


"""simple docstring"""







def _A		(		lowercase	):







    """simple docstring"""
    a					=1
    a					=0
    a					=1
    a					=1
    a					=3
    a					=3
    a					=limit
    a					=0

    a					=""
    while counter != decimal + 1:
        if 4 * q + r - t < n * t:
            result += str(lowercase	)
            if counter == 0:
                result += "."

            if decimal == counter:
                break

            counter += 1
            a					=10 * (r - n * t)
            a					=((10 * (3 * q + r)) // t) - 10 * n
            q *= 10
            a					=nr
        else:
            a					=(2 * q + r) * l
            a					=(q * (7 * k) + 2 + (r * l)) // (t * l)
            q *= k
            t *= l
            l += 2
            k += 1
            a					=nn
            a					=nr
    return result
def _A		(		):







    """simple docstring"""
    print(f'''{calculate_pi(50	) = }'''	)
    import doctest

    doctest.testmod()


if __name__ == "__main__":
       main()



"""simple docstring"""







import logging
import random

import ray

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


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






class    __A       :






 """simple docstring"""




 def __init__(	self		)      ->  Dict:
     a					=False


 def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A				,					__A				,					__A		)      ->  Tuple:
     if not self.initialized:
         a					=RagRetriever(
             __A				,					question_encoder_tokenizer=__A				,					generator_tokenizer=__A				,					index=__A				,					init_retrieval=__A				,					)
         a					=True


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Dict:
     self.retriever.index.init_index()


 def    SCREAMING_SNAKE_CASE							(	self		)      ->  List[Any]:
     # delete the old self.retriever object before assigning the new index
     del self.retriever
     a					=False





 def    SCREAMING_SNAKE_CASE							(	self				,					__A				,					__A		)      ->  str:
     a	,   a					=self.retriever._main_retrieve(__A				,					__A		)
     a					=self.retriever.index.get_doc_dicts(__A		)
     return doc_ids, retrieved_doc_embeds, doc_dicts






class    __A       ( _SCREAMING_SNAKE_CASE    ):






 """simple docstring"""




 def __init__(	self				,					__A				,					__A				,					__A				,					__A				,					__A=None		)      ->  int:
     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				,					)

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


 @classmethod
 def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A=None				,					**__A		)      ->  Tuple:
     return super(__A				,					cls		).get_tokenizers(__A				,					__A				,					**__A		)


 @classmethod
 def    SCREAMING_SNAKE_CASE							(	cls				,					__A				,					__A				,					__A=None				,					**__A		)      ->  Optional[Any]:
     a					=kwargs.pop('''config'''				,					__A		) or RagConfig.from_pretrained(__A				,					**__A		)
     a					=RagTokenizer.from_pretrained(__A				,					config=__A		)
     a					=rag_tokenizer.question_encoder
     a					=rag_tokenizer.generator

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

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





 def    SCREAMING_SNAKE_CASE							(	self		)      ->  Union[str, 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					=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
         ]		)