
import logging
import random

import ray

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


__A				=logging.getLogger(__name__)




class     _SCREAMING_SNAKE_CASE     :



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



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase )				->       Optional[int]:
					if not self.initialized:
									lowerCamelCase_						=			RagRetriever(
									    lowercase						,  question_encoder_tokenizer=lowercase						,  generator_tokenizer=lowercase						,  index=lowercase						,  init_retrieval=lowercase						,  )
									lowerCamelCase_						=			True



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					self.retriever.index.init_index()



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       Any:
					lowerCamelCase_	,    lowerCamelCase_						=			self.retriever._main_retrieve(lowercase						,  lowercase )
					return doc_ids, retrieved_doc_embeds




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def __init__(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase=None )				->       Dict:
					if index is not None and index.is_initialized() and len(lowercase ) > 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__(
					    lowercase						,  question_encoder_tokenizer=lowercase						,  generator_tokenizer=lowercase						,  index=lowercase						,  init_retrieval=lowercase						,  )
					lowerCamelCase_						=			retrieval_workers
					if len(self.retrieval_workers ) > 0:
									ray.get(
									    [
									        worker.create_rag_retriever.remote(lowercase						,  lowercase						,  lowercase						,  lowercase )
									        for worker in self.retrieval_workers
									    ] )



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



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



	@classmethod
	def     SCREAMING_SNAKE_CASE_(    cls						,  lowercase						,  lowercase						,  lowercase=None						,  **lowercase )				->       Optional[Any]:
					lowerCamelCase_						=			kwargs.pop("config"						,  lowercase ) or RagConfig.from_pretrained(lowercase						,  **lowercase )
					lowerCamelCase_						=			RagTokenizer.from_pretrained(lowercase						,  config=lowercase )
					lowerCamelCase_						=			rag_tokenizer.question_encoder
					lowerCamelCase_						=			rag_tokenizer.generator
					if indexed_dataset is not None:
									lowerCamelCase_						=			"custom"
									lowerCamelCase_						=			CustomHFIndex(config.retrieval_vector_size						,  lowercase )
					else:
									lowerCamelCase_						=			cls._build_index(lowercase )
					return cls(
					    lowercase						,  question_encoder_tokenizer=lowercase						,  generator_tokenizer=lowercase						,  retrieval_workers=lowercase						,  index=lowercase						,  )




from abc import ABC, abstractmethod
from argparse import ArgumentParser




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	@staticmethod
	@abstractmethod
	def     SCREAMING_SNAKE_CASE_(    lowercase )				->       int:
					raise NotImplementedError()



	@abstractmethod
	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					raise NotImplementedError()


