


'''simple docstring'''



import logging
import random

import ray

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


_lowercase    :    int			     =  logging.getLogger(__name__)






class  UpperCamelCase__:




				def __init__(	self    :				List[str]   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			False




				def   a__(	self    :				Dict   ,					lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				Union[str, Any]   ,					lowerCAmelCase    :				Any   ,					lowerCAmelCase    :				Any   )->  Union[str, Any]:

										"""simple docstring"""




										if not self.initialized:
																UpperCAmelCase         =			RagRetriever(
																    UpperCAmelCase__   ,					question_encoder_tokenizer=UpperCAmelCase__   ,					generator_tokenizer=UpperCAmelCase__   ,					index=UpperCAmelCase__   ,					init_retrieval=UpperCAmelCase__   ,					)
																UpperCAmelCase         =			True




				def   a__(	self    :				Dict   )->  Optional[Any]:

										"""simple docstring"""




										self.retriever.index.init_index()





				def   a__(	self    :				List[str]   ,					lowerCAmelCase    :				Dict   ,					lowerCAmelCase    :				int   )->  str:

										"""simple docstring"""




										UpperCAmelCase         =			self.retriever._main_retrieve(UpperCAmelCase__   ,					UpperCAmelCase__   )
										return doc_ids, retrieved_doc_embeds







class  UpperCamelCase__(     lowerCAmelCase ):




				def __init__(	self    :				Optional[int]   ,					lowerCAmelCase    :				Tuple   ,					lowerCAmelCase    :				Union[str, Any]   ,					lowerCAmelCase    :				List[Any]   ,					lowerCAmelCase    :				Union[str, Any]   ,					lowerCAmelCase    :				Optional[Any]=None   )->  Optional[Any]:

										"""simple docstring"""




										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__   ,					)
										UpperCAmelCase         =			retrieval_workers
										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    :				List[Any]   )->  int:

										"""simple docstring"""




										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    :				Any   ,					lowerCAmelCase    :				Any   ,					lowerCAmelCase    :				List[str]   )->  Any:

										"""simple docstring"""




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




				@classmethod
				def   a__(	cls    :				Dict   ,					lowerCAmelCase    :				Any   ,					lowerCAmelCase    :				Optional[Any]=None   ,					**lowerCAmelCase    :				str   )->  Dict:

										"""simple docstring"""




										return super(UpperCAmelCase__   ,					cls   ).get_tokenizers(UpperCAmelCase__   ,					UpperCAmelCase__   ,					**UpperCAmelCase__   )





				@classmethod
				def   a__(	cls    :				Dict   ,					lowerCAmelCase    :				Union[str, Any]   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				Any=None   ,					**lowerCAmelCase    :				Optional[Any]   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			kwargs.pop('''config'''   ,					UpperCAmelCase__   ) or RagConfig.from_pretrained(UpperCAmelCase__   ,					**UpperCAmelCase__   )
										UpperCAmelCase         =			RagTokenizer.from_pretrained(UpperCAmelCase__   ,					config=UpperCAmelCase__   )
										UpperCAmelCase         =			rag_tokenizer.question_encoder
										UpperCAmelCase         =			rag_tokenizer.generator
										if indexed_dataset is not None:
																UpperCAmelCase         =			'''custom'''
																UpperCAmelCase         =			CustomHFIndex(config.retrieval_vector_size   ,					UpperCAmelCase__   )
										else:
																UpperCAmelCase         =			cls._build_index(UpperCAmelCase__   )
										return cls(
										    UpperCAmelCase__   ,					question_encoder_tokenizer=UpperCAmelCase__   ,					generator_tokenizer=UpperCAmelCase__   ,					retrieval_workers=UpperCAmelCase__   ,					index=UpperCAmelCase__   ,					)









'''simple docstring'''



from ..utils import DummyObject, requires_backends






class  UpperCamelCase__(     metaclass=lowerCAmelCase ):
				__magic_name__       :   List[str]							= ["note_seq"]




				def __init__(	self    :				Any   ,					*lowerCAmelCase    :				List[str]   ,					**lowerCAmelCase    :				int   )->  Optional[int]:

										"""simple docstring"""




										requires_backends(self   ,					['''note_seq''']   )




				@classmethod
				def   a__(	cls    :				Dict   ,					*lowerCAmelCase    :				int   ,					**lowerCAmelCase    :				Optional[int]   )->  Dict:

										"""simple docstring"""




										requires_backends(cls   ,					['''note_seq''']   )





				@classmethod
				def   a__(	cls    :				int   ,					*lowerCAmelCase    :				Tuple   ,					**lowerCAmelCase    :				Optional[Any]   )->  Union[str, Any]:

										"""simple docstring"""




										requires_backends(cls   ,					['''note_seq''']   )





