






"""simple docstring"""



import logging
import random

import ray

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


__A		:       int  				=		logging.getLogger(__name__)





class 					_UpperCAmelCase       :




					def __init__(				self     :						int   )  ->  Dict:
							lowercase_ : Optional[int]							       =   False




					def   A  (				self     :						Dict    ,						A     :						Any    ,						A     :						Tuple    ,						A     :						List[str]    ,						A     :						Optional[int]   )  ->  Optional[Any]:
							if not self.initialized:
									lowercase_ : Optional[int]							       =   RagRetriever(
									    a_    ,						question_encoder_tokenizer=a_    ,						generator_tokenizer=a_    ,						index=a_    ,						init_retrieval=a_    ,						)
									lowercase_ : Dict							       =   True




					def   A  (				self     :						Optional[Any]   )  ->  Any:
							self.retriever.index.init_index()






					def   A  (				self     :						int    ,						A     :						List[Any]    ,						A     :						int   )  ->  int:
							lowercase_ : int							       =   self.retriever._main_retrieve(a_    ,						a_   )
							return doc_ids, retrieved_doc_embeds





class 					_UpperCAmelCase       ( _lowerCamelCase							):




					def __init__(				self     :						Union[str, Any]    ,						A     :						Union[str, Any]    ,						A     :						Tuple    ,						A     :						List[str]    ,						A     :						Dict    ,						A     :						Union[str, Any]=None   )  ->  Union[str, Any]:
							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_    ,						)
							lowercase_ : Union[str, Any]							       =   retrieval_workers
							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     :						List[str]   )  ->  Optional[int]:
							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     :						Optional[Any]    ,						A     :						Union[str, Any]    ,						A     :						Optional[Any]   )  ->  int:
							if len(self.retrieval_workers   ) > 0:
									# Select a random retrieval actor.
									lowercase_ : Union[str, Any]							       =   self.retrieval_workers[random.randint(0    ,						len(self.retrieval_workers   ) - 1   )]
									lowercase_ : int							       =   ray.get(random_worker.retrieve.remote(a_    ,						a_   )   )
							else:
									lowercase_ : str							       =   self._main_retrieve(a_    ,						a_   )
							return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(a_   )




					@classmethod
					def   A  (				cls     :						str    ,						A     :						Tuple    ,						A     :						Optional[int]=None    ,						**A     :						Optional[Any]   )  ->  Any:
							return super(a_    ,						cls   ).get_tokenizers(a_    ,						a_    ,						**a_   )






					@classmethod
					def   A  (				cls     :						List[str]    ,						A     :						int    ,						A     :						List[str]    ,						A     :						Dict=None    ,						**A     :						List[str]   )  ->  str:
							lowercase_ : Tuple							       =   kwargs.pop('''config'''    ,						a_   ) or RagConfig.from_pretrained(a_    ,						**a_   )
							lowercase_ : int							       =   RagTokenizer.from_pretrained(a_    ,						config=a_   )
							lowercase_ : Dict							       =   rag_tokenizer.question_encoder
							lowercase_ : List[str]							       =   rag_tokenizer.generator
							if indexed_dataset is not None:
									lowercase_ : Union[str, Any]							       =   """custom"""
									lowercase_ : Dict							       =   CustomHFIndex(config.retrieval_vector_size    ,						a_   )
							else:
									lowercase_ : List[Any]							       =   cls._build_index(a_   )
							return cls(
							    a_    ,						question_encoder_tokenizer=a_    ,						generator_tokenizer=a_    ,						retrieval_workers=a_    ,						index=a_    ,						)








'''simple docstring'''






from numpy import exp, pi, sqrt




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_ = 0.0       ,   lowerCAmelCase_ = 1.0		)->		int:





       '''simple docstring'''


       return 1 / sqrt(2 * pi * sigma**2		) * exp(-((x - mu) ** 2) / (2 * sigma**2)		)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



