code: 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 _lowercase					:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							def __init__(    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	False
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if not self.initialized:
  																					_lowercase				=	RagRetriever(
  																					    __SCREAMING_SNAKE_CASE     ,   question_encoder_tokenizer=__SCREAMING_SNAKE_CASE     ,   generator_tokenizer=__SCREAMING_SNAKE_CASE     ,   index=__SCREAMING_SNAKE_CASE     ,   init_retrieval=__SCREAMING_SNAKE_CASE     ,   )
  																					_lowercase				=	True
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														self.retriever.index.init_index()
  
  
  
  							def     _UpperCAmelCase       (    self  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														del self.retriever
  														_lowercase				=	False
  
  
  
  							def     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				,       _lowercase				=	self.retriever._main_retrieve(__SCREAMING_SNAKE_CASE     ,   __SCREAMING_SNAKE_CASE  )
  														_lowercase				=	self.retriever.index.get_doc_dicts(__SCREAMING_SNAKE_CASE  )
  														return doc_ids, retrieved_doc_embeds, doc_dicts
  
  
  
  
  
  
  class _lowercase					(       lowerCAmelCase_						):
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  							def __init__(    self     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase=None  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if index is not None and index.is_initialized() and len(__SCREAMING_SNAKE_CASE  ) > 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__(
  														    __SCREAMING_SNAKE_CASE     ,   question_encoder_tokenizer=__SCREAMING_SNAKE_CASE     ,   generator_tokenizer=__SCREAMING_SNAKE_CASE     ,   index=__SCREAMING_SNAKE_CASE     ,   init_retrieval=__SCREAMING_SNAKE_CASE     ,   )
  
  														_lowercase				=	retrieval_workers
  														_lowercase				=	question_encoder_tokenizer
  														_lowercase				=	generator_tokenizer
  														if len(self.retrieval_workers  ) > 0:
  																					ray.get(
  																					    [
  																					        worker.create_rag_retriever.remote(__SCREAMING_SNAKE_CASE     ,   __SCREAMING_SNAKE_CASE     ,   __SCREAMING_SNAKE_CASE     ,   __SCREAMING_SNAKE_CASE  )
  																					        for worker in self.retrieval_workers
  																					    ]  )
  
  
  
  							def     _UpperCAmelCase       (    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     _UpperCAmelCase       (    self     ,   UpperCAmelCase     ,   UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														if len(self.retrieval_workers  ) > 0:
  																					# Select a random retrieval actor.
  																					_lowercase				=	self.retrieval_workers[random.randint(0     ,   len(self.retrieval_workers  ) - 1  )]
  																					_lowercase				,       _lowercase				,       _lowercase				=	ray.get(
  																					    random_worker.retrieve.remote(__SCREAMING_SNAKE_CASE     ,   __SCREAMING_SNAKE_CASE  )  )
  														else:
  																					_lowercase				,       _lowercase				=	self._main_retrieve(__SCREAMING_SNAKE_CASE     ,   __SCREAMING_SNAKE_CASE  )
  																					_lowercase				=	self.index.get_doc_dicts(__SCREAMING_SNAKE_CASE  )
  														return retrieved_doc_embeds, doc_ids, doc_dicts
  
  
  
  							@classmethod
  							def     _UpperCAmelCase       (    cls     ,   UpperCAmelCase     ,   UpperCAmelCase=None     ,   **UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														return super(__SCREAMING_SNAKE_CASE     ,   cls  ).get_tokenizers(__SCREAMING_SNAKE_CASE     ,   __SCREAMING_SNAKE_CASE     ,   **__SCREAMING_SNAKE_CASE  )
  
  
  
  							@classmethod
  							def     _UpperCAmelCase       (    cls     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase=None     ,   **UpperCAmelCase  ):
  
  
  
  
  
  
  
  														'''simple docstring'''
  
  
  
  
  
