       for gap in gaps:
              for i in range(SCREAMING_SNAKE_CASE,len(SCREAMING_SNAKE_CASE				)				):
                     _UpperCAmelCase			     =  collection[i]
                     _UpperCAmelCase			     =  i
                     while j >= gap and collection[j - gap] > insert_value:
                            _UpperCAmelCase			     =  collection[j - gap]
                            j -= gap
                     if j != i:
                            _UpperCAmelCase			     =  insert_value
       return collection


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

    testmod()
    lowerCAmelCase_      =				input('''Enter numbers separated by a comma:\n''').strip()
    lowerCAmelCase_      =				[int(item) for item in user_input.split(''',''')]
    print(shell_sort(unsorted))






