




import logging
import random

import ray

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


UpperCAmelCase__    :							List[str]       		=     logging.getLogger(__name__)

class   UpperCAmelCase    :






   def __init__(						self :							Tuple  )->      Dict:
     _lowercase:			str      =     False






   def 			_a    (						self :							Tuple    ,       UpperCamelCase_ :							List[str]    ,       UpperCamelCase_ :							str    ,       UpperCamelCase_ :							Dict    ,       UpperCamelCase_ :							Dict  )->      Any:
     if not self.initialized:
       _lowercase:			Union[str, Any]      =     RagRetriever(
           UpperCamelCase_    ,       question_encoder_tokenizer=UpperCamelCase_    ,       generator_tokenizer=UpperCamelCase_    ,       index=UpperCamelCase_    ,       init_retrieval=UpperCamelCase_    ,       )
       _lowercase:			Optional[Any]      =     True






   def 			_a    (						self :							str  )->      List[Any]:
     self.retriever.index.init_index()






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






   def 			_a    (						self :							List[str]    ,       UpperCamelCase_ :							List[Any]    ,       UpperCamelCase_ :							List[Any]  )->      str:
     _lowercase  ,       _lowercase:			Dict      =     self.retriever._main_retrieve(UpperCamelCase_    ,       UpperCamelCase_  )
     _lowercase:			Optional[int]      =     self.retriever.index.get_doc_dicts(UpperCamelCase_  )
     return doc_ids, retrieved_doc_embeds, doc_dicts

class   UpperCAmelCase    (      A ):






   def __init__(						self :							Optional[int]    ,       UpperCamelCase_ :							Optional[Any]    ,       UpperCamelCase_ :							Tuple    ,       UpperCamelCase_ :							List[Any]    ,       UpperCamelCase_ :							int    ,       UpperCamelCase_ :							Optional[Any]=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_    ,       )

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






   @classmethod
   def 			_a    (						cls :							Dict    ,       UpperCamelCase_ :							Dict    ,       UpperCamelCase_ :							Union[str, Any]=None    ,       **UpperCamelCase_ :							str  )->      Optional[int]:
     return super(UpperCamelCase_    ,       cls  ).get_tokenizers(UpperCamelCase_    ,       UpperCamelCase_    ,       **UpperCamelCase_  )






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

     if indexed_dataset is not None:
       _lowercase:			Any      =     'custom'
       _lowercase:			Dict      =     CustomHFIndex(config.retrieval_vector_size    ,       UpperCamelCase_  )
     else:
       _lowercase:			List[Any]      =     cls._build_index(UpperCamelCase_  )

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






   def 			_a    (						self :							Tuple  )->      Tuple:
     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
     _lowercase:			List[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
         ]  )






from math import sqrt


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		list[int]:





  """simple docstring"""





  _lowercase:			Dict      =     [1]
  for i in range(2       ,  int(sqrt(UpperCamelCase				)				) + 1       ,  1				):
    if number % i == 0:
      values.append(UpperCamelCase				)
      if int(number // i				) != i:
        values.append(int(number // i				)				)
  return sorted(UpperCamelCase				)


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		bool:





  """simple docstring"""





  return sum(factors(UpperCamelCase				)				) > n


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		bool:





  """simple docstring"""





  _lowercase:			Dict      =     factors(UpperCamelCase				)
  _lowercase:			int      =     len(UpperCamelCase				)
  _lowercase:			Optional[int]      =     [[0 for i in range(number + 1				)] for j in range(r + 1				)]
  for i in range(r + 1				):
    _lowercase:			List[Any]      =     True

  for i in range(1       ,  number + 1				):
    _lowercase:			Optional[int]      =     False

  for i in range(1       ,  r + 1				):
    for j in range(1       ,  number + 1				):
      if j < values[i - 1]:
        _lowercase:			Optional[int]      =     subset[i - 1][j]
      else:
        _lowercase:			Optional[Any]      =     subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

  return subset[r][number] != 0


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		bool:





  """simple docstring"""





  return abundant(UpperCamelCase				) and not semi_perfect(UpperCamelCase				)


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   for number in (69, 70, 71):
      print(f'''{number} is {'' if weird(number) else 'not '}weird.''')
