import logging
import random

import ray

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


lowerCamelCase_    :		str							=	logging.getLogger(__name__)


class 						a__							:



			def __init__(						self   )			->   List[str]:
								__a		 =       False



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Any:
								if not self.initialized:
													__a		 =       RagRetriever(
													    UpperCAmelCase				,						question_encoder_tokenizer=UpperCAmelCase				,						generator_tokenizer=UpperCAmelCase				,						index=UpperCAmelCase				,						init_retrieval=UpperCAmelCase				,						)
													__a		 =       True



			def __SCREAMING_SNAKE_CASE							(						self   )			->   int:
								self.retriever.index.init_index()







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   str:
								__a ,				__a		 =       self.retriever._main_retrieve(UpperCAmelCase				,						UpperCAmelCase   )
								return doc_ids, retrieved_doc_embeds






class 						a__							(       __snake_case		):



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase=None   )			->   List[str]:
								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		 =       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 __SCREAMING_SNAKE_CASE							(						self   )			->   str:
								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				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Optional[int]:
								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		 =       ray.get(random_worker.retrieve.remote(UpperCAmelCase				,						UpperCAmelCase   )   )
								else:
													__a ,				__a		 =       self._main_retrieve(UpperCAmelCase				,						UpperCAmelCase   )
								return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCAmelCase   )



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						UpperCAmelCase				,						UpperCAmelCase=None				,						**UpperCAmelCase   )			->   Optional[int]:
								return super(UpperCAmelCase				,						cls   ).get_tokenizers(UpperCAmelCase				,						UpperCAmelCase				,						**UpperCAmelCase   )







			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase=None				,						**UpperCAmelCase   )			->   str:
								__a		 =       kwargs.pop('config'				,						UpperCAmelCase   ) or RagConfig.from_pretrained(UpperCAmelCase				,						**UpperCAmelCase   )
								__a		 =       RagTokenizer.from_pretrained(UpperCAmelCase				,						config=UpperCAmelCase   )
								__a		 =       rag_tokenizer.question_encoder
								__a		 =       rag_tokenizer.generator
								if indexed_dataset is not None:
													__a		 =       'custom'
													__a		 =       CustomHFIndex(config.retrieval_vector_size				,						UpperCAmelCase   )
								else:
													__a		 =       cls._build_index(UpperCAmelCase   )
								return cls(
								    UpperCAmelCase				,						question_encoder_tokenizer=UpperCAmelCase				,						generator_tokenizer=UpperCAmelCase				,						retrieval_workers=UpperCAmelCase				,						index=UpperCAmelCase				,						)





def 							lowerCAmelCase(     __lowerCamelCase						):
					if not all(char in '01' for char in bin_string						):
										raise ValueError('Non-binary value was passed to the function'						)
					if not bin_string:
										raise ValueError('Empty string was passed to the function'						)
					__a		 =       ''
					while len(__lowerCamelCase						) % 3 != 0:
										__a		 =       '0' + bin_string
					__a		 =       [
					    bin_string[index : index + 3]
					    for index in range(len(__lowerCamelCase						)						)
					    if index % 3 == 0
					]
					for bin_group in bin_string_in_3_list:
										__a		 =       0
										for index, val in enumerate(__lowerCamelCase						):
															oct_val += int(2 ** (2 - index) * int(__lowerCamelCase						)						)
										oct_string += str(__lowerCamelCase						)
					return oct_string


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

	testmod()




