


"""simple docstring"""






SCREAMING_SNAKE_CASE__				:  List[str]						= {
    "0000": "0",
    "0001": "1",
    "0010": "2",
    "0011": "3",
    "0100": "4",
    "0101": "5",
    "0110": "6",
    "0111": "7",
    "1000": "8",
    "1001": "9",
    "1010": "a",
    "1011": "b",
    "1100": "c",
    "1101": "d",
    "1110": "e",
    "1111": "f",
}




def        A_					(					UpperCAmelCase__				)     ->     str:
   a   :	Dict						=    str(UpperCAmelCase__				).strip()

   # Exceptions
   if not binary_str:
      raise ValueError('Empty string was passed to the function'				)
   a   :	Union[str, Any]						=    binary_str[0] == '-'
   a   :	Union[str, Any]						=    binary_str[1:] if is_negative else binary_str
   if not all(char in '01' for char in binary_str				):
      raise ValueError('Non-binary value was passed to the function'				)

   a   :	Optional[Any]						=    (
       '0' * (4 * (divmod(len(UpperCAmelCase__				)       ,   4				)[0] + 1) - len(UpperCAmelCase__				)) + binary_str
   )

   a   :	Dict						=    []
   for x in range(0       ,   len(UpperCAmelCase__				)       ,   4				):
      hexadecimal.append(BITS_TO_HEX[binary_str[x : x + 4]]				)
   a   :	Dict						=    '0x' + ''.join(UpperCAmelCase__				)

   return "-" + hexadecimal_str if is_negative else hexadecimal_str


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

 testmod()









"""simple docstring"""






import logging
import random

import ray

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


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


class 	A_       :





  """simple docstring"""

  def __init__(						self						)				->					Dict:
     a   :	List[str]						=    False
  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					List[str]:
     if not self.initialized:
        a   :	Dict						=    RagRetriever(
            __UpperCAmelCase  ,					question_encoder_tokenizer=__UpperCAmelCase  ,					generator_tokenizer=__UpperCAmelCase  ,					index=__UpperCAmelCase  ,					init_retrieval=__UpperCAmelCase  ,					)
        a   :	Dict						=    True
  def 							lowercase_     (						self						)				->					List[Any]:
     self.retriever.index.init_index()
  def 							lowercase_     (						self						)				->					List[Any]:
     # delete the old self.retriever object before assigning the new index
     del self.retriever
     a   :	Dict						=    False






  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Optional[int]:
     a	, a   :	List[str]						=    self.retriever._main_retrieve(__UpperCAmelCase  ,					__UpperCAmelCase						)
     a   :	List[Any]						=    self.retriever.index.get_doc_dicts(__UpperCAmelCase						)
     return doc_ids, retrieved_doc_embeds, doc_dicts



class 	A_       (     _UpperCAmelCase   ):





  """simple docstring"""

  def __init__(						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase=None						)				->					Tuple:
     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  ,					)

     a   :	Tuple						=    retrieval_workers
     a   :	List[str]						=    question_encoder_tokenizer
     a   :	Union[str, 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 							lowercase_     (						self						)				->					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 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Tuple:
     if len(self.retrieval_workers						) > 0:
        # Select a random retrieval actor.
        a   :	Dict						=    self.retrieval_workers[random.randint(0  ,					len(self.retrieval_workers						) - 1						)]
        a	, a	, a   :	Any						=    ray.get(
            random_worker.retrieve.remote(__UpperCAmelCase  ,					__UpperCAmelCase						)						)
     else:
        a	, a   :	str						=    self._main_retrieve(__UpperCAmelCase  ,					__UpperCAmelCase						)
        a   :	str						=    self.index.get_doc_dicts(__UpperCAmelCase						)
     return retrieved_doc_embeds, doc_ids, doc_dicts
  @classmethod
  def 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					__UpperCAmelCase=None  ,					**__UpperCAmelCase						)				->					int:
     return super(__UpperCAmelCase  ,					cls						).get_tokenizers(__UpperCAmelCase  ,					__UpperCAmelCase  ,					**__UpperCAmelCase						)
  @classmethod
  def 							lowercase_     (						cls  ,					__UpperCAmelCase  ,					__UpperCAmelCase  ,					__UpperCAmelCase=None  ,					**__UpperCAmelCase						)				->					str:
     a   :	Union[str, Any]						=    kwargs.pop('config'  ,					__UpperCAmelCase						) or RagConfig.from_pretrained(__UpperCAmelCase  ,					**__UpperCAmelCase						)
     a   :	Optional[Any]						=    RagTokenizer.from_pretrained(__UpperCAmelCase  ,					config=__UpperCAmelCase						)
     a   :	List[str]						=    rag_tokenizer.question_encoder
     a   :	int						=    rag_tokenizer.generator

     if indexed_dataset is not None:
        a   :	Optional[int]						=    'custom'
        a   :	str						=    CustomHFIndex(config.retrieval_vector_size  ,					__UpperCAmelCase						)
     else:
        a   :	Dict						=    cls._build_index(__UpperCAmelCase						)

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






  def 							lowercase_     (						self						)				->					List[Any]:
     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
     a   :	Optional[Any]						=    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
         ]						)





