






import logging
import random

import ray

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


A_			:Any	    =		logging.getLogger(__name__)




class 							__A	:





				"""simple docstring"""







				def __init__(					self       ):




									"""simple docstring"""

									__UpperCamelCase :   Optional[int]									=False


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									if not self.initialized:
														__UpperCamelCase :   int									=RagRetriever(
														    lowerCamelCase__				,    question_encoder_tokenizer=lowerCamelCase__				,    generator_tokenizer=lowerCamelCase__				,    index=lowerCamelCase__				,    init_retrieval=lowerCamelCase__				,    )
														__UpperCamelCase :   Tuple									=True


				def  __lowercase				(					self       ):




									"""simple docstring"""

									self.retriever.index.init_index()


				def  __lowercase				(					self       ):




									"""simple docstring"""

									del self.retriever
									__UpperCamelCase :   List[str]									=False







				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase						,							__UpperCamelCase :   Optional[Any]									=self.retriever._main_retrieve(lowerCamelCase__				,    lowerCamelCase__       )
									__UpperCamelCase :   Any									=self.retriever.index.get_doc_dicts(lowerCamelCase__       )
									return doc_ids, retrieved_doc_embeds, doc_dicts




class 							__A	(     a					):





				"""simple docstring"""







				def __init__(					self				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__=None       ):




									"""simple docstring"""

									if index is not None and index.is_initialized() and len(lowerCamelCase__       ) > 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__(
									    lowerCamelCase__				,    question_encoder_tokenizer=lowerCamelCase__				,    generator_tokenizer=lowerCamelCase__				,    index=lowerCamelCase__				,    init_retrieval=lowerCamelCase__				,    )

									__UpperCamelCase :   str									=retrieval_workers
									__UpperCamelCase :   Any									=question_encoder_tokenizer
									__UpperCamelCase :   Optional[Any]									=generator_tokenizer
									if len(self.retrieval_workers       ) > 0:
														ray.get(
														    [
														        worker.create_rag_retriever.remote(lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__       )
														        for worker in self.retrieval_workers
														    ]       )


				def  __lowercase				(					self       ):




									"""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  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

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


				@classmethod
				def  __lowercase				(					cls				,    lowerCamelCase__				,    lowerCamelCase__=None				,    **lowerCamelCase__       ):




									"""simple docstring"""

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


				@classmethod
				def  __lowercase				(					cls				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__=None				,    **lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   str									=kwargs.pop('config'				,    lowerCamelCase__       ) or RagConfig.from_pretrained(lowerCamelCase__				,    **lowerCamelCase__       )
									__UpperCamelCase :   Union[str, Any]									=RagTokenizer.from_pretrained(lowerCamelCase__				,    config=lowerCamelCase__       )
									__UpperCamelCase :   Dict									=rag_tokenizer.question_encoder
									__UpperCamelCase :   Optional[Any]									=rag_tokenizer.generator

									if indexed_dataset is not None:
														__UpperCamelCase :   Any									='custom'
														__UpperCamelCase :   str									=CustomHFIndex(config.retrieval_vector_size				,    lowerCamelCase__       )
									else:
														__UpperCamelCase :   Optional[Any]									=cls._build_index(lowerCamelCase__       )

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







				def  __lowercase				(					self       ):




									"""simple docstring"""

									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
									__UpperCamelCase :   Optional[int]									=self._build_index(self.config       )

									ray.get(
									    [
									        worker.create_rag_retriever.remote(
									            self.config				,    self.question_encoder_tokenizer				,    self.generator_tokenizer				,    lowerCamelCase__       )
									        for worker in self.retrieval_workers
									    ]       )









from typing import Callable






def 				A	(			a_   )	->		List[str]:

					def wrapper(a_   ):
										__UpperCamelCase :   Dict									=function_with_docstring.__doc__
										return target_function

					return wrapper