  
  														_lowercase				=	kwargs.pop("""config"""     ,   __SCREAMING_SNAKE_CASE  ) or RagConfig.from_pretrained(__SCREAMING_SNAKE_CASE     ,   **__SCREAMING_SNAKE_CASE  )
  														_lowercase				=	RagTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE     ,   config=__SCREAMING_SNAKE_CASE  )
  														_lowercase				=	rag_tokenizer.question_encoder
  														_lowercase				=	rag_tokenizer.generator
  
  														if indexed_dataset is not None:
  																					_lowercase				=	"""custom"""
  																					_lowercase				=	CustomHFIndex(config.retrieval_vector_size     ,   __SCREAMING_SNAKE_CASE  )
  														else:
  																					_lowercase				=	cls._build_index(__SCREAMING_SNAKE_CASE  )
  
  														return cls(
  														    __SCREAMING_SNAKE_CASE     ,   question_encoder_tokenizer=__SCREAMING_SNAKE_CASE     ,   generator_tokenizer=__SCREAMING_SNAKE_CASE     ,   retrieval_workers=__SCREAMING_SNAKE_CASE     ,   index=__SCREAMING_SNAKE_CASE     ,   )
  
  
  
  							def     _UpperCAmelCase       (    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
  														_lowercase				=	self._build_index(self.config  )
  
  														ray.get(
  														    [
  														        worker.create_rag_retriever.remote(
  														            self.config     ,   self.question_encoder_tokenizer     ,   self.generator_tokenizer     ,   __SCREAMING_SNAKE_CASE  )
  														        for worker in self.retrieval_workers
  														    ]  )
  
  
  
style_context: from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  A_:      int        =			{
      'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
      'tokenization_bart': ['BartTokenizer'],
  }
  
  try:
  				if not is_tokenizers_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      List[str]        =			['BartTokenizerFast']
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      int        =			[
  				    'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
  				    'BartForCausalLM',
  				    'BartForConditionalGeneration',
  				    'BartForQuestionAnswering',
  				    'BartForSequenceClassification',
  				    'BartModel',
  				    'BartPretrainedModel',
  				    'PretrainedBartModel',
  				]
  
  try:
  				if not is_tf_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      Any        =			[
  				    'TFBartForConditionalGeneration',
  				    'TFBartForSequenceClassification',
  				    'TFBartModel',
  				    'TFBartPretrainedModel',
  				]
  
  try:
  				if not is_flax_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				A_:      str        =			[
  				    'FlaxBartDecoderPreTrainedModel',
  				    'FlaxBartForCausalLM',
  				    'FlaxBartForConditionalGeneration',
  				    'FlaxBartForQuestionAnswering',
  				    'FlaxBartForSequenceClassification',
  				    'FlaxBartModel',
  				    'FlaxBartPreTrainedModel',
  				]
  
  if TYPE_CHECKING:
  				from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
  				from .tokenization_bart import BartTokenizer
  
  				try:
  								if not is_tokenizers_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .tokenization_bart_fast import BartTokenizerFast
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_bart import (
  								    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    BartForCausalLM,
  								    BartForConditionalGeneration,
  								    BartForQuestionAnswering,
  								    BartForSequenceClassification,
  								    BartModel,
  								    BartPretrainedModel,
  								    PretrainedBartModel,
  								)
  
  				try:
  								if not is_tf_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_tf_bart import (
  								    TFBartForConditionalGeneration,
  								    TFBartForSequenceClassification,
  								    TFBartModel,
  								    TFBartPretrainedModel,
  								)
  
  				try:
  								if not is_flax_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_flax_bart import (
  								    FlaxBartDecoderPreTrainedModel,
  								    FlaxBartForCausalLM,
  								    FlaxBartForConditionalGeneration,
  								    FlaxBartForQuestionAnswering,
  								    FlaxBartForSequenceClassification,
  								    FlaxBartModel,
  								    FlaxBartPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				A_:      Optional[Any]        =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  